pwc-archive/papers-with-abstracts huggingface dataset:
Short-and-Sparse Deconvolution Via Rank-One Constrained Optimization (ROCO)
Authors: Cheng Cheng, Wei Dai
Date: 2021-10-05 00:00:00

Short-and-sparse deconvolution (SaSD) aims to recover a short kernel and a long and sparse signal from their convolution. In the literature, formulations of blind deconvolution is either a convex programming via a matrix lifting of convolution, or a bilinear Lasso. Optimization solvers are typically based on bilinear factorizations. In this paper, we formulate SaSD as a non-convex optimization with a rank-one matrix constraint, hence referred to as Rank-One Constrained Optimization (ROCO). The solver is based on alternating direction method of multipliers (ADMM). It operates on the full rank-one matrix rather than bilinear factorizations. Closed form updates are derived for the efficiency of ADMM. Simulations include both synthetic data and real images. Results show substantial improvements in recovery accuracy (at least 19dB in PSNR for real images) and comparable runtime compared with benchmark algorithms based on bilinear factorization.
A signed pulse-train based image processor-array for parallel kernel convolution in vision sensors
Authors: Ahmad Reza Danesh, Mehdi Habibi
Date: 2021-10-05 00:00:00
Tasks: Edge Detection

Purpose- High speed image processing is a challenging task for real-time applications such as product quality control of manufacturing lines. Smart image sensors use an array of in-pixel processors to facilitate high-speed real-time image processing. These sensors are usually used to perform the initial low-level bulk image filtering and enhancement. Design- In this paper, using pulse-width modulated signals and regular nearest neighbor interconnections, a convolution image processor is presented. The presented processor is not only capable of processing arbitrary size kernels, but the kernel coefficients can be any arbitrary positive or negative floating number. Findings- The performance of the proposed architecture is evaluated on a FPGA platform. The peak signal-to-noise ratio (PSNR) metric is used to measure the computation error for different images, filters, and illuminations. Finally, the power consumption of the circuit in different operation conditions is presented. Originality/Value- The presented processor array can be used for high speed kernel convolution image processing tasks including arbitrary size edge detection and sharpening functions which require negative and fractional kernel values.
A 0.4 V, 19 pW Subthreshold Voltage Reference Generator Using Separate Line Sensitivity and Temperature Coefficient Correction Stages
Authors: Mohammad Azimi, Mehdi Habibi, Hamid-Reza Karimi-Alavijeh
Date: 2021-10-05 00:00:00
Tasks: Sensitivity

Sensor nodes and IoT systems require blocks that not only consume low power but also have good accuracy. Voltage reference generators are also considered important building blocks in sensor interface circuits. This paper presents a solution to increase the accuracy of low power subthreshold voltage generators by lowering the circuit sensitivity to temperature and supply voltage variations. The enhancement is achieved by using two separate stages for temperature coefficient (TC) and line sensitivity (LS) correction. A 0.18 {\mu}m standard CMOS process has been used for the proposed structure. The effects of parameter variations in the fabrication process are investigated using post-layout simulation and Monte Carlo analysis. In the supply voltage of 0.4 V to 2 V, an LS of 143.8 ppm/V is obtained. In typical corner conditions, the achieved TC is 7.45 ppm/{\circ}C over the temperature range of 0{\circ}C to 80{\circ}C. Due to process changes, and mainly affected by threshold voltage variations, the average TC can change to 39.2 ppm/{\circ}C. The minimum power consumption at 0{\circ}C and at a supply voltage of 0.4 V is 3.25 pW while the power consumption increases to 2.84 nW in 80{\circ}C and at the maximum supply voltage of 2 V.
RF Lens Antenna Array-Based One-Shot Coarse Pointing for Hybrid RF/FSO Communications
Authors: Hyung-Joo Moon, Hong-Bae Jeon, Chan-Byoung Chae
Date: 2021-10-05 00:00:00

