Miracle-dz/UniFAR-FTUs huggingface dataset:
Sedentary Behavior and Physical Function Decline in Older Women: Findings from the Women's Health Initiative

Sedentary behavior is associated with deleterious health outcomes. This study evaluated the association between sedentary time and physical function among postmenopausal women in the Women's Health Initiative Observational Study. Data for this prospective cohort study were collected between 1993–1998 (enrollment) and 2009, with an average of 12.3 follow-up years. Analyses included 61,609 women (aged 50–79 years at baseline). Sedentary time was estimated by questionnaire; physical function was measured using the RAND SF-36 physical function scale. Mixed-model analysis of repeated measures was used to estimate the relationship of sedentary time exposures and changes in physical function adjusting for relevant covariates. Compared to women reporting sedentary time of ≤6 hours/day, those with greater amounts of sedentary time (>6–8 hours/day, >8–11 hours/day, >11 hours/day) reported lower physical function between baseline and follow up (coefficient = −0.78, CI = −0.98, −0.57, −1.48, CI = −1.71, −1.25, −3.13, and CI = −3.36, −2.89, respectively P < 0.001). Sedentary time was strongly associated with diminished physical function and most pronounced among older women and those reporting the greatest sedentary time. Maintaining physical function with age may be improved by pairing messages to limit sedentary activities with those promoting recommended levels of physical activity.
A scalable availability model for Infrastructure-as-a-Service cloud

High availability is one of the key characteristics of Infrastructure-as-a-Service (IaaS) cloud. In this paper, we show a scalable method for availability analysis of large scale IaaS cloud using analytic models. To reduce the complexity of analysis and the solution time, we use an interacting Markov chain based approach. The construction and the solution of the Markov chains is facilitated by the use of a high-level Petri net based paradigm known as stochastic reward net (SRN). Overall solution is composed by iteration over individual SRN sub-model solutions. Dependencies among the sub-models are resolved using fixed-point iteration, for which existence of a solution is proved. We compare the solution obtained from the interacting sub-models with a monolithic model and show that errors introduced by decomposition are insignificant. Additionally, we provide closed form solutions of the sub-models and show that our approach can handle very large size IaaS clouds.
Introducing DOTUR, a Computer Program for Defining Operational Taxonomic Units and Estimating Species Richness

ABSTRACT Although copious qualitative information describes the members of the diverse microbial communities on Earth, statistical approaches for quantifying and comparing the numbers and compositions of lineages in communities are lacking. We present a method that addresses the challenge of assigning sequences to operational taxonomic units (OTUs) based on the genetic distances between sequences. We developed a computer program, DOTUR, which assigns sequences to OTUs by using either the furthest, average, or nearest neighbor algorithm for each distance level. DOTUR uses the frequency at which each OTU is observed to construct rarefaction and collector's curves for various measures of richness and diversity. We analyzed 16S rRNA gene libraries derived from Scottish and Amazonian soils and the Sargasso Sea with DOTUR, which assigned sequences to OTUs rapidly and reliably based on the genetic distances between sequences and identified previous inconsistencies and errors in assigning sequences to OTUs. An analysis of the two 16S rRNA gene libraries from soil demonstrated that they do not contain enough sequences to support a claim that they contain different numbers of bacterial lineages with statistical confidence (P > 0.05), nor do they contain enough sequences to provide a robust estimate of species richness when an OTU is defined as containing sequences that are no more than 3% different from each other. In contrast, the richness of OTUs at the 3% level in the Sargasso Sea collection began to plateau after the sampling of 690 sequences. We anticipate that an equivalent extent of sampling for soil would require sampling more than 10,000 sequences, almost 100 times the size of typical sequence collections obtained from soil.
Three-dimensional shape-adaptive discrete wavelet transforms for efficient object-based video coding

In this paper, we present an object-based coding scheme using 3D shape-adaptive discrete wavelet transforms (SA- DWT). Rather than straightforward extension of 2D SA-DWT, a novel way to handle the temporal wavelet transform using a motion model is proposed to achieve higher coding efficiency. Corresponding to this transform scheme, we use a 3D entropy coding algorithm called Motion-based Embedded Subband Coding with Optimized Truncation (ESCOT) to code the wavelet coefficients. Results show that ESCOT can achieve comparable coding performance with the state-of-the-art MPEG-4 verification model 13.0 while having the scalability and flexibility of the bitstream in low bit-rate object- based video coding. And in relative higher bit-rate, our coding approach outperforms MPEG-4 VM 13.0 by about 2.5 dB.
Quantitative Model Selection for Heterogeneous Time Series Learning

