pwc-archive/papers-with-abstracts huggingface dataset:
Space-Time Adaptive Processing for radars in Connected and Automated Vehicular Platoons
Authors: Zahra Esmaeilbeig, Kumar Vijay Mishra, Mojtaba Soltanalian
Date: 2023-09-13 00:00:00
Tasks: Scheduling

In this study, we develop a holistic framework for space-time adaptive processing (STAP) in connected and automated vehicle (CAV) radar systems. We investigate a CAV system consisting of multiple vehicles that transmit frequency-modulated continuous-waveforms (FMCW), thereby functioning as a multistatic radar. Direct application of STAP in a network of radar systems such as in a CAV may lead to excess interference. We exploit time division multiplexing (TDM) to perform transmitter scheduling over FMCW pulses to achieve high detection performance. The TDM design problem is formulated as a quadratic assignment problem which is tackled by power method-like iterations and applying the Hungarian algorithm for linear assignment in each iteration. Numerical experiments confirm that the optimized TDM is successful in enhancing the target detection performance.
Evaluation of Battery Energy Storage System to Provide Virtual Transmission Service
Authors: Qiushi Wang, Xingpeng Li
Date: 2023-09-13 00:00:00
Tasks: Scheduling

An immediate need in the transmission system is to find alternative solutions that improve system operation and defer the need for new transmission lines. This study comprehensively evaluates the performance and economic benefits of short-term operation of using battery energy storage systems (BESS) as virtual transmission (VT) to promote power transfer across distant regions. Specifically, this work implements various day-ahead energy scheduling models to analyze the impact of VT on system operation cost, network congestion, model computational time, and market performance. The performance of VT is compared with three alternative network congestion mitigation methods, including building new high-voltage physical transmission lines, cost-driven battery energy storage systems, and network reconfiguration, as well as combinations of two of the aforementioned methods. The benchmark dayahead scheduling model is a traditional security-constrained unit commitment model without system upgrades or other network congestion mitigation. Numerical simulations conducted on the IEEE 24-bus system demonstrate that VT provides a 14% operational cost reduction and 34% congested line relief compared to the base case. Compared to other examined schemes, VT is the only one comparable to physical transmission lines that can provide satisfying congestion relief and operation cost reduction without significantly sacrificing computing time and load payment.
Optimizing Deep Decarbonization Pathways in California with Power System Planning Using Surrogate Level-based Lagrangian Relaxation
Authors: Osten Anderson, Nanpeng Yu, Mikhail Bragin
Date: 2023-09-13 00:00:00
Tasks: Computational Efficiency

With California's ambitious goal to achieve decarbonization of the electrical grid by the year 2045, significant challenges arise in power system investment planning. Existing modeling methods and software focus on computational efficiency, which is currently achieved by simplifying the associated unit commitment formulation. This may lead to unjustifiable inaccuracies in the cost and constraints of gas-fired generation operations, and may affect both the timing and the extent of investment in new resources, such as renewable energy and energy storage. To address this issue, this paper develops a more detailed and rigorous mixed-integer model, and more importantly, a solution methodology utilizing surrogate level-based Lagrangian relaxation to overcome the combinatorial complexity that results from the enhanced level of model detail. This allows us to optimize a model with approximately 12 million binary and 100 million total variables in under 48 hours. The investment plan is compared with those produced by E3's RESOLVE software, which is currently employed by the California Energy Commission and California Public Utilities Commission. Our model produces an investment plan that differs substantially from that of the existing method and saves California over 12 billion dollars over the investment horizon.
Distribution Grid Line Outage Identification with Unknown Pattern and Performance Guarantee
Authors: Chenhan Xiao, Yizheng Liao, Yang Weng
Date: 2023-09-10 00:00:00
Tasks: Change Point Detection

Line outage identification in distribution grids is essential for sustainable grid operation. In this work, we propose a practical yet robust detection approach that utilizes only readily available voltage magnitudes, eliminating the need for costly phase angles or power flow data. Given the sensor data, many existing detection methods based on change-point detection require prior knowledge of outage patterns, which are unknown for real-world outage scenarios. To remove this impractical requirement, we propose a data-driven method to learn the parameters of the post-outage distribution through gradient descent. However, directly using gradient descent presents feasibility issues. To address this, we modify our approach by adding a Bregman divergence constraint to control the trajectory of the parameter updates, which eliminates the feasibility problems. As timely operation is the key nowadays, we prove that the optimal parameters can be learned with convergence guarantees via leveraging the statistical and physical properties of voltage data. We evaluate our approach using many representative distribution grids and real load profiles with 17 outage configurations. The results show that we can detect and localize the outage in a timely manner with only voltage magnitudes and without assuming a prior knowledge of outage patterns.
Dynamics of cell-type transition mediated by epigenetic modifications
Authors: Rongsheng Huang, Qiaojun Situ, Jinzhi Lei
Date: 2023-09-13 00:00:00

