Miracle-dz/UniFAR-FTUs huggingface dataset:
Trends in cardiovascular complications of diabetes.

CONTEXT Despite reductions in cardiovascular disease (CVD) mortality over the past few decades, it is unclear whether adults with and without diabetes have experienced similar declines in CVD risk. OBJECTIVE To determine whether adults with and without diabetes experienced similar declines in incident CVD in 1950-1995. DESIGN, SETTING, AND PARTICIPANTS Participants aged 45-64 years from the Framingham Heart Study original and offspring cohorts who attended examinations in 1950-1966 ("earlier" time period; 4118 participants, 113 with diabetes) and 1977-1995 ("later" time period; 4063 participants, 317 with diabetes). Incidence rates of CVD among those with and without diabetes were compared between the earlier and later periods. MAIN OUTCOME MEASURES Myocardial infarction, coronary heart disease death, and stroke. RESULTS Among participants with diabetes, the age- and sex-adjusted CVD incidence rate was 286.4 per 10,000 person-years in the earlier period and 146.9 per 10,000 in the later period, a 49.3% (95% confidence interval [CI], 16.7%-69.4%) decline. Among participants without diabetes, the age- and sex-adjusted incidence rate was 84.6 per 10,000 person-years in the earlier period and 54.3 per 10,000 person-years in the later period, a 35.4% (95% CI, 25.3%-45.4%) decline. Hazard ratios for diabetes as a predictor of incident CVD were not different in the earlier vs later periods. CONCLUSIONS We report a 50% reduction in the rate of incident CVD events among adults with diabetes, although the absolute risk of CVD is 2-fold greater than among persons without diabetes. Adults with and without diabetes have benefited similarly during the decline in CVD rates over the last several decades. More aggressive treatment of CVD risk factors and further research on diabetes-specific factors contributing to CVD risk are needed to further reduce the high absolute risk of CVD still experienced by persons with diabetes.
Validation of a queueing model with classes of customers

There have been many queueing models of computer systems published in the literature, but relatively few studies have appeared in which the models have been validated on actual systems. A procedure is described which permits an analyst to obtain values for the parameters of queueing models using measured statistics of operational computer systems. The particular model which was validated is the first queueing network model which will permit more than one class of customer. Partitioning a computer system environment into classes of timesharing and batch jobs appears to be useful, and the model was validated with these classes on an IBM 370/155-2 and an IBM 370/168.
Factor Models and the Shape of the Term Structure

he present paper analyses a broad range of one- and multifactor models of the term structure of interest rates. We assess the influence of the number of factors, mean reversion, and the factor probability distributions on the term structure shapes the models generate, and use spread options as an aggregate measure of the relative importance assigned to rising and falling forward rate curves by the models considered. We derive valuation formulas for these contingent claims in the multifactor Gaussian and CIR-models. Our main result is that the specification of mean reversion and the number of factors are both much more important for the relative movements of interest rates than the distributional characteristics of the factors. To the extent that interest rate risk depends on the movements of different parts of the term structure relative to one another rather than on shifts of its absolute level, the distributional assumption on the factor dynamics is found to be essentially irrelevant.
Adaptive Blind Multi-ChannelEqualization for Multiple SignalsSeparationYe

This paper investigates adaptive blind equalization for multiple-input and multiple-output (MIMO) channels and its application to blind separation of multiple signals received by antenna arrays in communication systems. The performance analysis is presented for the CMA equalizer used in MIMO channels. Our analysis results indicate that double innnite-length MIMO-CMA equalizer can recover one of input signals, remove the intersymbol interference (ISI), and suppress the rest signals. In particular, for the MIMO FIR channels satisfying certain conditions, the MIMO-CMA FIR equalizer is able to remove the ISI and co-channel interference regardless of the initial setting of the blind equalizer. To recover all input signals simultaneously, a novel MIMO channel blind equalization algorithm is developed in this paper. The global convergence of the new algorithm for MIMO channels is proved. Hence, the new blind equalization algorithm for MIMO channels can be applied to separate and equalize the signals received by antenna arrays in communication systems. Finally, Computer simulations are presented to connrm our analysis and illustrate the performance of the new algorithm.
Giving and Receiving Awards

Awards in the form of orders, medals, decorations, prizes, and titles are ubiquitous in monarchies and republics, private organizations, and not-for-profit and profit-oriented firms. Nevertheless, this kind of nonmaterial extrinsic incentive has been given little attention in the social sciences, including psychology. The demand for awards relies on an individual's desire for distinction, and the supply of awards is governed by the desire to motivate. The technique of analytic narratives is used to show that a number of empirically testable propositions about awards are consistent with observable data.
Indoor positioning: A key enabling technology for IoT applications

