pwc-archive/papers-with-abstracts huggingface dataset:
Investigating salient representations and label Variance in Dimensional Speech Emotion Analysis
Authors: Vikramjit Mitra, Jingping Nie, Erdrin Azemi
Date: 2023-12-17 00:00:00
Tasks: Emotion Recognition, Speech Emotion Recognition

Representations derived from models such as BERT (Bidirectional Encoder Representations from Transformers) and HuBERT (Hidden units BERT), have helped to achieve state-of-the-art performance in dimensional speech emotion recognition. Despite their large dimensionality, and even though these representations are not tailored for emotion recognition tasks, they are frequently used to train large speech emotion models with high memory and computational costs. In this work, we show that there exist lower-dimensional subspaces within the these pre-trained representational spaces that offer a reduction in downstream model complexity without sacrificing performance on emotion estimation. In addition, we model label uncertainty in the form of grader opinion variance, and demonstrate that such information can improve the models generalization capacity and robustness. Finally, we compare the robustness of the emotion models against acoustic degradations and observed that the reduced dimensional representations were able to retain the performance similar to the full-dimensional representations without significant regression in dimensional emotion performance.
Risk of Cascading Collisions in Network of Vehicles with Delayed Communication
Authors: Guangyi Liu, Christoforos Somarakis, Nader Motee
Date: 2023-12-28 00:00:00
Tasks: Autonomous Vehicles

This paper establishes and explores a framework to analyze the risk of cascading failures in a platoon of autonomous vehicles, accounting for communication time-delays and input uncertainty. Our proposed framework yields closed-form expressions for cascading collisions, which we quantify using the coherent Average Value-at-Risk ($\AVAR$) to assess the cascading effect of vehicle collisions within the platoon. We investigate how factors such as network connectivity, system dynamics, communication delays, and uncertainty contribute to the emergence of cascading failures. Our findings are extended to standard communication graphs with symmetries, allowing us to evaluate the risk of cascading collisions from a platoon design perspective. Furthermore, by discovering the boundedness of the inter-vehicle distances, we reveal the best achievable risk of cascading collision with general graph topologies, which is further specified for special communication graph, such as the complete graph. Our theoretical results pave the way for the development of a safety-aware framework aimed at mitigating the risk of cascading collisions in vehicle platoons.
Properties of Immersions for Systems with Multiple Limit Sets with Implications to Learning Koopman Embeddings
Authors: Zexiang Liu, Necmiye Ozay, Eduardo D. Sontag
Date: 2023-12-28 00:00:00

Linear immersions (such as Koopman eigenfunctions) of a nonlinear system have wide applications in prediction and control. In this work, we study the properties of linear immersions for nonlinear systems with multiple omega-limit sets. While previous research has indicated the possibility of discontinuous one-to-one linear immersions for such systems, it has been unclear whether continuous one-to-one linear immersions are attainable. Under mild conditions, we prove that any continuous immersion to a class of systems including finite-dimensional linear systems collapses all the omega-limit sets, and thus cannot be one-to-one. Furthermore, we show that this property is also shared by approximate linear immersions learned from data as sample size increases and sampling interval decreases. Multiple examples are studied to illustrate our results.
Symbolic Models for Interconnected Impulsive Systems
Authors: Sadek Belamfedel Alaoui, Adnane Saoud, Pushpak Jagtap, Abdalla Swikir
Date: 2023-12-28 00:00:00

In this paper, we present a compositional methodology for constructing symbolic models of nonlinear interconnected impulsive systems. Our approach relies on the concept of "alternating simulation function" to establish a relationship between concrete subsystems and their symbolic models. Assuming some small-gain type conditions, we develop an alternating simulation function between the symbolic models of individual subsystems and those of the nonlinear interconnected impulsive systems. To construct symbolic models of nonlinear impulsive subsystems, we propose an approach that depends on incremental input-to-state stability and forward completeness properties. Finally, we demonstrate the advantages of our framework through a case study.
Battery model impact on time-optimal co-design for electric racing cars: review and application
Authors: Giorgio Riva, Stefano Radrizzani, Giulio Panzani
Date: 2023-12-28 00:00:00
Tasks: energy management, Management

