Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of Spirituality, Family, and Self Efficacy on Teacher Effectiveness.

HEFFNER, JUDITH. Influence of Spirituality, Family, and Self Efficacy on Teacher Effectiveness. (Under the direction of Dr. Lance D. Fusarelli.) Spirituality, family influence, and teacher efficacy are the focal points of this research project. Research supports family influence as a powerful support in career selection and the positive effects spirituality has on a teacher’s personal and professional life. Self efficacy possessed by a teacher gives them an “I can do attitude. ” They don’t let children fail. This mixed method research project surveyed National Teacher of the Year winners and finalists on measures of spirituality, family, and self efficacy. Seven participants were interviewed and were located in various regions across the United States, to attain richer data to support the quantitative data gained from the surveys. Previous to responding to the surveys, the participants were asked to answer questions about their background such as education, family, and teaching as a calling, ministry/missionary, and years of teaching. Of the ten questions asked on the spirituality survey, the lowest positive rating was 82.34% while the highest was 100%, 7 of 10 responses were above 96%. The survey on family yielded positive rating from 85.29% to 88.57%, while several respondents rated positively in the 70% range. Respondents rated themselves high in self efficacy at 88.24%. Participants believed their spirituality led them to know themselves; believe life is a positive experience; and feel good about the future. Those respondents to the family survey valued communication between family members, closeness with family members, and the family’s ability to resolve conflict. The self efficacy survey yielded rich data from the participants and elicited these responses about their belief in their ability to affect change, effectively dealing with unexpected events, able to handle whatever comes my way, and solution oriented. © Copyright 2016 by Judith Maples Heffner
Additional file 10: of Genome-wide analysis of lectin receptor-like kinases in Populus

The amino acid sequence alignment and conserved motifs of G-type PtLecRLKs. (A) Amino acid sequence alignment via CLUSTALW. To determine conserved amino acid regions, 50Â % sequence identity was used as a cutoff. Note that the amino acid sequence in the protein kinase domain is highly conserved in most G-type PtLecRLKs whereas the other domains such as S-locus glycoprotein, EGF, PAN, TM and signal peptide varied. Highly conserved motifs are boxed with labels a, b, c, d and e. The red arrows indicate truncated bulb lectin domains. The blue arrows indicate the truncated protein kinase domains. (B) The conserved motif sequences marked on panel A. Sequence logo was generated from the consensus amino acid sequence over 50Â % sequence identity. (PPTX 3107 kb)
Dynamic Hedging with a Deterministic Local Volatility Function Model

We compare the dynamic hedging performance of the deterministic local volatility function approach with the implied/constant volatility method. Using an example in which the underlying price follows an absolute diffusion process, we illustrate that hedge parameters computed from the implied/constant volatility method can have significant error even though the implied volatility method is able to calibrate the current option prices of different strikes and maturities. In particular the delta hedge parameter produced by the implied/constant volatility method is consistently significantly larger than that of the exact delta when the underlying price follows an absolute diffusion. In order to compute a better hedge parameter, accurate estimation of the local volatility function in a region surrounding the current asset price is crucial. We illustrate that a suitably implemented volatility function method can estimate this local volatility function sufficiently accurately to generate more accurate hedge parameters. Hedging using this volatility function for the absolute diffusion example leads to a smaller average absolute hedging error when compared with using the implied/constant volatility rate. When comparing the hedging performance in the S&P 500 index option market as well as the S&P 500 futures option market, we similarly observe that the delta hedge parameter from the implied/constant volatility method is typically greater than that using the volatility function approach. Examination of the hedging error reveals that using a larger delta factor greater than that of the true volatility yields more positive
Preparing to Reach: Selecting an Adaptive Long-Latency Feedback Controller

In a voluntary movement, the nervous system specifies not only the motor commands but also the gains associated with reaction to sensory feedback. For example, suppose that, during reaching, a perturbation tends to push the hand to the left. With practice, the brain not only learns to produce commands that predictively compensate for the perturbation but also increases the long-latency reflex gain associated with leftward displacements of the arm. That is, the brain learns a feedback controller. Here, we wondered whether, during the preparatory period before the reach, the brain engaged this feedback controller in anticipation of the upcoming movement. If so, its signature might be present in how the motor system responds to perturbations in the preparatory period. Humans trained on a reach task in which they adapted to a force field. During the preparatory period before the reach, we measured how the arm responded to a pulse to the hand that was either in the direction of the upcoming field, or in the opposite direction. Reach adaptation produced an increase in the long-latency (45–100 ms delay) feedback gains with respect to baseline, but only for perturbations that were in the same direction as the force field that subjects expected to encounter during the reach. Therefore, as the brain prepares for a reach, it loads a feedback controller specific to the upcoming reach. With adaptation, this feedback controller undergoes a change, increasing the gains for the expected sensory feedback.
Extended Named Entities Annotation on OCRed Documents: From Corpus Constitution to Evaluation Campaign

