pwc-archive/papers-with-abstracts huggingface dataset:
Decentralized Input and State Estimation for Multi-agent System with Dynamic Topology and Heterogeneous Sensor Network
Authors: Zida Wu, Ankur Mehta
Date: 2024-09-30 00:00:00
Tasks: State Estimation

A crucial challenge in decentralized systems is state estimation in the presence of unknown inputs, particularly within heterogeneous sensor networks with dynamic topologies. While numerous consensus algorithms have been introduced, they often require extensive information exchange or multiple communication iterations to ensure estimation accuracy. This paper proposes an efficient algorithm that achieves an unbiased and optimal solution comparable to filters with full information about other agents. This is accomplished through the use of information filter decomposition and the fusion of inputs via covariance intersection. Our method requires only a single communication iteration for exchanging individual estimates between agents, instead of multiple rounds of information exchange, thus preserving agents' privacy by avoiding the sharing of explicit observations and system equations. Furthermore, to address the challenges posed by dynamic communication topologies, we propose two practical strategies to handle issues arising from intermittent observations and incomplete state estimation, thereby enhancing the robustness and accuracy of the estimation process. Experiments and ablation studies conducted in both stationary and dynamic environments demonstrate the superiority of our algorithm over other baselines. Notably, it performs as well as, or even better than, algorithms that have a global view of all neighbors.
Adaptive bias for dissensus in nonlinear opinion dynamics with application to evolutionary division of labor games
Authors: Tyler M. Paine, Anastasia Bizyaeva, Michael R. Benjamin
Date: 2024-09-21 00:00:00

This paper addresses the problem of adaptively controlling the bias parameter in nonlinear opinion dynamics (NOD) to allocate agents into groups of arbitrary sizes for the purpose of maximizing collective rewards. In previous work, an algorithm based on the coupling of NOD with an multi-objective behavior optimization was successfully deployed as part of a multi-robot system in an autonomous task allocation field experiment. Motivated by the field results, in this paper we propose and analyze a new task allocation model that synthesizes NOD with an evolutionary game framework. We prove sufficient conditions under which it is possible to control the opinion state in the group to a desired allocation of agents between two tasks through an adaptive bias using decentralized feedback. We then verify the theoretical results with a simulation study of a collaborative evolutionary division of labor game.
Scalable Multi-agent Reinforcement Learning for Factory-wide Dynamic Scheduling
Authors: Jaeyeon Jang, Diego Klabjan, Han Liu, Nital S. Patel, Xiuqi Li, Balakrishnan Ananthanarayanan, Husam Dauod, Tzung-Han Juang
Date: 2024-09-20 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling

Real-time dynamic scheduling is a crucial but notoriously challenging task in modern manufacturing processes due to its high decision complexity. Recently, reinforcement learning (RL) has been gaining attention as an impactful technique to handle this challenge. However, classical RL methods typically rely on human-made dispatching rules, which are not suitable for large-scale factory-wide scheduling. To bridge this gap, this paper applies a leader-follower multi-agent RL (MARL) concept to obtain desired coordination after decomposing the scheduling problem into a set of sub-problems that are handled by each individual agent for scalability. We further strengthen the procedure by proposing a rule-based conversion algorithm to prevent catastrophic loss of production capacity due to an agent's error. Our experimental results demonstrate that the proposed model outperforms the state-of-the-art deep RL-based scheduling models in various aspects. Additionally, the proposed model provides the most robust scheduling performance to demand changes. Overall, the proposed MARL-based scheduling model presents a promising solution to the real-time scheduling problem, with potential applications in various manufacturing industries.
Friedkin-Johnsen Model with Diminishing Competition
Authors: Luca Ballotta, Áron Vékássy, Stephanie Gil, Michal Yemini
Date: 2024-09-19 00:00:00
Tasks: model

