pwc-archive/papers-with-abstracts huggingface dataset:
An Efficient Distributed Nash Equilibrium Seeking with Compressed and Event-triggered Communication
Authors: Xiaomeng Chen, Wei Huo, Yuchi Wu, Subhrakanti Dey, Ling Shi
Date: 2023-11-23 00:00:00

Distributed Nash equilibrium (NE) seeking problems for networked games have been widely investigated in recent years. Despite the increasing attention, communication expenditure is becoming a major bottleneck for scaling up distributed approaches within limited communication bandwidth between agents. To reduce communication cost, an efficient distributed NE seeking (ETC-DNES) algorithm is proposed to obtain an NE for games over directed graphs, where the communication efficiency is improved by event-triggered exchanges of compressed information among neighbors. ETC-DNES saves communication costs in both transmitted bits and rounds of communication. Furthermore, our method only requires the row-stochastic property of the adjacency matrix, unlike previous approaches that hinged on doubly-stochastic communication matrices. We provide convergence guarantees for ETC-DNES on games with restricted strongly monotone mappings and testify its efficiency with no sacrifice on the accuracy. The algorithm and analysis are extended to a compressed algorithm with stochastic event-triggered mechanism (SETC-DNES). In SETC-DNES, we introduce a random variable in the triggering condition to further enhance algorithm efficiency. We demonstrate that SETC-DNES guarantees linear convergence to the NE while achieving even greater reductions in communication costs compared to ETC-DNES. Finally, numerical simulations illustrate the effectiveness of the proposed algorithms.
Unconstrained learning of networked nonlinear systems via free parametrization of stable interconnected operators
Authors: Leonardo Massai, Danilo Saccani, Luca Furieri, Giancarlo Ferrari-Trecate
Date: 2023-11-23 00:00:00

This paper characterizes a new parametrization of nonlinear networked incrementally $L_2$-bounded operators in discrete time. The distinctive novelty is that our parametrization is \emph{free} -- that is, a sparse large-scale operator with bounded incremental $L_2$ gain is obtained for any choice of the real values of our parameters. This property allows one to freely search over optimal parameters via unconstrained gradient descent, enabling direct applications in large-scale optimal control and system identification. Further, we can embed prior knowledge about the interconnection topology and stability properties of the system directly into the large-scale distributed operator we design. Our approach is extremely general in that it can seamlessly encapsulate and interconnect state-of-the-art Neural Network (NN) parametrizations of stable dynamical systems. To demonstrate the effectiveness of this approach, we provide a simulation example showcasing the identification of a networked nonlinear system. The results underscore the superiority of our free parametrizations over standard NN-based identification methods where a prior over the system topology and local stability properties are not enforced.
5G Edge Vision: Wearable Assistive Technology for People with Blindness and Low Vision
Authors: Tommy Azzino, Marco Mezzavilla, Sundeep Rangan, Yao Wang, John-Ross Rizzo
Date: 2023-11-23 00:00:00

In an increasingly visual world, people with blindness and low vision (pBLV) face substantial challenges in navigating their surroundings and interpreting visual information. From our previous work, VIS4ION is a smart wearable that helps pBLV in their day-to-day challenges. It enables multiple artificial intelligence (AI)-based microservices such as visual scene processing, navigation, and vision-language inference. These microservices require powerful computational resources and, in some cases, stringent inference times, hence the need to offload computation to edge servers. This paper introduces a novel video streaming platform that improves the capabilities of VIS4ION by providing real-time support of the microservices at the network edge. When video is offloaded wirelessly to the edge, the time-varying nature of the wireless network requires the use of adaptation strategies for a seamless video service. We demonstrate the performance of an adaptive real-time video streaming platform through experimentation with an open-source 5G deployment based on open air interface (OAI). The experiments demonstrate the ability to provide the microservices robustly in time-varying network loads.
Integration demand response aggregator and wind power aggregators with generation units and energy storages in wholesale electricity markets
Authors: Ramin Nourollahi, Rasoul Esmaeilzadeh
Date: 2023-11-23 00:00:00

