Miracle-dz/UniFAR-FTUs huggingface dataset:
BMC Medical Informatics and Decision Making

Background: There is uncertainty about how GPs should convey information about treatment effectiveness to their patients in the context of cardiovascular disease. Hence we study the concordance of decisions based on one of four single information formats for treatment effectiveness with subsequent decisions based on all four formats combined with a pictorial representation. Methods: A randomized study comprising 1,169 subjects aged 40–59 in Odense, Denmark. Subjects were randomized to receive information in terms of absolute risk reduction (ARR), relative risk reduction (RRR), number needed to treat (NNT), or prolongation of life (POL) without heart attack, and were asked whether they would consent to treatment. Subsequently the same information was conveyed with all four formats jointly accompanied by a pictorial presentation of treatment effectiveness. Again, subjects should consider consent to treatment. Results: After being informed about all four formats, 52%–79% of the respondents consented to treatment, depending on level of effectiveness and initial information format. Overall, ARR gave highest concordance, 94% (95% confidence interval (91%; 97%)) between initial and final decision, but ARR was not statistically superior to the other formats. Conclusion
Group Therapy Goals: A Comparison of Group Therapy Providers and Male Inmates

ABSTRACT This study was designed to identify important process and content goals from the perspective of inmates and compare these goals to the goals identified by group therapists in a previous study conducted by Winterowd, Morgan, and Ferrell (2001). Utilizing survey data from 156 incarcerated adult males, an initial confirmatory factor analysis indicated that the factor structure for inmates’ goal importance ratings did not precisely mirror the group therapists’ ratings in Winterowd et al. A subsequent exploratory factor analysis revealed a five-factor model with the following goals: Self-Exploration and Coping Skills; Group Relationship Building and Cooperation; Substance Abuse; Prosocial Behavior and Healthy Lifestyle; and Institutional Relationships. Overall, these goals match the primary goals identified as important by therapists in Winterowd et al. However, inmates did not appear to draw distinctions between some of the goals identified by therapists. Implications for group therapy with male inmates are highlighted.
Explaining Majority and Minority Trust in the Police

This article tests the contribution that social capital theory, performance theory, and the procedural justice-based model can make towards explaining the trust of majority and minority group members in the police. The central research questions are: (1) do the same factors determine their levels of trust? and (2) are the effects parallel? To answer these questions, we carried out regression analyses on data collected from majority and minority group members living in Belgium (960 face-to-face interviews). The results show that, although the three theories offer explanatory elements for members of both majority and minority groups, the explanation of their trust in the police is not identical. Implications for research and theorization are discussed.
Grid mapping in dynamic road environments: Classification of dynamic cell hypothesis via tracking

We propose a method capable of acquiring an occupancy grid map-based representation of the local, static driving environment around an intelligent vehicle in the presence of dynamic objects. These corrupt the representation due to violating the underlying static-world assumptions of common grid mapping algorithms and are therefore detected and filtered from the map. For this purpose, a subsequent step is suggested that identifies, clusters and merges dynamic cell hypothesis in a novel way. Thereafter, an Interacting-Multiple-Model-Unscented-Kalman-Probabilistic-Data-Association (IMM-UK-PDA) tracker is used to classify of whether cell movements behave consistently with possible movement characteristics of real dynamic objects or are just generated by noise or newly observed static environment. In opposition to many other approaches, the method explicitly combines information of newly occupied and free areas, completes the shape of only partly visible dynamic objects and uses an advanced object tracking scheme to clean the grid from dynamic object corruptions. The method is evaluated with grids generated by an automotive radar and stereo camera in real traffic environments.
Optimization by a quantum reinforcement algorithm

A reinforcement algorithm solves a classical optimization problem by introducing a feedback to the system which slowly changes the energy landscape and converges the algorithm to an optimal solution in the configuration space. Here, we use this strategy to concentrate (localize) preferentially the wave function of a quantum particle, which explores the configuration space of the problem, on an optimal configuration. We examine the method by solving numerically the equations governing the evolution of the system, which are similar to the nonlinear Schrodinger equations, for small problem sizes. In particular, we observe that reinforcement increases the minimal energy gap of the system in a quantum annealing algorithm. Our numerical simulations and the latter observation show that such kind of quantum feedbacks might be helpful in solving a computationally hard optimization problem by a quantum reinforcement algorithm.
Effect of continuous positive airway pressure on ventricular ectopy in heart failure patients with obstructive sleep apnoea

