Miracle-dz/UniFAR-FTUs huggingface dataset:
Evidence for a geomagnetic effect in the CODALEMA radio data

The CODALEMA experiment detects the cosmic air showers with an hybrid detector composed of an array of scintillators and an array of antennas triggered by the particle array. We will first describe the experiment and the data analysis then we will focus on the sky distribution of the events detected by the antennas which presents a strong asymmetry. We will show that this asymmetry is well described and quantified at first order by an emission of the shower electric field given by the cross product of the shower axis with the geomagnetic field vector. The physical origin of this term is explained in another talk in this conference.
Effect of the severity of autism on parental marital satisfaction

Parents soon find out that having a child creates many changes in their lives. Unfortunately, when that child happens to be developmentally disabled, the changes are much more difficult. The hypothesis of this study is that as the severity of autism goes up (as measured by the Gilliam Autism Rating Scale) the level of marital satisfaction goes down (as measured by the Dyadic Adjustment Scale). This was a quantitative research project whose goal was to compare the level of autism and it's affect on the parental relationship. Further, it sought to look at different family attributes of the subject child to see how they affected the marital satisfaction as well. The findings indicate that the ' hypothesis was correct, that the level of severity of autism, does negatively affect the parental marital satisfaction. Therefore, these findings indicate that social workers and other researchers must make studying the effects that a developmentally disabled child has on the marriage and the ways to best support these families a
Machine Learning Approach to Evaluate MultiLingual Summaries

The present paper introduces a new MultiLing text summary evaluation method. This method relies on machine learning approach which operates by combining multiple features to build models that predict the human score (overall responsiveness) of a new summary. We have tried several single and “ensemble learning” classifiers to build the best model. We have experimented our method in summary level evaluation where we evaluate each text summary separately. The correlation between built models and human score is better than the correlation between baselines and manual score.
Body size and fatness of free‐living baboons reflect food availability and activity levels

We used morphometric techniques and isotope‐labeled water to investigate the influence of abundant, accessible food and resultant low activity levels on body size and fatness in free‐living adolescent and adult baboons as compared to animals in the same population that experienced more typical, wild‐feeding conditions. Females that had access to abundant food from a nearby garbage dump averaged 16.7 kg body mass, 50% more than their wild‐feeding counterparts in adjacent home ranges. Little of the difference was due to lean mass: the animals with an accessible abundance of food averaged 23.2% body fat in contrast to 1.9% for the wild‐feeding animals. Significant differences between feeding conditions were found for all measured skinfolds and for upper arm circumference but not for linear measurements. Differences between feeding conditions were less for males than for females, perhaps reflecting persistent effects of nutritional conditions during the first eight years of life before dispersal from the group of birth. The difference in fatness between feeding conditions was similar to the difference between humans with frank obesity and those that are considered lean, but in both cases the percentages of body fat in the baboons were considerably less than those observed in humans. In levels of fatness, the relatively sedentary animals resembled their counterparts in group‐housed captive conditions. © 1993 Wiley‐Liss, Inc.
The Danish IPIP-NEO-120: A free, validated five-factor measure of personality

Abstract This paper reports the results of a translation and validation of a Danish language version of the IPIP-NEO-120 constructed by Johnson. The IPIP-NEO-120 is a public domain 120-item inventory that measures personality constructs similar to those in the Revised NEO Personality Inventory. The inventory was developed to provide researchers with a free inventory with a shorter format than the earlier 300-item IPIP-NEO and hence enable more researchers to include a personality measure in their research. The validation study reported in this paper was conducted to expand the IPIP-NEO-120 into the Danish context and provide Danish researchers with a free, validated personality measure based on the Five-Factor Model directly applicable to cross-national research. The psychometric properties of the Danish IPIP-NEO-120 were tested in a sample of veterinary students and graduates (N = 525) from a Danish university. The factor structure, internal consistency, and factor intercorrelations obtained in the Danish sample closely resembled those obtained in Johnson’s validation of the original IPIP-NEO-120, and the results indicate that the Danish measure corresponds well to the English language original.
Construction of Iso-Contours, Bisectors, and Voronoi Diagrams on Triangulated Surfaces

