pwc-archive/papers-with-abstracts huggingface dataset:
Integrated Sensing and Communication enabled Doppler Frequency Shift Estimation and Compensation
Authors: Jinzhu Jia, Zhiqing Wei, Ruiyun Zhang, Lin Wang
Date: 2023-10-11 00:00:00
Tasks: Integrated sensing and communication, ISAC

Despite the millimeter wave technology fulfills the low-latency and high data transmission, it will cause severe Doppler Frequency Shift (DFS) for high-speed vehicular network, which tremendously damages the communication performance. In this paper, we propose an Integrated Sensing and Communication (ISAC) enabled DFS estimation and compensation algorithm. Firstly, the DFS is coarsely estimated and compensated using radar detection. Then, the designed preamble sequence is used to accurately estimate and compensate DFS. In addition, an adaptive DFS estimator is designed to reduce the computational complexity. Compared with the traditional DFS estimation algorithm, the improvement of the proposed algorithm is verified in bit error rate and mean square error performance by simulation results.
Adaptive Distributionally Robust Planning for Renewable-Powered Fast Charging Stations Under Decision-Dependent EV Diffusion Uncertainty
Authors: Yujia Li, Feng Qiu, Yixuan Chen, Yunhe Hou
Date: 2023-10-11 00:00:00
Tasks: ARC

When deploying fast charging stations (FCSs) to support long-distance trips of electric vehicles (EVs), there exist indirect network effects: while the gradual diffusion of EVs directly influences the timing and capacities of FCS allocation, the decisions for FCS allocations, in turn, impact the drivers' willingness to adopt EVs. This interplay, if neglected, can result in uncovered EVs and security issues on the grid side and even hinder the effective diffusion of EVs. In this paper, we explicitly incorporate this interdependence by quantifying EV adoption rates as decision-dependent uncertainties (DDUs) using decision-dependent ambiguity sets (DDASs). Then, a two-stage decision-dependent distributionally robust FCS planning (D$^3$R-FCSP) model is developed for adaptively deploying FCSs with on-site sources and expanding the coupled distribution network. A multi-period capacitated arc cover-path cover (MCACPC) model is incorporated to capture the EVs' recharging patterns to ensure the feasibility of FCS locations and capacities. To resolve the nonlinearity and nonconvexity, the D$^3$R-FCSP model is equivalently reformulated into a single-level mixed-integer linear programming by exploiting its strong duality and applying the McCormick envelope. Finally, case studies highlight the superior out-of-sample performances of our model in terms of security and cost-efficiency. Furthermore, the byproduct of accelerated EV adoption through an implicit positive feedback loop is highlighted.
Integrated Sensing and Communication Neighbor Discovery for MANET with Gossip Mechanism
Authors: Zhiqing Wei, Chenfei Li, Yanpeng Cui, Xu Chen, Zeyang Meng, Zhiyong Feng
Date: 2023-10-11 00:00:00
Tasks: Integrated sensing and communication, ISAC, Q-Learning

Mobile Ad hoc Network (MANET), supporting Machine-Type Communication(MTC), has a strong demand for rapid networking. Neighbor Discovery (ND) is a key initial step in configuring MANETs and faces a serious challenge in decreasing convergence time. Integrated Sensing and Communication (ISAC), as one of the potential key technologies in the 6th Generation (6G) mobile networks, can obtain the sensing data as the priori information to accelerate ND convergence. In order to further reduce the convergence time of ND, this paper introduces the ISAC-enabled gossip mechanism into the ND algorithm. The prior information acquired by ISAC reduces the information redundancy brought by the gossip mechanism and thus decreases the probability of collision, which further improves convergence speed. The average number of discovered nodes within a given period is derived, which is applied as the critical metric to evaluate the performance of ND algorithms. The simulation results confirm the correctness of the theoretical derivation and show that the interplay between the prior mechanisms and the gossip mechanism significantly reduces the convergence time. In addition, to solve the problem of imperfect sensing information, reinforcement learning is applied. Under the constraints of the convergence condition, the non-Reply and non-Stop Algorithm based on Gossip and Q-learning (GQ-nRnS) proposed in this paper not only ensures the completeness of ND, but also maintains a high convergence speed of ND. Compared with the Q-learning-based ND algorithm (Q-ND), the average convergence time of the GQ-nRnS algorithm is reduced by about 66.4%.
Dynamic Brain Networks with Prescribed Functional Connectivity
Authors: Umberto Casti, Giacomo Baggio, Danilo Benozzo, Sandro Zampieri, Alessandra Bertoldo, Alessandro Chiuso
Date: 2023-10-11 00:00:00
Tasks: Functional Connectivity

