Miracle-dz/UniFAR-FTUs huggingface dataset:
Structure and New Substructure of α-Ti2O3: X-ray Diffraction and Theoretical Study

1 Faculté de Chimie, Laboratoire Sciences des matériaux, Université des Sciences et de la Technologie Houari-Boumediene, USTHB, Algérie. 1, 2 Département de Chimie, Laboratoire Chimie Physique Moléculaire et Macromoléculaire (LCPMM), Université Blida1, Algérie. 3 Département de Physique, Laboratoire de Physique Théorique et Interaction Rayonnement Matière, Université Blida1, Algérie. 4 CNRS, ISCR (Institut des Sciences Chimiques de Rennes), Université de Rennes 1, France.
Relationship between Interprofessional Communication and Sharing Information and Nurses' Burnout and Work Autonomy

Interprofessional communication can positively improve clinical outcomes. Aim: to examine the relationship between interprofessional communication and sharing information and nurses' burnout and work autonomy. Methods: Design: cross-sectional correlational. Sampling: all physicians and nurses working in critical care units at Damanhour National Medical Institute, Egypt (N=167). Tools: self-administered questionnaires composed of four parts, namely: Communication and Sharing Information (CSI) scale; Maslach Burnout Inventory-Human Services Scale (MBI-HSS); Global Work Autonomy Scale (GWA); and a demographic data sheet. Results: the findings showed that sharing of medical information and nurses-physicians communication were the highest dimensions. High mean of emotional exhaustion; depersonalization; and personal accomplishment dimensions of burnout; as well as total work autonomy. Significant differences were found between age and interprofessional communication and sharing information and both burnout dimensions (emotional exhaustion) and (depersonalization). No significant differences were found between all demographic characteristics and both nurses' burnout (personal accomplishment) and total work autonomy. An intermediate positive correlation was found between total communication and sharing information and burnout dimensions: emotional exhaustion and depersonalization and total work autonomy. Conclusion: total communication and sharing information are positively correlated to both burnout subscales: emotional exhaustion and depersonalization; and total work autonomy. However, total communication and sharing information and burnout subscale (personal accomplishment) are not correlated. Recommendations: strategies that foster patient-centered communication; and development of policies to equalize power dynamics; and enhance teamwork should be implemented.
Alternative method for the transformation of Capsicum species

Capsicum species have a high economic value and have been serving as an important food crop worldwide for a long time. It is the second most signi icant crop in the family of Solanaceae after tomato [1]. The studying of pepper genes is having major barriers due to most of the Capsicum known to be recalcitrant to plant regeneration in vitro, and to genetic transformation with Agrobacterium tumefaciens [2]. A. tumefaciens mediated transformation in Capsicum species relies on the plant variety and showing low transformational ef iciencies. Current studies indicate that inding A. tumefaciens mediated transformational protocols that can be applied across a wide range of Capsicum genotypes seems unrealistic at this moment [3,4].
Opcode 와 Windows API 를 사용한 멀웨어 탐지 Malware Detection Method using Opcode and windows API Calls

We proposed malware detection method, which use the feature vector that consist of Opcode(operation code) and Windows API Calls extracted from executable files. And, we implemented our feature vector and measured the performance of it by using Bernoulli Naïve Bayes and K-Nearest Neighbor classifier. In experimental result, when using the K-NN classifier with the proposed method, we obtain 95.21% malware detection accuracy. It was better than existing methods using only either Opcode or Windows API Calls.
Reduced Reflex Autonomic Responses Following Intradetrusor OnabotulinumtoxinA Injections: A Pre-/Post-study in Individuals With Cervical and Upper Thoracic Spinal Cord Injury

