pwc-archive/papers-with-abstracts huggingface dataset:
Joint Data Collection and Sensor Positioning in Multi-UAV-Assisted Wireless Sensor Network
Authors: Mingyue Zhu, Zhiqing Wei, Chen Qiu, Wangjun Jiang, Huici Wu, Zhiying Feng
Date: 2023-08-14 00:00:00
Tasks: Position

Due to the high mobility and easy deployment, unmanned aerial vehicles (UAVs) have attracted much attention in the field of wireless communication and positioning. To meet the challenges of lack of infrastructure coverage, uncertain sensor position and large amount of sensing data collection in wireless sensor network (WSN), this paper presents an efficient joint data collection and sensor positioning scheme for WSN supported by multiple UAVs. Specifically, a UAV is set as the main UAV to collect data, and other UAVs are used as auxiliary UAVs for sensor positioning using time difference of arrival (TDoA). A mixed-integer non-convex optimization problem with uncertain sensor position is established. The goal is to minimize the average positioning error of all sensors by jointly optimizing the UAV trajectories, sensor transmission schedule and positioning observation points (POPs). To solve this optimization model, the original problem is decomposed into two sub-problems based on the path discrete method. Firstly, the block coordinate descent (BCD) and successive convex approximation (SCA) techniques are applied to iteratively optimize the trajectory of the main UAV and the sensor transmission schedule, so as to maximize the minimum amount of data uploaded by the sensor. Then, based on the trajectory of the main UAV, a particle swarm optimization (PSO)-based algorithm is designed to optimize the POPs of UAVs. Finally, the spline curve is applied to generate the trajectories of auxiliary UAVs. The simulation results show that the proposed scheme can meet the requirements of data collection and has a good positioning performance.
Distance preservation in state-space methods for detecting causal interactions in dynamical systems
Authors: Matthew O'Shaughnessy, Mark Davenport, Christopher Rozell
Date: 2023-08-13 00:00:00

We analyze the popular ``state-space'' class of algorithms for detecting casual interaction in coupled dynamical systems. These algorithms are often justified by Takens' embedding theorem, which provides conditions under which relationships involving attractors and their delay embeddings are continuous. In practice, however, state-space methods often do not directly test continuity, but rather the stronger property of how these relationships preserve inter-point distances. This paper theoretically and empirically explores state-space algorithms explicitly from the perspective of distance preservation. We first derive basic theoretical guarantees applicable to simple coupled systems, providing conditions under which the distance preservation of a certain map reveals underlying causal structure. Second, we demonstrate empirically that typical coupled systems do not satisfy distance preservation assumptions. Taken together, our results underline the dependence of state-space algorithms on intrinsic system properties and the relationship between the system and the function used to measure it -- properties that are not directly associated with causal interaction.
Challenges and Opportunities for Second-life Batteries: A Review of Key Technologies and Economy
Authors: Xubo Gu, Hanyu Bai, Xiaofan Cui, Juner Zhu, Weichao Zhuang, Zhaojian Li, Xiaosong Hu, Ziyou Song
Date: 2023-08-13 00:00:00
Tasks: Explainable Models

