Miracle-dz/UniFAR-FTUs huggingface dataset:
European Heart Rhythm Association Practical Guide on the use of non-vitamin K antagonist anticoagulants in patients with non-valvular atrial fibrillation

Advisors:, Azhar Ahmad, M.D. (Boehringer Ingelheim Pharma), Jutta Heinrich-Nols, M.D. (Boehringer Ingelheim Pharma), Susanne Hess, M.D. (Bayer Healthcare Pharmaceuticals), Markus Müller, M.D., Ph.D. (Pfizer Pharma), Felix Münzel, Ph.D. (Daiichi-Sankyo Europe), Markus Schwertfeger, M.D. (Daiichi-Sankyo Europe), Martin Van Eickels, M.D. (Bayer Healthcare Pharmaceuticals), and Isabelle Richard-Lordereau, M.D. (Bristol Myers Squibb/Pfizer)
Diagnostic accuracy, internal consistency, and convergent validity of the Greek version of the patient health questionnaire 9 in diagnosing depression in rheumatologic disorders

The Patient Health Questionnaire 9 (PHQ‐9) was developed to screen for depressive disorders in community, primary care, and medical settings. We aimed to estimate its diagnostic accuracy, internal consistency, reliability, and convergent validity in diagnosing major depressive disorder (MDD) in Greek patients with rheumatologic disorders.
Local Difference Probability (LDP)-Based Environment Adaptive Algorithm for Unmanned Ground Vehicle

This paper presents a new vision sensor-based road-following method for unmanned vehicles. Usually, the performance obtained with such methods is limited by several factors like image quality according to camera types (charge-coupled device CCD/CMOS), mounted camera position, stereo-/single-vision sensors, structured/unstructured environment, and image noise (illumination and shadow). Existing road-following algorithms for unmanned vehicles perform well, given a certain number of satisfied constraints, so there is a lack of flexibility in their use in real-world situations. The currently proposed local-difference-probability-based method overcomes most of these constraints assuring flexibility in real-world environments
Improved RELAX-PHAF autofocus algorithm of hybrid SAR

In this paper, an improved RELAX-PHAF autofocus method is introduced. First, select the range lines of the high signal to clutter ratio (SCR) to improve the product high-order ambiguity function (PHAF) autofocus accuracy. Second, use the RELAX to substitute the FFT in the computing from multilag high order instantaneous moment to multilag PHAF. Due to the superresolution ability of RELAX, the RELAX-PHAF has improved accuracy than the original PHAF. The improved RELAX-PHAF autofocus method is a good method for the hybrid strip-map/spotlight SAR. Keywords-Hybrid SAR; autofocus; RELAX; product highorder ambiguity function (PHAF); RELAX-PHAF
CAGE: Combinatorial Analysis of Gene-Cluster Evolution

Much important evolutionary activity occurs in gene clusters, where a copy of a gene may be free to acquire new functions. Current computational methods to extract evolutionary information from sequence data for such clusters are suboptimal, in part because accurate sequence data are often lacking in these genomic regions, making existing methods difficult to apply. We describe a new method for reconstructing the recent evolutionary history of gene clusters, and evaluate its performance on both simulated data and actual human gene clusters.
Small object detection neurons in female hoverflies

While predators such as dragonflies are dependent on visual detection of moving prey, social interactions make conspecific detection equally important for many non-predatory insects. Specialized ‘acute zones’ associated with target detection have evolved in several insect groups and are a prominent male-specific feature in many dipteran flies. The physiology of target selective neurons associated with these specialized eye regions has previously been described only from male flies. We show here that female hoverflies (Eristalis tenax) have several classes of neurons within the third optic ganglion (lobula) capable of detecting moving objects smaller than 1°. These neurons have frontal receptive fields covering a large part of the ipsilateral world and are tuned to a broad range of target speeds and sizes. This could make them suitable for detecting targets under a range of natural conditions such as required during predator avoidance or conspecific interactions.
Forecasting patterns of soil erosion in arid lands from Landsat MSS data

