pwc-archive/papers-with-abstracts huggingface dataset:
Fast Assignment in Asset-Guarding Engagements using Function Approximation
Authors: Neelay Junnarkar, Emmanuel Sin, Peter Seiler, Douglas Philbrick, Murat Arcak
Date: 2024-04-11 00:00:00

This letter considers assignment problems consisting of n pursuers attempting to intercept n targets. We consider stationary targets as well as targets maneuvering toward an asset. The assignment algorithm relies on an n x n cost matrix where entry (i, j) is the minimum time for pursuer i to intercept target j. Each entry of this matrix requires the solution of a nonlinear optimal control problem. This subproblem is computationally intensive and hence the computational cost of the assignment is dominated by the construction of the cost matrix. We propose to use neural networks for function approximation of the minimum time until intercept. The neural networks are trained offline, thus allowing for real-time online construction of cost matrices. Moreover, the function approximators have sufficient accuracy to obtain reasonable solutions to the assignment problem. In most cases, the approximators achieve assignments with optimal worst case intercept time. The proposed approach is demonstrated on several examples with increasing numbers of pursuers and targets.
Interaction networks in persistent Lotka-Volterra communities
Authors: Lyle Poley, Tobias Galla, Joseph W. Baron
Date: 2024-04-12 00:00:00

A central concern of community ecology is the interdependence between interaction strengths and the underlying structure of the network upon which species interact. In this work we present a solvable example of such a feedback mechanism in a generalised Lotka-Volterra dynamical system. Beginning with a community of species interacting on a network with arbitrary degree distribution, we provide an analytical framework from which properties of the eventual `surviving community' can be derived. We find that highly-connected species are less likely to survive than their poorly connected counterparts, which skews the eventual degree distribution towards a preponderance of species with low degree, a pattern commonly observed in real ecosystems. Further, the average abundance of the neighbours of a species in the surviving community is lower than the community average (reminiscent of the famed friendship paradox). Finally, we show that correlations emerge between the connectivity of a species and its interactions with its neighbours. More precisely, we find that highly-connected species tend to benefit from their neighbours more than their neighbours benefit from them. These correlations are not present in the initial pool of species and are a result of the dynamics.
Strategic Interactions between Large Language Models-based Agents in Beauty Contests
Authors: Siting Estee Lu
Date: 2024-04-12 00:00:00

The growing adoption of large language models (LLMs) presents potential for deeper understanding of human behaviours within game theory frameworks. Addressing research gap on multi-player competitive games, this paper examines the strategic interactions among multiple types of LLM-based agents in a classical beauty contest game. LLM-based agents demonstrate varying depth of reasoning that fall within a range of level-0 to 1, which are lower than experimental results conducted with human subjects, but they do display similar convergence pattern towards Nash Equilibrium (NE) choice in repeated setting. Further, through variation in group composition of agent types, I found environment with lower strategic uncertainty enhances convergence for LLM-based agents, and having a mixed environment comprises of LLM-based agents of differing strategic levels accelerates convergence for all. Higher average payoffs for the more intelligent agents are usually observed, albeit at the expense of less intelligent agents. The results from game play with simulated agents not only convey insights on potential human behaviours under specified experimental set-ups, they also offer valuable understanding of strategic interactions among algorithms.
Estimation and Inference for Three-Dimensional Panel Data Models
Authors: Guohua Feng, Jiti Gao, Fei Liu, Bin Peng
Date: 2024-04-12 00:00:00
Tasks: Time Series

Hierarchical panel data models have recently garnered significant attention. This study contributes to the relevant literature by introducing a novel three-dimensional (3D) hierarchical panel data model, which integrates panel regression with three sets of latent factor structures: one set of global factors and two sets of local factors. Instead of aggregating latent factors from various nodes, as seen in the literature of distributed principal component analysis (PCA), we propose an estimation approach capable of recovering the parameters of interest and disentangling latent factors at different levels and across different dimensions. We establish an asymptotic theory and provide a bootstrap procedure to obtain inference for the parameters of interest while accommodating various types of cross-sectional dependence and time series autocorrelation. Finally, we demonstrate the applicability of our framework by examining productivity convergence in manufacturing industries worldwide.
One Factor to Bind the Cross-Section of Returns
Authors: Nicola Borri, Denis Chetverikov, Yukun Liu, Aleh Tsyvinski
Date: 2024-04-11 00:00:00

