Miracle-dz/UniFAR-FTUs huggingface dataset:
German-Language Version of the Compensatory Health Belief Scale

This study constitutes the first psychometric examination of the German-language version of the Compensatory Health Belief (CHB) scale to assess the belief that unhealthy behavior can be compensated for by engaging in healthy behavior. Data from four different Swiss samples of 1,571 adolescents/students (age range 15 – 55 years), collected between 2007 and 2009, were used. Confirmatory factor analysis did not support either the hypothesized 4-factor structure or a second-order factor structure with a latent overall variable for the German-language CHB scale. These results support the inconsistent patterns of CHBs found across European cultures. Thus, the development of behavior-specific scales might be of advantage. Further recommendations for an improvement of the measurement of compensatory health beliefs are discussed.
Experience with parallel computing on the AN2 network

Technology trends make it attractive to use workstations connected by a local area network as a multicomputing platform for parallel applications. Achieving acceptable application performance in such a workstation cluster using commodity components requires good support from system software and network hardware. This paper describes our experience with parallel programming in a workstation cluster and the implications for operating systems and network adaptor design. Our cluster consists entirely of commercially-available hardware: 24 Alpha workstations connected by a 155 Mbit/s AN2 ATM network. We present results from running several parallel applications on this cluster. Our cluster demonstrates both excellent low-level communication performance and good overall application performance that compares favorably with dedicated multicomputers like the IBM SP2.
Hausdorff distance under translation for points and balls

We study the shape matching problem under the Hausdorff distance and its variants. In the first part of the article, we consider two sets A,B of balls in Rd, d=2,3, and wish to find a translation t that minimizes the Hausdorff distance between A+t, the set of all balls in A shifted by t, and B. We consider several variants of this problem. First, we extend the notion of Hausdorff distance from sets of points to sets of balls, so that each ball has to be matched with the nearest ball in the other set. We also consider the problem in the standard setting, by computing the Hausdorff distance between the unions of the two sets (as point sets). Second, we consider either all possible translations t (as is the standard approach), or consider only translations that keep the balls of A+t disjoint from those of B. We propose several exact and approximation algorithms for these problems. In the second part of the article, we note that the Hausdorff distance is sensitive to outliers, and thus consider two variants that are more robust: the root-mean-square (rms) and the summed Hausdorff distance. We propose efficient approximation algorithms for computing the minimum rms and the minimum summed Hausdorff distances under translation, between two point sets in Rd. In order to obtain a fast algorithm for the summed Hausdorff distance, we propose a deterministic efficient dynamic data structure for maintaining an &epsis;-approximation of the 1-median of a set of points in Rd, under insertions and deletions.
Requirements Traceability Technologies Selection for Industry

The ultimate goal for researchers to develop a requirements traceability (RT) technology is transferring it from academy to industry. Academic researchers and industrial practitioners both expect that more and more technologies can be transferred. However, there is always a wide gap between RT research and practice. In this thesis, I'll propose a novel model that focuses on evaluating the RT technologies transfer maturity. A reasonable maturity evaluation model is proposed to measure to what extent an academic RT technology must achieve before it is able to be transferred to industry. Based on the model, an initial progress is made in selecting a suitable RT technology for industrial practitioners.
Genome-Wide Survey of Pseudogenes in 80 Fully Re-sequenced Arabidopsis thaliana Accessions

Pseudogenes (Ψs), including processed and non-processed Ψs, are ubiquitous genetic elements derived from originally functional genes in all studied genomes within the three kingdoms of life. However, systematic surveys of non-processed Ψs utilizing genomic information from multiple samples within a species are still rare. Here a systematic comparative analysis was conducted of Ψs within 80 fully re-sequenced Arabidopsis thaliana accessions, and 7546 genes, representing ∼28% of the genomic annotated open reading frames (ORFs), were found with disruptive mutations in at least one accession. The distribution of these Ψs on chromosomes showed a significantly negative correlation between Ψs/ORFs and their local gene densities, suggesting a higher proportion of Ψs in gene desert regions, e.g. near centromeres. On the other hand, compared with the non-Ψ loci, even the intact coding sequences (CDSs) in the Ψ loci were found to have shorter CDS length, fewer exon number and lower GC content. In addition, a significant functional bias against the null hypothesis was detected in the Ψs mainly involved in responses to environmental stimuli and biotic stress as reported, suggesting that they are likely important for adaptive evolution to rapidly changing environments by pseudogenization to accumulate successive mutations.
Effects of Task Consideration Order on Decentralized Task Allocation Using Time-Variant Response Thresholds

