Miracle-dz/UniFAR-FTUs huggingface dataset:
The NiCad Clone Detector

The NiCad Clone Detector is a scalable, flexible clone detection tool designed to implement the NiCad (Automated Detection of Near-Miss Intentional Clones) hybrid clone detection method in a convenient, easy-to-use command- line tool that can easily be embedded in IDEs and other environments. It takes as input a source directory or directories to be checked for clones and a configuration file specifying the normalization and filtering to be done, and provides output results in both XML form for easy analysis and HTML form for convenient browsing. NiCad handles a range of languages and normalizations, and is designed to be easily extensible using a component-based plugin architecture. It is scalable to very large systems and has been used to analyze, for example, all 47 releases of FreeBSD (60 million lines) as a single system.
Controlled synthesis of uniform and monodisperse upconversion core/mesoporous silica shell nanocomposites for bimodal imaging.

Here we report the design and controlled synthesis of monodisperse and precisely size-controllable UCNP@mSiO(2) nanocomposites smaller than 50 nm by directly coating a mesoporous silica shell (mSiO(2)) on upconversion nanocrystals NaYF(4):Tm/Yb/Gd (UCNPs), which can be used as near-infrared fluorescence and magnetic resonance imaging (MRI) agents and a platform for drug delivery as well. Some key steps such as transferring hydrophobic UCNPs to the water phase by using cetyltrimethylammonium bromide (CTAB), removal of the excess amount of CTAB, and temperature-controlled ultrasonication treatment should be adopted and carefully monitored to obtain uniform upconversion core/mesoporous silica shell nanocomposites. The excellent performance of the core-shell-structured nanocomposite in near-infrared fluorescence and magnetic resonance imaging was also demonstrated.
A Novel Recurrent Neural Network with Finite-Time Convergence for Linear Programming

In this letter, a novel recurrent neural network based on the gradient method is proposed for solving linear programming problems. Finite-time convergence of the proposed neural network is proved by using the Lyapunov method. Compared with the existing neural networks for linear programming, the proposed neural network is globally convergent to exact optimal solutions in finite time, which is remarkable and rare in the literature of neural networks for optimization. Some numerical examples are given to show the effectiveness and excellent performance of the new recurrent neural network.
RX-P873, a Novel Protein Synthesis Inhibitor, Accumulates in Human THP-1 Monocytes and Is Active against Intracellular Infections by Gram-Positive (Staphylococcus aureus) and Gram-Negative (Pseudomonas aeruginosa) Bacteria

ABSTRACT The pyrrolocytosine RX-P873, a new broad-spectrum antibiotic in preclinical development, inhibits protein synthesis at the translation step. The aims of this work were to study RX-P873's ability to accumulate in eukaryotic cells, together with its activity against extracellular and intracellular forms of infection by Staphylococcus aureus and Pseudomonas aeruginosa, using a pharmacodynamic approach allowing the determination of maximal relative efficacies (Emax values) and bacteriostatic concentrations (Cs values) on the basis of Hill equations of the concentration-response curves. RX-P873's apparent concentration in human THP-1 monocytes was about 6-fold higher than the extracellular one. In broth, MICs ranged from 0.125 to 0.5 mg/liter (S. aureus) and 2 to 8 mg/liter (P. aeruginosa), with no significant shift in these values against strains resistant to currently used antibiotics being noted. In concentration-dependent experiments, the pharmacodynamic profile of RX-P873 was not influenced by the resistance phenotype of the strains. Emax values (expressed as the decrease in the number of CFU from that in the initial inoculum) against S. aureus and P. aeruginosa reached more than 4 log units and 5 log units in broth, respectively, and 0.7 log unit and 2.7 log units in infected THP-1 cells, respectively, after 24 h. Cs values remained close to the MIC in all cases, making RX-P873 more potent than antibiotics to which the strains were resistant (moxifloxacin, vancomycin, and daptomycin for S. aureus; ciprofloxacin and ceftazidime for P. aeruginosa). Kill curves in broth showed that RX-P873 was more rapidly bactericidal against P. aeruginosa than against S. aureus. Taken together, these data suggest that RX-P873 may constitute a useful alternative for infections involving intracellular bacteria, especially Gram-negative species.
Random Mutagenesis of the Complement Factor 5a (C5a) Receptor N Terminus Provides a Structural Constraint for C5a Docking*

