pwc-archive/papers-with-abstracts huggingface dataset:
Collision Avoidance for Elliptical Agents with Control Barrier Function Utilizing Supporting Lines
Authors: Koju Nishimoto, Riku Funada, Tatsuya Ibuki, Mitsuji Sampei
Date: 2022-04-28 00:00:00
Tasks: Collision Avoidance

This paper presents a collision avoidance method for elliptical agents traveling in a two-dimensional space. We first formulate a separation condition for two elliptical agents utilizing a signed distance from a supporting line of an agent to the other agent, which renders a positive value if two ellipses are separated by the line. Because this signed distance could yield a shorter length than the actual distance between two ellipses, the supporting line is rotated so that the signed distance from the line to the other ellipse is maximized. We prove that this maximization problem renders the signed distance equivalent to the actual distance between two ellipses, hence not causing the conservative evasive motion. Then, we propose the collision avoidance method utilizing novel control barrier functions incorporating a gradient-based update law of a supporting line. The validity of the proposed methods is evaluated in the simulations.
TDD frame design for interference handling in mobile IAB networks
Authors: Victor Farias Monteiro, Fco. Rafael Marques Lima, Darlan Cavalcante Moreira, Diego Aguiar Sousa, Tarcisio Ferreira Maciel, Behrooz Makki, Ritesh Shreevastav, Hans Hannu
Date: 2022-04-27 00:00:00

Integrated access and backhaul (IAB) is envisioned as a possible solution to address the need for network densification in situations where fiber connection is not viable. In Release 18, 3rd generation partnership project (3GPP) will take another step on extending IAB capabilities. With mobile IAB (mIAB), IAB nodes can be deployed within vehicles, e.g., buses. As in every new technology, performance assessment and evaluation of the impact of interference is of interest for industry and academia. In this paper, we present contributions on those topics. First, we evaluate the performance of mIAB compared to fiber connected deployments. Moreover, we study the impact of interference on the performance of mIAB networks and propose a solution based on inserting silent slots on the time division duplex (TDD) frame pattern. According to our simulation results, mIAB is capable of improving performance of onboard user equipments (UEs) without harming too much the quality of service (QoS) of surrounding UEs. Furthermore, we show that the TDD frame pattern should be carefully designed to account for scenarios with different levels of interference.
Efficient Estimation of Structural Models via Sieves
Authors: Yao Luo, Peijun Sang
Date: 2022-04-28 00:00:00

We propose a class of sieve-based efficient estimators for structural models (SEES), which approximate the solution using a linear combination of basis functions and impose equilibrium conditions as a penalty to determine the best-fitting coefficients. Our estimators avoid the need to repeatedly solve the model, apply to a broad class of models, and are consistent, asymptotically normal, and asymptotically efficient. Moreover, they solve unconstrained optimization problems with fewer unknowns and offer convenient standard error calculations. As an illustration, we apply our method to an entry game between Walmart and Kmart.
Impulse response estimation via flexible local projections
Authors: Haroon Mumtaz, Michele Piffer
Date: 2022-04-27 00:00:00
Tasks: regression

This paper introduces a flexible local projection that generalizes the model by Jord\'a (2005) to a non-parametric setting using Bayesian Additive Regression Trees. Monte Carlo experiments show that our BART-LP model is able to capture non-linearities in the impulse responses. Our first application shows that the fiscal multiplier is stronger in recession than in expansion only in response to contractionary fiscal shocks, but not in response to expansionary fiscal shocks. We then show that financial shocks generate effects on the economy that increase more than proportionately in the size of the shock when the shock is negative, but not when the shock is positive.
Individual characteristics associated with risk and time preferences: A multi country representative survey
Authors: Thomas Meissner, Xavier Gassmann, Corinne Faure, Joachim Schleich
Date: 2022-04-28 00:00:00

