Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey and Experimental Comparison of Distributed SPARQL Engines for Very Large RDF Data

Distributed SPARQL engines promise to support very large RDF datasets by utilizing shared-nothing computer clusters. Some are based on distributed frameworks such as MapReduce; others implement proprietary distributed processing; and some rely on expensive preprocessing for data partitioning. These systems exhibit a variety of trade-offs that are not well-understood, due to the lack of any comprehensive quantitative and qualitative evaluation. In this paper, we present a survey of 22 state-of-the-art systems that cover the entire spectrum of distributed RDF data processing and categorize them by several characteristics. Then, we select 12 representative systems and perform extensive experimental evaluation with respect to preprocessing cost, query performance, scalability and workload adaptability, using a variety of synthetic and real large datasets with up to 4.3 billion triples. Our results provide valuable insights for practitioners to understand the trade-offs for their usage scenarios. Finally, we publish online our evaluation framework, including all datasets and workloads, for researchers to compare their novel systems against the existing ones.
Construction of stochastic context trees for genetical texts

A method has been developed for constructing a tree source model for genetic text generation. Model visualisation in the form of suffix (context) trees provides a new way of context analysis of symbol sequences. Estimation of the stochastic complexity of the data in the frame of the model serves as a criterion for the model's ascertainment. The model and complexity values are used for analysis of genetic texts. The software realisation of this algorithm enables to reveal statistical properties of genetic sequences based on an information measure. The program developed is available via Internet at http://wwwmgs.bionet. nsc.ru/mgs/programs/complexity/.
Are cognitive changes the first symptoms of Huntington’s disease? A study of gene carriers

BACKGROUND Huntington’s disease is a neurodegenerative disorder due to an excessive number of CAG repeats in the IT15 gene on chromosome 4. The first symptoms are typically choreic movements or psychiatric disorders, whereas global cognitive decline generally becomes obvious later. This study was aimed at detecting early subtle cognitive deficits in asymptomatic gene carriers. METHODS As part of the testing procedure for predictive diagnosis of Huntington’s disease, 91 asymptomatic at risk candidates had a neuropsychological examination, evaluating global efficiency, attention, memory (Wechsler memory scale and California verbal learning test), and executive functions. RESULTS The groups of carriers (n=42) and non-carriers (n=49) differed only on a few memory variables. When we considered the group of gene carriers as a whole, significant correlations emerged between the number of CAG repeats and (a) performance on several tests of executive functions, and (b) performance on the hard pairs associates of the Wechsler memory scale. Further analysis of performance on this memory subtest led to the division of the group of carriers into two subgroups, without any overlap. The performance of subjects without cognitive deficits (n=32) was similar to that of non-carriers on all tests. The subjects with cognitive deficits (n=10) differed from both carriers without cognitive deficits and non-carriers over a wide array of variables measuring executive functions and memory. Moreover, qualitative aspects of the performance of carriers with cognitive deficits in the California verbal learning test closely resembled those of patients diagnosed as having Huntington’s disease. Conclusion—This suggests that these subjects already have Huntington’s disease, despite a total lack of motor and psychiatric signs. An ongoing follow up study is testing the prediction that they will develop the full range of symptoms of the disease earlier than carriers without cognitive deficits.
Design of Adaptive Deblocking Filter for H.264/AVC Decoder SOC

Abstract In this article, a design for the adaptive deblocking filter is proposed. To understand the real-time performance, a FILTER unit that can process eight pixels beside an edge simultaneously is applied in this design to increase filtering efficiency, and local memory is used to store all temporary data generated by the FILTER to reduce access to system bus. The filter makes every 4×4 sample block pipelined through the process units and achieves an efficiency of 80% for both the FILTER unit and the bus access unit. It can fulfill filtering process for a crystallographic information file (CIF, 352×288) format picture in 95 k clock cycles. The proposed design is part of a H.264/AVC decoder system-on-chip (SOC), which is fabricated in 0.18 μm complementary metal oxide semiconductor (CMOS) process. The filter module consists of 60 k gates and 25.7 kb static random access memory (SRAM) and it can filter a macro-block in 240 clock cycles.
Dlx1&2 and Mash1 transcription factors control striatal patterning and differentiation through parallel and overlapping pathways

Here we define the expression of ≈100 transcription factors in progenitors and neurons of the developing basal ganglia. We have begun to elucidate the transcriptional hierarchy of these genes with respect to the Dlx homeodomain genes, which are essential for differentiation of most GABAergic projection neurons of the basal ganglia. This analysis identified Dlx‐dependent and Dlx‐independent pathways. The Dlx‐independent pathway depends in part on the function of the Mash1 b‐HLH transcription factor. These analyses define core transcriptional components that differentially specify the identity and differentiation of the striatum, nucleus accumbens, and septum. J. Comp. Neurol. 512:556–572, 2009. © 2008 Wiley‐Liss, Inc.
Computer-Aided Diagnosis Applied to 3-D US of Solid Breast Nodules by Using Principal Component Analysis and Image Retrieval

