Miracle-dz/UniFAR-FTUs huggingface dataset:
Risks for all-cause mortality, cardiovascular disease, and diabetes associated with the metabolic syndrome: a summary of the evidence.

OBJECTIVE In recent years, several major organizations have endorsed the concept of the metabolic syndrome and developed working definitions for it. How well these definitions predict the risk for adverse events in people with the metabolic syndrome is only now being learned. The purpose of this study was to summarize the estimates of relative risk for all-cause mortality, cardiovascular disease, and diabetes reported from prospective studies in samples from the general population using definitions of the metabolic syndrome developed by the National Cholesterol Education Program (NCEP) and World Health Organization (WHO). RESEARCH DESIGN AND METHODS The author reviewed prospective studies from July 1998 through August 2004. RESULTS For studies that used the exact NCEP definition of the metabolic syndrome, random-effects estimates of combined relative risk were 1.27 (95% CI 0.90-1.78) for all-cause mortality, 1.65 (1.38-1.99) for cardiovascular disease, and 2.99 (1.96-4.57) for diabetes. For studies that used the most exact WHO definition of the metabolic syndrome, the fixed-effects estimates of relative risk were 1.37 (1.09-1.74) for all-cause mortality and 1.93 (1.39-2.67) for cardiovascular disease; the fixed-effects estimate was 2.60 (1.55-4.38) for coronary heart disease. CONCLUSIONS These estimates suggest that the population-attributable fraction for the metabolic syndrome, as it is currently conceived, is approximately 6-7% for all-cause mortality, 12-17% for cardiovascular disease, and 30-52% for diabetes. Further research is needed to establish the use of the metabolic syndrome in predicting risk for death, cardiovascular disease, and diabetes in various population subgroups.
Accelerating Fully Homomorphic Encryption in Hardware

We present a custom architecture for realizing the Gentry-Halevi fully homomorphic encryption (FHE) scheme. This contribution presents the first full realization of FHE in hardware. The architecture features an optimized multi-million bit multiplier based on the Schonhage Strassen multiplication algorithm. Moreover, a number of optimizations including spectral techniques as well as a precomputation strategy is used to significantly improve the performance of the overall design. When synthesized using 90 nm technology, the presented architecture achieves to realize the encryption, decryption, and recryption operations in 18.1 msec, 16.1 msec, and 3.1 sec, respectively, and occupies a footprint of less than 30 million gates.
Distributed Dynamic Replica Placement and Request Redirection in Content Delivery Networks

The content delivery networks (CDN) paradigm is based on the idea to transparently move third-party content closer to the users. More specifically, content is replicated on CDN servers which are located close to the final users, and user requests are redirected to the "best" replica (e.g., the closest) in a transparent way, so that users perceive a better content access service. In this paper we address user requests redirection and replica placement in CDNs. Differently from previous solutions our scheme considers the two problems jointly and relies on distributed and localized schemes that can be implemented with little complexity and overhead, thus providing a new overall solution that effectively trades-off among the number of replicas, their utilization (i.e., how many users requests they serve), the distance from the best replica and the number of replica adds and removals. An OPNET based thorough performance evaluation has allowed us to assess the effectiveness of the proposed solution. By properly tuning the distributed heuristics parameters the CDN provider can have a strict control on the CDN network operations so that the desired trade-off between all the relevant performance metrics is achieved.
Virtualized Execution Runtime for FPGA Accelerators in the Cloud

FPGAs offer high performance coupled with energy efficiency, making them extremely attractive computational resources within a cloud ecosystem. However, to achieve this integration and make them easy to program, we first need to enable users with varying expertise to easily develop cloud applications that leverage FPGAs. With the growing size of FPGAs, allocating them monolithically to users can be wasteful due to potentially low device utilization. Hence, we also need to be able to dynamically share FPGAs among multiple users. To address these concerns, we propose a methodology and a runtime system that together simplify the FPGA application development process by providing: 1) a clean abstraction with high-level APIs for easy application development; 2) a simple execution model that supports both hardware and software execution; and 3) a shared memory-model which is convenient to use for the programmers. Akin to an operating system on a computer, our lightweight runtime system enables the simultaneous execution of multiple applications by virtualizing computational resources, i.e., FPGA resources and on-board memory, and offers protection facilities to isolate applications from each other. In this paper, we illustrate how these features can be developed in a lightweight manner and quantitatively evaluate the performance overhead they introduce on a small set of applications running on our proof of concept prototype. Our results demonstrate that these features only introduce marginal performance overheads. More importantly, by sharing resources for simultaneous execution of multiple user applications, our platform improves FPGA utilization and delivers higher aggregate throughput compared to accessing the device in a time-shared manner.
Insights Into Layering in the Cratonic Lithosphere Beneath Western Australia

