Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk management profile of etoricoxib: an example of personalized medicine

The development of nonsteroidal anti-inflammatory drugs (NSAIDs) selective for cyclooxygenase (COX)-2 (named coxibs) has been driven by the aim of reducing the incidence of serious gastrointestinal (GI) adverse events associated with the administration of traditional (t) NSAIDs – mainly dependent on the inhibition of COX-1 in GI tract and platelets. However, their use has unravelled the important protective role of COX-2 for the cardiovascular (CV) system, mainly through the generation of prostacyclin. In a recent nested-case control study, we found that patients taking NSAIDs (both coxibs and tNSAIDs) had a 35% increase risk of myocardial infarction. The increased incidence of thrombotic events associated with profound inhibition of COX-2-dependent prostacyclin by coxibs and tNSAIDs can be mitigated, even if not obliterated, by a complete suppression of platelet COX-1 activity. However, most tNSAIDs and coxibs are functional COX-2 selective for the platelet (ie, they cause a profound suppression of COX-2 associated with insufficient inhibition of platelet COX-1 to translate into inhibition of platelet function), which explains their shared CV toxicity. The development of genetic and biochemical markers will help to identify the responders to NSAIDs or who are uniquely susceptible at developing thrombotic or GI events by COX inhibition. We will describe possible strategies to reduce the side effects of etoricoxib by using biochemical markers of COX inhibition, such as whole blood COX-2 and the assessment of prostacyclin biosynthesis in vivo.
Mood-congruent true and false memory: Effects of depression

The Deese/Roediger-McDermott paradigm was used to investigate the effect of depression on true and false recognition. In this experiment true and false recognition was examined across positive, neutral, negative, and depression-relevant lists for individuals with and without a diagnosis of major depressive disorder. Results showed that participants with major depressive disorder falsely recognised significantly more depression-relevant words than non-depressed controls. These findings also parallel recent research using recall instead of recognition and show that there are clear mood congruence effects for depression on false memory performance.
IRIT at TREC 2002: Filtering Track

The experiments we undertaken this year for TREC2002 Filtering track, are focussed on threshold calibration. We proposed a new approach to calibrate the dissemination threshold in an adaptive information ltering. It consists of optimizing a utility function represented by a linearized form of the probability distributions of the scores of the relevant and the non-relevant documents already ltered. The proles are learned using the same method used last year. It is based on a reinforcement algorithm. We submitted results on three tasks: adaptive, batch and routing.
One Rule to Grow Them All: A General Theory of Neuronal Branching and Its Practical Application

Understanding the principles governing axonal and dendritic branching is essential for unravelling the functionality of single neurons and the way in which they connect. Nevertheless, no formalism has yet been described which can capture the general features of neuronal branching. Here we propose such a formalism, which is derived from the expression of dendritic arborizations as locally optimized graphs. Inspired by Ramón y Cajal's laws of conservation of cytoplasm and conduction time in neural circuitry, we show that this graphical representation can be used to optimize these variables. This approach allows us to generate synthetic branching geometries which replicate morphological features of any tested neuron. The essential structure of a neuronal tree is thereby captured by the density profile of its spanning field and by a single parameter, a balancing factor weighing the costs for material and conduction time. This balancing factor determines a neuron's electrotonic compartmentalization. Additions to this rule, when required in the construction process, can be directly attributed to developmental processes or a neuron's computational role within its neural circuit. The simulations presented here are implemented in an open-source software package, the “TREES toolbox,” which provides a general set of tools for analyzing, manipulating, and generating dendritic structure, including a tool to create synthetic members of any particular cell group and an approach for a model-based supervised automatic morphological reconstruction from fluorescent image stacks. These approaches provide new insights into the constraints governing dendritic architectures. They also provide a novel framework for modelling and analyzing neuronal branching structures and for constructing realistic synthetic neural networks.
Characterization of the EEMBC Benchmark Suite

EEMBC addresses the needs of embedded designers by providing a diverse suite of processor benchmarks organized into categories that span numerous real-world applications. Research done at North Carolina State University investigates the benchmark suites through the use of benchmark characterization to create a description of each workload. Benchmark characterization involves re-describing a workload as a set of quantifiable abstract attributes. Designers can then use these measured attributes to determine program similarity. By combining these characteristics with their knowledge of the actual application, designers can select the most relevant benchmarks from the EEMBC suite to test the potential in situ performance of an embedded processor. We used a mixture of design characteristics including instructions/clock (IPC), branch mis-prediction ratios
Spatio-Temporal LAI Modelling by Integrating Climate and MODIS LAI Data in a Mesoscale Catchment

