Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic and Interactive Synthesis of Code Snippets

Author(s): Galenson, Joel David | Advisor(s): Bodik, Rastislav; Sen, Koushik | Abstract: Many code fragments are difficult to write. For example, using new and unfamiliar APIs can be a complex task with a steep learning curve. In addition, implementing a complex data structure requires discovering and understanding all of the corner cases. And more and more end users with little to no formal training are trying to write code, whether they be scientists writing simulations or kids writing mobile apps. For all of these reasons and more, programming is a difficult task, which leads to bugs and delays in software.There are many tools that help programmers find code fragments involving complex APIs, but many are somewhat inexpressive and rely on static information. We present a new technique, which we call CodeHint, that generates and evaluates code at runtime and hence can synthesize real-world Java code that involves I/O, reflection, native calls, and other advanced language features. Our approach is dynamic (giving accurate results and allowing programmers to reason about concrete executions), easy-to-use (supporting a wide range of correctness specifications), and interactive (allowing users to refine the candidate code snippets). We evaluate CodeHint and show that its algorithms are efficient and that in two user studies it improves programmer productivity by more than a factor of two.As the second contribution, programmers and end users often find it easy to explain an algorithm on a whiteboard or with pictures in a textbook but struggle to write the code correctly. We propose a new methodology that allows users to program by demonstrating how an algorithm proceeds on concrete inputs. To reduce the burden of these demonstrations on the user, we have developed pruning algorithms to remove ambiguities in the demonstrations and control flow inference algorithms to infer missing conditionals in demonstrations. These two techniques take advantage of the knowledge encoded in the user's partial correctness condition. We show that this approach is effective in practice by analyzing its performance on several common algorithms.
Fitting multiple models to multiple images with minimal user interaction

Utilising the broad range of information which human observers bring to bear when interpreting their visual environment is currently infeasible for artificial vision systems. We propose instead a method for modelling compound structures which intelligently divides this prior information into that which may be applied by the system and that which may not. Models are fitted to the input data on the basis of 2D and 3D image-based measures, but also as directed by a prior whose specification is split between the human and the system. Importantly this split is carried out in a manner which minimises the human input required.
Analysing the evolution of social aspects of open source software ecosystems

Empirical software engineering is concerned with statistical studies that aim to understand and improve certain aspects of the soft- ware development process. Many of these focus on the evolution and maintenance of evolving software projects. They rely on repository min- ing techniques to extract relevant data from software repositories or other data sources frequently used by software developers. We enlarge these empirical studies by exploring social software engineering, study- ing the developer community, including the way developers work, coop- erate, communicate and share information. The underlying hypothesis is that social aspects signicantly inuence the way in which the software project will evolve over time. We present some preliminary results of an empirical study we are carrying out on the dierent types of activities of the community involved in the GNOME open source ecosystem, and we discuss suggestions for future work.
Communication in Abstract State Machines

Up to recently the majority of applications of the Abstract State Machines method for design and verification of computational systems used the shared variable approach. However in particular with distributed systems only various forms of communication may be available to share information. We define communicating ASMs by using instead of shared locations an explicit, abstract concept of Sending/Receiving messages which can be added to existing ASM execution engines. We aim to provide a definition which is a conservative extension of traditional ASMs, uniformly captures synchronous and asynchronous communication and is not bound to any specific message passing mechanism but can be instantiated to the major communication concepts in the literature. We illustrate the concept by an application to synchronous and asynchronous networks of communicating processes.
Edwardsiella tarda-Induced Cytotoxicity Depends on Its Type III Secretion System and Flagellin

ABSTRACT Many Gram-negative bacteria utilize a type III secretion system (T3SS) to translocate virulence proteins into host cells to cause diseases. In responding to infection, macrophages detect some of the translocated proteins to activate caspase-1-mediated cell death, called pyroptosis, and secretion of proinflammatory cytokines to control the infection. Edwardsiella tarda is a Gram-negative enteric pathogen that causes hemorrhagic septicemia in fish and both gastrointestinal and extraintestinal infections in humans. In this study, we report that the T3SS of E. tarda facilitates its survival and replication in murine bone marrow-derived macrophages, and E. tarda infection triggers pyroptosis of infected macrophages from mice and fish and increased secretion of the cytokine interleukin 1β in a T3SS-dependent manner. Deletion of the flagellin gene fliC of E. tarda results in decreased cytotoxicity for infected macrophages and does not attenuate its virulence in a fish model of infection, whereas upregulated expression of FliC in the fliC mutant strain reduces its virulence. We propose that the host controls E. tarda infection partially by detecting FliC translocated by the T3SS, whereas the bacteria downregulate the expression of FliC to evade innate immunity.
Building 2D wide-area site models from single- and multipass single-polarization SAR data

