Miracle-dz/UniFAR-FTUs huggingface dataset:
Opposing Effects of Low Molecular Weight Heparins on the Release of Inflammatory Cytokines from Peripheral Blood Mononuclear Cells of Asthmatics

Background T-cell-mediated inflammatory cytokines, such as interleukin (IL)-4, IL-5, IL-13 and tumor necrosis factor-alpha (TNF-α), play an important role in the initiation and progression of inflammatory airways diseases. Low-molecular-weight heparins (LMWHs), widely used anticoagulants, possess anti-inflammatory properties making them potential treatment options for inflammatory diseases, including asthma. In the current study, we investigated the modulating effects of two LMWHs (enoxaparin and dalteparin) on the release of cytokines from stimulated peripheral blood mononuclear cells (PBMCs) of asthmatic subjects to identify the specific components responsible for the effects. Methods PBMCs from asthmatic subjects (consist of ~75% of T-cells) were isolated from blood taken from ten asthmatic subjects. The PBMCs were pre-treated in the presence or absence of different concentrations of LMWHs, and were then stimulated by phytohaemagglutinin for the release of IL-4, IL-5, IL-13 and TNF-α. LMWHs were completely or selectively desulfated and their anticoagulant effect, as well as the ability to modulate cytokine release, was determined. LMWHs were chromatographically fractionated and each fraction was tested for molecular weight determination along with an assessment of anticoagulant potency and effect on cytokine release. Results Enoxaparin inhibited cytokine release by more than 48%, whereas dalteparin increased their release by more than 25%. The observed anti-inflammatory effects of enoxaparin were independent of their anticoagulant activities. Smaller fractions, in particular dp4 (four saccharide units), were responsible for the inhibitory effect of enoxaparin. Whereas, the larger fractions, in particular dp22 (twenty two saccharide units), were associated with the stimulatory effect of dalteparin. Conclusion Enoxaparin and dalteparin demonstrated opposing effects on inflammatory markers. These observed effects could be due to the presence of structurally different components in the two LMWHs arising from different methods of depolymerisation. This study provides a platform for further studies investigating the usefulness of enoxaparin in various inflammatory diseases.
NEPS technical report for science: Scaling results of starting cohort 4 in 11th grade

The National Educational Panel Study (NEPS) aims at investigating the development of competences across the whole life span and designs tests for assessing these different competence domains. In order to evaluate the quality of the competence tests, a wide range of analyses have been performed based on item response theory (IRT). This paper describes the data on scientific literacy for starting cohort 3 in grade 6. Besides presenting descriptive statistics for the data, the scaling model applied to estimate competence scores and analyses performed to investigate the quality of the scale as well as the results of these analyses are also explained. The science test in grade 6 originally consisted of 27 multiple choice and complex multiple choice items and covered two knowledge domains as well as three different contexts. The test was administered to 4,871 students. A Partial Credit Model was used for scaling the data. Item fit statistics, differential item functioning, Rasch-homogeneity, and the tests’ dimensionality were evaluated to ensure the quality of the test. Two items had to be eliminated due to insufficient item discrimination. The results of the remaining 25 items illustrate good item fit values and measurement invariance across various subgroups. Moreover, the test showed a high reliability. As the correlations between the two knowledge domains are very high in a multidimensional model, the assumption of unidimensionality seems adequate. Among the challenges of this test is the lack of very difficult items. But overall, the results emphasize the good psychometric properties of the science test, thus supporting the estimation of reliable scientific literacy scores. In this paper, the data available in the Scientific Use File are described and the ConQuest-Syntax for scaling the data is provided. (IPN/Orig.)
Robust multispectral palmprint identification system by jointly using contourlet decomposition & Gabor Filter response

In current society, reliable identification and verification of individuals are becoming more and more necessary tasks for many fields, not only in police environment, but also in civilian applications, such as access control or financial transactions. Biometric systems are used nowadays in these fields, offering greater convenience and several advantages over traditional security methods based on something that you know (password) or something that you have (keys). In this paper, we propose an efficient online personal identification system based on Multi-Spectral Palmprint (MSP) images using Contourlet Transform (CT) and Gabor Filter (GF) response. In this study, the spectrum image is characterized by the contourlet coefficients sub-bands. Then, we use the Hidden Markov Model (HMM) for modeling the observation vector. In addition, the same spectrum is filtered by the Gabor filter. The real and imaginary responses of the filtering image are used to create another observation vector. Subsequently, the two sub-systems are integrated in order to construct an efficient multi-modal identification system based on matching score level fusion. Our experimental results show the effectiveness and reliability of the proposed method, which brings both high identification and accuracy rate.
Tradeoff of FPGA Design of a Floating-point Library for Arithmetic Operators

