Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrating source code search into git client for effective retrieving of change history

In order to achieve effective development management, it is important to manipulate and understand the change histories of source code in a repository. Although general version control systems provide change history manipulation, these systems are restricted to line-based and textual operations such as grep and diff. As such, these systems cannot follow the syntax/semantics of the source code. While various studies have examined querying and searching source codes, these methods cannot follow historical changes. The key concept of this paper is the integration of a source code search technique into Git commands that manipulate historical data in a repository. This paper presents MJgit, a prototype tool for achieving the above goal. In order to evaluate the proposed tool, we conducted a performance experiment using actual software repositories.
Detection of Anionic Antimicrobial Peptides in Ovine Bronchoalveolar Lavage Fluid and Respiratory Epithelium

ABSTRACT Three small antimicrobial anionic peptides (AP) were originally isolated from an ovine pulmonary surfactant. However, their presence in bronchoalveolar lavage (BAL) fluid and tissues of the respiratory tract is unknown. In this study, we made affinity-purified rabbit polyclonal and mouse monoclonal antibodies to synthetic H-DDDDDDD-OH. Antibody specificity was assessed by a competitive enzyme-linked immunosorbent assay (ELISA), and the exact epitope binding sites were determined with analog peptides synthesized on derivatized cellulose. These antibodies were used to detect AP in BAL fluid by ELISA and in respiratory tissues by Western blot analysis and immunocytochemistry. BAL fluid from 25 sheep contained 0.83 ± 0.33 mM AP (mean ± standard deviation; range, 0.10 to 1.59 mM) and was antimicrobial. The presence of AP in BAL fluid was confirmed by reverse-phase high-pressure liquid chromatography fractionation followed by matrix-assisted laser desorption/ionization (MALDI) mass spectrometry on those fractions which were positive by competitive ELISA and demonstrated antimicrobial activity. In Western blots, polyclonal antibody PAB96-1 and monoclonal antibody 1G9-1C2 (5.0 μg/ml) detected four bands in solubilized turbinate and tracheal epithelial cells (53.7, 31.2, 28.0, and 25.7 kDa) and five bands in lung homogenates (53.5, 37.1, 31.2, 28.0, and 25.7 kDa). Only a single band was seen in solubilized liver and small-intestine homogenates, and no bands were seen in blots containing BAL fluid, albumin, or kidney or spleen homogenates. In pulmonary-tissue sections, both antibodies PAB96-1 and 1G9-1C2 identified accumulated protein in the apical cytoplasm of the bronchial and bronchiolar epithelia, in the cytoplasm of pulmonary endothelial cells, and in an occasional alveolar macrophage. As a first step in identifying a candidate AP precursor gene(s), degenerate oligonucleotides representing all possible coding combinations for H-GADDDDD-OH and H-DDDDDDD-OH were synthesized and used to probe Southern blots of sheep genomic DNA. Following low-stringency washes and a 2-day exposure, strongly hybridizing bands could be identified. One degenerate oligonucleotide, SH87, was used as a hybridization probe to screen a sheep phage genomic library. Two independent phage contained the H-GADDDDD-OH coding sequence as part of a larger predicted protein. AP may originate as part of an intracellular precursor protein, with multistep processing leading to the release of the heptapeptide into mucosal secretions. There it may interact with other innate pulmonary defenses to prevent microbial infection.
Smartwatch-based Early Gesture Detection 8 Trajectory Tracking for Interactive Gesture-Driven Applications

The paper explores the possibility of using wrist-worn devices (specifically, a smartwatch) to accurately track the hand movement and gestures for a new class of immersive, interactive gesture-driven applications. These interactive applications need two special features: (a) the ability to identify gestures from a continuous stream of sensor data early--i.e., even before the gesture is complete, and (b) the ability to precisely track the hand's trajectory, even though the underlying inertial sensor data is noisy. We develop a new approach that tackles these requirements by first building a HMM-based gesture recognition framework that does not need an explicit segmentation step, and then using a per-gesture trajectory tracking solution that tracks the hand movement only during these predefined gestures. Using an elaborate setup that allows us to realistically study the table-tennis related hand movements of users, we show that our approach works: (a) it can achieve 95% stroke recognition accuracy. Within 50% of gesture, it can achieve a recall value of 92% for 10 novice users and 93% for 15 experienced users from a continuous sensor stream; (b) it can track hand movement during such strokeplay with a median accuracy of 6.2 cm.
The origins of island size scaling in heterogeneous film growth