The integration of various energy resources with wind farms, results in improved market-aware management of wind generation variations. Consequently, the proposed method minimizes the negative impact associated with wind generation deviations on the electricity market and energy systems. This paper integrates the multiple energy resources of a smart grid with wind producers to form a virtual power plant (VRP). One of the primary issues of integrating electricity markets in multi-electricity markets such as the Day-Ahead (DHM), intraday (INT), and real-time market (RLT) is the participation of a VRP. The proposed VRP was formed by the integration of the on-site generation (ONG), energy storage system (ENS), wind power aggregator (WPG), and demand response (DRE) contracts. Furthermore, The DRE includes detailed contracts with shiftable loads and load curtailment load aggregators. An effective method to manage the uncertainty of a VRP in order to attain optimal strategy and offering is provided in this article. The p-robust method is the proposed solution to measure the regret of VRP in the offering strategys formation. In the modeled scenario-based program, the p-robust approach is used for controlling the relative regret of a VRP in a multi-market operation under uncertainty. The obtained results demonstrate that a 6.81% reduction in estimated profit can result in a 45.75% reduction in relative regret of VRP.
Threat-Based Resource Allocation Strategy for Target Tracking in a Cognitive Radar Network
Authors: Jiye Lee, J. H Park
Date: 2023-11-23 00:00:00

Cognitive radar is developed to utilize the feedback of its operating environment obtained from a beam to make resource allocation decisions by solving optimization problems. Previous works focused on target tracking accuracy by designing an evaluation metric for an optimization problem. However, in a real combat situation, not only the tracking performance of the target but also its operational perspective should be considered. In this study, the usage of threats in the allocation of radar resource is proposed for a cognitive radar framework. Resource allocation regarding radar dwell time is considered to reflect the operational importance of target effects. The dwell time allocation problem is solved using a Second-Order Cone Program (SOCP). Numerical simulations are performed to verify the effectiveness of the proposed framework.
SIMBa: System Identification Methods leveraging Backpropagation
Authors: Loris Di Natale, Muhammad Zakwan, Philipp Heer, Giancarlo Ferrari-Trecate, Colin N. Jones
Date: 2023-11-23 00:00:00

This manuscript details and extends the SIMBa toolbox (System Identification Methods leveraging Backpropagation) presented in previous work, which uses well-established Machine Learning tools for discrete-time linear multi-step-ahead state-space System Identification (SI). SIMBa leverages linear-matrix-inequality-based free parametrizations of Schur matrices to guarantee the stability of the identified model by design. In this paper, backed up by novel free parametrizations of Schur matrices, we extend the toolbox to show how SIMBa can incorporate known sparsity patterns or true values of the state-space matrices to identify without jeopardizing stability. We extensively investigate SIMBa's behavior when identifying diverse systems with various properties from both simulated and real-world data. Overall, we find it consistently outperforms traditional stable subspace identification methods, and sometimes significantly, especially when enforcing desired model properties. These results hint at the potential of SIMBa to pave the way for generic structured nonlinear SI. The toolbox is open-sourced on https://github.com/Cemempamoi/simba.
To Transmit or Not to Transmit: Optimal Sensor Schedule for Remote State Estimation of Discrete-Event Systems
Authors: Yingying Liu, Jin Hu, Yongxia Yang, Wei Duan
Date: 2023-11-23 00:00:00
Tasks: Decision Making, State Estimation

