Miracle-dz/UniFAR-FTUs huggingface dataset:
Model-based scaling of the streamwise energy density in high-Reynolds-number turbulent channels

Abstract We study the Reynolds-number scaling and the geometric self-similarity of a gain-based, low-rank approximation to turbulent channel flows, determined by the resolvent formulation of McKeon & Sharma (J. Fluid Mech., vol. 658, 2010, pp. 336–382), in order to obtain a description of the streamwise turbulence intensity from direct consideration of the Navier–Stokes equations. Under this formulation, the velocity field is decomposed into propagating waves (with single streamwise and spanwise wavelengths and wave speed) whose wall-normal shapes are determined from the principal singular function of the corresponding resolvent operator. Using the accepted scalings of the mean velocity in wall-bounded turbulent flows, we establish that the resolvent operator admits three classes of wave parameters that induce universal behaviour with Reynolds number in the low-rank model, and which are consistent with scalings proposed throughout the wall turbulence literature. In addition, it is shown that a necessary condition for geometrically self-similar resolvent modes is the presence of a logarithmic turbulent mean velocity. Under the practical assumption that the mean velocity consists of a logarithmic region, we identify the scalings that constitute hierarchies of self-similar modes that are parameterized by the critical wall-normal location where the speed of the mode equals the local turbulent mean velocity. For the rank-1 model subject to broadband forcing, the integrated streamwise energy density takes a universal form which is consistent with the dominant near-wall turbulent motions. When the shape of the forcing is optimized to enforce matching with results from direct numerical simulations at low turbulent Reynolds numbers, further similarity appears. Representation of these weight functions using similarity laws enables prediction of the Reynolds number and wall-normal variations of the streamwise energy intensity at high Reynolds numbers ( ${Re}_{\tau } \approx 1{0}^{3} {\unicode{x2013}} 1{0}^{10} $ ). Results from this low-rank model of the Navier–Stokes equations compare favourably with experimental results in the literature.
Surface–Volume–Surface Electric Field Integral Equation for Magneto-Quasi-Static Analysis of Complex 3-D Interconnects

A novel single-source surface-volume-surface integral equation is proposed for accurate broadband resistance and inductance extraction in 3-D interconnects. The new equation originates in the volume integral equation (VIE) traditionally used for magneto-quasi-static modeling of current flow in 3-D wires. The latter is reduced to a surface integral equation by representing the electric field inside each conductor segment as a superposition of cylindrical waves emanating from the conductor's boundary. As no approximation is utilized and all underlying boundary conditions and pertinent equations are satisfied in the reduction, the new integral equation is rigorously equivalent to the solution of the traditional volume electric field integral equation. The rigorous nature of the proposed single-source surface integral equation is corroborated numerically. In this paper, a detailed description of the method of moments discretization for the proposed surface integral equation is also presented. Numerical solution of the proposed surface integral equation for a 12-conductor bond-wire package is used to demonstrate the accuracy of the method and its computational benefits compared to the traditional solution based on the VIE.
A role dynamic management algorithm for prolonging lifetime in Wireless Sensor Networks

This article presents a role dynamic management algorithm for sensor devices. The network scenario considers a wireless sensor network in which each node can assume different roles. Specifically, attention is focused on two different roles: the active and the passive role. When a sensor device assumes an active role it uses a greater amount of energy greater than the passive role. The scope of this work is to introduce a dynamic management strategy to better distribute the energy in the network varying dynamically roles of the nodes under different mobility conditions. This dynamic algorithm is applied to an energy-efficient protocol based on a cross-layered approach (ESR and EMAC) and which ensures a good node lifetime. Two different algorithms have been developed to ensure a dynamic management of the node roles with the ESR and EMAC approach. Both these approaches have been evaluated through a simulation tool OMNeT++ and their performance have been compared with the original ESR and EMAC.
Ni 2 1 transport by the human Na 1 / Ca 2 1 exchanger expressed in Sf 9 cells

Egger, M. , A. Ruknudin, E. Niggli, W. J. Lederer, and D. H. Schulze. Ni21 transport by the human Na1/Ca21 exchanger expressed in Sf9 cells. Am. J. Physiol. 276 (Cell Physiol. 45): C1184–C1192, 1999. —The mechanism of Ni21 block of the Na1/Ca21 exchanger was examined in Sf9 cells expressing the human heart Na1/Ca21 exchanger (NCX1NACA1). As predicted from the reported actions of Ni21, its application reduced extracellular Na1-dependent changes in intracellular Ca21 concentration (measured by fluo 3 fluorescence changes). However, contrary to expectation, the reduced fluorescence was accompanied by measured 63Ni21 entry. The 63Ni21 entry was observed in Sf9 cells expressing the Na1/Ca21 exchanger but not in control cells. The established sequential transport mechanism of the Na1/Ca21 exchanger could be compatible with these results if one of the two ion translocation steps is blocked by Ni21 and the other permits Ni21 translocation. We conclude that, because Ni21 entry was inhibited by extracellular Ca21 and enhanced by extracellular Na1, the Ca21 translocation step moved Ni21, whereas the Na1 translocation step was inhibited by Ni21. A model is presented to discuss these findings.
A sybilproof distributed identity management for P2P networks

