Miracle-dz/UniFAR-FTUs huggingface dataset:
A more accurate analytical model on blocking probability of multicast networks

Multicast communication is one of the most important collective communication operations and is highly demanded in telecommunication environments and scalable parallel and distributed computing systems. In this paper, we consider the issue of supporting multicast in the widely used a three-stage Clos network or /spl upsi/(m,n,r) network. We improve a previously proposed analytical model (Yang and Wang 1998) for the blocking probability of the /spl upsi/(m,n,r) multicast network by introducing more reasonable assumptions based on the properties of multicast communication and the Clos network. We also compare the improved analytical model with the simulation results under three typical routing control strategies. As can be seen, the improved model matches better with the simulation results and further confirms that a /spl upsi/(m,n,r) network with a comparable cost to a permutation network is almost nonblocking for multicast connections.
Investigations of Online/Offline Tests for Integrated Sensors in the Operating Phase

The integration of analog and/or digital circuits into more fields of application has increased the demand for reliability verification of a system in the operating phase. It is known that sensor parameter variation in analog circuits leads to faulty results. Determining whether an operation point is to be tolerated needs to be handled in the operation phase. The proposed methods achieve high fault coverage. A temperature measurement system is the example used in this experiment. A comparison is done with other test methods, supply current supervision and the correlation of the output voltage to the supply current. Key-Words: online test, offline, test, operation phase, Idd supervision
Context-Awareness and Learning Fusion Method of Fluid Property Sensor Networks Based on Fuzzy Entropy

Fluid sensor network is very difficult to make context-awareness and learning fusion because there is a variety of complex dynamic uncertainties involved ranging from information redundancy, information complementary, to information instability. This paper introduces a fuzzy entropy method into context-awareness and learning fusion method of fluid property sensor networks. First, the architecture of fluid property sensor network is analyzed, and based on it the context characteristics are described. Second, by the introduction of fuzzy entropy, the learning fusion method of fluid property sensor networks is proposed, where the fusion hierarchy of context information is discussed and the fusion algorithm is also illustrated. Third, an example is presented for verification of the proposed model, where the multiple sensor information fusion based on fuzzy logic analysis method can effectively tackle uncertain information. At last, some interesting conclusions are carried out and future researching directions are also indicated at the end of the paper.
Evaluation of three neural network models using Wisconsin breast cancer database

A major class of problems in medical science involves the diagnosis of a disease based upon various tests performed upon the patient. Cancer is a complex and clinical heterogeneous disease. The research into the diagnosis and treatment of cancer has become an important issue for the scientific community. The objective of cancer classification is to design a classifier to categorize the tissue samples into pre-defined classes (e.g. tumor and normal) using the gene expression levels produced by micro array techniques. The automatic diagnosis of breast cancer is an important, real-world medical problem. This paper evaluates the three neural network models namely 1. Adaptive Resonance Theory Based Neural Network (ART), 2. Self Organizing Map Based Neural Network (SOM) and 3.Back Propagation Neural network (BPN) using the Wisconsin breast cancer database.
Review on Vision based Human Activity Analysis

Recognizing human actions are important in various real time applications. Review on human activity analysis is provided in three sections. The first section in this paper presents an overall classification to Human activity analysis from feature extraction to recognition systems. In the second section a survey is included which provides technical information to activity analysis. Finally a brief description of databases which came across in survey is also included. The overall purpose of this paper is to provide a basic understanding to human activity analysis and to analyze the major challenge in human activity analysis. General Terms Human Activity Recognition (HAR), Feature Extraction, Feature Representation, Classification, Recognition
Optimal Set of 360-Degree Videos for Viewport-Adaptive Streaming

With the decreasing price of Head-Mounted Displays (HMDs), 360-degree videos are becoming popular. The streaming of such videos through the Internet with state of the art streaming architectures requires, to provide high immersion feeling, much more bandwidth than the median user's access bandwidth. To decrease the need for bandwidth consumption while providing high immersion to users, scientists and specialists proposed to prepare and encode 360-degree videos into quality-variable video versions and to implement viewport-adaptive streaming. Quality-variable versions are different versions of the same video with non-uniformly spread quality: there exists some so-called Quality Emphasized Regions (QERs). With viewport-adaptive streaming the client, based on head movement prediction, downloads the video version with the high quality region closer to where the user will watch. In this paper we propose a generic theoretical model to find out the optimal set of quality-variable video versions based on traces of head positions of users watching a 360-degree video. We propose extensions to adapt the model to popular quality-variable version implementations such as tiling and offset projection. We then solve a simplified version of the model with two quality levels and restricted shapes for the QER. With this simplified model, we show that an optimal set of four quality-variable video versions prepared by a streaming server, together with a perfect head movement prediction, allow for 45% bandwidth savings to display video with the same average quality as state of the art solutions or allows an increase of 102% of the displayed quality for the same bandwidth budget.
Perfusion computed tomography in patients with stroke thrombolysis