This letter studies the Friedkin-Johnsen (FJ) model with diminishing competition, or stubbornness. The original FJ model assumes that each agent assigns a constant competition weight to its initial opinion. In contrast, we investigate the effect of diminishing competition on the convergence point and speed of the FJ dynamics. We prove that, if the competition is uniform across agents and vanishes asymptotically, the convergence point coincides with the nominal consensus reached with no competition. However, the diminishing competition slows down convergence according to its own rate of decay. We study this phenomenon analytically and provide upper and lower bounds on the convergence rate. Further, if competition is not uniform across agents, we show that the convergence point may not coincide with the nominal consensus point. Finally, we evaluate our analytical insights numerically.
Graph-based Scalable Sampling of 3D Point Cloud Attributes
Authors: Shashank N. Sridhara, Eduardo Pavez, Ajinkya Jayawant, Antonio Ortega, Ryosuke Watanabe, Keisuke Nonaka
Date: 2024-10-01 00:00:00
Tasks: Attribute

3D Point clouds (PCs) are commonly used to represent 3D scenes. They can have millions of points, making subsequent downstream tasks such as compression and streaming computationally expensive. PC sampling (selecting a subset of points) can be used to reduce complexity. Existing PC sampling algorithms focus on preserving geometry features and often do not scale to handle large PCs. In this work, we develop scalable graph-based sampling algorithms for PC color attributes, assuming the full geometry is available. Our sampling algorithms are optimized for a signal reconstruction method that minimizes the graph Laplacian quadratic form. We first develop a global sampling algorithm that can be applied to PCs with millions of points by exploiting sparsity and sampling rate adaptive parameter selection. Further, we propose a block-based sampling strategy where each block is sampled independently. We show that sampling the corresponding sub-graphs with optimally chosen self-loop weights (node weights) will produce a sampling set that approximates the results of global sampling while reducing complexity by an order of magnitude. Our empirical results on two large PC datasets show that our algorithms outperform the existing fast PC subsampling techniques (uniform and geometry feature preserving random sampling) by 2dB. Our algorithm is up to 50 times faster than existing graph signal sampling algorithms while providing better reconstruction accuracy. Finally, we illustrate the efficacy of PC attribute sampling within a compression scenario, showing that pre-compression sampling of PC attributes can lower the bitrate by 11% while having minimal effect on reconstruction.
Decoding Complexity-Rate-Quality Pareto-Front for Adaptive VVC Streaming
Authors: Angeliki Katsenou, Vignesh V Menon, Adam Wieckowski, Benjamin Bross, Detlev Marpe
Date: 2024-09-27 00:00:00

Pareto-front optimization is crucial for addressing the multi-objective challenges in video streaming, enabling the identification of optimal trade-offs between conflicting goals such as bitrate, video quality, and decoding complexity. This paper explores the construction of efficient bitrate ladders for adaptive Versatile Video Coding (VVC) streaming, focusing on optimizing these trade-offs. We investigate various ladder construction methods based on Pareto-front optimization, including exhaustive Rate-Quality and fixed ladder approaches. We propose a joint decoding time-rate-quality Pareto-front, providing a comprehensive framework to balance bitrate, decoding time, and video quality in video streaming. This allows streaming services to tailor their encoding strategies to meet specific requirements, prioritizing low decoding latency, bandwidth efficiency, or a balanced approach, thus enhancing the overall user experience. The experimental results confirm and demonstrate these opportunities for navigating the decoding time-rate-quality space to support various use cases. For example, when prioritizing low decoding latency, the proposed method achieves decoding time reduction of 14.86% while providing Bjontegaard delta rate savings of 4.65% and 0.32dB improvement in the eXtended Peak Signal-to-Noise Ratio (XPSNR)-Rate domain over the traditional fixed ladder solution.
A Survey on Multimodal Benchmarks: In the Era of Large AI Models
Authors: Lin Li, Guikun Chen, Hanrong Shi, Jun Xiao, Long Chen
Date: 2024-09-21 00:00:00
Tasks: Benchmarking, Survey

