Interpretation of anomalous temperature dependence of anti-Stokes photoluminescence at GaInP2/GaAs interface

The anomalous temperature dependence of anti-Stokes photoluminescence (ASPL) at the GaInP2/GaAs interface was studied. A localized state luminescence model was employed to interpret the temperature-dependence of the peak position of the ASPL. The results show that the localization of the up-converted carriers plays an important role in radiative recombination producing the ASPL. The studies also show that the microscopic mechanism of thermal quenching of ASPL at GaInP2/GaAs interface is unmasked.published_or_final_versio

Eye movement patterns during the recognition of three-dimensional objects: Preferential fixation of concave surface curvature minima

This study used eye movement patterns to examine how high-level shape information is used during 3D object recognition. Eye movements were recorded while observers either actively memorized or passively viewed sets of novel objects, and then during a subsequent recognition memory task. Fixation data were contrasted against different algorithmically generated models of shape analysis based on: (1) regions of internal concave or (2) convex surface curvature discontinuity or (3) external bounding contour. The results showed a preference for fixation at regions of internal local features during both active memorization and passive viewing but also for regions of concave surface curvature during the recognition task. These findings provide new evidence supporting the special functional status of local concave discontinuities in recognition and show how studies of eye movement patterns can elucidate shape information processing in human vision

Lorenz System Parameter Determination and Application to Break the Security of Two-channel Chaotic Cryptosystems

This paper describes how to determine the parameter values of the chaotic Lorenz system used in a two-channel cryptosystem. The geometrical properties of the Lorenz system are used firstly to reduce the parameter search space, then the parameters are exactly determined, directly from the ciphertext, through the minimization of the average jamming noise power created by the encryption process.Comment: 5 pages, 5 figures Preprint submitted to IEEE T. Cas II, revision of authors name spellin

Sequential inference methods for non-homogeneous poisson processes with state-space prior

© 2018 IEEE. The Non-homogeneous Poisson process is a point process with time-varying intensity across its domain, the use of which arises in numerous areas in signal processing and machine learning. However, applications are largely limited by the intractable likelihood function and the high computational cost of existing inference schemes. We present a sequential inference framework that utilises generative Poisson data and sequential Markov Chain Monte Carlo (SMCMC) algorithm to enable online inference in various applications. The proposed model is compared to competing methods on synthetic datasets and tested with real-world financial data

Light-Cone Quantization and Hadron Structure

In this talk, I review the use of the light-cone Fock expansion as a tractable and consistent description of relativistic many-body systems and bound states in quantum field theory and as a frame-independent representation of the physics of the QCD parton model. Nonperturbative methods for computing the spectrum and LC wavefunctions are briefly discussed. The light-cone Fock state representation of hadrons also describes quantum fluctuations containing intrinsic gluons, strangeness, and charm, and, in the case of nuclei, "hidden color". Fock state components of hadrons with small transverse size, such as those which dominate hard exclusive reactions, have small color dipole moments and thus diminished hadronic interactions; i.e., "color transparency". The use of light-cone Fock methods to compute loop amplitudes is illustrated by the example of the electron anomalous moment in QED. In other applications, such as the computation of the axial, magnetic, and quadrupole moments of light nuclei, the QCD relativistic Fock state description provides new insights which go well beyond the usual assumptions of traditional hadronic and nuclear physics.Comment: LaTex 36 pages, 3 figures. To obtain a copy, send e-mail to [email protected]

Class reconstruction driven adversarial domain adaptation for hyperspectral image classification

We address the problem of cross-domain classification of hyperspectral image (HSI) pairs under the notion of unsupervised domain adaptation (UDA). The UDA problem aims at classifying the test samples of a target domain by exploiting the labeled training samples from a related but different source domain. In this respect, the use of adversarial training driven domain classifiers is popular which seeks to learn a shared feature space for both the domains. However, such a formalism apparently fails to ensure the (i) discriminativeness, and (ii) non-redundancy of the learned space. In general, the feature space learned by domain classifier does not convey any meaningful insight regarding the data. On the other hand, we are interested in constraining the space which is deemed to be simultaneously discriminative and reconstructive at the class-scale. In particular, the reconstructive constraint enables the learning of category-specific meaningful feature abstractions and UDA in such a latent space is expected to better associate the domains. On the other hand, we consider an orthogonality constraint to ensure non-redundancy of the learned space. Experimental results obtained on benchmark HSI datasets (Botswana and Pavia) confirm the efficacy of the proposal approach

Sharp endpoint estimates for some operators associated with the Laplacian with drift in Euclidean space.

Let $v \ne 0$ be a vector in $\R^n$. Consider the Laplacian on $\R^n$ with drift $\Delta_{v} = \Delta + 2v\cdot \nabla$ and the measure $d\mu(x) = e^{2 \langle v, x \rangle} dx$, with respect to which $\Delta_{v}$ is self-adjoint. %Let $d$ and $\nabla$ denote the Euclidean distance and the gradient operator on $\R^n$. Consider the space $(\R^n, d,d\mu)$, which has the property of exponential volume growth. This measure has exponential growth with respect to the Euclidean distance. We study weak type $(1, 1)$ and other sharp endpoint estimates for the Riesz transforms of any order, and also for the vertical and horizontal Littlewood-Paley-Stein functions associated with the heat and the Poisson semigroups

Sequential Dynamic Leadership Inference Using Bayesian Monte Carlo Methods

Hierarchy and leadership interactions commonly occur in animal groups, crowds of people and in vehicle motions. Such interactions are often affected by one or more individuals who possess key domain information (e.g. final destination, environmental constraints and best routes) or pertinent traits (e.g. better navigation, sensing and decision making capabilities) compared with the rest of the group. This paper presents a framework for the automatic identification of group structure and leadership from noisy sensory observations of tracked groups. Accordingly, a new leader-follower model is developed which assumes the dynamics of the group to be a multivariate Ornstein–Uhlenbeck process with the designated leader(s) drifting to the destination and followers reverting to the leaders’ state. Sequential Monte Carlo (SMC) approaches, and specifically the sequential Markov chain Monte Carlo (SMCMC) approach, are adopted to infer, probabilistically, the evolving leadership structure. A Rao-Blackwellisation scheme is employed such that the kinematic state of the objects in the group is inferred in closed form by Kalman filtering. Experiments show that the proposed techniques can successfully determine the leadership structures in challenging scenarios with a corresponding enhancement in tracking accuracy through direct consideration of the leadership interactions of the group

Estimates for operators related to the sub-Laplacian with drift in Heisenberg groups

In the Heisenberg group of dimension 2n+1, we consider the sub-Laplacian witha drift in the horizontal coordinates. There is a related measure for whichthis operator is symmetric.The corresponding Riesz transforms are known to be L^p boundedwith respect to this measure.We prove that the Riesz transforms of order 1 are also of weak type (1,1),and that this is false for order 3 and above. Further, we consider the relatedmaximal Littlewood-Paley-Stein operators and prove the weak type (1,1) forthose of order 1 and disprove it for higher orders