Motivated by the recent advances on internet of things (IoT) and the importance that location information has on many application scenarios, this article offers references to theoretical and localization-algorithmic tools that can be utilised in connection with IoT. We develop this discussion from basic to sophisticated localization techniques covering also some less-intuitive notions of localization, e.g. semantic positioning, for which we provide a novel solution which overcome the problem of privacy. We analyze the localization problem from a mathematical perspective; reviewing the most common and best-performing class of localization methods based on optimization and algebraic approaches and we discuss benefits of location information in a wireless system. In this regard we discuss few concrete applications scenario currently under investigation in the largest EU project on IoT, namely the FP-7 Butler project, how location information is one of the key enabling technology in the IoT. In addition to the theoretical aspect, this article provides references to the pervasive localization system architecture using the smart sensors developed within the Butler project.
Improvements to the Sensitivity of Gravitational Clustering for Multiple Neuron Recordings

We outline two improvements to the technique of gravitational clustering for detection of neuronal synchrony, which are capable of improving the method's detection of weak synchrony with limited data. The advantages of the enhancements are illustrated using data with known levels of synchrony and different interspike interval distributions. The novel simulation method described can easily generate such test data. An important dependence of the sensitivity of gravitational clustering to the interspike interval distribution of the analysed spike trains is described.
Calculation-Delay Tolerant Predictive Current Controller for Three-Phase Inverters

This work presents an improved deadbeat predictive current controller for grid-tie inverters that addresses issues related to implementation delays. The total delay is composed of the integer computational delay and a fractional delay, which is taken into account in the design of the controller algorithm, to improve its performance and robustness. The control strategy, based on a model that includes these delays, employs state feedback and a prediction observer in order to obtain a true two-sample ripple-free deadbeat response. System robustness can be adjusted with an appropriate selection of the location of the observer poles, at the expense of reducing control bandwidth. The proposed control scheme is both simple and computationally efficient since only few operations are required to include the delay in the algorithm. Experimental results show an improvement of the dynamic response even when mismatch in the load-inductance value estimation occurs.
Robust multiple model adaptive control: Modified using ν‐gap metric

A new robust adaptive control method is proposed, which removes the deficiencies of the classic robust multiple model adaptive control (RMMAC) using benefits of the ν‐gap metric. First, the classic RMMAC design procedure cannot be used for systematic design for unstable plants because it uses the Baram Proximity Measure, which cannot be calculated for open‐loop unstable plants. Next, the %FNARC method which is used as a systematic approach for subdividing the uncertainty set makes the RMMAC structure being always companion with the µ‐synthesis design method. Then in case of two or more uncertain parameters, the model set definition in the classic RMMAC is based on cumbersome ad hoc methods. Several methods based on ν‐gap metric for working out the mentioned problems are presented in this paper. To demonstrate the benefits of the proposed RMMAC method, two benchmark problems subject to unmodeled dynamics, stochastic disturbance input and sensor noise are considered as case studies. The first case‐study is a non‐minimum‐phase (NMP) system, which has an uncertain NMP zero; the second case‐study is a mass‐spring‐dashpot system that has three uncertain real parameters. In the first case‐study, five robust controller design methods (H2, H∞, QFT, H∞ loop‐shaping and µ‐synthesis) are implemented and it is shown via extensive simulations that RMMAC/ν/QFT method improves disturbance‐rejection, when compared with the classic RMMAC. In the second case‐study, two robust controller design methods (QFT and mixed µ‐synthesis) are applied and it is shown that the RMMAC/ν/QFT method improves disturbance‐rejection, when compared with RMMAC/ν/mixed−µ. Copyright © 2010 John Wiley & Sons, Ltd.
Second-Order Queries for Rule-Based Data Access : Technical Report 3019

Rules and ontologies can be used to enrich a database system with advanced data access capabilities. The success of this paradigm has led to a number of languages such as DL-Lite, Datalog+/- and OWL RL. The two major approaches to answering queries under constraints expressed in such languages are forward-chaining (materialization) and backward-chaining (query rewriting). The latter is typically focused on first-order queries that have only limited expressivity. We propose a querying formalism based on monadic second-order logic which subsumes and goes beyond conjunctive queries and regular path queries, but still has a decidable query subsumption problem. We devise methods for rewriting rule sets to queries in this new formalism and we show that query entailment in most of the established rule-based approaches can be decided by combining two methods: (i) bottom-up forward-chaining computation w.r. t. a rule set with the bounded treewidth model property and (ii) top-down second-order query rewriting w.r. t. a rewritable rule set
Determination of Cloud Parameters from SCIAMACHY Data for the Correction of Tropospheric Trace Gases