The rapid evolution of Multimodal Large Language Models (MLLMs) has brought substantial advancements in artificial intelligence, significantly enhancing the capability to understand and generate multimodal content. While prior studies have largely concentrated on model architectures and training methodologies, a thorough analysis of the benchmarks used for evaluating these models remains underexplored. This survey addresses this gap by systematically reviewing 211 benchmarks that assess MLLMs across four core domains: understanding, reasoning, generation, and application. We provide a detailed analysis of task designs, evaluation metrics, and dataset constructions, across diverse modalities. We hope that this survey will contribute to the ongoing advancement of MLLM research by offering a comprehensive overview of benchmarking practices and identifying promising directions for future work. An associated GitHub repository collecting the latest papers is available.
From Linguistic Giants to Sensory Maestros: A Survey on Cross-Modal Reasoning with Large Language Models
Authors: Shengsheng Qian, Zuyi Zhou, Dizhan Xue, Bing Wang, Changsheng Xu
Date: 2024-09-19 00:00:00
Tasks: Survey

Cross-modal reasoning (CMR), the intricate process of synthesizing and drawing inferences across divergent sensory modalities, is increasingly recognized as a crucial capability in the progression toward more sophisticated and anthropomorphic artificial intelligence systems. Large Language Models (LLMs) represent a class of AI algorithms specifically engineered to parse, produce, and engage with human language on an extensive scale. The recent trend of deploying LLMs to tackle CMR tasks has marked a new mainstream of approaches for enhancing their effectiveness. This survey offers a nuanced exposition of current methodologies applied in CMR using LLMs, classifying these into a detailed three-tiered taxonomy. Moreover, the survey delves into the principal design strategies and operational techniques of prototypical models within this domain. Additionally, it articulates the prevailing challenges associated with the integration of LLMs in CMR and identifies prospective research directions. To sum up, this survey endeavors to expedite progress within this burgeoning field by endowing scholars with a holistic and detailed vista, showcasing the vanguard of current research whilst pinpointing potential avenues for advancement. An associated GitHub repository that collects the relevant papers can be found at https://github.com/ZuyiZhou/Awesome-Cross-modal-Reasoning-with-LLMs
Early Joint Learning of Emotion Information Makes MultiModal Model Understand You Better
Authors: MengYing Ge, Mingyang Li, Dongkai Tang, Pengbo Li, Kuo Liu, Shuhao Deng, Songbai Pu, Long Liu, Yang song, Tao Zhang
Date: 2024-09-12 00:00:00
Tasks: Emotion Recognition, Language Modeling, Language Modelling, Large Language Model, Multimodal Emotion Recognition

In this paper, we present our solutions for emotion recognition in the sub-challenges of Multimodal Emotion Recognition Challenge (MER2024). To mitigate the modal competition issue between audio and text, we adopt an early fusion strategy based on a large language model, where joint training of audio and text is conducted initially. And the joint Audio-Text modal feature will be late-fused with other unimodal features. In order to solve the problems of data insufficiency and class imbalance, We use multiple turns of multi-model voting for data mining. Moreover, to enhance the quality of audio features, we employ speech source separation to preprocess audios. Our model ranks \textbf{2nd} in both MER2024-SEMI and MER2024-NOISE, validating our method's effectiveness.
Geometric shape matching for recovering protein conformations from single-particle Cryo-EM data
Authors: Erik Jansson, Jonathan Krook, Klas Modin, Ozan Öktem
Date: 2024-10-01 00:00:00

We address recovery of the three-dimensional backbone structure of single polypeptide proteins from single-particle cryo-electron microscopy (Cryo-SPA) data. Cryo-SPA produces noisy tomographic projections of electrostatic potentials of macromolecules. From these projections, we use methods from shape analysis to recover the three-dimensional backbone structure. Thus, we view the reconstruction problem as an indirect matching problem, where a point cloud representation of the protein backbone is deformed to match 2D tomography data. The deformations are obtained via the action of a matrix Lie group. By selecting a deformation energy, the optimality conditions are obtained, which lead to computational algorithms for optimal deformations. We showcase our approach on synthetic data, for which we recover the three-dimensional structure of the backbone.
PREPARE: PREdicting PAndemic's REcurring Waves Amidst Mutations, Vaccination, and Lockdowns
Authors: Narges M. Shahtori, S. Farokh Atashzar
Date: 2024-09-30 00:00:00

