Miracle-dz/UniFAR-FTUs huggingface dataset:
Fe XXV and Fe XXVI Diagnostics of the Black Hole and Accretion Disk in Active Galaxies: Chandra Time-resolved Grating Spectroscopy of NGC 7314

We report the detection of Fe XXV and Fe XXVI Kα emission lines from a Chandra High Energy Transmission Grating Spectrometer observation of the narrow-line Seyfert 1 galaxy NGC 7314, made simultaneously with the Rossi X-Ray Timing Explorer. The lines are redshifted (cz ~ 1500 km s-1) relative to the systemic velocity and unresolved by the gratings. We argue that the lines originate in a near face-on (<7°) disk having a radial line emissivity flatter than r-2. Line emission from ionization states of Fe in the range ~Fe I up to Fe XXVI is observed. The ionization balance of Fe responds to continuum variations on timescales of less than 12.5 ks, supporting an origin of the lines close to the X-ray source. We present additional, detailed diagnostics from this rich data set. These results identify NGC 7314 as a key source to study in the future if we are to pursue reverberation mapping of spacetime near black hole event horizons. This is because it is first necessary to understand the ionization structure of accretion disks and the relation between the X-ray continuum and Fe Kα line emission. However, we also describe how our results are suggestive of a means of measuring black hole spin without a knowledge of the relation between the continuum and line emission. Finally, these data emphasize that one can study strong gravity with narrow (as opposed to very broad) disk lines. In fact, narrow lines offer higher precision, given sufficient energy resolution.
Efficient and tumble similar set retrieval

Set value attributes are a concise and natural way to model complex data sets. Modern Object Relational systems support set value attributes and allow various query capabilities on them. In this paper we initiate a formal study of indexing techniques for set value attributes based on similarity, for suitably defined notions of similarity between sets. Such techniques are necessary in modern applications such as recommendations through collaborative filtering and automated advertising. Our techniques are probabilistic and approximate in nature. As a design principle we create structures that make use of well known and widely used data structuring techniques, as a means to ease integration with existing infrastructure. We show how the problem of indexing a collection of sets based on similarity can be reduced to the problem of indexing suitably encoded (in a way that preserves similarity) binary vectors in Hamming space thus, reducing the problem to one of similarity query processing in Hamming space. Then, we introduce and analyze two data structure primitives that we use in cooperation to perform similarity query processing in a Hamming space. We show how the resulting indexing technique can be optimized for properties of interest by formulating constraint optimization problems based on the space one is willing to devote for indexing. Finally we present experimental results from a prototype implementation of our techniques using real life datasets exploring the accuracy and efficiency of our overall approach as well as the quality of our solutions to problems related to the optimization of the indexing scheme.
Leave-one-out Bounds for Support Vector Regression

The success of support vector machine (SVM) depends critically on the kernel and the parameters in it. One of the most reasonable approaches is to select the kernel and the parameters by minimizing the bound of leave-one-out (Loo) error. However, the computation of the Loo error is extremely time consuming. Therefore, an efficient strategy is to minimize an upper bound of the Loo error, instead of the error itself. In fact, for support vector classification (SVC), some famous bounds have been proposed. This paper is concerned with support vector regression (SVR). We derive two Loo bounds for two algorithms of SVR. In order to show the validity, preliminary experiments are also presented
A Program Transformation Framework for Multicore Software Reengineering

Multicore processors are becoming ubiquitous in embedded systems. To take advantage of multicore processor, a great number of previously designed embedded applications need reengineering processes before they are ported to run accurately and efficiently. Massive refactoring of sequential programs to multi-thread programs is required. Parallelisation refactoring is generally implemented by programmers. As multicore processors become more and more complex, the potential human error in the refactoring process increased dramatically. To provide the necessary assurance of correctness, a program transformation framework to validate the parallelised programs is proposed, which is a formal approach to transforming the multi-thread program while validating if the functional behaviour of the existing sequential program is preserved.
Gradient estimation for regenerative processes

ABSTRACT This paper is concerned with gradient estimationtechniques for steady-state performance measures as-sociated with regenerative stochastic processes. Theprincipal emphasis is on the discussion of conditionsunder which likelihood ratio methods and infinitesi-mal perturbation analysis techniques are valid.1 INTRODUCTION This paper is concerned with using simulation to esti-mate gradients of steady-state performance measuresin the regenerative process setting. This turns out tobe quite a rich class of stochastic processes from anapplications viewpoint, encompassing all irreduciblerecurrant discrete state space Markov processes aswell as a variety of more general discrete-event sys-t ems. In particular, a large class of generalized semi-Markov processes may be made regenerative by using‘(splitting” ideas from the theory of Harris recurrentmarkov chains; see, for example, Glynn (1989). Two different gradient estimation algorithms areexplored in this paper; likelihood ratio gradient esti-mations and estimations based on infinitesimal per-turbation analysis (IPA). The focus, in this paper, ison the tools needed to rigorously verify the mathe-matical validity of these techniques in the regenera-tive setting.2 STEADY-STATE LIKELIHOODRATIO GRADIENT ESTIMATIONLet W’ = (Wn : n ~ O) be a real-valued stochas-tic sequence. For each 0 E A =
A Feature Selection Approach for Automatic Music Genre Classification

