Miracle-dz/UniFAR-FTUs huggingface dataset:
Asserting and checking determinism for multithreaded programs

The trend towards processors with more and more parallel cores is increasing the need for software that can take advantage of parallelism. The most widespread method for writing parallel software is to use explicit threads. Writing correct multithreaded programs, however, has proven to be quite challenging in practice. The key difficulty is non-determinism. The threads of a parallel application may be interleaved non-deterministically during execution. In a buggy program, non-deterministic scheduling will lead to non-deterministic results - some interleavings will produce the correct result while others will not. We propose an assertion framework for specifying that regions of a parallel program behave deterministically despite non-deterministic thread interleaving. Our framework allows programmers to write assertions involving pairs of program states arising from different parallel schedules. We describe an implementation of our deterministic assertions as a library for Java, and evaluate the utility of our specifications on a number of parallel Java benchmarks. We found specifying deterministic behavior to be quite simple using our assertions. Further, in experiments with our assertions, we were able to identify two races as true parallelism errors that lead to incorrect non-deterministic behavior. These races were distinguished from a number of benign races in the benchmarks.
Rooster Collagen Extracts from Rooster by-Products

Collagen associated hydrolysates have desirable characteristics for various industrial applications [1,2]. They have been used as important biomaterial in medical applications because of their biodegradability and weak antigenicity [3]. In addition, intracellular and extra-cellular self-assembly of polypeptide chains and the cross-links between adjacent molecules help collagen fibrils to withstand physical stress [4]. Collagen is also effective in decreasing cooking loss of deli products containing chunked chicken meat by increasing protein-protein bindings, and has the potential to improve the quality characteristics of deli rolls by improving their texture [5]. Further, Mittal [6] showed that collagen hydrolysates can be used as binders or extenders in meat emulsions. Moreover, collagen can also be used to make natural casings for sausages and frankfurters [7] and to fill periodontal bony pockets [8]. Collagen is however, deficient in the essential amino acid tryptophan and limited in some essential amino acids such as lysine, threonine and methionine [9].
A joint learning based face hallucination approach for low quality face image

This paper describes a novel method for single-image super-resolution (SR) based on a neighbor embedding technique which uses coupled feature spaces under surveillance scenarios. For surveillance face images, traditional neighbor embedding SR approaches could not offer counterintuitive results because consistency between high resolution images and low resolution images is destroyed by serious noise which caused by environmental impact factors and large distance between the camera and objects. In order to reinforce the consistency, we extend the learning space from single to a coupled feature space that combine image intensity feature and contour model. The contour model describes facial contour information as images generated from original low resolution ones. Simulation experiments show that this proposed approach could provide competitive results in simulation experiments in subjective and objective quality. Even in surveillance scenario the proposed method outperforms the traditional methods.
Category-theoretic models of Linear Abadi & Plotkin Logic

This paper presents a sound and complete category-theoretic notion of models for Linear Abadi & Plotkin Logic (Birkedal et al., 2006), a logic suitable for reasoning about parametricity in combination with recursion. A subclass of these called parametric LAPL structures can be seen as an axiomatization of domain theoretic models of parametric polymorphism, and we show how to solve general (nested) recursive domain equations in these. Parametric LAPL structures constitute a general notion of model of parametricity in a setting with recursion. In future papers we will demonstrate this by showing how many dierent models of parametricity and recursion give rise to parametric LAPL structures, including Simpson and Rosolini's set theoretic models (Rosolini and Simpson, 2004), a syntactic model based on Lily (Pitts, 2000, Bierman et al., 2000) and a model based on admissible pers over a reflexive domain (Birkedal et al., 2007).
Event-related potentials during mental rotation tasks in patients with first-episode depression

Background The ability to mentally rotate images is impaired in depressed patients but the electrophysiological abnormalities in the brain related to this impairment remain unclear. Aim To compare the event-related potentials (ERPs) of depressed patients and control subjects during the completion of a mental rotation (MR) task. Methods Thirty-two inpatients and outpatients with first-episode depression and twenty-nine control subjects were administered an MR task that presented test stimuli at different angles of orientation. During the test ERPs were measured in four regions of the brain (PZ, CZ, P3 and P4). Outcome variables included the error rate, reaction time, P500 latency and maximum P500 amplitude. Results Compared to control subjects, patients with depression completing the MR test had a significantly longer mean (sd) reaction time (689 [98] ms vs. 569 [55] ms; t=4.36, p<0.001) and a significantly higher mean percent error rate (30.2% [11.4%] vs. 20.3% [7.2%]; t=3.61, p=0.015); these differences were also significant at each of the four orientation angles assessed. The ERP assessment during the MR test found that patients had a non-significant increase in the overall P500 latency and a significant reduction in the mean maximum P500 amplitude at each of the four brain regions assessed. For both patients and controls the error rate, reaction time, P500 latency and P500 amplitude increased significantly in a stepwise fashion as the angle of orientation of the presented stimulus increased from 0° to 180°. In the control group there was a positive peak in the averaged ERP waveforms at about 700 ms that was not present in the patient group. Conclusion Our study confirms previous work on the usefulness of MR tests to assess the cognitive deficits in depression. We find that the electrophysiological measures provided by ERP assessments during MR tasks, particularly maximum P500 amplitude and maximum P700 amplitude, are potential biological markers for depression. Prospective studies that assess changes in these measures over the course of a depressive illness will be needed to confirm their usefulness.
Behavior of Access Modifiers in Java Programs

