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

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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 ...

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 ...

Foundation models are highly versatile neural-network architectures capable of processing different data types, such as text and images, and generalizing across various tasks like classification and generation. Inspired by this success, we propose Foundati ...

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 ...

The 2015 Paris agreement was established to limit Greenhouse gas (GHG) global warming below 1.5°C above preindustrial era values. Knowledge of climate sensitivity to GHG levels is central for formulating effective climate policies, yet its exact value is s ...

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 ...

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 ...

Over the past two decades, the joint manifestation of global warming and rapid urbanization has significantly increased the occurrence of heatwaves and the formation of urban heat islands in temperate cities. Consequently, this synergy has amplified the fr ...

Abstract. Wind-blown snow is a frequent phenomenon in high-elevation and polar regions which impacts the surface energy and mass balance of these areas. Loose surface snow gets eroded and transported by wind, which influences the snow particles' physical p ...

Genetic variation is instrumental for adaptation to changing environments but it is unclear how it is structured and contributes to adaptation in pelagic species lacking clear barriers to gene flow. Here, we applied comparative genomics to extensive transc ...

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 ...

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 ...

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 ...

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. ...

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

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TimeGPT in load forecasting: A large time series model perspective

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

Foundation neural-networks quantum states as a unified Ansatz for multiple hamiltonians

Musculoskeletal motor control with reinforcement learning

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

network-based constraint to evaluate climate sensitivity

Anatomy-Informed Multimodal Learning for Myocardial Infarction Prediction

Topics in statistical physics of high-dimensional machine learning

Quantifying urban climate response to large-scale forcing modified by local boundary layer effects

Identifying airborne snow metamorphism with stable water isotopes

Comparative Population Transcriptomics Provide New Insight into the Evolutionary History and Adaptive Potential of World Ocean Krill

Stable parameterization of continuous and piecewise-linear functions

Fusing physics-based and deep learning models for prognostics

Implicit phonetic information modeling for speech emotion recognition

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

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