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liesvarastranta

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arxiv_cs_category

like 3

2303.04253

Lijing Zhu

Lijing Zhu, Qizhen Lan, Alvaro Velasquez, Houbing Song, Acharya Kamal, Qing Tian, Shuteng Niu

SKGHOI: Spatial-Semantic Knowledge Graph for Human-Object Interaction Detection

10 pages, 3 figures, 2 tables

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Detecting human-object interactions (HOIs) is a challenging problem in computer vision. Existing techniques for HOI detection heavily rely on appearance-based features, which may not capture other essential characteristics for accurate detection. Furthermore, the use of transformer-based models for sentiment representation of human-object pairs can be computationally expensive. To address these challenges, we propose a novel graph-based approach, SKGHOI (Spatial-Semantic Knowledge Graph for Human-Object Interaction Detection), that effectively captures the sentiment representation of HOIs by integrating both spatial and semantic knowledge. In a graph, SKGHOI takes the components of interaction as nodes, and the spatial relationships between them as edges. Our approach employs a spatial encoder and a semantic encoder to extract spatial and semantic information, respectively, and then combines these encodings to create a knowledge graph that captures the sentiment representation of HOIs. Compared to existing techniques, SKGHOI is computationally efficient and allows for the incorporation of prior knowledge, making it practical for use in real-world applications. We demonstrate the effectiveness of our proposed method on the widely-used HICO-DET datasets, where it outperforms existing state-of-the-art graph-based methods by a significant margin. Our results indicate that the SKGHOI approach has the potential to significantly improve the accuracy and efficiency of HOI detection, and we anticipate that it will be of great interest to researchers and practitioners working on this challenging task.

[{'version': 'v1', 'created': 'Tue, 7 Mar 2023 21:52:10 GMT'}, {'version': 'v2', 'created': 'Wed, 15 Mar 2023 02:52:14 GMT'}]

2023-03-16

[['Zhu', 'Lijing', ''], ['Lan', 'Qizhen', ''], ['Velasquez', 'Alvaro', ''], ['Song', 'Houbing', ''], ['Kamal', 'Acharya', ''], ['Tian', 'Qing', ''], ['Niu', 'Shuteng', '']]

2004.03442

Nicolo Pollini

Nicol\`o Pollini

Fail-safe optimization of viscous dampers for seismic retrofitting

Earthquake Engineering & Structural Dynamics 2020

10.1002/eqe.3319

cs.CE math.OC

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

This paper presents a new optimization approach for designing minimum-cost fail-safe distributions of fluid viscous dampers for seismic retrofitting. Failure is modeled as either complete damage of the dampers or partial degradation of the dampers' properties. In general, this leads to optimization problems with large number of constraints. Thus, the use of a working-set optimization algorithm is proposed. The main idea is to solve a sequence of relaxed optimization sub-problems with a small sub-set of all constraints. The algorithm terminates once a solution of a sub-problem is found that satisfies all the constraints of the problem. The retrofitting cost is minimized with constraints on the inter-story drifts at the peripheries of frame structures. The structures considered are subjected to a realistic ensemble of ground motions, and their response is evaluated with time-history analyses. The transient optimization problem is efficiently solved with a gradient-based sequential linear programming algorithm. The gradients of the response functions are calculated with a consistent adjoint sensitivity analysis procedure. Promising results attained for 3-D irregular frames are presented and discussed. The numerical results highlight the fact that the optimized layout and size of the dampers can change significantly even for moderate levels of damage.

[{'version': 'v1', 'created': 'Tue, 7 Apr 2020 14:46:04 GMT'}]

2020-07-16

[['Pollini', 'Nicolò', '']]

2303.09338

Laurent Alonso

Laurent Alonso

Uniform random generations and rejection method(I) with binomial majorant

cs.DM math.CO

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We present three simple algorithms to uniformly generate `Fibonacci words' (i.e., some words that are enumerated by Fibonacci numbers), Schr{\"o}der trees of size $n$ and Motzkin left factors of size $n$ and final height $h$. These algorithms have an average complexity of $O(n)$ in the unit-cost RAM model.

[{'version': 'v1', 'created': 'Thu, 16 Mar 2023 14:19:31 GMT'}]

2023-03-17

[['Alonso', 'Laurent', '']]

1304.6530

Goce Chadzitaskos

Goce Chadzitaskos

Parabolic Strip Telescope

4 pages, 7 figures

astro-ph.IM physics.optics

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We present a proposal of a new type of telescopes using a rotating parabolic strip as the primary mirror. It is the most principal modification of the design of telescopes from the times of Galileo and Newton. In order to demonstrate the basic idea, the image of an artificial constellation observed by this kind of telescope was reconstructed using the techniques described in this article. As a working model of this new telescope, we have used an assembly of the primary mirror---a strip of acrylic glass parabolic mirror 40 cm long and 10 cm wid shaped as a parabolic cylinder of focal length 1 m---and an artificial constellation, a set of 5 apertures in a distance of 5 m illuminated from behind. In order to reconstruct the image, we made a series of snaps, each after a rotation of the constellation by 15 degrees. Using Matlab we reconstructed the image of the artificial constellation.

[{'version': 'v1', 'created': 'Wed, 24 Apr 2013 09:45:04 GMT'}]

2013-04-25

[['Chadzitaskos', 'Goce', '']]

1206.4755

Omar El Ayach

Omar El Ayach, Steven W. Peters, Robert W. Heath Jr

The Practical Challenges of Interference Alignment

submitted to IEEE Wireless Communications Magazine

IEEE Wireless Communications Magazine, vol. 20, no. 1, pp. 35-42, February 2012

cs.IT math.IT

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Interference alignment (IA) is a revolutionary wireless transmission strategy that reduces the impact of interference. The idea of interference alignment is to coordinate multiple transmitters so that their mutual interference aligns at the receivers, facilitating simple interference cancellation techniques. Since IA's inception, researchers have investigated its performance and proposed improvements, verifying IA's ability to achieve the maximum degrees of freedom (an approximation of sum capacity) in a variety of settings, developing algorithms for determining alignment solutions, and generalizing transmission strategies that relax the need for perfect alignment but yield better performance. This article provides an overview of the concept of interference alignment as well as an assessment of practical issues including performance in realistic propagation environments, the role of channel state information at the transmitter, and the practicality of interference alignment in large networks.

[{'version': 'v1', 'created': 'Thu, 21 Jun 2012 01:36:49 GMT'}]

2013-04-15

[['Ayach', 'Omar El', ''], ['Peters', 'Steven W.', ''], ['Heath', 'Robert W.', 'Jr']]

1804.05667

Yingli Wang

Yingli Wang, Qingpeng Zhang, and Xiaoguang Yang

Evolution of the Chinese Guarantee Network under Financial Crisis and Stimulus Program

30pages, 8 figures, 1 table

Nature Communications volume 11, Article number: 2693 (2020)

10.1038/s41467-020-16535-8

q-fin.RM cs.CE

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Our knowledge about the evolution of guarantee network in downturn period is limited due to the lack of comprehensive data of the whole credit system. Here we analyze the dynamic Chinese guarantee network constructed from a comprehensive bank loan dataset that accounts for nearly 80% total loans in China, during 01/2007-03/2012. The results show that, first, during the 2007-2008 global financial crisis, the guarantee network became smaller, less connected and more stable because of many bankruptcies; second, the stimulus program encouraged mutual guarantee behaviors, resulting in highly reciprocal and fragile network structure; third, the following monetary policy adjustment enhanced the resilience of the guarantee network by reducing mutual guarantees. Interestingly, our work reveals that the financial crisis made the network more resilient, and conversely, the government bailout degenerated network resilience. These counterintuitive findings can provide new insight into the resilience of real-world credit system under external shocks or rescues.

[{'version': 'v1', 'created': 'Mon, 2 Apr 2018 09:27:09 GMT'}, {'version': 'v2', 'created': 'Sat, 14 Dec 2019 01:25:24 GMT'}, {'version': 'v3', 'created': 'Sat, 9 May 2020 10:41:14 GMT'}, {'version': 'v4', 'created': 'Mon, 1 Jun 2020 10:32:45 GMT'}, {'version': 'v5', 'created': 'Tue, 2 Jun 2020 13:06:36 GMT'}]

2020-06-03

[['Wang', 'Yingli', ''], ['Zhang', 'Qingpeng', ''], ['Yang', 'Xiaoguang', '']]

1711.03694

Junting Zhang

Junting Zhang, Chen Liang, C.-C. Jay Kuo

A Fully Convolutional Tri-branch Network (FCTN) for Domain Adaptation

Accepted by ICASSP 2018

cs.CV

http://creativecommons.org/licenses/by-nc-sa/4.0/

A domain adaptation method for urban scene segmentation is proposed in this work. We develop a fully convolutional tri-branch network, where two branches assign pseudo labels to images in the unlabeled target domain while the third branch is trained with supervision based on images in the pseudo-labeled target domain. The re-labeling and re-training processes alternate. With this design, the tri-branch network learns target-specific discriminative representations progressively and, as a result, the cross-domain capability of the segmenter improves. We evaluate the proposed network on large-scale domain adaptation experiments using both synthetic (GTA) and real (Cityscapes) images. It is shown that our solution achieves the state-of-the-art performance and it outperforms previous methods by a significant margin.

[{'version': 'v1', 'created': 'Fri, 10 Nov 2017 05:01:28 GMT'}, {'version': 'v2', 'created': 'Tue, 27 Feb 2018 01:43:35 GMT'}]

2018-02-28

[['Zhang', 'Junting', ''], ['Liang', 'Chen', ''], ['Kuo', 'C. -C. Jay', '']]

2010.05340

Taras Lehinevych

Ihor Protsenko, Taras Lehinevych, Dmytro Voitekh, Ihor Kroosh, Nick Hasty, Anthony Johnson

Self-attention aggregation network for video face representation and recognition

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Models based on self-attention mechanisms have been successful in analyzing temporal data and have been widely used in the natural language domain. We propose a new model architecture for video face representation and recognition based on a self-attention mechanism. Our approach could be used for video with single and multiple identities. To the best of our knowledge, no one has explored the aggregation approaches that consider the video with multiple identities. The proposed approach utilizes existing models to get the face representation for each video frame, e.g., ArcFace and MobileFaceNet, and the aggregation module produces the aggregated face representation vector for video by taking into consideration the order of frames and their quality scores. We demonstrate empirical results on a public dataset for video face recognition called IJB-C to indicate that the self-attention aggregation network (SAAN) outperforms naive average pooling. Moreover, we introduce a new multi-identity video dataset based on the publicly available UMDFaces dataset and collected GIFs from Giphy. We show that SAAN is capable of producing a compact face representation for both single and multiple identities in a video. The dataset and source code will be publicly available.

[{'version': 'v1', 'created': 'Sun, 11 Oct 2020 20:57:46 GMT'}]

2020-10-13

[['Protsenko', 'Ihor', ''], ['Lehinevych', 'Taras', ''], ['Voitekh', 'Dmytro', ''], ['Kroosh', 'Ihor', ''], ['Hasty', 'Nick', ''], ['Johnson', 'Anthony', '']]

2303.07646

Siddartha Reddy Mr

Thummaluru Siddartha Reddy, Sundeep Prabhakar Chepuri, and Pierre Borgnat

Clustering with Simplicial Complexes

cs.LG eess.SP

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

In this work, we propose a new clustering algorithm to group nodes in networks based on second-order simplices (aka filled triangles) to leverage higher-order network interactions. We define a simplicial conductance function, which on minimizing, yields an optimal partition with a higher density of filled triangles within the set while the density of filled triangles is smaller across the sets. To this end, we propose a simplicial adjacency operator that captures the relation between the nodes through second-order simplices. This allows us to extend the well-known Cheeger inequality to cluster a simplicial complex. Then, leveraging the Cheeger inequality, we propose the simplicial spectral clustering algorithm. We report results from numerical experiments on synthetic and real-world network data to demonstrate the efficacy of the proposed approach.

[{'version': 'v1', 'created': 'Tue, 14 Mar 2023 06:11:57 GMT'}]

2023-03-15

[['Reddy', 'Thummaluru Siddartha', ''], ['Chepuri', 'Sundeep Prabhakar', ''], ['Borgnat', 'Pierre', '']]

2102.06164

Roberto Vega

Roberto Vega, Pouneh Gorji, Zichen Zhang, Xuebin Qin, Abhilash Rakkunedeth Hareendranathan, Jeevesh Kapur, Jacob L. Jaremko, Russell Greiner

Sample Efficient Learning of Image-Based Diagnostic Classifiers Using Probabilistic Labels

To appear in the Proceedings of the 24 th International Conference on Artificial Intelligence and Statistics (AISTATS) 2021, San Diego,California, USA. PMLR: Volume 130

cs.CV cs.LG

http://creativecommons.org/licenses/by/4.0/

Deep learning approaches often require huge datasets to achieve good generalization. This complicates its use in tasks like image-based medical diagnosis, where the small training datasets are usually insufficient to learn appropriate data representations. For such sensitive tasks it is also important to provide the confidence in the predictions. Here, we propose a way to learn and use probabilistic labels to train accurate and calibrated deep networks from relatively small datasets. We observe gains of up to 22% in the accuracy of models trained with these labels, as compared with traditional approaches, in three classification tasks: diagnosis of hip dysplasia, fatty liver, and glaucoma. The outputs of models trained with probabilistic labels are calibrated, allowing the interpretation of its predictions as proper probabilities. We anticipate this approach will apply to other tasks where few training instances are available and expert knowledge can be encoded as probabilities.

[{'version': 'v1', 'created': 'Thu, 11 Feb 2021 18:13:56 GMT'}]

2021-02-12

[['Vega', 'Roberto', ''], ['Gorji', 'Pouneh', ''], ['Zhang', 'Zichen', ''], ['Qin', 'Xuebin', ''], ['Hareendranathan', 'Abhilash Rakkunedeth', ''], ['Kapur', 'Jeevesh', ''], ['Jaremko', 'Jacob L.', ''], ['Greiner', 'Russell', '']]

2203.15063

Daniel Claudino

Daniel Claudino

The Basics of Quantum Computing for Chemists

Added email address in v2

quant-ph physics.chem-ph

http://creativecommons.org/licenses/by/4.0/

The rapid and successful strides in quantum chemistry in the past decades can be largely credited to a conspicuous synergy between theoretical and computational advancements. However, the architectural computer archetype that enabled such a progress is approaching a state of more stagnant development. One of the most promising technological avenues for the continuing progress of quantum chemistry is the emerging quantum computing paradigm. This revolutionary proposal comes with several challenges, which span a wide array of disciplines. In chemistry, it implies, among other things, a need to reformulate some of its long established cornerstones in order to adjust to the operational demands and constraints of quantum computers. Due to its relatively recent emergence, much of quantum computing may still seem fairly nebulous and largely unknown to most chemists. It is in this context that here we review and illustrate the basic aspects of quantum information and their relation to quantum computing insofar as enabling simulations of quantum chemistry. We consider some of the most relevant developments in light of these aspects and discuss the current landscape when of relevance to quantum chemical simulations in quantum computers.

[{'version': 'v1', 'created': 'Mon, 28 Mar 2022 20:10:00 GMT'}, {'version': 'v2', 'created': 'Wed, 30 Mar 2022 13:20:28 GMT'}]

2022-03-31

[['Claudino', 'Daniel', '']]

1902.06382

Chengcheng Li

Chengcheng Li, Zi Wang, Xiangyang Wang, Hairong Qi

Single-shot Channel Pruning Based on Alternating Direction Method of Multipliers

Submitted to ICIP 2019

cs.CV

http://creativecommons.org/licenses/by/4.0/

Channel pruning has been identified as an effective approach to constructing efficient network structures. Its typical pipeline requires iterative pruning and fine-tuning. In this work, we propose a novel single-shot channel pruning approach based on alternating direction methods of multipliers (ADMM), which can eliminate the need for complex iterative pruning and fine-tuning procedure and achieve a target compression ratio with only one run of pruning and fine-tuning. To the best of our knowledge, this is the first study of single-shot channel pruning. The proposed method introduces filter-level sparsity during training and can achieve competitive performance with a simple heuristic pruning criterion (L1-norm). Extensive evaluations have been conducted with various widely-used benchmark architectures and image datasets for object classification purpose. The experimental results on classification accuracy show that the proposed method can outperform state-of-the-art network pruning works under various scenarios.

[{'version': 'v1', 'created': 'Mon, 18 Feb 2019 03:07:59 GMT'}]

2019-02-19

[['Li', 'Chengcheng', ''], ['Wang', 'Zi', ''], ['Wang', 'Xiangyang', ''], ['Qi', 'Hairong', '']]

1809.02535

Geevarghese George

G. George, I. Kriuchevskyi, H. Meyer, J. Baschnagel, J. P. Wittmer

Shear-stress fluctuations in free-standing polymer films

15 pages, 14 figures, accepted to Phys. Rev. E (Nov 2018) https://journals.aps.org/pre/accepted/b9077R77J041fb19b5843389c816aa554b5d02468

Phys. Rev. E 98, 062502 (2018)

10.1103/PhysRevE.98.062502

cond-mat.soft cond-mat.mtrl-sci cond-mat.stat-mech physics.comp-ph

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Using molecular dynamics simulation of a polymer glass model we investigate free-standing polymer films focusing on the in-plane shear modulus $\mu$ and the corresponding shear-stress relaxation modulus $G(t)$ as functions of temperature $T$, film thickness $H$ (tuned by means of the lateral box size $L$) and sampling time $\Delta t$. Various observables are seen to vary linearly with $1/H$ demonstrating thus the (to leading order) linear superposition of bulk and surface properties. In agreement with recent studies on three-dimensional polymer glass-formers, $\mu$ and $G(t)$ are found to decrease continuously with $T$. A jump-singularity is not observed. Confirming the time-translational invariance of our systems, the $\Delta t$-dependence of $\mu$ is traced back to $G(t)$.