In this paper, we consider stable stochastic linear systems modeling whole-brain resting-state dynamics. We parametrize the state matrix of the system (effective connectivity) in terms of its steady-state covariance matrix (functional connectivity) and a skew-symmetric matrix $S$. We examine how the matrix $S$ influences some relevant dynamic properties of the system. Specifically, we show that a large $S$ enhances the degree of stability and excitability of the system, and makes the latter more responsive to high-frequency inputs.
Operating-Envelopes-Aware Decentralized Welfare Maximization for Energy Communities
Authors: Ahmed S. Alahmed, Guido Cavraro, Andrey Bernstein, Lang Tong
Date: 2023-10-11 00:00:00

We propose an operating-envelope-aware, prosumer-centric, and efficient energy community that aggregates individual and shared community distributed energy resources and transacts with a regulated distribution system operator (DSO) under a generalized net energy metering tariff design. To ensure safe network operation, the DSO imposes dynamic export and import limits, known as dynamic operating envelopes, on end-users' revenue meters. Given the operating envelopes, we propose an incentive-aligned community pricing mechanism under which the decentralized optimization of community members' benefit implies the optimization of overall community welfare. The proposed pricing mechanism satisfies the cost-causation principle and ensures the stability of the energy community in a coalition game setting. Numerical examples provide insights into the characteristics of the proposed pricing mechanism and quantitative measures of its performance.
A Digital Twin Approach for Adaptive Compliance in Cyber-Physical Systems: Case of Smart Warehouse Logistics
Authors: Nan Zhang, Rami Bahsoon, Nikos Tziritas, Georgios Theodoropoulos
Date: 2023-10-11 00:00:00

Engineering regulatory compliance in complex Cyber-Physical Systems (CPS), such as smart warehouse logistics, is challenging due to the open and dynamic nature of these systems, scales, and unpredictable modes of human-robot interactions that can be best learnt at runtime. Traditional offline approaches for engineering compliance often involve modelling at a higher, more abstract level (e.g. using languages like SysML). These abstract models only support analysis in offline-designed and simplified scenarios. However, open and complex systems may be unpredictable, and their behaviours are difficult to be fully captured by abstract models. These systems may also involve other business goals, possibly conflicting with regulatory compliance. To overcome these challenges, fine-grained simulation models are promising to complement abstract models and support accurate runtime predictions and performance evaluation with trade-off analysis. The novel contribution of this work is a Digital Twin-oriented architecture for adaptive compliance leveraging abstract goal modelling, fine-grained agent-based modelling and runtime simulation for managing compliance trade-offs. A case study from smart warehouse logistics is used to demonstrate the approach considering safety and productivity trade-offs.
Decentralization of Energy Systems with Blockchain: Bridging Top-down and Bottom-up Management of the Electricity Grid
Authors: Sakshi Mishra, Roohallah Khatami, Yu Christine Chen
Date: 2023-10-11 00:00:00
Tasks: Decision Making, energy trading, Management

