pwc-archive/papers-with-abstracts huggingface dataset:
Del Visual al Auditivo: Sonorización de Escenas Guiada por Imagen
Authors: María Sánchez, Laura Fernández, Julián Arias, Mateo Cámara, Giulia Comini, Adam Gabrys, José Luis Blanco, Juan Ignacio Godino, Luis Alfonso Hernández
Date: 2024-02-02 00:00:00
Tasks: Contrastive Learning

Recent advances in image, video, text and audio generative techniques, and their use by the general public, are leading to new forms of content generation. Usually, each modality was approached separately, which poses limitations. The automatic sound recording of visual sequences is one of the greatest challenges for the automatic generation of multimodal content. We present a processing flow that, starting from images extracted from videos, is able to sound them. We work with pre-trained models that employ complex encoders, contrastive learning, and multiple modalities, allowing complex representations of the sequences for their sonorization. The proposed scheme proposes different possibilities for audio mapping and text guidance. We evaluated the scheme on a dataset of frames extracted from a commercial video game and sounds extracted from the Freesound platform. Subjective tests have evidenced that the proposed scheme is able to generate and assign audios automatically and conveniently to images. Moreover, it adapts well to user preferences, and the proposed objective metrics show a high correlation with the subjective ratings.
An Intra-BRNN and GB-RVQ Based END-TO-END Neural Audio Codec
Authors: Linping Xu, Jiawei Jiang, Dejun Zhang, Xianjun Xia, Li Chen, Yijian Xiao, Piao Ding, Shenyi Song, Sixing Yin, Ferdous Sohel
Date: 2024-02-02 00:00:00
Tasks: Quantization

Recently, neural networks have proven to be effective in performing speech coding task at low bitrates. However, under-utilization of intra-frame correlations and the error of quantizer specifically degrade the reconstructed audio quality. To improve the coding quality, we present an end-to-end neural speech codec, namely CBRC (Convolutional and Bidirectional Recurrent neural Codec). An interleaved structure using 1D-CNN and Intra-BRNN is designed to exploit the intra-frame correlations more efficiently. Furthermore, Group-wise and Beam-search Residual Vector Quantizer (GB-RVQ) is used to reduce the quantization noise. CBRC encodes audio every 20ms with no additional latency, which is suitable for real-time communication. Experimental results demonstrate the superiority of the proposed codec when comparing CBRC at 3kbps with Opus at 12kbps.
A Lyapunov theory demonstrating a fundamental limit on the speed of systems consolidation
Authors: Alireza Alemi, Emre R. F. Aksay, Mark S. Goldman
Date: 2024-02-02 00:00:00

The nervous system reorganizes memories from an early site to a late site, a commonly observed feature of learning and memory systems known as systems consolidation. Previous work has suggested learning rules by which consolidation may occur. Here, we provide conditions under which such rules are guaranteed to lead to stable convergence of learning and consolidation. We use the theory of Lyapunov functions, which enforces stability by requiring learning rules to decrease an energy-like (Lyapunov) function. We present the theory in the context of a simple circuit architecture motivated by classic models of learning in systems consolidation mediated by the cerebellum. Stability is only guaranteed if the learning rate in the late stage is not faster than the learning rate in the early stage. Further, the slower the learning rate at the late stage, the larger the perturbation the system can tolerate with a guarantee of stability. We provide intuition for this result by mapping the consolidation model to a damped driven oscillator system, and showing that the ratio of early- to late-stage learning rates in the consolidation model can be directly identified with the (square of the) oscillator's damping ratio. This work suggests the power of the Lyapunov approach to provide constraints on nervous system function.
Contingency Detection in Modern Power Systems: A Stochastic Hybrid System Method
Authors: Shuo Yuan, Le Yi Wang, George Yin, Masoud H. Nazari
Date: 2024-02-02 00:00:00
Tasks: State Space Models

