Miracle-dz/UniFAR-FTUs huggingface dataset:
An Empirical Study of User Support Tools in Open Source Software

End users’ positive response is essential for the success of any software. This is true for both commercial and Open Source Software (OSS). OSS is popular not only because of its availability, which is usually free but due to the user support it provides, generally through public platforms. The study model of this research establishes a relationship between OSS user support and available support tools. To conduct this research, we used a dataset of 100 OSS projects in different categories and examined five user support tools provided by different OSS projects. The results show that project trackers, user mailing lists, and updated versions have a significant role in gaining user support. However, we were unable to find a significant association between user support and documentation, as well as between user support and the troubleshooting guidelines provided by OSS projects.
Anti-windup design for discrete-time systems with time delay via predictor-based extended state observer

A novel anti-windup design is proposed for discrete-time systems with time delay and actuator saturation. Based on a generalized predictor to estimate the undelayed system output, a modified model-based extended state observer (MESO) is designed to simultaneously estimate the system state and disturbance, which becomes an anti-windup compensator when actuator saturation appears. Accordingly, a feedback controller is analytically designed to stabilize the system and reject disturbance. By proposing the desired closed-loop transfer function for the set-point tracking, a pre-filter is designed to ensure no steady-state output tracking error. Using the generalized sector condition and generalized free-weighting-matrix (GFWM), a sufficient condition guaranteeing the stability of the closed-loop system is established in terms of linear matrix inequalities (LMIs). The effectiveness of the proposed control design is demonstrated by an illustrative example from the literature.
Distribution Discrepancy Maximization Metric Learning for Ship Classification in Synthetic Aperture Radar Images

Supervised learning techniques are widely used in the task of ship classification in synthetic aperture radar (SAR) images in recent years. Learning distance metrics that describe the underlying distribution between data points based on the distance metric learning (DML) methods can further improve the performance of ship classification in SAR images. Traditional supervised DML methods usually learn distance metrics based on pairwise constraints, but ignore the importance of inter-class distribution discrepancy. In this study, we propose a novel DML method named distribution discrepancy maximization metric learning (DDMML) algorithm, which maximizes the maximum mean discrepancy (MMD) between different categories in the process of learning distance metrics. We adopt a high-resolution SAR ship database for experimental evaluation. The experimental results show that the proposed method outperforms the state-of-the-art DML methods.
Deep Ordinal Classification for Automatic Cloud Assessment

We develop a deep neural network for ordinal classification and use this technique for the automatic cloud cover assessment (ACCA) of satellite images. We adopt a VGG+ResNet approach with a novel loss function to solve this problem. Results using Quicklook images from Centre for Remote Imaging, Sensing and Processing (CRISP) are promising, as approximately 97.3% of all sub-scenes are correctly labelled, potentially even higher when one ordinal error is tolerated.
Whole Genome Analysis of a Single Scottish Deerhound Dog Family Provides Independent Corroboration That a SGK3 Coding Variant Leads to Hairlessness

The breeds of domestic dog, Canis lupus familiaris, display a range of coat types with variation in color, texture, length, curl, and growth pattern. One trait of interest is that of partial or full hairlessness, which is found in a small number of breeds. While the standard for some breeds, such as the Xoloitzcuintli, requires sparse hair on their extremities, others are entirely bald, including the American Hairless Terrier. We identified a small, rare family of Scottish Deerhounds in which coated parents produced a mixed litter of coated and hairless offspring. To identify the underlying variant, we performed whole genome sequencing of the dam and five offspring, comparing single nucleotide polymorphisms and small insertions/deletions against an established catalog of 91 million canine variants. Of 325 homozygous alternative alleles found in both hairless dogs, 56 displayed the expected pattern of segregation and only a single, high impact variant within a coding region was observed: a single base pair insertion in exon two of SGK3 leading to a potential frameshift, thus verifying recently published findings. In addition, we observed that gene expression levels between coated and hairless dogs are similar, suggesting a mechanism other than non-sense mediated decay is responsible for the phenotype.
Remote Sensing For Assessing Drought Insurance Claims in Central Europe