In this paper we present an analysis of the suitability of four different feature sets which are currently employed to represent music signals in the context of the automatic music genre classification. To such an aim, feature selection is carried out through genetic algorithms, and it is applied to multiple feature vectors generated from different segments of the music signal. The feature sets used in this paper, which encompass time-domain and frequency-domain characteristics of the music signal, comprise: short-time Fourier transform, Mel frequency cepstral coefficient, beat-related features, pitch-related features, inter-onset interval histogram coefficients, rhythm histograms and statistical spectrum descriptors. The classification is based on the use of multiple feature vectors and an ensemble approach, according to time and space decomposition strategies. Feature vectors are extracted from music segments from the beginning, middle and end parts of the music signal (time-decomposition). Despite music genre classification being a multi-class problem, we accomplish the task using a combination of binary classifiers, whose results are merged to produce the final music genre label (space decomposition). Experiments were carried out on two databases: the Latin Music Database, which contains 3,227 music pieces categorized into ten musical genres; the ISMIR'2004 genre contest database which contains 1,458 music pieces categorized into six popular western musical genres. The experimental results have shown that the feature sets have different importance according to the part of the music signal from where the feature vectors are extracted. Furthermore, the ensemble approach provides better results than the individual segments in most cases. For high-dimensional feature sets, the feature selection provides a compact but discriminative feature subset which has an interesting trade-off between classification accuracy and computational effort.
Extinction properties of the X-ray bright/optically faint afterglow of GRB 020405

Received ; accepted Abstract. We present an optical-to-X-ray spectral analysis of the aft erglow of GRB 020405. The optical spectral energy dis- tribution not corrected for the extragalactic extinction i s significantly below the X-ray extrapolation of the single p owerlaw spectral model suggested by multiwavelength studies. We investigate whether considerable extinction could explain t he ob- served spectral "mismatch" by testing several types of extinction curves. For the first time we test extinction curves co mputed with time-dependent numerical simulations of dust grains destruction by the burst radiation. We find that an extinction law weakly dependent on wavelength can reconcile the unabsorbed optical and X-ray data with the expected synchrotron spectrum. A gray extinction law can be provided by a dust grain size distribution biased toward large grains.
High density impulse noise removal based on linear mean-median filter

This paper presents Linear Mean-Median (LMM) filter that used to reduce impulse noise. LMM filter is a combination between Mean and Median filter. Wherein, linear value is acquired from the linearity between mean and median value. Mean and Median filter are only applied for free-noise pixel on the 3×3 windows that has been sorted from the smallest to the largest value. The mean value is obtained from the average value of all free-noise pixels without including the median pixel position. Meanwhile, median pixel is the middle position of the pixel that has been sorted. LMM uses nine sample pixels to determine a pixel for replacement a corrupted pixel. Our filter also provides the impulse noise prediction systems that serve as a facilitator to give information about noise content. If the noise is greater than 30%, the performance of LMM filter needs to be improved by an adaptive rank order mean filters. The filtering results have shown satisfactory results in terms of the quality result and the computation time process. A good image quality can be evidenced by PSNR (Peak Signal to Noise Ratio). Our methods always have higher PSNR value than the comparison methods. In addition, the speed computation time of our method is faster than the comparison method.
Reliable Management of Virtualized Resources Using Fault Trees

The new trends in distributed computing has changed the way we do computing when talking about cloud infrastructures or high-performance computing. Resource virtualization technologies enabled elasticity of resource provisioning and management through easy replication of virtual nodes or virtual machine migration. In order to provide high availability and reliability in such distributed environments where resources are managed and served in form of virtual machines, specific load balancing and fault strategies are needed. Based on fault tree analysis concepts, we propose a distributed and autonomous approach to manage faults using fault agents able to asses and predict for each virtualized node, its state of fault or future fault. Accordingly, each node can take a decision about accepting future jobs, delegate jobs to own replicated instances or start a live migration process as a second strategy for assuring availability and continuity of the service.
ARPushPush: Augmented Reality Game in Indoor Environment *