In this work, we investigate how the order in which tasks are considered to be acted upon by probabilistic agents with time-variant response thresholds within a fully decentralized multiagent system may affect task allocation, task fulfillment levels, specialization tendencies, and system robustness. The tested ordering schemas are: (1) ascending subjective action threshold, (2) descending objective task stimulus, (3) descending action probability P as defined by (Theraulaz, Bonabeau, and Deneubourg 1998) which combines both subjective threshold and objective stimulus, and (4) a random ordering to serve as a baseline for comparison. As the behavior of real-world systems tends to stem from complex interactions of multiple system aspects, we expand our analysis by further breaking down each of these ordering approaches into positive vs. negative reinforcement under different learning and forgetting rates for updating agent response thresholds.
Diabetes-specific social learning variables and self-care behaviors among persons with type II diabetes.

This study assessed the relationship between diabetes-specific social learning factors and diabetes self-care. Predictor variables were collected within the categories of knowledge (e.g., behavioral demonstrations, pencil-and-paper tests), beliefs/expectations (e.g., self-efficacy, self-motivation), skills (e.g., problem-solving and refusal skills), and environmental support (e.g., barriers to adherence, family support). Different results emerged across the three areas of the regimen assessed: diet, exercise, and glucose testing. Multiple-regression analyses revealed that the social learning variables consistently improved the prediction of self-care beyond that attributable to demographic variables but that the categories of social learning variables most closely related to self-care varied across regimen areas. These findings suggest that programs to enhance diabetes self-care could beneficially focus on life-style behaviors and employ strategies to increase regimen-related expectations and diabetes-specific social and problem-solving skills.
U-band wideband receiver front-end

A 40-60 GHz wideband receiver front-end, consisting of a two-stage low noise amplifier (LNA) and a downconverter, is presented. The downconverter comprises a subharmonic mixer with a pair of anti-parallel diodes in asymmetric bilateral finline, and a local oscillator (LO) doubler-amplifier chain to provide the necessary LO power to the subharmonic mixer. Thus the U-band front-end operates with a LO frequency range of 10-15 GHz. The circuits are fabricated on 254 mum thick Rogers Duroid 5880 substrate in hybrid integrated circuit technology. Measurement results show the gain of LNA is around 24 dB, the conversion loss of the downconverter is in the range of 8-15 dB, and the front-end has a conversion gain greater than 6 dB over the range of 40-60 GHz.
N A ] 2 8 D ec 2 01 7 Parallel Active Subspace Decomposition for Scalable and Efficient Tensor Robust Principal Component Analysis

Tensor robust principal component analysis (TRPCA) has received a substantial amount of attention in various fields. Most existing methods, normally relying on tensor nuclear norm minimization, need to pay an expensive computational cost due to multiple singular value decompositions (SVDs) at each iteration. To overcome the drawback, we propose a scalable and efficient method, named Parallel Active Subspace Decomposition (PASD), which divides the unfolding along each mode of the tensor into a columnwise orthonormal matrix (active subspace) and another small-size matrix in parallel. Such a transformation leads to a nonconvex optimization problem in which the scale of nulcear norm minimization is generally much smaller than that in the original problem. Furthermore, we introduce an alternating direction method of multipliers (ADMM) method to solve the reformulated problem and provide rigorous analyses for its convergence and suboptimality. Experimental results on synthetic and real-world data show that our algorithm is more accurate than the state-of-the-art approaches, and is orders of magnitude faster.
Contribution of Reynolds stress distribution to the skin friction in wall-bounded flows

A simple expression is derived of the componential contributions that different dynamical effects make to the frictional drag in turbulent channel, pipe and plane boundary layer flows. The local skin friction can be decomposed into four parts, i.e., laminar, turbulent, inhomogeneous and transient components, the second of which is a weighted integral of the Reynolds stress distribution. It is reconfirmed that the near-wall Reynolds stress is primarily important for the prediction and control of wall turbulence. As an example, the derived expression is used for an analysis of the drag modification by the opposition control and by the uniform wall blowing/suction.
Cross-Listing and Capital Investment Decisions 1

This study examines how cross-listing in the U.S. relates to the efficiency of capital investment decisions. We find that U.S. cross-listed firms exhibit greater investment efficiency than foreign firms not cross-listing in the U.S. We then explore whether this finding is related to the type of cross-listing (exchange vs. non-exchange), the strength of the home country disclosure environment, and the level of analyst following. We observe a stronger association between cross-listing and investment efficiency for exchange-listed ADRs, subject to U.S. disclosure and enforcement requirements, than for non-exchange listed ADRs. We further find that this association is largely concentrated in exchange-listed firms from weaker home country disclosure environments where the potential benefits of cross-listing in the U.S. are greater. Finally, we document that the association between cross-listing and investment efficiency is generally stronger for firms with high analyst following. Additional sensitivity tests indicate the findings continue to hold when controlling for potential endogeneity using the Heckman procedure, a propensity matched sample, adding additional controls for earnings quality and level of disclosure, and weighting observations from each country equally. Our findings provide new evidence regarding the possible internal benefits to cross-listing. For instance, we provide evidence that actual managerial decisions, specifically capital investment efficiency, are associated with cross-listing.
Task Management under Change and Uncertainty Constraint Solving Experience with the CALO Project