For the accurate retrieval of tropospheric trace gases from SCIAMACHY the availability of cloud parameters is crucial. In this paper we present a new algorithm for the retrieval of cloud fraction from SCIAMACHY data, the Heidelberg Iterative Cloud Retrieval Utilities (HICRU). The reliability of the results are proven through intercomparision with cloud fractions from the FRESCO algorithm and images from Meteosat. The new algorithm for SCIAMACHY is based on the already well validated HICRU algorithm developed for the retrieval of cloud fractions from GOME data. We use broad band spectrometers with high spatial resolution, the Polarization Monitoring Devices. The retrieval applies the widely used threshold method. Our sophisticated, iterative calculation of thresholds including image sequence analysis makes the algorithm also reliable in regions like deserts, which requires a very accurate set of thresholds.
A Sensed-Point-Oriented Geographic Routing for Camera Networks

Camera networks have been paid much attention in sensor networks since those network are useful to visualize a phenomenon sensed by other sensors for temperature, humidity, and light. When a user selects a point of an area deployed cameras in an application, a geographic routing is best used as a routing of the camera network. In conventional geographic routing, the destination of a message should be the location of a node. However, this is not suitable for sensors that cover a wide or directed sensing area such as cameras. To cope with this problem, we propose a modified geographic routing scheme called SenriGan. SenriGan accommodates specifying a sensed point as a destination instead of using a location of a node. In this demonstration, we show a prototype system of SenriGan.
NCSU-SAS-Ning: Candidate Generation and Feature Engineering for Supervised Lexical Normalization

User generated content often contains non-standard words that hinder effective automatic text processing. In this paper, we present a system we developed to perform lexical normalization for English Twitter text. It first generates candidates based on past knowledge and a novel string similarity measurement and then selects a candidate using features learned from training data. The system has a constrained mode and an unconstrained mode. The constrained mode participated in the W-NUT noisy English text normalization competition (Baldwin et al., 2015) and achieved the best F1 score.
SYSTOOL — An online learning tool for signals and systems

SYSTOOL is an online learning tool for real or virtual courses in signals and systems. It provides a collection of Java applets for interactive simulation and visualisation of linear systems. While based on a traditional textbook, SYSTOOL is accessible online though the Virtual University of Bavaria. This article describes structure and content of this learning tool and presents selected modules.
Falling on Sensitive Ears

Spoken word recognition is characterized by multiple activation of sound patterns that are consistent with the acoustic-phonetic input. Recently, an extreme form of multiple activation was observed: Bilingual listeners activated words from both languages that were consistent with the input. We explored the degree to which bilingual multiple activation may be constrained by fine-grained acoustic-phonetic information. In a head-mounted eyetracking experiment, we presented Spanish-English bilinguals with spoken Spanish words having word-initial stop consonants with either English- or Spanish-appropriate voice onset times. Participants fixated interlingual distractors (nontarget pictures whose English names shared a phonological similarity with the Spanish targets) more frequently than control distractors when the target words contained English-appropriate voice onset times. These results demonstrate that fine-grained acoustic-phonetic information and a precise match between input and representation are critical for parallel activation of two languages.
An Essential Role for Frizzled5 in Neuronal Survival in the Parafascicular Nucleus of the Thalamus

Frizzled5 (Fz5), a putative Wnt receptor, is expressed in the retina, hypothalamus, and the parafascicular nucleus (PFN) of the thalamus. By constructing Fz5 alleles in which β-galactosidase replaces Fz5 or in which Cre-mediated recombination replaces Fz5 with alkaline phosphatase, we observe that Fz5 is required continuously and in a cell autonomous manner for the survival of adult PFN neurons, but is not required for proliferation, migration, or axonal growth and targeting of developing PFN neurons. A motor phenotype associated with loss of Fz5 establishes a role for the PFN in sensorimotor coordination. Transcripts coding for Wnt9b, the likely Fz5 ligand in vivo, and β-catenin, a mediator of canonical Wnt signaling, are both downregulated in the Fz5 −/− PFN, implying a positive feedback mechanism in which Wnt signaling is required to maintain the expression of Wnt signaling components. These data suggest that defects in Wnt–Frizzled signaling could be the cause of neuronal loss in degenerative CNS diseases.
Metabolic Basis for Thyroid Hormone Liver Preconditioning: Upregulation of AMP-Activated Protein Kinase Signaling

