Miracle-dz/UniFAR-FTUs huggingface dataset:
Geometrical and mechanical aspects of structure and flexibility in proteins

Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  You may not further distribute the material or use it for any profit-making activity or commercial gain  You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Downloaded from orbit.dtu. dk on: Jan 11, 2020
SURVEY OF HEALTH RISK BEHAVIORS IN COLLEGE STUDENTS

[Objective]To study the epidemiological characteristics of health risk behaviors among college students and the influential factors. [Methods]A questionnaire survey of health risk behaviors was conducted among 505 college students in Sichuan,including freshmen and seniors. [Results]Among the college students,health risk behaviors were common.The prevalence of cigarette smoking was 41.92%,the rate of alcohol using was 31.99% in recent 30 days,11.07% reported having experienced sexual intercourse,9.64% were addicted to internet,and 10.46% had used violated medicamentum.Most of the risk behaviors were more common significantly among males than females,but there was no remarkable difference in grades. [Conclusions]It's necessary to strengthen health education,and more attention should be paid to the health risk behaviors of college students.
MODIFIED COOPERATIVE ACCESS WITH RELAY’S DATA PROTOCOL USING SMART ANTENNA

In this paper, a new protocol named Modified Cooperative Access with Relay’s Data (MCARD) for wireless local area networks is proposed. MCARD gives remote stations chance to send their information by using intermediate stations (relays) to Access Point. In the MCARD, a relay and source stations send their information simultaneously to the access point and relay station, respectively, using a smart antenna technique. A uniform circular array of 12 half-wave dipole antenna elements is considered to control the array beam-forming of both relay and source stations. Analytical and simulation results show that MCARD can significantly improve system quality of service (QOS) in terms of throughput and delay under ideal channel conditions.
Testing of a background classification algorithm for use with dual-polarized radars in determining precipitation type at the surface

The NWS is required to provide precipitation type guidance within 200 km of all radars. This is a rather difficult challenge primarily because radars cannot see all the way to the surface and so elevated melting layers may not be detected and because surface observations (ASOS) are rather sparsely spaced. Additionally, ASOS are known to have some biases in detecting freezing rain and are unable to detect ice pellets. A new winter surface classification algorithm is under development at NSSL. This poster presents details important to the development of one phase (the generation of a background class) of this algorithm.
A Probabilistic Model For Recommending Music Based on Acoustic Features and Social Data

The “Cold Start” problem is a well known issue in Collaborative Filtering recommendation systems, associated to the moment when a new item or user is added to a given collection, because the system has no historical information of interaction between existing and new elements and it still need to incorporate these elements into the recommendation algorithm. This work addresses one possible solution for the case where new songs are added to a dataset of a music recommendation system, by proposing a probabilistic model for inference based on the songs’ acoustic/timbre features. This model was first proposed for tagging music with semantic labels but is here suggested as being suitable for predicting user interactions with new songs. The experiments were conducted using a selection of Brazilian popular music and the results show that the proposed method compares favorably to Logistic Regression.
13.7 Material Studies of GaN on Diamond

Group4 Labs has developed and made commercially available for the first time, a composite semiconductor wafer that comprises of heteroepitaxial Gallium Nitride (GaN) compound semiconductor films atomically attached to polycrystalline chemical vapor deposited (CVD) diamond substrate. This GaN-on-Diamond wafer system enables extremely rapid, efficient, passive, and costeffective heat extraction from the heat-generating heteroepitaxial device layers since the GaN heating layers are less than twenty nanometers from the highly thermal conductive diamond.
KNOWLEDGE-BASED APPROACH TO WINOGRAD SCHEMA CHALLENGE