The goal of CALO (Cognitive Assistant that Learns and Organizes) is to design an automated personal assistant to support a busy high-level knowledge worker. Operating in an inherently dynamic and uncertain domain, CALO relies on constraint technology in several components of its architecture. We outline the challenges and opportunities presented by constraint solving in the presence of change and uncertainty, embodied in CALO’s personalized time management and task reasoning and execution systems.
Deep learning in acoustic modeling for Automatic Speech Recognition and Understanding - an overview -

This paper will discuss the progress made in Automatic Speech Recognition and Understanding (ASRU) by applying Deep Learning (DL) in the frame of acoustic modeling. After explaining the concept of DL, specific algorithms like Restricted Bolzmann Machine (RBM), Convolutional Neural Network (CNN), Autoencoder (AE), Deep Belief Network (DBN), will be presented and evaluated. Experiments in the academic research but also in the industry with DL structures concerning Phone Recognition and Large Vocabulary Continuous Speech Recognition (LVCSR) will be highlighted, confirming the usefulness of the DL framework in ASRU. Some considerations about the future of this new and effective machine learning paradigm will conclude the paper.
Motivations of Organizational Citizenship Behavior's Altruism: Through the Perspective of Guanxi

Despite the growing number of studies on the motivations of organizational citizenship behavior(OCB) in work context, there are few studies which take Chinese culture into account such as guanxi. Based on literature review and theoretical analysis, authors discuss the motivations of OCB's altruism from a perspective of guanxi in Chinese organizations and argue OCB can be triggered by al truistic or self-interested motivations that are produced by Confucianism and guanxi culture. By this way, it is necessary to provide reference to expand the future researches on OCB and improve the practice of management in Chinese context.
Event Processing and Stream Reasoning with ETALIS

Event Processing (EP) is concerned with detection of situations under time constraints that are of a particular business interest. We face today a paradigm shift toward the real time information processing, and EP has therefore spawned significant attention in science and technology. Due to omnipresence of events, EP is becoming a central aspect of new distributed systems such as cloud computing and grid systems, mobile and sensor-based systems, as well as a number of application areas including financial services, business intelligence, social and collaborative networking, click stream analysis and many others. However, there are a number of issues to be considered in order to enable effective event-based computation. A language for describing event patterns needs to feature a well-defined semantics. It also needs to be rich enough to express important classes of event patterns. Pattern matching should be supported in both, query-driven and eventdriven modes. A number of other event operations, such as event aggregation, filtering, translation, enrichment and splitting, should be supported too. Since EP is a real time processing task, an EP language needs to feature an efficient execution model. Finally, processing only events is not sufficient in many applications. To detect complex situations of interest, EP needs to be enhanced by background knowledge. This knowledge captures the domain of interest. Its purpose is to be evaluated during detection of events in order to on the fly enrich events with relevant background information; to detect more complex situations; to reason about events and propose certain intelligent recommendations; or to accomplish event classification, clustering, filtering and so forth. The ETALIS Language for Events (ELE) is a declarative rule-based language for EP. It supports the above mentioned features, and goes beyond the state of the art by providing stream reasoning capabilities. In this thesis, we first review related literature and extract requirements for modern EP systems. Then we present ELE as a novel expressive formalism that fulfils these requirements. Further on, we show how deductive stream reasoning capabilities of ELE, together with its EP capabilities, have the potential to provide powerful real time intelligence. We give a few extensions of the core ELE. A number of examples and use case scenarios are developed to show the power of the proposed EP framework. We provide a prototype implementation of the language, and present evaluation results for implemented scenarios. Finally, we summarise the results of this thesis and outline our view of the emerging future work.
Networks of Gene Sharing among 329 Proteobacterial Genomes Reveal Differences in Lateral Gene Transfer Frequency at Different Phylogenetic Depths

