pwc-archive/papers-with-abstracts huggingface dataset:
Efficient Probabilistic Assessment of Power System Resilience Using the Polynomial Chaos Expansion Method with Enhanced Stability
Authors: Aidan Gerkis, Xiaozhe Wang
Date: 2025-01-16 00:00:00

Increasing frequency and intensity of extreme weather events motivates the assessment of power system resilience. The random nature of power system failures during these events mandates probabilistic resilience assessment, but state-of-the-art methods are computationally inefficient. In this paper, an enhanced PCE method to quantify power system resilience based on the extended AC Cascading Failure Model (AC-CFM) model is proposed. To address repeatability issues arising from PCE computation with different sample sets, we propose a novel experiment design method. Numerical studies on the IEEE 39-bus system illustrate the improved repeatability and convergence of the method. The enhanced PCE method is then used to efficiently assess the system's resilience and propose adaptation measures.
Greening the Grid: Electricity Market Clearing with Consumer-Based Carbon Cost
Authors: Wenqian Jiang, Line Roald
Date: 2025-01-16 00:00:00

To enhance decarbonization efforts in electric power systems, we propose a novel electricity market clearing model that internalizes the allocation of emissions from generations to loads and allows for consideration of consumer-side carbon costs. Specifically, consumers can not only bid for power but also assign a cost to the carbon emissions incurred by their electricity use. These carbon costs provide consumers, ranging from carbon-agnostic to carbon-sensitive, with a tool to actively manage their roles in carbon emission mitigation. By incorporating carbon allocation and consumer-side carbon costs, the market clearing is influenced not solely by production and demand dynamics but also by the allocation of carbon emission responsibilities. To demonstrate the effect of our proposed model, we conduct a case study comparing market clearing outcomes across various percentages of carbon-sensitive consumers with differing carbon costs.
Crossover-BPSO Driven Multi-Agent Technology for Managing Local Energy Systems
Authors: Hafiz Majid Hussain, Ashfaq Ahmad. Pedro H. J. Nardelli
Date: 2025-01-16 00:00:00
Tasks: Scheduling

This article presents a new hybrid algorithm, crossover binary particle swarm optimization (crBPSO), for allocating resources in local energy systems via multi-agent (MA) technology. Initially, a hierarchical MA-based architecture in a grid-connected local energy setup is presented. In this architecture, task specific agents operate in a master-slave manner. Where, the master runs a well-formulated optimization routine aiming at minimizing costs of energy procurement, battery degradation, and load scheduling delay. The slaves update the master on their current status and receive optimal action plans accordingly. Simulation results demonstrate that the proposed algorithm outperforms selected existing ones by 21\% in terms average energy system costs while satisfying customers' energy demand and maintaining the required quality of service.
Excitable dynamics and coral reef formation: A simple model of macro-scale structure development
Authors: Miguel Álvarez-Alegría, Pablo Moreno-Spiegelberg, Manuel A. Matías, Damià Gomila
Date: 2025-01-17 00:00:00

In this work, we demonstrate that key aspects of the dynamical behavior of coral reefs at the macro scale, which evolve over time scales of centuries, can be accurately described using a model that integrates a few fundamental ecological and physical mechanisms. The model displays excitable behavior generating, among other dynamical regimes, traveling pulses and waves, which result in the formation of spatial structures resembling those observed in real reefs, without involving a classical pattern formation mechanism, like the Turing scenario. We conduct an in-depth exploration of the bifurcations exhibited by the model as a function of the two most ecologically significant parameters. This establishes the groundwork for using the presented model as a tool to explain coral reef formation.
A wideband amplifying and filtering reconfigurable intelligent surface for wireless relay
Authors: Lijie Wu, Qun Yan Zhou, Jun Yan Dai, Siran Wang, Junwei Zhang, Zhen Jie Qi, Hanqing Yang, Ruizhe Jiang, Zheng Xing Wang, Huidong Li, Zhen Zhang, Jiang Luo, Qiang Cheng, Tie Jun Cui
Date: 2024-12-31 00:00:00