For more than a century, the grid has operated in a centralized top-down fashion. However, as distributed energy resources (DERs) penetration grows, the grid edge is increasingly infused with intelligent computing and communication capabilities. Thus, the bottom-up approach to grid operations inclined toward decentralizing energy systems will likely gain momentum alongside the existing centralized paradigm. Decentralization refers to transferring control and decision-making from a centralized entity (individual, organization, or group thereof) to a distributed network. It is not a new concept - in energy systems context or otherwise. In the energy systems context, however, the complexity of this multifaceted concept increases manifolds due to two major reasons - i) the nature of the commodity being traded (the electricity) and ii) the enormity of the traditional electricity sector's structure that builds, operates, and maintains this capital-intensive network. In this work, we aim to highlight the need for and outline a credible path toward restructuring the current operational architecture of the electricity grid in view of the ongoing decentralization trends with an emphasis on peer-to-peer energy trading. We further introduce blockchain technology in the context of decentralized energy systems problems. We also suggest that blockchain is an effective technology for facilitating the synergistic operations of top-down and bottom-up approaches to grid management.
Analyzing Distribution System Load Flow Through Linearization of Non-Holomorphic Functions
Authors: Ibrahim Habiballah, Wasiu Sulaimon, Fahad Al-Ismail
Date: 2023-10-10 00:00:00

This letter presents a novel non-iterative power flow solution for radial distribution systems. In the pursuit of a linear power flow solution that seamlessly integrates into other power system operations, an approximate solution via complex linearization of non-holomorphic functions, making no assumptions about the network's parameters was developed. This approach can be readily adapted to different load models, and its accuracy is comparable to other established conventional radial load flow analysis tools.
Barrier States Theory for Safety-Critical Multi-Objective Control
Authors: Hassan Almubarak, Nader Sadegh, Evangelos A. Theodorou
Date: 2023-10-10 00:00:00

Multi-objective safety-critical control entails a diligent design to avoid possibly conflicting scenarios and ensure safety. This paper addresses multi-objective safety-critical control through a novel approach utilizing barrier states (BaS) to integrate safety into control design. It introduces the concept of safety embedded systems, where the safety condition is integrated with barrier functions to characterize a dynamical subsystem that is incorporated into the original model for control design. This approach reformulates the control problem to focus on designing a control law for an unconstrained system, ensuring that the barrier state remains bounded while achieving other performance objectives. The paper demonstrates that designing a stabilizing controller for the safety embedded system guarantees the safe stabilization of the original safety-critical system, effectively mitigating conflicts between performance and safety constraints. This approach enables the use of various legacy control methods from the literature to develop safe control laws. Moreover, it explores how this method can be applied to enforce input constraints and extend traditional control techniques to incorporate safety considerations. Additionally, the paper introduces input-to-state safety (ISSf) through barrier states for analyzing robust safety under bounded input disturbances and develops the notion of input-to-state safe stability (IS$^3$) for analyzing and designing robustly safe stabilizing feedback controls. The proposed techniques and concepts are used in various examples including the design of proportional-integral-derivative-barrier (PIDB) control for adaptive cruise control.
Adversarial optimization leads to over-optimistic security-constrained dispatch, but sampling can help
Authors: Charles Dawson, Chuchu Fan
Date: 2023-10-10 00:00:00
Tasks: Adversarial Attack, Diversity

To ensure safe, reliable operation of the electrical grid, we must be able to predict and mitigate likely failures. This need motivates the classic security-constrained AC optimal power flow (SCOPF) problem. SCOPF is commonly solved using adversarial optimization, where the dispatcher and an adversary take turns optimizing a robust dispatch and adversarial attack, respectively. We show that adversarial optimization is liable to severely overestimate the robustness of the optimized dispatch (when the adversary encounters a local minimum), leading the operator to falsely believe that their dispatch is secure. To prevent this overconfidence, we develop a novel adversarial sampling approach that prioritizes diversity in the predicted attacks. We find that our method not only substantially improves the robustness of the optimized dispatch but also avoids overconfidence, accurately characterizing the likelihood of voltage collapse under a given threat model. We demonstrate a proof-of-concept on small-scale transmission systems with 14 and 57 nodes.
A Reaction-Diffusion-Chemotaxis Model for Human Population Dynamics over Fractal Terrains
Authors: Benjamin M. Alessio, Ankur Gupta
Date: 2023-10-11 00:00:00