Background: Obstructive sleep apnoea (OSA) elicits a number of cardiovascular perturbations that could lead acutely or chronically to increased ventricular ectopy in patients with heart failure (HF). We tested the hypothesis that treatment of OSA with continuous positive airway pressure (CPAP) in patients with HF would reduce the frequency of ventricular premature beats (VPBs) during sleep in association with reduced sympathetic nervous system activity. Methods: Following optimisation of medical treatment, 18 HF patients with OSA and >10 VPBs per hour of sleep were randomised to a control group (n = 8) or a treatment group who received CPAP (n = 10). The frequency of VPBs and urinary norepinephrine (noradrenaline) concentrations during total sleep time were determined at baseline and after 1 month. Results: Control patients did not experience any significant changes in apnoea-hypopnoea index (AHI), mean nocturnal O2 saturation, or the frequency of VPBs. In contrast, there was a significant reduction in AHI (p<0.001), an increase in minimum O2 saturation (p = 0.05), a reduction in urinary norepinephrine concentrations (p = 0.009), and a 58% reduction in the frequency of VPBs during total sleep (from mean (SE) 170 (65) to 70 (28) per hour, p = 0.011) after 1 month of CPAP treatment. Conclusions: In patients with HF, treatment of co-existing OSA by CPAP reduces the frequency of VPBs during sleep. These data suggest that reductions in VPBs and other ventricular arrhythmias through treatment of OSA might improve the prognosis in patients with HF.
Effect of Nitrate on Nodulation and Nitrogen Fixation of Soybean

1.1 Biological nitrogen fixation and nitrogen nutrition in soybean plants Biological nitrogen fixation is one of the most important processes for ecosystem to access available N for all living organisms. Although N2 consists 78% of atmosphere, but the triple bond between two N atoms is very stable, and only a few group of prokaryotes can fix N2 to ammonia by the enzyme nitrogenase. Annual rate of natural nitrogen fixation is estimated about 232 x 106 t, and the 97% depends on biological nitrogen fixation (Bloom, 2011). This exceeds the rate of chemical nitrogen fertilizer uses about 100 x 106 t฀in 2009. Soybean can use N2, though symbiosis with nitrogen fixing soil bacteria, rhizobia, to make root nodules for harboring them. Soybean (Glycine max [L.] Merr.) is a major grain legume crop for feeding humans and livestock. It serves as an important oil and protein source for large population residing in Asia and the American continents. The current global soybean production was 231 x 106 t in 2008 (FAOSTAT). It is a crop predominantly cultivated in U.S.A. , Brazil, Argentina and China, which together contribute nearly 87 percent of the total world produce in 2008. Soybean has become the raw materials for diversity of agricultural and industrial uses. Soybean seeds contain a high proportion of protein, about 40% based on dry weight, therefore, they require a large amount of nitrogen to get a high yield. About 8 kg N is required for 100 kg of soybean seed production. Soybean can use atmospheric dinitrogen (N2) by nitrogen fixation of root nodules associated with soil bacteria, rhizobia. Soybean plants can absorb combined nitrogen such as nitrate for their nutrition either from soil mineralized N or fertilizer N. It is well known that heavy supply of nitrogen fertilizer often causes the inhibition of nodulation and nitrogen fixation. Therefore, only a little or no nitrogen fertilizer is
3D SMoSIFT: three-dimensional sparse motion scale invariant feature transform for activity recognition from RGB-D videos

Abstract. Human activity recognition based on RGB-D data has received more attention in recent years. We propose a spatiotemporal feature named three-dimensional (3D) sparse motion scale-invariant feature transform (SIFT) from RGB-D data for activity recognition. First, we build pyramids as scale space for each RGB and depth frame, and then use Shi-Tomasi corner detector and sparse optical flow to quickly detect and track robust keypoints around the motion pattern in the scale space. Subsequently, local patches around keypoints, which are extracted from RGB-D data, are used to build 3D gradient and motion spaces. Then SIFT-like descriptors are calculated on both 3D spaces, respectively. The proposed feature is invariant to scale, transition, and partial occlusions. More importantly, the running time of the proposed feature is fast so that it is well-suited for real-time applications. We have evaluated the proposed feature under a bag of words model on three public RGB-D datasets: one-shot learning Chalearn Gesture Dataset, Cornell Activity Dataset-60, and MSR Daily Activity 3D dataset. Experimental results show that the proposed feature outperforms other spatiotemporal features and are comparative to other state-of-the-art approaches, even though there is only one training sample for each class.
On the UTBB SOI MOSFET performance improvement in quasi-double-gate regime

This work investigates the simultaneous electrostatic improvement and performance enhancement of UTBB SOI MOSFETs obtained in quasi-double-gate (QDG) regime (i.e. simultaneously biasing gate and substrate (or ground plane) as Vsub=k*Vg) as a strong function of k-multiplication factor, when compared to a standard single-gate mode. QDG mode is demonstrated to allow threshold voltage tuning and on-current enhancement without off-state current degradation, of interest for digital applications (e.g. switches). Improved performance in QDG mode combined with lowered DIBL and enhanced gain are of interest for high-precision low-frequency analog applications. The work finally quantifies the resulting gate area decrease in QDG mode, potentially exploitable in actual circuit implementations.
Accurate person tracking through changing poses for multi-view action recognition

