Miracle-dz/UniFAR-FTUs huggingface dataset:
Calibration of the INCA model in a Mediterranean forested catchment: the effect of hydrological inter-annual variability in an intermittent stream

Abstract. Mediterranean regions are characterised by a stream hydrology with a marked seasonal pattern and high inter-annual variability. Accordingly, soil N processes and leaching of solutes in Mediterranean regions also show a marked seasonality, occurring in pulses as soils re-wet following rain. The Integrated Nitrogen Catchment model (INCA) was applied to Fuirosos, a Mediterranean catchment located in NE Spain using hydrological data and streamwater nitrate and ammonium concentrations collected from 1999 to 2002. This study tested the model under Mediterranean climate conditions and assessed the effect of the high inter-annual variability on the ability of INCA to simulate discharge and N fluxes. The model was calibrated for the whole three-year period and the n coefficients of determinion (r2) between simulated and observed data were 0.54 and 0.1 for discharge and nitrate temporal dynamics, respectively. Ammonium dynamics were simulated poorly and the linear regression between observed and simulated data was not significant statistically. To assess the effect of inter-annual variability on INCA simulations, the calibration process was run separately for two contrasting hydrological years: a dry year with a total rainfall of 525 mm and a wet year with a total of 871 mm. The coefficients of determination for the correlation between observed and simulated discharge for these two periods were 0.67 (p Keywords: environmental modelling, intermittent stream, Mediterranean climate, Fuirosos, hydrology, nitrate
Din din! The (Semantic) Turkey is served!

From its first introduction in this same conference, the original prototype of the Semantic Bookmarking tool Semantic Turkey has undergone a deep and extensive revision process, breaking the boundaries of its original intents and going more and more towards an extensible platform for Knowledge Management and Acquisition based on Semantic Web technologies. Following its recent official release, we discuss here the main innovations of this system, its potential applications and future plans for its improvement.
Who is in control? Clinicians’ view on their role in self-management approaches: a qualitative metasynthesis

Objective To explore clinician perceptions of involvement in delivery of self-management approaches. Setting All healthcare settings. Design EBSCO, Scopus and AMED databases were searched, in July 2013, for peer-reviewed studies in English reporting original qualitative data concerning perceptions of clinicians regarding their involvement in or integration of a self-management approach. Of 1930 studies identified, 1889 did not meet the inclusion criteria. Full text of 41 studies were reviewed by two independent reviewers; 14 papers were included for metasynthesis. Findings and discussion sections were imported into Nvivo-10 and coded line-by-line. Codes were organised into descriptive themes and cross-checked against original sources to check interpretation, and refined iteratively until findings represented an agreed understanding. Studies were appraised for quality. Results Delivering self-management in practice appeared to be a complex process for many clinicians. The issue of ‘control’ arose in all studies, both in the qualitative data and authors’ interpretations. The first theme: Who is in control? —represented ways clinicians talked of exercising control over patients and the control they expected patients to have over their condition. The second theme: Changing clinician views—reflected what appeared to be an essential transformation of practice experienced by some clinicians in the process of integrating self-management approaches into the practice. A range of challenges associated with shifting towards a self-management approach were reflected in the third theme, Overcoming challenges to change. Tensions appeared to exist around forming partnerships with patients. Strategies found helpful in the process of change included: dedicating time to practice reciprocity in communication style, peer support and self-reflection. Conclusions A consistent finding across studies is that ‘control’ is a key feature of how self-management is viewed by clinicians. They described challenges associated with the paradigm shift required to share or let go of control. Future research should identify whether strategies described by clinicians are key to successful self-management.
fMRI differences between subjects with low and high responses to alcohol during a stop signal task.

BACKGROUND A low level of response (i.e., a low LR) to alcohol is a genetically influenced phenotype that predicts later alcoholism. While the low LR reflects, at least in part, a low brain response to alcohol, the physiological underpinnings of the low LR have only recently been addressed. METHODS Forty-nine drinking but not yet alcoholic matched pairs of 18- to 25-year-old subjects (N = 98; 53% women) with low and high LRs as established in separate alcohol challenges were evaluated in 2 event-related functional magnetic resonance imaging (fMRI) sessions (placebo and approximately 0.7 ml/kg of alcohol) while performing a validated stop signal task. The high and low LR groups had identical blood alcohol levels during the alcohol session. RESULTS Significant high versus low LR group and LR group × condition effects were observed in blood oxygen level-dependent (BOLD) signal during error and inhibitory processing, despite similar LR group performance on the task. In most clusters with significant (corrected p < 0.05, clusters > 1,344 μl) LR group × alcohol/placebo condition interactions, the low LR group demonstrated relatively less, whereas the high LR group demonstrated more, error and inhibition-related activation after alcohol compared with placebo. CONCLUSIONS This is one of the first fMRI studies to demonstrate significant differences between healthy groups with different risks of a future life-threatening disorder. The results may suggest a brain mechanism that contributes to how a low LR might enhance the risk of future heavy drinking and alcohol dependence.
Wrapper Syntax for Example-based Machine Translation

