pwc-archive/papers-with-abstracts huggingface dataset:
Robust Data-Driven Predictive Control for Mixed Platoons under Noise and Attacks
Authors: Shuai Li, Chaoyi Chen, Haotian Zheng, Jiawei Wang, Qing Xu, Jianqiang Wang, Keqiang Li
Date: 2024-11-21 00:00:00

Controlling mixed platoons, which consist of both connected and automated vehicles (CAVs) and human-driven vehicles (HDVs), poses significant challenges due to the uncertain and unknown human driving behaviors. Data-driven control methods offer promising solutions by leveraging available trajectory data, but their performance can be compromised by noise and attacks. To address this issue, this paper proposes a Robust Data-EnablEd Predictive Leading Cruise Control (RDeeP-LCC) framework based on data-driven reachability analysis. The framework over-approximates system dynamics under noise and attack using a matrix zonotope set derived from data, and develops a stabilizing feedback control law. By decoupling the mixed platoon system into nominal and error components, we employ data-driven reachability sets to recursively compute error reachable sets that account for noise and attacks, and obtain tightened safety constraints of the nominal system. This leads to a robust data-driven predictive control framework, solved in a tube-based control manner. Numerical simulations and human-in-the-loop experiments demonstrate that the RDeeP-LCC method significantly improves robustness against noise and attacks, while enhancing tracking accuracy, control efficiency, energy economy, and driving comfort.
Weak synchronization in heterogeneous multi-agent systems
Authors: Anton A. Stoorvogel, Ali Saberi, Zhenwei Liu
Date: 2024-11-21 00:00:00

In this paper, we propose a new framework for synchronization of heterogeneous multi agent system which we refer to as weak synchronization. This new framework of synchronization is based on achieving the network stability in the absence of any information on communication network including the connectivity. Here by network stability, we mean that in the basic setup of a multi-agent system, we require that the signals exchanged over the network converge to zero. As such if the network happens to have a directed spanning tree then we obtain classical synchronization. Moreover, we design protocols which achieve weak synchronization for any network without making any kind of assumptions on communication network. If the network happens to have a directed spanning tree, then we obtain classical synchronization. However, if this is not the case then we describe in detail in this paper what kind of synchronization properties are preserved in the system and the output of the different agents can behave.
ScAlN-on-SiC Ku-Band Solidly-Mounted Bidimensional Mode Resonators
Authors: Luca Colombo, Luca Spagnuolo, Kapil Saha, Gabriel Giribaldi, Pietro Simeoni, Matteo Rinaldi
Date: 2024-11-20 00:00:00

This letter reports on Solidly-Mounted Bidimensional Mode Resonators (S2MRs) exploiting a highly-optimized Sezawa mode in 30% Scandium-doped Aluminum Nitride (ScAlN) on Silicon Carbide (SiC) and operating near 16 GHz. Experimental results demonstrate mechanical quality factors (Qm) as high as 380, Bode quality factors (QBode) approaching 500, electromechanical coupling coefficients (kt2) of 4.5%, an overall Figure of Merit (FOM = Qm kt2) exceeding 17, and power handling greater than 20 dBm for devices closely matched to 50 ohms. To the best of the authors' knowledge, S2MRs exhibit the highest Key Performance Indicators (KPIs) among solidly mounted resonators in the Ku band, paving the way for the integration of nanoacoustic devices on fast substrates with high-power electronics, tailored for military and harsh-environment applications.
Assessing the Impact of Electric Vehicle Charging on Residential Distribution Grids
Authors: Elias Raffoul, Xingpeng Li
Date: 2024-11-20 00:00:00

