Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Detection Of Emerging Flux Regions In Consecutive Michelson Doppler Imager Magnetograms

In this paper, we present a novel method to detect Emerging Flux Regions (EFRs) in consecutive Michelson Doppler Imager (MDI) magnetograms. To our knowledge, this is the first developed technique for automatically detecting EFRs. The method includes several steps. First, the projection distortion on the MDI magnetograms is corrected. Second, the bipolar regions are extracted by applying multi-scale circular harmonic filters. Third, the extracted bipolar regions are traced in consecutive MDI frames by Kalman filter as candidate EFRs. Fourth, the properties, such as positive and negative magnetic fluxes and distance between two polarities, are measured in each frame. Finally, a feature vector is constructed for each bipolar region using the measured properties, and the Support Vector Machine (SVM) classifier is applied to distinguish EFRs from other regions. Experimental results show that the detection rate of EFRs is 96.4% and of nonEFRs is 98.0%, and the false alarm rate is 25.7%, based on all the available MDI magnetograms in 2001 and 2002. Index Terms – Emerging flux region, Michelson Doppler Imager, circular harmonic filter, Kalman filter, and solar features
Visible and Infrared Face Identification via Sparse Representation

We present a facial recognition technique based on facial sparse representation. A dictionary is learned from data, and patches extracted from a face are decomposed in a sparse manner onto this dictionary. We particularly focus on the design of dictionaries that play a crucial role in the final identification rates. Applied to various databases and modalities, we show that this approach gives interesting performances. We propose also a score fusion framework that allows quantifying the saliency classifiers outputs and merging them according to these saliencies.
Emission lines from rotating proto-stellar jets with variable velocity profiles. I. Three-dimensio

Using the Yguazu-a three-dimensional hydrodynamic code, we have computed a set of numerical simulations of heavy, supersonic, radiatively cooling jets including var iabilities in both the ejection direction (precession) and the jet velocity (intermittence). In order to investigate the effects of jet rotation on the shape of the line profiles, we also i ntroduce an initial toroidal rotation velocity profile, in agreement with some r ecent observational evidence found in jets from T Tauri stars which seems to support the presence of a rotation velocity pattern inside the jet beam, near the jet production region. Since th e Yguazu- a code includes an atomic/ionic network, we are able to compute the emission coeffi cients for several emission lines, and we generate line profiles for the H �, (O I)�6300, (S II)�6716 and (N II)�6548 lines. Using initial parameters that are suitable for the DG Tau microjet, we show that the computed radial velocity shift for the medium-velocity component of the line profile as a function of distance from the jet axis is strikingly simi lar for rotating and non-rotating jet models. These findings lead us to put forward some caveats on the interpretation of the observed radial velocity distribution from a few outflows from yo ung stellar objects, and we claim that these data should not be di rectly used as a doubtless confirmation of the magnetocentri fugal wind acceleration models.
The Nocturnal Return of Neglected Regrets: Deficits in Regret Anticipation Predict Insomnia Symptoms

Cognitive accounts of insomnia generally assume that excessive mental activity at bedtime constitutes a central impediment to the process of falling asleep, yet the affective aspects of this mental hyperactivity still remain largely unex- plored. The present study explored whether failures in regret anticipation might lead to a rise of regret-related counterfac- tual thoughts and emotions at bedtime, thereby hindering or interrupting sleep. A sample of 105 undergraduate students completed the newly developed Regret Anticipation Failures Scale (RAFS), the Bedtime Counterfactual Processing Ques- tionnaire (BCPQ), and the Insomnia Severity Index (ISI). Analyses revealed that both regret anticipation failures and bed- time counterfactual processing were related to insomnia severity and that the effect of regret anticipation failures on in- somnia was mediated by the frequency of regret-related thoughts and emotions at bedtime. These findings reveal for the first time that failures in antecedent-focused emotion regulation may contribute to sleeping difficulties.
K2 variable catalogue – II. Machine learning classification of variable stars and eclipsing binaries in K2 fields 0–4

We are entering an era of unprecedented quantities of data from current and planned survey telescopes. To maximize the potential of such surveys, automated data analysis techniques are required. Here we implement a new methodology for variable star classification, through the combination of Kohonen Self-Organizing Maps (SOMs, an unsupervised machine learning algorithm) and the more common Random Forest (RF) supervised machine learning technique. We apply this method to data from the K2 mission fields 0–4, finding 154 ab-type RR Lyraes (10 newly discovered), 377 δ Scuti pulsators, 133 γ Doradus pulsators, 183 detached eclipsing binaries, 290 semidetached or contact eclipsing binaries and 9399 other periodic (mostly spot-modulated) sources, once class significance cuts are taken into account. We present light-curve features for all K2 stellar targets, including their three strongest detected frequencies, which can be used to study stellar rotation periods where the observed variability arises from spot modulation. The resulting catalogue of variable stars, classes, and associated data features are made available online. We publish our SOM code in PYTHON as part of the open source PYMVPA package, which in combination with already available RF modules can be easily used to recreate the method.
Optical and wireless hybrid access networks: Design and optimization

