arXiv subjects

Peng Ren

Publications and source records attributed to Peng Ren.

48 records · Page 3Linked to original sources

arXiv2021

Oil Spill SAR Image Segmentation via Probability Distribution Modelling

Segmentation of marine oil spills in Synthetic Aperture Radar (SAR) images is a challenging task because of the complexity and irregularities in SAR images. In this work, we aim to develop an effective segmentation method which addresses marine oil spill identification in SAR images by investigating the distribution representation of SAR images. To seek effective oil spill segmentation, we revisit the SAR imaging mechanism in order to attain the probability distribution representation of oil spill SAR images, in which the characteristics of SAR images are properly modelled. We then exploit the distribution representation to formulate the segmentation energy functional, by which oil spill characteristics are incorporated to guide oil spill segmentation. Moreover, the oil spill segmentation model contains the oil spill contour regularisation term and the updated level set regularisation term which enhance the representational power of the segmentation energy functional. Benefiting from the synchronisation of SAR image representation and oil spill segmentation, our proposed method establishes an effective oil spill segmentation framework. Experimental evaluations demonstrate the effectiveness of our proposed segmentation framework for different types of marine oil spill SAR image segmentation.

cs.LG↗

arXiv2021

Fractional order graph neural network

This paper proposes fractional order graph neural networks (FGNNs), optimized by the approximation strategy to address the challenges of local optimum of classic and fractional graph neural networks which are specialised at aggregating information from the feature and adjacent matrices of connected nodes and their neighbours to solve learning tasks on non-Euclidean data such as graphs. Meanwhile the approximate calculation of fractional order gradients also overcomes the high computational complexity of fractional order derivations. We further prove that such an approximation is feasible and the FGNN is unbiased towards global optimization solution. Extensive experiments on citation networks show that FGNN achieves great advantage over baseline models when selected appropriate fractional order.

cs.LG↗

arXiv2020

Controlling the non-linear emission of upconversion nanoparticles to enhance super-resolution imaging performance

Upconversion nanoparticles (UCNPs) exhibit unique optical properties such as photo-emission stability, large anti-Stokes shift, and long excited-state lifetimes, allowing significant advances in a broad range of applications from biomedical sensing to super-resolution microscopy. In recent years, progress on nanoparticle synthesis led to the development of many strategies for enhancing their upconversion luminescence, focused in particular on heavy doping of lanthanide ions and core-shell structures. In this article, we investigate the non-linear emission properties of fully Yb-based core-shell UCNPs and their impact on the super-resolution performance of stimulated excitation-depletion (STED) microscopy and super-linear excitation-emission (uSEE) microscopy. Controlling the power-dependent emission curve enables us to relax constraints on the doping concentrations and to reduce the excitation power required for accessing sub-diffraction regimes. We take advantage of this feature to implement multiplexed super-resolution imaging of a two-sample mixture.

physics.app-ph↗

arXiv2020

Realized volatility and parametric estimation of Heston SDEs

We present a detailed analysis of \emph{observable} moments based parameter estimators for the Heston SDEs jointly driving the rate of returns $R_t$ and the squared volatilities $V_t$. Since volatilities are not directly observable, our parameter estimators are constructed from empirical moments of realized volatilities $Y_t$, which are of course observable. Realized volatilities are computed over sliding windows of size $\varepsilon$, partitioned into $J(\varepsilon)$ intervals. We establish criteria for the joint selection of $J(\varepsilon)$ and of the sub-sampling frequency of return rates data. We obtain explicit bounds for the $L^q$ speed of convergence of realized volatilities to true volatilities as $\varepsilon \to 0$. In turn, these bounds provide also $L^q$ speeds of convergence of our observable estimators for the parameters of the Heston volatility SDE. Our theoretical analysis is supplemented by extensive numerical simulations of joint Heston SDEs to investigate the actual performances of our moments based parameter estimators. Our results provide practical guidelines for adequately fitting Heston SDEs parameters to observed stock prices series.

q-fin.CP↗

arXiv2019

Deep Adversarial Learning in Intrusion Detection: A Data Augmentation Enhanced Framework

