pwc-archive/papers-with-abstracts huggingface dataset:
Kernelized offset-free data-driven predictive control for nonlinear systems
Authors: Thomas Oliver de Jong, Mircea Lazar
Date: 2024-11-27 00:00:00

This paper presents a kernelized offset-free data-driven predictive control scheme for nonlinear systems. Traditional model-based and data-driven predictive controllers often struggle with inaccurate predictors or persistent disturbances, especially in the case of nonlinear dynamics, leading to tracking offsets and stability issues. To overcome these limitations, we employ kernel methods to parameterize the nonlinear terms of a velocity model, preserving its structure and efficiently learning unknown parameters through a least squares approach. This results in a offset-free data-driven predictive control scheme formulated as a nonlinear program, but solvable via sequential quadratic programming. We provide a framework for analyzing recursive feasibility and stability of the developed method and we demonstrate its effectiveness through simulations on a nonlinear benchmark example.
A potassium ion channel simulated with a universal neural network potential
Authors: Timothy T. Duignan
Date: 2024-11-28 00:00:00

Potassium ion channels are critical components of biology. They conduct potassium ions across the cell membrane with remarkable speed and selectivity. Understanding how they do this is crucially important for applications in neuroscience, medicine, and materials science. However, many fundamental questions about the mechanism they use remain unresolved, partly because it is extremely difficult to computationally model due to the scale and complexity of the necessary simulations. Here, the selectivity filter (SF) of the KcsA potassium ion channel is simulated using Orb-D3, a recently released universal neural network potential. A previously unreported hydrogen bond between water in the SF and the T75 hydroxyl side group at the entrance to the SF is observed. This hydrogen bond appears to stabilize water in the SF, enabling a soft knock-on transport mechanism where water is co-transported through the SF with a reasonable conductivity (80 $\pm$ 20 pS). Carbonyl backbone flipping is also observed at new sites in the SF. This work demonstrates the potential of universal neural network potentials to provide insights into previously intractable questions about complex systems far outside their training data distribution.
Topological Approach for Data Assimilation
Authors: Max M. Chumley, Firas A. Khasawneh
Date: 2024-11-12 00:00:00
Tasks: Topological Data Analysis

Many dynamical systems are difficult or impossible to model using high fidelity physics based models. Consequently, researchers are relying more on data driven models to make predictions and forecasts. Based on limited training data, machine learning models often deviate from the true system states over time and need to be continually updated as new measurements are taken using data assimilation. Classical data assimilation algorithms typically require knowledge of the measurement noise statistics which may be unknown. In this paper, we introduce a new data assimilation algorithm with a foundation in topological data analysis. By leveraging the differentiability of functions of persistence, gradient descent optimization is used to minimize topological differences between measurements and forecast predictions by tuning data driven model coefficients without using noise information from the measurements. We describe the method and focus on its capabilities performance using the chaotic Lorenz 63 system as an example and we also show that the method works on a higher dimensional example with the Lorenz 96 system.
Optimization and Dynamic Analysis of a Vibro-Impact Nonlinear Energy Sink with Electromagnetic Coil for Vibration Suppression and Energy Harvesting
Authors: Ali Abdollahi
Date: 2024-11-12 00:00:00

This study investigates a system comprising a linear oscillator (LO) equipped with a Vibro-Impact Nonlinear Energy Sink (VI-NES) and a coil. The LO's damping and stiffness coefficients are represented by ( C ) and ( k ), respectively, while a ball inside the LO moves within a cavity, colliding with the walls at both ends. The primary focus is on optimizing the cavity length (( L_c )) and the coefficient of restitution (( \kappa )) and coil parameter () using a Genetic Algorithm (GA). The motion equations for the LO incorporating the VI-NES are derived, considering the electromagnetic force (( F_e )) and contact force (( F_c )). The study also explores the energy harvesting circuit, which generates electric power by connecting a load resistance to the coil, transforming mechanical energy into electrical energy. The dynamic behavior of the system is analyzed, highlighting the transition from predictable patterns to chaotic responses. Efficiency metrics are defined to measure the VI-NES's performance in absorbing and dissipating excitation energy. The optimization formulation addresses the influence of initial conditions, proposing a multi-objective approach to handle uncertainties. Validation against existing studies confirms the reliability of the proposed optimization method. The results demonstrate that optimizing both ( L_c ) and ( \kappa ) using GA effectively minimizes the amplitude response of the LO, outperforming approximate methods.
Assessing the physical risks of climate change for the financial sector: a case study from Mexico's Central Bank
Authors: Francisco Estrada, Miguel A. Altamirano del Carmen, Oscar Calderon-Bustamante, W. J. Wouter Botzen, Serafin Martinez-Jaramillo, Stefano Battiston
Date: 2024-11-28 00:00:00

