Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical simulation method for slow unsteady flows near to local heat release regions

The present paper studies gas dynamic processes which can be observed at laser radiation absorption in air flows of atmospheric pressure after spark breakdown, when a blast wave generated as a result of practically instantaneous energy emission moved to a significant distance from the energy emission area. Moreover, it is assumed that at the initial instant of time (from which the simulation starts) gas inside the heated cloud is moving with a velocity of environment, while the pressure is completely even. The assumption of the local thermodynamic equilibrium (LTE) is used, since the air is at atmospheric pressure and the typical temperatures are 2000-20000 K. Gas is considered to be viscous, heat conductive, selectively radiating and absorbing radiation. The assumption of the LTE allows to use the data regarding thermophysical, transfer and optical properties calculated and tabulated for air at atmospheric pressure within the whole range of temperatures which is of practical interest. The choice of small-scale heated domains for the analysis (typical radius of the heat release region is ~ 0.3 0.5 cm) Abstract
Transplayer: Timbre Style Transfer with Flexible Timbre Control

Music timbre style transfer aims at replacing the instrument timbre in a solo recording with another instrument, while preserving the musical content. Existing GAN-based methods can only achieve timbre style transfer between two given timbres. Inspired by the practice in voice conversion, we propose TransPlayer, which uses an autoencoder model with one-hot representations of instruments as the condition, and a Diffwave model trained especially for music synthesis. We evaluate our model in both the one-to-one transfer task and the many-to-many transfer task. The results prove that our method is able to provide one-to-one style transfer outputs comparable with the existing GAN-based method, and can transfer among multiple timbres with only one single model.
Investigation of the Ability of Crocin to Treat Skin Cancer Chemically Induced in Mice via the Inhibition of the Wnt/β-Catenin and Fibrotic Pathway

Background The Wnt pathway is a major pathway in the pathogenesis of skin cancer. Moreover, crocin is one of the carotenoid compounds present in the flowers of gardenia and crocus. Crocin is responsible for the characteristic color of saffron. Aims This study was conducted to discover the therapeutic effects of crocin against skin cancer induced in mice by blocking the Wnt pathway with subsequent effects on inflammation and fibrosis. Methods For the induction of skin cancer in mice, the application of DMBA and Croton oil was used. The dorsal skin was used for the evaluation of the gene and protein expression of TGF-β, SMAD, Wnt, β-catenin, TNF-α, and NFκB. Part of the skin is stained with Mallory trichrome. Results The use of crocin for treating skin cancer mice significantly reduced both the number of tumors and the number of scratches. In addition, crocin inhibited epidermal hyperplasia. Finally, crocin reduced the gene expression and protein levels of Wnt, β-catenin, SMAD, NFκB; TGF-β and TNF-α. Conclusions Crocin produced therapeutic effects against skin cancer induced in mice by blocking the expression of Wnt followed by blocking the pro-inflammatory pathway through downregulation of NFκB and TNF-α. In addition, crocin blocked the fibrosis pathway via the downregulation of TGF-β.
Partioned predictive mean matching as a large data multilevel imputation technique.

Large scale assessment data often has a multilevel structure. When dealing with missing values, such structures need to be taken into account to prevent underestimation of the intraclass correlation. We evaluate predictive mean matching (PMM) as a multilevel imputation technique and compare it to other imputation approaches for multilevel data. We propose partitioned predictive mean matching (PPMM) as an extension to the PMM algorithm to divide the big data multilevel problem into manageable parts that can be solved by standard predictive mean matching. We show that PPMM can be a very effective imputation approach for large multilevel datasets and that both PPMM and PMM yield plausible inference for continuous, ordered categorical, or even dichotomous multilevel data. We conclude that both the performance of PMM and PPMM is often comparable to dedicated methods for multilevel data.
An efficient FPGA‐based implementation of UWB radar system for through‐wall imaging