Maintaining tissue homeostasis requires appropriate regulation of stem cell differentiation. The Waddington landscape posits that gene circuits in a cell form a potential landscape of different cell types, wherein cells follow attractors of the probability landscape to develop into distinct cell types. However, how adult stem cells achieve a delicate balance between self-renewal and differentiation remains unclear. We propose that random inheritance of epigenetic states plays a pivotal role in stem cell differentiation and present a hybrid model of stem cell differentiation induced by epigenetic modifications. Our comprehensive model integrates gene regulation networks, epigenetic state inheritance, and cell regeneration, encompassing multi-scale dynamics ranging from transcription regulation to cell population. Through model simulations, we demonstrate that random inheritance of epigenetic states during cell divisions can spontaneously induce cell differentiation, dedifferentiation, and transdifferentiation. Furthermore, we investigate the influences of interfering with epigenetic modifications and introducing additional transcription factors on the probabilities of dedifferentiation and transdifferentiation, revealing the underlying mechanism of cell reprogramming. This \textit{in silico} model provides valuable insights into the intricate mechanism governing stem cell differentiation and cell reprogramming and offers a promising path to enhance the field of regenerative medicine.
Temporal compressive edge imaging enabled by a lensless diffuser camera
Authors: Ze Zheng, Baolei Liu, Jiaqi Song, Lei Ding, Xiaolan Zhong, David Mcgloin, Fan Wang
Date: 2023-09-13 00:00:00
Tasks: Edge Detection

Lensless imagers based on diffusers or encoding masks enable high-dimensional imaging from a single shot measurement and have been applied in various applications. However, to further extract image information such as edge detection, conventional post-processing filtering operations are needed after the reconstruction of the original object images in the diffuser imaging systems. Here, we present the concept of a temporal compressive edge detection method based on a lensless diffuser camera, which can directly recover a time sequence of edge images of a moving object from a single-shot measurement, without further post-processing steps. Our approach provides higher image quality during edge detection, compared with the conventional post-processing method. We demonstrate the effectiveness of this approach by both numerical simulation and experiments. The proof-of-concept approach can be further developed with other image post-process operations or versatile computer vision assignments toward task-oriented intelligent lensless imaging systems.
A Stein's Method Approach to the Linear Noise Approximation for Stationary Distributions of Chemical Reaction Networks
Authors: Theodore W. Grunberg, Domitilla Del Vecchio
Date: 2023-09-14 00:00:00

Stochastic Chemical Reaction Networks are continuous time Markov chain models that describe the time evolution of the molecular counts of species interacting stochastically via discrete reactions. Such models are ubiquitous in systems and synthetic biology, but often have a large or infinite number of states, and thus are not amenable to computation and analysis. Due to this, approximations that rely on the molecular counts and the volume being large are commonly used, with the most common being the Reaction Rate Equations and the Linear Noise Approximation. For finite time intervals, Kurtz established the validity of the Reaction Rate Equations and Linear Noise Approximation, by proving law of large numbers and central limit theorem results respectively. However, the analogous question for the stationary distribution of the Markov chain model has remained mostly unanswered, except for chemical reaction networks with special structures or bounded molecular counts. In this work, we use Stein's Method to obtain sufficient conditions for the stationary distribution of an appropriately scaled Stochastic Chemical Reaction Network to converge to the Linear Noise Approximation as the system size goes to infinity. Our results give non asymptotic bounds on the error in the Reaction Rate Equations and in the Linear Noise Approximation as applied to the stationary distribution. As a special case, we give conditions under which the global exponential stability of an equilibrium point of the Reaction Rate Equations is sufficient to obtain our error bounds, thus permitting one to obtain conclusions about the Markov chain by analyzing the deterministic Reaction Rate Equations.
Novel Smart N95 Filtering Facepiece Respirator with Real-time Adaptive Fit Functionality and Wireless Humidity Monitoring for Enhanced Wearable Comfort
Authors: Kangkyu Kwon, Yoon Jae Lee, Yeongju Jung, Ira Soltis, Chanyeong Choi, Yewon Na, Lissette Romero, Myung Chul Kim, Nathan Rodeheaver, Hodam Kim, Michael S. Lloyd, Ziqing Zhuang, William King, Susan Xu, Seung-Hwan Ko, Jinwoo Lee, Woon-Hong Yeo
Date: 2023-09-09 00:00:00

