Miracle-dz/UniFAR-FTUs huggingface dataset:
Massive Star Evolution in Different Environments

We review the properties of massive star evolution in dif-ferent environments, where the major environmental factor is metallicity. Comparisons between evolutionary models and observations of massive OB, WR stars and related objects are presented. We also review several observations asking for future improvements of stellar models and theoretical developments in this respect. We summarize evolutionary scenarios for the most massive stars and try to clarify recent questions regarding their evolutionary status as core-H or core-He burning objects. Another environmental eﬀect, which might aﬀect stellar evolution is a cluster environment with a high stellar density. As test cases of massive star evolution in dense clusters we summarize recent work on the densest known resolved young clusters: R136, NGC 3603, and the three Galactic Center (GC) star clusters – the central cluster, Quintuplet and the “Arches” cluster. For the central cluster we present new comparisons between stellar parameters of emission line stars derived by Najarro et al. (1994, 1997), and appropriate evolutionary models. From their parameters we argue that most of these stars can be regarded as WNL stars, and do hence not necessarily represent a peculiar class. We suggest that some apparent diﬀerences with well known WR stars can be understood in terms of their core burning stage and/or other changes due to a high metallicity. Based on our present knowledge we conclude that in young clusters with central stellar densities up to ρ c ∼ 10 5 − 6 M ⊙ pc − 3 no compelling evidence for a secondary eﬀect inﬂuencing the evolution of massive stars has yet been found.
Second parameter globulars and dwarf spheroidals around the Local Group massive galaxies: What can they evidence?

We suggest that the majority of the "young", so-called "second parameter" globular clusters (SPGCs) have origi- nated in the outer Galactic halo due to a process other than a tidal disruption of the dwarf spheroidal (dSph) galaxies. Basic observational evidence regarding both the dSphs and the SPGCs, coupled with the latest data about a rather large relative num- ber of such clusters among globulars in M 33 and their low portion in M 31, seems to be consistent with the suspected process. It might have taken place within the system of the most massive galaxies of the Local Group (LG) at the earliest stages of their formation and evolution. We argue that the origin and basic characteristics of the SPGCs can naturally be explained as a result of mass outflow from M 31, during and due to formation of its Pop. II stars, and subsequent accretion of gas onto its massive companions, the Galaxy and M 33. An amount of the gas accreted onto the Milky Way is expected to have been quite enough for the formation in the outer Galactic halo not only of the clusters under consideration but also a number of those dSph galaxies which are as young as the SPGCs. A less significant, but notable mass transfer from the starbursting protoGalaxy to the massive members of the LG might have occurred, too.
Comparative Study of Feature Extraction Techniques for Face Recognition System

recognition is one of the most active research areas in computer vision and pattern recognition. This paper compares the different face recognition techniques like visual face recognition, thermal face recognition, eigenface approach and feature extraction techniques like geometry-based feature extraction(Gabor wavelet transform), appearance based techniques, color segmentation based techniques and template based feature extraction. PCA is used in extracting the relevant information in human face. Face images are projected on to the face space which encodes the variation among known face images. This paper discusses feature extraction techniques with pros and cons. Performances of these techniques are different with various factors such as face expression variation, illumination variation, noise and orientation. Visual face recognition systems perform relatively reliably under controlled illumination conditions. Thermal face recognition systems are advantageous for detecting disguised faces or when there is no control over illumination. Thermal images of individuals wearing eyeglasses may be poor performance since eyeglasses block the infrared emissions around the eyes, which are important features for recognition. Keywordsace recognition, face feature extraction, PCA, Gabor wavelet transform, template based feature extraction.
SEAMLESS ACCESS TO MOBILE SERVICES FOR THE MOBILE USER

While available technologies provide us with base enablers of the ambient intelligence vision, there is still a long way to go before offering robust ambient intelligence systems to consumers, which requires advances in most areas relating to computer science. Our contribution in the area focuses on development support for ambient intelligence software systems, introducing a service-based middleware for anytime, anywhere access to content and services in the mobile environment. Exploitation of the middleware is illustrated in the context of a mobile user accessing services in various situations.
The Capitalization of Interest Subsidies: Evidence from Sweden

We analyze by far the most extensive data base yet employed in estimating capitalization of below-market interest rates into asset prices: nearly 300,000 sales of owner-occupied homes in Sweden from 1981 to 1993 with 27,000 including government subsidized interest rates. Our estimates indicate very clearly that interest subsidies are capitalized into house prices. The below-market financing parameter is consistently significant in all model specifications, irrespective of assumptions about the degree of foresight, representation of the age structure and interest rate measure for all ten regions that we have studied. In our favored model specification the estimated capitalization coefficients center on unity, indicating full capitalization.
Memory probes: exploring retrospective user experience through traces of use on cherished objects

