Miracle-dz/UniFAR-FTUs huggingface dataset:
Combined use of exhaled nitric oxide and airway hyperresponsiveness in characterizing asthma in a large population survey.

The aim of the present study was to see whether measurements of airway hyperresponsiveness (AHR) and nitric oxide (NO) in exhaled air (ENO) either separately or in combination, could differentiate between asthmatics and healthy control subjects in a population based survey. In central Norway 8,571 adolescents participated in a large-scale epidemiological survey (Young Helseundersøkelsen i Nord-Trøndelag (Health Survey in North-Trøndelag; HUNT). Asthmatic symptoms when exposed to pollen, pets or house-dust were reported by 7.8% (suspected asthmatics), while 56% reported no asthmatic or allergic symptoms (control subjects). From these respective groups 151 and 213 adolescents were investigated with allergy screening, measurements of exhaled and nasal NO, and methacholine challenge test. AHR (provocative dose of methacholine causing a 20% fall in forced expiratory volume in one second (PD20) <2 mg) was confirmed in 75% of the suspected asthmatics versus 25% of the control subjects, whereas 52% versus 20% had elevated levels of ENO (> or =8 parts per billion (ppb)). ENO and dose response ratio to methacholine (DRR) were positively correlated (r=0.41, p<0.001). ENO was significantly elevated in atopic versus nonatopic suspected asthmatics (11.7 ppb and 5.6 ppb respectively, p<0.001). Suspected asthmatics with both AHR and atopy had the highest levels of ENO (14.2 ppb). It is concluded that measurements of nitric oxide in exhaled air alone are not a useful tool in diagnosing asthma in population surveys, but that the combination of airway hyperresponsiveness and elevated nitric oxide in exhaled air is a very specific finding for allergic asthma. The use of dose response ratio to methacholine did not provide any additional information to the provocative dose of methacholine causing a 20% fall in forced expiratory volume in one second in this study.
Richness-preserving manga screening

Due to the tediousness and labor intensive cost, some manga artists have already employed computer-assisted methods for converting color photographs to manga backgrounds. However, existing bitonal image generation methods usually produce unsatisfactory uniform screening results that are not consistent with traditional mangas, in which the artist employs a rich set of screens. In this paper, we propose a novel method for generating bitonal manga backgrounds from color photographs. Our goal is to preserve the visual richness in the original photograph by utilizing not only screen density, but also the variety of screen patterns. To achieve the goal, we select screens for different regions in order to preserve the tone similarity, texture similarity, and chromaticity distinguishability. The multi-dimensional scaling technique is employed in such a color-to-pattern matching for maintaining pattern dissimilarity of the screens. Users can control the mapping by a few parameters and interactively fine-tune the result. Several results are presented to demonstrate the effectiveness and convenience of the proposed method.
Advection Dominated Accretion Flows. A Toy Disk Model

A toy model of a disk undergoing steady state accretion onto a black hole is presented. The disk is in a hydrostatic equilibrium for all radii r > r_{in}, with the inner disk radius located between the marginally stable and marginally bound orbits: r_{ms} > r_{in} > r_{mb}. Matter flows from the disk through a narrow cusp at r_{ms} and falls freely into the black hole, carrying with it no thermal energy. At radii larger than r_{out} the disk is assumed to radiate away all locally generated heat, and therefore the disk is geometrically thin for r > r_{out}. We assume that no heat generated in the inner disk, with r_{out} > r > r_{in} can be radiated away, i.e. the disk is 100% advective, and it becomes geometrically thick in this range of radii. All enthalpy of the thick disk is used up to press the inner disk radius towards the marginally bound orbit, and to lower the efficiency of conversion of accreted mass into radiation generated only for r > r_{out}, by assumption. Conservation laws of mass, angular momentum and energy make it possible to calculate the inner thick disk radius r_{in} for any specified value of its outer radius r_{out}. As the nature of disk viscosity is not known there is some freedom in choosing the shape of the thick disk, subject to several general conditions, which include the hydrostatic equilibrium everywhere for r > r_{in}. The main purpose of this toy model is to emphasize the effect the disk thickness has on lowering the energetic efficiency of a black hole accretion.
QBI: an iconic query system for inexpert users

We present a general purpose query interface for inexpert users based on the manipulation of icons. The user perceives the reality of interest as structured in classes and attributes while the system internally maintains a schema rich of semantic information. The query language, fully visual, is based on the select and project paradigm that has been proven to be easy to understand. No path specification is required for composing a query. Automatic feedbacks based on natural language generation and cardinality constraints analysis help the user in specifying his/her requests.
Séance 34B Development of Interactive Tools for the Exploration of Large Geographic Databases