Advection of entities induced by gradients in attractant concentration fields is observed via diffusiophoresis in colloids and via chemotaxis in microorganisms. Mathematically, both diffusiophoresis and chemotaxis follow similar mathematical descriptions and display a variety of interesting behaviors that are not observed through other transport mechanisms. However, the application of such a mathematical framework has largely been restricted to soft matter research. In this article, we argue that this framework is more general and can be expanded to study human population dynamics. We assert that human populations also migrate chemotactically, but by sensing concentrations gradients in attractants such as resource availability, social connections, and safety indices. Therefore, we extend the Fisher-KPP reaction-diffusion model, foundational to human population dynamics, to incorporate chemotactic advection. Furthermore, we introduce a fractal terrain to better mimic the human dispersal phenomena. Simulations demonstrate that, by including chemotaxis of a population toward attractants which are dispersed heterogenously over fractal terrains, population hotspots can appear from from initially uniformly dispersed states whereas Fisher-KPP without chemotaxis predicts a persistent tendency toward population uniformity. Varying the chemotactic migration yields fine control over inter- or intra-population segregation and thus the population growth rates may be substantially altered by considering the population-attractant coupling. This framework may be useful for characterizing historical population separations, and furthermore is particularly pertinent for predicting emergence of new population hotspots as climate change is expected to cause large-scale human displacement, which may be dictated by chemotactic movement of humans due to evolving concentration gradients in safety indices.
Identification and Estimation of a Semiparametric Logit Model using Network Data
Authors: Brice Romuald Gueyap Kounga
Date: 2023-10-11 00:00:00

This paper studies the identification and estimation of a semiparametric binary network model in which the unobserved social characteristic is endogenous, that is, the unobserved individual characteristic influences both the binary outcome of interest and how links are formed within the network. The exact functional form of the latent social characteristic is not known. The proposed estimators are obtained based on matching pairs of agents whose network formation distributions are the same. The consistency and the asymptotic distribution of the estimators are proposed. The finite sample properties of the proposed estimators in a Monte-Carlo simulation are assessed. We conclude this study with an empirical application.
Empirical Analysis of the Impact of Legal Tender Digital Currency on Monetary Policy -Based on China's Data
Authors: Ruimin Song, TIntian Zhao, Chunhui Zhou
Date: 2023-10-11 00:00:00

This paper takes the development of China's Central bank digital currencies as a perspective, theoretically analyses the impact mechanism of the issuance and circulation of Central bank digital currencies on China's monetary policy and various variables of the money multiplier; at the same time, it selects the quarterly data from 2010 to 2022, and examines the impact of the Central bank digital currencies on the money supply multiplier through the establishment of the VECM model. The research results show that: the issuance of China's Central bank digital currencies will have an impact on the effectiveness of monetary policy and intermediary indicators; and have a certain positive impact on the narrow money multiplier and broad money multiplier. Based on theoretical analyses and empirical tests, this paper proposes that China should explore a more effective monetary policy in the context of Central bank digital currencies in the future on the premise of steadily promoting the development of Central bank digital currencies.
Lung Cancer in Argentina: A Modelling Study of Disease and Economic Burden
Authors: Andrea Alcaraz, Federico Rodriguez Cairoli, Carla Colaci, Constanza Silvestrini, Carolina Gabay, Natalia Espinola
Date: 2023-10-10 00:00:00

