Miracle-dz/UniFAR-FTUs huggingface dataset:
Continual Learning of Generative Models with Limited Data: From Wasserstein-1 Barycenter to Adaptive Coalescence

Learning generative models is challenging for a network edge node with limited data and computing power. Since tasks in similar environments share a model similarity, it is plausible to leverage pretrained generative models from other edge nodes. Appealing to optimal transport theory tailored toward Wasserstein-1 generative adversarial networks (WGANs), this study aims to develop a framework that systematically optimizes continual learning of generative models using local data at the edge node while exploiting adaptive coalescence of pretrained generative models. Specifically, by treating the knowledge transfer from other nodes as Wasserstein balls centered around their pretrained models, continual learning of generative models is cast as a constrained optimization problem, which is further reduced to a Wasserstein-1 barycenter problem. A two-stage approach is devised accordingly: 1) the barycenters among the pretrained models are computed offline, where displacement interpolation is used as the theoretic foundation for finding adaptive barycenters via a "recursive" WGAN configuration and 2) the barycenter computed offline is used as metamodel initialization for continual learning, and then, fast adaptation is carried out to find the generative model using the local samples at the target edge node. Finally, a weight ternarization method, based on joint optimization of weights and threshold for quantization, is developed to compress the generative model further. Extensive experimental studies corroborate the effectiveness of the proposed framework.
First-principles study of the surface of silica and sodium silicate glasses

We use ab initio molecular dynamics simulations to investigate the properties of the dry surface of pure silica and sodium silicate glasses. The surface layers are defined based on the atomic distributions along the direction (z−direction) perpendicular to the surfaces. We show that these surfaces have a higher concentration of dangling bonds as well as twomembered (2M) rings than the bulk samples. Increasing concentration of Na2O reduces the proportion of structural defects. From the vibrational density of states, one concludes that 2M rings have a unique vibrational signature at a frequency ≈ 850 cm, compatible with experimental findings. We also find that, due to the presence of surfaces, the atomic vibration in the z−direction is softer than for the two other directions. The electronic density of states shows clear the differences between the surface and interior and we can attribute these to specific structural units. Finally, the analysis of the electron localization function allows to get insight on the influence of local structure and the presence of Na on the nature of chemical bonding in the glasses. ∗Corresponding author: simona.ispas@umontpellier.fr
A novel DL approach to PE malware detection: exploring Glove vectorization, MCC_RCNN and feature fusion

In recent years, malware becomes more threatening. Concerning the increasing malware variants, there comes Machine Learning (ML)-based and Deep Learning (DL)-based approaches for heuristic detection. Nevertheless, the prediction accuracy of both needs to be improved. In response to the above issues in the PE malware domain, we propose the DL-based approaches for detection and use static-based features fed up into models. The contributions are as follows: we recapitulate existing malware detection methods. That is, we propose a vec-torized representation model of the malware instruction layer and semantic layer based on Glove. We implement a neural network model called MCC_RCNN (Malware Detection and Recurrent Convolutional Neural Network), comprising of the combination with CNN and RNN. Moreover, we provide a description of feature fusion in static behavior levels. With the numerical results generated from several comparative experiments towards evaluating the Glove-based vectoriza-tion, MCC_RCNN-based classification methodology and feature fusion stages, our proposed classification methods can obtain a higher prediction accuracy than the other baseline methods.
A Categorical Semantics for Bounded Petri Nets

We provide a categorical semantics for bounded Petri nets, both in the collective- and individual-token philosophy. In both cases, we describe the process of bounding a net internally, by just constructing new categories of executions of a net using comonads, and externally, using lax-monoidal-lax functors. Our external semantics is non-local, meaning that tokens are endowed with properties that say something about the global state of the net. We then prove, in both cases, that the internal and external constructions are equivalent, by using machinery built on top of the Grothendieck construction. The individual-token case is harder, as it requires a more explicit reliance on abstract methods.
Single Neuron Segmentation Using Graph-Based Global Reasoning with Auxiliary Skeleton Loss from 3D Optical Microscope Images