This paper introduces a new stochastic hybrid system (SHS) framework for contingency detection in modern power systems (MPS). The framework uses stochastic hybrid system representations in state space models to expand and facilitate capability of contingency detection. In typical microgrids (MGs), buses may contain various synchronous generators, renewable generators, controllable loads, battery systems, regular loads, etc. For development of SHS models in power systems, this paper introduces the concept of dynamic and non-dynamic buses. By converting a physical power grid into a virtual linearized state space model and representing contingencies as random switching of system structures and parameters, this paper formulates the contingency detection problem as a joint estimation problem of discrete event and continuous states in stochastic hybrid systems. This method offers unique advantages, including using common measurement signals on voltage and current synchrophasors to detect different types and locations of contingencies, avoiding expensive local direct fault measurements and detecting certain contingencies that cannot be directly measured. The method employs a small and suitably-designed probing signal to sustain the ability of persistent contingency detection. Joint estimation algorithms are presented with their proven convergence and reliability properties. Examples that use an IEEE 5-bus system demonstrate the main ideas and derivation steps. Simulation case studies on an IEEE 33-bus system are used for detecting transmission line faults and sensor interruptions.
Active Support of Inverters for Improving Short-Term Voltage Security in 100% IBRsPenetrated Power Systems
Authors: Yinhong Lin, Bin Wang, Qinglai Guo, Haotian Zhao, Hongbin Sun
Date: 2024-02-02 00:00:00

Due to the energy crisis and environmental pollution, the installed capacity of inverter-based resources (IBRs) in power grids is rapidly increasing, and grid-following control (GFL) is the most prevalent at present. Meanwhile, grid-forming control-based (GFM) devices have been installed in the grid to provide active support for frequency and voltage. In the future GFL devices combined with GFM will be promising, especially in power systems with high penetration or 100% IBRs. When a short-circuit fault occurs in the grid, the controlled current source characteristic of the GFL devices leads to insufficient dynamic voltage support (DVS), while the GFM devices usually reduce the internal voltage to limit the current. Thus, deep voltage sags and undesired disconnections of IBRs may occur. Moreover, due to the dispersed locations and the control strategies' diversity of IBRs, the voltage support of different devices may not be fully coordinated, which is not conducive to short-term voltage security (STVS). To address this issue, a control scheme based on the simulation of transient characteristics of synchronous machines (SMs) is proposed. Then, a new fault ride-through strategy (FRT) is proposed based on the characteristic differences between GFL and GFM devices, and an optimization model of multi-device control parameters is formulated to meet the short-term voltage security constraints (SVSCs) and device capacity constraints. Finally, a fast solution method based on analytical modeling is proposed for the model. Test results based on the doublegenerator-one-load system, the IEEE 14-bus system, and other systems of different sizes show that the proposed method can effectively enhance the active support capability of GFL and GFM to the grid voltage, and avoid the large-scale disconnection of IBRs
Quadrotor Takeoff Trajectory Planning in a One-Dimensional Uncertain Wind-field Aided by Wind-Sensing Infrastructure
Authors: Nicholas Kakavitsas, Artur Wolek
Date: 2024-02-02 00:00:00
Tasks: Position, Trajectory Planning

This paper investigates optimal takeoff trajectory planning for a quadrotor modeled with vertical-plane rigid body dynamics in an uncertain, one-dimensional wind-field. The wind-field varies horizontally and propagates across an operating region with a known fixed speed. The operating area of the quadrotor is equipped with wind-sensing infrastructure that shares noisy anemometer measurements with a centralized trajectory planner. The measurements are assimilated via Gaussian process regression to predict the wind at unsampled locations and future time instants. A minimum-time optimal control problem is formulated for the quadrotor to take off and reach a desired vertical-plane position in the presence of the predicted wind-field. The problem is solved using numerical optimal control. Several examples illustrate and compare the performance of the trajectory planner under varying wind conditions and sensing characteristics.
Robust Commutation Design: Applied to Switched Reluctance Motors
Authors: Max van Meer, Gert Witvoet, Tom Oomen
Date: 2024-02-02 00:00:00