Urodynamic studies (UDS) can provoke autonomic dysreflexia (AD) in individuals with spinal cord injury (SCI) at and above the sixth thoracic spinal segment potentially leading to profound vagally mediated heart rate (HR) reductions. In this study,1 we test the hypothesis that intradetrusor onabotulinumtoxinA injections will improve HR and its variability (HRV) responses to UDS in individuals with cervical and thoracic SCI. A total of 19 participants with chronic SCI (5 women, mean age 42.5 ± 7.9 years) with confirmed neurogenic detrusor overactivity underwent UDS before (i.e., baseline) and 1 month after intradetrusor onabotulinumtoxinA (200 U) injections (post-treatment). Continuous electrocardiography and blood pressure (BP) recordings were used to assess RR-interval, time, and frequency domain metrics of HRV (a surrogate marker of autonomic nervous system activity), and AD pre- and post-treatment. UDS pre-treatment resulted in increased RR-interval as well as time and frequency domain metrics of HRV. Vagally mediated increases in high-frequency (HF) power during UDS were larger in participants with cervical compared to upper thoracic SCI. Post-treatment, UDS had no effect on RR-interval and significantly reduced instances of bradycardia. Furthermore, intradetrusor onabotulinumtoxinA injections significantly reduced time domain metrics of HRV and HF power responses to UDS across all participants. Changes in HRV during UDS could be a potential indicator of improved autonomic cardiovascular function following interventions such as intradetrusor onabotulinumtoxinA injections.
Resting-state functional MRI signal fluctuations are correlated with brain amyloid-Î² deposition

Mounting evidence suggests that amyloid-{beta} (A{beta}) and vascular etiologies are intertwined in the pathogenesis of Alzheimer's disease. Spontaneous fluctuations of the brain blood-oxygen-level-dependent (BOLD) signal, as measured by resting-state functional MRI (rs-fMRI), have been shown to be associated with neuronal activities as well as cerebrovascular hemodynamics. Nevertheless, it is unclear if rs-fMRI BOLD fluctuations are associated with brain A{beta} deposition in individuals with an elevated risk of Alzheimer's disease. We recruited 33 patients with amnestic mild cognitive impairment who underwent rs-fMRI and positron emission tomography (PET). The A{beta} standardized uptake value ratio (SUVR) was calculated with cortical white matter as the reference region to improve sensitivity for cortical A{beta} quantification. We calculated the amplitudes of low-frequency fluctuations (ALFF) of local BOLD signals in the frequency band of 0.01-0.08 Hz. Applying physiological/vascular signal regression in stepwise increasing levels on the rs-fMRI data, we examined whether local correlations between ALFF and brain A{beta} deposition were driven by vascular hemodynamics, spontaneous neuronal activities, or both. We found that ALFF and A{beta} SUVR were negatively correlated in brain regions involving the default-mode and visual networks, with peak correlation at the precuneus, and angular, lingual, and fusiform gyri. Regions with higher ALFF had less A{beta} accumulation. The correlated cluster sizes in MNI space were reduced from 3018 mm3 with no physiological/vascular regression to 1072 mm3 with strong physiological/vascular regression, with mean cluster r values at approximately -0.47. Results demonstrate that both vascular hemodynamics and neuronal activities, as reflected by BOLD fluctuations, are negatively associated with brain A{beta} deposition. These findings further imply that local brain blood fluctuations due to either vascular hemodynamics or neuronal activities can affect A{beta} homeostasis.
Estimation of local time-varying reproduction numbers in noisy surveillance data

A valuable metric in understanding local infectious disease dynamics is the local time-varying reproduction number, i.e. the expected number of secondary local cases caused by each infected individual. Accurate estimation of this quantity requires distinguishing cases arising from local transmission from those imported from elsewhere. Realistically, we can expect identification of cases as local or imported to be imperfect. We study the propagation of such errors in estimation of the local time-varying reproduction number. In addition, we propose a Bayesian framework for estimation of the true local time-varying reproduction number when identification errors exist. And we illustrate the practical performance of our estimator through simulation studies and with outbreaks of COVID-19 in Hong Kong and Victoria, Australia. This article is part of the theme issue ‘Technical challenges of modelling real-life epidemics and examples of overcoming these’.
6-Month Follow Up of 8679 Hospitalized COVID-19 Patients in Germany: A Nationwide Cohort Study