The financial sector is increasingly concerned with the physical risks of climate change, but economic and financial impact representations are still developing, particularly for chronic risks. Mexico's Central Bank conducted a comprehensive assessment using a suite of global models to evaluate both physical and transition risks. We present the analysis concerning with chronic physical risks, underlining innovations such as the use of a recent integrated assessment model that enables grid-cell level analysis and differentiates urban and non-urban areas, capturing the local effects of climate change more accurately. The model includes multiple damage functions and a probabilistic climate model for encompassing analyses and detailed economic impact insights. Under the Current Policies scenario, economic losses could exceed 35 percent of Mexico's GDP by 2100. Accounting for the urban heat island effect, losses could surpass 20 trillion (USD) in present value, over ten times Mexico's 2024 GDP. However, implementing a scenario aligned with the Paris Agreement significantly reduces these losses, showcasing the benefits of international mitigation efforts, though substantial residual impacts persist. This study emphasizes integrating chronic physical risks into financial evaluations, proposing new approaches, metrics, and methods that exploit detailed, spatially explicit measures to improve risk and loss estimation and facilitate communication.
The role of inhibitory neuronal variability in modulating phase diversity between coupled networks
Authors: Katiele V. P. Brito, Joana M. G. L. Silva, Claudio R. Mirasso, Fernanda S. Matias
Date: 2024-11-29 00:00:00
Tasks: Diversity

Neuronal heterogeneity, characterized by the presence of a multitude of spiking neuronal patterns, is a widespread phenomenon throughout the nervous system. In particular, the brain exhibits strong variability among inhibitory neurons. Despite the huge neuronal heterogeneity across brain regions, which in principle could decrease synchronization, cortical areas coherently oscillate during various cognitive tasks. Therefore, the functional significance of neuronal heterogeneity remains a subject of active investigation. Previous studies typically focus on the role of heterogeneity in the dynamic properties of only one population. Here, we explore how different types of inhibitory neurons can contribute to the diversity of the phase relations between two cortical areas. This research sheds light on the potential impact of local properties, such as neuronal variability, on communication between distant brain regions. We show that both homogeneous and heterogeneous inhibitory networks can exhibit phase diversity and nonintuitive regimes such as anticipated synchronization (AS) and phase bistability. It has been proposed that the bi-stable phase could be related to bi-stable perception, such as in the Necker cube. Moreover, we show that heterogeneity enlarges the region of zero-lag synchronization and bistability. We also show that the parameter controlling inhibitory heterogeneity modulates the transition from the usual delayed synchronization regime (DS) to AS. Finally, we show that the inhibitory heterogeneity drives the internal dynamics of the free-running population. Therefore, we suggest a possible mechanism to explain when the DS-AS transition occurs via zero-lag synchronization or bi-stability.
Canonical correlation analysis of stochastic trends via functional approximation
Authors: Massimo Franchi, Iliyan Georgiev, Paolo Paruolo
Date: 2024-11-29 00:00:00

