Miracle-dz/UniFAR-FTUs huggingface dataset:
Competitive and Non-Competitive Goal Structures: An Analysis of Motivation and Achievement among Elementary Band Students

This study examined the effects of competitive and non-competitive goal structures, level of music self-esteem, and pre-post administration on dependent measures of student achievement and motivation in music. Forty- eight, 5th- and 6th-grade band students were randomly assigned to either a competitive (rewards based on absolute standards) or non-competitive (all participants rewarded for typing their best) goal structure. Goal structures were operationalised as music contest settings; all students prepared and performed accompanied solos for adjudication. Pre-test and post-test data were obtained for two measures of music achievement (aural-visual discrimination, performance) and two measures of music motivation (attribution orientation, intrinsic interest). Data were analysed using split-plot analysis of variance (ANOVA) and analysis of covariance (ANCOVA) procedures with an alpha significance level of t)S. Results indicated no significant goal structure effects. Music self-esteem did have significant effects on motivation outcomes, but not on achievement. Similarly, there were significant pre-test versus post-test differences for motivation, but not achievement. Students' future goal structure preference (competitive versus non-competitive) was significantly related to the type of goal structure they had experienced within this study. Implications of these findings are discussed.
Bridging the semantic gaps of GPU acceleration for scale-out CNN-based big data processing: Think big, see small

Convolutional Neural Networks (CNNs) have substantially advanced the state-of-the-art accuracies of object recognition, which is the core function of a myriad of modern multimedia processing techniques such as image/video processing, speech recognition, and natural language processing. GPU-based accelerators gained increasing attention because a large amount of highly parallel neurons in CNN naturally matches the GPU computation pattern. In this work, we perform comprehensive experiments to investigate the performance bottlenecks and overheads of current GPU acceleration platform for scale-out CNN-based big data processing. In our characterization, we observe two significant semantic gaps: framework gap that lies between CNN-based data processing workflow and data processing manner in distributed framework; and the standalone gap that lies between the uneven computation loads at different CNN layers and fixed computing capacity provisioning of current GPU acceleration library. To bridge these gaps, we propose D3NN, a Distributed, Decoupled, and Dynamically tuned GPU acceleration framework for modern CNN architectures. In particular, D3NN features a novel analytical model that enables accurate time estimation of GPU accelerated CNN processing with only 5-10% error. Our evaluation results show the throughput of standalone processing node using D3NN gains up to 3.7× performance improvement over current standalone GPU acceleration platform. Our CNN-oriented GPU acceleration library with built-in dynamic batching scheme achieves up to 1.5× performance improvement over the non-batching scheme and outperforms the state-of-the-art deep learning library by up to 28% (performance mode) ~ 67% (memory-efficient mode).
Tangent space discriminant analysis for feature extraction

In this paper, a novel method called tangent space discriminant analysis is proposed for dimensionality reduction and feature extraction. Differing from the recently proposed manifold learning methods completely operating on raw feature space, TSDA completely uses the local tangent space to represent the local within-class geometry and local between-class geometry. Assume that the face images of different people reside on different intrinsically low-dimensional sub-manifolds, TSDA is developed to preserve the locality of each sub-manifold and simultaneously maximize the local separability of different sub-manifolds by using local tangent space alignment. Experimental results show that TSDA achieves higher recognition rates than a few the state-of-the-art techniques.
Computer-mediated communication: writing to speak without foreign language anxiety?

This dissertation is based on a research study that investigates the relationship between computer-mediated communication (CMC) and foreign language (FL) anxiety. While all four skills (reading, writing, listening, speaking) can trigger anxiety, speaking in particular has been found to make learners anxious. Since FL anxiety is usually associated with avoidance behavior (e.g. lack of classroom participation, missing class), it can be difficult for anxious learners to learn to use the FL communicatively. Research in the field of CMC has suggested that electronic discussions can be especially beneficial for students suffering from FL anxiety because they are thought to provide a low-pressure environment. Participants in this study were enrolled in five sections of third-semester German at the University of Texas at Austin. Over the course of one semester, six small-group discussions were scheduled in each section, which were conducted in three different modalities. For the treatment groups, two sections each participated either in synchronous (n = 21) or asynchronous CMC (n = 23). A fifth section of third semester German was chosen as the control group and engaged in face-to-face discussions (n = 12). This study was designed as a repeated-measures study, with pretests and posttests, to collect quantitative as well as qualitative data in order to investigate the following research questions: (1) How, if at all, do the different treatments types affect learners' anxiety and self-confidence in using German? and (2) Does the modality of interaction have any effect on students' changes in FL anxiety levels? The major findings of this study were fourfold. First, quantitative analyses revealed that students' self-ratings of their anxiety and self-confidence levels during each discussion did not change significantly differently between the three treatment groups. The study did collect, however, insightful qualitative data providing a list of possible explanations for the difference in ratings, such as group dynamics, technical difficulties and the topic. Second, a pre- and posttest comparison showed a significant reduction in anxiety about speaking German across all three treatment groups. Again, the modality did not have any effect on these changes; rather, this changed appears to be due to the practice the treatment discussions provided. Third, participants who initially suffered from high anxiety levels experienced a significantly greater reduction in FL anxiety than students with initially low or medium levels of anxiety. Fourth, students became less concerned with formal accuracy; instead, their awareness of the communicative aspects of the discussions grew and affected their anxiety and self-confidence levels.
Quality in trauma care: improving the discharge procedure of patients by means of Lean Six Sigma.