We propose a method to resolve anaphoric pronouns in the framework of Winograd Schema Challenge (WSC) by means of SemETAP—a knowledge-based semantic analyzer. WSC is a modern version of the famous Turing test. Its objective is to check a machine’s ability to exhibit intelligent behavior indistinguishable from that of a human. In contrast to other approaches to WSC, which are based on machine learning, our method uses explicit knowledge. An important advantage of this approach is that it gives an opportunity to provide an explanation of the result understandable for humans. SemETAP interprets the text using both linguistic and extralinguistic (background) knowledge. The former is stored in the grammar and the dictionary of the ETAP-4 system, and the latter is provided by the SemETAP ontology, inference rules and the repository of individuals. We show how this knowledge is used for resolving WSC. At the moment, the performance of the algorithm is not high—54%. This is due to the incompleteness of the background knowledge supplied to the system. It is shown, however, that if the background knowledge is complete and accurate enough, the WSC test is resolved well and it is easily understandable why the system arrived at a particular conclusion.
Fast Monte Carlo Simulation for Pricing Covariance Swap under Correlated Stochastic Volatility Models

The modeling and pricing of covariance swap derivatives under correlated stochastic volatility models are studied. The pricing problem of the derivative under the case of discrete sampling covariance is mainly discussed, by efficient Control Variate Monte Carlo simulation. Based on the closed form solutions derived for approximate models with correlated deterministic volatility by partial differential equation method, two kinds of acceleration methods are therefore proposed when the volatility processes obey the Hull-White stochastic processes. Through analyzing the moments for the underlying processes, the efficient control volatility under the approximate model is constructed to make sure the high correlation between the control variate and the problem. The numerical results illustrate the high efficiency of the control variate Monte Carlo method; the results coincide with the theoretical results. The idea in the paper is also applicable for the valuation of other financial derivatives with discrete features under multi-factor models.
SOME COMPLEX STRUCTURES ON PRODUCTS OF HOMOTOPY SPHERES

IN 1948 H. Hopf ([6]) introduced the complex manifolds, that nowadays are known as Hopf manifolds. Their construction is quite simple: they are nothing but the quotient of C! , 0 by the group of automorphisms, generated by the transformation z -+ cz, where c is a complex number with lcl # 1. Differentiably, they are the product S’ x Sznml of a circle and a (2n 1)-sphere, and complex analytically they are homogeneous manifolds and fibre bundles over the complex projective space P,1, with fibre an elliptic curve. For n B 2 they do not carry any Kahler structure, hence a fortiori no projective algebraic structure.
SEXUAL VIOLENCE DECONSTRUCTED :

The complex universe created by Sade has conditioned the Western imagination in such a way that his figure has been vindicated by the different aesthetic and philosophical perspectives inhabiting the last century. Due to his potential of transgression Sade has become a cultural icon with some relevance for the evolution of feminism in the last decades. It is the purpose of this article to draw a critical line starting with the masculine tradition and analyzing later the different feminist rewritings that begins with Beauvoir’s revision (1955), followed by the essay The Sadeian Woman (1979) by Angela Carter, ending with Rikki Ducornet’s The Fan-Maker’s Inquisition. A Novel of the Marquis de Sade (1999).
ON ANALYSIS OF MARTINGALE RESIDUALS IN LIFETIME MODELS

Residual analysis is used commonly in statistical tests of model fit, i.e. of good correspondence between data and model. We shall recall the notion of martingale residuals defined for the case of lifetime models based on failure intensities. Analysis of these residual processes has already been studied by many authors. Nevertheless, Bayes approach to this problem is just developing. We shall present a Bayes procedure of estimation in models containing nonparametric components. Further, Bayes construction of residual processes and model goodness-of-fit assessing will be proposed and demonstrated on an example with Cox’s regression model.
Comparing Meta-Classifiers for Automatic Music Genre Classification

Automatic music genre classification is the problem of associating mutually-exclusive labels to audio tracks. This process fosters the organization of collections and facilitates searching and marketing music. One approach for automatic music genre classification is to use diverse vector representations for each track, and then classify them individually. After that, a majority voting system can be used to infer a single label to the whole track. In this work, we evaluated the impact of changing the majority voting system to a meta-classifier. The classification results with the meta-classifier showed statistically significant improvements when related to the majority-voting classifier. This indicates that the higher-level information used by the meta-classifier might be relevant for automatic music genre classification.
Are word-initial sibilant-sonorant clusters especial? Behavioural and neural correlates differentiate sonority-conforming #FR and #SR clusters in English Introduction When considering the pair blick-nbick, languages with word-initial consonant clusters can be divided into those that will allow for n