This paper proposes a novel canonical correlation analysis for semiparametric inference in $I(1)/I(0)$ systems via functional approximation. The approach can be applied coherently to panels of $p$ variables with a generic number $s$ of stochastic trends, as well as to subsets or aggregations of variables. This study discusses inferential tools on $s$ and on the loading matrix $\psi$ of the stochastic trends (and on their duals $r$ and $\beta$, the cointegration rank and the cointegrating matrix): asymptotically pivotal test sequences and consistent estimators of $s$ and $r$, $T$-consistent, mixed Gaussian and efficient estimators of $\psi$ and $\beta$, Wald tests thereof, and misspecification tests for checking model assumptions. Monte Carlo simulations show that these tools have reliable performance uniformly in $s$ for small, medium and large-dimensional systems, with $p$ ranging from 10 to 300. An empirical analysis of 20 exchange rates illustrates the methods.
Warfare Ignited Price Contagion Dynamics in Early Modern Europe
Authors: Emile Esmaili, Michael J. Puma, Francis Ludlow, Poul Holm, Eva Jobbova
Date: 2024-11-28 00:00:00

Economic historians have long studied market integration and contagion dynamics during periods of warfare and global stress, but there is a lack of model-based evidence on these phenomena. This paper uses an econometric contagion model, the Diebold-Yilmaz framework, to examine the dynamics of economic shocks across European markets in the early modern period. Our findings suggest that key periods of violent conflicts significantly increased food price spillover across cities, causing widespread disruptions across Europe. We also demonstrate the ability of this framework to capture relevant historical dynamics between the main trade centers of the period.
Contrasting the optimal resource allocation to cybersecurity and cyber insurance using prospect theory versus expected utility theory
Authors: Chaitanya Joshi, Jinming Yang, Sergeja Slapnicar, Ryan K L Ko
Date: 2024-11-28 00:00:00
Tasks: Decision Making

Protecting against cyber-threats is vital for every organization and can be done by investing in cybersecurity controls and purchasing cyber insurance. However, these are interlinked since insurance premiums could be reduced by investing more in cybersecurity controls. The expected utility theory and the prospect theory are two alternative theories explaining decision-making under risk and uncertainty, which can inform strategies for optimizing resource allocation. While the former is considered a rational approach, research has shown that most people make decisions consistent with the latter, including on insurance uptakes. We compare and contrast these two approaches to provide important insights into how the two approaches could lead to different optimal allocations resulting in differing risk exposure as well as financial costs. We introduce the concept of a risk curve and show that identifying the nature of the risk curve is a key step in deriving the optimal resource allocation.
Quantifying Global Food Trade: A Net Caloric Content Approach to Food Trade Network Analysis
Authors: Xiaopeng Wang, Chengyi Tu, Shuhao Chen, Sicheng Wang, Ying Fan, Samir Suweis, Paolo D'Odorico
Date: 2024-11-28 00:00:00

As the global population and the per capita demand for resource intensive diets continues to grow, the corresponding increase in food demand challenges the global food system, enhancing its reliance on trade. Most previous research typically constructed either unweighted networks or weighted solely by tonnage to represent food trade, and focused on bilateral trade relationships between pairs of countries. This study investigates the properties of global food trade constructed in terms of total food calories associated with all the main food products exchanged along each trade link (edge of the food trade network). Utilizing data from the Food and Agriculture Organization between 1986 and 2022, we construct a directed, weighted network of net caloric flows between countries. This approach highlights the importance of considering nutritional value in discussions of food security and trade policies, offering a more holistic view of global food trade dynamics. Our analysis reveals significant heterogeneity in trade patterns, with certain countries emerging as major exporters or importers of food calories. Moreover, we employ network measures, including network connectivity, network heterogeneity, network modularity, and node correlation similarity, to elucidate the structural dynamics of global net food calorie trade networks that are relevant to the stability and resilience of the global food system. Our work provides a more nuanced understanding of global food trade dynamics, emphasizing the need for comprehensive strategies to enhance the resilience and sustainability of food trade networks.
Condorcet-Consistent Choice Among Three Candidates
Authors: Felix Brandt, Chris Dong, Dominik Peters
Date: 2024-11-29 00:00:00