In this paper, we propose ARPushPush, a novel augmented reality game for indoor environment, which uses vision-based tracking and user’s hand gestures. The systems using trackers are too expensive and the calibration between a camera and a tracker, which is indispensable for vision-based interaction, is not easy. The proposed system only uses two cameras for tracking markers and augmenting virtual objects, respectively. The tracking camera detects ARToolKit markers attached on the ceiling and calculates the pose of user’s head on the fly [11]. The HMD camera detects markers attached to the back of user’s hand for interaction and gives augmented view to a user. The transformation matrix between two cameras is updated when markers are visible in each camera. To support wide working area effectively, we propose an algorithm to train multiple markers on the ceiling. In the ARPushPush based on our framework, a user acts to move blocks augmented in a virtual maze to predefined places. The proposed framework allows a user to work in wide area, to interact naturally, and to collaborate with other users.
An Overview of the Health and Retirement Study

This paper examines the scientific, public policy, and organizational background out of which the Health and Retirement Study emerged. It describes the evolution of the major parameters of the survey and the unique planning structure designed to ensure that the substantive insights of the research community were fully reflected in the content of the database, highlights key survey innovations contained in the HRS, and provides a preliminary assessment of the quality of the data as reflected by sample size, sample composition, response rate, and survey content. The paper also describes the several types of administrative data that are expected to be added to the HRS data: earnings and benefits from Social Security files, and health insurance and pension data from the employers of survey respondents.
A wearable computing system for wireless communication and gesture based human computer interface

The paper presents a prototype for a two way peer to peer communication and advanced human computer interface (HCI). This could be in the form of a wearable ring equipped with laser, infrared and other radio capabilities that enable the human beings communicate with people and other smart devices in a much natural way. The ring ensembles two way peer to peer laser communication, NFC (Near Field Communication) enabled authentication, wearable laser gesture control for remote devices. The ring houses electronic systems, which enable the user to use natural gestures such as waving a hand, nodding head etc. to activate a remote device mile away. The user can instantaneously transmit, encrypted data to another ring, without the need for internet or any other industrial ecosystem based communication. More natural and customized symbols can be trained and tracked into a computer system, for it to function as naturally as possible, without the user clicking, or interacting by any other legacy mechanisms.
A Monte Carlo algorithm for cold start recommendation

Recommendation systems have been widely used in E-commerce sites, social networks, etc. One of the core tasks in recommendation systems is to predict the users' ratings on items. Although many models and algorithms have been proposed, how to make accurate prediction for new users with extremely few rating records still remains a big challenge, which is called the cold start problem. Many existing methods utilize additional information, such as social graphs, to cope with the cold start problem. However, the side information may not always be available. In contrast to such methods, we propose a more general solution to address the cold start problem based on the observed user rating records only. Specifically we define a random walk on a bipartite graph of users and items to simulate the preference propagation among users, in order to alleviate the data sparsity problem for cold start users. Then we propose a Monte Carlo algorithm to estimate the similarity between different users. This algorithm takes a precomputation approach, and thus can efficiently compute the user similarity given any new user for rating prediction. In addition, our algorithm can easily handle dynamic updates and can be parallelized naturally, which are crucial for large recommendation systems. Theoretical analysis is presented to demonstrate the efficiency and effectiveness of our algorithm, and extensive experiments also confirm our theoretical findings.
Cluster load balancing algorithms comparison

This paper deals with the comparison of different load balancing strategies for massively parallel computations on clusters. There are four different strategies presented - static versus dynamic load balancing applied to master-worker and two-level master-worker models. Algorithms were applied to the problem of large matrix multiplication. The obtained results can be generalized to other problems, which require massively parallel computation.
Probing the Low-Mass Stellar End of the η Chamaeleontis Cluster

We have identified three faint new members of the η Chamaeleontis cluster. Spectral types of the new members are estimated to be ~M5, based on their TiO band strengths and broadband colors. With an age of 5-8 Myr for the cluster, masses of these new members are estimated to be ~0.08 M☉. All three display strong Li 6708 Å absorption and Hα emission features, including one with an Hα emission equivalent width of ~60 Å, along with He I 6678 and 7605 Å emission features that are characteristics of classical T Tauri stars.
Massive MIMO transmission with pilot reuse in single cell

We propose pilot reuse (PR) in single cell for massive multiuser multiple-input multiple-output (MIMO) transmission to reduce the pilot overhead. For the spatially correlated Rayleigh fading channels, we establish a relationship between the channel spatial correlations and the channel power angle spectrum when the base station antenna number tends to infinity. With this channel model, we first show that the sum mean square error of the channel estimation (MSE-CE) can be minimized if the channel angle of arrival intervals of the user terminals reusing the pilots are non-overlapping, which shows the feasibility of PR in massive MIMO channels with constrained channel angular spreads. Then we design the pilot scheduler under the minimum MSE-CE criterion. With the channel estimation error due to PR taken into account, we also develop the closed-form robust uplink receiver and downlink precoder that minimize the sum MSE of the signal detection. Simulation results show that the proposed PR scheme provides significant performance gains over the conventional orthogonal training scheme in terms of the net spectral efficiency.
Super-Lie n-algebra extensions, higher WZW models and super-p-branes with tensor multiplet fields

