Treating Noise and Anomalies in Vehicle Trajectories From an Experiment With a Swarm of Drones

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Humans and animals both use acoustic signals for vocal communication. The advent of self-supervised learning (SSL) has enabled neural networks to learn robust and general feature representations through the intrinsic acoustic structure of input signals, wi ...

Genetic variation plays a crucial role in human disease susceptibility and molecular pathophysiology. This thesis explores genetic determinants of disease using advanced computational approaches, including statistical modeling, network analysis, and deep l ...

Incorporating pixel contextual information is critical for accurate segmentation. In this paper, we show that an effective way to incorporate contextual information is through a patch-based classifier. This patch classifier is trained to identify classes p ...

Animals, including humans, interact with the external environment primarily through motion. Replicating their motor control skills in artificial embodied agents is a major objective of artificial intelligence research. This thesis presents a collection of ...

The dynamics of neuron populations commonly evolve on low-dimensional manifolds. Thus, we need methods that learn the dynamical processes over neural manifolds to infer interpretable and consistent latent representations. We introduce a representation lear ...

Triboelectric nanogenerators (TENGs) serve as a key technology in flexible electronic devices, especially tactile sensors, enabling flexible robotic manipulators to effectively interact with their external environment. Persistent challenges include inadequ ...

Machine learning models have made significant progress in load forecasting, but their forecast accuracy is limited in cases where historical load data is scarce. Inspired by the outstanding performance of large language models (LLMs) in computer vision and ...

While the accurate description of redox reactions remains a challenge for first-principles calculations, it has been shown that extended Hubbard functionals (DFT+U+V) can provide a reliable approach, mitigating self-interaction errors, in materials with st ...

The growing deployment of low-cost, distributed sensor networks in environmental and biomedical domains has enabled continuous, large-scale health monitoring. However, these systems often face challenges related to degraded data quality caused by sensor dr ...

Climate change has led to stronger warming in the Arctic, causing higher ground temperatures and extensive permafrost thaw. Retrogressive Thaw Slumps (RTSs) represent one of the most rapid and considerable geomorphological changes in permafrost regions, oc ...

In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

Goal: In patients with coronary artery disease, the prediction of future cardiac events such as myocardial infarction (MI) remains a major challenge. In this work, we propose a novel anatomy-informed multimodal deep learning framework to predict future MI ...

We present a chance-constrained model predictive control (MPC) framework under Gaussian mixture model (GMM) uncertainty. Specifically, we consider the uncertainty that arises from predicting future behaviors of moving obstacles, which may exhibit multiple ...

Physics-based and data-driven models for remaining useful lifetime (RUL) prediction typically suffer from two major challenges that limit their applicability to complex real-world domains: (1) the incompleteness of physics-based models and (2) the limited ...

IntroductionRecent efforts have been made to apply machine learning and deep learning approaches to the automated classification of schizophrenia using structural magnetic resonance imaging (sMRI) at the individual level. However, these approaches are less ...

Protein post-translational modifications (PTMs) play a crucial role in countless biological processes, profoundly modulating protein properties on both spatial and temporal scales. Protein PTMs have also emerged as reliable biomarkers for several diseases. ...

This study investigates the efficacy of utilizing embedding spaces to model phonetic information in emotion utterances for speech emotion recognition. Our approach involves implicit modeling of phone information by deriving phone-based embeddings from netw ...

The application of unsupervised domain adaptation (UDA)-based fault diagnosis methods has shown significant efficacy in industrial settings, facilitating the transfer of operational experience and fault signatures between different operating conditions, di ...

Rectified-linear-unit (ReLU) neural networks, which play a prominent role in deep learning, generate continuous and piecewise-linear (CPWL) functions. While they provide a powerful parametric representation, the mapping between the parameter and function s ...

Water quality prediction in the spatially heterogeneous environment is challenging as the importance of water quality parameters (WQPs) and the performance of prediction models may vary across space. Thus, this study proposed spatially adaptive machine lea ...

Treating Noise and Anomalies in Vehicle Trajectories From an Experiment With a Swarm of Drones

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Transferability of Learnt Speech Representations for Decoding Non-Human Vocal Communication

Learning the language of the genome: AI- and data-driven decoding of genetic variants and disease mechanisms

Beyond Pixels: Semi-supervised Semantic Segmentation with a Multi-scale Patch-Based Multi-label Classifier

Musculoskeletal motor control with reinforcement learning

MARBLE: interpretable representations of neural population dynamics using geometric deep learning

Intelligent Soft Robotic System for Sensing and Recognition via Triboelectric‐Based Multi‐Sensor Fusion

TimeGPT in load forecasting: A large time series model perspective

Teaching oxidation states to neural networks

Efficient Unsupervised Domain Adaptation Regression for Spatial-Temporal Sensor Fusion

Detecting mass wasting of Retrogressive Thaw Slumps in spaceborne elevation models using deep learning

Topics in statistical physics of high-dimensional machine learning

Anatomy-Informed Multimodal Learning for Myocardial Infarction Prediction

Recursively Feasible Chance-Constrained Model Predictive Control Under Gaussian Mixture Model Uncertainty

Fusing physics-based and deep learning models for prognostics

Bridging structural MRI with cognitive function for individual level classification of early psychosis via deep learning

Deep Learning-Assisted Single-Molecule Detection of Protein Post-translational Modifications with a Biological Nanopore

Implicit phonetic information modeling for speech emotion recognition

Learnable Wavelet Transform and Domain Adversarial Learning for Enhanced Bearing Fault Diagnosis

Stable parameterization of continuous and piecewise-linear functions

Spatially adaptive machine learning models for predicting water quality in Hong Kong