Programmable metasurfaces have garnered significant attention due to their exceptional ability to manipulate electromagnetic (EM) waves in real time, leading to the emergence of a prominent area in wireless communication, namely reconfigurable intelligent surfaces (RISs), to control the signal propagation and coverage. However, the existing RISs usually suffer from limited operating distance and band interference, which hinder their practical applications in wireless relay and communication systems. To overcome the limitations, we propose an amplifying and filtering RIS (AF-RIS) to enhance the in-band signal energy and filter the out-of-band signal of the incident EM waves, ensuring the miniaturization of the RIS array and enabling its anti-interference ability. In addition, each AF-RIS element is equipped with a 2-bit phase control capability, further endowing the entire array with great beamforming performance. An elaborately designed 4*8 AF-RIS array is presented by integrating the power dividing and combining networks, which substantially reduces the number of amplifiers and filters, thereby reducing the hardware costs and power consumption. Experimental results showcase the powerful capabilities of AF-RIS in beam-steering, frequency selectivity, and signal amplification. Therefore, the proposed AF-RIS holds significant promise for critical applications in wireless relay systems by offering an efficient solution to improve frequency selectivity, enhance signal coverage, and reduce hardware size.
Prediction Sets and Conformal Inference with Interval Outcomes
Authors: Weiguang Liu, Áureo de Paula, Elie Tamer
Date: 2025-01-17 00:00:00
Tasks: Prediction

Given data on a scalar random variable $Y$, a prediction set for $Y$ with miscoverage level $\alpha$ is a set of values for $Y$ that contains a randomly drawn $Y$ with probability $1 - \alpha$, where $\alpha \in (0,1)$. Among all prediction sets that satisfy this coverage property, the oracle prediction set is the one with the smallest volume. This paper provides estimation methods of such prediction sets given observed conditioning covariates when $Y$ is \textit{censored} or \textit{measured in intervals}. We first characterise the oracle prediction set under interval censoring and develop a consistent estimator for the shortest prediction {\it interval} that satisfies this coverage property.These consistency results are extended to accommodate cases where the prediction set consists of multiple disjoint intervals. We use conformal inference to construct a prediction set that achieves finite-sample validity under censoring and maintains consistency as sample size increases, using a conformity score function designed for interval data. The procedure accommodates the prediction uncertainty that is irreducible (due to the stochastic nature of outcomes), the modelling uncertainty due to partial identification and also sampling uncertainty that gets reduced as samples get larger. We conduct a set of Monte Carlo simulations and an application to data from the Current Population Survey. The results highlight the robustness and efficiency of the proposed methods.
Applying AHP and FUZZY AHP Management Methods to Assess the Level of Financial and Digital Inclusion
Authors: Bogdan Marza, Renate-Doina Bratu, Razvan Serbu, Sebastian Emanuel Stan, Camelia Oprean-Stan
Date: 2025-01-17 00:00:00
Tasks: Management

In today's world, marked by social distancing and lockdowns, the development of digital financial services is becoming increasingly important, but there is little empirical work documenting the most important factors that contribute to the process of financial and digital inclusion. Because the speed with which states adapt to digital financial services is critical, we must ask how prepared states are for this transition and how far they have progressed in terms of financial and digital inclusion. In this context, the goal of this article is, on the one hand, to propose a financial responsibility process framework capable of raising awareness of the most important harmonized key levels of financial and digital inclusion process that, when properly managed, can lead to achieving an optimal level of financial responsibility, and, on the other hand, to assess the financial and digital inclusion process of two different age groups of individuals who are active in the financial environment (15-34 and 35-59 age groups). The Analytical Hierarchy Process AHP and Fuzzy AHP approaches are proposed as a framework for assessing the mechanism of financial and digital inclusion in five East Central European countries. The findings reflect differences between the analyzed countries in terms of the key levels of financial and digital inclusion (where digital and financial education are the most important levels), with Croatia, Czech Republic, and Poland being the most integrated and Romania being the least. According to the findings, as a country or region's level of financial and digital inclusion increases, so does its level of financial responsibility. This research can be a useful tool in raising awareness about the importance of directed behavior for financial responsibility, particularly for policymakers.
The Evolution of Unobserved Skill Returns in the U.S.: A New Approach Using Panel Data
Authors: Lance Lochner, Youngmin Park, Youngki Shin
Date: 2025-01-17 00:00:00

