Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Temperature Trends over India

The impact of climate change is projected to have different effects within and between countries. Information about such change is required at global, regional and basin scales for a variety of purposes. An investigation was carried out to identify trends in temperature time series of 125 stations distributed over the whole of India. The non-parametric Mann-Kendall test was applied to detect monotonic trends in annual average and seasonal temperatures. Three variables related to temperature, viz. Mean, mean maximum and mean minimum, were considered for analysis on both an annual and a seasonal basis. Each year was divided into four principal seasons, viz. Winter, pre-monsoon, monsoon and post-monsoon. The percentages of significant trends obtained for each parameter in the different seasons are presented. Temperature anomalies are plotted, and it is observed that annual mean temperature, mean maximum temperature and mean minimum temperature have increased at the rate of 0.42, 0.92 and 0.09°C (100 year) −1, respectively. On a regional basis, stations of southern and western India show a rising trend of 1.06 and 0.36°C (100 year)−1, respectively, while stations of the north Indian plains show a falling trend of –0.38°C (100 years)−1. The seasonal mean temperature has increased by 0.94°C (100 year)−1 for the post-monsoon season and by 1.1°C (100 year) −1 for the winter season.
Application of SIFT and advanced RANSAC algorithm on image registration

There have been great advances in object recognition and image registration, through the match of invariant local image feature. With the feature of invariance to affine, 3D projection, scaling and rotation, illumination changes, image translation, the Scale Invariant Features Transform(SIFT)is commonly used in object recognition. The RANdom Sample Consensus(RANSAC)is widely used as robust estimator, as a standard in the field of computer vision. In the process of R-RANSAC, the Randomized hypothesis evaluation)RANSAC, a modification is made to RANSAC by checking data points sequentially, while the standard RANSAC check all the data points in the model verification step. Own to this improvement, hypotheses with low support can be rejected before all points are considered. In addition, the Sequential Probability Ratios Test(SPRT)is used to minimize R-RANSAC runtime.
Computation of Flow Past Complex Geometries Using MAC Algorithm on Body-Fitted Coordinates

Abstract The study aims to report an algorithm based on co-ordinate transformation to compute complex flows. A two-dimensional computational code is developed using transformation of governing incompressible flow equations by employing body-fitted co-ordinates. The complex flows may involve external flows past obstacles having curved surfaces or internal flows confined in curved domains. The transformed governing equations are discretized using MAC algorithm. The code has been validated by comparing variation of Strouhal number with Reynolds number for flow past a circular cylinder.
Augmented and Normal Equations System in Mehrotra's Primal-dual Algorithm

In this paper 1 we compare two variants of Mehrotra’s primal dual algorithm which are based on the augmented and normal equations system, respectively. An implementation of corresponding algorithms in the package MATHEMATICA, version 4.1, is used for the comparison. Numerical examples are reported applying the program on some Netlib test problems.
Chemo-Kinematic Survey of z ∼ 1 Star Forming Galaxies using Keck OSIRIS LGS-AO

Abstract We present first results from the Intermediate Redshift OSIRIS Chemo-Kinematic Survey (IROCKS) of z ∼ 1 star forming galaxies (Mieda et al. in prep). We have targeted Hα and [NII] emission lines in J-band and have spatially resolved the galaxies at sub-kilo parsec scale. We have combined our sample with deep HST continuum images, and are able to reveal the dynamics, morphologies, metallicity distribution, emission-line diagnostics, and star formation rates of galaxies spanning this crucial z ∼ 1 epoch.
Stabilized Reconstruction in Signal and Image Processing: II. Iterative Reconstruction with and Without Constraint–Interactive Implementation

Part II is essentially devoted to the iterative reconstruction procedures that can easily be implemented in signal and image processing when the stability conditions are fulfilled. The application of the regularization principle introduced in part I for deconvolution is examined in this context. As the convergence of the method of conjugate gradients is then superlinear, this technique proves to be very well suited to solving the least-squares problem without constraint. If need be, the non-negativity constraint can be taken into account by slightly modifying the algorithm of steepest descent with fixed step, i.e. the Bialy-Jacobi iteration. Implemented in an appropriate manner, these methods may also provide the interesting part of the spectrum of the operator A∗ A to be inverted. This last point is particularly useful for conducting the error analysis. The exploration of the eigenspaces is also possible. As far as the implementation of the regularization principle is concerned, the theoretical .
Comparison of empirical study power in sample size calculation approaches for cluster randomized trials with varying cluster sizes – a continuous outcome endpoint