[{'version': 'v1', 'created': 'Fri, 7 Sep 2018 15:22:31 GMT'}, {'version': 'v2', 'created': 'Wed, 28 Nov 2018 09:30:10 GMT'}]

2018-12-12

[['George', 'G.', ''], ['Kriuchevskyi', 'I.', ''], ['Meyer', 'H.', ''], ['Baschnagel', 'J.', ''], ['Wittmer', 'J. P.', '']]

2112.01830

Longbing Cao

Longbing Cao and Chengzhang Zhu

Table2Vec: Automated Universal Representation Learning to Encode All-round Data DNA for Benchmarkable and Explainable Enterprise Data Science

24 pages, 16 figures, 1 table

cs.LG cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Enterprise data typically involves multiple heterogeneous data sources and external data that respectively record business activities, transactions, customer demographics, status, behaviors, interactions and communications with the enterprise, and the consumption and feedback of its products, services, production, marketing, operations, and management, etc. A critical challenge in enterprise data science is to enable an effective whole-of-enterprise data understanding and data-driven discovery and decision-making on all-round enterprise DNA. We introduce a neural encoder Table2Vec for automated universal representation learning of entities such as customers from all-round enterprise DNA with automated data characteristics analysis and data quality augmentation. The learned universal representations serve as representative and benchmarkable enterprise data genomes and can be used for enterprise-wide and domain-specific learning tasks. Table2Vec integrates automated universal representation learning on low-quality enterprise data and downstream learning tasks. We illustrate Table2Vec in characterizing all-round customer data DNA in an enterprise on complex heterogeneous multi-relational big tables to build universal customer vector representations. The learned universal representation of each customer is all-round, representative and benchmarkable to support both enterprise-wide and domain-specific learning goals and tasks in enterprise data science. Table2Vec significantly outperforms the existing shallow, boosting and deep learning methods typically used for enterprise analytics. We further discuss the research opportunities, directions and applications of automated universal enterprise representation and learning and the learned enterprise data DNA for automated, all-purpose, whole-of-enterprise and ethical machine learning and data science.

[{'version': 'v1', 'created': 'Fri, 3 Dec 2021 10:39:25 GMT'}]

2021-12-06

[['Cao', 'Longbing', ''], ['Zhu', 'Chengzhang', '']]

1905.01391

Jonah Casebeer

Jonah Casebeer, Michael Colomb, Paris Smaragdis

Deep Tensor Factorization for Spatially-Aware Scene Decomposition

5 pages, 5 figures, accepted to WASPAA 2019

cs.SD cs.LG eess.AS stat.ML

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We propose a completely unsupervised method to understand audio scenes observed with random microphone arrangements by decomposing the scene into its constituent sources and their relative presence in each microphone. To this end, we formulate a neural network architecture that can be interpreted as a nonnegative tensor factorization of a multi-channel audio recording. By clustering on the learned network parameters corresponding to channel content, we can learn sources' individual spectral dictionaries and their activation patterns over time. Our method allows us to leverage deep learning advances like end-to-end training, while also allowing stochastic minibatch training so that we can feasibly decompose realistic audio scenes that are intractable to decompose using standard methods. This neural network architecture is easily extensible to other kinds of tensor factorizations.

[{'version': 'v1', 'created': 'Fri, 3 May 2019 23:58:56 GMT'}, {'version': 'v2', 'created': 'Thu, 26 Sep 2019 23:39:19 GMT'}]

2019-09-30

[['Casebeer', 'Jonah', ''], ['Colomb', 'Michael', ''], ['Smaragdis', 'Paris', '']]

0909.0599

R Doomun

Md. Rabiul Islam, Md. Fayzur Rahman

Codebook Design Method for Noise Robust Speaker Identification based on Genetic Algorithm

5 Pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS 2009, ISSN 1947 5500, Impact factor 0.423,http://sites.google.com/site/ijcsis/

International Journal of Computer Science and Information Security, IJCSIS, Vol. 4, No. 1 & 2, August 2009, USA

ISSn 1947 5500

cs.SD cs.NE

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

In this paper, a novel method of designing a codebook for noise robust speaker identification purpose utilizing Genetic Algorithm has been proposed. Wiener filter has been used to remove the background noises from the source speech utterances. Speech features have been extracted using standard speech parameterization method such as LPC, LPCC, RCC, MFCC, (delta)MFCC and (delta)(delta) MFCC. For each of these techniques, the performance of the proposed system has been compared. In this codebook design method, Genetic Algorithm has the capability of getting global optimal result and hence improves the quality of the codebook. Comparing with the NOIZEOUS speech database, the experimental result shows that 79.62 percent accuracy has been achieved.

[{'version': 'v1', 'created': 'Thu, 3 Sep 2009 08:49:54 GMT'}]

2009-09-04

[['Islam', 'Md. Rabiul', ''], ['Rahman', 'Md. Fayzur', '']]

2302.05312

Carol Scott

Carol F. Scott, Gabriela Marcu, Riana Elyse Anderson, Mark W. Newman, and Sarita Schoenebeck

Trauma-Informed Social Media: Towards Solutions for Reducing and Healing Online Harm

20 pages, 2 figures. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record will be published in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

10.1145/3544548.3581512

cs.HC

http://creativecommons.org/licenses/by-nc-nd/4.0/

Social media platforms exacerbate trauma, and many users experience various forms of trauma unique to them (e.g., doxxing and swatting). Trauma is the psychological and physical response to experiencing a deeply disturbing event. Platforms' failures to address trauma threaten users' well-being globally, especially amongst minoritized groups. Platform policies also expose moderators and designers to trauma through content they must engage with as part of their jobs (e.g., child sexual abuse). We consider how a trauma-informed approach might help address or decrease the likelihood of (re)experiencing trauma online. A trauma-informed approach to social media recognizes that everyone likely has a trauma history and that trauma is experienced at the individual, secondary, collective, and cultural levels. This paper proceeds by detailing trauma and its impacts. We then describe how the six trauma-informed principles can be applied to social media design, content moderation, and companies. We conclude by offering recommendations that balance platform responsibility and accountability with well-being and healing for all.

[{'version': 'v1', 'created': 'Fri, 10 Feb 2023 15:17:48 GMT'}]

2023-02-13

[['Scott', 'Carol F.', ''], ['Marcu', 'Gabriela', ''], ['Anderson', 'Riana Elyse', ''], ['Newman', 'Mark W.', ''], ['Schoenebeck', 'Sarita', '']]

2004.13950

Zhibin Gao

Jia Song, Luyu Wang, Liang Zhang, Kaiqi Wu, Wenheng Wu, and Zhibin Gao

Structures and Properties of $\beta$-Titanium Doping Trace Transition Metal Elements: a Density Functional Theory Study

19 pages, 4 figures, 3 tables

cond-mat.mtrl-sci cond-mat.mes-hall physics.comp-ph

http://creativecommons.org/licenses/by/4.0/

We systematically calculate the structure, formation enthalpy, formation free energy, elastic constants and electronic structure of Ti$_{0.98}$X$_{0.02}$ system by density functional theory (DFT) simulations to explore the effect of transition metal X (X=Ag, Cd, Co, Cr, Cu, Fe, Mn, Mo, Nb, Ni, Pd, Rh, Ru, Tc, and Zn) on the stability mechanism of $\beta$-titanium. Based on our calculations, the results of formation enthalpy and free energy show that adding trace X is beneficial to the thermodynamic stability of $\beta$-titanium. This behavior is well explained by the density of state (DOS). However, the tetragonal shear moduli of Ti$_{0.98}$X$_{0.02}$ systems are negative, indicating that $\beta$-titanium doping with a low concentration of X is still elastically unstable at 0 K. Therefore, we theoretically explain that $\beta$-titanium doping with trace transition metal X is unstable in the ground state.

[{'version': 'v1', 'created': 'Wed, 29 Apr 2020 04:04:20 GMT'}]

2020-04-30

[['Song', 'Jia', ''], ['Wang', 'Luyu', ''], ['Zhang', 'Liang', ''], ['Wu', 'Kaiqi', ''], ['Wu', 'Wenheng', ''], ['Gao', 'Zhibin', '']]

1408.3742

Timothy H. Boyer

Timothy H. Boyer

Classical Interaction of a Magnet and a Point Charge: The Shockley-James Paradox

25 pages

Phyical Review E 91, 013201(11) (2015)

10.1103/PhysRevE.91.013201

physics.class-ph quant-ph

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

It is pointed out that Coleman and Van Vleck make a major blunder in their discussion of the Shockly-James paradox by designating relativistic hidden mechanical momentum as the basis for resolution of the paradox. This blunder has had a wide influence in the current physics literature, including erroneous work on the Shockley-James paradox, on Mansuripur's paradox, on the motion of a magnetic moment, on the Aharonov-Bohm phase shift, and on the Aharonov-Casher phase shift. Although hidden mechanical momentum is indeed dominant for non-interacting particles moving in a closed orbit under the influence of an external electric field, the attention directed toward hidden mechanical momentum represents a fundamental misunderstanding of the classical electromagnetic interaction between a multiparticle magnet and an external point charge. In the interacting multiparticle situation, the external charge induces an electrostatic polarization of the magnet which leads to an internal electromagnetic momentum in the magnet where both the electric and magnetic fields for the momentum are contributed by the magnet particles. This internal electromagnetic momentum for the interacting multiparticle situation is equal in magnitude and opposite in direction compared to the familiar external electromagnetic momentum where the electric field is contributed by the external charged particle and the magnetic field is that due to the magnet. In the present article, the momentum balance of the Shockley-James situation for a system of a magnet and a point charge is calculated in detail for a magnet model consisting of two interacting point charges which are constrained to move in a circular orbit on a frictionless ring with a compensating negative charge at the center.

[{'version': 'v1', 'created': 'Sat, 16 Aug 2014 15:29:32 GMT'}]

2015-04-07

[['Boyer', 'Timothy H.', '']]

2103.14581

Yazhou Yao

Yazhou Yao, Tao Chen, Guosen Xie, Chuanyi Zhang, Fumin Shen, Qi Wu, Zhenmin Tang, and Jian Zhang

Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation

accepted by IEEE Conference on Computer Vision and Pattern Recognition, 2021

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Semantic segmentation aims to classify every pixel of an input image. Considering the difficulty of acquiring dense labels, researchers have recently been resorting to weak labels to alleviate the annotation burden of segmentation. However, existing works mainly concentrate on expanding the seed of pseudo labels within the image's salient region. In this work, we propose a non-salient region object mining approach for weakly supervised semantic segmentation. We introduce a graph-based global reasoning unit to strengthen the classification network's ability to capture global relations among disjoint and distant regions. This helps the network activate the object features outside the salient area. To further mine the non-salient region objects, we propose to exert the segmentation network's self-correction ability. Specifically, a potential object mining module is proposed to reduce the false-negative rate in pseudo labels. Moreover, we propose a non-salient region masking module for complex images to generate masked pseudo labels. Our non-salient region masking module helps further discover the objects in the non-salient region. Extensive experiments on the PASCAL VOC dataset demonstrate state-of-the-art results compared to current methods.

[{'version': 'v1', 'created': 'Fri, 26 Mar 2021 16:44:03 GMT'}]

2021-03-29

[['Yao', 'Yazhou', ''], ['Chen', 'Tao', ''], ['Xie', 'Guosen', ''], ['Zhang', 'Chuanyi', ''], ['Shen', 'Fumin', ''], ['Wu', 'Qi', ''], ['Tang', 'Zhenmin', ''], ['Zhang', 'Jian', '']]

physics/0204054

Alexei Buzulutskov

A. Bondar, A. Buzulutskov, L. Shekhtman, V. Snopkov, A. Vasiljev

Triple GEM operation in compressed He and Kr

9 pages, 8 figures. To be published in Nucl. Instr. and Meth. A

Nucl.Instrum.Meth. A493 (2002) 8-15

10.1016/S0168-9002(02)01414-6

Preprint Budker INP 2002-15

physics.ins-det

We study the performance of the triple GEM (Gas Electron Multiplier) detector in pure noble gases He and Kr at high pressures, varying from 1 to 15 atm. The operation in these gases is compared to that recently studied in Ne, Ar and Xe. It turned out that light noble gases, He and Ne, have superior performance: the highest gain, approaching 10^5, and an unusual gain dependence on pressure. In particular, the maximum gain in He and Ne does not decrease with pressure, in contrast to Ar, Kr and Xe. These results are of high relevance for understanding basic mechanisms of electron avalanching in noble gases and for applications in cryogenic particle detectors, X-ray imaging and neutron detectors.

[{'version': 'v1', 'created': 'Thu, 18 Apr 2002 11:19:02 GMT'}, {'version': 'v2', 'created': 'Mon, 5 Aug 2002 09:50:24 GMT'}]

2009-11-07

[['Bondar', 'A.', ''], ['Buzulutskov', 'A.', ''], ['Shekhtman', 'L.', ''], ['Snopkov', 'V.', ''], ['Vasiljev', 'A.', '']]

2301.03783

John Evans

Ryan M. Aronson and John A. Evans

Divergence-Conforming Isogeometric Collocation Methods for the Incompressible Navier-Stokes Equations

math.NA cs.NA

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We develop two isogeometric divergence-conforming collocation schemes for incompressible flow. The first is based on the standard, velocity-pressure formulation of the Navier-Stokes equations, while the second is based on the rotational form and includes the vorticity as an unknown in addition to the velocity and pressure. We describe the process of discretizing each unknown using B-splines that conform to a discrete de Rham complex and collocating each governing equation at the Greville abcissae corresponding to each discrete space. Results on complex domains are obtained by mapping the equations back to a parametric domain using structure-preserving transformations. Numerical results show the promise of the method, including accelerated convergence rates of the three field, vorticity-velocity-pressure scheme when compared to the two field, velocity-pressure scheme.

[{'version': 'v1', 'created': 'Tue, 10 Jan 2023 04:46:14 GMT'}]

2023-01-11

[['Aronson', 'Ryan M.', ''], ['Evans', 'John A.', '']]

2010.03172

Jiawei He

Joseph Marino, Lei Chen, Jiawei He, Stephan Mandt

Improving Sequential Latent Variable Models with Autoregressive Flows

Published at Machine Learning Journal

Mach Learn (2021)

10.1007/s10994-021-06092-6

cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We propose an approach for improving sequence modeling based on autoregressive normalizing flows. Each autoregressive transform, acting across time, serves as a moving frame of reference, removing temporal correlations, and simplifying the modeling of higher-level dynamics. This technique provides a simple, general-purpose method for improving sequence modeling, with connections to existing and classical techniques. We demonstrate the proposed approach both with standalone flow-based models and as a component within sequential latent variable models. Results are presented on three benchmark video datasets, where autoregressive flow-based dynamics improve log-likelihood performance over baseline models. Finally, we illustrate the decorrelation and improved generalization properties of using flow-based dynamics.

[{'version': 'v1', 'created': 'Wed, 7 Oct 2020 05:14:37 GMT'}, {'version': 'v2', 'created': 'Tue, 8 Mar 2022 05:32:41 GMT'}]

2022-03-09

[['Marino', 'Joseph', ''], ['Chen', 'Lei', ''], ['He', 'Jiawei', ''], ['Mandt', 'Stephan', '']]

2010.00159

Boris Blinov

Liudmila A. Zhukas, Maverick J. Millican, Peter Svihra, Andrei Nomerotski, Boris B. Blinov

Direct Observation of Ion Micromotion in a Linear Paul Trap

6 pages, 5 figues

Phys. Rev. A 103, 023105 (2021)

10.1103/PhysRevA.103.023105

physics.atom-ph

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

In this paper, direct observation of micromotion for multiple ions in a laser-cooled trapped ion crystal is discussed along with a novel measurement technique for micromotion amplitude. Micromotion is directly observed using a time-resolving, single-photon sensitive camera that provides both fluorescence and position data for each ion on the nanosecond time scale. Micromotion amplitude and phase for each ion in the crystal are measured, allowing this method to be sensitive to tilts and shifts of the ion chain from the null of the radiofrequency quadrupole potential in the linear trap. Spatial resolution makes this micromotion detection technique suitable for complex ion configurations, including two-dimensional geometries. It does not require any additional equipment or laser beams, and the modulation of the cooling lasers or trap voltages is not necessary for detection, as it is in other methods.

[{'version': 'v1', 'created': 'Thu, 1 Oct 2020 01:01:26 GMT'}, {'version': 'v2', 'created': 'Wed, 21 Apr 2021 17:40:16 GMT'}]

2021-04-22

[['Zhukas', 'Liudmila A.', ''], ['Millican', 'Maverick J.', ''], ['Svihra', 'Peter', ''], ['Nomerotski', 'Andrei', ''], ['Blinov', 'Boris B.', '']]

2302.00155

Abel C. H. Chen

Abel C. H. Chen

How to Prove the Optimized Values of Hyperparameters for Particle Swarm Optimization?

in Chinese language

cs.NE cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

In recent years, several swarm intelligence optimization algorithms have been proposed to be applied for solving a variety of optimization problems. However, the values of several hyperparameters should be determined. For instance, although Particle Swarm Optimization (PSO) has been applied for several applications with higher optimization performance, the weights of inertial velocity, the particle's best known position and the swarm's best known position should be determined. Therefore, this study proposes an analytic framework to analyze the optimized average-fitness-function-value (AFFV) based on mathematical models for a variety of fitness functions. Furthermore, the optimized hyperparameter values could be determined with a lower AFFV for minimum cases. Experimental results show that the hyperparameter values from the proposed method can obtain higher efficiency convergences and lower AFFVs.

[{'version': 'v1', 'created': 'Wed, 1 Feb 2023 00:33:35 GMT'}]

2023-02-02

[['Chen', 'Abel C. H.', '']]

2104.13876

Li Yang

Li Yang, Yan Xu, Shaoru Wang, Chunfeng Yuan, Ziqi Zhang, Bing Li, Weiming Hu

PDNet: Toward Better One-Stage Object Detection With Prediction Decoupling

IEEE Transactions on Image Processing, 2022

10.1109/TIP.2022.3193223

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Recent one-stage object detectors follow a per-pixel prediction approach that predicts both the object category scores and boundary positions from every single grid location. However, the most suitable positions for inferring different targets, i.e., the object category and boundaries, are generally different. Predicting all these targets from the same grid location thus may lead to sub-optimal results. In this paper, we analyze the suitable inference positions for object category and boundaries, and propose a prediction-target-decoupled detector named PDNet to establish a more flexible detection paradigm. Our PDNet with the prediction decoupling mechanism encodes different targets separately in different locations. A learnable prediction collection module is devised with two sets of dynamic points, i.e., dynamic boundary points and semantic points, to collect and aggregate the predictions from the favorable regions for localization and classification. We adopt a two-step strategy to learn these dynamic point positions, where the prior positions are estimated for different targets first, and the network further predicts residual offsets to the positions with better perceptions of the object properties. Extensive experiments on the MS COCO benchmark demonstrate the effectiveness and efficiency of our method. With a single ResNeXt-64x4d-101-DCN as the backbone, our detector achieves 50.1 AP with single-scale testing, which outperforms the state-of-the-art methods by an appreciable margin under the same experimental settings.Moreover, our detector is highly efficient as a one-stage framework. Our code is public at https://github.com/yangli18/PDNet.