We propose a new non-linear single-factor asset pricing model $r_{it}=h(f_{t}\lambda_{i})+\epsilon_{it}$. Despite its parsimony, this model represents exactly any non-linear model with an arbitrary number of factors and loadings -- a consequence of the Kolmogorov-Arnold representation theorem. It features only one pricing component $h(f_{t}\lambda_{I})$, comprising a nonparametric link function of the time-dependent factor and factor loading that we jointly estimate with sieve-based estimators. Using 171 assets across major classes, our model delivers superior cross-sectional performance with a low-dimensional approximation of the link function. Most known finance and macro factors become insignificant controlling for our single-factor.
Uniform Inference in High-Dimensional Threshold Regression Models
Authors: Jiatong Li, Hongqiang Yan
Date: 2024-04-11 00:00:00
Tasks: regression, Time Series, valid

We develop uniform inference for high-dimensional threshold regression parameters, allowing for either cross-sectional or time series data. We first establish Oracle inequalities for prediction errors and $\ell_1$ estimation errors for the Lasso estimator of the slope parameters and the threshold parameter, accommodating heteroskedastic non-subgaussian error terms and non-subgaussian covariates. Next, we derive the asymptotic distribution of tests involving an increasing number of slope parameters by debiasing (or desparsifying) the Lasso estimator in cases with no threshold effect and with a fixed threshold effect. We show that the asymptotic distributions in both cases are the same, allowing us to perform uniform inference without specifying whether the true model is a linear or threshold regression. Finally, we demonstrate the consistent performance of our estimator in both cases through simulation studies, and we apply the proposed estimator to analyze two empirical applications.
Natural disasters and social entrepreneurship: An attention-based view
Authors: Shihao Wei, Christopher Boudreaux, Zhongfeng Su, Zhan Wu
Date: 2024-04-12 00:00:00

Drawing on the attention based view, this study explores the joint effects of natural disaster intensity at the country level with personal attributes in terms of gender, human capital, and fear of failure on the likelihood to enter social entrepreneurship. Using data on 107,386 observations across 30 countries, we find that natural disaster intensity has a positive effect on individuals likelihood to engage in social entrepreneurship. In addition, the effect of natural disaster intensity is greater for males, individuals lacking human capital, and those who fear failure. Our study helps elaborate on the antecedents of social entrepreneurship and extends the consequences of natural disasters to entrepreneurship at the individual level.
Istanbul Flower Auction: The Need for Speed
Authors: Isa Hafalir, Onur Kesten, Donglai Luo, Katerina Sherstyuk, Cong Tao
Date: 2024-04-12 00:00:00

We examine a unique auction format used in the Istanbul flower market, which could transform into either Dutch or English auction depending on bidders' bidding behaviors. By introducing a time cost that reduces the value of a perishable good as time passes, we explore how this hybrid auction format accommodates the desire for speed via an adaptive starting price. We show that the Istanbul Flower Auction outperforms both the Dutch and English auctions in terms of the auctioneer's utility. With numerical analysis, we also illustrate the Istanbul Flower Auction's superiority in terms of social welfare and auction duration. Our results highlight the critical role of auction design in improving welfare when the duration of the auction process plays a role.
Accel-NASBench: Sustainable Benchmarking for Accelerator-Aware NAS
Authors: Afzal Ahmad, Linfeng Du, Zhiyao Xie, Wei zhang
Date: 2024-04-09 00:00:00
Tasks: Benchmarking, Neural Architecture Search

