Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep learning-based classification of fine hand movements from low frequency EEG

The classification of different fine hand movements from electroencephalogram (EEG) signals represents a relevant research challenge, e.g., in BCI applications for motor rehabilitation. Here, we analyzed two different datasets where fine hand movements (touch, grasp, palmar, and lateral grasp) were performed in a self-paced modality. We trained and tested a newly proposed CNN, and we compared its classification performance with two well-established machine learning models, namely, shrinkage-linear discriminant analysis (LDA) and Random Forest (RF). Compared to previous literature, we included neuroscientific evidence, and we trained our Convolutional Neural Network (CNN) model on the so-called movement-related cortical potentials (MRCPs). They are EEG amplitude modulations at low frequencies, i.e., (0.3,3) Hz that have been proved to encode several properties of the movements, e.g., type of grasp, force level, and speed. We showed that CNN achieved good performance in both datasets (accuracy of 0.70±0.11 and 0.64±0.10, for the two datasets, respectively), and they were similar or superior to the baseline models (accuracy of 0.68±0.10 and 0.62±0.07 with sLDA; accuracy of 0.70±0.15 and 0.61±0.07 with RF, with comparable performance in precision and recall). In addition, compared to the baseline, our CNN requires a faster pre-processing procedure, paving the way for its possible use in online BCI applications.
ROLL: Visual Self-Supervised Reinforcement Learning with Object Reasoning

Current image-based reinforcement learning (RL) algorithms typically operate on the whole image without performing object-level reasoning. This leads to inefficient goal sampling and ineffective reward functions. In this paper, we improve upon previous visual self-supervised RL by incorporating object-level reasoning and occlusion reasoning. Specifically, we use unknown object segmentation to ignore distractors in the scene for better reward computation and goal generation; we further enable occlusion reasoning by employing a novel auxiliary loss and training scheme. We demonstrate that our proposed algorithm, ROLL (Reinforcement learning with Object Level Learning), learns dramatically faster and achieves better final performance compared with previous methods in several simulated visual control tasks. Project video and code are available at this https URL.
The effect of shelter availability on foraging in Atlantic salmon (Salmo salar)

i Acknowledgments . ii List of Tables . vii List of Figures . viii List of Appendices . x Introduction . 1 Predation risk and avoidance . 1 Daily activity budgets . 6 Population differences in foraging behaviour . 7 Effect of predation on diet and physiological processes . 8 Study species . 10 Environmental influence on foraging and shelter use . 11 Lake Ontario’s Atlantic salmon . 13 Research Objectives and Hypotheses . 14 Methods . 16
Robust Policies via Mid-Level Visual Representations: An Experimental Study in Manipulation and Navigation

Vision-based robotics often separates the control loop into one module for perception and a separate module for control. It is possible to train the whole system end-to-end (e.g. with deep RL), but doing it "from scratch" comes with a high sample complexity cost and the final result is often brittle, failing unexpectedly if the test environment differs from that of training. We study the effects of using mid-level visual representations (features learned asynchronously for traditional computer vision objectives), as a generic and easy-to-decode perceptual state in an end-to-end RL framework. Mid-level representations encode invariances about the world, and we show that they aid generalization, improve sample complexity, and lead to a higher final performance. Compared to other approaches for incorporating invariances, such as domain randomization, asynchronously trained mid-level representations scale better: both to harder problems and to larger domain shifts. In practice, this means that mid-level representations could be used to successfully train policies for tasks where domain randomization and learning-from-scratch failed. We report results on both manipulation and navigation tasks, and for navigation include zero-shot sim-to-real experiments on real robots.
Pneumothorax récidivants chez un patient atteint de dystrophie musculaire congénitale avec déficit en collagène VI