TransBooster is a wrapper technology designed to improve the performance of wide-coverage machine translation systems. Using linguistically motivated syntactic information, it automatically decomposes source language sentences into shorter and syntactically simpler chunks, and recomposes their translation to form target language sentences. This generally improves both the word order and lexical selection of the translation. To date, TransBooster has been successfully applied to rule-based MT, statistical MT, and multi-engine MT. This paper presents the application of TransBooster to Example-Based Machine Translation. In an experiment conducted on test sets extracted from Europarl and the Penn II Treebank we show that our method can raise the BLEU score up to 3.8% relative to the EBMT baseline. We also conduct a manual evaluation, showing that TransBooster-enhanced EBMT produces a better output in terms of fluency than the baseline EBMT in 55% of the cases and in terms of accuracy in 53% of the cases.
Executing multiple group-by query in a MapReduce approach

Facing more and more generated information, data analysis software meets the challenge of processing large volume of data. The arrival of MapReduce provides a chance to utilize commodity hardware for processing large data set in parallel. In this paper, we focus on a special type of data analysis query, namely, multiple group-by query. We give an initial implementation of multiple group-by query based on MapReduce model. Considering the ignorable communication cost, we then propose an optimized version based on MapCombineReduce model, which addresses this issue. Our optimized version shows a better accelerating ability and a better scalability than the initial version.
Faculty Learning at Boundaries to Broker Racial Equity

ABSTRACT On many college campuses, faculty are being called on to act as change agents for racial equity. Through narrative inquiry analysis using the theoretical constructs of boundary crossing and boundary objects, this case study examined learning among faculty (n = 12) who attempted to broker structural changes at their universities through participation in action research. The findings illustrate that boundary negotiation across the “silos” of academic departments and administrative offices to promote racial equity involves perspective taking (regarding current norms and discourse) and perspective making (about equity as a legitimate institutional goal). These findings contribute to the literature on faculty agency and organizational learning for racial equity in higher education by identifying boundary objects as cultural tools that facilitate faculty learning and agency.
Recovering Missing Depth Information from Microsoft ’ s Kinect

This paper presents a technique to estimate missing depth information obtained from the Microsoft Kinect. Due to the limitations of the depth sensing technology used by the Kinect, a significant loss and noise incurs when capturing depth information. The paper describes the steps needed to go from raw depth, as captured by Kinect, to a point cloud representation of the recovered missing data. Our main contribution in this paper is the use of an iterative diffusion method that accounts for both the known depth values and RGBD segmentation results to recover missing depth information. The algorithm proposed is highly parallelizable and is capable of being implemented for real-time processing by utilizing the Graphical Processing Unit.
Deep belief network based CRF for spoken language understanding

The key task in spoken language understanding research is the semantic tagging of sequences. Deep belief networks have recently shown great performance in word-labeling tasks while conditional random field has been a successful approach to model probabilities of sequences in a global fashion. In contrast to CRFs, DBNs are optimized based on a tag-by-tag likelihood in a locally normalized way and may suffer from the label bias problem. In this paper, we combine the DBN and CRF by employing the CRF model on top hidden layer of the DBN. This DBN-CRF architecture can explicitly model the dependencies of the output labels with transition features, and can be trained with a global sequence-level objective function. Experiments on ATIS corpus show that the new model outperforms CRFs and DBNs by 4.9% and 3.8% respectively. After effectively pre-training with additional unlabeled data, the results can be state-of-the-art, compared to the recent RNN-CRF model.
An Ensemble Smoother with Error Estimates

A smoother introduced earlier by van Leeuwen and Evensen is applied to a problem in which real observations are used in an area with strongly nonlinear dynamics. The derivation is new, but it resembles an earlier derivation by van Leeuwen and Evensen. Again a Bayesian view is taken in which the prior probability density of the model and the probability density of the observations are combined to form a posterior density. The mean and the covariance of this density give the variance-minimizing model evolution and its errors. The assumption is made that the prior probability density is a Gaussian, leading to a linear update equation. Critical evaluation shows when the assumption is justified. This also sheds light on why Kalman filters, in which the same approximation is made, work for nonlinear models. By reference to the derivation, the impact of model and observational biases on the equations is discussed, and it is shown that Bayes’s formulation can still be used. A practical advantage of the ensemble smoother is that no adjoint equations have to be integrated and that error estimates are easily obtained. The present application shows that for process studies a smoother will give superior results compared to a filter, not only owing to the smooth transitions at observation points, but also because the origin of features can be followed back in time. Also its preference over a strong-constraint method is highlighted. Furthermore, it is argued that the proposed smoother is more efficient than gradient descent methods or than the representer method when error estimates are taken into account.
Fast iterative solver for convection‐diffusion systems with spectral elements