Security is one of the essential features promised by Java system especially for Internet computing. It is because Java programs running in the computer are not only localized in the desktop, and harmful programs can easily penetrate into the computer system through Internet if the system does not have enough security protection. In order to provide information security, Java language provides different access modifiers to individual objects and members to control the access control among various objects. The enforcement of access control totally relies on the Java Virtual Machine. However, complex routines and thread synchronization are required to perform verification process for access modifiers. It generates software overhead. Based on the analysis for verification process with different access modifiers, a simpler and more efficient hardware modification can reduce software overhead to support the verification process. This paper presents the statistic analysis for verification frequency of different access modifiers in Java programs.
APPLICATION OF COMPRESSED SAMPLING FOR SPECTRUM SENSING AND CHANNEL ESTIMATION IN WIDEBAND COGNITIVE RADIO NETWORKS

In the last few years Compressed Sampling (CS) has been well used in the area of signal processing and image compression. Recently, CS has been earning a great interest in the area of wireless communication networks. CS exploits the sparsity of the signal processed for digital acquisition to reduce the number of measurement, which leads to reductions in the size, power consumption, processing time and processing cost. This article presents application of CS for the spectrum sensing and channel estimation in Cognitive Radio (CR) networks. Basic approach of CS is introduced first, and then scheme for spectrum sensing and channel estimation for CR is discussed. First, fast and efficient compressed spectrum sensing (CSS) scheme is proposed to detect wideband spectrum, where samples are taken at sub-Nyquist rate and signal acquisition is terminated automatically once the samples are sufficient for the best spectral recovery and then, after the spectrum sensing, in the second phase notion of multipath sparsity is formalized and a novel approach based on Orthogonal Matching Pursuit (OMP) is discussed to estimate sparse multipath channels for CR networks. The effectiveness of the proposed scheme is demonstrated through comparisons with the existing conventional spectrum sensing and channel estimation methods.
Exploiting mutual coupling by means of analog-digital zero forcing

In this paper, the mutual coupling effect among antenna elements for the downlink multiuser multiple-input-single-output (MU-MISO) is studied. Different from conventional knowledge that the mutual coupling effect usually degrades the system performance, a joint analog-digital precoding scheme is proposed so that the system can benefit from this effect. Linear precoding approaches are applied in the digital domain, while in the analog domain, convex optimization is applied to determine the value of each load impedance such that the resulting noise amplification factor for the precoder is minimized. Simulation results show that the proposed analog-digital precoding schemes can achieve a significant performance gain over conventional precoding approaches with fixed mutual coupling.
Analysis of slow convergence regions in adaptive systems

We examine convergence properties of errors in a class of adaptive systems that corresponds to adaptive control of linear time-invariant plants with state variables accessible. We demonstrate the existence of a sticking region in the error space where the state errors move with a finite velocity independent of their magnitude. We show that these properties are also exhibited by adaptive systems with closed-loop reference models which have been demonstrated to exhibit improved transient performance as well as those that include an integral control in the inner-loop. A simulation study is included to illustrate the size of this sticking region and its dependence on various system parameters.
Statistical Properties Demand as Much Attention as Object Features

Recent studies have argued that the statistical properties of a set of visual items can be extracted with little or even no cost. In the present study, observers (N = 188) performed a color task and an orientation task, and the attention effect was measured as the advantage of pre-cueing one of the two tasks. The color and orientation tasks required participants to report either an object feature or the mean of a 4×4 array (i.e., statistical property). The pre-cueing advantages were approximately equal regardless of the nature of the tasks (object features vs. statistical properties), providing evidence that statistical properties are not perceived with zero cost, but demand as much attention as object features.
Specialized Rehabilitation Programs for Children and Adolescents with Severe Disabling Chronic Pain: Indications, Treatment and Outcomes