The N terminus of G protein-coupled receptors has been implicated in binding to peptide hormones. We have used random saturation mutagenesis to identify essential residues in the N terminus of the human complement factor 5a receptor (C5aR). In a library of N-terminal mutant C5aR molecules screened for activation by C5a, residues 24-30 of the C5aR showed a marked propensity to mutate to cysteine, most likely indicating that sulfhydryl groups at these positions are appropriately situated to form disulfide interactions with the unpaired Cys27 of human C5a. This presumptive spatial constraint allowed the ligand to be computationally docked to the receptor to form a model of the C5a/C5aR interaction. When the N-terminal mutant C5aR library was rescreened with C5a C27R, a ligand incapable of disulfide interactions, no individual position in the N terminus was essential for receptor signaling. However, the region 19-29 was relatively highly conserved in the functional mutants, further demonstrating that this region of the C5aR makes a productive physiologic interaction with the C5a ligand.
Detection and Quantification of Formalin Adulteration in Cow Milk Using Near Infrared Spectroscopy Combined with Multivariate Analysis

In order to increase the shelf life for long distance transportation of milk, formalin is added as an adulterant to milk. That is highly toxic causes liver and kidney damages. A new Near Infrared spectroscopy (NIR) combined with multivariate analysis was developed to detect as well as to quantify the level of formalin adulteration in cow milk. In this study four different types of cow milk samples were collected from Nizwa regions of Sultanate of Oman and were investigated. Those cow milk samples were then adulterated with formalin at eight different percentage levels: 0%, 1%, 3%, 5%, 7%, 9%, 11%, 13% and 17% of formalin. All samples were measured using NIR spectroscopy in absorption mode in the wavelength range from 700-2500 nm, at 2 cm-1 resolution and using a 0.2 mm path length CaF2 sealed cell. The multivariate methods like Principle component analysis (PCA), partial least discriminant analysis (PLS-DA) and partial least regression analysis (PLS) were applied for statistical analysis of the obtained NIR spectral data. PLS-DA model was used to check the discrimination between the pure and formalin adulterated milk samples. For PLSDA model the R-square value obtained was 0.969 with 0.086 RMSE (Root mean square error). Furthermore, PLS regression model was also built to quantify the levels formalin adulterant in cow milk samples. The PLS regression model was obtained with the R-square 93% and with 1.38 RMSECV(Root mean square error of cross validation) value having good prediction with RMSEP (Root mean square error of prediction) value 1.50 and correlation of 0.95. This newly developed method is non-destructive, cheap, no need of much sample preparation and having sensitivity level less than 2% level of formalin adulteration.
Divide and Translate Legal Text Sentence by Using Its Logical Structure

Translating legal text is generally considered to be difficult because legal text has some characteristics that make it different from other daily-use documents and legal text is usually long and complicated. In order boost the legal text translation quality, splitting an input sentence becomes mandatory. In this paper, we propose a novel method based on the logical structure of legal text sentence for dividing and translating legal text. We use a statistical learning method-Conditional Random Fields (CRFs) with rich linguistic information to recognize the logical structure of legal text sentence. We adapt the logical structure of legal text sentence to divide the sentence. By doing so, translation quality improves. Our experiments show that our approach can achieve better result for both Japanese-English and English-Japanese legal text translation by BLEU, NIST and TER score.
Average Counting via Approximate Histograms -- Preliminary Report

In this paper, we propose a novel method of solving the averaging problem for distributed Wireless Sensors Networks. This method is based on approximated histograms of measurements made by a sensor network. Our method uses a set of probabilistic counters and allows to find the approximation of the average of a set of measures done by sensor network with arbitrary, controlled by two parameters, precision. The exchange of information is based on broadcasting method exploiting extreme propagation technique. Our method require O(D) rounds, where D is the diameter of the network. We analyze both kind of errors which occurs in our method: discretization errors caused by the histogram representation of the data and errors due to the probabilistic nature of the used counters.
Microbial sulfur transformations in sediments from Subglacial Lake Whillans

