Miracle-dz/UniFAR-FTUs huggingface dataset:
Roles of Perceived Behavioral Control and Self-Efficacy to Volunteer Tourists

Volunteer tourism, as an alternative to mass tourism, has grown significantly since the 1970s, sparking a growing research interest in the subject. However, little research has been conducted about Asian volunteer tourists. The purpose of this study is to compare the strength of perceived behavioral control with self-efficacy to predict volunteer tourists' intentions within the theory of planned behavior. Meta-analysis is also used to examine the effect size of the independent variables derived from the theory of planned behavior. Study results indicate that self-efficacy is a stronger predictor than perceived behavioral control in predicting volunteer tourists' future behavioral intentions.
An adaptive dictionary encoding scheme for SOC data buses

As bus lengths on multi-hundred-million transistor SOCs (systems-on-a-chip) grow and as inter-wire capacitances of sub-0.1 /spl mu/m technologies increase, the resulting high switching capacitances of buses (and interconnects in general) have a non-negligible impact on the power consumption of a whole SOC. In this paper, we address this problem by introducing our bus encoding technique 'ADES' that minimizes the power consumption of data buses through a dictionary-based encoding technique. We show that our technique saves between 18% and 40% of bus energy compared to the non-encoded cases using a large set of (freely-accessible) real-world applications. Furthermore, we compare our technique to the best-known data bus encoding techniques to date and show that it exceeds all of them in energy savings for the same set of applications. The additional hardware effort for our bus en/decoder is thereby very small.
The Prediction on Exercise Behavior of College Student's Exercise Attitude

In order to explore the current situation of college students' attitudes towards exercises,and the relationship between attitudes towards exercises and exercise behavior,this paper,adopted the Scale Of Attitudes Towards Exercises,Physical Activity Rating Scale-3,tests 891 college students from 7 colleges of Heilongjiang Province from grade one to grade four,and attains main conclusions as follows:Generally speaking,the physical exercise attitude of college students is more positive because of their higher total score,and the score of the rest of scale is higher than average level except behavior intention and perceived behavior control.The behavior attitudes,behavior intent,feeling of exercise,and subject standard of male college students are significantly higher than that of female students;however,it is reversed on behavior cognition.There was a significant difference in different grade students at sub-scales about behavior habit,feeling of exercise,and perceived behavior control.Male college students are more intensive,frequent,and longer than female college students.Junior students was significantly higher than freshmen and senior students in training time;sophomore and junior students was significantly higher than freshmen in the exercise frequency,the sophomore students were significantly higher than junior and senior students in exercise intensity.The attitudes towards exercises can effectively predict college students' exercise behavior including exercise intensity,exercise time and exercise frequency.
Predicting Local Quality of a Sequence-Structure Alignment

Although protein structure prediction has made great progress in recent years, a protein model derived from automated prediction methods is subject to various errors. As methods for structure prediction develop, a continuing problem is how to evaluate the quality of a protein model, especially to identify some well-predicted regions of the model, so that the structural biology community can benefit from the automated structure prediction. It is also important to identify badly-predicted regions in a model so that some refinement measurements can be applied to it. We present two complementary techniques, FragQA and PosQA, to accurately predict local quality of a sequence-structure (i.e. sequence-template) alignment generated by comparative modeling (i.e. homology modeling and threading). FragQA and PosQA predict local quality from two different perspectives. Different from existing methods, FragQA directly predicts cRMSD between a continuously aligned fragment determined by an alignment and the corresponding fragment in the native structure, while PosQA predicts the quality of an individual aligned position. Both FragQA and PosQA use an SVM (Support Vector Machine) regression method to perform prediction using similar information extracted from a single given alignment. Experimental results demonstrate that FragQA performs well on predicting local fragment quality, and PosQA outperforms two top-notch methods, ProQres and ProQprof. Our results indicate that (1) local quality can be predicted well; (2) local sequence evolutionary information (i.e. sequence similarity) is the major factor in predicting local quality; and (3) structural information such as solvent accessibility and secondary structure helps to improve the prediction performance.
Computing the Nearest Neighbor Transform Exactly with Only Double Precision

The nearest neighbor transform of a binary image assigns to each pixel the index of the nearest black pixel - it is the discrete analog of the Voronoi diagram. Implementations that compute the transform use numerical calculations to perform geometric tests, so they may produce erroneous results if the calculations require more arithmetic precision than is available. Liotta, Preparata, and Tamassia, in 1999, suggested designing algorithms that not only minimize time and space resources, but also arithmetic precision. A simple algorithm using double precision can compute the nearest neighbor transform: compare the squared distances of each pixel to all black pixels, but this is inefficient when many pixels are black. We develop and implement efficient algorithms, computing the nearest neighbor transform of an image in linear time with respect to the number of pixels, while still using only double precision.
A proposal to monitor muscle contraction through the change of electrical impedance inside a muscle

