Teaching oxidation states to neural networks

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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 ...

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 ...

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 ...

Distributed optimization is the standard way of speeding up machine learning training, and most of the research in the area focuses on distributed first-order, gradient-based methods. Yet, there are settings where some computationally-bounded nodes may not ...

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 ...

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 ...

This dataset supports the findings reported in the manuscript titled: Predicting rates of manganese oxide reduction from thermodynamic driving forces and structural properties, submitted to Nature Communications. Understanding the kinetics of manganese oxi ...

Direct light olefin synthesis from CO2 hydrogenation is a new pathway to decarbonize the chemical industry. Inspired by the promising catalytic activity of Ga2O3-based catalysts in alkane dehydrogenation, this study reveals that optimizing Ga content in th ...

In the pursuit of durable lithium–sulfur (Li–S) batteries, considerable research has been devoted to extending cycle life by developing advanced cathode materials. However, there exists a common overcharging problem which leads to the disfunction of Li–S b ...

Lead halide perovskites have emerged as highly efficient solar cell materials. However, to date, the most promising members of this class are polymorphs in which a wide-band-gap delta phase competes with the photoactive perovskite alpha form and the intrin ...

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 ...

Electrolytic hydrogen is identified as a crucial component in the desired decarbonisation of the chemical industry, utilizing renewable energy to split water into hydrogen and oxygen. Water electrolysis still requires important scientific advances to impro ...

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 ...

Here we provide the synthetic spindle datasets of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the synthetic generated training dataset of simulated muscle spindles during arm passive movements gene ...

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 ...

Probing the architecture of neuronal circuits and the principles that underlie their functional organization remains an important challenge of modern neurosciences. This holds true, in particular, for the inference of neuronal connectivity from large-scale ...

In supramolecular materials, multiple weak binding groups can act as a single collective unit when confined to a localized volume, thereby producing strong but dynamic bonds between material building blocks. This principle of multivalency provides a versat ...

Auditory research aims in general to lead to understanding of physiological processes. By contrast, the state of the art in automatic speech processing (notably recognition) is dominated by large pre-trained models that are meant to be used as black-boxes. ...

Teaching oxidation states to neural networks

Chattez avec Graph Search

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

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

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

Hybrid Decentralized Optimization: Leveraging Both First- and Zeroth-Order Optimizers for Faster Convergence

Musculoskeletal motor control with reinforcement learning

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

Predicting rates of manganese oxide reduction from thermodynamic driving forces and structural properties

Selective light olefin synthesis with high ethylene abundance via CO2 hydrogenation over (Ga-In)2O3/SSZ-13 catalysts

Insights into the Overcharge-Induced Failure Mechanism of Lithium–Sulfur Batteries

Force-Matching-Based Approach for the Generation of Polarizable and Nonpolarizable Force Fields Applied to CsPbI3

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

Operando evidence on the chirality-enhanced oxygen evolution reaction in intrinsically chiral electrocatalysts

Topics in statistical physics of high-dimensional machine learning

Task-driven neural network models predict neural dynamics of proprioception: Synthetic muscle spindle datasets

Anatomy-Informed Multimodal Learning for Myocardial Infarction Prediction

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

Ensemble learning and ground-truth validation of synaptic connectivity inferred from spike trains

Rationally Designing the Supramolecular Interfaces of Nanoparticle Superlattices with Multivalent Polymers

Training a Filter-Based Model of the Cochlea in the Context of Pre-Trained Acoustic Models