Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling the effects of spirituality/religion on patients’ perceptions of living with HIV/AIDS

BackgroundSpirituality/religion is an important factor in health and illness, but more work is needed to determine its link to quality of life in patients with HIV/AIDS.ObjectiveTo estimate the direct and indirect effects of spirituality/religion on patients’ perceptions of living with HIV/AIDS.DesignIn 2002 and 2003, as part of a multicenter longitudinal study of patients with HIV/AIDS, we collected extensive demographic, clinical, and behavioral data from chart review and patient interviews. We used logistic regression and path analysis combining logistic and ordinary least squares regression.SubjectsFour hundred and fifty outpatients with HIV/AIDS from 4 sites in 3 cities.MeasuresThe dependent variable was whether patients felt that life had improved since being diagnosed with HIV/AIDS. Spirituality/religion was assessed by using the Duke Religion Index, Functional Assessment of Chronic Illness Therapy—Spiritual Well-Being—Expanded, and Brief RCOPE measures. Mediating factors included social support, self-esteem, healthy beliefs, and health status/health concerns.ResultsApproximately one-third of the patients felt that their life was better now than it was before being diagnosed with HIV/AIDS. A 1-SD increase in spirituality/religion was associated with a 68.50% increase in odds of feeling that life has improved—29.97% due to a direct effect, and 38.54% due to indirect effects through healthy beliefs (29.15%) and health status/health concerns (9.39%). Healthy beliefs had the largest effect on feeling that life had improved; a 1-SD increase in healthy beliefs resulted in a 109.75% improvement in feeling that life changed.ConclusionsIn patients with HIV/AIDS, the level of spirituality/religion is associated, both directly and indirectly, with feeling that life is better now than previously. Future research should validate our new conceptual model using other samples and longitudinal studies. Clinical education interventions should focus on raising awareness among clinicians about the importance of spirituality/religion in HIV/AIDS.
Comparing Three Algorithms of Implementing ON/OFF Aggregation Model

ON/OFF aggregation model is one of the efficient and accurate models for self-similar network traffic generation. In this paper we propose and compare three algorithms of implementing ON/OFF aggregation model, based on Cavium OCTEON CN3860 network processor, aimed to achieve highbandwidth and real-time network traffic generation. The model is implemented in a multi-thread approach, in a token-bucket approach and in a discrete-event approach. The Hurst parameter of synthetic traffic is estimated by wavelet estimator, and the validation of the last algorithm is empirically discussed and proved. Keywords-ON/OFF aggregation model; self-similarity; network traffic simulation
Umbrella contingencies in security-constrained optimal power flow

The notion of umbrella contingencies is rigor- ously defined in the context of the security-constrained opti- mal power flow (SCOPF) problem in both its deterministic and probabilistic forms. The set of umbrella contingencies is a subset of the set of credible contingencies that is suffi- cient to attain levels of security and economic performance identical or nearly identical to those found when all credible contingencies are considered. This paper shows how to iden- tify the members of the umbrella contingency set from the norms of the Lagrange multiplier vectors associated with the post-contingency power balance relations of the SCOPF. A case study of the deterministic SCOPF investigates the range of validity of the set of umbrella contingencies as the system demand varies. In addition, we analyze the validity of the umbrella contingency set when lowly ranked contingencies are left out in a stochastic SCOPF problem.
Online Hybrid Task Scheduling in Reconfigurable Systems

This paper mainly discusses online tasks scheduling problem on hybrid CPU-FPGA reconfigurable systems. In these systems, hybrid tasks may be binary codes executed on CPU as well as hardware logic circuits implemented on FPGA. Tasks scheduling algorithms of conventional operating systems are not suitable for scheduling hybrid tasks on CPU-FPGA architecture. Based on a real reconfigurable system prototype, we present a task scheduler model and correlative algorithm for scheduling software, hardware and hybrid tasks. This algorithm combines tasks allocation, tasks placement with tasks migration. Simulation results have demonstrated this algorithm provides preferable scheduling performance and reduces the scheduling rejection rate by making use of the great flexibility of hybrid tasks.
Fast Packet Classification Using Multi-Dimensional Encoding

