pwc-archive/papers-with-abstracts huggingface dataset:
NS3TCG: NS3 Topology and Code Generator
Authors: Rishav Halder, Sridhar Mundra
Date: 2020-02-20 00:00:00
Proceeding: International Conference on Recent Innovations in Electrical, Electronics & Communication Engineering (ICRIEECE) 2020 2

Construction of Network Simulator version 3 (NS3)
code and topology in easy and quick way are one of the main
issues. In the world of computer networks, network simulator acts
as a program to simulate network from which we can realize the
behaviour of a network. In this modern era, the computer
networks are getting complex which are becoming difficult to
understand and to implement the network in NS3 platform. Our
designed NS3 Topology and Code Generator (NS3TCG) tool can
minimize the difficulty level as it can construct topology and
generate NS3 code in C++ language, with minimum number of
inputs about the network like, numbers of nodes, Internet Protocol
(IP) address, Wireless or Wired or Mixed Network, delay rate,
etcetera (etc). Our networking tool works mainly on two
algorithms; one to dynamically generate NS3 code where as the
other, to dynamically construct network topology. With the help of
Graphical User Interference (GUI), we can understand the
structure of any complicated network very easily and on the other
hand, NS3 code generated by our tool can be further compiled and
executed to obtain the simulation of the topology. As per our
knowledge, NS3TCG is the first work in the direction of
generating NS3 code and topology, which makes our networking
tool unique and innovative.
A Dataset for Web-Scale Knowledge Base Population
Authors: Michael Glass, Alfio Gliozzo
Date: 2018-06-03 00:00:00
Proceeding: European Semantic Web Conference 2018 6
Tasks: Benchmarking, Knowledge Base Population

For many domains, structured knowledge is in short supply,
while unstructured text is plentiful. Knowledge Base Population (KBP)
is the task of building or extending a knowledge base from text, and
systems for KBP have grown in capability and scope. However, existing
datasets for KBP are all limited by multiple issues: small in size, not
open or accessible, only capable of benchmarking a fraction of the KBP
process, or only suitable for extracting knowledge from title-oriented documents (documents that describe a particular entity, such as Wikipedia
pages). We introduce and release CC-DBP, a web-scale dataset for training and benchmarking KBP systems. The dataset is based on Common
Crawl as the corpus and DBpedia as the target knowledge base. Critically, by releasing the tools to build the dataset, we enable the dataset
to remain current as new crawls and DBpedia dumps are released. Also,
the modularity of the released tool set resolves a crucial tension between
the ease that a dataset can be used for a particular subtask in KBP and
the number of different subtasks it can be used to train or benchmark.
ACWA: An AI-driven Cyber-Physical Testbed for Intelligent Water Systems
Authors: Feras A. Batarseh, Ajay Kulkarni, Chhayly Sreng, Justice Lin, Siam Maksud
Date: 2023-09-27 00:00:00
Tasks: Decision Making, Management

This manuscript presents a novel state-of-the-art cyber-physical water testbed, namely: The AI and Cyber for Water and Agriculture testbed (ACWA). ACWA is motivated by the need to advance water supply management using AI and Cybersecurity experimentation. The main goal of ACWA is to address pressing challenges in the water and agricultural domains by utilising cutting-edge AI and data-driven technologies. These challenges include Cyberbiosecurity, resources management, access to water, sustainability, and data-driven decision-making, among others. To address such issues, ACWA consists of multiple topologies, sensors, computational nodes, pumps, tanks, smart water devices, as well as databases and AI models that control the system. Moreover, we present ACWA simulator, which is a software-based water digital twin. The simulator runs on fluid and constituent transport principles that produce theoretical time series of a water distribution system. This creates a good validation point for comparing the theoretical approach with real-life results via the physical ACWA testbed. ACWA data are available to AI and water domain researchers and are hosted in an online public repository. In this paper, the system is introduced in detail and compared with existing water testbeds; additionally, example use-cases are described along with novel outcomes such as datasets, software, and AI-related scenarios.
Managing extreme AI risks amid rapid progress
Authors: Yoshua Bengio, Geoffrey Hinton, Andrew Yao, Dawn Song, Pieter Abbeel, Trevor Darrell, Yuval Noah Harari, Ya-Qin Zhang, Lan Xue, Shai Shalev-Shwartz, Gillian Hadfield, Jeff Clune, Tegan Maharaj, Frank Hutter, Atılım Güneş Baydin, Sheila Mcilraith, Qiqi Gao, Ashwin Acharya, David Krueger, Anca Dragan, Philip Torr, Stuart Russell, Daniel Kahneman, Jan Brauner, Sören Mindermann
Date: 2023-10-26 00:00:00

