A method to study alterations in networks of structural connectivity

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Understanding the structure of real data is paramount in advancing modern deep-learning methodologies. Natural data such as images are believed to be composed of features organized in a hierarchical and combinatorial manner, which neural networks capture d ...

Implicit neural representations map a shape-specific latent code and a 3D coordinate to its corresponding signed distance (SDF) value. However, this approach only offers a single level of detail. Emulating low levels of detail can be achieved with shallow ...

Low-light photography produces images with low signal-to-noise ratios due to limited photons. In such conditions, common approximations like the Gaussian noise model fall short, and many denoising techniques fail to remove noise effectively. Although deep- ...

A search is presented for the pair production of new heavy resonances, each decaying into a top quark (t) or antiquark and a gluon (g). The analysis uses data recorded with the CMS detector from proton-proton collisions at a center-of-mass energy of 13 TeV ...

Machine learning and artificial intelligence methods have achieved remarkable success, matching and even surpassing human capabilities in various complex tasks. However, many demonstrations have generally neglected a critical part of the intelligence that ...

Recent progress in computer vision has been driven by a simple yet transformative observation: the potential of 30-year-old neural networks can be unlocked by massively scaling their parameters and the amount of human-labeled training data. This breakthrou ...

This paper proposes a novel approach to improve the performance of distributed nonlinear control systems while preserving stability by leveraging Deep Neural Networks (DNNs). We build upon the Neural System Level Synthesis (Neur-SLS) framework and introduc ...

Background: Compared with conventional unimodal analysis, understanding how brain function and structure relate to one another opens a new biologically relevant assessment of neural mechanisms. However, how function-structure dependencies (FSDs) evolve thr ...

In this paper, we use synthetic data and propose a new method to reconstruct high-resolution face images from facial templates in a template inversion attack against face recognition systems. We use a pre-trained face generator network to generate syntheti ...

Sonosensitive perfluorocarbon F(8)TAC(18)-PFOB emulsion is under development to enhance heating, increase thermal contrast, and reduce treatment times during focused ultrasound tumor ablation of highly perfused tissue. The emulsion previously showed enhanc ...

The objective of meta-learning is to exploit knowledge obtained from observed tasks to improve adaptation to unseen tasks. Meta-learners are able to generalize better when they are trained with a larger number of observed tasks and with a larger amount of ...

We introduce universal solution manifold network (USM-Net), a novel surrogate model, based on artificial neural networks (ANNs), which applies to differential problems whose solution depends on physical and geometrical parameters. We employ a mesh-less arc ...

Lack of awareness of cognitive impairment (i.e. anosognosia) could be a key factor for distinguishing between neuropsychological postCOVID-19 condition phenotypes. In this context, the 2-fold aim of the present study was to (i) establish the prevalence of ...

The current diagnostic criteria for multiple sclerosis (MS) lack specificity, and this may lead to misdiagnosis, which remains an issue in present-day clinical practice. In addition, conventional biomarkers only moderately correlate with MS disease progres ...

Affective inertia represents the lasting impact of transient emotions at one time point on affective state at a subsequent time point. Here we describe the neural underpinnings of inertia following negative emotions elicited by sad events in movies. Using ...

Multiple sclerosis (MS) is an inflammatory and neurodegenerative disease characterized by diffuse and focal areas of tissue loss. Conventional MRI techniques such as T1-weighted and T2-weighted scans are generally used in the diagnosis and prognosis of the ...

Decentralized training of deep learning models enables on-device learning over networks, as well as efficient scaling to large compute clusters. Experiments in earlier works reveal that, even in a data-center setup, decentralized training often suffers fro ...

The anisotropy of brain white matter microstructure manifests itself in orientational-dependence of various MRI contrasts, and can result in significant quantification biases if ignored. Understanding the origins of this orientation-dependence could enhanc ...

Diffusion MRI (dMRI) represents one of the few methods for mapping brain fiber orientations non-invasively. Unfortunately, dMRI fiber mapping is an indirect method that relies on inference from measured diffusion patterns. Comparing dMRI results with other ...

At the typical spatial resolution of MRI in the human brain, approximately 60–90% of voxels contain multiple fiber populations. Quantifying microstructural properties of distinct fiber populations within a voxel is therefore challenging but necessary. Whil ...

A method to study alterations in networks of structural connectivity

Chattez avec Graph Search

A phase transition in diffusion models reveals the hierarchical nature of data

A Latent Implicit 3D Shape Model for Multiple Levels of Detail

Dark Noise Diffusion: Noise Synthesis for Low-Light Image Denoising

Search for Pair Production of Heavy Particles Decaying to a Top Quark and a Gluon in the Lepton Plus Jets Final State in Proton-proton Collisions at √s=13 Tev

Sequential Reasoning with Socially Caused Beliefs

Exploiting Representation Similarities in Self-Supervised Learning for Vision Tasks

Optimal distributed control with stability guarantees by training a network of neural closed-loop maps

Longitudinal Analysis of Brain Function-Structure Dependencies in 22q11.2 Deletion Syndrome and Psychotic Symptoms

Inversion of Deep Facial Templates using Synthetic Data

Perfluorocarbon emulsion enhances MR-ARFI displacement and temperature in vitro: Evaluating the response with MRI, NMR, and hydrophone

Dif-MAML: Decentralized Multi-Agent Meta-Learning

Universal Solution Manifold Networks (USM-Nets): Non-Intrusive Mesh-Free Surrogate Models for Problems in Variable Domains

Functional connectivity underlying cognitive and psychiatric symptoms in post-COVID-19 syndrome: is anosognosia a key determinant?

Cortical lesions, central vein sign, and paramagnetic rim lesions in multiple sclerosis: Emerging machine learning techniques and future avenues

Dynamic functional brain networks underlying the temporal inertia of negative emotions

Multi-Compartment Diffusion Mri, T2 Relaxometry And Myelin Water Imaging As Neuroimaging Descriptors For Anomalous Tissue Detection

Consensus Control for Decentralized Deep Learning

Measuring compartmental T-2-orientational dependence in human brain white matter using a tiltable RF coil and diffusion-T-2 correlation MRI

Comparison of diffusion MRI and CLARITY fiber orientation estimates in both gray and white matter regions of human and primate brain

Resolving bundle-specific intra-axonal T2 values within a voxel using diffusion-relaxation tract-based estimation