Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the missing link: an empirical study of software fixes

Many papers have been published on analysis and prediction of software faults and/or failures, but few addressed the software fixes made to correct the faults and prevent failures from reoccurring. This paper contributes towards filling this gap by focusing on empirical characterization of software fixes. The results are based on the data extracted from a safety–critical NASA mission. In particular, 21 large‐scale software components (which together constitute over 8000 files and millions of lines of code) were analysed. The unique characteristic of this work is the fact that links were established from software faults (i.e. the root causes) to (potential or observed) failures and consequently to fixes made to correct these faults. Specifically, for the fixes associated with individual failures, the spread across software components and types of software artifacts being fixed was studied. Our results showed that significant number of software failures required fixes in multiple software components and/or multiple software artifacts (i.e. 15% and 26%, respectively). The results also showed that the patterns of software components that were often fixed together were significantly affected by the software architecture. Furthermore, the types of fixed software artifacts were highly correlated with fault type and they had different distributions for prerelease and post‐release failures. Copyright © 2013 John Wiley & Sons, Ltd.
Early Striatal Dendrite Deficits followed by Neuron Loss with Advanced Age in the Absence of Anterograde Cortical Brain-Derived Neurotrophic Factor

Brain-derived neurotrophic factor (BDNF), a member of the neurotrophin family, modulates neuronal survival, differentiation, and synaptic function. Reduced BDNF expression in the cortex caused by mutation of the huntingtin gene has been suggested to play a role in the striatal degeneration observed in Huntington's disease. BDNF expression rises dramatically in the cortex during the first few weeks of postnatal life in mice. Previously, it has been impossible to study the specific long-term effects of BDNF absence on CNS structures because of the early postnatal lethality of BDNF-/- mice. Mice harboring a floxed BDNF gene were bred with Emx1IREScre/+ mice to generate Emx-BDNFKO mice that lack cortical BDNF but are viable. Adult Emx-BDNFKO mice display a hindlimb clasping phenotype similar to that observed in mouse models of Huntington's disease. The striatum of postnatal Emx-BDNFKO mice was reduced in volume compared with controls, and the most abundant neuron type of the striatum, medium spiny neurons (MSNs), had shrunken cell somas, thinner dendrites, and fewer dendritic spines at 35 d of age. Although significant striatal neuron losses were not detected at 35 or 120 d postnatal, 35% of striatal neurons were missing in Emx-BDNFKO mice aged beyond 1 year. Thus, cortical BDNF, although not required for the generation or near-term survival of MSN, is necessary for normal striatal neuron dendrite morphology during the period when BDNF expression rises in the cortex. Furthermore, a long-term in vivo requirement for cortical BDNF in supporting the survival of MSNs is revealed.
Expression of HLA-B27 in transgenic mice is dependent on the mouse H-2D genes

HLA-B27 transgenic mice in the context of various H-2 haplotypes were produced. A high expression of the HLA-B27 antigen was observed in mice homozygous for H-2b, H-2f, H-2s, H-2p, H-2r, and H-2k haplotypes. Mice of the H-2v haplotype expressed HLA-B27 at an intermediate level. Expression of HLA-B27 was minimal in mice of the H-2q and H-2d haplotypes. This was observed both on the B10 background and in DBA/2 or BALB/c mice. Only minimal expression of HLA-B27 could be detected in B10.PL (KuDd) or B10.RKDB (KkSkDdLb) mice, indicating that the low level of HLA-B27 expression maps to the H-2D gene or a very closely linked gene. Integration and transcription of the HLA-B27 gene does not appear to be different between high-expressing haplotypes and low- expressing haplotypes as determined by Southern and Northern blot analysis. However, expression of HLA-B27 on the cell surface correlated with the amount of HLA-B27 and beta 2M that could be immunoprecipitated with an anti-B27 antibody. Therefore, the association of the B27 heavy chain with endogenous beta 2M and subsequent expression on the cell surface are disrupted in mice with some class I H-2D genes. Possible mechanisms that might contribute to this defect in assembly, transport, and expression of class I molecules are discussed.
A Node Linkage Approach for Sequential Pattern Mining

