pwc-archive/papers-with-abstracts huggingface dataset:
Taylor-Expansion-Based Robust Power Flow in Unbalanced Distribution Systems: A Hybrid Data-Aided Method
Authors: Sungjoo Chung, Ying Zhang, Zhaoyu Wang, Fei Ding
Date: 2024-07-27 00:00:00
Tasks: Computational Efficiency, regression

Traditional power flow methods often adopt certain assumptions designed for passive balanced distribution systems, thus lacking practicality for unbalanced operation. Moreover, their computation accuracy and efficiency are heavily subject to unknown errors and bad data in measurements or prediction data of distributed energy resources (DERs). To address these issues, this paper proposes a hybrid data-aided robust power flow algorithm in unbalanced distribution systems, which combines Taylor series expansion knowledge with a data-driven regression technique. The proposed method initiates a linearization power flow model to derive an explicitly analytical solution by modified Taylor expansion. To mitigate the approximation loss that surges due to the DER integration and bad data, we further develop a data-aided robust support vector regression approach to estimate the errors efficiently. Comparative analysis in the 13-bus and 123-bus IEEE unbalanced feeders shows that the proposed algorithm achieves superior computational efficiency, with guaranteed accuracy and robustness against outliers.
B-ISAC: Backscatter Integrated Sensing and Communication for IoE Applications
Authors: Zongyao Zhao, Yuhan Dong, Tiankuo Wei, Xinke Tang, Xiao-Ping Zhang, Zhenyu Liu
Date: 2024-07-27 00:00:00
Tasks: Integrated sensing and communication, ISAC, TAG

The integration of backscatter communication (BackCom) technology with integrated sensing and communication (ISAC) technology not only enhances the system sensing performance, but also enables low-power information transmission. This is expected to provide a new paradigm for communication and sensing in internet of everything (IoE) applications. In this paper, we propose a novel cognitive wireless system called backscatter-ISAC (B-ISAC) and develop a joint beamforming framework for different stages (task modes). This system can achieve cognitive spectrum sharing between legacy communication, backscatter communication and sensing functions. We derive communication performance metrics of the system in terms of the signal-to-interference-plus-noise ratio (SINR) and communication rate, and derive sensing performance metrics of the system in terms of probability of detection, error of linear least squares (LS) estimation, and the error of linear minimum mean square error (LMMSE) estimation. The proposed joint beamforming framework consists of three stages: tag detection, tag estimation, and communication enhancement. We develop corresponding joint beamforming schemes aimed at enhancing the performance objectives of their respective stages by solving complex non-convex optimization problems. Extensive simulation results demonstrate the effectiveness of the proposed joint beamforming schemes. The proposed B-ISAC system has broad application prospect in next generation IoE scenarios.
Impact of Transmission Dynamics and Treatment Uptake, Frequency and Timing on the Cost-effectiveness of Directly Acting Antivirals for Hepatitis C Virus Infection
Authors: Soham Das, Ajit Sood, Vandana Midha, Arshdeep Singh, Pranjl Sharma, Varun Ramamohan
Date: 2024-07-27 00:00:00

Cost-effectiveness analyses, based on decision-analytic models of disease progression and treatment, are routinely used to assess the economic value of a new intervention and consequently inform reimbursement decisions for the intervention. Many decision-analytic models developed to assess the economic value of highly effective directly acting antiviral (DAA) treatments for the hepatitis C virus (HCV) infection do not incorporate the transmission dynamics of HCV, accounting for which is required to estimate the number of downstream infections prevented by curing an infection. In this study, we develop and validate a comprehensive agent-based simulation (ABS) model of HCV transmission dynamics in the Indian context and use it to: (a) quantify the extent to which the cost-effectiveness of a DAA is underestimated - as a function of its uptake rate - if disease transmission dynamics are not considered in a cost-effectiveness analysis model; and (b) quantify the impact of the frequency and timing of treatment with DAAs, also as a function of their uptake rate, within a disease surveillance period on its cost-effectiveness. The process of accomplishing the above research objectives also motivated the development of a novel random sampling and allocation based approach, along with associated theoretical grounding, to estimate individual-level outcomes within an ABS that incurs substantially lower computational expense than the benchmark incremental accumulation approach.
Network-Based Epidemic Control Through Optimal Travel and Quarantine Management
Authors: Mahtab Talaei, Apostolos I. Rikos, Alex Olshevsky, Laura F. White, Ioannis Ch. Paschalidis
Date: 2024-07-27 00:00:00
Tasks: Management

