Miracle-dz/UniFAR-FTUs huggingface dataset:
Statistical inverse analysis and stochastic modeling of transition

A computational method is introduced to infer statistical information on boundarylayer perturbations upstream of laminar-turbulent transition in a supersonic boundary layer. The method uses the intermittency function as a basis, which specifies the amount of time a flow is turbulent at a given streamwise location. The methods yields a joint probability density function for amplitude and frequency of boundary-layer perturbations upstream of transition. It relies on linear stability theory to link the probability density function with the intermittency. In order to infer parameters governing the function, we perform a statistical inverse analysis using the Markov chain Monte Carlo method. The approach is applied to a synthetic test case and to experimental data. The knowledge of the location of laminar to turbulent transition is essential for numerous engineering applications. For a hypersonic vehicle, an accurate prediction of the transition location may allow to precisely define the dimensions of the vehicle’s thermal protection system. Transition does not only affect the performance of an atmospheric re-entry vehicle, it also plays a key role in the safety of the vehicle and its payload. The development of credible engineering models for the prediction of laminar-turbulent transition is therefore an important task. The transition location depends on a large number of parameters and is, consequently, difficult to predict. In particular, perturbations in the boundary layer upstream of transition are known to affect the transition process. Except in studies performed within a carefully controlled laboratory environment, transition is a highly random process due to its strong sensitivity to these perturbations. Instead of trying to determine fixed values of the most important governing parameters, in particular those relating to boundary-layer perturbations, we should characterize them in a statistical way in order to account for the stochastic nature of transition. The laminar-turbulent transition process may be divided into three stages: receptivity, linear disturbance evolution and nonlinear breakdown to turbulence. Most transition modeling approaches are deterministic and rely on empirical data (Langtry & Menter 2009), but they do not capture the stochastic nature of the physical processes active during these stages. Only the nonlinear breakdown stage, including the formation of turbulent spots, has been modeled in a probabilistic way (Vinod & Govindarajan 2004; Pyecnik et al. 2011).
The temporal characteristics of the TeV Gamma emission from Mkn 501 in 1997 - Part II: Results from HEGRA CT1 and CT2

We present the results on the TeV gamma emission from Mkn 501 in 1997 obtained with the standalone Cherenkov telescopes CT1 and CT2 (threshold > 1.2 and > 1.0 resp.) of the HEGRA collaboration. The CT1 lightcurve has the most complete coverage of all TeV observations of Mkn 501 in 1997 due to the additional observations made under the presence of moonlight. CT2 - at the time of these observations a second generation Cherenkov telescope with relatively low imaging resolution - is a well tested instrument and its 85 hours of observational data on Mkn 501 in 1997 are useful for consistency checks and provide some additional daily data points. The Mkn 501 lightcurve data show significant correlation with RXTE ASM X-ray data consistent with no time-lag. The spectral analysis shows a steepening spectrum extending beyond 10 TeV. No change of the spectral slope with the variation of the intensity was found. The results presented here are consistent with the results from the HEGRA Cherenkov telescope system presented in part I of this paper.
Model-based Pose Estimation in an Urban Environment

This paper addresses pose estimation problem for augmented reality applications in an urban environment. The pose is estimated based on the known wire-frame models of buildings and line segments detected on the images. One of key problems for model-based pose estimation is to establish the correspondence between the model and detected features. The main contribution of this paper is a robust matching method that combines geometric and intensity constraints to establish accurate correspondence for pose estimation. Our pose estimation method is based on the EKF framework and the experiments show that the proposed method can estimated camera pose reliably even for abrupt and large inter motions.
Enhanced Partial Expansion A

When solving instances of problem domains that feature a large branching factor, A* may generate a large number of nodes whose cost is greater than the cost of the optimal solution. We designate such nodes as surplus. Generating surplus nodes and adding them to the OPEN list may dominate both time and memory of the search. A recently introduced variant of A* called Partial Expansion A* (PEA*) deals with the memory aspect of this problem. When expanding a node n, PEA* generates all of its children and puts into OPEN only the children with f = f (n). n is re-inserted in the OPEN list with the f -cost of the best discarded child. This guarantees that surplus nodes are not inserted into OPEN. In this paper, we present a novel variant of A* called Enhanced Partial Expansion A* (EPEA*) that advances the idea of PEA* to address the time aspect. Given a priori domain- and heuristic- specific knowledge, EPEA* generates only the nodes with f = f(n). Although EPEA* is not always applicable or practical, we study several variants of EPEA*, which make it applicable to a large number of domains and heuristics. In particular, the ideas of EPEA* are applicable to IDA* and to the domains where pattern databases are traditionally used. Experimental studies show significant improvements in run-time and memory performance for several standard benchmark applications. We provide several theoretical studies to facilitate an understanding of the new algorithm.
FUZZY RULE BASED CLASSIFIER FOR SOFTWARE QUALITY DATA