The work presented in this paper aims to develop a low‐cost ultra‐wideband (UWB) radar system that has the capability to image through the wall of a human target. The proposed radar system relies heavily on a field‐programmable gate array (FPGA) platform. The Xilinx FPGA Kintex‐7 (KC705) board is employed to integrate the main functionalities of the radar system, such as the generation and acquisition of a UWB signal. The generated signal is a monocycle signal, which has an ultra‐wide bandwidth. Due to the UWB nature of this signal, the radar system achieves better penetration ability, as well as high imaging resolution. However, the major challenge of a UWB radar system lies in the stage of digitization, as it requires a high sampling rate analog‐to‐digital converter (ADC). In fact, such a chip is very expensive. Hence, to reach a high sampling resolution using a low‐cost and low‐speed ADC, an efficient sampling strategy is implemented. In contrast to other UWB sampling methods, which require a hardware delay line chip, the new sampling scheme depends only on the FPGA firmware to realize a combination of real‐time and equivalent‐time sampling, which provides better jitter performance. Finally, to demonstrate the imaging capability, experimental tests are conducted in an indoor environment while human targets are located in different places. The measurement results revealed that the proposed radar system has the ability to provide 2D images that accurately determine the location of the target.
Rôle des MDD de terroir dans la construction de la légitimité des distributeurs

Cet article mobilise l’approche néo-institutionnelle pour étudier l’effet des MDD de terroir sur la légitimité territoriale des distributeurs. Les résultats d’une étude quantitative auprès de consommateurs de la marque Reflets de France du groupe Carrefour, montrent que la MDD de terroir influence positivement la légitimité territoriale des distributeurs à travers l’image terroir perçue et l’attitude. Nous montrons par ailleurs que l’effet de l’image terroir perçue est plus important que l’effet de l’attitude envers la marque. Les conséquences managériales pour les enseignes se situent au niveau des stratégies d’ancrage territorial et de proximité largement déployées aujourd’hui par la plupart des distributeurs. Pour les PME et les acteurs locaux, les résultats mettent en lumière les avantages d’une coopération concertée avec les enseignes pour valoriser le territoire.
Singularity Analysis of Parallel Robot with Six Degrees-Of- Freedom of Six Legs

This paper suggests a mathematical solution to parallel manipulator with six degrees of freedom and follows the movement on a specific track to evaluate the singularity position. Mathematical equation was derived based on the fundamental principles of the coordinate and orientation vectors of the moving platform center. According to this equation, each leg length was calculated. It means that the independent coordinates (linkage length) was calculated with respect to the dependent coordinates (coordinate and orientation of the moving platform center) by using (MathCad 14) software. By solving the forward kinematics, the coordinate and orientation of the moving platform center were evaluated when this center was moving in a specific track. The center of moving platform moved between two points. When the robot moved using animated program turns the moving platform to imaginary at (position 4), when lose control of the robot and this is a big problem occurs in special cases when the movement of the robot is called singularity.
Silicene: Silicon-Based Topological Materials

Silicene is a silicon cousin of graphene. The ground state shows the quantum spin Hall effect since silicene has a relatively large spin-orbit interaction. Silicene undergoes topological phase transitions into various topological insulators by applying external field such as electric field, photo-irradiation and exchange field. Examples are the exchange-induced quantum anomalous Hall insulator and the photo-induced quantum anomalous Hall insulator. Between two distinctive topological insulators there emerges a semimetal or a metal whose existence is protected topologically by the bulk-edge correspondence. Examples are spin polarized semimetals and single Dirac-cone states. The single Dirac-cone state is a remarkable state containing only one type of massless Dirac fermions and three types of massive ones.
Fast algorithm for target feature extraction based on connected component analysis

Based on the idea of sub-region,a fast algorithm for Connected Component Analysis(CCA)is proposed to extract fea-tures of video targets.Through scanning an image for only one time,the algorithm can describe the equivalences between sub-re-gions belonging to the same connected component with a multi-tree.And at the same time,features of a target are extracted from leaf nodes to the root node and stored in the root node.It can be demonstrated that the proposed algorithm shows low space and high efficiency by analysis and experimentation.
The Influence of Social Media Usage on Loyalty with Customer Satisfaction & Brand Trust as Mediation Variable