Motivated by the swift global transmission of infectious diseases, we present a comprehensive framework for network-based epidemic control. Our aim is to curb epidemics using two different approaches. In the first approach, we introduce an optimization strategy that optimally reduces travel rates. We analyze the convergence of this strategy and show that it hinges on the network structure to minimize infection spread. In the second approach, we expand the classic SIR model by incorporating and optimizing quarantined states to strategically contain the epidemic. We show that this problem reduces to the problem of matrix balancing. We establish a link between optimization constraints and the epidemic's reproduction number, highlighting the relationship between network structure and disease dynamics. We demonstrate that applying augmented primal-dual gradient dynamics to the optimal quarantine problem ensures exponential convergence to the KKT point. We conclude by validating our approaches using simulation studies that leverage public data from counties in the state of Massachusetts.
Adaptive Target Tracking Using Retrospective Cost Input Estimation
Authors: Shashank Verma, Sneha Sanjeevini, E. Dogan Sumer, Dennis S. Bernstein
Date: 2024-07-26 00:00:00
Tasks: Autonomous Vehicles, Collision Avoidance, State Estimation

Target tracking of surrounding vehicles is essential for collision avoidance in autonomous vehicles. Our approach to target tracking is based on causal numerical differentiation on relative position data to estimate relative velocity and acceleration. Causal numerical differentiation is useful for a wide range of estimation and control problems with application to robotics and autonomous systems. The present paper extends prior work on causal numerical differentiation based on retrospective cost input estimation (RCIE). Since the variance of the input-estimation error and its correlation with the state-estimation error (the sum of the variance and the correlation is denoted as $\widetilde{V}$) used in the Kalman filter update are unknown, the present paper considers an adaptive discrete-time Kalman filter, where $\widetilde{V}_k$ is updated at each time step $k$ to minimize the difference between the sample variance of the innovations and the variance of the innovations given by the Kalman filter. The performance of this approach is shown to reach the performance of numerical differentiation based on RCIE with the best possible fixed value of $\widetilde{V}_k$. The proposed method thus eliminates the need to determine the best possible fixed value for $\widetilde{V}_k$. Finally, RCIE with an adaptive Kalman filter is applied to target tracking of a vehicle using simulated data from CarSim.
State estimation for a class of nonlinear time-varying uncertain system under multiharmonic disturbance
Authors: Alexey A. Margun, Van H. Bui, Alexey A. Bobtsov, Denis V. Efimov
Date: 2024-07-25 00:00:00
Tasks: State Estimation

The paper considers the observer synthesis for nonlinear, time-varying plants with uncertain parameters under multiharmonic disturbance. It is assumed that the relative degree of the plant is known, the regressor linearly depends on the state vector and may have a nonlinear relationship with the output signal. The proposed solution consists of three steps. Initially, an unknown input state observer is synthesized. This observer, however, necessitates the measurement of output derivatives equal to the plant's relative degree. To relax this limitation, an alternative representation of the observer is introduced. Further, based on this observer, the unknown parameters and disturbances are reconstructed using an autoregression model and the dynamic regressor extension and mixing (DREM) approach. This approach allows the estimates to be obtained in a finite time. Finally, based on these estimates, an observer has been constructed that does not require measurements of the output derivatives. The effectiveness and efficiency of this solution are demonstrated through a computer simulation.
GUI Element Detection Using SOTA YOLO Deep Learning Models
Authors: Seyed Shayan Daneshvar, Shaowei Wang
Date: 2024-08-07 00:00:00
Tasks: 2D Object Detection, Code Generation, Contour Detection, Deep Learning, Edge Detection, GUI Element Detection, Object, object-detection, Object Detection