Textural features have been shown to be valuable in tumor diagnosis. This study combines three practical textural features in ultrasound (US) images, i.e. spatial gray-level dependence matrices (SGLDMs), gray-level difference matrix (GLDM) and auto-covariance matrix, to identify breast tumor as benign or malignant. The textural features were extracted from 147 3-D ultrasound cases and each case composes a volume of interest (VOI). Usually, the larger region of interest (ROI) sub-image contains considerable textural information. Thus the feature vector extraction utilizes only the adjacent frames with the largest ROI sub-image. The textural features always perform as a high dimensional vector that is unfavorable to differentiate breast tumors in practice. The principal component analysis (PCA) is used to reduce the dimension of textural feature vector and then the image retrieval technique was utilized to differentiate between benign and malignant tumors. The proposed computer-aided diagnosis (CAD) system differentiates solid breast nodules with a relatively high accuracy in the US imaging and helps inexperienced operators avoid misdiagnosis
An exploratory study of the relationship between learning styles and academic performance among students in different nursing programs

Abstract Background: Learning style is a major consideration in planning for effective and efficient instruction and learning. Learning style has been shown to influence academic performance in the previous research. Little is known about Taiwanese students’ learning styles, particularly in the field of nursing education. Aim: This purpose of this study was to identify the relationship between learning styles and academic performance among nursing students in a 5-year associate degree of nursing (ADN) program and a 2-year bachelor of science in nursing (BSN) program in Taiwan. Methods/Design: This study employed a descriptive and exploratory design. The Chinese version of the Myers–Briggs type indicator Form M was an instrument. Data such as grade point average were obtained from the Office of Academic Affairs and the Registrar computerized records. Descriptive statistics, one-way analysis of variance and chi-square statistical analysis were used to explore the relationship between academic performance and learning style in Taiwanese nursing students. Results/Findings: The study sample included 285 nursing students: 96 students in a 2-year BSN program, and 189 students in a 5-year ADN program. Two common learning styles were found: Introversion, sensing, thinking, and judging; and introversion, sensing, feeling, and judging. A sensing–judging pair was identified in 43.3% of the participants. Academic performance was significantly related to learning style (p < 0.05, df = 15). Conclusion: The results of this study can help educators devise classroom and clinical instructional strategies that respond to individual needs in order to maximize academic performance and enhance student success. A large sample is recommended for further research. Understanding the learning style preferences of students can enhance learning for those who are under performing in their academic studies, thereby enhancing nursing education.
Direct Construction of Complete Merged Processes

Merged processes (MPs) are a recently proposed condensed representation of a Petri net’s behaviour similar to branching processes (unfoldings), which copes well not only with concurrency but also with other sources of state space explosion like sequences of choices. They are by orders of magnitude more compact than traditional unfoldings, and yet can be used for efficient model checking. However, constructing complete MPs is difficult, and the only known algorithm is based on building a (potentially much larger) complete unfolding prefix of a Petri net, whose nodes are then merged. Obviously, this significantly reduces their appeal as a representation that can be used for practical model checking. In this paper, we develop an algorithm that avoids constructing the intermediate unfolding prefix and builds a complete merged process directly from a safe Petri net. In particular, a challenging problem of truncating a merged process is solved.
Structural Basis for CD44 Recognition by ERM Proteins*

CD44 is an important adhesion molecule that functions as the major hyaluronan receptor which mediates cell adhesion and migration in a variety of physiological and pathological processes. Although full activity of CD44 requires binding to ERM (ezrin/radixin/moesin) proteins, the CD44 cytoplasmic region, consisting of 72 amino acid residues, lacks the Motif-1 consensus sequence for ERM binding found in intercellular adhesion molecule (ICAM)-2 and other adhesion molecules of the immunoglobulin superfamily. Ultracentrifugation sedimentation studies and circular dichroism measurements revealed an extended monomeric form of the cytoplasmic peptide in solution. The crystal structure of the radixin FERM domain complexed with a CD44 cytoplasmic peptide reveals that the KKKLVIN sequence of the peptide forms a β strand followed by a short loop structure that binds subdomain C of the FERM domain. Like Motif-1 binding, the CD44 β strand binds the shallow groove between strand β5C and helix α1C and augments the β sheet β5C-β7C from subdomain C. Two hydrophobic CD44 residues, Leu and Ile, are docked into a hydrophobic pocket with the formation of hydrogen bonds between Asn of the CD44 short loop and loop β4C-β5C from subdomain C. This binding mode resembles that of NEP (neutral endopeptidase 24.11) rather than ICAM-2. Our results reveal a characteristic versatility of peptide recognition by the FERM domains from ERM proteins, suggest a possible mechanism by which the CD44 tail is released from the cytoskeleton for nuclear translocation by regulated intramembrane proteolysis, and provide a structural basis for Smad1 interactions with activated CD44 bound to ERM protein.
Solution-Processable Carbon Nanotube Devices for Biosensing Applications