Abstract A model for forecasting large-scale patterns of soil erosion and deposition from Landsat MSS data in arid grazing lands is presented. The model is based on the erosion cell mosaic approach and exploits the high degree of temporal and spatial autocorrelation in the erosion process on flat alluvial plains. Erosion and deposition are first mapped using Pickup and Nelson's soil stability index. As a landscape degrades, the variance and autocorrelation function of this index change. These trends represent changes in the physical behaviour of the system and can be explained by it. The model used for forecasting change is a first-order simultaneous autoregressive (s.a.r.) process which can reproduce changes in mean, variance and spatial autocorrelation. This model expresses each pixel value as a function of those of its neighbours plus a noise term. The forecasting procedure involves fitting a s.a. r. model to an area, obtaining the values of the underlying pattern (noise) series by inverse filtering and.
A Scalable User Fairness Model for Adaptive Video Streaming Over SDN-Assisted Future Networks

The growing demand for online distribution of high quality and high throughput content is dominating today's Internet infrastructure. This includes both production and user-generated media. Among the myriad of media distribution mechanisms, HTTP adaptive streaming (HAS) is becoming a popular choice for multi-screen and multi-bitrate media services over heterogeneous networks. HAS applications often compete for network resources without any coordination between each other. This leads to quality of experience (QoE) fluctuations on delivered content, and unfairness between end users, while new network protocols, technologies, and architectures, such as software defined networking (SDN), are being developed for the future Internet. The programmability, flexibility, and openness of these emerging developments can greatly assist the distribution of video over the Internet. This is driven by the increasing consumer demands and QoE requirements. This paper introduces a novel user-level fairness model UFair and its hierarchical variant UFairHA, which orchestrate HAS media streams using emerging network architectures and incorporate three fairness metrics (video quality, switching impact, and cost efficiency) to achieve user-level fairness in video distribution. UFairHA has also been implemented in a purpose-built SDN testbed using open technologies, including OpenFlow. Experimental results demonstrate the performance and feasibility of our design for video distribution over future networks.
Impact of crystallinity of High-k oxides on Vt instabilities of NMOS devices assessed by physical and electrical measurements

This paper investigates the impact of crystallinity of HfO2 oxides on VT instabilities. Wet etch rate measurements enhances a critical thickness tHK C for HfO2 which marks the transition between a monoclinic crystalline phase to a near amorphous state, both clearly identified by ATR FTIR. Using electrical measurements and modeling, it is demonstrated that this transition from the crystalline phase to an amorphous state is accompanied by a strong reduction of the density of bulk HfO2 defects responsible for electron trapping, Prevents the crystallization of an high-k layer is therefore fundamental to improve its BTI reliability.
PleioGRiP: genetic risk prediction with pleiotropy

MOTIVATION Although several studies have used Bayesian classifiers for risk prediction using genome-wide single nucleotide polymorphism (SNP) datasets, no software can efficiently perform these analyses on massive genetic datasets and can accommodate multiple traits. RESULTS We describe the program PleioGRiP that performs a genome-wide Bayesian model search to identify SNPs associated with a discrete phenotype and uses SNPs ranked by Bayes factor to produce nested Bayesian classifiers. These classifiers can be used for genetic risk prediction, either selecting the classifier with optimal number of features or using an ensemble of classifiers. In addition, PleioGRiP implements an extension to the Bayesian search and classification and can search for pleiotropic relationships in which SNPs are simultaneously associated with two or more distinct phenotypes. These relationships can be used to generate connected Bayesian classifiers to predict the phenotype of interest either using genetic data alone or in combination with the secondary phenotype(s). AVAILABILITY PleioGRiP is implemented in Java, and it is available from http://hdl.handle. net/2144/4367. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Geographical representation of radial orbit perturbations due to ocean tides: Implications for satellite altimetry