[{'version': 'v1', 'created': 'Wed, 28 Apr 2021 16:48:04 GMT'}, {'version': 'v2', 'created': 'Thu, 1 Dec 2022 13:00:49 GMT'}]

2022-12-02

[['Yang', 'Li', ''], ['Xu', 'Yan', ''], ['Wang', 'Shaoru', ''], ['Yuan', 'Chunfeng', ''], ['Zhang', 'Ziqi', ''], ['Li', 'Bing', ''], ['Hu', 'Weiming', '']]

1910.04848

Xiao-Yue Gong

James B. Orlin, Xiao-Yue Gong

A Fast Max Flow Algorithm

35 pages

cs.DS cs.CC

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

In 2013, Orlin proved that the max flow problem could be solved in $O(nm)$ time. His algorithm ran in $O(nm + m^{1.94})$ time, which was the fastest for graphs with fewer than $n^{1.06}$ arcs. If the graph was not sufficiently sparse, the fastest running time was an algorithm due to King, Rao, and Tarjan. We describe a new variant of the excess scaling algorithm for the max flow problem whose running time strictly dominates the running time of the algorithm by King et al. Moreover, for graphs in which $m = O(n \log n)$, the running time of our algorithm dominates that of King et al. by a factor of $O(\log\log n)$.

[{'version': 'v1', 'created': 'Thu, 10 Oct 2019 20:58:12 GMT'}]

2019-10-14

[['Orlin', 'James B.', ''], ['Gong', 'Xiao-Yue', '']]

physics/9712022

Rui Vilela Mendes

V.I. Man'ko and R.Vilela Mendes (Grupo de Fisica-Matematica, Complexo Interdisciplinar, Univ. Lisboa, Lisboa, Portugal)

Non-commutative time-frequency tomography

13 pages Latex, 10 ps-figures e-mail: [email protected]

Physics Letters A 263 (1999) 53 - 61

physics.data-an

The characterization of non-stationary signals requires joint time and frequency information. However, time (t) and frequency (omega) being non-commuting variables there cannot be a joint probability density in the (t,omega) plane and the time-frequency distributions, that have been proposed, have difficult interpretation problems arising from negative or complex values and spurious components. As an alternative we propose to obtain time-frequency information by looking at the marginal distributions along rotated directions in the (t,omega) plane. The rigorous probability interpretation of the marginal distributions avoids all interpretation ambiguities. Applications to signal analysis and signal detection are discussed as well as an extension of the method to other pairs of non-commuting variables.

[{'version': 'v1', 'created': 'Fri, 12 Dec 1997 17:18:49 GMT'}]

2007-05-23

[["Man'ko", 'V. I.', '', 'Grupo de Fisica-Matematica, Complexo\n Interdisciplinar, Univ. Lisboa, Lisboa, Portugal'], ['Mendes', 'R. Vilela', '', 'Grupo de Fisica-Matematica, Complexo\n Interdisciplinar, Univ. Lisboa, Lisboa, Portugal']]

1611.01794

Katanya Kuntz

Katanya B. Kuntz, Trevor A. Wheatley, Hongbin Song, James G. Webb, Mohamed A. Mabrok, Elanor H. Huntington, and Hidehiro Yonezawa

Ultra-wide frequency response measurement of an optical system with a DC photo-detector

10 pages, 7 figures

10.1364/OE.25.000573

physics.ins-det physics.optics quant-ph

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Precise knowledge of an optical device's frequency response is crucial for it to be useful in most applications. Traditional methods for determining the frequency response of an optical system (e.g. optical cavity or waveguide modulator) usually rely on calibrated broadband photo-detectors or complicated RF mixdown operations. As the bandwidths of these devices continue to increase, there is a growing need for a characterization method that does not have bandwidth limitations, or require a previously calibrated device. We demonstrate a new calibration technique on an optical system (consisting of an optical cavity and a high-speed waveguide modulator) that is free from limitations imposed by detector bandwidth, and does not require a calibrated photo-detector or modulator. We use a low-frequency (DC) photo-detector to monitor the cavity's optical response as a function of modulation frequency, which is also used to determine the modulator's frequency response. Knowledge of the frequency-dependent modulation depth allows us to more precisely determine the cavity's characteristics (free spectral range and linewidth). The precision and repeatability of our technique is demonstrated by measuring the different resonant frequencies of orthogonal polarization cavity modes caused by the presence of a non-linear crystal. Once the modulator has been characterized using this simple method, the frequency response of any passive optical element can be determined.

[{'version': 'v1', 'created': 'Sun, 6 Nov 2016 15:22:07 GMT'}]

2017-08-02

[['Kuntz', 'Katanya B.', ''], ['Wheatley', 'Trevor A.', ''], ['Song', 'Hongbin', ''], ['Webb', 'James G.', ''], ['Mabrok', 'Mohamed A.', ''], ['Huntington', 'Elanor H.', ''], ['Yonezawa', 'Hidehiro', '']]

1210.6044

Andrew Lucas

Andrew Lucas, Ching Hua Lee

Multistable binary decision making on networks

v3: mostly published version; v2: fixed minor textual errors; 21 pages, 8 figures, 1 table

Physical Review E87 (2013) 032806

10.1103/PhysRevE.87.032806

physics.soc-ph cond-mat.stat-mech cs.SI stat.AP

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We propose a simple model for a binary decision making process on a graph, motivated by modeling social decision making with cooperative individuals. The model is similar to a random field Ising model or fiber bundle model, but with key differences on heterogeneous networks. For many types of disorder and interactions between the nodes, we predict discontinuous phase transitions with mean field theory which are largely independent of network structure. We show how these phase transitions can also be understood by studying microscopic avalanches, and describe how network structure enhances fluctuations in the distribution of avalanches. We suggest theoretically the existence of a "glassy" spectrum of equilibria associated with a typical phase, even on infinite graphs, so long as the first moment of the degree distribution is finite. This behavior implies that the model is robust against noise below a certain scale, and also that phase transitions can switch from discontinuous to continuous on networks with too few edges. Numerical simulations suggest that our theory is accurate.

[{'version': 'v1', 'created': 'Mon, 22 Oct 2012 20:00:12 GMT'}, {'version': 'v2', 'created': 'Sun, 9 Dec 2012 19:54:04 GMT'}, {'version': 'v3', 'created': 'Fri, 15 Mar 2013 03:56:25 GMT'}]

2017-01-20

[['Lucas', 'Andrew', ''], ['Lee', 'Ching Hua', '']]

2003.11330

Xiwei Liu

Jingzhu Wang, Xiwei Liu

Global $\mu$-stability and finite-time control of octonion-valued neural networks with unbounded delays

math.DS cs.SY eess.SY

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Octonion-valued neural networks (OVNNs) are a type of neural networks for which the states and weights are octonions. In this paper, the global $\mu$-stability and finite-time stability problems for octonion-valued neural networks are considered under unbounded and asynchronous time-varying delays. To avoid the non-communicative and non-associative multiplication feature of the octonions, we firstly decompose the OVNNs into eight real-valued neural networks (RVNNs) equivalently. Through the use of generalized norm and the Cauchy convergence principle, we obtain the sufficient criteria which assure the existence, uniqueness of the equilibrium point and global $\mu$-stability of OVNNs. By adding controllers, the criteria to ensure the finite-time stability for OVNNs are presented by dividing the analysis of finite-time stability process into two phases. Furthermore, we also prove the adaptive finite time stability theory of above networks. At last, the simulation results of specified examples is given to substantiate the effectiveness and correctness of the theoretical results.

[{'version': 'v1', 'created': 'Wed, 25 Mar 2020 11:21:22 GMT'}]

2020-03-26

[['Wang', 'Jingzhu', ''], ['Liu', 'Xiwei', '']]

2204.14001

Sanghyun Kim

Sanghyun Kim, Jungyun Byun, Kwansik Park

Machine Learning-Based GPS Multipath Detection Method Using Dual Antennas

Submitted to ASCC 2022

10.23919/ASCC56756.2022.9828175

cs.NI cs.LG eess.SP

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

In urban areas, global navigation satellite system (GNSS) signals are often reflected or blocked by buildings, thus resulting in large positioning errors. In this study, we proposed a machine learning approach for global positioning system (GPS) multipath detection that uses dual antennas. A machine learning model that could classify GPS signal reception conditions was trained with several GPS measurements selected as suggested features. We applied five features for machine learning, including a feature obtained from the dual antennas, and evaluated the classification performance of the model, after applying four machine learning algorithms: gradient boosting decision tree (GBDT), random forest, decision tree, and K-nearest neighbor (KNN). It was found that a classification accuracy of 82%-96% was achieved when the test data set was collected at the same locations as those of the training data set. However, when the test data set was collected at locations different from those of the training data, a classification accuracy of 44%-77% was obtained.

[{'version': 'v1', 'created': 'Wed, 6 Apr 2022 04:19:57 GMT'}]

2022-08-10

[['Kim', 'Sanghyun', ''], ['Byun', 'Jungyun', ''], ['Park', 'Kwansik', '']]

1603.01612

Seng Fatt Liew

Brandon Redding, Seng Fatt Liew, Yaron Bromberg, Raktim Sarma, Hui Cao

Evanescently coupled multimode spiral spectrometer

10 pages, 7 figures, in submission

Optica Vol. 3, pp. 956-962 (2016)

10.1364/OPTICA.3.000956

physics.ins-det physics.optics

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We designed a high-resolution compact spectrometer based on an evanescently-coupled multimode spiral waveguide. Interference between the modes in the waveguide forms a wavelength-dependent speckle pattern which is used as a fingerprint to identify the input wavelength after calibration. Evanescent coupling between neighboring arms of the spiral results in a non-resonant broad-band enhancement of the spectral resolution. Experimentally, we demonstrated a resolution of 0.01 nm at a wavelength of 1520 nm using a 250 {\mu}m radius spiral structure. Spectra containing 40 independent spectral channels are recovered simultaneously and the operation bandwidth is significantly increased by applying compressive sensing to sparse spectra reconstruction.

[{'version': 'v1', 'created': 'Fri, 4 Mar 2016 19:33:35 GMT'}, {'version': 'v2', 'created': 'Thu, 30 Jun 2016 22:03:14 GMT'}]

2016-08-26

[['Redding', 'Brandon', ''], ['Liew', 'Seng Fatt', ''], ['Bromberg', 'Yaron', ''], ['Sarma', 'Raktim', ''], ['Cao', 'Hui', '']]

1910.09729

Katharina Kann

Katharina Kann

Grammatical Gender, Neo-Whorfianism, and Word Embeddings: A Data-Driven Approach to Linguistic Relativity

cs.CL

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The relation between language and thought has occupied linguists for at least a century. Neo-Whorfianism, a weak version of the controversial Sapir-Whorf hypothesis, holds that our thoughts are subtly influenced by the grammatical structures of our native language. One area of investigation in this vein focuses on how the grammatical gender of nouns affects the way we perceive the corresponding objects. For instance, does the fact that key is masculine in German (der Schl\"ussel), but feminine in Spanish (la llave) change the speakers' views of those objects? Psycholinguistic evidence presented by Boroditsky et al. (2003, {\S}4) suggested the answer might be yes: When asked to produce adjectives that best described a key, German and Spanish speakers named more stereotypically masculine and feminine ones, respectively. However, recent attempts to replicate those experiments have failed (Mickan et al., 2014). In this work, we offer a computational analogue of Boroditsky et al. (2003, {\S}4)'s experimental design on 9 languages, finding evidence against neo-Whorfianism.

[{'version': 'v1', 'created': 'Tue, 22 Oct 2019 02:16:47 GMT'}]

2019-10-23

[['Kann', 'Katharina', '']]

1606.00212

Xi Chen

Xi Chen and Fazle Hussain and Zhen-Su She

Bulk flow scaling for turbulent channel and pipe flows

4 pages, 4 figures

10.1209/0295-5075/115/34001

physics.flu-dyn

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We report a theory deriving bulk flow scaling for canonical wall-bounded flows. The theory accounts for the symmetries of boundary geometry (flat plate channel versus circular pipe) by a variational calculation for a large-scale energy length, which characterizes its bulk flow scaling by a simple exponent, i.e. $m=4$ for channel and 5 for pipe. The predicted mean velocity shows excellent agreement with several dozen sets of quality empirical data for a wide range of the Reynolds number (Re), with a universal bulk flow constant $\kappa\approx0.45$. Predictions for dissipation and turbulent transport in the bulk flow are also given, awaiting data verification.

[{'version': 'v1', 'created': 'Wed, 1 Jun 2016 10:49:28 GMT'}]

2016-09-21

[['Chen', 'Xi', ''], ['Hussain', 'Fazle', ''], ['She', 'Zhen-Su', '']]

1801.05926

Hao Wang

Hao Wang, Mario Diaz, Flavio P. Calmon, Lalitha Sankar

The Utility Cost of Robust Privacy Guarantees

cs.IT cs.CR math.IT

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Consider a data publishing setting for a data set with public and private features. The objective of the publisher is to maximize the amount of information about the public features in a revealed data set, while keeping the information leaked about the private features bounded. The goal of this paper is to analyze the performance of privacy mechanisms that are constructed to match the distribution learned from the data set. Two distinct scenarios are considered: (i) mechanisms are designed to provide a privacy guarantee for the learned distribution; and (ii) mechanisms are designed to provide a privacy guarantee for every distribution in a given neighborhood of the learned distribution. For the first scenario, given any privacy mechanism, upper bounds on the difference between the privacy-utility guarantees for the learned and true distributions are presented. In the second scenario, upper bounds on the reduction in utility incurred by providing a uniform privacy guarantee are developed.

[{'version': 'v1', 'created': 'Thu, 18 Jan 2018 03:48:27 GMT'}, {'version': 'v2', 'created': 'Thu, 10 May 2018 00:12:16 GMT'}]

2018-05-11

[['Wang', 'Hao', ''], ['Diaz', 'Mario', ''], ['Calmon', 'Flavio P.', ''], ['Sankar', 'Lalitha', '']]

2301.08486

Nader Bshouty

Nader H. Bshouty

Superpolynomial Lower Bounds for Learning Monotone Classes

cs.DS

http://creativecommons.org/licenses/by/4.0/

Koch, Strassle, and Tan [SODA 2023], show that, under the randomized exponential time hypothesis, there is no distribution-free PAC-learning algorithm that runs in time $n^{\tilde O(\log\log s)}$ for the classes of $n$-variable size-$s$ DNF, size-$s$ Decision Tree, and $\log s$-Junta by DNF (that returns a DNF hypothesis). Assuming a natural conjecture on the hardness of set cover, they give the lower bound $n^{\Omega(\log s)}$. This matches the best known upper bound for $n$-variable size-$s$ Decision Tree, and $\log s$-Junta. In this paper, we give the same lower bounds for PAC-learning of $n$-variable size-$s$ Monotone DNF, size-$s$ Monotone Decision Tree, and Monotone $\log s$-Junta by~DNF. This solves the open problem proposed by Koch, Strassle, and Tan and subsumes the above results. The lower bound holds, even if the learner knows the distribution, can draw a sample according to the distribution in polynomial time, and can compute the target function on all the points of the support of the distribution in polynomial time.

[{'version': 'v1', 'created': 'Fri, 20 Jan 2023 09:31:46 GMT'}, {'version': 'v2', 'created': 'Mon, 30 Jan 2023 11:21:23 GMT'}]

2023-01-31

[['Bshouty', 'Nader H.', '']]

2301.03275

Samarth Vadia

Samarth Vadia, Johannes Scherzer, Kenji Watanabe, Takashi Taniguchi, Alexander H\"ogele

Magneto-optical chirality in a coherently coupled exciton-plasmon system

6 pages, 4 figures; 10 pages Supplementary Information

Nano Letters 2023

10.1021/acs.nanolett.2c04246

physics.optics cond-mat.mes-hall cond-mat.mtrl-sci

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Chirality is a fundamental asymmetry phenomenon, with chiral optical elements exhibiting asymmetric response in reflection or absorption of circularly polarized light. Recent realizations of such elements include nanoplasmonic systems with broken mirror symmetry and polarization-contrasting optical absorption known as circular dichroism. An alternative route to circular dichroism is provided by spin-valley polarized excitons in atomically thin semiconductors. In the presence of magnetic fields, they exhibit an imbalanced coupling to circularly polarized photons and thus circular dichroism. Here, we demonstrate that polarization-contrasting optical transitions associated with excitons in monolayer WSe$_2$ can be transferred to proximal plasmonic nanodisks by coherent coupling. The coupled exciton-plasmon system exhibits magneto-induced circular dichroism in a spectrally narrow window of Fano interference, which we model in a master equation framework. Our work motivates exciton-plasmon interfaces as building blocks of chiral metasurfaces for applications in information processing, non-linear optics and sensing.

[{'version': 'v1', 'created': 'Mon, 9 Jan 2023 11:19:34 GMT'}]

2023-01-10

[['Vadia', 'Samarth', ''], ['Scherzer', 'Johannes', ''], ['Watanabe', 'Kenji', ''], ['Taniguchi', 'Takashi', ''], ['Högele', 'Alexander', '']]

1803.03839

Kunihiro Wasa

Kunihiro Wasa and Takeaki Uno

Efficient Enumeration of Bipartite Subgraphs in Graphs

10.1007/978-3-319-94776-1_38

cs.DS

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Subgraph enumeration problems ask to output all subgraphs of an input graph that belongs to the specified graph class or satisfy the given constraint. These problems have been widely studied in theoretical computer science. As far, many efficient enumeration algorithms for the fundamental substructures such as spanning trees, cycles, and paths, have been developed. This paper addresses the enumeration problem of bipartite subgraphs. Even though bipartite graphs are quite fundamental and have numerous applications in both theory and application, its enumeration algorithms have not been intensively studied, to the best of our knowledge. We propose the first non-trivial algorithms for enumerating all bipartite subgraphs in a given graph. As the main results, we develop two efficient algorithms: the one enumerates all bipartite induced subgraphs of a graph with degeneracy $k$ in $O(k)$ time per solution. The other enumerates all bipartite subgraphs in $O(1)$ time per solution.