Background: Data on long-term outcomes of hospitalized COVID-19 patients are scarce. Objective: To provide a detailed account of hospitalized COVID-19 patients until 180 days after their initial hospitalization. Design: Nationwide cohort study using claims data from the German Local Health Care Funds, the health insurer of one-third of the German population. Setting: Germany. Patients: Adult patients hospitalized in Germany between Feb 1 and April 30, 2020 with PCR-confirmed COVID-19 and a related principal diagnosis. Measurements: Patient characteristics and ventilation status, in-hospital, 30-, 90- and 180-day mortality measured from admission, and 180-day readmission measured from discharge. Multivariable logistic regression model of independent risk factors for 180-day mortality. Results: Of 8679 patients (median age, 72 years), 2161 (24.9%) died during the index hospitalization. 30-day mortality was 23.9% (2073/8679), 90-day mortality 27.9% (2425/8679), and 180-day mortality 29.6% (2566/8679). The latter was 52.3% (1472/2817) for patients aged [≥] 80 years, and 53.0% for patients who had been ventilated invasively (853/1608). Risk factors for 180-day mortality included coagulopathy, BMI [≥] 40, and age. Female sex was a protective factor. Of 6235 patients discharged alive, 1668 patients were readmitted a total of 2551 times within 180 days, resulting in an overall readmission rate of 26.8%. Limitations: We could not stratify patients by ICU treatment as it is not coded separately. Furthermore, we cannot exclude residual confounding in our analysis of risk factors nor determine causality between risk factors and long-term mortality given the observational nature of this study. Conclusion: This nationwide cohort study of hospitalized COVID-19 patients in Germany found considerable long-term mortality and readmission rates, especially among patients with coagulopathy. Close follow-up after hospital discharge may improve long-term outcome.
Fostering learners' self-regulation and collaboration skills and strategies for mobile language learning beyond the classroom

Many language learners need to be supported in acquiring a second or foreign language quickly and effectively across learning environments beyond the classroom. The chapter argues that support should focus on the development of two vital learning skills, namely being able to self-regulate and to collaborate effectively in the learning process. We base our argumentation on the theoretical lenses of self-regulated learning (SRL) and collaborative learning in the context of mobile situated learning that can take place in a variety of settings. The chapter examines a sample of selected empirical studies within the field of mobile-assisted language learning with a twofold aim. Firstly, the studies are analyzed in order to understand the role of learner self-regulation and collaboration while acquiring a new language beyond the classroom. Secondly, we aim to provide a deeper understanding of any mechanisms provided to develop or support language learners’ self-regulated and collaborative learning skills. Finally, we propose that fostering SRL and collaborative learning skills and strategies will benefit from recent advances in the fields of learning analytics and artificial intelligence, coupled with the use of mobile technologies and self-monitoring mechanisms. The ultimate aim is to enable the provision of individual adaptive learning paths to facilitate language learning beyond the classroom.
Automatic Post-Editing for Translating Chinese Novels to Vietnamese

Automatic post-editing (APE) is an important remedy for reducing errors of raw translated texts that are produced by machine translation (MT) systems or software-aided translation. In this paper, we present the first attempt to tackle the APE task for Vietnamese. Specifically, we construct the first large-scale dataset of 5M Vietnamese translated and corrected sentence pairs. We then apply strong neural MT models to handle the APE task, using our constructed dataset. Experimental results from both automatic and human evaluations show the effectiveness of the neural MT models in handling the Vietnamese APE task.
Quantization of Deep Neural Networks for Accurate EdgeComputing

