pwc-archive/papers-with-abstracts huggingface dataset:
A Neural-Inspired Architecture for EEG-Based Auditory Attention Detection
Authors: Siqi Cai, Peiwen Li, Enze Su, Qi Liu, Longhan Xie
Date: 2022-06-01 00:00:00
Proceeding: IEEE Transactions on Human-Machine Systems 2022 6
Tasks: Brain Computer Interface, Data Visualization, EEG, EEG Signal Classification, Electroencephalogram (EEG)

Humans have the ability to focus on one of the sound sources in a noisy scene, which is critical for everyday communication. Auditory attention detection (AAD) seeks to detect selective attention from one’s brain signals. For AAD to be useful in brain–computer interface applications, new approaches with low computational cost, high classification performance, and low latency are required to be developed. In this study, we proposed a novel neural-inspired architecture to mimic the neural computation and coding strategy in the brain for electroencephalography-based AAD. We validated our model through data visualization, and conducted experiments on two publicly available databases. For both KUL and DTU databases, it outperforms both linear and convolutional neural network (CNN) models with consistent improvements from 1 s to 5 s decision windows in terms of detection accuracy. Although the accuracy of the proposed neural-inspired model is inferior to the state-of-the-art spatio-spectral feature (SSF)-CNN model, the computational cost of our model is less than 1% of SSF-CNN’s. Moreover, the neural-inspired decoder is more hardware friendly and energy-efficient due to its biological computing scheme. Overall, the proposed neural-inspired architecture realizes a fast, accurate, and low energy expenditure AAD, which is a big step forward towards practical neuro-steered hearing aids.
Multi-Scale Convolutional Mask Network for Hyperspectral Unmixing
Authors: JinXu MingmingXu ShanweiLiu HuiSheng ZhiruYang
Date: 2024-01-10 00:00:00
Proceeding: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2024 1
Tasks: Hyperspectral Unmixing

Deep learning has gained popularity in hyperspectral unmixing (HU) applications recently due to its powerful learning and data-fitting capabilities. As an unmixing baseline network, the autoencoder (AE) framework performs well in HU by automatically learning low-dimensional embeddings and reconstructing data. Nevertheless, there are spectral variability and nonlinear mixing problems in the highly mixed region of hyperspectral images, which can cause interference to structures using only AE. Therefore, inspired by the effectiveness of mask modeling, we propose a multiscale convolutional mask network (MsCM-Net) for HU with two new strategies. First, we propose a mixed region mask strategy suitable for the HU task, and a multiscale convolutional AE is adopted as the unmixing baseline network to apply the mask strategy, making the method more robust in solving ill-posed unmixing problems. In addition, a new initialization strategy is used in which vertex component analysis (VCA) is combined with density-based spatial clustering of applications with noise (DBSCAN) to mitigate the impact of outliers and noise on initialization. The proposed MsCM-Net performs more accurately than state-of-the-art methods by comparison experiments on one synthetic and three real hyperspectral data sets. The effectiveness of the mixed region mask strategy and DBSCAN-VCA initialization is also demonstrated by ablation experiments.
SkipPLUS: Skip the First Few Layers to Better Explain Vision Transformers
Authors: Faridoun Mehri, Mohsen Fayyaz, Mahdieh Soleymani Baghshah, Mohammad Taher Pilehvar
Date: 2024-06-17 00:00:00
Proceeding: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2024 6

Despite their remarkable performance, the explainability of Vision Transformers (ViTs) remains a challenge. While forward attention-based token attribution techniques have become popular in text processing, their suitability for ViTs hasn’t been extensively explored. In this paper, we compare these methods against state-of-the-art input attribution methods from the Vision literature, revealing their limitations due to improper aggregation of information across layers. To address this, we introduce two general techniques, PLUS and SkipPLUS, that can be composed with any input attribution method to more effectively aggregate information across layers while handling noisy layers. Through comprehensive and quantitative evaluations of faithfulness and human interpretability on a variety of ViT architectures and datasets, we demonstrate the effectiveness of PLUS and SkipPLUS, establishing a new state-of-the-art in white-box token attribution. We conclude with a comparative analysis highlighting the strengths and weaknesses of the best versions of all the studied methods. The code used in this paper is freely available at https://github.com/NightMachinery/SkipPLUS-CVPR-2024.
基于深度强化学习的无人机集群实时航路规划
Authors: 宋海伟
Date: 2024-05-03 00:00:00
Proceeding: 航天电子对抗 2024 5

