pwc-archive/papers-with-abstracts huggingface dataset:
Reconfigurable Intelligent Surface Assisted Railway Communications: A survey
Authors: Aline Habib, Ammar El Falou, Charlotte Langlais, Marion Berbineau
Date: 2023-07-10 00:00:00
Tasks: Blocking, Survey

The number of train passengers and the demand for high data rates to handle new technologies such as video streaming and IoT technologies are continuously increasing. Therefore the exploration of millimeter waves (mmWave) band is a key technology to meet this demand. However, the high penetration loss makes mmWave very sensitive to blocking, limiting its coverage area. One promising, efficient, and low-cost solution is the reconfigurable intelligent surface (RIS). This paper reviews the state of the art of RIS for railway communications in the mmWave context. First, we present the different types of RIS and review some optimization algorithms used in the literature to find the RIS phase shift. Then, we review recent works on RIS in the railway domain and provide future directions.
Understanding Counterspeech for Online Harm Mitigation
Authors: Yi-Ling Chung, Gavin Abercrombie, Florence Enock, Jonathan Bright, Verena Rieser
Date: 2023-07-01 00:00:00

Counterspeech offers direct rebuttals to hateful speech by challenging perpetrators of hate and showing support to targets of abuse. It provides a promising alternative to more contentious measures, such as content moderation and deplatforming, by contributing a greater amount of positive online speech rather than attempting to mitigate harmful content through removal. Advances in the development of large language models mean that the process of producing counterspeech could be made more efficient by automating its generation, which would enable large-scale online campaigns. However, we currently lack a systematic understanding of several important factors relating to the efficacy of counterspeech for hate mitigation, such as which types of counterspeech are most effective, what are the optimal conditions for implementation, and which specific effects of hate it can best ameliorate. This paper aims to fill this gap by systematically reviewing counterspeech research in the social sciences and comparing methodologies and findings with computer science efforts in automatic counterspeech generation. By taking this multi-disciplinary view, we identify promising future directions in both fields.
Spoofing-Resilient LiDAR-GPS Factor Graph Localization with Chimera Authentication
Authors: Adam Dai, Tara Minda, Ashwin Kanhere, Grace Gao
Date: 2023-07-10 00:00:00
Tasks: Position, State Estimation

Many vehicle platforms typically use sensors such as LiDAR or camera for locally-referenced navigation with GPS for globally-referenced navigation. However, due to the unencrypted nature of GPS signals, all civilian users are vulner-able to spoofing attacks, where a malicious spoofer broadcasts fabricated signals and causes the user to track a false position fix. To protect against such GPS spoofing attacks, Chips-Message Robust Authentication (Chimera) has been developed and will be tested on the Navigation Technology Satellite 3 (NTS-3) satellite being launched later this year. However, Chimera authentication is not continuously available and may not provide sufficient protection for vehicles which rely on more frequent GPS measurements. In this paper, we propose a factor graph-based state estimation framework which integrates LiDAR and GPS while simultaneously detecting and mitigating spoofing attacks experienced between consecutive Chimera authentications. Our proposed framework combines GPS pseudorange measurements with LiDAR odometry to provide a robust navigation solution. A chi-squared detector, based on pseudorange residuals, is used to detect and mitigate any potential GPS spoofing attacks. We evaluate our method using real-world LiDAR data from the KITTI dataset and simulated GPS measurements, both nominal and with spoofing. Across multiple trajectories and Monte Carlo runs, our method consistently achieves position errors under 5 m during nominal conditions, and successfully bounds positioning error to within odometry drift levels during spoofed conditions.
Exploiting an External Microphone for Binaural RTF-Vector-Based Direction of Arrival Estimation for Multiple Speakers
Authors: Daniel Fejgin, Simon Doclo
Date: 2023-07-10 00:00:00
Tasks: Direction of Arrival Estimation