Within the framework of the Quaero project, we proposed a new definition of named entities, based upon an extension of the coverage of named entities as well as the structure of those named entities. In this new definition, the extended named entities we proposed are both hierarchical and compositional. In this paper, we focused on the annotation of a corpus composed of press archives, OCRed from French newspapers of December 1890. We present the methodology we used to produce the corpus and the characteristics of the corpus in terms of named entities annotation. This annotated corpus has been used in an evaluation campaign. We present this evaluation, the metrics we used and the results obtained by the participants.
Learning Relational Features with Backward Random Walks Submitted for Blind Review

A path learning algorithm (PRA) has been recently proposed that addresses link prediction tasks on heterogenous graphs using learned combinations of labeled paths. Unlike most statistical relational learning methods, this approach scales to large data sets. In this paper, we extend PRA is terms of expressive power, while maintaining its high scalability. Mainly, we propose to compute backward random walks to efficiently model paths that correspond to first order rules with constants in this framework. In addition, we extend PRA to consider long relational paths. An empirical study is conducted on the tasks of graph-based knowledge base inference and coordinate term extraction from parsed text. Our results show that learning paths with constants improves performance on both tasks, and that modeling long paths significantly improves performance on coordinate term extraction. Overall, the enhanced path learning algorithm gives competitive or superior performance both in terms of prediction quality and computing efficiency compared with alternative approaches. Keywords-knowledge-base; random walks; logical inference
Studies on inhibition of H2O2 induced TGF–β1 expression in peripheral blood mononuclear cells by novel pyridine appended lutein derivative

Lutein a hydroxy carotenoid belonging to xanthophyll group having a hydroxylgroup attached to either end of the molecule, making it react more strongly with singlet oxygen than other carotenoids. In the present study, Lutein was modified by appending pyridine moiety for increased antioxidant activity and its ability to inhibit H2O2 induced TGF-β1 expression in peripheral blood mononuclear cells was investigated. Lutein was treated with Isonicotinic acid (Pyridine-4-carboxylic acid) in thepresence of DCC and DMF as solvent medium to obtain pyridine appended Lutein (2.28mg/ml). The pyridine appended Lutein was purified using silica gel-G (60–120mesh) column, further by HPLC and fractions were eluted at the rate of 1ml/min using water, acetonitrile, methanol and dichloromethane as mobile phase in the ratio 0.5:9.5:67.5:22.5. A major peak was obtained at 454nm with retention time of 1.60min. In dose dependent study, Pyridine appended Lutein at 0. 42µmoles showed a maximum of 91.6% of DPPH radical scavenging activity in comparison to unmodified Lutein (80.76%). Unmodified Lutein showed maximum activity (86.18% at 1.25µmoles) with 2.97 folds higher than Pyridine appended Lutein. TGF–β1 expression in H2O2 (3mM) treated peripheral blood mononuclear cells was inhibited by 83.14% (152.1pg/ml) and 76.21% (214.62pg/ml) at 0. 92µmoles of Pyridine appended Lutein and unmodified Lutein respectively compared to H2O2 treated cells (902.15pg/ml) without test samples. In conclusion, Pyridine appended Lutein showed higher DPPH radical scavenging activity and inhibited H2O2 induced TGF–β1 expression in peripheral blood mononuclear cells at low concentrations than unmodified Lutein.
A handwritten Chinese characters recognition method based on sample set expansion and CNN

Convolutional neural networks (CNN) is a powerful technology for classification of visual inputs. However, both the scale and quality of the training set are an important factor to the performance of a learned system. In real applications, it is generally difficult to obtain a high-quality and large-scale handwritten Chinese characters sample set. Insufficient samples of handwritten Chinese characters would cause poor recognition performance. In this paper, we propose a handwritten Chinese character recognition method based on dataset expansion and CNNs. Firstly, the topology of proposed Convolutional neural networks model is addressed. Then, several dataset expansion techniques are utilized to expand the scale of available samples, which include random elastic deformation, shear transformation and rotation within a small range, etc. A series of experiments on the HCL2000 Chinese character handwriting database have shown that our method can effectively improve the recognition performance, with a reduction in error rate of 35.01%, verified the effectiveness of our proposed approach.
Social Capital and the Viability of Nonprofit Firms: Evidence from Norwegian Savings Banks