Objectives: Lung cancer remains a significant global public health challenge and is still one of the leading cause of cancer-related death in Argentina. This study aims to assess the disease and economic burden of lung cancer in the country. Study design: Burden of disease study Methods. A mathematical model was developed to estimate the disease burden and direct medical cost attributable to lung cancer. Epidemiological parameters were obtained from local statistics, the Global Cancer Observatory, the Global Burden of Disease databases, and a literature review. Direct medical costs were estimated through micro-costing. Costs were expressed in US dollars (US$), April 2023 (1 US$ =216.38 argentine pesos). A second-order Monte Carlo simulation was performed to estimate the uncertainty. Results: Considering approximately 10,000 deaths, 12,000 incident cases, and 14,000 5-year prevalent cases, the economic burden of lung cancer in Argentina in 2023 was estimated to be US$ 556.20 million (396.96 -718.20), approximately 1.4% of the total healthcare expenditure for the country. The cost increased with a higher stage of the disease and the main driver was the drug acquisition (80%). 179,046 Disability-adjusted life years could be attributable to lung cancer representing the 10% of the total cancer. Conclusion: The disease and economic burden of lung cancer in Argentina implies a high cost for the health system and would represent 19% of the previously estimated economic burden for 29 cancers in Argentina.
Damping Density of an Absorptive Shoebox Room Derived from the Image-Source Method
Authors: Sebastian J. Schlecht, Karolina Prawda, Rudolf Rabenstein, Maximilian Schäfer
Date: 2023-10-11 00:00:00

The image-source method is widely applied to compute room impulse responses (RIRs) of shoebox rooms with arbitrary absorption. However, with increasing RIR lengths, the number of image sources grows rapidly, leading to slow computation. In this paper, we derive a closed-form expression for the damping density, which characterizes the overall multi-slope energy decay. The omnidirectional energy decay over time is directly derived from the damping density. The resulting energy decay model accurately matches the late reverberation simulated via the image-source method. The proposed model allows the fast stochastic synthesis of late reverberation by shaping noise with the energy envelope. Simulations of various wall damping coefficients demonstrate the model's accuracy. The proposed model consistently outperforms the energy decay prediction accuracy compared to a state-of-the-art approximation method. The paper elaborates on the proposed damping density's applicability to modeling multi-sloped sound energy decay, predicting reverberation time in non-diffuse sound fields, and fast frequency-dependent RIR synthesis.
Magnitude-and-phase-aware Speech Enhancement with Parallel Sequence Modeling
Authors: Yuewei Zhang, Huanbin Zou, Jie Zhu
Date: 2023-10-11 00:00:00
Tasks: Speech Enhancement

In speech enhancement (SE), phase estimation is important for perceptual quality, so many methods take clean speech's complex short-time Fourier transform (STFT) spectrum or the complex ideal ratio mask (cIRM) as the learning target. To predict these complex targets, the common solution is to design a complex neural network, or use a real network to separately predict the real and imaginary parts of the target. But in this paper, we propose to use a real network to estimate the magnitude mask and normalized cIRM, which not only avoids the significant increase of the model complexity caused by complex networks, but also shows better performance than previous phase estimation methods. Meanwhile, we devise a parallel sequence modeling (PSM) block to improve the RNN block in the convolutional recurrent network (CRN)-based SE model. We name our method as magnitude-and-phase-aware and PSM-based CRN (MPCRN). The experimental results illustrate that our MPCRN has superior SE performance.
VSANet: Real-time Speech Enhancement Based on Voice Activity Detection and Causal Spatial Attention
Authors: Yuewei Zhang, Huanbin Zou, Jie Zhu
Date: 2023-10-11 00:00:00
Tasks: Action Detection, Activity Detection, Multi-Task Learning, Speech Enhancement

The deep learning-based speech enhancement (SE) methods always take the clean speech's waveform or time-frequency spectrum feature as the learning target, and train the deep neural network (DNN) by reducing the error loss between the DNN's output and the target. This is a conventional single-task learning paradigm, which has been proven to be effective, but we find that the multi-task learning framework can improve SE performance. Specifically, we design a framework containing a SE module and a voice activity detection (VAD) module, both of which share the same encoder, and the whole network is optimized by the weighted loss of the two modules. Moreover, we design a causal spatial attention (CSA) block to promote the representation capability of DNN. Combining the VAD aided multi-task learning framework and CSA block, our SE network is named VSANet. The experimental results prove the benefits of multi-task learning and the CSA block, which give VSANet an excellent SE performance.