See Saver (doi:10.1093/awx020) for a scientific commentary on this article. The extent to which CT perfusion imaging variables before and after reperfusion therapy for stroke predict the length of subsequent disability-free life is unclear. Kawano et al. show that saving a millilitre of penumbra translates into benefits equivalent to more than a week of disability-free life.
Electrochemistry-based maskless nanofabrication

A review of technological approaches for 2D and 3D nanostructuring of semiconductor compounds by using radiation treatment and electrochemical etching is presented. We demonstrate novel spatial nanoarchitectures based on III-V and II-VI compounds as well as two-dimensional metallo-dielectric structures realized in different geometries. It is shown that photoelectrochemical etching of GaN combined with preliminary low-dose low-energy focused-ion-beam treatment of the sample surface allows one to fabricate in a controlled fashion arrays of nanowires and nanowalls as well as ultrathin membranes and supporting nanocolumns in the same technological route. Possible electronic and photonic applications of the elaborated nanostructures are discussed.
A Self-Tuning Configurable Cache

The memory hierarchy of a system can consume up to 50% of microprocessor system power. Previous work has shown that tuning a configurable cache to a particular application can reduce memory subsystem energy by 62% on average. We introduce a self-tuning cache that performs transparent runtime cache tuning, thus relieving the application designer and/or compiler from predetermining an application's cache configuration. The self-tuning cache applies tuning at a determined tuning interval. A good interval balances tuning process energy overhead against the energy overhead of running in a sub-optimal cache configuration, which we show wastes much energy. We present a self-tuning cache that dynamically varies the tuning interval, resulting in average energy reduction of as much as 29%, falling within 13% of an oracle-based optimal method.
Integration of Fuzzy C-Means with Correlation Template and Active Contour for Brain Lesion Segmentation in Diffusion-Weighted MRI

This study proposed automatic detection and segmentation of brain lesion in diffusion-weighted magnetic resonance images (DWI) based on Fuzzy C-Means (FCM). Due to noises and intensity inhomogeneity, FCM technique fails in producing accurate results. Active contour and correlation template are integrated to overcome this problem. The brain lesions are acute stroke and solid tumor foe hyperintense lesions, and necrosis and chronic stroke for hypointense lesions. The proposed analysis framework has been validated by using Jaccard (AO), Dice, false negative rate (FNR) and false positive rate (FPR). FCM with correlation template provides more accurate results compared with FCM with active contour. The results are 0.547, 0.258, 0.192 and 0.687 for Jaccard, FPR, FNR and Dice indices. This method also can segment the lesions precisely.
Trends in dermoscopy use in the UK: results from surveys in 2003 and 2012

Background: Dermoscopy is a useful tool to aid diagnosis of pigmented and non-pigmented skin lesions, as well as many other dermatological conditions. Use of dermoscopy is increasing worldwide, but to date, there are no reported data on attitudes of dermatologists in the United Kingdom (UK) towards dermoscopy. Objective: To determine current attitudes of UK dermatologists towards dermoscopy and assess how these attitudes have changed over the last decade. Methods: In October 2012, an online survey was sent to members of British Association of Dermatologists over a 12-week period. Data were subsequently compared with data from a similar UK nationwide paper questionnaire distributed to members in 2003. Results: The 2003 survey collected 292 responses (uptake 42%), and in 2012 there were 209 responses (22%), predominantly from consultants and registrars. In 2012, 86% respondents reported increased use of dermoscopy over the previous decade with 98.5% of respondents reporting regular clinical use of dermoscopy, compared with 54% in 2003. Overall, 81% respondents in 2012 had received dermoscopy training, mainly from UK-based courses (62% of respondents) but increasingly via Internet-based resources (30% vs. 7% in 2003). However, 39% respondents lacked confidence when making a diagnosis based on their interpretation of dermoscopy findings. Conclusions: Over the last decade, use of dermoscopy has increased amongst UK dermatologists and the majority of respondents now employ dermoscopy in daily clinical practice. However, the use of dermoscopy in the dermatology community overall is not known and for those individuals there is a continued need for education.
Recombinant Mycobacterium bovis BCG Expressing the Sm14 Antigen of Schistosoma mansoni Protects Mice from Cercarial Challenge