The sustainable mobility trend touches the racing world as well, from the hybridization of Formula 1 (F1) and Le Mans Hypercars to the fully electric Formula E racing class. In this scenario, the research community is studying how to push electric racing vehicles to their limit, combining vehicle dynamics and energy management, to successfully solve the minimum lap time problem. Recently, this class of problems has been enlarged towards optimal sizing, with a particular interest in batteries, which represent the main bottleneck for electric vehicle performance. In this work, starting from a thorough review of literature approaches, we define a general optimization framework of minimum lap and race time problems for electric vehicles, suitable to figure out the impact of different modeling choices on both problem structure and optimal variables profiles. Exploiting a case study on Generation 3 (Gen 3) of Formula E cars, we delve into the impact of battery models' complexity on both optimal sizing and optimal battery usage. We show how highly detailed models are necessary to study the evolution of both battery and vehicle control variables during the race, while, simple models are more than sufficient to address the battery sizing problem.
LEO Satellite and RIS: Two Keys to Seamless Indoor and Outdoor Localization
Authors: Pinjun Zheng, Xing Liu, Yuchen Zhang, Jiguang He, Gonzalo Seco-Granados, Tareq Y. Al-Naffouri
Date: 2023-12-28 00:00:00
Tasks: Indoor Localization, Outdoor Localization, Position

The contemporary landscape of wireless technology underscores the critical role of precise localization services. Traditional global navigation satellite systems (GNSS)-based solutions, however, fall short when it comes to indoor environments, and existing indoor localization techniques such as electromagnetic fingerprinting methods face challenges of additional implementation costs and limited coverage. This article explores an innovative solution that blends low Earth orbit (LEO) satellites with reconfigurable intelligent surfaces (RISs), unlocking its potential for realizing seamless indoor and outdoor localization (SIOL) with global coverage. After a comprehensive review of the distinctive characteristics of LEO satellites and RISs, we showcase their potential for SIOL applications through two case studies on position error bound evaluation. Finally, we discuss system architectures and highlight open challenges in such systems.
Blind System Identification in Linear Parameter-Varying Systems
Authors: Javad Zahedi Moghaddam, Hamidreza Momeni, Mojtaba Danesh
Date: 2023-12-28 00:00:00
Tasks: Scheduling

Blind System Identification (BSI) is used to extract a system model whenever input data is not attainable. Therefore, the input data and system model should be estimated simultaneously. Because of nonlinearities in a large number of systems, BSI problem is usually challenging to solve. In this paper, an innovative solution is proposed to deal with the BSI problem in nonlinear systems using the properties of the Linear Parameter-Varying (LPV) systems and Hidden Markov Models (HMM). More specifically, assuming scheduling variable is not measurable, the dynamic of the LPV system is approximated. To solve the BSI problem in this context, LPV structure is modeled as an HMM network and a modified Quasi-Static combination of Viterbi and Baum-Welch algorithms (QSVBW) is proposed to estimate the nonlinear mappings and scheduling variable signal. The applicability and the performance of the suggested QSVBW algorithm has been justified by numerical studies.
Lyapunov-Krasovskii Functionals of Robust Type for the Stability Analysis in Time-Delay Systems
Authors: Tessina H. Scholl
Date: 2023-12-27 00:00:00
Tasks: LEMMA

