Elemental estimators for the Generalized Extreme Value tail

In a companion paper (McRobie(2013) arxiv:1304.3918), a simple set of `elemental' estimators was presented for the Generalized Pareto tail parameter. Each elemental estimator: involves only three log-spacings; is absolutely unbiased for all values of the tail parameter; is location- and scale-invariant; and is valid for all sample sizes $N$, even as small as $N= 3$. It was suggested that linear combinations of such elementals could then be used to construct efficient unbiased estimators. In this paper, the analogous mathematical approach is taken to the Generalised Extreme Value (GEV) distribution. The resulting elemental estimators, although not absolutely unbiased, are found to have very small bias, and may thus provide a useful basis for the construction of efficient estimators.

On the relation of slow feature analysis and Laplacian eigenmaps

The past decade has seen a rise of interest in Laplacian eigenmaps (LEMs) for nonlinear dimensionality reduction. LEMs have been used in spectral clustering, in semisupervised learning, and for providing efficient state representations for reinforcement learning. Here, we show that LEMs are closely related to slow feature analysis (SFA), a biologically inspired, unsupervised learning algorithm originally designed for learning invariant visual representations. We show that SFA can be interpreted as a function approximation of LEMs, where the topological neighborhoods required for LEMs are implicitly defined by the temporal structure of the data. Based on this relation, we propose a generalization of SFA to arbitrary neighborhood relations and demonstrate its applicability for spectral clustering. Finally, we review previous work with the goal of providing a unifying view on SFA and LEMs. © 2011 Massachusetts Institute of Technology.

Generalized power method for sparse principal component analysis

In this paper we develop a new approach to sparse principal component analysis (sparse PCA). We propose two single-unit and two block optimization formulations of the sparse PCA problem, aimed at extracting a single sparse dominant principal component of a data matrix, or more components at once, respectively. While the initial formulations involve nonconvex functions, and are therefore computationally intractable, we rewrite them into the form of an optimization program involving maximization of a convex function on a compact set. The dimension of the search space is decreased enormously if the data matrix has many more columns (variables) than rows. We then propose and analyze a simple gradient method suited for the task. It appears that our algorithm has best convergence properties in the case when either the objective function or the feasible set are strongly convex, which is the case with our single-unit formulations and can be enforced in the block case. Finally, we demonstrate numerically on a set of random and gene expression test problems that our approach outperforms existing algorithms both in quality of the obtained solution and in computational speed. © 2010 Michel Journée, Yurii Nesterov, Peter Richtárik and Rodolphe Sepulchre.

Synchronization with partial state feedback on SO(n)

In this paper we consider the problem of constructing a distributed feedback law to achieve synchronization for a group of k agents whose states evolve on SO(n) and which exchange only partial state information along communication links. The partial state information is given by the action of the state on reference vectors in ℝn. We propose a gradient based control law which achieves exponential local convergence to a synchronization configuration under a rank condition on a generalized Laplacian matrix. Furthermore, we discuss the case of time-varying reference vectors and provide a convergence result for this case. The latter helps reach synchronization, requiring less communication links and weaker conditions on the instantaneous reference vectors. Our methods are illustrated on an attitude synchronization problem where agents exchange only their relative positions observed in the respective body frames. ©2009 IEEE.

Generalized phenomenological model for the viscoelasticity of idealized asphalts

A generalized theory for the viscoelastic behavior of idealized bituminous mixtures (asphalts) is presented. The mathematical model incorporates strain rate and temperature dependency as well as nonmonotonic loading and unloading with shape recovery. The stiffening effect of the aggregate is included. The model is of phenomenological nature. It can be calibrated using a relatively limited set of experimental parameters, obtainable by uniaxial tests. It is shown that the mathematical model can be represented as a special nonlinear form of the Burgers model. This facilitates the derivation of numerical algorithms for solving the constitutive equations. A numerical scheme is implemented in a user material subroutine (UMAT) in the finite-element analysis (FEA) code ABAQUS. Simulation results are compared with uniaxial and indentation tests on an idealized asphalt mix. © 2014 American Society of Civil Engineers.

Spin-Hall effect in the generalized honeycomb lattice with Rashba spin-orbit interaction

We study the spin-Hall effect in a generalized honeycomb lattice, which is described by a tight-binding Hamiltonian including the Rashba spin-orbit coupling and inversion-symmetry breaking terms brought about by a uniaxial pressure. The calculated spin-Hall conductance displays a series of exact or approximate plateaus for isotropic or anisotropic hopping integral parameters, respectively. We show that these plateaus are a consequence of the various Fermi-surface topologies when tuning epsilon(F). For the isotropic case, a consistent two-band analysis, as well as a Berry-phase interpretation. are also given. (C) 2009 Elsevier B.V. All rights reserved.

Generalized Viscoplastic Modeling of Debris Flow

Various concepts have been proposed or used in the development of rheological models for debris flow. The earliest model developed by Bagnold was based on the concept of the “dispersive” pressure generated by grain collisions. Bagnold’s concept appears to be theoretically sound, but his empirical model has been found to be inconsistent with most theoretical models developed from non-Newtonian fluid mechanics. Although the generality of Bagnold’s model is still at issue, debris-flow modelers in Japan have generally accepted Takahashi’s formulas derived from Bagnold’s model. Some efforts have recently been made by theoreticians in non-Newtonian fluid mechanics to modify or improve Bagnold’s concept or model. A viable rheological model should consist both of a rate-independent part and a rate-dependent part. A generalized viscoplastic fluid (GVF) model that has both parts as well as two major rheological properties (i.e., the normal stress effect and soil yield criterion) is shown to be sufficiently accurate, yet practical, for general use in debris-flow modeling. In fact, Bagnold’s model is found to be only a particular case of the GVF model. Analytical solutions for (steady) uniform debris flows in wide channels are obtained from the GVF model based on Bagnold’s simplified assumption of constant grain concentration.

Practical Evaluation of Security against Generalized Interpolation Attack

Interpolation attack was presented by Jakobsen and Knudsen at FSE'97. Interpolation attack is effective against ciphers that have a certain algebraic structure like the PURE cipher which is a prototype cipher, but it is difficult to apply the attack to real-world ciphers. This difficulty is due to the difficulty of deriving a low degree polynomial relation between ciphertexts and plaintexts. In other words, it is difficult to evaluate the security against interpolation attack. This paper generalizes the interpolation attack. The generalization makes easier to evaluate the security against interpolation attack. We call the generalized interpolation attack linear sum attack. We present an algorithm that evaluates the security of byte-oriented ciphers against linear sum attack. Moreover, we show the relationship between linear sum attack and higher order differential attack. In addition, we show the security of CRYPTON, E2, and RIJNDAEL against linear sum attack using the algorithm.

Prediction of simultaneously large and opposite generalized Goos-Hanchen shifts for TE and TM light beams in an asymmetric double-prism configuration

It is predicted that large and opposite generalized Goos-Hanchen (GGH) shifts may occur simultaneously for TE and TM light beams upon reflection from an asymmetric double-prism configuration when the angle of incidence is below but near the critical angle for total reflection, which may lead to interesting applications in optical devices and integrated optics. Numerical simulations show that the magnitude of the GGH shift can be of the order of beam's width.