Vegetation is often represented by the leaf area index (LAI) in many ecological, hydrological and meteorological land surface models. However, the spatio-temporal dynamics of the vegetation are important to represent in these models. While the widely applied methods, such as the Canopy Structure Dynamic Model (CSDM) and the Double Logistic Model (DLM) are solely based on cumulative daily mean temperature data as input, a new spatio-temporal LAI prediction model referred to as the Temperature Precipitation Vegetation Model (TPVM) is developed that also considers cumulative precipitation data as input into the modelling process. TPVM as well as CDSM and DLM model performances are compared and evaluated against filtered LAI data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The calibration/validation results of a cross-validation performed in the meso-scale Attert catchment in Luxembourg indicated that the DLM and TPVM generally provided more realistic and accurate LAI data. The TPVM performed superiorly for the agricultural land cover types compared to the other two models, which only used the temperature data. The Pearson's correlation coefficient (CC) between TPVM and the field measurement is 0.78, compared to 0.73 and 0.69 for the DLM and CSDM, respectively. The phenological metrics were derived from the TPVM model to investigate the interaction between the climate variables and the LAI variations. These interactions illustrated the dominant control of temperature on the LAI dynamics for deciduous forest cover, and a combined influence of temperature with precipitation for the agricultural land use areas.
Computers and Mathematics with Applications Accelerated staggered coupling schemes for problems of thermoelasticity at finite strains

This paper introduces a fully implicit partitioned couplin g scheme for problems of thermoelasticity at finite strains u tilizing the p-version of the finite element method. The mechanical and th e thermal fields are partitioned into symmetric subproblems where algorithmic decoupling has been obtained by means of an isot hermal operator-split. Numerical relaxation methods have be n implemented to accelerate the convergence of the algorithm . Such methods are well-known from coupled fluid-structure i nt raction problems leading to highly e fficient algorithms. Having studied the influence of three di fferent strategies: polynomial prediction methods, numerical relaxation with constant relaxation co efficients, its dynamic variant with a residual based relaxatio n coefficient and a variant of a reduced order model quasi-Newton method, we present several numerical simulations of quasi-static p roblems investigating the performance of accelerated coupling sch emes. c © 2012 Published by Elsevier Ltd.
The seed bank longevity index revisited: limited reliability evident from a burial experiment and database analyses.

BACKGROUND AND AIMS Seed survival in the soil contributes to population persistence and community diversity, creating a need for reliable measures of soil seed bank persistence. Several methods estimate soil seed bank persistence, most of which count seedlings emerging from soil samples. Seasonality, depth distribution and presence (or absence) in vegetation are then used to classify a species' soil seed bank into persistent or transient, often synthesized into a longevity index. This study aims to determine if counts of seedlings from soil samples yield reliable seed bank persistence estimates and if this is correlated to seed production. METHODS Seeds of 38 annual weeds taken from arable fields were buried in the field and their viability tested by germination and tetrazolium tests at 6 month intervals for 2.5 years. This direct measure of soil seed survival was compared with indirect estimates from the literature, which use seedling emergence from soil samples to determine seed bank persistence. Published databases were used to explore the generality of the influence of reproductive capacity on seed bank persistence estimates from seedling emergence data. KEY RESULTS There was no relationship between a species' soil seed survival in the burial experiment and its seed bank persistence estimate from published data using seedling emergence from soil samples. The analysis of complementary data from published databases revealed that while seed bank persistence estimates based on seedling emergence from soil samples are generally correlated with seed production, estimates of seed banks from burial experiments are not. CONCLUSIONS The results can be explained in terms of the seed size-seed number trade-off, which suggests that the higher number of smaller seeds is compensated after germination. Soil seed bank persistence estimates correlated to seed production are therefore not useful for studies on population persistence or community diversity. Confusion of soil seed survival and seed production can be avoided by separate use of soil seed abundance and experimental soil seed survival.
Simulating bagging without bootstrapping

Bagging is a well-known and widely used ensemble method. It operates by sequentially bootstrapping the data set and invoking a base classifier on these different bootstraps. By learning several models (and combining them), it tends to increase predictive accuracy, while sacrificing efficiency. Due to this it becomes slow for large scale data sets. In this paper we propose a method that simulates bagging. Instead of bootstrapping the data and computing statistics for each attribute on each bootstrap, we only compute each statistic for the original training set, and verify how the statistic would be distributed if it were computed from resampled sets. We then generate the statistic for bootstrapped versions by sampling from that distribution. This procedure has the effect of reducing the computational complexity of refining a node in bagging from O(N*I) to O(N+I), where N is the number of instances and I the number of bagging iterations. An experimental evaluation reveals, however, that due to unfavorable constant factors hidden in those formulas, efficiency gain becomes only really worthwhile when dealing with large data sets. Thus, efficient generation of random values according to the required distribution is crucial in order to truly exploit this improvement.
ClusTex: Information Extraction from HTML Pages