Les dystrophies musculaires avec deficit en collagenes VI constituent des affections rares avec une presentation clinique tres variable, couvrant un large spectre intermediaire allant des formes congenitales, de type Ullrich, aux formes tardives de type Bethlem. Elles sont dues a des mutations des genes COL6A1 , COL6A2 ou COL6A3 . Elles ont en commun de se traduire par un deficit moteur et des retractions progressifs. L’atteinte respiratoire participe a la gravite de l’affection pour certains patients. Elle est due au deficit du diaphragme et des muscles intercostaux, a la retraction de la cage thoracique, et a la scoliose. Une atteinte parenchymateuse pulmonaire liee a la fragilite de la matrice extracellulaire favorisant la survenue de pneumothorax est suspectee.
Transductive Zero-Shot Learning using Cross-Modal CycleGAN

In Computer Vision, Zero-Shot Learning (ZSL) aims at classifying unseen classes -- classes for which no matching training image exists. Most of ZSL works learn a cross-modal mapping between images and class labels for seen classes. However, the data distribution of seen and unseen classes might differ, causing a domain shift problem. Following this observation, transductive ZSL (T-ZSL) assumes that unseen classes and their associated images are known during training, but not their correspondence. As current T-ZSL approaches do not scale efficiently when the number of seen classes is high, we tackle this problem with a new model for T-ZSL based upon CycleGAN. Our model jointly (i) projects images on their seen class labels with a supervised objective and (ii) aligns unseen class labels and visual exemplars with adversarial and cycle-consistency objectives. We show the efficiency of our Cross-Modal CycleGAN model (CM-GAN) on the ImageNet T-ZSL task where we obtain state-of-the-art results. We further validate CM-GAN on a language grounding task, and on a new task that we propose: zero-shot sentence-to-image matching on MS COCO.
Negative isotropic seismic moment tensors, migrating and cyclic seismicity during the 2018 summit collapse at K\={\i}lauea caldera

The 2018 rift zone eruption of K\=ilauea volcano was accompanied by a remarkable and episodic collapse of its summit. Between May-August the eruption and collapse sequence included over 70,000 earthquakes (M$\geq$0) and 54 major earthquakes (M$\geq$5). We analyzed the seismicity in the K\=ilauea summit region and estimated seismic full moment tensors with their uncertainties for the 54 M$\geq$5 events. These events occurred at almost daily intervals and were accompanied by intense seismicity which was concentrated between 0-3 km depths beneath the Halema`uma`u pit crater. The hypocenters reveal partial elliptical patterns (map view) that migrated downward by $\sim$200 m. The moment tensors reveal remarkably consistent mechanisms, with negative isotropic source types and localized uncertainties, and vertical P-axis orientations. From the moment tensors we derived Poisson's ratios which are variable ($\nu=0.1-0.3)$ for the first half of the collapse events and converged to $\nu\sim0.28$ from June 26 onward.
Spontaneous atomic crystallization via diffractive dephasing in optical cavities

The design of an experiment on the spontaneous crystallization of a laser-cooled, but thermal atomic cloud into a hexagonally structured phase is discussed. Atomic interaction is mediated by the dipole potential of an optical lattice formed spontaneously in a multi-mode degenerate cavity from single-mode longitudinal pumping. The length scale of the structure is given by the diffractive dephasing between the spontaneous sidebands and the on-axis pump. A linear stability indicates that the transition can be observed in a cavity of moderate finesse compatible with having the cavity mirrors outside the vacuum cell. A new anti-reflection coated cell has been assembled for this purpose.
Gradings for nilpotent Lie algebras

We present a constructive approach to torsion-free gradings of Lie algebras. Our main result is the computation of a maximal grading. Given a Lie algebra, using its maximal grading we enumerate all of its torsion-free gradings as well as its positive gradings. As applications, we classify gradings in low dimension, we consider the enumeration of Heintze groups, and we give methods to find bounds for non-vanishing $\ell^{q,p}$ cohomology.
Learning language variations in news corpora through differential embeddings

There is an increasing interest in the NLP community in capturing variations in the usage of language, either through time (i.e., semantic drift), across regions (as dialects or variants) or in different social contexts (i.e., professional or media technolects). Several successful dynamical embeddings have been proposed that can track semantic change through time. Here we show that a model with a central word representation and a slice-dependent contribution can learn word embeddings from different corpora simultaneously. This model is based on a star-like representation of the slices. We apply it to The New York Times and The Guardian newspapers, and we show that it can capture both temporal dynamics in the yearly slices of each corpus, and language variations between US and UK English in a curated multi-source corpus. We provide an extensive evaluation of this methodology.
An analysis of variations in isentropic melt productivity