A new description of the growth mechanism for the islands in submonolayer film coverage is presented which goes beyond the usual mean-field approximations. We show that the scaling behaviour observed during heterogeneous growth simulations is explained by the existence of capture zones for each island which remain unchanged throughout the evolution. These capture zones are in fact the Voronoi polygons for the island centres and so represent the departure of the local island environment from the average. The relevance and range of applicability of this description for homogeneous nucleation systems are discussed.
Beyond Semantic Attributes: Discrete Latent Attributes Learning for Zero-Shot Recognition

In this letter, we propose a novel approach for learning semantics-driven attributes, which are discriminative for zero-shot visual recognition. Latent attributes are derived in a principled manner, aiming at maintaining class-level semantic relatedness and attribute-wise balancedness. Unlike existing methods that binarize learned real-valued attributes via a quantization stage, we directly learn the optimal binary attributes by effectively addressing a discrete optimization problem. Particularly, we propose a class-wise discrete descent algorithm, based on which latent attributes of each class are learned iteratively. Moreover, we propose to simultaneously predict multiple attributes from low-level features via multioutput neural networks (MONN), which can model intrinsic correlation among attributes and make prediction more tractable. Extensive experiments on two standard datasets clearly demonstrate the superiority of our method over the state-of-the-arts.
Performance of the UroVysion ® FISH assay for the diagnosis of malignant effusions using two cutoff strategies

The cytological examination of cavity fluids has limited sensitivity in the diagnosis of malignancy. Aneuploidy, which is commonly observed in neoplastic cells, could potentially be used as an ancillary diagnostic tool. To evaluate the detection of aneuploid cells in cavitary effusion samples using the fluorescence in situ hybridization (FISH) assay UroVysion® with some adaptations and two different cutoff strategies. Seventy samples of pleural or peritoneal fluid with positive (n = 40), negative (n = 15), or suspicious (n = 15) oncotic cytology were subjected to FISH assay with the multitarget UroVysion® kit, which is composed of probes that hybridize to the centromeric region of chromosomes 3, 7, and 17 and to the locus 9p21. FISH performance was evaluated using two different cutoffs: (1) the manufacturer's cutoff (M‐FISH) and 2) a proposed cutoff (P‐FISH). Using M‐FISH, the diagnostic sensitivity was 57.1%, specificity 87.5%, and accuracy 60.0%; with P‐FISH, the sensitivity was 87.3%, specificity 71.4%, and accuracy 85.7%. When combined with cytology, the sensitivity, specificity, and accuracy were 88.0%, 83.3%, and 87.8%, respectively. Malignant cells presented a predominance of chromosomal gains. The UroVysion® test using the P‐FISH cutoff was effective in demonstrating aneuploid cells in all malignant effusions, confirming the diagnosis of malignancy even in cases with suspicious cytology.
Stylogenetics: clustering-based stylistic analysis of literary corpora

Current advances in shallow parsing allow us to use results from this field in stylogenetic research, so that a new methodology for the automatic analysis of literary texts can be developed. The main pillars of this methodology which is borrowed from topic detection research are (i) using more complex features than the simple lexical features suggested by traditional approaches, (ii) using authors or groups of authors as a prediction class, and (iii) using clustering methods to indicate the differences and similarities between authors (i.e. stylogenetics). On the basis of the stylistic genome of authors, we try to cluster them into closely related and meaningful groups. We report on experiments with a literary corpus of five million words consisting of representative samples of female and male authors. Combinations of syntactic, token-based and lexical features constitute a profile that characterizes the style of an author. The stylogenetics methodology opens up new perspectives for literary analysis, enabling and necessitating close cooperation between literary scholars and computational linguists.
New Insights Towards Developing Recommender Systems