[{'version': 'v1', 'created': 'Sat, 10 Mar 2018 17:08:51 GMT'}]

2018-07-03

[['Wasa', 'Kunihiro', ''], ['Uno', 'Takeaki', '']]

1502.03118

Jo\~ao Marcos Salvi Sakamoto

Jo\~ao M. S. Sakamoto, Gefeson M. Pacheco, Cl\'audio Kitano, and Bernhard R. Tittmann

Geometrical parameter analysis of the high sensitivity fiber optic angular displacement sensor

10 pages, 12 figures

Appl. Opt. 53, 8436-8443 (2014)

10.1364/AO.53.008436

physics.optics

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

In this work, we present an analysis of the influence of the geometrical parameters on the sensitivity and linear range of the fiber optic angular displacement sensor, through computational simulations and experiments. The geometrical parameters analyzed were the lens focal length, the gap between fibers, the fibers cladding radii, the emitting fiber critical angle (or, equivalently, the emitting fiber numerical aperture), and the standoff distance (distance between the lens and the reflective surface). Besides, we analyzed the sensor sensitivity regarding any spurious linear displacement. The simulation and experimental results showed that the parameters which play the most important roles are the emitting fiber core radius, the lens focal length, and the light coupling efficiency, while the remaining parameters have little influence on sensor characteristics. This paper was published in Applied Optics and is made available as an electronic reprint with the permission of OSA. The paper can be found at the following URL on the OSA website: http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-53-36-8436. Systematic or multiple reproduction or distribution to multiple locations via electronic or other means is prohibited and is subject to penalties under law.

[{'version': 'v1', 'created': 'Tue, 10 Feb 2015 21:16:40 GMT'}]

2015-02-12

[['Sakamoto', 'João M. S.', ''], ['Pacheco', 'Gefeson M.', ''], ['Kitano', 'Cláudio', ''], ['Tittmann', 'Bernhard R.', '']]

2104.12800

Stanislav \v{Z}ivn\'y

Alex Brandts and Stanislav \v{Z}ivn\'y

Beyond PCSP (1-in-3,NAE)

Full version of an ICALP 2021 paper

Information and Computation 289, Part A, 104954 (2022)

10.1016/j.ic.2022.104954

cs.CC cs.DM

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The promise constraint satisfaction problem (PCSP) is a recently introduced vast generalisation of the constraint satisfaction problem (CSP) that captures approximability of satisfiable instances. A PCSP instance comes with two forms of each constraint: a strict one and a weak one. Given the promise that a solution exists using the strict constraints, the task is to find a solution using the weak constraints. While there are by now several dichotomy results for fragments of PCSPs, they all consider (in some way) symmetric PCSPs. 1-in-3-SAT and Not-All-Equal-3-SAT are classic examples of Boolean symmetric (non-promise) CSPs. While both problems are NP-hard, Brakensiek and Guruswami showed [SICOMP'21] that given a satisfiable instance of 1-in-3-SAT one can find a solution to the corresponding instance of (weaker) Not-All-Equal-3-SAT. In other words, the PCSP template (1-in-3,NAE) is tractable. We focus on non-symmetric PCSPs. In particular, we study PCSP templates obtained from the Boolean template (t-in-k,NAE) by either adding tuples to t-in-k or removing tuples from NAE. For the former, we classify all templates as either tractable or not solvable by the currently strongest known algorithm for PCSPs, the combined basic LP and affine IP relaxation of Brakensiek, Guruswami, Wrochna, and \v{Z}ivn\'y [SICOMP'20]. For the latter, we classify all templates as either tractable or NP-hard.

[{'version': 'v1', 'created': 'Mon, 26 Apr 2021 18:00:41 GMT'}, {'version': 'v2', 'created': 'Sat, 12 Feb 2022 13:27:46 GMT'}, {'version': 'v3', 'created': 'Sun, 28 Aug 2022 19:14:35 GMT'}]

2023-01-31

[['Brandts', 'Alex', ''], ['Živný', 'Stanislav', '']]

0712.2529

David A. Kessler

Isaac Freund and David A. Kessler

Singularities in Speckled Speckle

10.1364/OL.33.000479

physics.optics

Speckle patterns produced by random optical fields with two (or more) widely different correlation lengths exhibit speckle spots that are themselves highly speckled. Using computer simulations and analytic theory we present results for the point singularities of speckled speckle fields: optical vortices in scalar (one polarization component) fields; C points in vector (two polarization component) fields. In single correlation length fields both types of singularities tend to be more{}-or{}-less uniformly distributed. In contrast, the singularity structure of speckled speckle is anomalous: for some sets of source parameters vortices and C points tend to form widely separated giant clusters, for other parameter sets these singularities tend to form chains that surround large empty regions. The critical point statistics of speckled speckle is also anomalous. In scalar (vector) single correlation length fields phase (azimuthal) extrema are always outnumbered by vortices (C points). In contrast, in speckled speckle fields, phase extrema can outnumber vortices, and azimuthal extrema can outnumber C points, by factors that can easily exceed $10^{4}$ for experimentally realistic source parameters.

[{'version': 'v1', 'created': 'Sat, 15 Dec 2007 18:23:01 GMT'}]

2009-11-13

[['Freund', 'Isaac', ''], ['Kessler', 'David A.', '']]

1701.02578

Yanting Ma

Yanting Ma, Yue M. Lu, Dror Baron

Multiprocessor Approximate Message Passing with Column-Wise Partitioning

This document contains complete details of the previous version (i.e., arXiv:1701.02578v1), which was accepted for publication in ICASSP 2017

cs.IT math.IT

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Solving a large-scale regularized linear inverse problem using multiple processors is important in various real-world applications due to the limitations of individual processors and constraints on data sharing policies. This paper focuses on the setting where the matrix is partitioned column-wise. We extend the algorithmic framework and the theoretical analysis of approximate message passing (AMP), an iterative algorithm for solving linear inverse problems, whose asymptotic dynamics are characterized by state evolution (SE). In particular, we show that column-wise multiprocessor AMP (C-MP-AMP) obeys an SE under the same assumptions when the SE for AMP holds. The SE results imply that (i) the SE of C-MP-AMP converges to a state that is no worse than that of AMP and (ii) the asymptotic dynamics of C-MP-AMP and AMP can be identical. Moreover, for a setting that is not covered by SE, numerical results show that damping can improve the convergence performance of C-MP-AMP.

[{'version': 'v1', 'created': 'Tue, 10 Jan 2017 13:24:20 GMT'}, {'version': 'v2', 'created': 'Mon, 30 Jan 2017 17:01:34 GMT'}]

2017-01-31

[['Ma', 'Yanting', ''], ['Lu', 'Yue M.', ''], ['Baron', 'Dror', '']]

1907.12368

Armaan Kaur

Armaan Kaur, Jaspal Kaur Saini, Divya Bansal

Detecting Radical Text over Online Media using Deep Learning

The Paper consists of 7 pages with 5 figures. The paper is accepted in Intelligent Information Feed Workshop of 25th ACM SIGKDD Conference 2019 for oral presentation

cs.IR cs.LG stat.ML

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Social Media has influenced the way people socially connect, interact and opinionize. The growth in technology has enhanced communication and dissemination of information. Unfortunately,many terror groups like jihadist communities have started consolidating a virtual community online for various purposes such as recruitment, online donations, targeting youth online and spread of extremist ideologies. Everyday a large number of articles, tweets, posts, posters, blogs, comments, views and news are posted online without a check which in turn imposes a threat to the security of any nation. However, different agencies are working on getting down this radical content from various online social media platforms. The aim of our paper is to utilise deep learning algorithm in detection of radicalization contrary to the existing works based on machine learning algorithms. An LSTM based feed forward neural network is employed to detect radical content. We collected total 61601 records from various online sources constituting news, articles and blogs. These records are annotated by domain experts into three categories: Radical(R), Non-Radical (NR) and Irrelevant(I) which are further applied to LSTM based network to classify radical content. A precision of 85.9% has been achieved with the proposed approach

[{'version': 'v1', 'created': 'Mon, 22 Jul 2019 17:27:37 GMT'}, {'version': 'v2', 'created': 'Tue, 30 Jul 2019 19:07:10 GMT'}]

2019-08-01

[['Kaur', 'Armaan', ''], ['Saini', 'Jaspal Kaur', ''], ['Bansal', 'Divya', '']]

1911.12196

Lennon \'O N\'araigh

Lennon \'O N\'araigh and Daniel R. Jansen van Vuuren

Linear and Nonlinear Stability Analysis in Microfluidic Systems

23 pages, 13 figures

physics.flu-dyn

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

In this article we use analytical and numerical modeling to describe parallel viscous two-phase flows in microchannels. The focus is on idealized two-dimensional geometries, with a view to validating the various methodologies for future work in three dimensions. In the first instance, we use analytical Orr--Sommerfeld theory to describe the linear instability which governs the formation of small-amplitude waves in such systems. We then compare the results of this analysis with an in-house Computational Fluid Dynamics (CFD) solver called TPLS. Excellent agreement between the theoretical analysis and TPLS is obtained in the regime of small-amplitude waves. We continue the numerical simulations beyond the point of validity of the Orr--Sommerfeld theory. In this way, we illustrate the generation of nonlinear interfacial waves and reverse entrainment of one fluid phase into the other. We justify our simulations further by comparing the numerical results with corresponding results from a commercial CFD code. This comparison is again extremely favourable -- this rigorous validation paves the way for future work using TPLS or commercial codes to perform extremely detailed three-dimensional simulations of flow in microchannels.

[{'version': 'v1', 'created': 'Wed, 27 Nov 2019 14:55:55 GMT'}]

2019-11-28

[['Náraigh', 'Lennon Ó', ''], ['van Vuuren', 'Daniel R. Jansen', '']]

2105.07364

Yu Shen

Yu Shen, Sijie Zhu, Taojiannan Yang, Chen Chen, Delu Pan, Jianyu Chen, Liang Xiao, Qian Du

BDANet: Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite Images

arXiv admin note: text overlap with arXiv:2010.14014

10.1109/TGRS.2021.3080580

cs.CV cs.LG eess.IV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Fast and effective responses are required when a natural disaster (e.g., earthquake, hurricane, etc.) strikes. Building damage assessment from satellite imagery is critical before relief effort is deployed. With a pair of pre- and post-disaster satellite images, building damage assessment aims at predicting the extent of damage to buildings. With the powerful ability of feature representation, deep neural networks have been successfully applied to building damage assessment. Most existing works simply concatenate pre- and post-disaster images as input of a deep neural network without considering their correlations. In this paper, we propose a novel two-stage convolutional neural network for Building Damage Assessment, called BDANet. In the first stage, a U-Net is used to extract the locations of buildings. Then the network weights from the first stage are shared in the second stage for building damage assessment. In the second stage, a two-branch multi-scale U-Net is employed as backbone, where pre- and post-disaster images are fed into the network separately. A cross-directional attention module is proposed to explore the correlations between pre- and post-disaster images. Moreover, CutMix data augmentation is exploited to tackle the challenge of difficult classes. The proposed method achieves state-of-the-art performance on a large-scale dataset -- xBD. The code is available at https://github.com/ShaneShen/BDANet-Building-Damage-Assessment.

[{'version': 'v1', 'created': 'Sun, 16 May 2021 06:13:28 GMT'}]

2022-02-16

[['Shen', 'Yu', ''], ['Zhu', 'Sijie', ''], ['Yang', 'Taojiannan', ''], ['Chen', 'Chen', ''], ['Pan', 'Delu', ''], ['Chen', 'Jianyu', ''], ['Xiao', 'Liang', ''], ['Du', 'Qian', '']]

2103.15578

Venkat Margapuri

Venkat Margapuri and Mitchell Neilsen

Classification of Seeds using Domain Randomization on Self-Supervised Learning Frameworks

cs.CV cs.AI cs.LG

http://creativecommons.org/licenses/by/4.0/

The first step toward Seed Phenotyping i.e. the comprehensive assessment of complex seed traits such as growth, development, tolerance, resistance, ecology, yield, and the measurement of pa-rameters that form more complex traits is the identification of seed type. Generally, a plant re-searcher inspects the visual attributes of a seed such as size, shape, area, color and texture to identify the seed type, a process that is tedious and labor-intensive. Advances in the areas of computer vision and deep learning have led to the development of convolutional neural networks (CNN) that aid in classification using images. While they classify efficiently, a key bottleneck is the need for an extensive amount of labelled data to train the CNN before it can be put to the task of classification. The work leverages the concepts of Contrastive Learning and Domain Randomi-zation in order to achieve the same. Briefly, domain randomization is the technique of applying models trained on images containing simulated objects to real-world objects. The use of synthetic images generated from a representational sample crop of real-world images alleviates the need for a large volume of test subjects. As part of the work, synthetic image datasets of five different types of seed images namely, canola, rough rice, sorghum, soy and wheat are applied to three different self-supervised learning frameworks namely, SimCLR, Momentum Contrast (MoCo) and Build Your Own Latent (BYOL) where ResNet-50 is used as the backbone in each of the networks. When the self-supervised models are fine-tuned with only 5% of the labels from the synthetic dataset, results show that MoCo, the model that yields the best performance of the self-supervised learning frameworks in question, achieves an accuracy of 77% on the test dataset which is only ~13% less than the accuracy of 90% achieved by ResNet-50 trained on 100% of the labels.

[{'version': 'v1', 'created': 'Mon, 29 Mar 2021 12:50:06 GMT'}]

2021-03-30

[['Margapuri', 'Venkat', ''], ['Neilsen', 'Mitchell', '']]

2101.00774

Fengbin Zhu

Fengbin Zhu, Wenqiang Lei, Chao Wang, Jianming Zheng, Soujanya Poria, Tat-Seng Chua

Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Open-domain Question Answering (OpenQA) is an important task in Natural Language Processing (NLP), which aims to answer a question in the form of natural language based on large-scale unstructured documents. Recently, there has been a surge in the amount of research literature on OpenQA, particularly on techniques that integrate with neural Machine Reading Comprehension (MRC). While these research works have advanced performance to new heights on benchmark datasets, they have been rarely covered in existing surveys on QA systems. In this work, we review the latest research trends in OpenQA, with particular attention to systems that incorporate neural MRC techniques. Specifically, we begin with revisiting the origin and development of OpenQA systems. We then introduce modern OpenQA architecture named "Retriever-Reader" and analyze the various systems that follow this architecture as well as the specific techniques adopted in each of the components. We then discuss key challenges to developing OpenQA systems and offer an analysis of benchmarks that are commonly used. We hope our work would enable researchers to be informed of the recent advancement and also the open challenges in OpenQA research, so as to stimulate further progress in this field.

[{'version': 'v1', 'created': 'Mon, 4 Jan 2021 04:47:46 GMT'}, {'version': 'v2', 'created': 'Fri, 23 Apr 2021 07:25:37 GMT'}, {'version': 'v3', 'created': 'Sat, 8 May 2021 16:16:50 GMT'}]

2021-05-11

[['Zhu', 'Fengbin', ''], ['Lei', 'Wenqiang', ''], ['Wang', 'Chao', ''], ['Zheng', 'Jianming', ''], ['Poria', 'Soujanya', ''], ['Chua', 'Tat-Seng', '']]

1805.10333

Nico G\"o{\ss}ling

N. G\"o{\ss}ling and S. Doclo

Relative Transfer Function Estimation Exploiting Spatially Separated Microphones in a Diffuse Noise Field

To appear in the Proc. of IWAENC2018

10.1109/IWAENC.2018.8521295

eess.AS cs.SD

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Many multi-microphone speech enhancement algorithms require the relative transfer function (RTF) vector of the desired speech source, relating the acoustic transfer functions of all array microphones to a reference microphone. In this paper, we propose a computationally efficient method to estimate the RTF vector in a diffuse noise field, which requires an additional microphone that is spatially separated from the microphone array, such that the spatial coherence between the noise components in the microphone array signals and the additional microphone signal is low. Assuming this spatial coherence to be zero, we show that an unbiased estimate of the RTF vector can be obtained. Based on real-world recordings experimental results show that the proposed RTF estimator outperforms state-of-the-art estimators using only the microphone array signals in terms of estimation accuracy and noise reduction performance.

[{'version': 'v1', 'created': 'Fri, 25 May 2018 19:08:08 GMT'}, {'version': 'v2', 'created': 'Wed, 11 Jul 2018 11:27:18 GMT'}, {'version': 'v3', 'created': 'Thu, 12 Jul 2018 11:21:33 GMT'}]

2022-11-22

[['Gößling', 'N.', ''], ['Doclo', 'S.', '']]

1409.5308

Mohammed El-Kebir

Mohammed El-Kebir and Gunnar W. Klau

Solving the Maximum-Weight Connected Subgraph Problem to Optimality

11th DIMACS implementation challenge

cs.DS

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Given an undirected node-weighted graph, the Maximum-Weight Connected Subgraph problem (MWCS) is to identify a subset of nodes of maximalsum of weights that induce a connected subgraph. MWCS is closely related to the well-studied Prize Collecting Steiner Tree problem and has many applications in different areas, including computational biology, network design and computer vision. The problem is NP-hard and even hard to approximate within a constant factor. In this work we describe an algorithmic scheme for solving MWCS to provable optimality, which is based on preprocessing rules, new results on decomposing an instance into its biconnected and triconnected components and a branch-and-cut approach combined with a primal heuristic. We demonstrate the performance of our method on the benchmark instances of the 11th DIMACS implementation challenge consisting of MWCS as well as transformed PCST instances.

[{'version': 'v1', 'created': 'Thu, 18 Sep 2014 14:15:45 GMT'}, {'version': 'v2', 'created': 'Mon, 1 Dec 2014 22:13:05 GMT'}]

2014-12-03

[['El-Kebir', 'Mohammed', ''], ['Klau', 'Gunnar W.', '']]

quant-ph/0511016

G David Forney Jr.

G. David Forney, Jr., Markus Grassl, and Saikat Guha

Convolutional and tail-biting quantum error-correcting codes

30 pages. Submitted to IEEE Transactions on Information Theory. Minor revisions after first round of reviews

IEEE Transactions on Information Theory, vol. 53, no. 3, March 2007, pp. 865-880

10.1109/TIT.2006.890698

quant-ph cs.IT math.IT

Rate-(n-2)/n unrestricted and CSS-type quantum convolutional codes with up to 4096 states and minimum distances up to 10 are constructed as stabilizer codes from classical self-orthogonal rate-1/n F_4-linear and binary linear convolutional codes, respectively. These codes generally have higher rate and less decoding complexity than comparable quantum block codes or previous quantum convolutional codes. Rate-(n-2)/n block stabilizer codes with the same rate and error-correction capability and essentially the same decoding algorithms are derived from these convolutional codes via tail-biting.

