pwc-archive/papers-with-abstracts huggingface dataset:
Fast convolution algorithm for state space models
Authors: Gregory Beylkin
Date: 2024-11-22 00:00:00
Tasks: State Space Models

We present an unconditionally stable algorithm for applying matrix transfer function of a linear time invariant system (LTI) in time domain. The state matrix of an LTI system used for modeling long range dependencies in state space models (SSMs) has eigenvalues close to $1$. The standard recursion defining LTI system becomes unstable if the $m\times m$ state matrix has just one eigenvalue with absolute value even slightly greater than 1. This may occur when approximating a state matrix by a structured matrix to reduce the cost of matrix-vector multiplication from $\mathcal{O}\left(m^{2}\right)$ to $\mathcal{O}\left(m\right)$ or $\mathcal{O}\left(m\log m\right).$ We introduce an unconditionally stable algorithm that uses an approximation of the rational transfer function in the z-domain by a matrix polynomial of degree $2^{N+1}-1$, where $N$ is chosen to achieve any user-selected accuracy. Using a cascade implementation in time domain, applying such transfer function to compute $L$ states requires no more than $2L$ matrix-vector multiplications (whereas the standard recursion requires $L$ matrix-vector multiplications). However, using unconditionally stable algorithm, it is not necessary to assure that an approximate state matrix has all eigenvalues with absolute values strictly less than 1 i.e., within the desired accuracy, the absolute value of some eigenvalues may possibly exceed $1$. Consequently, this algorithm allows one to use a wider variety of structured approximations to reduce the cost of matrix-vector multiplication and we briefly describe several of them to be used for this purpose.
Soil Characterization of Watermelon Field through Internet of Things: A New Approach to Soil Salinity Measurement
Authors: Md. Naimur Rahman, Shafak Shahriar Sozol, Md. Samsuzzaman, Md. Shahin Hossin, Mohammad Tariqul Islam, S. M. Taohidul Islam, Md. Maniruzzaman
Date: 2024-11-22 00:00:00

In the modern agricultural industry, technology plays a crucial role in the advancement of cultivation. To increase crop productivity, soil require some specific characteristics. For watermelon cultivation, soil needs to be sandy and of high temperature with proper irrigation. This research aims to design and implement an intelligent IoT-based soil characterization system for the watermelon field to measure the soil characteristics. IoT based developed system measures moisture, temperature, and pH of soil using different sensors, and the sensor data is uploaded to the cloud via Arduino and Raspberry Pi, from where users can obtain the data using mobile application and webpage developed for this system. To ensure the precision of the framework, this study includes the comparison between the readings of the soil parameters by the existing field soil meters, the values obtained from the sensors integrated IoT system, and data obtained from soil science laboratory. Excessive salinity in soil affects the watermelon yield. This paper proposes a model for the measurement of soil salinity based on soil resistivity. It establishes a relationship between soil salinity and soil resistivity from the data obtained in the laboratory using artificial neural network (ANN).
Finding "Good Views" of Electrocardiogram Signals for Inferring Abnormalities in Cardiac Condition
Authors: Hyewon Jeong, Suyeol Yun, Hammaad Adam
Date: 2024-11-11 00:00:00
Tasks: Arrhythmia Detection, Contrastive Learning

Electrocardiograms (ECGs) are an established technique to screen for abnormal cardiac signals. Recent work has established that it is possible to detect arrhythmia directly from the ECG signal using deep learning algorithms. While a few prior approaches with contrastive learning have been successful, the best way to define a positive sample remains an open question. In this project, we investigate several ways to define positive samples, and assess which approach yields the best performance in a downstream task of classifying arrhythmia. We explore spatiotemporal invariances, generic augmentations, demographic similarities, cardiac rhythms, and wave attributes of ECG as potential ways to match positive samples. We then evaluate each strategy with downstream task performance, and find that learned representations invariant to patient identity are powerful in arrhythmia detection. We made our code available in: https://github.com/mandiehyewon/goodviews_ecg.git
Probabilistic Forecasting of Radiation Exposure for Spaceflight
Authors: Rutuja Gurav, Elena Massara, Xiaomei Song, Kimberly Sinclair, Edward Brown, Matt Kusner, Bala Poduval, Atilim Gunes Baydin
Date: 2024-11-11 00:00:00

