Miracle-dz/UniFAR-FTUs huggingface dataset:
Memory Based Collaborative Filtering with Lucene

Memory Based Collaborative Filtering is a widely used approach to provide recommendations. It exploits similarities between ratings across a population of users by forming a weighted vote to predict unobserved ratings. Bespoke solutions are frequently adopted to deal with the problem of high quality recommendations on large data sets. A disadvantage of this approach, however, is the loss of generality and flexibility of the general collaborative filtering systems. In this paper, we have developed a methodology that allows one to build a scalable and effective collaborative filtering system on top of a conventional full-text search engine such as Apache Lucene.
Race condition detection for debugging of shared-memoryparallel programs

This thesis addresses theoretical and practical aspects of the dynamic detecting and debugging of race conditions in shared-memory parallel programs. To reason about race conditions, we present a formal model that characterizes actual, observed, and potential behaviors of the program. The actual behavior precisely represents the program execution, the observed behavior represents partial information that can be reasonably recorded, and the potential behavior represents alternate executions possibly allowed by nondeterministic timing variations. These behaviors are used to characterize different types of race conditions, general races and data races, which pertain to different classes of parallel programs and require different detection techniques. General races apply to programs intended to be deterministic; data races apply to nondeterministic programs containing critical sections. We prove that, for executions using synchronization powerful enough to implement two-process mutual exclusion, locating every general race or data race is an NP-hard problem. However, for data races, we show that detecting only a subset of all races is sufficient for debugging. We also prove that, for weaker types of synchronization, races can be efficiently located. We present post-mortem algorithms for detecting race conditions as accurately as possible, given the constraint of limited run-time information. We characterize those races that are direct manifestations of bugs and not artifacts caused by other races, imprecise run-time traces (causing false races to appear real), or unintentional synchronization (caused by shared-memory references). Our techniques analyze the observed behavior to conservatively locate races that either did occur or had the potential of occurring, and that were unaffected by any other race in the execution. Finally, we describe a prototype data race detector that we used to analyze a sample collection of parallel programs. Experiments indicate that our techniques effectively pinpoint non-artifact races, directing the programmer to parts of the execution containing direct manifestations of bugs. In all programs analyzed, our techniques reduced hundreds to thousands of races down to four or fewer that required investigation.
Precarious Economies: Exploring the Use of Environmental Indicators to Predict Economic Instability

On August 5th, 2011 the Standard and Poor’s (SP low, in the case of the U.S.) could be useful environmental indicators of a country's future fiscal performance. Moreover, these environmental indicators may be a sort of “early warning” system that can predict a nation’s financial collapse before it is predicted by standard financial indicators (such as debt levels). This article suggests a possible mechanism for such a link, and uses these indicators to speculate which other AAA+ countries may be the next to encounter fiscal challenges that lead to credit downgrades.
Limiting shape for directed percolation models

We consider directed first-passage and last-passage percolation on the nonnegative lattice Z d + , d ≥ 2, with i.i. d. weights at the vertices. Under certain moment conditions on the common distribution of the weights, the limits g(x) = lim n→ ∞ n- 1 T(|nx|) exist and are constant a.s. for x ∈ R d + , where T(z) is the passage time from the origin to the vertex z ∈ Z d + . We show that this shape function g is continuous on R d + , in particular at the boundaries. In two dimensions, we give more precise asymptotics for the behavior of g near the boundaries; these asymptotics depend on the common weight distribution only through its mean and variance. In addition we discuss growth models which are naturally associated to the percolation processes, giving a shape theorem and illustrating various possible types of behavior with output from simulations.
Supplementation with Omega-3 Fatty Acids in Psychiatric Disorders: A Review of Literature Data

A new application for omega-3 fatty acids has recently emerged, concerning the treatment of several mental disorders. This indication is supported by data of neurobiological research, as highly unsaturated fatty acids (HUFAs) are highly concentrated in neural phospholipids and are important components of the neuronal cell membrane. They modulate the mechanisms of brain cell signaling, including the dopaminergic and serotonergic pathways. The aim of this review is to provide a complete and updated account of the empirical evidence of the efficacy and safety that are currently available for omega-3 fatty acids in the treatment of psychiatric disorders. The main evidence for the effectiveness of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) has been obtained in mood disorders, in particular in the treatment of depressive symptoms in unipolar and bipolar depression. There is some evidence to support the use of omega-3 fatty acids in the treatment of conditions characterized by a high level of impulsivity and aggression and borderline personality disorders. In patients with attention deficit hyperactivity disorder, small-to-modest effects of omega-3 HUFAs have been found. The most promising results have been reported by studies using high doses of EPA or the association of omega-3 and omega-6 fatty acids. In schizophrenia, current data are not conclusive and do not allow us either to refuse or support the indication of omega-3 fatty acids. For the remaining psychiatric disturbances, including autism spectrum disorders, anxiety disorders, obsessive-compulsive disorder, eating disorders and substance use disorder, the data are too scarce to draw any conclusion. Concerning tolerability, several studies concluded that omega-3 can be considered safe and well tolerated at doses up to 5 g/day.
Adaptive Image Reconstruction Using Information Measures