A key benefit of modular learning is the ability to apply different algorithms to suit the characteristics of each subtask. This approach requires methods for task decomposition, classifier fusion, and matching of subproblems to learning techniques. In this paper, we present a new method for technique selection from a "repertoire" of statistical learning architectures (specifically, artificial neural networks and Bayesian networks) and methods (Bayesian learning, mixture models, and gradient learning). We first discuss the problem of learning heterogeneous time series, such as sensor data from multiple modalities. We then explain how to construct composite learning systems by selecting model components. Finally, we outline the design of a composite learning system for geospatial monitoring problems and present an application (precision agriculture) that demonstrates its potential benefits.
The 2001 and 2003 Tax Rate Reductions: An Overview and Estimate of the Taxable Income Response

The Economic Growth and Tax Relief Reconciliation Act of 2001 (EGTRRA) and the Jobs and Growth Tax Relief Reconciliation Act of 2003 (JGTRRA) incorporated the main elements of the Bush Administration's tax proposals. The principal feature of this legislation was the reduction in individual income tax rates. Reducing marginal tax rates was intended to improve the economic incentives to work and invest, reduce the other economic distortions associated with high tax rates, lower overall tax burdens and improve the prospects for economic growth. The paper examines the effects of the lower marginal tax rates by estimating the response of reported taxable income to the lower rates. Using a panel of tax returns spanning the enactment of EGTRRA and JGTRRA, the paper estimates a taxable income elasticity in the base model of about 0.4, with estimates for other specifications and samples ranging from about 0.2 to 0.7.
The Scirun Inverse Eeg Pipeline | a Modeling and Simulation System for Cortical Mapping and Source Localization

The goal of the inverse problem in electroencephalography is to determine electrical activity within the cranial volume, based on potential measurements taken from the scalp. In this paper, we discuss our implementation of a exible, extensible system to solve the inverse EEG problem. Our implementation is constructed on top of the SCIRun problem solving environment, a exible framework for implementing large-scale scientiic and engineering applications. We leverage the power of this underlying software architecture in the design and implementation of our inverse EEG pipeline. We apply our inverse EEG software system to a test subject's P300 auditory response.
Asymptotically Optimal Importance Sampling and Stratification for Pricing Path‐Dependent Options

This paper develops a variance reduction technique for Monte Carlo simulations of path‐dependent options driven by high‐dimensional Gaussian vectors. The method combines importance sampling based on a change of drift with stratified sampling along a small number of key dimensions. The change of drift is selected through a large deviations analysis and is shown to be optimal in an asymptotic sense. The drift selected has an interpretation as the path of the underlying state variables which maximizes the product of probability and payoff—the most important path. The directions used for stratified sampling are optimal for a quadratic approximation to the integrand or payoff function. Indeed, under differentiability assumptions our importance sampling method eliminates variability due to the linear part of the payoff function, and stratification eliminates much of the variability due to the quadratic part of the payoff. The two parts of the method are linked because the asymptotically optimal drift vector frequently provides a particularly effective direction for stratification. We illustrate the use of the method with path‐dependent options, a stochastic volatility model, and interest rate derivatives. The method reveals novel features of the structure of their payoffs.
Brain MR Image Segmentation using Tabu Search and Hidden Markov Random Field Model

In this paper, we propose a hybrid Tabu Expectation Maximization (TEM) Algorithm for segmentation of Brain Magnetic Resonance (MR) images in both supervised and unsupervised framewrok. Gaussian Hidden Markov Random Field (GHMRF) is used to model the available degraded image. In supervised framework, the apriori image MRF model parameters as well as the GHMRF model parameters are assumed to be known. The class labels are estimated using the Maximum a Posteriori (MAP) estimation criterion. In unsupervised framework, the problem of model parameter estimation and label estimation is formulated in Expectation Maximization (EM) framework. The labels are estimated using the proposed Tabu Search algorithm while the model parameters are the maximum likelihood estimates. Our proposed algorithm yields results with arbitrary initial paramater set and thus overcomes the problem of proper choice of initial parameters. The results obtained are comparable with the results obtained by using the algorithm proposed by Zhang et.al. [15] , where the Iterated Conditional Mode (ICM) algorithm is used for computing the MAP estimates.
The Interactive Monitoring and Control (IMAC) Design Methodology: Application and Empirical Results

The Interactive Monitoring and Control (IMaC) methodology is a design methodology for operator interfaces to complex engineering systems. This four-step methodology helps designers tailor human-computer interaction to communicate monitoring and control activity requirements to the operator. This paper presents the methodology and associated design principles as well as the results of an empirical study assessing the effectiveness of a proof-of-concept operator interface. The study found that an interface designed by the IMaC methodology resulted in superior operator performance when compared to a conventional interface.
Communication-efficient Distributed Estimation and Inference for Transelliptical Graphical Models