One of the critical steps in improving accurate single neuron reconstruction from three-dimensional (3D) optical microscope images is the neuronal structure segmentation. However, they are always hard to segment due to the lack in quality. Despite a series of attempts to apply convolutional neural networks (CNNs) on this task, noise and disconnected gaps are still challenging to alleviate with the neglect of the non-local features of graph-like tubular neural structures. Hence, we present an end-to-end segmentation network by jointly considering the local appearance and the global geometry traits through graph reasoning and a skeleton-based auxiliary loss. The evaluation results on the Janelia dataset from the BigNeuron project demonstrate that our proposed method exceeds the counterpart algorithms in performance.
Comparison of Three Physical—Cognitive Training Programs in Healthy Older Adults: A Study Protocol for a Monocentric Randomized Trial

(1) Combining aerobic, coordination and cognitive training allows for more improved physical and cognitive performance than when performed separately. A Nordic walking (NW) and two cognitive-motor circuit training programs (CT-c and CT-fit) are compared. CT-c and CT-fit stimulate cognition differently: CT-c, is through conventional complex coordination training performed in single and dual-task conditions; CT-fit, incorporates it into complex goal-directed actions, implemented by fitness gaming technology (2) The aim is to determine whether CT-fit brings additional benefits to cognition compared to more traditional training. (3) Forty-five healthy independent living community dwellers participants (65–80 years) will be included after a general medical examination. The main exclusion criteria are signs of cognitive impairments (Mini–Mental State Examination < 26/30) and physical impairments. Pre and post-tests will be performed to assess: cognitive functions (Montreal Cognitive Assessment; Trail Making Test; Stroop task, working memory test, Rey Complex Figure copy task, Oral Trail Making Test, and dual-task); motor fitness (Bipedal and unipedal balance test, gait assessments, Time Up and Go, chair sit and reach test and four-square stepping test); and physical fitness (10 m incremental shuttle walking test, maximal handgrip force, Timed-Stands test). (4) Incorporating cognitive demands into complex, goal-directed actions using fitness gaming technology should be the best solution to optimize training benefits.
Total Scattering Cross Section of Solids for Cold and Epithermal Neutrons

Simple expressions are given which accurately describe the total cross section of solids at cold and epithermal neutron energies. They are intended to provide a consistent procedure for the evaluation of solid state effects, which must be substracted from the experimental data when information is wanted on intrinsic nuclear properties. The formulae presented here are the asymptotic forms to which the full expressions reduce in those energy limits. A Debye model is adopted to describe the frequency spectrum of the solid. The present results are compared with some commonly used expressions as well as with the exact formalism. The effect of temperature is discussed and numerical calculations are presented for some cases.
Non-contact measurement technique of the vapor pressure of liquid and high temperature solid materials

Here is reported a new scheme to accurately determine the vapor pressure of undercooled, liquid, and high temperature solid materials. The method relies on an imaging technique that measures the time variation of the radius of an electrostatically levitated sample. This scheme, compared to other techniques, offers unique opportunity to explore not only the liquid above the melting point but also the undercooled states of highly reactive materials in a contamination free environment. This was exemplified in this paper with titanium. For the first time, we report the vapor pressure $(V_{\rm p})$ of its liquid phase over a large temperature range, covering the undercooled region. Over the 1700 to 2050 K temperature range, it was measured as Log $V_{\rm p}(T) = 9.154 - 17978\ T^{-1}$ (3%). Similarly, for high temperature solid titanium, the vapor pressure could be expressed as Log $V_{\rm p}(T) = 16.634 - 32960\ T^{-1}$ (6%) over the 1770 to 1940 K temperature interval. From these data, the average latent heats of vaporization and sublimation were calculated respectively as 344.8 kJ/kg (8%) and 632.1 kJ/kg (6%) respectively.
Green fabrication of antibacterial polymer/silver nanoparticle nanohybrids by dual-spinneret electrospinning