Many scientific and engineering applications require to perform a large number of arithmetic operations that must be computed in an efficient manner using a high precision and a large dynamic range. Commonly, these applications are implemented on personal computers taking advantage of the floating-point arithmetic to perform the computations and high operational frequencies. However, most common software architectures execute the instructions in a sequential way due to the von Neumann model and, consequently, several delays are introduced in the data transfer between the program memory and the Arithmetic Logic Unit (ALU). There are several mobile applications which require to operate with a high performance in terms of accuracy of the computations and execution time as well as with low power consumption. Modern Field Programmable Gate Arrays (FPGAs) are a suitable solution for high performance embedded applications given the flexibility of their architectures and their parallel capabilities, which allows the implementation of complex algorithms and performance improvements. This paper describes a parameterizable floating-point library for arithmetic operators based on FPGAs. A general architecture was implemented for addition/subtraction and multiplication and two different architectures based on the Goldschmidt’s and the Newton-Raphson algorithms were implemented for division and square root. Additionally, a tradeoff analysis of the hardware implementation was performed, which enables the designer to choose, for general purpose applications, the suitable bit-width representation and error associated, as well as the area cost, elapsed time and power consumption for each arithmetic operator. Synthesis results have demonstrated the effectiveness of the implemented cores on commercial FPGAs and showed that the most critical parameter is the dedicated Digital Signal Processing (DSP) slices consumption. Simulation results were addressed to compute the mean square error (MSE) and maximum absolute error demonstrating the correctness of the implemented floating-point library and achieving and experimental error analysis. The Newton-Raphson algorithm achieves similar MSE results as the Goldschmidt’s algorithm, operating with similar frequencies; however, the first one saves more logic area and dedicated DSP blocks.
Efficient solution algorithm of non-equilibrium Green ’ s functions in atomistic tight binding representation

INTRODUCTION With the shrinking dimension of electronic devices, quantum transport attracts increasing interest. The non-equilibrium Green’s function (NEGF) formalism [1] provides a very general framework for quantum transport, but it is numerically expensive when applied on atomistic tight binding representations. So far, computational burden (in memory and CPU time) limits the maximum diameter of nanowires that are solvable within atomistic NEGF to about 8nm. Several methods are developed to reduce the numerical costs. However, these methods are either limited to effective mass model [2] or to small cross-sections perpendicular to the transport direction [3]. In this work, it is demonstrated how to apply the concept of low rank approximation [4] to the NEGF method in atomistic tight binding representation. Using this method, transport in a 12nm squared Si nanowire is solved. METHOD All electrons are represented in atomistic multi band tight binding models. If a device consists of several millions of atoms, tight binding descriptions yield millions of electronic states. Only a small fraction of these states contribute to charge transport. In this method, the NEGF equations of the tight binding Hilbert space of rank N are transformed into a Hilbert space of rank n that is spanned by only these relevant states. The maximum achievable speed up is given by (n/N) and the maximum reduction of required memory by (n/N). First, the energy dependent contact selfenergies are calculated within the transfer matrix method of [5] and folded into the device Hamiltonian to represent an open system through the nonhermitian Hamiltonians H(E) for each considered energy E. For every H(E), those n right sided eigenstates are calculated that have eigenvalues closest to E. These eigenstates represent the n columns of a rectangular transformation matrix T (N rows and n columns). The NEGF equations are transformed by T into the smaller Hilbert space and solved therein.
Electrophysiological correlates of anticipation and emotional memory

This thesis investigated the role of anticipation as a mediating factor in the Emotion-Enhanced Memory (EEM) phenomenon. Using behavioural and ERP measures, three anticipatory conditions were explored: Informative, No-Cue and Non-Informative. The primary objective was to determine how far the pre-stimulus-Dm (Ps-Dm) effect is a reliable indicator of emotional memory encoding under different levels of anticipation, and if the preparatory process explanation accounts for any effects. This study also aimed to determine if there is an association between anticipatory activity at the pre- and post- stimulus phase, and the related behavioural outcome. One behavioural and three ERP studies were conducted to measure the difference due to memory (Dm) effect during an anticipatory phase. The Dm effect distinguishes between neural activity of subsequently remembered and forgotten items, providing an index of successful encoding. We employed an S1-S2 (Stimulus 1: Cues - Stimulus 2: Pictures) Cueing-Subsequent Memory Paradigm. Upper case letters (O, X, Z) served as cue stimuli (S1). Emotional and neutral images selected from International Affective Picture System (IAPS) were used as S2. Findings revealed a Dm effect for informative as well as for non-informative cue conditions when participants anticipated high-arousal emotionally negative pictures. This effect was found during the 400-600ms time window only when the cue remained on the screen. This effect was not significant for the studies in which the arousal level of anticipated negative pictures was mixed. Moreover, the behavioral findings mirrored the neural activity in this particular study. However, in rest of the studies, behavioral results could not corroborate neural activity. The results of the present set of experiments highlighted that emotional memory might be formed without specific information about the content or valence of imminent pictures.
An Improved Cellular Genetic Algorithm with Evolutionary Rules for 3D Animation Modeling Design