Extended human presence beyond low-Earth orbit (BLEO) during missions to the Moon and Mars will pose significant challenges in the near future. A primary health risk associated with these missions is radiation exposure, primarily from galatic cosmic rays (GCRs) and solar proton events (SPEs). While GCRs present a more consistent, albeit modulated threat, SPEs are harder to predict and can deliver acute doses over short periods. Currently NASA utilizes analytical tools for monitoring the space radiation environment in order to make decisions of immediate action to shelter astronauts. However this reactive approach could be significantly enhanced by predictive models that can forecast radiation exposure in advance, ideally hours ahead of major events, while providing estimates of prediction uncertainty to improve decision-making. In this work we present a machine learning approach for forecasting radiation exposure in BLEO using multimodal time-series data including direct solar imagery from Solar Dynamics Observatory, X-ray flux measurements from GOES missions, and radiation dose measurements from the BioSentinel satellite that was launched as part of Artemis~1 mission. To our knowledge, this is the first time full-disk solar imagery has been used to forecast radiation exposure. We demonstrate that our model can predict the onset of increased radiation due to an SPE event, as well as the radiation decay profile after an event has occurred.
Quantity versus Diversity: Influence of Data on Detecting EEG Pathology with Advanced ML Models
Authors: Martyna Poziomska, Marian Dovgialo, Przemysław Olbratowski, Paweł Niedbalski, Paweł Ogniewski, Joanna Zych, Jacek Rogala, Jarosław Żygierewicz
Date: 2024-11-13 00:00:00
Tasks: Diversity, EEG

This study investigates the impact of quantity and diversity of data on the performance of various machine-learning models for detecting general EEG pathology. We utilized an EEG dataset of 2,993 recordings from Temple University Hospital and a dataset of 55,787 recordings from Elmiko Biosignals sp. z o.o. The latter contains data from 39 hospitals and a diverse patient set with varied conditions. Thus, we introduce the Elmiko dataset - the largest publicly available EEG corpus. Our findings show that small and consistent datasets enable a wide range of models to achieve high accuracy; however, variations in pathological conditions, recording protocols, and labeling standards lead to significant performance degradation. Nonetheless, increasing the number of available recordings improves predictive accuracy and may even compensate for data diversity, particularly in neural networks based on attention mechanism or transformer architecture. A meta-model that combined these networks with a gradient-boosting approach using handcrafted features demonstrated superior performance across varied datasets.
Comprehensive Methodology for Sample Augmentation in EEG Biomarker Studies for Alzheimers Risk Classification
Authors: Veronica Henao Isaza, David Aguillon, Carlos Andres Tobon Quintero, Francisco Lopera, John Fredy Ochoa Gomez
Date: 2024-11-20 00:00:00
Tasks: EEG

Background: Dementia, marked by cognitive decline, is a global health challenge. Alzheimer's disease (AD), the leading type, accounts for ~70% of cases. Electroencephalography (EEG) measures show promise in identifying AD risk, but obtaining large samples for reliable comparisons is challenging. Objective: This study integrates signal processing, harmonization, and statistical techniques to enhance sample size and improve AD risk classification reliability. Methods: We used advanced EEG preprocessing, feature extraction, harmonization, and propensity score matching (PSM) to balance healthy non-carriers (HC) and asymptomatic E280A mutation carriers (ACr). Data from four databases were harmonized to adjust site effects while preserving covariates like age and sex. PSM ratios (2:1, 5:1, 10:1) were applied to assess sample size impact on model performance. The final dataset underwent machine learning analysis with decision trees and cross-validation for robust results. Results: Balancing sample sizes via PSM significantly improved classification accuracy, ranging from 0.92 to 0.96 across ratios. This approach enabled precise risk identification even with limited samples. Conclusion: Integrating data processing, harmonization, and balancing techniques improves AD risk classification accuracy, offering potential for other neurodegenerative diseases.
Automatic EEG Independent Component Classification Using ICLabel in Python
Authors: Arnaud Delorme, Dung Truong, Luca Pion-Tonachini, Scott Makeig
Date: 2024-11-20 00:00:00
Tasks: Component Classification, EEG

