Miracle-dz/UniFAR-FTUs huggingface dataset:
Arterial stiffness in postmenopausal women: determinants of pulse wave velocity

Objective To investigate the degree and potential cardiovascular determinants of arterial stiffness, assessed by aortic pulse wave velocity (PWV) measurements, and to relate arterial stiffness to absolute 10–12-year risks of stroke, coronary heart disease and death, as estimated by available risk functions, in postmenopausal women. Method We performed a cross-sectional study among 385 postmenopausal women, aged 50–74 years, sampled from the general population. Arterial stiffness was assessed non-invasively by measurement of aortic PWV using applanation tonometry. Information on health was obtained by medical history, registration of current medication, and physical examination. Height, weight, waist and hip circumferences, fasting glucose, total and high-density lipoprotein (HDL) cholesterol, triglycerides, resting blood pressure, and heart rate were measured. Three risk scores were used to estimate, for each individual, the absolute risk of stroke, coronary heart disease, and death within 10–12 years as a function of their cardiovascular risk factor profile. The relationship between PWV and these risk scores was subsequently determined. Results Significant positive relationships with PWV were found for body mass index, fasting glucose, diabetes mellitus, and triglycerides in analyses adjusted for age, mean arterial blood pressure, and heart rate. Height and HDL cholesterol were inversely related to PWV. The risks of stroke, coronary heart disease, and death increased with increasing PWV in a linear graded manner. Conclusions This cross-sectional study among postmenopausal women provides evidence that most of the established cardiovascular risk factors are determinants of aortic PWV. Increased PWV marks an increased risk of stroke, coronary heart disease, and death within 10–12 years.
Contrasting Patterns of Polymorphism and Divergence on the Z Chromosome and Autosomes in Two Ficedula Flycatcher Species

In geographic areas where pied and collared flycatchers (Ficedula hypoleuca and F. albicollis) breed in sympatry, hybridization occurs, leading to gene flow (introgression) between the two recently diverged species. Notably, while such introgression is observable at autosomal loci it is apparently absent at the Z chromosome, suggesting an important role for genes on the Z chromosome in creating reproductive isolation during speciation. To further understand the role of Z-linked loci in the formation of new species, we studied genetic variation of the two species from regions where they live in allopatry. We analyzed patterns of polymorphism and divergence in introns from 9 Z-linked and 23 autosomal genes in pied and collared flycatcher males. Average variation on the Z chromosome is greatly reduced compared to neutral expectations based on autosomal diversity in both species. We also observe significant heterogeneity between patterns of polymorphism and divergence at Z-linked loci and a relative absence of polymorphisms that are shared by the two species on the Z chromosome compared to the autosomes. We suggest that these observations may indicate the action of recurrent selective sweeps on the Z chromosome during the evolution of the two species, which may be caused by sexual selection acting on Z-linked genes. Alternatively, reduced variation on the Z chromosome could result from substantially higher levels of introgression at autosomal than at Z-linked loci or from a complex demographic history, such as a population bottleneck.
BLIND SOURCE SEPARATION USING WAVELETS

This paper is the implementation of the source separation using wavelets. In this paper, the problem considered is the enhancement and separation of speech signals corrupted by environmental acoustic noise, interferences and other speakers using array of microphones containing at least two microphones. This work presents the implementation of the blind source separation using ICA (Independent Component Analysis). ICA is a recently developed method in which the goal is to find a linear representation of non-gaussian data so that the components are statiscally independent, or independent as possible. Such a representation seems to capture the essential structure of the data in many applications, including feature extraction and signal separation. The ICA algorithm that uses wavelets is used to exploit the structure in the signals of interest and thus learn the source separation more efficiently. We propose a new algorithm for blind source separation(BSS), in which frequency-domain ICA and time-domain ICA are combined to achieve a superior source-separation performances.
SAR-ADC design for neural recording system with energy efficient quaternary capacitive DAC switching scheme

This paper presents energy efficient 4-bit successive approximation register analog to digital converter (SAR-ADC) for neural recording front end interface of neural prosthetic system(Brain machine interface). The energy efficient quaternary capacitive switching scheme (QCS) in the implementation of capacitive digital to analog converter (C-DAC) is employed which makes the energy consumption in the C-DAC independent of the output digital code. The proposed technique achieves a 50% reduction in the average energy consumption. The design is implemented in 0.25um standard complementary metal-oxide semiconductor technology (CMOS).
Ranking objects by following paths in entity-relationship graphs