Deep neural networks have demonstrated their great potential in recent years, exceeding the performance of human experts in a wide range of applications. Due to their large sizes, however, compression techniques such as weight quantization and pruning are usually applied before they can be accommodated on the edge. It is generally believed that quantization leads to performance degradation, and plenty of existing works have explored quantization strategies aiming at minimum accuracy loss. In this paper, we argue that quantization, which essentially imposes regularization on weight representations, can sometimes help to improve accuracy. We conduct comprehensive experiments on three widely used applications: fully connected network for biomedical image segmentation, convolutional neural network for image classification on ImageNet, and recurrent neural network for automatic speech recognition, and experimental results show that quantization can improve the accuracy by 1%, 1.95%, 4.23% on the three applications respectively with 3.5x-6.4x memory reduction.
Class Equilibrium using Coulomb's Law

Projection algorithms learn a transformation function to project the data from input space to the feature space, with the objective of increasing the inter-class distance. However, increasing the inter-class distance can affect the intra-class distance. Maintaining an optimal inter-class separation among the classes without affecting the intra-class distance of the data distribution is a challenging task. In this paper, inspired by the Coulomb's law of Electrostatics, we propose a new algorithm to compute the equilibrium space of any data distribution where the separation among the classes is optimal. The algorithm further learns the transformation between the input space and equilibrium space to perform classification in the equilibrium space. The performance of the proposed algorithm is evaluated on four publicly available datasets at three different resolutions. It is observed that the proposed algorithm performs well for lowresolution images.
The Design of the User Interfaces for Privacy Enhancements for Android

We present the design and design rationale for the user interfaces for Privacy Enhancements for Android (PE for Android). These UIs are built around two core ideas, namely that developers should explicitly declare the purpose of why sensitive data is being used, and these permission-purpose pairs should be split by first party and third party uses. We also present a taxonomy of purposes and ways of how these ideas can be deployed in the existing Android ecosystem.
Speeding up Computational Morphogenesis with Online Neural Synthetic Gradients

A wide range of modern science and engineering applications are formulated as optimization problems with a system of partial differential equations (PDEs) as constraints. These PDE-constrained optimization problems are typically solved in a standard discretize-then-optimize approach. In many industry applications that require high-resolution solutions, the discretized constraints can easily have millions or even billions of variables, making it very slow for the standard iterative optimizer to solve the exact gradients. In this work, we propose a general framework to speed up PDE-constrained optimization using online neural synthetic gradients (ONSG) with a novel two-scale optimization scheme. We successfully apply our ONSG framework to computational morphogenesis, a representative and challenging class of PDE-constrained optimization problems. Extensive experiments have demonstrated that our method can significantly speed up computational morphogenesis (also known as topology optimization), and meanwhile maintain the quality of final solution compared to the standard optimizer. On a large-scale 3D optimal design problem with around 1,400,000 design variables, our method achieves up to 7.5x speedup while producing optimized designs with comparable objectives.
Oriented Bounding Boxes for Small and Freely Rotated Objects

A novel object detection method is presented that handles freely rotated objects of arbitrary sizes, including tiny objects as small as $2 \times 2$ pixels. Such tiny objects appear frequently in remotely sensed images, and present a challenge to recent object detection algorithms. More importantly, current object detection methods have been designed originally to accommodate axis-aligned bounding box detection, and therefore fail to accurately localize oriented boxes that best describe freely rotated objects. In contrast, the proposed convolutional neural network (CNN) -based approach uses potential pixel information at multiple scale levels without the need for any external resources, such as anchor boxes. The method encodes the precise location and orientation of features of the target objects at grid cell locations. Unlike existing methods that regress the bounding box location and dimension, the proposed method learns all the required information by classification, which has the added benefit of enabling oriented bounding box detection without any extra computation. It thus infers the bounding boxes only at inference time by finding the minimum surrounding box for every set of the same predicted class labels. Moreover, a rotation-invariant feature representation is applied to each scale, which imposes a regularization constraint to enforce covering the 360° range of in-plane rotation of the training samples to share similar features. Evaluations on the xView and dataset for object detection in aerial images (DOTA) data sets show that the proposed method uniformly improves performance over existing state-of-the-art methods.
Probing lipid membrane bending mechanics using gold nanorod tracking