We formalize higher-dimensional and higher gauge WZW-type sigma-model local prequantum field theory, and discuss its rationalized/perturbative description in (super-)Lie n-algebra homotopy theory (the true home of the "FDA"-language used in the supergravity literature). We show generally how the intersection laws for such higher WZW-type σ-model branes (open brane ending on background brane) are encoded precisely in (super-)L∞-extension theory and how the resulting "extended (super-)space-times" formalize spacetimes containing σ-model brane condensates. As an application we prove in Lie n-algebra homotopy theory that the complete super-p-brane spectrum of superstring/M-theory is realized this way, including the pure σ-model branes (the "old brane scan") but also the branes with tensor multiplet worldvolume fields, notably the D-branes and the M5-brane. For instance the degree-0 piece of the higher symmetry algebra of 11-dimensional (11D) spacetime with an M2-brane condensate turns out to be the "M-theory super-Lie algebra". We also observe that in this formulation there is a simple formal proof of the fact that type IIA spacetime with a D0-brane condensate is the 11D sugra/M-theory spacetime, and of (prequantum) S-duality for type IIB string theory. Finally we give the non-perturbative description of all this by higher WZW-type σ-models on higher super-orbispaces with higher WZW terms in stacky differential cohomology.
The Effects of Learning in Interactive Monetary Policy Committees

We develop a theoretical framework for studying the effects of interaction on the quaJity of decision-making by monetary policy committees. We show that interaction, i.e. increasing one's expertise through an exchange of views, is most likely not to result in interdependent voting behaviour.Therefore, and in contrast to earlier literature, we find that interaction is beneficial for the collective outcome.
An auxiliary variable method for Langevin based MCMC algorithms

Markov Chain Monte Carlo sampling algorithms are efficient Bayesian tools to explore complicated posterior distributions. However, sampling in large scale problems remains a challenging task since the Markov chain is very sensitive to the dependencies between the signal samples. In this paper, we are mainly interested in Langevin based MCMC sampling algorithms that allow us to speed up the convergence by controlling the direction of sampling and/or exploiting the correlation structure of the target signal. However, these techniques may sometimes fail to explore efficiently the target space because of poor mixing properties of the chain or the high cost of each iteration. By adding some auxiliary variables, we show that the resulting conditional distribution of the target signal is much simpler to explore by using these algorithms. Experiments performed in the context of multicomponent image restoration illustrate that the proposed approach can achieve substantial performance improvement compared with standard algorithms.
Structural characteristics and stellar composition of low surface brightness disk galaxies

We present UBVI surface photometry of a sample of low surface brightness (LSB) disk galaxies. LSB disk galaxies are fairly well described as exponential disks with no preferred value for either scale length, central surface brightness, or rotational velocity. Indeed, the distribution of scale lengths is indistinguishable from that of high surface brightness spirals, indicating that dynamically similar galaxies (e.g., those with comparable Rv^2) exist over a large range in surface density. These LSB galaxies are strikingly blue. The complete lack of correlation between central surface brightness and color rules out any fading scenario. Similarly, the oxygen abundances inferred from HII region spectra are uncorrelated with color so the low metallicities are not the primary cause of the blue colors. While these are difficult to interpret in the absence of significant star formation, the most plausible scenario is a stellar population with a young mean age stemming from late formation and subsequent slow evolution. These properties suggest that LSB disks formed from low initial overdensities with correspondingly late collapse times.
Quantitative proteomics of mitochondrial membrane proteins by sodium dodecyl sulphate polyacrylamide gel electrophoresis, 16O/18O stable isotope labeling and linear ion trap mass spectrometry

Summary: In spite of recent developments, the global analysis of membrane subproteomes is still a common obstacle. In this work we explore whether quantification of mitochondrial membrane proteins can be achieved by SDS-PAGE separation followed by in-gel digestion, 16 O/ 18 O stable isotope labeling and peptide-centric identification and quantification by linear ion trap mass spectrometry. Using this approach we were able to identify more than fifty membrane proteins among a total of more than one hundred, from a preparation of 100 µg crude mitochondrial membranes. A part of this proteome was split into two and subjected to comparative analysis by 16 O/ 18 O stable isotope labeling. A very narrow distribution of ratios was obtained, showing no evident deviations from the 1:1 ratio in more than two hundred quantifications. Our data demonstrated that significant changes in protein expression levels higher than 1.5-fold or lower than 0.6-fold could be detected at the p < 0.05 confidence le vel. Our results suggest that this method could be suitable for the large-scale identification and relative quantification of mitochondrial membrane proteomes, as well as other protein membrane preparations.