Wide area site models are useful for delineating regions of interest and assisting in tasks like monitoring and change detection. They are also useful in registering a newly acquired image to an existing one of the same site, or to a map. This paper presents an algorithm for building a 2D wide area site model from high resolution, single polarization synthetic aperture radar (SAR) data. A three stage algorithm, involving detection of bright pixels, statistical segmentation of the data into homogeneous regions, and labeling/validation of segmentation results, is used for this task. Constant false alarm rate (CFAR) detectors are used for detecting bright pixels. Under assumptions of a suitable model for the statistical distribution of single polarization intensity or complex data, maximum likelihood labeling is used for initial segmentation. Knowledge of the acquisition parameters and other geometric cues are used to refine the initial segmentation and to extract man-made objects like buildings, and their shadows, as well as roads, from these images. When data from multiple passes of the same site is available, site models yield feature points which can be used to register the different images. In case complete information regarding the radar location, heading, and depression angle are available, the multiple views can be registered prior to site model construction, leading to improved performance. Site models are also useful for SAR data compression, where possible targets, man-made objects, and their neighborhoods are compressed losslessly and the background regions are compressed using lossy schemes.
Slide localization in video sequence by using a rapid and suitable segmentation in marginal space

The paper is interested in indexing lecture videos for semantic search of learning material. We present a comparative study between DCT, Grayscale and Marginal space using classical k-means technique. For stable segmentation we propose to introduce an automatic threshold based on moment preservation. We discuss the suitability of each space on different images and then focus on educational video frames that are not so predominant in color and present the best transformation technique for segmentation of lecture video frame. We also present a technique to localize the slide based on some heuristics.
Mechanism-based treatments for Alzheimer's disease

Treatment for Alzheimer's disease is entering a new and exciting phase, with several new drugs beginning clinical trials. Many of these new therapies are based on our best current understanding of the pathogenesis of Alzheimer's disease, and are designed to try to either slow or halt the progression of the disease. There are several different theories underlying the current efforts, and these are briefly reviewed. Therapies directed against some aspect of β-amyloid formation, against neurofibrillary tangle formation and against the inflammatory response are all considered, as are the problems associated with each area. It is as yet unclear which, if any, of these approaches will be successful, but the high level of activity in each of these three fields provides some hope that an effective treatment for Alzheimer's disease is on the horizon.
Pubertal Characteristics in Girls of Qazvin Province, Iran

Objective Puberty is a critical time between childhood and adulthood. Many studies have reported that the mean age of breast development is decreasing. The aim of this study was to provide updated data on the pubertal development of girls and to evaluate precocious puberty in our population. Methods This cross sectional study was conducted in 6 to 16 year old school girls during 2009-2010 in Qazvin. 2240 healthy girls from all geographical regions with every socioeconomic status were selected by a stratified multistage cluster design to obtain representative sample of population. A questionnaire including demographic data, anthropometric measurements, secondary sexual characteristics, menarche status and its onset was filled out for every participant. Secondary sexual characteristics including breast development (B1–5) and pubic hair (PH1–5) were evaluated according to Marshal and Tanner recommendation. Findings The mean±SD of height, weight, and BMI of participants was 139.7±14.5, 36.1±12.9 and 17.9±3.7 respectively. The mean age (10th – 90th percentile) of B2 and PH2 were 9.71(7.67–11.4) and 9.82 years (7.84–11.42) respectively. Mean age of menstruation was 12.52 years. The mean BMI was significantly higher in pubertal females comparing to prepubertal girls (P<0.001). Average duration of puberty (the time from initiation of puberty to menarche) was 2.81 years. Conclusion The mean age of pubertal onset in girls living in Qazvin is 9.71 years. Menarche occurs at mean age of 12.52 and onset of puberty earlier than 6.24 years will be precocious. We found that girls in Qazvin had a slightly earlier age of initiation of puberty and of menarche in comparison with other studies in Iran.
Classification of mammographic masses using support vector machines and Bayesian networks