Background: Cluster randomized trials (CRTs) are a popular trial design. In most CRTs, researchers assume equal cluster sizes when calculating sample sizes. When clusters vary, assuming equal sized clusters may result in low study power. There are two common approaches to sample size calculations for varying cluster sizes. One approach uses a harmonic mean ( m H ) of cluster sizes, while the other incorporates the squared coefficient of variation ( cv 2 ) of cluster sizes. We performed simulations to compare empirical power between the two methods as well as the arithmetic mean method for a continuous endpoint. Study design: We considered cluster sizes that follow uniform distributions and performed 20,000 simulations under each scenario. Endpoints were analyzed using: 1) an individual-level linear regression model with Gaussian random intercepts for clusters; 2) an individual-level t -statistic with cluster-robust standard errors; 3) a generalized estimating equations (GEE) model with exchangeable correlation structure; and 4) a GEE model with independent correlation structure and robust standard errors. Results: When the Gaussian random effects or the GEE model with exchangeable correlation structure was considered, the m H method had 80% power. The cv 2 method had power of 85%–88%. However, when the data were analyzed using a t -statistic or the GEE model with independent correlation structure, the power of cv 2 method was 80%. The m H method produced power of 71%–76%. Conclusion: The performance of the sample size methods depends on the data analysis approaches. The degree of disparity in power depends also on the intracluster correlation coefficient. These findings emphasize the maxim that researchers should consider methods of analysis when designing CRTs to allow for appropriate sample size calculations.
Camera-Based Instant Feedback Systems

In this work we describe Instant Feedback Systems (IFS), that are designed to collect students’ responses to a series of multiple-choice questions. An IFS enables the educators to improve their teaching by generating real-time feedback from the students measuring their level of knowledge or the level of clarity of the lesson. A number of IFS are known using wired mini-keyboards, infrared clickers, SMS, Internet-based devices, etc. Our long-term goal is to create an IFS system based on the use of digital cameras. In the frame of camera-based IFS implementation, every student attending the class receives a specially designed card containing a predefined number of color markers and a 2D barcode. When later the students are asked a multiple-choice question, they answer by physically raising their cards. The 2D barcode on the card is used to encode the student’s ID, whereas the 2D orientation of the card encodes the number of the student’s answer. A number of images of the class are grabbed by one or more IFS cameras and processed by a computer, which generates a list of student grades. Reliability of several card designs, different camera placements and different Optical Character Recognition (OCR) algorithms is analysed. The feasibility and accuracy of the presented IFS approach is evaluated by means of MATLAB-based simulations as well as real life images. We conclude that the presented card recognition process demonstrates feasibility and efficiency of the camera-based IFS approach. KeywordsImage processing, 2D barcode, Color Recognition, OCR, Instant Feedback System
A Parallel Domain Decomposition Algorithm for Simulating Blood Flow with Incompressible Navier-Stokes Equations with Resistive Boundary Condition

We introduce and study a parallel domain decomposition algorithm for the simulation of blood flow in compliant arteries using a fully-coupled system of nonlinear partial differential equations consisting of a linear elasticity equation and the incompressible Navier-Stokes equations with a resistive outflow boundary condition. The system is discretized with a finite element method on unstructured moving meshes and solved by a Newton-Krylov algorithm preconditioned with an overlapping restricted additive Schwarz method. The resistive outflow boundary condition plays an interesting role in the accuracy of the blood flow simulation and we provide a numerical comparison of its accuracy with the standard pressure type boundary condition. We also discuss the parallel performance of the implicit domain decomposition method for solving the fully coupled nonlinear system on a supercomputer with a few hundred processors. AMS subject classifications: 74F10, 65M55, 35R37, 65Y05, 68W10
A Coupled Model to Simulate Snow Behavior on Roads

Abstract To develop a decision-making tool for road management in winter, a numerical model resulting from the coupling of a soil model and a snow model was developed and validated using experimental results from a comprehensive experimental field campaign during three winters (1997/98, 1998/99, and 1999/2000). The coupling of the models has been done through an implicit calculation of the conduction flux between snow and road. An equivalent thermal resistance has been used to take into account the different road–snow interface configurations. For this purpose, a parameterization of water-saturated snow was introduced. This model permits the simulation of the snow behavior on a road, and it takes into account different interfacial configurations according to snow and road types and the snowpack evolution (freezing, melting, grain type). Comparisons of experimental and simulated results for typical snowfall events or over the entire winter showed that the model was able to simulate road surface temperature.
Projected score methods for approximating conditional scores

SUMMARY This paper extends the projected score methods of Small & McLeish (1989). It is shown that the conditional score function may be approximated, with arbitrarily small stochastic error, in terms of a natural basis for the space of centred likelihood ratios. The utility of using this basis is established by identifying a U-statistic representation theorem and a class of expectation identities for the basis elements, making higher order asymptotics more tractable. The results are applied to a canonical exponential family model, where it is shown that the projected scores with estimated nuisance parameters can provide an accurate approximation to the conditional score function.
Stability analysis for T–S fuzzy systems with interval time‐varying delays and nonlinear perturbations