The liver is a major organ responsible for most functions of cellular metabolism and a mediator between dietary and endogenous sources of energy for extrahepatic tissues. In this context, adenosine-monophosphate- (AMP-) activated protein kinase (AMPK) constitutes an intrahepatic energy sensor regulating physiological energy dynamics by limiting anabolism and stimulating catabolism, thus increasing ATP availability. This is achieved by mechanisms involving direct allosteric activation and reversible phosphorylation of AMPK, in response to signals such as energy status, serum insulin/glucagon ratio, nutritional stresses, pharmacological and natural compounds, and oxidative stress status. Reactive oxygen species (ROS) lead to cellular AMPK activation and downstream signaling under several experimental conditions. Thyroid hormone (L-3,3′,5-triiodothyronine, T3) administration, a condition that enhances liver ROS generation, triggers the redox upregulation of cytoprotective proteins affording preconditioning against ischemia-reperfusion (IR) liver injury. Data discussed in this work suggest that T3-induced liver activation of AMPK may be of importance in the promotion of metabolic processes favouring energy supply for the induction and operation of preconditioning mechanisms. These include antioxidant, antiapoptotic, and anti-inflammatory mechanisms, repair or resynthesis of altered biomolecules, induction of the homeostatic acute-phase response, and stimulation of liver cell proliferation, which are required to cope with the damaging processes set in by IR.
Medial temporal contributions to successful face‐name learning

The brain mechanisms that enable us to form durable associations between different types of information are not completely understood. Although the hippocampus is widely thought to play a substantial role in forming associations, the role of surrounding cortical regions in the medial temporal lobe, including perirhinal and parahippocampal cortex, is controversial. Using anatomically constrained functional magnetic resonance imaging, we assessed medial temporal contributions to learning arbitrary associations between faces and names. By sorting learning trials based on subsequent performance in associative and item‐specific memory tests, we characterized brain activity associated with successful face‐name associative learning. We found that right hippocampal activity was greater when corresponding face‐name associations were subsequently remembered than when only a face or a name, but not both, were remembered, or when single‐item information or associative information was not remembered. Neither perirhinal nor parahippocampal cortex encoding activity differed across these same conditions. Furthermore, right hippocampal activity during successful face‐name association learning was strongly correlated with activity in cortical regions involved in multimodal integration, supporting the idea that interactions between the hippocampus and neocortex contribute to associative memory. These results specifically implicate the hippocampus in associative memory formation, in keeping with theoretical formulations in which contributions to across‐domain binding differ among brain structures in the medial temporal region. Hum Brain Mapp 33:1717–1726, 2012. © 2011 Wiley Periodicals, Inc.
Decentralized recovery of sparse signals for sensor network applications

In this paper, we consider the problem of distributed l1 regularized quadratic optimization in a large-scale sensor network setting. Specifically, we consider sensor nodes which can measure only a part of the entire measurement vector and whose communication capabilities are limited to only their neighboring nodes. We formulate the l1-optimization problem as bound constrained quadratic optimization and develop a distributed, gossip-based algorithm using the projected-gradient approach. The sensor nodes reach a consensus on the gradient to be used for vector update at each step of the optimization algorithm. Finally we analyze the performance of the proposed algorithm using synthetic data and compare it with a standard l1 solver.
Quasars at z = 6: The Survival of the Fittest

The Sloan Digital Sky Survey has detected luminous quasars at very high redshift, z > 6. Follow-up observations indicate that at least some of these quasars are powered by supermassive black holes (SMBHs), with masses in excess of 109 M☉. SMBHs, therefore, seem to have already existed when the universe was less than 1 Gyr old and the bulk of galaxy formation had yet to take place. Here we investigate the extent to which accretion and dynamical processes influence the early growth of SMBHs. We assess the impact of (1) black hole mergers, (2) the influence of the merger efficiency, and (3) the negative contribution due to dynamical effects, which can kick black holes out of their host halos (gravitational recoil). We find that if accretion is always limited by the Eddington rate via a thin disk, the maximum allowed radiative efficiency (or spin) to reproduce the luminosity function at z = 6 is ϵ = 0.12 (or = 0.8), against the adverse effect of the gravitational recoil. Dynamical effects unquestionably cannot be neglected in studies of high-redshift SMBHs. If black holes can accrete at a supercritical rate during an early phase, reproducing the observed SMBH mass values is not an issue, even in the case that the recoil velocity is in the upper limit range, as the mass ratios of merging binaries are skewed toward low values, where the gravitational recoil effect is very mild. We propose that SMBH growth at early times is very selective, and efficient only for black holes hosted in high density peak halos.
3D face reconstruction by KLT feature extraction and model consistency match refining and growing

In this paper a new method for the reconstruction of 3D face model is proposed. The method starts with corner extraction and matching using KLT (Kanade-Lucas-Tomasi) approach. Then we apply special algorithm to find and refine erroneous matches. The proposed refining algorithm is a two stage algorithm. In the first stage, we find incorrect matches by sorting displacement vectors. The second stage of the refining process is performed by calculating affine model between two stereo images. In this stage the match points which are not consistent with affine model are considered as incorrect matches. To find match points for the remaining pixels of stereo images, we used match point growing algorithm which uses the refined points and their matches as seed in recursive method. When the match points are obtained, they are used for 3D face model extraction. To remove noise in the reconstructed 3D image, we used adaptive Gaussian filter. Experimental results showed the promise of the algorithm.