针 对 无 人 机 城 市 任 务 复 杂 化 场 景 ,提 出 了 一 种 基 于 深 度 强 化 学 习 的 无 人 机 集 群  实 时 航 路 规 划 算 法 ,通 过 对 威 胁 、障 碍 物 等 进 行 统 一 化 建 模 ,构 建 集 群 航 路 规 划 模 型 ,将 无 人  机 传 感 器 探 测 信 息 作 为 深 度 神 经 网 络 的 状 态 输 入 ,优 化 了 无 人 机 航 路 规 划 的 能 耗 和 成 功 率 ,  通 过 仿 真 ,证 明 了 算 法 的 有 效 性 和 实 用 性 。
LoCATe-GAT: Modeling Multi-Scale Local Context and Action Relationships for Zero-Shot Action Recognition
Authors: Sandipan Sarma, Divyam Singal, Arijit Sur
Date: 2024-11-27 00:00:00
Proceeding: IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) 2024 11
Tasks: Action Recognition, Graph Attention, Scene Understanding, Zero-Shot Action Recognition

The increasing number of actions in the real world makes it difficult for traditional deep-learning models to recognize unseen actions. Recently, pretrained contrastive image-based visual-language (I-VL) models have been adapted for efficient “zero-shot” scene understanding. Pairing such models with transformers to implement temporal modeling has been rewarding for zero-shot action recognition (ZSAR). However, the significance of modeling the local spatial context of objects and action environments remains unexplored. In this work, we propose a ZSAR framework called LoCATe-GAT, comprising a novel Local Context-Aggregating Temporal transformer (LoCATe) and a Graph Attention Network (GAT). Specifically, image and text encodings extracted from a pretrained I-VL model are used as inputs for LoCATe-GAT. Motivated by the observation that object-centric and environmental contexts drive both distinguishability and functional similarity between actions, LoCATe captures multi-scale local context using dilated convolutional layers during temporal modeling. Furthermore, the proposed GAT models semantic relationships between classes and achieves a strong synergy with the video embeddings produced by LoCATe. Extensive experiments on four widely-used benchmarks – UCF101, HMDB51, ActivityNet, and Kinetics – show we achieve state-of-the-art results. Specifically, we obtain relative gains of 3.8% and 4.8% on these datasets in conventional and 16.6% on UCF101in generalized ZSAR settings. For large-scale datasets like ActivityNet and Kinetics, our method achieves a relative gain of 31.8% and 27.9%, respectively, over the previous methods. Additionally, we gain 25.3% and 18.4%
on UCF101 and HMDB51 as per the recent “TruZe” evaluation protocol.
Capacity Maximization for MIMO Channels Assisted by Beyond-Diagonal RIS
Authors: Emil Björnson, Özlem Tuğfe Demir
Date: 2024-11-27 00:00:00

Reconfigurable intelligent surfaces (RISs) can improve the capacity of wireless communication links by passively beamforming the impinging signals in desired directions. This feature has been demonstrated both analytically and experimentally for conventional RISs, consisting of independently reflecting elements. To further enhance reconfigurability, a new architecture called beyond-diagonal RIS (BD-RIS) has been proposed. It allows for controllable signal flows between RIS elements, resulting in a non-diagonal reflection matrix, unlike the conventional RIS architecture. Previous studies on BD-RIS-assisted communications have predominantly considered single-antenna transmitters/receivers. One recent work provides an iterative capacity-improving algorithm for multiple-input multiple-output (MIMO) setups but without providing geometrical insights. In this paper, we derive the first closed-form capacity-maximizing BD-RIS reflection matrix for a MIMO channel. We describe how this solution pairs together propagation paths, how it behaves when the signal-to-noise ratio is high, and what capacity is achievable with ideal semi-unitary channel matrices. The analytical results are corroborated numerically.
Quantifying information stored in synaptic connections rather than in firing patterns of neural networks
Authors: Xinhao Fan, Shreesh P Mysore
Date: 2024-11-26 00:00:00