Nanohybrids from waterborne polyurethane (WPU), poly(vinyl alcohol) (PVA) and silver nanoparticles (AgNPs) with ultrasmall sizes (5.1 ± 0.6 nm) are facilely obtained by directly one-step dual-spinneret electrospinning fabrication from water. SEM and TEM images indicated the nanofibre morphology as designed. Moreover, the elemental distribution mapping indicated the good mixture of WPU/PVA and PVA/AgNPs nanofibers in the mats via such dual-spinneret technique. Also the thermal stability and biocidal activity of the hybrid nanofibers were greatly enhanced by the addition of WPU component and incorporation of Ag nanoparticles without increase of cytotoxicity.
Oxynitride materials for solar water splitting

Water splitting to form hydrogen and oxygen over a heterogeneous photocatalyst using solar energy is a promising process for clean and renewable hydrogen production. In recent years, numerous attempts have been made for the development of photocatalysts that work under visible light irradiation to efficiently utilize solar energy. This article reviews recent research progress in the development of visible light-driven photocatalysts, focusing on the refinement of oxynitride materials. They harvest visible photons (~450–700 nm) and work as stable photocatalysts for water reduction and oxidation under visible light. Oxynitrides with d ^0 electronic configuration can be successfully applied to a two-step water-splitting system, which can harvest a wide range of visible photons (~660 nm), in the presence of an iodate/iodide shuttle redox mediator. Also d ^10-type oxynitrides of GaN–ZnO and ZnGeN_2–ZnO solid solutions can achieve functionality as photocatalysts for overall water-splitting under visible light without noticeable degradation.
Pollen sterols are associated with phylogeny and environment but not with pollinator guilds

Summary Phytosterols are primary plant metabolites that have fundamental structural and regulatory functions. They are also essential nutrients for phytophagous insects, including pollinators, that cannot synthesize sterols. Despite the well‐described composition and diversity in vegetative plant tissues, few studies have examined phytosterol diversity in pollen. We quantified 25 pollen phytosterols in 122 plant species (105 genera, 51 families) to determine their composition and diversity across plant taxa. We searched literature and databases for plant phylogeny, environmental conditions, and pollinator guilds of the species to examine the relationships with pollen sterols. 24‐methylenecholesterol, sitosterol and isofucosterol were the most common and abundant pollen sterols. We found phylogenetic clustering of twelve individual sterols, total sterol content and sterol diversity, and of sterol groupings that reflect their underlying biosynthesis pathway (C‐24 alkylation, ring B desaturation). Plants originating in tropical‐like climates (higher mean annual temperature, lower temperature seasonality, higher precipitation in wettest quarter) were more likely to record higher pollen sterol content. However, pollen sterol composition and content showed no clear relationship with pollinator guilds. Our study is the first to show that pollen sterol diversity is phylogenetically clustered and that pollen sterol content may adapt to environmental conditions.
Metal Derivatives of Heterocyclic Thioamides: Synthesis and Crystal Structures of Copper Complexes with 1-Methyl-1,3-imidazoline-2-thione and 1,3-Imidazoline-2-thione

Reactions of copper(I) halides (Cl, Br, I) with 1-methyl-1, 3-imidazoline-2-thione (mimzSH) in 1 : 2 molar ratio yielded sulfur-bridged dinuclear [Cu2X2(μ-S-mimzSH)2(η1-S-mimzSH)2] (X = I, 1, Br, 2; Cl, 3) complexes. Copper(I) iodide with 1,3-imidazoline-2-thione (imzSH2) and Ph3P in 1 : 1 : 1 molar ratio has also formed a sulfur-bridged dinuclear [Cu2I2(μ-S-imzSH2)2(PPh3)2] (4) complex. The central Cu(μ-S)2Cu cores form parallelograms with unequal Cu–S bond distances {2.324(2), 2.454(3) A} (1); {2.3118(6), 2.5098(6) A} (2); {2.3075(4), 2.5218(4) A} (3); {2.3711(8), 2.4473(8) A} (4). The Cu···Cu separations, 2.759–2.877A in complexes 1–3 are much shorter than 3.3446A in complex 4. The weak intermolecular interactions {H2CH···S# (2); CH···Cl# (3); NH···I# (4)} between dimeric units in complexes 2–4 lead to the formation of linear 1D polymers.
A combined sensor for simultaneous high resolution 2‐D imaging of oxygen and trace metals fluxes

