Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Calculation of Process Metrics and their Bug Prediction Capabilities

Identifying fault-prone code parts is useful for the developers to help reduce the time required for locating bugs. It is usually done by characterizing the already known bugs with certain kinds of metrics and building a predictive model from the data. For the characterization of bugs, software product and process metrics are the most popular ones. The calculation of product metrics is supported by many free and commercial software products. However, tools that are capable of computing process metrics are quite rare. In this study, we present a method of computing software process metrics in a graph database. We describe the schema of the database created and we present a way to readily get the process metrics from it. With this technique, process metrics can be calculated at the file, class and method levels. We used GitHub as the source of the change history and we selected 5 open-source Java projects for processing. To retrieve positional information about the classes and methods, we used SourceMeter, a static source code analyzer tool. We used Neo4j as the graph database engine, and its query language - cypher - to get the process metrics. We published the tools we created as open-source projects on GitHub. To demonstrate the utility of our tools, we selected 25 release versions of the 5 Java projects and calculated the process metrics for all of the source code elements (files, classes and methods) in these versions. Using our previous published bug database, we built bug databases for the selected projects that contain the computed process metrics and the corresponding bug numbers for files and classes. (We published these databases as an online appendix.) Then we applied 13 machine learning algorithms on the database we created to find out if it is feasible for bug prediction purposes. We achieved F-measure values on average of around 0.7 at the class level, and slightly better values of between 0.7 and 0.75 at the file level. The best performing algorithm was the RandomForest method for both cases.
Annexin/S100A Protein Family Regulation through p14ARF-p53 Activation: A Role in Cell Survival and Predicting Treatment Outcomes in Breast Cancer

The annexin family and S100A associated proteins are important regulators of diverse calcium-dependent cellular processes including cell division, growth regulation and apoptosis. Dysfunction of individual annexin and S100A proteins is associated with cancer progression, metastasis and cancer drug resistance. This manuscript describes the novel finding of differential regulation of the annexin and S100A family of proteins by activation of p53 in breast cancer cells. Additionally, the observed differential regulation is found to be beneficial to the survival of breast cancer cells and to influence treatment efficacy. We have used unbiased, quantitative proteomics to determine the proteomic changes occurring post p14ARF-p53 activation in estrogen receptor (ER) breast cancer cells. In this report we identified differential regulation of the annexin/S100A family, through unique peptide recognition at the N-terminal regions, demonstrating p14ARF-p53 is a central orchestrator of the annexin/S100A family of calcium regulators in favor of pro-survival functions in the breast cancer cell. This regulation was found to be cell-type specific. Retrospective human breast cancer studies have demonstrated that tumors with functional wild type p53 (p53wt) respond poorly to some chemotherapy agents compared to tumors with a non-functional p53. Given that modulation of calcium signaling has been demonstrated to change sensitivity of chemotherapeutic agents to apoptotic signals, in principle, we explored the paradigm of how p53 modulation of calcium regulators in ER+ breast cancer patients impacts and influences therapeutic outcomes.
The validity and reliability of the Portuguese versions of three tools used to diagnose delirium in critically ill patients

OBJECTIVES: The objectives of this study are to compare the sensitivity and specificity of three diagnostic tools for delirium (the Intensive Care Delirium Screening Checklist, the Confusion Assessment Method for Intensive Care Units and the Confusion Assessment Method for Intensive Care Units Flowsheet) in a mixed population of critically ill patients, and to validate the Brazilian Portuguese Confusion Assessment Method for Intensive Care Units. METHODS: The study was conducted in four intensive care units in Brazil. Patients were screened for delirium by a psychiatrist or neurologist using the Diagnostic and Statistical Manual of Mental Disorders. Patients were subsequently screened by an intensivist using Portuguese translations of the three tools. RESULTS: One hundred and nineteen patients were evaluated and 38.6% were diagnosed with delirium by the reference rater. The Confusion Assessment Method for Intensive Care Units had a sensitivity of 72.5% and a specificity of 96.2%; the Confusion Assessment Method for Intensive Care Units Flowsheet had a sensitivity of 72.5% and a specificity of 96.2%; the Intensive Care Delirium Screening Checklist had a sensitivity of 96.0% and a specificity of 72.4%. There was strong agreement between the Confusion Assessment Method for Intensive Care Units and the Confusion Assessment Method for Intensive Care Units Flowsheet (kappa coefficient = 0.96). CONCLUSION: All three instruments are effective diagnostic tools in critically ill intensive care unit patients. In addition, the Brazilian Portuguese version of the Confusion Assessment Method for Intensive Care Units is a valid and reliable instrument for the assessment of delirium among critically ill patients.
Compiling for memory emergency