Because of its high directivity, free-space optical (FSO) communication offers a number of advantages. It can, however, give rise to major system difficulties concerning alignment between two terminals. During the link-acquisition step (a.k.a. coarse pointing), a ground station can be prevented from acquiring optical links due to pointing errors and insufficient information about unmanned aerial vehicle locations. We propose, in this letter, a radio-frequency (RF) lens antenna array to increase the performance of coarse pointing in hybrid RF/FSO communications. The proposed algorithm using a novel closed-form angle estimator, compared to conventional methods, reduces the minimum outage probability by over a thousand times.
Reputation and Punishment sustain cooperation in the Optional Public Goods Game
Authors: Shirsendu Podder, Simone Righi, Francesca Pancotto
Date: 2021-10-05 00:00:00

Cooperative behaviour has been extensively studied as a choice between cooperation and defection. However, the possibility to not participate is also frequently available. This type of problem can be studied through the optional public goods game. The introduction of the "Loner" strategy, allows players to withdraw from the game, which leads to a cooperator-defector-loner cycle. While prosocial punishment can help increase cooperation, anti-social punishment -- where defectors punish cooperators -- causes its downfall in both experimental and theoretical studies. In this paper, we introduce social norms that allow agents to condition their behaviour to the reputation of their peers. We benchmark this both with respect to the standard optional public goods game and to the variant where all types of punishment are allowed. We find that a social norm imposing a more moderate reputational penalty for opting out than for defecting, increases cooperation. When, besides reputation, punishment is also possible, the two mechanisms work synergically under all social norms that do not assign to loners a strictly worse reputation than to defectors. Under this latter setup, the high levels of cooperation are sustained by conditional strategies, which largely reduce the use of pro-social punishment and almost completely eliminate anti-social punishment.
A Weight of Evidence approach to classify nanomaterials according to the EU Classification, Labelling and Packaging regulation criteria
Authors: Gianpietro Basei, Alex Zabeo, Kirsten Rasmussen, Georgia Tsiliki, Danail Hristozov
Date: 2021-10-05 00:00:00
Tasks: Classification

In the context of the EU Horizon 2020 GRACIOUS project, we proposed a quantitative Weight of Evidence (WoE) approach for hazard classification of nanomaterials (NMs). This approach is based on the requirements of the European Regulation on Classification, Labelling and Packaging of Substances and Mixtures (the CLP Regulation), which implements the United Nations' Globally Harmonized System of Classification and Labelling of Chemicals (UN GHS) in the European Union. The goal of this WoE methodology is to facilitate classification of NMs according to CLP criteria, following the decision trees defined in ECHA's CLP regulatory guidance. The proposed methodology involves the following stages: (1) collection of data for different NMs related to the endpoint of interest: each study related to each NM is referred as a Line of Evidence (LoE); (2) computation of weighted scores for each LoE: each LoE is weighted by a score calculated based on agreed data quality and completeness criteria defined in the GRACIOUS project; (3) comparison and integration of the weighed LoEs for each NM: A Monte Carlo resampling approach is adopted to quantitatively and probabilistically integrate the weighted evidence; and (4) assignment of each NM to a hazard class: according to the results, each NM is assigned to one of the classes defined by the CLP regulation. Furthermore, to facilitate the integration and the classification of the weighted LoEs, an R tool was developed. Finally, the approach was tested against an endpoint relevant to CLP (Aquatic Toxicity) using data retrieved from the eNanoMapper database, results obtained were consistent to results in ECHA registration dossiers and in recent literature
Heritable Nongenetic Information That is Independent of DNA and That Governs Organismal Development, Tissue Regeneration, and Tumor Architecture
Authors: David H Nguyen, Daniel Gallegos
Date: 2021-10-04 00:00:00
Tasks: Diversity, Form

