Supplementary files for Machine learning for histological annotation and quantification of cortical layers

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The chemical industry faces mounting pressure to develop more sustainable processes while accelerating innovation to address climate challenges. Traditional discovery approaches in chemistry follow an iterative cycle: forming hypotheses about new molecules ...

This paper explores self-supervised disentangled representation learning within sequential data, focusing on separating time-indep- endent and time-varying factors in videos. We propose a new model that breaks the usual independence assumption between thos ...

As machine learning models evolve, maintaining transparency demands more human-centric explainable AI techniques. Counterfactual explanations, with roots in human reasoning, identify the minimal input changes needed to obtain a given output, and hence, are ...

In this thesis, we address a fundamental challenge in modern deep learning: uncertainty estimation. While deep neural networks have achieved remarkable success across diverse and impactful applications-from robotics to large language models and advanced in ...

Key cellular functions depend on the transduction of extracellular mechanical signals by specialized membrane receptors, including adhesion G-protein coupled receptors (aGPCRs). While recent structural studies support aGPCR activation through shedding of t ...

This thesis contributes to the modelling of multivariate rare events by combining extreme value theory and graphical modelling. The central theme involves structural equation models (SEMs), which are powerful tools used to express causal extremal dependenc ...

Creatine (Cr) is an organic acid essential for recycling ATP, important in tissues with high energy demand such as muscle or brain. Cr is synthesized in a 2-step pathway by the enzymes AGAT and GAMT, and transported by SLC6A8 (also called CrT). Cerebral Cr ...

Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...

In this paper, three machine learning (ML)-based approaches—XGBoost, Artificial Neural Network (ANN), and Random Forest algorithms—are compared for the localization of lightning through the analysis of lightninginduced voltages on power transmission lines. ...

This dataset was created as part of the PhD project Machine Understanding of Architectural Space: From Analytical to Generative Applications at EPFL Media and Design Lab (LDM). It was derived from the Cubicasa5K dataset. The dataset contains preprocessed d ...

Irregular sampling of time series in electronic health records (EHRs) is one of the main challenges for developing machine learning models. Additionally, the pattern of missing values in certain clinical variables is not at random but depends on the decisi ...

Drug design is both a time consuming and expensive endeavour. Computational strategies offer viable options to address this task; deep learning approaches in particular are indeed gaining traction for their capability of dealing with chemical structures. A ...

We experimentally study the spectral lasing response of on-chip InP network random lasers under illumination of different input image shapes. Deep-learning models have become increasingly omipresent throughout society. However, they are blighted by exponen ...

Progressive gait impairment is common among aging adults. Remote phenotyping of gait during daily living has the potential to quantify gait alterations and evaluate the effects of interventions that may prevent disability in the aging population. Here, we ...

Computational modelling is important in the life sciences, but students may not be greatly exposed to it in typical curricula.This paper describes an innovative, one-semester Master's level course in cell biology that uses computer simulations as the prima ...

Sociability refers to an animal's ease of living amongst members of its own species. This trait is essential for ethologically important social interactions like courtship, mating, and child rearing, as well as collective behaviors like group decision-maki ...

Translation elongation plays an important role in regulating protein concentrations in the cell, and dysregulation of this process has been linked to several human diseases. In this study, we use data from ribo-seq experiments to model ribosome densities, ...

Progress in digital pathology is hindered by high-resolution images and the prohibitive cost of exhaustive localized annotations. The commonly used paradigm to categorize pathology images is patch-based processing, which often incorporates multiple instanc ...

This thesis is dedicated to developing innovative methodologies that improve elemental quantification in scanning transmission electron microscopy (STEM) using energy-dispersive X-ray spectroscopy (EDXS). The primary motivation stems from a geochemistry pr ...

Aging compromises hematopoietic and immune system functions, making older adults especially susceptible to hematopoietic failure, infections and tumor development, and thus representing an important medical target for a broad range of diseases. During agin ...

Supplementary files for Machine learning for histological annotation and quantification of cortical layers

Chattez avec Graph Search

Integrating AI across the Chemistry Discovery Cycle: Advancing Sustainable Chemistry through Digital Methods

Sequential Representation Learning via Static-Dynamic Conditional Disentanglement

Towards Unifying Evaluation of Counterfactual Explanations: Leveraging Large Language Models for Human-Centric Assessments

Uncertainty in Modern Deep Learning

Molecular characterization of an adhesion GPCR signal transduction using Graph-neural networks

Structural equation models for multivariate extremes

Heterozygous females from a rat model for creatine transporter deficiency reveal altered behavioral response to stressors, normal body weight and slight metabolic changes

Machine learning-aided generative molecular design

An Efficient Machine Learning Model for Lightning Localization via Lightning-Induced Voltages on Transmission Lines

Floorplan Isovist Dataset

TEE4EHR: Transformer event encoder for better representation learning in electronic health records

AMCG: a graph dual atomic-molecular conditional molecular generator

Image sensitive spectral response of semiconductor random network lasers

Self-supervised learning of wrist-worn daily living accelerometer data improves the automated detection of gait in older adults

Can you Teach Cell Biology using Computer Simulations?

Neural mechanisms of learned sociability in Drosophila

Towards improving full-length ribosome density prediction by bridging sequence and graph-based representations

ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification

New tools for phase segmentation and denoising of analytical STEM data for enhanced chemical sensitivity, applied to Earth mantle research

Induction of mitochondrial recycling reverts age-associated decline of the hematopoietic and immune systems