There has been a continued growth in the sales of mobile and embedded devices, in spite of the economic recession in many parts of the world. Many of these devices operate under tight memory bounds. Past research dealt with this problem and proposed both hardware and software solutions oriented toward reducing memory space requirements of embedded and mobile applications. One of the common characteristics of most of these prior efforts is that they assume the memory space available to an embedded application is fixed for the entire execution. Unfortunately, this is not a valid assumption in many execution environments since a typical embedded/mobile platform can have multiple applications executing concurrently and sharing a common memory space. As a result, the amount of memory available to a particular application can vary during execution. A particularly interesting scenario is what we call "memory emergency", where the size of the memory available to an application suddenly drops. If the application is not written to cope with this emergency scenario, the result would normally be a premature termination due to insufficient memory. In this paper, we propose compiler-based solutions to this memory emergency problem. The proposed compiler support modifies a given application code assuming a memory emergency model and reduces memory space demand (when necessary) by recomputing data values, thereby performing a tradeoff between memory space reduction and performance overhead. Our goal is to be able to work with the reduced memory space but minimize the performance overhead it brings. We evaluate the proposed approaches using twelve array-based embedded benchmarks. Our experimental analysis shows that the proposed approaches are very successful in responding to many memory emergency scenarios.
Atom diffusion and bonding behaviour between titania nanofilm and glass substrate

Abstract TiO2 nanofilms added with cerium ion were coated on the glass substrate using the sol–gel procedure. The films thickness with different coating times increased from 40 to 200 nm. The films were characterised by SEM, EDS and FT-IR. The results indicated that the surface morphology of TiO2 nanofilms improved with the coating times increasing and Ce4+ addition. Moreover, in the process of heat treatment, atom interdiffusion and chemical bonding occurred between TiO2 nanofilms and substrates.
Multisource transfer learning for host-pathogen protein interaction prediction in unlabeled tasks

We consider the problem of building a predictive model for host-pathogen protein interactions, when there are no known interactions available. Our goal is to predict the protein protein interactions (PPIs) between the plant host Arabidopsis thaliana and the bacterial species Salmonella typhimurium. Our method based on transfer learning, utilizes labeled data i.e known interactions from other species (we call these the source tasks). The first challenge is to pick the best instances from the source tasks, such that the resultant model when applied on the target task generates high confidence predictions. Towards this, we use the instance reweighting technique Kernel Mean Matching (KMM). The reweighted instances are used to build a kernelized support vector machine (SVM) model, which is applied on the target data. This brings forth the second challenge selecting appropriate hyperparameters while building a model for a task with no labeled data. For the purposes of evaluation, we apply our method on a task where we have some labeled data available. We find that choice of the right source examples makes a significant difference in performance on the target task.
Antidepressant-Like Effects of Fractions Prepared from Danzhi-Xiaoyao-San Decoction in Rats with Chronic Unpredictable Mild Stress: Effects on Hypothalamic-Pituitary-Adrenal Axis, Arginine Vasopressin, and Neurotransmitters

The aim of the present study was to investigate the antidepressant-like effects of two fractions, including petroleum ether soluble fraction (Fraction A, FA) and water-EtOH soluble fraction (Fraction B, FB) prepared from the Danzhi-xiaoyao-san (DZXYS) by using chronic unpredictable mild stress-induced depressive rat model. The results indicated that DZXYS could ameliorate the depression-like behavior in chronic stress model of rats. The inhibition of hyperactivity of HPA axis and the modulation of monoamine and amino acid neurotransmitters in the hippocampus may be the important mechanisms underlying the action of DZXYS antidepressant-like effect in chronically stressed rats.
Candidate Gene Resequencing in a Large Bicuspid Aortic Valve-Associated Thoracic Aortic Aneurysm Cohort: SMAD6 as an Important Contributor

Bicuspid aortic valve (BAV) is the most common congenital heart defect. Although many BAV patients remain asymptomatic, at least 20% develop thoracic aortic aneurysm (TAA). Historically, BAV-related TAA was considered as a hemodynamic consequence of the valve defect. Multiple lines of evidence currently suggest that genetic determinants contribute to the pathogenesis of both BAV and TAA in affected individuals. Despite high heritability, only very few genes have been linked to BAV or BAV/TAA, such as NOTCH1, SMAD6, and MAT2A. Moreover, they only explain a minority of patients. Other candidate genes have been suggested based on the presence of BAV in knockout mouse models (e.g., GATA5, NOS3) or in syndromic (e.g., TGFBR1/2, TGFB2/3) or non-syndromic (e.g., ACTA2) TAA forms. We hypothesized that rare genetic variants in these genes may be enriched in patients presenting with both BAV and TAA. We performed targeted resequencing of 22 candidate genes using Haloplex target enrichment in a strictly defined BAV/TAA cohort (n = 441; BAV in addition to an aortic root or ascendens diameter ≥ 4.0 cm in adults, or a Z-score ≥ 3 in children) and in a collection of healthy controls with normal echocardiographic evaluation (n = 183). After additional burden analysis against the Exome Aggregation Consortium database, the strongest candidate susceptibility gene was SMAD6 (p = 0.002), with 2.5% (n = 11) of BAV/TAA patients harboring causal variants, including two nonsense, one in-frame deletion and two frameshift mutations. All six missense mutations were located in the functionally important MH1 and MH2 domains. In conclusion, we report a significant contribution of SMAD6 mutations to the etiology of the BAV/TAA phenotype.
Robust Transceiver Design and Waveform Synthesis for Wideband MIMO Radar