BACKGROUND The University Medical Center Groningen is a level I trauma center in the northern part of the Netherlands. Sixty-three percent of all the patients admitted at the Trauma Nursing Department (TND) are acute patients who are admitted directly after trauma. In 2006 and 2007, the University Medical Center Groningen was not always capable of admitting all trauma patients to the TND due to the relatively high-bed occupation. Therefore, the reduction of the average length of stay (LOS) formed the objective of the project described in this study. METHODS We used the process-focused method of Lean Six Sigma to reduce hospital stay by improving the discharge procedure of patients in the care processes and eliminating waste and waiting time. We used the "Dutch Appropriateness Evaluation Protocol" to identify the possible causes of inappropriate hospital stay. The average LOS of trauma patients at the TND at the beginning of the project was 10.4 days. RESULTS Thirty percent of the LOS was unnecessary. The main causes of the inappropriate hospital stay were delays in several areas. The implementation of the improvement plan reduced almost 50% of the inappropriate hospital stay, enabling the trauma center to admit almost all trauma patients to the TND. After the implementation of the improvements, the average LOS was 8.5 days. CONCLUSION Our study shows that Lean Six Sigma is an effective method to reduce inappropriate hospital stay, thereby improving the quality and financial efficiency of trauma care.
Driving the Adoption of Enterprise Architecture Inside Small Companies - Lessons Learnt from a Long Term Case Study

Including Enterprise Architecture (EA) as part of an organisationâs processes is an important milestone in reaching higher maturity levels because it will drive the a long term alignment of IT and business dimensions. This paper explores some important questions related to the introduction of EA inside very small enterprises or entities (VSE), referring to the very common situation where the IT department has limited resources even possibly in a larger organisation. We provide a number of elements of answers on how EA can successfully be deployed in VSE based on a study of approaches already developed by others complemented by our extensive experience in helping such kind of companies to improve their IT practices and adopt EA. The paper is illustrated on a multi-year case study. It has also a special focus on the ISO29110 standard directed towards VSE and possible ways to evolve it to take EA into account in more advanced maturity profiles under preparation.
Exploiting Implementation Diversity and Partial Connection of Routers in Application-Specific Network-on-Chip Topology Synthesis

This paper proposes a novel application-specific Network-on-Chip (NoC) topology synthesis method, in which the partial connection and the implementation diversity of routers are exploited. NoC has emerged as a promising solution to future system-on-chip (SoC), and many researchers have focused on the automatic synthesis of NoC topology. In our observation, those NoC topology synthesis methods resemble the logic synthesis in the following sense: both the NoC topology synthesis and the logic synthesis determine the connections among the components where the components are the routers in the former and the logic cells in the latter. However, an outstanding difference is that the existing NoC topology synthesis methods consider only a single implementation for each size of router, whereas modern logic synthesis tools utilize multiple implementations of a cell to produce better netlist by the feature called technology mapping. To tackle this drawback, we propose a novel NoC topology synthesis methodology where the implementation diversity of routers is exploited to produce optimal topologies in terms of area and/or power consumption. Two different approaches, the post-process approach and the in-process approach, are proposed for exploiting the implementation diversity to provide flexibility between synthesis time and design quality. Also, the proposed method for characterizing and modeling routers makes it feasible to consider the implementation diversity even when the partial connection of routers is considered during the synthesis. Compared to the method in which the implementation diversity is exploited but the partial connection is not, the experimental results demonstrate that the proposed method can reduce the power consumption by up to 67.8% and 40.0% on average. On the other hand, compared to the method in which the partial connection is exploited but the implementation diversity is not, the power consumption is reduced by up to 12.0% and 3.4% on average.
BiNet: Trust Sub-network Extraction Using Binary Ant Colony Algorithm in Contextual Social Networks

