Miracle-dz/UniFAR-FTUs huggingface dataset:
The GRID scale: a new tool for measuring service mixed satisfaction

Purpose The purpose of this study is to evaluate a bidimensional tool to measure overall service satisfaction: the evaluative space grid (GRID scale). The GRID scale provides a common measure for both positivity and negativity through 25 grid cells. The authors propose to use the GRID scale as an integrated measure of both satisfaction and dissatisfaction to capture mixed reactions or ambivalence. Design/methodology/approach Within a cross-sectional between-subjects survey design, this study compares overall satisfaction with bank services as measured on the GRID scale versus a traditional semantic differential (SD) scale. Findings The results show that the GRID scale performs as well as the SD scale with respect to different criteria, such as reliability and discriminant, convergent, nomological and predictive validity. However, it allows to measure separately indifference and ambivalence. Practical implications Such a distinction assists decision-makers with recommendations on different strategies not only to create customer loyalty based on satisfaction but also to encourage them to think how to decrease the levels of dissatisfaction and ambivalence. Originality/value The GRID scale would address survey needs of every business suffering from average performances. This tool provides them better in-depth overall satisfaction information, especially regarding the “middle-ground” customers.
The Crucial Role of Working Memory in Intellectual Functioning

Cognitive psychology offers an important contribution to the understanding of the mechanisms underlying intelligence. In this paper, we synthesize the research showing that, among the different cognitive mechanisms associated with intelligence, working memory has a particularly high explanatory power, especially when considered in its active component involving not only the maintenance (as in short-term memory) but also the manipulation of information. The paper considers two main implications of this finding for the applied and clinical fields. For a start, we examine how intelligence tests take into consideration working memory. Secondly, we consider the highly debated literature on the effects of working memory training on intellectual performance. Theoretical and applied implications for the relationship between working memory and intelligence are discussed.
Deep-Learning-Based Classification for DTM Extraction from ALS Point Cloud

Airborne laser scanning (ALS) point cloud data are suitable for digital terrain model (DTM) extraction given its high accuracy in elevation. Existing filtering algorithms that eliminate non-ground points mostly depend on terrain feature assumptions or representations; these assumptions result in errors when the scene is complex. This paper proposes a new method for ground point extraction based on deep learning using deep convolutional neural networks (CNN). For every point with spatial context, the neighboring points within a window are extracted and transformed into an image. Then, the classification of a point can be treated as the classification of an image; the point-to-image transformation is carefully crafted by considering the height information in the neighborhood area. After being trained on approximately 17 million labeled ALS points, the deep CNN model can learn how a human operator recognizes a point as a ground point or not. The model performs better than typical existing algorithms in terms of error rate, indicating the significant potential of deep-learning-based methods in feature extraction from a point cloud.
Suinn Test Anxiety Behavior Scale: Normative Data for Graduate Students in Education

The Suinn Test Anxiety Behavior Scale was administered to both graduate and undergraduate students to reassess earlier findings (Suinn, 1969). The primary purpose of this study was to provide normative data for graduate students (N = 153). Such information does not appear in Suinn's 1969 article. Means, standard deviations, and percentiles are reported for each sex, as well as for the total graduate sample. Females reported significantly higher test anxiety. Test-retest reliability over a 6-wk. interval was . 73. In general, the findings for the graduate students were similar to Suinn's findings for undergraduates.
Associations Between Variants Near a Monoaminergic Pathways Gene (PHOX2B) and Amygdala Reactivity: A Genome-Wide Functional Imaging Study

As the amygdala is part of the phylogenetic old brain, and its anatomical and functional properties are conserved across species, it is reasonable to assume genetic influence on its activity. A large corpus of candidate gene studies indicate that individual differences in amygdala activity may be caused by genetic variants within monoaminergic signaling pathways such as dopamine, serotonin, and norepinephrine. However, to our knowledge, the use of genome-wide data to discover genetic variants underlying individual differences in adult amygdala activity is novel. In the present study, the combination of genome-wide data and functional imaging phenotypes from an emotional faces task yielded a significant association between rs10014254 and the amygdala using a region of interest approach. This single nucleotide polymorphism is located in a regulatory region upstream of the Paired-like homeobox 2b (PHOX2B) gene; therefore it could affect the expression of this gene. PHOX2B regulates the expression of enzymes necessary for the synthesis of several monoamines and is essential for the development of the autonomic nervous system. However, an attempt to replicate the finding in an independent sample from North America did not succeed. The synthesis of functional magnetic resonance imaging (fMRI) and genome-wide data takes a hypothesis-free approach as to which genetic variants are of interest. Therefore, we believe that an undirected finding within such a plausible region is of interest, and that our results add further support to the hypothesis that monoaminergic signaling pathways play a central role in regulating amygdala activity.
Guaranteed Bounds on the Kullback–Leibler Divergence of Univariate Mixtures

The Kullback-Leibler (KL) divergence between two mixture models is a fundamental primitive in many signal processing tasks. Since the KL divergence of mixtures does not admit a closed-form formula, it is in practice either estimated using costly Monte-Carlo stochastic integration or approximated. We present a fast and generic method that builds algorithmically closed-form lower and upper bounds on the entropy, the cross-entropy and the KL divergence of univariate mixtures. We illustrate the versatile method by reporting on our experiments for approximating the KL divergence between Gaussian mixture models.
Hsp90 promotes kinase evolution Authors: Jennifer Lachowiec, Department of Genome Sciences, Molecular and Cellular Biology Program, University of Washington, Seattle

Heat-shock protein 90 (Hsp90) promotes the maturation and stability of its client proteins, including many kinases. In doing so, Hsp90 may allow its clients to accumulate mutations as previously proposed by the capacitor hypothesis. If true, Hsp90 clients should show increased evolutionary rate compared to non-clients; however, other factors, such as gene expression and protein connectivity, may confound or obscure the chaperone’s putative contribution. Here, we compared the evolutionary rates of many Hsp90 clients and non-clients in the human protein kinase superfamily. We show that Hsp90 client status promotes evolutionary rate independently of, but in a small magnitude similar to that of gene expression and protein connectivity. Hsp90’s effect on kinase evolutionary rate was detected across mammals, specifically relaxing purifying selection. Hsp90 clients also showed increased nucleotide diversity and harbored more damaging variation than non-client kinases across humans. These results are consistent with the central argument of the capacitor hypothesis that interaction with the chaperone allows its clients to harbor genetic variation. Hsp90 client status is thought to be highly dynamic with as few as one amino acid change rendering a protein dependent on the chaperone. Contrary to this expectation, we found that across protein kinase phylogeny Hsp90 client status tends to be gained, maintained, and shared among closely related kinases. We also infer that the ancestral protein kinase was not an Hsp90 client. Taken together, our results suggest that Hsp90 played an important role in shaping the kinase superfamily. Introduction The conserved heat shock protein Hsp90 facilitates the proper folding and stability of its substrates (clients) (Taipale, et al. 2010), many of which are kinases with important roles in growth and development. Hsp90 perturbation increases the penetrance of expressed genetic variants and reveals cryptic genetic variation in genetically divergent populations of plant, fly, yeast, and fish (Rutherford and Lindquist 1998; Queitsch, et al. 2002; Yeyati, et al. 2007; Jarosz and Lindquist 2010). In worms, naturally varying Hsp90 levels predict mutation penetrance with lower Hsp90 levels resulting in greater penetrance (Burga, et al. 2011; Casanueva, et al. 2012). These observations with traditional model organisms prompted the controversial hypothesis that Hsp90 plays an important evolutionary role, allowing genetic variation to remain phenotypically silent and releasing it in environments that perturb Hsp90 function (Rutherford and Lindquist . CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi. org/10.1101/006411 doi: bioRxiv preprint first posted online Jun. 19, 2014;
Roles of LAG3 and EGR2 in regulatory T cells

Regulatory T cells (Tregs) participate in the maintenance of tolerance to self-antigens and suppressive control of excessive immune responses to exogenous antigens. A lack or dysfunction of these cells is responsible for the pathogenesis and development of many autoimmune diseases. It is well known that CD4 Tregs play a major role in controlling immune responses and can be classified into two main populations: thymus-derived naturally occurring Tregs (nTregs) and induced Tregs (iTregs) generated from CD4+CD25− precursors in the peripheral lymphoid organs. The most extensively studied Tregs are the nTregs, which express the interleukin 2 (IL-2) receptor CD25 and the transcription factor Forkhead box P3 (Foxp3). On the other hand, iTregs contain multiple heterogeneous subsets, including interleukin (IL)-10-producing CD4 type I Tregs (Tr1 cells) and transforming growth factor -β-producing Th3 cells, and so on. However, the extent of the contribution of iTregs to immunoregulation in normal animals has been difficult to evaluate because of the lack of suitable cell surface markers. It has been found recently that IL-10-secreting iTregs can be delineated as CD4+CD25−Foxp3− T cells that characteristically express both the lymphocyte activation gene-3 (LAG3) and the early growth response gene-2 (EGR2). In this review, opinions about the roles of LAG3 and EGR2 in Tregs are presented.
Characterization of all nonlinear stabilizing controllers via observer based kernel representations