This thesis deals with the problem of designing and synthesizing waveforms that are optimal, both in a signal-to-noise-and-interference ratio (SNIR), and in a system hardware design perspective, i.e., to synthesize time domain waveforms with a low peak-to-average power ratio (PAPR), or even constant modulus. In the first part of the thesis, we investigate the possibility to suppress interference for wideband multiple-input multiple-output radar, by exploiting the spectral properties of the transmit signals. The idea is to use tunable filters at the transmitter and receiver sides, and to derive the optimal power spectral density that enhances the system performance in terms of the SNIR, for a given scenario. Herein, the focus is to suppress active jamming interference, and especially deceptive jamming. The proposed method is extended to invoke imperfections in the given scenario. Two robust optimization methods are evaluated: one that utilizes a Taylor series expansion of the SNIR, and one that exploits a worst-case SNIR maximization. In the second part of the thesis, we utilize the results obtained in the first part to synthesize time domain signals that achieve certain hardware restrictions. By using the technique of partial transmit sequence, we synthesize signals that achieve optimal spectral properties and that experience a low PAPR. Finally, we show that if we allow the power spectrum to deviate somewhat from its desired shape, a further reduction of the PAPR, or even a constant modulus signal is possible. The proposed method can be used to design a time domain signal with any predefined power spectrum, if there is no design restriction except for the PAPR on the time domain signal waveform.
Development of simulation program for Transient Stability Analysis in Korean nuclear power system

This paper presents details of the behavior of doubly-fed induction generators that are operating at different operation points following system disturbances. Detailed models for doubly-fed induction machine are well known but study based on each individual unit in a wind farm during power system disturbances like fault is not that well reported. When the wind generators are spread over the wind farm, different types of disturbance can result in different impacts experienced by the units. Clustering the units based on coherency of these impacts, to arrive at equivalent model, is proposed in this paper. Case studies are presented to illustrate this approach using DIgSILENT PowerFactory simulations.
Wave Propagation Correlations between Finite Element Simulations and Tests for Enhanced Structural Health Monitoring

Ultrasonic Lamb waves are frequently used in non-destructive testing of elastic thin plate-like structures. Modern real-time Structure Health Monitoring (SHM) systems take advantage of their long travel distance of propagation and their capacity to detect surface and also internal defects. Signal interpretation involved in the damage diagnosis process becomes a very difficult task due to dispersive and multimode characteristics of these guided waves, making numerical simulation a valuable component to understand wave generation and propagation issues. The work here presented is concerned with implementation of an efficient analysis methodology through the finite element method in order to study Lamb wave propagation in homogeneous elastic plates using phased array piezoelectric transducers operating simultaneously as wave transmitters and receivers. A detailed study of the influence on the pulse-echo response of different aspects has been conducted in order to provide modelling guidelines. Additionally, a specific postprocessing procedure has been implemented which allows to selectively study and display each propagating mode separately. Experiments have been performed on aluminium plates, using different excitation frequencies, and PZT voltage variation was found to be in good correlation with that computed from numerical simulations. The developed methodology includes the wave generation using PZTs, wave propagation in an isotropic and homogeneous plate, interaction with the plate boundaries, and wave signals in panel surface captured by PZT elements including the generation of image sequences for both fundamental symmetric and anti-symmetric Lamb wave modes.
Learning Grammar with Explicit Annotations for Subordinating Conjunctions

Data-driven approach for parsing may suffer from data sparsity when entirely unsupervised. External knowledge has been shown to be an effective way to alleviate this problem. Subordinating conjunctions impose important constraints on Chinese syntactic structures. This paper proposes a method to develop a grammar with hierarchical category knowledge of subordinating conjunctions as explicit annotations. Firstly, each part-of-speech tag of the subordinating conjunctions is annotated with the most general category in the hierarchical knowledge. Those categories are human-defined to represent distinct syntactic constraints, and provide an appropriate starting point for splitting. Secondly, based on the data-driven state-split approach, we establish a mapping from each automatic refined subcategory to the one in the hierarchical knowledge. Then the data-driven splitting of these categories is restricted by the knowledge to avoid over refinement. Experiments demonstrate that constraining the grammar learning by the hierarchical knowledge improves parsing performance significantly over the baseline.
Introduction of sensor spectral response into image fusion methods. Application to wavelet-based methods