This study aims to determine (1) the effect of social media usage on customer satisfaction on wardah products. (2) The effect of social media usage on Brand Trust on Wardah products. (3) Influence of Customer Satisfaction on Customer Loyalty of Wardah products. (4) The effect of Brand Trust on @wardahbeauty Instagram customer loyalty. (5) The effect of social media usage on Wardah product customer loyalty. (6) The effect of social media usage on customer loyalty mediated by customer satisfaction. (7) The effect of social media usage on customer loyalty with brand trust as media. The population in this study were all followers of Instagram @wardahbeauty and the sample in this study were users of wardah products with a minimum of one purchase. The number of samples in this study were 131 respondents. The data in this study were collected through the distribution of online questionnaires and data processing was carried out using the SmartPLS software. The results of this study indicate that (1) social media usage has a positive and significant effect on customer satisfaction. (2) social media usage has a positive and significant effect on Brand Trust. (3) Customer satisfaction on brand loyalty has a positive and significant influence. (4) Brand Trust has a positive and significant relationship with Customer Loyalty. (5) Social media usage does not have a significant effect on Customer Loyalty. (6) Social media usage has a positive and significant relationship to customer loyalty with customer satisfaction as a mediation. (7) Social media usage has a positive and significant relationship to customer loyalty with brand trust as a mediation.
Structure and Assembly of the Proteus mirabilis Flagellar Motor by Cryo-Electron Tomography

Proteus mirabilis is a Gram-negative Gammaproteobacterium and a major causative agent of urinary tract infections in humans. It is characterized by its ability to switch between swimming motility in liquid media and swarming on solid surfaces. Here, we used cryo-electron tomography and subtomogram averaging to reveal the structure of the flagellar motor of P. mirabilis at nanometer resolution in intact cells. We found that P. mirabilis has a motor that is structurally similar to those of Escherichia coli and Salmonella enterica, lacking the periplasmic elaborations that characterize other more specialized gammaproteobacterial motors. In addition, no density corresponding to stators was present in the subtomogram average suggesting that the stators are dynamic. Finally, several assembly intermediates of the motor were seen that support the inside-out assembly pathway.
Bayesian modeling of university first-year students' grades after placement test

ABSTRACT University first-year students grades are naturally correlated with the scores obtained at placement tests. Often this characteristic leads the university grades in the first exams to be asymmetrically distributed. Motivated by the analysis of grades of the basic Statistics examination of first-year students, we discuss informative priors for the shape parameter of the skew-normal model, a class of distribution which account for several degree of asymmetry. Our proposed prior leads to closed-form full-conditional posterior distributions, particularly useful in Markov Chain Monte Carlo simulation. A Gibbs sampling algorithm is discussed for the joint vector of parameters and the method is applied to a real data set from the School of Economics, University of Padua, Italy. Our analysis reveals that the correlation between the placement test and the grades of first-year students leads to a measurable positive skewness of the distribution of the university grades.
Cyber Threat Intelligence Mining for Proactive Cybersecurity Defense: A Survey and New Perspectives

Today’s cyber attacks have become more severe and frequent, which calls for a new line of security defenses to protect against them. The dynamic nature of new-generation threats, which are evasive, resilient, and complex, makes traditional security systems based on heuristics and signatures struggle to match. Organizations aim to gather and share real-time cyber threat information and then turn it into threat intelligence for preventing attacks or, at the very least, responding quickly in a proactive manner. Cyber Threat Intelligence (CTI) mining, which uncovers, processes, and analyzes valuable information about cyber threats, is booming. However, most organizations today mainly focus on basic use cases, such as integrating threat data feeds with existing network and firewall systems, intrusion prevention systems, and Security Information and Event Management systems (SIEMs), without taking advantage of the insights that such new intelligence can deliver. In order to make the most of CTI so as to significantly strengthen security postures, we present a comprehensive review of recent research efforts on CTI mining from multiple data sources in this article. Specifically, we provide and devise a taxonomy to summarize the studies on CTI mining based on the intended purposes (i.e., cybersecurity-related entities and events, cyber attack tactics, techniques and procedures, profiles of hackers, indicators of compromise, vulnerability exploits and malware implementation, and threat hunting), along with a comprehensive review of the current state-of-the-art. Lastly, we discuss research challenges and possible future research directions for CTI mining.
ScaleMix: Intra- And Inter-Layer Multiscale Feature Combination for Change Detection

