Automatic identification of atmospheric bioaerosol using real-time imaging and fluorescence

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Current progress in artificial intelligence is centered around so-called large language models that consist of neural networks processing long sequences of high-dimensional vectors called tokens. Statistical physics provides powerful tools to study the fun ...

Background and Objective: Cardiovascular diseases, a leading cause of noncommunicable disease-related deaths, require early and accurate detection to improve patient outcomes. Taking advantage of advances in machine learning and deep learning, multiple app ...

In order to fight hunger and meet the food demands of a constantly growing human population, drastic changes need to be introduced in the agricultural sector. To achieve that in a sustainable manner, current agricultural practices should be improved and th ...

The increasing accumulation of medical data brings the hope of data-driven medical decision-making, but data's increasing complexity—as text or images in electronic health records—calls for complex models, such as machine learning. Here, we review how mach ...

This thesis is concerned with improving the interpretability of network summaries in both a theoretical and an applied framework, with an application to clinically relevant problems in neuroscience. The theoretical part of this thesis is concerned with gra ...

The EPFL Graph Platform is an open-source data infrastructure designed for academic institutions. It organizes educational and institutional data into a semantically interconnected knowledge graph, making it accessible through a graph-based search engine a ...

In recent decades, the rapid growth of cloud computing has transformed the technological landscape and profoundly impacted our daily lives, driven by the demand for secure, flexible, and cost-effective computing solutions. To meet these demands, computing ...

Identification of spot-like structures in large, noisy microscopy images is a crucial step for many life-science applications. Imaging-based spatial transcriptomics (iST), in particular, relies on the precise detection of millions of transcripts in low sig ...

Holography enables quantitative phase sensing through light wave interference, allowing interesting imaging modalities applied in computational microscopy where phase shift is used as a way to generate image contrast. Recovering both amplitude and phase di ...

This paper studies the problem of interconnected agents collaborating to track a dynamic state from partially informative observations, where the dynamic state evolves according to a slowly varying finite-state Markov chain. Although the centralized versio ...

Recent studies show that a reproducing kernel Hilbert space (RKHS) is not a suitable space to model functions by neural networks as the curse of dimensionality (CoD) cannot be evaded when trying to approximate even a single ReLU neuron (Bach, 2017). In thi ...

With the significant increase in photovoltaic (PV) electricity generation, more attention has been given to PV power forecasting. Indeed, accurate forecasting allows power grid operators to better schedule and dispatch their assets, such as energy storage ...

Reasoning about distance is indispensable for establishing or avoiding contact in manipulation tasks. To this end, we present an online approach for learning implicit representations of signed distance using piecewise polynomial basis functions. Starting f ...

In recent years, the field of Natural Language Processing has seen significant revolution by the introduction of Transformers, a stack of multiple layers of attention and non-linearity, capable of performing almost any task and the backbone for large found ...

Origami-inspired structures facilitate the design of compliant and compact robots. However, physical origami robots possess inherent material compliance and mechanical imperfections, presenting challenges in modeling and redundant actuation for accurate co ...

Automatic identification of atmospheric bioaerosol using real-time imaging and fluorescence

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Bilinear Sequence Regression: A Model for Learning from Long Sequences of High-Dimensional Tokens

FADE: Forecasting for anomaly detection on ECG

Nanoparticle-modified bacteria for agricultural applications

From Prediction to Prescription: Machine Learning and Causal Inference for the Heterogeneous Treatment Effect

Nonparametric network summaries

GraphOntology Dataset

Multi-Objective Management for Many-Core Computing Servers

Spotiflow: accurate and efficient spot detection for fluorescence microscopy with deep stereographic flow regression

Cross-Net: Joint In-Line Holographic Image Reconstruction and Refocusing

Fundamental Social Learning Scaling Law for Tracking Hidden Markov Models

Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks

Intraday solar irradiance forecasting using public cameras

Online Learning of Continuous Signed Distance Fields Using Piecewise Polynomials

Discovering meaningful units from text sequences

Data-Driven Kinematic Modeling of Physical Origami Robots