Software quality estimation based on measured attributes from previous similar products is an active field of research. Such estimation models must inevitably handle imprecision and uncertainty and hence soft computing techniques are gaining popularity. This paper presents a Fuzzy rule based classifier for software quality data and its performance is compared with Bayesian classifier. The fuzzy rules have been generated using Fuzzy C-Means clustering. The objectives of this paper are threefold. First, Fuzzy C-Means algorithm is applied to a set of Software Quality data and clusters are generated. The nearest data points to each cluster centroid are used to generate optimum set of fuzzy rules which are refined with the help of train data. These rules are used to label the clusters generated by Fuzzy C-Means algorithm Second, these fuzzy rules are used to classify the test data. Third, naïve Bayes classification method is applied to classify the test data. Confusion matrix is generated and results show that the performance of both the classification methods is comparable.
SIMPLE METHODS FOR "STAINING WITH LEAD" AT HIGH pH IN ELECTRON MICROSCOPY

The lead hydroxide stain of Watson (1958) used for increasing contrast in thin sections for electron microscopy has found acceptance in many laboratories. However, this stain has an unfortunate tendency to form precipitates (probably of lead carbonate (5)) on exposure to the air, thus contaminating the sections and irritating the observer. This drawback has led to the development of several modifications (2, 3) of the original method of staining and the use of ingenious devices (4, 5) for preventing exposure to air and consequent precipitate formation. We offer the following alternative methods which, we believe, are simpler to perform than those hitherto described. They have the additional advantages mentioned below. The methods are based on the observation that highly alkaline solutions of lead salts (pH > 11.5) yield relatively stable solutions which stain rapidly and intensely, thus obviating the hazard of precipitation to a marked degree. The methods have these additional advantages: the staining solutions are easily and rapidly prepared, are simply stored, and are stable for long periods of time. Furthermore, they can be efficiently used, many grids being treated simultaneously, without excessive precautions being taken against lead carbonate precipitation. Finally, "difficult" material, embedded in media which characteristically yield rather low contrast, such as epoxide resins, can be rapidly and easily stained. "C lean" preparations, of high contrast, are routinely obtained. As will be discussed later, it is thought that in these highly alkaline staining solutions lead is present as an hydroxide complex anion (plumbite ion) and that this anion is responsible for the staining. The methods of preparation are based on this hypothesis. Two methods for preparing the staining solutions have been found useful:
Maximising Business Returns to Corporate Social Responsibility Communication: An Empirical Test

Based on a classic conceptual model of corporate social responsibility (CSR) communication, we developed an empirical research study to test how several aspects of CSR message content (i.e., issue importance, impact, motives, fit, commitment) are associated with external support responses (i.e., purchase, advocacy). We also tested the moderating role of stakeholder&#8208; and company&#8208;specific factors (i.e., issue support and industry, respectively) in the proposed model. Data were collected from 302 participants who evaluated the same CSR information displayed in the websites of a fictitious bank and a fictitious restaurant chain. The findings suggest that better perceptions on how the CSR message reinforces issue importance, corporate CSR impact and altruistic motives lead to higher purchase and advocacy intentions. CSR fit is related only to advocacy, while CSR commitment does not have any significant impact on participants&#8217; responses. Some new interdependence relationships are also identified among issue importance, motives, fit, and commitment. The moderating role of issue support and industry is confirmed.
An Implementation of Object-Based Storage System Access Control Based on IBE

Object-based Storage System (OBSS) is the ideal solution to improve performance of large-scale storage systems by virtue of distributed storage architecture. However, existing OBSS adopts complex security scheme, and takes little consideration on how to reduce the overhead of storage security. Most of traditional Access Control Based on Certificate (ACBC) will generate abundant certificate, which will lend to the Meta-Data Server (MDS) overload. So we introduce Identity-Based Encryption (IBE) to the OBSS, and proposed a novel Access Control Based on IBE (ACBI). In ACBI the public key can be calculated according with their identity, and need not maintain public key certificate for each entity. Thereby ACBI can significantly reduce the overhead of certificate management. At the same time, ACBI associated the access control list (ACL) with the object, the users will not required to ask the MDS for authorization, they can directly access the storage device. The storage system depends on user’s identity to authenticate user's access permission, which simplifying the access control process. The experiment results demonstrated that security overhead of ACBI on MDS is only 48.7% in comparison to ACBC. Meanwhile, ACBI made the security module load of OBSS only employ 74.5% response time compared to ACBC.
Depolarization of polymorphonuclear leukocytes by Porphyromonas (Bacteroides) gingivalis 381 in the absence of respiratory burst activation