Lateral gene transfer (LGT) is an important mechanism of natural variation among prokaryotes. Over the full course of evolution, most or all of the genes resident in a given prokaryotic genome have been affected by LGT, yet the frequency of LGT can vary greatly across genes and across prokaryotic groups. The proteobacteria are among the most diverse of prokaryotic taxa. The prevalence of LGT in their genome evolution calls for the application of network-based methods instead of tree-based methods to investigate the relationships among these species. Here, we report networks that capture both vertical and horizontal components of evolutionary history among 1,207,272 proteins distributed across 329 sequenced proteobacterial genomes. The network of shared proteins reveals modularity structure that does not correspond to current classification schemes. On the basis of shared protein-coding genes, the five classes of proteobacteria fall into two main modules, one including the alpha-, delta-, and epsilonproteobacteria and the other including beta- and gammaproteobacteria. The first module is stable over different protein identity thresholds. The second shows more plasticity with regard to the sequence conservation of proteins sampled, with the gammaproteobacteria showing the most chameleon-like evolutionary characteristics within the present sample. Using a minimal lateral network approach, we compared LGT rates at different phylogenetic depths. In general, gene evolution by LGT within proteobacteria is very common. At least one LGT event was inferred to have occurred in at least 75% of the protein families. The average LGT rate at the species and class depth is about one LGT event per protein family, the rate doubling at the phylum level to an average of two LGT events per protein family. Hence, our results indicate that the rate of gene acquisition per protein family is similar at the level of species (by recombination) and at the level of classes (by LGT). The frequency of LGT per genome strongly depends on the species lifestyle, with endosymbionts showing far lower LGT frequencies than free-living species. Moreover, the nature of the transferred genes suggests that gene transfer in proteobacteria is frequently mediated by conjugation.
Evidence for cure by adjuvant therapy in colon cancer: observations based on individual patient data from 20,898 patients on 18 randomized trials.

PURPOSE Limited data are available on the time course of treatment failures (recurrence and/or death), the nature and duration of adjuvant treatment benefit, and long-term recurrence rates in patients with resected stage II and III colon cancer. METHODS The data set assembled by the Adjuvant Colon Cancer Endpoints Group, a collection of individual patient data from 18 trials and more than 20,800 patients testing fluorouracil-based adjuvant therapy in patients with stage II or III colon cancer, was analyzed. RESULTS A significant overall survival (OS) benefit of adjuvant therapy was consistent over the 8-year follow-up period. The risk of recurrence in patients treated with adjuvant chemotherapy never exceeds that of control patients, signifying that adjuvant therapy cures some patients, as opposed to delaying recurrence. After 5 years, recurrence rates were less than 1.5% per year, and after 8 years, they were less than 0.5% per year. Significant disease-free survival (DFS) benefit from adjuvant chemotherapy was observed in the first 2 years. After 2 years, DFS rates in treated and control patients were not significantly different, and after 4 years, no trend toward benefit was demonstrated. This benefit was primarily driven by patients with stage III disease. CONCLUSION Adjuvant chemotherapy provides significant DFS benefit, primarily by reducing the recurrence rate, within the first 2 years of adjuvant therapy with some benefit in years 3 to 4, translating into long-term OS benefit. This reflects the curative role of chemotherapy in the adjuvant setting. After 5 years, recurrence rates in patients treated on clinical trials are low, and after 8 years, they are minimal; thus, long-term follow-up for recurrence is of little value.
Knowledge discovery through visualising using virtual reality

Knowledge discovery and visualisation are important techniques for discovering and understanding patterns in large data sets. The study presents the development of a three dimensional virtual reality model that assists the user to visually explore structures and relationships in the collected data. The three dimensional model uses a combination of nodes and paths to represent objects/actors and the strength/direction of the measured characteristic. Virtual reality provides the mechanism for manipulation of the developed model in real time. Specifically, we demonstrate the potential of this visual tool by having postgraduate students, organised into focus groups, engage in a knowledge discovery exercise with data collected in an academic work environment. The method adopted to convert the identified relationships into a virtual reality model is discussed, as well as some of the additional features that could be incorporated using such a method.
Forecasting Depression in Bipolar Disorder

Bipolar disorder is characterized by recurrent episodes of mania and depression and affects about 1% of the adult population. The condition can have a major impact on an individual's ability to function and is associated with a long-term risk of suicide. In this paper, we report on the use of self-rated mood data to forecast the next week's depression ratings. The data used in the study have been collected using SMS text messaging and comprises one time series of approximately weekly mood ratings for each patient. We find a wide variation between series: some exhibit a large change in mean over the monitored period and there is a variation in correlation structure. Almost half of the time series are forecast better by unconditional mean than by persistence. Two methods are employed for forecasting: exponential smoothing and Gaussian process regression. Neither approach gives an improvement over a persistence baseline. We conclude that the depression time series from patients with bipolar disorder are very heterogeneous and that this constrains the accuracy of automated mood forecasting across the set of patients. However, the dataset is a valuable resource and work remains to be done that might result in clinically useful information and tools.