Promoting recommender systems in real-world applications requires deep investigations with emphasis on their next generation. This survey offers a comprehensive and systematic review on recommender system development lifecycles to enlighten researchers and practitioners. The paper conducts statistical research on published recommender systems indexed by Web of Science to get an overview of the state of the art. Based on the reviewed findings, we introduce taxonomies driven by the following five phases: initiation (architecture and data acquisition techniques), design (design types and techniques), development (implementation methods and algorithms), evaluation (metrics and measurement techniques) and application (domains of applications). A layered framework of recommender systems containing market strategy, data, recommender core, interaction, security and evaluation is proposed. Based on the framework, the existing advanced humanized techniques emerged from computational intelligence and some inspiring insights from computational economics and machine learning are provided for researchers to expand the novel aspects of recommender systems.
Analytical Modelling of Elastic-Plastic Interference Fit Joints

The interference fit is a widely process, used to produce a joint assembly of two parts achieved by friction. It is a popular type of joint in machine elements and it is found in many field applications related to the automotive, aerospace, oil and gas and shipbuilding industries. Consequently, a better understanding of its limit behavior together with an accurate evaluation of the residual stresses generated by this process are very important for the design of mechanical components requiring high optimized performance. This work focuses on the analytical development of the residual stresses when the two assembled parts are circular cylinders and deform elasto-plastically under plane strain condition. The constitutive law that governs their material strain hardening behavior is assumed to follow a general power law which also covers the particular cases of elastic perfectly plastic and bilinear hardening. To validate the developed analytical model (AM), the finite element method (FEM) was used and the results showed good agreement between the two approaches. The obtained results show that the stresses increase when the interference value increases causing maximum stress intensity at the inner surface of the two assembled parts to exceed their material yield stress. After hollow shaft plastic collapse, any increase in interference results in a small increase in residual contact pressure with a large increase of the equivalent stresses localized at the hollow shaft inner surface. The method could be used to determine the maximum value of the interference in order to prevent assembly failure.
PolSAR Image Classification Using Polarimetric-Feature-Driven Deep Convolutional Neural Network

Polarimetric synthetic aperture radar (PolSAR) image classification is an important application. Advanced deep learning techniques represented by deep convolutional neural network (CNN) have been utilized to enhance the classification performance. One current challenge is how to adapt deep CNN classifier for PolSAR classification with limited training samples, while keeping good generalization performance. This letter attempts to contribute to this problem. The core idea is to incorporate expert knowledge of target scattering mechanism interpretation and polarimetric feature mining to assist deep CNN classifier training and improve the final classification performance. A polarimetric-feature-driven deep CNN classification scheme is established. Both classical roll-invariant polarimetric features and hidden polarimetric features in the rotation domain are used to drive the proposed deep CNN model. Comparison studies validate the efficiency and superiority of the proposal. For the benchmark AIRSAR data, the proposed method achieves the state-of-the-art classification accuracy. Meanwhile, the convergence speed from the proposed polarimetric-feature-driven CNN approach is about 2.3 times faster than the normal CNN method. For multitemporal UAVSAR data sets, the proposed scheme achieves comparably high classification accuracy as the normal CNN method for train-used temporal data, while for train-not-used data it obtains an average of 4.86% higher overall accuracy than the normal CNN method. Furthermore, the proposed strategy can also produce very promising classification accuracy even with very limited training samples.
A 10.1” 183- $\mu \text{W}$ /electrode, 0.73-mm2/sensor High-SNR 3-D Hover Sensor Based on Enhanced Signal Refining and Fine Error Calibrating Techniques