Due to the increasing volume of Electric Vehicles in automotive markets and the limited lifetime of onboard lithium-ion batteries (LIBs), the large-scale retirement of LIBs is imminent. The battery packs retired from Electric Vehicles still own 70%-80% of the initial capacity, thus having the potential to be utilized in scenarios with lower energy and power requirements to maximize the value of LIBs. However, spent batteries are commonly less reliable than fresh batteries due to their degraded performance, thereby necessitating a comprehensive assessment from safety and economic perspectives before further utilization. To this end, this paper reviews the key technological and economic aspects of second-life batteries (SLBs). Firstly, we introduce various degradation models for first-life batteries and identify an opportunity to combine physics-based theories with data-driven methods to establish explainable models with physical laws that can be generalized. However, degradation models specifically tailored to SLBs are currently absent. Therefore, we analyze the applicability of existing battery degradation models developed for first-life batteries in SLB applications. Secondly, we investigate fast screening and regrouping techniques and discuss the regrouping standards for the first time to guide the classification procedure and enhance the performance and safety of SLBs. Thirdly, we scrutinize the economic analysis of SLBs and summarize the potentially profitable applications. Finally, we comprehensively examine and compare power electronics technologies that can substantially improve the performance of SLBs, including high-efficiency energy transformation technologies, active equalization technologies, and technologies to improve reliability and safety.
A Fog-based Smart Agriculture System to Detect Animal Intrusion
Authors: Jinpeng Miao, Dasari Rajasekhar, Shivakant Mishra, Sanjeet Kumar Nayak, Ramanarayan Yadav
Date: 2023-08-12 00:00:00
Tasks: Cloud Computing, Edge-computing

Smart agriculture is one of the most promising areas where IoT-enabled technologies have the potential to substantially improve the quality and quantity of the crops and reduce the associated operational cost. However, building a smart agriculture system presents several challenges, including high latency and bandwidth consumption associated with cloud computing, frequent Internet disconnections in rural areas, and the need to keep costs low for farmers. This paper presents an end-to-end, fog-based smart agriculture infrastructure that incorporates edge computing and LoRa-based communication to address these challenges. Our system is deployed to transform traditional agriculture land of rural areas into smart agriculture. We address the top concern of farmers - animals intruding - by proposing a solution that detects animal intrusion using low-cost PIR sensors, cameras, and computer vision. In particular, we propose three different sensor layouts and a novel algorithm for predicting animals' future locations. Our system can detect animals before they intrude into the field, identify them, predict their future locations, and alert farmers in a timely manner. Our experiments show that the system can effectively and quickly detect animal intrusions while maintaining a much lower cost than current state-of-the-art systems.
Conformance Testing for Stochastic Cyber-Physical Systems
Authors: Xin Qin, Navid Hashemi, Lars Lindemann, Jyotirmoy V. Deshmukh
Date: 2023-08-12 00:00:00
Tasks: Conformal Prediction

Conformance is defined as a measure of distance between the behaviors of two dynamical systems. The notion of conformance can accelerate system design when models of varying fidelities are available on which analysis and control design can be done more efficiently. Ultimately, conformance can capture distance between design models and their real implementations and thus aid in robust system design. In this paper, we are interested in the conformance of stochastic dynamical systems. We argue that probabilistic reasoning over the distribution of distances between model trajectories is a good measure for stochastic conformance. Additionally, we propose the non-conformance risk to reason about the risk of stochastic systems not being conformant. We show that both notions have the desirable transference property, meaning that conformant systems satisfy similar system specifications, i.e., if the first model satisfies a desirable specification, the second model will satisfy (nearly) the same specification. Lastly, we propose how stochastic conformance and the non-conformance risk can be estimated from data using statistical tools such as conformal prediction. We present empirical evaluations of our method on an F-16 aircraft, an autonomous vehicle, a spacecraft, and Dubin's vehicle.
Concurrent Probabilistic Control Co-Design and Layout Optimization of Wave Energy Converter Farms using Surrogate Modeling
Authors: Saeed Azad, Daniel R. Herber
Date: 2023-08-11 00:00:00

