Miracle-dz/UniFAR-FTUs huggingface dataset:
Three-dimensional speckle tracking echocardiography for the evaluation of segmental myocardial deformation

Background : Although the feasibility of three-dimensional (3D) speckle tracking echocardiography (STE) for the evaluation of myocardial function has been demonstrated, the poor reproducibility of strain measurements obtained with 3D STE as compared to two-dimensional (2D) STE has been controversially discussed. Aim of this study was to demonstrate the benefit of longitudinal strain analysis by 3D STE as compared to the established 2D STE techniques. Methods : 2D and 3D STE was performed in 30 volunteers with normal systolic left ventricular (LV) function using cardiac ultrasound systems from two different vendors (Vivid E9 and iE33 xMATRIX). Global and segmental longi - tudinal strain (GLS, SLS) values were analyzed for 2D STE using respective software packages (Vivid E9: EchoPAC AFI; iE33 xMATRIX: QLAB CMQ 9.0). Measurements for 3D STE were performed using specific software for Vivid E9 (EchoPAC 4DAutoLVQ) and unspecific software for iE33 xMATRIX (TomTec Imaging Systems 4D left ventricular Analysis). Intra-, interobserver and test-retest variability as well as times for acquisition and analysis were compared between 2D and 3D STE techniques. Results : The reliability of SLS measurements using 3D STE was non-inferior to the measurements obtained by 2D STE, with perpetual constant results in all tests (ICC SLS 3D 0.78 – 0.94 vs . SLS 2D 0.73 – 0.93). Agreements between SLS values were better when vendor specific 2D and 3D software was applied. GLS measurements showed inhomogeneous results for both techniques (ICC GLS 3D 0.40 – 0.93 vs . GLS 2D 0.13 – 0.91). Acquisition time was significantly shorter for 3D datasets than for 2D datasets (3D 51.0 ± 10.66 sec vs . 2D 91.0 ± 10.9 sec, p < .01). Conclusion : 3D STE is a time-saving technology for the evaluation of myocardial deformation in daily clinical practice, generating results that are comparable to the conventional 2D STE techniques. SLS obtained by 3D STE seems to be a more robust parameter than GLS for the assessment of myocardial deformation, especially when vendor specific software packages are used.
Real-time face recognition in HD videos: Algorithms and framework

Computation of high dimensional data in real-time is a challenging task. In this paper, we consider the problem of detecting and recognizing faces in a real-time high definition video. We show time and accuracy of various incremental dimensionality reduction and incremental learning algorithms for face recognition and present a system architecture for implementing these algorithms. Further, we show that approximation in face detection can speed up the recognition time with minor loss in accuracy. We present our speedup results with the help of low cost GPUs. We also design a benchmark high definition multi subject video database and show our results over this benchmark.
Monetary Policy, House Prices, and Consumption in China: A National and Regional Study

This paper studies the relationships among monetary policy, house prices, and consumption in China from both national and regional perspectives. Using a panel vector autoregression (VAR) model and a counterfactual simulation method, we find that monetary policy has a significant effect on consumption but with a regional pattern, in terms of the magnitude and the housing wealth channel. It is found that in the middle southern and the western cities, the monetary policy has strong effects on consumption while the house prices have minimal contribution to the monetary policy transmission to household consumption. By contrast, in the Tier-1 and the eastern cities, house prices play a more important role in monetary policy transmission; even household consumption is less sensitive to monetary policy changes.
Automatic and Transparent Resource Contention Mitigation for Improving Large-Scale Parallel File System Performance

