Miracle-dz/UniFAR-FTUs huggingface dataset:
Scheduling for SPARQL Endpoints

When providing public access to data on the Semantic Web, publishers have various options that include downloadable dumps, Web APIs, and SPARQL endpoints. Each of these methods is most suitable for particular scenarios. SPARQL provides the richest access capabilities and is the most suitable option when granular access to the data is needed. However, SPARQL expressivity comes at the expense of high evaluation cost. The potentially large variance in the cost of different SPARQL queries makes guaranteeing consistently good quality of service a very difficult task. Current practices to enhance the reliability of SPARQL endpoints, such as query timeouts and limiting the number of results returned, are far from ideal. They can result in under utilisation of resources by rejecting some queries even when the available resources are sitting idle and they do not isolate “well-behaved” users from “illbehaved” ones and do not ensure fair sharing among different users. In similar scenarios, where unpredictable contention for resources exists, scheduling algorithms have proven to be effective and to significantly enhance the allocation of resources. To the best of our knowledge, using scheduling algorithms to organise query execution at SPARQL endpoints has not been studied. In this paper, we study, and evaluate through simulation, the applicability of a few algorithms to scheduling queries received at a SPARQL endpoint.
The Impact of Personalisation on Human-Robot Interaction in Learning Scenarios

Advancements in Human-Robot Interaction involve robots being more responsive and adaptive to the human user they are interacting with. For example, robots model a personalised dialogue with humans, adapting the conversation to accommodate the user's preferences in order to allow natural interactions. This study investigates the impact of such personalised interaction capabilities of a human companion robot on its social acceptance, perceived intelligence and likeability in a human-robot interaction scenario. In order to measure this impact, the study makes use of an object learning scenario where the user teaches different objects to the robot using natural language. An interaction module is built on top of the learning scenario which engages the user in a personalised conversation before teaching the robot to recognise different objects. The two systems, i.e. with and without the interaction module, are compared with respect to how different users rate the robot on its intelligence and sociability. Although the system equipped with personalised interaction capabilities is rated lower on social acceptance, it is perceived as more intelligent and likeable by the users.
Dominant negative retinoid X receptor beta inhibits retinoic acid-responsive gene regulation in embryonal carcinoma cells

Retinoid X receptors (RXRs) heterodimerize with multiple nuclear hormone receptors and are thought to exert pleiotropic functions. To address the role of RXRs in retinoic acid- (RA) mediated gene regulation, we designed a dominant negative RXR beta. This mutated receptor, termed DBD-, lacked the DNA binding domain but retained the ability to dimerize with partner receptors, resulting in formation of nonfunctional dimers. DBD- was transfected into P19 murine embryonal carcinoma (EC) cells, in which reporters containing the RA-responsive elements (RAREs) were activated by RA through the activity of endogenous RXR-RA receptor (RAR) heterodimers. We found that DBD- had a dominant negative activity on the RARE reporter activity in these cells. P19 clones stably expressing DBD- were established; these clones also failed to activate RARE-driven reporters in response to RA. Further, these cells were defective in RA-induced mRNA expression of Hox-1.3 and RAR beta, as well as in RA-induced down-regulation of Oct3 mRNA. Gel mobility shift assays demonstrated that RA treatment of control P19 cells induces RARE-binding activity, of which RXR beta is a major component. However, the RA-induced binding activity was greatly reduced in cells expressing DBD-. By genomic footprinting, we show that RA treatment induces in vivo occupancy of the RARE in the endogenous RAR beta gene in control P19 cells but that this occupancy is not observed with the DBD- cells. These data provide evidence that the dominant negative activity of DBD- is caused by the lack of receptor binding to target DNA. Finally, we show that in F9 EC cells expression of DBD- leads to inhibition of the growth arrest that accompanies RA-induced differentiation. Taken together, these results demonstrate that RXR beta and partner receptors play a central role in RA-mediated gene regulation and in the control of growth and differentiation in EC cells.
The moderating effects of need for cognition on drivers of customer loyalty