We present a class of nonlinear adaptive image restoration filters which may be steered to preserve sharp edges and contrasts in the restorations. From a theoretical point of view we discuss the associated variational problems and prove existence of solutions in certain Sobolev spaces W1,p or in a BV-space. The degree of regularity of the solution may be understood as a mathematical explanation of the heuristic properties of the designed filters.
The teacher and student diversity: problems, challenges and opportunities

This paper explores the issues of student diversity as it relates to the difficulties and challenges it poses to the design and delivery of instruction. It also looks at the educational movements which advocate the benefits of diversity in both a broad social sense, and specifically in reference to its impact on the education system and teachers in particular. Throughout the discussion 'student diversity' is used to refer to both the behavioural and psychological characteristics which contribute to individual uniqueness, and to the myriad of social and cultural differences which exist in the community at large and which are also reflected in the school population. It concludes with a discussion of the impediments which exist to inhibit the capacity of teachers to respond appropriately and effectively to student diversity and diverse student needs.
Large-Scale Graph Analytics in Aster 6: Bringing Context to Big Data Discovery

Graph analytics is an important big data discovery technique. Applications include identifying influential employees for retention, detecting fraud in a complex interaction network, and determining product affinities by exploiting community buying patterns. Specialized platforms have emerged to satisfy the unique processing requirements of large-scale graph analytics; however, these platforms do not enable graph analytics to be combined with other analytics techniques, nor do they work well with the vast ecosystem of SQL-based business applications. Teradata Aster 6.0 adds support for large-scale graph analytics to its repertoire of analytics capabilities. The solution extends the multi-engine processing architecture with support for bulk synchronous parallel execution, and a specialized graph engine that enables iterative analysis of graph structures. Graph analytics functions written to the vertex-oriented API exposed by the graph engine can be invoked from the context of an SQL query and composed with existing SQL-MR functions, thereby enabling data scientists and business applications to express computations that combine large-scale graph analytics with techniques better suited to a different style of processing. The solution includes a suite of pre-built graph analytic functions adapted for parallel execution.
A Regional Model for Malaria Vector Developmental Habitats Evaluated Using Explicit, Pond-Resolving Surface Hydrology Simulations

Dynamical malaria models can relate precipitation to the availability of vector breeding sites using simple models of surface hydrology. Here, a revised scheme is developed for the VECTRI malaria model, which is evaluated alongside the default scheme using a two year simulation by HYDREMATS, a 10 metre resolution, village-scale model that explicitly simulates individual ponds. Despite the simplicity of the two VECTRI surface hydrology parametrization schemes, they can reproduce the sub-seasonal evolution of fractional water coverage. Calibration of the model parameters is required to simulate the mean pond fraction correctly. The default VECTRI model tended to overestimate water fraction in periods subject to light rainfall events and underestimate it during periods of intense rainfall. This systematic error was improved in the revised scheme by including the a parametrization for surface run-off, such that light rainfall below the initial abstraction threshold does not contribute to ponds. After calibration of the pond model, the VECTRI model was able to simulate vector densities that compared well to the detailed agent based model contained in HYDREMATS without further parameter adjustment. Substituting local rain-gauge data with satellite-retrieved precipitation gave a reasonable approximation, raising the prospects for regional malaria simulations even in data sparse regions. However, further improvements could be made if a method can be derived to calibrate the key hydrology parameters of the pond model in each grid cell location, possibly also incorporating slope and soil texture.
Relative Frequencies of Blue Stragglers in Galactic Globular Clusters: Constraints for the Formation Mechanisms