Dynamically monitoring muscle contraction is an important task in sports training and rehabilitation programs. Internal muscles monitoring are specially challenging because they cannot be accessed by palpation or surface EMG. It is well known that blood flow through skeletal muscle can increase 15-to 25-fold during extreme exercise compared to its rest state. Additionally, at rest, some muscle capillaries have little or no blood flowing, but during strenuous exercise, all the capillaries open. With regard to electrical properties, blood resistivity is about half of muscle resistivity. Thus, global muscle resistivity have a tendency to change when it goes from rest to activity state. Recent results suggest that resistivity changes are related to contraction rate. Bearing that in mind, Electrical Impedance Tomography turns out to be an interesting signal to monitor muscle contraction by sensing resistivity changes related to muscle activity. If low intensity current is injected and electric potentials are measured through electrodes attached on skin, resistivity maps can be obtained using this data to solve an ill-posed inverse problem. In this work the electrical impedance was measured while a muscle was under repetitive contractions. Significant changes of electrical impedance were found in the frequency of contractions.
Destroy Stock Return Predictability ?

We study the out-of-sample and post-publication return-predictability of 82 characteristics that are identified in published academic studies. The average out-of-sample decay due to statistical bias is about 10%, but not statistically different from zero. The average post-publication decay, which we attribute to both statistical bias and price pressure from aware investors, is about 35%, and statistically different from both 0% and 100%. Our findings point to mispricing as the source of predictability. Post-publication, stocks in characteristic portfolios experience higher volume, variance, and short interest, and higher correlations with portfolios that are based on published characteristics. Consistent with costly (limited) arbitrage, post-publication return declines are greater for characteristic portfolios that consist of stocks with low idiosyncratic risk.
Computational antisense oligo prediction with a neural network model

MOTIVATION The expression of a gene can be selectively inhibited by antisense oligonucleotides (AOs) targeting the mRNA. However, if the target site in the mRNA is picked randomly, typically 20% or less of the AOs are effective inhibitors in vivo. The sequence properties that make an AO effective are not well understood, thus many AOs need to be tested to find good inhibitors, which is time consuming and costly. So far computational models have been based exclusively on RNA structure prediction or motif searches while ignoring information from other aspects of AO design into the model. RESULTS We present a computational model for AO prediction based on a neural network approach using a broad range of input parameters. Collecting sequence and efficacy data from AO scanning experiments in the literature generated a database of 490 AO molecules. Using a set of derived parameters based on AO sequence properties we trained a neural network model. The best model, an ensemble of 10 networks, gave an overall correlation coefficient of 0.30 (p=10(-8)). This model can predict effective AOs (>50% inhibition of gene expression) with a success rate of 92%. Using these thresholds the model predicts on average 12 effective AOs per 1000 base pairs, making it a stringent yet practical method for AO prediction.
6D object localization and obstacle detection for collision-free manipulation with a mobile service robot

The main goal of mobile service robots is to assist human beings in everyday life. Instead of limiting treatment to just artificial, structured lab areas, such systems must in particular be able to operate well in human, unstructured environments. In this paper we present an approach to recognize and localize as well as reliably and robustly manipulate objects in the presence of obstacles. Our method was implemented, fully integrated and presented on the 2009 CeBIT technology show in Hanover, Germany using the experimental robot of the DESIRE project.
The Westerbork SINGS survey - II Polarization, Faraday rotation, and magnetic fields

A sample of large northern Spitzer Infrared Nearby Galaxies Survey (SINGS) galaxies has recently been observed with the Westerbork Synthesis Radio Telescope (WSRT). We present observations of the linearly polarized radio continuum emission in this WSRT-SINGS galaxy sample. Of the 28 galaxies treated in this paper, 21 are detected in polarized radio continuum at 18- and 22-cm wavelengths. We utilize the rotation measure synthesis (RM-Synthesis) method, as implemented by Brentjens & de Bruyn (2005, A&A, 441, 1217), to coherently detect polarized emission from a large fractional bandwidth, while simultaneously assessing the degree of Faraday rotation experienced by the radiation along each line-of-sight. This represents the first time that the polarized emission and its Faraday rotation have been systematically probed down to ~10 μ Jy beam -1 RMs for a large sample of galaxies. Non-zero Faraday rotation is found to be ubiquitous in all of the target fields, from both the Galactic foreground and the target galaxies themselves. In this paper, we present an overview of the polarized emission detected in each of the WSRT-SINGS galaxies. The most prominent trend is a systematic modulation of the polarized intensity with galactic azimuth, such that a global minimum in the polarized intensity is seen toward the kinematically receding major axis. The implied large-scale magnetic field geometry is discussed in a companion paper. A second novel result is the detection of multiple nuclear Faraday depth components that are offset to both positive and negative RM by 100–200 rad m -2 in all targets that host polarized (circum-)nuclear emission.
Strategic system comparisons via targeted relevance judgments