Change detection (CD) aims at finding change objects from bi-temporal images, which has wide applications in different vision tasks. Previous CD methods focus more on fusing inter-layer multiscale features while ignoring the intra-layer multiscale characteristics, which hurts the integrity of change objects with different sizes. In this paper, we propose to mix intra- and inter-layer multiscale features to generate more complete change regions. To realize intra-layer multi-scale, we propose inception difference module (IDM), which employs convolutional filters with different sizes, absolute differences, and residual connections to capture intra-layer multiscale characteristics. To capture inter-layer multiscale, we propose a residual network refinement module (RNR) to fuse the features from the highest layer to the lowest layer and generate finely detailed change predictions. Our method can capture complete changes of different sizes by considering the multiscale characteristics of intra- and inter-layer simultaneously. Experiments on two benchmark datasets reveal that our method outperforms six state-of-the-art change detectors.
Learning To Regularized Resource Allocation with Budget Constraints

Online resource allocation problem with budget constraints has a wide range of applications in network science and operation research. In this problem, the decision maker needs to make actions that consume resources to accumulate rewards. Contrary to prior work, we introduce a non-linear and non-separable regularizer to this problem that acts on the total resource consumption. The motivation for introducing the regularizer is allowing the decision maker to tradeoff the reward maximization and metrics optimization such as load-balancing and fairness that appears frequently in practical needs. Our goal is to simultaneously maximize additively separable rewards and the value of a non-separable regularizer without violating resource budget constraints. We develop a primal-dual-type online algorithm for this problem in the online learning setting and confirm its no-regret guarantee and zero constraint violations for stochastic i.i. d. input models. Furthermore, the general convex resource consumption functions allow our model to be more applicable. Numerical experiments are conducted to demonstrate the theoretical guarantee of our algorithm.
Vision-Related Quality of Life following Combined Cataract and Minimally Invasive Glaucoma Surgery or Cataract Surgery Alone in Glaucoma Patients

This study examined glaucoma patients after undergoing combined cataract and minimally invasive glaucoma surgery (MIGS), microhook ab interno trabeculotomy and goniotomy with the Kahook Dual Blade (KDB), or cataract surgery alone, and it then evaluates their vision-related quality of life (VR-QOL) following the procedure. A total of 75 eyes of 75 consecutive glaucoma patients in this prospective cohort study underwent phacoemulsification (Phaco) or phaco and MIGS (Phaco-TLO) between October 2019 and March 2022. In all cases, the National Eye Institute Visual Function Questionnaire (VFQ-25) was used to evaluate the 20 eyes in the Phaco group and the 55 eyes in the Phaco-TLO group before and at 2 months after surgery. There was a significant increase in the visual acuity (logMAR) at 2 months post-operatively (Phaco group; 0.34 ± 0.10 to −0.07 ± 0.1, p < 0.0001, Phaco-TLO group; 0.37 ± 0.43 to 0.09 ± 0.32, p < 0.0001). The median (25–75th percentile) total VFQ scores in the Phaco group before and at 2 months after surgery were 71.1 (62.4–80.6) and 79.4 (69.0–84.0), respectively. (p = 0.006). The median (25–75th percentile) total VFQ scores in the Phaco-TLO group before and at 2 months after surgery were 69.8 (55.3–78.6) and 74.7 (65.1–83.3), respectively. (p = 0.005). Glaucoma patients who underwent not only cataract surgery alone but also combined cataract surgery and MIGS exhibited significant improvement in the VR-QOL.
STACKMAPS: A Visualization Technique for Diabetic Retinopathy Grading