Introduction When considering the pair blick-nbick, languages with word-initial consonant clusters can be divided into those that will allow for nbick as a possible word and those that will not (Chomsky & Halle, 1968). For the latter group, the sequence #/nb/ in nbick is ill-formed: there exists a sonority restriction in the grammaticality of initial clusters in languages such as English and French due to the Sonority Principle (C&H, 1968 inter alia). Languages with sonority-restricted word-initial consonant clusters specifically limit the combinations of permitted consonants to obstruent-sonorant (or obstruent-liquid) sequences (Clements, 1990; Scheer, 2012). For ease of exposition we will refer to these languages as #TR languages, where T is short for obstruent and R for sonorant (Scheer, 2012). When empirically investigating the role of sonority in the contribution to initial cluster well-formedness in #TR languages, one potential experimental confound relates to the documented phenomenon of perceptual deafness to unattested and sonority-violating sequences (Dupoux et al., 1999). Because of this, existing experiments investigating the neural response of initial #nonTR clusters in #TR languages have only been indirect: the only existing electroencephalography (EEG) event-related potential (ERP) study on #nonTR relied on eliciting differential responses to sequences with and without an epenthetic vowel (Wagner et al., 2012). This indirect inference method does not address the grammatical status of initial clusters beyond the success rate of specific-token perception. This paper focuses specifically on the comparison of word-initial nonsibilant fricative-sonorant (henceforth #FR) and sibilant-sonorant (#SR) clusters in English (Table 1). We include glides, liquids and nasals as sonorants: hence canonically “unperceivable” cluster sequences (/fn/) can also be tokens. Methodologically, we address aforementioned challenges by presenting two novel paradigms to facilitate investigations of all types of word-initial clusters in #TR languages: a dichotic listening study and an EEG study. Topically we explore behavioural and neurophysiological correlates to sonority sequencing in initial cluster perception and processing. Preliminary pilot results (n=9 behavioural, n=4 neural; data collection in progress) indicate a differential processing response between #FR and #SR clusters despite their shared rising sonority slope.
3A: mAchine learning Algorithm Applied to emotions in melodies

At every moment, innumerable emotions can indicate and provide questions about daily attitudes. These emotions can interfere or stimulate different goals. Whether in school, home or social life, the environment increases the itinerant part of the process of attitudes. The musician is also passive of these emotions and incorporates them into his compositions for various reasons. Thus, the musical composition has innumerable sources, for example, academic formation, experiences, influences and perceptions of the musical scene. In this way, this work develops the mAchine learning Algorithm Applied to emotions in melodies (3A). The 3A recognizes the musician’s melodies in real time to generate accompaniment melody. As input, The 3A used MIDI data from a synthesizer to generate accompanying MIDI output or sound file by the programming language Chuck. Initially in this work, it is using the Gregorian modes for each intention of composition. In case, the musician changes the mode or tone, the 3A has an adaptation to continuing the musical sequence. Currently, The 3A uses artificial neural networks to predict and adapt melodies. It started from mathematical series for the formation of melodies that present interesting results for both mathematicians and musicians.
Correction of longitudinal aberrations in goniometric measurement with a multispectral camera

