Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling and Estimation of Spatiotemporal Surface Dynamics Applied to a Middle Himalayan Region

and timely estimation of the spatiotemporal surface dynamics is very important for natural resource planning and disaster mitigation. This paper discusses a novel technique to assess the patterns of the surfaces of a particular severe landslide susceptible zone (Kullu-Larji-Rampur geological window, near Aut village, district Mandi, Himachal Pradesh, India; N 31°44'34.78'' E 77°12'29.02''). The spatiotemporal surface dynamics of this region, spanning over last 20 years (1989 - 2009), has been modelled using Landsat TM images acquired during summers of 1989, 2000 and 2009. The proposed technique uses image processing to derive regression models of selected area segments, these models are then used to measure area under the curve to estimate the surface area changes. The surface area changes thus obtained have also been validated by standard method of pixel counting. Principal component analysis has been done in order to understand the correlations amongst the estimated parameters, namely; segment lengths, percentage area change and the area change in the first (1989-2000) and second (2000-2009) decades. The results obtained show a fair degree of accuracy as compared to the standard method of pixel counting.
Coping with uncertainty: an expedition into the field of new transport technology

Many decisions taken by mankind are rational only to a limited extent. This holds for individual travel behaviour, but also for long-range strategic decisions on transport systems or transport technology. In any decision problem, coping with uncertainty is the most critical element. The introduction of new transport technology is surrounded by uncertainty. For example, there is uncertainty about the pace and extent of adoption of new technology and there is uncertainty about the impact of new technology in terms of increased sustainability or increased efficiency. This article maps uncertainty surrounding new transport technology and identifies ways in which to deal with uncertainty in policy making. The findings will be illustrated with electric vehicles, particularly with two specific strategies to deal with uncertainty: interactive technology watching and experimentation in a market niche. The paper concludes with a discussion of success factors that influence the outcomes of such strategies.
A Comparison of Split-Half and Multilevel Methods to Assess the Reliability of Progress Monitoring Outcomes

One consideration for selecting progress monitoring tools is the reliability in which the instrument measures student response to instruction. Researchers and vendors establish reliability of growth using two analytic methods: (a) calculating slopes to even and odd observations for each student and correlating the resulting slopes (split-half), and (b) estimating a multilevel model and computing the ratio between true and observed variance in growth estimates. It is unclear whether reliability estimates from either analytic method systematically differ. Therefore, it is also unclear whether recommendations regarding the requisite length of time to collect data to achieve sufficient levels of reliability would vary based on the analytic method used to calculate coefficients. Results from this study indicate that the multilevel method yielded systematically higher reliability estimates compared with the split-half method. Bootstrapping confirmed that differences were statistically significant. Researchers and practitioners should be aware that split-half and multilevel reliability estimates are not interchangeable and recommendations regarding the frequency and duration educators should follow when collecting data may depend on the manner in which reliability is estimated.
The Pharmacokinetic-Pharmacodynamic Model of Azithromycin for Lipopolysaccharide-Induced Depressive-Like Behavior in Mice

A mechanism-based model was developed to describe the time course of lipopolysaccharide-induced depressive-like behavior and azithromycin pharmacodynamics in mice. The lipopolysaccharide-induced disease progression was monitored by lipopolysaccharide, proinflammatory cytokines, and kynrenine concentration in plasma. The depressive-like behavior was investigated by forced swimming test and tail suspension test. Azithromycin was selected to inhibit the surge of proinflammatory cytokines induced by lipopolysaccharide. Disease progression model and azithromycin pharmacodynamics were constructed from transduction and indirect response models. A delay in the onset of increased proinflammatory cytokines, kynrenine, and behavior test compared to lipopolysaccharide was successfully characterized by series transduction models. The inhibition of azithromycin on proinflammatory cytokines was described by an indirect response model. After lipopolysaccharide challenging, the proinflammatory cytokines, kynrenine and behavior tests would peak approximately at 3, 12, and 24 h respectively, and then the time courses slowly declined toward a baseline state after peak response. During azithromycin administration, the peak levels of proinflammatory cytokines, kynrenine and behavior indexes decreased. Model parameters indicated that azithromycin significantly inhibited the proinflammatory cytokines level in plasma and improved the depressive-like behavior induced by inflammation. The integrated model for disease progression and drug intervention captures turnovers of proinflammatory cytokines, kynrenine and the behavior results in the different time phases and conditions.
How Kinetic Property Shapes Novelty Perceptions