This paper propose ClusTex, a system which employs clustering techniques for automatic information extraction from HTML documents containing semi- structured data. Using domain-specific information provided by the user, ClusTex parses and tokenizes the data from an HTML document, partitions it into clusters containing similar elements, and estimates an extraction rule based on the pattern of occurrence of data tokens. The extraction rule is then used to refine clusters, and finally the output is reported. To demonstrate the effectiveness of this approach, the proposed approach is tested by conducting experiments on the University of Calgary Web-site; the results prove comparable to those reported in the literature.
Energy-Efficient SINR-Based Routing for Multihop Wireless Networks

In this paper, we develop an energy-efficient routing scheme that takes into account the interference created by existing flows in the network. The routing scheme chooses a route such that the network expends the minimum energy satisfying with the minimum constraints of flows. Unlike previous works, we explicitly study the impact of routing a new flow on the energy consumption of the network. Under certain assumptions on how links are scheduled, we can show that our proposed algorithm is asymptotically (in time) optimal in terms of minimizing the average energy consumption. We also develop a distributed version of the algorithm. Our algorithm automatically detours around a congested area in the network, which helps mitigate network congestion and improve overall network performance. Using simulations, we show that the routes chosen by our algorithm (centralized and distributed) are more energy efficient than the state of the art.
Double Layer Based Multi-label Classifier Chain

In multi-label learning, each training example is associated with a set of labels and the task is to predict the proper label set for each unseen instance. The widely known binary relevance method (BR) for multi-label classification considers each label as an independent binary problem. It is ignored in the literature due to inadequacy of not considering label correlations. In this paper, we present our double layer based classifier chains method (DCC), which overcomes dis- advantages of BR and inherits the benefit of classifier chain method (CC). This algorithm decomposes the multi-label classification problem into two classification processes to generate classifier chain. Each classifier in the chain is respon- sible for learning and predicting the binary association of the label given the attribute space expanded by all prior binary relevance predictions in the chain. This chaining allows DCC to take into account correlations in the label space. We also extend this approach further in an ensemble framework. An extensive evaluation covers a broad range of multi-label datasets with a variety of evaluation measures specifically designed for multi-label classification. Experiments on bench- mark datasets validate the effectiveness of proposed approach comparing with state-of-art methods in terms of average ranking. �
QoS Management: Differentiating Services and Practical Views

Quality of service management support is a key factor in developing new products. This paper presents and implements an architecture for sharing Internet access among several users supporting QoS management based on differentiated services philosophy. For implementation details, existing Linux traffic control capabilities are mentioned in the paper, but a high level abstraction application has been developed for administrators to configure QoS management. Several billing schemes are presented, based on time consumption or amount of transferred data. Two different events have been investigated: a) bandwidth distribution and limitation as isolated problems and b) based on an ADSL Internet access, the influence of its asymmetrical nature and a simple way to minimize it is proposed. The output results show that the developed architecture behaves at optimum in bandwidth distribution as well as in bandwidth limitation
On the Use of Software Visualization to Support Concern Modularization Analysis

Most of the methods for concern modularity analysis rely on the exploration of crosscutting concerns directly in source code artifacts. However, as both the code size and the number of concerns increase, analyses of concern properties become an even more cumbersome and time-consuming task. In this paper, we propose a visual representation of crosscutting concerns using a software visualization infrastructure called SourceMiner. The infrastructure facilitates concern modularization analysis based on the following concepts: inheritance tree and package-class-method structure. The analysis based on each of these concepts is supported by the views and its resources available in the infrastructure. An example of use illustrates the infrastructure support to visually analyze the concern modularity of an open source code.
State and unknown input estimation for nonlinear systems described by Takagi-Sugeno models with unmeasurable premise variables

This paper presents a new method to synthesize observers for continuous time nonlinear systems described by Takagi-Sugeno (TS) model with unmeasurable premise variables. First, convergence conditions are established in order to guarantee the convergence of the state estimation error. These conditions are given in Linear Matrix Inequality (LMI) formulation. Secondly, a classical Proportional Integral Observer (PIO) is extended to the considered nonlinear systems in order to estimate the state and the unknown inputs (UI).
A scalable approach to optimal block scheduling