In the research of computer vision and machine perception, 3D objects are usually represented by 2-manifold triangular meshes M. In this paper, we present practical and efficient algorithms to construct iso-contours, bisectors, and Voronoi diagrams of point sites on M, based on an exact geodesic metric. Compared to euclidean metric spaces, the Voronoi diagrams on M exhibit many special properties that fail all of the existing euclidean Voronoi algorithms. To provide practical algorithms for constructing geodesic-metric-based Voronoi diagrams on M, this paper studies the analytic structure of iso-contours, bisectors, and Voronoi diagrams on M. After a necessary preprocessing of model M, practical algorithms are proposed for quickly obtaining full information about iso--contours, bisectors, and Voronoi diagrams on M. The complexity of the construction algorithms is also analyzed. Finally, three interesting applications-surface sampling and reconstruction, 3D skeleton extraction, and point pattern analysis-are presented that show the potential power of the proposed algorithms in pattern analysis.
Influence of Perceived Parent and Peer Endorsement on Adolescent Smoking Intentions: Parents Have More Say, But Their Influence Wanes as Kids Get Older

Purpose The aim of the study is to investigate how adolescents' perception of parents' and peers' smoking approval influences adolescent smoking intention, and how age affects this influence in a Swiss sample of adolescents. To know the influence of age can help to develop specific prevention programs tailored to the age groups needs. Method in a cross sectional survey, students aged between 11 and 14 from public and private middle schools in the Italian region of Switzerland (Ticino) answered questions on smoking habits, parents' and peers' approval and intention to smoke. Results peers' and parents' approval significantly influence students' smoking intention, and students' age significantly moderates this relation: the effect of parents' approval decreases for older adolescents, while the effect of peers' approval increases with age. No difference is found between girls and boys, while non-Swiss are more likely to smoke than Swiss students. Conclusions as literature suggests, results evidence the role parents play during early adolescence. Prevention programs targeting parent-child communication in early adolescence for preventing children's tobacco consumption are strongly supported.
A compact representation of terms and extensional equality at the exp-log normal form of types

Lambda calculi with algebraic data types appear at the core of functional programming languages, but still pose theoretical challenges today: for instance, even in the presence of the simplest non-trivial data type, the sum type, we do not know how to assign a unique canonical normal form to a class of beta-eta-equal programs. In this paper, we present the exp-log normal form of types---derived from the representation of exponential polynomials via the unary exponential and logarithmic functions---that any type built from arrows, products, and sums, can be isomorphically mapped to, but that systematically minimizes the number of necessary sums in the type. We then reduce the standard beta-eta equational theory of the lambda calculus to a specialized version of itself, while preserving completeness of the equality of terms. Finally, we describe an alternative, more canonical, representation of terms of the lambda calculus with sums, together with a Coq-implemented type-directed partial evaluator into/from our new term calculus. This is the first heuristic for deciding interesting cases of beta-eta-equality that relies only on syntactic comparison of normal forms, and not on performing program analysis of the involved terms.
Neural Networks based on Quantum Gated Nodes

On the basis of analyzing the principles of the quantum rotation gates and quantum controlled-NOT gates, a novel quantum neural networks model is proposed in our paper. In this model, the input information is expressed by the qubits, which, as the control qubits after rotated by the rotation gate, control the qubits in the hidden layer to reverse. The qubits in the hidden layer, as the control qubits after rotated by the rotation gate, control the qubits in the output layer to reverse. The networks output is described by the probability amplitude of state  1 | in the output layer. The learning algorithm is derived and discussed based on the gradient descent algorithm. It has been shown in two application examples of pattern recognition and function approximation that this new quantum neural network model is superior to the standard BP networks with regard to their convergence rate, number of iterations, approximation ability, and robustness. Keywordsquantum computing, quantum neural networks, quantum gate, learning algorithm
Out-of-label suppression dictionary learning with cluster regularization

This paper addresses the problem of learning a discriminative dictionary from training signals. Given a structured dictionary, each atom of which has its corresponding label, one signal should be mainly constructed by its closely associated atoms. Besides the representations for the same class ought to be very close to form a cluster. Thus we present out-of-label suppression dictionary model with cluster regularization to amplify the discriminative power. Upon out-of-label suppression, we don't adopt l0-norm or l1-norm for regularization. Meanwhile, two simple classifier are developed to take full advantage of the learnt dictionary. The effectiveness of the proposed dictionary model has been evaluated on two popular visual benchmarks.
The sociology of the limit: Reformulating the question of migration through Van Gennep