Bacteroides spp. may contribute to the chronicity of mixed infections by affecting the normal functions of polymorphonuclear leukocytes (PMN). This study evaluated the physiologic and biochemical responses of human peripheral blood PMN to a variety of strains of the oral periodontal pathogen Porphyromonas (Bacteroides) gingivalis. Strain 381 and ATCC type strain 33277 caused rapid and lasting depolarization of the electrochemical potential that exists across the PMN membrane by a mechanism that was independent of activation of the pertussis toxin-sensitive G protein or protein kinase C. Membrane depolarization did not initiate increases in intracellular calcium or respiratory burst activation, and activity was not inhibited by surface proteolysis or sugars. However, membrane depolarization was associated with inhibition of PMN responses to the chemotactic peptide N-formylmethionyl leucyl phenylalanine. Membrane-depolarizing activity was isolated with the outer membrane of strain 381 by surface extraction of the bacteria by using Zwittergent 3,14, followed by Sephacryl S-200 gel filtration chromatography. The partially purified outer membrane components were heat stable, were not inhibited by tosyl-lysine chloromethyl ketone, and inhibited N-formylmethionyl leucyl phenylalanine-stimulated superoxide production. The results suggest that outer membrane components of P. gingivalis 381 and 33277 have porinlike activity that can depolarize PMN membranes and immobilize PMN responses to chemotactic peptides. This may prove to be an important virulence characteristic of these strains.
An overview of limited feedback in wireless communication systems

It is now well known that employing channel adaptive signaling in wireless communication systems can yield large improvements in almost any performance metric. Unfortunately, many kinds of channel adaptive techniques have been deemed impractical in the past because of the problem of obtaining channel knowledge at the transmitter. The transmitter in many systems (such as those using frequency division duplexing) can not leverage techniques such as training to obtain channel state information. Over the last few years, research has repeatedly shown that allowing the receiver to send a small number of information bits about the channel conditions to the transmitter can allow near optimal channel adaptation. These practical systems, which are commonly referred to as limited or finite-rate feedback systems, supply benefits nearly identical to unrealizable perfect transmitter channel knowledge systems when they are judiciously designed. In this tutorial, we provide a broad look at the field of limited feedback wireless communications. We review work in systems using various combinations of single antenna, multiple antenna, narrowband, broadband, single-user, and multiuser technology. We also provide a synopsis of the role of limited feedback in the standardization of next generation wireless systems.
Navigating the Blue Waters : Online Failure Prediction in the Petascale Era

At the scale of todays large scale systems, fault tolerance is no longer an option, but a necessity. As the size of supercomputers increases, so does the probability of a single component failure within a time frame. With a system MTBF of less than one day and predictions that future systems will experience delays of couple of hours between failures, current fault tolerance strategies face serious limitations. Checkpointing is currently an area of significant research, however, even when implemented in a satisfactory manner, there is still a considerable loss of computation time due to frequent application roll-backs. With the growing operation cost of extreme scale supercomputers like Blue Waters, the act of predicting failures to prevent the loss of computation hours becomes cumbersome and presents a couple of challenges not encountered for smaller systems. In this paper, we present a novel methodology for truly online failure prediction for the Blue Water system. We analyze its results and show that some failures types can be predicted with over 60% recall and a precision of over 85%. The failures wich represent the main bottlenecks are discussed in detail and possible solutions are proposed by investigating different prediction methods. We show to what extent online failure prediction is a possibility at petascale and what are the challenges in achieving an effective fault prediction mechanism for Blue Waters.
The Contingent Valuation of Natural Parks: Assessing the Warmglow Propensity Factor