To achieve net-zero carbon emissions, electrification in the transportation sector plays an important role. Significant increase of electric vehicles (EV) has been observed nationally and globally. While the transition to EVs presents substantial environmental benefits, it would lead to several challenges to the power grid due to EV charging activities. Growing EVs greatly increase peak loads on residential grids, particularly during evening charging periods. This surge can result in operational challenges, including greater voltage drops, increased power losses, and potential overloading violations, compromising grid reliability and efficiency. This study focuses on determining ampacity violations, and analyzing line loading levels in a 240-bus distribution system with 1120 customers, located in the Midwest U.S. By simulating a range of charging scenarios and evaluating EV chargers with varying power capacities under different distribution system voltage levels, this research aims to identify lines at risk of ampacity violations for various EV charging penetration rates up to 100%. The findings will provide valuable insights for utilities and grid operators, informing strategies for voltage level adjustments and necessary infrastructure reinforcements to effectively accommodate the growing energy demands associated with widespread EV adoption.
Does the square-root price impact law belong to the strict universal scalings?: quantitative support by a complete survey of the Tokyo stock exchange market
Authors: Yuki Sato, Kiyoshi Kanazawa
Date: 2024-11-21 00:00:00
Tasks: All

Universal power laws have been scrutinised in physics and beyond, and a long-standing debate exists in econophysics regarding the strict universality of the nonlinear price impact, commonly referred to as the square-root law (SRL). The SRL posits that the average price impact $I$ follows a power law with respect to transaction volume $Q$, such that $I(Q) \propto Q^{\delta}$ with $\delta \approx 1/2$. Some researchers argue that the exponent $\delta$ should be system-specific, without universality. Conversely, others contend that $\delta$ should be exactly $1/2$ for all stocks across all countries, implying universality. However, resolving this debate requires high-precision measurements of $\delta$ with errors of around $0.1$ across hundreds of stocks, which has been extremely challenging due to the scarcity of large microscopic datasets -- those that enable tracking the trading behaviour of all individual accounts. Here we conclusively support the universality hypothesis of the SRL by a complete survey of all trading accounts for all liquid stocks on the Tokyo Stock Exchange (TSE) over eight years. Using this comprehensive microscopic dataset, we show that the exponent $\delta$ is equal to $1/2$ within statistical errors at both the individual stock level and the individual trader level. Additionally, we rejected two prominent models supporting the nonuniversality hypothesis: the Gabaix-Gopikrishnan-Plerou-Stanley and the Farmer-Gerig-Lillo-Waelbroeck models. Our work provides exceptionally high-precision evidence for the universality hypothesis in social science and could prove useful in evaluating the price impact by large investors -- an important topic even among practitioners.
Multiscale Markowitz
Authors: Revant Nayar, Raphael Douady
Date: 2024-11-21 00:00:00
Tasks: Portfolio Optimization

Traditional Markowitz portfolio optimization constrains daily portfolio variance to a target value, optimising returns, Sharpe or variance within this constraint. However, this approach overlooks the relationship between variance at different time scales, typically described by $\sigma(\Delta t) \propto (\Delta t)^{H}$ where $H$ is the Hurst exponent, most of the time assumed to be \(\frac{1}{2}\). This paper introduces a multifrequency optimization framework that allows investors to specify target portfolio variance across a range of frequencies, characterized by a target Hurst exponent $H_{target}$, or optimize the portfolio at multiple time scales. By incorporating this scaling behavior, we enable a more nuanced and comprehensive risk management strategy that aligns with investor preferences at various time scales. This approach effectively manages portfolio risk across multiple frequencies and adapts to different market conditions, providing a robust tool for dynamic asset allocation. This overcomes some of the traditional limitations of Markowitz, when it comes to dealing with crashes, regime changes, volatility clustering or multifractality in markets. We illustrate this concept with a toy example and discuss the practical implementation for assets with varying scaling behaviors.
Long-term predictive models for mosquito borne diseases: a narrative review
Authors: Marcio Maciel Bastos, Luiz Max Carvalho, Eduardo Correa Araujo, Flávio Codeço Coelho
Date: 2024-11-20 00:00:00