A voting rule is a Condorcet extension if it returns a candidate that beats every other candidate in pairwise majority comparisons whenever one exists. Condorcet extensions have faced criticism due to their susceptibility to variable-electorate paradoxes, especially the reinforcement paradox (Young and Levenglick, 1978) and the no-show paradox (Moulin, 1988). In this paper, we investigate the susceptibility of Condorcet extensions to these paradoxes for the case of exactly three candidates. For the reinforcement paradox, we establish that it must occur for every Condorcet extension when there are at least eight voters and demonstrate that certain refinements of maximin, a voting rule originally proposed by Condorcet (1785), are immune to this paradox when there are at most seven voters. For the no-show paradox, we prove that the only homogeneous Condorcet extensions immune to it are refinements of maximin. We also provide axiomatic characterizations of maximin and two of its refinements, Nanson's rule and leximin, highlighting their suitability for three-candidate elections.
Money Burning Improves Mediated Communication
Authors: Yi Liu, Yang Yu
Date: 2024-11-29 00:00:00

This paper explores the problem of mediated communication enhanced by money-burning tactics for commitment power. In our model, the sender has state-independent preferences and can design a communication mechanism that both transmits messages and burns money. We characterize the sender's maximum equilibrium payoff, which has clear geometric interpretations and is linked to two types of robust Bayesian persuasion. We demonstrate that, generically, the money-burning tactic \emph{strictly} improves the sender's payoff for almost all prior beliefs where commitment is valuable for the sender. Furthermore, our communication model directly applies to Web 3.0 communities, clarifying the commitment value within these contexts.
Dynamic matching games: stationary equilibria under varying commitments
Authors: Nadia Guiñazú, Pablo Neme, Jorge Oviedo
Date: 2024-11-28 00:00:00

This paper examines equilibria in dynamic two-sided matching games, extending Gale and Shapley's foundational model to a non-cooperative, decentralized, and dynamic framework. We focus on markets where agents have utility functions and commitments vary. Specifically, we analyze a dynamic matching game in which firms make offers to workers in each period, considering three types of commitment: (i) no commitment from either side, (ii) firms' commitment, and (iii) workers' commitment. Our results demonstrate that stable matchings can be supported as stationary equilibria under different commitment scenarios, depending on the strategies adopted by firms and workers. Furthermore, we identify key conditions, such as discount factors, that influence agents' decisions to switch partners, thereby shaping equilibrium outcomes.
AudioSetCaps: An Enriched Audio-Caption Dataset using Automated Generation Pipeline with Large Audio and Language Models
Authors: Jisheng Bai, Haohe Liu, Mou Wang, Dongyuan Shi, Wenwu Wang, Mark D. Plumbley, Woon-Seng Gan, Jianfeng Chen
Date: 2024-11-28 00:00:00
Tasks: Audio captioning, Audio to Text Retrieval, Caption Generation, Retrieval, Text Retrieval

With the emergence of audio-language models, constructing large-scale paired audio-language datasets has become essential yet challenging for model development, primarily due to the time-intensive and labour-heavy demands involved. While large language models (LLMs) have improved the efficiency of synthetic audio caption generation, current approaches struggle to effectively extract and incorporate detailed audio information. In this paper, we propose an automated pipeline that integrates audio-language models for fine-grained content extraction, LLMs for synthetic caption generation, and a contrastive language-audio pretraining (CLAP) model-based refinement process to improve the quality of captions. Specifically, we employ prompt chaining techniques in the content extraction stage to obtain accurate and fine-grained audio information, while we use the refinement process to mitigate potential hallucinations in the generated captions. Leveraging the AudioSet dataset and the proposed approach, we create AudioSetCaps, a dataset comprising 1.9 million audio-caption pairs, the largest audio-caption dataset at the time of writing. The models trained with AudioSetCaps achieve state-of-the-art performance on audio-text retrieval with R@1 scores of 46.3% for text-to-audio and 59.7% for audio-to-text retrieval and automated audio captioning with the CIDEr score of 84.8. As our approach has shown promising results with AudioSetCaps, we create another dataset containing 4.1 million synthetic audio-language pairs based on the Youtube-8M and VGGSound datasets. To facilitate research in audio-language learning, we have made our pipeline, datasets with 6 million audio-language pairs, and pre-trained models publicly available at https://github.com/JishengBai/AudioSetCaps.
TS3-Codec: Transformer-Based Simple Streaming Single Codec
Authors: Haibin Wu, Naoyuki Kanda, Sefik Emre Eskimez, Jinyu Li
Date: 2024-11-27 00:00:00
Tasks: Audio Compression