This paper empirically analyzes how individual characteristics are associated with risk aversion, loss aversion, time discounting, and present bias. To this end, we conduct a large-scale demographically representative survey across eight European countries. We elicit preferences using incentivized multiple price lists and jointly estimate preference parameters to account for their structural dependencies. Our findings suggest that preferences are linked to a variety of individual characteristics such as age, gender, and income as well as some personal values. We also report evidence on the relationship between cognitive ability and preferences. Incentivization, stake size, and the order of presentation of binary choices matter, underlining the importance of controlling for these factors when eliciting economic preferences.
Bunching and Taxing Multidimensional Skills
Authors: Job Boerma, Aleh Tsyvinski, Alexander P. Zimin
Date: 2022-04-28 00:00:00

We characterize optimal policy in a multidimensional nonlinear taxation model with bunching. We develop an empirically relevant model with cognitive and manual skills, firm heterogeneity, and labor market sorting. We first derive two conditions for the optimality of taxes that take into account bunching. The first condition $-$ a stochastic dominance optimal tax condition $-$ shows that at the optimum the schedule of benefits dominates the schedule of distortions in terms of second-order stochastic dominance. The second condition $-$ a global optimal tax formula $-$ provides a representation that balances the local costs and benefits of optimal taxation while explicitly accounting for global incentive constraints. Second, we use Legendre transformations to represent our problem as a linear program. This linearization allows us to solve the model quantitatively and to precisely characterize bunching. At an optimum, 10 percent of workers is bunched. We introduce two notions of bunching $-$ blunt bunching and targeted bunching. Blunt bunching constitutes 30 percent of all bunching, occurs at the lowest regions of cognitive and manual skills, and lumps the allocations of these workers resulting in a significant distortion. Targeted bunching constitutes 70 percent of all bunching and recognizes the workers' comparative advantage. The planner separates workers on their dominant skill and bunches them on their weaker skill, thus mitigating distortions along the dominant skill dimension.
Dynamic screening
Authors: David Lagziel, Ehud Lehrer
Date: 2022-04-28 00:00:00

We study dynamic screening problems where elements are subjected to noisy evaluations and, in every stage, some of the elements are rejected while the remaining ones are independently re-evaluated in subsequent stages. We prove that, ceteris paribus, the quality of a dynamic screening process is not monotonic in the number of stages. Specifically, we examine the accepted elements' values and show that adding a single stage to a screening process may produce inferior results, in terms of stochastic dominance, whereas increasing the number of stages substantially leads to a first-best outcome.
Une version polyatomique de l'algorithme Frank-Wolfe pour résoudre le problème LASSO en grandes dimensions
Authors: Adrian Jarret, Matthieu Simeoni, Julien Fageot
Date: 2022-04-28 00:00:00
Tasks: Astronomy, Radio Interferometry

Nous nous int\'eressons \`a la reconstruction parcimonieuse d'images \`a l'aide du probl\`eme d'optimisation r\'egularis\'e LASSO. Dans de nombreuses applications pratiques, les grandes dimensions des objets \`a reconstruire limitent, voire emp\^echent, l'utilisation des m\'ethodes de r\'esolution proximales classiques. C'est le cas par exemple en radioastronomie. Nous d\'etaillons dans cet article le fonctionnement de l'algorithme \textit{Frank-Wolfe Polyatomique}, sp\'ecialement d\'evelopp\'e pour r\'esoudre le probl\`eme LASSO dans ces contextes exigeants. Nous d\'emontrons sa sup\'eriorit\'e par rapport aux m\'ethodes proximales dans des situations en grande dimension avec des mesures de Fourier, lors de la r\'esolution de probl\`emes simul\'es inspir\'es de la radio-interf\'erom\'etrie. -- We consider the problem of recovering sparse images by means of the penalised optimisation problem LASSO. For various practical applications, it is impossible to rely on the proximal solvers commonly used for that purpose due to the size of the objects to recover, as it is the case for radio astronomy. In this article we explain the mechanisms of the \textit{Polyatomic Frank-Wolfe algorithm}, specifically designed to minimise the LASSO problem in such challenging contexts. We demonstrate in simulated problems inspired from radio-interferometry the preeminence of this algorithm over the proximal methods for high dimensional images with Fourier measurements.
Sensing RISs: Enabling Dimension-Independent CSI Acquisition for Beamforming
Authors: Jieao Zhu, Kunzan Liu, Zhongzhichao Wan, Linglong Dai, Tie Jun Cui, H. Vincent Poor
Date: 2022-04-28 00:00:00
Tasks: parameter estimation