Inspired by the widespread theory of complete-type Lyapunov-Krasovskii functionals, the article considers an alternative class of Lyapunov-Krasovskii functionals that intends to achieve less conservative robustness bounds. These functionals share the same structure as the functionals of complete type, and also they share to be defined via their derivative along solutions of the nominal system. The defining equation for the derivative, however, is chosen differently: the Lyapunov equation, which forms the template for the defining equation of complete-type Lyapunov-Krasovskii functionals, is replaced by the template of an algebraic Riccati equation. Properties of the proposed Lyapunov-Krasovskii functionals of robust type are proven in the present article. Moreover, existence conditions are derived from the infinite-dimensional Kalman-Yakubovich-Popov lemma, combined with a splitting approach. The concept is tailored to sector-based absolute stability problems, and the obtainable robustness bounds are strongly related to the small-gain theorem, the complex stability radius, passivity theorems, the circle criterion, and integral quadratic constraints with constant multipliers, where, however, the nominal system itself has a time delay.
Dynamic model of tissue electroporation on the basis of biological dispersion and Joule heating
Authors: Raul Guedert, Daniella L. L. S. Andrade, Jéssica Rodrigues, Guilherme B. Pintarelli, Daniela O. H. Suzuki
Date: 2023-12-27 00:00:00

Electroporation is a complex, iterative, and nonlinear phenomenon that is often studied by numerical simulations. In recent years, tissue electroporation simulations have been performed using static models. However, the results of a static model simulation are restricted to a fixed protocol signature of the pulsed electric field. This paper describes a novel dynamic model of tissue electroporation that also includes tissue dispersion and temperature to allow time-domain simulations. We implemented the biological dispersion of potato tubers and thermal analysis in a commercial finite element method software. A cell electroporation model was adapted to account for the increase in tissue conductivity. The model yielded twelve parameters, divided into three dynamic states of electroporation. Thermal analysis describes the dependence of tissue conductivity on temperature. The model parameters were evaluated using experiments with vegetal tissue (Solanum tuberosum) under electrochemotherapy protocols. The proposed model can accurately predict the conductivity of tissue under electroporation from 10 kV/m to 100 kV/m. A negligible thermal effect was observed at 100 kV/m, with a 0.89 {\deg}C increase. We believe that the proposed model is suitable for describing the electroporation current on a tissue scale and also for providing a hint on the effects on the cell membrane.
Autonomous Driving using Residual Sensor Fusion and Deep Reinforcement Learning
Authors: Amin Jalal Aghdasian, Amirhossein Heydarian Ardakani, Kianoush Aqabakee, Farzaneh Abdollahi
Date: 2023-12-27 00:00:00
Tasks: Autonomous Driving, Decision Making, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Self-Driving Cars, Sensor Fusion

This paper proposes a novel approach by integrating sensor fusion with deep reinforcement learning, specifically the Soft Actor-Critic (SAC) algorithm, to develop an optimal control policy for self-driving cars. Our system employs a two-branch fusion method for vehicle image and tracking sensor data, leveraging the strengths of residual structures and identity mapping to enhance agent training. Through comprehensive comparisons, we demonstrate the efficacy of information fusion and establish the superiority of our selected algorithm over alternative approaches. Our work advances the field of autonomous driving and demonstrates the potential of reinforcement learning in enabling intelligent vehicle decision-making.
Observation-based Optimal Control Law Learning with LQR Reconstruction
Authors: Chendi Qu, Jianping He, Xiaoming Duan
Date: 2023-12-27 00:00:00

Designing controllers to generate various trajectories has been studied for years, while recently, recovering an optimal controller from trajectories receives increasing attention. In this paper, we reveal that the inherent linear quadratic regulator (LQR) problem of a moving agent can be reconstructed based on its trajectory observations only, which enables one to learn the optimal control law of the agent autonomously. Specifically, the reconstruction of the optimization problem requires estimation of three unknown parameters including the target state, weighting matrices in the objective function and the control horizon. Our algorithm considers two types of objective function settings and identifies the weighting matrices with proposed novel inverse optimal control methods, providing the well-posedness and identifiability proof. We obtain the optimal estimate of the control horizon using binary search and finally reconstruct the LQR problem with above estimates. The strength of learning control law with optimization problem recovery lies in less computation consumption and strong generalization ability. We apply our algorithm to the future control input prediction and the discrepancy loss is further derived. Numerical simulations and hardware experiments on a self-designed robot platform illustrate the effectiveness of our work.
Structural Diagnosability Analysis of Switched and Modular Battery Packs
Authors: Fatemeh Hashemniya, Arvind Balachandran, Erik Frisk, Mattias Krysander
Date: 2023-12-27 00:00:00