This paper considers the problem of optimal sensor schedules for remote state estimation of discrete-event systems. In this setting, the sensors observe information from the plant and transmit the observable information to the receiver or estimator selectively. A transmission mechanism decides whether the observable information is transmitted or not, according to an information transmission policy, such that the receiver has sufficient information to satisfy the purpose of decision-making. To construct such a transmission mechanism, we first construct a non-deterministic dynamic observer that contains all feasible information transmission policies. Then, we show that the information updating rule of the dynamic observer indeed yields the state estimate from the receiver's point of view. Finally, we propose an approach to extract a specific information transmission policy, realized by a finite-state automaton, from the dynamic observer while satisfying some desired observation properties. To reduce transmission-related costs, we also require that the sensors transmit events as few as possible. A running example is provided to illustrate the proposed procedures.
Dynamic Operating Envelopes Embedded Peer-to-Peer-to-Grid Energy Trading
Authors: Zhisen Jiang, Ye Guo, Hongbin Sun, Jianxiao Wang
Date: 2023-11-23 00:00:00
Tasks: energy trading

A novel decentralized peer-to-peer-to-grid (P2P2G) trading mechanism considering distribution network integrity is proposed. In order to direct prosumers' peer-to-peer (P2P) trading behavior to be grid-friendly, the proposed method incorporates Dynamic Operating Envelopes (DOEs) into the existing P2P2G trading. Moreover, DOEs are determined through negotiations between the distribution system operator (DSO) and prosumers alongside the process of P2P trading, avoiding compromising prosumers' privacy and network parameters leakage. To reduce communication costs during P2P trading, a variant of the alternating direction method of multipliers (ADMM), i.e., communication-censored ADMM (COCA) is used to solve the P2P2G trading problem. Finally, the DOE price is shown to be comprised of several economically interpretable components. Simulations validate the effectiveness of the proposed mechanism.
Federated Learning Assisted Distributed Energy Optimization
Authors: Yuhan Du, Nuno Mendes, Simin Rasouli, Javad Mohammadi, Pedro Moura
Date: 2023-11-23 00:00:00
Tasks: Decision Making, Distributed Optimization, energy management, Federated Learning, Management

The increased penetration of distributed energy resources and the adoption of sensing and control technologies are driving the transition from our current centralized electric grid to a distributed system controlled by multiple entities (agents). The Transactive Energy Community (TEC) serves as an established example of this transition. Distributed energy management approaches can effectively address the scalability, resilience, and privacy requirements of the evolving grid. In this context, the accuracy of agents' estimations becomes crucial for the performance of distributed and multi-agent decision-making paradigms. This paper specifically focuses on integrating Federated Learning (FL) with the multi-agent energy management procedure. FL is utilized to forecast agents' local energy generation and demand, aiming to accelerate the convergence of the distributed decision-making process. To enhance energy aggregation in TECs, we propose an FL-assisted distributed Consensus + Innovations approach. The results demonstrate that employing FL significantly reduces errors in predicting net power demand. The improved forecast accuracy, in turn, introduces less error in the distributed optimization process, thereby enhancing its convergence behavior.
Information dynamics of $in\; silico$ EEG Brain Waves: Insights into oscillations and functions
Authors: Gustavo Menesse, Joaquin J. Torres
Date: 2023-11-23 00:00:00
Tasks: EEG, Rhythm

The relation between EEG rhythms, brain functions, and behavioral correlates is well-established. Some mechanisms underlying rhythm generation are understood, enabling the replication of brain rhythms $in\; silico$. This allows to explore relations between neural oscillations and specific neuronal circuits, helping to decipher the functional properties of brain waves. Integrated information Decomposition ($\Phi$-ID) framework relates dynamical regimes with informational properties, providing deeper insights into neuronal dynamic functions. Here, we investigate wave emergence in an excitatory/inhibitory (E/I) balanced network of IF neurons with short-term synaptic plasticity producing a diverse range of EEG-like rhythms, from low $\delta$ waves to high-frequency oscillations. Through $\Phi$-ID, we analyze the network's information dynamics elucidating the system's suitability for robust information transfer, storage, and parallel operation. Our study identifies also regimes that may resemble pathological states due to poor informational properties and high randomness. We found that $in\; silico$ $\beta$ and $\delta$ waves are associated with maximum information transfer in inhibitory and excitatory neuron populations, and the coexistence of excitatory $\theta$, $\alpha$, and $\beta$ waves associated to information storage. Also, high-frequency oscillations can exhibit either high or poor informational properties, shedding light on discussions regarding physiological versus pathological high-frequency oscillations. Our study demonstrates that dynamical regimes with similar oscillations may exhibit different information dynamics. Finally, our findings suggest that the use of information dynamics in both model and experimental data analysis, could help discriminate between oscillations associated with cognitive functions and those linked to neuronal disorders.
Self-organized biodiversity in biotic resource systems
Authors: Ju Kang, Shijie Zhang, Yiyuan Niu, Xin Wang
Date: 2023-11-23 00:00:00