One of the primary challenges impeding the progress of Neural Architecture Search (NAS) is its extensive reliance on exorbitant computational resources. NAS benchmarks aim to simulate runs of NAS experiments at zero cost, remediating the need for extensive compute. However, existing NAS benchmarks use synthetic datasets and model proxies that make simplified assumptions about the characteristics of these datasets and models, leading to unrealistic evaluations. We present a technique that allows searching for training proxies that reduce the cost of benchmark construction by significant margins, making it possible to construct realistic NAS benchmarks for large-scale datasets. Using this technique, we construct an open-source bi-objective NAS benchmark for the ImageNet2012 dataset combined with the on-device performance of accelerators, including GPUs, TPUs, and FPGAs. Through extensive experimentation with various NAS optimizers and hardware platforms, we show that the benchmark is accurate and allows searching for state-of-the-art hardware-aware models at zero cost.
Full-Duplex Beyond Self-Interference: The Unlimited Sensing Way
Authors: Ziang Liu, Ayush Bhandari, Bruno Clerckx
Date: 2024-04-12 00:00:00
Tasks: Quantization

The success of full-stack full-duplex communication systems depends on how effectively one can achieve digital self-interference cancellation (SIC). Towards this end, in this paper, we consider unlimited sensing framework (USF) enabled full-duplex system. We show that by injecting folding non-linearities in the sensing pipeline, one can not only suppress self-interference but also recover the signal of interest (SoI). This approach leads to novel design of the receiver architecture that is complemented by a modulo-domain channel estimation method. We then demonstrate the advantages of modulo ADC by analyzing the relationship between quantization noise, quantization bits, and dynamic range. Numerical experiments show that the USF enabled receiver structure can achieve up to 40 dB digital SIC by using as few as 4-bits per sample. Our method outperforms the previous approach based on adaptive filters when it comes to SoI reconstruction, detection, and digital SIC performance.
Rate-Splitting Multiple Access for Non-Orthogonal Unicast Multicast: An Experimental Study
Authors: Xinze Lyu, Sundar Aditya, Bruno Clerckx
Date: 2024-04-12 00:00:00

Non-orthogonal unicast multicast (NOUM) is a variant of multi-antenna multi-user communications where the users desire a shared message (multicast) in addition to their respective unique messages (unicast). The multicast rate is capped in many emerging NOUM applications, such as live-event broadcasting, location-based services and vehicular communications. Given this constraint, we experimentally show that when the user channels are highly correlated, Rate-Splitting Multiple Access (RSMA)-based NOUM can meet the multicast rate while supporting a larger unicast sum rate than multi-user linear precoding (MULP)-based NOUM and orthogonal unicast multicast.
SemHARQ: Semantic-Aware HARQ for Multi-task Semantic Communications
Authors: Jiangjing Hu, Fengyu Wang, Wenjun Xu, Hui Gao, Ping Zhang
Date: 2024-04-12 00:00:00
Tasks: Feature Importance, Vehicle Re-Identification

Intelligent task-oriented semantic communications (SemComs) have witnessed great progress with the development of deep learning (DL). In this paper, we propose a semantic-aware hybrid automatic repeat request (SemHARQ) framework for the robust and efficient transmissions of semantic features. First, to improve the robustness and effectiveness of semantic coding, a multi-task semantic encoder is proposed. Meanwhile, a feature importance ranking (FIR) method is investigated to ensure the important features delivery under limited channel resources. Then, to accurately detect the possible transmission errors, a novel feature distortion evaluation (FDE) network is designed to identify the distortion level of each feature, based on which an efficient HARQ method is proposed. Specifically, the corrupted features are retransmitted, where the remaining channel resources are used for incremental transmissions. The system performance is evaluated under different channel conditions in multi-task scenarios in Internet of Vehicles. Extensive experiments show that the proposed framework outperforms state-of-the-art works by more than 20% in rank-1 accuracy for vehicle re-identification, and 10% in vehicle color classification accuracy in the low signal-to-noise ratio regime.
Optic Fingerprint: Enhancing Security in Visible Light Communication Networks
Authors: Chen Xuanbang, Liu Ziqi, Zhang Xun, Wang Yuhao, Shi Dayu, Liu Xiaodong
Date: 2024-04-12 00:00:00

