Harnessing data science to improve molecular structure elucidation from tandem mass spectrometry

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Federated Learning (FL) has emerged as a transformative paradigm in machine learning, enabling collaborative model training across decentralized devices while preserving data privacy. However, FL's success is highly contingent on the quality and integrity ...

Non-regular or irregular statistical problems are those that do not satisfy a set of standard regularity conditions that allow useful theoretical properties of inferential procedures to be proven. Non-regular problems are prevalent; a classical example is ...

Why should we confine land cover classes to rigid and arbitrary definitions? Land cover mapping is a central task in remote sensing image processing, but the rigorous class definitions can sometimes restrict the transferability of annotations between datas ...

Neurodegenerative diseases, such as Alzheimer's, Parkinson's, and Huntington's, afflict tens of millions of patients worldwide. They are characterized by protein aggregation and progressive neuronal loss, leading to cognitive and motor impairments, and ult ...

Latest advances in super-resolution microscopy allow the study of subcellular features at the level of single proteins, which could lead to discoveries in fundamental biological processes, specifically in cell signaling mediated by membrane receptors. Desp ...

The transition matrix, frequently abbreviated as T-matrix, contains the complete information in a linear approximation of how a spatially localized object scatters an incident field. The T-matrix is used to study the scattering response of an isolated obje ...

This thesis explores the innovative integration of Discrete Choice Modeling (DCM) and Machine Learning (ML), specifically Neural Networks (NN), within the transportation sector. DCM, with its highly interpretable and hand-designed models grounded in robust ...

Over the past decade, computational atomistic modeling, driven by machine learning (ML), has become indispensable to scientific endeavors, improving our understanding and accelerating the search for compounds with enhanced properties. Traditional atomistic ...

The training of automatic speech recognition (ASR) with little to no supervised data remains an open question. In this work, we demonstrate that streaming Transformer-Transducer (TT) models can be trained from scratch in consumer and accessible GPUs in the ...

Without the ability to collect, access and analyze data, most of nowadays research would be impossible. Without data to learn from, the field of machine learning (ML) would not exist. However, much of the particularly useful data---medical records, human b ...

Epileptic seizure detection and monitoring is critical in healthcare, particularly for individuals requiring continuous oversight. Current methods, primarily based on electroencephalogram (EEG) technologies, face limitations due to their complexity in term ...

WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models ############# Authors: Valentin Gabeff, Marc Russwurm, Devis Tuia & Alexander Mathis Affiliation: EPFL Date: January, 2024 Link to the article: ...

This work presents the changing abundance of surface functional groups (SFGs) on polystyrene (PS) upon weathering within one or a few molecular monolayers from a molecular point of view. PS particles were aged by exposing it to a gas flow of typically (5 % ...

Molecular behavior can change dramatically at interfaces, and these changes become even more pronounced when molecules are confined within nanoscale dimensions. In living organisms, nanoscale channels play a crucial role in selectively transporting molecul ...

Deep metric learning (DML) has shown to be effective for content-based image retrieval (CBIR) in remote sensing (RS). Most of the DML methods for CBIR rely on a high number of annotated images to accurately learn model parameters of deep neural networks (D ...

The perception of autonomous vehicles using radars has attracted increased research interest due its ability to operate in fog and bad weather. However, training radar models is hindered by the cost and difficulty of annotating largescale radar data. To ov ...

The increasing demand for precision, reproducibility, and scalability in scientific research has driven the development of advanced robotic systems for laboratory automation. This thesis presents the design, implementation, and validation of a mobile robot ...

The accurate description of the structural and thermodynamic properties of ferroelectrics has been one of the most remarkable achievements of density functional theory (DFT). However, running large simulation cells with DFT is computationally demanding, wh ...

Harnessing data science to improve molecular structure elucidation from tandem mass spectrometry

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Robust and Personalized Federated Learning: Handling Data Corruption and Heterogeneity

Non-regular Inference: Universal Inference and Discrete Profiling

Learning transferable land cover semantics for open vocabulary interactions with remote sensing images

Intelligent and Self-Driving Microscopy of Protein Aggregation in Neurodegenerative Diseases

Spatial and stoichiometric in situ analysis of biomolecular oligomerization at single-protein resolution

T-matrix representation of optical scattering response: Suggestion for a data format

Bridging Discrete Choice Modeling and Neural Networks: Unifying Statistical Rigor and Predictive Power

Integrating symmetry and physical constraints into atomic-scale machine learning

Fast Streaming Transducer ASR Prototyping via Knowledge Distillation with Whisper

Privacy and Confidentiality in Machine Learning and Data Analysis: Understanding Risks and Developing Protections

Distributed Machine Learning Targeting Embedded Systems for Epilepsy Detection

WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models

Natural and simulated weathering of polystyrene: A molecular view of the polymeric interface

Optical imaging of molecules and their dynamics from surfaces to nanoscale confinement

Annotation Cost-Efficient Active Learning for Deep Metric Learning-Driven Remote Sensing Image Retrieval

Bootstrapping Autonomous Driving Radars with Self-Supervised Learning

Materials science experiments by robotic automation

Modeling the ferroelectric phase transition in barium titanate with DFT accuracy and converged sampling