What determines biodiversity in nature is a prominent issue in ecology, especially in biotic resource systems that are typically devoid of cross-feeding. Here, we show that by incorporating pairwise encounters among consumer individuals within the same species, a multitude of consumer species can self-organize to coexist in a well-mixed system with one or a few biotic resource species. The coexistence modes can manifest as either stable steady states or self-organized oscillations. Importantly, all coexistence states are robust to stochasticity, whether employing the stochastic simulation algorithm or individual-based modeling. Our model quantitatively illustrates species distribution patterns across a wide range of ecological communities and can be broadly used to explain biodiversity in many biotic resource systems.
The prebiotic emergence of biological evolution
Authors: Charles D. Kocher, Ken A. Dill
Date: 2023-11-22 00:00:00

The origin of life must have been preceded by Darwin-like evolutionary dynamics that could propagate it. How did that adaptive dynamics arise? And from what prebiotic molecules? Using evolutionary invasion analysis, we develop a universal framework for describing any origin story for evolutionary dynamics. We find that cooperative autocatalysts, i.e. autocatalysts whose per-unit reproductive rate grows as their population increases, have the special property of being able to cross a barrier that separates their initial degradation-dominated state from a growth-dominated state with evolutionary dynamics. For some model parameters, this leap to persistent propagation is likely, not rare. We apply this analysis to the Foldcat Mechanism, wherein peptides fold and help catalyze the elongation of each other. Foldcats are found to have cooperative autocatalysis and be capable of emergent evolutionary dynamics.
Reproducible Aggregation of Sample-Split Statistics
Authors: David M. Ritzwoller, Joseph P. Romano
Date: 2023-11-23 00:00:00

Statistical inference is often simplified by sample-splitting. This simplification comes at the cost of the introduction of randomness not native to the data. We propose a simple procedure for sequentially aggregating statistics constructed with multiple splits of the same sample. The user specifies a bound and a nominal error rate. If the procedure is implemented twice on the same data, the nominal error rate approximates the chance that the results differ by more than the bound. We illustrate the application of the procedure to several widely applied econometric methods.
Measurement Error and Counterfactuals in Quantitative Trade and Spatial Models
Authors: Bas Sanders
Date: 2023-11-23 00:00:00
Tasks: counterfactual, Uncertainty Quantification

Counterfactuals in quantitative trade and spatial models are functions of the current state of the world and the model parameters. Common practice treats the current state of the world as perfectly observed, but there is good reason to believe that it is measured with error. This paper provides tools for quantifying uncertainty about counterfactuals when the current state of the world is measured with error. I recommend an empirical Bayes approach to uncertainty quantification, and show that it is both practical and theoretically justified. I apply the proposed method to the settings in Adao, Costinot, and Donaldson (2017) and Allen and Arkolakis (2022) and find non-trivial uncertainty about counterfactuals.
Was Javert right to be suspicious? Marginal Treatment Effects with Duration Outcomes
Authors: Santiago Acerenza, Vitor Possebom, Pedro H. C. Sant'Anna
Date: 2023-11-23 00:00:00
Tasks: valid