Structured P2P networks are vulnerable to the sybil attack. In this attack, a misbehaving person generates a huge number of node identifiers and possibly chooses some of them in order to disrupt availability or integrity in the P2P network. In order to circumvent this attack, one person should be able to obtain only a limited set of identifiers and should not be able to choose them. Moreover, due to the distributed architecture of P2P networks, this limitation should not be managed by a centralized system. In this paper, we propose such a sybilproof distributed identity management system which is based on invitations, and thus rely on social relationships between users.
Reasoning with Cause and Effect in Intrusion Detection

detection is an essential tool to protect hacking and unauthorized access in computer networks nowadays. Mechanisms used to attack keep evolving as the internet technology is improving. Hence, the task of differentiating authorized and unauthorized access has become more and more challenging. The modeling of network intrusion domain and causal reasoning for the intrusion detection has been proposed in this paper to address the security issues of a network. Bayesian network modeling with causal knowledge-driven approach has been selected for a network intrusion domain. Reasoning capabilities of Bayesian network have been adapted to perform detection and analysis in the domain.There are two main problems to be addressed in this paper: the first problem is to model the network intrusion domain and the second problem is to perform causal reasoning for intrusion detection and analysis. A methodology has been proposed to solve the two problems mentioned above. Intrusion detection is viewed as fault diagnosis in causal reasoning, and the analysis of the effect is viewed as fault prognosis. To address the first problem under causal knowledge-driven approach, we propose Bayesian network for the modeling of network intrusion domain. The second problem is addressed by applying the powerful reasoning capabilities of Bayesian network. The capabilities of causal reasoning using Bayesian network have not been fully discovered in the domain of intrusion detection. This research work is to bridge the gap.
Strategic 'morphing' and the survivability of e-commerce firms

This research develops an evolutionary game theory-motivated framework that helps academic researchers and industry practitioners to understand the "strategic morphing" of DotComs. Our framework applies an analogy from the theory of bio-diversity and genetic survivability in population ecology to different species in a highly competitive organic biome. In the e-commerce context, however, we emphasize survivability in terms of a set of characteristic categories and constructs that provide explanatory power for firm "strategic fitness" in competition with other firms and in the marketplace in general. We illustrate our framework using mini-case studies on DotCom success and failure.
European Exchange Option Pricing in Exponential Lévy Model

In this article we apply two methods to price the exchange options in exponential Levy model, where the jump parts of two driving processes are assumed to have mapping relations. One method is pricing under the minimal entropy martingale measure(MEMM) of 2-dimensional market, and the other one is Margrabe's method which has been examined to be correct for Black-Scholes model. We compare the results of two methods and find that Margrabe's method is incorrect for exponential Levy model with jumps except special states of the model.
Big data clustering validity

Nowadays we communicate in a digital universe. In fact the amount of data (structured and unstructured) is exploding. That's what we call Big Data. The voluminous data are in the most of cases noisy and overlapping, their clustering makes critical challenges. In addition validating resulting partitions is a serious problem. In this paper we present a new fuzzy validity index able to interpret the best partition of Big Data clustering. Called Fuzzy Validity Index with Noise-Overlap Separation (FVINOS), this new technique provides sufficient interpretation of the properties of the Big Data by detecting the overall geometric structure within and between clusters. The main contribution of FVINOS is to define a crisp and fuzzy clustering validation taking in account the structure of Big Data sets.
Unequal Initial Energy Assignment in Wireless Sensor Networks

In multi-hop clustering Wireless Sensor Networks (WSNs), Cluster Head (CH) role is rotated among sensor nodes in order to balance their residual energy usages. However, to forward data from CHs to sink or Base Station (BS), CH nodes that are placed near to the sink consume more energy than CH nodes that are further away. This is known as “Energy Hole” problem in WSNs. This paper proposes an algorithm called “Unequal Initial Energy Assignment” or UIEA to determine initial energy for sensors based on their distances to the sink. This unequal energy assignment is to assign different values of initial energy to sensors that belong to different regions in the network field. Since CHs perform data aggregation, this approach allows sensors to operate using same cluster sizes throughout deployed network field, which is one of the important requirements for some specific applications. Performance from simulation shows that UIEA can get more detail and precise data after aggregation by deploying smaller cluster size than average EC cluster size while the Stable Operation Period (SOP) is approximately the same as EC solution.
Biological mass spectrometry: a primer.