In order to inspire and assist designers to create novel 3D animation modelings, an improved cellular genetic algorithm with evolutionary rules is proposed in this paper and applied in 3D animation modeling design. In this algorithm, ACIS rule expressions are used for deforming the initial 3D animation modeling created by Maya or 3D Max, and human-computer interaction which used expert knowledge to determine fitness function values of individuals is adopted to evaluate generated modelings. Tree-structure encoding is used for the generation and evolution of ACIS rule expressions and an evolutionary rule based on expert knowledge is introduced to reduce the number of human-computer interactions. Besides, a prototype system, called 3D Animation Modeling Design System, is developed based on the proposed algorithm. The experimental results show that the proposed algorithm can automatically and efficiently generate a series of creative 3D animation modelings and reduce the number of human-computer interactions to some extent.
topographic data for volcanology studies at Kilauea Volcano , Hawaii : An assessment using airborne lidar data

We use airborne lidar data for the summit area of Kilauea Caldera, Hawaii, to explore the utility of topographic data collected by the TOPSAR airborne interferometric radar for volcanology studies. The lidar data are processed to a spatial resolution of 1 m/pixel, compared to TOPSAR with a spatial resolution of 5 m. Over a variety of fresh volcanic surfaces (pahoehoe and aa lava flows, ash falls and fluvial fans), TOPSAR data are shown to have a typical vertical offset compared to the lidar data of no more than ~2–3 m. Larger differences between the two data sets and TOPSAR data drop-outs are found to be concentrated around steep scarps such as the walls of pit craters and ground cracks associated with the Southwest Rift Zone. A comparison of these two data sets is used to explore the utility of TOPSAR to interpret the topography of volcanic features close to the spatial resolution of TOPSAR, such as spatter ramparts, fractures, a perched lava flow, and eroded ash deposits. Comparison of the TOPSAR elevation and the lidar first-return minus the return from the ground surface (the so-called ‘‘bald Earth’’ data) for vegetated areas reveals TOPSAR penetration into the tree canopy is typically at least 10% and no more than ~50%, although a wide range of penetration values from 0% to 90% has been identified. Our results are significant because they show that TOPSAR data for volcanoes can reliably be used to measure regional slopes and the thickness of lava flows, and have value for the validation of coarser spatial resolution digital elevation data (such as SRTM) in areas where lidar data have not been collected. D 2005 Elsevier Inc. All rights reserved.
Sparse denoising with learned composite structured dictionaries

In the sparse representation field recent studies using composite dictionaries have shown encouraging results in performing noise removal. In this paper we look at dictionary composition in the particular case of dictionaries structured as a union of orthonormal bases. Our study focuses on denoising performance, providing new algorithms that outperform existing solutions, and also speed, resulting in different algorithms that execute a lot faster with a negligible denoising penalty.
Feature Selection for Multiclass Discrimination via Mixed-Integer Linear Programming

We reformulate branch-and-bound feature selection employing L/sub /spl infin// or particular L/sub p/ metrics, as mixed-integer linear programming (MILP) problems, affording convenience of widely available MILP solvers. These formulations offer direct influence over individual pairwise interclass margins, which is useful for feature selection in multiclass settings.
Digital holographic microscopy – innovative and non-destructive analysis of living cells

s of proposals for the book titled: Microscopy: Science, Technology, Applications and Education Digital holography is a novel technique that has been developed recently to study living cells. The technique is an innovative, non-destructive method with possibilities to study living cells over time. We are investigating cell number, growth, viability and death of adherent cells using digital holography, which is a novel, label-free, imaging technique for biological applications. We have recently demonstrated that digital holography is highly comparable to the conventional manual cell counting method using a hemocytometer (Molder et al., 2008). Digital holography is a method that gives us information about the refractive index of cells, which can change under different circumstances. The technique is cheap, fast and simple to use. The unique measurable parameters are the cell number, cell area, thickness and volume, which can be transformed to proliferation, migration, viability and cell death. The digital holographic images produced can provide both quantitative and qualitative phase information from a single hologram. Future applications can include real-time cell monitoring of various parameters of cells of different diseases in response to clinically relevant compounds.
Construction of Data Flow Networks for Tracking in Augmented Reality Applications

