pwc-archive/papers-with-abstracts huggingface dataset:
MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning
Authors: Ming Zhou, Ziyu Wan, Hanjing Wang, Muning Wen, Runzhe Wu, Ying Wen, Yaodong Yang, Weinan Zhang, Jun Wang
Date: 2021-06-05 00:00:00
Tasks: Atari Games, CPU, Distributed Computing, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL), rllib

Population-based multi-agent reinforcement learning (PB-MARL) refers to the series of methods nested with reinforcement learning (RL) algorithms, which produces a self-generated sequence of tasks arising from the coupled population dynamics. By leveraging auto-curricula to induce a population of distinct emergent strategies, PB-MARL has achieved impressive success in tackling multi-agent tasks. Despite remarkable prior arts of distributed RL frameworks, PB-MARL poses new challenges for parallelizing the training frameworks due to the additional complexity of multiple nested workloads between sampling, training and evaluation involved with heterogeneous policy interactions. To solve these problems, we present MALib, a scalable and efficient computing framework for PB-MARL. Our framework is comprised of three key components: (1) a centralized task dispatching model, which supports the self-generated tasks and scalable training with heterogeneous policy combinations; (2) a programming architecture named Actor-Evaluator-Learner, which achieves high parallelism for both training and sampling, and meets the evaluation requirement of auto-curriculum learning; (3) a higher-level abstraction of MARL training paradigms, which enables efficient code reuse and flexible deployments on different distributed computing paradigms. Experiments on a series of complex tasks such as multi-agent Atari Games show that MALib achieves throughput higher than 40K FPS on a single machine with $32$ CPU cores; 5x speedup than RLlib and at least 3x speedup than OpenSpiel in multi-agent training tasks. MALib is publicly available at https://github.com/sjtu-marl/malib.
Spatial spread of COVID-19 outbreak in Italy using multiscale kinetic transport equations with uncertainty
Authors: Giulia Bertaglia, Walter Boscheri, Giacomo Dimarco, Lorenzo Pareschi
Date: 2021-06-14 00:00:00
Tasks: Uncertainty Quantification

In this paper we introduce a space-dependent multiscale model to describe the spatial spread of an infectious disease under uncertain data with particular interest in simulating the onset of the COVID-19 epidemic in Italy. While virus transmission is ruled by a SEIAR type compartmental model, within our approach the population is given by a sum of commuters moving on a extra-urban scale and non commuters interacting only on the smaller urban scale. A transport dynamic of the commuter population at large spatial scales, based on kinetic equations, is coupled with a diffusion model for non commuters at the urban scale. Thanks to a suitable scaling limit, the kinetic transport model used to describe the dynamics of commuters, within a given urban area coincides with the diffusion equations that characterize the movement of non-commuting individuals. Because of the high uncertainty in the data reported in the early phase of the epidemic, the presence of random inputs in both the initial data and the epidemic parameters is included in the model. A robust numerical method is designed to deal with the presence of multiple scales and the uncertainty quantification process. In our simulations, we considered a realistic geographical domain, describing the Lombardy region, in which the size of the cities, the number of infected individuals, the average number of daily commuters moving from one city to another, and the epidemic aspects are taken into account through a calibration of the model parameters based on the actual available data. The results show that the model is able to describe correctly the main features of the spatial expansion of the first wave of COVID-19 in northern Italy.
Fast symplectic integrator for Nesterov-type acceleration method
Authors: Shin-itiro Goto, Hideitsu Hino
Date: 2021-06-01 00:00:00
Tasks: Vocal Bursts Type Prediction