The methodology of parallel coordinate geometry is presented and adapted for the visualization of multidimensional geographic information. Large, multidimensional spatiotemporal data sets common in the environmental sciences require exploratory methods of analysis in addition to more deductive approaches. Representation techniques for such an inductive approach must facilitate visualization of more than three dimensions at one time. The dynamic parallel coordinate plot is introduced as a tool well designed for the visualization of multivariate analysis tasks, in particular automated classification techniques such as those found in data mining. The effectiveness of this tool for interpretation and refinement of such classifications is then demonstrated anecdotally using classed multivariate climate observations.
Visualizing Large‐scale Parallel Communication Traces Using a Particle Animation Technique

Large‐scale scientific simulations require execution on parallel computing systems in order to yield useful results in a reasonable time frame. But parallel execution adds communication overhead. The impact that this overhead has on performance may be difficult to gauge, as parallel application behaviors are typically harder to understand than the sequential types. We introduce an animation‐based interactive visualization technique for the analysis of communication patterns occurring in parallel application execution. Our method has the advantages of illustrating the dynamic communication patterns in the system as well as a static image of MPI (Message Passing Interface) utilization history. We also devise a data streaming mechanism that allows for the exploration of very large data sets. We demonstrate the effectiveness of our approach scaling up to 16 thousand processes using a series of trace data sets of ScaLAPACK matrix operations functions.
Registration based on projective reconstruction technique for augmented reality systems

In AR systems, registration is one of the most difficult problems currently limiting their application. In this paper, we propose a simple registration method using projective reconstruction. This method consists of two steps: embedding and tracking. Embedding involves specifying four points to build the world coordinate system on which a virtual object will be superimposed. In tracking, a projective reconstruction technique is used to track these four specified points to compute the model view transformation for augmentation. This method is simple, as only four points need to be specified at the embedding stage and the virtual object can then be easily augmented onto a real scene from a video sequence. In addition, it can be extended to a scenario using the projective matrix that has been obtained from previous registration results using the same AR system. The proposed method has three advantages: 1) it is fast because the linear least square method can be used to estimate the related matrix in the algorithm and it is not necessary to calculate the fundamental matrix in the extended case. 2) A virtual object can still be superimposed on a related area even if some parts of the specified area are occluded during the whole process. 3) This method is robust because it remains effective even when not all the reference points are detected during the whole process, as long as at least six pairs of related reference points correspondences can be found. Some experiments have been conducted to validate the performance of the proposed method.
Anemia as an Independent Predictor of Adverse Cardiac Outcomes in Patients with Atrial Fibrillation

Background: Anemia and echocardiographic systolic and diastolic parameters are useful predictors of cardiovascular outcomes in patients with atrial fibrillation (AF). However, no studies have evaluated the use of anemia for predicting cardiovascular outcome in AF patients when the important echocardiographic parameters are known. Therefore, this study was designed to evaluate whether low hemoglobin is a useful parameter for predicting poor cardiac outcome after adjustment for important echocardiographic parameters in AF patients. Methods: Index beat method was used to measure echocardiographic parameters in 166 patients with persistent AF. Cardiac events were defined as death and hospitalization for heart failure. The association of hemoglobin with adverse cardiac events was assessed by Cox proportional hazards model. Results: The 49 cardiac events identified in this population included 21 deaths and 28 hospitalizations for heart failure during an average follow-up of 20 months (25th-75th percentile: 14-32 months). Multivariable analysis showed that increased left ventricular mass index (LVMI) and decreased body mass index, estimated glomerular filtration rate, and hemoglobin (hazard ratio 0.827; P = 0.015) were independently associated with increased cardiac events. Additionally, tests of a Cox model that included important clinic variables, LVMI, left ventricular ejection fraction, and the ratio of transmitral E-wave velocity to early diastolic mitral annulus velocity showed that including hemoglobin significantly increased value in predicting adverse cardiac events (P = 0.010). Conclusions: Hemoglobin is a useful parameter for predicting adverse cardiac events, and including hemoglobin may improve the prognostic prediction of conventional clinical and echocardiographic parameters in patients with AF.
From beliefs to practice: An examination of students' and teachers' views about effective classroom management