Actions by humans in real-world settings involve large changes in the person's pose and the relative orientation with respect to the camera. Person tracking algorithms often fail under such conditions, since they work by detecting and tracking people in a few known poses (typically standing). Further, due to occlusions and similarity of clothing with background, foreground silhouettes are typically very noisy. We present an approach which address these problems by first accurately tracking a person through changing pose and broken foreground blobs. During the tracking process we also estimate the relative orientation and scale of the person. We represent the pose of the person in each track window using a grid-of-centroids model, and recognize the action by matching with a set of keyposes, in each frame. We tested our approach in a dataset collected in a real grocery store, and report better than ≈82.5% accuracy for frame-by-frame recognition of actions.
Evaluating Children for Instructional Purposes

*Donald D. Hammill Ed.D. , is professor of special education, Temple University, Philadelphia, Pennsylvania. TO ENSURE the most successful instruction of children with learning disabilities, the teacher must have considerable understanding of each pupil’s psychoeducational strengths and weaknesses. No superficial overview of a child’s performance will suffice. Knowledge of IQ, reading grade level, or neurological status, while interesting, does not provide enough information with which either to establish appropriate goals or to construct a reality-based training program for a specific child. Therefore, the implementation in the schools of an effective, educationally oriented evaluation program is of the utmost importance for a successful instructional experience for children with learning disorders.
DMA-circular: an enhanced high level programmable DMA controller for optimized management of on-chip local memories

This paper presents DMA-circular, a novel DMA controller for optimized memory management for on-chip local memories. DMA-circular embeds the functionality of caches into the DMA controller and applies aggressive optimizations using novel hardware. DMA-circular anticipates the computation requirements in terms of data transfers and performs buffer management for data that is mapped to the local memory. The explicit hardware support accelerates the most common actions related to the management of a local memory while the cache functionalities enable a high level of programmability for the DMA-circular. The evaluation is done on several high performance kernels from the NAS benchmark suite. Compared to traditional DMA controllers, results show speedups from 1.20x to 2x, keeping the control code overhead under 15% of the kernels' execution time and also reducing the energy consumption up to 40%.
Tackling the complexity of requirements engineering process improvement by partitioning the improvement task

Software process improvement is a complex and expensive endeavor requiring extensive resources and long term commitment. In the present study software process improvement (SPI) problems in small organizations were tackled by dividing the overall effort into three subtopics: technical infrastructure, working practices, and management infrastructure. Partitioning the SPI efforts into these three subtopics makes it apparent that all of these topics do not need to be tackled in the same way but some tasks can, e.g., be outsourced. In this paper the results of an investigation into the use of the model above in requirements engineering (RE) process improvement are reported from three industrial case studies. A domain specific method was constructed independently of the utilizing companies, i.e., outsourced, and it was then used in SPI efforts in the companies to establish a solid infrastructure for basic RE in a short period of time, with limited resources, and without previous expertise in RE. It is argued that the suggested partitioning can both lower the threshold for initiating software process improvement efforts in industry and increase the likelihood of successfully completing them.
Stochastic Processes Are Key Determinants of Short-Term Evolution in Influenza A Virus

Understanding the evolutionary dynamics of influenza A virus is central to its surveillance and control. While immune-driven antigenic drift is a key determinant of viral evolution across epidemic seasons, the evolutionary processes shaping influenza virus diversity within seasons are less clear. Here we show with a phylogenetic analysis of 413 complete genomes of human H3N2 influenza A viruses collected between 1997 and 2005 from New York State, United States, that genetic diversity is both abundant and largely generated through the seasonal importation of multiple divergent clades of the same subtype. These clades cocirculated within New York State, allowing frequent reassortment and generating genome-wide diversity. However, relatively low levels of positive selection and genetic diversity were observed at amino acid sites considered important in antigenic drift. These results indicate that adaptive evolution occurs only sporadically in influenza A virus; rather, the stochastic processes of viral migration and clade reassortment play a vital role in shaping short-term evolutionary dynamics. Thus, predicting future patterns of influenza virus evolution for vaccine strain selection is inherently complex and requires intensive surveillance, whole-genome sequencing, and phenotypic analysis.
Mixtures of regression models for time course gene expression data: evaluation of initialization and random effects