The widespread emergence of the COVID-19 pandemic has transformed our lifestyle, and facial respirators have become an essential part of daily life. Nevertheless, the current respirators possess several limitations such as poor respirator fit because they are incapable of covering diverse human facial sizes and shapes, potentially diminishing the effect of wearing respirators. In addition, the current facial respirators do not inform the user of the air quality within the smart facepiece respirator in case of continuous long-term use. Here, we demonstrate the novel smart N-95 filtering facepiece respirator that incorporates the humidity sensor and pressure sensory feedback-enabled self-fit adjusting functionality for the effective performance of the facial respirator to prevent the transmission of airborne pathogens. The laser-induced graphene (LIG) constitutes the humidity sensor, and the pressure sensor array based on the dielectric elastomeric sponge monitors the respirator contact on the face of the user, providing the sensory information for a closed-loop feedback mechanism. As a result of the self-fit adjusting mode along with elastomeric lining, the fit factor is increased by 3.20 and 5 times at average and maximum respectively. We expect that the experimental proof-of-concept of this work will offer viable solutions to the current commercial respirators to address the limitations.
Entropy-based machine learning model for diagnosis and monitoring of Parkinson's Disease in smart IoT environment
Authors: Maksim Belyaev, Murugappan Murugappan, Andrei Velichko, Dmitry Korzun
Date: 2023-08-28 00:00:00
Tasks: EEG

The study presents the concept of a computationally efficient machine learning (ML) model for diagnosing and monitoring Parkinson's disease (PD) in an Internet of Things (IoT) environment using rest-state EEG signals (rs-EEG). We computed different types of entropy from EEG signals and found that Fuzzy Entropy performed the best in diagnosing and monitoring PD using rs-EEG. We also investigated different combinations of signal frequency ranges and EEG channels to accurately diagnose PD. Finally, with a fewer number of features (11 features), we achieved a maximum classification accuracy (ARKF) of ~99.9%. The most prominent frequency range of EEG signals has been identified, and we have found that high classification accuracy depends on low-frequency signal components (0-4 Hz). Moreover, the most informative signals were mainly received from the right hemisphere of the head (F8, P8, T8, FC6). Furthermore, we assessed the accuracy of the diagnosis of PD using three different lengths of EEG data (150-1000 samples). Because the computational complexity is reduced by reducing the input data. As a result, we have achieved a maximum mean accuracy of 99.9% for a sample length (LEEG) of 1000 (~7.8 seconds), 98.2% with a LEEG of 800 (~6.2 seconds), and 79.3% for LEEG = 150 (~1.2 seconds). By reducing the number of features and segment lengths, the computational cost of classification can be reduced. Lower-performance smart ML sensors can be used in IoT environments for enhances human resilience to PD.
Maximizing the performance for microcomb based microwave photonic transversal signal processors
Authors: Yang Sun, Jiayang Wu, Yang Li, Xingyuan Xu, Guanghui Ren, Mengxi Tan, Sai Tak Chu, Brent E. Little, Roberto Morandotti, Arnan Mitchell, David J. Moss
Date: 2023-09-10 00:00:00

Microwave photonic (MWP) transversal signal processors offer a compelling solution for realizing versatile high-speed information processing by combining the advantages of reconfigurable electrical digital signal processing and high-bandwidth photonic processing. With the capability of generating a number of discrete wavelengths from micro-scale resonators, optical microcombs are powerful multi-wavelength sources for implementing MWP transversal signal processors with significantly reduced size, power consumption, and complexity. By using microcomb-based MWP transversal signal processors, a diverse range of signal processing functions have been demonstrated recently. In this paper, we provide a detailed analysis for the processing inaccuracy that is induced by the imperfect response of experimental components. First, we investigate the errors arising from different sources including imperfections in the microcombs, the chirp of electro-optic modulators, chromatic dispersion of the dispersive module, shaping errors of the optical spectral shapers, and noise of the photodetector. Next, we provide a global picture quantifying the impact of different error sources on the overall system performance. Finally, we introduce feedback control to compensate the errors caused by experimental imperfections and achieve significantly improved accuracy. These results provide a guide for optimizing the accuracy of microcomb-based MWP transversal signal processors.
Causal inference in network experiments: regression-based analysis and design-based properties
Authors: Mengsi Gao, Peng Ding
Date: 2023-09-14 00:00:00
Tasks: Causal Inference, regression

Network experiments are powerful tools for studying spillover effects, which avoid endogeneity by randomly assigning treatments to units over networks. However, it is non-trivial to analyze network experiments properly without imposing strong modeling assumptions. We show that regression-based point estimators and standard errors can have strong theoretical guarantees if the regression functions and robust standard errors are carefully specified to accommodate the interference patterns under network experiments. We first recall a well-known result that the H\'ajek estimator is numerically identical to the coefficient from the weighted-least-squares fit based on the inverse probability of the exposure mapping. Moreover, we demonstrate that the regression-based approach offers three notable advantages: its ease of implementation, the ability to derive standard errors through the same regression fit, and the potential to integrate covariates into the analysis to improve efficiency. Recognizing that the regression-based network-robust covariance estimator can be anti-conservative under nonconstant effects, we propose an adjusted covariance estimator to improve the empirical coverage rates.
Computer says 'no': Exploring systemic bias in ChatGPT using an audit approach
Authors: Louis Lippens
Date: 2023-09-14 00:00:00
Tasks: Chatbot, Language Modeling, Language Modelling