The characteristics of internal lithospheric discontinuities carry crucial information regarding the origin and evolution of the lithosphere. However, the formation and mechanisms of the midlithosphere discontinuity (MLD) are still enigmatic and controversial. We investigate the midlithospheric discontinuities beneath the Archean Western Australian Craton, which represents one of the oldest continents on the globe, using a novel receiver‐based reflectivity approach combined with other geophysical information comprising tomographic P and S wave velocity, radial anisotropy, electrical resistivity, and heat flow data. The MLD is rather shallow with a depth of 68–82 km. Multiple prominent discontinuities are observed in the lithospheric mantle using constructed high‐frequency (0.5–4 Hz) P wave reflectivities. These multiple discontinuities coincide well with the broad‐scale reduction of relative P and SV wave velocities at the top of the graded transition zone from the lithosphere to the asthenosphere. Strong radial anisotropy in the upper lithosphere mantle tends to be weak across the MLD, which might reflect quasi‐laminar lithospheric heterogeneity behavior with a horizontal correlation length that is greater than its vertical correlation length. Broad‐scale electrical resistivity variations show little coherence with the MLD. Given these various geophysical observations, the upper lithosphere exhibits rigid and elastic properties above the MLD, while the lower lithosphere tends to be ductile and rheological or viscous. A model comprising quasi‐laminar lithospheric heterogeneity could effectively represent the MLD characteristics beneath the Archean continent.
Spectrum sensing for cognitive radio using quantized data fusion and Hidden Markov model

Most of the radio frequency spectrum is not being utilized efficiently. The utilization can be improved by including unlicensed users to exploit the radio frequency spectrum by not creating any interference to the primary users. For Cognitive Radio, the main issue is to sense and then identify all spectrum holes present in the environment. In this paper, we are proposing the Quantized data fusion sensing which is applied through the Hidden Markov Model (HMM). It does not need any kind of synchronizing signals from the Primary user as well as with the secondary transmitter in a working condition. Simulation results with error rates are improved by the activity of Primary User (PU) and have been presented.
Local linear dynamics assignment in IDA-PBC for underactuated mechanical systems

The problem of finding a set of design parameters in the well-known IDA-PBC approach for a class of nonlinear underactuated mechanical systems to realize desired time behavior of the closed loop Port-Hamiltonian system in a transparent way is considered. Using a local coordinate transformation, the effect of the homogeneous solution of the potential energy matching PDE is isolated. By comparison of desired local linear dynamics with the parametrized linearization of the closed loop dynamics a set of linear equations for the IDA-PBC design parameters is derived. Besides the possibility to assign predefined dynamics the definiteness check of the potential energy can be omitted. With an Acrobot-type mechanical system the design steps are illustrated and simulations validate the performance of the approach.
Trend Detection in Text Collections using Latent Dirichlet Allocation Submitted for Blind Review

Algorithms that enable the process of automatically mining distinct topics in document collections have become increasingly important due to their applications in many fields and the extensive growth of the number of documents in many domains. Traditionally, the task of topic discovery has been mainly addressed through algorithms that work on a snapshot view of the documents, which ignores the temporal characteristics of the collection. In a significan t number of collections, the documents are temporal in nature and this temporal dimension can influence the topic discovery process. In this paper, we propose a generative model based on latent Dirichlet allocation that integrates the temporal ordering of the documents into the generative process in an iterative fashion. The document collection is divided into time segments and the topics discovered in each segment is propagated to influence the topic discovery in the subsequent time segments. We conduct experiments on the collection of academic papers from CiteSeer repository . In addition to the textual content of the documents, we augment the text corpus with the addition of user queries and tags and integrate the citation graph to boost the topica l terms. The experiment results show that we can effectively detect distinct topics and their evolution over time.
Genomics of deep‐sea and sub‐seafloor microbes

Over two‐thirds of the surface of the earth is covered by oceans, which have an average depth of about 3800 m. As each drop of ocean water contains > 105 cells, the > 1030 microbial cells in the ocean represent the largest reservoir of microbes on earth (Whitman et al., 1998). Communities of bacteria, archaea, protists and unicellular fungi account for most of the oceanic biomass and metabolism. Marine microbes are known to play an essential role in the global cycling of nitrogen, carbon, oxygen, phosphorous, iron, sulfur and trace elements (Karl, 2007).
Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages

We propose an efficient nonparametric strategy for learning a message operator in expectation propagation (EP), which takes as input the set of incoming messages to a factor node, and produces an outgoing message as output. This learned operator replaces the multivariate integral required in classical EP, which may not have an analytic expression. We use kernel-based regression, which is trained on a set of probability distributions representing the incoming messages, and the associated outgoing messages. The kernel approach has two main advantages: first, it is fast, as it is implemented using a novel two-layer random feature representation of the input message distributions; second, it has principled uncertainty estimates, and can be cheaply updated online, meaning it can request and incorporate new training data when it encounters inputs on which it is uncertain. In experiments, our approach is able to solve learning problems where a single message operator is required for multiple, substantially different data sets (logistic regression for a variety of classification problems), where it is essential to accurately assess uncertainty and to efficiently and robustly update the message operator.
Characteristics of Sea‐Effect Clouds and Precipitation Over the Sea of Japan Region as Observed by A‐Train Satellites

Prolific winter (December‐January‐February) snowfall occurs over northwest Japan due to frequent sea‐effect precipitation that develops during cold‐air outbreaks over the Sea of Japan (SOJ). Knowledge of sea‐effect clouds and precipitation across the SOJ region has historically been constrained, however, by limited offshore in situ observations and remote‐sensing limitations. This paper uses sensors from National Aeronautics and Space Administration (NASA)'s A‐Train Satellite Constellation to examine winter sea‐effect properties in the SOJ region. The analysis shows that cloud and precipitation occurrence generally increases across the SOJ from Asia to Japan, as potential sea‐effect periods with an along‐orbit mean sea surface to 850‐hPa temperature difference ≥13 °C comprise a majority of the total clouds and precipitation. Sea‐effect clouds and precipitation occur most frequently in an arc‐shaped area that extends from the western SOJ, where the Japan‐Sea Polar‐Airmass Convergence Zone (JPCZ) is common, to the coast of Honshu, and then northward to Hokkaido. Radar, lidar, and column water path statistics along A‐Train orbital tracks show that sea‐effect precipitation is deepest along the central Honshu coast and becomes shallower but more frequent with northward extent. Precipitation amount and frequency maximize along the coast and adjacent mountains but decline with inland extent, most abruptly downstream of higher mountain barriers. This work illustrates that air‐sea interactions, coastal geometry, and regional topography strongly modulate cloud and precipitation patterns during sea‐effect periods in the SOJ region.
Estimating Bulk Entrainment With Unaggregated and Aggregated Convection

To investigate how entrainment is influenced by convective organization, we use the ICON (ICOsahedral Nonhydrostatic) model in a radiative‐convective equilibrium framework, with a 1 km spatial grid mesh covering a 600 by 520 km2 domain. We analyze two simulations, with unaggregated and aggregated convection, and find that, in the lower free troposphere, the bulk entrainment rate increases when convection aggregates. The increase of entrainment rate with aggregation is caused by a strong increase of turbulence in the close environment of updrafts, masking other effects like the increase of updraft size and of static stability with aggregation. Even though entrainment rate increases with aggregation, updraft buoyancy reduction through entrainment decreases because aggregated updrafts are protected by a moist shell. Parameterizations that wish to represent mesoscale convective organization would need to model this moist shell.
Diagnosing MJO hindcast biases in NCAR CAM3 using nudging during the DYNAMO field campaign

This study evaluates the Madden–Julian Oscillation (MJO) hindcast skill and investigates the hindcast biases in the dynamic and thermodynamic fields of the National Center for Atmospheric Research Community Atmosphere Model version 3. The analysis is based on the October 2011 MJO event observed during the Dynamics of the Madden–Julian Oscillation field campaign. The model captures the MJO initiation but, compared to the observations, the hindcast has a faster MJO phase speed, a dry relative humidity bias, a stronger zonal wind shear, and a weaker MJO peak amplitude. The MJO hindcast is then nudged toward the European Centre for Medium‐Range Weather Forecast Reanalysis fields of temperature, specific humidity, horizontal winds, and surface pressure. The nudging tendencies highlight the model physics parameterization biases, such as not enough convective diabatic heating during the MJO initiation, not enough upper tropospheric stratiform condensation, and lower tropospheric reevaporation during the mature and decay phases and a strong zonal wind shear during the MJO evolution. To determine the role of temperature, specific humidity, and horizontal winds in the model physics parameterization errors, six additional nudging experiments are carried out, with either one or two of the fields allowed to evolve freely while the others are nudged. Results show that convection and precipitation increase when temperature or specific humidity are unconstrained and decrease when horizontal winds evolve freely or temperature alone is constrained to reanalysis. Budget analysis of moist static energy shows that the nudging tendency compensates for different process biases during different MJO phases. The diagnosis of such nudging tendencies provides a unique objective way to identify model physics biases, which usefully guides the model physics parameterization development.
Exploring the relationships between students' learning styles and social media use in educational settings