We discuss the main properties of the Galactic globular cluster (GC) blue straggler stars (BSSs), as inferred from our new catalog containing nearly 3000 BSSs. The catalog has been extracted from the photometrically homogeneous V versus (B-V) color-magnitude diagrams (CMDs) of 56 GCs, based on Wide Field Planetary Camera 2 images of their central cores. In our analysis, we used consistent relative distances based on the same photometry and calibration. The number of BSSs has been normalized to obtain relative frequencies (FBSS) and specific densities (NS) using different stellar populations extracted from the CMD. The cluster FBSS is significantly smaller than the relative frequency of field BSSs. We find a significant anticorrelation between the BSS relative frequency in a cluster and its total absolute luminosity (mass). There is no statistically significant trend between the BSS frequency and the expected collision rate. The value of FBSS does not depend on other cluster parameters, apart from a mild dependence on the central density. Post-core-collapse clusters act like normal clusters as far as the BSS frequency is concerned. We also show that the BSS luminosity function for the most luminous clusters is significantly different, with a brighter peak and extending to brighter luminosities than in the less luminous clusters. These results imply that the efficiency of BSS production mechanisms and their relative importance vary with the cluster mass.
Production of B Cell Stimulatory Factor-2 and Interferon Y in the Central Nervous System during Viral Meningitis and Encephalitis Evaluation in a Murine Model Infection and in Patients Brief Definitive Report

The production of antibodies within the central nervous system (CNS) is a characteristic finding in primary and postinfectious viral meningoencephalitis. Igs, particularly IgG, are increased in cerebrospinal fluid (CSF), and isoelectrofocusing of CSF shows a series of so-called "oligoclonal" bands of IgG, which can be absorbed by viral antigens (1, 2) . Furthermore, inflammatory cells in brain parenchyma and meninges are composed of significant numbers of B lymphocytes (3) . The mechanisms leading to activation and differentiation of B cells within the CNS have not been investigated so far. In general, B cell responses result from.intercellular communications between B cells, T cells, and monocytes/macrophages . T-B cell cooperation involves distinct T cell-derived helper factors such as IFN-y and B cellstimulating factor-2 (BSF2) (4) . BSF2, which has also been named IL-6, is identical to IFN02, as well as to the 26-kD factor (5) . In the present report, we demonstrate that these cytokines are also produced within the CNS in viral meningitis and encephalitis . Synthesis of BSF2 and IFN-y in the CNS may trigger generation of antiviral antibodies by B lymphocytes/plasma cells having invaded the CNS in viral diseases .
Direct and inverse solution for a stimulus adaptation problem using SVR

Adapting stimuli to stabilize neural responses is an important problem in the context of cortical prostheses. This paper describes two approaches for stimulus adaptation using support vector regression (SVR). One approach involves the solution of an inverse problem and it is shown that for linear SVR an analytical solution exists. The proposed algorithms are evaluated in conjunction with different preprocessing methods an three datasets recorded from the barrel cortex of anasthesized rats.
Pairwise Neural Machine Translation Evaluation

We present a novel framework for machine translation evaluation using neural networks in a pairwise setting, where the goal is to select the better translation from a pair of hypotheses, given the reference translation. In this framework, lexical, syntactic and semantic information from the reference and the two hypotheses is compacted into relatively small distributed vector representations, and fed into a multi-layer neural network that models the interaction between each of the hypotheses and the reference, as well as between the two hypotheses. These compact representations are in turn based on word and sentence embeddings, which are learned using neural networks. The framework is flexible, allows for efficient learning and classification, and yields correlation with humans that rivals the state of the art.
A measurement study of TCP over RPL in low-power and lossy networks

Low-power and lossy networks (LLNs) comprised of thousands of embedded networking devices can be used in a variety of applications, such as smart grid automated metering infrastructures (AMIs) and wireless sensor networks. Connecting these LLNs to the Internet has even greater potential, leading to the emerging concept of the Internet of Things (IoT). With the goal of integrating LLNs into IoT, the IETF has recently standardized RPL and 6LoWPAN to allow the use of IPv6 on LLNs. Although there already exist several studies on the the performance of RPL and embedded IPv6 stack in LLN, performance measurement and characterization of TCP over RPL in multihop LLNs is yet to be studied. In this article, we present a comprehensive experimental study on the performance of TCP over RPL in an embedded IPv6-based LLN running over a 30-node multihop IEEE 802.15. 4 testbed network. Our results and findings are aimed at investigating how embedded TCP interoperates with common Linux TCP and underlying RPL (and vice versa), which furthers our understanding of the performance trade-offs when choosing TCP over RPL in IPv6-based LLNs.
Analysis of Second Language Acquisition (SLA) Speech Perception Model & the Perception of Second Language Prosody

This paper provides a critical review on the major models of speech perception in second language (L2) acquisition. It is argued that some new models, such as L2LP and ASP, have more explanatory power for L2 speech perception. However, due to the different theoretical frameworks, objectives and hypotheses in these models, it is difficult to integrate these models into one which is universally applicable. Although most of these models were proposed for accounting for the perception of L2 segments, they can also be applied in the perception of L2 prosody. When these models are used in examining L2 speech prosody, the prosodic systems of both L1 and L2 should be thoroughly investigated first.
Software Component Models from a Technical perspective

