Miracle-dz/UniFAR-FTUs huggingface dataset:
Obstructive Sleep Apnea and Lipid Abnormalities

BACKGROUND: There has been a great interest in the interaction between obstructive sleep apnea (OSA) and metabolic dysfunction, but there is no consistent data suggesting that OSA is a risk factor for dyslipidemia. AIM: The aim of this cross-sectional study was to evaluate the prevalence of lipid abnormalities in patients suspected of OSA, referred to our sleep laboratory for polysomnography. MATERIAL AND METHODS: Two hundred patients referred to our hospital with suspected OSA, and all of them underwent for standard polysomnography. All patients with respiratory disturbance index (RDI) above 15 were diagnosed with OSA. In the morning after 12 hours fasting, the blood sample was collected from all patients. Blood levels of triglycerides, total cholesterol, high-density lipoprotein cholesterol (HDL) and low-density lipoprotein cholesterol (LDL), were determined in all study patients. In the study, both OSA positive and OSA negative patients were divided according to the body mass index (BMI) in two groups. The first group with BMI ≤ 30 kg/m^2 and the second group with BMI > 30 kg/m^2. RESULTS: OSA positive patients with BMI ≤ 30 kg/m^2 had statistically significant higher levels of triglycerides and total cholesterol, and statistically significant lower level of HDL compared to OSA negative patients with BMI ≤ 30. There were no statistically significant differences in age and LDL levels between these groups. OSA positive patients with BMI > 30 kg/m^2 had higher levels of triglycerides, total cholesterol and LDL and lower levels of HDL versus OSA negative patients with BMI > 30 kg/m^2, but without statistically significant differences. CONCLUSION: OSA and obesity are potent risk factors for dyslipidemias. OSA could play a significant role in worsening of lipid metabolism in non-obese patients. But in obese patients, the extra weight makes the metabolic changes of lipid metabolism, and the role of OSA is not that very important like in non-obese patients.
Energy efficient resource allocation in wireless systems with control channel overhead

This paper presents a novel resource scheduling technique named Time Compression Mode (TCoM) that allows energy aware schedulers to decrease the energy consumption within orthogonal frequency division multiple access (OFDMA) type systems - for example the Long Term Evolution (LTE) system. TCoM allows users whose energy consumption is dominated by control channel overhead, to reduce their overall energy expenditure by reducing the number of resource blocks (RBs) allocated to them. This leads to an overall reduction in the used energy due to the decrease in energy used for overhead transmission. To ensure correct operation, TCoM has to be coupled with an energy aware scheduler. This combination not only reduces the used energy, but is also able to increase the achieved user data rate. Simulation results show that the data rate performance is equivalent for 86.8% of the users, however the remaining 13.2% enjoy approximately double the data rate delivered by the benchmark. The overall used energy is decreased by 26% on average. These results are achieved for an future base station (BS) overall hardware efficiency model.
Dissection of the B10.D2 anti-H-2Kb cytolytic T lymphocyte receptor repertoire

The B10.D2 cytolytic T lymphocyte (CTL) receptor repertoire specific for the H-2Kb alloantigen has been studied by determining the reactivity patterns of monoclonal CTL against a panel of seven different H-2Kb mutants. The repertoire is extremely diverse and contains a minimum of approximately 50 different specificities against unique antigenic determinants on the H-2Kb molecule. Each specificity appears at a maximum frequency of 1 in 18,000 CTL precursors. These studies have also served to dissect the antigenic composition of the H- 2Kb molecule. Very few CTL clonotypes share recognition of all the mutants, thereby indicating the lack of conservation of a b-type antigenic region. In addition, the degree to which each mutant shares antigenic determinants with the standard H-2Kb molecule has been determined.
Design and implementation of Envision - a visual programming system

In the following work we will present the concept and initial implementation of Envision. Envision combines the features of an integrated development environment with the functionality of an information system to offer a unified tool for software engineers. The system is independent of a specific programming language and offers a visual programming approach for developing applications. Envision is designed for general purpose development and targets professional programmers that work on software projects of all scales. We first describe the basic requirements for the Envision platform. They define a flexible framework which can be extended and customized to provide a wide variety of functionality used in everyday software engineering. The core of this work consists of designing a modular architecture for Envision and implementing a first version of the system. The focus of the current implementation is on mechanisms for visual object oriented programming. We describe the modules that constitute Envision and outline how they achieve the requirements and goals set earlier. The current state of the platform and the progress regarding visual programming is illustrated using examples from the running tool. Finally we outline our plans for further development of Envision in the areas of visual interaction, semantic version control, statistics, software verification and others.
Managing people and learning – major challenge for modern managers