Biological polymers undergo numerous significant and fascinating interactions, such as post-translational modifications, non-covalent associations and conformational changes. A valuable parameter for the characterization of a biopolymer is molecular weight. Modern methods of mass spectrometry, including electrospray ionization and matrix-assisted laser desorption ionization mass spectrometry, are ideally suited for the accurate determination of the molecular weight of a biopolymer of interest. Molecular weight measurements are now routinely utilized in the qualitative and quantitative analysis of macromolecules. In many cases small sample quantities (i.e. a few micrograms) limit the utility of nuclear magnetic resonance spectroscopy and X-ray crystallography in obtaining structural information. Thus, mass spectrometry offers an attractive alternative to the more traditional bioanalytical methods for rapid and sensitive measurements. The ultimate goal of these experiments is to obtain sufficient information in order to map the complex molecular circuitry which operates within the cell. In the analysis of complex mixtures mass spectrometry is even more powerful when utilized in conjunction with separation methods. Herein we present some of the aspects of modern biological mass spectrometry for the investigation of large molecules. For more advanced or detailed technical descriptions we refer the reader to a number of recently published reports.
EXAMINING INFORMATION-GRAPHICS: EXTRACTING IMPORTANT INFORMATION FROM USER-WRITTEN QUERIES TO HELP DEVELOP AN EFFECTIVE RETRIEVAL SYSTEM

Information-graphics (non-pictorial graphics such as bar charts and line graphs that depict attributes of entities and relations among entities) have attracted increased attention from both the research and industrial worlds. While a lot of attention has been given in information retrieval research to retrieving textual documents, relatively little work has been done to retrieve information-graphics in response to user-written queries. Common image retrieval techniques, such as Image Meta search and Contentbased Image Retrieval (CBIR), work well for ranking ordinary images and pictures but often fall short when dealing with information-graphics since they do not take into account their structure and content. This research attempts to analyze a user’s query to identify the content of the independent and dependent axes of graphs that might be relevant to the query and to identify the kind of high-level message that would be conveyed by relevant graphs. Natural language processing techniques are used to extract features from the query and machine learning is used to build a model for hypothesizing the content of the axes and the intended message. Results have shown that the constructed models can achieve an accuracy of about 81%.
Transmit beamforming for frequency-selective channels with decision-feedback equalization

In this paper, we propose beamforming schemes for frequency-selective channels with decision-feedback equalization (DFE) at the receiver and with, respectively, perfect and quantized channel state information (CSI) at the transmitter. For beamforming with perfect CSI and infinite impulse response (IIR) beamforming Alters (BFFs) we provide a closed-form expression for the optimum BFFs. We also devise two efficient numerical methods for recursive calculation of the optimum finite impulse response (FIR) BFFs with perfect CSI. For beamforming with quantized CSI and finite-rate feedback channel, we propose a global vector quantization (GVQ) algorithm for codebook design. This algorithm is deterministic and independent of initial conditions and does not impose any constraints on the number of transmit and receive antennas, the antenna correlation, or the fading statistics. Our simulation results for typical GSM/EDGE channels show that in general short FIR BFFs are sufficient to closely approach the performance of IIR BFFs even in severely frequency-selective channels. Furthermore, finite-rate feedback beamforming with only a few feedback bits achieves significant performance gains over single-antenna transmission, transmit antenna selection, and optimized delay diversity in frequency-selective fading.
Exploratory 3D geovisual analytics

In this paper, we extend our generic ldquoGeoAnalyticsrdquo visualization (GAV) component toolkit, based on the principles behind Visual Analytics (VA), to also support time-oriented, geographically referenced and multivariate attribute volumetric data. GAV includes components that support a mixture of technologies from the three data visualization fields: information visualization (InfoVis), geovisualization (GeoVis) and scientific visualization (SciVis). Our research concentrates on visual user interface (VUI) techniques through dynamic and direct data manipulation that permit the visual analytical process to become more interactive and focused. This paper encourages synergies between well-known information- and volume data visualization methods applied in a multiple-linked and coordinated views interface. We address challenges for improved data interaction techniques with volumetric data and the need for immediate response. Varieties of explorative data analysis (EDA) tasks and the possibility to view the information simultaneously from different perspectives and scenarios are discussed. The effectiveness of our geovisual analytics framework is demonstrated in a tailor-made volume data explorer (VDE) application that integrates InfoVis, GeoVis and SciVis visualization methods assembled from GAV components. VDE facilitates dynamic exploration and correlation of temporal ocean space temperature and salinity data supplied in a NetCDF format from NOAA. This real-world phenomenon that corresponds to a huge volumetric data set comprises more than 31 million values for a time period of 12 months in 1994.
Facial Expression Recognition

Biometrics-Unique and Diverse Applications in Nature, Science, and Technology provides a unique sampling of the diverse ways in which biometrics is integrated into our lives and our technology. From time immemorial, we as humans have been intrigued by, perplexed by, and entertained by observing and analyzing ourselves and the natural world around us. Science and technology have evolved to a point where we can empirically record a measure of a biological or behavioral feature and use it for recognizing patterns, trends, and or discrete phenomena, such as individuals' and this is what biometrics is all about. Understanding some of the ways in which we use biometrics and for what specific purposes is what this book is all about.
Fractional excretions of albumin and IgG are the best predictors of progression in primary glomerulonephritis.