Wave energy converters (WECs) are a promising candidate for meeting the increasing energy demands of today's society. It is known that the sizing and power take-off (PTO) control of WEC devices have a major impact on their performance. In addition, to improve power generation, WECs must be optimally deployed within a farm. While such individual aspects have been investigated for various WECs, potential improvements may be attained by leveraging an integrated, system-level design approach that considers all of these aspects. However, the computational complexity of estimating the hydrodynamic interaction effects significantly increases for large numbers of WECs. In this article, we undertake this challenge by developing data-driven surrogate models using artificial neural networks and the principles of many-body expansion. The effectiveness of this approach is demonstrated by solving a concurrent plant (i.e., sizing), control (i.e., PTO parameters), and layout optimization of heaving cylinder WEC devices. WEC dynamics were modeled in the frequency domain, subject to probabilistic incident waves with farms of $3$, $5$, $7$, and $10$ WECs. The results indicate promising directions toward a practical framework for array design investigations with more tractable computational demands.
A Compressive Sensing Based Method for Harmonic State Estimation
Authors: Saeed Nasiri
Date: 2023-08-11 00:00:00
Tasks: Compressive Sensing, State Estimation

Power quality monitoring has become a vital need in modern power systems owing to the need for agile operation and troubleshooting scheme. On the other hand, the nature of load in modern power system is changing in many ways. Digital loads, which are mostly relied on power electronic equipment, may distort the quality of power flowing through the network. Moreover, one of the most critical objectives of smart grids is to improve quality of services delivered to customers, alongside with security, reliability and efficiency. To this end, a novel method based on compressive sensing is proposed in this paper to detect the source and the magnitude of the harmonics. The method takes advantages of compressive sensing theory in such a way that a real-time monitoring of harmonic distortion is obtained with a limited number of measurements. The efficacy of the method is checked by means of various simulations on IEEE 118 bus test system. The results show the capabilities of the method in both noisy and noise-free conditions.
Evolving division of labor in a response threshold model
Authors: José F. Fontanari, Viviane M. de Oliveira, Paulo R. A. Campos
Date: 2023-08-14 00:00:00

The response threshold model explains the emergence of division of labor (i.e., task specialization) in an unstructured population by assuming that the individuals have different propensities to work on different tasks. The incentive to attend to a particular task increases when the task is left unattended and decreases when individuals work on it. Here we derive mean-field equations for the stimulus dynamics and show that they exhibit complex attractors through period-doubling bifurcation cascades when the noise disrupting the thresholds is small. In addition, we show how the fixed threshold can be set to ensure specialization in both the transient and equilibrium regimes of the stimulus dynamics. However, a complete explanation of the emergence of division of labor requires that we address the question of where the threshold variation comes from, starting from a homogeneous population. We then study a structured population scenario, where the population is divided into a large number of independent groups of equal size, and the fitness of a group is proportional to the weighted mean work performed on the tasks during a fixed period of time. Using a winner-take-all strategy to model group competition and assuming an initial homogeneous metapopulation, we find that a substantial fraction of workers specialize in each task, without the need to penalize task switching.
Quantile Time Series Regression Models Revisited
Authors: Christis Katsouris
Date: 2023-08-12 00:00:00
Tasks: regression, Time Series, Time Series Regression

This article discusses recent developments in the literature of quantile time series models in the cases of stationary and nonstationary underline stochastic processes.
Economic complexity and the sustainability transition: A review of data, methods, and literature
Authors: Bernardo Caldarola, Dario Mazzilli, Lorenzo Napolitano, Aurelio Patelli, Angelica Sbardella
Date: 2023-08-14 00:00:00
Tasks: Articles

Economic Complexity (EC) methods have gained increasing popularity across fields and disciplines. In particular, the EC toolbox has proved particularly promising in the study of complex and interrelated phenomena, such as the transition towards a greener economy. Using the EC approach, scholars have been investigating the relationship between EC and sustainability, proposing to identify the distinguishing characteristics of green products and to assess the readiness of productive and technological structures for the sustainability transition. This article proposes to review and summarize the data, methods, and empirical literature that are relevant to the study of the sustainability transition from an EC perspective. We review three distinct but connected blocks of literature on EC and environmental sustainability. First, we survey the evidence linking measures of EC to indicators related to environmental sustainability. Second, we review articles that strive to assess the green competitiveness of productive systems. Third, we examine evidence on green technological development and its connection to non-green knowledge bases. Finally, we summarize the findings for each block and identify avenues for further research in this recent and growing body of empirical literature.
The four types of stablecoins: A comparative analysis
Authors: Matthias Hafner, Marco Henriques Pereira, Helmut Dietl, Juan Beccuti
Date: 2023-08-14 00:00:00
Tasks: Management