Safety, reliability, and durability are targets of all engineering systems, including Li-ion batteries in electric vehicles. This paper focuses on sensor setup exploration for a battery-integrated modular multilevel converter (BI-MMC) that can be part of a solution to sustainable electrification of vehicles. BI-MMC contains switches to convert DC to AC to drive an electric machine. The various configurations of switches result in different operation modes, which in turn, pose great challenges for diagnostics. The study explores diverse sensor arrangements and system configurations for detecting and isolating faults in modular battery packs. Configurations involving a minimum of two modules integrated into the pack are essential to successfully isolate all faults. The findings indicate that the default sensor setup is insufficient for achieving complete fault isolability. Additionally, the investigation also demonstrates that current sensors in the submodules do not contribute significantly to fault isolability. Further, the results on switch positions show that the system configuration has a significant impact on fault isolability. A combination of appropriate sensor data and system configuration is important in achieving optimal diagnosability, which is a paramount objective in ensuring system safety.
Multilayer control of synchronization and cascading failures in power grids
Authors: Simona Olmi, Lucia Valentina Gambuzza, Mattia Frasca
Date: 2023-12-27 00:00:00

In this work, we propose a control scheme for power grids subject to large perturbations that cause the failure of a node of the grid. Under such circumstances, the system may lose synchrony and, in addition, a cascade of line failures can be triggered as an effect of the flow redistribution that activates the protection mechanisms equipped on each line of the grid. To devise a control action for addressing this problem, we adopt a multi-layer network-based description of the power grid that incorporates an overflow condition to model the possibility of cascading failures. The two other layers of the structure are devoted to the control, one implements the distributed proportional control law, and the other the integral control law. To exemplify the application of our model, we study the Italian high-voltage power grid for different parameters and topologies of the control layers.
Joint Planning of Active Distribution Network and EV Charging Stations Considering Vehicle-to-Grid Functionality and Reactive Power Support
Authors: Yongheng Wang, Xinwei Shen, Yan Xu
Date: 2023-12-26 00:00:00

This paper proposes a collaborative planning model for the active distribution network (ADN) and electric vehicle (EV) charging stations that fully considers the vehicle-to-grid (V2G) function and reactive power support of EVs in different regions. This paper employs a sequential decomposition method based on the physical characteristics of the problem, breaking down the holistic problem into two sub-problems for solution. Subproblem I optimizes the charging and discharging behavior of the autopilot electric vehicles (AEVs) using a mixed-integer linear programming (MILP) model. Subproblem II uses a mixed-integer second-order cone programming (MISOCP) model to plan the ADN and retrofit or construct V2G charging stations (V2GCS), as well as multiple distributed generation resources (DGRs). The paper also analyzes the impact of the bi-directional active-reactive power interaction of V2GCS on ADN planning. The presented model was tested in the 47 nodes ADN in Longgang District, Shenzhen, China, and the IEEE 33 nodes ADN, demonstrating that decomposition can significantly improve the speed of solving large-scale problems while maintaining accuracy with low AEV penetration.
Single particle algorithms to reveal cellular nanodomain organization
Authors: Pierre Parutto, Jennifer Heck, Martin Heine, David Holcman
Date: 2023-12-28 00:00:00
Tasks: Super-Resolution