Online Social Networks (OSNs) have become an integral part of daily life in recent years. OSNs contain important participants, the trust relations between participants, and the contexts in which participants interact with each other. All of these have a great influence on the prediction of the trust between a source participant and a target participant, which is important for a participant's decision-making process in many applications, such as seeking service providers. However, predicting the trust from a source participant to a target one based on the whole social network is not really feasible. Thus, prior to trust prediction, the extraction of a small-scale sub-network containing most of the important nodes and contextual information with a high density rate could make trust prediction more efficient and effective. However, extracting such a sub-network has been proved to be an NP-Complete problem. To address this challenging problem, we propose BiNet: a social context-aware trust sub-network extraction model to search for near-optimal solutions effectively and efficiently. In this model, we first capture important factors that affect the trust between participants in OSNs. Next, we define a utility function to measure the trust factors of each node in a social network. At last, we design a novel binary ant colony algorithm with newly designed initialization and mutation processes for sub-network extraction incorporating the utility function. The experiments, conducted on two popular datasets of Epinion and Slash dot, demonstrate that our approach can extract sub-networks covering important participants and contextual information while keeping a high density rate. Our approach is superior to the state-of-the-art approaches in terms of the quality of extracted sub-networks within the same execution time.
Monocyte-Directed RNAi Targeting CCR2 Improves Infarct Healing in Atherosclerosis-Prone Mice

Background— Exaggerated and prolonged inflammation after myocardial infarction (MI) accelerates left ventricular remodeling. Inflammatory pathways may present a therapeutic target to prevent post-MI heart failure. However, the appropriate magnitude and timing of interventions are largely unknown, in part because noninvasive monitoring tools are lacking. Here, we used nanoparticle-facilitated silencing of CCR2, the chemokine receptor that governs inflammatory Ly-6Chigh monocyte subset traffic, to reduce infarct inflammation in apolipoprotein E–deficient (apoE−/−) mice after MI. We used dual-target positron emission tomography/magnetic resonance imaging of transglutaminase factor XIII (FXIII) and myeloperoxidase (MPO) activity to monitor how monocyte subset–targeted RNAi altered infarct inflammation and healing. Methods and Results— Flow cytometry, gene expression analysis, and histology revealed reduced monocyte numbers and enhanced resolution of inflammation in infarcted hearts of apoE−/− mice that were treated with nanoparticle-encapsulated siRNA. To follow extracellular matrix cross-linking noninvasively, we developed a fluorine-18–labeled positron emission tomography agent (18F-FXIII). Recruitment of MPO-rich inflammatory leukocytes was imaged with a molecular magnetic resonance imaging sensor of MPO activity (MPO-Gd). Positron emission tomography/magnetic resonance imaging detected anti-inflammatory effects of intravenous nanoparticle-facilitated siRNA therapy (75% decrease of MPO-Gd signal; P<0.05), whereas 18F-FXIII positron emission tomography reflected unimpeded matrix cross-linking in the infarct. Silencing of CCR2 during the first week after MI improved ejection fraction on day 21 after MI from 29% to 35% (P<0.05). Conclusion— CCR2-targeted RNAi reduced recruitment of Ly-6Chigh monocytes, attenuated infarct inflammation, and curbed post-MI left ventricular remodeling.
P1G-4 Through-Wall Communication of Low-Rate Digital Data Using Ultrasound

Wireless communication is ineffective for communicating through a solid steel wall due to the shielding effect of the metal. In some cases, holes can be made in the wall to allow wires to pass through, enabling the transport of electronic data. However, holes are often undesirable because they can reduce the integrity of the wall. This paper describes several approaches for using ultrasound to communicate low-rate digital data through a steel wall. The techniques minimize the complexity and power consumption of the communications hardware on the side of the wall from which the data is being sent, supporting applications in which a sensor may be either permanently embedded in a structure or is difficult to reach for servicing. Both pulsed and continuous-wave ultrasound techniques are described. Experimental data is presented showing the performance of the techniques when implemented using 1 MHz transducers mounted on a 15.24 cm (6 inch) thick steel wall. The results show that data rates on the order of 500 bits per second are readily available using simple communications techniques. Higher rates are possible if equalization is used to mitigate the effects of the multipath propagation within the steel block
Streaming Algorithms for Partitioning Integer Sequences

We study the problem of partitioning integer sequences in the one-pass data streaming model. Given is an input stream of integers $X \in \{0, 1, \dots, m \}^n$ of length $n$ with maximum element $m$, and a parameter $p$. The goal is to output the positions of separators splitting the input stream into $p$ contiguous blocks such that the maximal weight of a block is minimized. We show that computing an optimal solution requires linear space, and we design space efficient $(1+\epsilon)$-approximation algorithms for this problem following the parametric search framework. We demonstrate that parametric search can be successfully applied in the streaming model, and we present more space efficient refinements of the basic method. All discussed algorithms require space $O( \frac{1}{\epsilon} \mathrm{polylog} (m,n,\frac{1}{\epsilon}))$, and we prove that the linear dependency on $\frac{1}{\epsilon}$ is necessary for any possibly randomized one-pass streaming algorithm that computes a $(1+\epsilon)$-approximation.
An untapped bacterial cellulolytic community enriched from coastal marine sediment under anaerobic and thermophilic conditions.