ICLabel is an important plug-in function in EEGLAB, the most widely used software for EEG data processing. A powerful approach to automated processing of EEG data involves decomposing the data by Independent Component Analysis (ICA) and then classifying the resulting independent components (ICs) using ICLabel. While EEGLAB pipelines support high-performance computing (HPC) platforms running the open-source Octave interpreter, the ICLabel plug-in is incompatible with Octave because of its specialized neural network architecture. To enhance cross-platform compatibility, we developed a Python version of ICLabel that uses standard EEGLAB data structures. We compared ICLabel MATLAB and Python implementations to data from 14 subjects. ICLabel returns the likelihood of classification in 7 classes of components for each ICA component. The returned IC classifications were virtually identical between Python and MATLAB, with differences in classification percentage below 0.001%.
Comparison of Tiny Machine Learning Techniques for Embedded Acoustic Emission Analysis
Authors: Uditha Muthumala, Yuxuan Zhang, Luciano Sebastian Martinez-Rau, Sebastian Bader
Date: 2024-11-22 00:00:00
Tasks: Structural Health Monitoring

This paper compares machine learning approaches with different input data formats for the classification of acoustic emission (AE) signals. AE signals are a promising monitoring technique in many structural health monitoring applications. Machine learning has been demonstrated as an effective data analysis method, classifying different AE signals according to the damage mechanism they represent. These classifications can be performed based on the entire AE waveform or specific features that have been extracted from it. However, it is currently unknown which of these approaches is preferred. With the goal of model deployment on resource-constrained embedded Internet of Things (IoT) systems, this work evaluates and compares both approaches in terms of classification accuracy, memory requirement, processing time, and energy consumption. To accomplish this, features are extracted and carefully selected, neural network models are designed and optimized for each input data scenario, and the models are deployed on a low-power IoT node. The comparative analysis reveals that all models can achieve high classification accuracies of over 99\%, but that embedded feature extraction is computationally expensive. Consequently, models utilizing the raw AE signal as input have the fastest processing speed and thus the lowest energy consumption, which comes at the cost of a larger memory requirement.
3D-Mem: 3D Scene Memory for Embodied Exploration and Reasoning
Authors: Yuncong Yang, Han Yang, Jiachen Zhou, Peihao Chen, Hongxin Zhang, Yilun Du, Chuang Gan
Date: 2024-11-23 00:00:00
Proceeding: CVPR 2025 1
Tasks: Management

Constructing compact and informative 3D scene representations is essential for effective embodied exploration and reasoning, especially in complex environments over extended periods. Existing representations, such as object-centric 3D scene graphs, oversimplify spatial relationships by modeling scenes as isolated objects with restrictive textual relationships, making it difficult to address queries requiring nuanced spatial understanding. Moreover, these representations lack natural mechanisms for active exploration and memory management, hindering their application to lifelong autonomy. In this work, we propose 3D-Mem, a novel 3D scene memory framework for embodied agents. 3D-Mem employs informative multi-view images, termed Memory Snapshots, to represent the scene and capture rich visual information of explored regions. It further integrates frontier-based exploration by introducing Frontier Snapshots-glimpses of unexplored areas-enabling agents to make informed decisions by considering both known and potential new information. To support lifelong memory in active exploration settings, we present an incremental construction pipeline for 3D-Mem, as well as a memory retrieval technique for memory management. Experimental results on three benchmarks demonstrate that 3D-Mem significantly enhances agents' exploration and reasoning capabilities in 3D environments, highlighting its potential for advancing applications in embodied AI.
Sampling weights of deep neural networks
Authors: Erik Lien Bolager, Iryna Burak, Chinmay Datar, Qing Sun, Felix Dietrich
Date: 2023-06-29 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Transfer Learning