Diverse microbial assemblages inhabit subglacial aquatic environments. While few of these environments have been sampled, data reveal that subglacial organisms gain energy for growth from reduced minerals containing nitrogen, iron, and sulfur. Here we investigate the role of microbially mediated sulfur transformations in sediments from Subglacial Lake Whillans (SLW), Antarctica, by examining key genes involved in dissimilatory sulfur oxidation and reduction. The presence of sulfur transformation genes throughout the top 34 cm of SLW sediments changes with depth. SLW surficial sediments were dominated by genes related to known sulfur-oxidizing chemoautotrophs. Sequences encoding the adenosine-5′-phosphosulfate (APS) reductase gene, involved in both dissimilatory sulfate reduction and sulfur oxidation, were present in all samples and clustered into 16 distinct operational taxonomic units. The majority of APS reductase sequences (74%) clustered with known sulfur oxidizers including those within the “Sideroxydans” and Thiobacillus genera. Reverse-acting dissimilatory sulfite reductase (rDSR) and 16S rRNA gene sequences further support dominance of “Sideroxydans” and Thiobacillus phylotypes in the top 2 cm of SLW sediments. The SLW microbial community has the genetic potential for sulfate reduction which is supported by experimentally measured low rates (1.4 pmol cm-3d-1) of biologically mediated sulfate reduction and the presence of APS reductase and DSR gene sequences related to Desulfobacteraceae and Desulfotomaculum. Our results also infer the presence of sulfur oxidation, which can be a significant energetic pathway for chemosynthetic biosynthesis in SLW sediments. The water in SLW ultimately flows into the Ross Sea where intermediates from subglacial sulfur transformations can influence the flux of solutes to the Southern Ocean.
Evolving Best-Response Strategies for Market-Driven Agents Using Aggregative Fitness GA

There are very few existing works that adopt genetic algorithms (GAs) for evolving the most successful strategies for different negotiation situations. Furthermore, these works did not explicitly model the influence of market dynamics. The contribution of this work is developing bargaining agents that can both: 1) react to different market situations by adjusting their amounts of concessions and 2) evolve their best-response strategies for different market situations and constraints using an aggregative fitness GA (AFGA). While many existing negotiation agents only optimize utilities, the AFGA in this work is used to evolve best-response strategies of negotiation agents that optimize their utilities, success rates, and negotiation speed in different market situations. Given different constraints and preferences of agents in optimizing utilities, success rates, and negotiation speed, different best-response strategies can be evolved using the AFGA. A testbed consisting of both: 1) market-driven agents (MDAs)-negotiation agents that make adjustable amounts of concessions taking into account market rivalry, outside options, and time preferences and 2) GA-MDAs-MDAs augmented with an AFGA, was implemented. Empirical results show that GA-MDAs achieved higher utilities, higher success rates, and faster negotiation speed than MDAs in a wide variety of market situations.
A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization

In modern large-scale machine learning applications, the training data are often partitioned and stored on multiple machines. It is customary to employ the "data parallelism" approach, where the aggregated training loss is minimized without moving data across machines. In this paper, we introduce a novel distributed dual formulation for regularized loss minimization problems that can directly handle data parallelism in the distributed setting. This formulation allows us to systematically derive dual coordinate optimization procedures, which we refer to as Distributed Alternating Dual Maximization (DADM). The framework extends earlier studies described in (Boyd et al., 2011; Ma et al., 2015a; Jaggi et al., 2014; Yang, 2013) and has rigorous theoretical analyses. Moreover with the help of the new formulation, we develop the accelerated version of DADM (Acc-DADM) by generalizing the acceleration technique from (Shalev-Shwartz and Zhang, 2014) to the distributed setting. We also provide theoretical results for the proposed accelerated version and the new result improves previous ones (Yang, 2013; Ma et al., 2015a) whose runtimes grow linearly on the condition number. Our empirical studies validate our theory and show that our accelerated approach significantly improves the previous state-of-the-art distributed dual coordinate optimization algorithms.
Texture-based classification of atherosclerotic carotid plaques