We introduce a solver and preconditioning technique based on Domain Decomposition and the Fast Diagonalization Method that can be applied to tensor product based discretizations of the steady convection–diffusion equation. The method is based on a Robin–Robin interface preconditioner coupled to a fast diagonalization solver which is used to efficiently eliminate the interior degrees of freedom and perform subsidiary subdomain solves. Using a spectral element discretization, we first apply our technique to constant wind problems, and then propose a means for applying the technique as a preconditioner for variable wind problems. We demonstrate that iteration counts are mildly dependent on changes in mesh size and convection strength. © 2009 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq, 2011
Children imitate!: appreciating recycling in participatory design with children

The cooperative design practices as well as the participatory research tradition and contextual design have inspired the researchers of a relatively new and challenging design context, i.e. design with children for children. An ample literature base of its own has been generated on the subject already. However, the phenomenon of children imitating each other's work in the design sessions has been largely disregarded in current research. This article sheds light on the practices of 'recycling', originally characterised as 'imitation', in the drawings produced by children during participatory design workshops in a school setting. The article suggests that instead of ignoring the issue of imitation and recycling, practitioners might start to appreciate it; both when planning design sessions as well as when making interpretations and judgments on the basis of the results produced by children. Implications for practice and future research are discussed.
Spectrophotometry of 237 Stars in 7 Open Clusters

Photoelectric spectrophotometry is presented for 237 stars in the Hyades, Pleiades, Alpha Persei, Praesepe, Coma Berenices, IC~4665, and M39 open clusters. 16 bandpasses were observed from 350 nm to 778 nm, with 3.2 to 6.4 nm bandwidth. Data are standardized to the Hayes-Latham system to mutual accuracy of 0.016 mag per passband. By comparison to SIMBAD-available BV and ubvy colors on the one hand, and to 1993 Kurucz synthetic spectra on the other, we show our data to be accurate to 0.01 mag for the 13 passbands at or above 400 nm, and 0.02 mag for the 3 passbands below 400 nm. We also show that the scatter in the fits of the spectrophotometric colors and the uvby filter colors is a reasonable way to identify the observations of which specific stars are accurate to 1-sigma, 2-sigma, . The very good agreement between the models and these data verifies the accuracy of these data, and also verifies the usefulness of the Kurucz models to define spectrophotometry for stars in this temperature range (>5000 K). These data define accurate spectrophotometry of bright, open cluster stars that can be used as a secondary flux calibration for CCD-based spectrophotometric surveys.
Establishment Decision Rules for Turnover of Nursing Staff - Applying Rough Set Theory

The research investigated the rules relevant with leaving reasons and personal characteristic variables of nursing staff by rough set theory (RST). RST can discover the important facts hidden in the data and describe them with natural language. That is it can describe the information provided by researchers in a broad sense and delete the redundant part of original data for the decision rules setting. It is expected the research can establish rules related with leaving reasons and personal characteristics variables of nursing staff. The study conducted analysis on 458 persons' data that include six attributes - age, marriage status, education, seniority, service department and dimission reasons to propose concerned suggestions and comments based on the research result as reference for Hospital A and other hospitals as well as relevant academic institutions.
Analyzing and Visualizing Log Files: A Computational Science of Usability

Researchers in human-computer interactions know that software can easily be instrumented to create a trace of user events2 in the interface (which we call a log file) for later analysis. Using these data for studying usability or other HCI questions is certainly attractive. • The data are cheap – data gathering is totally automated. • The data are gathered discretely, so the possibility of a Hawthorne effect is diminished. • Data can be collected outside of the laboratory, while the user is engaged in real tasks, so the results have greater ecological validity. The problem has always been what to do with the volume of data that gets generated. With so much data, it's difficult to determine what's useful and how to display the useful portion in a meaningful way.
Bimodal Effect of Lithium Plasma Levels on Hippocampal Glutamate Concentrations in Bipolar II Depression: A Pilot Study

