Miracle-dz/UniFAR-FTUs huggingface dataset:
A pr 2 00 0 Accretion Disk Boundary Layers Around Neutron Stars : X-ray Production in Low-Mass X-ray Binaries

The boundary layer where the accretion disk meets the star is expected to be the dominant source of high-energy radiation in low-mass X-ray binaries which contain weakly magnetized accreting neutron stars. We present solutions for the structure of the boundary layer in such a system. We find that the main portion of the boundary layer gas is hot (∼> 10 K), has low density, and is radially and vertically extended. It will emit a large luminosity in X-rays, mainly produced by Comptonization of soft photons which pass through the hot gas. The gas is generally optically thick to scattering but optically thin to absorption. Energy is transported by viscosity from the rapidly rotating outer part of the boundary layer to the slowly rotating inner part, and this has the important effect of concentrating the energy dissipation in the dense, optically thick zone close to the stellar surface. Advection of energy also plays an important role in the energy balance. We explore the dependence of the boundary layer structure on the mass accretion rate and rotation rate of the star. We also examine the effects of changes in the α viscosity parameter and the viscosity prescription. Radiation pressure is the dominant source of pressure in the boundary layer. The radiation flux in the boundary layer is a substantial fraction of the Eddington limiting flux even for luminosities well below (∼ 0.01 times) the Eddington luminosity LEdd for spherically symmetric accretion. At luminosities near LEdd, the boundary layer expands radially, and has a radial extent larger than one stellar radius. This radial expansion increases the surface area of the boundary layer and allows it to radiate a larger total luminosity. Based on the temperatures and optical depths which characterize the boundary layer, we expect that Comptonization will produce a power-law also Space Research Institute, Moscow, Russia
Edinburgh Research Explorer Detection of growth-related QTL in turbot (Scophthalmus maximus)

Background: The turbot ( Scophthalmus maximus ) is a highly appreciated European aquaculture species. Growth related traits constitute the main goal of the ongoing genetic breeding programs of this species. The recent construction of a consensus linkage map in this species has allowed the selection of a panel of 100 homogeneously distributed markers covering the 26 linkage groups (LG) suitable for QTL search. In this study we addressed the detection of QTL with effect on body weight, length and Fulton ’ s condition factor. Results: Eight families from two genetic breeding programs comprising 814 individuals were used to search for growth related QTL using the panel of microsatellites available for QTL screening. Two different approaches, maximum likelihood and regression interval mapping, were used in order to search for QTL. Up to eleven significant QTL were detected with both methods in at least one family: four for weight on LGs 5, 14, 15 and 16; five for length on LGs 5, 6, 12, 14 and 15; and two for Fulton ’ s condition factor on LGs 3 and 16. In these LGs an association analysis was performed to ascertain the microsatellite marker with the highest apparent effect on the trait, in order to test the possibility of using them for marker assisted selection. Conclusions: The use of regression interval mapping and maximum likelihood methods for QTL detection provided consistent results in many cases, although the high variation observed for traits mean among families made it difficult to evaluate QTL effects. Finer mapping of detected QTL, looking for tightly linked markers to the causative mutation, and comparative genomics are suggested to deepen in the analysis of QTL in turbot so they can be applied in marker assisted selection programs.
Evaluation plan for system-wide benefits of an airport surface-operation automation concept

The predicted growth in air travel requires capacity enhancement in the National Airspace System (NAS), and congestion at key airports has been recognized as one of the most prominent problem areas. With flights operating at limits dictated by operational requirements associated with current airport configurations, airport expansion plans involving addition of new runways and taxiways are in place to increase the airports' capacities. However, the expansion projects necessarily increase the complexity of the airport configurations, which tends to penalize the efficiency of the system, partially offsetting the capacity-related benefits of the investments. The surface operation automation research (SOAR) concept has been proposed as a collaborative concept to provide automation for surface-traffic management (STM) and the flight deck to enhance the operational efficiency in complex airport environments, thus softening the unintended penalties associated with airport expansions. STM automation is based on the ground-operation situation awareness and flow efficiency (GO-SAFE) concept previously reported to ease runway access conflicts, especially in situations where active-runway crossings constitute a significant taxi delay problem. To help achieve the potential GO-SAFE benefits, the flight-deck automation for reliable ground operation (FARGO) concept has been proposed to provide the necessary flight-deck automation for enabling precision taxi control to comply with GO-SAFE advisories. Development and evaluation of the SOAR concept is being pursued in a 5-year program. A previous publication has documented an initial evaluation of the concept based on computer simulations of surface operations at a single hub airport. Expanding on this previous evaluation, this paper describes a NAS-wide evaluation effort to assess the system-wide benefits of the SOAR concept.
The Relationship Between Interpersonal Victimization and Women’s Criminal Sentencing: A Latent Class Analysis

