Survey-propagation decimation through distributed local computations

18 pages, 10 figuresWe discuss the implementation of two distributed solvers of the random K-SAT problem, based on some development of the recently introduced survey-propagation (SP) algorithm. The first solver, called the \"SP diffusion algorithm\", diffuses as dynamical information the maximum bias over the system, so that variable nodes can decide to freeze in a self-organized way, each variable making its decision on the basis of purely local information. The second solver, called the \"SP reinforcement algorithm\", makes use of time-dependent external forcing messages on each variable, which let the variables get completely polarized in the direction of a solution at the end of a single convergence. Both methods allow us to find a solution of the random 3-SAT problem in a range of parameters comparable with the best previously described serialized solvers. The simulated time of convergence towards a solution (if these solvers were implemented on a distributed device) grows as log(N)

Les sillons corticaux sont des biomarqueurs neuro-développementaux régionaux du diagnostic de la schizophrénie

The human cortical brain is folded and made of bumps called gyri, separated by grooves called sulci. Folding is highly variable among individuals, and making sense of this variability has been a long-standing goal. Part of this variability can be described by clinically relevant parameters. Here, we learn to predict a mental disorder with a likely neurodevelopmental imprint---schizophrenia---from the cortical folds by using as volume inputs cortical skeletons, which are negative casts of the brain. We are looking for local folding descriptors, not global ones; thus, we scatter the learning over 24 sulcal bilateral regions on the two hemispheres and apply an ensemble method to each region. We found significant correlations between cortical folding and schizophrenia in the precentral, temporal, and occipital regions and the collateral fissure

Unsupervised Representation Learning of Cingulate Cortical Folding Patterns

International audienceThe human cerebral cortex is folded, making sulci and gyri over the whole cortical surface. Folding presents a very high inter-subject variability, and some neurodevelopmental disorders are correlated to local folding structures, named folding patterns. However, it is tough to characterize these patterns manually or semi-automatically using geometric distances. Here, we propose a new methodology to identify typical folding patterns. We focus on the cingulate region, known to have a clinical interest, using so-called skeletons (3D representation of folding patterns). We compare two models, β − V AE and SimCLR, in an unsupervised setting to learn a relevant representation of these patterns. We add a decoder to SimCLR to be able to analyse latent space. Specifically, we leverage the data augmentations used in SimCLR to propose a novel kind of augmentations based on folding topology. We then apply a clustering on the latent space. Cluster folding averages, interpolation in the latent space and reconstructions reveal new pattern structures. This structured representation shows that unsupervised learning can help in the discovery of still unknown patterns. We will gain further insights into folding patterns by using new priors in the unsupervised algorithms and integrating other brain data modalities. Code and experiments are available at github.com/neurospin-projects/2021 jchavas lguillon deepcingulate

Unsupervised Representation Learning of Cingulate Cortical Folding Patterns

International audienceThe human cerebral cortex is folded, making sulci and gyri over the whole cortical surface. Folding presents a very high inter-subject variability, and some neurodevelopmental disorders are correlated to local folding structures, named folding patterns. However, it is tough to characterize these patterns manually or semi-automatically using geometric distances. Here, we propose a new methodology to identify typical folding patterns. We focus on the cingulate region, known to have a clinical interest, using so-called skeletons (3D representation of folding patterns). We compare two models, β − V AE and SimCLR, in an unsupervised setting to learn a relevant representation of these patterns. We add a decoder to SimCLR to be able to analyse latent space. Specifically, we leverage the data augmentations used in SimCLR to propose a novel kind of augmentations based on folding topology. We then apply a clustering on the latent space. Cluster folding averages, interpolation in the latent space and reconstructions reveal new pattern structures. This structured representation shows that unsupervised learning can help in the discovery of still unknown patterns. We will gain further insights into folding patterns by using new priors in the unsupervised algorithms and integrating other brain data modalities. Code and experiments are available at github.com/neurospin-projects/2021 jchavas lguillon deepcingulate

Optimiser l'apprentissage contrastif pour la détection de motifs de sillons corticaux