BACKGROUND Proteinuria is the most sensitive predictor of development of progressive renal insufficiency, with increasing focus on the composition of proteinuria, particularly high molecular weight proteins such as immunoglobulin G (IgG) (molecular weight 150 kDa). Differing methods of assessing excretion of proteinuria molecules have limited interpretation of results. We aimed to assess the utility of available indices of IgG, total proteinuria and albumin excretions as predictors of chronic kidney disease (CKD) progression in patients with primary glomerulonephritis. METHODS We recruited 97 patients with primary glomerulonephritis and measured 24-h urinary protein excretion, 24-h urinary albumin excretion, selectivity index, albumin:creatinine ratio, urinary IgG:creatinine ratio, fractional excretion of albumin (FE Alb) and fractional excretion of IgG (FE IgG) at baseline. The composite endpoint was developing stage 5 CKD, requiring RRT or death. Receiver operating characteristics curve analysis was used to assess the value of each measure in predicting outcome. From this analysis, high- and low-risk patient groups according to each measure were established. These were then tested using Kaplan-Meier and Cox survival analysis. RESULTS During a median follow-up of 7.07 years, 23 patients developed the primary endpoint. FE IgG and FE Alb were the most sensitive predictive tests. The hazard ratios (HR) of developing the primary endpoint using FE IgG [HR 37.1 (95% CI 8.6-158.8)] and FE Alb [HR 35.2 (95% CI 8.2-150.8)] cut-offs were double those using the other measures. CONCLUSIONS FE IgG and FE Alb are superior to conventional measures of proteinuria in predicting outcome in patients with primary glomerulonephritis, possibly because they are more accurate indicators of impairment of glomerular permselectivity. FE Alb should be used, in conjunction with other measures of proteinuria, in future studies of prediction of CKD progression.
Five new species of Myrsidea Waterston (Phthiraptera: Menoponidae) from tanagers (Passeriformes: Thraupidae) in Panama

3 Corresponding author Abstract There are 23 species of Myrsidea recognized from passerine thraupid hosts. Five new species parasitic on members of this avian family are described. They and their type hosts are Myrsidea rozsai ex the Thick-billed Euphonia, Euphonia laniirostris d'Orbigny & Lafresnaye, M. cruickshanki ex the Carmiol's Tanager, Chlorothraupis carmioli (Lawrence), M. patersoni ex the Grey-headed Tanager, Eucometis penicillata (Spix), M. pagei ex the Crimson-backed Tanager, Ramphocelus dimidiatus Lafresnaye, and M. valimi ex the Tawny-capped Euphonia, Euphonia anneae Cassin. The first species is placed in the bonariensis species group, while the remaining four are placed in the fusca species group. Sequences of a portion of the mitochondrial cytochrome oxidase I (COI) gene were compared to other species of Myrsidea and were highly divergent.
Distributional Evidence and Beyond: the Success and Limitations of Machine Learning in Chinese Word Segmentation

In this paper, we argue that the key to the success of the current state-of-art statistical learning algorithms for Chinese word segmentation (CWS) mostly lies in their optimal weighting of non-overlapping distributional evidence in the corpora. The utilization of distributional evidence is more essential than the learning algorithm. We further analyze the characteristics of distributional evidence for CWS, under the framework of Zipf’s law and summarize the limitation of statistical learning in CWS as the feature absence problem, which may be apparent yet usually neglected. Making a connection between theoretical/empirical linguistics and CWS, we suggest that the study and development of a generative word formation system may be beneficial for both the science and engineering of CWS. We wrap up the discussion after reviewing some recent works that are already on this line.
The Italian Particle "ne": Corpus Construction and Analysis

The Italian particle ne exhibits interesting anaphoric properties that have not been yet explored in depth from a corpus and computational linguistic perspective. We provide: (i) an overview of the phenomenon; (ii) a set of annotation schemes for marking up occurrences of ne; (iii) the description of a corpus annotated for this phenomenon ; (iv) a first assessment of the resolution task. We show that the schemes we developed are reliable, and that the actual distribution of partitive and non-partitive uses of ne is inversely proportional to the amount of attention that the two different uses have received in the linguistic literature. As an assessment of the complexity of the resolution task, we find that a recency-based baseline yields an accuracy of less than 30% on both development and test data.