This study releases an adaptable framework that can provide insights to policymakers to predict the complex recurring waves of the pandemic in the medium postemergence of the virus spread, a phase marked by rapidly changing factors like virus mutations, lockdowns, and vaccinations, offering a way to forecast infection trends and stay ahead of future outbreaks even amidst uncertainty. The proposed model is validated on data from COVID-19 spread in Germany.
Tannenbaum's gain-margin optimization meets Polyak's heavy-ball algorithm
Authors: Wuwei Wu, Jie Chen, Mihailo R. Jovanović, Tryphon T. Georgiou
Date: 2024-09-30 00:00:00

The paper highlights a relatively unknown link between algorithm design in optimization and control synthesis in robust control. Specifically, quadratic optimization can be recast as a regulation problem within the framework of $\mathcal{H}_\infty$ control. From this vantage point, the optimality of Polyak's fastest heavy-ball algorithm can be ascertained as a solution to a gain margin optimization problem. The approach is independent of Polyak's original and brilliant argument, yet simpler, and relies on the foundational work by Tannenbaum that introduced and solved the gain margin optimization via Nevanlinna--Pick interpolation theory. The link between first-order optimization methods and robust control theory sheds new light into limits of algorithmic performance for such methods, and suggests a new framework where similar computational problems can be systematically studied and algorithms optimized. In particular, it raises the question as to whether periodically scheduled algorithms can achieve faster rates for quadratic optimization, in a manner analogous to periodic control that extends gain margin beyond that of time-invariant control. This turns out not to be the case, due to the analytic obstruction of a transmission zero that is inherent in causal optimization algorithms. Interestingly, this obstruction can be removed with implicit algorithms, cast in a similar manner as feedback regulation problems with causal, but not strictly causal dynamics, thereby devoid of the transmission zero at infinity and able to achieve superior convergence rates. The confluence of the fields of optimization algorithms and control provides a frame to tackle questions pertaining to speed, accuracy, distributed computation, and so forth, and to delineate respective limits to performance and tradeoffs in a systematic manner, utilizing the formalism of robust control.
A Krasnoselskii-Mann Proximity Algorithm for Markowitz Portfolios with Adaptive Expected Return Level
Authors: Yizun Lin, Yongxin He, Zhao-Rong Lai
Date: 2024-09-20 00:00:00
Tasks: Portfolio Optimization

Markowitz's criterion aims to balance expected return and risk when optimizing the portfolio. The expected return level is usually fixed according to the risk appetite of an investor, then the risk is minimized at this fixed return level. However, the investor may not know which return level is suitable for her/him and the current financial circumstance. It motivates us to find a novel approach that adaptively optimizes this return level and the portfolio at the same time. It not only relieves the trouble of deciding the return level during an investment but also gets more adaptive to the ever-changing financial market than a subjective return level. In order to solve the new model, we propose an exact, convergent, and efficient Krasnoselskii-Mann Proximity Algorithm based on the proximity operator and Krasnoselskii-Mann momentum technique. Extensive experiments show that the proposed method achieves significant improvements over state-of-the-art methods in portfolio optimization. This finding may contribute a new perspective on the relationship between return and risk in portfolio optimization.
Invertible ResNets for Inverse Imaging Problems: Competitive Performance with Provable Regularization Properties
Authors: Clemens Arndt, Judith Nickel
Date: 2024-09-20 00:00:00
Tasks: Image Reconstruction