In addressing physical layer security issues, hardware fingerprinting has been proven to be a reliable method. Additionally, Visible Light Communication (VLC) technology offers a solution to the spectrum congestion in next-generation wireless communications and is noteworthy for its high security. However, there is currently a lack of a comprehensive and systematic description of the hardware fingerprints and their extraction mechanisms for VLC devices. This study aims to bridge this gap by thoroughly analyzing the hardware fingerprints of VLC devices and proposing an innovative extraction mechanism, thereby enhancing the security and reliability of the physical layer. An Optic Fingerprint (OF) model is proposed based on the LED's inherent circuit characteristics, capable of extracting and processing unique feature vectors with high precision. Through extensive experiments, we demonstrate the model's efficacy, achieving up to 99.3% accuracy in identifying the same manufactured white LEDs under variable conditions, marking a significant improvement in authentication robustness and interference resistance.
Intelligent Reflecting Surface-Enabled Anti-Detection for Secure Sensing and Communications
Authors: Beixiong Zheng, Xue Xiong, Tiantian Ma, Jie Tang, Derrick Wing Kwan Ng, A. Lee Swindlehurst, Rui Zhang
Date: 2024-04-12 00:00:00

The ever-increasing reliance on wireless communication and sensing has led to growing concerns over the vulnerability of sensitive information to unauthorized detection and interception. Traditional anti-detection methods are often inadequate, suffering from limited adaptability and diminished effectiveness against advanced detection technologies. To overcome these challenges, this article presents the intelligent reflecting surface (IRS) as a groundbreaking technology for enabling flexible electromagnetic manipulation, which has the potential to revolutionize anti-detection in both electromagnetic stealth/spoofing (evading radar detection) and covert communications (facilitating secure information exchange). We explore the fundamental principles of IRS and its advantages over traditional anti-detection techniques and discuss various design challenges associated with implementing IRS-based anti-detection systems. Through the examination of case studies and future research directions, we provide a comprehensive overview of the potential of IRS technology to serve as a formidable shield in the modern wireless landscape.
Energy-modified Leverage Sampling for Radio Map Construction via Matrix Completion
Authors: Hao Sun, Junting Chen
Date: 2024-04-12 00:00:00
Tasks: Matrix Completion

This paper explores an energy-modified leverage sampling strategy for matrix completion in radio map construction. The main goal is to address potential identifiability issues in matrix completion with sparse observations by using a probabilistic sampling approach. Although conventional leverage sampling is commonly employed for designing sampling patterns, it often assigns high sampling probability to locations with low received signal strength (RSS) values, leading to a low sampling efficiency. Theoretical analysis demonstrates that the leverage score produces pseudo images of sources, and in the regions around the source locations, the leverage probability is asymptotically consistent with the RSS. Based on this finding, an energy-modified leverage probability-based sampling strategy is investigated for efficient sampling. Numerical demonstrations indicate that the proposed sampling strategy can decrease the normalized mean squared error (NMSE) of radio map construction by more than 10% for both matrix completion and interpolation-assisted matrix completion schemes, compared to conventional methods.
SNAKE-fMRI: A modular fMRI data simulator from the space-time domain to k-space and back
Authors: Pierre-Antoine Comby, Alexandre Vignaud, Philippe Ciuciu
Date: 2024-04-12 00:00:00
Tasks: Image Reconstruction

We propose a new, modular, open-source, Python-based 3D+time fMRI data simulation software, \emph{SNAKE-fMRI}, which stands for \emph{S}imulator from \emph{N}eurovascular coupling to \emph{A}cquisition of \emph{K}-space data for \emph{E}xploration of fMRI acquisition techniques.Unlike existing tools, the goal here is to simulate the complete chain of fMRI data acquisition, from the spatio-temporal design of evoked brain responses to various multi-coil k-space data 3D sampling strategies, with the possibility of extending the forward acquisition model to various noise and artifact sources while remaining memory-efficient.By using this \emph{in silico} setup, we are thus able to provide realistic and reproducible ground truth for fMRI reconstruction methods in 3D accelerated acquisition settings and explore the influence of critical parameters, such as the acceleration factor and signal-to-noise ratio~(SNR), on downstream tasks of image reconstruction and statistical analysis of evoked brain activity.We present three scenarios of increasing complexity to showcase the flexibility, versatility, and fidelity of \emph{SNAKE-fMRI}: From a temporally-fixed full 3D Cartesian to various 3D non-Cartesian sampling patterns, we can compare -- with reproducibility guarantees -- how experimental paradigms, acquisition strategies and reconstruction methods contribute and interact together, affecting the downstream statistical analysis.
Mental Stress Detection: Development and Evaluation of a Wearable In-Ear Plethysmography
Authors: Hika Barki, Wan-Young Chung
Date: 2024-04-12 00:00:00
Tasks: Math, Mental Stress Detection