In face of climate change and increasing urbanization, the predictive mosquito-borne diseases (MBD) transmission models require constant updates. Thus, is urgent to comprehend the driving forces of this non stationary behavior, observed through spatial and incidence expansion. We observed that temperature is a critical driver in predictive models for MBD transmission, also being consistently used in multiple reviewed papers with considerable incidence predictive capacity. Rainfall, however, have more subtle importance as moderate precipitation creates breeding sites for mosquitoes, but excessive rainfall can reduce larvae populations. We highlight the frequent use of mechanistic models, particularly those that integrate temperature-dependent biological parameters of disease transmission in incidence proxies as the Vectorial Capacity (VC) and temperature-based basic reproduction number $R_0(t)$, for example. These models show the importance of climate variables, but the socio-demographic factors are often not considered. This gap is a significant opportunity for future research to incorporate socio-demographic data into long-term predictive models for more comprehensive and reliable forecasts. With this survey, we outline the most promising paths to be followed by long-term MBD transmission research and highlighting the potential facing challenges. Thus, we offer a valuable foundation for enhancing disease forecasting models and supporting more effective public health interventions, specially in the long term.
Competitive binding of Activator-Repressor in Stochastic Gene Expression
Authors: Amit Kumar Das, Debabrata Biswas
Date: 2024-11-20 00:00:00

Regulation of gene expression is the consequence of interactions between the promoter of the gene and the transcription factors (TFs). In this paper, we explore the features of a genetic network where the TFs (activators and repressors) bind the promoter in a competitive way. We develop an analytical theory that offers detailed reaction kinetics of the competitive activator-repressor system which could be the powerful tools for extensive study and analysis of the genetic circuit in future research. Moreover, the theoretical approach helps us to find a most probable set of parameter values which was unavailable in experiments. We study the noisy behaviour of the circuit and compare the profile with the network where the activator and repressor bind the promoter non-competitively. We further notice that, due to the effect of transcriptional reinitiation in the presence of the activator and repressor molecules, there exits some anomalous characteristic features in the mean expressions and noise profiles. We find that, in presence of the reinitiation the noise in transcriptional level remains low while it is higher in translational level than the noise when the reinitiation is absent. In addition, it is possible to reduce the noise further below the Poissonian level in competitive circuit than the non-competitive one with the help of some noise reducing parameters.
Dynamic spatial interaction models for a leader's resource allocation and followers' multiple activities
Authors: Hanbat Jeong
Date: 2024-11-21 00:00:00
Tasks: counterfactual

This paper introduces a novel spatial interaction model to explore the decision-making processes of two types of agents-a leader and followers-with central and local governments serving as empirical representations. The model accounts for three key features: (i) resource allocations from the leader to the followers and the resulting strategic interactions, (ii) followers' choices across multiple activities, and (iii) interactions among these activities. We develop a network game to examine the micro-foundations of these processes. In this game, followers engage in multiple activities, while the leader allocates resources by monitoring the externalities arising from followers' interactions. The game's unique NE is the foundation for our econometric framework, providing equilibrium measures to understand the short-term impacts of changes in followers' characteristics and their long-term consequences. To estimate the agent payoff parameters, we employ the QML estimation method and examine the asymptotic properties of the QML estimator to ensure robust statistical inferences. Empirically, we investigate interactions among U.S. states in public welfare expenditures (PWE) and housing and community development expenditures (HCDE), focusing on how federal grants influence these expenditures and the interactions among state governments. Our findings reveal positive spillovers in states' PWEs, complementarity between the two expenditures within states, and negative cross-variable spillovers between them. Additionally, we observe positive effects of federal grants on both expenditures. Counterfactual simulations indicate that federal interventions lead to a 6.46% increase in social welfare by increasing the states' efforts on PWE and HCDE. However, due to the limited flexibility in federal grants, their magnitudes are smaller than the proportion of federal grants within the states' total revenues.
Wavelet Analysis of Cryptocurrencies -- Non-Linear Dynamics in High Frequency Domains
Authors: Tatsuru Kikuchi
Date: 2024-11-21 00:00:00