This article situates Van Gennep’s Rites de passage in the contemporary field of migration studies, arguing that his second and third chapters allow us to reformulate how we conceptualize contemporary migratory encounters. Rather than reading Van Gennep’s classical text through the prevalent Turnerian lens of “liminality” in migration, I reread it as offering a conceptualization of migration as deriving from society’s establishment on a delimited piece of earth, constituting itself against and in relation to its own limits. Society’s limit then becomes the center of social inquiry, while the strangers at the gate are seen as an integral part of these fundamental relations between a social grouping and the delimited space it occupies. The article further suggests that Van Gennep’s sociology of the limit offers the field of migration studies three practical analytical contributions: (a) its notion of the multiplicity of limits and sublimits emerging throughout the migratory encounter; (b) its emphasis on the sanctification of the limit, which opens up the possibility of analyzing the relations between migration and emotions; and (c) its pointing to the potential cycle of dread elicited by this sanctification—a notion highly relevant to analyzing contemporary migratory encounters.
Identification and analysis of chunks in software projects

Most software systems undergo continuous change in different phases of their lifecycle such as development or maintenance. Ideally, such changes should correspond to a system’s modular design. However, some changes span across more than one component thereby resulting in discrepancies between design and implementation. In such cases, making a change to one component requires changes to other components leading to an increase in time and effort to make changes to a software system as it evolves. This thesis investigates: 1) an approach to observe how components change together by identifying tightly coupled changes known as chunks, 2) whether there are any trends in how chunks evolve over time, and 3) whether chunks can help identify design issues in a software system. In this work, a family of algorithms is proposed to identify independently changing chunks from change data obtained from mining version history repositories of three large software systems – Moodle, Eclipse, and Company-X. A comprehensive analysis of certain characteristics of the resulting chunks is conducted. In addition, evolution of chunks with respect to size in terms of number of files within a chunk, and percentage of changes crossing a chunk are studied. Lastly, a pragmatic interpretation of the results to identify necessary code refactoring or system redesign is presented.
Modelling constraints on the emission inventory and on vertical dispersion for CO and SO 2 in the Mexico City Metropolitan Area using Solar FTIR and zenith sky UV spectroscopy

Emissions of air pollutants in and around urban areas lead to negative health impacts on the population. To estimate these impacts, it is important to know the sources and transport mechanisms of the pollutants accurately. Mexico City has a large urban fleet in a topographically constrained basin leading to high levels of carbon monoxide ( CO). Large point sources of sulfur dioxide (SO2) surrounding the basin lead to episodes with high concentrations. An Eulerian grid model (CAMx) and a particle trajectory model ( FLEXPART) are used to evaluate the estimates of CO and SO2 in the current emission inventory using mesoscale meteorological simulations from MM5. Vertical column measurements of CO are used to constrain the total amount of emitted CO in the model and to identify the most appropriate vertical dispersion scheme. Zenith sky UV spectroscopy is used to estimate the emissions of SO2 from a large power plant and the Popocatepetl volcano. Results suggest that the models are able to identify correctly large point sources and that both the power plant and the volcano impact the MCMA. Modelled concentrations of CO based on the current emission inventory match observations suggesting that the current total emissions estimate is correct. Possible adjustments to the spatial and temporal distribution can be inferred from model results. Accurate source and dispersion modelling provides feedback for development of the emission inventory, verification of transport processes in air quality models and guidance for policy decisions.
On modeling information retrieval with probabilistic inference

This article examines and extends the logical models of information retrieval in the context of probability theory. The fundamental notions of term weights and relevance are given probabilistic interpretations. A unified framework is developed for modeling the retrieval process with probabilistic inference. This new approach provides a common conceptual and mathematical basis for many retrieval models, such as the Boolean, fuzzy set, vector space, and conventional probabilistic models. Within this framework, the underlying assumptions employed by each model are identified, and the inherent relationships between these models are analyzed. Although this article is mainly a theoretical analysis of probabilistic inference for information retrieval, practical methods for estimating the required probabilities are provided by simple examples.
Effect of Principle Component Analysis and Support Vector Machine in Software Fault Prediction

: Machine Learning (ML) approaches have a great impact in fault prediction. Demand for producing quality assured software in an organization has been rapidly increased during the last few years. This leads to increase in development of machine learning algorithms for analyzing and classifying the data sets, which can be used in constructing models for predicting the important quality attributes such as fault proneness. Defective modules in software project have a considerable risk which reduces the quality of the software. This paper mainly addresses the software fault prediction using hybrid Support Vector Machine (SVM) classifier. We conduct a comparative study using the WEKA tool for three different levels of software metrics (package level, class level and method level) with hybrid SVM classifiers using feature selection techniques such as Principle Component Analysis (PCA). The experiments are carried out on the datasets such as NASA KC1 method level data set, NASA KC1 class level dataset and Eclipse dataset for package level metrics. The feature selection techniques evolved by experiments shows that Principle Component Analysis (PCA) with hybrid SVM performs better than other feature selection techniques.
Collaborative knowledge capture in ontologies