This work explores the use of solution processable electrolyte gated carbon nanotube transistor on a flexible substrate for biosensing applications. Biocompatibility of CNTs, their use as an impedance electrode is investigated. Different sensor architectures for single and multiple sensor arrays are developed. Using this as a platform several classes of biosensors targeted towards biochemicals such as ions, enzymes, neurotransmitters and DNA are developed.
Secure Authentication Watermarking for Binary Images using Pattern Matching

Summary In image authentication watermarking, hidden data is inserted into an image to detect any accidental or malicious image alteration. In the literature, quite a small number of cryptography based secure authentication methods are available for binary images. In a cryptography based authentication watermarking, a message authentication code (or digital signature) of the whole image is computed and the resulting code is inserted into the image itself. This paper proposes a new authentication watermarking method for binary images. The main idea is to use the prioritized sub-blocks by pattern matching scheme to embed the code. Shuffling is applied before embedding to equalize the uneven embedding capacity. It detects any alteration while maintaining good visual quality for all types of binary images. The security of the algorithm lies only on the secrecy of a secret or private keys used.
Effects of relationship education on couple communication and satisfaction: A randomized controlled trial with low-income couples.

OBJECTIVE Although preventive educational interventions for couples have been examined in more than 100 experimental studies, the value of this work is limited by reliance on economically advantaged populations and by an absence of data on proposed mediators and moderators. Data from the Supporting Healthy Marriage Project-a randomized, controlled trial of relationship education for couples living with low incomes-were therefore analyzed to test whether intervention effects on relationship satisfaction would be mediated by observational assessments of relationship communication and whether any such effects would be moderated by couples' pretreatment risk. METHOD Within the larger sample of Supporting Healthy Marriage Project couples randomized to a relationship education or no-treatment control condition, the present analyses focus on the 1,034 couples who provided (a) data on sociodemographic risk at baseline, (b) observational data on couple communication 12 months after randomization, and (c) reports of relationship satisfaction 30 months after randomization. RESULTS Intervention couples reported higher satisfaction at 30 months than control couples, regardless of their level of pretreatment risk. Among higher risk couples, the intervention improved observed communication as well. Contrary to prediction, treatment effects on satisfaction were not mediated by improvements in communication, and improvements in communication did not translate into greater satisfaction. CONCLUSIONS Relationship education programs produce small improvements in relationship satisfaction and communication, particularly for couples at elevated sociodemographic risk. The absence of behavioral effects on satisfaction indicates, however, that the mechanisms by which couples may benefit from relationship education are not yet well understood.
Local label learning (LLL) for subcortical structure segmentation: Application to hippocampus segmentation

Automatic and reliable segmentation of subcortical structures is an important but difficult task in quantitative brain image analysis. Multi‐atlas based segmentation methods have attracted great interest due to their promising performance. Under the multi‐atlas based segmentation framework, using deformation fields generated for registering atlas images onto a target image to be segmented, labels of the atlases are first propagated to the target image space and then fused to get the target image segmentation based on a label fusion strategy. While many label fusion strategies have been developed, most of these methods adopt predefined weighting models that are not necessarily optimal. In this study, we propose a novel local label learning strategy to estimate the target image's segmentation label using statistical machine learning techniques. In particular, we use a L1‐regularized support vector machine (SVM) with a k nearest neighbor (kNN) based training sample selection strategy to learn a classifier for each of the target image voxel from its neighboring voxels in the atlases based on both image intensity and texture features. Our method has produced segmentation results consistently better than state‐of‐the‐art label fusion methods in validation experiments on hippocampal segmentation of over 100 MR images obtained from publicly available and in‐house datasets. Volumetric analysis has also demonstrated the capability of our method in detecting hippocampal volume changes due to Alzheimer's disease. Hum Brain Mapp 35:2674–2697, 2014. © 2013 Wiley Periodicals, Inc.
Influence of processing parameters on microstructure and mechanical response of a high-pressure die cast aluminum alloy