This paper is concerned with the problem of the robust stability for T–S fuzzy systems with interval time‐varying delay and nonlinear perturbations. The key features of the approach include the introduction of uncorrelated augmented matrix items into the Lyapunov functional and the use of some appropriate integral inequalities. Unlike the existing methodologies, the proposed approach involves neither free weighting matrices nor any model transformation. It can, however, lead to much less conservative stability criteria than the existing ones for the systems under consideration. The cases of perturbations are assumed to both nonlinear time‐varying perturbations and time‐varying uncertainties in linear fractional form. Numerical examples show that the obtained results are less conservative than the existing ones in the literature. Copyright © 2009 John Wiley & Sons, Ltd.
On a unified treatment of kinetics and diffusion, and a connection to a nonlocal boltzmann equation

Fick's law of diffusion has been generalized to include kinetic processes, the transport term of the Boltzmann equation, and nonlocal interaction processes. It is shown that the collision interaction term can be obtained by the introduction of a quantum stochastic potential equation. Some approximations of a nonlocal Boltzmann equation can be solved exactly. The solutions can be applied to problems of molecular pattern in biology.
Numerical Study of Noise from Isotropic Turbulence

A numerical study of sound radiation by isotropic turbulence is carried out by combining turbulence simulation with Lighthill's acoustic analogy. In the first study we analyze sound generation by decaying isotropic turbulence obtained both with 643 Direct Numerical Simulation (DNS) and 163 Large Eddy Simulation (LES). Both simulations lead to similar results for acoustic power, in agreement with the numerical results of Sarkar and Hussaini, but slightly different from theoretical predictions of Proudman and Lilley. In the second study we analyze sound generation by forced stationary turbulence, simulated with 1283 DNS using a forcing scheme which preserves turbulence structure. The acoustic power computed from the stationary turbulence is in good agreement with results obtained for decaying isotropic turbulence. The acoustic spectrum shows that the characteristic frequency of the generated sound is approximately four times the inverse eddy turnover time. The contributions of different turbulence scales to the generated noise are computed separately from filtered velocity fields. For the low Reynolds number turbulence analyzed, the scales which most contribute to noise generation are 2–3 times smaller than the energy-containing scales and lie between the energy and dissipation-rate spectral peaks.
High-resolution neutron transmission measurements on /sup 235/U, /sup 239/Pu, and /sup 238/U

High-resolution transmission measurements have been made on three sample thicknesses of both /sup 235/U and /sup 239/Pu at liquid nitrogen temperature and also on three of /sup 238/U at room temperature using neutrons from the water-moderated ORELA target. The data on /sup 235/U and /sup 239/Pu from 1 to 10,000 eV were obtained using /sup 6/Li glass scintillation detectors at 17.909-and 80.394-m flight paths. The /sup 238/U data from 1 to 100 keV were obtained using a new NE 110 proton-recoil scintillation detector at a 210.558-m flight path.
Solving 3D Time-Fractional Diffusion Equations by High-Performance Parallel Computing

Abstract Numerically solving time-fractional diffusion equations, especially in three space dimensions, is a daunting computational task. This is due to the huge requirements of both computation time and memory storage. Compared with solving integer-ordered diffusion equations, the costs for time and storage both increase by a factor that equals the number of time steps involved. Aiming to overcome these two obstacles, we study in this paper three programming techniques: loop unrolling, vectorization and parallelization. For a representative numerical scheme that adopts finite differencing and explicit time integration, the performance-enhancing techniques are indeed shown to dramatically reduce the computation time, while allowing the use of many CPU cores and thereby a large amount of memory storage. Moreover, we have developed simple-to-use performance models that support our empirical findings, which are based on using up to 8192 CPU cores and 12.2 terabytes.
A Report on Null Horizons in Relativity

This is a review paper on isolated, distorted and time-dependent null horizons by providing up-date information on their role in black hole physics. Geometry of totally umbilical null hyper surfaces has been used to establish an interrelation between these three types of horizons in a unified manner. Distorted horizons describe the near isolated black holes which are dis- torted by the presence of faraway matter. On the other hand, time-dependent null horizons are modeled by a family of totally umbilical null hyper surfaces. A sketch of the proofs of the most important results is presented together with sufficient related references.
Modeling Predictors of Latent Classes in Regression Mixture Models