Switched Reluctance Motors (SRMs) are cost-effective electric actuators that utilize magnetic reluctance to generate torque, with torque ripple arising from unaccounted manufacturing defects in the rotor tooth geometry. This paper aims to design a versatile, resource-efficient commutation function for accurate control of a range of SRMs, mitigating torque ripple despite manufacturing variations across SRMs and individual rotor teeth. The developed commutation function optimally distributes current between coils by leveraging the variance in the torque-current-angle model and is designed with few parameters for easy integration on affordable hardware. Monte Carlo simulations and experimental results show a tracking error reduction of up to 31% and 11%, respectively. The developed approach is beneficial for applications using a single driver for multiple systems and those constrained by memory or modeling effort, providing an economical solution for improved tracking performance and reduced acoustic noise.
Simulation Framework for Vehicle and Electric Scooter Interaction
Authors: Zhitong He, Lingxi Li
Date: 2024-02-02 00:00:00

The number of shared micro-mobility services such as electric scooters (e-scooters) has an increasing trend due to the advantages of high efficiency and low cost in short-range travel in urban areas. However, due to the unique characteristics of moving behavior, it is commonly seen that e-scooters may share the road with other motor vehicles. The lack of protection may lead to severe injury for e-scooter riders. The scenario where an e-scooter crosses an intersection or makes a lane change while interacting with an approaching vehicle was commonly seen in real-life traffic data. Such scenarios are hazardous because the intention and behavior of the e-scooter may vary significantly based on the traffic environment conditions. Furthermore, some other vehicles may occlude the presence of the moving e-scooter, which can result in an unexpected collision. In this paper, we propose a simulation platform to mimic the interactions between vehicles and e-scooters. Several traffic scenarios are studied via qualitative and quantitative analysis. The proposed framework is shown to be valuable and efficient for the general risk analysis for vehicle and e-scooter interactions (VEI).
Eco-driving under localization uncertainty for connected vehicles on Urban roads: Data-driven approach and Experiment verification
Authors: Eunhyek Joa, Eric Yongkeun Choi, Francesco Borrelli
Date: 2024-02-01 00:00:00
Tasks: Model Predictive Control

This paper addresses the eco-driving problem for connected vehicles on urban roads, considering localization uncertainty. Eco-driving is defined as longitudinal speed planning and control on roads with the presence of a sequence of traffic lights. We solve the problem by using a data-driven model predictive control (MPC) strategy. This approach involves learning a cost-to-go function and constraints from state-input data. The cost-to-go function represents the remaining energy-to-spend from the given state, and the constraints ensure that the controlled vehicle passes the upcoming traffic light timely while obeying traffic laws. The resulting convex optimization problem has a short horizon and is amenable for real-time implementations. We demonstrate the effectiveness of our approach through real-world vehicle experiments. Our method demonstrates $12\%$ improvement in energy efficiency compared to the traditional approaches, which plan longitudinal speed by solving a long-horizon optimal control problem and track the planned speed using another controller, as evidenced by vehicle experiments.
The Fragile Nature of Road Transportation Systems
Authors: Linghang Sun, Yifan Zhang, Cristian Axenie, Margherita Grossi, Anastasios Kouvelas, Michail A. Makridis
Date: 2024-02-01 00:00:00

Major cities worldwide experience problems with the performance of their road transportation systems, and the continuous increase in traffic demand presents a substantial challenge to the optimal operation of urban road networks and the efficiency of traffic control strategies. The operation of transportation systems is widely considered to display fragile property, i.e., the loss in performance increases exponentially with the linearly increasing magnitude of disruptions. Meanwhile, the risk engineering community is embracing the novel concept of antifragility, enabling systems to learn from historical disruptions and exhibit improved performance under black swan events. In this study, based on established traffic models, namely fundamental diagrams and macroscopic fundamental diagrams, we first conducted a rigorous mathematical analysis to prove the fragile nature of the systems theoretically. Subsequently, we propose a skewness-based indicator that can be readily applied to cross-compare the degree of fragility for different networks solely dependent on the MFD-related parameters. At last, by taking real-world stochasticity into account, we implemented a numerical simulation with realistic network data to bridge the gap between the theoretical proof and the real-world operations, to reflect the potential impact of uncertainty on the fragility of the systems. This work aims to demonstrate the fragile nature of road transportation systems and help researchers better comprehend the necessity to consider explicitly antifragile design for future traffic control strategies.
Income distribution in Thailand is scale-invariant
Authors: Thitithep Sitthiyot, Kanyarat Holasut
Date: 2024-02-02 00:00:00