Numerous studies of the tumor microenvironment, interspecies xenografting, and limb regeneration suggest the existence of a Tissue Spatial Code (TSC) that controls tissue structure in a quasi-epigenetic fashion. The term 'epigenetic' is an inadequate label for the concept that encompasses the TSC, because this information does not act upon DNA or chromatin via post-translational modifications (i.e. DNA methylation, histone acetylation). A broader term is needed to capture the diversity of three-dimensional (3D) spatial codes in biology. One such term is Heritable Nongenetic Information (HNI, pronounced 'honey'), which encompasses the TSC. The term 'heritable' is appropriate, because this information is passed onto offspring, otherwise it would have disappeared during evolution. Another reason for the heritability of HNI is that the spatial information observed in tissues is not reducible to the laws of physics, meaning structures like epithelial tubes or neural circuits do not spontaneously form in an aqueous solution; pre-existing physiological information in the microenvironment is necessary. In summary, HNI is defined as: heritable, instructional information that affects molecular-, cellular-, tissue-, or organ-level function without having to post-translationally modify DNA or chromatin in order to be inherited.
QoS-aware Big Service Composition using Distributed Co-Evolutionary Algorithm
Authors: Avik Dutta, Chandrashekar Jatoth, G R Gangadharan, Ugo Fiore
Date: 2021-09-14 00:00:00
Proceeding: Concurrency and Computation Practice and Experience 2021 9
Tasks: Diversity, Multiobjective Optimization, Service Composition

Big services are collections of interrelated web services across virtual and physical domains, processing Big Data. Existing service selection and composition algorithms fail to achieve the global optimum solution in a reasonable time. In this paper, we design an efficient quality of service‐aware big service composition methodology using a distributed co‐evolutionary algorithm. In our proposed model, we develop a distributed NSGA‐III for finding the optimal Pareto front and a distributed multi‐objective Jaya algorithm for enhancing the diversity of solutions. The distributed co‐evolutionary algorithm finds the near‐optimal solution in a fast and scalable way.
Enhancement of Anime Imaging Enlargement using Modified Super-Resolution CNN
Authors: Tanakit Intaniyom, Warinthorn Thananporn, Kuntpong Woraratpanya
Date: 2021-10-05 00:00:00
Tasks: Super-Resolution

Anime is a storytelling medium similar to movies and books. Anime images are a kind of artworks, which are almost entirely drawn by hand. Hence, reproducing existing Anime with larger sizes and higher quality images is expensive. Therefore, we proposed a model based on convolutional neural networks to extract outstanding features of images, enlarge those images, and enhance the quality of Anime images. We trained the model with a training set of 160 images and a validation set of 20 images. We tested the trained model with a testing set of 20 images. The experimental results indicated that our model successfully enhanced the image quality with a larger image-size when compared with the common existing image enlargement and the original SRCNN method.
A Survey of Fish Tracking Techniques Based on Computer Vision
Authors: Weiran Li, Zhenbo Li, Fei Li, Meng Yuan, Chaojun Cen, Yanyu Qi, Qiannan Guo, You Li
Date: 2021-10-06 00:00:00
Tasks: Image Enhancement, Transfer Learning

Fish tracking is a key technology for obtaining movement trajectories and identifying abnormal behavior. However, it faces considerable challenges, including occlusion, multi-scale tracking, and fish deformation. Notably, extant reviews have focused more on behavioral analysis rather than providing a comprehensive overview of computer vision-based fish tracking approaches. This paper presents a comprehensive review of the advancements of fish tracking technologies over the past seven years (2017-2023). It explores diverse fish tracking techniques with an emphasis on fundamental localization and tracking methods. Auxiliary plugins commonly integrated into fish tracking systems, such as underwater image enhancement and re-identification, are also examined. Additionally, this paper summarizes open-source datasets, evaluation metrics, challenges, and applications in fish tracking research. Finally, a comprehensive discussion offers insights and future directions for vision-based fish tracking techniques. We hope that our work could provide a partial reference in the development of fish tracking algorithms.
Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer
Authors: Yining Ma, Jingwen Li, Zhiguang Cao, Wen Song, Le Zhang, Zhenghua Chen, Jing Tang
Date: 2021-10-06 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Traveling Salesman Problem