Purpose – While satisfaction, value, image, and credibility are commonly assumed to drive customer loyalty, there is nevertheless reason to question whether their effects vary across groups of consumers. This paper seeks to explore how individuals with contrasting need-for-cognition (NFC) levels differ in using memory-based information when forming behavioral intentions towards a current service provider. Design/methodology/approach – The authors tested the hypotheses by means of survey data from customers of retail banks, and applied two-group analysis using structural equation modeling (SEM) to test the moderating effects of NFC. Findings – Satisfaction positively affects loyalty for high NFCs, but not for low NFCs. Image is insignificant in both groups. Value positively affects loyalty for low NFCs, but not for high NFCs. Credibility has a positive effect for low NFCs, but not for high NFCs. Research limitations/implications – The limited sample size affects the power of the test methodology, but Chow-.
A Dynamic Clustering Algorithm Based on PSO and Its Application in Fuzzy Identification

A dynamic clustering algorithm based on Particle Swarm Optimization (PSO) algorithm is proposed, in which a novel coding and operation on the basis of standard PSO is introduced and DB Index rule is used to determine the validity of clustering. The simulation results illustrate its veracity and efficiency. In the first place, the proper fuzzy rule number and exact premise parameters can be obtained by using the dynamic clustering algorithm to identify fuzzy models, and result parameters by the least squared method (LSM). The effectiveness and practicability is demonstrated by the simulation results of the Box-Jenkins gas furnace data comparing with other methods.
Improving Project Performance with Role Driven Computational Support System

This paper describes a role driven computational support for software project lifecycle development. For a role to achieve its goal it needs some support. In our system we build a group of support agents of different types around each abstract role (i.e. scheduler, document deliverable, bookkeeper, etc). These support agents interact among each other and with those of other roles that share tasks dependencies. Agents in the system are not restricted by predefined schemes they can join and/or leave the coalition. The objective of the system is threefold: first, to present individuals with awareness, precise views of who is doing what and when, and more specifically activities evolution; second, to provide grounding algorithms for conceptual role(activity) dynamics and consistencies in a distributed environment; and third, to facilitate the control and optimization of activities accomplishment in organizations.
Joint interference coordination and relay cell expansion in LTE-Advanced networks

Deploying relay nodes is foreseen a cost-efficient solution to combat the severe propagation loss at cell edge. However, relay cell coverage is limited by the low transmit power, limited antenna capabilities and wireless backhaul link bottleneck which may lead to load imbalances and hence low resource utilization efficiency. Further challenges in relay deployments are attributed to increased interference levels in the network compared with macrocell-only deployments, causing degradation of the user throughput. In this context, relay cell coverage expansion and interference coordination techniques are expected to improve the performance of relay deployments. In this study, we analyze the impact of the additional interference due to the relay node transmissions. Jointly with our previous study on cell expansion, we show how the interference coordination mitigates the interference from relays and hence optimizes the system performance. Comprehensive system level simulations are carried out within the LTE-Advanced framework. Results confirm that the interference imposed by relay nodes can be reduced. Furthermore, both cell edge throughput gains and cell average throughput gains are observed.
The Staphylococcal Ferritins Are Differentially Regulated in Response to Iron and Manganese and via PerR and Fur