Relevance judgments are used to compare text retrieval systems. Given a collection of documents and queries, and a set of systems being compared, a standard approach to forming judgments is to manually examine all documents that are highly ranked by any of the systems. However, not all of these relevance judgments provide the same benefit to the final result, particularly if the aim is to identify which systems are best, rather than to fully order them. In this paper we propose new experimental methodologies that can significantly reduce the volume of judgments required in system comparisons. Using rank-biased precision, a recently proposed effectiveness measure, we show that judging around 200 documents for each of 50 queries in a TREC-scale system evaluation containing over 100 runs is sufficient to identify the best systems.
Using UIML to Automate Generation of Usability Prototypes and Tactical Software

Human Computer Interface (HCI) design and implementation for advanced tactical systems is a complex process. With the growing need for systems to be interoperable across the Services, it has become necessary to devise innovative methods of creating, importing, and exporting HCIs while maintaining the capability to quickly and efficiently transition due to system/software upgrades. We discuss the User Interface Markup Language (UIML), which is an open eXtensible Markup Language (XML)-based language intended as a universal interchange language between tools, and LiquidUI, which is a comprehensive HCI engineering tool suite funded in part by the Navy to address the issues above as well as others.
Undo as concurrent inverse in group editors

As an important mechanism for error recovery and exploration of alternatives in interactive and collaborative applications, an undo facility should have the capability of undoing any operation at any time. However, supporting undo in collaborative applications is technically challenging and none of the existing group undo solutions is able to offer such a capability. In this article, we contribute an undo solution with such a capability for group text editors. The basic idea is to interpret an undo command as a concurrent inverse operation by means of operational transformation. To cope with the high complexity of group undo, a generic undo framework has been adopted to separate undo policy from the undo mechanism and to separate transformation control algorithms from transformation functions. The proposed undo solution consists of a generic transformation control algorithm that is capable of generating, transforming, and representing valid inverse operations in any context, and a set of transformation functions that are capable of preserving undo-related transformation conditions and properties. Formal proofs are provided to show the correctness of the undo transformation control algorithm in achieving the required undo effect, undo property, and consistency properties. Solutions to the known undo puzzles are provided to show soundness of the transformation functions. A Web-based group text editor REDUCE (REal-time Distributed Unconstrained Cooperative Editing) has been implemented to demonstrate the feasibility and usability of the proposed undo and other technical solutions. The proposed undo solution is generally applicable to collaborative applications that support concurrent insertion and deletion on shared documents consisting of one or multiple dimensions of linearly ordered data objects with positional references.
Particle Filtering

Optimal filtering: The filtering problem involves the estimation of the state vector at time k, given all the measurements up to and including time k, which we denote by z1:k. In a Bayesian setting, this problem can be formalized as the computation of the distribution p(xk|z1:k), which can be done recursively in two steps. In the prediction step, p(xk|z1:k−1) is computed from the filtering distribution p(xk−1|z1:k−1) at time k − 1:
Interactive Simulation and Rendering of Heterogeneous Deformable Bodies

In this paper, we present an interactive method for physics-based simulation and rendering of deformable bodies exhibiting heterogeneous material properties. To fully exploit the capabilities of consumer class computer systems, a CPU simulation engine is run in parallel with a GPU render engine. On the simulation side, we have developed a numerical multigrid solver for the governing equations of motion of deformable bodies. This solver allows for the combination of implicit solution methods and varying material parameters into an unconditionally stable simulation support system. The deformable model is rendered using a high-resolution surface mesh, which is subsequently updated on the GPU to exploit parallelism and memory bandwidth. Rendering of the deformed mesh can thus be performed without any additional bus-transfer. The interactive system is open to a variety of different applications ranging from surgery simulation, medical imaging and solid mechanics to computer animation and virtual sculpting.
Batch Mode Active Learning for Object Detection Based on Maximum Mean Discrepancy

Various active learning methods have been proposed for image classification problems, while very little work addresses object detection. Measuring the informativeness of an image based on its object windows is a key problem in active learning for object detection. In this paper, an image selection method to select the most representative images is proposed based on measuring their object window distributions by Maximum Mean Discrepancy (MMD). Then an active learning method for object detection is introduced based on MMD-based image selection. Experimental results show that MMD-based image selection can improve object detection performance compared to random image selection. The proposed active learning method based on MMD image selection also outperforms a classical active learning method and passive learning method.
Spiral of Political Learning: The Reciprocal Relationship of News Media Use and Political Knowledge Among Adolescents