Large language models offer significant potential for increasing labour productivity, such as streamlining personnel selection, but raise concerns about perpetuating systemic biases embedded into their pre-training data. This study explores the potential ethnic and gender bias of ChatGPT, a chatbot producing human-like responses to language tasks, in assessing job applicants. Using the correspondence audit approach from the social sciences, I simulated a CV screening task with 34,560 vacancy-CV combinations where the chatbot had to rate fictitious applicant profiles. Comparing ChatGPT's ratings of Arab, Asian, Black American, Central African, Dutch, Eastern European, Hispanic, Turkish, and White American male and female applicants, I show that ethnic and gender identity influence the chatbot's evaluations. Ethnic discrimination is more pronounced than gender discrimination and mainly occurs in jobs with favourable labour conditions or requiring greater language proficiency. In contrast, gender discrimination emerges in gender-atypical roles. These findings suggest that ChatGPT's discriminatory output reflects a statistical mechanism echoing societal stereotypes. Policymakers and developers should address systemic bias in language model-driven applications to ensure equitable treatment across demographic groups. Practitioners should practice caution, given the adverse impact these tools can (re)produce, especially in selection decisions involving humans.
Measuring Higher-Order Rationality with Belief Control
Authors: Wei James Chen, Meng-Jhang Fong, Po-Hsuan Lin
Date: 2023-09-14 00:00:00

Determining an individual's strategic reasoning capability based solely on choice data is a complex task. This complexity arises because sophisticated players might have non-equilibrium beliefs about others, leading to non-equilibrium actions. In our study, we pair human participants with computer players known to be fully rational. This use of robot players allows us to disentangle limited reasoning capacity from belief formation and social biases. Our results show that, when paired with robots, subjects consistently demonstrate higher levels of rationality and maintain stable rationality levels across different games compared to when paired with humans. This suggests that strategic reasoning might indeed be a consistent trait in individuals. Furthermore, the identified rationality limits could serve as a measure for evaluating an individual's strategic capacity when their beliefs about others are adequately controlled.
The Fiscal Cost of Public Debt and Government Spending Shocks
Authors: Venance Riblier
Date: 2023-09-14 00:00:00

This paper investigates how the cost of public debt shapes fiscal policy and its effect on the economy. Using U.S. historical data, I show that when servicing the debt creates a fiscal burden, the government responds to spending shocks by limiting debt issuance. As a result, the initial shock triggers only a limited increase in public spending in the short run, and even leads to spending reversal in the long run. Under these conditions, fiscal policy loses its ability to stimulate economic activity. This outcome arises as the fiscal authority limits its own ability to borrow to ensure public debt sustainability. These findings are robust to several identification and estimation strategies.
The effect of housewife labor on gdp calculations
Authors: Saadet Yagmur Kumcu
Date: 2023-09-11 00:00:00

In this study, the evolutionary development of labor has been tried to be revealed based on theoretical analysis. Using the example of gdp, which is an indicator of social welfare, the economic value of the labor of housewives was tried to be measured with an empirical modeling. To this end; first of all, the concept of labor was questioned in orthodox (mainstream) economic theories; then, by abstracting from the labor-employment relationship, it was examined what effect the labor of unpaid housewives who are unemployed in the capitalist system could have on gdp. In theoretical analysis; It has been determined that the changing human profile moves away from rationality and creates limited rationality and, accordingly, a heterogeneous individual profile. Women were defined as the new example of heterogeneous individuals, as those who best fit the definition of limited rational individuals because they prefer to be housewives. In the empirical analysis of the study, housewife labor was taken into account as the main variable. In the empirical analysis of the study; In the case of Turkiye, using turkstat employment data and the atkinson inequality scale; the impact of housewife labor on gdp was calculated. The results of the theoretical and empirical analysis were evaluated in the context of labor-employment independence.
Quota Mechanisms: Finite-Sample Optimality and Robustness
Authors: Ian Ball, Deniz Kattwinkel
Date: 2023-09-14 00:00:00
Tasks: Sensitivity

A quota mechanism, such as a mandatory grading curve, links together multiple decisions. We analyze the performance of quota mechanisms when the number of linked decisions is finite and the designer has imperfect knowledge of the type distribution. Using a new optimal transport approach, we derive an ex-post decision error guarantee for quota mechanisms. This guarantee cannot be improved by any mechanisms without transfers. We quantify the sensitivity of quota mechanisms to errors in the designer's estimate of the type distribution. Finally, we show that quotas are robust to agents' beliefs about each other.