Economists disagree about the factors driving the substantial increase in residual wage inequality in the US over the past few decades. To identify changes in the returns to unobserved skills, we make a novel assumption about the dynamics of skills rather than about the stability of skill distributions across cohorts, as is standard. We show that our assumption is supported by data on test score dynamics for older workers in the HRS. Using survey data from the PSID and administrative data from the IRS and SSA, we estimate that the returns to unobserved skills $declined$ substantially in the late-1980s and 1990s despite an increase in residual inequality. Accounting for firm-specific pay differences yields similar results. Extending our framework to consider occupational differences in returns to skill and multiple unobserved skills, we further show that skill returns display similar patterns for workers employed in each of cognitive, routine, and social occupations. Finally, our results suggest that increasing skill dispersion, driven by rising skill volatility, explains most of the growth in residual wage inequality since the 1980s.
The Structure of Scenarios
Authors: Guido Fioretti
Date: 2025-01-16 00:00:00

Scenarios elicit possibilities that may be ignored otherwise, as well as causal relations between them. Even when too little information is available to assess reliable probabilities, the structure of linkages between evoked alternatives and perceived consequences can be analyzed by highlighting shared consequences of different alternatives or, conversely, diverging consequences of apparently similar alternatives. While according to current practice this structure is analyzed by exploring four possibilities obtained by crossing two macro-features, I illustrate the wider possibilities enabled by hypergraph analysis. An application is discussed.
How to Avoid Both the Repugnant and Sadistic Conclusions without Dropping Standard Axioms in Population Economics
Authors: Norihito Sakamoto
Date: 2025-01-17 00:00:00
Tasks: Ethics

This study investigates possibility and impossibility results of the repugnant and sadistic conclusions in population ethics and economics. The repugnant conclusion says that an enormous population with very low well-being is socially better than any smaller population with sufficiently high well-being. The sadistic conclusion says that adding individuals with negative well-being to a society is socially better than adding individuals with positive well-being to it. Previous studies have often found it challenging to avoid both undesirable conclusions. However, I demonstrate that a class of acceptable social welfare orderings can easily prevent these conclusions while adhering to standard axioms, such as anonymity, strong Pareto, Pigou-Dalton transfer, and extended continuity. Nevertheless, if the avoidance requirements for the repugnant and sadistic conclusions are strengthened, it is possible to encounter new impossibility results. These results reveal essential conflicts between the independence axiom and the avoidance of the weak repugnant conclusion when evaluating well-being profiles with different populations.
A Class of Practical and Acceptable Social Welfare Orderings That Satisfy the Principles of Aggregation and Non-Aggregation: Reexamination of the Tyrannies of Aggregation and Non-Aggregation
Authors: Norihito Sakamoto
Date: 2025-01-17 00:00:00

This paper revisits impossibility results on the tyrannies of aggregation and non-aggregation. I propose two aggregation principles (quantitative aggregation and ratio aggregation) and investigate theoretical implications. As a result, I show that quantitative aggregation and minimal non-aggregation are incompatible while ratio aggregation and minimal non-aggregation are compatible under the assumption of standard axioms in social choice theory. Furthermore, this study provides a new characterization of the leximin rule by using replication invariance and the strong version of non-aggregation. Finally, I propose a class of practical and acceptable social welfare orderings that satisfy the principles of aggregation and non-aggregation, which has various advantages over the standard rank-discounted generalized utilitarianism.
The Impossibility of the Almost Pareto Principles
Authors: Norihito Sakamoto
Date: 2025-01-17 00:00:00