[{'version': 'v1', 'created': 'Wed, 2 Nov 2005 21:23:21 GMT'}, {'version': 'v2', 'created': 'Tue, 7 Nov 2006 17:59:18 GMT'}]

2012-08-27

[['Forney,', 'G. David', 'Jr.'], ['Grassl', 'Markus', ''], ['Guha', 'Saikat', '']]

2206.10096

Xuxin Chen

Xuxin Chen, Ke Zhang, Neman Abdoli, Patrik W. Gilley, Ximin Wang, Hong Liu, Bin Zheng, Yuchen Qiu

Transformers Improve Breast Cancer Diagnosis from Unregistered Multi-View Mammograms

cs.CV cs.AI eess.IV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Deep convolutional neural networks (CNNs) have been widely used in various medical imaging tasks. However, due to the intrinsic locality of convolution operation, CNNs generally cannot model long-range dependencies well, which are important for accurately identifying or mapping corresponding breast lesion features computed from unregistered multiple mammograms. This motivates us to leverage the architecture of Multi-view Vision Transformers to capture long-range relationships of multiple mammograms from the same patient in one examination. For this purpose, we employ local Transformer blocks to separately learn patch relationships within four mammograms acquired from two-view (CC/MLO) of two-side (right/left) breasts. The outputs from different views and sides are concatenated and fed into global Transformer blocks, to jointly learn patch relationships between four images representing two different views of the left and right breasts. To evaluate the proposed model, we retrospectively assembled a dataset involving 949 sets of mammograms, which include 470 malignant cases and 479 normal or benign cases. We trained and evaluated the model using a five-fold cross-validation method. Without any arduous preprocessing steps (e.g., optimal window cropping, chest wall or pectoral muscle removal, two-view image registration, etc.), our four-image (two-view-two-side) Transformer-based model achieves case classification performance with an area under ROC curve (AUC = 0.818), which significantly outperforms AUC = 0.784 achieved by the state-of-the-art multi-view CNNs (p = 0.009). It also outperforms two one-view-two-side models that achieve AUC of 0.724 (CC view) and 0.769 (MLO view), respectively. The study demonstrates the potential of using Transformers to develop high-performing computer-aided diagnosis schemes that combine four mammograms.

[{'version': 'v1', 'created': 'Tue, 21 Jun 2022 03:54:21 GMT'}]

2022-06-22

[['Chen', 'Xuxin', ''], ['Zhang', 'Ke', ''], ['Abdoli', 'Neman', ''], ['Gilley', 'Patrik W.', ''], ['Wang', 'Ximin', ''], ['Liu', 'Hong', ''], ['Zheng', 'Bin', ''], ['Qiu', 'Yuchen', '']]

2206.12052

Xia Jiang

Xia Jiang, Jian Zhang, Xiaoyu Shi and Jian Cheng

Learning the policy for mixed electric platoon control of automated and human-driven vehicles at signalized intersection: a random search approach

IEEE Transactions on Intelligent Transportation Systems (2023)

10.1109/TITS.2023.3242678

eess.SY cs.RO cs.SY

http://creativecommons.org/licenses/by/4.0/

The upgrading and updating of vehicles have accelerated in the past decades. Out of the need for environmental friendliness and intelligence, electric vehicles (EVs) and connected and automated vehicles (CAVs) have become new components of transportation systems. This paper develops a reinforcement learning framework to implement adaptive control for an electric platoon composed of CAVs and human-driven vehicles (HDVs) at a signalized intersection. Firstly, a Markov Decision Process (MDP) model is proposed to describe the decision process of the mixed platoon. Novel state representation and reward function are designed for the model to consider the behavior of the whole platoon. Secondly, in order to deal with the delayed reward, an Augmented Random Search (ARS) algorithm is proposed. The control policy learned by the agent can guide the longitudinal motion of the CAV, which serves as the leader of the platoon. Finally, a series of simulations are carried out in simulation suite SUMO. Compared with several state-of-the-art (SOTA) reinforcement learning approaches, the proposed method can obtain a higher reward. Meanwhile, the simulation results demonstrate the effectiveness of the delay reward, which is designed to outperform distributed reward mechanism} Compared with normal car-following behavior, the sensitivity analysis reveals that the energy can be saved to different extends (39.27%-82.51%) by adjusting the relative importance of the optimization goal. On the premise that travel delay is not sacrificed, the proposed control method can save up to 53.64% electric energy.

[{'version': 'v1', 'created': 'Fri, 24 Jun 2022 03:05:19 GMT'}]

2023-02-07

[['Jiang', 'Xia', ''], ['Zhang', 'Jian', ''], ['Shi', 'Xiaoyu', ''], ['Cheng', 'Jian', '']]

1212.6209

Joshua Shaevitz

Yi Deng, Philip Coen, Mingzhai Sun, Joshua W. Shaevitz

Efficient Multiple Object Tracking Using Mutually Repulsive Active Membranes

18 pages, 6 figures, 1 table

10.1371/journal.pone.0065769

q-bio.QM cs.CV physics.bio-ph

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Studies of social and group behavior in interacting organisms require high-throughput analysis of the motion of a large number of individual subjects. Computer vision techniques offer solutions to specific tracking problems, and allow automated and efficient tracking with minimal human intervention. In this work, we adopt the open active contour model to track the trajectories of moving objects at high density. We add repulsive interactions between open contours to the original model, treat the trajectories as an extrusion in the temporal dimension, and show applications to two tracking problems. The walking behavior of Drosophila is studied at different population density and gender composition. We demonstrate that individual male flies have distinct walking signatures, and that the social interaction between flies in a mixed gender arena is gender specific. We also apply our model to studies of trajectories of gliding Myxococcus xanthus bacteria at high density. We examine the individual gliding behavioral statistics in terms of the gliding speed distribution. Using these two examples at very distinctive spatial scales, we illustrate the use of our algorithm on tracking both short rigid bodies (Drosophila) and long flexible objects (Myxococcus xanthus). Our repulsive active membrane model reaches error rates better than $5\times 10^{-6}$ per fly per second for Drosophila tracking and comparable results for Myxococcus xanthus.

[{'version': 'v1', 'created': 'Wed, 26 Dec 2012 16:30:40 GMT'}]

2015-06-12

[['Deng', 'Yi', ''], ['Coen', 'Philip', ''], ['Sun', 'Mingzhai', ''], ['Shaevitz', 'Joshua W.', '']]

1511.05265

Siqi Sun

Sheng Wang, Siqi Sun and Jinbo Xu

AUC-maximized Deep Convolutional Neural Fields for Sequence Labeling

Under review as a conference paper at ICLR 2016

stat.ML cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Deep Convolutional Neural Networks (DCNN) has shown excellent performance in a variety of machine learning tasks. This manuscript presents Deep Convolutional Neural Fields (DeepCNF), a combination of DCNN with Conditional Random Field (CRF), for sequence labeling with highly imbalanced label distribution. The widely-used training methods, such as maximum-likelihood and maximum labelwise accuracy, do not work well on highly imbalanced data. To handle this, we present a new training algorithm called maximum-AUC for DeepCNF. That is, we train DeepCNF by directly maximizing the empirical Area Under the ROC Curve (AUC), which is an unbiased measurement for imbalanced data. To fulfill this, we formulate AUC in a pairwise ranking framework, approximate it by a polynomial function and then apply a gradient-based procedure to optimize it. We then test our AUC-maximized DeepCNF on three very different protein sequence labeling tasks: solvent accessibility prediction, 8-state secondary structure prediction, and disorder prediction. Our experimental results confirm that maximum-AUC greatly outperforms the other two training methods on 8-state secondary structure prediction and disorder prediction since their label distributions are highly imbalanced and also have similar performance as the other two training methods on the solvent accessibility prediction problem which has three equally-distributed labels. Furthermore, our experimental results also show that our AUC-trained DeepCNF models greatly outperform existing popular predictors of these three tasks.

[{'version': 'v1', 'created': 'Tue, 17 Nov 2015 03:21:43 GMT'}, {'version': 'v2', 'created': 'Thu, 19 Nov 2015 22:45:31 GMT'}]

2015-11-23

[['Wang', 'Sheng', ''], ['Sun', 'Siqi', ''], ['Xu', 'Jinbo', '']]

0905.1910

Patrick Valageas

P. Valageas

Quasi-linear regime and rare-event tails of decaying Burgers turbulence

32 pages

Phys. Rev. E 80, 016305 (2009)

10.1103/PhysRevE.80.016305

cond-mat.stat-mech astro-ph.CO physics.flu-dyn

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We study the decaying Burgers dynamics in $d$ dimensions for random Gaussian initial conditions. We focus on power-law initial energy spectra, such that the system shows a self-similar evolution. This is the case of interest for the "adhesion model" in cosmology and a standard framework for "decaying Burgers turbulence". We briefly describe how the system can be studied through perturbative expansions at early time or large scale (quasi-linear regime). Next, we develop a saddle-point method, based on spherical instantons, that allows to obtain the asymptotic probability distributions $\cP(\eta_r)$ and $\cP(\ctheta_r)$, of the density and velocity increment over spherical cells, reached in the quasi-linear regime. Finally, we show how this approach can be extended to take into account the formation of shocks and we derive the rare-event tails of these probability distributions, at any finite time and scale. This also gives the high-mass tail of the mass function of point-like singularities (shocks in the one dimensional case).

[{'version': 'v1', 'created': 'Tue, 12 May 2009 17:03:35 GMT'}]

2010-01-19

[['Valageas', 'P.', '']]

1311.4528

Aamir Younis Raja

R Aamir, A Chernoglazov, C J Bateman, A P H Butler, P H Butler, N G Anderson, S T Bell, R K Panta, J L Healy, J L Mohr, K Rajendran, M F Walsh, N de Ruiter, S P Gieseg, T Woodfield, P F Renaud, L Brooke, S Abdul-Majid, M Clyne, R Glendenning, P J Bones, M Billinghurst, C Bartneck, H Mandalika, R Grasset, N Schleich, N Scott, S J Nik, A Opie, T Janmale, D N Tang, D Kim, R M Doesburg, R Zainon, J P Ronaldson, N J Cook, D J Smithies, K Hodge

MARS spectral molecular imaging of lamb tissue: data collection and image analysis

11 pages, 6 figs

10.1088/1748-0221/9/02/P02005

physics.med-ph

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Spectral molecular imaging is a new imaging technique able to discriminate and quantify different components of tissue simultaneously at high spatial and high energy resolution. Our MARS scanner is an x-ray based small animal CT system designed to be used in the diagnostic energy range (20 to 140 keV). In this paper, we demonstrate the use of the MARS scanner, equipped with the Medipix3RX spectroscopic photon-processing detector, to discriminate fat, calcium, and water in tissue. We present data collected from a sample of lamb meat including bone as an illustrative example of human tissue imaging. The data is analyzed using our 3D Algebraic Reconstruction Algorithm (MARS-ART) and by material decomposition based on a constrained linear least squares algorithm. The results presented here clearly show the quantification of lipid-like, water-like and bone-like components of tissue. However, it is also clear to us that better algorithms could extract more information of clinical interest from our data. Because we are one of the first to present data from multi-energy photon-processing small animal CT systems, we make the raw, partial and fully processed data available with the intention that others can analyze it using their familiar routines. The raw, partially processed and fully processed data of lamb tissue along with the phantom calibration data can be found at [http://hdl.handle.net/10092/8531].

[{'version': 'v1', 'created': 'Mon, 18 Nov 2013 20:39:18 GMT'}, {'version': 'v2', 'created': 'Fri, 24 Jan 2014 04:14:55 GMT'}]

2014-02-19

[['Aamir', 'R', ''], ['Chernoglazov', 'A', ''], ['Bateman', 'C J', ''], ['Butler', 'A P H', ''], ['Butler', 'P H', ''], ['Anderson', 'N G', ''], ['Bell', 'S T', ''], ['Panta', 'R K', ''], ['Healy', 'J L', ''], ['Mohr', 'J L', ''], ['Rajendran', 'K', ''], ['Walsh', 'M F', ''], ['de Ruiter', 'N', ''], ['Gieseg', 'S P', ''], ['Woodfield', 'T', ''], ['Renaud', 'P F', ''], ['Brooke', 'L', ''], ['Abdul-Majid', 'S', ''], ['Clyne', 'M', ''], ['Glendenning', 'R', ''], ['Bones', 'P J', ''], ['Billinghurst', 'M', ''], ['Bartneck', 'C', ''], ['Mandalika', 'H', ''], ['Grasset', 'R', ''], ['Schleich', 'N', ''], ['Scott', 'N', ''], ['Nik', 'S J', ''], ['Opie', 'A', ''], ['Janmale', 'T', ''], ['Tang', 'D N', ''], ['Kim', 'D', ''], ['Doesburg', 'R M', ''], ['Zainon', 'R', ''], ['Ronaldson', 'J P', ''], ['Cook', 'N J', ''], ['Smithies', 'D J', ''], ['Hodge', 'K', '']]

2210.16841

Adit Magotra

Adit Magotra

Actionable Phrase Detection using NLP

cs.CL

http://creativecommons.org/licenses/by/4.0/

Actionable sentences are terms that, in the most basic sense, imply the necessity of taking a specific action. In Linguistic terms, they are steps to achieve an operation, often through the usage of action verbs. For example, the sentence, `Get your homework finished by tomorrow` qualifies as actionable since it demands a specific action (In this case, finishing homework) to be taken. In contrast, a simple sentence such as, `I like to play the guitar` does not qualify as an actionable phrase since it simply states a personal choice of the person instead of demanding a task to be finished. In this paper, the aim is to explore if Actionables can be extracted from raw text using Linguistic filters designed from scratch. These filters are specially catered to identifying actionable text using Transfer Learning as the lead role. Actionable Detection can be used in detecting emergency tasks during a crisis, Instruction accuracy for First aid and can also be used to make productivity tools like automatic ToDo list generators from conferences. To accomplish this, we use the Enron Email Dataset and apply our Linguistic filters on the cleaned textual data. We then use Transfer Learning with the Universal Sentence Encoder to train a model to classify whether a given string of raw text is actionable or not.

[{'version': 'v1', 'created': 'Sun, 30 Oct 2022 13:37:49 GMT'}]

2022-11-01

[['Magotra', 'Adit', '']]

1806.01829

Daniel Lum

Daniel J. Lum

Characterizing High-Dimensional Optical Systems with Applications in Compressive Sensing and Quantum Data Locking

PhD thesis, Univ. Rochester (2018)

quant-ph physics.comp-ph

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

This University of Rochester Physics Ph.D. dissertation introduces concepts in compressive sensing, quantum entanglement, FMCW LiDAR, and quantum data locking. Additionally, the appendix serves as a thorough reference for those interested in applying the alternating direction method of multipliers (ADMM) to optimize an augmented Lagrangian and can easily be tailored to specific optimization problems. In particular, I show how fast Hadamard transforms and the ADMM can be used for $L^1$-minimization with different sparse-basis transforms along with total-variation minimization of both images and video. The simple examples given demonstrate how to minimize high-dimensional problems with little memory overhead. The original version of this dissertation can be accessed through ProQuest.

[{'version': 'v1', 'created': 'Tue, 5 Jun 2018 17:37:32 GMT'}, {'version': 'v2', 'created': 'Wed, 6 Jun 2018 13:12:59 GMT'}]

2018-06-07

[['Lum', 'Daniel J.', '']]

hep-ph/0703320

Tomasz Pierzcha{\l}a

L. Bonnet, T. Pierzchala, K. Piotrzkowski, P. Rodeghiero (UCL Louvain-la-Neuve, Belgium)

GASTOF: Ultra-fast ToF forward detector for exclusive processes at the LHC

6 pages, 3 figures, presented at the conference ''Physics at LHC'', Krakow, June 2006

ActaPhys.Polon.B38:477-482,2007

CP3-06-18

hep-ph physics.ins-det

GASTOF (Gas Time-of-Flight) detector is a Cherenkov detector proposed for very precise (10--20 ps) arrival time measurements of forward protons at some 420 m from the central detectors of CMS and ATLAS. Such an excellent time resolution will allow by z-by-timing technique for precise measurement of the z-coordinate of the event vertex in exclusive production at the LHC, when two colliding protons are scattered at very small angles. In the paper we present first GASTOF prototype, simulations of its performance as well as first tests using a cosmic muon telescope.

[{'version': 'v1', 'created': 'Fri, 30 Mar 2007 19:38:38 GMT'}]

2008-11-26

[['Bonnet', 'L.', '', 'UCL\n Louvain-la-Neuve, Belgium'], ['Pierzchala', 'T.', '', 'UCL\n Louvain-la-Neuve, Belgium'], ['Piotrzkowski', 'K.', '', 'UCL\n Louvain-la-Neuve, Belgium'], ['Rodeghiero', 'P.', '', 'UCL\n Louvain-la-Neuve, Belgium']]

2203.11278

Yiming Zeng

Yiming Zeng, Shahin Khobahi, Mojtaba Soltanalian

One-Bit Compressive Sensing: Can We Go Deep and Blind?

IEEE SIGNAL PROCESSING LETTERS,2022

IEEE Signal Processing Letters, vol. 29, pp. 1629-1633, 2022

10.1109/LSP.2022.3187318

eess.SP cs.IT cs.LG math.IT

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

One-bit compressive sensing is concerned with the accurate recovery of an underlying sparse signal of interest from its one-bit noisy measurements. The conventional signal recovery approaches for this problem are mainly developed based on the assumption that an exact knowledge of the sensing matrix is available. In this work, however, we present a novel data-driven and model-based methodology that achieves blind recovery; i.e., signal recovery without requiring the knowledge of the sensing matrix. To this end, we make use of the deep unfolding technique and develop a model-driven deep neural architecture which is designed for this specific task. The proposed deep architecture is able to learn an alternative sensing matrix by taking advantage of the underlying unfolded algorithm such that the resulting learned recovery algorithm can accurately and quickly (in terms of the number of iterations) recover the underlying compressed signal of interest from its one-bit noisy measurements. In addition, due to the incorporation of the domain knowledge and the mathematical model of the system into the proposed deep architecture, the resulting network benefits from enhanced interpretability, has a very small number of trainable parameters, and requires very small number of training samples, as compared to the commonly used black-box deep neural network alternatives for the problem at hand.