In this paper, we propose an object ranking method for search and recommendation. By selecting schema-level paths and following them in an entity-relationship graph, it can incorporate diverse semantics existing in the graph. Utilizing this kind of graph-based data models has been recognized as a reasonable way for dealing with heterogeneous data. However, previous work on ranking models using graphs has some limitations. In order to utilize a variety of semantics in multiple types of data, we define a schema path as a basic component of the ranking model. By following the path or a combination of paths, relevant objects could be retrieved or recommended. We present some preliminary experiments to evaluate our method. In addition, we discuss several interesting challenges that can be considered in future work.
Evaluating Airlines Safety Using Fuzzy Simulation Technique

In this paper, a model based on fuzzy simulation technique and hierarchical structure analysis is developed to evaluate the airlines safety. With the use of linguistic terms, subjective assessments of qualitative safety measures are represented with fuzzy variable. The relative weights of safety attributes are assessed using a pairwise comparison process. Based on the concept of optimality, fuzzy simulation approach is employed to dispose of the evaluation model and compute the expected values of the airline. Then the shortcoming of the traditional analytic hierarchy process (AHP) is made up. Finally, an empirical study on Chinese three major domestic airlines is conducted to demonstrate the effectiveness of the approach.
Speech-Assisted Self Portraits on Mobile Devices

Self-portraiture is an important activity in photography. Yet, it remains difficult for mobile users who often capture self-portraits spontaneously. This demo presents a prototype that combines computer vision with audio interface to complement the conventional visual interaction, thus assisting users to capture better-framed self-portraits. ACM Classification: H5.2. Information interfaces and presentation: User Interfaces
A (Somewhat) New Solution to the Binding Problem

A gas turbine cycle in which air is compressed to superatmospheric pressure, water is circulated in heat exchange relation with the compressed air to convert the sensible heat therein to heated water, the compressed air is humidified using the heated water, the humidified air is heated with sensible heat in the turbine exhaust gases, and a solid fuel such as coal is converted to a motive fluid for driving the gas turbine by substoichiometrically burning solid fuel under superatmospheric pressure using oxygen from the heated humidified air for combustion of the solid fuel and to produce combustible fuel gas and char, above stoichiometrically and adiabatically burning the char under superatmospheric pressure by supplying the heated humidified air in sufficient quantity to provide oxygen for combustion of the char and to control the temperature of such combustion, thereby to produce char combustion gases containing oxygen, cleaning the combustible fuel gas and the char combustion gasses to remove particulate materials therefrom, and burning the combustible fuel gas under superatmospheric pressure, oxygen for combustion of the gases being supplied by the char combustion gases and the heated humidified air, and the temperature of such gas combustion being controlled by the quantity of heated humidified air supplied for burning the fuel gas and the char combustion gases, the products of such gas combustion providing the motive fluid for driving the turbine.
Humanoid 3D Gait Generation Based on Inverted Pendulum Model

A planning method for humanoid walking is proposed in this paper. In this method, IPM (inverted pendulum model) is used as a dynamic model for humanoid robots. Whereby ZMP (zero moment point) constraints of the robot are analyzed in the IPM motion, and the COG (center of gravity) motion of IPM is to approximate the COG motion of robots. The kinematic model of the robot for walking planning is based on a typical model with 12 DOFs (degree of freedom) of legs. After the robot COG motion and two legs motion are generated, and 3D kinematic constraints are satisfied, the angles of every DOFs are obtained. These angular trajectories are used to control the real robots. Simulation experiments are conducted to verify the effectiveness of our proposed walking planning method.
Emotion-based sequence of family photos

This paper presents a method for the automatic creation of slideshows from family photo collections based on the emotions of a given group of people. The user specifies the desired person(s) to be included in the slideshow. A natural image sequence is formed based on people's emotions and several other, user-defined image similarity attributes, in order to form meaningful slideshow transitions. This process makes use of a new image dissimilarity function, which can integrate various attribute combinations and preferences, making the system highly user-adaptable and flexible.
Physical Analytics: A New Frontier for (Indoor) Location Research

The large body of research on localization has been driven by the goal of providing users with location-based services, be it mapping and navigation or local alerts and advertisements. However, as we discuss in this paper, location information can over time provide deep insight about about a user, going well beyond the location domain itself. We argue that unlocking the wealth of information available from location is valuable and represents a new and promising frontier for location-related research, especially in the indoor domain. We call this Physical Analytics, analogous to Online Analytics, with footsteps taking the place of a clickstream. We describe research opportunities, challenges, and our initial investigation in Physical Analytics.
Visualizing large-scale atomistic simulations in ultra-resolution immersive environments