Formation, maintenance and physiology of high-density protein-enriched organized nanodomains, first observed in electron microscopy images, remains challenging to investigate due to their small sizes. However, these regions regulate molecular trafficking, assembly and sorting required for higher cell functions, such as communication or plastic changes. Over the past ten years, super-resolution single-particle trajectories (SPTs) have been used to sample these sub-cellular environments at a nanometer resolution for both membrane and soluble proteins. We present here data analysis developments and algorithms that convert high-throughput molecular trajectories into maps of molecular density, diffusion and local drift organization. These approaches transform intrinsic trajectory properties into statistics of the underlying cellular organization. The automatic identification of large numbers of high-density regions allows quantifying their boundary location and organization, their stability over time and their ability to transiently retain molecules. To conclude recent automated algorithms can now be used to extract biophysical parameters of sub-cellular nanodomains over a large amount of trajectories.
Linear and nonlinear causality in financial markets
Authors: Haochun Ma, Davide Prosperino, Alexander Haluszczynski, Christoph Räth
Date: 2023-12-18 00:00:00
Tasks: Causal Inference, Management, PAIR TRADING

Identifying and quantifying co-dependence between financial instruments is a key challenge for researchers and practitioners in the financial industry. Linear measures such as the Pearson correlation are still widely used today, although their limited explanatory power is well known. In this paper we present a much more general framework for assessing co-dependencies by identifying and interpreting linear and nonlinear causalities in the complex system of financial markets. To do so, we use two different causal inference methods, transfer entropy and convergent cross-mapping, and employ Fourier transform surrogates to separate their linear and nonlinear contributions. We find that stock indices in Germany and the U.S. exhibit a significant degree of nonlinear causality and that correlation, while a very good proxy for linear causality, disregards nonlinear effects and hence underestimates causality itself. The presented framework enables the measurement of nonlinear causality, the correlation-causality fallacy, and motivates how causality can be used for inferring market signals, pair trading, and risk management of portfolios. Our results suggest that linear and nonlinear causality can be used as early warning indicators of abnormal market behavior, allowing for better trading strategies and risk management.
Whales in Space: Experiencing Aquatic Animals in Their Natural Place with the Hydroambiphone
Authors: James P. Crutchfield, David D. Dunn, Alexandra M. Jurgens
Date: 2023-12-27 00:00:00

Recording the undersea three-dimensional bioacoustic sound field in real-time promises major benefits to marine behavior studies. We describe a novel hydrophone array -- the hydroambiphone (HAP) -- that adapts ambisonic spatial-audio theory to sound propagation in ocean waters to realize many of these benefits through spatial localization and acoustic immersion. Deploying it to monitor the humpback whales (Megaptera novaeangliae) of southeast Alaska demonstrates that HAP recording provides a qualitatively-improved experience of their undersea behaviors; revealing, for example, new aspects of social coordination during bubble-net feeding. On the practical side, spatialized hydrophone recording greatly reduces post-field analytical and computational challenges -- such as the "cocktail party problem" of distinguishing single sources in a complicated and crowded auditory environment -- that are common to field recordings. On the scientific side, comparing the HAP's capabilities to single-hydrophone and nonspatialized recordings yields new insights into the spatial information that allows animals to thrive in complex acoustic environments. Spatialized bioacoustics markedly improves access to the humpbacks' undersea acoustic environment and expands our appreciation of their rich vocal lives.
Development of Choice Model for Brand Evaluation
Authors: Marina Kholod, Nikita Mokrenko
Date: 2023-12-28 00:00:00
Tasks: model

Consumer choice modeling takes center stage as we delve into understanding how personal preferences of decision makers (customers) for products influence demand at the level of the individual. The contemporary choice theory is built upon the characteristics of the decision maker, alternatives available for the choice of the decision maker, the attributes of the available alternatives and decision rules that the decision maker uses to make a choice. The choice set in our research is represented by six major brands (products) of laundry detergents in the Japanese market. We use the panel data of the purchases of 98 households to which we apply the hierarchical probit model, facilitated by a Markov Chain Monte Carlo simulation (MCMC) in order to evaluate the brand values of six brands. The applied model also allows us to evaluate the tangible and intangible brand values. These evaluated metrics help us to assess the brands based on their tangible and intangible characteristics. Moreover, consumer choice modeling also provides a framework for assessing the environmental performance of laundry detergent brands as the model uses the information on components (physical attributes) of laundry detergents.