This study proposes a new efficiency requirement, a minimal almost weak Pareto principle, which says that x is socially better than y whenever the only one individual never prefers y to x, and all the others prefers x to y. Then, I show that even if the Pareto principle is modified into this harmless form, that seems sufficiently acceptable in the setting of social choice with variable population sizes or incomplete preferences, it violates acyclicity. Furthermore, it is shown that under this framework, a modified Pareto indifference and usual weak Pareto are inconsistent. These results are serious because they have a wide range of applications, not only to population economics and intergenerational equity analysis, but also to welfare evaluations of incomplete preferences and multi-dimensional well-being. In order to solve these problems, it is necessary to impose very strong assumptions on various contexts of social choice problems.
Checking Cheap Talk
Authors: Ian Ball, Xin Gao
Date: 2025-01-16 00:00:00

We consider a sender--receiver game in which the receiver's action is binary and the sender's preferences are state-independent. The state is multidimensional. The receiver can select one dimension of the state to check (i.e., observe) before choosing his action. We identify a class of influential equilibria in which the sender's message reveals which components of the state are highest, and the receiver selects one of these components to check. The sender can benefit from communication if and only if she prefers one of these equilibria to the no-communication outcome. Similar equilibria exist when the receiver can check multiple dimensions.
Consistencies of Beliefs
Authors: Ziv Hellman, Miklós Pintér
Date: 2025-01-16 00:00:00

Type spaces are analysed, and we identify three types of consistency of the players' beliefs in a type space: consistency, universal consistency, and strong consistency. Furthermore, we propose a new interpretation of a type space, defining it as a collection of the players' beliefs. This new interpretation introduces a radically different perspective on key theoretic notions, such as the prior, common prior, and posterior. In this framework: the terms of prior, common prior, and posterior are replaced by and interpreted as adequate aggregation of beliefs, consistency of the players' beliefs, and beliefs respectively. Through examples, we demonstrate that, under the proposed approach to type spaces, the ex-ante stage is not only meaningless but nonsensical. This is because, strong consistency of the players' beliefs cannot be witnessed by any probability distribution over the state space (i.e., a concrete common prior).
The R-Vessel-X Project
Authors: Abir Affane, Mohamed Amine Chetoui, Jonas Lamy, Guillaume Lienemann, Raphaël Peron, P. Beaurepaire, Guillaume Dollé, Marie-Ange Lèbre, Benoit Magnin, Odyssée Merveille, Mathilde Morvan, Phuc Ngo, Thibault Pelletier, Hugo Rositi, Stéphanie Salmon, Julien Finet, Bertrand Kerautret, Nicolas Passat, Antoine Vacavant
Date: 2025-01-17 00:00:00
Tasks: Anatomy, Segmentation

1) Objectives: This technical report presents a synthetic summary and the principal outcomes of the project R-Vessel-X ("Robust vascular network extraction and understanding within hepatic biomedical images") funded by the French Agence Nationale de la Recherche, and developed between 2019 and 2023. 2) Material and methods: We used datasets and tools publicly available such as IRCAD, Bullitt or VascuSynth toobtain real or synthetic angiographic images. The main contributions lie in the field of 3D angiographic image analysis: filtering, segmentation, modeling and simulation, with a specific focus on the liver. 3) Results: We paid a particular attention to open-source software diffusion of the developed methods, by means of 3D Slicer plugins for the liver anatomy segmentation (SlicerRVXLiverSegmentation) and vesselness filtering (Slicer-RVXVesselnessFilters), and an online demo for the generation of synthetic and realistic vessels in 2D and 3D (OpenCCO). 4) Conclusion: The R-Vessel-X project provided extensive research outcomes, covering various topics related to 3D angiographic image analysis, such as filtering, segmentation, modeling and simulation. We also developed open-source and free softwares so that the research communities in biomedical engineering can use these results in their future research.
Enhancing AI Transparency: XRL-Based Resource Management and RAN Slicing for 6G ORAN Architecture
Authors: Suvidha Mhatre, Ferran Adelantado, Kostas Ramantas, Christos Verikoukis
Date: 2025-01-17 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, Explainable artificial intelligence, Explainable Artificial Intelligence (XAI), Management