In hearing aid applications, an important objective is to accurately estimate the direction of arrival (DOA) of multiple speakers in noisy and reverberant environments. Recently, we proposed a binaural DOA estimation method, where the DOAs of the speakers are estimated by selecting the directions for which the so-called Hermitian angle spectrum between the estimated relative transfer function (RTF) vector and a database of prototype anechoic RTF vectors is maximized. The RTF vector is estimated using the covariance whitening (CW) method, which requires a computationally complex generalized eigenvalue decomposition. The spatial spectrum is obtained by only considering frequencies where it is likely that one speaker dominates over the other speakers, noise and reverberation. In this contribution, we exploit the availability of an external microphone that is spatially separated from the hearing aid microphones and consider a low-complexity RTF vector estimation method that assumes a low spatial coherence between the undesired components in the external microphone and the hearing aid microphones. Using recordings of two speakers and diffuse-like babble noise in acoustic environments with mild reverberation and low signal-to-noise ratio, simulation results show that the proposed method yields a comparable DOA estimation performance as the CW method at a lower computational complexity.
Ferroelectric MirrorBit-Integrated Field-Programmable Memory Array for TCAM, Storage, and In-Memory Computing Applications
Authors: Paritosh Meihar, Rowtu Srinu, Sandip Lashkare, Ajay Kumar Singh, Halid Mulaosmanovic, Veeresh Deshpande, Stefan Dünkel, Sven Beyer, Udayan Ganguly
Date: 2023-07-10 00:00:00
Tasks: Computational Efficiency

In-memory computing on a reconfigurable architecture is the emerging field which performs an application-based resource allocation for computational efficiency and energy optimization. In this work, we propose a Ferroelectric MirrorBit-integrated field-programmable reconfigurable memory. We show the conventional 1-Bit FeFET, the MirrorBit, and MirrorBit-based Ternary Content-addressable memory (MCAM or MirrorBit-based TCAM) within the same field-programmable array. Apart from the conventional uniform Up and Down polarization states, the additional states in the MirrorBit are programmed by applying a non-uniform electric field along the transverse direction, which produces a gradient in the polarization and the conduction band energy. This creates two additional states, thereby, creating a total of 4 states or 2-bit of information. The gradient in the conduction band resembles a Schottky barrier (Schottky diode), whose orientation can be configured by applying an appropriate field. The TCAM operation is demonstrated using the MirrorBit-based diode on the reconfigurable array. The reconfigurable array architecture can switch from AND-type to NOR-type and vice-versa. The AND-type array is appropriate for programming the conventional bit and the MirrorBit. The MirrorBit-based Schottky diode in the NOR-array resembles a crossbar structure, which is appropriate for diode-based CAM operation. Our proposed memory system can enable fast write via 1-bit FeFET, the dense data storage capability by Mirror-bit technology and the fast search capability of the MCAM. Further, the dual configurability enables power, area and speed optimization making the reconfigurable Fe-Mirrorbit memory a compelling solution for In-memory and associative computing.
Deep and Decentralized Multi-Agent Coverage of a Target with Unknown Distribution
Authors: Hossein Rastgoftar
Date: 2023-07-10 00:00:00

This paper proposes a new architecture for multi-agent systems to cover an unknowingly distributed fast, safely, and decentralizedly. The inter-agent communication is organized by a directed graph with fixed topology, and we model agent coordination as a decentralized leader-follower problem with time-varying communication weights. Given this problem setting, we first present a method for converting communication graph into a neural network, where an agent can be represented by a unique node of the communication graph but multiple neurons of the corresponding neural network. We then apply a mass-cetric strategy to train time-varying communication weights of the neural network in a decentralized fashion which in turn implies that the observation zone of every follower agent is independently assigned by the follower based on positions of in-neighbors. By training the neural network, we can ensure safe and decentralized multi-agent coordination of coverage control. Despite the target is unknown to the agent team, we provide a proof for convergence of the proposed multi-agent coverage method.
On IMU preintegration: A nonlinear observer viewpoint and its application
Authors: Bowen Yi, Ian R. Manchester
Date: 2023-07-09 00:00:00
Tasks: parameter estimation