Issue 4, volume 24 offers many interesting academic contributions and implications for practitioners. Simonin tested N-loop learning in international strategic alliances, Poell, and Van Der Kroght focused on HRD, Hillon and Boje discussed dialectics in the context of higher education, Sasaki emphasized the importance of systems intelligence in knowledge management and Bozoglu et al. investigated error management practices in two state university departments. Analysis of their contribution is offered in this paper.
Clustering based Voltage Control Areas for Localized Reactive Power Management in Deregulated Power System

— In this paper, a new K-means clustering based approach for identification of voltage control areas is developed. Voltage control areas are important for efficient reactive power management in power systems operating under deregulated environment. Although, voltage control areas are formed using conventional hierarchical clustering based method, but the present paper investigate the capability of K-means clustering for the purpose of forming voltage control areas. The proposed method is tested and compared for IEEE 14 bus and IEEE 30 bus systems. The results show that this K-means based method is competing with conventional hierarchical approach
A Secure QOS Distributed Routing Protocol for Hybrid Wireless Networks

The succeeding wireless network is Hybrid Wireless Networks. It can provide Quality of Service (QoS) requirements in real time transmission for wireless application. But it stream including critical mission application like military use or emergency recovery. Hybrid wireless networks is unified mobile ad-hoc network (MANET) and wireless infrastructure networks. It inherits invalid reservation and race condition problem in Mobile ad-hoc network (MANET). Whereas open medium and wide distribution of node make vulnerable to malicious attackers in Hybrid wireless networks. How to secure the Quality of Service (QoS) routing in Hybrid wireless networks. In this paper, we propose a Secure QoS-Oriented Distributed routing protocol (SQOD) to upgrade the secure Quality of Service (QoS) routing in Hybrid wireless networks. SQOD contain two contrivances: 1.QoS-Oriented Distributed Routing Protocol (QOD)-to reduce transmission delay, transmission time. And also increase wireless network transmission throughput. 2. Enhanced Adaptive ACKnowledgment (EAACK)-implement a new intrusion-detection system for Hybrid wireless networks. It protect Hybrid wireless networks from attacks that have higher malicious behavior detection rate. Analytical and simulation result based on the real human mobility mode. SQOD can provide high secure performance in terms of Intrusiondetection, overhead, transmission delay.
Towards complex visual surveillance algorithms on smart cameras

One of the drawbacks of currently available smart camera solutions is the gap in comfort and power between software development on the PC and on the embedded system side. This paper describes efforts of bringing high level computer vision software from PCs to the constrained environments of smart cameras. We show that software development with Intel's Open Source Library OpenCV on smart cameras is feasable and nearly as comfortable as on the PC side, and that processing speeds are sufficiently high for real-time processing. We elaborate on porting efforts of the well known KLT tracking algorithm to a VC4465 smart camera. In addition, we compare the differences in acurracy between the fixed point code and the floating point code of the KLT implementation. Finally, by describing a complex pedestrian tracking algorithm which is suited for crowded scenes, we highlight the practical challenges of running high level vision software on a device with limited computing resources.
Audio concept classification with Hierarchical Deep Neural Networks

Audio-based multimedia retrieval tasks may identify semantic information in audio streams, i.e., audio concepts (such as music, laughter, or a revving engine). Conventional Gaussian-Mixture-Models have had some success in classifying a reduced set of audio concepts. However, multi-class classification can benefit from context window analysis and the discriminating power of deeper architectures. Although deep learning has shown promise in various applications such as speech and object recognition, it has not yet met the expectations for other fields such as audio concept classification. This paper explores, for the first time, the potential of deep learning in classifying audio concepts on User-Generated Content videos. The proposed system is comprised of two cascaded neural networks in a hierarchical configuration to analyze the short- and long-term context information. Our system outperforms a GMM approach by a relative 54%, a Neural Network by 33%, and a Deep Neural Network by 12% on the TRECVID-MED database.
Architectural Modeling of Ozone Widget Framework End-User Compositions (CMU-ISR-14-108)

Ozone Widget Framework (OWF) is an event-based web platform for lightweight integration of widget applications. This technical report presents a formal model of OWF’s widget composition mechanism. First, we present a detailed description of Ozone’s end user composition mechanism. Then, we describe our architectural modeling approach and its value for analysis of OWF widget compositions. We go through the process of creating an architectural style to represent assemblies of Ozone widgets, reviewing modeling decision points and style alternatives. This work was supported in part by the Office of Naval Research (ONR-N000140811223). Additional support was provided by the Center for Computational Analysis of Social and Organizational Systems (CASOS). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Office of Naval Research, or the U.S. government.
KALAKA-3: a database for the recognition of spoken European languages on YouTube audios

