Miracle-dz/UniFAR-FTUs huggingface dataset:
Will HITECH Heal Patient Data Hemorrhages?

Hemorrhages of confidential patient health data create privacy and security concerns. While the US HIPAA legislation on privacy and security went into effect over five years ago, healthcare information security remains a significant concern as organizations migrate to electronic health records. The recent HITECH legislation aimed at accelerating this migration contained mandates for greater security, including the addition of new requirements on breach reporting. We overview this legislation and examine a recently collected sample of inadvertently disclosed files found on internet-based file-sharing networks. We characterize the security risk of these files and also present evidence of the threat by analyzing user-issued searches. Our analysis indicates that the threat and vulnerability for the healthcare sector continued, even as HITECH became effective.
Dynamic Optimal Taxation with Private Information

We study dynamic optimal taxation in a class of economies with private information. Constrained optimal allocations in these environments are complicated and history-dependent. Yet, we show that they can be attained as competitive equilibria in market economies supplemented with simple tax systems. The market structure in these economies is identical to that in Bewley (1986): agents can trade current consumption and risk-free claims to future consumption, subject to a budget constraint and a debt limit. The tax system describes additional transfers that the agents must make to the government. It conditions them upon only two observable characteristics of an agent: their accumulated stock of claims, or wealth, and their current labour income. It implies optimal tax functions that are not additively separable in these variables. The marginal wealth tax is negatively correlated with income and its expected value is generally positive. The marginal income tax is decreasing in wealth.
Efficient MapReduce Kernel k-Means for Big Data Clustering

Data clustering is an unsupervised learning task that has found many applications in various scientific fields. The goal is to find subgroups of closely related data samples (clusters) in a set of unlabeled data. A classic clustering algorithm is the so-called k-Means. It is very popular, however, it is also unable to handle cases in which the clusters are not linearly separable. Kernel k-Means is a state of the art clustering algorithm, which employs the kernel trick, in order to perform clustering on a higher dimensionality space, thus overcoming the limitations of classic k-Means regarding the non linear separability of the input data. It has recently received a distributed implementation, named Trimmed Kernel k-Means, following the MapReduce distributed computing model. In addition to performing the computations in a distributed manner, Trimmed Kernel k-Means also trims the kernel matrix, in order to reduce the memory requirements and improve performance. The trimming of each row of the kernel matrix is achieved by attempting to estimate the cardinality of the cluster that the corresponding sample belongs to, and removing the kernel matrix entries connecting the sample to samples that probably belong to another cluster. The Spark cluster computing framework was used for the distributed implementation. In this paper, we present a distributed clustering scheme that is based on Trimmed Kernel k-Means, which employs subsampling, in order to be able to efficiently perform clustering on an extremely large dataset. The results indicate that the proposed method run much faster than the original Trimmed Kernel k-Means, while still providing clustering performance competitive with other state of the art kernel approaches.
Psip1/Ledgf p52 Binds Methylated Histone H3K36 and Splicing Factors and Contributes to the Regulation of Alternative Splicing

Increasing evidence suggests that chromatin modifications have important roles in modulating constitutive or alternative splicing. Here we demonstrate that the PWWP domain of the chromatin-associated protein Psip1/Ledgf can specifically recognize tri-methylated H3K36 and that, like this histone modification, the Psip1 short (p52) isoform is enriched at active genes. We show that the p52, but not the long (p75), isoform of Psip1 co-localizes and interacts with Srsf1 and other proteins involved in mRNA processing. The level of H3K36me3 associated Srsf1 is reduced in Psip1 mutant cells and alternative splicing of specific genes is affected. Moreover, we show altered Srsf1 distribution around the alternatively spliced exons of these genes in Psip1 null cells. We propose that Psip1/p52, through its binding to both chromatin and splicing factors, might act to modulate splicing.
Proactive loss recovery using forward segment redundancy in optical burst-switched (OBS) networks

Data burst contentions occur in optical burst-switched (OBS) networks due to one-way signaling and bufferless nature of the core network. A single burst loss containing multiple TCP segments can trigger drastic reactions from the corresponding TCP sources. TCP sender interprets segment loss as network congestion, resulting in throttling send rate. In this paper, we propose a new forward segment redundancy (FSR) mechanism that minimizes segment loss during burst contentions in the core and also recovers from segment loss in the forward direction using redundant segments placed in each data burst. In FSR mechanism, redundant TCP segments are added to each burst assembled at the ingress node before transmission to the destination. The segment losses due to contentions are minimized using a modified burst segmentation mechanism, and most lost segments are recovered in the forward direction using redundant segments. Using FSR, we observe significant improvement in TCP performance over OBS networks.
Wanted : Element Retrieval Users