ABSTRACT Motor sealing brackets were fabricated by pressurized die casting (PDC) process under 408 MPa pressure via semisolid thick slurry of Aluminum Alloy 6061 base metal and 20% powder-chip of similar composition (average 500 μm). Initially, 81 microns powder (40% by weight) and micro-size chips (60% by weight) were mixed by stirring near 300°C until the diffusion of powder takes place and then refined by ball milling process. Influence of thick semisolid slurry modified by powder-chip reinforcement was investigated according to unmelted chip distribution inside the formed cavities, reinforcement incorporation factor (RIF), density measurement, blister formation along the surfaces and microhardness. The obtained results reveal that the unmelted particles significantly improved the microhardness value and density as 134.85 HV and 2.71 g/cm3, respectively, also with the reduction in the size of cavities. Refined microstructures were observed at several locations of the components due to the effect of processing parameters.
The latest on IgG4-RD: a rapidly emerging disease

Purpose of reviewProvide an update of the developments in IgG4 related disease within the last year in terms of nomenclature, diagnostic criteria and clinical features. Recent findingsThe number of diseases associated with IgG4 related disease continues to grow. The entities of idiopathic orbital inflammation, idiopathic cervical fibrosis, eosinophilic angiocentric fibrosis, reactive nodular fibrous pseudotumor, sclerosing mesenteritis, and membranous glomerulonephritis should all be added to the list of diseases associated with the IgG4 related disease spectrum. The issues of nomenclature and diagnostic criteria for IgG4 related disease have important recent developments as well. SummaryThe diagnosis of IgG4 related disease continues to be challenging. Increasing recognition of IgG4 related disease has led to a large body of literature on organ sites of involvement. Understanding how the disease manifests itself is critical to diagnosis and ultimately treatment.
A theory of ocular dominance.

DOMINANCE in the broadest sense consists of any sort of physiological preeminence, priority or preferential activity of one member of any bilateral pair of structures in the body. Thus, to write right handed or left handed is to have a dominant hand. To start walking, or climbing or putting on one's trousers always with the same foot is to have a dominant foot. The discovery of dominance is lost in antiquity. Handedness has been known for so long that there are words in common speech, such as the Frenchgauche, and the English "gawky," which allude to it. Eyedness, however, is supposed to have received its first scientific mention in 1861, by G. M. Humphrey. In neurology, a first order expectation is that the lateralities (sidednesses) of the various dominances in a given individual will agree. This is because one primary laterality is in position to dictate the lateralities of
Demon algorithms and their application to optimization problems

We introduce four new general optimization algorithms based on the 'demon' algorithm from statistical physics and the simulated annealing (SA) optimization method. These algorithms reduce the computation time per trial without significant effect on the quality of solutions found. Any SA annealing schedule or move generation function can be used. The algorithms are tested on traveling salesman problems including Grotschel's 442-city problem (1984) with results comparable to SA. Applications to the Boltzmann machine are considered.
Packet fragmentation in Wi-Fi ad hoc networks with correlated channel failures

We present an analytical method for estimating the saturation throughput of an 802.11 ad hoc LAN called also the Wi-Fi (wireless fidelity) LAN in the presence of noise distorting transmitted frames. This is the first method that allows analytical study of the Wi-Fi network performance with consideration of correlated channel failures usually inherent to wireless channels. Within the study, we consider the possible packet fragmentation that can be adopted to reduce the performance waste caused by noise-induced distortions. In addition to the throughput, our method allows estimation of the probability of a packet rejection occurring when the number of packet transmission retries attains its limit. The obtained numerical results of investigating Wi-Fi LANs by the developed method are validated by simulation and show high estimation accuracy as well as the method efficiency in determining the optimal fragmentation threshold.
Mathematical modelling of electronic contact mechanisms in silicon photovoltaic cells

In screen-printed silicon-crystalline solar cells, the contact resistance of a thin interfacial glass layer between the silicon and the silver electrode plays a limiting role for electron transport. The motivation of this project is to gain increased understanding of the transport mechanisms of the electrons across this layer, which can be exploited to provide higher performance crystalline silicon solar cells. Our methodology throughout is to formulate and analyse mathematical models for the electron transport, based on the drift diffusion equations. In the first chapter we outline the problem and provide a summary of relevant theory. In Chapter 2 we formulate a one-dimensional model for electron transport across the glass layer, that we solve both numerically and by employing asymptotic techniques. Chapter 3 extends the model presented in Chapter 2 to two dimensions. To solve the two-dimensional model numerically we devise and validate a new spectral method. The short circuiting of current through thinner regions of the glass layer enables us to find limiting asymptotic expressions for the average current density for two different canonical glass layer profiles. In Chapter 4 we include quantum mechanical effects into the one-dimensional model outlined in Chapter 2 and find that they have a negligible effect on the contact resistance of the glass layer. We model the boundary effects present at the silicon emitter-glass interface in Chapter 5. Finally, in Chapter 6 we summarise our key results, suggest possible future work, and outline the implications of our work to crystalline silicon solar cell manufacturers.