In this paper, we compare two state-of-the-art classification techniques characterizing masses as either benign or malignant, using a dataset consisting of 271 cases (131 benign and 140 malignant), containing both a MLO and CC view. For suspect regions in a digitized mammogram, 12 out of 81 calculated image features have been selected for investigating the classification accuracy of support vector machines (SVMs) and Bayesian networks (BNs). Additional techniques for improving their performance were included in their comparison: the Manly transformation for achieving a normal distribution of image features and principal component analysis (PCA) for reducing our high-dimensional data. The performance of the classifiers were evaluated with Receiver Operating Characteristics (ROC) analysis. The classifiers were trained and tested using a k-fold cross-validation test method (k=10). It was found that the area under the ROC curve (Az) of the BN increased significantly (p=0.0002) using the Manly transformation, from Az = 0.767 to Az = 0.795. The Manly transformation did not result in a significant change for SVMs. Also the difference between SVMs and BNs using the transformed dataset was not statistically significant (p=0.78). Applying PCA resulted in an improvement in classification accuracy of the naive Bayesian classifier, from Az = 0.767 to Az = 0.786. The difference in classification performance between BNs and SVMs after applying PCA was small and not statistically significant (p=0.11).
Molecular mechanisms orchestrating the stem cell response to translational scaffolds

A 2013 Perspective in Science titled “Deconstructing Dimensionality” noted the importance of fiber morphology on cell phenotype, concluding with the statement “Identifying the mechanisms by which cells assess the nature of their environment will advance basic cell biology and facilitate the development of synthetic matrices for specific tissue engineering applications. ” Nanofibers have revolutionized scaffold-based approaches for musculoskeletal tissues; demonstrating surprising efficacy over promoting mesenchymal stem cell, MSC, differentiation down multiple musculoskeletal lineages. Understanding the fundamental mechanisms involved will allow the future design of nanofiber-based scaffolds to target a lineage with specificity. This article focuses on how three geometry sensors: focal adhesions, membrane associated vesicle stabilizing and trafficking proteins, and adherens junctions; potentially regulate MSC lineage commitment in response to bio-instructive nanofibers.
A new parallel genetic algorithm

One problem of propagating the globally fittest individual via neighbourhood evolution in both the island model and the cellular model of existing parallel genetic algorithms (PGAs) is that the migration of the globally best individual is delayed to non-adjacent processors. This may cause an inferior search in those sub-populations. The propagation delay of the globally best individual is proportional to the network distance between two processors. Delayed migration of the best individual in PGAs is an essential deviation from the sequential version of the genetic algorithm, in which the best individuals are always used to compete with other individuals. To solve this problem, this paper proposes an extended version of the island PGA called the Virtual Community PGA (VC-PGA). The VC-PGA is applied in a case study of optimizing the parameters of a backpropagation neural network classifier.
Automatic Statistics Extraction for Amateur Soccer Videos

Amateur soccer statistics have interesting applications such as providing insights to improve team performance, individual coaching, monitoring team progress and personal or team entertainment. Professional soccer statistics are extracted with labor intensive expensive manual effort which is not realistic for amateur matches. In this paper we develop a solution that automatically extracts action-related soccer statistics from a static camera pointed at the pitch. We implement a solution to player localization and action classification problem in human action recognition. Our method does not rely on player tracking, sliding windows, super voxels or construction of multiple hypotheses. Our work is developed with actual application in mind and a fully functional recognition pipeline is implemented, specifically tailored to meet the inherent challenges of action-rich soccer video.
Combining Naive Bayes and Decision Tables

We investigate a simple semi-naive Bayesian ranking method that combines naive Bayes with induction of decision tables. Naive Bayes and decision tables can both be trained efficiently, and the same holds true for the combined semi-naive model. We show that the resulting ranker, compared to either component technique, frequently significantly increases AUC. For some datasets it significantly improves on both techniques. This is also the case when attribute selection is performed in naive Bayes and its semi-naive variant.
Directing Mesenchymal Stem Cells with Gold Nanowire Arrays

Controlling cell organization is still a major bottleneck in biointerface engineering when the material dimensions decrease to the nanoscale. Here, Au nanowire‐patterned array platforms with multiscale design from the macroscale to the nanoscale are developed for studying human bone marrow–derived mesenchymal stem cell (hBM‐MSC) response. When the angle of the Au nanowires on glass is increased from 0° to 90°, hBM‐MSC arrangement exhibits a transition from a unidirectional distribution induced by a vector response to a bimodal polarization pattern. The degree of cell vector response and elongation decreases with increasing nanowire angles from 0° to 90°. Further, it is demonstrated that the specific cell adhesion and organization are dependent on the surface micro/nanotopography, which is greatly enhanced by introducing stem cell‐material affinity differences. An ideal model and new insights into a deeper understanding of cell–nano‐biointerface interactions are provided.
A Novel Form of Transcriptional Silencing by Sum1-1 Requires Hst1 and the Origin Recognition Complex