Latent class analysis was used to identify subgroups of incarcerated women differentiated by experiences of child abuse and intimate partner violence victimization. The abuse subscales of the Childhood Trauma Questionnaire and the Abuse Behavior Inventory were used as indicators. Data were drawn from a probability sample of 217 incarcerated women housed in two state-level prisons in North Carolina. Four classes were extracted—Low Victimization, Child Abuse, Lifetime Physical and Psychological Abuse, and Lifetime Sexual Abuse; classes were then related to current sentence length, holding criminological predictors constant. Women in the Child Abuse and Lifetime Sexual Abuse classes had significantly longer sentences, despite the lack of significant between-class differences on criminological predictors. Results suggest that certain victimization patterns, notably experiences of sexual violence, were associated with sentencing disparities despite the use of structured sentencing grids. Recommendations for trauma-informed sentencing are provided.
Computerized Detection of Lung Nodules by Means of “Virtual Dual-Energy” Radiography

Major challenges in current computer-aided detection (CADe) schemes for nodule detection in chest radiographs (CXRs) are to detect nodules that overlap with ribs and/or clavicles and to reduce the frequent false positives (FPs) caused by ribs. Detection of such nodules by a CADe scheme is very important, because radiologists are likely to miss such subtle nodules. Our purpose in this study was to develop a CADe scheme with improved sensitivity and specificity by use of “virtual dual-energy” (VDE) CXRs where ribs and clavicles are suppressed with massive-training artificial neural networks (MTANNs). To reduce rib-induced FPs and detect nodules overlapping with ribs, we incorporated the VDE technology in our CADe scheme. The VDE technology suppressed rib and clavicle opacities in CXRs while maintaining soft-tissue opacity by use of the MTANN technique that had been trained with real dual-energy imaging. Our scheme detected nodule candidates on VDE images by use of a morphologic filtering technique. Sixty morphologic and gray-level-based features were extracted from each candidate from both original and VDE CXRs. A nonlinear support vector classifier was employed for classification of the nodule candidates. A publicly available database containing 140 nodules in 140 CXRs and 93 normal CXRs was used for testing our CADe scheme. All nodules were confirmed by computed tomography examinations, and the average size of the nodules was 17.8 mm. Thirty percent (42/140) of the nodules were rated “extremely subtle” or “very subtle” by a radiologist. The original scheme without VDE technology achieved a sensitivity of 78.6% (110/140) with 5 (1165/233) FPs per image. By use of the VDE technology, more nodules overlapping with ribs or clavicles were detected and the sensitivity was improved substantially to 85.0% (119/140) at the same FP rate in a leave-one-out cross-validation test, whereas the FP rate was reduced to 2.5 (583/233) per image at the same sensitivity level as the original CADe scheme obtained (Difference between the specificities of the original and the VDE-based CADe schemes was statistically significant). In particular, the sensitivity of our VDE-based CADe scheme for subtle nodules (66.7% = 28/42) was statistically significantly higher than that of the original CADe scheme (57.1% = 24/42). Therefore, by use of VDE technology, the sensitivity and specificity of our CADe scheme for detection of nodules, especially subtle nodules, in CXRs were improved substantially.
ADAPTIVE BLIND MULTIUSER DETECTION UNDER IMPULSIVE NOISE USING PRINCIPAL COMPONENTS

In this paper we consider blind signal detection for an asynchronous code division multiple access (CDMA) system with Principal component analysis (PCA) in impulsive noise. The blind multiuser detector requires no training sequences compared with the conventional multiuser detection receiver. The proposed PCA blind multiuser detector is robust when compared with knowledge based signature waveforms and the timing of the user of interest. PCA is a statistical method for reducing the dimension of data set, spectral decomposition of the covariance matrix of the dataset i.e first and second order statistics are estimated. Principal component analysis makes no assumption on the independence of the data vectors PCA searches for linear combinations with the largest variances and when several linear combinations are needed, it considers variances in decreasing order of importance. PCA improves SNR of signals used for differential side channel analysis. In different to other approaches, the linear minimum mean-square-error (MMSE) detector is obtained blindly; the detector does not use any training sequence like in subspace methods to detect multi user receiver. The algorithm need not estimate the subspace rank in order to reduce the computational complexity. Simulation results show that the new algorithm offers substantial performance gains over the traditional subspace methods.
On Joint Source and Channel Coding Using Trellis Coded CPM: Analytical Bounds on the Channel Distortion

Joint source and channel coding (JSCC) using trellis coded quantization (TCQ) in conjunction with trellis coded continuous phase modulation (CPM) is studied. The channel is assumed to be the additive white gaussian noise (AWGN) channel. Analytical bounds on the channel distortion for the investigated systems with maximum-likelihood sequence detection (MLSD) are developed. The bounds are based on the transfer function technique, which was modified and generalized to include continuous-amplitude discrete-time signals. For a memoryless uniform source, the constructed bounds for the investigated systems are shown to be asymptotically tight for increasing channel signal-to-noise ratio (SNR) values. For a memoryless nonuniform source, the constructed bounds are not as tight as the one for the uniform source, however, it still can be used as an indication to how the system performs. It is concluded that the minimum Euclidean distance of the system alone is not enough to evaluate the performance of the considered systems. The number of error events having minimum Euclidean distance and the total distortion caused by those error events also affect the asymptotic performance. This work provides an analysis tool for the investigated systems. The analysis method is very general. It may be applied to any trellis based JSCC schemes.
Towards information retrieval evaluation with reduced and only positive judgements

This paper proposes a document distance-based approach to automatically expand the number of available relevance judgements when those are limited and reduced to only positive judgements. This may happen, for example, when the only available judgements are extracted from a list of references in a published clinical systematic review. We show that evaluations based on these expanded relevance judgements are more reliable than those using only the initially available judgements. We also show the impact of such an evaluation approach as the number of initial judgements decreases.
Exception Handling Education Approach Incorporating Role- Changing Brainstorming Technique in HCI Design Process

Human Computer Interaction (HCI) is a discipline dealing with the study, planning, and design of interactions between humans and computers. The prevalent use of digital devices is increasing the need for research and education on HCI. Paying a close attention to HCI is important as poorly designed user interfaces can lead to unexpected problems. This work is focused on a new education method geared towards reducing errors and exceptions while developing a program, incorporating role-changing brainstorming techniques during HCI design process. According to the proposed approach, two evaluation elements, including the ability to find errors and exceptions and the ability to present alternative solutions when faced with these errors, are considered. Through a role-changing brainstorming based design exploration, a low-fi prototype was developed to set up an assessment method incorporating quantitative and qualitative factors for a team-based HCI project.
Machine learned ranking of entity facets

The research described in this paper forms the backbone of a service that enables the faceted search experience of the Yahoo! search engine. We introduce an approach for a machine learned ranking of entity facets based on user click feedback and features extracted from three different ranking sources. The objective of the learned model is to predict the click-through rate on an entity facet. In an empirical evaluation we compare the performance of gradient boosted decision trees (GBDT) against a linear combination of features on two different click feedback models using the raw click-through rate (CTR), and click over expected clicks (COEC). The results show a significant improvement in retrieval performance, in terms of discounted cumulated gain, when ranking entity facets with GBDT trained on the COEC model. Most notably this is true when evaluated against the CTR test set.
GENOME-WIDE TRANSCRIPTOME ANALYSIS OF HYDROGEN PRODUCTION IN SYNECHOCYSTIS : TOWARDS THE IDENTIFICATION OF NEW PLAYERS

We report the development of new and robust tools for facile integration and meaningful representation of the high throughput data acquired during the study of the genome-wide responses to environmental challenges or genetic modifications of the best-studied cyanobacterium Synechocystis sp. PCC6803, which has the potential for the photoproduction of sustainable bio-fuels such as hydrogen. These new tools comprise (i) the new design of pan-genomic oligonucleotide DNA microarray to monitor the transcriptome responses of not only the circular chromosome (3.57 Mb; 3,317 genes) but also the 7 plasmids (0.38 Mb; 408 genes altogether), and (ii) the SVGMapping a computational method to map omic experimental data onto custom-made templates, depicting metabolic pathways, cellular structures and/or biological processes. We validated our tools, through the identification and representation of the global transcriptome changes triggered by the deletion of abrB2, the gene we recently found to encode the repressor of the hydrogenase hox operon directing the production of hydrogen. We show here, for the first time, that AbrB2 is a master regulator that regulates (mostly negatively) a large number of chromosomal genes involved in regulation, metal transport and protection against oxidative stress, as well as numerous plasmid genes of as yet unknown function. That AbrB2 regulates 12 regulator genes in addition to the hox operon suggesting that the regulation of hydrogen production might be more complex than anticipated a hypothesis currently tested in our laboratory. Furthermore, our data suggest that plasmids carry several genes of unknown function, which have been overlooked so far, are involved in hydrogen metabolism, among other important cellular processes mentioned above. Consequently, we believe that the present report will stimulate both basic and applied researches on plasmid functions and their likely relation with hydrogen production.
Next generation X-ray detectors for in-house XRD