The inertial measurement unit (IMU) preintegration approach nowadays is widely used in various robotic applications. In this article, we revisit the preintegration theory and propose a novel interpretation to understand it from a nonlinear observer perspective, specifically the parameter estimation-based observer (PEBO). We demonstrate that the preintegration approach can be viewed as recursive implementation of PEBO in moving horizons, and that the two approaches are equivalent in the case of perfect measurements. We then discuss how these findings can be used to tackle practical challenges in estimation problems. As byproducts, our results lead to a novel hybrid sampled-data observer design and an approach to address statistical optimality for PEBO in presence of noise.
Automated Stability Analysis of Piecewise Affine Dynamics Using Vertices
Authors: Pouya Samanipour, Hasan A. Poonawala
Date: 2023-07-08 00:00:00
Tasks: valid

This paper presents an automated algorithm to analyze the stability of piecewise affine (PWA) dynamical systems due to their broad applications. We parametrize the Lyapunov function as a PWA function, with polytopic regions defined by the PWA dynamics. Using this parametrization, Stability conditions can be expressed as linear constraints restricted to polytopes so that the search for a Lyapunov function involves solving a linear program. However, a valid Lyapunov function might not be found given these polytopic regions. A natural response is to increase the size of the parametrization of the Lyapunov function by dividing regions and solving the new linear program. This paper proposes two new methods to divide each polytope into smaller ones. The first approach divides a polytope based on the sign of the derivative of the candidate Lyapunov function, while the second divides it based on the change in the vector field of the PWA dynamical system. In addition, we propose using Delaunay triangulation to achieve automated division of regions and preserve the continuity of the PWA Lyapunov function. Examples involving learned models and explicit MPC controllers demonstrate that the proposed method of dividing regions leads to valid Lyapunov functions with fewer regions than existing methods, reducing the computational time taken for stability analysis
Self-Excited Dynamics of Discrete-Time Lur'e Models with Affinely Constrained, Piecewise-C1 Feedback Nonlinearities
Authors: Juan Paredes, Omran Kouba, Dennis S. Bernstein
Date: 2023-07-07 00:00:00
Tasks: All

Self-excited systems (SES) arise in numerous applications, such as fluid-structure interaction, combustion, and biochemical systems. In support of system identification and digital control of SES, this paper analyzes discrete-time Lur'e models with affinely constrained, piecewise-C1 feedback nonlinearities. The main result provides sufficient conditions under which a discrete-time Lur'e model is self-excited in the sense that its response is 1) bounded for all initial conditions, and 2) nonconvergent for almost all initial conditions.
Coordinate-based neural representations for computational adaptive optics in widefield microscopy
Authors: Iksung Kang, Qinrong Zhang, Stella X. Yu, Na Ji
Date: 2023-07-07 00:00:00

Widefield microscopy is widely used for non-invasive imaging of biological structures at subcellular resolution. When applied to complex specimen, its image quality is degraded by sample-induced optical aberration. Adaptive optics can correct wavefront distortion and restore diffraction-limited resolution but require wavefront sensing and corrective devices, increasing system complexity and cost. Here, we describe a self-supervised machine learning algorithm, CoCoA, that performs joint wavefront estimation and three-dimensional structural information extraction from a single input 3D image stack without the need for external training dataset. We implemented CoCoA for widefield imaging of mouse brain tissues and validated its performance with direct-wavefront-sensing-based adaptive optics. Importantly, we systematically explored and quantitatively characterized the limiting factors of CoCoA's performance. Using CoCoA, we demonstrated the first in vivo widefield mouse brain imaging using machine-learning-based adaptive optics. Incorporating coordinate-based neural representations and a forward physics model, the self-supervised scheme of CoCoA should be applicable to microscopy modalities in general.
Dynamics of the securities market in the information asymmetry context: developing a methodology for emerging securities markets
Authors: Kostyantyn Anatolievich Malyshenko, Majid Mohammad Shafiee, Vadim Anatolievich Malyshenko, Marina Viktorovna Anashkina
Date: 2023-07-09 00:00:00