Detection of Graphical User Interface (GUI) elements is a crucial task for automatic code generation from images and sketches, GUI testing, and GUI search. Recent studies have leveraged both old-fashioned and modern computer vision (CV) techniques. Oldfashioned methods utilize classic image processing algorithms (e.g. edge detection and contour detection) and modern methods use mature deep learning solutions for general object detection tasks. GUI element detection, however, is a domain-specific case of object detection, in which objects overlap more often, and are located very close to each other, plus the number of object classes is considerably lower, yet there are more objects in the images compared to natural images. Hence, the studies that have been carried out on comparing various object detection models, might not apply to GUI element detection. In this study, we evaluate the performance of the four most recent successful YOLO models for general object detection tasks on GUI element detection and investigate their accuracy performance in detecting various GUI elements.
Blind and robust estimation of adaptive optics point spread function and diffuse halo with sharp-edged objects
Authors: Anthony Berdeu
Date: 2024-07-31 00:00:00

Context . Initially designed to detect and characterise exoplanets, extreme adaptive optics (AO) systems open a new window onto the Solar System by resolving its small bodies. Nonetheless, their study remains limited by the accuracy of the knowledge of the AO-corrected point spread function (AO-PSF) that degrades their image and produces a bright halo, potentially hiding faint moons in their close vicinity. Aims . To overcome the random nature of AO-PSFs, I aim to develop a method that blindly recovers the PSF and its faint structured extensions directly into the data of interest, without any prior on the instrument or the object's shape. The objectives are both to deconvolve the object and to properly estimate and remove its surrounding halo to highlight potential faint companions. Methods . My method first estimated the PSF core via a parametric model fit, under the assumption of a sharp-edged flat object. Then, the resolved object and the PSF extensions were alternatively deconvolved with a robust method, insensitive to model outliers, such as cosmic rays or unresolved moons. Finally, the complex halo produced by the AO system was modelled and removed from the data. Results . The method is validated on realistic simulations with an on-sky AO-PSF from the SPHERE/ZIMPOL instrument. On real data, the proposed blind deconvolution algorithm strongly improves the image sharpness and retrieves details on the surface of asteroids. In addition, their moons are visible in all tested epochs despite important variability in turbulence conditions. Conclusions . My method shows the feasibility of retrieving the complex features of AO-PSFs directly from the data of interest. It paves the way towards more precise studies of asteroid surfaces and the discovery and characterisation of Solar System moons in archival data or with future instruments on extremely large telescopes with ever more complex AO-PSFs.
Learning Physics-Consistent Material Behavior from Dynamic Displacements
Authors: Zhichao Han, Mohit Pundir, Olga Fink, David S. Kammer
Date: 2024-07-25 00:00:00
Tasks: Relation, Stress-Strain Relation

Accurately modeling the mechanical behavior of materials is crucial for numerous engineering applications. The quality of these models depends directly on the accuracy of the constitutive law that defines the stress-strain relation. However, discovering these constitutive material laws remains a significant challenge, in particular when only material deformation data is available. To address this challenge, unsupervised machine learning methods have been proposed to learn the constitutive law from deformation data. Nonetheless, existing approaches have several limitations: they either fail to ensure that the learned constitutive relations are consistent with physical principles, or they rely on boundary force data for training which are unavailable in many in-situ scenarios. Here, we introduce a machine learning approach to learn physics-consistent constitutive relations solely from material deformation without boundary force information. This is achieved by considering a dynamic formulation rather than static equilibrium data and applying an input convex neural network (ICNN). We validate the effectiveness of the proposed method on a diverse range of hyperelastic material laws. We demonstrate that it is robust to a significant level of noise and that it converges to the ground truth with increasing data resolution. We also show that the model can be effectively trained using a displacement field from a subdomain of the test specimen and that the learned constitutive relation from one material sample is transferable to other samples with different geometries. The developed methodology provides an effective tool for discovering constitutive relations. It is, due to its design based on dynamics, particularly suited for applications to strain-rate-dependent materials and situations where constitutive laws need to be inferred from in-situ measurements without access to global force data.
An Uncertainty-aware Deep Learning Framework-based Robust Design Optimization of Metamaterial Units
Authors: Zihan Wang, Anindya Bhaduri, Hongyi Xu, Liping Wang
Date: 2024-07-19 00:00:00
Tasks: Robust Design