In this paper, explicit stable integrators based on symplectic and contact geometries are proposed for a non-autonomous ordinarily differential equation (ODE) found in improving convergence rate of Nesterov's accelerated gradient method. Symplectic geometry is known to be suitable for describing Hamiltonian mechanics, and contact geometry is known as an odd-dimensional counterpart of symplectic geometry. Moreover, a procedure, called symplectization, is a known way to construct a symplectic manifold from a contact manifold, yielding Hamiltonian systems from contact ones. It is found in this paper that a previously investigated non-autonomous ODE can be written as a contact Hamiltonian system. Then, by symplectization of a non-autonomous contact Hamiltonian vector field expressing the non-autonomous ODE, novel symplectic integrators are derived. Because the proposed symplectic integrators preserve hidden symplectic and contact structures in the ODE, they should be more stable than the Runge-Kutta method. Numerical experiments demonstrate that, as expected, the second-order symplectic integrator is stable and high convergence rates are achieved.
A novel fully 3D, microfluidic-oriented, gel-based and low cost stretchable soft sensor
Authors: Mohsen Annabestani, Pouria Esmaili-Dokht, Seyyed Ali Olianasab, Nooshin Orouji, Zeinab Alipour, Mohammad Hossein Sayad, Kimia Rajabi, Barbara Mazzolai, Mehdi Fardmanesh
Date: 2021-06-13 00:00:00

In this paper, a novel fully 3D, microfluidic-oriented, gel-based, and low-cost highly stretchable resistive sensors have been presented. By the proposed method we are able to measure and discriminate all of the stretch, twist, and pressure features by a single sensor which is the potential that we have obtained from the fully 3D structure of our sensor. Against previous sensors which all have used EGaIn as the conductive material of their sensor, we have used low-cost, safe, and ubiquitous glycol-based gel instead. To show the functionality of the proposed sensor some FEM simulations, a set of the designed experimental tests were done which showed the linear, accurate, and durable operation of the proposed sensor. Finally, the sensor was put through its paces on the knee, elbow, and wrist of a female test subject. Also, to evaluate the pressure functionality of the sensor, a fully 3D active foot insole was developed, fabricated, and evaluated. All of the results show promising features for the proposed sensor to be used in real-world applications like rehabilitation, wearable devices, soft robotics, smart clothing, gait analysis, AR/VR, etc.
Few-shot learning of new sound classes for target sound extraction
Authors: Marc Delcroix, Jorge Bennasar Vázquez, Tsubasa Ochiai, Keisuke Kinoshita, Shoko Araki
Date: 2021-06-14 00:00:00
Tasks: Few-Shot Learning, Target Sound Extraction

Target sound extraction consists of extracting the sound of a target acoustic event (AE) class from a mixture of AE sounds. It can be realized using a neural network that extracts the target sound conditioned on a 1-hot vector that represents the desired AE class. With this approach, embedding vectors associated with the AE classes are directly optimized for the extraction of sound classes seen during training. However, it is not easy to extend this framework to new AE classes, i.e. unseen during training. Recently, speech, music, or AE sound extraction based on enrollment audio of the desired sound offers the potential of extracting any target sound in a mixture given only a short audio signal of a similar sound. In this work, we propose combining 1-hot- and enrollment-based target sound extraction, allowing optimal performance for seen AE classes and simple extension to new classes. In experiments with synthesized sound mixtures generated with the Freesound Dataset (FSD) datasets, we demonstrate the benefit of the combined framework for both seen and new AE classes. Besides, we also propose adapting the embedding vectors obtained from a few enrollment audio samples (few-shot) to further improve performance on new classes.
A Dataset of Dynamic Reverberant Sound Scenes with Directional Interferers for Sound Event Localization and Detection
Authors: Archontis Politis, Sharath Adavanne, Daniel Krause, Antoine Deleforge, Prerak Srivastava, Tuomas Virtanen
Date: 2021-06-13 00:00:00
Tasks: Sound Event Localization and Detection