The performance of optical/wireless hybrid networks is largely limited by the wireless part. To reduce the wireless transmission, we configure virtual channels in the optical infrastructure as “shortcuts” between selected gateways. Adding virtual channels changes the network topology and affects the optimality of gateway deployment. Meanwhile, the gateway locations dictate the traffic distribution that affects the selection of gateways for virtual-channel placement. We propose to jointly optimize the gateways and virtual channels, and formulate the optimization as a mixed integer programming problem. In addition, we design an efficient heuristic algorithm based on k-means clustering to iteratively optimize these two aspects. Our schemes are evaluated using various network settings (network size, number of gateways, etc.). In particular, a random topology with Ethernet traffic is studied for delay and throughput. Numerical results show that our design and optimization effectively reduce the wireless transmission, which leads to better scalability, smaller traffic delay, and larger throughput.
Object Unitization and Associative Memory Formation Are Supported by Distinct Brain Regions

While it has been established that the medial temporal lobe (MTL) is critical for successful memory formation, the precise contribution of one of the key MTL subregions, the perirhinal cortex (PrC), has been the subject of intense focus and debate. Although this region has been implicated in nonassociative item encoding, recent neuroimaging data have revealed that it also contributes to the binding of specific item-related associations (e.g., an object–color association). Based on neuropsychological evidence that associative memory for unitized word pairs does not require the hippocampus, it has been proposed that PrC contributes to associative memory formation by means of unitization. However, the role of PrC in unitization processes remains unclear. Here, we used fMRI to assess the involvement of PrC in object unitization as well as in successful episodic encoding of item-related details. Our results show that while PrC activation linearly tracks the amount of item-related information successfully encoded, it is not modulated by object unitization demands. Instead, the present data reveal that unitization of object fragments may be accomplished in visual/ventral temporal processing stages before PrC. Indeed, we observed a gradual transition from unitization to successful memory formation across the ventral visual pathway. This suggests that PrC may specifically serve to encode item-related event details, whereas their perceptual integration is established along preceding processing stages.
Saliva as A Diagnostic Tool

This review examines the diagnostic application of saliva for systemic diseases. As a diagnostic fluid, saliva offers distinctive advantages over serum because it can be collected noninvasively by individuals with modest training. And also saliva may provide a cost-effective approach for screening of large populations. Analysis of saliva may be useful for the diagnosis of hereditary disorders, autoimmune diseases, malignant and infectious diseases, and endocrine disorders, as well as in the assessment of therapeutic levels of drugs and the monitoring of illicit drug use.
The Compass Acceptance Model for the analysis and evaluation of mobile services

User acceptance is increasingly regarded as a critical success factor for mobile services. Although several acceptance models exist and help to increase understanding the different influencing factors on user acceptance, they are not suitable to support the development of mobile services. This paper introduces the Compass Acceptance Model (CAM), which is especially designed for the analysis and evaluation of the user acceptance for mobile services. Fundamental design criteria are the applicability during the whole product life cycle, a balanced consideration of relevant influencing factors, the use as a permanent controlling instrument and the adaptability to the individual requirements of a service. The CAM helps to verify the perception concerning user acceptance or to understand the user (types of users and behaviour patterns) and the implication of service design better. The insights assist in considering explicitly the user acceptance in the design of a mobile service.
EM-Based Inference of True Labels Using Confidence Judgments

We have developed a method for accurately inferring true labels from labels provided by crowdsourcing workers, with the aid of self-reported confidence judgments in their labels. Although confidence judgments can be useful information for estimating the quality of the provided labels, some workers are overconfident about the quality of their labels while others are underconfident. To address this problem, we extended the Dawid-Skene model and created a probabilistic model that considers the differences among workers in their accuracy of confidence judgments. Results of experiments using actual crowdsourced data showed that incorporating workers' confidence judgments can improve the accuracy of inferred labels.
Virtual Reality: A Novel Approach in Rehabilitating People with Schizophrenia

: Since the introduction of schizophrenia, different approaches have been developed to ameliorate the symptoms or improve the quality of life in patients who are diagnosed with schizophrenia. Currently, scientists around the world are developing new techniques to treat and rehabilitate people with schizophrenia. One of these techniques is virtual reality, which benefits the patient through simulating the experience of acting in a variety of settings and interacting with virtual characters. In this study, we aimed to briefly introduce this technique to scholars in the field of psychiatry and rehabilitation.
FTCloudSim: a simulation tool for cloud service reliability enhancement mechanisms