Proportional to the scale increases in HPC systems, many scientific applications are becoming increasingly data intensive, and parallel I/O has become one of the dominant factors impacting the large-scale HPC application performance. On a typical large-scale HPC system, we have observed that the lack of a global workload coordination coupled with the shared nature of storage systems cause load imbalance and resource contention over the end-to-end I/O paths resulting in severe performance degradation. I/O load imbalance on HPC systems is generally a self-inflicted wound and mostly occurs between the I/O paths and resources consumed by each individual job. In this paper, we introduce TAPP-IO, a dynamic, shared load balancing framework for mitigating resource contention. TAPP-IO extends our previous work and solves two major limitations: First, it transparently intercepts file creation calls during runtime to balance the workload over all available storage targets. The usage of TAPP-IO requires no application source code modifications and is independent from any I/O middleware. The framework can be applied to almost any HPC platform and is suitable for systems that lack a centralized file system resource manager. Second, the framework proposes a new placement strategy to support not only file-per-process I/O, but also single shared file I/O. This opens the door to a new class of scientific applications that can leverage the placement library for improved performance. We demonstrate the effectiveness of our integration on the Titan system at the Oak Ridge National Laboratory. Our experiments with a synthetic benchmark and real-world HPC workload show that, even in a noisy production environment, TAPP-IO can improve large-scale application performance significantly.
A Critical Overview of Interpretative Phenomenological Analysis: A Contemporary Qualitative Research Approach

Context: Interpretative Phenomenological Analysis (IPA) has become a dominant qualitative research methodology in many academic disciplines. The desire to understand the theoretical underpinnings of this research approach is evident. Objective: This paper is aimed at providing an overview and limitations of IPA. This paper will hopefully equip researchers when deciding on the appropriate research methodology to their research topic. Methods: A range of literature on qualitative research approach and phenomenology is reviewed. The relevant literatures on the theoretical underpinnings of IPA are examined. Results: The article illuminates that IPA represents a highly useful methodology in providing a rich and nuance insight into the experiences of research participants. Conclusion: IPA is a forward-looking research approach that adopts a flexible and versatile design to understand people’s experiences.
Sign language based SMS generator for hearing impaired people

Sign Language provides information by using hand gestures and other body parts for projecting thoughts and meanings. It does not have any theoretical form and producing signs using video approach is very expensive, hence sign animation is best explanation. This paper explains implementation of SMS generator for deaf people in Indian Sign Language. For this system, three tools have been developed: the first one includes advanced visual interface for user (for a deaf person to indicate sign sequence), second one has a language translator (for sequence generation of words containing Sign Language into English to send SMS), third for SMS (Short Message Service) into speech translation system. The graphic interface permits a sequence of signs to be characterized by means of various services. An animation module has been added for playing the sign actions through the GIF file.
Formal definition of edge computing: An emphasis on mobile cloud and IoT composition

Under the Edge computing umbrella, mobile cloud computing is an emerging area where two trends come together to compose its major pillars. On one hand, the virtualization affecting the data centers hypervisors. On the other hand, device's mobility, especially Smart Phones, which proved to be the most effective and convenient tools in human life. This emerging area is then changing the game in terms of mobility of workspaces and the interaction with the connected devices and sensors. This paper provides a formal specification of the Mobile cloud component using the n-calculus. The proposed model defines the mobile cloud component, the virtual device representation, and interaction that leads to application offloading and device composition. This paper describe our contribution that enables the composition of virtual devices from physical devices, sensors, and actuators available on the network. Moreover, we present a model of application offloading and virtual devices networking on mobile clouds. Our architectural model is inspired from the Cloudlet based system. In addition to the formal specifications and architecture this paper presents a case studies showing the structural congruence between a locally executed application and an offloaded version of that same application.
Algorithms for the Vold-Kalman multiorder tracking filter

The paper deals with the Vold-Kalman (VK) multiorder tracking filter. The basic principle of the Vold-Kalman (VK) order tracking filter for only one sinusoidal component of a signal was published many times while an algorithm of tracking more than one harmonic component was not published yet. It is assumed that the frequency of the tracked component is known for each sample of the signal. The multiorder tracking filtration allows the decoupling of the crossing or close orders, i.e. two harmonic signals which are differing in frequency except a certain time moment when both signals can have the same frequency. The analytical calculation method can track only one or two orders, while the PCG method provides a tool for simultaneous decoupling of more than the two orders.
A comparison of semiglobal and local dense matching algorithms for surface reconstruction