We introduce a probability distribution, combined with an efficient sampling algorithm, for weights and biases of fully-connected neural networks. In a supervised learning context, no iterative optimization or gradient computations of internal network parameters are needed to obtain a trained network. The sampling is based on the idea of random feature models. However, instead of a data-agnostic distribution, e.g., a normal distribution, we use both the input and the output training data to sample shallow and deep networks. We prove that sampled networks are universal approximators. For Barron functions, we show that the $L^2$-approximation error of sampled shallow networks decreases with the square root of the number of neurons. Our sampling scheme is invariant to rigid body transformations and scaling of the input data, which implies many popular pre-processing techniques are not required. In numerical experiments, we demonstrate that sampled networks achieve accuracy comparable to iteratively trained ones, but can be constructed orders of magnitude faster. Our test cases involve a classification benchmark from OpenML, sampling of neural operators to represent maps in function spaces, and transfer learning using well-known architectures.
Reinforcement Learning: A Survey
Authors: L. P. Kaelbling, M. L. Littman, A. W. Moore
Date: 1996-05-01 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Survey

This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word ``reinforcement.'' The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.
Devils in Middle Layers of Large Vision-Language Models: Interpreting, Detecting and Mitigating Object Hallucinations via Attention Lens
Authors: Zhangqi Jiang, Junkai Chen, Beier Zhu, Tingjin Luo, Yankun Shen, Xu Yang
Date: 2024-11-23 00:00:00
Proceeding: CVPR 2025 1
Tasks: Hallucination

Hallucinations in Large Vision-Language Models (LVLMs) significantly undermine their reliability, motivating researchers to explore the causes of hallucination. However, most studies primarily focus on the language aspect rather than the visual. In this paper, we address how LVLMs process visual information and whether this process causes hallucination. Firstly, we use the attention lens to identify the stages at which LVLMs handle visual data, discovering that the middle layers are crucial. Moreover, we find that these layers can be further divided into two stages: ''visual information enrichment'' and ''semantic refinement'' which respectively propagate visual data to object tokens and interpret it through text. By analyzing attention patterns during the visual information enrichment stage, we find that real tokens consistently receive higher attention weights than hallucinated ones, serving as a strong indicator of hallucination. Further examination of multi-head attention maps reveals that hallucination tokens often result from heads interacting with inconsistent objects. Based on these insights, we propose a simple inference-time method that adjusts visual attention by integrating information across various heads. Extensive experiments demonstrate that this approach effectively mitigates hallucinations in mainstream LVLMs without additional training costs. Code is available at https://github.com/ZhangqiJiang07/middle_layers_indicating_hallucinations.
LetsTalk: Latent Diffusion Transformer for Talking Video Synthesis
Authors: Haojie Zhang, Zhihao Liang, Ruibo Fu, Zhengqi Wen, Xuefei Liu, Chenxing Li, JianHua Tao, Yaling Liang
Date: 2024-11-24 00:00:00
Tasks: Diversity, Image Animation, Video Generation