Convolution Neural Networks (CNN) excelled humans in many classification tasks, including medical imaging applications. However, model interpretation is still an active research area. In this paper, we address the model interpretation problem for the Diabetic Retinopathy grading task. We propose a novel visualization method called StackMaps. Our proposed method is class-agnostic, which fits the diabetic retinopathy grading problem better than other alternatives. Moreover, unlike previous visualization methods, StackMaps gets rid of the dependency on gradients. Our StackMaps technique gets the most significant feature maps at the last convolutional layer after one forward pass. We can also get the significant feature maps from lower layers using beam search guided by the feature maps obtained from the previous layer. Finally, we compute the final map as the sum of the significant feature maps obtained at each layer. We evaluate StackMaps against other state-of-the-art visualization methods qualitatively and quantitatively. We used the FGADR dataset to define our experimental setup. We show that StackMaps achieves better visual interpretation and lesion localization.
Fast Low-Latency Convolution by Low-Rank Tensor Approximation

In this paper we consider fast time-domain convolution, exploiting low-rank properties of an impulse response (IR). This reduces the computational complexity, speeding up the convolution, without introducing latency. Previous work has considered a truncated singular value decomposition (SVD) of a two-dimensional matricization, or reshaping, of the IR. We here build upon this idea, by providing an algorithm for convolution with a three-dimensional tensorization of the IR. We provide simulations using real-life acoustic room impulse responses (RIRs) of various lengths, convolving them with music, as well as speech signals. The proposed algorithm is shown to outperform the comparable existing algorithm in terms of signal quality degradation, for all considered scenarios, without increasing the computational complexity, or the memory usage.
Decoding Musical Pitch from Human Brain Activity with Automatic Voxel-Wise Whole-Brain FMRI Feature Selection

Decoding models seek to infer stimulus or task information from neural activity and play a central role in brain-computer interfaces. However, the high spatial resolution of fMRI means that the number of available features far exceeds the number of trials in a typical experiment. Although a common approach is to restrict features to a priori-defined regions of interest, related information present in other brain regions are consequently omitted. Here, we propose a two-stage thresholding approach that automatically pools relevant voxels from the whole-brain to enhance decoding performance. Testing on an fMRI dataset of 20 subjects, we show that our approach significantly improves regression performance in decoding musical pitch value by 2-fold compared to restricting voxels to the auditory cortex. We further examine properties of the selected voxels, and compare performance between random forest and convolutional neural network decoders.
LOCATIONS OF OUT-OF-PLANE EQUILIBRIUM POINTS IN THE ELLIPTIC RESTRICTED THREE-BODY PROBLEM UNDER RADIATION AND OBLATENESS EFFECTS

This study deals with the generalization of the Elliptic Restricted Three-Body Problem (ER3BP) by considering the effects of radiation and oblate spheroid primaries. This may illustrate a gas giant exoplanet orbiting its host star with eccentric orbit. In the three dimensional case, this generalization may possess two additional equilibrium points (L6,7, out-of-plane). We determine the existence of L6,7 in ER3BP under the effects of radiation (bigger primary) and oblateness (small primary). We analytically derive the locations of L6,7 and assume initial approximations of (μ− 1, ± √ 3A2), where μ and A2 are the mass parameter and oblateness factor, respectively. The fixed locations are then determined. Our results show that the locations of L6,7 are periodic and affected by A2 and the radiation factor (q1).
G-Frames and operator valued-frames in Hilbert C*-modules

g-frames and fusion frames in Hilbert C*-modules have been dened by the second author and B. Khosravi in (15) and operator-valued frames in Hilbert C*-modules have been dened by Kaftal et al in (11). We show that every operator- valued frame is a g-frame, we also show that in Hilbert C*-modules tensor product of orthonormal basis is an orthonormal basis and tensor product of g-frames is a g-frame, we get some relations between their g-frame operators, and we study tensor product of operator-valued frames in Hilbert C*-modules.