This study examines whether income distribution in Thailand has a property of scale invariance or self-similarity across years. By using the data on income shares by quintile and by decile of Thailand from 1988 to 2021, the results from 306-pairwise Kolmogorov-Smirnov tests indicate that income distribution in Thailand is statistically scale-invariant or self-similar across years with p-values ranging between 0.988 and 1.000. Based on these empirical findings, this study would like to propose that, in order to change income distribution in Thailand whose pattern had persisted for over three decades, the change itself cannot be gradual but has to be like a phase transition of substance in physics.
Response Theory via Generative Score Modeling
Authors: Ludovico Theo Giorgini, Katherine Deck, Tobias Bischoff, Andre Souza
Date: 2024-02-01 00:00:00
Tasks: Time Series

We introduce an approach for analyzing the responses of dynamical systems to external perturbations that combines score-based generative modeling with the Generalized Fluctuation-Dissipation Theorem (GFDT). The methodology enables accurate estimation of system responses, including those with non-Gaussian statistics. We numerically validate our approach using time-series data from three different stochastic partial differential equations of increasing complexity: an Ornstein-Uhlenbeck process with spatially correlated noise, a modified stochastic Allen-Cahn equation, and the 2D Navier-Stokes equations. We demonstrate the improved accuracy of the methodology over conventional methods and discuss its potential as a versatile tool for predicting the statistical behavior of complex dynamical systems.
Data-driven model selection within the matrix completion method for causal panel data models
Authors: Sandro Heiniger
Date: 2024-02-02 00:00:00
Tasks: Matrix Completion, Model Selection, parameter estimation, valid, Variable Selection

Matrix completion estimators are employed in causal panel data models to regulate the rank of the underlying factor model using nuclear norm minimization. This convex optimization problem enables concurrent regularization of a potentially high-dimensional set of covariates to shrink the model size. For valid finite sample inference, we adopt a permutation-based approach and prove its validity for any treatment assignment mechanism. Simulations illustrate the consistency of the proposed estimator in parameter estimation and variable selection. An application to public health policies in Germany demonstrates the data-driven model selection feature on empirical data and finds no effect of travel restrictions on the containment of severe Covid-19 infections.
The prices of renewable commodities: A robust stationarity analysis
Authors: Manuel Landajo, María José Presno
Date: 2024-02-01 00:00:00

This paper addresses the problem of testing for persistence in the effects of the shocks affecting the prices of renewable commodities, which have potential implications on stabilization policies and economic forecasting, among other areas. A robust methodology is employed that enables the determination of the potential presence and number of instant/gradual structural changes in the series, stationarity testing conditional on the number of changes detected, and the detection of change points. This procedure is applied to the annual real prices of eighteen renewable commodities over the period of 1900-2018. Results indicate that most of the series display non-linear features, including quadratic patterns and regime transitions that often coincide with well-known political and economic episodes. The conclusions of stationarity testing suggest that roughly half of the series are integrated. Stationarity fails to be rejected for grains, whereas most livestock and textile commodities do reject stationarity. Evidence is mixed in all soft commodities and tropical crops, where stationarity can be rejected in approximately half of the cases. The implication would be that for these commodities, stabilization schemes would not be recommended.
A simple method for joint evaluation of skill in directional forecasts of multiple variables
Authors: Thitithep Sitthiyot, Kanyarat Holasut
Date: 2024-02-02 00:00:00