Recently, Transformer has become a prevailing deep architecture for solving vehicle routing problems (VRPs). However, it is less effective in learning improvement models for VRP because its positional encoding (PE) method is not suitable in representing VRP solutions. This paper presents a novel Dual-Aspect Collaborative Transformer (DACT) to learn embeddings for the node and positional features separately, instead of fusing them together as done in existing ones, so as to avoid potential noises and incompatible correlations. Moreover, the positional features are embedded through a novel cyclic positional encoding (CPE) method to allow Transformer to effectively capture the circularity and symmetry of VRP solutions (i.e., cyclic sequences). We train DACT using Proximal Policy Optimization and design a curriculum learning strategy for better sample efficiency. We apply DACT to solve the traveling salesman problem (TSP) and capacitated vehicle routing problem (CVRP). Results show that our DACT outperforms existing Transformer based improvement models, and exhibits much better generalization performance across different problem sizes on synthetic and benchmark instances, respectively.
Secure Byzantine-Robust Distributed Learning via Clustering
Authors: Raj Kiriti Velicheti, Derek Xia, Oluwasanmi Koyejo
Date: 2021-10-06 00:00:00
Tasks: Clustering, Federated Learning, Privacy Preserving

Federated learning systems that jointly preserve Byzantine robustness and privacy have remained an open problem. Robust aggregation, the standard defense for Byzantine attacks, generally requires server access to individual updates or nonlinear computation -- thus is incompatible with privacy-preserving methods such as secure aggregation via multiparty computation. To this end, we propose SHARE (Secure Hierarchical Robust Aggregation), a distributed learning framework designed to cryptographically preserve client update privacy and robustness to Byzantine adversaries simultaneously. The key idea is to incorporate secure averaging among randomly clustered clients before filtering malicious updates through robust aggregation. Experiments show that SHARE has similar robustness guarantees as existing techniques while enhancing privacy.
No-Press Diplomacy from Scratch
Authors: Anton Bakhtin, David Wu, Adam Lerer, Noam Brown
Date: 2021-10-06 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Starcraft

Prior AI successes in complex games have largely focused on settings with at most hundreds of actions at each decision point. In contrast, Diplomacy is a game with more than 10^20 possible actions per turn. Previous attempts to address games with large branching factors, such as Diplomacy, StarCraft, and Dota, used human data to bootstrap the policy or used handcrafted reward shaping. In this paper, we describe an algorithm for action exploration and equilibrium approximation in games with combinatorial action spaces. This algorithm simultaneously performs value iteration while learning a policy proposal network. A double oracle step is used to explore additional actions to add to the policy proposals. At each state, the target state value and policy for the model training are computed via an equilibrium search procedure. Using this algorithm, we train an agent, DORA, completely from scratch for a popular two-player variant of Diplomacy and show that it achieves superhuman performance. Additionally, we extend our methods to full-scale no-press Diplomacy and for the first time train an agent from scratch with no human data. We present evidence that this agent plays a strategy that is incompatible with human-data bootstrapped agents. This presents the first strong evidence of multiple equilibria in Diplomacy and suggests that self play alone may be insufficient for achieving superhuman performance in Diplomacy.
Geometric and Physical Quantities Improve E(3) Equivariant Message Passing
Authors: Johannes Brandstetter, Rob Hesselink, Elise van der Pol, Erik J Bekkers, Max Welling
Date: 2021-10-06 00:00:00
Proceeding: ICLR 2022 4
Tasks: Graph Property Prediction

Including covariant information, such as position, force, velocity or spin is important in many tasks in computational physics and chemistry. We introduce Steerable E(3) Equivariant Graph Neural Networks (SEGNNs) that generalise equivariant graph networks, such that node and edge attributes are not restricted to invariant scalars, but can contain covariant information, such as vectors or tensors. This model, composed of steerable MLPs, is able to incorporate geometric and physical information in both the message and update functions. Through the definition of steerable node attributes, the MLPs provide a new class of activation functions for general use with steerable feature fields. We discuss ours and related work through the lens of equivariant non-linear convolutions, which further allows us to pin-point the successful components of SEGNNs: non-linear message aggregation improves upon classic linear (steerable) point convolutions; steerable messages improve upon recent equivariant graph networks that send invariant messages. We demonstrate the effectiveness of our method on several tasks in computational physics and chemistry and provide extensive ablation studies.
Compositional Q-learning for electrolyte repletion with imbalanced patient sub-populations
Authors: Aishwarya Mandyam, Andrew Jones, Jiayu Yao, Krzysztof Laudanski, Barbara Engelhardt
Date: 2021-10-06 00:00:00
Tasks: Decision Making, Navigate, OpenAI Gym, Q-Learning, reinforcement-learning, Reinforcement Learning (RL), Sequential Decision Making