Molecular Dynamics is becoming a principle methodology in the study of nanoscale systems, paving the way for innovations in battery design and alternative fuel applications. With the increasing availability of computational power and advances in modeling, atomistic simulations are rapidly growing in scale and complexity. Despite the plethora of molecular visualization techniques, visualizing and exploring large-scale atomistic simulations remain difficult. Existing molecular representations are not perceptually scalable and often adopt a rigid definition of surfaces, making them inappropriate for nanostructured materials where boundaries are inherently ill-defined. In this paper, we present an application for the interactive visualization and exploration of large-scale atomistic simulations in ultra-resolution immersive environments. We employ a hybrid representation which combines solid ball-and-stick glyphs with volumetric surfaces to visually convey the uncertainty in molecular boundaries at the nanoscale. We also describe a scalable, distributed GPU ray-casting implementation capable of rendering complex atomistic simulations with millions of atoms in real-time.
Financial Time-Series Forecasting based on a Neural Network with Weighted Fuzzy Membership Functions and the Takagi-Sugeno Fuzzy Model

This paper proposes financial time-series forecasting using a feature selection method based on the non-overlap area distribution measurement method supported in a neural network with weighted fuzzy membership functions (NEWFM) and the Takagi- Sugeno (T-S) fuzzy model. The non-overlap area distribution measurement method selects the minimum number of features with the highest performance by removing the worst features one by one. This paper uses CPPn,m (current price position on day n: percentage of the difference between the price on day n and the moving average of the past m days' prices from day n-1) as a technical indicator. The performance result improves from 58.35% to 58.86% whenCPPn,5 is added to a minimum number of features that are selected by the non-overlap area distribution measurement method. The T-S fuzzy model can provide the weighted average defuzzification method to represent a trend line such as a financial time series. This paper gene rally demonstrates fluctuations similar to a financial time series of the daily KOSPI's trend line by the defuzzification method.
Sparse coding of natural images using a prior on edge co-occurences

Oriented edges in images commonly occur in co-linear and co-circular arrangements, obeying the "good continuation law" of Gestalt psychology. The human visual system appears to exploit this property of images, with contour detection, line completion, and grouping performance well predicted by such an "association field" between edge elements [1, 2]. In this paper, we show that an association field of this type can be used to enhance the sparse representation of natural images. First, we define the SparseLets framework as an efficient representation of images based on a discrete wavelet transform. Second, we extract second-order information about edge co-occurrences from a set of images of natural scenes. Finally, we incorporate this prior information into our framework and show that it allows for the extraction of features relevant to natural scenes, like a round shape. This novel approach points the way to practical computer vision algorithms with human-like performance.
Retinoic Acid Induces Pdx 1-Positive Endoderm in Differentiating Mouse Embryonic Stem Cells

We have generated an embryonic stem (ES) cell line in which sequences encoding green fluorescent protein (GFP) were targeted to the locus of the pancreaticduodenal homeobox gene (Pdx1). Analysis of chimeric embryos derived from blastocyst injection of Pdx1 ES cells demonstrated that the pattern of GFP expression was consistent with that reported for the endogenous Pdx1 gene. By monitoring GFP expression during the course of ES cell differentiation, we have shown that retinoic acid (RA) can regulate the commitment of ES cells to form Pdx1 pancreatic endoderm. RA was most effective at inducing Pdx1 expression when added to cultures at day 4 of ES differentiation, a period corresponding to the end of gastrulation in the embryo. RT-PCR analysis showed that Pdx1-positive cells from day 8 cultures expressed the early endoderm markers Ptf1a, Foxa2, Hnf4 , Hnf1 , and Hnf6, consistent with the notion that they corresponded to the early pancreatic endoderm present in the embryonic day 9.5 mouse embryo. These results demonstrate the utility of Pdx1 ES cells as a tool for monitoring the effects of factors that influence pancreatic differentiation from ES cells. Diabetes 54:301–305, 2005
Overview: Video recognition from handcrafted method to deep learning method

With the development of information technology, the automatic recognition of human action from video becomes a very popular research topic. In this paper, we review recent state-of-the-art of human action recognition methods in videos. First, we compare several notable handcrafted methods. Then we introduce some deep learning action recognition models. As deep learning becomes hot spot of research in recent years, more and more papers have utilized this method to explore the spatiotemporal features representation. We find that the deep learning methods outperform handcrafted methods at large scale recognition especially in cluttered background. But the networks still have much disadvantage. We expect our overview provides a fairly clear guidance for future research in this domain.
On the training of DNN-based average voice model for speech synthesis

Adaptability and controllability are the major advantages of statistical parametric speech synthesis (SPSS) over unit-selection synthesis. Recently, deep neural networks (DNNs) have significantly improved the performance of SPSS. However, current studies are mainly focusing on the training of speaker-dependent DNNs, which generally requires a significant amount of data from a single speaker. In this work, we perform a systematic analysis of the training of multi-speaker average voice model (AVM), which is the foundation of adaptability and controllability of a DNN-based speech synthesis system. Specifically, we employ the i-vector framework to factorise the speaker specific information, which allows a variety of speakers to share all the hidden layers. And the speaker identity vector is augmented with linguistic features in the DNN input. We systematically analyse the impact of the implementations of i-vectors and speaker normalisation.
Convex optimization of battery energy storage station in a micro-grid