In this study, the viability of assessing drought insurance claims via remote sensing is explored. Time series of satellite images from the Sentinel-2 mission and weather data from the European Climate Assessment & Dataset are used to fit classifiers on historical loss data from an agricultural insurance. Two different approaches for training classifiers are explored, designing neural networks to learn directly on the time series and transforming the data to a fixed-size representation to enable the use of other methods. It is shown that in this case the second approach yields much better results, as careful feature engineering combined with more rigid methods like gradient boosting leads to less overfitting compared to the neural network approach.Compared to existing approaches, the proposed methods allow for analyzing the situation on a per-field level while using high resolution imagery (Sentinel-2) over a large geographic area.
Role of glycosylation on the ensemble of conformations in the MUC1 immunodominant epitope

MUC1 is a membrane glycoprotein, which in adenocarninomas is overexpressed and exhibits truncated O‐glycosylation. Overexpression and altered glycosylation make MUC1 into a candidate for immunotherapy. Monoclonal antibodies directed against MUC1 frequently bind an immunodominant epitope that contains a single site for O‐glycosylation. Glycosylation with tumor carbohydrate antigens such as the Tn‐antigen (GalNAc‐O‐Ser/Thr) results in antibodies binding with higher affinity. One proposed model to explain the enhanced affinity of antibodies for the glycosylated antigen is that the addition of a carbohydrate alters the conformational properties, favoring a binding‐competent state. The conformational effects associated with Tn glycosylation of the MUC1 antigen was investigated using solution‐state NMR and molecular dynamics. NMR experiments revealed distinct substructures of the glycosylated MUC1 peptides compared with the unglycosylated peptide. Molecular dynamics simulations of the MUC1 glycopeptide and peptide revealed distinguishing differences in their conformational preferences.
Measuring Alcohol Use-Related Shame and Guilt: Development and Validation of the Perceptions of Drinking Scale

Abstract Background: The dispositional tendency to experience guilt is inversely related to disordered alcohol use, while dispositional shame-proneness appears to share a positive relationship with alcohol problems. Objective: In order to further research in this domain, a new measure of alcohol use-related shame and guilt is described. Methods: Using exploratory and confirmatory factor analysis (CFA), the psychometric properties of the Perceptions of Drinking Scale (PODS) were validated across two independent samples (Sample 1 N = 293, Sample 2 N = 429). Results: A four factor model of the PODS was identified in exploratory factor analysis. The hypothesized four-factor PODS model was validated in an independent sample using CFA (RMSEA = .046; CFI = .99; TLI = .99). Alcohol use-related shame and guilt were reliably differentiated, and test re-test stability, divergent and convergent validity was established. Alcohol use-related shame was not clearly related to taking action to address problematic alcohol use, but was positively related with measures of negative affect and using avoidance-based coping strategies. Conversely, alcohol use-related guilt was generally unrelated to measures of negative affect and was clearly associated with the taking of action to address problematic alcohol use. Conclusions: The Perceptions of Drinking Scale has good psychometric properties and also appears to reliably distinguish between experiences of alcohol use-related shame and guilt. Both alcohol use-related shame and guilt appear to be positively associated with the contemplation of changing one’s alcohol use-related behaviors. Only alcohol use-related guilt was clearly linked to the taking of action to address problematic drinking behavior.
Accessibility-Free Active Learning for Hyperspectral Image Classification