This report presents the dataset and baseline of Task 3 of the DCASE2021 Challenge on Sound Event Localization and Detection (SELD). The dataset is based on emulation of real recordings of static or moving sound events under real conditions of reverberation and ambient noise, using spatial room impulse responses captured in a variety of rooms and delivered in two spatial formats. The acoustical synthesis remains the same as in the previous iteration of the challenge, however the new dataset brings more challenging conditions of polyphony and overlapping instances of the same class. The most important difference of the new dataset is the introduction of directional interferers, meaning sound events that are localized in space but do not belong to the target classes to be detected and are not annotated. Since such interfering events are expected in every real-world scenario of SELD, the new dataset aims to promote systems that deal with this condition effectively. A modified SELDnet baseline employing the recent ACCDOA representation of SELD problems accompanies the dataset and it is shown to outperform the previous one. The new dataset is shown to be significantly more challenging for both baselines according to all considered metrics. To investigate the individual and combined effects of ambient noise, interferers, and reverberation, we study the performance of the baseline on different versions of the dataset excluding or including combinations of these factors. The results indicate that by far the most detrimental effects are caused by directional interferers.
Low-Rank Projections of GCNs Laplacian
Authors: Nathan Grinsztajn, Philippe Preux, Edouard Oyallon
Date: 2021-06-04 00:00:00
Proceeding: ICLR Workshop GTRL 2021 5
Tasks: Community Detection

In this work, we study the behavior of standard models for community detection under spectral manipulations. Through various ablation experiments, we evaluate the impact of bandpass filtering on the performance of a GCN: we empirically show that most of the necessary and used information for nodes classification is contained in the low-frequency domain, and thus contrary to images, high frequencies are less crucial to community detection. In particular, it is sometimes possible to obtain accuracies at a state-of-the-art level with simple classifiers that rely only on a few low frequencies.
Understanding Information Spreading Mechanisms During COVID-19 Pandemic by Analyzing the Impact of Tweet Text and User Features for Retweet Prediction
Authors: Pervaiz Iqbal Khan, Imran Razzak, Andreas Dengel, Sheraz Ahmed
Date: 2021-05-26 00:00:00

COVID-19 has affected the world economy and the daily life routine of almost everyone. It has been a hot topic on social media platforms such as Twitter, Facebook, etc. These social media platforms enable users to share information with other users who can reshare this information, thus causing this information to spread. Twitter's retweet functionality allows users to share the existing content with other users without altering the original content. Analysis of social media platforms can help in detecting emergencies during pandemics that lead to taking preventive measures. One such type of analysis is predicting the number of retweets for a given COVID-19 related tweet. Recently, CIKM organized a retweet prediction challenge for COVID-19 tweets focusing on using numeric features only. However, our hypothesis is, tweet text may play a vital role in an accurate retweet prediction. In this paper, we combine numeric and text features for COVID-19 related retweet predictions. For this purpose, we propose two CNN and RNN based models and evaluate the performance of these models on a publicly available TweetsCOV19 dataset using seven different evaluation metrics. Our evaluation results show that combining tweet text with numeric features improves the performance of retweet prediction significantly.
i-Pulse: A NLP based novel approach for employee engagement in logistics organization
Authors: Rachit Garg, Arvind W Kiwelekar, Laxman D Netak, Akshay Ghodake
Date: 2021-05-24 00:00:00

Although most logistics and freight forwarding organizations, in one way or another, claim to have core values. The engagement of employees is a vast structure that affects almost every part of the company's core environmental values. There is little theoretical knowledge about the relationship between firms and the engagement of employees. Based on research literature, this paper aims to provide a novel approach for insight around employee engagement in a logistics organization by implementing deep natural language processing concepts. The artificial intelligence-enabled solution named Intelligent Pulse (I-Pulse) can evaluate hundreds and thousands of pulse survey comments and provides the actionable insights and gist of employee feedback. I-Pulse allows the stakeholders to think in new ways in their organization, helping them to have a powerful influence on employee engagement, retention, and efficiency. This study is of corresponding interest to researchers and practitioners.
Decision making under uncertainty for deploying battery storage as a non-wire alternative in distribution networks
Authors: Marc Barbar, Dharik S. Mallapragada, Robert Stoner
Date: 2021-06-14 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty

The growing demand for electricity in emerging markets and developing economies (EMDE) such as India is causing loading and congestion problems on distribution networks, particularly in urban locations, that adversely impact sustainable development and economic growth. Electric utilities in these economies face unique constraints regarding raising capital required to upgrade their congested networks. Battery storage has emerged as a non-wire alternative (NWA) to feeder upgrades. This article presents a flexible valuation framework for battery storage use in distribution networks and its application in the context of EMDE distribution network planning. We evaluate the value of storage as an NWA using a multi-stage decision making process that combines system optimization with markov-decision processes (MDP) to identify the least-cost network upgrade strategy under demand growth uncertainty. When applied to feeders in Delhi, India, the approach highlights the cost-effectiveness of battery storage to manage load growth while deferring network investments. Across the low, medium and high battery storage capital cost projections for 2030, we estimate that 18 to 29 GWh of battery storage capacity could be deployed to defer 11,752 to 15,914 km of medium voltage distribution feeder lines that are loaded at 60\% or more of their ampere capacity in 2030, resulting in 12 to 16\% capital cost savings. Interestingly, the lowering storage capital costs does not always increase NWA storage deployment, due to network capacity constraints limiting opportunities for off-peak storage charging.
Scenarios of future Indian electricity demand accounting for space cooling and electric vehicle adoption
Authors: Marc Barbar, Dharik Mallapragada, Meia Alsup, Robert Stoner
Date: 2021-06-14 00:00:00

India is expected to witness rapid growth in electricity use over the next two decades. Here, we introduce a custom regression model to project electricity consumption in India over the coming decades, which includes a bottom-up estimate of electricity consumption for two major growth drivers, air conditioning, and vehicle electrification. The model projections are available at a customizable level of spatial aggregation at an hourly temporal resolution, which makes them useful as inputs to long-term electricity infrastructure planning studies. The approach is used to develop electricity consumption data sets spanning various technology adoption and growth scenarios up to the year 2050 in five-year increments. The aim of the data is to provide a range of scenarios for India's demand growth given new technology adoption. With long-term hourly demand projections serving as an essential input for electricity infrastructure modeling, this data publication enables further work on energy efficiency, generation, and transmission expansion planning for a fast-growing and increasingly important region from a global climate mitigation perspective.
Resilient and Distributed Discrete Optimal Transport with Deceptive Adversary: A Game-Theoretic Approach
Authors: Jason Hughes, Juntao Chen
Date: 2021-06-14 00:00:00

Optimal transport (OT) is a framework that can be used to guide the optimal allocation of a limited amount of resources. The classical OT paradigm does not consider malicious attacks in its formulation and thus the designed transport plan lacks resiliency to an adversary. To address this concern, we establish an OT framework that explicitly accounts for the adversarial and stealthy manipulation of participating nodes in the network during the transport strategy design. Specifically, we propose a game-theoretic approach to capture the strategic interactions between the transport planner and the deceptive attacker. We analyze the properties of the established two-person zero-sum game thoroughly. We further develop a fully distributed algorithm to compute the optimal resilient transport strategies, and show the convergence of the algorithm to a saddle-point equilibrium. Finally, we demonstrate the effectiveness of the designed algorithm using case studies.
A Multi Polarization Square Patch Antenna with a Reconfigurable Feeding Network
Authors: Roozbeh Rezaeipour, Ramezanali Sadeghzadeh
Date: 2021-06-14 00:00:00

A multi-polarization square patch antenna with a reconfigurable feeding network is presented in this paper. The reconfigurable feeding network of this antenna is implemented on an FR-4 substrate by a Wilkinson power divider and a branch line coupler which perform amplitude distribution in the feeding network. Besides, two switching circuits which consist of one PIN diode (BAR63-02w) and its DC biasing circuit manage the RF signal flow on this feeding network. These switching circuits control the phase of the RF signal applied to the square patch, so it can provide linear polarization, left-hand and right-hand circular polarization at 2.45 GHz which has many applications in wireless networks. The simulated and measured results are presented which illuminate acceptable axial ratio bandwidth (ARBW) for both right-hand and left-hand circular polarization in (2.38-2.48 GHz) and minimum -10 dB return loss at 2.45 GHz.
Robust Speed Control Methodology for Variable Speed Wind Turbines
Authors: Ammar Al-Jodah, Marwah Alwan
Date: 2021-06-13 00:00:00