Artificial Intelligence (AI) is progressing rapidly, and companies are shifting their focus to developing generalist AI systems that can autonomously act and pursue goals. Increases in capabilities and autonomy may soon massively amplify AI's impact, with risks that include large-scale social harms, malicious uses, and an irreversible loss of human control over autonomous AI systems. Although researchers have warned of extreme risks from AI, there is a lack of consensus about how exactly such risks arise, and how to manage them. Society's response, despite promising first steps, is incommensurate with the possibility of rapid, transformative progress that is expected by many experts. AI safety research is lagging. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems. In this short consensus paper, we describe extreme risks from upcoming, advanced AI systems. Drawing on lessons learned from other safety-critical technologies, we then outline a comprehensive plan combining technical research and development with proactive, adaptive governance mechanisms for a more commensurate preparation.
An Invitation to Hypercomplex Phase Retrieval: Theory and Applications
Authors: Roman Jacome, Kumar Vijay Mishra, Brian M. Sadler, Henry Arguello
Date: 2023-10-20 00:00:00
Tasks: Retrieval

Hypercomplex signal processing (HSP) provides state-of-the-art tools to handle multidimensional signals by harnessing intrinsic correlation of the signal dimensions through Clifford algebra. Recently, the hypercomplex representation of the phase retrieval (PR) problem, wherein a complex-valued signal is estimated through its intensity-only projections, has attracted significant interest. The hypercomplex PR (HPR) arises in many optical imaging and computational sensing applications that usually comprise quaternion and octonion-valued signals. Analogous to the traditional PR, measurements in HPR may involve complex, hypercomplex, Fourier, and other sensing matrices. This set of problems opens opportunities for developing novel HSP tools and algorithms. This article provides a synopsis of the emerging areas and applications of HPR with a focus on optical imaging.
Distributed Delay-Tolerant Strategies for Equality-Constraint Sum-Preserving Resource Allocation
Authors: Mohammadreza Doostmohammadian, Alireza Aghasi, Maria Vrakopoulou, Hamid R. Rabiee, Usman A. Khan, Themistoklis Charalambou
Date: 2023-10-27 00:00:00
Tasks: All, CPU, Distributed Optimization, Quantization, Scheduling

This paper proposes two nonlinear dynamics to solve constrained distributed optimization problem for resource allocation over a multi-agent network. In this setup, coupling constraint refers to resource-demand balance which is preserved at all-times. The proposed solutions can address various model nonlinearities, for example, due to quantization and/or saturation. Further, it allows to reach faster convergence or to robustify the solution against impulsive noise or uncertainties. We prove convergence over weakly connected networks using convex analysis and Lyapunov theory. Our findings show that convergence can be reached for general sign-preserving odd nonlinearity. We further propose delay-tolerant mechanisms to handle general bounded heterogeneous time-varying delays over the communication network of agents while preserving all-time feasibility. This work finds application in CPU scheduling and coverage control among others. This paper advances the state-of-the-art by addressing (i) possible nonlinearity on the agents/links, meanwhile handling (ii) resource-demand feasibility at all times, (iii) uniform-connectivity instead of all-time connectivity, and (iv) possible heterogeneous and time-varying delays. To our best knowledge, no existing work addresses contributions (i)-(iv) altogether. Simulations and comparative analysis are provided to corroborate our contributions.
Experimental Validation for Distributed Control of Energy Hubs
Authors: Varsha Behrunani, Philipp Heer, John Lygeros
Date: 2023-10-27 00:00:00
Tasks: energy management, Management