Forecasts for key macroeconomic variables are almost always made simultaneously by the same organizations, presented together, and used together in policy analyses and decision-makings. It is therefore important to know whether the forecasters are skillful enough to forecast the future values of those variables. Here a method for joint evaluation of skill in directional forecasts of multiple variables is introduced. The method is simple to use and does not rely on complicated assumptions required by the conventional statistical methods for measuring accuracy of directional forecast. The data on GDP growth and inflation forecasts of three organizations from Thailand, namely, the Bank of Thailand, the Fiscal Policy Office, and the Office of the National Economic and Social Development Council as well as the actual data on GDP growth and inflation of Thailand between 2001 and 2021 are employed in order to demonstrate how the method could be used to evaluate the skills of forecasters in practice. The overall results indicate that these three organizations are somewhat skillful in forecasting the direction-of-changes of GDP growth and inflation when no band and a band of +/- 1 standard deviation of the forecasted outcome are considered. However, when a band of +/- 0.5% of the forecasted outcome is introduced, the skills in forecasting the direction-of-changes of GDP growth and inflation of these three organizations are, at best, little better than intelligent guess work.
Recent Advances on Uniqueness of Competitive Equilibrium
Authors: Alexis Akira Toda, Kieran James Walsh
Date: 2024-02-01 00:00:00

This article reviews the recent advances in the uniqueness and multiplicity of competitive equilibria in models arising in mathematical economics, finance, macroeconomics, and trade.
MACE CT Reconstruction for Modular Material Decomposition from Energy Resolving Photon-Counting Data
Authors: Natalie M. Jadue, Madhuri Nagare, Jonathan S. Maltz, Gregery T. Buzzard, Charles A. Bouman
Date: 2024-02-01 00:00:00
Tasks: Computed Tomography (CT), CT Reconstruction

X-ray computed tomography (CT) based on photon counting detectors (PCD) extends standard CT by counting detected photons in multiple energy bins. PCD data can be used to increase the contrast-to-noise ratio (CNR), increase spatial resolution, reduce radiation dose, reduce injected contrast dose, and compute a material decomposition using a specified set of basis materials. Current commercial and prototype clinical photon counting CT systems utilize PCD-CT reconstruction methods that either reconstruct from each spectral bin separately, or first create an estimate of a material sinogram using a specified set of basis materials and then reconstruct from these material sinograms. However, existing methods are not able to utilize simultaneously and in a modular fashion both the measured spectral information and advanced prior models in order to produce a material decomposition. We describe an efficient, modular framework for PCD-based CT reconstruction and material decomposition using on Multi-Agent Consensus Equilibrium (MACE). Our method employs a detector proximal map or agent that uses PCD measurements to update an estimate of the pathlength sinogram. We also create a prior agent in the form of a sinogram denoiser that enforces both physical and empirical knowledge about the material-decomposed sinogram. The sinogram reconstruction is computed using the MACE algorithm, which finds an equilibrium solution between the two agents, and the final image is reconstructed from the estimated sinogram. Importantly, the modularity of our method allows the two agents to be designed, implemented, and optimized independently. Our results on simulated data show a substantial (450%) CNR boost vs conventional maximum likelihood reconstruction when applied to a phantom used to evaluate low contrast detectability.
DRL-Based Dynamic Channel Access and SCLAR Maximization for Networks Under Jamming
Authors: Abdul Basit, Muddasir Rahim, Georges Kaddoum, Tri Nhu Do, Nadir Adam
Date: 2024-02-02 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning

This paper investigates a deep reinforcement learning (DRL)-based approach for managing channel access in wireless networks. Specifically, we consider a scenario in which an intelligent user device (iUD) shares a time-varying uplink wireless channel with several fixed transmission schedule user devices (fUDs) and an unknown-schedule malicious jammer. The iUD aims to harmoniously coexist with the fUDs, avoid the jammer, and adaptively learn an optimal channel access strategy in the face of dynamic channel conditions, to maximize the network's sum cross-layer achievable rate (SCLAR). Through extensive simulations, we demonstrate that when we appropriately define the state space, action space, and rewards within the DRL framework, the iUD can effectively coexist with other UDs and optimize the network's SCLAR. We show that the proposed algorithm outperforms the tabular Q-learning and a fully connected deep neural network approach.