Developing a system of indicators that reflects the degree to which the securities market fulfils its key functions, is essential to assess the level of its development. In the conditions of asymmetric information it can also provide effective policies for securities market development. This paper is aimed to develop a set of indicators to assess the securities market performance, especially in the asymmetric information context. To this goal, we selected the Russian securities market as a case of asymmetric information context, in comparison with other post-Soviet countries, to investigate its success and failure in fulfilling its key functions. Regarding this, we developed research hypotheses and we conducted a normative research method, based on an ideal model of market functioning that is used as a criterion for testing the hypotheses. The results offer an original scale for assessing the performance of securities market of its functions. The findings also help practitioners with effective policy making in securities market regulation and its development toward its ideal state. The key contribution of this research is in developing a new scale for determining the performance and efficiency of the securities market, based on the conditions of information asymmetry.
A Regional Analysis of Electric LDV Portfolio Choices by Vehicle Manufacturers
Authors: Aditya Ramji, Hanif Tayarani
Date: 2023-07-07 00:00:00

Global light duty electric vehicle (EV) sales exceeded 10.5 million units in 2022, with a year-on-year growth of 55%, but these trends differ regionally. Despite the robust growth, upfront purchase price remains a challenge for consumers in different regions, and thus, OEMs make technology choices to respond to market needs. This paper examines the electrification portfolio choices of three major automotive manufacturers (OEMs) in different regions of the world, including Europe, Americas, Asia Pacific, and Africa/Middle-East. The analysis focuses on trends in dominant segments for Battery Electric Vehicles (BEV) and Plug-in Hybrid Electric Vehicles (PHEV), as well as battery chemistry choices. Regional differences show a trend towards SUVs for both BEVs and PHEVs. Tesla's dominance in the BEV market influences battery chemistry choices. Average battery sizes for BEVs remain similar in Europe and Americas, but lower in Asia Pacific and Africa/Middle East.
Fatal errors and misuse of mathematics in the Hong-Page Theorem and Landemore's epistemic argument
Authors: Álvaro Romaniega
Date: 2023-07-10 00:00:00
Tasks: Diversity

In the pursuit of understanding collective intelligence, the Hong-Page Theorems have been presented as cornerstones of the interplay between diversity and ability. However, upon rigorous examination, there seem to be inherent problems and misinterpretations within these theorems. H\'el\`ene Landemore's application of these results in her epistemic argument and her political proposal showcases a invalid use of mathematical principles. This paper critically dissects the Hong-Page Theorems, revealing significant inconsistencies and oversights, and underscores the indispensable role of ``ability" in group problem-solving contexts. This paper aims not to undermine the importance of diversity, but rather to highlight the dangers of misusing mathematical principles and the necessity for a more careful analysis of mathematical results when applying them to social sciences.d the necessity for a more nuanced comprehension of mathematical results when applying them to social sciences.
A Belief-Based Characterization of Reduced-Form Auctions
Authors: Xu Lang
Date: 2023-07-09 00:00:00
Tasks: Form

We study games of chance (e.g., pokers, dices, horse races) in the form of agents' first-order posterior beliefs about game outcomes. We ask for any profile of agents' posterior beliefs, is there a game that can generate these beliefs? We completely characterize all feasible joint posterior beliefs from these games. The characterization enables us to find a new variant of Border's inequalities (Border, 1991), which we call a belief-based characterization of Border's inequalities. It also leads to a generalization of Aumann's Agreement Theorem. We show that the characterization results are powerful in bounding the correlation of agents' joint posterior beliefs.
Harnessing the Power of Swarm Satellite Networks with Wideband Distributed Beamforming
Authors: Juan Carlos Merlano Duncan, Vu Nguyen Ha, Jevgenij Krivochiza, Rakesh Palisetty, Geoffrey Eappen, Juan Andres Vasquez, Wallace Alves Martins, Symeon Chatzinotas, Björn Ottersten
Date: 2023-07-10 00:00:00