Neural audio codecs (NACs) have garnered significant attention as key technologies for audio compression as well as audio representation for speech language models. While mainstream NAC models are predominantly convolution-based, the performance of NACs with a purely transformer-based, and convolution-free architecture remains unexplored. This paper introduces TS3-Codec, a Transformer-Based Simple Streaming Single Codec. TS3-Codec consists of only a stack of transformer layers with a few linear layers, offering greater simplicity and expressiveness by fully eliminating convolution layers that require careful hyperparameter tuning and large computations. Under the streaming setup, the proposed TS3-Codec achieves comparable or superior performance compared to the codec with state-of-the-art convolution-based architecture while requiring only 12% of the computation and 77% of bitrate. Furthermore, it significantly outperforms the convolution-based codec when using similar computational resources.
Upsampling Improvement for Overfitted Neural Coding
Authors: Pierrick Philippe, Théo Ladune, Gordon Clare, Félix Henry, Théophile Blard, Thomas Leguay
Date: 2024-11-28 00:00:00
Tasks: Image Compression

Neural image compression, based on auto-encoders and overfitted representations, relies on a latent representation of the coded signal. This representation needs to be compact and uses low resolution feature maps. In the decoding process, those latents are upsampled and filtered using stacks of convolution filters and non linear elements to recover the decoded image. Therefore, the upsampling process is crucial in the design of a neural coding scheme and is of particular importance for overfitted codecs where the network parameters, including the upsampling filters, are part of the representation. This paper addresses the improvement of the upsampling process in order to reduce its complexity and limit the number of parameters. A new upsampling structure is presented whose improvements are illustrated within the Cool-Chic overfitted image coding framework. The proposed approach offers a rate reduction of 4.7%. The code is provided.
Spectrum Efficiency and Processing Latency Trade-offs in Panel-Based LIS
Authors: Lina Tinnerberg, Dumitra Iancu, Ove Edfors, Liang Liu, Juan Vidal Alegría
Date: 2024-11-28 00:00:00

The next generation wireless systems will face stringent new requirements, including ultra-low latency, high data rates and enhanced reliability. Large Intelligent Surfaces, is one proposed solution that has the potential to solve these high demands. The real-life deployment of such systems involves different design considerations with non-trivial trade-offs. This paper investigates the trade-off between spectral efficiency and processing latency, considering different antenna distribution schemes and detection algorithms. A latency model for the physical layer processing has been developed with realistic hardware parameters. The simulation results using an in-door environment show that distributing the antennas throughout the scenario improves the overall reliability, while the impact on the latency is limited when zero-forcing (ZF) detection is used. On the other hand, changing the detection algorithm to maximum-ratio combining (MRC) may reduce the latency significantly, even if a larger number of antennas are needed to achieve a similar spectrum efficiency as ZF detection.
A Novel Design Method for Digital FIR/IIR Filters Based on the Shuffle Frog-Leaping Algorithm
Authors: D. Jiménez-Galindo, P. Casaseca-de-la-Higuera, Luis M. San-José-Revuelta
Date: 2024-11-28 00:00:00
Tasks: global-optimization

The design of both FIR and IIR digital filters is a multi-variable optimization problem, where traditional algorithms fail to obtain optimal solutions. A modified Shuffled Frog Leaping Algorithm (SFLA) is here proposed for the design of FIR and IIR discrete-time filters as close as possible to the desired filter frequency response. This algorithm can be considered a type of memetic algorithm. In this paper, simulations prove the obtained filters outperform those designed using the traditional bilinear Z transform (BZT) method with elliptic approximation. Besides, results are close to, and even slightly better, than those reported in recent bio-inspired approaches using algorithms such as particle swarm optimization (PSO), differential evolution (DE) and regularized global optimization (RGA).