Summary Component-based Software Development is an approach that has many benefits, such as improving application developer productivity, reducing costs and complexity. Programming within this approach is like assembly rather than development, which reduce skill requirements, and allow expertise focus on domain problems. The foundation of any CBSD methodology is its underlying component model, which defines what components are, how they can be constructed, and specifies the standards and conventions that are needed to enable composition of independently developed component. This paper presents a survey of the current available component technologies with focus on the technical perspective of each component model in order to have better understanding for developing a new component model. We have categorized them based on Distributed Application Support and Interaction mechanism.
Disruption of the Smad7 gene enhances CCI4-dependent liver damage and fibrogenesis in mice

Transforming growth factor‐β (TGF‐β) signalling is induced in liver as a consequence of damage and contributes to wound healing with transient activation, whereas it mediates fibrogenesis with long‐term up‐regulation in chronic disease. Smad‐dependent TGF‐β effects are blunted by antagonistic Smad7, which is transcriptionally activated as an immediate early response upon initiation of TGF‐β signalling in most cell types, thereby providing negative feedback regulation. Smad7 can be induced by other cytokines, e.g. IFN‐γ, leading to a crosstalk of these signalling pathways. Here we report on a novel mouse strain, denoted S7ΔE1, with a deletion of exon I from the endogenous smad7 gene. The mice were viable and exhibited normal adult liver architecture. To obtain insight into Smad7‐depend‐ent protective effects, chronic liver damage was induced in mice by carbon tetrachloride (CCI4) administration. Subsequent treatment, elevated serum liver enzymes indicated enhanced liver damage in mice lacking functional Smad7. CCI4‐dependent Smad2 phosphoryla‐tion was pronounced in S7ΔE1 mice and accompanied by increased numbers of α‐smooth muscle actin positive ‘activated’ HSCs. There was evidence for matrix accumulation, with elevated collagen deposition as assessed morphometrically in Sirius red stained tissue and confirmed with higher levels of hydroxyproline in S7ΔE1 mice. In addition, the number of CD43 positive infiltrating lymphocytes as well as of apoptotic hepatocytes was increased. Studies with primary hepatocytes from S7ΔE1 and wild‐type mice indicate that in the absence of functional Smad7 protein, hepatocytes are more sensitive for TGF‐β effects resulting in enhanced cell death. Furthermore, S7ΔE1 hepatocytes display increased oxidative stress and cell damage in response to CCI4, as measured by reactive oxygen species production, glutathione depletion, lactate dehydrogenase release and lipid peroxidation. Using an ALK‐5 inhibitor all investigated CCI4 effects on hepatocytes were blunted, confirming participation of TGF‐β signalling. We conclude that Smad7 mediates a protective effect from adverse TGF‐β signalling in damaged liver, re‐iterating its negative regulatory loop on signalling.
Tamper Difficult Technique for Data Residing on Passive Storage

For storage service, irrespective of the form of the relationship between data owner and storage holder, how can a data owner extract evidence on corrupting its inquilinous data remains to be resolved, especially when the data owner is not the storage holder. Up to now, no technique can offer self-detection technique for data deletion residing on raw disk that is directly exposed to attackers. In this paper, we devote our efforts to building such a mechanism in an untrusted storage place (passive repository) by means of file-chunks entangled method. This provides a foundation for constructing trustworthy virtual network disk storage tool in distributed environment.
Automatic Object Recognition and Registration of Dynamic Construction Equipment from a 3D Point Cloud

This paper introduces a model-based automatic objec t recognition and registration framework to assist heavy equipment operators in rapidly perceiv ing 3D working environment at dynamic construction sites. A video camera and a laser scanner were util ized in this study to rapidly recognize and registe r dynamic target objects in a 3D space by dynamically separating target object’s point cloud data from a background scene for a quick computing process. A s mart scan data updating method has been developed which only updates the dynamic target object’s poin t cloud data while keeping the previously scanned static work environments. Extracted target areas co ntaining 3D point clouds were orthographically projected into a series of 2D planes with a rotatio n center located in the target’s vertical-middle li ne. Prepared 2D templates were compared to these 2D pla nes by extracting SURF features. Point cloud bundles of the target were recognized, and followed by the prepared CAD model’s registration to the templates. The field experimental results show that e proposed rapid workspace modeling method can significantly improve heavy construction equipment operations and automated equipment control by rapidly modeling dynamic target objects in a 3D vie w.