A multispectral camera featuring a monochrome camera and a filter wheel with 19 narrow band filters covering the visible wavelength range is considered. Longitudinal aberrations cannot be avoided in such cameras: they are caused by the wavelength dependency of refraction indices in optical components and by the different parameters of the color filters. Longitudinal aberrations are analyzed for a multispectral camera acquiring images from different viewing angles (goniometric imaging). The images are deformed geometrically because of the different positions of the camera. The images are thus rectified in order to be consistent to one viewing point, yet become less sharp due to this rectification. This work presents a method for the correction of longitudinal aberrations in goniometric multispectral measurements. The blur in rectified goniometric images is measured using point spread functions and its different components are analyzed. The longitudinal aberrations are compensated by deconvolving the multispectral images.
16A.6 GUST FRONT VS. NON-GUST FRONT THUNDERSTORMS: APPLICATION OF THE MULTI-RESPONSE PERMUTATION PROCEDURE (MRPP) TO ASSESS RADAR STORM CHARACTERISTICS AND ENVIRONMENTAL CONDITIONS

The purpose of this paper is to highlight the use of a non-classical, rigorous statistical technique, called the Multi-Response Permutation Procedure (MRPP), to differentiate gust front (GF) and non-gust front (NGF) thunderstorms observed during the 2002 International H2O Project (IHOP_2002). Given that this mesoscale feature has a considerable influence on thunderstorm development and behavior with the potential to initiate secondary convection, it is essential, particularly for short-term forecasting, to know which storms will produce a gust front. However, this research did not focus on gust front forecasting, rather, the objective was to identify significant variables that distinguished GF/NGF storms, in terms of storm radar characteristics and environmental conditions, using a probability approach (though the preliminary results could be used as predictors in gust front development).
Target Localization in Synthetic Aperture Sonar Imagery using Convolutional Neural Networks

Automatic target recognition (ATR) in synthetic aperture sonar (SAS) is usually performed in two stages: object detection and target classification. The detector aims to localize all the potential targets whereas the classifier distinguishes between real targets and false alarms. The probability of detection at this first stage must be the highest as possible to ensure that targets are not missed. Unfortunately, this generally implies a significant false alarm rate. Therefore, the challenge of the second stage, classification, is to drastically reduce the number of false alarms while keeping the detected targets. Using a large database of SAS images, efficient CNN classifiers have been demonstrated for underwater target classification tasks. In this paper, we suggest applying a pretrained classification CNN for localizing targets in SAS images. In so doing, we show the feasibility of target detection and classification in one-step using CNNs.
Before the Occupational Safety and Health Administration United States Department of Labor Comments of Cardno ChemRisk on OSHA’s Discussion of the Adequacy of Sampling and Analytical Methods for Measuring Respirable Crystalline Silica at Exposure Levels of 25 and 50 μg/m

In the Notice of Proposed Rulemaking (NPRM) to adopt a standard for Occupational Exposure to Crystalline Silica, 78 Fed. Reg. 56274 (September 12, 2013), and in the associated Preliminary Economic Analysis (PEA), OSHA presents its justification for why the analytical methods for respirable crystalline silica (RCS) are adequate to accurately measure exposures at the proposed permissible exposure limit (PEL) of 50 μg/m and at an action level of 25 μg/m. The overall justification is misguided and inaccurate because it fails to adequately characterize the various sources of sampling and analytical error associated with the sampling and analysis methods used for respirable crystalline silica. In addition, there is one instance of a calculation error in OSHA’s analysis that mistakenly leads the Agency to conclude that precision is as good at lower silica loadings as at higher ones. The result is that OSHA has not made a supportable showing that RCS exposures can be accurately measured with acceptable precision at the concentrations of the proposed PEL and action level. The existence of sampling and analytical methods that can accurately and precisely measure respirable crystalline silica at the proposed PEL is the first step in determining the technological feasibility of the rule. If the sampling and analytical methods do not meet the necessary standards of accuracy and precision, then it is impractical to accurately determine the effectiveness of the engineering controls to meet the proposed PEL. According to NIOSH (1995), the necessary standard of accuracy and precision that a sampling and analytical method should meet is to give a result that is within +25% of the true concentrations at a 95% confidence level. At exposure levels of 25 and 50 μg/m, that standard cannot be met.
Assessment, Selection and Demonstration of Technologies and Equipment for Measuring Welder Exposure to Ozone during GMAW (MIG Welding) on Aluminum Alloys