Our daily interactions with objects can not only leave traces of use on the objects but also leave memories in our minds. These human traces on objects are potential cues that can trigger our autobiographical memories and connect us to social networks. The first aim of this paper is to demonstrate what might be a suitable method of inquiry into the way materials can enrich dialogues about remembered experiences derived from human traces left on cherished possessions. The second aim is to investigate how the accumulation of human traces on objects influences people’s remembering and usage. The design of our research artifacts, Memory Probes, was situated in relation to three spectra of paired values: (1) the familiarity and strangeness of tool use, (2) the definiteness and ambiguity of data capture, and (3) the objective and subjective reality of interpretation. Our field study revealed a transactive nature between traces of interaction with possessions and memories in the owners’ minds. It also informed us of how gradual and curiosity-driven understanding could become a methodological nuance when we are empathetically engaged in a collaborative way of knowing with other participants. To conclude, several implications for designing products that can participate in our everyday reminiscing and meaning-making are proposed.
Erratum: An H surge and X-ray jet Magnetic properties and velocity patterns

We described simultaneous observations of a surge in Hβ and an X-ray jet in NOAA 8100 on November 1, 1997. We found that the Hβ surge was spatially coincident with the X-ray jet. They occurred at the site where the pre-existing magnetic flux was “cancelled” by a newly emerging flux of opposite polarity. At the base of the surge we identified surge-flaring in the Hβ filtergrams, and both blueshifts and redshifts in the Hβ Dopplergrams. The X-ray jet appeared about 2 hours after the first appearance of the surge. The surge consisted of two ejecting threads. Initially, these two components were twisted together, then became untwisted before the appearance of the Xray jet. This example presents an alternative scenario of plasma ejection. The magnetic reconnection in the lower atmosphere, which was responsible for the Hβ surge, created the twisted surge threads; the X-ray jet likely resulted from a fast reconnection in the upper atmosphere, which took place well after the Hβ surge.
Bank/Sovereign Risk Spillovers in the European Debt Crisis

This paper investigates contagion between bank risk and sovereign risk in Europe over the period 2006-2011. Since this period covers various stages of the banking and sovereign crisis, it offers a fertile ground to analyze bank/sovereign risk spillovers. We define contagion as excess correlation, i.e. correlation between banks and sovereigns over and above what is explained by common factors, using CDS spreads at the bank and at the sovereign level. Moreover, we investigate the determinants of contagion by analyzing bank-specific as well as country-specific variables and their interaction. We provide empirical evidence that various contagion channels are at work, including a strong home bias in bank bond portfolios, using the EBA's disclosure of sovereign exposures of banks. We find that banks with a weak capital and/or funding position are particularly vulnerable to risk spillovers. At the country level, the debt ratio is the most important driver of contagion.
Nonlinear Diiusion Scale-spaces: from the Continuous to the Discrete Setting

A survey on continuous, semidiscrete and discrete well-posedness and scale-space results for a class of nonlinear diiusion lters is presented. This class does not require any monotony assumption (comparison principle) and, thus, allows image restoration as well. The theoretical results include existence , uniqueness, continuous dependence on the initial image, maximum-minimum principles, average grey level invariance, smoothing Lyapunov functionals, and convergence to a constant steady state.
Training robust acoustic models using features of pseudo-speakers generated by inverse CMLLR transformations

In this paper a novel speech feature generationbased acoustic model training method is proposed. For decades, speaker adaptation methods have been widely used. All existing adaptation methods need adaptation data. However, our proposed method creates speaker-independent acoustic models that cover not only known but also unknown speakers. We do this by adopting inverse maximum likelihood linear regression (MLLR) transformation-based feature generation, and then train our models using these features. First we obtain MLLR transformation matrices from a limited number of existing speakers. Then we extract the bases of the MLLR transformation matrices using PCA. The distribution of the weight parameters to express the MLLR transformation matrices for the existing speakers are estimated. Next we generate pseudo-speaker MLLR transformations by sampling the weight parameters from the distribution, and apply the inverse of the transformation to the normalized existing speaker features to generate the pseudospeakers’ features. Finally, using these features, we train the acoustic models. Evaluation results show that the acoustic models which are created are robust for unknown speakers.
Modelling Architectural Decisions under Changing Requirements

One of the approaches for documenting software architecture is to treat it as a set of architectural design decisions. Such decisions are always made in the context of requirements that must be fulfilled and in the context of decisions that were made before. Currently, models for representing architectural decisions are mainly concentrated on showing the decision making process of the initial architectural design. However, decisions that have been made in such a process may need to be changed during further evolution and maintenance of the software architecture, typically in response to the new or changed requirements. A graphical modelling notation for documenting architectural decisions (Maps of Architectural Decisions) has been developed by our team. In this paper, it is presented how this notation could be used to model architectural decisions under changing requirements. It is proposed how one decision change could be effectively propagated through the rest of the architectural decision model and how a rigorous and tool-supported process of updating such models could be organized.
The effect of frequent or occasional dialysis-associated hypotension on survival of patients on maintenance haemodialysis.