Hansmann (1996) points out that a surprisingly large number of nonprofit firms operate in developed economies, often in sectors where they compete with for-profit firms. In this paper, we shed new light on the viability of nonprofit firms by studying the survival of nonprofit savings banks in the Norwegian banking industry after branching deregulations in the mid-1980s. We propose that banks’ survival is related to the level of social capital in the local communities in which the banks operate. Using newspaper readership and charity donations to proxy for the amount of social capital, we estimate that an increase in social capital from the minimum to the maximum level observed in the sample increases the probability of survival for the average bank by approximately 10 percentage points at the time of deregulation. The result is robust to controls for, among others, bank equity, bank competition, population age and education. Our findings suggest that social capital may facilitate collective decision-making and the alignment of stakeholders’ preferences.
A node allocation system for deploying JavaEE systems on Grids

This paper presents the design, implementation and evaluation of a node allocation system for deploying Java EE systems on Grids. The node allocation system we propose considers the proximity to clients for a better application response time and relies on the Grid nodes cooperation to find the resources respecting the Java EE middleware architecture. We evaluate our solution on the Frensh Grid, the Grid5000.
Challenges and opportunities of ‘good governance’ for drug policy: the case of the development of Belgian drug policy between 1996 and 2003

Abstract Aims: Studying the characteristics of the science-policy nexus in the field of drugs emerged in parallel with an interest in the principles of good governance. Centre-stage is the processes through which policy is informed, especially with regard to the role of non-government actors, contrasting with the widely accepted view that drug policymaking is a prerogative of governments. Through the lens of what is defined as good governance in drug policy, this article examines the processes underpinning the drug-policy change in Belgium between 1996 and 2003. Methods: The paper is based on an analysis of 164 policy documents and 1067 newspaper articles, and 55 interviews with a range of stakeholders including policymakers, professionals, scientists and journalists. Findings: Some distinctive features were found relating to the mechanisms through which evidence was mobilised and eventually informed policy change in Belgium. Evidence-imbued leadership, evaluation, coordination, the engagement of different stakeholders and the role of the parliament played key roles. Conclusions: Several characteristics of good governance make the use of evidence more likely. However, governance processes seem to be challenged in highly sensitive discussions (e.g. on cannabis policy). Another challenge may be how the principles of good governance can be consolidated in the long term.
35 Work in Progress Framewordk for Real . Time Sumulations Anders Backman 41 Surface Construction with Near Last Square Acceleration based on Vertex Normals on Tringular Meshes Tony Barrera

Work in dynamic image based rendering has been presented by Kanade et al. [4] and Matusik et al. [5] previously. We present an alternative implementation that allows us to have a very inexpensive process of creating dynamic image-based renderings of digitally recorded photo realistic, real-life objects. Together with computer vision algorithms, the image-based objects are visualized using the Relief Texture Mapping algorithm presented by Oliveira et al [6]. As the relief engine requires depth information for all Texels representing the recorded object in an arbitrary view, a recording solution making depth extraction possible is required. Our eyes use binocular vision to produce disparities in depth, which also is the most effortless technique of producing stereovision. By using two digital video cameras, the dynamic object is recorded in stereo in different views to cover its whole volume. As the depth information from all views are generated, the different views from the image-based object are textured on a pre-defined bounding box and relief textured into a three dimensional representation by applying the known depth disparities.
Accelerating feature extraction for patch-based Multi-View Stereo algorithm

In this paper, we present a novel parallel implementation of HARRIS and DOG detector on GPU for feature extractions of Patch-based Multi-View Stereo (PMVS) algorithm in image sequence. With the Compute Unified Device Architecture(CUDA)-enabled GPU, the acceleration is significant and it obtains a 34 times performance boost comparing to a CPU implementation. We adopt the hardware built-in bilinear interpolation of texture to shorten the time for image resampling while doing image transformations. While lots of time can be saved by using our improved PMVS Algorithm with GPU-based feature extraction, experimental results also show that our implementation can obtain fine details for building accurate object and scene models.
Emotional Nonacceptance and Anxiety Sensitivity in Relation to Anxious Arousal, Social Anxiety, and Depressive Symptoms Among African American Young Adults

Past work suggests African American college students may be a segment of the college population at heightened risk for anxiety and depressive symptoms; however, there remains an overarching need to broaden our understanding of individual difference variables that may be related to adverse emotional states. The purpose of the present study was to test a theoretically driven interactive model of two individual difference variables (anxiety sensitivity and emotional nonacceptance) in relation to anxiety and depressive symptoms among 133 African American undergraduate students (76.7% female, Mage = 20.9, SD = 5.6; age range = 18-53 years). Results indicated that anxiety sensitivity was positively associated with anxious arousal symptoms at higher levels of emotional nonacceptance (β = 2.1), and more strongly associated with depressive symptoms among individuals endorsing low levels of emotional nonacceptance (β = 3.8). The present data provide novel evidence suggesting there is clinically relevant interplay between anxiety sensitivity and emotional nonacceptance. These findings aid in better understanding the expression of anxiety and depressive symptoms among this population and may represent transdiagnostic constructs of relevance for early intervention.
Leveraging Simultaneous Multithreading for Adaptive Thermal Control