Internet routers need to classify incoming packets quickly into flows in order to support features such as Internet security, virtual private networks and quality of service (QoS). Packet classification uses information contained in the packet header, and a predefined rule table in the routers. Packet classification of multiple fields is generally a difficult problem. Hence, researchers have proposed various algorithms. This study proposes a multidimensional encoding method in which parameters such as the source IP address, destination IP address, source port, destination port and protocol type are placed in a multidimensional space. Similar to the previously best known algorithm, i.e., bitmap intersection, multi-dimensional encoding is based on the multi-dimensional range lookup approach, in which rules are divided into several multidimensional collision-free rule sets. These sets are then used to form the new coding vector to replace the bit vector of the bitmap intersection algorithm. The average memory storage of this encoding is ominus (L-N-logN) for each dimension, where L denotes the number of collision-free rule sets, and N represents the number of rules. The multi-dimensional encoding practically requires much less memory than bitmap intersection algorithm. Additionally, the computation needed for this encoding is as simple as bitmap intersection algorithm. The low memory requirement of the proposed scheme means that it not only decreases the cost of packet classification engine, but also increases the classification performance, since memory access represents the performance bottleneck in the packet classification engine implementation using a network processor.
Methods to identify the prey of invertebrate predators in terrestrial field studies

Abstract Predation is an interaction during which an organism kills and feeds on another organism. Past and current interest in studying predation in terrestrial habitats has yielded a number of methods to assess invertebrate predation events in terrestrial ecosystems. We provide a decision tree to select appropriate methods for individual studies. For each method, we then present a short introduction, key examples for applications, advantages and disadvantages, and an outlook to future refinements. Video and, to a lesser extent, live observations are recommended in studies that address behavioral aspects of predator–prey interactions or focus on per capita predation rates. Cage studies are only appropriate for small predator species, but often suffer from a bias via cage effects. The use of prey baits or analyses of prey remains are cheaper than other methods and have the potential to provide per capita predation estimates. These advantages often come at the cost of low taxonomic specificity. Molecular methods provide reliable estimates at a fine level of taxonomic resolution and are free of observer bias for predator species of any size. However, the current PCR‐based methods lack the ability to estimate predation rates for individual predators and are more expensive than other methods. Molecular and stable isotope analyses are best suited to address systems that include a range of predator and prey species. Our review of methods strongly suggests that while in many cases individual methods are sufficient to study specific questions, combinations of methods hold a high potential to provide more holistic insights into predation events. This review presents an overview of methods to researchers that are new to the field or to particular aspects of predation ecology and provides recommendations toward the subset of suitable methods to identify the prey of invertebrate predators in terrestrial field research.
G-Optimal Design with Laplacian Regularization

In many real world applications, labeled data are usually expensive to get, while there may be a large amount of unlabeled data. To reduce the labeling cost, active learning attempts to discover the most informative data points for labeling. Recently, Optimal Experimental Design (OED) techniques have attracted an increasing amount of attention. OED is concerned with the design of experiments that minimizes variances of a parameterized model. Typical design criteria include D-, A-, and E-optimality. However, all these criteria are based on an ordinary linear regression model which aims to minimize the empirical error whereas the geometrical structure of the data space is not well respected. In this paper, we propose a novel optimal experimental design approach for active learning, called Laplacian G-Optimal Design (LapGOD), which considers both discriminating and geometrical structures. By using Laplacian Regularized Least Squares which incorporates manifold regularization into linear regression, our proposed algorithm selects those data points that minimizes the maximum variance of the predicted values on the data manifold. We also extend our algorithm to nonlinear case by using kernel trick. The experimental results on various image databases have shown that our proposed LapGOD active learning algorithm can significantly enhance the classification accuracy if the selected data points are used as training data.
Automatic Segmentation of Pulmonary Artery ( PA ) Using Customized Level Set Method in 3 D ( CTA ) Images