This paper describes the main features of KALAKA-3, a speech database specifically designed for the development and evaluation of language recognition systems. The database provides TV broadcast speech for training, and audio data extracted from YouTube videos for tuning and testing. The database was created to support the Albayzin 2012 Language Recognition Evaluation, which featured two language recognition tasks, both dealing with European languages. The first one involved six target languages (Basque, Catalan, English, Galician, Portuguese and Spanish) for which there was plenty of training data, whereas the second one involved four target languages (French, German, Greek and Italian) for which no training data was provided. Two separate sets of YouTube audio files were provided to test the performance of language recognition systems on both tasks. To allow open-set tests, these datasets included speech in 11 additional (Out-Of-Set) European languages. The paper also presents a summary of the results attained in the evaluation, along with the performance of state-of-the-art systems on the four evaluation tracks defined on the database, which demonstrates the extreme difficulty of some of them. As far as we know, this is the first database specifically designed to benchmark spoken language recognition technology on YouTube audios.
Pedestrian detection via Wavelet Fractal Signature and Support Vector Machine

This paper presents a novel pedestrian detection approach. Firstly, Haar wavelet is employed to transform the input image into its sub-patterns with different resolution. And then, some relevant wavelet sub-patterns are selected to compute the fractal vector in different scales. Next, this fractal vector is assembled to be a Wavelet Fractal Signature vector, which is utilized in Support Vector Machine classifier. To validate this approach, some experiments based on Daimler's Pedestrian Detection Benchmark are conducted; the experimental results show that the proposed approach has the advantages of compact feature expression form and high detection rate.
A variable step-size proportionate affine projection algorithm for network echo cancellation

For a network canceler, whose input is mainly speech, the proportionate affine projection algorithm (PAPA) is expected to present faster convergence speed than the existing proportionate NLMS algorithms. However, the performance criteria of fast convergence speed conflicts with low steady-state misalignment when a constant step-size parameter is applied. In this article we introduce a variable step-size technique into the PAPA in order to achieve both fast convergence speed and low steady-state misalignment. Simulation results show that the proposed approach can greatly improve the steady-state misalignment without sacrificing the fast convergence speed of the PAPA.
Retinal channelrhodopsin-2-mediated activity in vivo evaluated with manganese-enhanced magnetic resonance imaging

Purpose Ectopic expression of light-sensitive proteins, such as channelrhodopsin-2, represent a novel approach for restoring light-detection capabilities to degenerated retina. A noninvasive method that can detect light-mediated activities of such light-sensitive proteins in the retina in vivo would be important for correlating expression patterns and retinal function. In this study, we tested the hypothesis that retinal uptake of manganese, measured noninvasively with manganese-enhanced magnetic resonance imaging (MEMRI), is a biomarker of channelrhodopsin-2-mediated activity in vivo. Methods The eyes of 3-month-old rd1/rd1 mice were either untreated (“uninjected,” negative control) or injected intravitreally with either saline (“saline,” negative control) or adeno-associated virus carrying a fusion construct of channelopsin-2 (Chop2) and green fluorescent protein (GFP; “Chop2-GFP”). MEMRI examination was performed 2 months later on either dark or continuous bright blue light-exposed mice to assess the distribution and extent of manganese uptake in the retina and optic nerve. In separate experiments, MEMRI was used to map laminar accumulation of manganese vertically through the retina. For comparison, Chop2-GFP expression was evaluated in whole mounts and vertical sections of virus-infected retinas and optic nerve. Results In the two control groups (regardless of lighting exposure) and between the control groups and the dark-exposed virus-treated eyes, retinal and optic nerve uptake of manganese did not differ. In light-exposed virus-treated eyes, manganese uptake in the retina and optic nerve was significantly greater relative to the other groups. In a retinal cross-section, manganese accumulation in light-exposed virus-treated eyes was spatially matched with Chop2-GFP expression in the optic nerve and all remaining retinal layers except the inner nuclear layer. Conclusions First-time evidence is presented indicating the usefulness of measuring intraretinal manganese accumulation as a noninvasive biomarker of channelrhodopsin-2-mediated activity in vivo.
Legacy System Evolution towards Service-Oriented Architecture

Although Service-Oriented Architecture (SOA) has become popular in recent years, the majority of legacy systems are still not SOA enabled. The increase in the amount of information that companies must handle has resulted in a considerable increase in the complexity of the legacy systems that store this information. While moving to a serviceoriented architecture platform can help in handling this increase, at the same time it is important to preserve the investment of many years of tuning and debugging of the legacy assets as much as possible. Several techniques exist for modernizing legacy systems towards service-oriented architecture. In this paper we present a survey of the various approaches to moving legacy systems to the SOA environment. We discuss the various approaches and methods and highlight their strengths and weaknesses, with an eye to assisting the decision process when undertaking a new modernization project.
Static Checking of Dynamically-Varying Security Policies in Database-Backed Applications