Document centric information retrieval is used every day by people all over the world. It is an application well studied, well understood, and of which there is a sound user model. Element retrieval, on the other hand, is a new field of research, with no identified applications, no users, and without a user model. Some of the methodological issues in element retrieval are identified. The standard document collection (the INEX / IEEE collection) is shown to be unsuitable for element retrieval, and the question is raised – does such a suitable collection exist? Some characteristics of querying behavior are identified, and the question raised – will users ever use structural hints in their queries? Examining the judgments and metrics, it is shown that the judgments are inconsistent and the metrics do not measure the same things. It is suggested that identifying an application of element retrieval could resolve some of these issues. Aspects of the application could (and should) be modeled, resulting is a more sound field of element retrieval. Alternatively, whatever it is, users don’t want it, judges can’t judge it, and the metrics can’t measure it.
3-D visualizations of coastal bathymetry by utilization of airborne TOPSAR polarized data

Abstract Multi-frequency C and L bands in the TOPSAR data have been utilized to reconstruct three-dimensional (3-D) bathymetry pattern. The main objective of this study is to utilize fuzzy arithmetic to reduce the errors arising from speckle in synthetic aperture radar (SAR) data when constructing ocean bathymetry from polarized SAR data. In doing so, two 3-D surface models, the Volterra algorithm and a fuzzy B-spline (FBS) algorithm, which construct a global topological structure between the data points, were used to support an approximation to the real surface. Volterra algorithm was used to express the non-linearity of TOPSAR data intensity gradient based on the action balance equation (ABC). In this context, a first-order kernel of Volterra algorithm was used to express ABC equation. The inverse of Volterra algorithm then performed to simulate 2-D current velocities from CVV and LHH band. Furthermore, the 2-D continuity equation then used to estimate the water depth. In order to reconstruct 3-D bathymetry pattern, the FBS has been performed to water depth information which was estimated from 2-D continuity equation. The best reconstruction of coastal bathymetry of the test site in Kuala Terengganu, Malaysia, was obtained with polarized L and C bands SAR acquired with HH and VV polarizations, respectively. With 10 m spatial resolution of TOPSAR data, bias of –0.004 m, the standard error mean of 0.023 m, r 2 value of 0.95, and 90% confidence intervals in depth determination was obtained with LHH band.
Learner Reflections on Extensive Reading Materials

This paper presents a summary and analysis of qualitative data on learner preferences in extensive reading materials. The study used data collected during one academic year from three convenience samples (intact classes) totaling 94 students, of which 87 both responded and agreed to allow the researcher-instructor to use responses for research. The learners were all first-year students in majors other than English in a required English Reading course at a private university in Japan. The learners were required to choose extensive reading materials, read, and describe them every week, and to complete five guided reflections over two semesters as part of the reading course. The data presented is from the responses to several questions related to how the learners chose readers, the types of books they liked to read, and what made for good or interesting books. Results support the principles of providing variety, choice, and easy materials for extensive reading.
The Balance between Sphingosine and Sphingosine-1-Phosphate Is Decisive for Mast Cell Activation after Fc∈ Receptor I Triggering

Over the last few years, sphingolipids have been identified as potent second messenger molecules modulating cell growth and activation. A newly emerging facet to this class of lipids suggests a picture where the balance between two counterregulatory lipids (as shown in the particular example of ceramide and sphingosine-1-phosphate in T lymphocyte apoptosis) determines the cell fate by setting the stage for various protein signaling cascades. Here, we provide a further example of such a decisive balance composed of the two lipids sphingosine and sphingosine-1-phosphate that determines the allergic responsiveness of mast cells. High intracellular concentrations of sphingosine act as a potent inhibitor of the immunoglobulin (Ig)E plus antigen–mediated leukotriene synthesis and cytokine production by preventing activation of the mitogen-activated protein kinase pathway. In contrast, high intracellular levels of sphingosine-1-phosphate, also secreted by allergically stimulated mast cells, activate the mitogen-activated protein kinase pathway, resulting in hexosaminidase and leukotriene release, or in combination with ionomycin, give cytokine production. Equivalent high concentrations of sphingosine-1-phosphate are dominant over sphingosine as they counteract its inhibitory potential. Therefore, it might be inferred that sphingosine-kinase is pivotal to the activation of signaling cascades initiated at the Fc∈ receptor I by modulating the balance of the counterregulatory lipids.
New Results on Single-Channel Speech Separation Using Sinusoidal Modeling

We present new results on single-channel speech separation and suggest a new separation approach to improve the speech quality of separated signals from an observed mixture. The key idea is to derive a mixture estimator based on sinusoidal parameters. The proposed estimator is aimed at finding sinusoidal parameters in the form of codevectors from vector quantization (VQ) codebooks pre-trained for speakers that, when combined, best fit the observed mixed signal. The selected codevectors are then used to reconstruct the recovered signals for the speakers in the mixture. Compared to the log-max mixture estimator used in binary masks and the Wiener filtering approach, it is observed that the proposed method achieves an acceptable perceptual speech quality with less cross-talk at different signal-to-signal ratios. Moreover, the method is independent of pitch estimates and reduces the computational complexity of the separation by replacing the short-time Fourier transform (STFT) feature vectors of high dimensionality with sinusoidal feature vectors. We report separation results for the proposed method and compare them with respect to other benchmark methods. The improvements made by applying the proposed method over other methods are confirmed by employing perceptual evaluation of speech quality (PESQ) as an objective measure and a MUSHRA listening test as a subjective evaluation for both speaker-dependent and gender-dependent scenarios.
Primal-Dual Interior-Point Methods for Self-Scaled Cones

In this paper we continue the development of a theoretical foundation for efficient primal-dual interior-point algorithms for convex programming problems expressed in conic form, when the cone and its associated barrier are self-scaled (see Yu. E. Nesterov and M. J. Todd, Math. Oper. Res., 22 (1997), pp. 1--42). The class of problems under consideration includes linear programming, semidefinite programming, and convex quadratically constrained, quadratic programming problems. For such problems we introduce a new definition of affine-scaling and centering directions. We present efficiency estimates for several symmetric primal-dual methods that can loosely be classified as path-following methods. Because of the special properties of these cones and barriers, two of our algorithms can take steps that typically go a large fraction of the way to the boundary of the feasible region, rather than being confined to a ball of unit radius in the local norm defined by the Hessian of the barrier.
Towards fast and reliable candidate peptide indexing for Tandem Mass Spectrometry data

Many algorithms for protein and peptide identification from Tandem Mass Spectrometry (MS/MS) data have been published. The majority of such methods are based on sequence or spectral database search: experimental spectra are matched to theoretical spectra characterizing known peptide sequences. Hits define candidate peptides. In this context, the development of reliable candidate peptide indexing and ranking techniques for accelerated pre-processing of large datasets still remains a challenge.
An automated approach for clustering an ensemble of NMR-derived protein structures into conformationally related subfamilies.

Unlike structures determined by X-ray crystallography, which are deposited in the Brookhaven Protein Data Bank (Abola et al., 1987) as a single structure, each NMR-derived structure is often deposited as an ensemble containing many structures, each consistent with the restraint set used. However, there is often a need to select a single 'representative' structure, or a 'representative' subset of structures, from such an ensemble. This is useful, for example, in the case of homology modelling or when compiling a relational database of protein structures. It has been shown that cluster analysis, based on overall fold, followed by selection of the structure closest to the centroid of the largest cluster, is likely to identify a structure more representative of the ensemble than the commonly used minimized average structure (Sutcliffe, 1993). Two approaches to the problem of clustering ensembles of NMR-derived structures have been described. One of these (Adzhubei et al., 1995) performs the pairwise superposition of all structures using C a atoms to generate a set of r.m.s. distances. After cluster analysis based on these distances, a user-defined cut-off is required to determine the final membership of clusters and therefore the representative structures. The other approach (Diamond, 1995) uses collective superpositions and rigid-body transformations. Again, the position at which to draw a cut-off based on the particular clustering pattern was not addressed. Whenever fixed values are used for the cut-off in clustering, there is a danger of missing 'true' clusters under the threshold imposed by the rigid cut-off value. Considering the highly diverse nature of NMR-derived ensembles of proteins, it would seem most appropriate to avoid the use of predefined values for determining clusters. In fact, of the 302 ensembles we have studied, the average pairwise r.m.s. distance across an ensemble varied from 0.29 to 11.3 A (mean value 3.0, SD 1.9 A). Here we present an automated method for cut-off determination that avoids the dangers of using fixed values for this purpose. We have developed a computer program that automatically, systematically and rapidly (i) clusters an ensemble of structures into a set of conformationally related subfamilies, and (ii) selects a representative structure from each cluster. The program uses the method of average linkage to define how clusters are built up, followed by the application of a penalty function that seeks to minimize simultaneously the number of clusters
Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models

Latent Gaussian models (LGMs) are widely used in statistics and machine learning. Bayesian inference in non-conjugate LGMs is difficult due to intractable integrals involving the Gaussian prior and nonconjugate likelihoods. Algorithms based on variational Gaussian (VG) approximations are widely employed since they strike a favorable balance between accuracy, generality, speed, and ease of use. However, the structure of the optimization problems associated with these approximations remains poorly understood, and standard solvers take too long to converge. We derive a novel dual variational inference approach that exploits the convexity property of the VG approximations. We obtain an algorithm that solves a convex optimization problem, reduces the number of variational parameters, and converges much faster than previous methods. Using real-world data, we demonstrate these advantages on a variety of LGMs, including Gaussian process classification, and latent Gaussian Markov random fields.
First impressions through the constructs of impression management