This paper proposes an efficient customized level s t algorithm to segment Pulmonary Artery (PA) tree in 3D pulmonary Computed Tomography Angiography (CTA) images. In this algorithm, to reduce the search are a th lung regions from the original image are first segmented an the heart region is extracted by selecting the regions between the lungs. A pre-processing algorithm based on Hessian m trix and its eigenvalues is used to remove the connectiv ity between the pulmonary artery and other nearby pulmonary org ans. To extract the pulmonary artery tree, we first use a r egion growing method initialized by a seed point which is automatically selected within the pulmonary artery trunk in the heart region. In the second step, the segmentation of the pulmonary artery is performed using a customized 3D level set algorithm, using the output of region grower as the initial contour. We use a new stopping criterion for the pr oposed level set algorithm, a consideration often neglecte d in many level set implementations. To validate and assess t he robustness of the method, 20 CT angiography dataset s w re used (10 free pulmonary embolism scans and 10 CT wi th pulmonary emboli). A very good agreement with the v isual judgment was obtained in both normal and positive p ulmonary emboli CT scans.
Contrasting multi‐site genotypic distributions among discordant quantitative phenotypes: the APOA1/C3/A4/A5 gene cluster and cardiovascular disease risk factors

Most tests of association between DNA sequence variation and quantitative phenotypes in samples of randomly chosen individuals rely on specification of genotypic strata followed by comparison of phenotypes across these strata. This strategy often succeeds when phenotypic differences are caused by one or two single nucleotide polymorphisms (SNPs) among the surveyed markers. However, when multiple‐SNP haplotypes account for observed phenotypic variation, identification of the best partitioning requires examination of an inordinate number of SNP combinations. An alternative approach is to rank individuals by their phenotypic measures and ask whether attributes of the genotypic variation show a non‐random distribution along this phenotypic ranking. One simple version of this strategy selects the top and bottom tails of the distribution, and then tests whether genotypes from these two samples are drawn from a single population. This framework does not require the recovery of phased haplotypes and allows contrasts between large numbers of sites at once. We use a method based on this approach to identify associations between plasma triglyceride level, a risk factor for cardiovascular disease, and multi‐site genotypes located in the APOA1/C3/A4/A5 cluster of apolipoprotein genes in unrelated individuals (1,071 African‐American females, 780 African‐American males, 1,036 European‐American females, and 930 European‐American males) sampled from four US cities as part of the Coronary Artery Risk Development in Young Adults (CARDIA) study. Method performance is investigated using simulations that model genealogical variation and different genetic architectures. Results indicate that this multi‐site test can identify genotype‐phenotype associations with reasonable power, including those generated by some simple epistatic models. Genet. Epidemiol. 2006. © 2006 Wiley‐Liss, Inc.
Minimum Spanning Trees and Single Linkage Cluster Analysis

Minimum spanning trees (MST) and single linkage cluster analysis (SLCA) are explained and it is shown that all the information required for the SLCA of a set of points is contained in their MST. Known algorithms for finding the MST are discussed. They are efficient even when there are very many points; this makes a SLCA practicable when other methods of cluster analysis are not. The relevant computing procedures are published in the Algorithm section of the same issue of Applied Statistics. The use of the MST in the interpretation of vector diagrams arising in multivariate analysis is illustrated by an example.
Visualizing transplanted muscle flaps using minimally invasive multi-electrode bioimpedance spectroscopy

We describe here an imaging system that uses bioimpedance spectroscopy with multi-electrode array to indicate the state of muscle flap regions under the array. The system is able to differentiate between different health states in the tissue and give early information about the location and size of ischemic sub-regions. The array will be 4*8 electrodes with the spacing of 5mm between the electrodes (the number of electrodes and the spacing may vary). The electrodes are minimally invasive short stainless steel needles, that penetrate 0.3 mm into the tissue with the goal of achieving a wet electric contact. We combine 32 configurations of 4-electrode multi-frequency impedance measurements to derive a health-state map for the transplanted flap. The imaging method is tested on a model consisting of 2 tissues and FEM software (Finite Element Method -COMSOL Multiphysics based) is used to conduct the measurements virtually. Dedicated multichannel bioimpedance measurement equipment has already been developed and tested, that cover the frequency range from 100 Hz to 1 MHz.
Learning Exploration Policies with Models Conference on Automated Learning and Discovery (CONALD'98)