A cornerstone of our understanding of both biological and artificial neural networks is that they store information in the strengths of connections among the constituent neurons. However, in contrast to the well-established theory for quantifying information encoded by the firing patterns of neural networks, little is known about quantifying information encoded by its synaptic connections. Here, we develop a theoretical framework using continuous Hopfield networks as an exemplar for associative neural networks, and data that follow mixtures of broadly applicable multivariate log-normal distributions. Specifically, we analytically derive the Shannon mutual information between the data and singletons, pairs, triplets, quadruplets, and arbitrary n-tuples of synaptic connections within the network. Our framework corroborates well-established insights about storage capacity of, and distributed coding by, neural firing patterns. Strikingly, it discovers synergistic interactions among synapses, revealing that the information encoded jointly by all the synapses exceeds the 'sum of its parts'. Taken together, this study introduces an interpretable framework for quantitatively understanding information storage in neural networks, one that illustrates the duality of synaptic connectivity and neural population activity in learning and memory.
Electromagnetic Formation Flying with State and Input Constraints Using Alternating Magnetic Field Forces
Authors: Sumit S. Kamat, T. Michael Seigler, Jesse B. Hoagg
Date: 2024-11-25 00:00:00

This article presents a feedback control algorithm for electromagnetic formation flying with constraints on the satellites' states and control inputs. The algorithm combines several key techniques. First, we use alternating magnetic field forces to decouple the electromagnetic forces between each pair of satellites in the formation. Each satellite's electromagnetic actuation system is driven by a sum of amplitude-modulated sinusoids, where amplitudes are controlled in order to prescribe the time-averaged force between each pair of satellites. Next, the desired time-averaged force is computed from a optimal control that satisfies state constraints (i.e., no collisions and an upper limit on intersatellite speeds) and input constraints (i.e., not exceeding satellite's apparent power capability). The optimal time-averaged force is computed using a single relaxed control barrier function that is obtained by composing multiple control barrier functions that are designed to enforce each state and input constraint. Finally, we demonstrate the satellite formation control method in a numerical simulation.
Zero-Order Control Barrier Functions for Sampled-Data Systems with State and Input Dependent Safety Constraints
Authors: Xiao Tan, Ersin Das, Aaron D. Ames, Joel W. Burdick
Date: 2024-11-26 00:00:00
Tasks: Collision Avoidance

We propose a novel zero-order control barrier function (ZOCBF) for sampled-data systems to ensure system safety. Our formulation generalizes conventional control barrier functions and straightforwardly handles safety constraints with high-relative degrees or those that explicitly depend on both system states and inputs. The proposed ZOCBF condition does not require any differentiation operation. Instead, it involves computing the difference of the ZOCBF values at two consecutive sampling instants. We propose three numerical approaches to enforce the ZOCBF condition, tailored to different problem settings and available computational resources. We demonstrate the effectiveness of our approach through a collision avoidance example and a rollover prevention example on uneven terrains.
FunGrasp: Functional Grasping for Diverse Dexterous Hands
Authors: Linyi Huang, HUI ZHANG, Zijian Wu, Sammy Christen, Jie Song
Date: 2024-11-24 00:00:00

Functional grasping is essential for humans to perform specific tasks, such as grasping scissors by the finger holes to cut materials or by the blade to safely hand them over. Enabling dexterous robot hands with functional grasping capabilities is crucial for their deployment to accomplish diverse real-world tasks. Recent research in dexterous grasping, however, often focuses on power grasps while overlooking task- and object-specific functional grasping poses. In this paper, we introduce FunGrasp, a system that enables functional dexterous grasping across various robot hands and performs one-shot transfer to unseen objects. Given a single RGBD image of functional human grasping, our system estimates the hand pose and transfers it to different robotic hands via a human-to-robot (H2R) grasp retargeting module. Guided by the retargeted grasping poses, a policy is trained through reinforcement learning in simulation for dynamic grasping control. To achieve robust sim-to-real transfer, we employ several techniques including privileged learning, system identification, domain randomization, and gravity compensation. In our experiments, we demonstrate that our system enables diverse functional grasping of unseen objects using single RGBD images, and can be successfully deployed across various dexterous robot hands. The significance of the components is validated through comprehensive ablation studies. Project page: https://hly-123.github.io/FunGrasp/ .
Towards Improved Objective Perceptual Audio Quality Assessment -- Part 1: A Novel Data-Driven Cognitive Model
Authors: Pablo M. Delgado, Jürgen Herre
Date: 2024-11-27 00:00:00
Tasks: Audio Quality Assessment, Prediction