A high-signal-to-noise ratio (SNR) inductor-free 3-D hover sensor is presented. This paper solved the low-signal component issue, which is the biggest problem in 3-D hover sensing. For this purpose, we propose a power- and cost-effective high-voltage driving technique in the self-capacitance sensing scheme (SCSS) and lateral resolution optimization of a touch panel. In addition, the huge panel offsets in the SCSS from both vertical panel capacitance (CSV) and horizontal panel capacitance (CSH) can effectively be eliminated by exploiting the panel’s natural characteristics, without using other costly resources. Therefore, in the proposed design, the total calibration block is minimized only for parasitic capacitance mismatches. Last, by adopting new driving scheme, two-phase simultaneous sensing is enabled to increase the SNR further. The proposed hover sensing system achieved a 39-dB SNR at a 1-cm hover point under a 240-Hz scan rate condition in noise experiments, while consuming 183 $\mu \text{W}$ /electrode and 0.73 mm2/sensor, which are the lowest power per electrode performance and the smallest die-area per sensor performance, respectively, in comparison to the state-of-the-art 3-D hover systems.
Silencing of RNA Polymerases II and III-Dependent Transcription by the KRAB Protein Domain of KOX1, a Krüppel-Type Zinc Finger Factor

The so-called KRAB domain, which is present in about one third of the vertebrate Kruppel-type zinc finger factors, has previously been shown to inhibit transcription in cis when tethered to promoter regions. Here we analyze this effect with fusions of the KRAB domain derived from KOX1/ZNF10 zinc finger protein to the heterologous DNA binding domains of both LexA and GAL4 factors. In transfected human cells, repression of reporter gene transcription is observed not only from proximal promoter positions, but also when KRAB is tethered to DNA at a remote position more than 1.8 kb downstream of the initiation site of transcription. Furthermore, KRAB-mediated silencing over short and long distances is not restricted to RNA polymerase II, since transcription by RNA polymerase III is also repressed. However, transcription by RNA polymerase I and by phage T7 RNA polymerase in mammalian cells are not significantly influenced by the KRAB domain. These latter results may indicate that repression by the KRAB domain, at least under our assay conditions, involves specific inhibition of some component(s) of RNA polymerase II and III transcription, rather than inducing some gross physical alteration of template chromatin structure.
QGesture

Many HCI applications, such as volume adjustment in a gaming system, require quantitative gesture measurement for metrics such as movement distance and direction. In this paper, we propose QGesture, a gesture recognition system that uses CSI values provided by COTS WiFi devices to measure the movement distance and direction of human hands. To achieve high accuracy in measurements, we first use phase correction algorithm to remove the phase noise in CSI measurements. We then propose a robust estimation algorithm, called LEVD, to estimate and remove the impact of environmental dynamics. To separate gesture movements from daily activities, we design simple gestures with unique characteristics as preambles to determine the start of the gesture. Our experimental results show that QGesture achieves an average accuracy of 3 cm in the measurement of movement distance and more than 95% accuracy in the movement direction detection in the one-dimensional case. Furthermore, it achieves an average absolute direction error of 15 degrees and an average accuracy of 3.7 cm in the measurement of movement distance in the two-dimensional case.
WeChat Public Account Use Improves Clinical Control of Cough-Variant Asthma: A Randomized Controlled Trial

Background WeChat is a convenient and popular social medium, and it seems to be an appropriate platform for education and management of patients. This study sought to identify usefulness in clinical control of cough-variant asthma (CVA). Material/Methods A randomized controlled trial was conducted among 80 CVA patients. After being assigned to either the traditional group (TG) or the WeChat group (WG), they received the same inhalation therapy, but patients in WG received additional education and instruction via our public account on the WeChat application. Questionnaires on asthma and chronic cough, data on pulmonary function, blood-related items, follow-up adherence, and Emergency Department (ED) visits were collected at the initial visit and at 3 months. Results A total of 67 participants completed the trial for analysis. FEV1/predicted and FEV1/FVC were significantly increased in WG (p<0.001; p=0.012) after 3 months. PD20-FEV1 was increased in both groups compared with baseline, but more pronounced in WG (p=0.004). ACQ-7 scores were improved in both groups (p=0.024; p<0.001). Participants allocated to WG experienced a greater improvement in AQLQ and LCQ scores, and between-group differences were significant at 3 months (p=0.040; p=0.001). Furthermore, we observed decreases in blood eosinophil count and FeNO in WG (p=0.048; p=0.014), and WG presented better follow-up compliance (p=0.034). Conclusions Using WeChat as part of treatment and management of CVA can help patients learn about their disease and medications, as well as improve disease control and therapy outcomes.
A fast SIFT parallel match algorithm combined with pipeline technology on multi-core DSP