Lipid bilayer membranes undergo rapid bending undulations with wavelengths from tens of nanometers to tens of microns due to thermal fluctuations. Here, we probe such undulations and the membranes’ mechanics by measuring the time-varying orientation of single gold nanorods (GNRs) adhered to the membrane, using high-speed dark field microscopy. In a lipid vesicle, such measurements allow the determination of the membrane’s viscosity, bending rigidity, and tension as well as the friction coefficient for sliding of the monolayers over one another. The in-plane rotation of the GNR is hindered by undulations in a tension dependent manner, consistent with simulations. The motion of single GNRs adhered to the plasma membrane of living cultured cells similarly reveals the membrane’s complex physics and coupling to the cell’s actomyosin cortex.
Detecting new edge types in a temporal network model

Networks representing complex systems in nature and society usually involve multiple interaction types. These types suggest essential information on the interactions between components, but not all of the existing types are usually discovered. Therefore, detecting the undiscovered edge types is crucial for deepening our understanding of the network structure. Although previous studies have discussed the edge label detection problem, we still lack effective methods for uncovering previously-undetected edge types. Here, we develop an effective technique to detect undiscovered new edge types in networks by leveraging a novel temporal network model. Both analytical and numerical results show that the prediction accuracy of our method is perfect when the model networks’ time parameter approaches infinity. Furthermore, we find that when time is finite, our method is still significantly more accurate than the baseline.
Agile (data) science: a (draft) manifesto

Science has a data management problem, as well as a project management problem. While industrial-grade data science teams have embraced the agile mindset, and adopted or created all kind of tools to create reproducible workflows, academia-based science is still (mostly) mired in a mindset that is focused on a single final product (a paper), without focusing on incremental improvement, on any specific problem or customer, or, paying any attention reproducibility. In this report we argue towards the adoption of the agile mindset and agile data science tools in academia, to make a more responsible, and over all, reproducible science.
Chern number and Berry curvature for Gaussian mixed states of fermions

We generalize the concept of topological invariants for mixed states based on the ensemble geometric phase (EGP) introduced for one-dimensional lattice models to two dimensions. In contrast to the geometric phase for density matrices suggested by Uhlmann, the EGP leads a proper Chern number for Gaussian, finite-temperature or non-equilibrium steady states. The Chern number can be expressed as an integral of the Berry curvature of the so-called fictitious Hamiltonian, constructed from single-particle correlations, over the two-dimensional Brillouin zone. For the Chern number to be non-zero the fictitious Hamiltonian has to break time-reversal symmetry.
Quartic multifractality and finite-size corrections at the spin quantum Hall transition

The spin-quantum Hall transition (or class C transition in two dimensions) represents one of the few localization-delocalization transitions for which some of the critical exponents are known exactly. Not known, however, is the multifractal spectrum, τ q , which describes the system-size scaling of inverse participation ratios P q , i.e., the q -moments of critical wavefunction amplitudes. We here report simulations based on the class C Chalker-Coddington network and demonstrate that τ q is (essentially) a quartic polynomial in q . Analytical results ﬁx all prefactors except the quartic curvature that we obtain as γ = (2 . 22 ± 0 . 15) · 10 − 3 . In order to achieve the necessary accuracy in the presence of sizable corrections to scaling, we have analyzed the evolution with system size of the entire P q -distribution function. As it turns out, in a sizable window of q -values this distribution function exhibits a (single-parameter) scaling collapse already in the pre-asymptotic regime, where ﬁnite-size corrections are not negligible. This observation motivates us to propose a novel approach for extracting τ q based on concepts borrowed from the Kolmogorov-Smirnov test of mathematical statistics. We believe that our work provides the conceptual means for high-precision investigations of multifractal spectra also near other localization-delocalization transitions of current interest, especially the integer (class A) quantum Hall e ﬀ ect.