[{'version': 'v1', 'created': 'Sun, 13 Mar 2022 16:06:56 GMT'}, {'version': 'v2', 'created': 'Thu, 22 Sep 2022 03:26:11 GMT'}]

2022-09-23

[['Zeng', 'Yiming', ''], ['Khobahi', 'Shahin', ''], ['Soltanalian', 'Mojtaba', '']]

1711.10921

Shiv Ram Dubey

Swalpa Kumar Roy, Bhabatosh Chanda, Bidyut B. Chaudhuri, Dipak Kumar Ghosh, Shiv Ram Dubey

Local Jet Pattern: A Robust Descriptor for Texture Classification

Accepted in Multimedia Tools and Applications, Springer

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Methods based on local image features have recently shown promise for texture classification tasks, especially in the presence of large intra-class variation due to illumination, scale, and viewpoint changes. Inspired by the theories of image structure analysis, this paper presents a simple, efficient, yet robust descriptor namely local jet pattern (LJP) for texture classification. In this approach, a jet space representation of a texture image is derived from a set of derivatives of Gaussian (DtGs) filter responses up to second order, so called local jet vectors (LJV), which also satisfy the Scale Space properties. The LJP is obtained by utilizing the relationship of center pixel with the local neighborhood information in jet space. Finally, the feature vector of a texture region is formed by concatenating the histogram of LJP for all elements of LJV. All DtGs responses up to second order together preserves the intrinsic local image structure, and achieves invariance to scale, rotation, and reflection. This allows us to develop a texture classification framework which is discriminative and robust. Extensive experiments on five standard texture image databases, employing nearest subspace classifier (NSC), the proposed descriptor achieves 100%, 99.92%, 99.75%, 99.16%, and 99.65% accuracy for Outex_TC-00010 (Outex_TC10), and Outex_TC-00012 (Outex_TC12), KTH-TIPS, Brodatz, CUReT, respectively, which are outperforms the state-of-the-art methods.

[{'version': 'v1', 'created': 'Sun, 26 Nov 2017 17:18:46 GMT'}, {'version': 'v2', 'created': 'Sun, 8 Jul 2018 12:18:38 GMT'}, {'version': 'v3', 'created': 'Tue, 4 Dec 2018 02:01:15 GMT'}]

2018-12-05

[['Roy', 'Swalpa Kumar', ''], ['Chanda', 'Bhabatosh', ''], ['Chaudhuri', 'Bidyut B.', ''], ['Ghosh', 'Dipak Kumar', ''], ['Dubey', 'Shiv Ram', '']]

2202.05477

Chul Min Kim Ph.D.

Chul Min Kim and Sang Pyo Kim

Vacuum Birefringence in a Supercritical Magnetic Field and a Subcritical Electric Field

10.1140/epjc/s10052-023-11243-1

astro-ph.HE hep-ph physics.plasm-ph

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Recent ultra-intense lasers of subcritical fields and proposed observations of the x-rays polarization from highly magnetized neutron stars of supercritical fields have attracted attention to vacuum birefringence, a unique feature of nonlinear electrodynamics. We propose a formulation of vacuum birefringence that incorporates the effects of the weaker electric field added to the extremely strong magnetic field. To do so, we first derive a closed analytical expression for the one-loop effective Lagrangian for the combined magnetic and electric fields by using an explicit formula of the one-loop effective Lagrangian for an arbitrarily strong magnetic field. We then employ the expression to derive the polarization and magnetization of the vacuum, from which the permittivity and permeability for weak probe fields are obtained. Finally, we find the refractive indices and the associated polarization vectors for the case of parallel magnetic and electric fields. The proposed formulation predicts that an electric field along the magnetic field reduces the birefringence and rotates the polarization vectors. Such effects should be taken into account for accurate polarimetry of the x-rays from magnetized neutron stars, which will prove the fundamental aspect of the strong field quantum electrodynamics (QED) and explore the extreme fields of astrophysical bodies.

[{'version': 'v1', 'created': 'Fri, 11 Feb 2022 06:38:47 GMT'}]

2023-02-06

[['Kim', 'Chul Min', ''], ['Kim', 'Sang Pyo', '']]

2204.09379

Andrew R. Liddle

Marina Cort\^es, Stuart A. Kauffman, Andrew R. Liddle, and Lee Smolin

Biocosmology: Biology from a cosmological perspective

28 pages. See also companion paper arXiv:2204.09378

physics.hist-ph astro-ph.CO gr-qc

http://creativecommons.org/licenses/by-sa/4.0/

The Universe contains everything that exists, including life. And all that exists, including life, obeys universal physical laws. Do those laws then give adequate foundations for a complete explanation of biological phenomena? We discuss whether and how cosmology and physics must be modified to be able to address certain questions which arise at their intersection with biology. We show that a universe that contains life, in the form it has on Earth, is in a certain sense radically non-ergodic, in that the vast majority of possible organisms will never be realized. We argue from this that complete explanations in cosmology require a mixture of reductionist and functional explanations.

[{'version': 'v1', 'created': 'Wed, 20 Apr 2022 10:45:47 GMT'}, {'version': 'v2', 'created': 'Thu, 28 Apr 2022 15:48:39 GMT'}]

2022-04-29

[['Cortês', 'Marina', ''], ['Kauffman', 'Stuart A.', ''], ['Liddle', 'Andrew R.', ''], ['Smolin', 'Lee', '']]

2109.14181

Hans De Sterck

Hans De Sterck and Yunhui He and Oliver A. Krzysik

Anderson Acceleration as a Krylov Method with Application to Asymptotic Convergence Analysis

this version resubmitted to journal on Nov 22, 2022

math.NA cs.LG cs.NA math.OC

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Anderson acceleration (AA) is widely used for accelerating the convergence of nonlinear fixed-point methods $x_{k+1}=q(x_{k})$, $x_k \in \mathbb{R}^n$, but little is known about how to quantify the convergence acceleration provided by AA. As a roadway towards gaining more understanding of convergence acceleration by AA, we study AA($m$), i.e., Anderson acceleration with finite window size $m$, applied to the case of linear fixed-point iterations $x_{k+1}=M x_{k}+b$. We write AA($m$) as a Krylov method with polynomial residual update formulas, and derive recurrence relations for the AA($m$) polynomials. Writing AA($m$) as a Krylov method immediately implies that $k$ iterations of AA($m$) cannot produce a smaller residual than $k$ iterations of GMRES without restart (but without implying anything about the relative convergence speed of (windowed) AA($m$) versus restarted GMRES($m$)). We find that the AA($m$) residual polynomials observe a periodic memory effect where increasing powers of the error iteration matrix $M$ act on the initial residual as the iteration number increases. We derive several further results based on these polynomial residual update formulas, including orthogonality relations, a lower bound on the AA(1) acceleration coefficient $\beta_k$, and explicit nonlinear recursions for the AA(1) residuals and residual polynomials that do not include the acceleration coefficient $\beta_k$. Using these recurrence relations we also derive new residual convergence bounds for AA(1) in the linear case, demonstrating how the per-iteration residual reduction $||r_{k+1}||/||r_{k}||$ depends strongly on the residual reduction in the previous iteration and on the angle between the prior residual vectors $r_k$ and $r_{k-1}$. We apply these results to study the influence of the initial guess on the asymptotic convergence factor of AA(1), and to study AA(1) residual convergence patterns.

[{'version': 'v1', 'created': 'Wed, 29 Sep 2021 03:53:15 GMT'}, {'version': 'v2', 'created': 'Fri, 24 Feb 2023 00:03:36 GMT'}]

2023-02-27

[['De Sterck', 'Hans', ''], ['He', 'Yunhui', ''], ['Krzysik', 'Oliver A.', '']]

2011.02565

Anand Kamat

Anand Kamat and Doina Precup

Diversity-Enriched Option-Critic

cs.LG cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Temporal abstraction allows reinforcement learning agents to represent knowledge and develop strategies over different temporal scales. The option-critic framework has been demonstrated to learn temporally extended actions, represented as options, end-to-end in a model-free setting. However, feasibility of option-critic remains limited due to two major challenges, multiple options adopting very similar behavior, or a shrinking set of task relevant options. These occurrences not only void the need for temporal abstraction, they also affect performance. In this paper, we tackle these problems by learning a diverse set of options. We introduce an information-theoretic intrinsic reward, which augments the task reward, as well as a novel termination objective, in order to encourage behavioral diversity in the option set. We show empirically that our proposed method is capable of learning options end-to-end on several discrete and continuous control tasks, outperforms option-critic by a wide margin. Furthermore, we show that our approach sustainably generates robust, reusable, reliable and interpretable options, in contrast to option-critic.

[{'version': 'v1', 'created': 'Wed, 4 Nov 2020 22:12:54 GMT'}]

2020-11-06

[['Kamat', 'Anand', ''], ['Precup', 'Doina', '']]

physics/0604125

Jean-Francois Roch

Loc Le Xuan (LPQM), Fran\c{c}ois Marquier (EM2C), Dominique Chauvat (LPQM, PALMS), Sophie Brasselet (LPQM), Fran\c{c}ois Treussart (LPQM), Sandrine Perruchas (PMC), C\'edric Tard (PMC), Thierry Gacoin (PMC), Jean-Fran\c{c}ois Roch (LPQM)

Balanced homodyne detection in second-harmonic generation microscopy

9 pages to appear in Applied Physics Letters

Applied Physics Letters 89 (2006) 121118

10.1063/1.2356375

physics.optics

We demonstrate the association of two-photon nonlinear microscopy with balanced homodyne detection for investigating second harmonic radiation properties at nanoscale dimensions. Variation of the relative phase between second-harmonic and fundamental beams is retrieved, as a function of the absolute orientation of the nonlinear emitters. Sensitivity down to approximately 3.2 photon/s in the spatio-temporal mode of the local oscillator is obtained. This value is high enough to efficiently detect the coherent second-harmonic emission from a single KTiOPO4 crystal of sub-wavelength size.

[{'version': 'v1', 'created': 'Sat, 15 Apr 2006 18:45:13 GMT'}, {'version': 'v2', 'created': 'Wed, 20 Sep 2006 09:18:24 GMT'}]

2016-08-16

[['Xuan', 'Loc Le', '', 'LPQM'], ['Marquier', 'François', '', 'EM2C'], ['Chauvat', 'Dominique', '', 'LPQM, PALMS'], ['Brasselet', 'Sophie', '', 'LPQM'], ['Treussart', 'François', '', 'LPQM'], ['Perruchas', 'Sandrine', '', 'PMC'], ['Tard', 'Cédric', '', 'PMC'], ['Gacoin', 'Thierry', '', 'PMC'], ['Roch', 'Jean-François', '', 'LPQM']]

1704.06444

Lun Wang

Lun Wang, Damai Dai, Jie Jiang, Tong Yang, Xiaoke Jiang, Zekun Cai, Yang Li, Xiaoming Li

FISF: Better User Experience using Smaller Bandwidth for Panoramic Virtual Reality Video

cs.MM

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The panoramic video is widely used to build virtual reality (VR) and is expected to be one of the next generation Killer-Apps. Transmitting panoramic VR videos is a challenging task because of two problems: 1) panoramic VR videos are typically much larger than normal videos but they need to be transmitted with limited bandwidth in mobile networks. 2) high-resolution and fluent views should be provided to guarantee a superior user experience and avoid side-effects such as dizziness and nausea. To address these two problems, we propose a novel interactive streaming technology, namely Focus-based Interactive Streaming Framework (FISF). FISF consists of three parts: 1) we use the classic clustering algorithm DBSCAN to analyze real user data for Video Focus Detection (VFD); 2) we propose a Focus-based Interactive Streaming Technology (FIST), including a static version and a dynamic version; 3) we propose two optimization methods: focus merging and prefetch strategy. Experimental results show that FISF significantly outperforms the state-of-the-art. The paper is submitted to Sigcomm 2017, VR/AR Network on 31 Mar 2017 at 10:44:04am EDT.

[{'version': 'v1', 'created': 'Fri, 21 Apr 2017 08:41:21 GMT'}]

2017-04-24

[['Wang', 'Lun', ''], ['Dai', 'Damai', ''], ['Jiang', 'Jie', ''], ['Yang', 'Tong', ''], ['Jiang', 'Xiaoke', ''], ['Cai', 'Zekun', ''], ['Li', 'Yang', ''], ['Li', 'Xiaoming', '']]

1507.02048

Chaofan Ma

Chaofan Ma, Wei Liang, and Meng Zheng

A Connectivity-Aware Approximation Algorithm for Relay Node Placement in Wireless Sensor Networks

14 pages, 24 figures

cs.NI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

In two-tiered Wireless Sensor Networks (WSNs) relay node placement is one of the key factors impacting the network energy consumption and the system overhead. In this paper, a novel connectivity-aware approximation algorithm for relay node placement in WSNs is proposed to offer a major step forward in saving system overhead. Specifically, a unique Local Search Approximation Algorithm (LSAA) is introduced to solve the Relay Node Single Cover (RNSC) problem. In this proposed LSAA approach, the sensor nodes are allocated into groups and then a local Set Cover (SC) for each group is achieved by a local search algorithm. The union set of all local SCs constitutes a SC of the RNSC problem. The approximation ratio and the time complexity of the LSAA are analyzed by rigorous proof. Additionally, the LSAA approach has been extended to solve the relay node double cover problem. Then, a Relay Location Selection Algorithm (RLSA) is proposed to utilize the resulting SC from LSAA in combining RLSA with the minimum spanning tree heuristic to build the high-tier connectivity. As the RLSA searches for a nearest location to the sink node for each relay node, the high-tier network built by RLSA becomes denser than that by existing works. As a result, the number of added relay nodes for building the connectivity of the high-tier WSN can be significantly saved. Simulation results clearly demonstrate that the proposed LSAA outperforms the approaches reported in literature and the RLSA-based algorithm can noticeably save relay nodes newly deployed for the high-tier connectivity.

[{'version': 'v1', 'created': 'Wed, 8 Jul 2015 07:14:56 GMT'}, {'version': 'v2', 'created': 'Thu, 9 Jul 2015 02:31:27 GMT'}]

2015-07-10

[['Ma', 'Chaofan', ''], ['Liang', 'Wei', ''], ['Zheng', 'Meng', '']]

1903.02723

Zhenliang Zhang

Zhenliang Zhang, Cong Wang, Dongdong Weng, Yue Liu, Yongtian Wang

Symmetrical Reality: Toward a Unified Framework for Physical and Virtual Reality

IEEE VR Poster

cs.HC

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

In this paper, we review the background of physical reality, virtual reality, and some traditional mixed forms of them. Based on the current knowledge, we propose a new unified concept called symmetrical reality to describe the physical and virtual world in a unified perspective. Under the framework of symmetrical reality, the traditional virtual reality, augmented reality, inverse virtual reality, and inverse augmented reality can be interpreted using a unified presentation. We analyze the characteristics of symmetrical reality from two different observation locations (i.e., from the physical world and from the virtual world), where all other forms of physical and virtual reality can be treated as special cases of symmetrical reality.

[{'version': 'v1', 'created': 'Thu, 7 Mar 2019 04:29:50 GMT'}]

2019-03-08

[['Zhang', 'Zhenliang', ''], ['Wang', 'Cong', ''], ['Weng', 'Dongdong', ''], ['Liu', 'Yue', ''], ['Wang', 'Yongtian', '']]

2209.00448

Ehsan Qasemi

Ehsan Qasemi, Alessandro Oltramari

Intelligent Traffic Monitoring with Hybrid AI

IJCAI Workshop on Artificial Intelligence for Autonomous Driving (AI4AD) 2022

cs.AI cs.CL

http://creativecommons.org/licenses/by/4.0/

Challenges in Intelligent Traffic Monitoring (ITMo) are exacerbated by the large quantity and modalities of data and the need for the utilization of state-of-the-art (SOTA) reasoners. We formulate the problem of ITMo and introduce HANS, a neuro-symbolic architecture for multi-modal context understanding, and its application to ITMo. HANS utilizes knowledge graph technology to serve as a backbone for SOTA reasoning in the traffic domain. Through case studies, we show how HANS addresses the challenges associated with traffic monitoring while being able to integrate with a wide range of reasoning methods

[{'version': 'v1', 'created': 'Wed, 31 Aug 2022 17:47:22 GMT'}]

2022-09-02

[['Qasemi', 'Ehsan', ''], ['Oltramari', 'Alessandro', '']]

2008.09753

Tai-Xiang Jiang

Yi-Si Luo, Xi-Le Zhao, Tai-Xiang Jiang, Yu-Bang Zheng, Yi Chang

Unsupervised Hyperspectral Mixed Noise Removal Via Spatial-Spectral Constrained Deep Image Prior

cs.CV

http://creativecommons.org/licenses/by-nc-nd/4.0/

Recently, convolutional neural network (CNN)-based methods are proposed for hyperspectral images (HSIs) denoising. Among them, unsupervised methods such as the deep image prior (DIP) have received much attention because these methods do not require any training data. However, DIP suffers from the semi-convergence behavior, i.e., the iteration of DIP needs to terminate by referring to the ground-truth image at the optimal iteration point. In this paper, we propose the spatial-spectral constrained deep image prior (S2DIP) for HSI mixed noise removal. Specifically, we incorporate DIP with a spatial-spectral total variation (SSTV) term to fully preserve the spatial-spectral local smoothness of the HSI and an $\ell_1$-norm term to capture the complex sparse noise. The proposed S2DIP jointly leverages the expressive power brought from the deep CNN without any training data and exploits the HSI and noise structures via hand-crafted priors. Thus, our method avoids the semi-convergence behavior, showing higher stabilities than DIP. Meanwhile, our method largely enhances the HSI denoising ability of DIP. To tackle the proposed denoising model, we develop an alternating direction multiplier method algorithm. Extensive experiments demonstrate that the proposed S2DIP outperforms optimization-based and supervised CNN-based state-of-the-art HSI denoising methods.