Stablecoins have gained significant popularity recently, with their market cap rising to over $180 billion. However, recent events have raised concerns about their stability. In this paper, we classify stablecoins into four types based on the source and management of collateral and investigate the stability of each type under different conditions. We highlight each type's potential instabilities and underlying tradeoffs using agent-based simulations. The results emphasize the importance of carefully evaluating the origin of a stablecoin's collateral and its collateral management mechanism to ensure stability and minimize risks. Enhanced understanding of stablecoins should be informative to regulators, policymakers, and investors alike.
Contagion Effects of the Silicon Valley Bank Run
Authors: Dong Beom Choi, Paul Goldsmith-Pinkham, Tanju Yorulmazer
Date: 2023-08-12 00:00:00

This paper analyzes the contagion effects associated with the failure of Silicon Valley Bank (SVB) and identifies bank-specific vulnerabilities contributing to the subsequent declines in banks' stock returns. We find that uninsured deposits, unrealized losses in held-to-maturity securities, bank size, and cash holdings had a significant impact, while better-quality assets or holdings of liquid securities did not help mitigate the negative spillovers. Interestingly, banks whose stocks performed worse post-SVB also experienced lower returns in the previous year, following Federal Reserve interest rate hikes. Stock investors appeared to anticipate risks associated with uninsured deposit reliance, but did not foresee the realization of implied losses. While mid-sized banks experienced particular stress immediately after the SVB failure, over time negative spillovers became widespread except for the largest banks.
The Disagreement Dividend
Authors: Giampaolo Bonomi
Date: 2023-08-12 00:00:00

We study how disagreement influences team performance in a dynamic game with positive production externalities. Players can hold different views about the productivity of the available production technologies. This disagreement results in different technology and effort choices -- "optimistic" views induce higher effort than "skeptical" views. Views are changed when falsified by evidence. With a single technology available, optimists exert more effort early on if the team also includes skeptics. With sufficiently strong externalities, a disagreeing team produces, on average, more than any like-minded team. With multiple technologies, disagreement over which technology works best motivates everyone to exert more effort.
The Cycle of Value The Cycle of Value -- A Conservationist Approach to Economics
Authors: Nick Harkiolakis
Date: 2023-08-08 00:00:00

A representation of economic activity in the form of a law of conservation of value is presented based on the definition of value as potential to act in an environment. This allows the encapsulation of the term as a conserved quantity throughout transactions. Marginal value and speed of marginal value are defined as derivatives of value and marginal value, respectively. Traditional economic statements are represented here as cycles of value where value is conserved. Producer-consumer dyads, shortage and surplus, as well as the role of the value in representing the market and the economy are explored. The role of the government in the economy is also explained through the cycles of value the government is involved in. Traditional economic statements and assumptions produce existing hypotheses as outcomes of the law of conservation of value.
Visitors Out! The Absence of Away Team Supporters as a Source of Home Advantage in Football
Authors: Federico Fioravanti, Fernando Delbianco, Fernando Tohmé
Date: 2023-08-03 00:00:00

We seek to gain more insight into the effect of the crowds on the Home Advantage by analyzing the particular case of Argentinean football (also known as soccer), where for more than ten years, the visiting team fans were not allowed to attend the games. Additionally, during the COVID-19 lockdown, a significant number of games were played without both away and home team fans. The analysis of more than 20 years of matches of the Argentinean tournament indicates that the absence of the away team crowds was beneficial for the Top 5 teams during the first two years after their attendance was forbidden. An additional intriguing finding is that the lack of both crowds affects significantly all the teams, to the point of turning the home advantage into home `disadvantage' for most of the teams.
Exploring the Nexus between Exhaustible Human Resources and Economic Development in China: An Application of the Hotelling Model
Authors: Zhiwei Yang
Date: 2023-06-21 00:00:00