ABSTRACT The Sm14 antigen of Schistosoma mansoni was cloned and expressed in Mycobacterium bovis BCG as a fusion with the Mycobacterium fortuitum β-lactamase protein under the control of its promoter, pBlaF*; the protein was localized in the bacterial cell wall. The rBCG-Sm14 strain was shown to be relatively stable in cultured murine and bovine monocytes in terms of infectivity, bacterial persistence, and plasmid stability. The immunization of mice with rBCG-Sm14 showed no induction of anti-Sm14 antibodies; however, splenocytes of immunized mice released increased levels of gamma interferon upon stimulation with recombinant Sm14 (rSm14), indicating an induction of a Th1-predominant cellular response against Sm14. Mice immunized with one or two doses of rBCG-Sm14 and challenged with live S. mansoni cercaria showed a 48% reduction in worm burden, which was comparable to that obtained by immunization with three doses of rSm14 purified from Escherichia coli. The data presented here further enhance the status of Sm14 as a promising candidate antigen for the control of schistosomiasis and indicate that a one-dose regimen of rBCG-Sm14 could be considered a convenient means to overcome many of the practical problems associated with the successful implementation of a multiple-dose vaccine schedule in developing countries.
Cross-Sectional Study of Hepatitis A Virus Infection in the Pantanal Population before Vaccine Implementation in Brazil: Usage of Non-Invasive Specimen Collection

Population-based prevalence studies are essential tools for screening of hepatitis A and provide important data on susceptible groups. However, surveillance in isolated communities is difficult because of the limited access to these areas and the need for blood sample collection. This study aimed to determine the anti-HAV prevalence using oral fluid samples to provide an alternative tool for epidemiological studies that might be useful for vaccination-related decisions. The study population was composed of 224 volunteers from South Pantanal, aged 3 to 86 years old. This study was performed using oral fluids, previously standardized for anti-HAV antibody detection, which were collected using a ChemBio device. Eluates were tested using modified commercial EIA to detect anti-HAV antibodies. The overall prevalence was 79.1%, corresponding to 178 reactive EIA tests out of 224 samples. The age stratified data revealed a prevalence of 47.8% between 0–10 years, 84% in 11–20 years and 91.9% in subjects older than 21 years. Results indicate that hepatitis A prevalence was higher in adolescents and adults, corroborating the literature reports. Thus, oral fluid samples could replace serum in HAV epidemiological studies in isolated communities as they are efficient at detecting anti-HAV antibodies.
Improving the selective cancer killing ability of ZnO nanoparticles using Fe doping

Abstract This work reports a new method to improve our recent demonstration of zinc oxide (ZnO) nanoparticles (NPs) selectively killing certain human cancer cells, achieved by incorporating Fe ions into the NPs. Thoroughly characterized cationic ZnO NPs (∼6 nm) doped with Fe ions (Zn1-x Fe x O, x = 0–0.15) were used in this work, applied at a concentration of 24 μg/ml. Cytotoxicity studies using flow cytometry on Jurkat leukemic cancer cells show cell viability drops from about 43% for undoped ZnO NPs to 15% for ZnO NPs doped with 7.5% Fe. However, the trend reverses and cell viability increases with higher Fe concentrations. The non-immortalized human T cells are markedly more resistant to Fe-doped ZnO NPs than cancerous T cells, confirming that Fe-doped samples still maintain selective toxicity to cancer cells. Pure iron oxide samples displayed no appreciable toxicity. Reactive oxygen species generated with NP introduction to cells increased with increasing Fe up to 7.5% and decreased for >7.5% doping.
Stress syndromes.

Some methodological problems in epidemiological research on stress syndromes are reviewed. It is pointed out that syndromes defined on the basis of physiological findings may be difficult to relate to psychosocial problems. This is exemplified by the early asymptomatic stages of hypertension. Men with this syndrome seem to report fewer life problems than other men. This could be due to systematic underreporting. It is shown that different illnesses may have different psychosocial correlates. Research has shown that early endocrinological response patterns during stress periods may be beneficial in the long-term perspective.
Optimized Rollback and Re-computation

Large data processing tasks can be effected using workflow management systems. When either the input data or the programs in the pipeline are modified, the workflow must be re-executed to ensure that the final output data is updated to reflect the changes. Since such re-computation can consume substantial resources, optimizing the system to avoid redundant computation is desirable. In the case of a workflow, the dependency relationships between files are specified at the outset and can be leveraged to track which programs need to be re-executed when particular files change. Current distributed systems cannot provide such functionality when no predefined workflows exist. In this paper, we present an architecture that provides functionality to produce both correct output as well as fast re-execution by leveraging the provenance of data to propagate changes along an implicit dependency graph. We explore the tradeoff between storage and availability by presenting a performance analysis of our rollback and re-execution scheme.
Multi‐parametric phospho‐flow cytometry: A crucial tool for T lymphocyte signaling studies