Abstract. Encouraged by the growing interest in automatic 3D image-based reconstruction, the development and improvement of robust stereo matching techniques is one of the most investigated research topic of the last years in photogrammetry and computer vision. The paper is focused on the comparison of some stereo matching algorithms (local and global) which are very popular both in photogrammetry and computer vision. In particular, the Semi-Global Matching (SGM), which realizes a pixel-wise matching and relies on the application of consistency constraints during the matching cost aggregation, will be discussed. The results of some tests performed on real and simulated stereo image datasets, evaluating in particular the accuracy of the obtained digital surface models, will be presented. Several algorithms and different implementation are considered in the comparison, using freeware software codes like MICMAC and OpenCV, commercial software (e.g. Agisoft PhotoScan) and proprietary codes implementing Least Square e Semi-Global Matching algorithms. The comparisons will also consider the completeness and the level of detail within fine structures, and the reliability and repeatability of the obtainable data.
Report e Mitotic Checkpoint Component Mad 1 Controls Integrin Secretion and Cell Migration

Jun Wan,1,2 Fen Zhu,1 Lauren M. Zasadil,1,3 Jiaquan Yu,4 Lei Wang,5 Adam Johnson,5 Erwin Berthier,4 David J. Beebe,4,6 Anjon Audhya,5,6 and Beth A. Weaver1,6,* 1Department of Cell and Regenerative Biology 2Physiology Training Program 3Molecular and Cellular Pharmacology Training Program 4Department of Biomedical Engineering 5Department of Biomolecular Chemistry 6Carbone Cancer Center University of Wisconsin, Madison, WI 53705, USA
Reference proteome map of buckwheat (Fagopyrum esculentum and Fagopyrum tataricum) leaf and stem cultured under light or dark

Buckwheat sprouts are a vegetable to provide health benefit with their nutritionally important substances. Despite several reports on describing buckwheat as preventive medicine, little has been studied on the developmental physiology of buckwheat in protein level. Thus, we attempted for the first time to examine the proteomic profiles of leaf and stem from 7-day-old etiolated sprouts of common (Fagopyrum esculentum Moench.) and tatary buckwheat (Fagopyrum tataricum Gaertn.) seedlings under light and dark. By the gelbased proteomic approach, 166 unique proteins were identified and six proteins were commonly found from both buckwheat species in which 79 and 81 proteins were exclusively belonged to common and tatary buckwheat, respectively. The most abundant proteins were assigned to metabolism and cytoplasmic proteins were dominated by the classification of molecular function and intracellular localization. The sprouting leaf of 7-day-old etiolated common buckwheat seedlings showed light yellow under dark, suggesting the inhibition of light-dependent protochlorophyllide reductase leading to the reduction of chlorophyll biosynthesis. Light-inhibited storage protein, 13S globulin 3, was commonly found in the leaves of both buckwheat species under dark. In particular, the unknown pentatricopeptide repeat-containing proteins were identified in light-induced leaf of common and tatary buckwheat and also identified in dark-induced stem of tatary buckwheat. Thus, it suggests that the different and specific regulators are involved in the sterility and fertility from common and tatary buckwheat in a specific light-dependent manner. The reference proteomic map of buckwheat will give insight for understanding buckwheat physiology and application to buckwheat industry.
Coronary Artery Calcification in the Asian Population: An Overview of the Results from the Korea Initiatives on Coronary Artery Calcification Registry