As the battery energy storage station has the function of improving the fluctuations of power output and regulating system's economic benefit caused by time-of-use pricing, the optimization model of battery energy storage station is proposed to stabilize the power output and to maximize profit of the micro-grid with the known output model of each distributed generator. In order to avoid the disadvantages of dynamic programming algorithm and genetic algorithm, we transform the optimization model to a convex optimization problem and obtain the optimal scheduling policy. Take a specified data in the north area as example, a comparison diagram on power output with and without the use of battery energy storage station shows effectiveness of the model and suitability of using convex optimization algorithm to solve the problem.
Selective Mesh Refinement for Interactive Terrain Rendering Selective Mesh Reenement for Interactive Terrain Rendering

Terrain surfaces are often approximated by geometric meshes to permit eecient rendering. This paper describes how the complexity of an approximating irregular mesh can be varied across its domain in order to minimise the number of displayed facets while ensuring that the rendered surface meets predetermined resolution requirements. We rst present a generalised scheme to represent a mesh over a continuous range of resolutions using the output from conventional single-resolution approximation methods. We then describe an algorithm which extracts a surface from this representation such that the resolution of the surface is enhanced only in speciic areas of interest. We prove that the extracted surface is complete, minimal, satisses the given resolution constraints and meets the Delaunay triangulation criterion if possible. In addition, we present a method of performing smooth visual transitions between selectively-reened meshes to permit eecient animation of a terrain scene.
Robot localization method by acoustical signal identification

Non-speech audio is a non-explored characteristic in robot localization but due to its potentiality it can yield a valuable information together with other sensorial systems. In this work, a novel robot localization method is proposed based on audio signal pattern recognition with extracted features from signal identification. To reinforce the localization, avoiding ambiguity and reducing uncertainty, a sensorial system is used aboard the robot to compute the angle between itself and the sound source. This method can be generalized to any non-speech sound signal because the acoustical meaning and the room geometry are related.
Uncertainty and Sensitivity Assessments of GPS and GIS Integrated Applications for Transportation

Uncertainty and sensitivity analysis methods are introduced, concerning the quality of spatial data as well as that of output information from Global Positioning System (GPS) and Geographic Information System (GIS) integrated applications for transportation. In the methods, an error model and an error propagation method form a basis for formulating characterization and propagation of uncertainties. They are developed in two distinct approaches: analytical and simulation. Thus, an initial evaluation is performed to compare and examine uncertainty estimations from the analytical and simulation approaches. The evaluation results show that estimated ranges of output information from the analytical and simulation approaches are compatible, but the simulation approach rather than the analytical approach is preferred for uncertainty and sensitivity analyses, due to its flexibility and capability to realize positional errors in both input data. Therefore, in a case study, uncertainty and sensitivity analyses based upon the simulation approach is conducted on a winter maintenance application. The sensitivity analysis is used to determine optimum input data qualities, and the uncertainty analysis is then applied to estimate overall qualities of output information from the application. The analysis results show that output information from the non-distance-based computation model is not sensitive to positional uncertainties in input data. However, for the distance-based computational model, output information has a different magnitude of uncertainties, depending on position uncertainties in input data.
Approximation algorithms for geometric tour and network design problems (extended abstract)

Red-Blue Separation Problem (RBSP): Consider the problem of finding a minimum-perimeter Jordan curve (necessarily, a simple polygon) that separates a set of “red” points, R, from a set of “blue” points, B. This problem is seen to be NP-hard, using a reduction from the Euclidean traveling salesman problem [3, 12]. (Replace each city in the TSP instance by a pair of points, one red and one blue, very close together.) While Euclidean TSP can be approximated to within a factor of 1.5 times optimal (using Christofides’ heuristic [9]), the seemingly related RBSP has defied previous attempts to devise a provably good approximation algorithm. We provide an O(log m) approximation bound algorithm for RBSP, where m < n is the minimum number of sides
AN APPROACH FOR IDS BY COMBINING SVM AND ANT COLONY ALGORITHM

This piece of work researches the intrusion detection problem of the network sanctuary; the primary task is to classify network behavior as normal or abnormal while reducing misclassification. In this paper, two efficient data mining algorithms are combined together to detect the network intrusion. Combining SVM and Ant colony (CSVAC) used for well-organized data classification, this technique takes the advantage of both the algorithm while avoiding their weaknesses. This algorithm is implemented and evaluated using standard benchmark KDDCUP99 data set. Experimental results drastically well produce superior results than the other algorithm in terms of accuracy rate and run time efficiency, and this algorithm able to detect the new types of attacks