This article demonstrates a new substantive finding: that kinetic property in advertising, defined as direction changes in the paths of moving on-screen ad elements, enhances consumer judgments of product novelty. Across six studies, the authors first outline an inference-based theory as to why the novelty-enhancing effect of kinetic property manifests: kinetic property generates impressions of how visually lively an ad is, which leads to inferences of product atypicality and, consequently, higher novelty judgments. Second, they demonstrate boundary conditions by showing that (1) the positive effect for kinetic property is evident with incremental (and not radical) innovations, (2) the effect dissipates when figure-ground contrast in the ad makes kinetic property less discriminable, (3) contextual adaptation to kinetic property can mitigate this effect, and (4) kinetic property enhances novelty judgments primarily when product category characteristics such as perceived market dynamism match with kinetic property–based executions. The authors offer substantive implications for firms marketing new products as well as for multimedia advertising.
Segmentation of White Blood Cells through Nucleus Mark Watershed Operations and Mean Shift Clustering

This paper presents a novel method for segmentation of white blood cells (WBCs) in peripheral blood and bone marrow images under different lights through mean shift clustering, color space conversion and nucleus mark watershed operation (NMWO). The proposed method focuses on obtaining seed points. First, color space transformation and image enhancement techniques are used to obtain nucleus groups as inside seeds. Second, mean shift clustering, selection of the C channel component in the CMYK model, and illumination intensity adjustment are employed to acquire WBCs as outside seeds. Third, the seeds and NMWO are employed to precisely determine WBCs and solve the cell adhesion problem. Morphological operations are further used to improve segmentation accuracy. Experimental results demonstrate that the algorithm exhibits higher segmentation accuracy and robustness compared with traditional methods.
Brain activation during a social attribution task in adolescents with moderate to severe traumatic brain injury

The ability to make accurate judgments about the mental states of others, sometimes referred to as theory of mind (ToM), is often impaired following traumatic brain injury (TBI), and this deficit may contribute to problems with interpersonal relationships. The present study used an animated social attribution task (SAT) with functional magnetic resonance imaging (fMRI) to examine structures mediating ToM in adolescents with moderate to severe TBI. The study design also included a comparison group of matched, typically developing (TD) adolescents. The TD group exhibited activation within a number of areas that are thought to be relevant to ToM, including the medial prefrontal and anterior cingulate cortex, fusiform gyrus, and posterior temporal and parietal areas. The TBI subjects had significant activation within many of these same areas, but their activation was generally more intense and excluded the medial prefrontal cortex. Exploratory regression analyses indicated a negative relation between ToM-related activation and measures of white matter integrity derived from diffusion tensor imaging, while there was also a positive relation between activation and lesion volume. These findings are consistent with alterations in the level and pattern of brain activation that may be due to the combined influence of diffuse axonal injury and focal lesions.
Optimized online learning for QoE prediction

Quality of Experience (QoE) consists of a set of indicators that show the perceived satisfaction of using a multimedia (or other kind of) service by the end user. Being so the QoE presents a subjective metric and the only relevant mechanisms for measuring such indicators are subjective tests. Due to the fact that subjective tests are an expensive, impractical and in cases of live streaming a close to impossible exercise we set out on a twofold task to address this issue. First we set out to build prediction models using traditional Machine Learning (ML) techniques based on subjective test data. Second we explore an approach for reduction of the training dataset that will minimize the need for subjective data whilst keeping the prediction models as accurate as possible. For the first goal we used supervised learning based classification algorithms and we came up with high accuracy (over ninety percent) for the prediction models. To address the issue of high cost training data we developed a novel approach in reducing the training dataset while keeping a high accuracy of the classifiers. The reduction method provides a grading mechanism for unseen data. By having this mechanism in the online learning platform we can optimize the process of asking for user feedback by looking for the most significant cases, and therefore improving the gain on the trade-off between more feedback and more accuracy.
Estimating Historical California Precipitation Phase Trends Using Gridded Precipitation, Precipitation Phase, and Elevation Data