Cardiovascular disease (CVD) is a global concern and is considered a major cause of global morbidity and mortality. Approximately 17.5 million people died from CVD in 2012, which represents one-third of all global deaths [1]. CVD is associated with several diseases; coronary artery disease (CAD) and cerebrovascular disease, such as stroke, account for over two-thirds of all mortalities related to CVD [2,3]. Of these deaths, around 7.4 million people died because of CAD. Prior epidemiologic evidence indicates that the prevalence and severity of CAD vary according to ethnicity and race [3-9]. Noticeably, at least threequarters of the world’s CAD-related mortalities occur in lowand middle-income countries [1]. Commonly, the prevalence and severity of CAD are influenced by traditional risk factors (RFs), which contribute to CAD development and progression. In the context of CAD prevention, various risk prediction alcc This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. CVIA 2017;1(2):89-98 pISSN 2508-707X / eISSN 2508-7088 REVIEW ARTICLE CVIA
The New View of Animal Phylogeny

▪ Abstract Molecular tools have profoundly rearranged our understanding of metazoan phylogeny. Initially based on the nuclear small ribosomal subunit (SSU or 18S) gene, recent hypotheses have been corroborated by several sources of data (including the nuclear large ribosomal subunit, Hox genes, mitochondrial gene order, concatenated mitochondrial genes, and the myosin II heavy chain gene). Herein, the evidence supporting our current understanding is discussed on a clade by clade basis. Bilaterian animals consist of three clades: Deuterostomia, Lophotrochozoa, and Ecdysozoa. Each clade is supported by molecular and morphological data. Deuterostomia is smaller than traditionally recognized, consisting of hemichordates, echinoderms, chordates, and Xenoturbella (an enigmatic worm-like animal). Lophotrochozoa groups animals with a lophophore feeding apparatus (Brachiopoda, Bryozoa, and Phoronida) and trochophore larvae (e.g., annelids and mollusk), as well as several other recognized phyla (e.g., platyhelmin t.
Prey stage preference by different stages of Phytoseiulus persimilis (Acari: Phytoseiidae) to Tetranychus urticae (Acari: Tetranychidae) on rose

Abstract Phytoseiulus persimilis Athias-Henriot is a commercialized predatory mite that is commonly used in ornamental greenhouses to control spider mites. In this research, prey stage preference of this predator on rose leaf disc was studied in laboratory conditions (25 ± 1°C, 75 ± 5% RH and 16L: 8D hour photoperiod). The preference index for each predator stage was calculated by Manly's β index. Comparison of the mean preference index for protonymphs, deutonymphs and females of P. persimilis showed that all stages of this predator showed a significant preference for eggs of T. urticae. The predator protonymphs and deutonymphs preferred prey eggs, followed by prey larvae and protonymphs, and the female predators preferred prey eggs, followed by prey protonymphs and deutonymphs.
Study on tunneling current through barrier height using scanning tunneling microscopy

The electrical properties of viologen derivatives were studied of the tunneling current characteristics on the length of the viologen derivatives using self-assembling techniques and ultra high vacuum scanning tunneling microscopy (UHV-STM). The current-voltage (I-V) and differential conductance (dl/dV-V) characteristics were measured while the electrical properties of the formed monolayer were scanned by using a scanning tunneling spectroscopy (STS). Also, we investigated to study electrons movement which is caused by height variation because we can see the organic materials barrier height and STM tip by organic materials energy gap (Eg). The total effective barrier height Phi of viologen derivatives VC8SH, VC10SH, HSC8VC8SH, and HSC10VC10SH SAMs were calculated to be 1.076 eV, 1.86 eV, 1.85 eV, 2.28 eV, respectively.
ML-NA: A Machine Learning Based Node Performance Analyzer Utilizing Straggler Statistics