Reinforcement learning can greatly proot from world models updated by experience and used for computing policies. Fast discovery of near-optimal policies, however, requires to focus on \useful" experiences. Using an additional exploration model, we learn an exploration policy maximizing \exploration rewards" for visits of states that promise information gain. We augment this approach by an extension of Kaelbling's Interval Estimation algorithm to the model-based case. Experimental results in stochastic environments demonstrate advantages of this hybrid approach.
Quantitative Determination of Lercanidipine Enantiomers in Commercial Formulations by Capillary Electrophoresis

An enantioselective method based on capillary electrophoresis (CE) using cyclodextrin (CD) as chiral selector was developed and validated for determination of lercanidipine (LER) enantiomers, a drug calcium channel blocker which exerts antihypertensive effects of long duration, in a pharmaceutical formulation. Optimum separation of LER enantiomers was obtained on a 50 cm × 50 μm id capillary using a sodium acetate buffer solution 200 mmol/L pH 4.0 containing 10 mmol/L of 2,3,6-o-methyl-β-cyclodextrin (TM-β-CD) as background electrolyte. The capillary temperature and voltage were 15°C and 25 kV, respectively, hydrodynamic injection and detection at 237 nm. Linearity was obtained in the range 12.5–100 μg/mL for both enantiomers (r ≥ 0.995). The RSD (%) and relative errors (E, %) obtained in precision and accuracy studies (intraday and interday) were lower than 5%. After validation, the method was applied to quantify the enantiomers of LER in commercial tablets and the results were satisfactory in terms of accuracy and precision, both less than 5%. Therefore, this method was found to be appropriate for enantioselective quality control of LER enantiomers in pharmaceutical formulations.
Changes in bioavailability and tissue distribution of zinc caused by magnesium deficiency in rats

The effect of a Mg-deficient diet (200 mg Mg/kg feed) on the bioavailability of dietary Zn and the concentration of this cation in plasma, whole blood, skeletal muscle, kidney, heart and brain of Wistar rats was studied after 7, 35, 42, 49, 56, 63 and 70 d. Mg deficiency significantly decreased Zn in whole blood on day 42 of the experiment, but there was no significant change in plasma Zn throughout the 70 d study period. The Mg-deficient diet significantly increased intestinal absorption of Zn, Zn balance, and Zn concentration in femur and kidney, but decreased Zn concentration in the heart despite the increase in dry weight of this organ. No change was found in brain Zn concentration.
Saliency-based image quality assessment metric

Traditional image quality assessment (IQA) metrics are most based on the discrepancy between reference image and the distorted image does not correlate well with the perceptive mechanism of human visual system (HVS). In this paper, we found from the experimental investigations that perceptible quality distortions can lead to some measurable changes in the visual saliency (VS) map of the image, and then proposed a novel and effective full reference image quality assessment method by means of VS. In our method, VS serves two functions which one is as a feature for computing the distorted image's local quality map and the other is used for a weight to reflect the importance of a local region when pooling. However, VS map alone sometimes does not work quite well when image's contrast change or color distortion, so we also merged the gradient similarity map and chrominance similarity map into the IQA. We named our proposed IQA metric as visual saliency-based metric (VSM). The experiments carried out on a benchmark datasets demonstrate that our proposed VSM outperform most of the state-of-the-art IQA metrics in terms of the prediction accuracy.
The Acquisitions of Information Technology Firms by M&A Intents: An Empirical Analysis