The amount of melt generated per unit pressure drop during adiabatic upwelling, the isentropic melt productivity, cannot be determined directly from experiments and is commonly assumed to be constant or to decrease as melting progresses. From analysis of one– and two–component systems and from calculations based on a thermodynamic model of peridotite partial melting, we show that productivity for reversible adiabatic (i.e. isentropic) depressurization melting is never constant; rather, productivity tends to increase as melting proceeds. Even in a one–component system with a univariant solid–liquid boundary, the 1/T dependence of (∂S/∂T)P and the downward curvature of the solidus (due to greater compressibility of liquids relative to minerals) lead to increased productivity with increasing melt fraction during batch fusion (and even for fractional fusion in some cases). Similarly, for multicomponent systems, downward curvature of contours of equal melt fraction between the solidus and the liquidus contributes to an increase in productivity as melting proceeds. In multicomponent systems, there is also a lever–rule relationship between productivity and the compositions of coexisting liquid and residue such that productivity is inversely related to the compositional distance between coexisting bulk solid and liquid. For most geologically relevant cases, this quantity decreases during progressive melting, again contributing to an increase in productivity with increasing melting. These results all suggest that the increases in productivity with increasing melt fraction (punctuated by drops in productivity upon exhaustion of each phase from the residue) predicted by thermodynamic modelling of melting of typical mantle peridotites using MELTS are neither artifacts nor unique properties of the model, but rather general consequences of adiabatic melting of upwelling mantle.
Community detection in network neuroscience

Many real-world networks, including nervous systems, exhibit meso-scale structure. This means that their elements can be grouped into meaningful sub-networks. In general, these sub-networks are unknown ahead of time and must be "discovered" algorithmically using community detection methods. In this article, we review evidence that nervous systems exhibit meso-scale structure in the form of communities, clusters, and modules. We also provide a set of guidelines to assist users in applying community detection methods to their own network data. These guidelines focus on the method of modularity maximization but, in many cases, are general and applicable to other techniques.
Nematicidal efficacies of several organic and microbial fertilizers against Root-knot nematodes (Meloidogyne spp.) in organic tomato farming

Bu calisma, organik domates (Gokce cesidi) uretiminde Kok-ur nematodlari (Meloidogyne incognita ve M. javanica)’na karsi organik ve mikrobiyal gubre veya nematisidal ozellik gosteren bazi bitki ekstraktlarinin kullanim olanaklari ve etkinliklerinin karsilastirilmasi amaciyla yurutulmustur. Uygulamalarda ilacli kontrol olarak Fenamiphos (Nemacur® EC 400) etkili maddeli nematisit, sarimsak ekstrakti (NemGuard®), Paecilomyces lilacinus strain 251’in sporlarini iceren (Bioact® WG 400g) biyopreparat, susam yagi (Nemax®), Quillaja saponaria bitkisinin ekstrakti (QLAgri® 35), kekik ekstrakti (Bionem®), Neem (Azadirachta indica) ekstrakti (NeemAzal®-T/S) ve bazi preparatlarin ikili kombinasyonlari kullanilmistir. Sera denemeleri, tesaduf bloklari deneme desenine gore 12 karakterli ve 4 tekerrurlu olmak uzere 2010-2011 yillari guz (Agustos-Ocak) uretim donemlerinde yurutulmustur. Deneme sonucunda, yapilan tum uygulamalarin verim degeri, ur skala degeri ve topraktaki kok-ur nematodu (M. incognita ve M. javanica) 2. donem larva sayilari uzerine etkileri degerlendirilmistir. Denemeler sonunda, 2010’da Bioact®+ QLAgri® 35 (%37.83) ve Nemax®+ QLAgri® 35 (%36.87) uygulamalari, 2011’de Nemax® (%10.84) ve NemGuard® (%9.62) uygulamalari Kok-ur nematodlari (M. incognita and M. javanica)’nin olusturdugu urlari kontrol bitkilerine gore onemli olcude azaltmistir. Uygulamalarin verim uzerine etkileri acisindan, 2010’da Agro-full Nemaflash® (%27.50) ve Nemax®+Bioact® (%21.19) uygulamalari, 2011’de Bioact®+QLAgri® 35 (%23.67) ve Nemax®+Bioact® (%14.29) uygulamalari kontrol bitkilerine gore verimde onemli artislar saglamistir. Sonuc olarak, organik domates yetistiriciliginde zararli olan M. incognita ve M. javanica ile mucadelede, susam yagi uygulamasinin tek basina ve bazi bitki ekstraktlari veya biyopreparat ile kombine uygulamalari oldukca etkili bir yontem olabilecektir
KAAACI Standardization Committee Report on the procedures and applications of the diagnostic tests for drug allergy