The speed of traditional match phase of Scale invariant feature transformation(SIFT) descants in some cases. For example, the speed descants straightforward when the number of feature points is small. Besides it will lead a lower execution efficiency if it is transplanted to the Digital Signal Processing(DSP) platform immediately. So a fast SIFT parallel matching algorithm combined with pipeline technology on multi-core DSP is proposed in this paper. Firstly, the algorithm divides the image into a 3∗3 grid image, then marks each grid. Secondly, the algorithm executes grids quantization parallelly. Finally the algorithm matches grids of the two images parallelly. And this algorithm leverages the parallel architecture, pipeline technology, and mixed scheduling mode of the multi-core DSP. Finally, experiments show that this algorithm can reduce the time consumption under the almost same accuracy rate as traditional method.
Switching controller design to swing-up a two-link underactuated robot

In the control engineering field it has shown that the dynamical behaviors are mostly non-linear. Meanwhile, the solution for facing these challenges seem to be not feasible or despite simulations, they just don't come to the good results. Therefore, in this research a practical approach is used which can lead a system to any operating point without considering its region of attraction. In other words, this solution presents a way for finding a path which brings a two-link robot system from one equilibrium point to another. Two-link pendulums are widely used as an example of highly nonlinear underactuated mechanical systems. The limited working space of an inverted two-link pendulum despite other mechanical systems such as cart and pendulum system requires some consideration for the controller design. Actually, in a cart and pendulum system one of its degree of freedom (the linear position of the cart) may vary from minus infinity to plus infinity which is a great advantage for controller design but such a thing does not exist in the case of two-link inverted pendulum. By using the proposed method, a swing-up controller has been successfully employed. In order to control the two-link robot, feedforward control and feedback control have been used. The feedforward control is achieved by Discrete Mechanics and Optimal Control (DMOC) method. In order to optimize the path of objective, Hamiltonian method has been employed. As a verification of this method, experimental and simulation results have been compared. Regarding the implementation of the two-link robot, due to the lack of accuracy in dynamic parameters, the feedback control has been used.
Interaction between nitric oxide and renal α1-adrenoreceptors mediated vasoconstriction in rats with left ventricular hypertrophyin Wistar Kyoto rats

Left ventricular hypertrophy (LVH) is associated with decreased responsiveness of renal α1-adrenoreceptors subtypes to adrenergic agonists. Nitric oxide donors are known to have antihypertrophic effects however their impact on responsiveness of renal α1-adrenoreceptors subtypes is unknown. This study investigated the impact of nitric oxide (NO) and its potential interaction with the responsiveness of renal α1-adrenoreceptors subtypes to adrenergic stimulation in rats with left ventricular hypertrophy (LVH). This study also explored the impact of NO donor on CSE expression in normal and LVH kidney. LVH was induced using isoprenaline and caffeine in drinking water for 2 weeks while NO donor (L-arginine, 1.25g/Lin drinking water) was given for 5 weeks. Intrarenal noradrenaline, phenylephrine and methoxamine responses were determined in the absence and presence of selective α1-adrenoceptor antagonists, 5- methylurapidil (5-MeU), chloroethylclonidine (CeC) and BMY 7378. Renal cortical endothelial nitric oxide synthase mRNA was upregulated 7 fold while that of cystathione γ lyase was unaltered in the NO treated LVH rats (LVH-NO) group compared to LVH group. The responsiveness of renal α1A, α1B and α1D-adrenoceptors in the low dose and high dose phases of 5-MeU, CEC and BMY7378 to adrenergic agonists was increased along with cGMP in the kidney of LVH-NO group. These findings suggest that exogenous NO precursor up-regulated the renal eNOS/NO/cGMP pathway in LVH rats and resulted in augmented α1A, α1B and α1D adrenoreceptors responsiveness to the adrenergic agonists. There is a positive interaction between H2S and NO production in normal animals but this interaction appears absent in LVH animals.