A new sandwich sensor, consisting of an O2 planar optode overlain by a thin (90 µm) DGT layer is presented. This sensor can simultaneously resolve 2‐D O2 dynamics and trace metal fluxes in benthic substrates at a high spatial resolution. The DGT layer accumulates metals on a small particle size (0.2 µm) chelating resin and records the locally induced trace metal flux during the deployment, whereas the planar optode resolves the O2 dynamic in near real time at the same location in the sediment. Despite its ultrathin composition, the DGT layer has high carrying capacity for trace metals with no saturation problems during application to typical coastal‐ or contaminated sediments. Combined with laser ablation, accumulated metal fluxes could be resolved at a resolution of ~200 µm, whereas the O2 images had a resolution of ~100 µm. A 2‐D diffusion‐reaction model showed that the enhanced smearing and reduced response time of the O2 signal associated with the additional DGT layer were marginal. To test sensor performance at realistic conditions, it was applied to an artificial burrow system consisting of permeable dialysis tubing flushed with oxygenated seawater. The measurements demonstrated localized mobilization of Ni, Cu, and Pb close to the burrow wall, where O2 was elevated. The latter was also confirmed for Cu and Pb in natural sediments irrigated by the polychaete Hediste diversicolor. The sandwich sensor has great potential for investigating interrelations between O2 dynamics and metal mobilization in complex benthic systems such as burrows, rhizospheres, permeable sands, and microbial mats.
Sex‐specific associations between life‐history traits and a novel reproductive polymorphism in the Pacific field cricket

Associations between heritable polymorphisms and life‐history traits, such as development time or reproductive investment, may play an underappreciated role in maintaining polymorphic systems. This is because selection acting on a particular morph could be bolstered or disrupted by correlated changes in life history or vice versa. In a Hawaiian population of the Pacific field cricket (Teleogryllus oceanicus), a novel mutation (flatwing) on the X‐chromosome is responsible for a heritable polymorphism in male wing structure. We used laboratory cricket colonies fixed for male wing morph to investigate whether males and females bearing the flatwing or normal‐wing (wild‐type) allele differed in their life‐history traits. We found that flatwing males developed faster and had heavier testes than normal‐wings, whereas flatwing homozygous females developed slower and had lighter reproductive tissues than normal‐wing homozygous females. Our results advance our understanding of the evolution of polymorphisms by demonstrating that the genetic change responsible for a reproductive polymorphism can also have consequences for fundamental life‐history traits in both males and females.
Mid-infrared (λ ∼ 2–6 µm) measurements of the refractive indices of GaAs and AlAs

AlAs and GaAs dispersion properties in the mid-IR (λ ~ 2–6 µm) spectral region have been measured for the first time by modelling reflectivity spectra from a GaAs/AlAs 'distributed Bragg reflector' in the near- (1–1.7 µm) and mid- (2–6 µm) IR spectral ranges. The near-IR spectra are first modelled, using existing dispersion data, for layer thickness calibrations. Modelling the mid-IR reflectivity then allows the mid-IR dispersion properties to be extracted. The resulting mid-IR dispersion curves can be accurately parameterized with a 'single effective oscillator' model and represent significant improvement over previous theoretical estimates.
Impaired Immune Function in Patients with Chronic Postsurgical Hypoparathyroidism: Results of the EMPATHY Study.