As future energy systems become more decentralised due to the integration of renewable energy resources and storage technologies, several autonomous energy management and peer-to-peer trading mechanisms have been recently proposed for the operation of energy hub networks based on optimization and game theory. However, most of these strategies have been tested either only in simulated environments or small prosumer units as opposed to larger energy hubs. This simulation reality gap has hindered large-scale implementation and practical application of these method. In this paper, we aim to experimentally validate the performance of a novel multi-horizon distributed model predictive controller for an energy hub network by implementing the controller on a complete network of hubs comprising of a real energy hub inter-faced with multiple virtual hubs. The experiments are done using two different network topologies and the controller shows promising results in both setups.
An Overview on IEEE 802.11bf: WLAN Sensing
Authors: Rui Du, Haocheng Hua, Hailiang Xie, Xianxin Song, Zhonghao Lyu, Mengshi Hu, Narengerile, Yan Xin, Stephen McCann, Michael Montemurro, Tony Xiao Han, Jie Xu
Date: 2023-10-20 00:00:00
Tasks: Quantization

With recent advancements, the wireless local area network (WLAN) or wireless fidelity (Wi-Fi) technology has been successfully utilized to realize sensing functionalities such as detection, localization, and recognition. However, the WLANs standards are developed mainly for the purpose of communication, and thus may not be able to meet the stringent requirements for emerging sensing applications. To resolve this issue, a new Task Group (TG), namely IEEE 802.11bf, has been established by the IEEE 802.11 working group, with the objective of creating a new amendment to the WLAN standard to meet advanced sensing requirements while minimizing the effect on communications. This paper provides a comprehensive overview on the up-to-date efforts in the IEEE 802.11bf TG. First, we introduce the definition of the 802.11bf amendment and its formation and standardization timeline. Next, we discuss the WLAN sensing use cases with the corresponding key performance indicator (KPI) requirements. After reviewing previous WLAN sensing research based on communication-oriented WLAN standards, we identify their limitations and underscore the practical need for the new sensing-oriented amendment in 802.11bf. Furthermore, we discuss the WLAN sensing framework and procedure used for measurement acquisition, by considering both sensing at sub-7GHz and directional multi-gigabit (DMG) sensing at 60 GHz, respectively, and address their shared features, similarities, and differences. In addition, we present various candidate technical features for IEEE 802.11bf, including waveform/sequence design, feedback types, as well as quantization and compression techniques. We also describe the methodologies and the channel modeling used by the IEEE 802.11bf TG for evaluation. Finally, we discuss the challenges and future research directions to motivate more research endeavors towards this field in details.
TorchAudio 2.1: Advancing speech recognition, self-supervised learning, and audio processing components for PyTorch
Authors: Jeff Hwang, Moto Hira, Caroline Chen, Xiaohui Zhang, Zhaoheng Ni, Guangzhi Sun, Pingchuan Ma, Ruizhe Huang, Vineel Pratap, Yuekai Zhang, Anurag Kumar, Chin-Yun Yu, Chuang Zhu, Chunxi Liu, Jacob Kahn, Mirco Ravanelli, Peng Sun, Shinji Watanabe, Yangyang Shi, Yumeng Tao, Robin Scheibler, Samuele Cornell, Sean Kim, Stavros Petridis
Date: 2023-10-27 00:00:00
Tasks: Self-Supervised Learning, Speech Enhancement, speech-recognition, Speech Recognition

TorchAudio is an open-source audio and speech processing library built for PyTorch. It aims to accelerate the research and development of audio and speech technologies by providing well-designed, easy-to-use, and performant PyTorch components. Its contributors routinely engage with users to understand their needs and fulfill them by developing impactful features. Here, we survey TorchAudio's development principles and contents and highlight key features we include in its latest version (2.1): self-supervised learning pre-trained pipelines and training recipes, high-performance CTC decoders, speech recognition models and training recipes, advanced media I/O capabilities, and tools for performing forced alignment, multi-channel speech enhancement, and reference-less speech assessment. For a selection of these features, through empirical studies, we demonstrate their efficacy and show that they achieve competitive or state-of-the-art performance.
Traffic smoothing using explicit local controllers
Authors: Amaury Hayat, Arwa Alanqary, Rahul Bhadani, Christopher Denaro, Ryan J. Weightman, Shengquan Xiang, Jonathan W. Lee, Matthew Bunting, Anish Gollakota, Matthew W. Nice, Derek Gloudemans, Gergely Zachar, Jon F. Davis, Maria Laura Delle Monache, Benjamin Seibold, Alexandre M. Bayen, Jonathan Sprinkle, Daniel B. Work, Benedetto Piccoli
Date: 2023-10-27 00:00:00
Tasks: Management

