Miracle-dz/UniFAR-FTUs huggingface dataset:
Geodesic motion on groups of diffeomorphisms with H1 metric as geometric generalised Lagrangian mean theory

ABSTRACT Generalized Lagrangian mean theories are used to analyse the interactions between mean flows and fluctuations, where the decomposition is based on a Lagrangian description of the flow. A systematic geometric framework was recently developed by Gilbert and Vanneste who cast the decomposition in terms of intrinsic operations on the group of volume preserving diffeomorphisms or on the full diffeomorphism group. In this setting, the mean of an ensemble of maps can be defined as the Riemannian centre of mass on either of these groups. We apply this decomposition in the context of Lagrangian averaging where equations of motion for the mean flow arise via a variational principle from a mean Lagrangian, obtained from the kinetic energy Lagrangian of ideal fluid flow via a small amplitude expansion for the fluctuations. We show that the Euler-α equations arise as Lagrangian averaged Euler equations when using the -geodesic mean on the volume preserving diffeomorphism group of a manifold without boundaries, imposing a “Taylor hypothesis”, which states that first order fluctuations are transported as a vector field by the mean flow, and assuming that fluctuations are statistically nearly isotropic. Similarly, the EPDiff equations arise as the Lagrangian averaged Burgers' equations using the same argument on the full diffeomorphism group. A serious drawback of this construction is that the assumptions of Lie transport of the fluctuation vector field and isotropy of fluctuations cannot persist except for an asymptotically vanishing interval of time. To remedy the problem of persistence of isotropy, we suggest adding strong mean-reverting stochastic term to the Taylor hypothesis and identify a scaling regime in which the inclusion of the stochastic term leads to the same averaged equations up to a constant.
On High-Latency Bowtie Data Streaming

In this paper, we consider applications that read sequential data from n input files and write the result into m output files, which encompasses many types of external-memory sorting, database join/group queries, and MapReduce computation. We call this I/O model bowtie streaming and develop novel algorithms for modeling its throughput, maximizing sequential run lengths, and obtaining optimal multi-pass split/merge factors under non-trivial stream-switching (i.e., seek) delay. Based on these developments, we build a platform called Tuxedo for general bowtie computation and show that it is able to perform external-memory sorting with a million times fewer attempted seeks than Hadoop and two orders of magnitude fewer than highly optimized external-memory frameworks STXXL and nsort.
Quadratic points on modular curves

In this paper we determine the quadratic points on the modular curves X0(N), where the curve is non-hyperelliptic, the genus is 3, 4 or 5, and the Mordell–Weil group ofJ0(N)is finite. The values of Nare 34, 38, 42, 44, 45, 51, 52, 54, 55, 56, 63, 64, 72, 75, 81.As well as determining the non-cuspidal quadratic points, we give the j-invariants of the elliptic curves parametrized by those points, and determine if they have complex multiplication or are quadratic Q-curves.
Adaptive step up tests for the false discovery rate (FDR) under independence and dependence

The false discovery rate (FDR) is widely used and accepted as error criterion in multiple hypothesis testing and much effort has been done to develop multiple tests which control the FDR under different model assumptions. The FDR is often used when the number of false rejections of a multiple test is allowed to be a reasonable portion of all rejections. It is defined as the expected portion of false rejections among all rejections. The most famous multiple test with respect to the FDR is the linear step-up test of Benjamini and Hochberg [2] which controls the FDR under different assumptions, but still does not exhausts it and thus may have a lack of power. Therefore, the adaptive step-up test of Storey et al. [61] has been proposed which includes an estimation of the portion of true null hypotheses. In the introductory first chapter we introduce the basic model assumptions for this thesis and give a brief summary of well known results. Chapter 2 provides three central lemmas from which we derive various results concerning the FDR of non adaptive step-up tests, the critical values of step-up tests with FDR control and the asymptotic FDR of adaptive step-up tests. We consider several dependence structures of the p-values, including independence, a reverse martingale structure, positive regression dependence and arbitrary dependence. In Chapter 3 we extend the results of Storey [59, 60] to p-values of null hypotheses whose marginal distributions are stochastically larger than the uniform distribution on [0,1] which may occur for one sided hypotheses. We motivate a new class of estimators for adaptive step-up tests and show that the common estimation of the portion of true null hypotheses is not appropriate in this case. In Chapter 4 we establish a new sufficient condition for finite sample FDR control of adaptive step-up tests under independence and we prove that a slightly modified estimator from Chapter 3 satisfies this condition. It turns out that the selection of the estimator for the adaptive step-up test may even be performed in a data dependent manner. A reasonable selection method is discussed in a practical guide. Chapter 5 is devoted to finite sample FDR control of adaptive step-up tests under a specific kind of block dependence.
Ellipsometry investigation of perovskite/pyrochlore PZT thin film stacks

