CLaiMS-Net: cortical lesions assessment in multiple sclerosis patients via a convolutional neural network and a single 3T MRI acquisition

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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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Electronic computers have evolved drastically over the past years with an ever-growing demand for improved performance. However, the transfer of information from memory and high energy consumption have emerged as issues that require solutions. Optical tech ...

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, ...

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 ...

Despite the therapeutical advancements in the surgical treatment of primary intra-axial neoplasms, which determined both a significative improvement in OS and QoL and a reduction in the incidence of surgery-induced major neurological deficits, nowadays pat ...

Detecting learners in need of support is a complex process for both teachers and machines. Most prior work has devised visualization tools that allow teachers to do so by analyzing educational indicators. Other recent efforts have been devoted to models th ...

Stereo matching aiming to perceive the 3-D geometry of a scene facilitates numerous computer vision tasks used in advanced driver assistance systems (ADAS). Although numerous methods have been proposed for this task by leveraging deep convolutional neural ...

Introduction: Cerebral palsy (CP) is the major cause of motor and cognitive impairments during childhood. CP can result from direct or indirect structural injury to the developing brain. In this study, we aimed to describe brain damage and behavioural alte ...

Multivariate time series forecasting poses challenges as the variables are intertwined in time and space, like in the case of traffic signals. Defining signals on graphs relaxes such complexities by representing the evolution of signals over a space using ...

The need for efficient Convolutional Neural Network (CNNs) targeting embedded systems led to the popularization of Binary Neural Networks (BNNs), which significantly reduce execution time and memory requirements by representing the operands using only one ...

How does the brain process and memorize information? We all know that the neuron (also known as nerve cell) is the processing unit in the brain. But how do neurons work together in networks? The connectivity structure of neural networks plays an important ...

CLaiMS-Net: cortical lesions assessment in multiple sclerosis patients via a convolutional neural network and a single 3T MRI acquisition

Chattez avec Graph Search

Nonparametric network summaries

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

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

FADE: Forecasting for anomaly detection on ECG

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

Online Learning of Continuous Signed Distance Fields Using Piecewise Polynomials

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

Programming the scalable optical learning operator with spatial-spectral optimization

Towards Robust Vision Models

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

Connectome-Based Neurosurgery in Primary Intra-Axial Neoplasms: Beyond the Traditional Modular Conception of Brain Architecture for the Preservation of Major Neurological Domains and Higher-Order Cognitive Functions

How Close are Predictive Models to Teachers in Detecting Learners at Risk?

Adversarial Confidence Estimation Networks for Robust Stereo Matching

Experimental cerebral palsy causes microstructural brain damage in areas associated to motor deficits but no spatial memory impairments in the developing rat

Traffic Signal Prediction on Transportation Networks Using Spatio-Temporal Correlations on Graphs

Accelerating Inference on Binary Neural Networks with Digital RRAM Processing

Axonal damage explains part of and extends beyond the diffuse pathology evidenced by T1 mapping in normal-appearing white matter of multiple sclerosis patients

The spectrum of brain MRI findings of methanol intoxication after the methanol affair in the Czech Republic

Neural assemblies as core elements for modeling neural networks in the brain