A novel type X-ray detector, called PILATUS, has been developed at the Paul Scherrer Institut in Switzerland during the last decade. PILATUS detectors are two-dimensional hybrid pixel array detectors, which operate in single-photon counting mode. PILATUS detectors feature a very wide dynamic range (1:1 000 000), very short readout time (<3.0 ms), no readout noise, and very high counting rate (>2×106counts/s/pixel). In addition, a lower energy threshold can be set in order to suppress fluorescence background from the sample, thus a very good signal-to-noise ratio is achieved. The combination of these features for area detectors is unique and thus the PILATUS detectors are considered to be the next generation X-ray detectors. The basic building block of all the detectors is the PILATUS module having an active area of 83.8×33.5 mm2. The PILATUS 100K is a complete detector system with one module. PILATUS detector systems can have other configurations, including large area systems consisting of 20 to 60 modules that can cover up to an area of 431×448 mm2. Such large systems are mainly used for macromolecular structure determination, such as protein crystallography and small angle X-ray scattering. The PILATUS 100K detector can be easily adapted to many systems; the single-module detector is integrated to an in-house X-ray diffraction (XRD) system. Examples of XRD measurements with the PILATUS 100K detector are given.
We have a DREAM: distributed reactive programming with consistency guarantees

The reactive programming paradigm has been proposed to simplify the development of reactive systems. It relies on programming primitives to express dependencies between data items and on runtime/middleware support for automated propagation of changes. Despite this paradigm is receiving increasing attention, defining the precise semantics and the consistency guarantees for reactive programming in distributed environments is an open research problem. This paper targets such problem by studying the consistency guarantees for the propagation of changes in a distributed reactive system. In particular, it introduces three propagation semantics, namely causal, glitch free, and atomic, providing different trade-offs between costs and guarantees. Furthermore, it describes how these semantics are concretely implemented in a Distributed REActice Middleware (DREAM), which exploits a distributed event-based dispatching system to propagate changes. We compare the performance of DREAM in a wide range of scenarios. This allows us to study the overhead introduced by the different semantics in terms of network traffic and propagation delay and to assess the efficiency of DREAM in supporting distributed reactive systems.
specificity using self-organizing maps Integrating and mining the chromatin landscape of cell-type Material Supplemental

Material Supplemental http://genome.cshlp. org/content/suppl/2013/10/23/gr.158261. 113.DC1. html References http://genome.cshlp. org/content/23/12/2136.full. html#ref-list-1 This article cites 25 articles, 11 of which can be accessed free at: Open Access Open Access option. Genome Research Freely available online through the License Commons Creative . http://creativecommons.org/licenses/by-nc/3.0/ License (Attribution-NonCommercial 3.0 Unported), as described at , is available under a Creative Commons Genome Research This article, published in
Planar Manhattan Local Minimal and Critical Networks

One-dimensional branching extremals of Lagrangian-type functionals are considered. Such extremals appear as solutions to the classical Steiner problem on a shortest network, i.e., a connected system of paths that has the smallest total length among all the networks spanning a given finite set of terminal points in the plane. In the present paper, the Manhattan-length functional is investigated, with Lagrangian equal to the sum of the absolute values of projections of the velocity vector onto the coordinate axes. Such functionals are useful in problems arising in electronics, robotics, chip design, etc. In this case, in contrast to the case of the Steiner problem, local minimality does not imply extremality (however, each extreme network is locally minimal). A criterion of extremality is presented, which shows that the extremality with respect to the Manhattan-length functional is a global topological property of networks. Bibliography: 95 titles.
Adaptive Work Placement for Query Processing on Heterogeneous Computing Resources

The hardware landscape is currently changing from homogeneous multi-core systems towards heterogeneous systems with many different computing units, each with their own characteristics. This trend is a great opportunity for data-base systems to increase the overall performance if the heterogeneous resources can be utilized efficiently. To achieve this, the main challenge is to place the right work on the right computing unit. Current approaches tackling this placement for query processing assume that data cardinalities of intermediate results can be correctly estimated. However, this assumption does not hold for complex queries. To overcome this problem, we propose an adaptive placement approach being independent of cardinality estimation of intermediate results. Our approach is incorporated in a novel adaptive placement sequence. Additionally, we implement our approach as an extensible virtualization layer, to demonstrate the broad applicability with multiple database systems. In our evaluation, we clearly show that our approach significantly improves OLAP query processing on heterogeneous hardware, while being adaptive enough to react to changing cardinalities of intermediate query results.
A Multi-objective Robust Operating Model for Supply Chain with Uncertain Market Supplies and Demands

A dynamic robust operating model is proposed to deal with multiple incommensurable goals for a multi-product, multi-stage supply chain with uncertain market demands and raw material supplies. The uncertainties are modeled as a number of discrete scenarios with known probabilities. The supply chain operating model is constructed as a multi-objective dynamic programming problem to satisfy several conflict objectives, such as coordination of supply chain, the maximum profit of all participants, and robustness of decision to uncertain product demands and raw material supplies. The result of a numerical example proved effectively that the inclusion of robustness measures as part of objectives can significantly reduce the variability of objective values to uncertain supplies and demands