In this study, we perform some analysis for the probability distributions in the space of frequency and time variables. However, in the domain of high frequencies, it behaves in such a way as the highly non-linear dynamics. The wavelet analysis is a powerful tool to perform such analysis in order to search for the characteristics of frequency variations over time for the prices of major cryptocurrencies. In fact, the wavelet analysis is found to be quite useful as it examine the validity of the efficient market hypothesis in the weak form, especially for the presence of the cyclical persistence at different frequencies. If we could find some cyclical persistence at different frequencies, that means that there exist some intrinsic causal relationship for some given investment horizons defined by some chosen sampling scales. This is one of the characteristic results of the wavelet analysis in the time-frequency domains.
Process and Policy Insights from an Intercomparison of Open Electricity System Capacity Expansion Models
Authors: Greg Schivley, Aurora Barone, Michael Blackhurst, Patricia Hidalgo-Gonzalez, Jesse Jenkins, Oleg Lugovoy, Qian Luo, Michael J. Roberts, Rangrang Zheng, Cameron Wade, Matthias Fripp
Date: 2024-11-21 00:00:00

This study performs a detailed intercomparison of four open-source electricity capacity expansion models - Temoa, Switch, GenX, and USENSYS - to evaluate 1) how closely the results of these models align when inputs and configurations are harmonized, and 2) the degree to which varying model configurations affect outputs. We harmonize the inputs to each model using PowerGenome and use clearly defined scenarios (policy conditions) and configurations (model setup choices). This allows us to isolate how differences in model structure affect policy outcomes and investment decisions. Our framework allows each model to be tested on identical assumptions for policy, technology costs, and operational constraints, allowing us to focus on differences that arise from inherent model structures. Key findings highlight that, when harmonized, models produce very similar capacity portfolios under current policies and net-zero scenarios, with less than 1% difference in system costs for most configurations. This agreement among models allows us to focus on how configuration choices affect model results. For instance, configurations with unit commitment constraints or economic retirement yield different investments and system costs compared to simpler configurations. Our findings underscore the importance of aligning input data and transparently defining scenarios and configurations to provide robust policy insights.
The Role of Central Banks in Advancing Sustainable Finance
Authors: A T M Omor Faruq, Md Toufiqul Huq
Date: 2024-11-14 00:00:00

This paper examines the pivotal role central banks play in advancing sustainable finance, a crucial component in addressing global environmental and social challenges. As supervisors of financial stability and economic growth, central banks have dominance over the financial system to influence how a country moves towards sustainable economy. The chapter explores how central banks integrate sustainability into their monetary policies, regulatory frameworks, and financial market operations. It highlights the ways in which central banks can promote green finance through sustainable investment principles, climate risk assessments, and green bond markets. Additionally, the chapter examines the collaborative efforts between central banks, governments, and international institutions to align financial systems with sustainability goals. By investigating case studies and best practices, the chapter provides a comprehensive understanding of the strategies central banks employ to foster a resilient and sustainable financial landscape. The findings underscore the imperative for central banks to balance traditional mandates with the emerging necessity to support sustainable development, ultimately contributing to the broader agenda of achieving global sustainability targets.
Non-Allais Paradox and Context-Dependent Risk Attitudes
Authors: Edward Honda, Keh-Kuan Sun
Date: 2024-11-21 00:00:00

We provide and axiomatize a representation of preferences over lotteries that generalizes the expected utility model. Our representation is consistent with the violations of the independence axiom that we observe in the laboratory experiment that we conduct. The violations differ from the Allais Paradox in that they are incompatible with some of the most prominent non-expected utility models. Our representation can be interpreted as a decision-maker with context-dependent attitudes to risks and allows us to generate various types of realistic behavior. We analyze some properties of our model, including specifications that ensure preferences for first-order stochastic dominance. We test whether subjects in our experiment exhibit the type of context-dependent risk attitudes that arise in our model.
Flow methods for cooperative games with generalized coalition configuration
Authors: Encarnacion Algaba, Eric Remila, Philippe Solal
Date: 2024-11-20 00:00:00

