Entities on the Web : Resolution, Matching and Profiling

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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 ...

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 ...

Vision-language models (VLMs) are typically composed of a vision encoder, e.g. CLIP, and a language model (LM) that interprets the encoded features to solve downstream tasks. Despite remarkable progress, VLMs are subject to several shortcomings due to the ...

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 ...

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 ...

Creators Meystre Julie Olivier Burri Contributors Jean Jacquemier Description This dataset contains 7 QuPath projects. The raw data images linked to these projects and located in other Zenodo datasets need to be downloaded as well. The raw data contains im ...

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. ...

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 ...

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 ...

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 ...

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 ...

Network clustering tackles the problem of identifying sets of nodes (communities) that have similar connection patterns. However, in many scenarios, nodes also have attributes that are correlated with the clustering structure. Thus, network information (ed ...

We propose a machine learning-based method to build a system of differential equations that approximates the dynamics of 3D electromechanical models for the human heart, accounting for the dependence on a set of parameters. Specifically, our method permits ...

Characterizing left ventricle (LV) systolic function in the presence of an LV assist device (LVAD) is extremely challenging. We developed a framework comprising a deep neural network (DNN) and a 0D model of the cardiovascular system to predict parameters o ...

Background Lung auscultation is fundamental to the clinical diagnosis of respiratory disease. However, auscultation is a subjective practice and interpretations vary widely between users. The digitization of auscultation acquisition and interpretation is a ...

The optimization of therapeutic antibodies is time-intensive and resource-demanding, largely because of the low-throughput screening of full-length antibodies (approximately 1 x 10(3) variants) expressed in mammalian cells, which typically results in few o ...

In presence of sparse noise we propose kernel regression for predicting output vectors which are smooth over a given graph. Sparse noise models the training outputs being corrupted either with missing samples or large perturbations. The presence of sparse ...

Entities on the Web : Resolution, Matching and Profiling

Chat with Graph Search

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

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

BRAVE: Broadening the Visual Encoding of Vision-Language Models

Floorplan Isovist Dataset

Machine learning-aided generative molecular design

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

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

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

AMCG: a graph dual atomic-molecular conditional molecular generator

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

Image sensitive spectral response of semiconductor random network lasers

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

Exact recovery and Bregman hard clustering of node-attributed Stochastic Block Model

A machine learning method for real-time numerical simulations of cardiac electromechanics

Deep Neural Network to Accurately Predict Left Ventricular Systolic Function Under Mechanical Assistance

Deep learning diagnostic and risk-stratification pattern detection for COVID-19 in digital lung auscultations: clinical protocol for a case-control and prospective cohort study

Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning

Kernel Regression for Graph Signal Prediction in Presence of Sparse Noise