Self-supervised Dense Representation Learning for Live-Cell Microscopy with Time Arrow Prediction

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In a matter of years, single-cell omics has matured from a pioneering technique employed by just a handful of specialized laboratories to become a ubiquitous feature of biological research and a key driver of scientific discovery. The widespread adoption a ...

Machine learning for protein modeling faces significant challenges due to proteins' inherently dynamic nature, yet most graph-based machine learning methods rely solely on static structural information. Recently, the growing availability of molecular dynam ...

Explainable Artificial Intelligence (XAI) is an emerging field in AI that aims to address the opaque nature of machine learning models. Furthermore, it has been shown that XAI can be used to extract input-output relationships, making them a useful tool in ...

Ash dieback, caused by the fungal pathogen Hymenoscyphus fraxineus, is devastating ash tree populations across U.K. and Europe, with projections indicating that up to 80% of ash trees may die as a result of the disease. The extensive loss of this keystone ...

Bioelectromagnetics published by Wiley Periodicals LLC on behalf of Bioelectromagnetics Society.Electrical stimulation of peripheral nerves via implanted electrodes has been shown to be a promising approach to restore sensation, movement, and autonomic fun ...

Clickstream data from digital learning environments provide valuable insights into student behavior but are challenging to interpret due to their granularity. Prior methods mainly relied on handcrafted features, expert labeling, clustering, or supervised m ...

Fluorescence lifetime imaging microscopy (FLIM) is a powerful tool to discriminate fluorescent molecules or probe their nanoscale environment. Traditionally, FLIM uses time-correlated single-photon counting (TCSPC), which is precise but intrinsically low-t ...

Recreating real-world objects and scenes in a visually plausible manner and making them editable with intuitive instructions is central to Virtual and Augmented Reality (VR/AR) applications. This research lies at the intersection of neural rendering, 3D co ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimen ...

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 ...

Deep learning has revolutionized the field of computer vision, a success largely attributable to the growing size of models, datasets, and computational power. Simultaneously, a critical pain point arises as several computer vision applications are deploye ...

Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D com ...

In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely positioned and displaying notable inter-object correlations. This can creat ...

In this thesis we explore the applications of projective geometry, a mathematical theory of the relation between 3D scenes and their 2D images, in modern learning-based computer vision systems. This is an interesting research question which contradicts the ...

Computing servers have played a key role in developing and processing emerging compute-intensive applications in recent years. Consolidating multiple virtual machines (VMs) inside one server to run various applications introduces severe competence for limi ...

Cells are the smallest operational units of living systems. Through synthesis of various biomolecules and exchange of signals with the environment, cells tightly regulate their composition to realize a specific functional state. The transformation of a cel ...

The field of biometrics, and especially face recognition, has seen a wide-spread adoption the last few years, from access control on personal devices such as phones and laptops, to automated border controls such as in airports. The stakes are increasingly ...

Self-supervised Dense Representation Learning for Live-Cell Microscopy with Time Arrow Prediction

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A clinical road map for single-cell omics

Boosting Protein Graph Representations through Static-Dynamic Fusion

Human interpretable structure-property relationships in chemistry using explainable machine learning and large language models

Leaf Level Ash Dieback Disease Detection and Online Severity Estimation With UAVs

Characterization of Machine Learning-Based Surrogate Models of Neural Activation Under Electrical Stimulation

ClickSight: Interpreting Student Clickstreams to Reveal Insights on Learning Strategies via LLMs

Wide-field fluorescence lifetime imaging of single molecules with a gated single-photon camera

Towards Visually-Plausible and Controllable 3D Representations

Aggregating Spatial and Photometric Context for Photometric Stereo

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Robust machine learning for neuroscientific inference

The multimodality cell segmentation challenge: toward universal solutions

Topics in statistical physics of high-dimensional machine learning

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Incorporating Projective Geometry into Deep Learning

CloudProphet: A Machine Learning-Based Performance Prediction for Public Clouds

Deciphering the nature of cell state transitions in single cells using quantitative modeling of temporal dynamics

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms