Miracle-dz/UniFAR-FTUs huggingface dataset:
THE X-RAY POINT-SOURCE POPULATION OF NGC 1365: THE PUZZLE OF TWO HIGHLY-VARIABLE ULTRALUMINOUS X-RAY SOURCES

We present 26 point-sources discovered with Chandra within 200″ (≈20 kpc) of the center of the barred supergiant galaxy NGC 1365. The majority of these sources are high-mass X-ray binaries, containing a neutron star or a black hole accreting from a luminous companion at a sub-Eddington rate. Using repeat Chandra and XMM-Newton, as well as optical observations, we discuss in detail the natures of two highly variable ultraluminous X-ray sources (ULXs): NGC 1365 X1, one of the most luminous ULXs known since the ROSAT era, which is X-ray variable by a factor of 30, and NGC 1365 X2, a newly discovered transient ULX, variable by a factor of >90. Their maximum X-ray luminosities ((3–5) × 1040 erg s−1, measured with Chandra) and multiwavelength properties suggest the presence of more exotic objects and accretion modes: accretion onto intermediate mass black holes (IMBHs) and beamed/super-Eddington accretion onto solar-mass compact remnants. We argue that these two sources have black hole masses higher than those of the typical primaries found in X-ray binaries in our Galaxy (which have masses of <20 M☉), with a likely black-hole mass of 40–60 M☉ in the case of NGC 1365 X1 with a beamed/super-Eddington accretion mode, and a possible IMBH in the case of NGC 1365 X2 with M = 80–500 M☉.
Hand Motion and Grasping: Capturing, Recognizing and Synthesizing

In this paper we review the various previous methods in vision-based hand motion analysis and hand motion synthesis and grasping. We present our two solutions to the problem. First, we present an articulated hand pose estimation scheme, in which we improve the regression forest based methods by incorporating high-level hand part feature and hand motion constraints. Second, we present a framework for joint 6D palm pose tracking and hand gesture recognition. Based on the algorithms developed above, we have built several real-time systems to enable human-computer interaction with bare hands, such as virtual object manipulation and hand gesture recognition. We also discuss methods to generate hand motion and grasping movement for Virtual Humans.
Novel Approach for Salient Region Detection

: Salient region detection in image is useful for applications like adaptive compression, image segmentation and region-based image retrieval. Many of existing saliency detection techniques do not pay much attention to the noise problem. In this paper the proposed model uses L0 smoothing filter to reduce the effect of noise and achieve a better performance. The important role in our framework is played by the L0 smoothing filter. The L0 filter is extremely helpful in characterizing fundamental image constituents, i.e. salient edges and can simultaneously reduce insignificant details, thus producing more accurate boundary information for background merging. The saliency maps in this paper are extracted by three features: the color spatial variance, border measurement, and region contrast. The color spatial variance is utilized to calculate the degree of color distribution. Based on the observation that salient objects are less likely to be connected with the borders of an image, the boundary information of each segment is used to determine the salient region. Last but not least, a region contrast method is used for extracting global contrast used to achieve full resolution map with well defined boundaries. Thus this method will give more accurate results as compared to other methods.
Extensions to Orthogonal Matching Pursuit for Compressed Sensing

Compressed Sensing (CS) provides a set of mathematical results showing that sparse signals can be exactly reconstructed from a relatively small number of random linear measurements. A particularly appealing greedy-approach to signal reconstruction from CS measurements is the so called Orthogonal Matching Pursuit (OMP). We propose two modifications to the basic OMP algorithm, which can be handy in different situations.
Source coding with delayed side information

For memoryless sources, delayed side information at the decoder does not improve the rate-distortion function. However, this is not the case for more general sources with memory, as demonstrated by a number of works focusing on the special case of (delayed) feedforward. In this paper, a setting is studied in which the side information is delayed and the encoder is informed about the side information sequence. Assuming a hidden Markov model for the sources, at first, a single-letter characterization is given for the set-up where the side information delay is arbitrary and known at the encoder, and the reconstruction at the destination is required to be (near) lossless. Then, with delay equal to zero or one source symbol, a single-letter characterization is given of the rate-distortion function for the case where side information may be delayed or not, unbeknownst to the encoder. Finally, an example for a binary source is provided.
The Geometry of and Mass Accretion Rate through the Maser Accretion Disk in NGC 4258

A maximum likelihood analysis of the NGC 4258 maser positions and velocities reveals a ~2 σ deviation from Keplerian motion in the projected rotation curve of the high-velocity features, corresponding to a ~9 km s-1, or 0.8%, flattening of the LOS velocities with respect to Keplerian motion over the range of the high-velocity masers. While there are a number of potential explanations for this flattening, we argue for pure Keplerian rotation in an inclination-warped disk on the basis of the ability of this model to explain a number of otherwise puzzling features of the system. A radial gradient in the disk inclination of 0.034 mas-1 is not only consistent with the observed rotation curve, but it generates a bowl along the near edge of the disk that naturally explains the otherwise puzzling narrow spread in the declinations of the systemic masers. It also explains the existence and location of an apparently recurring flare among the systemic masers. There is no significant evidence for non-Keplerian rotation in the inclination-warped disk. An additional implication of the inclination warp is that the disk rises in front of and obscures the central engine at a disk radius of about 8.3 mas, or 0.29 pc. By comparing the observed X-ray column to conditions in the disk at this radius, we argue that the disk must be atomic at 0.29 pc. Hence, we conclude that the molecular-to-atomic transition occurs just beyond the outermost maser at 0.28 pc, and from this we infer an accretion rate of ~10-4α M☉ yr-1, where α (≲1) is the standard dimensionless parameterization of the kinematic viscosity. Our model suggests that most of the observed X-ray column arises in the warped accretion disk at 0.29 pc and that the maser emission is truncated at large radii predominantly as a result of the molecular-to-atomic phase transition originally proposed by Neufeld & Maloney. The inferred accretion rate is consistent with the jet-dominated accretion models of Yuan et al.
Market Freeze and Recovery: Trading Dynamics under Optimal Intervention by a Market-Maker-of-Last-Resort

In the context of a search model of asset trading with adverse selection, we demonstrate that trading of a financial asset will cease, when its average quality drops sufficiently. A large player, however, can establish trading again, if he removes a sufficiently large quantity of bad assets which involves assuming losses. Most importantly, we show that such a player does not have to intervene immediately: a mere announcement today of intervening in the future can cause markets to function again. This announcement effect gives rise to a trade-off between the size and the timing of the intervention. The optimal policy balances the (social) costs of transfers against the costs of illiquid markets. If the former are small, it is optimal to ensure that markets function continuously. This is optimally achieved by intervening immediately, but at a minimum scale. When the costs of transfers increase, it is however optimal to delay the intervention and increase its size.
Utility Independence in a Qualitative Decision Theory

Qualitative accounts of utility modeling and decision theory o er the prospect of rea soning about preference and decision making without requiring hard to obtain numerical probabilities and utilities It is plausible that such accounts can be found because qualita tive criteria in particular dominance seems to play a large role in human decision making the formal quantitative apparatus of maxi mum expected utility tends to be invoked only in the most critical most nely balanced cases In this paper we show how non probabilistic independence concepts such as preferential independence and utility independence can be integrated with other sources of quantita tive information It turns out that there are some subtleties involved in making sense of these ideas in a logical framework The main contribution of this paper is to demonstrate these subtleties and then give semantics that avoid many of the problems We then argue that knowledge of utility independence can be a useful addition to the qualitative rea soner s tool kit
Parallel image processing in heterogeneous computing network systems

We present a uniform construct of parallel programming for a set of image processing tasks based on our distributed computing primitive (DCP) concept. Our target architecture is a heterogeneous computing network system consisting of various high performance workstations connected through a local area network. We show that DCP has advantages over non-primitive PVM-based parallel approaches in three aspects: ease-of-use, automation, and optimization.
Preferred but not Required: Examining Research Data Management Roles in Health Science Librarian Positions

Introduction: Research data management (RDM) is being recognized as an increasingly important role for librarians. In this paper, the role of health science librarians in supporting research data management endeavors is examined. Methods: All job postings currently (as of April 5th, 2018) available on the University of Toronto’s Faculty of Information (iSchool) job site were analyzed to identify positions related to health science librarianship. The job responsibilities and descriptions were then examined to identify instances where research data management was mentioned. Results: Thirty-two postings from the search results were identified as meeting the inclusion criteria. Of these thirty-two health science librarian postings which were included in the analysis, eight included supporting research data management services, in some capacity, as part of the position description. Discussion/ Conclusion: Through the job posting analysis, a picture emerges where RDM is not consistently seen as a role for health science librarians. However, the literature indicates that in many instances, research data management is already being done by health science librarians, and is a trend which is likely to continue in the future. As such, it is important that research data management services start being acknowledged and reflected in education and job description opportunities.
Mechanisms of AXL overexpression and function in Imatinib-resistant chronic myeloid leukemia cells

AXL is a receptor tyrosine kinase of the TAM family, the function of which is poorly understood. We previously identified AXL overexpression in Imatinib (IM)-resistant CML cell lines and patients. The present study was conducted to investigate the role of AXL and the mechanisms underlying AXL overexpression in Tyrosine Kinase Inhibitor (TKI)-resistant CML cells. We present evidence that high AXL expression level is a feature of TKI-resistant CML cells and knockdown of AXL sensitized TKI-resistant cells to IM. In addition, expression of wild-type AXL but not a dominant negative form of AXL confers IM-sensitive CML cells the capacity to resist IM effect. AXL overexpression required PKCα and β and constitutive activation of ERK1/2. Accordingly, GF109203X a PKC inhibitor, U0126 a MEK1 inhibitor and PKCα/β knockdown restore sensitivity to IM while PKCα or PKCβ overexpression in CML cells promotes protection against IM-induced cell death. Finally, using luciferase promoter activity assays we established that AXL is regulated transcriptionally through the AP1 transcription factor. Our findings reveal an unexpected role of AXL in resistance to TKI in CML cells, identify the molecular mechanisms involved in its overexpression and support the notion that AXL is a new marker of resistance to TKI in CML.
Effets d’un court entraînement de pleine conscience sur la douleur et la cognition : une étude expérimentale randomisée en double aveugle

iii Table des matières . v Liste des tableaux . viii Liste des figures . ix Liste des sigles . x Remerciements . xi Chapitre 1 : Problématique et contexte théorique . 12 1. Définitions, usages et opérationnalisation du concept de pleine conscience . 12 1.1.1 La pleine conscience dans le bouddhisme . 13 1.1.2 Les interventions cliniques basées sur la pleine conscience . 17 1.1.3 La thérapie basée sur l’engagement et l’acceptation . 23 1.1.4 Thérapie comportementale dialectique . 27 1.1. 5 La pleine conscience langerienne . 28 1.1. 6 Les approches neurologiques de la PC . 30 1.1. 7 Définition unifiée . 35 1.2 Les courtes interventions de pleine conscience : revue systématique . 41 1.3 Douleur chronique, interférence douloureuse et pleine conscience . 52 1.4 Objectifs et hypothèses . 56 Chapitre 2 Article soummis au Journal of Pain : Effects of brief mindfulness interventions on the interference induced by heat pain on cognition in healthy individuals: a randomized, dualblind experimental study . 58 Analysis . 74
Learning Relational Features with Backward Random Walks

The path ranking algorithm (PRA) has been recently proposed to address relational classification and retrieval tasks at large scale. We describe Cor-PRA, an enhanced system that can model a larger space of relational rules, including longer relational rules and a class of first order rules with constants, while maintaining scalability. We describe and test faster algorithms for searching for these features. A key contribution is to leverage backward random walks to efficiently discover these types of rules. An empirical study is conducted on the tasks of graph-based knowledge base inference, and person named entity extraction from parsed text. Our results show that learning paths with constants improves performance on both tasks, and that modeling longer paths dramatically improves performance for the named entity extraction task.
Boosted Optimization for Network Classification

In this paper we propose a new classification algorithm designed for application on complex networks motivated by algorithmic similarities between boosting learning and message passing. We consider a network classifier as a logistic regression where the variables define the nodes and the interaction effects define the edges. From this definition we represent the problem as a factor graph of local exponential loss functions. Using the factor graph representation it is possible to interpret the network classifier as an ensemble of individual node classifiers. We then combine ideas from boosted learning with network optimization algorithms to define two novel algorithms, Boosted Expectation Propagation (BEP) and Boosted Message Passing (BMP). These algorithms optimize the global network classifier performance by locally weighting each node classifier by the error of the surrounding network structure. We compare the performance of BEP and BMP to logistic regression as well state of the art penalized logistic regression models on simulated grid structured networks. The results show that using local boosting to optimize the performance of a network classifier increases classification performance and is especially powerful in cases when the whole network structure must be considered for accurate classification.
Developing an ankle-foot muscular model using Bayesian estimation for the influence of an ankle foot orthosis on muscles

The objective of this study is to develop an estimation model of foot-ankle muscular activity for designing an ankle-foot orthosis with a training function. We built a Bayesian network model [1] and chose three muscles to confirm its effectiveness. Such a model needs to include all factors affecting the gait, for example, speed reflex movements, joint angles and so forth. In an experiment, we examined the normal gait of a non-disabled subject. We measured the muscular activity of the lower foot muscles by electromyography, the joint angles by using statistical methods, and the sole pressure on each part of the sole. From this data, we obtained the causal relationship at every 10% level of these factors. Our model has three advantages. First, it can express the influences, which change throughout the gait, because we use 10% level nodes of each factor. Second, it can express the influences of factors, which are different for low and high muscular activity levels. Last, the model can compensate the missed estimations by estimating every 10% level muscle activity. In an evaluation of this model, we confirmed that this model can estimate all muscular activity level with an accuracy rate greater than 90%.
Cognitive Modeling in Software and Relation to Human Emotional Reasoning