Efficient audio quality assessment is vital for streamlining audio codec development. Objective assessment tools have been developed over time to algorithmically predict quality ratings from subjective assessments, the gold standard for quality judgment. Many of these tools use perceptual auditory models to extract audio features that are mapped to a basic audio quality score prediction using machine learning algorithms and subjective scores as training data. However, existing tools struggle with generalization in quality prediction, especially when faced with unknown signal and distortion types. This is particularly evident in the presence of signals coded using non-waveform-preserving parametric techniques. Addressing these challenges, this two-part work proposes extensions to the Perceptual Evaluation of Audio Quality (PEAQ - ITU-R BS.1387-1) recommendation. Part 1 focuses on increasing generalization, while Part 2 targets accurate spatial audio quality measurement in audio coding. To enhance prediction generalization, this paper (Part 1) introduces a novel machine learning approach that uses subjective data to model cognitive aspects of audio quality perception. The proposed method models the perceived severity of audible distortions by adaptively weighting different distortion metrics. The weights are determined using an interaction cost function that captures relationships between distortion salience and cognitive effects. Compared to other machine learning methods and established tools, the proposed architecture achieves higher prediction accuracy on large databases of previously unseen subjective quality scores. The perceptually-motivated model offers a more manageable alternative to general-purpose machine learning algorithms, allowing potential extensions and improvements to multi-dimensional quality measurement without complete retraining.
Optimizing Coverage in Convex Quadrilateral Regions with a Single UAV
Authors: Alexander Vavoulas, Nicholas Vaiopoulos, Konstantinos K. Delibasis, Harilaos G. Sandalidis
Date: 2024-11-27 00:00:00

The integration of unmanned aerial vehicles (UAVs) into next-generation wireless networks is a promising solution for providing flexible, efficient coverage. This paper explores the optimal deployment of a single UAV to cover an arbitrary convex quadrilateral region, utilizing a directional antenna with a tiltable beam that produces an elliptical coverage footprint. We examine two distinct coverage scenarios: (i) the largest inscribed ellipse, which maximizes coverage within the quadrilateral while excluding the boundary, and (ii) the smallest circumscribed ellipse, ensuring complete coverage of the entire area. The study formulates an optimization framework that accounts for path loss, signal-to-noise ratio (SNR), and energy consumption to determine the optimal altitude of the UAV. By employing a simplified path loss model, we derive the altitude that minimizes maximum path loss, while also analyzing the impact of antenna directivity on maximizing the minimum SNR at the coverage boundary. Additionally, the UAV's energy consumption is evaluated, considering the power demands during hovering, forward flight, and vertical takeoff. Numerical simulations are presented to illustrate the trade-offs between coverage effectiveness, communication performance, and energy efficiency across various environmental conditions and antenna configurations.
Scaled Relative Graph Analysis of Lur'e Systems and the Generalized Circle Criterion
Authors: Julius P. J. Krebbekx, Roland Tóth, Amritam Das
Date: 2024-11-27 00:00:00

Scaled Relative Graphs (SRGs) provide a novel graphical frequency-domain method for the analysis of nonlinear systems. However, we show that the current SRG analysis suffers from a pitfall that limit its applicability in analyzing practical nonlinear systems. We overcome this pitfall by modifying the SRG of a linear time invariant operator, combining the SRG with the Nyquist criterion, and apply our result to Lur'e systems. We thereby obtain a generalization of the celebrated circle criterion, which deals with a broader class of nonlinearities, and provides (incremental) $L_2$-gain performance bounds.
Benchmarking Agility and Reconfigurability in Satellite Systems for Tropical Cyclone Monitoring
Authors: Brycen D. Pearl, Logan P. Gold, Hang Woon Lee
Date: 2024-11-27 00:00:00
Tasks: Benchmarking, Earth Observation, Scheduling