This paper applies the Hotelling model to the context of exhaustible human resources in China. We find that over-exploitation of human resources occurs under conditions of restricted population mobility, rigid wage levels, and increased foreign trade demand elasticity. Conversely, the existence of technological replacements for human resources or improvements in the utilization rate of human resources leads to conservation. Our analysis provides practical insights for policy-making towards sustainable development.
Long-term Effects of Temperature Variations on Economic Growth: A Machine Learning Approach
Authors: Eugene Kharitonov, Oksana Zakharchuk, Lin Mei
Date: 2023-06-17 00:00:00

This study investigates the long-term effects of temperature variations on economic growth using a data-driven approach. Leveraging machine learning techniques, we analyze global land surface temperature data from Berkeley Earth and economic indicators, including GDP and population data, from the World Bank. Our analysis reveals a significant relationship between average temperature and GDP growth, suggesting that climate variations can substantially impact economic performance. This research underscores the importance of incorporating climate factors into economic planning and policymaking, and it demonstrates the utility of machine learning in uncovering complex relationships in climate-economy studies.
When Deep Learning Meets Multi-Task Learning in SAR ATR: Simultaneous Target Recognition and Segmentation
Authors: Chenwei Wang, Jifang Pei, Zhiyong Wang, Yulin Huang, Junjie Wu, Haiguang Yang, Jianyu Yang
Date: 2023-08-14 00:00:00
Tasks: Decoder, Deep Learning, Learning Theory, Multi-Task Learning, Segmentation

With the recent advances of deep learning, automatic target recognition (ATR) of synthetic aperture radar (SAR) has achieved superior performance. By not being limited to the target category, the SAR ATR system could benefit from the simultaneous extraction of multifarious target attributes. In this paper, we propose a new multi-task learning approach for SAR ATR, which could obtain the accurate category and precise shape of the targets simultaneously. By introducing deep learning theory into multi-task learning, we first propose a novel multi-task deep learning framework with two main structures: encoder and decoder. The encoder is constructed to extract sufficient image features in different scales for the decoder, while the decoder is a tasks-specific structure which employs these extracted features adaptively and optimally to meet the different feature demands of the recognition and segmentation. Therefore, the proposed framework has the ability to achieve superior recognition and segmentation performance. Based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, experimental results show the superiority of the proposed framework in terms of recognition and segmentation.
Semantic Communications with Explicit Semantic Base for Image Transmission
Authors: Yuan Zheng, Fengyu Wang, Wenjun Xu, Miao Pan, Ping Zhang
Date: 2023-08-12 00:00:00
Tasks: Decoder, Image Reconstruction, Semantic Communication

Semantic communications, aiming at ensuring the successful delivery of the meaning of information, are expected to be one of the potential techniques for the next generation communications. However, the knowledge forming and synchronizing mechanism that enables semantic communication systems to extract and interpret the semantics of information according to the communication intents is still immature. In this paper, we propose a semantic image transmission framework with explicit semantic base (Seb), where Sebs are generated and employed as the knowledge shared between the transmitter and the receiver with flexible granularity. To represent images with Sebs, a novel Seb-based reference image generator is proposed to generate Sebs and then decompose the transmitted images. To further encode/decode the residual information for precise image reconstruction, a Seb-based image encoder/decoder is proposed. The key components of the proposed framework are optimized jointly by end-to-end (E2E) training, where the loss function is dedicatedly designed to tackle the problem of nondifferentiable operation in Seb-based reference image generator by introducing a gradient approximation mechanism. Extensive experiments show that the proposed framework outperforms state-of-art works by 0.5 - 1.5 dB in peak signal-to-noise ratio (PSNR) w.r.t. different signal-to-noise ratios (SNR).