Recently, an increasing number of companies have deployed their application services in the cloud. However, the cloud data center downtime has negative affected the quality of cloud service quality. Many researchers have studied the problem of cloud service reliability assurance. However, there is a shortage of tools that enable researchers to evaluate their new proposed cloud service reliability enhancement mechanisms. To fill this gap, we extend the basic functionalities of CloudSim and present FTCloudSim in this paper. FTCloudSim provides an extensible interface to help researchers implement new cloud service reliability enhancement mechanisms. In addition, FTCloudSim can also study the behavior of the new proposed mechanisms. We demonstrate the capabilities of FTCloudSim by using four reliability enhancement mechanisms. The results can indicate the benefits of our tool.
A Dynamic Topology Management Mechanism in Green Internet

In recent years, the rapid development of the Internet has provided people with more and more convenience. However, meanwhile a tremendous energy consumption problem and a low energy efficiency problem in the networks still exist. With the continuous expansion of the network scale, these problems are increasingly more severe. In order to achieve the "green" goal in the paper, first a networking model for green backbone network is built, then an innovative node structure and an innovative link structure are designed, furthermore, constitution of energy consumption in network device is analyzed, and a power model of the network device is set up, then according to the power-law principle, the values of network elements are evaluated from the three aspects of resource utilization rate, the impact degree on robustness of the network topology and the impact degree on communication quality, finally, a dynamic topology management mechanism in green Internet is proposed. Applicability and validity of the innovative mechanism with respect to energy saving and topology management are verified via prototype simulation and performance evaluation.
Osteoblasts Proliferation and Differentiation Stimulating Activities of the Main Components of Epimedii folium

Background: Osteoporosis is a disease of bones that leads to an increased risk of fracture. Epimedii Folium is commonly used for treating bone fractures and joint diseases for thousands of years in China. Methods: This study was aimed to screen active components, which might have the potency to stimulate osteoblasts proliferation and differentiation in Epimedii Folium. An HPLC method was established to analyze the main components in Epimedii Folium. The MTT and ALP methods were utilized for the assay of osteoblasts proliferation and differentiation activity. Bavachin, a flavonoid compound was treated as the positive control. Results: Totally eight compounds have been identified by comparing their retention time with correspondent standard substances. Icariside I and icariside II significantly stimulated cell proliferation and osteoblasts differentiation. All these compounds were found with a characterized flavonoid structure in each of their molecule backbones. Conclusion: These results lead to a hypothesis that flavonoid monoglycoside structure might be crucial to exhibit the activity. The structure–effect relationship of these compounds with flavonoid monoglycoside structure in mouse primary calvarial osteoblasts needs to be explored in further research. Abbreviations used: HPLC: High performance liquid chromatography, MTT: Methylthiazolyldiphenyl - tetrazolium bromide, ALP: Alkaline phosphatase
The bright side of transposons in crop evolution.

The past decades have revealed an unexpected yet prominent role of so-called 'junk DNA' in the regulation of gene expression, thereby challenging our view of the mechanisms underlying phenotypic evolution. In particular, several mechanisms through which transposable elements (TEs) participate in functional genome diversity have been depicted, bringing to light the 'TEs bright side'. However, the relative contribution of those mechanisms and, more generally, the importance of TE-based polymorphisms on past and present phenotypic variation in crops species remain poorly understood. Here, we review current knowledge on both issues, and discuss how analyses of massively parallel sequencing data combined with statistical methodologies and functional validations will help unravelling the impact of TEs on crop evolution in a near future.
ESTIMATION OF POSITIVE PARAMETERS IN FORM AND ROUGHNESS ASSESSMENT

The assessment of the geometry of artefacts in length metrology is concerned with estimating dimension, form and roughness. While all three are positive quantities, the evaluation of form and roughness are problematic, mainly because the values of the parameters to be measured are often of the same order as the measurement uncertainty. Furthermore, random effects associated with a measurement system will in general bias the estimate of parameter. This paper discusses these issues, using a Bayesian approach in which prior distributions are used to ensure parameter estimates are physically meaningful.
Similarity learning in nearest neighbor, positive semi-definitiveness and RELIEF algorithm

In this paper, we develop a similarity learning version of RELIEF algorithm, called RBS-PSD (for RELIEF-Based Similarity learning) where the learned similarity matrix is projected onto the set of positive, semi-definite matrices. Unfortunately, this algorithm does not perform very well in practice since it does not try to optimize the leave-one-out error or the 0–1 loss. This motivated us to develop its stricter version, called sRBS-PSD, which aims at reducing a cost function closer to the 0–1 loss. In the case of sRBS-PSD also, the similarity matrix is projected onto the set of positive, semi-definite matrices. Experiments reveal that this projection improves the performance for all of these algorithms. Furthermore, it has been shown that sRBS-PSD outperforms its counterparts for majority of the data sets.
Development and Implementation of Traffic Pattern Recognition Software Based on iOS Framework