A critical aspect of long-term open-pit mine planning consists in computing a production schedule based upon a block sequencing strategy. Such a schedule should specify when and if blocks should be extracted in such a way as to maximize NPV, while satisfying wall-slope and production capacity constraints. It is well known that this problem can be modeled with integer programming (IP). However, integer programming is not used in practice because the size of typical block models makes such problems intractable to standard IP solvers. In this article we describe a scalable IP-based methodology for solving very large (millions of blocks) instances of this problem. We show that embedding standard IP technologies in a local-search based algorithm we are able to obtain near-optimal solutions to large problems in reasonable time. This methodology has been tested in several mine wide block models. Biography Marcos Goycoolea obtained his PhD from the School of Industrial and Systems Engineering (ISYE) of Georgia Tech in 2006, and is currently an Assistant Professor at the Universidad Adolfo Ibañez School of Business in Santiago, Chile. He teaches Operations Management and Optimization to MBA and Graduate students and conducts research on integer programming methodologies and their application to natural resource management. His focus of research is on developing computational methods and software for large-scale optimization-based decision support systems. For more information visit his website at http://mgoycool.uai. cl.
An improved classification of B(e)-type stars

We review the classification criteria for the B(e)-type stars (B type stars with forbidden emission lines in their optical spectrum) and we express these in terms of physical character- istics of the stars and the circumstellar (CS) matter. We show on the basis of observations that these criteria can be met in differ- ent kinds of stars of different mass and different evolutionary stages. We propose that the name "B(e) phenomenon" is more appropriate than the name "B(e) stars". We propose the defini- tion of five classes of stars which show the B(e) phenomenon: (a) B(e) supergiants or "sgB(e) stars" (b) pre-main sequence B(e)-type stars or "HAeB(e) stars" (c) compact planetary nebulae B(e)-type stars or "cPNB(e) stars" (d) symbiotic B(e)-type stars or "SymB(e) stars" (e) unclassified B(e)-type stars or "unclB(e) stars" The primary and secondary classification criteria for each of these groups are defined. We also present lists of objects for each group, except for the SymB(e) stars. It is possible that some stars satisfy the criteria for more than one of the classes sgB(e), HAeB(e), cPNB(e) and SymB(e). In that case the evolutionary phase of the star is unclear and the star should be assigned to class unclB(e).
Far-UV FUSE spectra of peculiar magnetic cataclysmic variables

We present far-UV spectra of the three magnetic cataclysmic variables (MCVs) BY Cam, V1309 Ori and AE Aqr obtained with the FUSE satellite. These MCVs have revealed strongly unusual NV and CIV UV resonance lines. The FUSE spectra exhibit broad OVI lines as well as a strong NIII line at 991A, while the CIII 1175A line is nearly absent, supporting non-solar CNO abundances of the accreting matter in these sources. The spectrum of BY Cam shows molecular H2 lines which might be of circumstellar nature. The flaring activity of AE Aqr is also observed in the far-UV range. The radial velocities of the broad OVI components in AE Aqr are orbitally modulated and would indicate an emission region close to the magnetosphere.
Evidence for similar early but not late representation of possible and impossible objects

The perceptual processes that mediate the ability to efficiently represent object 3D structure are still not fully understood. The current study was aimed to shed light on these processes by utilizing spatially possible and impossible objects that could not be created in real 3D space. Despite being perceived as exceptionally unusual, impossible objects still possess fundamental Gestalt attributes and valid local depth cues that may support their initial successful representation. Based on this notion and on recent findings from our lab, we hypothesized that the initial representation of impossible objects would involve common mechanisms to those mediating typical object perception while the perceived differences between possible and impossible objects would emerge later along the processing hierarchy. In Experiment 1, participants preformed same/different classifications of two markers superimposed on a display containing two objects (possible or impossible). Faster reaction times were observed for displays in which the markers were superimposed on the same object (“object-based benefit”). Importantly, this benefit was similar for possible and impossible objects, suggesting that the representations of the two object categories rely on similar perceptual organization processes. Yet, responses for impossible objects were slower compared to possible objects. Experiment 2 was designed to examine the origin of this effect. Participants classified the location of two markers while exposure duration was manipulated. A similar pattern of performance was found for possible and impossible objects for the short exposure duration, with differences in accuracy between these two types of objects emerging only for longer exposure durations. Overall, these findings provide evidence that the representation of object structure relies on a multi-level process and that object impossibility selectively impairs the rendering of fine-detailed description of object structure.