International audienceThe human cerebral cortex has many bumps and grooves called gyri and sulci. Even though there is a high interindividual consistency for the main cortical folds, this is not the case when we examine the exact shapes and details of the folding patterns. Because of this complexity, characterizing the cortical folding variability and relating them to subjects' behavioral characteristics or pathologies is still an open scientific problem. Classical approaches include labeling a few specific patterns, either manually or semi-automatically, based on geometric distances, but the recent availability of MRI image datasets of tens of thousands of subjects makes modern deep-learning techniques particularly attractive. Here, we build a self-supervised deep-learning model to detect folding patterns in the cingulate region. We train a contrastive self-supervised model (SimCLR) on both Human Connectome Project (1101 subjects) and UKBioBank (21070 subjects) datasets with topological-based augmentations on the cortical skeletons, which are topological objects that capture the shape of the folds. We explore several backbone architectures (convolutional network, DenseNet, and PointNet) for the SimCLR. For evaluation and testing, we perform a linear classification task on a database manually labeled for the presence of the "double-parallel" folding pattern in the cingulate region, which is related to schizophrenia characteristics. The best model, giving a test AUC of 0.76, is a convolutional network with 6 layers, a 10-dimensional latent space, a linear projection head, and using the branch-clipping augmentation. This is the first time that a self-supervised deep learning model has been applied to cortical skeletons on such a large dataset and quantitatively evaluated. We can now envisage the next step: applying it to other brain regions to detect other biomarkers. The GitHub repository is publicly available on https://github.com/neurospin-projects/2022 jchavas cingulate inhibitory control

Supervised diagnosis prediction from cortical sulci: toward the discovery of neurodevelopmental biomarkers in mental disorders

International audienceRecent advances in machine learning applied to structural magnetic resonance imaging (sMRI) may highlight abnormalities in brain anatomy associated with mental disorders. These disorders are multifactorial, resulting from a complex combination of neurodevelopmental and environmental factors. In particular, such factors are present in cortical sulci, whose shapes are determined very early in brain development and are a valuable proxy for capturing specifically the neurodevelopmental contribution of brain anatomy. This paper explores whether the shapes of cortical sulci can be used for diagnosis prediction using deep learning models. These models are applied to three mental disorders (autism spectrum disorder, bipolar disorder, and schizophrenia) in largemulticentric datasets. We demonstrate that the neurodevelopmental underpinnings of these disorders can be captured withsMRI. Finally, we show the potential of visual explanations of models’ decisions in discovering biomarkers for mental disorders

Sub-nanosecond clock synchronization and trigger management in the nuclear physics experiment AGATA

International audienceThe new-generation spectrometer AGATA, the Advanced GAmma Tracking Array, requires sub-nanosecond clock synchronization among readout and front-end electronics modules that may lie hundred meters apart. We call GTS (Global Trigger and Synchronization System) the infrastructure responsible for precise clock synchronization and for the trigger management of AGATA. It is made of a central trigger processor and nodes, connected in a tree structure by means of optical fibers operated at 2Gb/s. The GTS tree handles the synchronization and the trigger data flow, whereas the trigger processor analyses and eventually validates the trigger primitives centrally. Sub-nanosecond synchronization is achieved by measuring two different types of round-trip times and by automatically correcting for phase-shift differences. For a tree of depth two, the peak-to-peak clock jitter at each leaf is 70 ps; the mean phase difference is 180 ps, while the standard deviation over such phase difference, namely the phase equalization repeatability, is 20 ps. The GTS system has run flawlessly for the two-year long AGATA campaign, held at the INFN Legnaro National Laboratories, Italy, where five triple clusters of the AGATA sub-array were coupled with a variety of ancillary detectors

High Brightness, Highly Directional Organic Light-Emitting Diodes as Light Sources for Future Light-Amplifying Prosthetics in the Optogenetic Management of Vision Loss

Optogenetic control of retinal cells transduced with light-sensitive channelrhodopsins can enable restoration of visual perception in patients with vision loss. However, a light intensity orders of magnitude higher than ambient light conditions is required to achieve robust cell activation. Relatively bulky wearable light amplifiers are currently used to deliver sufficient photon flux (>10(16) photons/cm(2)/s in a +/- 10 degrees emission cone) at a suitable wavelength (e.g., 600 nm for channelrhodopsin ChrimsonR). Here, ultrahigh brightness organic light-emitting diodes (OLEDs) with highly directional emission are developed, with the ultimate aim of providing high-resolution optogenetic control of thousands of retinal cells in parallel from a compact device. The orange-emitting phosphorescent OLEDs use doped charge transport layers, generate narrowband emission peaking at 600 nm, and achieve a luminance of 684 000 cd m(-2) at 15 V forward bias. In addition, tandem-stack OLEDs with a luminance of 1 152 000 cd m(-2) and doubled quantum efficiency are demonstrated, which greatly reduces electrical and thermal stress in these devices. At the photon flux required to trigger robust neuron firing in genetically modified retinal cells and when using heat sinking and realistic duty cycles (20% at 12.5 Hz), the tandem-stack OLEDs therefore show a greatly improved half-brightness lifetime of 800 h