The dissipation of stop-and-go waves attracted recent attention as a traffic management problem, which can be efficiently addressed by automated driving. As part of the 100 automated vehicles experiment named MegaVanderTest, feedback controls were used to induce strong dissipation via velocity smoothing. More precisely, a single vehicle driving differently in one of the four lanes of I-24 in the Nashville area was able to regularize the velocity profile by reducing oscillations in time and velocity differences among vehicles. Quantitative measures of this effect were possible due to the innovative I-24 MOTION system capable of monitoring the traffic conditions for all vehicles on the roadway. This paper presents the control design, the technological aspects involved in its deployment, and, finally, the results achieved by the experiment.
Event-Triggered Consensus for Continuous-Time Distributed Estimation
Authors: Irene Perez-Salesa, Rodrigo Aldana-Lopez, Carlos Sagues
Date: 2023-10-27 00:00:00
Tasks: State Estimation

Distributed sensor networks have gained interest thanks to the developments in processing power and communications. Event-triggering mechanisms can be useful in reducing communication between the nodes of the network, while still ensuring an adequate behaviour of the system. However, very little attention has been given to continuous-time systems in this context. In this work, we propose a strategy for distributed state estimation in sensor networks, based on average dynamic consensus of the continuous measurements. While communication between nodes is discrete and heavily reduced due to the event-triggering mechanism, our method ensures that the nodes are still able to produce a continuous estimate of the global average measurement and the state of the plant, within some tuneable error bounds.
FPGA-Based Implicit-Explicit Real-time Simulation Solver for Railway Wireless Power Transfer with Nonlinear Magnetic Coupling Components
Authors: Han Xu, Yangbin Zeng, Jialin Zheng, Kainan Chen, Weicheng Liu, Zhengming Zhao
Date: 2023-10-27 00:00:00

Railway Wireless Power Transfer (WPT) is a promising non-contact power supply solution, but constructing prototypes for controller testing can be both costly and unsafe. Real-time hardware-in-the-loop simulation is an effective and secure testing tool, but simulating the dynamic charging process of railway WPT systems is challenging due to the continuous changes in the nonlinear magnetic coupling components. To address this challenge, we propose an FPGA-based half-step implicit-explicit (IMEX) simulation solver. The proposed solver adopts an IMEX algorithm to solve the piecewise linear and nonlinear parts of the system separately, which enables FPGAs to solve nonlinear components while achieving high numerical stability. Additionally, we divide a complete integration step into two half-steps to reduce computational time delays. Our proposed method offers a promising solution for the real-time simulation of railway WPT systems. The novelty of our approach lies in the use of the IMEX algorithm and the half-step integration method, which significantly improves the accuracy and efficiency of the simulation. Our simulations and experiments demonstrate the effectiveness and accuracy of the proposed solver, which provides a new approach for simulating and optimizing railway WPT systems with nonlinear magnetic coupling components.
Probabilistic Multi-product Trading in Sequential Intraday and Frequency-Regulation Markets
Authors: Saeed Nordin, Abolfazl Khodadadi, Priyanka Shinde, Evelin Blom, Mohammad Reza Hesamzadeh, Lennart Söder
Date: 2023-10-26 00:00:00