We propose communication-efficient distributed estimation and inference methods for the transelliptical graphical model, a semiparametric extension of the elliptical distribution in the high dimensional regime. In detail, the proposed method distributes the $d$-dimensional data of size $N$ generated from a transelliptical graphical model into $m$ worker machines, and estimates the latent precision matrix on each worker machine based on the data of size $n=N/m$. It then debiases the local estimators on the worker machines and send them back to the master machine. Finally, on the master machine, it aggregates the debiased local estimators by averaging and hard thresholding. We show that the aggregated estimator attains the same statistical rate as the centralized estimator based on all the data, provided that the number of machines satisfies $m \lesssim \min\{N\log d/d,\sqrt{N/(s^2\log d)}\}$, where $s$ is the maximum number of nonzero entries in each column of the latent precision matrix. It is worth noting that our algorithm and theory can be directly applied to Gaussian graphical models, Gaussian copula graphical models and elliptical graphical models, since they are all special cases of transelliptical graphical models. Thorough experiments on synthetic data back up our theory.
Time-Efficient Protocols for Neighbor Discovery in Wireless Ad Hoc Networks

Neighbor discovery (ND) is a basic and crucial step for initializing wireless ad hoc networks. A fast, precise, and energy-efficient ND protocol has significant importance to subsequent operations in wireless networks. However, many existing protocols have a high probability of generating idle slots in their neighbor discovering processes, which prolongs the executing duration, thus compromising their performance. In this paper, we propose a novel randomized protocol FRIEND, which is a prehandshaking ND protocol, to initialize synchronous full-duplex wireless ad hoc networks. By introducing a prehandshaking strategy to help each node be aware of activities of its neighborhood, we significantly reduce the probabilities of generating idle slots and collisions. Moreover, with the development of single-channel full-duplex communication technology, we further decrease the processing time needed in FRIEND and construct the first fullduplex ND protocol. Our theoretical analysis proves that FRIEND can decrease the duration of ND by up to 48% in comparison with classical ALOHA-like protocols. In addition, we propose HD-FRIEND for half-duplex networks and variants of FRIEND for multihop and duty-cycled networks. Both theoretical analysis and simulation results show that FRIEND can adapt to various scenarios and significantly decrease the duration of ND.
Visual sentiment prediction with transfer learning and big data analytics for smart cities

Internet of things and social media platforms have changed the way people communicate and express themselves. People are now sharing their experiences and views in blogs, micro-blogs, comments, photos, videos, and other postings in social sites. It has been recognized that timely reactions to public opinions and sentiments and its proper use by city governments are of paramount importance to their missions. Despite of the widely recognized potential of sentiment analysis, relatively little is known about how to best harness its potential benefits for smart cities. The objective of this article is to help fill the void by reviewing the state of the art and opportunities of data sources and applications of sentiment analysis for smart cities. Additionally, The article explores deep features of photos shared by users in Twitter via transfer learning. Thus revealing interesting research opportunities and applications.
Using Interaction to Improve Intelligence : How Intelligent Systems Should Ask Users for Input

Intelligent systems will often need to collect input from users, to provide labels for training data or to correct mistakes the system makes. One interesting avenue of research is how to formulate the questions an intelligent system asks a user, in order to obtain the most accurate responses. In this paper, we study the impact of varying 5 dimensions of questions on response accuracy: indicating uncertainty, amount of context, level of context, suggesting an answer and asking for supplemental information. In a study of an email sorting task, we show that there is a combination that results in higher levels of accuracy than other combinations and validate this combination in a comparison to questions that a panel of HCI and email experts chose. The contributions of the paper are the approach to determine the best combination of dimensions, the validated combination, and a demonstration of how this type of question interaction can improve intelligent systems.
Popular song summarization using chorus section detection from audio signal

Music signal is a one-dimensional temporal sequence. It thus incurs difficulty for the listeners to quickly capturing the mostly attracting parts in popular songs, unless the listeners play the song until the ending. In order to improve the listening experience, music summarization, a tool to summarize the song using the most attractive sections, is needed. In the paper, a system and method is presented to summarize the popular songs by detecting the chorus sections from the input audio signal. The proposed summarization system uses the unique audio feature representation method, i.e. the octave-dependent probabilistic latent semantic analysis, and the chorus detection algorithm that combines the repeated segment extraction and chorus identification. The performance of music summarization is evaluated on the song database with the ground truth of chorus sections, i.e. the start and ending time-stamp of each chorus section. As we know, it is the first systematically evaluation of music summary performance. In terms of multiple metrics such as the boundary accuracy, precision, recall and F1, we show that the proposed system is much superior to the widely accepted methods.
Experimental analysis of traditional classification algorithms on bio medical dtatasets