ABSTRACT Staphylococcus aureus and Staphylococcus epidermidis ferritin (FtnA and SefA, respectively) homologues are antigenic and highly conserved. A previous study showed that ftnA is a component of the S. aureus PerR regulon with its transcription induced by elevated iron and repressed by PerR, which functions as a manganese-dependent transcriptional repressor. We have further investigated the role of iron and Fur in the regulation of PerR regulon genes ftnA (ferritin), ahpC (alkyl-hydroperoxidase), and mrgA (Dps homologue) and shown that iron has a major role in the regulation of the PerR regulon and hence the oxidative stress response, since in the presence of both iron and manganese, transcription of PerR regulon genes is induced above the repressed levels observed with manganese alone. Furthermore the PerR regulon genes are differentially regulated by metal availability and Fur. First, there is an additional level of PerR-independent regulation of ftnA under low-iron conditions which is not observed with ahpC and mrgA. Second, there is a differential response of these genes to Fur as ftnA expression is constitutive in a fur mutant, while ahpC expression is constitutive under low-Fe/Mn conditions but some repression of ahpC still occurs in the presence of manganese, whereas mrgA expression is still repressed in the fur mutant as in wild-type S. aureus, although there is a decrease in the overall level of mrgA transcription. These studies have also shown that FtnA expression is regulated by growth phase, but maximal transcription of ftnA differs dependent on the growth medium. Moreover, there are significant regulatory differences between the S. aureus and S. epidermidis ferritins, as sefA expression in contrast to that of ftnA is derepressed under low-Fe/Mn ion conditions.
High-performance long range obstacle detection using stereo vision

Reliable detection of obstacles at long range is crucial for the timely response to hazards by fast-moving safety-critical platforms like autonomous cars. We present a novel method for the joint detection and localization of distant obstacles using a stereo vision system on a moving platform. The approach is applicable to both static and moving obstacles and pushes the limits of detection performance as well as localization accuracy.
125 HUNTINGTON DISEASE

Huntington disease (HD) is a progressive neurodegenerative disorder with an established genetic origin and symptoms that are referable to specific regions of brain disease. Cellular and molecular techniques are rapidly elucidating the pathogenesis of the disorder and are leading to approaches designed to develop rational treatments. Thus, HD serves as a model for the future study of those psychiatric disorders in which abnormal brain function is thought to arise from predominantly genetic factors.
The effect of changes in cerebral blood flow on cognitive function during exercise

No studies have identified the direct effect of changes in cerebral blood flow (CBF) on cognitive function at rest and during exercise. In this study, we manipulated CBF using hypercapnic gas to examine whether an increase in CBF improves cognitive function during prolonged exercise. The speed and the accuracy of cognitive function were assessed using the Stroop color‐word test. After the Stroop test at rest, the subjects began exercising on a cycling ergometer in which the workload was increased by 0.5 kilopond every minute until a target heart rate of 140 beats/min was achieved. Then, the subjects continued to cycle at a constant rate for 50 min. At four time points during the exercise (0, 10, 20, 50 min), the subjects performed a Stroop test with and without hypercapnic respiratory gas (2.0% CO2), with a random order of the exposures in the two tests. Despite a decrease in the mean blood flow velocity in the middle cerebral artery (MCA Vmean), the reaction time for the Stroop test gradually decreased during the prolonged exercise without any loss of performance accuracy. In addition, the hypercapnia‐induced increase in MCA Vmean produced neither changes in the reaction time nor error in the Stroop test during exercise. These findings suggest that the changes in CBF are unlikely to affect cognitive function during prolonged exercise. Thus, we conclude that improved cognitive function may be due to cerebral neural activation associated with exercise rather than global cerebral circulatory condition.
High Prevalence of Metabolic Syndrome and Cardiovascular Disease Risk Among People with HIV on Stable ART in Southwestern Uganda.