Current Cloud clusters often consist of heterogeneous machine nodes, which can trigger performance challenges such as the task straggler problem, whereby a small subset of parallel tasks running abnormally slower than the other sibling ones. The straggler problem leads to extended job response and deteriorates system throughput. Poor performance nodes are more likely to engender stragglers, and can undermine straggler mitigation effectiveness. For example, as the dominant mechanism for straggler alleviation, speculative execution functions by creating redundant task replicas on other machine nodes as soon as a straggler is detected. When speculative copies are assigned onto the poor performance nodes, it is hard for them to catch up with the stragglers compared to replicas run on fast nodes. And due to the fact that the performance heterogeneity is caused not only by static attribute variations such as physical capacity, but also dynamic characteristic uctuations such as contention level, analyzing node performance is important yet challenging. In this paper we develop ML-NA, a Machine Learning based Node performance Analyzer. By leveraging historical parallel tasks execution log data, ML-NA classies cluster nodes into different categories and predicts their performance in the near future as a scheduling guide to improve speculation effectiveness and minimize task straggler generation. We consider MapReduce as a representative framework to perform our analysis, and use the published OpenCloud trace as a case study to train and to evaluate our model. Results show that ML-NA can predict node performance categories with an average accuracy up to 92.86%.
Dipole and bowtie nano-antenna for carbon nanotube (CNT) based infrared sensors

We report the fabrication and experimental characterization of two different designs of nanoantenna for carbon nanotube (CNT) based infrared (IR) sensors. By designing a nanosized antenna and integrating with CNT sensing element, the electric field intensity at the position of the sensing element can be enhanced and so a very sensitive infrared nanosensor is developed. The efficiency and characteristics of the two different nanoantenna geometries were studied experimentally by measuring the photocurrent response of the sensor before and after the nanoantenna is implemented. The experimental result has shown the nano antenna increases the photocurrent by more than 10-fold and the bowtie antenna produces a bigger enhancement factor. This implies that the electric field in the vicinity of the detector is enhanced by the nanoantenna. The proposed design and analysis enables a reliable method to enhance the efficiency of the ultra-small sensing elements, which are important to the development of a range of novel functional infrared devices and systems.
Adaptive Fuzzy Sliding Mode Control for Network-Based Nonlinear Systems With Actuator Failures

This paper investigates the robust control problem of nonlinear systems with unknown time-varying actuator faults over digital communication networks. In the study, a new adaptive sliding mode control (SMC) scheme is developed for the investigated nonlinear systems, where the unknown nonlinearity is approximated via the adaptive fuzzy mechanism in the presence of signal quantization. The proposed control law can compensate the time-varying faults and the quantization errors completely by injecting the quantizer parameter into the controller gain. Moreover, in this design, the adaptive fuzzy law is constructed via the quantized state information instead of using the exact original information. Under the proposed quantized SMC scheme, the closed-loop control systems are guaranteed to be asymptotically stable, and the reachability of the sliding surface can be ensured strictly. Finally, two examples are presented to show the effectiveness of the method.
Deep Learning : Effective Tool for Big Data Analytics

Currently, Deep Learning is a very active research area in pattern recognition and machine learning society. It has achieved unprecedented success in applications of essential fields such as Computer Vision, Speech and Audio Processing, and Natural Language Processing. Every day there are enormous amounts of data generated by multiple sources. So the term of data is converted to Big Data which face challenges in information acquisition and decision-making processes. Dealing with these data can be supported by Deep Learning capabilities, especially its ability to deal with both the labeled and unlabeled data which are often collected abundantly in Big Data. So Deep Learning is recently coming to play a significant role in reaching solutions for Big Data analytics. This survey discusses how Deep Learning architectures differ from convolutional structured architectures by illustrating the structure and learning aspects of most common used Deep Neural Networks. We will give an overview of Big Data and identify specific data analysis problems that can be addressed by Deep Learning. We will present some studies in Deep Learning that are used as a solution for data analysis. Finally, some Deep Learning challenges due to specific data analysis needs of Big Data will be showed. KeywordsDeep Learning; Big Data; Boltzmann Machine (BM); Auto-Encoders (AE); Deep Neural Networks (DNN); Deep Belief Networks (DBN); Deep Stacking Networks (DSN); Big Data Analytics.