The paper reports on our experience to in adapting emotional experiences of the software engineers in evolutionary design of software systems. The works here reported present development progress report in relation to the state-of art that need to create the multidisciplinary technologies, needed to establish best harmony engagement between human user the software application, based on human cognitive analysis. We approach the user best engagement from facial and voice analysis. And through it, we can measure (collectivized and quantified), and observe the user behavior, and accordingly enhance the engagement by generative interactive scenario. The approach has been experimented using famous literature person (Keni Miyazawa).
Monotonic Abstraction Techniques: from Parametric to Software Model Checking

Monotonic abstraction is a technique introduced in model checking parameterized distributed systems in order to cope with transitions containing global conditions within guards. The technique has been re-interpreted in a declarative setting in previou s papers of ours and applied to the verification of fault tolerant systems under the so-called ‘sto pping failures’ model. The declarative reinterpretation consists in logical techniques (quantifi er relativizations and, especially, quantifier instantiations) making sense in a broader context. In fact, we recently showed that such techniques can over-approximate array accelerations, so that they can be employed as a meaningful (and practically effective) component of CEGAR loops in software model checking too.
Optimizing energy output and layout costs for large wind farms using particle swarm optimization

The design of a wind farm involves several complex optimization problems. We consider the multi-objective optimization problem of maximizing the energy output under the consideration of wake effects and minimizing the cost of the turbines and land area used for the wind farm. We present an efficient particle swarm optimization algorithm that computes a set of trade-off solutions for the given task. Our algorithm can be easily integrated into the layout process for developing wind farms and gives designers new insights into the trade-off between energy output and land area.
A Neural Network System for Detection of Obstructive Sleep Apnea Through SpO2 Signal Features

Obstructive sleep apnea (OSA) is a common disorder in which individuals stop breathing during their sleep. These episodes last 10 seconds or more and cause oxygen levels in the blood to drop. Most of sleep apnea cases are currently undiagnosed because of expenses and practicality limitations of overnight polysomnography (PSG) at sleep labs, where an expert human observer is required. New techniques for sleep apnea classification are being developed by bioengineers for most comfortable and timely detection. In this study, we develop and validate a neural network (NN) using SpO2 measurements obtained from pulse oximetry to predict OSA. The results show that the NN is useful as a predictive tool for OSA with a high performance and improved accuracy, approximately 93.3%, which is better than reported techniques in the literature.
A Novel Corner Point Detector for Calibration Target Images Based on Grayscale Symmetry

The research of feature corner recognition for the calibration target is a crucial area for a vision-based measurement system. For the disability of detecting the corner’s location in a vehicle test system accurately, the theoretical errors based on Harris algorithm are analyzed. Experimental results show that the method can not be used in the vision-measured field as the corner cluster phenomenon. The novel corner detection algorithm based on grayscale symmetry is presented. The Gassian weighted symmetry detector is designed to illustrate the symmetrical feature around the optimal corner. Further Experimental results show that the detector proposed can be used to test the corner locations of the calibration target precisely.
Models for Dependability Analysis of Cloud Computing Architectures for Eucalyptus Platform

High availability in cloud computing services is essential for maintaining customer confidence and avoiding revenue losses due to SLA violation penalties. Since the software and hardware components of cloud infrastructures may have limited reliability, fault tolerance mechanisms are means of achieving the necessary dependability requirements. This paper investigates the benefits of a warm-standy replication mechanism in three architectures based on Eucalyptus cloud computing as well as the required acquisition cost. A heterogeneous hierarchical modeling approach is used to represent the redundancy strategy and to compare the availability for the architectures presented. Both hardware and software failures are considered in the proposed analytical models. The results highlight the improvements on the availability for the redundant architectures, the respective impact on the annual downtime and the related respective costs.
Recursive Evaluation of a Family of Compound Distributions

Compound distributions such as the compound Poisson and the compound negative binomial are used extensively in the theory of risk to model the distribution of the total claims incurred in a fixed period of time. The usual method of evaluating the distribution function requires the computation of many convolutions of the conditional distribution of the amount of a claim given that a claim has occurred. When the expected number of claims is large, the computation can become unwieldy even with modern large scale electronic computers. In this paper, a recursive definition of the distribution of total claims is developed for a family of claim number distributions and arbitrary claim amount distributions. When the claim amount is discrete, the recursive definition can be used to compute the distribution of total claims without the use of convolutions. This can reduce the number of required computations by several orders of magnitude for sufficiently large portfolios. Results for some specific distributions have been previously obtained using generating functions and Laplace transforms (see Panjer (1980) including discussion). The simple algebraic proof of this paper yields all the previous results as special cases.
Highway from hell: A case study on psychosocial impact monitoring

This presentation concerns the main results of a psychosocial monitoring of the construction stage of the final section of the Algarve Highway (the second most important in the country). The monitoring stage is part of the whole psychosocial impact assessment process, which concerns the analysis of both people’s perceptions regarding several situations implied in such an environmental event and the consequences these perceptions might produce with regard to the physical and psychological welfare.