The objectives of this study were to determine the epidemiology and correlates of cardiovascular disease (CVD) risk among Ugandans on first-line antiretroviral therapy (ART). We conducted a cross-sectional study at an HIV clinic in southwestern Uganda. We enrolled adult patients on non-nucleoside-based ART regimens for a minimum of 2 years. We collected anthropometric and clinical measurements, smoking history, and blood for fasting lipid profile and blood sugar (FBS). Outcomes of interest were (1) presence of metabolic syndrome (at least two of the following: FBS >100 mg/dL, blood pressure of ≥130/85 mmHg, triglycerides ≥150 mg/dL, HDL <40 mg/DL, or waist circumference ≥94 cm in males or ≥80 cm in females); and (2) a Framingham score correlating to >5% 10-year CVD risk. Of the 250 participants enrolled, metabolic syndrome was detected in 145/250 (58%) of participants (62% in females and 50% in males). Forty-three participants (17%) had a Framingham risk correlating to a 5% or greater risk for CVD within 10 years (26% in males and 13% in females). In multivariate analyses, being female (AOR 3.13; 95% CI: 1.0-9.70; p = 0.04) and over 40 years of age (AOR 1.78; 95% CI: 1.00-3.17; p = 0.05) was independently associated with having metabolic syndrome. We found no independent risk factors for a Framingham risk score 10-year risk exceeding 5%, or associations between ART regimen and CVD risk profiles. We conclude that metabolic abnormalities are common among patients on first-line ART in rural Uganda, and appear to be more common in women than men.
Realist review - a new method of systematic review designed for complex policy interventions

Evidence-based policy is a dominant theme in contemporary public services but the practical realities and challenges involved in using evidence in policy-making are formidable. Part of the problem is one of complexity. In health services and other public services, we are dealing with complex social interventions which act on complex social systems-things like league tables, performance measures, regulation and inspection, or funding reforms. These are not ‘magic bullets‘ which will always hit their target, but programmes whose effects are crucially dependent on context and implementation. Traditional methods of review focus on measuring and reporting on programme effectiveness, often find that the evidence is mixed or conflicting, and provide little or no clue as to why the intervention worked or did not work when applied in different contexts or circumstances, deployed by different stakeholders, or used for different purposes. This paper offers a model of research synthesis which is designed to work with complex social interventions or programmes, and which is based on the emerging ‘realist’ approach to evaluation. It provides an explanatory analysis aimed at discerning what works for whom, in what circumstances, in what respects and how. The first step is to make explicit the programme theory (or theories) - the underlying assumptions about how an intervention is meant to work and what impacts it is expected to have. We then look for empirical evidence to populate this theoretical framework, supporting, contradicting or modifying the programme theories as it goes. The results of the review combine theoretical understanding and empirical evidence, and focus on explaining the relationship between the context in which the intervention is applied, the mechanisms by which it works and the outcomes which are produced. The aim is to enable decision-makers to reach a deeper understanding of the intervention and how it can be made to work most effectively. Realist review does not provide simple answers to complex questions. It will not tell policy-makers or managers whether something works or not, but will provide the policy and practice community with the kind of rich, detailed and highly practical understanding of complex social interventions which is likely to be of much more use to them when planning and implementing programmes at a national, regional or local level.
Dual-polarity Grappa for Simultaneous Reconstruction and Ghost Correction of Epi Data

Purpose: We seek improved image quality from accelerated EPI data, particularly at ultra-high field. Certain arti-facts in EPI reconstructions can be attributed to nonlinear phase differences between data acquired using frequency encoding gradients of alternating polarity. These errors appear near regions of local susceptibility gradients, and typically cannot be corrected with conventional Nyquist ghost correction (NGC) methods. Methods: We propose a new reconstruction method that integrates ghost correction into the parallel imaging data recovery process. This is achieved through a pair of GRAPPA kernels that operate directly on the measured EPI data. The proposed Dual-Polarity GRAPPA (DPG) method estimates missing k-space data while simultaneously correcting inherent EPI phase errors. Results: Simulation results demonstrate that standard NGC is incapable of correcting higher-order phase errors, whereas the Dual-Polarity GRAPPA kernel approach successfully removes these errors. The presence of higher-order phase errors near regions of local susceptibility gradients is demonstrated with in vivo data. DPG reconstructions of in vivo 3T and 7T EPI data acquired near these regions show a marked improvement over conventional methods. Conclusion: This new parallel imaging method for reconstructing accelerated EPI data shows better resilience to inherent EPI phase errors, resulting in higher image quality in regions where higher-order EPI phase errors commonly occur .
Global Existence of Some Infinite Energy Solutions for a Perfect Incompressible Fluid