Reinforcement learning (RL) is an effective framework for solving sequential decision-making tasks. However, applying RL methods in medical care settings is challenging in part due to heterogeneity in treatment response among patients. Some patients can be treated with standard protocols whereas others, such as those with chronic diseases, need personalized treatment planning. Traditional RL methods often fail to account for this heterogeneity, because they assume that all patients respond to the treatment in the same way (i.e., transition dynamics are shared). We introduce Compositional Fitted $Q$-iteration (CFQI), which uses a compositional task structure to represent heterogeneous treatment responses in medical care settings. A compositional task consists of several variations of the same task, each progressing in difficulty; solving simpler variants of the task can enable efficient solving of harder variants. CFQI uses a compositional $Q$-value function with separate modules for each task variant, allowing it to take advantage of shared knowledge while learning distinct policies for each variant. We validate CFQI's performance using a Cartpole environment and use CFQI to recommend electrolyte repletion for patients with and without renal disease. Our results demonstrate that CFQI is robust even in the presence of class imbalance, enabling effective information usage across patient sub-populations. CFQI exhibits great promise for clinical applications in scenarios characterized by known compositional structures.
ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods
Authors: Victor Schmidt, Alexandra Sasha Luccioni, Mélisande Teng, Tianyu Zhang, Alexia Reynaud, Sunand Raghupathi, Gautier Cosne, Adrien Juraver, Vahe Vardanyan, Alex Hernandez-Garcia, Yoshua Bengio
Date: 2021-10-06 00:00:00
Proceeding: ICLR 2022 4
Tasks: Conditional Image Generation, Domain Adaptation, Image Generation, Unsupervised Domain Adaptation

Climate change is a major threat to humanity, and the actions required to prevent its catastrophic consequences include changes in both policy-making and individual behaviour. However, taking action requires understanding the effects of climate change, even though they may seem abstract and distant. Projecting the potential consequences of extreme climate events such as flooding in familiar places can help make the abstract impacts of climate change more concrete and encourage action. As part of a larger initiative to build a website that projects extreme climate events onto user-chosen photos, we present our solution to simulate photo-realistic floods on authentic images. To address this complex task in the absence of suitable training data, we propose ClimateGAN, a model that leverages both simulated and real data for unsupervised domain adaptation and conditional image generation. In this paper, we describe the details of our framework, thoroughly evaluate components of our architecture and demonstrate that our model is capable of robustly generating photo-realistic flooding.
Exploring the Common Principal Subspace of Deep Features in Neural Networks
Authors: Haoran Liu, Haoyi Xiong, Yaqing Wang, Haozhe An, Dongrui Wu, Dejing Dou
Date: 2021-10-06 00:00:00
Tasks: Image Reconstruction, Self-Supervised Learning

We find that different Deep Neural Networks (DNNs) trained with the same dataset share a common principal subspace in latent spaces, no matter in which architectures (e.g., Convolutional Neural Networks (CNNs), Multi-Layer Preceptors (MLPs) and Autoencoders (AEs)) the DNNs were built or even whether labels have been used in training (e.g., supervised, unsupervised, and self-supervised learning). Specifically, we design a new metric $\mathcal{P}$-vector to represent the principal subspace of deep features learned in a DNN, and propose to measure angles between the principal subspaces using $\mathcal{P}$-vectors. Small angles (with cosine close to $1.0$) have been found in the comparisons between any two DNNs trained with different algorithms/architectures. Furthermore, during the training procedure from random scratch, the angle decrease from a larger one ($70^\circ-80^\circ$ usually) to the small one, which coincides the progress of feature space learning from scratch to convergence. Then, we carry out case studies to measure the angle between the $\mathcal{P}$-vector and the principal subspace of training dataset, and connect such angle with generalization performance. Extensive experiments with practically-used Multi-Layer Perceptron (MLPs), AEs and CNNs for classification, image reconstruction, and self-supervised learning tasks on MNIST, CIFAR-10 and CIFAR-100 datasets have been done to support our claims with solid evidences. Interpretability of Deep Learning, Feature Learning, and Subspaces of Deep Features