This work proposes a new collaborative active and semi-supervised learning approach, named accessibility-free active learning (AFAL), for hyperspectral imaging classification. The proposed approach aims to tackle an existing problem in traditional active learning methods, that is, the fact that some selected samples are not accessible by oracles for assigning them pseudo labels, i.e., confident predictions for the classifier. The proposal specifically addresses this problem using superpixels in a self-training context. Specifically, AFAL first generates a set of candidates locally around the labeled pixels and then expands them to other subregions via a density peak-based augmentation strategy, in order to guarantee the confidence of pseudo labels. Our experimental results, obtained on two real and well-used hyperspectral images, reveal that the proposed scheme can lead to state-of-the-art performance.
Rumen-protected methionine supplementation during the peripartal period alters the expression of galectin genes associated with inflammation in peripheral neutrophils and secretion in plasma of Holstein cows

Abstract The work described in this research communication aimed to investigate whether rumen-protected methionine (Met) supplementation during the periparturient period would affect the expression of galectins in blood-derived neutrophils, and secretion of galectins, IL (interleukin)-1β, IL-6, myeloperoxidase (MPO), and glucose in plasma. Because supplementation of rumen-protected Met would alleviate inflammation and oxidative stress during the peripartal period, we hypothesized that enhancing Met supply would benefit the innate immune response at least in part by altering the expression of galectin genes associated with neutrophil activity and inflammation. Galectins (Gal) have an immuno-modulating effect acting like cell-surface receptors whose activation often results in signaling cascades stimulating cells such as neutrophils. This study revealed an association between Met supplementation and galectin expression and secretion. This implies that galectin expression and secretion can be modulated by Met supplementation. Further studies are needed to evaluate the regulation of galectin gene expression for therapeutic and dietary intervention in the peripartal cow.
Serum Levels of Leptin in Multiple Myeloma Patients and Its Relation to Angiogenic and Inflammatory Cytokines

Leptin was initially described as the regulator of food intake and calorie expenditure (1). Subsequent studies revealed that it is a pleiotropic cytokine also involved in the endocrine-reproductive, immune and hemopoietic systems (2). These conclusions have been derived in part from the study of leptinor leptin receptor-deficient mice in which, apart from obesity, there is suppression of the reproductive and thyroid function and stimulation of the hypothalamus-pituitary-adrenal axis (3). Regarding the immune system, leptin, which shows structural and functional similarities with interleukin-6, has been shown to alter the balance of T cell-derived cytokines in favor of a Th1 response by increasing the production of IL-2 and interferon gamma (1, 4). It also seems to regulate the response to inflammation by inhibiting monocytemacrophage-mediated responses and permitting lymphocyte-mediated responses (2), although some investigators sustain that leptin is a stimulator of human circulating monocytes (5). Leptin displays proliferative and antiapoptotic properties in T lymphocytes and other cells including tracheal epithelial cells, lung squamous cells and acute myeloid leukemia cells (6, 7). Hematopoiesis is affected by leptin deficiency in mice, which display a decrease in the number of circulating lymphocytes and an increase in the number of monocytes (8). Furthermore, leptin is expressed in bone marrow progenitor cells (CD34+) and stromal cells and in megakaryocytic cell lines (9, 10). It has also been characterized as an angiogenic molecule, inducing neovascularization in vitro and in vivo (11-13). It is apparent that, due to the multifunctional role of leptin, studies are needed to elucidate its possible involvement in various inflammatory, immunological and malignant disorders. Its proliferative, angiogenic properties and its similarities to IL-6 led us to question its role in multiple myeloma (MM). The aim of our study was to examine leptin levels in The International Journal of Biological Markers, Vol. 19 no. 1, pp. 52-57 © 2004 Wichtig Editore
X-ray photoelectron microscopy of the C 1s core level of free-standing single-wall carbon nanotube bundles

Core level photoemission spectra from a free-standing bundle of single-wall carbon nanotubes have been measured using a high-flux soft x-ray spectromicroscope. The good signal-to-noise ratio for the C 1s emission provides information on fundamental quantities such as the core-hole lifetime and binding energy, free from uncontrolled interactions between the nanotubes and the substrate or between the nanotubes and contaminants. We show that it is possible to distinguish chemically different nanotubes from the binding energy and line shape of the C 1s core level. This finding opens unique opportunities to probe in situ the response of the nanotube electronic properties and chemical activity to mechanical actions, doping, and functionalization.
Modeling of MEMS Micro-Mirror Using Input-output Data*