Reconfigurable intelligent surfaces (RISs) are envisioned as a potentially transformative technology for future wireless communications. However, RISs' inability to process signals and the attendant increased channel dimension have brought new challenges to RIS-assisted systems, including significantly increased pilot overhead required for channel estimation. To address these problems, several prior contributions that enhance the hardware architecture of RISs or develop algorithms to exploit the channels' mathematical properties have been made, where the required pilot overhead is reduced to be proportional to the number of RIS elements. In this paper, we propose a dimension-independent channel state information (CSI) acquisition approach in which the required pilot overhead is independent of the number of RIS elements. Specifically, in contrast to traditional signal transmission methods, where signals from the base station (BS) and the users are transmitted in different time slots, we propose a novel method in which signals are transmitted from the BS and the user simultaneously during CSI acquisition. With this method, an electromagnetic interference random field (IRF) will be induced on the RIS, and we propose the structure of sensing RIS to capture its features. Moreover, we develop three algorithms for parameter estimation in this system, in which one of the proposed vM-EM algorithm is analyzed with the fixed-point perturbation method to obtain an asymptotic achievable bound. In addition, we also derive the Cram\'er-Rao lower bound (CRLB) and an asymptotic expression for characterizing the best possible performance of the proposed algorithms. Simulation results verify that our proposed signal transmission method and the corresponding algorithms can achieve dimension-independent CSI acquisition for beamforming.
RIS-aided Joint Localization and Synchronization with a Single-Antenna Receiver: Beamforming Design and Low-Complexity Estimation
Authors: Alessio Fascista, Musa Furkan Keskin, Angelo Coluccia, Henk Wymeersch, Gonzalo Seco-Granados
Date: 2022-04-28 00:00:00
Tasks: Position, Robust Design

Reconfigurable intelligent surfaces (RISs) have attracted enormous interest thanks to their ability to overcome line-of-sight blockages in mmWave systems, enabling in turn accurate localization with minimal infrastructure. Less investigated are however the benefits of exploiting RIS with suitably designed beamforming strategies for optimized localization and synchronization performance. In this paper, a novel low-complexity method for joint localization and synchronization based on an optimized design of the base station (BS) active precoding and RIS passive phase profiles is proposed, for the challenging case of a single-antenna receiver. The theoretical position error bound is first derived and used as metric to jointly optimize the BS-RIS beamforming, assuming a priori knowledge of the user position. By exploiting the low-dimensional structure of the solution, a novel codebook-based robust design strategy with optimized beam power allocation is then proposed, which provides low-complexity while taking into account the uncertainty on the user position. Finally, a reduced-complexity maximum-likelihood based estimation procedure is devised to jointly recover the user position and the synchronization offset. Extensive numerical analysis shows that the proposed joint BS-RIS beamforming scheme provides enhanced localization and synchronization performance compared to existing solutions, with the proposed estimator attaining the theoretical bounds even at low signal-to-noise-ratio and in the presence of additional uncontrollable multipath propagation.
Efficient Approximation of Action Potentials with High-Order Shape Preservation in Unsupervised Spike Sorting
Authors: Majid Zamani, Christian Okreghe, Andreas Demosthenous
Date: 2022-04-28 00:00:00
Tasks: Spike Sorting