The space communications industry is challenged to develop a technology that can deliver broadband services to user terminals equipped with miniature antennas, such as handheld devices. One potential solution to establish links with ground users is the deployment of massive antennas in one single spacecraft. However, this is not cost-effective. Aligning with recent \emph{NewSpace} activities directed toward miniaturization, mass production, and a significant reduction in spacecraft launch costs, an alternative could be distributed beamforming from multiple satellites. In this context, we propose a distributed beamforming modeling technique for wideband signals. We also consider the statistical behavior of the relative geometry of the swarm nodes. The paper assesses the proposed technique via computer simulations, providing interesting results on the beamforming gains in terms of power and the security of the communication against potential eavesdroppers at non-intended pointing angles. This approach paves the way for further exploration of wideband distributed beamforming from satellite swarms in several future communication applications.
Thin-Film Lithium Niobate Acoustic Filter at 23.5 GHz with 2.38 dB IL and 18.2% FBW
Authors: Omar Barrera, Sinwoo Cho, Lezli Matto, Jack Kramer, Kenny Huynh, Vakhtang Chulukhadze, Yen-Wei Chang, Mark S. Goorsky, Ruochen Lu
Date: 2023-07-10 00:00:00

This work reports an acoustic filter at 23.5 GHz with a low insertion loss (IL) of 2.38 dB and a 3-dB fractional bandwidth (FBW) of 18.2%, significantly surpassing the state-of-the-art. The device leverages electrically coupled acoustic resonators in 100 nm 128{\deg} Y-cut lithium niobate (LiNbO3) piezoelectric thin film, operating in the first-order antisymmetric (A1) mode. A new film stack, namely transferred thin-film LiNbO3 on silicon (Si) substrate with an intermediate amorphous silicon (a-Si) layer, facilitates the record-breaking performance at millimeter-wave (mmWave). The filter features a compact footprint of 0.56 mm2. In this letter, acoustic and EM consideration, along with material characterization with X-ray diffraction and verified with cross-sectional electron microscopy are reported. Upon further development, the reported filter platform can enable various front-end signal-processing functions at mmWave.
Distributed Decisions on Optimal Load Balancing in Loss Networks
Authors: Qiong Liu, Chehao Wang, Ce Zheng
Date: 2023-07-10 00:00:00

When multiple users share a common link in direct transmission, packet loss and network collision may occur due to the simultaneous arrival of traffics at the source node. To tackle this problem, users may resort to an indirect path: the packet flows are first relayed through a sidelink to another source node, then transmitted to the destination. This behavior brings the problems of packet routing or load balancing: (1) how to maximize the total traffic in a collaborative way; (2) how self-interested users choose routing strategies to minimize their individual packet loss independently. In this work, we propose a generalized mathematical framework to tackle the packet and load balancing issue in loss networks. In centralized scenarios with a planner, we provide a polynomial-time algorithm to compute the system optimum point where the total traffic rate is maximized. Conversely, in decentralized settings with autonomous users making distributed decisions, the system converges to an equilibrium where no user can reduce their loss probability through unilateral deviation. We thereby provide a full characterization of Nash equilibrium and examine the efficiency loss stemming from selfish behaviors, both theoretically and empirically. In general, the performance degradation caused by selfish behaviors is not catastrophic; however, this gap is not monotonic and can have extreme values in certain specific scenarios.
Automotive Radar Mutual Interference Mitigation Based on Hough Transform in Time-Frequency Domain
Authors: Yanbing Li, Weichuan Zhang, Lianying Ji
Date: 2023-07-10 00:00:00
Tasks: Autonomous Driving, Line Detection

With the development of autonomous driving technology, automotive radar has received unprecedented attention due to its day-and-night and all-weather working capability. It is worthwhile to note that more and more vehicles are equipped with automotive radars, resulting in mutual interference between radars. The interference reduces radar target detection performance, making perception information unreliable. In this paper, a novel interference mitigation method based on power-weighted Hough transform is proposed for solving the radar mutual interference and improving the safety of autonomous driving systems. Firstly, the frequency modulation characteristics of interference signals and target echo signals are analyzed, and differences between the two signals are introduced. Secondly, based on the straight line detection technique, the power of the mutual interference signal in time-frequency domain is accumulated, and the accurate position of the interference is located. Finally, the target echo is recovered by autoregressive model. Compared with existing state-of-the-art methods, the proposed method has the ability to retain more useful signals after the interference mitigation, and achieve better interference detection robustness under low signal-to-noise ratio conditions. Simulation experiments and real scenario experiments verify the effectiveness of the proposed method and show its superiority.