CONTEXT despite the pivotal role of calcium signaling in immune response, little is known about immune function in patients affected by hypoparathyroidism. OBJECTIVE to evaluate immune function in hypoparathyroidism. DESIGN The Evaluation of iMmune function in Postsurgical and AuToimmune HYpoparathyroidism (NCT04059380) is a case-control cross-sectional study. SETTING Italian referral center. PATIENTS 20 patients with postsurgical hypoparathyroidism (12 females) and 20 age- and sex-matched controls. MAIN OUTCOME MEASURE(S) calcium metabolism assessment, PBMC profiling via flow cytometry, PTH receptor 1 (PTHr1) expression analysis using immunofluorescence and PrimeFlow RNA assay, gene expression analysis via real-time PCR, cytokine measurement, and evaluation of infectious disease frequency and severity. RESULTS immune cell profiling revealed decreased monocytes, regulatory, naive and total CD4 + T lymphocytes, which correlated with total calcium, ionized calcium, and PTH levels, in patients with hypoparathyroidism. Patients with hypoparathyroidism had higher CD3 -CD56 + natural killer (NK) cell count, which inversely correlated with calcium, PTH, and vitamin D levels. Furthermore, they exhibited decreased TNF and GM-CSF gene expression and decreased circulating TNF levels. Gene expression and immunofluorescence analysis confirmed PTHr1 expression in all PBMC lineages; however, the percentage of cells expressing PTHr1 was lower while the intensity of PTHr1 expression in monocytes, total T lymphocytes, CD8 +CD4 + and CD4 + T lymphocytes, and total NK cells were higher in patients with hypoparathyroidism. CONCLUSIONS this study describes for the first time the immune alterations in patients with hypoparathyroidism receiving conventional therapies, supporting the immunoregulatory role of PTH and proposing an explanation for the increased susceptibility to infections observed in epidemiological studies.
Insect species richness affects plant responses to multi‐herbivore attack

Summary Plants are often attacked by multiple insect herbivores. How plants deal with an increasing richness of attackers from a single or multiple feeding guilds is poorly understood. We subjected black mustard (Brassica nigra) plants to 51 treatments representing attack by an increasing species richness (one, two or four species) of either phloem feeders, leaf chewers, or a mix of both feeding guilds when keeping total density of attackers constant and studied how this affects plant resistance to subsequent attack by caterpillars of the diamondback moth (Plutella xylostella). Increased richness in phloem‐feeding attackers compromised resistance to P. xylostella. By contrast, leaf chewers induced a stronger resistance to subsequent attack by caterpillars of P. xylostella while species richness did not play a significant role for chewing herbivore induced responses. Attack by a mix of herbivores from different feeding guilds resulted in plant resistance similar to resistance levels of plants that were not previously exposed to herbivory. We conclude that B. nigra plants channel their defence responses stronger towards a feeding‐guild specific response when under multi‐species attack by herbivores of the same feeding guild, but integrate responses when simultaneously confronted with a mix of herbivores from different feeding guilds.
Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review

Human gait is a complex activity that requires high coordination between the central nervous system, the limb, and the musculoskeletal system. More research is needed to understand the latter coordination's complexity in designing better and more effective rehabilitation strategies for gait disorders. Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are among the most used technologies for monitoring brain activities due to portability, non-invasiveness, and relatively low cost compared to others. Fusing EEG and fNIRS is a well-known and established methodology proven to enhance brain–computer interface (BCI) performance in terms of classification accuracy, number of control commands, and response time. Although there has been significant research exploring hybrid BCI (hBCI) involving both EEG and fNIRS for different types of tasks and human activities, human gait remains still underinvestigated. In this article, we aim to shed light on the recent development in the analysis of human gait using a hybrid EEG-fNIRS-based BCI system. The current review has followed guidelines of preferred reporting items for systematic reviews and meta-Analyses (PRISMA) during the data collection and selection phase. In this review, we put a particular focus on the commonly used signal processing and machine learning algorithms, as well as survey the potential applications of gait analysis. We distill some of the critical findings of this survey as follows. First, hardware specifications and experimental paradigms should be carefully considered because of their direct impact on the quality of gait assessment. Second, since both modalities, EEG and fNIRS, are sensitive to motion artifacts, instrumental, and physiological noises, there is a quest for more robust and sophisticated signal processing algorithms. Third, hybrid temporal and spatial features, obtained by virtue of fusing EEG and fNIRS and associated with cortical activation, can help better identify the correlation between brain activation and gait. In conclusion, hBCI (EEG + fNIRS) system is not yet much explored for the lower limb due to its complexity compared to the higher limb. Existing BCI systems for gait monitoring tend to only focus on one modality. We foresee a vast potential in adopting hBCI in gait analysis. Imminent technical breakthroughs are expected using hybrid EEG-fNIRS-based BCI for gait to control assistive devices and Monitor neuro-plasticity in neuro-rehabilitation. However, although those hybrid systems perform well in a controlled experimental environment when it comes to adopting them as a certified medical device in real-life clinical applications, there is still a long way to go.