Sequential Pattern Mining is a widely addressed problem in data mining, with applications such as analyzing Web usage, examining purchase behavior, and text mining, among others. Nevertheless, with the dramatic increase in data volume, the current approaches prove inefficient when dealing with large input datasets, a large number of different symbols and low minimum supports. In this paper, we propose a new sequential pattern mining algorithm, which follows a pattern-growth scheme to discover sequential patterns. Unlike most pattern growth algorithms, our approach does not build a data structure to represent the input dataset, but instead accesses the required sequences through pseudo-projection databases, achieving better runtime and reducing memory requirements. Our algorithm traverses the search space in a depth-first fashion and only preserves in memory a pattern node linkage and the pseudo-projections required for the branch being explored at the time. Experimental results show that our new approach, the Node Linkage Depth-First Traversal algorithm (NLDFT), has better performance and scalability in comparison with state of the art algorithms.
Religion, culture and illness: a sociological study on religious coping in Iran

ABSTRACT The present article is based on an international study on meaning-making coping aimed at understanding the role of culture in coping. The larger study has been conducted among cancer patients in 10 countries. The present article is confined to the results obtained in our study in Iran and restricted to religious coping methods. Twenty-seven participants with various kinds of cancer were interviewed. The several religious coping methods found in the present study are categorised on the basis of RCOPE’s five basic religious functions. The study reveals, among others, the impact of cultural beliefs on certain religious coping methods, even among those who are not regarded as practicing Muslims. The study highlights the importance of investigating cultural and social context when exploring the use of the meaning-making coping strategies in different countries.
Neuropsychological Assessment in the Diagnosis of Alzheimer's Disease

Alzheimer's disease (AD) is the most common form of dementia in older adults. It represents a significant public health concern because of its associated personal, social, and economic burden. As such, AD is the focus of considerable research worldwide. This unit reviews the major cognitive and behavioral impairments associated with AD, and the practical application of current neuropsychological procedures used to assess these deficits. Curr. Protoc. Neurosci. 49:10.3. 1‐10.3. 8. © 2009 by John Wiley & Sons, Inc.
RAPID SPAN OR NODE RESTORATION IN IP NETWORKS USING VIRTUAL PROTECTION CYCLES1

- We describe a novel restoration strategy called the virtual protection cycle (p-cycle) for extremely fast restoration in IP networks. Originally, conceived for use in WDM and SONET transport networks, we outline the adaption of the p-cycle concept to an IP environment. In an IP router-based network, p-cycles are implemented as virtual circuits (VC) formed into closed loops. In the event of failure, packets which would normally have been disrupted are encapsulated and immediately diverted by an alternate routing table entry onto a p-cycle. These travel through the p-cycle until they have cleared the failure; the packets are then removed from the p-cycle and routed normally towards their destination. The normal IP routing protocol then develops a longer term update to routing tables network-wide. The p-cycle provides an immediate detour for the packets, preventing their loss, until conventional global routing reconvergence occurs. The aim of the paper is to explain the functioning of p-cycles for both node and span restoration and outline certain initial results on the problem of optimized design of p-cycle based IP networks.
Rotation and Translation Invariant Object Recognition with Tactile Sensors While Grasping

This paper presents a novel approach to recognize objects with feature descriptors invariant to movement and rotation of objects in hands during grasping. As an object is manipulated by hands without prior knowledge, tactile feedback can make up for the information loss caused by vision occlusion. But frequent movement and manipulations make it difficult to recognize shape and pose of the object. To solve this problem, Scale-Invariant Feature Transform (SIFT) technique from the computer vision is used to extract descriptors to represent rotation and translation invariant local features. The proposed system is validated by classifying objects with real data from tactile sensors mounted to a Barrett hand; average overall accuracy of 91.2% has been achieved.
Non-Rigid Registration for Automatic Fracture Segmentation