BACKGROUND While frequent or occasional symptomatic intradialytic hypotension (IDH) may influence patient well-being, its effects on survival-independent of comorbidities-has not previously been investigated. In this study, therefore, our objective was to assess the effect of frequent IDH (f-IDH) or occasional IDH (o-IDH) on survival. METHODS During a 10 month run-in period in 1998, 77 patients with f-IDH (> or =10 hypotensive events/10 months, responding only to medical intervention) and 101 patients with o-IDH (1 or 2 events/10 months) were identified among all 958 patients of a dialysis network. Eighty-five patients who had no hypotensive episodes (no-IDH) during this run-in phase served as controls. Patients were followed for a median of 27 months (range: 0.3-37) and survival of patients in the three groups was compared by log-rank test. Independent association of f-IDH and o-IDH with survival, compared with no-IDH, was assessed by a proportional hazards model that included patient demographics, laboratory data and antihypertensive medication as well as comorbidity. RESULTS Forty-five patients (58%) with f-IDH, 47 (47%) with o-IDH and 33 (39%) with no-IDH died during the follow-up. Mortality rates (deaths/100 patient years) were 37 (log-rank P = 0.013 vs no-IDH), 26 (log-rank P = 0.375 vs no-IDH) and 21 in the three groups, respectively. This indicates significantly decreased survival in patients with f-IDH as compared to those with no-IDH. In multivariate proportional hazards regression, however, where age, sex, time spent on dialysis, presence of coronary heart disease, diabetes, Kt/V, albumin level and use of beta-blockers, calcium-channel blockers and long-acting nitrates has been adjusted for, neither f-IDH nor o-IDH was associated with survival. CONCLUSIONS Mortality in patients with f-IDH is significantly higher than in those without such events. After adjustments for covariates, however, there is no independent effect of frequent or occasional episodes of IDH on mortality.
Think-an image based CAPTCHA mechanism (testifying human based on intelligence and knowledge)

There is a need for a CAPTCHA as a result of the abuse of automated 'bots' [6]. The problem of spamming resulted as the bots intruded into the internet [3]. However, there were many solutions provided for stopping these bots from entering the internet. The best and most feasible solution proposed till now has been the CAPTCHA or the fuzzy text recognition. But the latest news that hurt the organisations that use CAPTCHA is that the bots started recognizing these fuzzy texts. In this paper we propose a novel yet simple idea which can serve as a replacement to CAPTCHA. There have been several image based CAPTCHA's proposed and as well defeated [1]. But our Idea THINK is in the same lines yet is fool proof. In this paper we suggest that a real time image is used which will portray some action or show some object that the user is expected to identify the object and type the answer. No choices will be given to the user thereby eliminating the option of identifying the answer by probability.
Effect of asthma and six-months high-intensity interval training on heart rate variability during exercise in adolescents

ABSTRACT Little is known regarding the influence of asthma and exercise, and their interaction, on heart rate variability (HRV) in adolescents. Thirty-one adolescents with asthma (13.7±0.9 years; 21.9±3.9 kg·m−2; 19 boys, 12 girls) and thirty-three healthy adolescents (13.8±0.9 years; 20.3±3.2 kg·m−2; 16 boys, 17 girls) completed an incremental ramp test and three heavy-intensity constant-work-rate cycle tests. Thirteen adolescents (7 boys, 6 girls; 6 asthma, 7 control) completed six-months high-intensity interval training (HIIT) and were compared to age- and sex-matched controls. Standard time-domain, frequency-domain and non-linear indices of HRV were derived at baseline, three- and six-months. Asthma did not influence HRV at baseline or following HIIT. Total power, low frequency and normalised low frequency power, and sympathovagal balance increased at three-months in HIIT, subsequently declining towards baseline at six-months. Normalised high frequency power was reduced at three-months in both groups, which was sustained at six-months. No effects of HIIT were observed in the time-domain nor in the non-linear indices. HRV was not influenced by asthma, potentially because such derangements are a function of disease progression, severity or duration. HIIT may be associated with a short-term shift towards greater sympathetic predominance during exercise, perhaps caused by physiological overload and fatigue.
Analyses of information flow in the cycle of action and perception in an object-pursuit task of the humanoid robot