In analogy to the geographical representation of the zeroth-order radial orbit perturbations due to the static geopotential, similar relationships have been derived for radial orbit perturbations due to the ocean tides. At each location these perturbations are seen to be coherent with the tide height variations. The study of this singularity is of obvious importance to the estimation of ocean tides from satellite altimeter data. We derive analytical expressions for the sensitivity of altimeter derived ocean tide models to the ocean tide force model induced errors in the orbits of the altimeter satellite. In particular, we focus on characterizing and quantifying the nonresonant tidal orbit perturbations, which cannot be adjusted into the empirical accelerations or radial perturbation adjustments commonly used during orbit determination and in altimeter data processing. As an illustration of the utility of this technique, we study the differences between a TOPEX/POSEIDON-derived ocean tide model and the Cartwright and Ray 1991 Geosat model. This analysis shows that nearly 60 percent of the variance of this difference for M2 can be explained by the Geosat radial orbit error due to the omission of coefficients from the GEM-T2 background ocean tide model. For O1, K1, S2, and K2 the orbital effects account for approximately 10 to 40 percent of the variances of these differences. The utility of this technique to an assessment of the ocean tide induced errors in the TOPEX/POSEIDON-derived tide models is also discussed.
Cellular Automata for Decentralized Control of Self-Reconfigurable Robots

Introduction The key research questions in self-reconfiguring robotics are how to design and engineer robot systems capable of self-reconfiguration, and how to plan for such systems. Several interesting robot designs have been proposed [RV01, KR99, MKY98, TMK99, FK90, PCSC96, YDR00, SWC99, UK00]. For each of these systems, architecture-dependent algorithms that couple planning to the specific actuation have been proposed. This includes real breakthroughs in both centralized planning [KR99, YMK00] and decentralized planning [BBR, MYK01, TMK99]. This body of work has brought some real insight into planning and control. We believe we are at a point where we can step back and examine more general questions about self-reconfiguration planning in an architecture-independent way. It is important to examine architecture-independent algorithms that can be instantiated to many different systems because they have the potential of providing a more general sciencebase for the field. By outlining general principles for reconfiguration planning, we hope to learn how to better design hardware and control algorithms. In most existing self-reconfiguring robot systems, an individual module can move in general ways relative to a structure of modules, by traveling on the surface of the structure. By composing such movements, it is possible to generate a planner for shape transformation or for locomotion. The details for how to accomplish these goals are architecture-dependent. Automating this process, which is necessary to manage the individual degrees of freedom in robots made of many modules, has been the theme of the previous work in self-reconfiguration planning. We observe that we can abstract out three types of movement for the motion of one module on the surface of a structure made of lots of modules: (1) linear motion on plane of modules; (2) convex transitions into a different plane; and (3) concave transitions into a different plane. We use these abstractions as the basis of a general decentralized algorithm that can work for any system capable of such motion. In this paper we describe a distributed control algorithm for locomotion based on this abstract model.
Non-uniform fat-meshes for chip multiprocessors

This paper studies the traffic hot spots of mesh networks in the context of chip multiprocessors. To mitigate these effects, this paper describes a non-uniform fat-mesh extension to mesh networks, which are popular for chip multiprocessors. The fat-mesh is inspired by the fat-tree and dedicates additional links for connections with heavy traffic (e.g. near the center) with fewer links for lighter traffic (e.g. near the periphery). Two fat-mesh schemes are studied based on the traffic requirements of chip multiprocessors using dimensional ordered XY routing and a randomized XY-YX routing algorithms, respectively. Analytical fat-mesh models are constructed by theoretically presenting the expressions for the traffic requirements of personalized all-to-all traffic for both the raw message numbers and their normalized equivalents. We demonstrate how traffic scales for a traditional mesh compared to a non-uniform fat mesh.
Learning‐based Nonlinear Model Predictive Control to Improve Vision‐based Mobile Robot Path Tracking