Automatic segmentation of anatomical structures is often performed using model-based non-rigid registration methods. These algorithms work well when the images do not contain any large deviations from the normal anatomy. We have previously used such a method to generate patient specific models of hip bones for surgery simulation. The method that was used, the morphon method, registers two-or three-dimensional images using a multi-resolution deformation scheme. A prototype image is iteratively registered to a target image using quadrature filter phase difference to estimate the local displacement. The morphon method has in this work been extended to deal with automatic segmentation of fractured bones. Two features have been added. First, the method is modified such that multiple prototypes (in this case two) can be used. Second, normalised convolution is utilized for the displacement estimation, to guide the registration of the second prototype, based on the result of the registration of the first one.
An on-line learning mechanism for unsupervised classification and topology representation

An on-line learning mechanism is proposed for unsupervised data. Using a similarity threshold and local error based insertion criterion, the system is able to grow incrementally and to accommodate input patterns of online non-stationary data distribution. The definition of a utility parameter -"error-radius" - enables this system to learn the number of nodes needed to solve a task. The usage of a new technique for removing nodes in low probability density regions can separate the clusters with low-density overlaps and dynamically eliminate noise in the input data. Experiment results show that this system can report a reasonable number of clusters and represent the topological structure of unsupervised on-line data with no prior conditions such as a suitable number of nodes or a good initial codebook.
The G protein coupled receptor kinase 2 plays an essential role in beta-adrenergic receptor-induced insulin resistance.

AIMS Insulin (Ins) resistance (IRES) associates to increased cardiovascular risk as observed in metabolic syndrome. Chronic stimulation of beta-adrenergic receptors (betaAR) due to exaggerated sympathetic nervous system activity is involved in the pathogenesis of IRES. The cellular levels of G protein coupled receptor kinase 2 (GRK2) increase during chronic betaAR stimulation, leading to betaAR desensitization. We tested the hypothesis that GRK2 plays a role in betaAR-induced IRES. METHODS AND RESULTS We evaluated Ins-induced glucose uptake and signalling responses in vitro in cell overexpressing the beta(2)AR, the GRK2, or the catalytically dead mutant GRK2-DN. In a model of increased adrenergic activity, IRES and elevated cellular GRK2 levels, the spontaneously hypertensive rats (SHR) we performed the intravenous glucose tolerance test load. To inhibit GRK2, we synthesized a peptide based on the catalytical sequence of GRK2 conjugated with the antennapedia internalization sequence (Ant-124). Ins in human kidney embryonic (HEK-293) cells causes rapid accumulation of GRK2, tyrosine phosphorylation of Ins receptor substrate 1 (IRS1) and induces glucose uptake. In the same cell type, transgenic beta(2)AR overexpression causes GRK2 accumulation associated with significant deficit of IRS1 activation and glucose uptake by Ins. Similarly, transgenic GRK2 overexpression prevents Ins-induced tyrosine phosphorylation of IRS1 and glucose uptake, whereas GRK2-DN ameliorates glucose extraction. By immunoprecipitation, GRK2 binds IRS1 but not the Ins receptor in an Ins-dependent fashion, which is lost in HEK-GRK2 cells. Ant-124 improves Ins-induced glucose uptake in HEK-293 and HEK-GRK2 cells, but does not prevent GRK2/IRS1 interaction. In SHR, Ant-124 infusion for 30 days ameliorates IRES and IRS1 tyrosine phosphorylation. CONCLUSION Our results suggest that GRK2 mediates adrenergic IRES and that inhibition of GRK2 activity leads to increased Ins sensitivity both in cells and in animal model of IRES.
Corpus-based Learning in Stochastic OT-LFG – Experiments with a Bidirectional Bootstrapping Approach