The continual increase in microprocessor transistor densities has led to major challenges in on-chip temperature management. Examining how emerging architectural paradigms scale from a thermal-aware design perspective is critical for sustaining high-performance computing. In this paper we explore a novel dynamic thermal management technique for simultaneous multithreaded processors. Unlike prior studies, rather than testing general-purpose thermal management techniques applicable to all processor paradigms we propose to take advantage of SMT’s unique flexibility of having multiple threads. By selectively managing the execution of available threads we see an opportunity to adaptively counteract and prevent hot spots. Our work uses the Turandot simulator to model an SMTsupporting POWER5-like processor and the HotSpot 2.0 tool to simulate thermal behavior. With it, we examine the performance of our SMT-specific adaptive thread control mechanisms as compared to conventional dynamic thermal management techniques. We find that when multiple heterogeneous programs are available in the workload, thermal-aware issue policies provide a significant power-performance benefit; they average 44% ED reduction when aggressively operating near the thermally limited region. We observe the inherent tradeoffs between such performance advantages and thread fairness, and test this design as an instruction fetch policy as well as an adaptive register renaming technique.
Mechanism-Based PKI - A Real-Time Key Generation from Fingerprints

†1 This paper proposes a "mechanism-based PKI", in which only a mechanism for generating user's private keys is installed on a smart card. The private key is generated inside the smart card at the event that the legitimate user gives a "seed of private key" to his/her smart card in order to sign a document. The key exists nowhere except while users are signing a document. Thus, users no longer need to pay considerable attention to their smart cards. In addition, this paper also proposes a "statistical A/D conversion", which is an effective scheme to con- vert fingerprint to just one and the same ID in real-time. The statistical A/D conversion enables us to use fingerprint as a seed of private key. We construct an example system for real-time key generation form fingerprint. From some basic experiments that we carried out, the availability of the system is confirmed.
Pedestrian detection and tracking at crossroads

This paper presents a system for pedestrian detection and tracking by using image processing techniques. A very important issue in the field of intelligent transportation system is to prevent pedestrians from being hit by vehicles. Recently, a great number of vision-based techniques have been proposed for this purpose. In this paper, we propose a vision-based method which combines the use of a pedestrian model as well as the walking rhythm of pedestrians. Through integrating these spatial and temporal information grabbed by a vision system, we are able to develop a reliable pedestrian detection and tracking system that can always produce accurate results. Experimental results obtained using the real world cases have demonstrated that the proposed model is indeed superb.
A fuzzy rule-based and energy-efficient method for estimating the free size of parking places in smart cities by using wireless sensor networks

A fuzzy rule-based and energy-efficient method for estimating the free size of parking places in smart cities is proposed. In particular, the estimation is the output value of a fuzzy inference system (FIS) which was implemented on each wireless sensor mote (WSM) of a wireless sensor network (WSN) placed in the ground below the parking places and it was observed that it requires 8.9 Kbytes and 5 Kbytes of flash program memory and RAM respectively. Moreover, the fuzzy rules were formulated by using real numerical data obtained from the WSN. Next, the well known network simulator 2 (NS-2) was used for acquiring simulation results concerning the energy consumption of the battery of the WSMs. In order to model the error of the output value of the FIS and the energy consumption of the battery, a data fitting problem was solved and the cubic polynomial was fitted to the real and simulation data. As a result, a real-valued cost function was formed which was minimized to get the optimal values for the distance between the WSMs and the sampling period based on which the output of the FIS should be re-computed.
Spectral Human Flow Counting with RSSI in Wireless Sensor Networks

Human flow counting is a fundamental task in public space management. Counting flow correctly may help prevent overcrowding hazards and improve public safety. This study proposes an automated device-free flow counting system by exploiting radio frequency irregularity in a wireless sensor network. As people pass through the line-of-sight between transmitters and receivers, radio frequency transmission is disturbed and received signal strength indicator (RSSI) fluctuates at the receiving ends. Using RSSI fluctuation series, the system infers flow size without patrons' carrying any special devices. A wireless sensor network with HBE-Zigbex motes (IEEE 802.15.4) is set up to conduct experiments. Besides the mean and standard deviation of RSSI fluctuation series, Fourier spectral features are also employed as predictors of a machine learning algorithm. Experimental results show that spectral features improve the prediction accuracy significantly. The proposed method thus provides an alternative solution for the flow counting problem in addition to other video based solutions.