This paper presents a Learning‐based Nonlinear Model Predictive Control (LB‐NMPC) algorithm to achieve high‐performance path tracking in challenging off‐road terrain through learning. The LB‐NMPC algorithm uses a simple a priori vehicle model and a learned disturbance model. Disturbances are modeled as a Gaussian process (GP) as a function of system state, input, and other relevant variables. The GP is updated based on experience collected during previous trials. Localization for the controller is provided by an onboard, vision‐based mapping and navigation system enabling operation in large‐scale, GPS‐denied environments. The paper presents experimental results including over 3 km of travel by three significantly different robot platforms with masses ranging from 50 to 600 kg and at speeds ranging from 0.35 to 1.2 m/s (associated video at http://tiny.cc/RoverLearnsDisturbances). Planned speeds are generated by a novel experience‐based speed scheduler that balances overall travel time, path‐tracking errors, and localization reliability. The results show that the controller can start from a generic a priori vehicle model and subsequently learn to reduce vehicle‐ and trajectory‐specific path‐tracking errors based on experience.
Biosurveillance for Invasive Fungal Infections via Text Mining

Invasive fungal diseases (IFDs) cause more than 1,000 deaths in hos- pitals and cost the health system more than AUD100m in Australia each year. The most common life-threatening IFD is aspergillosis and a patient with this IFD typically has 12 days prolonged in-patient time in hospital and an 8% mor- tality rate. Surveillance and detection of IFDs irrespective of the stage of diag- nosis (i.e., early or late in disease) is important. We describe an application of text mining techniques, using machine learn- ing over a range of features, to automatically detect cases of patients with IFD from the text in the reports of CT scans performed on them. We focus on de- tecting the presence of aspergillosis; however, we anticipate the approach to be transferable to other diseases or conditions by training the text mining compo- nent over appropriate reports. Previous systems based on language technology have been deployed for processing radiology reports and for detecting hospital- acquired infection using language-processing technology, with significant suc- cess. Our approach differs by using a purely statistical/machine-learning ap- proach to the language technology, and by being trained and tested on data col- lected from a number of hospitals. We collected reports for 288 IFD and 291control patients from three different hospitals in Melbourne, Australia: Alfred Health, Melbourne Health, and Peter MacCallum Cancer Centre. We extracted a sample of 69 IFD and 49 control pa- tients to perform detailed analysis of the text with regard to IFD; each patient had possibly multiple scans (and associated reports), resulting in a total of 398 scan reports from IFD-positive patients and 83 scan reports from control pa- tients. We had medical experts annotate the patient-level classification on all scan reports at both sentence and report level: The annotators had to decide, for each sentence and report, whether it was positive, neutral, or negative with re- gards to IFD. We classify reports and patients as IFD-positive if they contain at least one positive sentence, and as negative otherwise. We used the Weka SVM implementation and employed a variety of text- and concept-based features, including bag-of-words, punctuation, UMLS concepts and negated contexts extracted using MetaMap. We also automatically extract-
EXPORTS OF ORGANIC CARBON IN BRITISH RIVERS

This study provides the first detailed estimate of riverine organic carbon fluxes in British rivers, as well as highlighting major gaps in organic carbon data in national archives. Existing data on organic carbon and suspended solids concentrations collected between 1989 and 1993, during routine monitoring by the River Purification Boards (RPBs) in Scotland and the National River Authorities (NRAs) in England and Wales, were used with annual mean flows to estimate fluxes of dissolved and particulate organic carbon (DOC and POC) in British rivers. Riverine DOC exports during 1993 varied from 7·7–103·5 kg ha−1 year−1, with a median flux of 31·9 kg ha−1 year−1 in the 85 rivers for which data were available. There was a trend for DOC fluxes to increase from the south and east to the north and west. A predictive model based on mean soil carbon storage in 17 catchments, together with regional precipitation totals, explained 94% of the variation in the riverine DOC exports in 1993. This model was used to predict riverine DOC fluxes in regions where no organic carbon data were available. Calculated and predicted fluxes were combined to produce an estimate for exports of DOC to tidal waters in British rivers during 1993 of 0·68±0·07 Mt. Of this total, rivers in Scotland accounted for 53%, England 38% and Wales 9%. Scottish blanket peats would appear to be the largest single source of DOC exports in British rivers. An additional 0·20 Mt of organic carbon were estimated to have been exported in particulate form in 1993, approximately two–thirds of which was contributed by English rivers. It is suggested that riverine losses of organic carbon have the potential to affect the long-term dynamics of terrestrial organic carbon pools in Britain and that rivers may regulate increases in soil carbon pools brought about by climate change. © 1997 by John Wiley & Sons, Ltd.
Type Level Clustering Evaluation: New Measures and a POS Induction Case Study