Portrait image animation using audio has rapidly advanced, enabling the creation of increasingly realistic and expressive animated faces. The challenges of this multimodality-guided video generation task involve fusing various modalities while ensuring consistency in timing and portrait. We further seek to produce vivid talking heads. To address these challenges, we present LetsTalk (LatEnt Diffusion TranSformer for Talking Video Synthesis), a diffusion transformer that incorporates modular temporal and spatial attention mechanisms to merge multimodality and enhance spatial-temporal consistency. To handle multimodal conditions, we first summarize three fusion schemes, ranging from shallow to deep fusion compactness, and thoroughly explore their impact and applicability. Then we propose a suitable solution according to the modality differences of image, audio, and video generation. For portrait, we utilize a deep fusion scheme (Symbiotic Fusion) to ensure portrait consistency. For audio, we implement a shallow fusion scheme (Direct Fusion) to achieve audio-animation alignment while preserving diversity. Our extensive experiments demonstrate that our approach generates temporally coherent and realistic videos with enhanced diversity and liveliness.
Towards Satellite Image Road Graph Extraction: A Global-Scale Dataset and A Novel Method
Authors: Pan Yin, Kaiyu Li, Xiangyong Cao, Jing Yao, Lei Liu, Xueru Bai, Feng Zhou, Deyu Meng
Date: 2024-11-23 00:00:00
Proceeding: CVPR 2025 1
Tasks: Autonomous Driving

Recently, road graph extraction has garnered increasing attention due to its crucial role in autonomous driving, navigation, etc. However, accurately and efficiently extracting road graphs remains a persistent challenge, primarily due to the severe scarcity of labeled data. To address this limitation, we collect a global-scale satellite road graph extraction dataset, i.e. Global-Scale dataset. Specifically, the Global-Scale dataset is $\sim20 \times$ larger than the largest existing public road extraction dataset and spans over 13,800 $km^2$ globally. Additionally, we develop a novel road graph extraction model, i.e. SAM-Road++, which adopts a node-guided resampling method to alleviate the mismatch issue between training and inference in SAM-Road, a pioneering state-of-the-art road graph extraction model. Furthermore, we propose a simple yet effective ``extended-line'' strategy in SAM-Road++ to mitigate the occlusion issue on the road. Extensive experiments demonstrate the validity of the collected Global-Scale dataset and the proposed SAM-Road++ method, particularly highlighting its superior predictive power in unseen regions. The dataset and code are available at \url{https://github.com/earth-insights/samroadplus}.
Building Confidence in Deep Generative Protein Design
Authors: Tianyuan Zheng, Alessandro Rondina, Pietro Liò
Date: 2024-11-27 00:00:00
Tasks: Protein Design

Deep generative models show promise for de novo protein design, but their effectiveness within specific protein families remains underexplored. In this study, we evaluate two 3D rigid-body generative methods, score matching and flow matching, to generate monomeric protein backbones in SE(3) space. Our goal is to provide new insights and build confidence in the broader applicability of deep generative models for protein design. The optimal amino acid sequences were predicted from the generated backbones, followed by side-chain homology modeling. Results demonstrated high structural integrity, with conserved key residues aligning with known proteins. Structural phylogenetic analysis shows evolutionary links between the generated samples and their protein family members. Further molecular dynamics simulations and protein-ligand docking confirm the dynamic stability and functional potential of these samples, with ligand binding inducing conformational changes consistent with those in wild-type proteins.
Graph-Based Orchestration of Service-Oriented Model-Based Control Systems
Authors: Julius Beerwerth, Hazem Ibrahim, Bianca Atodiresei, Lorenz Dörschel, Bassam Alrifaee
Date: 2024-11-27 00:00:00

This paper presents a novel graph-based method for adapting control system architectures at runtime. We use a service-oriented architecture as a basis for its formulation. In our method, adaptation is achieved by selecting the most suitable elements, such as filters and controllers, for a control system architecture to improve control systems objective based on a predefined cost function. Traditional configuration methods, such as state machines, lack flexibility and depend on a predefined control system architecture during runtime. Our graph-based method allows for dynamic changes in the control system architecture, as well as a change in its objective depending on the given system state. Our approach uses a weighted, directed graph to model the control system elements and their interaction. In a case-study with a three-tank system, we show that by using our graph-based method for architecture adaptation, the control system is more flexible, has lower computation time, and higher accuracy than traditional configuration methods.