This paper presents a novel approximation unit added to the conventional spike processing chain which provides an appreciable reduction of complexity of the high-hardware cost feature extractors. The use of the Taylor polynomial is proposed and modelled employing its cascaded derivatives to non-uniformly capture the essential samples in each spike for reliable feature extraction and sorting. Inclusion of the approximation unit can provide 3X compression (i.e. from 66 to 22 samples) to the spike waveforms while preserving their shapes. Detailed spike waveform sequences based on in-vivo measurements have been generated using a customized neural simulator for performance assessment of the approximation unit tested on six published feature extractors. For noise levels {\sigma}_N between 0.05 and 0.3 and groups of 3 spikes in each channel, all the feature extractors provide almost same sorting performance before and after approximation. The overall implementation cost when including the approximation unit and feature extraction shows a large reduction (i.e. up to 8.7X) in the hardware costly and more accurate feature extractors, offering a substantial improvement in feature extraction design.
Channel Modeling for Physically Secure Electro-Quasistatic In-Body to Out-of-Body Communication with Galvanic Tx and Multimodal Rx
Authors: Arunashish Datta, Mayukh Nath, Baibhab Chatterjee, Nirmoy Modak, Shreyas Sen
Date: 2022-04-27 00:00:00

Increasing number of devices being used in and around the human body has resulted in the exploration of the human body as a communication medium. In this paper, we design a channel model for implantable devices communicating outside the body using physically secure Electro-Quasistatic Human Body Communication. A galvanic receiver shows 5dB lower path loss than capacitive receiver when placed close to transmitter whereas a capacitive receiver has around 15dB lower path loss for larger separation between the transmitter and receiver. Finite Element Method (FEM) based simulations are used to analyze the communication channel for different receiver topologies and experimental data is used to validate the simulation results.
A Quantitative Analysis of Physical Security and Path Loss With Frequency for IBOB Channel
Authors: Arunashish Datta, Mayukh Nath, Baibhab Chatterjee, Shovan Maity, Shreyas Sen
Date: 2022-04-27 00:00:00

Security vulnerabilities demonstrated in implantable medical devices have opened the door for research into physically secure and low power communication methodologies. In this study, we perform a comparative analysis of commonly used ISM frequency bands and human body communication (HBC) for data transfer from in-body to out-of-body (IBOB). We develop a figure of merit (FoM) that comprises of the critical parameters to quantitatively compare the communication methodologies. We perform finite-element method (FEM)-based simulations and experiments to validate the FoM developed.
Link Budget Analysis for Free-Space Optical Satellite Networks
Authors: Jintao Liang, Aizaz U. Chaudhry, Eylem Erdogan, Halim Yanikomeroglu
Date: 2022-04-27 00:00:00

Free-space optical satellite networks (FSOSNs) will employ free-space optical links between satellites and between satellites and ground stations, and the link budget for optical inter-satellite links and optical uplink/downlink is analyzed in this paper. The satellites in these FSOSNs will have limited energy and thereby limited power, and we investigate the effect of link distance and link margin on optical inter-satellite link transmission power, and the effect of slant distance, elevation angle, and link margin on optical uplink/downlink transmission power. We model these optical links and compute the results for various parameters. We observe that the transmission power increases when the link distance increases for inter-satellite and uplink/downlink communications, while the transmission power decreases when the elevation angle increases for uplink/downlink transmission. We also observe an inverse relationship between link margin and link distance. Furthermore, we highlight some practical insights and design guidelines gained from this analysis.
PhysioGAN: Training High Fidelity Generative Model for Physiological Sensor Readings
Authors: Moustafa Alzantot, Luis Garcia, Mani Srivastava
Date: 2022-04-25 00:00:00
Tasks: Activity Recognition, Classification, Decoder, ECG Classification, Imputation, Vocal Bursts Intensity Prediction