Learning-based methods have demonstrated remarkable performance in solving inverse problems, particularly in image reconstruction tasks. Despite their success, these approaches often lack theoretical guarantees, which are crucial in sensitive applications such as medical imaging. Recent works by Arndt et al (2023 Inverse Problems 39 125018, 2024 Inverse Problems 40 045021) addressed this gap by analyzing a data-driven reconstruction method based on invertible residual networks (iResNets). They revealed that, under reasonable assumptions, this approach constitutes a convergent regularization scheme. However, the performance of the reconstruction method was only validated on academic toy problems and small-scale iResNet architectures. In this work, we address this gap by evaluating the performance of iResNets on two real-world imaging tasks: a linear blurring operator and a nonlinear diffusion operator. To do so, we extend some of the theoretical results from Arndt et al to encompass nonlinear inverse problems and offer insights for the design of large-scale performant iResNet architectures. Through numerical experiments, we compare the performance of our iResNet models against state-of-the-art neural networks, confirming their efficacy. Additionally, we numerically investigate the theoretical guarantees of this approach and demonstrate how the invertibility of the network enables a deeper analysis of the learned forward operator and its learned regularization.
Towards Event-Triggered NMPC for Efficient 6G Communications: Experimental Results and Open Problems
Authors: Jens Püttschneider, Julian Golembiewski, Niklas A. Wagner, Christian Wietfeld, Timm Faulwasser
Date: 2024-09-27 00:00:00
Tasks: Autonomous Vehicles, Model Predictive Control

Networked control systems enable real-time control and coordination of distributed systems, leveraging the low latency, high reliability, and massive connectivity offered by 5G and future 6G networks. Applications include autonomous vehicles, robotics, industrial automation, and smart grids. Despite networked control algorithms admitting nominal stability guarantees even in the presence of delays and packet dropouts, their practical performance still heavily depends on the specific characteristics and conditions of the underlying network. To achieve the desired performance while efficiently using communication resources, co-design of control and communication is pivotal. Although periodic schemes, where communication instances are fixed, can provide reliable control performance, unnecessary transmissions, when updates are not needed, result in inefficient usage of network resources. In this paper, we investigate the potential for co-design of model predictive control and network communication. To this end, we design and implement an event-triggered nonlinear model predictive controller for stabilizing a Furuta pendulum communicating over a tailored open radio access network 6G research platform. We analyze the control performance as well as network utilization under varying channel conditions and event-triggering criteria. Additionally, we analyze the network-induced delay pattern and its interaction with the event-triggered controller. Our results show that the event-triggered control scheme achieves similar performance to periodic control with reduced communication demand.
NL-COMM: Demonstrating Gains of Non-Linear Processing in Open-RAN Ecosystem
Authors: Chathura Jayawardena, Marcin Filo, George N. Katsaros, Konstantinos Nikitopoulos
Date: 2024-09-19 00:00:00

Multi-user multiple-input, multiple-output (MU-MIMO) designs can substantially increase wireless systems' achievable throughput and connectivity capabilities. However, existing MU-MIMO deployments typically utilize linear processing techniques that, despite their practical benefits, such as low computational complexity and easy integrability, can leave much of the available throughput and connectivity gains unexploited. They typically require many power-intensive antennas and RF chains to support a smaller number of MIMO streams, even when the transmitted information streams are of low rate. Alternatively, non-linear (NL) processing methods can maximize the capabilities of the MIMO channel. Despite their potential, traditional NL methods are challenged by high computational complexity and processing latency, making them impractical for real-time applications, especially in software-based systems envisioned for emerging Open Radio Access Networks (Open-RAN). Additionally, essential functionalities such as rate adaptation (RA) are currently unavailable for NL systems, limiting their practicality in real-world deployments. In this demo, we present the latest capabilities of our advanced NL processing framework (NL-COMM) in real-time and over-the-air, comparing them side-by-side with conventional linear processing. For the first time, NL-COMM not only meets the practical 5G-NR real-time latency requirements in pure software but also does so within a standard-compliant ecosystem. To achieve this, we significantly extended the NL-COMM algorithmic framework to support the first practical RA for NL processing. The demonstrated gains include enhanced connectivity by supporting four MIMO streams with a single base-station antenna, substantially increased throughput, and the ability to halve the number of base-station antennas without any performance loss to linear approaches.