Improving wind turbine efficiency is essential for reducing the costs of energy production. The highly nonlinear dynamics of the wind turbines and their uncertain operating conditions have posed many challenges for their control methods. In this work, a robust control strategy based on sliding mode and adaptive fuzzy disturbance observer is proposed for speed tracking in a variable speed wind turbine. First, the nonlinear mathematical model that describes the dynamics of the variable speed wind turbine is derived. This nonlinear model is then used to derive the control methodology and to find stability and robustness conditions. The control approach is designed to track the optimal wind speed that causes maximum energy extraction. The stability condition was verified using the Lyapunov stability theory. A simulation study was conducted to verify the method, and a comparative analysis was used to measure its effectiveness. The results showed a high tracking ability and robustness of the developed methodology. Moreover, higher power extraction was observed when compared to a classical control method.
Optimal Sensor Precision for Multi-Rate Sensing for Bounded Estimation Error
Authors: Niladri Das, Raktim Bhattacharya
Date: 2021-06-13 00:00:00

We address the problem of determining optimal sensor precisions for estimating the states of linear time-varying discrete-time stochastic dynamical systems, with guaranteed bounds on the estimation errors. This is performed in the Kalman filtering framework, where the sensor precisions are treated as variables. They are determined by solving a constrained convex optimization problem, which guarantees the specified upper bound on the posterior error variance. Optimal sensor precisions are determined by minimizing the l1 norm, which promotes sparseness in the solution and indirectly addresses the sensor selection problem. The theory is applied to realistic flight mechanics and astrodynamics problems to highlight its engineering value. These examples demonstrate the application of the presented theory to a) determine redundant sensing architectures for linear time invariant systems, b) accurately estimate states with low-cost sensors, and c) optimally schedule sensors for linear time-varying systems.
A Wideband Sliding Correlation Channel Sounder in 65 nm CMOS: Evaluation Board Performance
Authors: Dipankar Shakya, Ting Wu, Michael E. Knox, Theodore S. Rappaport
Date: 2021-06-12 00:00:00

Emerging applications such as wireless sensing, position location, robotics, and many more are driven by the ultra-wide bandwidths available at millimeter-wave (mmWave) and Terahertz (THz) frequencies. The characterization and efficient utilization of wireless channels at these extremely high frequencies require detailed knowledge of the radio propagation characteristics of the channels. Such knowledge is developed through empirical observations of operating conditions using wireless transceivers that measure the impulse response through channel sounding. Today, cutting-edge channel sounders rely on several bulky RF hardware components with complicated interconnections, large parasitics, and sub-GHz RF bandwidth. This paper presents a compact sliding correlation-based channel sounder baseband built on a monolithic integrated circuit (IC) using 65 nm CMOS, implemented as an evaluation board achieving a 2 GHz RF bandwidth. The IC is the worlds first gigabit-per-second channel sounder baseband implemented in low-cost CMOS. The presented single-board system can be employed at both the transmit and receive baseband to study multipath characteristics and path loss. Thus, the singleboard implementation provides an inexpensive and compact solution for sliding correlation-based channel sounding with 1 ns multipath delay resolution.
A piecewise ellipsoidal reachable set estimation method for continuous bimodal piecewise affine systems
Authors: Thuan Le Quang, Nam Phan Thanh, Simone Baldi
Date: 2021-06-12 00:00:00

In this work, the issue of estimation of reachable sets in continuous bimodal piecewise affine systems is studied. A new method is proposed, in the framework of ellipsoidal bounding, using piecewise quadratic Lyapunov functions. Although bimodal piecewise affine systems can be seen as a special class of affine hybrid systems, reachability methods developed for affine hybrid systems might be inappropriately complex for bimodal dynamics. This work goes in the direction of exploiting the dynamical structure of the system to propose a simpler approach. More specifically, because of the piecewise nature of the Lyapunov function, we first derive conditions to ensure that a given quadratic function is positive on half spaces. Then, we exploit the property of bimodal piecewise quadratic functions being continuous on a given hyperplane. Finally, linear matrix characterizations of the estimate of the reachable set are derived.