In this paper, a modeling method for electromagnetically-actuated micro-mirror using measured input and output data is presented. For the description of the behavior of micro-mirror, a Hammerstein model involved with hysteresis is proposed. In the Hammerstein model, the deflection mechanism is depicted by a linear dynamic model while the electromagnetic hysteresis is described by a modified Prandtl-Ishlinskii model. Subsequently, a recursive identification algorithm is applied for parameter estimation of the model. Then, the experimental results of modeling an electromagnetic micro-mirror are presented.
Recent advancements in understanding fin regeneration in zebrafish

Zebrafish have the remarkable ability to fully regenerate a lost appendage, faithfully restoring its size, shape and tissue patterning. Studies over the past decades have identified mechanisms underlying the formation, spatial organization, and regenerative growth of the blastema, a pool of proliferative progenitor cells. The patterning of newly forming tissue is tightly regulated to ensure proper rebuilding of anatomy. Precise niche regulation of retinoic acid and sonic hedgehog signaling ensures adherence to ray—interray boundaries. The molecular underpinnings of systems underlying re‐establishment of pre‐amputation size and shape (positional information) are also slowly starting to emerge. Osteoblasts play an important role as a cellular source of regenerating skeletal elements, and in zebrafish both osteoblast dedifferentiation as well as de novo osteoblast formation occurs. Both dedifferentiation and proliferation are tightly controlled, which makes it interesting to compare it to tumorigenesis, and to identify potential players involved in these processes.
Asynchronous Electric Drive Based on Cascade Multi-Level Frequency Converter

The paper considers the possibility of using cascade multi-level frequency converters in an asynchronous electric drive. The features of building a power part of the frequency converter are shown. The comparison of the elements in the power part of the frequency converter is done. The output voltage vectors depending on the switching of the power switches of the frequency converter are shown. The schemes of substitution are considered which allows simplify the mathematical models of the system. The study of transient processes with different voltage amplitudes of the source is carried out, the harmonic coefficient is shown.
A Dual-Polarized Magneto-Electric Dipole Antenna for Application to N77/N78 Band

A novel dual-polarized magneto-electric dipole antenna is developed for the next 5G wireless communications. The antenna is composed of four fishtail-shaped patches horizontally placed and four vertically placed patches shorted to the ground. The operation frequency band of the studied antenna is ranged from 3.05 to 4.42 GHz with SWR ≤1.5, covering the entire N77/N78 band for sub-6GHz 5G wireless communications. The measured gain varies from 7.09 to 9.36 dBi with high front-to-back ratios, meanwhile, very stable HPBWs at both horizontal plane (H-plane) and vertical plane (V-plane) within the frequency band of interest are found for the two polarizations, showing a promising candidate of this antenna for sub-6GHz 5G wireless communication applications.
A Multi-Task Deep Learning Framework Coupling Semantic Segmentation and Image Reconstruction for Very High Resolution Imagery

Semantic segmentation, especially for very high-resolution satellite data, is one of the pillar problems in the remote sensing community. Lately, deep learning techniques are the ones that set the state-of-the-art for a number of benchmark datasets, however, there are still a lot of challenges that need to be addressed, especially in the case of limited annotations. To this end, in this paper, we propose a novel framework based on deep neural networks that is able to address concurrently semantic segmentation and image reconstruction in an end to end training. Under the proposed formulation, the image reconstruction acts as a regularization, constraining efficiently the solution in the entire image domain. This self-supervised component helps significantly the generalization of the network for the semantic segmentation, especially in cases of a low number of annotations. Experimental results and the performed quantitative evaluation on the publicly available ISPRS (WGIII/4) dataset indicate the great potential of the developed approach.
Rotation-Invariant Latent Semantic Representation Learning for Object Detection in VHR Optical Remote Sensing Images