ABSTRACT In the yeast Saccharomyces cerevisiae, a and α mating-type information is stored in transcriptionally silenced cassettes called HML and HMR. Silencing of these loci, maintained by the formation of a specialized type of heterochromatin, requires trans-acting proteins andcis-acting elements. Proteins required for silencing include the Sir2 NAD+-dependent deacetylase, Sir3, and Sir4. Factors that bind to the cis elements atHMR and HML and that are important for silencing include the origin recognition complex (ORC). Mutations of any of these Sir proteins or combinations of cis elements result in loss of silencing. SUM1-1 was previously identified as a dominant mutation that restores silencing toHMR in the absence of either the Sir proteins or some of the cis elements. We have investigated the novel mechanism whereby Sum1-1 causes Sir-independent silencing at HMR and present the following findings: Sum1-1 requires the Sir2 homolog, Hst1, for silencing and most probably requires the NAD+-dependent deacetylase activity of this protein. Sum1-1 interacts strongly with ORC, and this strong interaction is dependent on HMR DNA. Furthermore, ORC is required for Sum1-1-mediated silencing atHMR. These observations lead to a model for Sum1-1 silencing of HMR in which Sum1-1 is recruited toHMR by binding to ORC. Sum1-1, in turn, recruits Hst1. Hst1 then deacetylates histones or other chromatin-associated proteins to cause chromatin condensation and transcriptional silencing.
TssA: The cap protein of the Type VI secretion system tail

The Type VI secretion system (T6SS) is a multiprotein and mosaic apparatus that delivers protein effectors into prokaryotic or eukaryotic cells. Recent data on the enteroaggregative Escherichia coli (EAEC) T6SS have provided evidence that the TssA protein is a key component during T6SS biogenesis. The T6SS comprises a trans‐envelope complex that docks the baseplate, a cytoplasmic complex that represents the assembly platform for the tail. The T6SS tail is structurally, evolutionarily and functionally similar to the contractile tails of bacteriophages. We have shown that TssA docks to the membrane complex, recruits the baseplate complex and initiates and coordinates the polymerization of the inner tube with that of the sheath. Here, we review these recent findings, discuss the variations within TssA‐like proteins, speculate on the role of EAEC TssA in T6SS biogenesis and propose future research perspectives.
Performance Assessment of Four Chimeric Trypanosoma cruzi Antigens Based on Antigen-Antibody Detection for Diagnosis of Chronic Chagas Disease

The performance of serologic tests in chronic Chagas disease diagnosis largely depends on the type and quality of the antigen preparations that are used for detection of anti-Trypanosoma cruzi antibodies. Whole-cell T. cruzi extracts or recombinant proteins have shown variation in the performance and cross-reactivity. Synthetic chimeric proteins comprising fragments of repetitive amino acids of several different proteins have been shown to improve assay performances to detect Chagasic infections. Here, we describe the production of four chimeric T. cruzi proteins and the assessment of their performance for diagnostic purposes. Circular Dichroism spectra indicated the absence of well-defined secondary structures, while polydispersity evaluated by Dynamic Light Scattering revealed only minor aggregates in 50 mM carbonate-bicarbonate (pH 9.6), demonstrating that it is an appropriate buffering system for sensitizing microplates. Serum samples from T. cruzi-infected and non-infected individuals were used to assess the performance of these antigens for detecting antibodies against T. cruzi, using both enzyme-linked immunosorbent assay and a liquid bead array platform. Performance parameters (AUC, sensitivity, specificity, accuracy and J index) showed high diagnostic accuracy for all chimeric proteins for detection of specific anti-T. cruzi antibodies and differentiated seropositive individuals from those who were seronegative. Our data suggest that these four chimeric proteins are eligible for phase II studies.
Selecting Best Answer: An Empirical Analysis on Community Question Answering Sites

A community question answering (CQA) site is a well-known online community, where user interacts on a wide variety of topics. To the best of our knowledge, the selection of a best answer for the question asked on the CQA site is done manually, which is traditional and tedious. In this paper, a model is developed for selecting best answer for the question asked on the CQA site. Instead of taking data related to question-answer only into account as done in manual process, this model takes both question-answer and answerers' data into account, which gives an insight view into the answers given by the experts that is more likely to be selected as the best answer. The presented approach analyzes StackOverflow Q&A posts with at least five answers to extract features for pattern identification using which the best answer is selected for the asked questions based on topic modeling and classifier. To evaluate correctness of the proposed model, a set of parameters are used, such as Receiver Operating Characteristics Area Under Curve, Precision Recall Area Under Curve, Gmean, and Accuracy. Results show that the proposed model is effective in predicting the best answer.