Children and adolescents with highly disabling chronic pain of high intensity and frequency are admitted to specialized pain rehabilitation programs. Some barriers to obtaining this specialized care include a lack of availability of treatment centers, a perceived social stigma and individual barriers such as socioeconomic status. Specialized rehabilitation programs for severe disabling chronic pain worldwide have similarities regarding admission criteria, structure and therapeutic orientation. They differ, however, regarding their exclusion criteria and program descriptions. The short- and long-term effectiveness of some rehabilitation programs is well documented. All countries should promote the establishment of future pediatric pain centers to improve the health care of children and adolescents suffering from severe chronic pain. Standardized reporting guidelines should be developed to describe treatments and outcomes to enable comparability across treatment centers.
Comparison of Naltrexone and Placebo in Treatment of Alcohol Dependence of Adolescents

Abstract This study assesses the efficacy and safety of long-term naltrexone treatment in alcohol dependence of adolescents. In a double-blind, placebo-controlled study, thirty patients, age 15 to 19 years, with chronic or episodic alcohol dependence were randomly allocated to treatment with naltrexone (50mg daily) or placebo for 90 days. Patients were assessed on the day treatment started and on days 30 and 90 by interview, self-report, questionnaire, and laboratory screening. At the end of treatment, 20 naltrexone treated and 10 placebo treated patients had been continuously abstinent (p = 0.0069). Naltrexone is an effective and well-tolerated pharmacological adjunct to psychosocial and behavioral treatment programs for the treatment of adolescent alcohol-dependent patients.
Stochastic Data Clustering

In 1961 Herbert Simon and Albert Ando [Econometrika, 29 (1961), pp. 111--138] published the theory behind the long-term behavior of a dynamical system that can be described by a nearly uncoupled matrix. Over the past fifty years this theory has been used in a variety of contexts, including queueing theory, brain organization, and ecology. In all of these applications, the structure of the system is known and the point of interest is the various stages the system passes through on its way to some long-term equilibrium. This paper looks at this problem from the other direction. That is, we develop a technique for using the evolution of the system to tell us about its initial structure, and then use this technique to develop an algorithm that takes the varied solutions from multiple data clustering algorithms to arrive at a single data clustering solution.
Kusk Object Dataset: Recording access to objects in food preparation

This study aims to construct a KUSK Dataset's extension that provides records of chef's touching and releasing action to objects, which we call “access to objects,” in his/her food preparation. The records of access to object are known as a key evidence for understanding chef's activity in food preparation. In the dataset, we provide object images as well as the records of access to object. The data are obtained by manual annotation and by automatic processing. As a result of annotation, we collected 4391 object images from 57 cooking observations. We also confirmed that the CNN-based state-of-the-art method reached 74.15% accuracy on average in recognizing objects on a cooking counter.
Nuclear pore complex assembly and maintenance in POM121- and gp210-deficient cells

So far, POM121 and gp210 are the only known anchoring sites of vertebrate nuclear pore complexes (NPCs) within the lipid bilayer of the nuclear envelope (NE) and, thus, are excellent candidates for initiating the NPC assembly process. Indeed, we demonstrate that POM121 can recruit several nucleoporins, such as Nup62 or Nup358, to ectopic assembly sites. It thus appears to act as a nucleation site for the assembly of NPC substructures. Nonetheless, we observed functional NPCs and intact NEs in severely POM121-depleted cells. Double knockdowns of gp210 and POM121 in HeLa cells, as well as depletion of POM121 from human fibroblasts, which do not express gp210, further suggest that NPCs can assemble or at least persist in a POM121- and gp210-free form. This points to extensive redundancies in protein–protein interactions within NPCs and suggests that vertebrate NPCs contain additional membrane-integral nucleoporins for anchorage within the lipid bilayer of the NE. In Stavru et al. (on p. 509 of this issue), we describe such an additional transmembrane nucleoporin as the metazoan orthologue of yeast Ndc1p.
Clinically significant religious impairment

Recent psychodiagnostic practice, as embodied in the DSM IV, requires that psychopathological features result in a "clinically significant impairment" to qualify as a "mental disorder" in many cases. The impairment must be in social, occupational, or other important areas of functioning. The current proposal is that clinicians should consider the potential impairment in religious functioning arising from mental disorders in diagnostic process. It is suggested that psychopathology may result in a clinically significant religious impairment that is defined as a reduced ability to perform religious activities, achieve religious goals, or to experience religious states, due to a psychological disorder. Various existing approaches to studying the relationship between religious functioning and psychopathology are briefly reviewed and roughly categorized as either focused on 'symptomatic religiosity' or reflecting a 'religiously sympathetic' posture. Yet, in both of these approaches, religion has predominantly been construed as an exogenous variable contributing to mental health in some fashion (for good, for ill, or for both). The current proposal suggests that clinicians should also consider religion in endogenous perspective. So construed, religion is a significant domain of adaptive functioning, which may be adversely impacted by psychopathology. A discussion of various clinical, research and ethical issues involved in realizing the proposal is provided.
Rural and regional Australia