The purpose of this study is to examine the views of teachers and students about effective classroom management. In studying the convergences between teachers’ and students’ beliefs, the study clarifies extant research on effective classroom management and broadens the perspective with which it is viewed. Within a mixed-methods design, the research comprised four complimentary sequences of data collection and analysis. Through surveys and focus groups, students explained they were more likely to behave well for teachers they respect and believe show genuine concern for student welfare and learning. In contrast, students tended to misbehave for teachers who tried to dominate, or who did not seem to care about students or their learning. Further, students appreciated teachers who held them accountable and yet offered them responsibility with support and structure. Students enjoyed and benefited from learning experiences that were varied, engaging and clearly articulated. Through surveys and interviews, teachers suggested that responsibility and care were fundamental to building effective learning contexts. Both teachers and students indicated that trust and encouragement were fundamental aspects of developing productive teaching and learning relationships, in addition to high expectations. Moreover, this research revealed that both students and the teachers have well articulated views on what constitutes effective classroom management. It shows quite clearly that whilst students’ actions and interactions are quite purposeful, they can also be productively managed by caring, commanding and compelling teachers. These findings are discussed in the context of current approaches to classroom management for prospective teachers, and national professional standards of practice for graduate and experienced teachers.
Shaping Tomorrow Today

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A Proactive Multi-type Context-Aware Recommender System in the Environment of Internet of Things

Currently recommender systems are incorporating context and social information of the user, producing context aware recommender systems. In the future, they will use implicit, local and personal information of the user from the Internet of Things, where anyone and anything will be connected at anytime and anywhere. Most recommender systems follow a request-response approach in which the recommendations are provided to the user upon his request. Recently a proactive recommender system - that pushes recommendations to the user when the current situation seems appropriate, without explicit user request - has been introduced in the research area of recommender systems. The fact that the future is for Internet of Things, and the emergence of proactivity concept leads to our system design, in which multi-type rather than one type of recommendations will be recommended proactively to the user in real time. In this paper, a design of a context aware recommender system that recommends different types of items proactively under the Internet of Things paradigm is proposed. A major part of this design is the context aware management system. In this system, we have used a neural network that will do the reasoning of the context to determine whether to push a recommendation or not and what type of items to recommend. The neural network inputs are derived virtually from the Internet of Things, and its outputs are scores for three types of recommendations, they are: gas stations, restaurants and attractions. These scores have been used to decide whether to push a recommendation or not, and what type of recommendations to push among these three types. The results of 5000 random contexts were tested. For an average of 98% of them, our trained neural network generated correct recommendation types in the correct times and contexts.
Divest Yourself of a Preconceived Idea: Transcription Factor ATF6 Is Not a Soluble Protein!

The unfolded protein response (UPR), an evolutionarily conserved transcriptional induction program that is coupled with intracellular signaling from the endoplasmic reticulum (ER) to the nucleus, is activated to cope with ER stress and to maintain the homeostasis of the ER. In 1996, we isolated a basic leucine zipper protein, which had been previously named activating transcription factor (ATF)6, as a candidate transcription factor responsible for the mammalian UPR. Subsequent analysis, however, was confounding. The problem was eventually tracked down to an unusual property of ATF6: rather than being a soluble nuclear protein, as expected for an active transcription factor, ATF6 was instead synthesized as a transmembrane protein embedded in the ER, which was activated by ER stress-induced proteolysis. ATF6 was thus unique: an ER stress sensor/transducer that is involved in all steps of the UPR, from the sensing step in the ER to the transcriptional activation step in the nucleus.
Quantifying the level of failure in a digital business ecosystem interactions

To ascertain the possible level of risk in a digital business ecosystem interaction, the initiating agent has to determine beforehand the probability of failure, the possible consequences of failure, and the loss of investment probability to its resources while interacting with the other agent. Out of these three constituents, the initiating agent can determine beforehand the probability of failure in interacting with an agent either by considering its past interaction history with it or by soliciting recommendations from other agents. In both cases, it is imperative for the agent who is either considering its past interaction history, or who is communicating a recommendation about the other agent, to know the accurate level of failure in interacting with the other agent. To achieve this, in this paper we propose a methodology by which the initiating agent of the interaction ascertains the level of failure in the interaction, after interacting with an agent.
Fatigue in Multiple Sclerosis: Misconceptions and Future Research Directions