Background: The hippocampus has been highly implicated in the pathophysiology of bipolar disorder (BD). Nevertheless, no study has longitudinally evaluated hippocampal metabolite levels in bipolar depression under treatment with lithium. Methods: Nineteen medication-free BD patients (78.9% treatment-naïve and 73.7% with BD type II) presenting an acute depressive episode and 17 healthy controls were studied. Patients were treated for 6 weeks with lithium in an open-label trial. N-acetyl aspartate (NAA), creatine, choline, myo-Inositol, and glutamate levels were assessed in the left hippocampus before (week 0) and after (week 6) lithium treatment using 3T proton magnetic resonance spectroscopy (1H-MRS). The metabolite concentrations were estimated using internal water as reference and voxel segmentation for partial volume correction. Results: At baseline, acutely depressed BD patients and healthy controls exhibited similar hippocampal metabolites concentrations, with no changes after 6 weeks of lithium monotherapy. A significant correlation between antidepressant efficacy and increases in NAA concentration over time was observed. Also, there was a significant positive correlation between the changes in glutamate concentrations over follow-up and plasma lithium levels at endpoint. Mixed effects model analysis revealed a bimodal effect of lithium plasma levels in hippocampal glutamate concentrations: levels of 0.2 to 0.49mmol/L (n=9) were associated with a decrease in glutamate concentrations, whereas the subgroup of BD subjects with “standard” lithium levels (≥0.50 mmol/L; n = 10) showed an overall increase in glutamate concentrations over time. Conclusions: These preliminary results suggest that lithium has a bimodal action in hippocampal glutamate concentration depending on the plasma levels.
Design and Operation of A Levitation Melting System

Levitation melting is a suitable technique to avoid the contamination by crucible material. This paper presents a design and operation of a levitation melting system. The illustrative workpiece is a 7-g aluminum. A finite element software is used in the coil design. The levitation melting system is constructed with a 2-kW resonant inverter to supply the necessary power. Simulation and experimental results have confirmed the validity of the levitation coil design and system operation.
Hyperspectral Anomaly Detection Through Sparse Representation With Tensor Decomposition-Based Dictionary Construction and Adaptive Weighting

Sparse representation-based methods, as an important branch of anomaly detection (AD) technologies for hyperspectral imagery (HSI), have attracted extensive attention. How to construct an overcomplete background dictionary containing all background categories and excluding anomaly signatures is the focus. Traditional background dictionary construction methods first convert HSI into a two-dimensional matrix composed of independent spectral vectors, and then execute the subsequent construction operations. In this way, only spectral anomalies can be excluded from the background dictionary, whereas spatial anomalies still exist. To alleviate this problem, this paper proposes a novel AD algorithm through sparse representation with tensor decomposition-based dictionary construction and adaptive weighting. It has three main advantages. First, tensor representation allows the spectral and spatial characteristics of HSI to be preserved simultaneously, and Tucker decomposition achieves excellent separation between the background part and anomaly part by distinguishing them along three modes. Second, the $K$ -means++ clustering operation is implemented on the background part so that the background dictionary used for sparse representation contains all background categories. Finally, an adaptive weighting matrix derived from the anomaly part further improves the distinction between background pixels and anomalies. Experiments on synthetic and real HSI datasets demonstrate the superiority of our proposed algorithm.
Does Learning a Complex Task Have to Be Complex?: A Study in Learning Decomposition

Many theories of skill acquisition have had considerable success in addressing the fine details of learning in relatively simple tasks, but can they scale up to complex tasks that are more typical of human learning in the real world? Some theories argue for scalability by making the implicit assumption that complex tasks consist of many smaller parts, which are learned according to basic learning principles. Surprisingly, there has been rather sparse empirical testing of this crucial assumption. In this article, we examine this assumption directly by decomposing the learning in the Kanfer-Ackerman Air-Traffic Controller Task (Ackerman, 1988) from the learning at the global level all the way down to the learning at the keystroke level. First, we reanalyze the data from Ackerman (1988) and show that the learning in this complex task does indeed reflect the learning of smaller parts at the keystroke level. Second, in a follow-up eye-tracking experiment, we show that a large portion of the learning at the keystroke level reflects the learning even at a lower, i.e., attentional level.
Achievement Goals, Implicit Theories, and Intrinsic Motivation: A Test of Domain Specificity Across Music, Visual Art, and Sports

The purpose of this study was to test the domain specificity of achievement goals across music, visual art, and sports specializations, as measured by Elliot’s 2 × 2 achievement goal framework. Participants in the study were 103 volunteer student teachers from a teacher training institute in Singapore specializing in music, visual art, and physical education. Data were collected via self-report questionnaires that included measures of (a) the 2 × 2 achievement goal orientation constructs; (b) incremental and entity beliefs among the participants in music, visual art, and sports; and (c) participants’ enjoyment, perceived competence, effort, and tension while being engaged in music, visual art, and sports. MANOVA analyses indicated that (a) achievement goals are domain-specific and are highest in participants’ area of specialization; (b) implicit theories can be generalized across the three specializations, with higher incremental beliefs than entity beliefs reported across all specializations; and (c) enjoyment was highest for those who specialized in that particular area. Finally, mastery-approach goals positively predicted enjoyment in each specialization.