This paper is concerned with the characterization of all nonlinear stabilizing controllers. Observer based kernel representations are introduced to avoid the difficulty which occurs in constructing the state-space realizations of input-output operators. By using observer based kernel representations, the parametrization of all the stabilizing plant-controller pairs is derived which is equivalent to the state-space parametrization. In addition the relationship between the kernel representation approach and the existing state-space approach is clarified.
Cortical projections to the superior colliculus in the macaque monkey: A retrograde study using horseradish peroxidase

The topical and laminar distribution of corticotectal cells, as well as their size and morphology, were studied in the macaque monkey with the horseradish peroxidase (HRP) technique. After HRP injections restricted primarily to the superficial layers of the colliculus, labelled cells were found in visual cortex (areas 17, 18, and 19) and both in the frontal eye field (area 8) and the adjacent part of premotor cortex (area 6). The clustering of labelled cells in visual cortex indicated that each of the anatomically and functionally distinct visual areas has its own set of collicular projections. When intermediate and deeper layers of the colliculus were injected, labelled cells were found also in posterior parietal cortex (area 7) where they were concentrated mainly on the posterior bank of the intraparietal fissure, in inferotemporal cortex (areas 20 and 21), in auditory cortex (area 22), in the somatosensory representation SII (anterior bank of sylvian fissure, area 2), in upper insular cortex (area 14), in motor cortex (area 4), in premotor cortex (area 6), and in prefrontal cortex (area 9). In the motor and premotor cortex, labelled cells formed a continuous band which appeared to stretch across finger‐hand‐arm‐shoulder‐neck representation. Similarly, the cluster of labelled cells in area 2 may correspond to the finger‐hand representation of SII. The cortical regions not containing labelled cells were the somatosensory representation SI (areas 3, 1 and 2) and the infraorbital cortex.
Inculcation of Values: A Necessity Today

The new generation is drifting away from its history and culture while crime and violence have spread to all spheres of life. Doubtless, the scientific discoveries have given rise to genuine optimism materialistic accomplishments, but the problems of inequity, conflicts, poverty, apathy and anxiety are on the rise. A teacher occupies a pivotal role, in orienting the students towards a proper direction instead of training in values. The family system in India has had a long tradition of imparting value education. With the presence of so many worldly-wise elders, it is but natural for the child to receive shelter and gain adequate knowledge to survive in the world
Estimating Diversifying Selection and Functional Constraint in the Presence of Recombination

Models of molecular evolution that incorporate the ratio of nonsynonymous to synonymous polymorphism (dN/dS ratio) as a parameter can be used to identify sites that are under diversifying selection or functional constraint in a sample of gene sequences. However, when there has been recombination in the evolutionary history of the sequences, reconstructing a single phylogenetic tree is not appropriate, and inference based on a single tree can give misleading results. In the presence of high levels of recombination, the identification of sites experiencing diversifying selection can suffer from a false-positive rate as high as 90%. We present a model that uses a population genetics approximation to the coalescent with recombination and use reversible-jump MCMC to perform Bayesian inference on both the dN/dS ratio and the recombination rate, allowing each to vary along the sequence. We demonstrate that the method has the power to detect variation in the dN/dS ratio and the recombination rate and does not suffer from a high false-positive rate. We use the method to analyze the porB gene of Neisseria meningitidis and verify the inferences using prior sensitivity analysis and model criticism techniques.
Server-based Inference of Internet Performance

We investigate the problem of inferring the packet loss characteristics of Internet links using server-based measurements. Unlike much of existing work on network tomography that is based on active probing, we make inferences based on passive observation of end-to-end client-server traffic. We start with a brief analysis of end-to-end packet loss rate over widearea Internet paths, as observed from a busy Web site. We find that the end-to-end packet loss rate correlates poorly with topological distance (i.e., hop count), remains stable for several minutes, and exhibits a limited degree of spatial locality. These findings suggest that passive network tomography would be both interesting and feasible. Our work on passive network tomography focuses on identifying lossy links (i.e., the trouble spots in the network). We have developed three techniques for this purpose based on Random Sampling, Linear Optimization, and Bayesian Inference using Gibbs Sampling, respectively. We evaluate the accuracy of these techniques using both simulations and Internet packet traces. We find that these techniques can identify most of the lossy links in the network with a manageable false positive rate. For instance, simulation results indicate that the Gibbs sampling technique has over 80% coverage with a false positive rate under 5%. Furthermore, this technique provides a confidence indicator on its inference. In the case of Internet traces, validating the inferences is a challenging problem. We present a method for indirect validation, which suggests that the false positive rate is manageable.
Deletion of the Aryl Hydrocarbon Receptor-associated Protein 9 Leads to Cardiac Malformation and Embryonic Lethality*