We identify the distributional and quantile marginal treatment effect functions when the outcome is right-censored. Our method requires a conditionally exogenous instrument and random censoring. We propose asymptotically consistent semi-parametric estimators and valid inferential procedures for the target functions. To illustrate, we evaluate the effect of alternative sentences (fines and community service vs. no punishment) on recidivism in Brazil. Our results highlight substantial treatment effect heterogeneity: we find that people whom most judges would punish take longer to recidivate, while people who would be punished only by strict judges recidivate at an earlier date than if they were not punished.
Narratives from GPT-derived Networks of News, and a link to Financial Markets Dislocations
Authors: Deborah Miori, Constantin Petrov
Date: 2023-11-24 00:00:00
Tasks: Articles, Community Detection

Starting from a corpus of economic articles from The Wall Street Journal, we present a novel systematic way to analyse news content that evolves over time. We leverage on state-of-the-art natural language processing techniques (i.e. GPT3.5) to extract the most important entities of each article available, and aggregate co-occurrence of entities in a related graph at the weekly level. Network analysis techniques and fuzzy community detection are tested on the proposed set of graphs, and a framework is introduced that allows systematic but interpretable detection of topics and narratives. In parallel, we propose to consider the sentiment around main entities of an article as a more accurate proxy for the overall sentiment of such piece of text, and describe a case-study to motivate this choice. Finally, we design features that characterise the type and structure of news within each week, and map them to moments of financial markets dislocations. The latter are identified as dates with unusually high volatility across asset classes, and we find quantitative evidence that they relate to instances of high entropy in the high-dimensional space of interconnected news. This result further motivates the pursued efforts to provide a novel framework for the systematic analysis of narratives within news.
Personalised incentives with constrained regulator's budget
Authors: Lucas Javaudin, Andrea Araldo, André de Palma
Date: 2023-11-24 00:00:00

We consider a regulator driving individual choices towards increasing social welfare by providing personal incentives. We formalise and solve this problem by maximising social welfare under a budget constraint. The personalised incentives depend on the alternatives available to each individual and on her preferences. A polynomial time approximation algorithm computes a policy within few seconds. We analytically prove that it is boundedly close to the optimum. We efficiently calculate the curve of social welfare achievable for each value of budget within a given range. This curve can be useful for the regulator to decide the appropriate amount of budget to invest. We extend our formulation to enforcement, taxation and non-personalised-incentive policies. We analytically show that our personalised-incentive policy is also optimal within this class of policies and construct close-to-optimal enforcement and proportional tax-subsidy policies. We then compare analytically and numerically our policy with other state-of-the-art policies. Finally, we simulate a large-scale application to mode choice to reduce CO2 emissions.
Impact of family-friendly HRM policies in organizational performance
Authors: Jose Maria Biedma Ferrer, Jose Aurelio Medina Garrido
Date: 2023-11-24 00:00:00
Tasks: Management

Purpose: The objective of this work is to analyze the impact of implementing work-family reconciliation measures on workers' perception and how this can influence their behavior, especially in their organizational performance. Design/methodology/approach: A literature review and the main research works related to the work-family conflict and reconciliation measures to overcome this conflict have been conducted to draw conclusions about their impact on worker performance. Contributions and results: This work proposes an integrative model that shows the existing relationships between work-family reconciliation and perceptual variables on one side, and those related to the worker's organizational behavior on the other. Perceptual variables such as stress, job satisfaction, and motivation are analyzed. Regarding variables related to the worker's organizational behavior, absenteeism, turnover, and performance are analyzed. The results of the analysis provide evidence that the existence of work-family reconciliation is perceived favorably by workers and improves their organizational behavior, especially their performance. Originality/Added value: This study integrates different perspectives related to the conflict and work-family reconciliation, from an eclectic vision. Thus, it contributes to existing literature with a more comprehensive approach to the investigated topic. Additionally, the proposed integrative model allows for useful conclusions for management from both a purely human resources management perspective and organizational productivity improvement. Keywords: Work-family conflict, work-family reconciliation, perceptual variables, organizational performance, human resources managements