Usual image fusion methods inject features from a high spatial resolution panchromatic sensor into every low spatial resolution multispectral band trying to preserve spectral signatures and improve spatial resolution to that of the panchromatic sensor. The objective is to obtain the image that would be observed by a sensor with the same spectral response (i.e., spectral sensitivity and quantum efficiency) as the multispectral sensors and the spatial resolution of the panchromatic sensor. But in these methods, features from electromagnetic spectrum regions not covered by multispectral sensors are injected into them, and physical spectral responses of the sensors are not considered during this process. This produces some undesirable effects, such as resolution overinjection images and slightly modified spectral signatures in some features. The authors present a technique which takes into account the physical electromagnetic spectrum responses of sensors during the fusion process, which produces images closer to the image obtained by the ideal sensor than those obtained by usual wavelet-based image fusion methods. This technique is used to define a new wavelet-based image fusion method.
On the potential of generic modeling for VANET data aggregation protocols

In-network data aggregation is a promising communication mechanism to reduce bandwidth requirements of applications in vehicular ad-hoc networks (VANETs). Many aggregation schemes have been proposed, often with varying features. Most aggregation schemes are tailored to specific application scenarios and for specific aggregation operations. Comparative evaluation of different aggregation schemes is therefore difficult. An application centric view of aggregation does also not tap into the potential of cross application aggregation. Generic modeling may help to unlock this potential. We outline a generic modeling approach to enable improved comparability of aggregation schemes and facilitate joint optimization for different applications of aggregation schemes for VANETs. This work outlines the requirements and general concept of a generic modeling approach and identifies open challenges.
Interfacial Thermal Resistance and Thermal Rectification in Graphene with Geometric Variations of Doped Nitrogen: A Molecular Dynamics Study

Using classical non-equilibrium molecular dynamics simulations (NEMD), the interfacial thermal resistance and thermal rectification of nitrogen-doped zigzag graphene (NDZG) are investigated. Two different structural models about nitrogen-doped graphene are constructed. It is found that the interfacial thermal resistance at the location of nitrogen-doping causes severe reduction in thermal conductivity of the NDZG. Thermal rectification of the triangular single-nitrogen-doped graphene (SNDG) decreases with increasing temperature. However, thermal rectification is not detected in the parallel various–nitrogen-doped graphene (VNDG). These results suggest that SNDG might be a promising structure for thermal device.
Multi-label classification for intelligent health risk prediction

A Multi-Label Problem Transformation Joint Classification (MLPTJC) method is developed to solve the multi-label classification problem for the health and disease risk prediction based on physical examination records. We adopt a multi-class classification problem transformation method to transform the multi-label classification problem to a multi-class classification problem. Then We propose a Joint Decomposition Subset Classifier method to reduce the infrequent label sets to deal with the imbalance learning problem. Based on MLPTJC, existing cost-sensitive multi-class classification algorithms can be used to train the prediction models. We conduct some experiments to evaluate the performance of the MLPTJC method. The Support Vector Machine (SVM) and Random Forest (RF) algorithms are used for multi-class classification learning. We use the 10-fold cross-validation and metrics such as Average Accuracy, Precision, Recall and F-measure to evaluate the performance. The real physical examination records were employed, which include 62 examination items and 110, 300 anonymous patients. 8 types of diseases were predicted. The experimental results show that the MLPTJC method has better performance in terms of accuracy.
Translating to Co-Büchi Made Tight, Unified, and Useful

We solve the longstanding open problems of the blow-up involved in the translations, when possible, of a nondeterministic Büchi word automaton (NBW) to a nondeterministic co-Büchi word automaton (NCW) and to a deterministic co-Büchi word automaton (DCW). For the NBW to NCW translation, the currently known upper bound is 2O(n log n) and the lower bound is 1.5n. We improve the upper bound to n2n and describe a matching lower bound of 2Ω(n). For the NBW to DCW translation, the currently known upper bound is 2O(n log n). We improve it to 2O(n), which is asymptotically tight. Both of our upper-bound constructions are based on a simple subset construction, do not involve intermediate automata with richer acceptance conditions, and can be implemented symbolically. We continue and solve the open problems of translating nondeterministic Streett, Rabin, Muller, and parity word automata to NCW and to DCW. Going via an intermediate NBW is not optimal and we describe direct, simple, and asymptotically tight constructions, involving a 2Θ(n) blow-up. The constructions are variants of the subset construction, providing a unified approach for translating all common classes of automata to NCW and DCW. Beyond the theoretical importance of the results, we point to numerous applications of the new constructions. In particular, they imply a simple subset-construction based translation, when possible, of LTL to deterministic Büchi word automata.