Programming the scalable optical learning operator with spatial-spectral optimization

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Since the 1960s, the prevalence of pollen allergy has increased worldwide in developed countries and this trend is continuing, exacerbated by climate change. In the 20th century, operational pollen monitoring networks have been set-up to respond to the nee ...

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 ...

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 ...

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 ...

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 ...

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 ...

Reaction additives are critical in dictating the outcomes of chemical processes making their effective screening vital for research. Conventional high-throughput experimentation tools can screen multiple reaction components rapidly. However, they are prohi ...

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 ...

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 ...

Recent advances in deep learning and Vision-Language Models (VLM) have enabled efficient transfer to downstream tasks even when limited labelled training data is available, as well as for text to be directly compared to image content. These properties of V ...

Deep convolutional neural networks (DCNNs) have significantly advanced the field of computer vision and are often considered as plausible models for the human visual system. However, they still have several vulnerabilities in terms of adversarial attacks, ...

Complex systems with multiple processes evolving on different temporal scales are naturally described by multilayer networks, where each layer represents a different timescale. In this work, we show how the multilayer structure shapes the generation and pr ...

Quantification of behavior is critical in diverse applications from neuroscience, veterinary medicine to animal conservation. A common key step for behavioral analysis is first extracting relevant keypoints on animals, known as pose estimation. However, re ...

Many-body systems at low temperature have revealed non-trivial phases of materials, such as spin liquids, which have found applications in the evolving fields of superconductivity, nanoelectronics, and quantum computing. Their exponentially large state spa ...

Quantitative analysis of microstructural features on the nanoscale, including precipitates, local chemical orderings (LCOs) or structural defects (e.g. stacking faults) plays a pivotal role in understanding the mechanical and physical responses of engineer ...

3D Fluorescence Molecular Tomography (FMT) is an important pre-clinical tool for quantifying and characterizing molecular markers for different diseases. Herein, we demonstrate, for the first time, 3D K-Space FMT reconstructions using a large time-gated SP ...

The interleaved regulator (implemented by IEEE TSN Asynchronous Traffic Shaping) is used in time-sensitive networks for reshaping the flows with per-flow contracts. When applied to an aggregate of flows that come from a FIFO system, an interleaved regulato ...

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 ...

Programming the scalable optical learning operator with spatial-spectral optimization

Chattez avec Graph Search

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

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

Nonparametric network summaries

FADE: Forecasting for anomaly detection on ECG

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

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

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

Bayesian optimisation for additive screening and yield improvements - beyond one-hot encoding

Online Learning of Continuous Signed Distance Fields Using Piecewise Polynomials

Intraday solar irradiance forecasting using public cameras

Prompt-guided and multimodal landscape scenicness assessments with vision-language models

Towards Robust Vision Models

Information propagation in Gaussian processes on multilayer networks

SuperAnimal pretrained pose estimation models for behavioral analysis

Exact Sampling for Classical and Quantum Many-Body Systems

3D deep learning for enhanced atom probe tomography analysis of nanoscale microstructures

3D fluorescence molecular tomography utilizing a novel SPAD camera

Network-calculus service curves of the interleaved regulator

Discovering meaningful units from text sequences