Mechanical metamaterials represent an innovative class of artificial structures, distinguished by their extraordinary mechanical characteristics, which are beyond the scope of traditional natural materials. The use of deep generative models has become increasingly popular in the design of metamaterial units. The effectiveness of using deep generative models lies in their capacity to compress complex input data into a simplified, lower-dimensional latent space, while also enabling the creation of novel optimal designs through sampling within this space. However, the design process does not take into account the effect of model uncertainty due to data sparsity or the effect of input data uncertainty due to inherent randomness in the data. This might lead to the generation of undesirable structures with high sensitivity to the uncertainties in the system. To address this issue, a novel uncertainty-aware deep learning framework-based robust design approach is proposed for the design of metamaterial units with optimal target properties. The proposed approach utilizes the probabilistic nature of the deep learning framework and quantifies both aleatoric and epistemic uncertainties associated with surrogate-based design optimization. We demonstrate that the proposed design approach is capable of designing high-performance metamaterial units with high reliability. To showcase the effectiveness of the proposed design approach, a single-objective design optimization problem and a multi-objective design optimization problem are presented. The optimal robust designs obtained are validated by comparing them to the designs obtained from the topology optimization method as well as the designs obtained from a deterministic deep learning framework-based design optimization where none of the uncertainties in the system are explicitly considered.
Comparative analysis of stationarity for Bitcoin and the S&P500
Authors: Yaoyue Tang, Karina Arias-Calluari, Michael S. Harré
Date: 2024-08-06 00:00:00
Tasks: Time Series

This paper compares and contrasts stationarity between the conventional stock market and cryptocurrency. The dataset used for the analysis is the intraday price indices of the S&P500 from 1996 to 2023 and the intraday Bitcoin indices from 2019 to 2023, both in USD. We adopt the definition of `wide sense stationary', which constrains the time independence of the first and second moments of a time series. The testing method used in this paper follows the Wiener-Khinchin Theorem, i.e., that for a wide sense stationary process, the power spectral density and the autocorrelation are a Fourier transform pair. We demonstrate that localized stationarity can be achieved by truncating the time series into segments, and for each segment, detrending and normalizing the price return are required. These results show that the S&P500 price return can achieve stationarity for the full 28-year period with a detrending window of 12 months and a constrained normalization window of 10 minutes. With truncated segments, a larger normalization window can be used to establish stationarity, indicating that within the segment the data is more homogeneous. For Bitcoin price return, the segment with higher volatility presents stationarity with a normalization window of 60 minutes, whereas stationarity cannot be established in other segments.
Towards principles of brain network organization and function
Authors: Suman Kulkarni, Dani S. Bassett
Date: 2024-08-05 00:00:00

The brain is immensely complex, with diverse components and dynamic interactions building upon one another to orchestrate a wide range of functions and behaviors. Understanding patterns of these complex interactions and how they are coordinated to support collective neural activity and function is critical for parsing human and animal behavior, treating mental illness, and developing artificial intelligence. Rapid experimental advances in imaging, recording, and perturbing neural systems across various species now provide opportunities and challenges to distill underlying principles of brain organization and function. Here, we take stock of recent progresses and review methods used in the statistical analysis of brain networks, drawing from fields of statistical physics, network theory and information theory. Our discussion is organized by scale, starting with models of individual neurons and extending to large-scale networks mapped across brain regions. We then examine the organizing principles and constraints that shape the biological structure and function of neural circuits. Finally, we describe current opportunities aimed at improving models in light of recent developments and at bridging across scales to contribute to a better understanding of brain networks.
Evolution of cooperation in the public goods game with Q-learning
Authors: Guozhong Zheng, Jiqiang Zhang, Shengfeng Deng, Weiran Cai, Li Chen
Date: 2024-07-29 00:00:00
Tasks: Decision Making, Imitation Learning, Q-Learning, Reinforcement Learning (RL)