Object detection in very high resolution (VHR) optical remote sensing images is a fundamental yet challenging problem for the field of remote sensing image analysis. The detection performance is heavily dependent on the representation capability of the extracted features. Recently, convolutional neural networks (CNNs) have made a breakthrough for various applications in nature images. However, it is problematic to directly apply CNN to perform object detection in VHR optical remote sensing images due to the problem of object rotation variations. To address this issue, a novel rotation invariant probabilistic Latent Semantic Analysis (RI-pLSA) model is proposed to learn latent semantic representations for object detection. This is achieved by imposing a rotation-invariant regularization term on the objective function of pLSA to enforce the learned representation from all rotations of the same sample to be as consistent as possible. Additionally, the proposed RI-pLSA model takes the CNN features as input, which generates more powerful semantic representation for object detection. Comprehensive experiments on a publicly available ten-class object detection dataset demonstrate the superiority and effectiveness of our method compared with state-of-the-arts.
Hybrid Network Assisted Dynamic Worker Recruitment Algorithm

With the wide application of mobile electronic devices such as smartphones, the emerging mobile crowd sensing (MCS) has gradually become an effective way of real-time sensing and information collection. In the MCS system, worker recruitment is a core and common research issue. The cold start problem limits the application of traditional MCS worker recruitment methods. Introducing social relationships can solve the cold start problem to some extent. Therefore, this paper borrows the idea of influence propagation on social networks and proposes a worker recruitment algorithm based on hybrid network mixing social network and communication network. The core idea is that first select seed workers according to the recruitment probability by using the communication network, then initiate the task spread on social networks and communication networks at the same time in a greedy way. The goal of worker recruitment is to maximize task spatial coverage. When calculating the probability of recruitment, this paper considers various factors such as worker's ability, sojourn time and worker's movement to improve the accuracy of recruitment probability. The experimental results on real datasets show that compared with the existing algorithms, the algorithm in this paper can guarantee the time constraint of the task and have better performance in terms of spatial coverage and running time.
Beam-Recursive Iterative Adaptive Approach for Scanning Radar Angular Superresolution

Angular resolution of scanning radar is constrained by the size of antenna aperture. Such coarse resolution can not satisfy the applications of microwave remote sensing that require high resolution. Iterative adaptive approach (IAA) is a recently introduced method for scanning radar angular super-resolution, which could notably improve the angular resolution and suppress the noise amplification. In this paper, we further this development, by presenting a beam-recursive I-AA, allowing for adjusting the regularization parameter adaptively and dynamically for varying scenario. Such implementation could effectively eliminate the artifacts on background when applying the batch IAA to resolve closely spaced strong targets. Moreover, the technique offers a promising potential that deserves further attention on computationally efficient implementation and real-time imaging along antenna beam scanning. Simulations are provided to validate the effectiveness of the proposed approach.
Genetic Algorithm Based GNSS-R Snow Water Equivalent Estimation

In this paper we propose a new snow water equivalent (SWE) estimation method using GNSS-R method. The forward model is established to describe the relationship between antenna height (snow depth), snow density and multipath error by using combination of pseudorange and carrier-phase of GNSS dual-frequency signals. As the function of antenna height and snow density, the forward model is used to construct the fitness function based on least squares principle. Then, the problem of inversion of antenna height and snow density is transformed into a conventional multi-variable function optimization problem. The genetic algorithm is used to find the minimum of the fitness function to obtain the optimal snow depth and snow density estimates. The Galileo satellite navigation system data of an experimental campaign conducted in Harbin, China was used to test the proposed method. The preliminary results show that the proposed method can achieve SWE estimation accuracy of about 4cm.