[{'version': 'v1', 'created': 'Sat, 22 Aug 2020 04:25:08 GMT'}, {'version': 'v2', 'created': 'Thu, 10 Jun 2021 14:22:11 GMT'}]

2021-06-11

[['Luo', 'Yi-Si', ''], ['Zhao', 'Xi-Le', ''], ['Jiang', 'Tai-Xiang', ''], ['Zheng', 'Yu-Bang', ''], ['Chang', 'Yi', '']]

2004.05849

Yanghong Liu

Yanghong Liu and Jia Lu and Tingting Li

MLPSVM:A new parallel support vector machine to multi-label learning

cs.LG stat.ML

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Multi-label learning has attracted the attention of the machine learning community. The problem conversion method Binary Relevance converts a familiar single label into a multi-label algorithm. The binary relevance method is widely used because of its simple structure and efficient algorithm. But binary relevance does not consider the links between labels, making it cumbersome to handle some tasks. This paper proposes a multi-label learning algorithm that can also be used for single-label classification. It is based on standard support vector machines and changes the original single decision hyperplane into two parallel decision hyper-planes, which call multi-label parallel support vector machine (MLPSVM). At the end of the article, MLPSVM is compared with other multi-label learning algorithms. The experimental results show that the algorithm performs well on data sets.

[{'version': 'v1', 'created': 'Mon, 13 Apr 2020 10:04:25 GMT'}]

2020-04-14

[['Liu', 'Yanghong', ''], ['Lu', 'Jia', ''], ['Li', 'Tingting', '']]

1810.08985

Abhishek Dubey

Sanchita Basak, Saptarshi Sengupta, Abhishek Dubey

Mechanisms for Integrated Feature Normalization and Remaining Useful Life Estimation Using LSTMs Applied to Hard-Disks

9 pages, 13 figures, 2 tables

Proceedings of IEEE Smartcomp 2019

cs.LG stat.ML

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

With emerging smart communities, improving overall system availability is becoming a major concern. In order to improve the reliability of the components in a system we propose an inference model to predict Remaining Useful Life (RUL) of those components. In this paper we work with components of backend data servers such as hard disks, that are subject to degradation. A Deep Long-Short Term Memory (LSTM) Network is used as the backbone of this fast, data-driven decision framework and dynamically captures the pattern of the incoming data. In the article, we discuss the architecture of the neural network and describe the mechanisms to choose the various hyper-parameters. Further, we describe the challenges faced in extracting effective training sets from highly unorganized and class-imbalanced big data and establish methods for online predictions with extensive data pre-processing, feature extraction and validation through online simulation sets with unknown remaining useful lives of the hard disks. Our algorithm performs especially well in predicting RUL near the critical zone of a device approaching failure. With the proposed approach we are able to predict whether a disk is going to fail in next ten days with an average precision of 0.8435. We also show that the architecture trained on a particular model can be used to predict RUL for devices in different models from same manufacturer through transfer learning.

[{'version': 'v1', 'created': 'Sun, 21 Oct 2018 16:24:46 GMT'}, {'version': 'v2', 'created': 'Tue, 12 Feb 2019 08:57:58 GMT'}, {'version': 'v3', 'created': 'Mon, 17 Jun 2019 00:41:38 GMT'}]

2019-06-18

[['Basak', 'Sanchita', ''], ['Sengupta', 'Saptarshi', ''], ['Dubey', 'Abhishek', '']]

1610.02237

Hilde Kuehne

Hilde Kuehne, Alexander Richard, Juergen Gall

Weakly supervised learning of actions from transcripts

33 pages, 9 figures, to appear in CVIU

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We present an approach for weakly supervised learning of human actions from video transcriptions. Our system is based on the idea that, given a sequence of input data and a transcript, i.e. a list of the order the actions occur in the video, it is possible to infer the actions within the video stream, and thus, learn the related action models without the need for any frame-based annotation. Starting from the transcript information at hand, we split the given data sequences uniformly based on the number of expected actions. We then learn action models for each class by maximizing the probability that the training video sequences are generated by the action models given the sequence order as defined by the transcripts. The learned model can be used to temporally segment an unseen video with or without transcript. We evaluate our approach on four distinct activity datasets, namely Hollywood Extended, MPII Cooking, Breakfast and CRIM13. We show that our system is able to align the scripted actions with the video data and that the learned models localize and classify actions competitively in comparison to models trained with full supervision, i.e. with frame level annotations, and that they outperform any current state-of-the-art approach for aligning transcripts with video data.

[{'version': 'v1', 'created': 'Fri, 7 Oct 2016 12:00:08 GMT'}, {'version': 'v2', 'created': 'Mon, 19 Jun 2017 09:25:13 GMT'}]

2017-06-20

[['Kuehne', 'Hilde', ''], ['Richard', 'Alexander', ''], ['Gall', 'Juergen', '']]

physics/0305019

Bernhard Kaufmann

Bernhard Kaufmann

Fitting a Sum of Exponentials to Numerical Data

physics.data-an

A finite sum of exponential functions may be expressed by a linear combination of powers of the independent variable and by successive integrals of the sum. This is proved for the general case and the connection between the parameters in the sum and the coefficients in the linear combination is highlighted. The fitting of exponential functions to a given data- set is therefore reduced to a multilinear approximation procedure. The results of this approximation do not only provide the necessary information to compute the factors in the exponents and the weights of the exponential terms but also they are used to estimate the errors in the factors.

[{'version': 'v1', 'created': 'Tue, 6 May 2003 11:26:45 GMT'}]

2007-05-23

[['Kaufmann', 'Bernhard', '']]

1905.05765

Arjun Kar

Vijay Balasubramanian, Matthew DeCross, Arjun Kar, Onkar Parrikar

Quantum Complexity of Time Evolution with Chaotic Hamiltonians

35+11 pages, 13 figures, improved motivation of cost factors, improved discussion of superoperator corrections

10.1007/JHEP01(2020)134

hep-th cs.CC quant-ph

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We study the quantum complexity of time evolution in large-$N$ chaotic systems, with the SYK model as our main example. This complexity is expected to increase linearly for exponential time prior to saturating at its maximum value, and is related to the length of minimal geodesics on the manifold of unitary operators that act on Hilbert space. Using the Euler-Arnold formalism, we demonstrate that there is always a geodesic between the identity and the time evolution operator $e^{-iHt}$ whose length grows linearly with time. This geodesic is minimal until there is an obstruction to its minimality, after which it can fail to be a minimum either locally or globally. We identify a criterion - the Eigenstate Complexity Hypothesis (ECH) - which bounds the overlap between off-diagonal energy eigenstate projectors and the $k$-local operators of the theory, and use it to show that the linear geodesic will at least be a local minimum for exponential time. We show numerically that the large-$N$ SYK model (which is chaotic) satisfies ECH and thus has no local obstructions to linear growth of complexity for exponential time, as expected from holographic duality. In contrast, we also study the case with $N=2$ fermions (which is integrable) and find short-time linear complexity growth followed by oscillations. Our analysis relates complexity to familiar properties of physical theories like their spectra and the structure of energy eigenstates and has implications for the hypothesized computational complexity class separations PSPACE $\nsubseteq$ BQP/poly and PSPACE $\nsubseteq$ BQSUBEXP/subexp, and the "fast-forwarding" of quantum Hamiltonians.

[{'version': 'v1', 'created': 'Tue, 14 May 2019 18:00:00 GMT'}, {'version': 'v2', 'created': 'Fri, 11 Oct 2019 03:30:59 GMT'}, {'version': 'v3', 'created': 'Wed, 3 Jun 2020 19:40:31 GMT'}]

2020-06-05

[['Balasubramanian', 'Vijay', ''], ['DeCross', 'Matthew', ''], ['Kar', 'Arjun', ''], ['Parrikar', 'Onkar', '']]

1605.09782

Jeff Donahue

Jeff Donahue, Philipp Kr\"ahenb\"uhl, Trevor Darrell

Adversarial Feature Learning

Published as a conference paper at ICLR 2017. Changelog: (v7) Table 2 results improved 1-2% due to averaging predictions over 10 crops at test time, as done in Noroozi & Favaro; Table 3 VOC classification results slightly improved due to minor bugfix. (See v6 changelog for previous versions.)

cs.LG cs.AI cs.CV cs.NE stat.ML

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The ability of the Generative Adversarial Networks (GANs) framework to learn generative models mapping from simple latent distributions to arbitrarily complex data distributions has been demonstrated empirically, with compelling results showing that the latent space of such generators captures semantic variation in the data distribution. Intuitively, models trained to predict these semantic latent representations given data may serve as useful feature representations for auxiliary problems where semantics are relevant. However, in their existing form, GANs have no means of learning the inverse mapping -- projecting data back into the latent space. We propose Bidirectional Generative Adversarial Networks (BiGANs) as a means of learning this inverse mapping, and demonstrate that the resulting learned feature representation is useful for auxiliary supervised discrimination tasks, competitive with contemporary approaches to unsupervised and self-supervised feature learning.

[{'version': 'v1', 'created': 'Tue, 31 May 2016 19:37:29 GMT'}, {'version': 'v2', 'created': 'Fri, 15 Jul 2016 19:52:42 GMT'}, {'version': 'v3', 'created': 'Mon, 18 Jul 2016 03:25:03 GMT'}, {'version': 'v4', 'created': 'Fri, 4 Nov 2016 18:40:47 GMT'}, {'version': 'v5', 'created': 'Fri, 6 Jan 2017 02:49:57 GMT'}, {'version': 'v6', 'created': 'Mon, 9 Jan 2017 05:38:18 GMT'}, {'version': 'v7', 'created': 'Mon, 3 Apr 2017 20:34:36 GMT'}]

2017-04-05

[['Donahue', 'Jeff', ''], ['Krähenbühl', 'Philipp', ''], ['Darrell', 'Trevor', '']]

1602.00812

Richard Moot

Richard Moot (LaBRI, CNRS)

The Grail theorem prover: Type theory for syntax and semantics

Modern Perspectives in Type Theoretical Semantics, Springer, 2016

cs.CL

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

As the name suggests, type-logical grammars are a grammar formalism based on logic and type theory. From the prespective of grammar design, type-logical grammars develop the syntactic and semantic aspects of linguistic phenomena hand-in-hand, letting the desired semantics of an expression inform the syntactic type and vice versa. Prototypical examples of the successful application of type-logical grammars to the syntax-semantics interface include coordination, quantifier scope and extraction.This chapter describes the Grail theorem prover, a series of tools for designing and testing grammars in various modern type-logical grammars which functions as a tool . All tools described in this chapter are freely available.

[{'version': 'v1', 'created': 'Tue, 2 Feb 2016 07:35:02 GMT'}, {'version': 'v2', 'created': 'Fri, 26 Aug 2016 07:04:29 GMT'}]

2016-08-29

[['Moot', 'Richard', '', 'LaBRI, CNRS']]

1705.09042

Yanxin Lu

Yanxin Lu, Swarat Chaudhuri, Chris Jermaine, David Melski

Data-Driven Program Completion

cs.PL

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We introduce program splicing, a programming methodology that aims to automate the commonly used workflow of copying, pasting, and modifying code available online. Here, the programmer starts by writing a "draft" that mixes unfinished code, natural language comments, and correctness requirements in the form of test cases or API call sequence constraints. A program synthesizer that interacts with a large, searchable database of program snippets is used to automatically complete the draft into a program that meets the requirements. The synthesis process happens in two stages. First, the synthesizer identifies a small number of programs in the database that are relevant to the synthesis task. Next it uses an enumerative search to systematically fill the draft with expressions and statements from these relevant programs. The resulting program is returned to the programmer, who can modify it and possibly invoke additional rounds of synthesis. We present an implementation of program splicing for the Java programming language. The implementation uses a corpus of over 3.5 million procedures from an open-source software repository. Our evaluation uses the system in a suite of everyday programming tasks, and includes a comparison with a state-of-the-art competing approach as well as a user study. The results point to the broad scope and scalability of program splicing and indicate that the approach can significantly boost programmer productivity.

[{'version': 'v1', 'created': 'Thu, 25 May 2017 04:34:39 GMT'}]

2017-05-26

[['Lu', 'Yanxin', ''], ['Chaudhuri', 'Swarat', ''], ['Jermaine', 'Chris', ''], ['Melski', 'David', '']]

1709.06604

Vidhya Tekken Valapil

Vidhya Tekken-Valapil and Sandeep S. Kulkarni

Derivation of Network Reprogramming Protocol with Z3

cs.DC

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Networks protocols are the heart of communication networks. An efficient network protocol does maximum utilization of the underlying network capabilities. Network Protocol synthesis is the process of synthesizing or deriving network specific protocols from the requirements of a given specific network. In this report, we present a step-by-step approach for the automated synthesis of network protocols from the network specifications. Using SMT solvers to automate the protocol generation is the key idea behind the presented synthesis approach. The protocols generated using this approach followed the most optimal way of data transmission for the given network requirements.

[{'version': 'v1', 'created': 'Tue, 19 Sep 2017 18:52:31 GMT'}]

2017-09-21

[['Tekken-Valapil', 'Vidhya', ''], ['Kulkarni', 'Sandeep S.', '']]

1304.2574

Albert Sunny

Albert Sunny, Joy Kuri and Anurag Kumar

An Analysis on the Inter-Cell Station Dependency Probability in an IEEE 802.11 Infrastructure WLANs

cs.NI cs.IT math.IT

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

In this document, we are primarily interested in computing the probabilities of various types of dependencies that can occur in a multi-cell infrastructure network.

[{'version': 'v1', 'created': 'Tue, 9 Apr 2013 13:13:33 GMT'}]

2013-04-10

[['Sunny', 'Albert', ''], ['Kuri', 'Joy', ''], ['Kumar', 'Anurag', '']]

2201.00184

Rathnakar Madhukar Yerraguntla

Sanjiva Prasad, R. Madhukar Yerraguntla, Subodh Sharma

Secure Information Flow Typing in LUSTRE

arXiv admin note: substantial text overlap with arXiv:2105.10687

cs.PL cs.LO

http://creativecommons.org/licenses/by/4.0/

Synchronous reactive data flow is a paradigm that provides a high-level abstract programming model for embedded and cyber-physical systems, including the locally synchronous components of IoT systems. Security in such systems is severely compromised due to low-level programming, ill-defined interfaces and inattention to security classification of data. By incorporating a Denning-style lattice-based secure information flow framework into a synchronous reactive data flow language, we provide a framework in which correct-and-secure-by-construction implementations for such systems may be specified and derived. In particular, we propose an extension of the Lustre programming framework with a security type system. The novelty of our type system lies in a symbolic formulation of constraints over security type variables, in particular the treatment of node calls, which allows us to reason about secure flow with respect to any security class lattice. The main theorem is the soundness of our type system with respect to the co-inductive operational semantics of Lustre, which we prove by showing that well-typed programs exhibit non-interference. Rather than tackle the full language, we first prove the non-interference result for a well-behaved sub-language called "Normalised Lustre" (NLustre), for which our type system is far simpler. We then show that Bourke et al.'s semantics-preserving "normalisation" transformations from Lustre to NLustre are security-preserving as well. This preservation of security types by the normalisation transformations is a property akin to "subject reduction" but at the level of compiler transformations. The main result that well-security-typed Lustre programs are non-interfering follows from a reduction to our earlier result of non-interference for NLustre via the semantics-preservation (of Bourke et al.) and type preservation results.

[{'version': 'v1', 'created': 'Sat, 1 Jan 2022 13:07:06 GMT'}]

2022-01-04

[['Prasad', 'Sanjiva', ''], ['Yerraguntla', 'R. Madhukar', ''], ['Sharma', 'Subodh', '']]

2207.11839

Ahmad Mustapha

Ahmad Mustapha, Wael Khreich, Wasim Masr

A Deep Dive into Deep Cluster

cs.CV

http://creativecommons.org/licenses/by-nc-nd/4.0/

Deep Learning has demonstrated a significant improvement against traditional machine learning approaches in different domains such as image and speech recognition. Their success on benchmark datasets is transferred to the real-world through pretrained models by practitioners. Pretraining visual models using supervised learning requires a significant amount of expensive data annotation. To tackle this limitation, DeepCluster - a simple and scalable unsupervised pretraining of visual representations - has been proposed. However, the underlying work of the model is not yet well understood. In this paper, we analyze DeepCluster internals and exhaustively evaluate the impact of various hyperparameters over a wide range of values on three different datasets. Accordingly, we propose an explanation of why the algorithm works in practice. We also show that DeepCluster convergence and performance highly depend on the interplay between the quality of the randomly initialized filters of the convolutional layer and the selected number of clusters. Furthermore, we demonstrate that continuous clustering is not critical for DeepCluster convergence. Therefore, early stopping of the clustering phase will reduce the training time and allow the algorithm to scale to large datasets. Finally, we derive plausible hyperparameter selection criteria in a semi-supervised setting.

[{'version': 'v1', 'created': 'Sun, 24 Jul 2022 22:55:09 GMT'}, {'version': 'v2', 'created': 'Mon, 10 Oct 2022 20:01:15 GMT'}]

2022-10-12

[['Mustapha', 'Ahmad', ''], ['Khreich', 'Wael', ''], ['Masr', 'Wasim', '']]

1802.02407

Sunayana Dutta

Sunayana Dutta, Marios C. Tsatsos, Saurabh Basu and Axel U. J. Lode

Management of the Correlations of Ultracold Bosons in Triple Wells

Supplementary Material is available

New J. Phys. 21 ( 2019 ) 053044

10.1088/1367-2630/ab117d

cond-mat.quant-gas physics.atom-ph quant-ph

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Ultracold interacting atoms are an excellent tool to study correlation functions of many-body systems that are generally eluding detection and manipulation. Herein, we investigate the ground state of bosons in a tilted triple-well potential and characterize the many-body state by the eigenvalues of its reduced one-body density matrix and Glauber correlation functions. We unveil how the interplay between the interaction strength and the tilt can be used to control the number of correlated wells as well as the fragmentation, i.e. the number of macroscopic eigenvalues of the reduced one-body density matrix.

[{'version': 'v1', 'created': 'Wed, 7 Feb 2018 13:11:50 GMT'}]

2019-06-12

[['Dutta', 'Sunayana', ''], ['Tsatsos', 'Marios C.', ''], ['Basu', 'Saurabh', ''], ['Lode', 'Axel U. J.', '']]

2206.02972

Adam Charles

Noga Mudrik, Yenho Chen, Eva Yezerets, Christopher J. Rozell, and Adam S. Charles

Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics

25 pages, 15 figures

stat.ML cs.LG eess.SP q-bio.NC stat.AP

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Learning interpretable representations of neural dynamics at a population level is a crucial first step to understanding how neural activity relates to perception and behavior. Models of neural dynamics often focus on either low-dimensional projections of neural activity, or on learning dynamical systems that explicitly relate to the neural state over time. We discuss how these two approaches are interrelated by considering dynamical systems as representative of flows on a low-dimensional manifold. Building on this concept, we propose a new decomposed dynamical system model that represents complex non-stationary and nonlinear dynamics of time-series data as a sparse combination of simpler, more interpretable components. The decomposed nature of the dynamics generalizes over previous switched approaches and enables modeling of overlapping and non-stationary drifts in the dynamics. We further present a dictionary learning-driven approach to model fitting, where we leverage recent results in tracking sparse vectors over time. We demonstrate that our model can learn efficient representations and smooth transitions between dynamical modes in both continuous-time and discrete-time examples. We show results on low-dimensional linear and nonlinear attractors to demonstrate that our decomposed dynamical systems model can well approximate nonlinear dynamics. Additionally, we apply our model to C. elegans data, illustrating a diversity of dynamics that is obscured when classified into discrete states.