This paper reports on experiments exploring the application of a Stochastic Optimality-Theoretic approach in the corpus-based learning of some aspects of syntax. Using the Gradual Learning Algorithm, the clausal syntax of German has to be learned from learning instances of clauses extracted from a corpus. The particular focus in the experiments was placed on the usability of a bidirectional approach, where parsing-directed, interpretive optimization is applied to determine the target candidate for a subsequent application of generation-directed, expressive optimization. The results show that a bidirectional bootstrapping approach is only slightly less effective than a fully supervised approach.
SWAT model calibration of a grid-based setup

Abstract. The eco-hydrological model SWAT (Soil and Water Assessment Tool) is a useful tool to simulate the effects of catchment processes and water management practices on the water cycle. For each catchment some model parameters (e.g. ground water delay time, ground water level) remain constant and therefore are used as constant values; other parameters such as soil types or land use are spatially variable and thus have to be spatially discretized. SWAT setup interfaces process input data to fit the data format requirements and to discretize the spatial characteristics of the catchment area. The primarily used configuration is the sub-watershed discretization scheme. This spatial setup method, however, results in a loss of spatial information which can be problematic for SWAT applications that require a spatially detailed description of the catchment area. At present no SWAT interface is available which provides the management of input and output data based on grid cells. To fill this gap, the authors developed a grid-based model interface. To perform hydrological studies, the SWAT user first calibrates the model to fit to the environmental and hydrological conditions of the catchment. Compared to the sub-watershed approach, the grid-based setup significantly increases model computation time and hence aggravates calibration according to established calibration guidelines. This paper describes how a conventional set of sub-watershed SWAT parameters can be used to calibrate the corresponding grid-based model. The procedure was evaluated in a sub-catchment of the River Elbe (Northern Germany). The simulation of daily discharge resulted in Nash-Sutcliffe efficiencies ranging from 0.76 to 0.78 and from 0.61 to 0.65 for the calibration and validation period respectively; thus model performance is satisfactory. The sub-watershed and grid configuration simulate comparable discharges at the catchment outlet (R2 = 0.99). Nevertheless, the major advantage of the grid-based set-up is an enhanced spatial description of landscape units inducing a more realistic spatial distribution of model output parameters.
Prolonging the Network Life Time in WSN through Computational Intelligence

In this paper, key constituents of computational intelligence (CI) are discussed and how different classes come under the paradigm of CI. The very need and importance of CI in the problem solving of different social fabrics of life is comprehensively elaborated in this article and is concluded that CI in turn depicts the nature's supremacy of being the ultimate intelligent optimizer for human made solutions. This concept has become more highlighted due to the discussion about the emergence of computational intelligence from electronics to communication, from signal processing to pattern recognition and from microprocessor design to stochastic system. Wireless Sensor Network is taken as a target domain to implement the computational intelligence for the optimization of different factors involved in energy aware routing. Comparative analysis of different classes in CI paradigm is done to find the most appropriate class for optimization of Clustering design and data aggregation techniques. This analytical survey article can prove as a guiding light for the future researcher to delve into more detail of CI and finding the optimal solution in different social fabric of life. It is concluded that this nature inspired paradigm has the key role for moving to the "optimized solutions" for almost every field of life.
Using Incremental Learning Algorithms in the Search for Minimaland

The approximation of nonlinear mappings by means of a smooth cooperation (by Gaussian mixture) of several linear experts has become a very popular approach in the connectionist, fuzzy and statistical communities. Two simultaneous types of tuning are required: one concerns the number of the local models whereas the other concerns the ne adjustment of the Gaus-sian zones and of the local linear models. The goal of this paper is rst to compare an incremental and a brute-force algorithm in order to build a minimal and eeective fuzzy model for regression problems and then to argue for the advantages gained by using the incremental approach. The two algorithms have been tested on the same benchmark: namely the prediction of Mackey-Glass chaotic time series. We will conjecture that fuzzy model architectures, when automatically learned at the structural and parametric level, present a huge number of local minima and that the use of incre-mental approach can help to travel in a more eecient way in this highly rugged search space.
Architecture of the Hyperledger Blockchain Fabric

