Nissl_5, Raw images for Machine learning for histological annotation and quantification of cortical layers

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The rapid evolution of Machine Learning (ML) workloads, particularly Deep Neural Networks (DNNs) and Transformer-based models, has intensified demands on computing architectures, highlighting the limitations of traditional von Neumann systems due to the me ...

The nematode Caenorhabditis elegans offers a uniquely tractable model for studying the relationships between neuronal activity, connectivity, and behavior. Despite its simplicity, key questions remain about how information flows through its nervous system ...

Metal ions are indispensable cofactors for many proteins and play crucial roles in numerous biological processes, yet the computational design of metalloproteins and the optimization of metal coordination sites poses significant challenges due to the comp ...

Background: Eating disorders (EDs) are serious psychiatric disorders characterized by impairments in neurocognition and altered brain structure. To date the majority of studies have investigated these in acutely ill or recovered individuals. Studying child ...

In real-world speech data processing, the scarcity of annotated data and the abundance of unlabelled speech data present a significant challenge. To address this, we propose an efficient data selection pipeline for fine-tuning ASR models by generating pseu ...

We investigate group fairness regularizers in federated learning, aiming to train a globally fair model in a distributed setting. Ensuring global fairness in distributed training presents unique challenges, as fairness regularizers typically involve probab ...

While classical in many theoretical settings-and in particular in statistical physics-inspired works-the assumption of Gaussian i.i.d. input data is often perceived as a strong limitation in the context of statistics and machine learning. In this study, we ...

The success of machine learning (ML) has been intimately linked with the availability of large amounts of data, typically collected from heterogeneous sources and processed on vast networks of computing devices (also called workers). Beyond accuracy, the u ...

We investigate the training dynamics of two-layer neural networks when learning multi-index target functions. We focus on multi-pass gradient descent (GD) that reuses the batches multiple times and show that it significantly changes the conclusion about wh ...

The brain and immune system communicate through complex bidirectional pathways, but the specificity by which the brain perceives or even remembers alterations in immune homeostasis is still poorly understood. Recent data revealed that immune-related inform ...

The Vision Transformer (ViT) excels in accuracy when handling high-resolution images, yet it confronts the challenge of significant spatial redundancy, leading to increased computational and memory requirements. To address this, we present the Localization ...

Objective. Brain-Computer Interfaces targeting post-stroke recovery of the upper limb employ mainly electroencephalography to decode movement-related brain activation. Recently hybrid systems including muscular activity were introduced. We compared the mot ...

Multi-object spectroscopic galaxy surveys typically make use of photometric and colour criteria to select their targets. That is not the case of Euclid, which will use the NISP slitless spectrograph to record spectra for every source over its field of view ...

Intelligent Fault Diagnosis (IFD) based on deep learning has proven to be an effective and flexible solution, attracting extensive research. Deep neural networks can learn rich representations from vast amounts of representative labeled data for various ap ...

Saliency models are image-based prediction models that estimate human visual attention. Such models, when applied to architectural spaces, could pave the way for design decisions where visual attention is taken into account. In this study, we tested the pe ...

Which cell types constitute brain circuits is a fundamental question, but establishing the correspondence across cellular data modalities is challenging. Bio-realistic models allow probing cause-and-effect and linking seemingly disparate modalities. Here, ...

This study investigates the use of a low cost microphone combined with state-of-the-art machine learning (ML) algorithms as online process monitoring to differentiate various materials and process regimes of Laser-Powder Bed Fusion (LPBF). Three processing ...

The objective of this study was to examine if patterns of resting-state brain activity and functional connectivity in cortical and subcortical regions in patients with early symptomatic amyotrophic lateral sclerosis (ALS) resemble those of behavioural vari ...

With the onset of large-scale astronomical surveys capturing millions of images, there is an increasing need to develop fast and accurate deconvolution algorithms that generalize well to different images. A powerful and accessible deconvolution method woul ...

Background. Latencies of motor evoked potentials (MEPs) can provide insights into the motor neuronal pathways activated by transcranial magnetic stimulation. Notwithstanding its clinical relevance, accurate, unbiased methods to automatize latency detection ...

Nissl_5, Raw images for Machine learning for histological annotation and quantification of cortical layers

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Stop Wasting your Cache! Bringing Machine Learning into Cache Computing

Behaviorally Relevant Information Content in Interneuron Activity

Engineering of stable and functional metalloproteins using evolutionary computation, deep learning and simulation

Neurobiology and Cognition in Girls at High-risk of Eating Disorders: Exploring Imaging-derived Trait Markers

Speech Data Selection for Efficient ASR Fine-Tuning using Domain Classifier and Pseudo-Label Filtering

Global Group Fairness in Federated Learning via Function Tracking

Gaussian universality of perceptrons with random labels

Brief Announcement: A Case for Byzantine Machine Learning

The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents

Investigations on the Ability of the Insular Cortex to Process Peripheral Immunosuppression

LF-ViT: Reducing Spatial Redundancy in Vision Transformer for Efficient Image Recognition

Brain and muscle derived features to discriminate simple hand motor tasks for a rehabilitative BCI: comparative study on healthy and post-stroke individuals

Euclid: Testing photometric selection of emission-line galaxy targets?

Calibrated Adaptive Teacher for Domain Adaptive Intelligent Fault Diagnosis

Saliency prediction in 360° architectural scenes: Performance and impact of daylight variations

Single-neuron models linking electrophysiology, morphology, and transcriptomics across cortical cell types

Differentiation of materials and laser powder bed fusion processing regimes from airborne acoustic emission combined with machine learning

Cortical and subcortical changes in resting-state neuronal activity and connectivity in early symptomatic ALS and advanced frontotemporal dementia

Deep learning-based galaxy image deconvolution

An automatized method to determine latencies of motor-evoked potentials under physiological and pathophysiological conditions