This research introduces an advanced Explainable Artificial Intelligence (XAI) framework designed to elucidate the decision-making processes of Deep Reinforcement Learning (DRL) agents in ORAN architectures. By offering network-oriented explanations, the proposed scheme addresses the critical challenge of understanding and optimizing the control actions of DRL agents for resource management and allocation. Traditional methods, both model-agnostic and model-specific approaches, fail to address the unique challenges presented by XAI in the dynamic and complex environment of RAN slicing. This paper transcends these limitations by incorporating intent-based action steering, allowing for precise embedding and configuration across various operational timescales. This is particularly evident in its integration with xAPP and rAPP sitting at near-real-time and non-real-time RIC, respectively, enhancing the system's adaptability and performance. Our findings demonstrate the framework's significant impact on improving Key Performance Indicator (KPI)-based rewards, facilitated by the ability to make informed multimodal decisions involving multiple control parameters by a DRL agent. Thus, our work marks a significant step forward in the practical application and effectiveness of XAI in optimizing ORAN resource management strategies.
Two-Stage Distributed Beamforming Design in Cell-Free Massive MIMO ISAC Systems
Authors: Leonardo Leyva, Daniel Castanheira, Adão Silva, Atílio Gameiro
Date: 2025-01-17 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrating radio-sensing functionalities into future cell-free (CF) wireless networks promises efficient resource utilization and facilitates the seamless roll-out of applications such as public safety and smart infrastructure. While the beamforming design problem for the CF integrated sensing and communication (ISAC) paradigm has been addressed in the literature, existing methods rely on centralized signal processing, leading to fronthaul load and scalability issues. This paper presents a two-stage beamforming design for the CF ISAC paradigm, aiming to significantly reduce the fronthaul load by distributing the signal processing tasks between the central unit (CU) and the access points (APs). The design optimizes the sum signal-to-interference-plus-noise ratio (SINR) for communication users, subject to per-AP power constraints and signal-to-noise ratio (SNR) requirements for radio-sensing purposes. The resulting optimization problems are non-convex and challenging to solve. To address this, we employ a majorization-minimization (MM) approach, which decomposes the problem into simpler convex subproblems. The results show that the two-stage beamforming design achieves performance comparable to centralized methods while substantially reducing the fronthaul load, thus minimizing data transmission requirements over the fronthaul network. This advancement highlights the potential of the proposed method to enhance the efficiency and scalability of cell-free MIMO ISAC systems.
A Novel Modulation Scheme Based on the Kramers--Kronig Relations for Optical IM/DD Systems
Authors: Xiaohe Dong, Kuokuo Zhang, Jiarui Zhang, Baoyin Yang, Caiming Sun
Date: 2025-01-17 00:00:00
Tasks: Sensitivity

The ever-growing demand for higher data rates in optical communication systems necessitates the development of advanced modulation formats capable of significantly enhancing system performance. In this work, we propose a novel modulation format derived from the Kramers--Kronig relations. This scheme effectively reduces the complexity of digital filtering and alleviates the demands on the digital-to-analog converter, offering a practical solution for high speed optical communication. The proposed modulation format was rigorously validated through experimental investigations using an optical wireless link. The results demonstrate a notable improvement in bit error rate (BER) performance and receiver sensitivity compared to PAM-4 and CAP-16 modulation schemes, with enhancements of 0.6 dB and 1.5 dB in receiver sensitivity, respectively. These improvements enable higher data transmission rates, positioning the Kramers--Kronig relations-based modulation format as a promising alternative to existing modulation techniques. Its potential to enhance the efficiency and capacity of optical communication systems is clearly evident. Future work will focus on extending its application to more complex scenarios, such as high-speed underwater optical communication systems, where advanced modulation formats are critical for overcoming bandwidth limitations.