There are indications that the morphology of atherosclerotic carotid plaques, obtained by high-resolution ultrasound imaging, has prognostic implications. The objective of this study was to develop a computer-aided system that will facilitate the characterization of carotid plaques for the identification of individuals with asymptomatic carotid stenosis at risk of stroke. A total of 230 plaque images were collected which were classified into two types: symptomatic because of ipsilateral hemispheric symptoms, or asymptomatic because they were not connected with ipsilateral hemispheric events. Ten different texture feature sets were extracted from the manually segmented plaque images using the following algorithms: first-order statistics, spatial gray level dependence matrices, gray level difference statistics, neighborhood gray tone difference matrix, statistical feature matrix, Laws texture energy measures, fractal dimension texture analysis, Fourier power spectrum and shape parameters. For the classification task a modular neural network composed of self-organizing map (SOM) classifiers, and combining techniques based on a confidence measure were used. Combining the classification results of the ten SOM classifiers inputted with the ten feature sets improved the classification rate of the individual classifiers, reaching an average diagnostic yield (DY) of 73.1%. The same modular system was implemented using the statistical k-nearest neighbor (KNN) classifier. The combined DY for the KNN system was 68.8%. The results of this paper show that it is possible to identify a group of patients at risk of stroke based on texture features extracted from ultrasound images of carotid plaques. This group of patients may benefit from a carotid endarterectomy whereas other patients may be spared from an unnecessary operation.
Quadratic Leaky Integrate-and-Fire Neural Network Tuned with an Evolution-Strategy for a Simulated 3D Biped Walking Controller

This paper presents the results of experiments in applying a spiking neural network to control the locomotion of a simulated biped robot. The neural model used in simulations was developed to allow for an analytic solution to a neuron's fire time, while maintaining a non-instant post-synaptic potential rise time. The synaptic weights and delays were tuned using an evolution-strategy. Simulation experiments demonstrate that already within about two thousand generations the biped is able to acquire a dynamic walk which allows it to walk upright for several metres.
Psychoanatomical substrates of Bálint's syndrome

Objectives: From a series of glimpses, we perceive a seamless and richly detailed visual world. Cerebral damage, however, can destroy this illusion. In the case of Bálint's syndrome, the visual world is perceived erratically, as a series of single objects. The goal of this review is to explore a range of psychological and anatomical explanations for this striking visual disorder and to propose new directions for interpreting the findings in Bálint's syndrome and related cerebral disorders of visual processing. Methods: Bálint's syndrome is reviewed in the light of current concepts and methodologies of vision research. Results: The syndrome affects visual perception (causing simultanagnosia/visual disorientation) and visual control of eye and hand movement (causing ocular apraxia and optic ataxia). Although it has been generally construed as a biparietal syndrome causing an inability to see more than one object at a time, other lesions and mechanisms are also possible. Key syndrome components are dissociable and comprise a range of disturbances that overlap the hemineglect syndrome. Inouye's observations in similar cases, beginning in 1900, antedated Bálint's initial report. Because Bálint's syndrome is not common and is difficult to assess with standard clinical tools, the literature is dominated by case reports and confounded by case selection bias, non-uniform application of operational definitions, inadequate study of basic vision, poor lesion localisation, and failure to distinguish between deficits in the acute and chronic phases of recovery. Conclusions: Studies of Bálint's syndrome have provided unique evidence on neural substrates for attention, perception, and visuomotor control. Future studies should address possible underlying psychoanatomical mechanisms at “bottom up” and “top down” levels, and should specifically consider visual working memory and attention (including object based attention) as well as systems for identification of object structure and depth from binocular stereopsis, kinetic depth, motion parallax, eye movement signals, and other cues.
Distributed decision making to improve link quality in coexisting TVWS secondary networks