Tropical cyclones (TCs) are highly dynamic natural disasters that travel vast distances and occupy a large spatial scale, leading to loss of life, economic strife, and destruction of infrastructure. The severe impact of TCs makes them crucial to monitor such that the collected data contributes to forecasting their trajectory and severity, as well as the provision of information to relief agencies. Among the various methods used to monitor TCs, Earth observation satellites are the most flexible, allowing for frequent observations with a wide variety of instruments. Traditionally, satellite scheduling algorithms assume nadir-directional observations, a limitation that can be alleviated by incorporating satellite agility and constellation reconfigurability -- two state-of-the-art concepts of operations (CONOPS) that extend the amount of time TCs can be observed from orbit. This paper conducts a systematic comparative analysis between both CONOPS to present the performance of each relative to baseline nadir-directional observations in monitoring TCs. A dataset of 100 historical TCs is used to provide a benchmark concerning real-world data through maximizing the number of quality observations. The results of the comparative analysis indicate that constellation reconfigurability allowing plane-change maneuvers outperforms satellite agility in the majority of TCs analyzed.
Combined Learning of Linear Parameter-Varying Models and Robust Control Invariant Sets
Authors: Sampath Kumar Mulagaleti, Alberto Bemporad
Date: 2024-11-27 00:00:00
Tasks: Scheduling

Dynamical models identified from data are frequently employed in control system design. However, decoupling system identification from controller synthesis can result in situations where no suitable controller exists after a model has been identified. In this work, we introduce a novel control-oriented regularization in the identification procedure to ensure the existence of a controller that can enforce constraints on system variables robustly. The combined identification algorithm includes: (i) the concurrent learning of an uncertain model and a nominal model using an observer; (ii) a regularization term on the model parameters defined as the size of the largest robust control invariant set for the uncertain model. To make the learning problem tractable, we consider nonlinear models in quasi Linear Parameter-Varying (qLPV) form, utilizing a novel scheduling function parameterization that facilitates the derivation of an associated uncertain linear model. The robust control invariant set is represented as a polytope, and we adopt novel results from polytope geometry to derive the regularization function as the optimal value of a convex quadratic program. Additionally, we present new model-reduction approaches that exploit the chosen model structure. Numerical examples on classical identification benchmarks demonstrate the efficacy of our approach. A simple control scheme is also derived to provide an example of data-driven control of a constrained nonlinear system.
Assessing Electricity Network Capacity Requirements for Industrial Decarbonisation in Great Britain
Authors: Ahmed Gailani, Peter Taylor
Date: 2024-11-26 00:00:00

Decarbonising the industrial sector is vital to reach net zero targets. The deployment of industrial decarbonisation technologies is expected to increase industrial electricity demand in many countries and this may require upgrades to the existing electricity network or new network investment. While the infrastructure requirements to support the introduction of new fuels and technologies in industry, such as hydrogen and carbon capture, utilisation and storage are often discussed, the need for investment to increase the capacity of the electricity network to meet increasing industrial electricity demands is often overlooked in the literature. This paper addresses this gap by quantifying the requirements for additional electricity network capacity to support the decarbonisation of industrial sectors across Great Britain (GB). The Net Zero Industrial Pathways model is used to predict the future electricity demand from industrial sites to 2050 which is then compared spatially to the available headroom across the distribution network in GB. The results show that network headroom is sufficient to meet extra capacity demands from industrial sites over the period to 2030 in nearly all GB regions and network scenarios. However, as electricity demand rises due to increased electrification across all sectors and industrial decarbonisation accelerates towards 2050, the network will need significant new capacity (71 GW + by 2050) particularly in the central, south, and north-west regions of England, and Wales. Without solving these network constraints, around 65% of industrial sites that are large point sources of emissions would be constrained in terms of electric capacity by 2040. These sites are responsible for 69% of industrial point source emissions.