Climate change projections for California indicate reduction in the percentage of precipitation that falls as snow, and increase in the percentage that falls as rain, due to warmer temperatures in the future. Previous work has shown overall decreases in rainfall / snowfall ratios for the western US over the last 60 years. Of interest in this study is the quantification of snow/rain ratios for smaller regions, specifically in California. Estimating cumulative precipitation phase ratios for specific regions is difficult due to large differences in local precipitation. In California, the high relief of the surface topography makes such estimates particularly difficult. The low spatial resolution of suitable precipitation/snow water equivalent monitoring stations contributes to the difficulty in quantifying the trends for sub-state sized regions of interest in this study. The present exploratory study develops and describes a methodology that uses readily available research data sets to produce gridded estimates of historical rainfall as a fraction of total precipitation for areas comprising the major water-supply watersheds of California. Using this methodology, statistically significant increases in the ratio of annual liquid to total precipitation are seen for large areas in the northern part of the State and northern Sierra over the water year. No significant annual trends are seen for regions in the central and southern portions of the Sierra. Future work extending the analysis to distinct elevation ranges and seasonality would provide more refined conclusions.
Double-Talk-Robust Prediction Error Identification Algorithms for Acoustic Echo Cancellation

The performance of an acoustic echo canceller may be severely degraded by the presence of a near-end signal. In such a double-talk situation, the variance of the echo path estimate typically increases, resulting in slow convergence or even divergence of the adaptive filter. This problem is usually tackled by equipping the echo canceller with a double-talk detector that freezes adaptation during near-end activity. Nevertheless, there is a need for more robust adaptive algorithms since the adaptive filter's convergence may be affected considerably in the time interval needed to detect double-talk. Moreover, in some applications, near-end noise may be continuously present and then the use of a double-talk detector becomes futile. Robustness to double-talk may be established by taking into account the near-end signal characteristics, which are, however, unknown and time varying. In this paper, we show how concurrent estimation of the echo path and an autoregressive near-end signal model can be performed using prediction error (PE) identification techniques. We develop a general recursive prediction error (RPE) identification algorithm and compare it to three existing algorithms from adaptive feedback cancellation. The potential benefit of the algorithms in a double-talk situation is illustrated by means of computer simulations. It appears that especially in the stochastic gradient case a huge improvement in convergence behavior can be obtained
Hecke Operator and S-Duality of N=4 ADE Gauge Theory on K3

We determine N = 4 partition functions on K3 for some ADE gauge groups, on the assumption that they are holomorphic. Our partition functions satisfy the gap condition and Montonen-Olive duality at the same time, like the SU(N) partition functions of Vafa and Witten. As a result, we reveal a close relation between Hecke operator and S-duality of N = 4 ADE gauge theory on K3.
Near-optimal phase retrieval of sparse vectors

In many areas of imaging science, it is difficult to measure the phase of linear measurements. As such, one often wishes to reconstruct a signal from intensity measurements, that is, perform phase retrieval. In several applications the signal in question is believed to be sparse. In this paper, we use ideas from the recently developed polarization method for phase retrieval and provide an algorithm that is guaranteed to recover a sparse signal from a number of phaseless linear measurements that scales linearly with the sparsity of the signal (up to logarithmic factors). This is particularly remarkable since it is known that a certain popular class of convex methods is not able to perform recovery unless the number of measurements scales with the square of the sparsity of the signal. This is a shorter version of a more complete publication that will appear elsewhere.1
The role of data exchange standards in developing automated fault disturbance monitoring

As the power systems become more complex due to integration of renewables and increased transmission demands, monitoring fault disturbances becomes more important task. Manual analysis of faults and related impacts on the system due to protective relay miss-operation or unintended operation is not sufficient to restore the grid in a timely manner. Hence, an automated approach is required. This paper describes a system developed for that purpose and deployed at several utilities in the USA at different levels of complexity. The barriers to implementation and development are addressed. The role of interoperability standards in overcoming the barriers is explained. While the use of IEC standards 61850 and 61970 is emphasized the focus is also on many other IEC and IEEE standards that need to be applied. The paper ends in a discussion of how such a solution may be built using standards for data exchange.
Weak and strong Taylor methods for numerical solutions of stochastic differential equations