Tools such as protein immunoblotting have proven benefits for investigating T lymphocyte signaling but have several drawbacks such as the number of cells required and the difficulty of distinguishing subset‐specific differences without expensive and invasive cell sorting. Recent advances in immunology and the identification of T lymphocyte sub‐populations making up only a very small fraction of the total population highlight the importance of studying signaling in those small subsets in a feasible, cost‐effective, high‐throughput manner. To this end, we have developed a simplified protocol to study both intracellular phosphorylation patterns of important signal transduction molecules concomitantly with T cell surface marker expression. A multi‐parametric analysis may allow the quantification of the phosphorylation of up to five signaling molecules in CD4 and CD8 T lymphocytes and their naïve, central memory, effector memory, and TEMRA subsets. This enables precise identification of subset‐specific signaling and alterations of signaling pathways in physiological and pathological situations. The importance of such detailed analysis is discussed. © 2013 International Society for Advancement of Cytometry
Agent-based resource management in hybrid wireless networks

Wireless networks are no more used for transfer of only voice but now also support data, video and multimedia. The key issue is to provide these services and maintain quality of service (QoS) in hybrid wireless networks. A very interesting problem is managing radio resources in a hybrid wireless network. The paper presents an agent-based scheme for efficient management of radio resources in hybrid wireless networks. Performance of the proposed scheme is measured in terms of successful handover rate between different wireless network architectures (e.g., WLAN, cellular), and also by the allocated bandwidth to admitted calls. Simulation results show that the proposed agent-based approach provides a 10% increase in the average allocated bandwidth obtained with conventional resource management schemes.
A high performance frequency offset estimator for OFDM

This paper proposes a simple method to enlarge the estimation range of conventional carrier frequency offset (CFO) estimation methods based on correlations among the identical parts of the preamble. A novel preamble is designed, which is composed of one regular OFDM training block with even numbers of identical parts and one irregular OFDM training block with odd numbers of identical parts. The initial estimates obtained over the two training blocks are next exploited to jointly estimate the CFO. By elaborately selecting the numbers of identical parts for the two training blocks, the proposed CFO estimator can estimate frequency offset over tens of the subcarrier spacing. Simulation results showed that the proposed CFO estimator satisfies the estimate range requirement for the practical OFDM systems, while achieving a very good estimate performance.
Polynomial Learning of Distribution Families

The question of polynomial learn ability of probability distributions, particularly Gaussian mixture distributions, has recently received significant attention in theoretical computer science and machine learning. However, despite major progress, the general question of polynomial learn ability of Gaussian mixture distributions still remained open. The current work resolves the question of polynomial learn ability for Gaussian mixtures in high dimension with an arbitrary fixed number of components. Specifically, we show that parameters of a Gaussian mixture distribution with fixed number of components can be learned using a sample whose size is polynomial in dimension and all other parameters. The result on learning Gaussian mixtures relies on an analysis of distributions belonging to what we call “polynomial families” in low dimension. These families are characterized by their moments being polynomial in parameters and include almost all common probability distributions as well as their mixtures and products. Using tools from real algebraic geometry, we show that parameters of any distribution belonging to such a family can be learned in polynomial time and using a polynomial number of sample points. The result on learning polynomial families is quite general and is of independent interest. To estimate parameters of a Gaussian mixture distribution in high dimensions, we provide a deterministic algorithm for dimensionality reduction. This allows us to reduce learning a high-dimensional mixture to a polynomial number of parameter estimations in low dimension. Combining this reduction with the results on polynomial families yields our result on learning arbitrary Gaussian mixtures in high dimensions.
Discriminant Projective Non-Negative Matrix Factorization

Projective non-negative matrix factorization (PNMF) projects high-dimensional non-negative examples X onto a lower-dimensional subspace spanned by a non-negative basis W and considers WT X as their coefficients, i.e., X≈WWT X. Since PNMF learns the natural parts-based representation Wof X, it has been widely used in many fields such as pattern recognition and computer vision. However, PNMF does not perform well in classification tasks because it completely ignores the label information of the dataset. This paper proposes a Discriminant PNMF method (DPNMF) to overcome this deficiency. In particular, DPNMF exploits Fisher's criterion to PNMF for utilizing the label information. Similar to PNMF, DPNMF learns a single non-negative basis matrix and needs less computational burden than NMF. In contrast to PNMF, DPNMF maximizes the distance between centers of any two classes of examples meanwhile minimizes the distance between any two examples of the same class in the lower-dimensional subspace and thus has more discriminant power. We develop a multiplicative update rule to solve DPNMF and prove its convergence. Experimental results on four popular face image datasets confirm its effectiveness comparing with the representative NMF and PNMF algorithms.
Aggregating signatures of MPEG-4 elementary streams

A complete MPEG-4 stream consists of many elementary streams, which may be generated by different authors. In the scenario of this paper, each author signs his own authentic elementary stream independently, and then an untrusted distributor aggregates these signatures into only one. Based on the unique signature, a client is able to verify the received MPEG-4 stream with the certificates of all the authors other than the certificate of the distributor. In addition, each author cannot deny what he has signed even if he is willing to admit a signature on another ES. This aggregated signature scheme is efficient in terms of transmission overhead and verification time since only one signature is processed in the client side.