The aryl hydrocarbon receptor-associated protein 9, ARA9 (also known as XAP2 or AIP1), is a chaperone that is found in complexes with certain xenobiotic receptors, such as the aryl hydrocarbon receptor (AHR) and the peroxisome proliferator-activated receptor α (PPARα). In an effort to better understand the physiological role of ARA9 outside of its role in xenobiotic signal transduction, we generated a null allele at the Ara9 locus in mice. Mice with a homozygous deletion of this gene die at various time points throughout embryonic development. Embryonic lethality is accompanied by decreased blood flow to head and limbs, as well as a range of heart deformations, including double outlet right ventricle, ventricular-septal defects, and pericardial edema. The early cardiovascular defects observed in Ara9-null mice suggest an essential role for the ARA9 protein in cardiac development. The observation that the developmental aberrations in Ara9-null mice are distinct from those observed for disrupted alleles at Ahr or Pparα indicates that the role of ARA9 in cardiac development is independent of its interactions with its known xenobiotic receptor partners.
DISTANCE ERROR ESTIMATION FOR RANGE IMAGING SENSORS

The recent development of range imaging technology has enabled 3D data capture with a high resolution and the ability to measure the distance to the corresponding object point in each pixel. Besides some promising advantages, there are a number of drawbacks with the major being the distance measurement precision and accuracy which are still limited due to large systematic and random errors of the range imaging sensors. In order to reduce the effect of the systematic errors, a system calibration is essential which includes the calibration of the sensor as a camera device and the calibration of the range camera as a metric machine. This paper deals with the latter and aims to present a distance calibration approach. The methodology involves the capture of the calibration plane by the range camera and two video cameras. The calibration procedure is applied on the Swissrange SR-3000 sensor.
Machine Learning Methods for Attack Detection in the Smart Grid

Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system and surmount constraints arising from the sparse structure of the problem in the proposed approach. Well-known batch and online learning algorithms (supervised and semisupervised) are employed with decision- and feature-level fusion to model the attack detection problem. The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods. The proposed algorithms are examined on various IEEE test systems. Experimental analyses show that machine learning algorithms can detect attacks with performances higher than attack detection algorithms that employ state vector estimation methods in the proposed attack detection framework.
Large-Scale Somatotopic Refinement via Functional Synapse Elimination in the Sensory Thalamus of Developing Mice

Functional synapse elimination and strengthening are crucial developmental processes in the formation of precise neuronal circuits in the somatosensory system, but the underlying alterations in topographical organization are not yet fully understood. To address this issue, we generated transgenic mice in which afferent fibers originating from the whisker-related brain region, called the maxillary principal trigeminal nucleus (PrV2), were selectively visualized with genetically expressed fluorescent protein. We found that functional synapse elimination drove and established large-scale somatotopic refinement even after the thalamic barreloid architecture was formed. Before functional synapse elimination, the whisker sensory thalamus was innervated by afferent fibers not only from the PrV2, but also from the brainstem nuclei representing other body parts. Most notably, only afferent fibers from PrV2 onto a whisker sensory thalamic neuron selectively survived and were strengthened, whereas other afferent fibers were preferentially eliminated via their functional synapse elimination. This large-scale somatotopic refinement was at least partially dependent on somatosensory experience. These novel results uncovered a previously unrecognized role of developmental synapse elimination in the large-scale, instead of the fine-scale, somatotopic refinement even after the initial segregation of the barreloid map.
Efficient Implementation of Scalar Multiplication for Elliptic Curve Cryptography using Ancient Indian Vedic Mathematics over GF(p)

Elliptic Curve Cryptography (ECC) is one of the safest standard algorithms, based on public-key, for providing the security in communication and networks. One of the most time consuming processes in ECC algorithm for encryption/decryption is the scalar multiplication, i.e., KP, where P is the text which is on the elliptic curve. This paper examines that computation can be speeded up by using Ancient Indian Vedic Mathematics. Coding is done using Verilog-HDL and downloaded on target device as Virtex 5. Our proposed work is six times faster than the previous work when applied in point doubling using Spartan3 as target device.