At present, user context perception, recognition and prediction is a hot research topic in smart community. As an important user context information, more and more researchers have studied the traffic pattern recognition, however, these studies cannot meet the requirement of high recognition accuracy, rich recognition categories and convenient implementation at the same time. Therefore, the purpose of this paper is to design a traffic pattern recognition system which can meet the above requirements. Due to the smart phone has a certain computing capacity, and its built-in sensors are more and more abundant, we develop the traffic pattern recognition software based on smart phone. This paper starts from the introduction of pattern recognition system and methods, and presents the research review of traffic pattern recognition. Then the classification method, Random Forests Model, and positioning technology based on smart phones applied in this paper are proposed. Lastly, the development of traffic pattern recognition software based on iOS framework is displayed in details.
INTEGRAL high energy detection of the transient IGR J11321–5311

Context. The transient hard X-ray source IGR J11321-5311 was discovered by INTEGRAL on June 2005, during observations of the Crux spiral arm. To date, this is the only detection of the source to be reported by any X/ γ -ray mission. Aims. To characterize the behaviour and hence the nature of the source through temporal and spectral IBIS analysis. Methods. Detailed spectral and temporal analysis has been performed using standard INTEGRAL software OSA v.5. 1. Results. To date, IGR J11321-5311 has been detected only once. It was active for ~3.5 h, a short and bright flare lasting ~1.5 h is evident in the IBIS light curve. It reached a peak flux of ~80 mCrab or 2.2 $\times$ 10 -9 erg cm -2 s -1 (20–300 keV), corresponding to a peak luminosity of ~1.1 $\times$ 10 37 erg s -1 (assuming a distance of 6.5 kpc). During the outburst, the source was detected with a significance of ~18 σ (20–300 keV) and ~8 σ (100–300 keV). The spectrum of the total outburst activity (17–300 keV) is best fitted by the sum of a power law ( Γ = 0.55 ± 0.18) plus a black body ( kT = 1.0$^{+0.2}_{-0.3}$ keV), with no evidence for a break up to 300 keV. A spectral analysis at Science Window level revealed an evident hardening of the spectrum through the outburst. The IBIS data were searched for pulsations with no positive result. Conclusions. The X-ray spectral shape and the flaring behaviour favour the hypothesis that IGR J11321-5311 is an Anomalous X-ray Pulsar, though a different nature can not be firmly rejected at the present stage.
Face Alignment Using Active Shape Model And Support Vector Machine

The Active Shape Model (ASM) is one of the most popular local texture models for face alignment. It applies in many fields such as locating facial features in the image, face synthesis, etc. However, the experimental results show that the accuracy of the classical ASM for some applications is not high. This paper suggests some improvements on the classical ASM to increase the performance of the model in the application: face alignment. Four of our major improvements include: i) building a model combining Sobel filter and the 2-D profile in searching face in image; ii) applying Canny algorithm for the enhancement edge on image; iii) Support Vector Machine (SVM) is used to classify landmarks on face, in order to determine exactly location of these landmarks support for ASM; iv)automatically adjust 2-D profile in the multi-level model based on the size of the input image. The experimental results on Caltech face database and Technical University of Denmark database (imm_face) show that our proposed improvement leads to far better performance.
An Application-Oriented Approach for Designing Heterogeneous Network-on-Chip

An on-chip interconnect is a critical shared resource that affects the performance-energy envelope of an entire multicore system. This aspect has led to a plethora of proposals in recent years for efficiently architecting the network-on-chip (NoC) substrate. However, most of these designs are agnostic to the actual application requirements in that, they attempt to optimize a generic set of objective functions such as latency and throughput and/or energy/power. Since not all applications demand similar resources from the underlying interconnection substrate, an alternative approach to designing an NoC is to utilize multiple networks each of which is specialized for common application requirements, and dynamically steer requests of each application to the network that matches the application’s requirements. To this end, we start with a top-down approach by analyzing the intrinsic communication requirements of several applications. Our key observation is that, although applications, in general, can be classified as either network bandwidth sensitive or latency sensitive, not all bandwidth (latency) sensitive applications are equally sensitive to bandwidth (latency). Following this, we propose a novel set of metrics that can dynamically classify applications as either bandwidth or latency sensitive to steer them into appropriate networks. We propose two separate heterogeneous networks in the on-chip interconnection substrate, where one network is tailored to optimize for bandwidth sensitive applications and the second network for latency sensitive applications. Within each sub-network, we prioritize applications based on their criticality of network resource demand. Simulations with nine different design of a 64-core 2D architecture demonstrate that our heterogeneous network architecture can improve the system throughput up to 34%, application throughput by 24% when compared to a competitive design. Further, the proposed design, while being competitive in performance to an iso-resource single network, can consume 47% lower energy.