Clustering is a central technique in NLP. Consequently, clustering evaluation is of great importance. Many clustering algorithms are evaluated by their success in tagging corpus tokens. In this paper we discuss type level evaluation, which reflects class membership only and is independent of the token statistics of a particular reference corpus. Type level evaluation casts light on the merits of algorithms, and for some applications is a more natural measure of the algorithm's quality. We propose new type level evaluation measures that, contrary to existing measures, are applicable when items are polysemous, the common case in NLP. We demonstrate the benefits of our measures using a detailed case study, POS induction. We experiment with seven leading algorithms, obtaining useful insights and showing that token and type level measures can weakly or even negatively correlate, which underscores the fact that these two approaches reveal different aspects of clustering quality.
Leveraging Contextual Information to Explore Posting and Linking Behaviors of Bloggers

The last decade has seen an explosion in blogging and the blogosphere is continuing to grow, having a large global reach and many vibrant communities. Researchers have been pouring over blog data with the goal of finding communities, tracking what people are saying, finding influencers, and using many social network analytic tools to analyze the underlying social networks embedded within the blogosphere. One of the key technical problems with analyzing large social networks such as those embedded in the blogosphere is that there are many links between individuals and we often do not know the context or meaning of those links. This is problematic because it makes it difficult if not impossible to tease out the true communities, their behavior, how information flows, and who the central players are (if any). This paper seeks to further our understanding of how to analyze large blog networks and what they can tell us. We analyze 1.24M blogs posted by 298K bloggers over a period of three weeks. These bloggers span private blog sites through large blog-sites such as live journal and blogspot. We first characterize the behavior of bloggers, validating some (but not all) common beliefs about how often bloggers post, how long their posts are, who they link to and how much reciprocity there is in links. We then take a look at bloggers from the larger blog sites to understand whether and how they differ in terms of these metrics. Finally, we extend our analysis to focus on contextual links: what is the textual content of the blog which had a link. We identify topics from the textual content of all the blog posts and use these to tag links based on the topics that were discussed in the blog.
NAPL Pool Dissolution in Stratified and Anisotropic Porous Formations

A two-dimensional numerical model is developed to study contaminant transport resulting from the dissolution of single- and multicomponent dense non-aqueous-phase liquid (DNAPL) pools in heterogeneous porous media. The aqueous-phase concentration of each dissolved component is assumed to undergo first-order decay as well as sorb under local equilibrium conditions. Pool shrinkage is accounted for by modeling the progressive reduction of the DNAPL pool surface area as a time-dependent boundary. Multicomponent pool dissolution is modeled using an effective solubility (or equilibrium aqueous solubility) relationship, where the non-aqueous-phase activity coefficient for each constituent is evaluated at each and every time step. Subsurface heterogeneities are depicted by an ideally stratified porous formation and by a statistically anisotropic aquifer. In the stratified formation, a multicomponent DNAPL pool is assumed to be formed at the interface between a sand layer and a clay layer, where DNAPL dissolution occurs simultaneously in both strata. The ground-water velocity inside the sand stratum is uniform in the longitudinal direction whereas the interstitial liquid in the aquitard is stagnant. In the statistically anisotropic aquifer, a single-component DNAPL pool is assumed to be formed on top of an impermeable bedrock, where DNAPL dissolution occurs in the aquifer only. Results from several model simulations indicate that dissolved contaminant concentrations in aquifers are reduced significantly in the presence of aquitards, and most importantly, the transfer of dissolved contaminants along the pool-water interface is slower within statistically anisotropic than within homogeneous aquifers.