SUMMARY Finite mixture models are routinely applied to time course microarray data. Due to the complexity and size of this type of data, the choice of good starting values plays an important role. So far initialization strategies have only been investigated for data from a mixture of multivariate normal distributions. In this work several initialization procedures are evaluated for mixtures of regression models with and without random effects in an extensive simulation study on different artificial datasets. Finally, these procedures are also applied to a real dataset from Escherichia coli. AVAILABILITY The latest release versions of R packages flexmix, gcExplorer and kernlab are always available from CRAN (http://cran.r-project.org/). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Effects of Standards on Self-Enhancing Interpretations of Ambiguous Social Comparison Information

When people receive feedback about how they and others have performed on a task-feedback that should be interpreted as implying equivalent performance-they only seem to exhibit a self-enhancement bias (erroneously believing they have outperformed others) when feedback about others' performance is unambiguous and feedback about their own performance is ambiguous (Klein, 2001). They do so by distorting estimates of their own performance upward. In 2 studies, we show how 2 types of standards moderate this effect. In Experiment 1, we show that people distort ambiguous self-performance feedback in a self-enhancing direction only when they receive information about their own and others'performance at the same time. In Experiment 2, we found that when people hold neutral prior beliefs about their ability (an internal standard), they exhibit self-enhancement in their performance assessments whenever at least one source of information (self or other) is ambiguous. Despite the wealth of literature that has demonstrated self-enhancement biases, it appears that the emergence of this bias is bounded in theoretically interesting ways when people are faced with performance feedback.
Stress, Coping, and Depression Among Elderly Korean Immigrants

Summary The effects of life stresses and social support on depressive symptoms in older Korean Americans (n = 67), recruited at senior centers and meal sites, were examined. Those who reported poorer health, who had more stressful life events, who were dissatisfied with help received from family members, and who reported few good friends were more likely to be depressed than those who did not. The impact of these factors on the quality of life of elderly Korean immigrants can be understood in the context of their immigration experience and Korean cultural values.
Development of the Reasons for Entrepreneurs’ Retirement Decision Inventory (RERDI) and Preliminary Evidence of Its Psychometric Properties in a French Sample

The purpose of the present research was to develop an original self-report questionnaire to assess the structure of the reasons underlying the retirement decision process among French entrepreneurs using the push pull anti-push anti-pull view. Three studies were conducted to examine the reliability and validity of the Reasons for Entrepreneurs’ Retirement Decision Inventory (RERDI). The first study involved creating a list of items leading to a preliminary version of the RERDI. In the second study, 167 French entrepreneurs were surveyed to test the factor structure of the RERDI and to select the final items leading to a 16-item inventory. This tool comprises four reliable subscales: (a) push, (b) pull, (c) anti-push, and (d) anti-pull, with adequate internal consistency. Using confirmatory factor analysis, this four-factor structure was confirmed in the third study (n = 255). The RERDI’s convergent validity and temporal stability were also confirmed. The importance of this inventory for further research and practice is discussed.
Control-theoretic Thermal Balancing for Clusters

Thermal management is critical for clusters because of the increasing power consumption of modern processors, compact server architectures and growing server density in data centers. Thermal balancing mitigates hot spots in a cluster through dynamic load distribution among servers. This paper presents the Control-theoretical Thermal Balancing (CTB) algorithms that employ feedback control loops to dynamically balance the temperature of different servers. CTB features optimal controllers designed based on a difference equation model that captures thermal dynamics of clusters. Stability analysis and simulation results demonstrate that CTB can provide effective temperature balancing when servers’ power consumption and ambient temperature deviate from estimation or change significantly at run-time.
The Predicate-Argument Structure of Discourse Connectives A Corpus-based Study

Discourse connectives can be analyzed as encoding predicate-argument relations whose arguments derive from the interpretation of discourse units. These arguments can be anaphoric or structural. Although structural arguments can be encoded in a parse tree, anaphoric arguments must be resolved by other means. A study of nine connectives, annotating the location, size, and syntactic type of their arguments, shows connective-specific patterns for each of these features. A preliminary study of inter-annotator consistency shows that it too varies by connective. Results of the corpus study will be used in the development of resolution algorithms for anaphoric connectives.
Scalable Parallel Algorithms for Boundary Control of Thermally Convective Flows

Optimal control of fluid flows is an important and computationally challenging problem. In this paper, we investigate the application of a class of parallel and fully coupled two-grid Lagrange-Newton-Krylov-Schwarz (LNKSz) algorithms for the boundary control of thermally convective flows. The investigation focuses on the use of a two-grid inexact Newton solver for the necessary optimality condition obtained from the optimization problem and the use of a Krylov subspace solver together with an efficient two-level overlapping Schwarz preconditioner for the Jacobian system. Our parallel numerical results show that the proposed method is scalable with respect to the number of processors, the grid size, and robust with respect to some physical parameters such as the Reynolds number and the Grashof number. We also show some large scale calculations involving several million unknowns obtained on a supercomputer with more than two thousand processors.