With the increasing integration of power plants into the frequency-regulation markets, the importance of optimal trading has grown substantially. This paper conducts an in-depth analysis of their optimal trading behavior in sequential day-ahead, intraday, and frequency-regulation markets. We introduce a probabilistic multi-product optimization model, derived through a series of transformation techniques. Additionally, we present two reformulations that re-frame the problem as a mixed-integer linear programming problem with uncertain parameters. Various aspects of the model are thoroughly examined to observe the optimal multi-product trading behavior of hydro power plant assets, along with numerous case studies. Leveraging historical data from Nordic electricity markets, we construct realistic scenarios for the uncertain parameters. Furthermore, we then proposed an algorithm based on the No-U-Turn sampler to provide probability distribution functions of cleared prices in frequency-regulation and day-ahead markets. These distribution functions offer valuable statistical insights into temporal price risks for informed multi-product optimal-trading decisions.
Inferring to C or not to C: Evolutionary games with Bayesian inferential strategies
Authors: Arunava Patra, Supratim Sengupta, Ayan Paul, Sagar Chakraborty
Date: 2023-10-27 00:00:00
Tasks: Bayesian Inference

Strategies for sustaining cooperation and preventing exploitation by selfish agents in repeated games have mostly been restricted to Markovian strategies where the response of an agent depends on the actions in the previous round. Such strategies are characterized by lack of learning. However, learning from accumulated evidence over time and using the evidence to dynamically update our response is a key feature of living organisms. Bayesian inference provides a framework for such evidence-based learning mechanisms. It is therefore imperative to understand how strategies based on Bayesian learning fare in repeated games with Markovian strategies. Here, we consider a scenario where the Bayesian player uses the accumulated evidence of the opponent's actions over several rounds to continuously update her belief about the reactive opponent's strategy. The Bayesian player can then act on her inferred belief in different ways. By studying repeated Prisoner's dilemma games with such Bayesian inferential strategies, both in infinite and finite populations, we identify the conditions under which such strategies can be evolutionarily stable. We find that a Bayesian strategy that is less altruistic than the inferred belief about the opponent's strategy can outperform a larger set of reactive strategies, whereas one that is more generous than the inferred belief is more successful when the benefit-to-cost ratio of mutual cooperation is high. Our analysis reveals how learning the opponent's strategy through Bayesian inference, as opposed to utility maximization, can be beneficial in the long run, in preventing exploitation and eventual invasion by reactive strategies.
Reducing the impact of non-ideal PRBS on microwave photonic random demodulators by low biasing the optical modulator via PRBS amplitude compression
Authors: Shiyang Liu, Yang Chen
Date: 2023-10-26 00:00:00
Tasks: compressed sensing

A novel method for reducing the impact of non-ideal pseudo-random binary sequence (PRBS) on microwave photonic random demodulators (RDs) in a photonics-assisted compressed sensing (CS) system is proposed. Different from the commonly used method that switches the bias point of the optical modulator in the RD between two quadrature transmission points to mix the signal to be sampled and the PRBS, this method employs a PRBS with lower amplitude to low bias the optical modulator so that the impact of non-ideal PRBS on microwave photonic RDs can be greatly reduced by compressing the amplitude of non-ideal parts of the PRBS. An experiment is performed to verify the concept. The optical modulator is properly low-biased via PRBS amplitude compression. The data rate and occupied bandwidth of the PRBS are 500 Mb/s and 1 GHz, while the multi-tone signals with a maximum frequency of 100 MHz are sampled at an equivalent sampling rate of only 50 MSa/s. The results show that the reconstruction error can be reduced by up to 85%. The proposed method can significantly reduce the requirements for PRBS in RD-based photonics-assisted CS systems, providing a feasible solution for reducing the complexity and cost of system implementation.
Locally Self-Adjustive Smoothing for Measurement Noise Reduction with Application to Automated Peak Detection
Authors: Keisuke Ozawa, Tomoya Itakura, Taisuke Ono
Date: 2023-10-23 00:00:00

Smoothing is widely used approach for measurement noise reduction in spectral analysis. However, it suffers from signal distortion caused by peak suppression. A locally self-adjustive smoothing method is developed that retains sharp peaks and less distort signals. The proposed method uses only one parameter that determines global smoothness, while balancing the local smoothness using data itself. Simulation and real experiments in comparison with existing convolution-based smoothing methods indicate both qualitatively and quantitatively improved noise reduction performance in practical scenarios. We also discuss parameter selection and demonstrate an application for the automated smoothing and detection of a given number of peaks from noisy measurement data.