We present a system for sound static checking of security policies for database-backed Web applications. Our tool checks a combination of access control and information flow policies, where the policies vary based on database contents. For instance, one or more database tables may represent an access control matrix, controlling who may read or write which cells of these and other tables. Using symbolic evaluation and automated theorem-proving, our tool checks these policies statically, requiring no program annotations (beyond the policies themselves) and adding no run-time overhead. Specifications come in the form of SQL queries as policies: for instance, an application's confidentiality policy is a fixed set of queries, whose results provide an upper bound on what information may be released to the user. To provide userdependent policies, we allow queries to depend on what secrets the user knows. We have used our prototype implementation to check several programs representative of the data-centric Web applications that are common today.
Evaluation of Synthetically Generated Airborne Image Datasets using Feature Detectors as Performance Metric

The use of synthetic datasets to develop, prototype and qualify new computer vision algorithms is currently not widely accepted, though highly sought after by the industry. This is due to lack of knowledge on how the results acquired with such datasets will transfer to real live performance. Therefore, this paper introduces an approach to evaluate modelled synthetic datasets against their real counterparts. In a use case, the performance of common feature detectors is evaluated using the repeatability metric against real and synthetic datasets. Based on resulting performances; general usability, rendering techniques and modelling efforts for generation of synthetic datasets are discussed.
Sub-millimeter T2 weighted fMRI at 7 T: comparison of 3D-GRASE and 2D SE-EPI

Functional magnetic resonance imaging (fMRI) allows studying human brain function non-invasively up to the spatial resolution of cortical columns and layers. Most fMRI acquisitions rely on the blood oxygenation level dependent (BOLD) contrast employing T*2 weighted 2D multi-slice echo-planar imaging (EPI). At ultra-high magnetic field (i.e., 7 T and above), it has been shown experimentally and by simulation, that T2 weighted acquisitions yield a signal that is spatially more specific to the site of neuronal activity at the cost of functional sensitivity. This study compared two T2 weighted imaging sequences, inner-volume 3D Gradient-and-Spin-Echo (3D-GRASE) and 2D Spin-Echo EPI (SE-EPI), with evaluation of their imaging point-spread function (PSF), functional specificity, and functional sensitivity at sub-millimeter resolution. Simulations and measurements of the imaging PSF revealed that the strongest anisotropic blurring in 3D-GRASE (along the second phase-encoding direction) was about 60% higher than the strongest anisotropic blurring in 2D SE-EPI (along the phase-encoding direction). In a visual paradigm, the BOLD sensitivity of 3D-GRASE was found to be superior due to its higher temporal signal-to-noise ratio (tSNR). High resolution cortical depth profiles suggested that the contrast mechanisms are similar between the two sequences, however, 2D SE-EPI had a higher surface bias owing to the higher T*2 contribution of the longer in-plane EPI echo-train for full field of view compared to the reduced field of view of zoomed 3D-GRASE.
Keeping and Losing Contrasts

In this paper, I will first show that contrasts aren't licensed in particular contexts because they are necessarily realized with more salient cues in those contexts than in contexts where they aren't licensed. Next, I will argue that the releases of consonants, particularly non-continuants, are not packages of particularly salient acoustic information about the consonant's identity but instead only one of many kinds of acoustic information about how the utterance is segmented prosodically. Prosodic segmentation helps the listener find words in the stream of speech. Finally, I will show that segments are usually perceived to be different from their neighbors, i.e. to contrast with them, except when the target sound is C 1 in a CVC 1 C 2 V string, which is instead often perceived to be the same as C 2 , i.e. to assimilate to C 2 . Both the general and specific effects of neighboring sounds phonetically explain where contrasts are kept or lost quite differently than the licensing by cue account.
Hamming Compatible Quantization for Hashing

Hashing is one of the effective techniques for fast Approximate Nearest Neighbour (ANN) search. Traditional single-bit quantization (SBQ) in most hashing methods incurs lots of quantization error which seriously degrades the search performance. To address the limitation of SBQ, researchers have proposed promising multi-bit quantization (MBQ) methods to quantize each projection dimension with multiple bits. However, some MBQ methods need to adopt specific distance for binary code matching instead of the original Hamming distance, which would significantly decrease the retrieval speed. Two typical MBQ methods Hierarchical Quantization and Double Bit Quantization retain the Hamming distance, but both of them only consider the projection dimensions during quantization, ignoring the neighborhood structure of raw data inherent in Euclidean space. In this paper, we propose a multi-bit quantization method named Hamming Compatible Quantization (HCQ) to preserve the capability of similarity metric between Euclidean space and Hamming space by utilizing the neighborhood structure of raw data. Extensive experiment results have shown our approach significantly improves the performance of various state-of-the-art hashing methods while maintaining fast retrieval speed.