We apply the results of Malliavin–Thalmaier–Watanabe for strong and weak Taylor expansions of solutions of perturbed stochastic differential equations (SDEs). In particular, we determine weight expressions for the Taylor coefficients of the expansion. The results are applied to LIBOR market models in order to find precise and quick algorithms. In contrast to methods such as Euler–Maruyama–Monte-Carlo for the full SDE, we obtain more tractable expressions for accurate pricing. In particular, we present a readily tractable alternative to ‘freezing the drift’ in LIBOR market models that has an accuracy similar to the Euler–Maruyama–Monte-Carlo scheme for the full LIBOR market model. Numerical examples underline our results.
Successful Learners of Irish as an L2: Motivation, Identity and Linguistic Mudes

Abstract This article presents the results of a small-scale research conducted for a master’s thesis on the motivation to learn Irish on the part of university students and members of the Gaelic society An Cumann Gaelach. In the light of questionnaires’ results and interviews, the emphasis is placed on the links between motivation, identity, and potential key moments in learners’ lives. Using an AMTB-type questionnaire (n=45), the author puts to the test Dörnyei’s Motivational Self System theory (2005) in the context of the learning of Irish by looking at the correlation between the motivational intensity of 45 students and six variables (Ideal L2 Self, Ought to Self, Ideal L2 Community, Instrumentality, Parental encouragement, and Role of teachers). The notion of Ideal L2 Self, or the capacity to picture oneself speaking an L2 in the future, clearly appears to be strongly correlated with the respondents’ motivational intensity (r=.75 p<.01), in accordance with Dörnyei’s model. However results concerning extrinsic factors differ from previous research, putting forward distinctive features of the learning of minority languages. The second phase of the research looks at the language learning narratives of three An Cumann Gaelach’s members through the qualitative analysis of three interview transcripts. The results clearly show that time spent in Irish summer colleges are linguistic mudes (Pujolar and Puigdevall 2015), or key-moments, which triggered the interest in the language for the three students interviewed.
Teachers in Schools with Low Socioeconomic Composition: Are They Really that Different?

This article aims to assess whether differences in teacher characteristics vary with differences in socioeconomic compositions of schools. We conducted correlation analyses on administrative data from the French-speaking education system in Belgium. This database regroups more than 20,000 teachers in 1,630 elementary schools. We selected indicators to measure the link between schools’ socioeconomic composition and a set of dimensions of teachers’ profile such as experience, job security, and stability. The results confirm that some of these dimensions are linked to the school composition. The findings highlight the relevance of considering segmentation of the school market when studying the topic.
A Family of Nonparametric Density Estimation Algorithms

A new methodology for density estimation is proposed. The methodology, which builds on the one developed by Tabak and Vanden‐Eijnden, normalizes the data points through the composition of simple maps. The parameters of each map are determined through the maximization of a local quadratic approximation to the log‐likelihood. Various candidates for the elementary maps of each step are proposed; criteria for choosing one includes robustness, computational simplicity, and good behavior in high‐dimensional settings. A good choice is that of localized radial expansions, which depend on a single parameter: all the complexity of arbitrary, possibly convoluted probability densities can be built through the composition of such simple maps. © 2012 Wiley Periodicals, Inc.
Power and Performance Analysis of GPU-Accelerated Systems

Graphics processing units (GPUs) provide significant improvements in performance and performance-per-watt as compared to traditional multicore CPUs. This energy-efficiency of GPUs has facilitated the use of GPUs in many application domains. Albeit energy efficient, GPUs consume non-trivial power independently of CPUs. Therefore, we need to analyze the power and performance characteristic of GPUs and their causal relation with CPUs in order to reduce the total energy consumption of the system while sustaining high performance. In this paper, we provide a power and performance analysis of GPU-accelerated systems for better understandings of these implications. Our analysis on a real system discloses that system energy can be reduced by 28% retaining a decrease in performance within 1% by controlling the voltage and frequency levels of GPUs. We show that energy savings can be achieved when GPU core and memory clock frequencies are appropriately scaled considering the workload characteristics. Another interesting finding is that voltage and frequency scaling of CPUs is trivial for total system energy reduction, and even should not be applied in state-of-the-art GPU-accelerated systems. We believe that these findings are useful to develop dynamic voltage and frequency scaling (DVFS) algorithms for GPU-accelerated systems.