Recent paradigm shifts from imitation learning to reinforcement learning (RL) is shown to be productive in understanding human behaviors. In the RL paradigm, individuals search for optimal strategies through interaction with the environment to make decisions. This implies that gathering, processing, and utilizing information from their surroundings are crucial. However, existing studies typically study pairwise games such as the prisoners' dilemma and employ a self-regarding setup, where individuals play against one opponent based solely on their own strategies, neglecting the environmental information. In this work, we investigate the evolution of cooperation with the multiplayer game -- the public goods game using the Q-learning algorithm by leveraging the environmental information. Specifically, the decision-making of players is based upon the cooperation information in their neighborhood. Our results show that cooperation is more likely to emerge compared to the case of imitation learning by using Fermi rule. Of particular interest is the observation of an anomalous non-monotonic dependence which is revealed when voluntary participation is further introduced. The analysis of the Q-table explains the mechanisms behind the cooperation evolution. Our findings indicate the fundamental role of environment information in the RL paradigm to understand the evolution of cooperation, and human behaviors in general.
Mitigating Farmland Biodiversity Loss: A Bio-Economic Model of Land Consolidation and Pesticide Use
Authors: Elia Moretti, Michael Benzaquen
Date: 2024-07-29 00:00:00
Tasks: Decision Making

Biodiversity loss driven by agricultural intensification is a pressing global issue, with significant implications for ecosystem stability and human well-being. Existing policy instruments have so far proven insufficient in halting this decline, which raises the need to explore the possible feedback loops that are pivotal to ecosystem degradation. We design a minimal integrated bio-economic agent-based model to qualitatively explore macro-level biodiversity trends, as influenced by individual farmer behavior within simple decision-making processes. Our model predicts further biodiversity decline under a business-as-usual scenario, primarily due to intensified land consolidation. We evaluate two policy options: reducing pesticide use and subsidizing small farmers. While pesticide reduction rapidly benefits biodiversity in the beginning, it eventually leads to increased land consolidation and further biodiversity loss. In contrast, subsidizing small farmers by reallocating a small fraction of existing subsidies, stabilizes farm sizes and enhances biodiversity in the long run. The most effective strategy results from combining both policies, leveraging pesticide reduction alongside targeted subsidies to balance economic pressures and consistently improve biodiversity.
Evolution of cooperation with Q-learning: the impact of information perception
Authors: Guozhong Zheng, Zhenwei Ding, Jiqiang Zhang, Shengfeng Deng, Weiran Cai, Li Chen
Date: 2024-07-29 00:00:00
Tasks: Diversity, Q-Learning

The inherent complexity of human beings manifests in a remarkable diversity of responses to intricate environments, enabling us to approach problems from varied perspectives. However, in the study of cooperation, existing research within the reinforcement learning framework often assumes that individuals have access to identical information when making decisions, which contrasts with the reality that individuals frequently perceive information differently. In this study, we employ the Q-learning algorithm to explore the impact of information perception on the evolution of cooperation in a two-person Prisoner's Dilemma game. We demonstrate that the evolutionary processes differ significantly across three distinct information perception scenarios, highlighting the critical role of information structure in the emergence of cooperation. Notably, the asymmetric information scenario reveals a complex dynamical process, including the emergence, breakdown, and reconstruction of cooperation, mirroring psychological shifts observed in human behavior. Our findings underscore the importance of information structure in fostering cooperation, offering new insights into the establishment of stable cooperative relationships among humans.