Overview. A blockchain is best understood in the model of state-machine replication [8], where a service maintains some state and clients invoke operations that transform the state and generate outputs. A blockchain emulates a “trusted” computing service through a distributed protocol, run by nodes connected over the Internet. The service represents or creates an asset, in which all nodes have some stake. The nodes share the common goal of running the service but do not necessarily trust each other for more. In a “permissionless” blockchain such as the one underlying the Bitcoin cryptocurrency, anyone can operate a node and participate through spending CPU cycles and demonstrating a “proof-of-work. ” On the other hand, blockchains in the “permissioned” model control who participates in validation and in the protocol; these nodes typically have established identities and form a consortium. A report of Swanson compares the two models [9].
Identification of prostate cancer biomarkers in urinary exosomes

Exosomes have recently appeared as a novel source of non-invasive cancer biomarkers since tumour-specific molecules can be found in exosomes isolated from biological fluids. We have here investigated the proteome of urinary exosomes by using mass spectrometry to identify proteins differentially expressed in prostate cancer patients compared to healthy male controls. In total, 15 control and 16 prostate cancer samples of urinary exosomes were analyzed. Importantly, 246 proteins were differentially expressed in the two groups. The majority of these proteins (221) were up-regulated in exosomes from prostate cancer patients. These proteins were analyzed according to specific criteria to create a focus list that contained 37 proteins. At 100% specificity, 17 of these proteins displayed individual sensitivities above 60%. Even though several of these proteins showed high sensitivity and specificity for prostate cancer as individual biomarkers, combining them in a multi-panel test has the potential for full differentiation of prostate cancer from non-disease controls. The highest sensitivity, 94%, was observed for transmembrane protein 256 (TM256; chromosome 17 open reading frame 61). LAMTOR proteins were also distinctly enriched with very high specificity for patient samples. TM256 and LAMTOR1 could be used to augment the sensitivity to 100%. Other prominent proteins were V-type proton ATPase 16 kDa proteolipid subunit (VATL), adipogenesis regulatory factor (ADIRF), and several Rab-class members and proteasomal proteins. In conclusion, this study clearly shows the potential of using urinary exosomes in the diagnosis and clinical management of prostate cancer.
Using ANOVA in a Microwave Round-Robin Comparison

This paper describes a procedure for comparing calibrated nonlinear radio-frequency (RF) measurements performed on a nonlinear device by five different measurement laboratories. The device under test (DUT) is a nonlinear active semiconductor device that is designed to generate a maximum number of harmonic tones. The goal is to obtain a simple automated method that detects if some laboratory had measurement problems during the measurement campaign. The developed comparison method is based on the analysis of variance (ANOVA) technique.
Interface Issues in Running Computer Architecture Tools via the World-Wide Web

An ability to quickly access and run computer architecture tools via the Internet would be very beneecial to users. Towards this end, we have developed a prototype network-computing system (the Purdue University Network-Computing Hubs) that allows designers, researchers, and students to access and run existing tools via standard worldwide web browsers. PUNCH currently provides access to a number of independently-developed tools for computer architecture research and education, and has been used by students in several undergraduate and graduate courses at Purdue and elsewhere. Users have logged more than 850,000 hits and have performed over 50,000 simulations. This paper focuses on user-interface issues that must be addressed when tools that were originally developed for local use are executed via a network-computing environment.
MODELLING SOLVENT-MEDIATED POLYMORPHIC TRANSITIONS DURING COOLING SOLUTION CRYSTALLIZATION

Abstract Polymorphism is one of the most important issues raised by the industrial crystallization of Active Pharmaceutical Ingredients (API). The dynamic modelling of polymorphic transformations is developed using Femlab® a multiphysics simulation software, which is applied to the resolution of the Populations Balance Equations (PBE) involved. The stable and metastable distinct populations of crystals are represented in a mono-dimensional framework (i.e. one particle size as internal variable.) The simulation reproduces experimental features associated with Ostwald's Rule of Stages, it allows explaining and analysing the conditions of occurrence of both stable and metastable forms of the solid product.