Intrusion detection systems (IDSs) play an important role in identifying malicious attacks and threats in networking systems. As fundamental tools of IDSs, learning based classification methods have been widely employed. When it comes to detecting network intrusions in small sample sizes (e.g., emerging intrusions), the limited number and imbalanced proportion of training samples usually cause significant challenges in training supervised and semi-supervised classifiers. In this paper, we propose a general network intrusion detection framework to address the challenges of both \emph{data scarcity} and \emph{data imbalance}. The novelty of the proposed framework focuses on incorporating deep adversarial learning with statistical learning and exploiting learning based data augmentation. Given a small set of network intrusion samples, it first derives a Poisson-Gamma joint probabilistic generative model to generate synthesised intrusion data using Monte Carlo methods. Those synthesised data are then augmented by deep generative neural networks through adversarial learning. Finally, it adopts the augmented intrusion data to train supervised models for detecting network intrusions. Comprehensive experimental validations on KDD Cup 99 dataset show that the proposed framework outperforms the existing learning based IDSs in terms of improved accuracy, precision, recall, and F1-score.

cs.CR↗

arXiv2017

BFDA: A Matlab Toolbox for Bayesian Functional Data Analysis

We provide a MATLAB toolbox, BFDA, that implements a Bayesian hierarchical model to smooth multiple functional data with the assumptions of the same underlying Gaussian process distribution, a Gaussian process prior for the mean function, and an Inverse-Wishart process prior for the covariance function. This model-based approach can borrow strength from all functional data to increase the smoothing accuracy, as well as estimate the mean-covariance functions simultaneously. An option of approximating the Bayesian inference process using cubic B-spline basis functions is integrated in BFDA, which allows for efficiently dealing with high-dimensional functional data. Examples of using BFDA in various scenarios and conducting follow-up functional regression are provided. The advantages of BFDA include: (1) Simultaneously smooths multiple functional data and estimates the mean-covariance functions in a nonparametric way; (2) flexibly deals with sparse and high-dimensional functional data with stationary and nonstationary covariance functions, and without the requirement of common observation grids; (3) provides accurately smoothed functional data for follow-up analysis.

stat.OT↗

arXiv2016

Efficient Bayesian hierarchical functional data analysis with basis function approximations using Gaussian-Wishart processes

Functional data are defined as realizations of random functions (mostly smooth functions) varying over a continuum, which are usually collected with measurement errors on discretized grids. In order to accurately smooth noisy functional observations and deal with the issue of high-dimensional observation grids, we propose a novel Bayesian method based on the Bayesian hierarchical model with a Gaussian-Wishart process prior and basis function representations. We first derive an induced model for the basis-function coefficients of the functional data, and then use this model to conduct posterior inference through Markov chain Monte Carlo. Compared to the standard Bayesian inference that suffers serious computational burden and unstableness for analyzing high-dimensional functional data, our method greatly improves the computational scalability and stability, while inheriting the advantage of simultaneously smoothing raw observations and estimating the mean-covariance functions in a nonparametric way. In addition, our method can naturally handle functional data observed on random or uncommon grids. Simulation and real studies demonstrate that our method produces similar results as the standard Bayesian inference with low-dimensional common grids, while efficiently smoothing and estimating functional data with random and high-dimensional observation grids where the standard Bayesian inference fails. In conclusion, our method can efficiently smooth and estimate high-dimensional functional data, providing one way to resolve the curse of dimensionality for Bayesian functional data analysis with Gaussian-Wishart processes.

stat.ME↗

arXiv2016

Oxygen vacancy induced room temperature metal-insulator transition in nickelates films and its potential application in photovoltaics