Biologically-inspired robots have been studied with respect to biological intelligence which can provide complex behaviors. The humanoid robot is a trend that has been used to investigate how the human body works (bipedal walking, for instance). In this research, however, we focus on on-demand planning and rescheduling within a dynamic environment. In the object pursuit task, experimental results demonstrated a online circulation between the internal controller and the environment to prevent fluctuations caused by an irregular noise, and to enable the robot to reach the target position without the endless loop of the identical motions that frequently occur in real time robotics.
Load-Dependent and -Independent Regulation of Proinflammatory Cytokine and Cytokine Receptor Gene Expression in the Adult Mammalian Heart

Background—Although previous studies have examined the effects of acute hemodynamic pressure overload on proinflammatory cytokine gene expression, the effects of sustained hemodynamic overloading have not been examined. Methods and Results—Sustained hemodynamic pressure overloading was produced in mice by transverse constriction of the aorta. Proinflammatory cytokine and cytokine receptor gene expression were determined by ribonuclease protection assays (RPA) at 6 hours and at 3, 7, 14 and 35 days after banding. M-mode echocardiography was used to assess left ventricular structure and function at identical time points. RPA showed that tumor necrosis factor (TNF), interleukin (IL)-1&bgr;, and IL-6 mRNA levels were maximal at 6 hours and returned to baseline levels within 72 hours. There was a significant increase in IL-1RII and IL-6R&agr; receptor mRNA levels after overloading but no significant increase in TNFR1, TNFR2, IL-1RI, or gp130 mRNA levels. The transient increase in expression of proinflammatory cytokine gene expression was not explained by changes in left ventricular loading conditions, left ventricular wall stress, desensitization of proinflammatory genes, or decreased nuclear factor-&kgr;B activation. It is interesting that transverse constriction of the aorta provoked an increase in the expression of tristetraprolin, a homeostatic zinc finger protein that is known to destabilize TNF mRNA. Conclusion—Sustained hemodynamic overloading provokes a transient increase in proinflammatory cytokine and cytokine receptor gene expression; however, the decrease in proinflammatory cytokine gene expression occurred in the absence of changes in loading conditions, suggesting that the expression of proinflammatory cytokines in the heart is regulated, at least in part, by load-dependent and load-independent mechanisms.
Structural Break Estimation for Nonstationary Time Series Models

This article considers the problem of modeling a class of nonstationary time series using piecewise autoregressive (AR) processes. The number and locations of the piecewise AR segments, as well as the orders of the respective AR processes, are assumed unknown. The minimum description length principle is applied to compare various segmented AR fits to the data. The goal is to find the “best” combination of the number of segments, the lengths of the segments, and the orders of the piecewise AR processes. Such a “best” combination is implicitly defined as the optimizer of an objective function, and a genetic algorithm is implemented to solve this difficult optimization problem. Numerical results from simulation experiments and real data analyses show that the procedure has excellent empirical properties. The segmentation of multivariate time series is also considered. Assuming that the true underlying model is a segmented autoregression, this procedure is shown to be consistent for estimating the location of the breaks.
Parallel Box: Visually Comparable Representation for Multivariate Data Analysis

In visual analytics, data comparison is a means of analyzing data. We developed Parallel Box to support the visual analysis of multivariate data by facilitating the flexible comparison of numerous multivariate items. To compare the data distributions of multivariate data, we combine cumulative bar charts and box plots, tools that are widely used in statistics. Using shadow expression based on the visual Gestalt principles of grouping, Parallel Box enables a direct comparison between either datasets or variables. We performed a social media analysis as a case study of Parallel Box. The results of our analysis confirm that Parallel Box is useful for visual analysis.
A Scalable, Accurate Hybrid Recommender System

Recommender systems apply machine learning techniques for filtering unseen information and can predict whether a user would like a given resource. There are three main types of recommender systems: collaborative filtering, content-based filtering, and demographic recommender systems. Collaborative filtering recommender systems recommend items by taking into account the taste (in terms of preferences of items) of users, under the assumption that users will be interested in items that users similar to them have rated highly. Content-based filtering recommender systems recommend items based on the textual information of an item, under the assumption that users will like similar items to the ones they liked before. Demographic recommender systems categorize users or items based on their personal attribute and make recommendation based on demographic categorizations. These systems suffer from scalability, data sparsity, and cold-start problems resulting in poor quality recommendations and reduced coverage. In this paper, we propose a unique cascading hybrid recommendation approach by combining the rating, feature, and demographic information about items. We empirically show that our approach outperforms the state of the art recommender system algorithms, and eliminates recorded problems with recommender systems.