Over the last decades, a large number of firms have undertaken mergers and acquisitions (M&As) to create synergies through increased production efficiency, increased market power, and quality improvements. Moreover, we have also recently witnessed that an increasing number of firms acquire information technology (IT) firms to create synergies from the customer side as well as the production side. In this study, we examine the post-merger risk of the acquiring firm, measured as its return volatility when IT firms are acquired, and seek to understand the dynamics surrounding M&A transactions. We also identify the conditions under which acquiring firms can mitigate the risks resulting from M&A transactions. We find that a strong run-up in risk occurs before M&A transactions are initiated, but this risk begins to decline over the post-merger years. However, we expect that post-merger risks tend to persist when firms seek M&A transactions with a customer-side motive, whereas this does not occur with a production-side motive. Moreover, we expect to find the conditions under which a firm can mitigate risk from the acquisition of IT firms contingent on its M&A motives.
CBRM: Computational Reputation Model for C2C e-Commerce Platform

Online shopping has become an important and popular way of life. With the rapid C2C platform development, the credit problem in online shopping was being discussed. Serious loss of credit will leads to bad online business environment. However, till now lots of researchers just focus on seller reputation model to solve credit problem, thus buyer reputation model is seldom being considered. To solve the above problem, we proposed a Computational Buyer Reputation Model for C2C e-Commerce platform, i.e. CBRM. Five buyer reputation indicators, including transactions amount, seller ratings, buyer operations, chargeback ratio and real name authentication, were integrated into CBRM. By using AHP to distribute various indicator weights, we can obtain numeric value of buyer reputation. Therefore, these values can be used for sellers to choose appropriate buyers to deal with. Experiment results showed the efficiency and effectiveness of CBRM.
All-optical Hall effect by the dynamic toroidal moment in a cavity-based metamaterial

Dynamic dipolar toroidal response is demonstrated by an optical plasmonic metamaterial composed of double disks. This response with a hotspot of localized E-field concentration is a well-behaved toroidal cavity mode that exhibits a large Purcell factor due to its deep-subwavelength mode volume. All-optical Hall effect (photovoltaic) due to this optical toroidal moment is demonstrated numerically, in mimicking the magnetoelectric effect in multiferroic systems. The result shows a promising avenue to explore various optical phenomena associated with this intriguing dynamic toroidal moment.
Hybrid Behavior Co-evolution and Structure Learning in Behavior-based Systems

Designing an intelligent situated agent is a difficult task as the designer must see the problem from the agent's standpoint considering all its sensors and actuators. We have devised a co-evolutionary/reinforcement learning hybrid method to automate the design of hierarchical behavior-based systems. In our approach, the design problem is decomposed into two separate parts: developing a repertoire of behaviors and organizing those behaviors in a structure. Mathematical formulation shows how to decompose the value of the structure to simpler components. These components can be estimated and used to find the optimal organization of behaviors during the agent's lifetime. Moreover, a novel co-evolutionary mechanism is suggested that evolves each type of behavior separately in their own genetic pool. Our method is applied to the decentralized multi-robot object lifting task which results in human-competitive performance.
Advancing Regional Monetary Cooperation: The Case of Fragile Financial Markets

1. Introduction PART I: DRIVERS OF REGIONAL MONETARY COOPERATION 2. Global Instability and 'Monetary Regionalism' 3. Monetary Policy Choices of Southern Economies 4. Fragile Financial Markets PART II: NEW PERSPECTIVES ON REGIONAL MONETARY COOPERATION AND INTEGRATION 5. A Modern Exchange Rate Theory Perspective 6. A Strategy to Achieve Macroeconomic Stability? 7. Asymmetric Regional Monetary Cooperation 8. Reconsidering Economic Costs PART III: REGIONAL MONETARY COOPERATION IN CMA, ASEAN/ASEAN+3, AND MERCOSUR 9. Case Selection and Data 10. Common Monetary Area (CMA) 11. Association of Southeast Asian Nations (ASEAN/ASEAN+3) 12. Common Market of the South (MERCOSUR) 13. CMA, ASEAN/ASEAN+3 and MERCOSUR Compared 14. Conclusions and Policy Recommendations