In recent developments, regulatory bodies are introducing flexible use of spectrum such as allowing the use of temporarily unoccupied VHF and UHF bands also known as television white spaces (TVWS). The networks exploiting such opportunity, hereafter called opportunistic networks, have equal right to access available TVWS. As a result, unhealthy competition to occupy the same spectrum would be inevitable. Hence, coexistence becomes a critical issue to be addressed. We propose a distributed coexistence algorithm to be implemented by TVWS networks sharing a single TVWS channel. The TVWS networks cooperate by sharing operational parameters in order to implement self enforcing coexistence algorithms. Through the knowledge of the exchanged parameters, devices dynamically select the appropriate transmit power that minimizes the mutual interference among the cooperating networks. This increases the efficiency the available spectrum being used by the opportunistic networks and at the same time it fosters green communications by avoiding unnecessary wastage of power that builds interference between the resource competing networks. We demonstrate by simulation that choosing of optimum transmit power vector, in which each element of the vector corresponds to the optimum transmit power of particular network, ensures minimum outage occurrence that any of the cooperating networks experience link quality below a specified threshold.
Fast Randomized Singular Value Thresholding for Low-Rank Optimization

Rank minimization can be converted into tractable surrogate problems, such as Nuclear Norm Minimization (NNM) and Weighted NNM (WNNM). The problems related to NNM, or WNNM, can be solved iteratively by applying a closed-form proximal operator, called Singular Value Thresholding (SVT), or Weighted SVT, but they suffer from high computational cost of Singular Value Decomposition (SVD) at each iteration. We propose a fast and accurate approximation method for SVT, that we call fast randomized SVT (FRSVT), with which we avoid direct computation of SVD. The key idea is to extract an approximate basis for the range of the matrix from its compressed matrix. Given the basis, we compute partial singular values of the original matrix from the small factored matrix. In addition, by developping a range propagation method, our method further speeds up the extraction of approximate basis at each iteration. Our theoretical analysis shows the relationship between the approximation bound of SVD and its effect to NNM via SVT. Along with the analysis, our empirical results quantitatively and qualitatively show that our approximation rarely harms the convergence of the host algorithms. We assess the efficiency and accuracy of the proposed method on various computer vision problems, e.g., subspace clustering, weather artifact removal, and simultaneous multi-image alignment and rectification.
Relationship Between Carotid Stenosis and Infarct Volume in Ischemic Stroke Patients

Background Stroke is a serious health problem all over the world. Ischemia causes 85% of strokes and 75% of these ischemic strokes occur within the area supplied by the internal carotid artery (ICA). Material/Methods This study included 47 acute stroke patients who were in the large-artery atherosclerosis group according to Trial of ORG 10172 in Acute Stroke Treatment (TOAST) classification and who had an infarct in the area supplied by the internal carotid artery. We sought to determine whether there was a significant correlation between the infarct volume of the patients as measured by diffusion-weighted magnetic resonance imaging (DW MRI), their National Institutes of Health Stroke Scale (NIHSS), and degree of carotid stenosis as identified by carotid computed tomography angiography (CTA). Results A significant correlation was observed between the percentage of carotid artery stenosis and infarct volume (p<0.001). In addition, there was a significant positive correlation between the NIHSS and infarct volume; the correlation was of moderate strength (r=0.366, p=0.001). Conclusions Our findings indicate that the percentage of carotid artery stenosis could be useful in predicting the infarct volume of the stroke.
3D motion vector coding with block base adaptive interpolation filter on H.264

Fractional pel motion compensation generally improves coding efficiency due to more precise motion accuracy and low path filtering effect in generating an image at fractional pel positions. In H.264, quarter pel motion compensation is applied, where the image at half pel position is generated by a 6 tap Wiener filter. And the adaptive interpolation filter technique, which adaptively changes filter characteristics for half pel positions has been proposed. That technique also changes the image at quarter pel positions, so it can be exploited to extend motion accuracy to be more precise. In this paper, a 3D motion vector coding (3DMVC) technique with block base adaptive interpolation filter (BAIF) is proposed. This paper also demonstrates the proposed method ensures filter data is successfully integrated into motion vector coding and outperforms the normal H.264.