A bacterial community with strong cellulose [filter paper (FP) and microcrystalline cellulose] degradation ability was isolated from the coastal marine environment. They were isolated under thermophilic (60 °C) and anaerobic cultivation conditions. The library of 16S rRNA gene clones revealed a total of 16 operational taxonomic units after 50 clones were surveyed. Sixty percent of the clones were most related to the type strain of Clostridium thermocellum with 16S rRNA gene identity around 87-89%. All of them showed extremely low sequence similarities and were novel at least in species level. The gene clone libraries of glycosyl hydrolase family 48 showed low gene and amino acid sequence similarities around 70-72%. The results indicated that the cellulose degradation systems in the specific environment have not been well studied. The enrichment could disrupt FP within 3 days in a basal medium. The cellulase activity of the community was comparable to that of C. thermocellum LQR1. The main fermentation products were ethanol, acetic acid and butyric acid. This work identified a novel microbial resource with a potential in lignocellulose conversion and biofuel production.
Developing a Psychophysical Measure to Assess Duration Discrimination in the Millisecond Range

Duration discrimination in the range of milliseconds is essential for various aspects of behavior and individual differences. The present paper addresses important methodological issues, such as type of stimuli, type of task, method for threshold estimation, and temporal sensitivity of the psychophysical procedure, that should be borne in mind when developing a sensitive and reliable duration discrimination task. Furthermore, it introduces a psychophysical approach for the assessment of individual differences in duration discrimination of extremely brief intervals in the subsecond range. Monte Carlo simulations provide clear evidence that this task is sensitive enough to correctly detect a true difference between temporal stimuli as small as 2 ms with a high probability. Further, the distributional properties of individual performance scores obtained from 534 participants by means of the introduced duration discrimination task are presented.
Faking it

Etude du materialisme metaphysique de Hobbes qui s'appuie sur une connaissance des personnes (S. Cavell) en tant que corps pensants, d'une part, et qui implique une ethique de la dissimulation au niveau de l'action politique collective, d'autre part.
Corpus-based approaches to processing the scope of negation cues: an evaluation of the state of the art

In this paper we summarize existing work on the recently introduced task of processing the scope of negation and modality cues; we analyse the scope model that existing systems can process, which is mainly the model reflected in the annotations of the biomedical corpus on which the systems have been trained; and we point out aspects of the scope finding task that would be different based on observations from a corpus from a different domain and nature.
In vitro generation of mature dopamine neurons by decreasing and delaying the expression of exogenous Nurr1

Neural stem/progenitor cell (NSC/NPC) cultures can be a source of dopamine (DA) neurons for experimental and transplantation purposes. Nurr1, a steroid receptor transcription factor, can overcome the limitations associated with differentiation of cultured NPCs into DA neurons. However, forced Nurr1 expression in NPC cultures generates non-neuronal and/or immature DA cells. We show here that the Nurr1 level and period of expression crucially affect the differentiation and maturation of Nurr1-induced DA neurons. Mature DA neurons were generated by manipulating Nurr1 expression patterns to resemble those in the developing midbrain.
A Triple-Loop Inductive Power Transmission System for Biomedical Applications

A triple-loop wireless power transmission (WPT) system equipped with closed-loop global power control, adaptive transmitter (Tx) resonance compensation (TRC), and automatic receiver (Rx) resonance tuning (ART) is presented. This system not only opposes coupling and load variations but also compensates for changes in the environment surrounding the inductive link to enhance power transfer efficiency (PTE) in applications such as implantable medical devices (IMDs). The Tx was built around a commercial off-the-shelf (COTS) radio-frequency identification (RFID) reader, operating at 13.56 MHz. A local Tx loop finds the optimal capacitance in parallel with the Tx coil by adjusting a varactor. A global power control loop maintains the received power at a desired level in the presence of changes in coupling distance, coil misalignments, and loading. Moreover, a local Rx loop is implemented inside a power management integrated circuit (PMIC) to avoid PTE degradation due to the Rx coil surrounding environment and process variations. The PMIC was fabricated in a 0.35- μm 4M2P standard CMOS process with 2.54 mm2 active area. Measurement results show that the proposed triple-loop system improves the overall PTE by up to 10.5% and 4.7% compared to a similar open- and single closed-loop system, respectively, at nominal coil distance of 2 cm. The added TRC and ART loops contribute 2.3% and 1.4% to the overall PTE of 13.5%, respectively. This is the first WPT system to include three loops to dynamically compensate for environment and circuit variations and improve the overall power efficiency all the way from the driver output in Tx to the load in Rx.