Generative models such as the variational autoencoder (VAE) and the generative adversarial networks (GAN) have proven to be incredibly powerful for the generation of synthetic data that preserves statistical properties and utility of real-world datasets, especially in the context of image and natural language text. Nevertheless, until now, there has no successful demonstration of how to apply either method for generating useful physiological sensory data. The state-of-the-art techniques in this context have achieved only limited success. We present PHYSIOGAN, a generative model to produce high fidelity synthetic physiological sensor data readings. PHYSIOGAN consists of an encoder, decoder, and a discriminator. We evaluate PHYSIOGAN against the state-of-the-art techniques using two different real-world datasets: ECG classification and activity recognition from motion sensors datasets. We compare PHYSIOGAN to the baseline models not only the accuracy of class conditional generation but also the sample diversity and sample novelty of the synthetic datasets. We prove that PHYSIOGAN generates samples with higher utility than other generative models by showing that classification models trained on only synthetic data generated by PHYSIOGAN have only 10% and 20% decrease in their classification accuracy relative to classification models trained on the real data. Furthermore, we demonstrate the use of PHYSIOGAN for sensor data imputation in creating plausible results.
Epistasis and Adaptation on Fitness Landscapes
Authors: Claudia Bank
Date: 2022-04-28 00:00:00

Epistasis occurs when the effect of a mutation depends on its carrier's genetic background. Despite increasing evidence that epistasis for fitness is common, its role during evolution is contentious. Fitness landscapes, mappings of genotype or phenotype to fitness, capture the full extent and complexity of epistasis. Fitness landscape theory has shown how epistasis affects the course and the outcome of evolution. Moreover, by measuring the competitive fitness of sets of tens to thousands of connected genotypes, empirical fitness landscapes have shown that epistasis is frequent and depends on the fitness measure, the choice of mutations for the landscape, and the environment in which it was measured. Here, I review fitness landscape theory and experiments and their implications for the role of epistasis in adaptation. I discuss theoretical expectations in the light of empirical fitness landscapes and highlight open challenges and future directions towards integrating theory and data, and incorporating ecological factors.
Portfolio Diversification Revisited
Authors: Charles Shaw
Date: 2022-04-28 00:00:00

We relax a number of assumptions in Alexeev and Tapon (2012) in order to account for non-normally distributed, skewed, multi-regime, and leptokurtic asset return distributions. We calibrate a Markov-modulated Levy process model to equity market data to demonstrate the merits of our approach, and show that the calibrated models do a good job of matching the empirical moments. Finally, we argue that much of the related literature on portfolio diversification relies on assumptions that are in tension with certain observable regularities and which, if ignored, may lead to underestimation of risk.
Response Ranking with Multi-types of Deep Interactive Representations in Retrieval-based Dialogues
Authors: Ruijian Xu, Chongyang Tao, Jiazhan Feng, Wei Wu, Rui Yan, Dongyan Zhao
Date: 2021-08-17 00:00:00
Proceeding: ACM Transactions on Information Systems 2021 8
Tasks: Conversational Response Selection, Retrieval

Building an intelligent dialogue system with the ability to select a proper response according to a multi-turn context is challenging in three aspects: (1) the meaning of a context–response pair is built upon language units from multiple granularities (e.g., words, phrases, and sub-sentences, etc.); (2) local (e.g., a small window around a word) and long-range (e.g., words across the context and the response) dependencies may exist in dialogue data; and (3) the relationship between the context and the response candidate lies in multiple relevant semantic clues or relatively implicit semantic clues in some real cases. However, existing approaches usually encode the dialogue with mono-type representation and the interaction processes between the context and the response candidate are executed in a rather shallow manner, which may lead to an inadequate understanding of dialogue content and hinder the recognition of the semantic relevance between the context and response. To tackle these challenges, we propose a representation[K]-interaction[L]-matching framework that explores multiple types of deep interactive representations to build context-response matching models for response selection. Particularly, we construct different types of representations for utterance–response pairs and deepen them via alternate encoding and interaction. By this means, the model can handle the relation of neighboring elements, phrasal pattern, and long-range dependencies during the representation and make a more accurate prediction through multiple layers of interactions between the context–response pair. Experiment results on three public benchmarks indicate that the proposed model significantly outperforms previous conventional context-response matching models and achieve slightly better results than the BERT model for multi-turn response selection in retrieval-based dialogue systems.