Constructing Ubiquitous Tracking setups that can integrate new and unknown sensors dynamically at runtime is a difficult problem. One issue is that sensors in general are not synchronized, which may lead to registration errors when measurements from multiple sensors are combined. The approach presented in this paper solves the measurement simultaneity problem by creating data flow networks that include interpolation/prediction components from spatial relationship graphs descriptions based on a system of spatial relationship patterns.
Computer Algebra Applied to a Solitary Waves Study

We apply Computer algebra techniques, such as algebraic computations of resultants and discriminants, certified drawing (with a guaranteed topology) of plane curves, to a problem in Fluid dynamics: We investigate ``capillary-gravity'' solitary waves in shallow water, relying on the framework of the Serre-Green-Naghdi equations. So, we deal with 2 dimensional surface waves, propagating in a shallow water of constant depth. By a differential elimination process, the study reduces to describing the solutions of an ordinary non linear first order differential equation, depending on two parameters. The paper is illustrated with examples and pictures computed with the computer algebra system Maple.
Truncated Qr Algorithms and the Solution of Large-scale Eigenvalue Problems

The QR algorithm has emerged as the general-purpose method of choice for computing the Schur decomposition of a matrix. For most large eigenvalue problems, however, the QR algorithm cannot be used because of the explicit storage of the matrix and because often only the action of the matrix upon a vector (or group of vectors) is available. Typically, only a small number of eigenvalues and the associated invariant subspace are required. This article considers a truncated QR algorithm. We show that a truncated QR algorithm is a generalization of Sorensen's implicitly restarted Arnoldi method to block Arnoldi reductions. Moreover, implicitly restarting an Arnoldi reduction is simultaneous iteration with an implicit projection step to accelerate convergence to the invariant subspace of interest. This is a generalization of the Rayleigh{Ritz procedure on a block Krylov subspace for a non Hermitian matrix. The moral of our story is that the large scale eigenvalue problem is intimately involved with the dense one.
The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data

Abstract The recent increase in data accuracy from high resolution accelerometers offers substantial potential for improved understanding and prediction of animal movements. However, current approaches used for analysing these multivariable datasets typically require existing knowledge of the behaviors of the animals to inform the behavioral classification process. These methods are thus not well‐suited for the many cases where limited knowledge of the different behaviors performed exist. Here, we introduce the use of an unsupervised learning algorithm. To illustrate the method's capability we analyse data collected using a combination of GPS and Accelerometers on two seabird species: razorbills (Alca torda) and common guillemots (Uria aalge). We applied the unsupervised learning algorithm Expectation Maximization to characterize latent behavioral states both above and below water at both individual and group level. The application of this flexible approach yielded significant new insights into the foraging strategies of the two study species, both above and below the surface of the water. In addition to general behavioral modes such as flying, floating, as well as descending and ascending phases within the water column, this approach allowed an exploration of previously unstudied and important behaviors such as searching and prey chasing/capture events. We propose that this unsupervised learning approach provides an ideal tool for the systematic analysis of such complex multivariable movement data that are increasingly being obtained with accelerometer tags across species. In particular, we recommend its application in cases where we have limited current knowledge of the behaviors performed and existing supervised learning approaches may have limited utility.
A family of models for spherical stellar systems

We describe a one-parameter family of models of stable sperical stellar systems in which the phase-space distribution function depends only on energy. The models have similar density profiles in their outer parts (rho propotional to r(exp -4)) and central power-law density cusps, rho proportional to r(exp 3-eta), 0 less than eta less than or = 3. The family contains the Jaffe (1983) and Hernquist (1990) models as special cases. We evaluate the surface brightness profile, the line-of-sight velocity dispersion profile, and the distribution function, and discuss analogs of King's core-fitting formula for determining mass-to-light ratio. We also generalize the models to a two-parameter family, in which the galaxy contains a central black hole; the second parameter is the mass of the black hole. Our models can be used to estimate the detectability of central black holes and the velocity-dispersion profiles of galaxies that contain central cusps, with or without a central black hole.
Towards integrability of topological strings. I. Three-forms on Calabi-Yau manifolds

The precise relation between Kodaira-Spencer path integral and a particular wave function in seven dimensional quadratic field theory is established. The special properties of three-forms in 6d, as well as Hitchin's action functional, play an important role. The latter defines a quantum field theory similar to Polyakov's formulation of 2d gravity; the curious analogy with world-sheet action of bosonic string is also pointed out.