Limited attention was paid to rural and regional Australia in the 2007 federal election. Only six of the government's eighteen marginal seats lay there and it seemed to have weathered the ‘rural backlash’ of previous years. The election campaign saw only a few explicitly rural policy differences between Government and Opposition. These involved broadband delivery, wheat marketing and climate change. Yet, the ALP made considerable inroads into the Coalition's non-metropolitan bastion. The swing to the ALP was greater there than the national average and it won more than half of the seats to change hands in rural and regional Australia. While the Coalition still holds a slight edge in non-metropolitan seats, many of these have now become more marginal.
The soft consensus model: dynamics, complexity, and modularity

We consider the network dynamics of the soft consensus model and we investigate the dynamical behavior of two important measures of network structure, complexity and modularity. The dynamical mechanism of the soft consensus model is driven by the minimization of a cost function combining a collective measure of dissensus with an individual mechanism of opinion changing aversion. The dissensus measure plays a key role in the model and induces a network of pairwise interactions between the individual opinions. The pairwise interaction coefficients take values in the unit interval and define the adjacency matrix of an undirected weighted graph. The measures of complexity and modularity express in two different ways how far the network structure is from some appropriate, transitive partitioning. We illustrate their dynamical behavior in the context of the soft consensus model by means of a number of computer simulations. 1 The soft consensus model Let {i = 1, . ,n} be an index set for a group of agents. The individual opinions of these agents are denoted by ri ∈ [0,1] with i = 1, . ,n. The soft consensus network model of group decision making [11,12,13,6,5,3,4] is based on a graph representation in which each node i encodes the individual opinion value ri, and each edge (i, j) encodes the interaction coefficient (strength of interaction) between agents i and j, expressed in the (0,1) interval: larger interaction values correspond to stronger interactions. Consider the decreasing sigmoid function σ(x) = 1 1 + eβ(x−α) ∈ (0,1) (1) with two free parameters 0 0. The former is a threshold parameter, σ(α) = 2 , and the latter controls the polarization of the sigmoidal response around the threshold value, σ (α) = − 4 . The interaction coefficients vi j (α,β) ∈ (0,1) defining the adjacency matrix of an undirected weighted graph are computed by applying the non-linear σ filter to the square distance between the two opinion values ri and r j , vi j = v ji = σ (
piggyBac‐mediated somatic transformation of the two‐spotted cricket, Gryllus bimaculatus

Transgenic insects have been artificially produced to study functions of interesting developmental genes, using insect transposons such as piggyBac. In the case of the cricket, however, transgenic animals have not yet been successfully artificially produced. In the present study, we examined whether the piggyBac transposon functions as a tool for gene delivery in embryos of Gryllus bimaculatus. We used either a piggyBac helper plasmid or a helper RNA synthesized in vitro as a transposase source. An excision assay revealed that the helper RNA was more effective in early Gryllus eggs to transpose a marker gene of eGFP than the helper plasmid containing the piggyBac transposase gene driven by the G. bimaculatus actin3/4 promoter. Further, only when the helper RNA was used, somatic transformation of the embryo with the eGFP gene was observed. These results suggest that the piggyBac system with the helper RNA may be effective for making transgenic crickets.
Prediction of Substrate-Enzyme-Product Interaction Based on Molecular Descriptors and Physicochemical Properties

It is important to correctly and efficiently predict the interaction of substrate-enzyme and to predict their product in metabolic pathway. In this work, a novel approach was introduced to encode substrate/product and enzyme molecules with molecular descriptors and physicochemical properties, respectively. Based on this encoding method, KNN was adopted to build the substrate-enzyme-product interaction network. After selecting the optimal features that are able to represent the main factors of substrate-enzyme-product interaction in our prediction, totally 160 features out of 290 features were attained which can be clustered into ten categories: elemental analysis, geometry, chemistry, amino acid composition, predicted secondary structure, hydrophobicity, polarizability, solvent accessibility, normalized van der Waals volume, and polarity. As a result, our predicting model achieved an MCC of 0.423 and an overall prediction accuracy of 89.1% for 10-fold cross-validation test.