This paper describes a new environment, COE, for capturing and formally representing expert knowledge for use in the Semantic Web. COE exploits the ease of use and rapid knowledge construction capabilities of the CmapTools concept mapping system and extends them to support the import and export of formal, machine-interpretable knowledge representations, such as OWL, across multiple ontologies. Pragati's ExpozT tool suite complements COE's ontology construction, browsing and navigation features by providing cluster-based search capabilities that expose existing reusable concepts relevant to the user's focus of attention.
Perceptions of the content and employability value of credentialed teaching certificates

ABSTRACT The purpose of this research was to gain a broader understanding of what department heads and doctoral students believe to be the value of credentialed teaching certificates. Using a survey methodology with participants (N= 450), the study focused on the extent to which a credentialed teaching certificate provides a competitive advantage when seeking employment, as well as the content (pedagogical knowledge) that is perceived to be important for such programmes. Using a cross-sectional survey design, results highlight significant differences between doctoral students and department heads regarding the content and value of a credentialed teaching certificate in higher education.
Potential for Inhalation Exposure to Engineered Nanoparticles from Nanotechnology-Based Cosmetic Powders

Background: The market of nanotechnology-based consumer products is rapidly expanding, and the lack of scientific evidence describing the accompanying exposure and health risks stalls the discussion regarding its guidance and regulation. Objectives: We investigated the potential for human contact and inhalation exposure to nanomaterials when using nanotechnology-based cosmetic powders and compare them with analogous products not marketed as nanotechnology based. Methods: We characterized the products using transmission electron microscopy (TEM) and laser diffraction spectroscopy and found nanoparticles in five of six tested products. TEM photomicrographs showed highly agglomerated states of nanoparticles in the products. We realistically simulated the use of cosmetic powders by applying them to the face of a human mannequin head while simultaneously sampling the released airborne particles through the ports installed in the mannequin’s nostrils. Results: We found that a user would be exposed to nanomaterial predominantly through nanoparticle-containing agglomerates larger than the 1–100-nm aerosol fraction. Conclusions: Predominant deposition of nanomaterial(s) will occur in the tracheobronchial and head airways—not in the alveolar region as would be expected based on the size of primary nanoparticles. This could potentially lead to different health effects than expected based on the current understanding of nanoparticle behavior and toxicology studies for the alveolar region.
Diffractive optics for high-resolution low-coherence digital holography

We study the properties of the recording of off-axis holograms when a 10 fs pulsed laser is used as illumination source. A proper optical design involving one diffractive lens outside a Michelson interferometer enables the recording of full-field off-axis holograms with high resolution and optical sectioning. We demonstrate our approach with some experimental results that show optical sectioning with a maximum resolution of 3.5 µm. We note that the axial resolution of the technique is reduced up to 9 µm when the object beam travels through a few millimeters of glass due to the pulse broadening along dispersive media.
IMPROVED CHANNEL ESTIMATION FOR OFDM SYSTEMS IN QUASI-STATIC CHANNELS

A low-complexity time-domain channel estimation technique for OFDM systems is proposed. It uses a training symbol (preamble) to estimate the channel impulse response (CIR) via circular cross-correlation. An efficient CIR search window technique is also proposed to optimise the accuracy of synchronisation and channel estimation. Simulation results in quasi-static channels show that the proposed method achieves near-ideal accuracy with a complexity which is much lower than that of the MMSE technique.
No-Reference Bitstream-Based Visual Quality Impairment Detection for High Definition H.264/AVC Encoded Video Sequences

Ensuring and maintaining adequate Quality of Experience towards end-users are key objectives for video service providers, not only for increasing customer satisfaction but also as service differentiator. However, in the case of High Definition video streaming over IP-based networks, network impairments such as packet loss can severely degrade the perceived visual quality. Several standard organizations have established a minimum set of performance objectives which should be achieved for obtaining satisfactory quality. Therefore, video service providers should continuously monitor the network and the quality of the received video streams in order to detect visual degradations. Objective video quality metrics enable automatic measurement of perceived quality. Unfortunately, the most reliable metrics require access to both the original and the received video streams which makes them inappropriate for real-time monitoring. In this article, we present a novel no-reference bitstream-based visual quality impairment detector which enables real-time detection of visual degradations caused by network impairments. By only incorporating information extracted from the encoded bitstream, network impairments are classified as visible or invisible to the end-user. Our results show that impairment visibility can be classified with a high accuracy which enables real-time validation of the existing performance objectives.