Fatigue is one of the most disabling side effects in people with multiple sclerosis. While this fact is well known, there has been a remarkable lack of progress in determining the pathophysiological mechanisms behind fatigue and the establishment of effective treatments. The main barrier has been the lack of a unified definition of fatigue that can be objectively tested with validated experimental models. In this “perspective article” we propose the use of the following model and definition of fatigue: the decrease in physical and/or mental performance that results from changes in central, psychological, and/or peripheral factors. These changes depend on the task being performed, the environmental conditions it is performed in, and the physical and mental capacity of the individual. Our definition and model of fatigue outlines specific causes of fatigue and how it affects task performance. We also outline the strengths and weaknesses of commonly used measures of fatigue and suggest, based on our model and definition, new research strategies, which should include multiple measures. These studies should be mechanistic with validated experimental models to determine changes in central, psychological, and/or peripheral factors that explain fatigue. The proposed new research strategies may lead to the identification of the origins of MS related fatigue and the development of new, more effective treatments.
Relational Markov Networks for Collective Information Extraction

Most information extraction (IE) systems treat separate potential extractions as independent. However, in many cases, considering influences between different potential extractions could improve overall accuracy. Statistical methods based on undirected graphical models, such as conditional random fields (CRFs), have been shown to be an effective approach to learning accurate IE systems. We present a new IE method that employs Relational Markov Networks, which can represent arbitrary dependencies between extractions. This allows for “collective information extraction” that exploits the mutual influence between possible extractions. Experiments on learning to extract protein names from biomedical text demonstrate the advantages of this approach.
BeppoSAX unveils the nuclear component in NGC 6240

The direct nuclear X-ray emission of the ultraluminous IRAS galaxy NGC 6240 has been observed for the first time by BeppoSAX. It is seen through an absorber with N_H=2*10^{24} cm^{-2}. The 2-10 keV X-ray luminosity >10^{44} erg s^{-1} definitely proves that a powerful AGN is hosted in this galaxy, despite the LINER optical appearance. Assuming the typical L_X/L_bol ratio for quasars, our result implies that it is the AGN and not the starburst that dominates the energy output of NGC 6240.
Energy cost and metabolic power in elite soccer: a new match analysis approach.

PURPOSE Video match analysis is used for the assessment of physical performances of professional soccer players, particularly for the identification of "high intensities" considered as "high running speeds. " However, accelerations are also essential elements setting metabolic loads, even when speed is low. We propose a more detailed assessment of soccer players' metabolic demands by video match analysis with the aim of also taking into account accelerations. METHODS A recent study showed that accelerated running on a flat terrain is equivalent to running uphill at constant speed, the incline being dictated by the acceleration. Because the energy cost of running uphill is known, this makes it possible to estimate the instantaneous energy cost of accelerated running, the corresponding instantaneous metabolic power, and the overall energy expenditure, provided that the speed (and acceleration) is known. Furthermore, the introduction of individual parameters makes it possible to customize performance profiles, especially as it concerns energy expenditure derived from anaerobic sources. Data from 399 "Serie-A" players (mean +/- SD; age = 27 +/- 4 yr, mass = 75.8 +/- 5.0 kg, stature = 1.80 +/- 0.06 m) were collected during the 2007-2008 season. RESULTS Mean match distance was 10,950 +/- 1044 m, and average energy expenditure was 61.12 +/- 6.57 kJ x kg(-1). Total distance covered at high power (>20 W x kg(-1)) amounted to 26% and corresponding energy expenditure to approximately 42% of the total. "High intensities" expressed as high-power output are two to three times larger than those based only on running speed. CONCLUSIONS The present approach for the assessment of top-level soccer players match performance through video analysis allowed us to assess instantaneous metabolic power, thus redefining the concept of "high intensity" on the basis of actual metabolic power rather than on speed alone.
Malware analysis and classification using Artificial Neural Network

Today major and serious threat on internet is malicious software or data which damage the system. Malware variants identification and classification is the one of the most important research problem in digital forensics. Malware binaries are set of instructions which may affect your system without your authority. Many researchers worked in this area mainly relied on specific API calls, sequences of bytes, statistic and dynamic analysis is used for detection and classification of malware. The proposed method malware is represented as 2Dimensional gray scale image is observed malware images of all the available variants and their texture similarity, which motivate to classify malware based on texture features. The texture plays a very significant role in identify and classify malware. The objective of this paper is to identify a behavior of malicious data based on global features using Gabor wavelet transform and GIST. The experiment done on Mahenhur dataset which includes 3131 binaries samples comprising 24 unique malware families. The algorithm has been implemented using feed forward Artificial Neural Networks (ANN) it gives their overview uniqueness. The experimental results are promising to effectively detecting and classifying malware with good accuracy 96.35 %.