This paper introduces the class of cooperative games with generalized coalition configuration. This new class of games corresponds to cooperative games with coalition configuration and restricted cooperation. A coalition configuration is a collection of coalitions covering the agent set. The restriction of cooperation between agents is represented by a set system on each element of the coalition configuration. A coalition profile is a list of feasible coalitions, one for each element of the coalition configuration. A coalition profile function associates a worth with each coalition profile. Based on this framework, we define and axiomatically characterize marginal values whose coefficients induce a unitary flow on the product digraph obtained from these set systems. Next, we propose a two-step procedure, inspired by Owen's procedure, to construct flow methods as above. Then, we show that the associated flow is decomposable into two flows. Finally, we use two axioms to characterize the flows that can be decomposed in this way, and hence the flow methods constructed using our procedure.
MVANet: Multi-Stage Video Attention Network for Sound Event Localization and Detection with Source Distance Estimation
Authors: Hengyi Hong, Qing Wang, Jun Du, Ruoyu Wei, Mingqi Cai, Xin Fang
Date: 2024-11-21 00:00:00
Tasks: Data Augmentation, Sound Event Localization and Detection

Sound event localization and detection with source distance estimation (3D SELD) involves not only identifying the sound category and its direction-of-arrival (DOA) but also predicting the source's distance, aiming to provide full information about the sound position. This paper proposes a multi-stage video attention network (MVANet) for audio-visual (AV) 3D SELD. Multi-stage audio features are used to adaptively capture the spatial information of sound sources in videos. We propose a novel output representation that combines the DOA with distance of sound sources by calculating the real Cartesian coordinates to address the newly introduced source distance estimation (SDE) task in the Detection and Classification of Acoustic Scenes and Events (DCASE) 2024 Challenge. We also employ a variety of effective data augmentation and pre-training methods. Experimental results on the STARSS23 dataset have proven the effectiveness of our proposed MVANet. By integrating the aforementioned techniques, our system outperforms the top-ranked method we used in the AV 3D SELD task of the DCASE 2024 Challenge without model ensemble. The code will be made publicly available in the future.
Sequence-to-Sequence Neural Diarization with Automatic Speaker Detection and Representation
Authors: Ming Cheng, Yuke Lin, Ming Li
Date: 2024-11-21 00:00:00
Tasks: Action Detection, Activity Detection, speaker-diarization, Speaker Diarization

This paper proposes a novel Sequence-to-Sequence Neural Diarization (S2SND) framework to perform online and offline speaker diarization. It is developed from the sequence-to-sequence architecture of our previous target-speaker voice activity detection system and then evolves into a new diarization paradigm by addressing two critical problems. 1) Speaker Detection: The proposed approach can utilize partially given speaker embeddings to discover the unknown speaker and predict the target voice activities in the audio signal. It does not require a prior diarization system for speaker enrollment in advance. 2) Speaker Representation: The proposed approach can adopt the predicted voice activities as reference information to extract speaker embeddings from the audio signal simultaneously. The representation space of speaker embedding is jointly learned within the whole diarization network without using an extra speaker embedding model. During inference, the S2SND framework can process long audio recordings blockwise. The detection module utilizes the previously obtained speaker-embedding buffer to predict both enrolled and unknown speakers' voice activities for each coming audio block. Next, the speaker-embedding buffer is updated according to the predictions of the representation module. Assuming that up to one new speaker may appear in a small block shift, our model iteratively predicts the results of each block and extracts target embeddings for the subsequent blocks until the signal ends. Finally, the last speaker-embedding buffer can re-score the entire audio, achieving highly accurate diarization performance as an offline system. Experimental results show that ...