[{'version': 'v1', 'created': 'Tue, 7 Jun 2022 02:25:38 GMT'}]

2022-06-08

[['Mudrik', 'Noga', ''], ['Chen', 'Yenho', ''], ['Yezerets', 'Eva', ''], ['Rozell', 'Christopher J.', ''], ['Charles', 'Adam S.', '']]

1503.07795

Marina Sokolova

Naveen Kumar Parachur Cotha and Marina Sokolova

Multi-Labeled Classification of Demographic Attributes of Patients: a case study of diabetics patients

16 pages, 9 tables

cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Automated learning of patients demographics can be seen as multi-label problem where a patient model is based on different race and gender groups. The resulting model can be further integrated into Privacy-Preserving Data Mining, where it can be used to assess risk of identification of different patient groups. Our project considers relations between diabetes and demographics of patients as a multi-labelled problem. Most research in this area has been done as binary classification, where the target class is finding if a person has diabetes or not. But very few, and maybe no work has been done in multi-labeled analysis of the demographics of patients who are likely to be diagnosed with diabetes. To identify such groups, we applied ensembles of several multi-label learning algorithms.

[{'version': 'v1', 'created': 'Thu, 26 Mar 2015 17:22:26 GMT'}]

2015-03-27

[['Cotha', 'Naveen Kumar Parachur', ''], ['Sokolova', 'Marina', '']]

2104.10908

Efi Efrati

Ana Silva, Eitan Ben Av and Efi Efrati

Explicit, time-reversible and symplectic integrator for Hamiltonians in isotropic uniformly curved geometries

math.NA cs.NA math-ph math.MP nlin.CD

http://creativecommons.org/licenses/by/4.0/

The kinetic term of the $N$-body Hamiltonian system defined on the surface of the sphere is non-separable. As a result, standard explicit symplectic integrators are inapplicable. We exploit an underlying hierarchy in the structure of the kinetic term to construct an explicit time-reversible symplectic scheme of second order. We use iterative applications of the method to construct a fourth order scheme and demonstrate its efficiency.

[{'version': 'v1', 'created': 'Thu, 22 Apr 2021 07:38:28 GMT'}]

2021-04-23

[['Silva', 'Ana', ''], ['Av', 'Eitan Ben', ''], ['Efrati', 'Efi', '']]

1401.5503

Spyros Themelis

Spyros I. Themelis

Two-color pump-probe dynamics of transitions between doubly excited states of Helium

10 pages, 5 figures

Balkan Physics Letters, BPL, 12 (3), pp. 158 - 167 (2004) - 1 July 2004

physics.atom-ph

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We discuss the dynamics of the two-photon resonant ionization of helium involving two autoionizing states in the presence of two-color laser fields. The first source is tuned around the transition from the ground state to the 2s2p 1Po autoionizing state, and the second couples the state 2s2p 1Po to the 2p2 1S autoionizing state. The laser coupling between the doubly excited states is shown to lead to modifications of the Beutler-Fano profile and the appearance of an Autler-Townes doublet. This double resonance effect between autoionizing states can be observed at moderate laser intensities easily attainable by currently operated sources.

[{'version': 'v1', 'created': 'Tue, 21 Jan 2014 22:04:46 GMT'}, {'version': 'v2', 'created': 'Thu, 23 Jan 2014 19:54:33 GMT'}]

2014-01-24

[['Themelis', 'Spyros I.', '']]

2105.00695

Indrit Nallbani

Indrit Nallbani, Reyhan Kevser Keser, Aydin Ayanzadeh, Nurullah \c{C}al{\i}k, Beh\c{c}et U\u{g}ur T\"oreyin

ResVGAE: Going Deeper with Residual Modules for Link Prediction

cs.LG cs.SI

http://creativecommons.org/licenses/by/4.0/

Graph autoencoders are efficient at embedding graph-based data sets. Most graph autoencoder architectures have shallow depths which limits their ability to capture meaningful relations between nodes separated by multi-hops. In this paper, we propose Residual Variational Graph Autoencoder, ResVGAE, a deep variational graph autoencoder model with multiple residual modules. We show that our multiple residual modules, a convolutional layer with residual connection, improve the average precision of the graph autoencoders. Experimental results suggest that our proposed model with residual modules outperforms the models without residual modules and achieves similar results when compared with other state-of-the-art methods.

[{'version': 'v1', 'created': 'Mon, 3 May 2021 09:05:46 GMT'}, {'version': 'v2', 'created': 'Thu, 4 Aug 2022 20:30:34 GMT'}]

2022-08-08

[['Nallbani', 'Indrit', ''], ['Keser', 'Reyhan Kevser', ''], ['Ayanzadeh', 'Aydin', ''], ['Çalık', 'Nurullah', ''], ['Töreyin', 'Behçet Uğur', '']]

physics/0410134

Philippe Cardin

Nathanael Schaeffer (LGIT), Philippe Cardin (LGIT)

Quasi-geostrophic kinematic dynamos at low magnetic Prandtl number

physics.class-ph astro-ph physics.geo-ph

Rapidly rotating spherical kinematic dynamos are computed using the combination of a quasi geostrophic (QG) model for the velocity field and a classical spectral 3D code for the magnetic field. On one hand, the QG flow is computed in the equatorial plane of a sphere and corresponds to Rossby wave instabilities of a geostrophic internal shear layer produced by differential rotation. On the other hand, the induction equation is computed in the full sphere after a continuation of the QG flow along the rotation axis. Differential rotation and Rossby-wave propagation are the key ingredients of the dynamo process which can be interpreted in terms of $\alpha\Omega$ dynamo. Taking into account the quasi geostrophy of the velocity field to increase its time and space resolution enables us to exhibit numerical dynamos with very low Ekman (rapidly rotating) and Prandtl numbers (liquid metals) which are asymptotically relevant to model planetary core dynamos.

[{'version': 'v1', 'created': 'Tue, 19 Oct 2004 08:04:25 GMT'}]

2007-05-23

[['Schaeffer', 'Nathanael', '', 'LGIT'], ['Cardin', 'Philippe', '', 'LGIT']]

1611.08575

Boris Tomasik

Boris Tomasik and Renata Kopecna

Event Shape Sorting: selecting events with similar evolution

6 pages, 4 figures, proceedings from XII Quark Confinement and the Hadron Spectrum, Thessaloniki, Grece, 28.8.2016-4.9.2016

10.1051/epjconf/201713713020

physics.data-an hep-ex hep-ph nucl-ex nucl-th

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We present novel method for the organisation of events. The method is based on comparing event-by-event histograms of a chosen quantity Q that is measured for each particle in every event. The events are organised in such a way that those with similar shape of the Q-histograms end-up placed close to each other. We apply the method on histograms of azimuthal angle of the produced hadrons in ultrarelativsitic nuclear collisions. By selecting events with similar azimuthal shape of their hadron distribution one chooses events which are likely that they underwent similar evolution from the initial state to the freeze-out. Such events can more easily be compared to theoretical simulations where all conditions can be controlled. We illustrate the method on data simulated by the AMPT model.

[{'version': 'v1', 'created': 'Fri, 25 Nov 2016 20:17:48 GMT'}]

2017-04-05

[['Tomasik', 'Boris', ''], ['Kopecna', 'Renata', '']]

2202.05598

Alex Gim\'enez-Romero

\`Alex Gim\'enez-Romero, Rosa Flaquer-Galm\'es and Manuel A. Matias

Vector-borne diseases with non-stationary vector populations: the case of growing and decaying populations

16 pages, 6 figures

Phys. Rev. E 106, 054402 (2022)

10.1103/PhysRevE.106.054402

q-bio.PE physics.bio-ph

http://creativecommons.org/licenses/by/4.0/

Since the last century, deterministic compartmental models have emerged as powerful tools to predict and control epidemic outbreaks, in many cases helping to mitigate their impacts. A key quantity for these models is the so-called Basic Reproduction Number, that measures the number of secondary infections produced by an initial infected individual in a fully susceptible population. Some methods have been developed to allow the direct computation of this quantity provided that some conditions are fulfilled, such that the model has a pre-pandemic disease-free equilibrium state. This condition is only fulfilled when the populations are stationary. In the case of vector-borne diseases, this implies that the vector birth and death rates need to be balanced, what is not fulfilled in many realistic cases in which the vector population grow or decrease. Here we develop a vector-borne epidemic model with growing and decaying vector populations and study the conditions under which the standard methods to compute $R_0$ work and discuss an alternative when they fail. We also show that growing vector populations produce a delay in the epidemic dynamics when compared to the case of the stationary vector population. Finally, we discuss the conditions under which the model can be reduced to the SIR model with fewer compartments and parameters, which helps in solving the problem of parameter unidentifiability of many vector-borne epidemic models.

[{'version': 'v1', 'created': 'Fri, 11 Feb 2022 13:32:10 GMT'}]

2022-11-03

[['Giménez-Romero', 'Àlex', ''], ['Flaquer-Galmés', 'Rosa', ''], ['Matias', 'Manuel A.', '']]

2103.11642

Matthew R Behrend

Matthew R. Behrend and Sean M. Robinson

A Batch Normalization Classifier for Domain Adaptation

cs.CV cs.LG

http://creativecommons.org/licenses/by/4.0/

Adapting a model to perform well on unforeseen data outside its training set is a common problem that continues to motivate new approaches. We demonstrate that application of batch normalization in the output layer, prior to softmax activation, results in improved generalization across visual data domains in a refined ResNet model. The approach adds negligible computational complexity yet outperforms many domain adaptation methods that explicitly learn to align data domains. We benchmark this technique on the Office-Home dataset and show that batch normalization is competitive with other leading methods. We show that this method is not sensitive to presence of source data during adaptation and further present the impact on trained tensor distributions tends toward sparsity. Code is available at https://github.com/matthewbehrend/BNC

[{'version': 'v1', 'created': 'Mon, 22 Mar 2021 08:03:44 GMT'}]

2021-03-23

[['Behrend', 'Matthew R.', ''], ['Robinson', 'Sean M.', '']]

2301.12521

Harshad Kalyankar

Harshad Kalyankar, Lutz Taubert, and Israel Wygnanski

Re-orienting the Turbulent flow over an Inclined Cylinder of Finite Aspect ratio

physics.flu-dyn

http://creativecommons.org/publicdomain/zero/1.0/

Turbulent flow around a swept back circular cylinder was investigated experimentally using one or two small sweeping jets emanating tangentially to the surface and orthogonal to the axis of the cylinder creating a yawing moment that overcomes the natural restoring force to the plane of symmetry. It appears from the integral force balance data that large yawing moment coefficients can be generated in this manner, and the results could be used in the orientation and attitude control of a refueling boom thus avoiding the H-shape control surfaces that are currently used on Air Force tankers. The interaction between the jet and the flow in the lee of the cylinder was mapped using surface oil flow visualization and 2D-Particle Image velocimetry technique. The sensitivity of the interaction and its outcome to the change in the azimuthal location of the actuators was investigated.

[{'version': 'v1', 'created': 'Sun, 29 Jan 2023 19:27:25 GMT'}]

2023-01-31

[['Kalyankar', 'Harshad', ''], ['Taubert', 'Lutz', ''], ['Wygnanski', 'Israel', '']]

2205.04536

Marco Rodas

Jeff Block, Yocelyne Gomez, Carolina Gonzalez, Miguel Magana, Marco Rodas and Academic Advisor and Mentor, Dr. Jongwook Woo

LA City Bike Lane Infrastructure and its Effects on Business Closures From 2016-2021

physics.soc-ph

http://creativecommons.org/licenses/by/4.0/

This paper analyzes the presence of bike lanes within the city of Los Angeles and how their presence could offer positive economic benefits to businesses. City wide shutdowns initiated in March of 2020 acted as a demand shock to local businesses. Over the next two years, businesses struggled to keep the doors open. We explored the possibility of bicycle infrastructure playing a role in supporting/insulating local businesses from closure and positive economic impacts. Our analysis shows that the businesses along bike lane in Los Angeles runs positively, and that COVID-19 is not related to, significantly.

[{'version': 'v1', 'created': 'Wed, 27 Apr 2022 06:40:09 GMT'}]

2022-05-11

[['Block', 'Jeff', ''], ['Gomez', 'Yocelyne', ''], ['Gonzalez', 'Carolina', ''], ['Magana', 'Miguel', ''], ['Rodas', 'Marco', ''], ['Advisor', 'Academic', ''], ['Mentor', '', ''], ['Woo', 'Dr. Jongwook', '']]

1811.06771

Bishoksan Kafle

Bishoksan Kafle, Graeme Gange, Peter Schachte, Harald Sondergaard, Peter J. Stuckey

Precondition Inference via Partitioning of Initial States

19 pages, 8 figures

cs.LO

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Precondition inference is a non-trivial task with several applications in program analysis and verification. We present a novel iterative method for automatically deriving sufficient preconditions for safety and unsafety of programs which introduces a new dimension of modularity. Each iteration maintains over-approximations of the set of \emph{safe} and \emph{unsafe} \emph{initial} states. Then we repeatedly use the current abstractions to partition the program's \emph{initial} states into those known to be safe, known to be unsafe and unknown, and construct a revised program focusing on those initial states that are not yet known to be safe or unsafe. An experimental evaluation of the method on a set of software verification benchmarks shows that it can solve problems which are not solvable using previous methods.

[{'version': 'v1', 'created': 'Fri, 16 Nov 2018 11:55:51 GMT'}]

2018-11-19

[['Kafle', 'Bishoksan', ''], ['Gange', 'Graeme', ''], ['Schachte', 'Peter', ''], ['Sondergaard', 'Harald', ''], ['Stuckey', 'Peter J.', '']]

2210.00094

Amin Ghiasi

Amin Ghiasi, Ali Shafahi, Reza Ardekani

Adaptive Weight Decay: On The Fly Weight Decay Tuning for Improving Robustness

cs.LG cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We introduce adaptive weight decay, which automatically tunes the hyper-parameter for weight decay during each training iteration. For classification problems, we propose changing the value of the weight decay hyper-parameter on the fly based on the strength of updates from the classification loss (i.e., gradient of cross-entropy), and the regularization loss (i.e., $\ell_2$-norm of the weights). We show that this simple modification can result in large improvements in adversarial robustness -- an area which suffers from robust overfitting -- without requiring extra data. Specifically, our reformulation results in 20% relative robustness improvement for CIFAR-100, and 10% relative robustness improvement on CIFAR-10 comparing to traditional weight decay. In addition, this method has other desirable properties, such as less sensitivity to learning rate, and smaller weight norms, which the latter contributes to robustness to overfitting to label noise, and pruning.

[{'version': 'v1', 'created': 'Fri, 30 Sep 2022 21:13:00 GMT'}]

2022-10-04

[['Ghiasi', 'Amin', ''], ['Shafahi', 'Ali', ''], ['Ardekani', 'Reza', '']]

1611.01890

Geoff Boeing

Geoff Boeing

OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks

peer-reviewed journal article

Computers, Environment and Urban Systems 65, 126-139

10.1016/j.compenvurbsys.2017.05.004

cs.SI physics.soc-ph

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Urban scholars have studied street networks in various ways, but there are data availability and consistency limitations to the current urban planning/street network analysis literature. To address these challenges, this article presents OSMnx, a new tool to make the collection of data and creation and analysis of street networks simple, consistent, automatable and sound from the perspectives of graph theory, transportation, and urban design. OSMnx contributes five significant capabilities for researchers and practitioners: first, the automated downloading of political boundaries and building footprints; second, the tailored and automated downloading and constructing of street network data from OpenStreetMap; third, the algorithmic correction of network topology; fourth, the ability to save street networks to disk as shapefiles, GraphML, or SVG files; and fifth, the ability to analyze street networks, including calculating routes, projecting and visualizing networks, and calculating metric and topological measures. These measures include those common in urban design and transportation studies, as well as advanced measures of the structure and topology of the network. Finally, this article presents a simple case study using OSMnx to construct and analyze street networks in Portland, Oregon.

[{'version': 'v1', 'created': 'Mon, 7 Nov 2016 04:41:18 GMT'}, {'version': 'v2', 'created': 'Tue, 8 Nov 2016 02:42:32 GMT'}, {'version': 'v3', 'created': 'Mon, 28 Nov 2016 00:51:19 GMT'}, {'version': 'v4', 'created': 'Thu, 9 Feb 2017 21:52:34 GMT'}, {'version': 'v5', 'created': 'Mon, 10 Jul 2017 19:20:02 GMT'}]

2017-07-12

[['Boeing', 'Geoff', '']]

2010.09672

Ansh Khurana

Soumajit Majumder, Ansh Khurana, Abhinav Rai, Angela Yao

Multi-Stage Fusion for One-Click Segmentation

A preprint of the accepted paper at GCPR 2020

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Segmenting objects of interest in an image is an essential building block of applications such as photo-editing and image analysis. Under interactive settings, one should achieve good segmentations while minimizing user input. Current deep learning-based interactive segmentation approaches use early fusion and incorporate user cues at the image input layer. Since segmentation CNNs have many layers, early fusion may weaken the influence of user interactions on the final prediction results. As such, we propose a new multi-stage guidance framework for interactive segmentation. By incorporating user cues at different stages of the network, we allow user interactions to impact the final segmentation output in a more direct way. Our proposed framework has a negligible increase in parameter count compared to early-fusion frameworks. We perform extensive experimentation on the standard interactive instance segmentation and one-click segmentation benchmarks and report state-of-the-art performance.

[{'version': 'v1', 'created': 'Mon, 19 Oct 2020 17:07:40 GMT'}, {'version': 'v2', 'created': 'Tue, 20 Oct 2020 12:52:55 GMT'}]

2020-10-21

[['Majumder', 'Soumajit', ''], ['Khurana', 'Ansh', ''], ['Rai', 'Abhinav', ''], ['Yao', 'Angela', '']]