Abstract The refractive index profile of PZT thin film perovskite/pyrochlore stacks was studied with a J. A. Woollam spectral ellipsometer. Evidence was found that self-polarization is determined by the interaction of perovskite sublayer with the bottom platinum electrode.
Measurement of Linear Attenuation Coefficient of Tc-99m using Planar Gamma Camera Image

Introduction: Attenuation is one of the most important factors which have an impact on the accuracy on quantitation of activity when using gamma camera image in a medium. Due to attenuation a number of photons are lost and there is also loss of counts which otherwise would have been included in the images. The purpose of this study is to estimate the linear attenuation coefficient for Tc-99m with water filled phantom using conjugate view method and transmission method and compare the obtained measurements with the reference values. Materials and Methods: To determine the linear attenuation coefficient of radionuclide Tc-99m using conjugate view method, the sensitivity of the gamma camera was determined. 30 MBq of Tc-99m was taken which was measured in dose calibrator and gamma camera images were acquired. Then the sensitivity was calculated as CPS/MBq. A Jaszczak phantom was filled up with water and a small sized cylindrical container filled with activity of 30 MBq Tc-99m liquid was placed inside the Jaszczak phantom centrally. The phantom was scanned on a dual head gamma camera and the images were taken and counts from the images were estimated. After estimation of all values, the linear attenuation coefficient was calculated using conjugate view formula. The linear attenuation coefficient was also measured from the transmission imaging. The transmission factor (TF) was determined by measuring the ratio of count rates obtained with Tc-99m under gamma camera, with and without the phantom. Results: The linear attenuation coefficient was calculated 0.1467 ± 0.0057 from conjugate view method and 0.1483 ± 0.0023 from transmission measurements. Both results were found within ±5% of the established value (0.15 cm-1). Conclusions: Correction for attenuation is required for the accurate estimation of activity. For conjugate view method, the attenuation coefficient was measured with a known activity of Tc-99m. The attenuation coefficient was also estimated by transmission measurements. The accuracy of the activity measurement is highly dependent on the accuracy of the attenuation coefficient used in the calculation. DOI: http://dx.doi. org/10.3329/bjnm.v17i1. 22493 Bangladesh J. Nuclear Med. 17(1): 61-66, January 2014
Modification on the electron field emission properties of diamond films: The effect of bias voltage applied in situ

In this work, we have systematically examined the effect of bias voltage applied in situ on the characteristics of diamond films. Raman spectroscopic and scanning electron microscopic examinations indicate that the applied positive bias voltage changes the thin films’ morphology and the Raman spectroscopy insignificantly, but markedly lowers their effective work function (Φe) and turn-on field (E0). The enhancement of the field emission properties of these films is assumed to result from the introduction of impurity and surface states. By contrast, the negative bias voltage applied during a chemical vapor deposition process leads to pronounced modification of the morphology of diamond films due to an etching effect. Such a process results in a fine granular structure for the diamond films, significantly improving their field emission behavior via the enhancement of field concentration effect.
A Summary of Attack Methods and Confidentiality Protection Measures for Fully Automated Remote Analysis Systems

This paper presents a summary of the current state of research on reducing the risk of disclosure related to what may be called “non‐traditional” outputs for statistical agencies. Whereas traditional outputs include frequency tables, magnitude tables and public use microdata files, non‐traditional outputs include outputs associated with user‐defined exploratory data analysis and statistical modelling offered through a remote analysis system. In remote analysis, a system accepts a query from an analyst, runs it on data held in a secure environment, and then returns the results to the analyst. There is a considerable current interest in fully automated remote analysis systems, because these have the potential to enable agencies to respond to growing researcher demand for more and more detailed data. In practice, a range of protective measures is most effective in remote analysis, and the choice of this range depends heavily on the context including the regulatory environment, the dataset itself, and the purpose of the access.
On the largest eigenvalue of a Hermitian random matrix model with spiked external source II. Higher rank cases

This is the second part of a study of the limiting distributions of the top eigenvalues of a Hermitian matrix model with spiked external source under a general external potential. The case when the external source is of rank one was analyzed in an earlier paper. In the present paper we extend the analysis to the higher rank case. If all the eigenvalues of the external source are less than a critical value, the largest eigenvalue converges to the right end-point of the support of the equilibrium measure as in the case when there is no external source. On the other hand, if an external source eigenvalue is larger than the critical value, then an eigenvalue is pulled off from the support of the equilibrium measure. This transition is continuous, and is universal, including the fluctuation laws, for convex potentials. For non-convex potentials, two types of discontinuous transitions are possible to occur generically. We evaluate the limiting distributions in each case for general potentials including those whose equilibrium measure have multiple intervals for their support.
Aesthetic Frequency Classifications