With the growing popularity of Web 2.0 tools in educational settings, it becomes important to investigate the influence of students' learning styles on the adoption and use of these emerging tools. Currently, there are only few studies addressing this issue and most of them are based on student self-reported data, e.g., preference, acceptance or attitude toward social media tools, captured by means of questionnaires. This paper explores the relationships between actual students' use of the Web 2.0 tools and their learning styles classified according to Felder-Silverman model. The context of the study is an undergraduate course on Web Applications' Design, with 45 enrolled students. Several machine learning algorithms for classification, association rule induction and feature selection are applied. Results show that learning styles have a limited influence on the students' level of interaction with each of the four Web 2.0 tools considered.
Contribution of Atlantic and Pacific Multidecadal Variability to Twentieth-Century Temperature Changes

AbstractRecent studies have suggested that significant parts of the observed warming in the early and the late twentieth century were caused by multidecadal internal variability centered in the Atlantic and Pacific Oceans. Here, a novel approach is used that searches for segments of unforced preindustrial control simulations from global climate models that best match the observed Atlantic and Pacific multidecadal variability (AMV and PMV, respectively). In this way, estimates of the influence of AMV and PMV on global temperature that are consistent both spatially and across variables are made. Combined Atlantic and Pacific internal variability impacts the global surface temperatures by up to 0.15°C from peak-to-peak on multidecadal time scales. Internal variability contributed to the warming between the 1920s and 1940s, the subsequent cooling period, and the warming since then. However, variations in the rate of warming still remain after removing the influence of internal variability associated with AMV .
A method for measuring human arm's mechanical impedance for assessment of motor rehabilitation

This research work aims to develop a new tool for assessment of human motor function with a focus on the upper limb. For that purpose, Simultaneous Sensing cum Actuating technology (SSA) will be used for measuring force, velocity, and especially mechanical impedance of the limb. Impedance measured at the hand point while the upper limb performing a motion task is a quality indicator of the limb's motor function. Conventional method of measuring mechanical impedance which requires a force and a motion sensor mounting on fixed base points is not applicable here. This method overcomes the difficulty. Here an electrical motor is used as sensor-actuator which carries a mechanical load (here human's limb); the motor plays the role of a sensor as well, so we calibrate the "transduction matrix" of the motor and will measure the mechanical impedance of the load through measuring the electrical impedance of the electrical motor. In this paper we explain how the appropriate apparatus was designed and tailored for this application and how the method was validated. At the end experimental result of measuring mechanical impedance of a human subject is presented.
Kappa Statistics for Multiple Raters Using Categorical Classifications

In order to assess the reliability of a given characterization of a subject it is often necessary to obtain multiple readings, usually but not always from different individuals or raters. The degree of agreement among the various raters gives some indication as to the consistency of the values. If agreement is high, we feel more confident the ratings reflect the actual circumstance. If agreement among the raters is low, we are less confident in the results. While several methods are available for measuring agreement when there are only two raters, this paper concentrates on presenting a generalized implementation of the Fleiss (1981) technique. This method can be utilized even in situations where there are more than two raters and/or categories. A review of the statistical theory behind the intraclass correlation coefficients and kappa statistics obtained when looking at the above situations is presented. SAS  code is provided which utilizes basic SAS procedures. INTRODUCTION Often we are faced with determining the measurement of interrater agreement when the ratings are on a categorical scale. When the number of raters is equal to two, this is easily accomplished by using SAS PROC CORR to get an estimate of the correlation coefficient. SAS 6.10 PROC FREQ with the AGREE option also provides an easy way to obtain the kappa statistic when there are only two raters. Fleiss describes a technique for obtaining interrater agreement when the number of raters is greater than or equal to two. This paper concentrates on the ability to obtain a measure of agreement when the number of raters is greater than two. It also concentrates on the technique necessary when the number of categories into which the ratings can fall is greater than two. BACKGROUND The data used in this paper to demonstrate the technique of calculating interrater agreement was compiled from a gastric graft versus host disease study. (Washington, et al, 1996, submitted). This research compares the results of three pathologists’ diagnoses of 51 different gastric biopsies. Each pathologist reviewed the gastric biopsies in a blinded fashion. The specific data used in the example in this paper involves degree of agreement on density of the inflammatory infiltrate in the lamina propria. The categories of degree of inflammation range from zero to three. METHODS Since the kappa statistic ( $ k ) was first proposed by Cohen (1960), variants have been proposed by others, including Scott (1955), Maxwell and Pilliner (1968), and Bangdiwala (1987). SAS code has also been presented by Gaccione (1993) to compute the kappa statistic. The various