This study examines the social intelligence process through the constructs of self-monitoring and impression management. With these terms set in place, this study observes how first impressions of a hotel experience can influence a guest's interpretation of a hotel stay. Two hundred and nine Ayres Suites guests responded to a questionnaire that asked the guests to state what impressions first impressions form during their hotel experience. Self-monitoring is an individual difference that refers to one's ability to accurately read social situations; it controls one's expressive behavior. Impression management refers to the behaviors used by individuals to influence the impressions that others have of them. This research examines how first impressions are formed in the hotel setting, and how guests' attitudes are formed based upon the brand and sales of the hotel. The guest may be partial to the brand or this may be a new experience. This judgment of the brand is automatic and assigned value to the brand; it begins to form the moment the guest steps into the hotel. The initial first impression is done from the guest's perspective. Other
Scalable Cloud-Based Analysis Framework for Medical Big-Data

Medical care can be improved by efficient analysis of the large amounts of data involved in patient care. Two important challenges for patient care big-data analysis are the need to cope with unstructured images and text (as well as structured data) and the need for efficient methods that scale for large amounts of data generated by the many patients. The framework described here is designed to cope with these challenges. Firstly it is composed of generic components where the initial stage can transform unstructured image or text data into a set of features for the latter stages. Secondly the machine learning analytics engine incorporates three optimizations which allow the framework to scale and deal with large amounts of unstructured data. The framework is evaluated using both image and text data, and shown to provide an efficient scalable solution.
Mozilla: Closing the Circle

Concurrent Versioning System (CVS) repositories and bug tracking systems are valuable sources of information to study the evolution of large open source software systems. However, being conceived for specific purposes, i.e., to support the development or trigger maintenance activities, they do neither allow an easy information browsing nor support the study of software evolution. For example, queries such as locating and browsing the faultiest methods are not provided. This paper addresses such issues and proposes an approach and a framework to consistently merge information extracted from source code, CVS repositories and bug reports. Our information representation exploits the property concepts of the FAMIX information exchange meta-model, allowing to represent, browse, and query, at different level of abstractions, the concept of interest. This allows the user to navigate back and forth from CVS modification reports to bug reports and to source code. As a result, concepts can be traced across different data sources to combine different conceptual abstractions for evolution analysis. This paper presents the analysis framework and approaches to populate it, tools developed and under development for it, as well as lessons learned while analyzing several releases of Mozilla.
Applying grey relational analysis to dimension reduction based on difference information theory

In this paper, a novel dimension reduction approach with the application of grey relational analysis based on difference information theory is proposed. In this approach, dimension reduction process is performed based on the calculation of the similarity between each pair of dimensions with the application of grey relational analysis. The effectiveness and efficiency of the proposed approach is proved by the experiments on real-world datasets.
Design pattern modelling and instantiation using DPML

We describe the Design Pattern Modelling Language, a notation supporting the specification of design pattern solutions and their instantiation into UML design models. DPML provides a set of modelling constructs allowing design pattern solutions to be modelled and reused. A corresponding notation links design pattern solution elements to UML model elements, verifying fulfilment of the design pattern in the UML model. A prototype tool is described, together with an evaluation of the language and tool.
The crystal structure of Nep1 reveals an extended SPOUT-class methyltransferase fold and a pre-organized SAM-binding site

Ribosome biogenesis in eukaryotes requires the participation of a large number of ribosome assembly factors. The highly conserved eukaryotic nucleolar protein Nep1 has an essential but unknown function in 18S rRNA processing and ribosome biogenesis. In Saccharomyces cerevisiae the malfunction of a temperature-sensitive Nep1 protein (nep1-1ts) was suppressed by the addition of S-adenosylmethionine (SAM). This suggests the participation of Nep1 in a methyltransferase reaction during ribosome biogenesis. In addition, yeast Nep1 binds to a 6-nt RNA-binding motif also found in 18S rRNA and facilitates the incorporation of ribosomal protein Rps19 during the formation of pre-ribosomes. Here, we present the X-ray structure of the Nep1 homolog from the archaebacterium Methanocaldococcus jannaschii in its free form (2.2 Å resolution) and bound to the S-adenosylmethionine analog S-adenosylhomocysteine (SAH, 2.15 Å resolution) and the antibiotic and general methyltransferase inhibitor sinefungin (2.25 Å resolution). The structure reveals a fold which is very similar to the conserved core fold of the SPOUT-class methyltransferases but contains a novel extension of this common core fold. SAH and sinefungin bind to Nep1 at a preformed binding site that is topologically equivalent to the cofactor-binding site in other SPOUT-class methyltransferases. Therefore, our structures together with previous genetic data suggest that Nep1 is a genuine rRNA methyltransferase.