Diagnostic methods for drug allergy include the patient’s history, in vivo skin test, in vitro laboratory test, and provocation test. How-ever, the history is often not reliable, procedures for in vivo and in vitro tests are not standardized, and provocation tests are some-times harmful to patients. Generally, skin prick and intradermal tests are useful for immediate reactions; in contrast, patch test and delayed reading of both skin prick and intradermal tests are helpful for delayed reactions. A drug provocation test is the gold stan-dard for both responses, and it is necessary to be aware of exact indications and contraindications with appropriate drugs, doses, and intervals. To date, several methods have been developed to detect culprit agents for drug hypersensitivity reactions, but they are neither completely well validated nor standardized. Based on this awareness and necessity, the Korean Academy of Asthma, Al-lergy and Clinical Immunology launched the Standardization Committee to review the international guidelines and the literature, and then developed the consensus report on the procedures and applications of diagnostic tests for drug allergy. (Allergy Asthma Respir Dis 2017;5:239-247)
Population Prevalence of Deleterious SGCE Variants

Background: Myoclonus-Dystonia (M-D) is a pleiotropic neuropsychiatric disorder of variable penetrance. Pathogenic variants in SGCE, a maternally imprinted gene, are the most frequent known genetic cause of M-D. The population prevalence of SGCE-linked M-D is unknown, the pathogenicity of SGCE variants identified in patients with M-D may be indeterminant, and SGCE variants predicted to be deleterious by in silico analysis may appear in patients undergoing whole-exome or whole-genome sequencing for seemingly unrelated disorders. The Genome Aggregation Database (gnomAD) v2 provides variant data on 125,748 exomes and 15,708 genomes from unrelated individuals sequenced as part of various disease-specific and population genetic studies. Methods: SGCE variants included in the gnomAD v2 dataset were analyzed with Combined Annotation Dependent Depletion (CADD), and database for nonsynonymous single nucleotide polymorphisms’ functional predictions (dbNSFP). We determined the frequency of annotated SGCE variants, ranked by scores of deleteriousness, within the gnomAD v2 dataset. Deleteriousness scores were compared to a subset of published disease associated SGCE pathogenic variants. Results: Within gnomAD v2, there were 56, 408, and 1250 alleles harboring SGCE variants with CADD scores greater than 30, 25, and 20, respectively. We estimate that approximately 1/348 individuals in the United States population harbors an SGCE variant with a CADD score ≥ 25. Discussion: SGCE M-D may be underdiagnosed due to pleiotropy, mild phenotypes, variable penetrance, and impaired access to genetic testing. Due to the high population prevalence of deleterious SGCE variants, caution should be used when asserting pathogenicity without co-segregation analyses and expert neurological examination of phenotypes within pedigrees. Highlights In silico analyses of a large population database of genetic variants revealed that over 0.2% of individuals in the United States harbor a highly deleterious SGCE variant. This finding suggests that M-D and minor phenotypic variants such as mild isolated myoclonus may be underdiagnosed.
Nano-antennes optiques pour l'exaltation et le contrôle de la fluorescence moléculaire dans des volumes sub-longueur d'onde.