Ozone is a by-product of arc welding on aluminum alloys and stainless steels. Assessment of exposure is difficult because emissions are short in duration and large in magnitude compared to background levels, and generally occur unpredictably during work activity. The welding environment is particularly harsh. This study applied a systematic process to identify technologies and sampling devices available in the marketplace and to select suitable candidates and reject unsuitable ones. Candidates suitable for study included colorimetric detector tubes, an Ultra-Violet (UV) air pollution analyzer, and person-portable instruments, one containing a heated semi-conductor sensor and the other an electrochemical sensor. These devices were exposed to welding plumes contained in a pre-conditioned plastic bag and to unconfined plumes during production welding (Gas Metal Arc Welding [GMAW] commonly known as Metal Inert Gas [MIG welding]) on aluminum alloys. Nitrogen dioxide is an interferent in the assessment of ozone exposure by almost all technologies. Particulates and unreacted metal atoms in the plume further complicate measurement of ozone. None of the measurement technologies overwhelmed the others in this application. The harshness of the welding environment eliminated several candidates. Colorimetric detector tubes provided the best compromise between performance and safety in proof-of-concept testing on welders.
Weighting Play and Learning in Interaction

Andrea Conci Dept. of Information Engineering And Computer Science University of Trento, Italy Via Sommarive, 5 38123 Povo (TN) andrea.conci. 1@unitn.it Cristina Core Dept. of Information Engineering And Computer Science University of Trento, Italy Via Sommarive, 5 38123 Povo (TN) cristina.core@unitn.it Fabio Morreale Dept. of Information Engineering And Computer Science University of Trento, Italy Via Sommarive, 5 38123 Povo (TN) morreale@disi.unitn.it
Forecast Customization System (FOCUS): A Multimodel Ensemble-Based Seasonal Climate Forecasting Tool for the Homogeneous Climate Zones of Myanmar

A Multi-Model Ensemble (MME) based seasonal rainfall forecast customization tool called FOCUS was developed for Myanmar in order to provide improved seasonal rainfall forecast to the country. The tool was developed using hindcast data from 7 Global Climate Models (GCMs) and observed rainfall data from 49 meteorological surface observatories for the period of 1982 to 2011 from the Department of Meteorology and Hydrology. Based on the homogeneity in terms of the rainfall received annually, the country was divided into six climatological zones. Three different operational MME techniques, namely, (a) arithmetic mean (AM-MME), (b) weighted average (WA-MME), and (c) supervised principal component regression (PCR-MME), were used and built-in to the tool developed. For this study, all 7 GCMs were initialized with forecast data of May month to predict the rainfall during June to September (JJAS) period, which is the predominant rainfall season for Myanmar. The predictability of raw GCMs, bias-corrected GCMs, and the MMEs was evaluated using RMSE, correlation coefficients, and standard deviations. The probabilistic forecasts for the terciles were also evaluated using the relative operating characteristics (ROC) scores, to quantify the uncertainty in the GCMs. The results suggested that MME forecasts have shown improved performance (RMSE = 1.29), compared to the raw individual models (ECMWF, which is comparatively better among the selected models) with RMSE = 4.4 and bias-corrected RMSE = 4.3, over Myanmar. Specifically, WA-MME (CC = 0.64) and PCR-MME (CC = 0.68) methods have shown significant improvement in the high rainfall (delta) zone compared with WA-MME (CC = 0.57) and PCR-MME (CC = 0.56) techniques for the southern zone. The PCR method suggests higher predictability skill for the upper tercile (ROC = 0.78) and lower tercile categories (ROC = 0.85) for the delta region and is less skillful over lower rainfall zones like dry zones with ROC = 0.6 and 0.63 for upper and lower terciles, respectively. The model is thus suggested to perform relatively well over the higher rainfall (Wet) zones compared to the lower (Dry) zone during the JJAS period.