The purpose of this study is to provide guidance on a process for including latent class predictors in regression mixture models. We first examine the performance of current practice for using the 1-step and 3-step approaches where the direct covariate effect on the outcome is omitted. None of the approaches show adequate estimates of model parameters. Given that Step 1 of the 3-step approach shows adequate results in class enumeration, we suggest using an alternative approach: (a) decide the number of latent classes without predictors of latent classes, and (b) bring the latent class predictors into the model with the inclusion of hypothesized direct covariate effects. Our simulations show that this approach leads to good estimates for all model parameters. The proposed approach is demonstrated by using empirical data to examine the differential effects of family resources on students’ academic achievement outcome. Implications of the study are discussed.
Analysis of complexity of primal-dual interior-point algorithms based on a new kernel function for linear optimization

Kernel functions play an important role in defining new search directions for primal-dual interior-point algorithm. In this paper, a new kernel function which its barrier term is integral type is proposed. We study the properties of the new kernel function, and give a primal-dual interior-point algorithm for solving linear optimization based on the new kernel function. Polynomial complexity of algorithm is analyzed. The iteration bounds both for large-update and for small-update methods are obtained, respectively. The iteration bound for small-update method is the best known complexity bound.
Bootstrap Analysis of DT-MRI Tractography Techniques: Streamlines and Tensorlines

Introduction Diffusion tensor MRI has been used to reconstruct white matter connections in the human brain [1,2]. The common approach in tracking the white matter fibers trajectories through the brain has been the use of the tensor major eigenvector in order to assess the propagation direction. As an alternative approach, tensorlines [3,4] was developed using the entire tensor, D, by defining a deflected vector:
A Guaranteed Cost Control for Stochastic Distribution System with Time-delay

This article investigates the guaranteed cost control problem for the stochastic distribution system with time-delay. Attention is focused on a memory state feedback control law based on Linear Matrix Inequality (LMI) with model transformation such that the closed-loop system is asymptotically stable and the guaranteed cost index is not more than a specied upper bound, which ensure that the system output Probability Density Function (PDF) follows the target probability density function. A comparative study of an example problem has show to illustrate the proposed method.
Effect of Similarity Measures for CBIR Using Bins Approach

This paper elaborates on the selection of suitable similarity measure for content based image retrieval. It contains the analysis done after the application of similarity measure named Minkowski Distance from order first to fifth. It also explains the effective use of similarity measure named correlation distance in the form of angle ‘cos θ’ between two vectors. Feature vector database prepared for this experimentation is based on extraction of first four moments into 27 bins formed by partitioning the equalized histogram of R, G and B planes of image into three parts. This generates the feature vector of dimension 27. Image database used in this work includes 2000 BMP images from 20 different classes. Three feature vector databases of four moments namely Mean, Standard deviation, Skewness and Kurtosis are prepared for three color intensities (R, G and B) separately. Then system enters in the second phase of comparing the query image and database images which makes of set of similarity measures mentioned above. Results obtained using all distance measures are then evaluated using three parameters PRCP, LSRR and Longest String. Results obtained are then refined and narrowed by combining the three different results of three different colors R, G and B using criterion 3. Analysis of these results with respect to similarity measures describes the effectiveness of lower orders of Minkowski distance as compared to higher orders. Use of Correlation distance also proved its best for these CBIR results.
General Structural Operational Semantics through Categorical Logic (Extended Abstract)

Certain principles are fundamental to operational semantics, regardless of the languages or idioms involved. Such principles include rule-based definitions and proof techniques for congruence results. We formulate these principles in the general context of categorical logic. From this general formulation we recover precise results for particular language idioms by interpreting the logic in particular categories. For instance, results for first-order calculi, such as CCS, arise from considering the general results in the category of sets. Results for languages involving substitution and name generation, such as the -calculus, arise from considering the general results in categories of sheaves and group actions. As an extended example, we develop a tyft/tyxt-like rule format for open bisimulation in the -calculus.
Discrete Decision Problems with Large Number of Criteria

Discrete decision problems with a large number of criteria (as compared to the number of alternatives) present specific difficulties, for example, most of decision alternatives tend to be Pareto-nondominated, some criteria might have binary character, etc. For these reasons, classical methods such as weighted sum aggregation or full utility elicitation are barely applicable for such problems. Methods that might be applied include reference point approaches, particularly if appropriately modified, equitable aggregation approaches and special hierarchical aggregation schemes. The paper presents descriptions and necessary modifications of such methods, together with associated concepts of objective versus subjective decision selection, compensatory versus non-compensatory criteria, preservation of Pareto-nondominance in hierarchical aggregation, etc. Examples show that methods effective in most difficult cases are based on reference point approaches combined with equitable aggregation and objective decision selection.