This Department will carry articles of high quality on all aspects of computation in statistics. Papers describing new algorithms, programs, or statistical packages will not contain listings of the program, although the completely documented program must be available from the author. Review of the paper will always include a running test of the program by the referee. The description of a program or package in this Department should not be construed as an endorsement of it by the American Statistical Association or its Committees, nor is any warranty implied about the validity of the program. The Editorial Committee will be pleased to confer with authors about the appropriateness of topics or drafts of possible articles.
Friedel Sum Rule as a Trace Formula

We examine the Friedel sum rule which states that the “excess charge” due to a singleimpurity potential in a metal is equal to a sum of phase shifts for scatterings of electronsby the impurity. For ﬁnite volume, the “excess charge” is given by the diﬀerence betweentotal numbers of levels in the Fermi sea with and without the impurity potential. However,a sequence of the “excess charge” for ﬁnite volume is not necessarily bounded in the inﬁnitevolume limit, as was pointed out by Kirsch. In order to circumvent this diﬃculty, we deﬁne“excess charge” directly for the inﬁnite volume. The Friedel sum rule is proven to holdfor the “excess charge” thus deﬁned.
Asymmetry of masking revisited: Generalization of masker and probe bandwidth

A band of noise masked by a tone is audible at a much lower intensity increment between masker alone and masker plus just-detectable probe than is a tone masked by a band of noise. To better understand the mechanisms responsible for this asymmetry of masking, we measured thresholds of probe signals ranging in bandwidth from 0 to 256 Hz in the presence of masking signals also ranging in bandwidth from 0 to 256 Hz. Masker and probe center frequencies were 1 kHz in all cases. Two versions of the experiment are reported: (1) masker intensity constant at 70 dB SPL and (2) masker intensity roved over the range 65–75 dB SPL. The results confirm and extend previous results. For a fixed masker bandwidth, threshold intensity increments are essentially constant, and accounted for by a model based on long-term average energy, so long as probe bandwidth does not exceed masker bandwidth. If probe bandwidth exceeds masker bandwidth, threshold intensity increments decrease sharply for all masker bandwidths tested. Roved-intensity results are consistent with the hypothesis that detection is based on long-term average energy for probe bandwidth equal to or less than masker bandwidth, but that for probe bandwidth greater than masker bandwidth other cues are utilized. In addition, we investigated the possible contribution of off-frequency listening by developing and presenting results from a frequency-selective model of peripheral masking. Model results argue against detection based on spectral cues associated with off-frequency listening for signal bandwidths less than a critical band. The conclusion is that detection must utilize the temporal structure of the signal for probe bandwidth greater than masker bandwidth.
The influence of the first term of an arithmetic progression

The goal of this paper is to study the discrepancy of the distribution of arithmetic sequences in arithmetic progressions. We will fix a sequence 𝒜={a(n)}n⩾1 of non‐negative real numbers in a certain class of arithmetic sequences. For a fixed integer a≠0, we will be interested in the behaviour of 𝒜 over the arithmetic progressions a mod q, on average over q. Our main result is that, for certain sequences of arithmetic interest, the value of a has a significant influence on this distribution, even after removing the first term of the progressions.
Numerical model for DGD estimation in optical transmission system

In this paper we investigate the nonlinear and polarization effects in optical transmission systems and its influence on transmitted pulses. The main attention is focused on fundamental description of refractive index in nonlinear birefringent environment. In general, optical fiber is nonlinear transmission medium. The mutual interaction between transmission medium and optical intensity induces changes in the refractive index resulting to nonlinear effects that can be polarization-dependent in presence of birefringence. The global effect has a significant impact on pulse propagation. The aim of this paper is to present a numerical model, which will be suitable for estimation of DGD (Differential Group Delay) parameter in nonlinear birefringence medium. The DGD is crucial for evaluation of the impact of PMD (Polarization Mode Dispersion) in high-bit-rates fiber-optic system. Our approach opens the novel opportunity for DGD estimation based on numerical model of optical pulse propagation in nonlinear birefringence medium. Numerical model is based on solving NLSE (Nonlinear Schrodinger Equation) through the SSFM (Split-Step Fourier Method). The model is compatible for various input parameters, different kinds of optical fibers and also for different types of modulation formats. The obtained results show the DGD for different system parameters (such as input power and wavelength) and for different fiber polarization characteristics (birefringence and mode coupling). The total pulse broadening is also calculated and illustrates how all degradation effects influence the performance of fiber-optic transmission system.
A hybrid algorithm for solving minimization problem over (R,S)-symmetric matrices with the matrix inequality constraint