Oxygen vacancy is intrinsically coupled with magnetic, electronic and transport properties of transition-metal oxide materials and directly determines their multifunctionality. Here, we demonstrate reversible control of oxygen content by post-annealing at temperature lower than 300 degree centigrade and realize the reversible metal-insulator transition in epitaxial NdNiO3 films. Importantly, over six orders of magnitude in the resistance modulation and a large change in optical band gap are demonstrated at room temperature without destroying the parent framework and changing the p-type conductive mechanism. Further study revealed that oxygen vacancies stabilized the insulating phase at room temperature is universal for perovskite nickelates films. Acting as electron donors, oxygen vacancies not only stabilize the insulating phase at room temperature, but also induce a large magnetization of ~50 emu/cm3 due to the formation of strongly correlated Ni2+ t2g6eg2 states. The band gap opening is an order of magnitude larger than that of the thermally driven metal-insulator transition and continuously tunable. Potential application of the newly found insulating phase in photovoltaics has been demonstrated in the nickelates-based heterojunctions. Our discovery opens up new possibilities for strongly correlated perovskite nickelates.

cond-mat.str-el↗

arXiv2015

Parametric Estimation from Approximate Data: Non-Gaussian Diffusions

We study the problem of parameters estimation in Indirect Observability contexts, where $X_t \in R^r$ is an unobservable stationary process parametrized by a vector of unknown parameters and all observable data are generated by an approximating process $Y^{\varepsilon}_t$ which is close to $X_t$ in $L^4$ norm. We construct consistent parameter estimators which are smooth functions of the sub-sampled empirical mean and empirical lagged covariance matrices computed from the observable data. We derive explicit optimal sub-sampling schemes specifying the best paired choices of sub-sampling time-step and number of observations. We show that these choices ensure that our parameter estimators reach optimized asymptotic $L^2$-convergence rates, which are constant multiples of the $L^4$ norm $|| Y^{\varepsilon}_t - X_t ||$.

math.PR↗

arXiv2015

Defect induced negative magnetoresistance and surface state immunity in topological insulator BiSbTeSe2

Absence of backscattering and occurrence of weak anti-localization are two characteristic features of topological insulators. We find that the introduction of defects results in the appearance of a negative contribution to magnetoresistance in the topological insulator BiSbTeSe2, at temperatures below 50 K. Our analysis shows that the negative magnetoresistance originates from an increase in the density of defect states created by introduction of disorder, which leaves the surface states unaffected. We find a decrease in the magnitude of the negative magnetoresistance contribution with increasing temperature and a robustness of the topological surface states to external disorder.

cond-mat.mes-hall↗

arXiv2013

Surface dominated transport in single crystalline nanoflake devices of topological insulator Bi1.5Sb0.5Te1.8Se1.2

We report experimental evidence of surface dominated transport in single crystalline nanoflake devices of topological insulator Bi1.5Sb0.5Te1.8Se1.2. The resistivity measurements show dramatic difference between the nanoflake devices and bulk single crystal. The resistivity and Hall analysis based on a two-channel model indicates that ~99% surface transport contribution can be realized in 200 nm thick BSTS nanoflake devices. Using standard bottom gate with SiO2 as a dielectric layer, pronounced ambipolar electric field effect was observed in devices fabricated with flakes of 100 - 200 nm thick. Moreover, angle-dependent magneto-resistances of a nanoflake device with thickness of 596 nm are fitted to a universal curve for the perpendicular component of the applied magnetic field. The value of phase coherence length obtained from 2D weak antilocalization fitting further confirmed the surface dominated transport. Our results open a path for realization of novel electric and spintronic devices based on the topological helical surface states.

cond-mat.mes-hall↗

arXiv2013

Beta-Ag2Te: A topological insulator with strong anisotropy

We present evidence of topological surface states in beta-Ag2Te through first-principles calculations and periodic quantum interference effect in single crystalline nanoribbon. Our first-principles calculations show that beta-Ag2Te is a topological insulator with a gapless Dirac cone with strong anisotropy. To experimentally probe the topological surface state, we synthesized high quality beta-Ag2Te nanoribbons and performed electron transport measurements. The coexistence of pronounced Aharonov-Bohm oscillations and weak Altshuler-Aronov-Spivak oscillations clearly demonstrates coherent electron transport around the perimeter of beta-Ag2Te nanoribbon and therefore the existence of metallic surface states, which is further supported by the temperature dependence of resistivity for beta-Ag2Te nanoribbons with different cross section areas. Highly anisotropic topological surface state of beta-Ag2Te suggests that the material is a promising material for fundamental study and future spintronic devices.

cond-mat.mes-hall↗