Mental stress is a prevalent condition that can have negative impacts on one's health. Early detection and treatment are crucial for preventing related illnesses and maintaining overall wellness. This study presents a new method for identifying mental stress using a wearable biosensor worn in the ear. Data was gathered from 14 participants in a controlled environment using stress-inducing tasks such as memory and math tests. The raw photoplethysmography data was then processed by filtering, segmenting, and transforming it into scalograms using a continuous wavelet transform (CWT) which are based on two different mother wavelets, namely, a generalized Morse wavelet and the analytic Morlet (Gabor) wavelet. The scalograms were then passed through a convolutional neural network classifier, GoogLeNet, to classify the signals as stressed or non-stressed. The method achieved an outstanding result using the generalized Morse wavelet with an accuracy of 91.02% and an F1-score of 90.95%. This method demonstrates promise as a reliable tool for early detection and treatment of mental stress by providing real-time monitoring and allowing for preventive measures to be taken before it becomes a serious issue.
Cepstral Analysis Based Artifact Detection, Recognition and Removal for Prefrontal EEG
Authors: Siqi Han, Chao Zhang, Jiaxin Lei, Qingquan Han, Yuhui Du, Anhe Wang, Shuo Bai, Milin Zhang
Date: 2024-04-12 00:00:00
Tasks: Artifact Detection, EEG, Emotion Classification

This paper proposes to use cepstrum for artifact detection, recognition and removal in prefrontal EEG. This work focuses on the artifact caused by eye movement. A database containing artifact-free EEG and eye movement contaminated EEG from different subjects is established. A cepstral analysis-based feature extraction with support vector machine (SVM) based classifier is designed to identify the artifacts from the target EEG signals. The proposed method achieves an accuracy of 99.62% on the artifact detection task and a 82.79% accuracy on the 6-category eye movement classification task. A statistical value-based artifact removal method is proposed and evaluated on a public EEG database, where an accuracy improvement of 3.46% is obtained on the 3-category emotion classification task. In order to make a confident decision of each 5s EEG segment, the algorithm requires only 0.66M multiplication operations. Compared to the state-of-the-art approaches in artifact detection and removal, the proposed method features higher detection accuracy and lower computational cost, which makes it a more suitable solution to be integrated into a real-time and artifact robust Brain-Machine Interface (BMI).
Source Invariance and Probabilistic Transfer: A Testable Theory of Probabilistic Neural Representations
Authors: Samuel Lippl, Raphael Gerraty, John Morrison, Nikolaus Kriegeskorte
Date: 2024-04-11 00:00:00

As animals interact with their environments, they must infer properties of their surroundings. Some animals, including humans, can represent uncertainty about those properties. But when, if ever, do they use probability distributions to represent their uncertainty? It depends on which definition we choose. In this paper, we argue that existing definitions are inadequate because they are untestable. We then propose our own definition. There are two reasons why existing definitions are untestable. First, they do not distinguish between representations of uncertainty and representations of variables merely related to uncertainty ('representational indeterminacy'). Second, they do not distinguish between probabilistic representations of uncertainty and merely "heuristic" representations of uncertainty. We call this 'model indeterminacy' because the underlying problem is that we do not have access to the animal's generative model. We define probabilistic representations by two properties: 1) they encode uncertainty regardless of the source of the uncertainty ('source invariance'), 2) they support the efficient learning of new tasks that would be more difficult to learn given non-probabilistic representations ('probabilistic task transfer'). Source invariance indicates that they are representations of uncertainty rather than variables merely related to uncertainty, thereby solving representational indeterminacy. Probabilistic task transfer indicates that they are probabilistic representations of uncertainty rather than merely heuristic representations, thereby solving model indeterminacy.