Data classification in medical field is distinct from that in other fields, because the medical data are heterogeneous, skewed and complex in nature and medical data classification involves multi class classification. In this paper we present the experimental analysis of well-known traditional classification algorithms on bio-medical datasets in order to observe their performance. This experimental analysis will provide deeper insight in designing the efficient classification algorithm for bio medical data.
A method using time series analysis for IEEE 802.11 WLANs channel forecasting

The growth of wireless network use has greatly increased research demand. Some applications, which are context-aware, must adapt to the environment. So, information on both environment characteristics and the device's hardware are crucial. In this work, a new method called Natural Adaptive Exponential Smoothing (NAES) is proposed. It describes and forecasts, in real time, IEEE 802.11 WLAN networks channel behavior. The NAES method is a variation of the exponential smoothing technique to compute the channel quality indicators, namely the Received Signal Strength (RSS) and the link quality. A comparison with the results obtained by the Trigg and Leach (TL) method shows that NAES outperforms TL method.
An SLA-aware load balancing scheme for cloud datacenters

One of the most important issues about cloud computing is how to achieve load balancing among thousands of virtual machines (VMs) in a large datacenter. In this paper, we propose a novel decentralized load balancing architecture, called tldlb (two-level decentralized load balancer). This distributed load balancer takes advantage of the decentralized architecture for providing scalability and high availability capabilities to service more cloud users. We also propose a neural network-based dynamic load balancing algorithm, called nn-dwrr (neural network-based dynamic weighted round-robin), to dispatch a large number of requests to different VMs, which are actually providing services. In nn-dwrr, we combine VM load metrics (CPU, memory, network bandwidth, and disk I/O utilizations) monitoring and neural network-based load prediction to adjust the weight of each VM. Experimental results support that our proposed load balancing algorithm, nn-dwrr, can be applied to a large cloud datacenter, and it is 1.86 times faster than the wrr, 1.49 times faster than the Capacity-based, and 1.21 times faster than the ANN-based load balancing algorithms in terms of average response time. In addition, tldlb can reduce the SLA (service-level agreement) violation rate via in-time activating VMs from a spare VM pool.
Effect of resistance training on dehydroepiandrestrone sulfate levels and fatigue in women with multiple sclerosis disease

Multiple Sclerosis (MS) is an autoimmune genetic disease that associated with chronic inflammation of the central nervous system, demyelization, axonal loss, and brain atrophy. Fatigue is a one of the most common problems in MS. Exercise may be effective to decrease fatigue in MS patients however it is not well known. The aim of this study was to examine the effects of 8 weeks resistance training (RT) on dehydroepiandrestrone sulfate (DHEA-S) levels and fatigue in women with multiple sclerosis disease. Twenty seven women with MS disease in a range of 20-40 year of old and EDSS lower than 5 participated in this study as the subject. Subjects were divided into control group (n=13) or training group (n=14) randomly. The training group performed progressive RT program, 3 days a week for 8 weeks. The control group was in absolute rest at the same time. Fatigue level was assessed by FSS questionnaire before and after training, and also, serum level of DHEA-S was measured by ELISA kits before and after training. The results showed that serum levels of DHEA-S increased and fatigue decreased after 8 weeks RT compare to the control group (P<0.05). In conclusion, the results suggest that RT with specific intensity and duration utilized in this study increases serum levels of DHEA-S and decreases fatigue in female patients with MS.
The design of sum-of-cisoids rayleigh fading channel simulators assuming non-isotropic scattering conditions

In this letter, we introduce the Riemann sum method (RSM) as an effective tool for the design of sum-of-cisoids (SOC) simulators for narrowband mobile Rayleigh fading channels under non-isotropic scattering conditions. We compare the performance of the RSM with that of the generalized method of equal areas (GMEA) and the Lp-norm method (LPNM), which were until now the only methods available for the design of SOC simulators for non-isotropic scattering channels. The obtained results indicate that the RSM is better suited than the GMEA and the LPNM to emulate the channel’s autocorrelation function (ACF), whereas the latter two methods are more precise regarding the approximation of the envelope distribution. The results also show that the benefits of increasing the number of cisoids are more significant in the case of the RSM than in the case of the GMEA and LPNM. Owing to its simplicity and good performance, the RSM can be used to design flexible simulation platforms for the laboratory analysis of mobile communication systems operating in non-isotropic scattering environments.