In this paper, we consider the following minimization problem:where , , , and are given. An efficient inequality relaxation technique is presented to relax the matrix inequality constraint so that there is an optimal solution which is (R,S)-symmetric that minimize , and also satisfies the corrected matrix inequality constraint. A hybrid algorithm with convergence analysis is given to solve this problem. Numerical examples show that the algorithm requires less CPU times when compared with some other methods.
Experiencing conceptual change about teaching: A case study from astronomy

Understanding faculty motivations for and barriers to change is an important component of facilitating instructional reform efforts to improve student learning. This case study describes the process of adoption of learner-centered instructional strategies by an astronomy faculty member, Ken, as viewed through the lens of conceptual change. Specifically, we applied the Cognitive Reconstruction of Knowledge Model (CRKM) to understand why Ken was willing to change his instructional strategies, what barriers to and supports for change existed, and how he and his students were impacted by this change. Ken’s statements and actions represented characteristics consistent with the CRKM. Notably, dissatisfaction, considered the primary motivator in many conceptual change models, was not of high importance in this case. Upon implementing learner-centered strategies, Ken’s students performed better on a measure of knowledge about stellar properties, which served to reinforce his motivation to continue with learner-ce.
Smoothing for linear systems erxcited by point processes with state–dependent rates

Smoothing for a linear system driven by a marked point process with a rate that depends on the state of the system is considered. A linear combination of the states is observed in additive white Gaussian noise. A smoother that uses estimation and detection is simulated on a computer and its mean squared error (MSE) is compared with the theoretical MSE of the optimal linear smoother and filter. The falso alarm rate is shown to depend strongly on the region of support of the mark distribution. When false alarms are low, the estimation/detection scheme has lower MSE than the optimal linear smoother.
A Novel Clustering Ensemble Method Based on One-Class Support Vector Machine

A clustering algorithm based on one-class support vector machine has been proposed recently. Because the kernel technique is used, this approach can appear preferable to the traditional k-means clustering. Clustering ensemble method can combine several divisions of all unlabeled data into a single clustering to gain the better clustering results. In this paper, the clustering ensemble method is applied to the clustering algorithm based one-class support vector machines. Several partitions of multiple runs with different random initial data sets are combined into a final clustering result. Experiments show that the new approach can improve the clustering performance.
Power-based Malware Detection in Linux Boot

We develop the first non-intrusive power-based malware detection method to ensure security of boot in an embedded system with the Intel Xeon-class CPU. Existing approaches to power-based malware detection are not effective for boot sequence because they consider a full power trace non-discriminately. Yet in a long boot power trace, security-relevant information is contained in a limited time interval. We demonstrate that the features based on optimal strategically-chosen phases are better in classification accuracy than those based on the full trace, under different feature and classifier selections. We investigate two threats: an untrusted device and a compromised kernel. We evaluate a supervised approach, requiring prior knowledge of malware, and an unsupervised approach, capable of zero-day detection. Identifying optimal phases for each attack leads to 10% improvement in average accuracy, compared to baseline classifiers trained with full trace features directly. We demonstrate an ensemble classification scheme which can be constructed with a reduced training cost (a 4.3X reduction). The classifier ensembles optimal phase classifiers of a limited, representative set of attacks. Our results show that the ensemble classifier improves average accuracy by 5% (to 77%) compared to the baseline full trace classifiers.
A GDP-driven model for the binary and weighted structure of the International Trade Network

Recent events such as the global financial crisis have renewed the interest in the topic of economic networks. One of the main channels of shock propagation among countries is the International Trade Network (ITN). Two important models for the ITN structure, the classical gravity model of trade (more popular among economists) and the fitness model (more popular among networks scientists), are both limited to the characterization of only one representation of the ITN. The gravity model satisfactorily predicts the volume of trade between connected countries, but cannot reproduce the missing links (i.e. the topology). On the other hand, the fitness model can successfully replicate the topology of the ITN, but cannot predict the volumes. This paper tries to make an important step forward in the unification of those two frameworks, by proposing a new gross domestic product (GDP) driven model which can simultaneously reproduce the binary and the weighted properties of the ITN. Specifically, we adopt a maximum-entropy approach where both the degree and the strength of each node are preserved. We then identify strong nonlinear relationships between the GDP and the parameters of the model. This ultimately results in a weighted generalization of the fitness model of trade, where the GDP plays the role of a ‘macroeconomic fitness’ shaping the binary and the weighted structure of the ITN simultaneously. Our model mathematically explains an important asymmetry in the role of binary and weighted network properties, namely the fact that binary properties can be inferred without the knowledge of weighted ones, while the opposite is not true.