This paper provides results on local and global existence for a class of solutions to the Euler equations for an incompressible, inviscid fluid. By considering a class of solutions which exhibits a characteristic growth at infinity we obtain an initial value problem for a nonlocal equation. We establish local well-posedness in all dimensions and persistence in time of these solutions for three and higher dimensions. We also examine a weaker class of global solutions.
Fast Computing Grammar-driven Convolution Tree Kernel for Semantic Role Labeling

Grammar-driven convolution tree kernel (GTK) has shown promising results for semantic role labeling (SRL). However, the time complexity of computing the GTK is exponential in theory. In order to speed up the computing process, we design two fast grammar-driven convolution tree kernel (FGTK) algorithms, which can compute the GTK in polynomial time. Experimental results on the CoNLL-2005 SRL data show that our two FGTK algorithms are much faster than the GTK.
DiVA: Using Application-Specific Policies to 'Dive' into Vector Approximations

In high-dimensional data domains, the performance of conventional tree-based access structures is occasionally outperformed by simple sequential scans. To this end, the introduction of approximation-based methods helped speed-up queries by providing compact representations of stored data. Approximation methods exploit vector quantization to index data mainly presumed to follow a uniform distribution. In real-world environments however, we mostly encounter both skewed data and query distributions. To address this dual challenge, we propose DiVA that combines the selective use of an approximation approach with an indexing mechanism to organize data subspaces in a high fan-out hierarchical structure. Moreover, DiVA reorganizes its own elements after receiving application hints regarding data access patterns. These hints or policies trigger the restructuring and possible expansion of DiVA so as to offer finer indexing granularity and improved access times in subspaces emerging as ‘hot-spots’. The novelty of our approach lies in the self-organizing nature of DiVA driven by application-provided policies; the latter effectively guide the refinement of DiVA’s elements as new data arrive, existing data are updated and the nature of query workloads continually changes. An extensive experimental evaluation using real data shows that DiVA reduces up-to 64% of the total number of I/Os if compared with state-of-art methods including the VA-file, GC-tree and A-tree.
Developing a Modified Directly Observed Therapy Intervention for Hepatitis C Treatment in a Methadone Maintenance Program: Implications for Program Replication

Background: Hepatitis C virus (HCV) is a prevalent chronic blood-borne infection among opioid-dependent patients on methadone maintenance treatment (MMT). Despite case reports and case–control studies, a randomized controlled trial (RCT) examining HCV treatment adherence in methadone-maintained patients is lacking and was the impetus for this ongoing RCT examining modified directly administered therapy for HCV treatment integrated within a MMT. Methods: Subjects were randomized 1:1 to receive HCV treatment as modified directly observed therapy (mDOT) into the MMT program or at a liver specialty clinic as self-administered therapy (SAT). Randomization was stratified based on HIV status and HCV genotype. Results: Twenty-one subjects to date have enrolled in this pilot study. The mDOT subjects have had greater success in starting treatment and 10 of the 12 mDOT subjects achieved early virologic response (EVR) at week 12 and 6 of those 10 achieved sustained virologic response (SVR). Of the nine SAT subjects, only three achieved EVR at week 12 and only one achieved SVR despite not completing the treatment. Conclusions: Hepatitis C treatment can be successfully integrated into a methadone maintenance clinic, and mDOT can be implemented with a methadone clinic’s existing nursing and medical staff. Patients struggling with concurrent substance use and mental illness comorbidity may be successfully addressed in such settings and facilitate access to and completion of treatment through the utilization of on-site clinical services for HCV treatment and adherence support with mDOT. The exact importance of site of services and adherence support remains a significant area for future investigation.