This study investigates the dynamics of the reciprocal influence of political knowledge and attentive news use. News media are an important source for political information and contribute to political learning. Yet, this process is optimized with increasing levels of pre-existing knowledge about the political world. In extant literature, mutual interdependence is often suggested, but empirical proof is scarce. We propose to conceptualize the relationship of knowledge and news use as an upward spiral. The model is tested on data from a three-wave panel survey among 888 adolescents using growth curve modeling. The results support the model of a spiral of political learning. Interestingly, the influence of political knowledge on news use is estimated to be higher than the other way round.
An architecture trade study for passive 10-km soil moisture measurements from low-Earth orbit

In 1999 NASA HQ, as a result of an internal NASA study on potential Earth Science Enterprise Post-2002 Missions, directed the hydrology community to focus on achieving a 10-km spatial resolution global soil moisture mission. This type of resolution represents a significant technological challenge for an L-band radiometer in Sun-synchronous low-Earth orbit. An engineering trade study has been completed to determine alternative system configurations that could achieve the science requirements and to identify the most appropriate technology investments and development path for NASA to pursue in order to bring about such a mission. The results of the study are presented here together with a short discussion of future efforts.
Do We Choose What We Desire? - Persuading Citizens to Make Consistent and Sustainable Mobility Decisions

A dilemma in urban mobility with tremendous effects on citizens’ wellbeing is the unconscious antipode between their shortand long-term goals. People do not anticipate all consequences of their modal choices and thus make decisions that might be incoherent with their desires, e.g. taking their own car due to convenience but causing a congested city. Omnipresent Information Systems on smartphones provide the necessary information and coordination capabilities to support people for sustainable and individually coherent mobility decisions on a mass scale. Building upon extant work in travel behavior and social psychology, a framework is proposed to coordinate research efforts in the development of persuading measures for sustainable mobility decisions. This framework accounts for user heterogeneity, motivation and wellbeing as influential dimensions in the mobility decision process. Tied to social influence the derived measures contribute to a behavioral change in people’s mobility behavior leading to a higher wellbeing level in urban areas.
CAIS: Community Based Annotation Insight Search in a Folksonomy Network

Folksonomy systems provide a way for users to share and organize bookmarks. The social relationship among users has become stronger with the rapid development of new technologies. Finding the leading objects has become an important topic. These research topics are always centered around finding the most popular pages or experts. In this paper, we propose a new notion of expertise, which we call user insight. User insight denotes the user¡¦s expertise in finding Web pages that are useful or have the potential to be popular pages before other users find them. To address the issue, we refer to three major types of Web pages, namely, isolated, well-known, and burgeoning. Burgeoning pages are exceptionally useful and attractive for users in a folksonomy system. In our paper, we build a time-based algorithm to estimate user insight. In addition, we discuss the social relationship within fan networks, and we propose a link-based algorithm called CAIS (Community-based Annotation Insight Search) to realize the reinforcement between users, communities and pages. Finally, we design several experiments to evaluate the performance of CAIS and compare it to other approaches. We prove that CAIS has a better performance for the user ranking of simulated data and real data from Delicious.
The TORNADO1 and TORNADO2 Genes Function in Several Patterning Processes during Early Leaf Development in Arabidopsis thaliana[W]

In multicellular organisms, patterning is a process that generates axes in the primary body plan, creates domains upon organ formation, and finally leads to differentiation into tissues and cell types. We identified the Arabidopsis thaliana TORNADO1 (TRN1) and TRN2 genes and their role in leaf patterning processes such as lamina venation, symmetry, and lateral growth. In trn mutants, the leaf venation network had a severely reduced complexity: incomplete loops, no tertiary or quaternary veins, and vascular islands. The leaf laminas were asymmetric and narrow because of a severely reduced cell number. We postulate that the imbalance between cell proliferation and cell differentiation and the altered auxin distribution in both trn mutants cause asymmetric leaf growth and aberrant venation patterning. TRN1 and TRN2 were epistatic to ASYMMETRIC LEAVES1 with respect to leaf asymmetry, consistent with their expression in the shoot apical meristem and leaf primordia. TRN1 codes for a large plant-specific protein with conserved domains also found in a variety of signaling proteins, whereas TRN2 encodes a transmembrane protein of the tetraspanin family whose phylogenetic tree is presented. Double mutant analysis showed that TRN1 and TRN2 act in the same pathway.