Part 1 Economic valuation, warmglow and preference modelling: values, valuation method and contingent valuation - an overview -the concept of total economic value, monetary valuation methods, the contingent valuation methodology, CV theoretical underpinning, conclusions evaluating CV measurement validity - separating the light from the heat - CV survey instrument and potential measurement bias, Hausman critics - the embedding valuation phenomena, the Kahnerman and Knetsch theory for CV embedding - the purchase of "moral satisfaction", conclusions modelling consumer preferences - the multiattribute model formulation, modelling individual consumer strategic behaviour - the Nash-Cournot equilibria and pareto optimality, exploring the properties of the voluntary contributions, conclusions. Part 2 Survey design and implementation: preliminary design research - scenario formulation, key design issues, preliminary survey design research, conclusions structure of the final survey instrument -section A, section B, section C, section D, section E, conclusions survey execution - the sampling design, interviewer training, field supervision and data validation, conclusions. Part 3 Analysis of the survey results: descriptive analysis of the survey responses - opinion with respect to governmental policy action and nature conservation issues, WTP responses, demographic and socio-economic responses, interviewer assessment responses, conclusions nonparametric testing procedures of the stated WTP responses - the survey formats, stated WTP responses - testing procedures, conclusions a univariate estimation of the stated WTP responses - the model, estimated WTP, sensitivity analysis of the WTP estimates, conclusions operationalization of the consumer motivations - factor analysis, empirical results, conclusions the valuation function - empirical model specification, estimation of the valuation function, sensitivity analysis of the stated WTP responses with respect to the motivation factor scores, the warmglow valuation transmission effect, what does the empirical evidence say about the structure of preferences? , conclusions conclusions. Part 4 Appendices and bibliographic references.
Reduce positive and negative falses from attacks collected from the deployment of distributed honeypot network

Current tools and systems of detecting vulnerabilities simply alert the administrator of attempted attacks against his network or system. However, generally, the huge number of alerts to analyze and the amount time required to update security rules after analyzing alerts provides time and opportunity for the attacker to inflict damages. Moreover, most of these tools generate positive and negative falses, which may be important to the attacked network. Otherwise, many solutions exist such as IPS, but it shows a great defect due, fundamentally, to false positives. Indeed, attackers often make IPS block a legitimate traffic when they detect its presence in the attacked network. In this paper we describe an automated algorithm that gives the ability to detect attacks before they occurrence, then reduce positive and negative falses rates. Moreover, we use a set of data related to malicious traffic captured using a network of honeypots to recognize potential threats sources.
Modulation of NKG2D Expression in Human CD8+ T Cells Corresponding with Tuberculosis Drug Cure

Background Biomarkers predicting tuberculosis treatment response and cure would facilitate drug development. This study investigated expression patterns of the co-stimulation molecule NKG2D in human tuberculosis and treatment to determine its potential usefulness as a host biomarker of tuberculosis drug efficacy. Methods Tuberculosis patients (n = 26) were recruited in Lahore, Pakistan, at diagnosis and followed up during treatment. Household contacts (n = 24) were also recruited. NKG2D expression was measured by qRT-PCR in RNA samples both ex vivo and following overnight mycobacterial stimulation in vitro. Protein expression of NKG2D and granzyme B was measured by flow cytometry. Results NKG2D expression in newly diagnosed tuberculosis patients was similar to household contacts in ex vivo RNA, but was higher following in vitro stimulation. The NKG2D expression was dramatically reduced by intensive phase chemotherapy, in both ex vivo blood RNA and CD8+ T cell protein expression, but then reverted to higher levels after the continuation phase in successfully treated patients. Conclusion The changes in NKG2D expression through successful treatment reflect modulation of the peripheral cytotoxic T cell response. This likely reflects firstly in vivo stimulation by live Mycobacterium tuberculosis, followed by the response to dead bacilli, antigen-release and finally immunopathology resolution. Such changes in host peripheral gene expression, alongside clinical and microbiological indices, could be developed into a biosignature of tuberculosis drug-induced cure to be used in future clinical trials.
Face Recognition Based on WKPCA

Principal Component Analysis is acknowledged one of the most powerful techniques for feature extraction. As a nonlinear form of Principal Component Analysis, Kernel Principal Component Analysis has been applied abroad in face recognition in recent years. However, the principal component analysis and kernel principal component analysis treat each dimension feature of face images as equals in feature extraction. But in fact, different features play different roles in face recognition. In this paper, we adopt a Gaussian distribution function as a weight function, which can give prominence to the key features in face recognition. The proposed method is combined with the kernel principal component analysis to calculate the weighted subspace. Experimental results on the normal ORL face database show the proposed method is effective.
Leveraging Non-Lexical Knowledge for the Linked Open Data Web

The Linked Data paradigm introduces the possibility to share machine-readable data across numerous Web resources, thus enabling applications that are traditionally only possible in corporate intranets to be realized on a Web scale. Due to the creation of an increasing number of publicly available Linked Open Data resources, the Web of Data has become a major application area for semantic technologies. This work introduces a recently published data set LON of non-lexical entities (NLEs) that can be used for numerous tasks of quantitative modeling on the Semantic Web. The size of the published data increases the magnitude of the public Linked Data significantly, yet we show how it can be seamlessly integrated into current application architectures for the Web of Data.