Les nano-antennes optiques permettent la manipulation, le confinement et l'exaltation des champs electromagnetiques dans des volumes sub-longueur d'onde. Les applications de ces nano-objets concernent des domaines varies tels que les nano-sources de lumiere, la photovoltaique, la microscopie, la spectroscopie. Les proprietes physiques de ces nano-antennes dependant essentiellement de leur nature, leurs tailles et leurs geometries, la caracterisation experimentale de ces nano-objets est essentielle car elle permet d'en ameliorer fortement le design et d'amplifier les reponses electromagnetiques. La problematique de ce travail de these concerne la caracterisation et l'exploitation des proprietes de nano-antennes optiques. Differentes techniques de caracterisation experimentale de nano-antennes ont ete developpees au cours de cette these: spectroscopie de correlation de fluorescence, suivi de dynamique temporelle de boites quantiques, spectroscopie sous saturation de fluorescence. Ces techniques ont ete appliques pour etudier differents types d'antennes optiques: microspheres dielectriques, nano-ouvertures simples et nano-ouvertures corruguees. Reciproquement, ces nano-antennes optiques ont ete utilisees pour ameliorer efficacement la detection de molecules fluorescentes en solution, avec des exaltations de fluorescence moleculaire superieures a un facteur 100 et un controle de la directivite d'emission de fluorescence, ouvrant ainsi de nouvelles opportunites en biophotonique.
Interplay between mitochondrial dysfunction and epigenetic alterations in environmental linked Parkinson's Disease

Parkinson's disease (PD) is a chronic age progressive neurodegenerative disease that affects about 1% of people over the age of 60. The hallmark pathological features of PD is characterized by the loss of 60-70% dopaminergic neurons, formation of Lewy bodies and aggregation of α-synuclein in the substantia nigra of the midbrain region. There are multiple factors that contribute to the origin of the disease and also these factors further trigger multiple pathways, thus prominently increasing the risk of incurring PD. Although the etiopathogenesis of PD is not very well understood, growing evidences suggest that mitochondrial dysfunction and oxidative stress play a central role in pathophysiological processes of PD. Many years of research has shown that incessant chronic exposure to several pesticides lead to mitochondria dysfunction and degeneration of dopaminergicc neuronal cells leading to PD. The primary target and goal of my Ph.D thesis was to particularly understand the specific role of mitochondrial complex I class of pesticides in causing dopaminergic neuronal damage. Our recently published study demonstrate that the green house pesticides, tebufenpyrad and pyridaben exposure cause a major adverse effect on mitochondrial function in cell based models of Parkinson’s disease. These pesticides are classified as mitochondrial complex I inhibitors and are functionally analogous to the Parkinsonian neurotoxicant rotenone, in terms of target and mode of action. Herein, we demonstrate that acute exposure of tebufenpyrad or pyridaben to the N27 neuronal cells, induced a time dependent loss of mitochondrial function and structural integrity by significant impairment of mitochondrial respiration, ATP production and redox status,suggesting that the pesticides neurotoxicity is associated with mitochondrial dysfunction oxidative damage in dopaminergic neuronal cells. In an effort to understand the molecular mechanisms underlying mitochondria dysfunction mediated apoptotic cell death, we examined the role of PKCδ a pro-apoptotic signaling kinase and
Capillary Tension in Rotting Ice Layers

Abstract : This study examines the moisture tension constituent relationship of ice near 0 C by measuring its moisture characteristic curve. In this novel approach, we held ice samples in a Tempe cell submerged in a cold bath and removed water from the ice veins through incremental increases in air pressure. Observed air entry and water re-entry pressures generally support an idealized flow path model of alternating vein segments and nodes. Re-entry of water into the air-filled ice samples occurred slowly over a period of weeks, suggesting low permeabilities on the order of 1 x 10(-18) m2. We conclude that ice desaturates and rewets in a manner similar to soils, with entry pressures on the order of silts.