Au Microdisk-Size Dependence of Quantum Dot Emission from the Hybrid Metal-Distributed Bragg Reflector Structures Employed for Single Photon Sources

We investigate metallic microdisk-size dependence of quantum dot （QD） spontaneous emission rate and micro- antenna directional emission effect for the hybrid metM-distributed Bragg reflector structures based on a particular single QD emission. It is found that the measured photolumineseence （PL） intensity is very sensitive to the size of metMlic disk, showing an enhancement factor of 11 when the optimal disk diameter is 2μm and the numerical aperture of microscope objective NA=0.5. It is found that for large metal disks, the Purcell effect is dominant for enhanced PL intensity, whereas for small size disks the main contribution comes from plasmon scattering at the disk edge within the light cone collected by the microscope objective.

IndRT-GCNets: Knowledge Reasoning with Independent Recurrent Temporal Graph Convolutional Representations

Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events,we propose an Independent Recurrent Temporal Graph Convolution Networks(IndRT-GCNets)framework to efficiently and accurately capture event attribute information.The framework models the knowledge graph sequences to learn the evolutionary represen-tations of entities and relations within each period.Firstly,by utilizing the temporal graph convolution module in the evolutionary representation unit,the framework captures the structural dependency relationships within the knowledge graph in each period.Meanwhile,to achieve better event representation and establish effective correlations,an independent recurrent neural network is employed to implement auto-regressive modeling.Furthermore,static attributes of entities in the entity-relation events are constrained andmerged using a static graph constraint to obtain optimal entity representations.Finally,the evolution of entity and relation representations is utilized to predict events in the next subsequent step.On multiple real-world datasets such as Freebase13(FB13),Freebase 15k(FB15K),WordNet11(WN11),WordNet18(WN18),FB15K-237,WN18RR,YAGO3-10,and Nell-995,the results of multiple evaluation indicators show that our proposed IndRT-GCNets framework outperforms most existing models on knowledge reasoning tasks,which validates the effectiveness and robustness.

Learning Sequential and Structural Dependencies Between Nucleotides for RNA N6-Methyladenosine Site Identification

N6-methyladenosine(m6A)is an important RNA methylation modification involved in regulating diverse biological processes across multiple species.Hence,the identification of m6A modification sites provides valuable insight into the biological mechanisms of complex diseases at the post-transcriptional level.Although a variety of identification algorithms have been proposed recently,most of them capture the features of m6A modification sites by focusing on the sequential dependencies of nucleotides at different positions in RNA sequences,while ignoring the structural dependencies of nucleotides in their threedimensional structures.To overcome this issue,we propose a cross-species end-to-end deep learning model,namely CR-NSSD,which conduct a cross-domain representation learning process integrating nucleotide structural and sequential dependencies for RNA m6A site identification.Specifically,CR-NSSD first obtains the pre-coded representations of RNA sequences by incorporating the position information into single-nucleotide states with chaos game representation theory.It then constructs a crossdomain reconstruction encoder to learn the sequential and structural dependencies between nucleotides.By minimizing the reconstruction and binary cross-entropy losses,CR-NSSD is trained to complete the task of m6A site identification.Extensive experiments have demonstrated the promising performance of CR-NSSD by comparing it with several state-of-the-art m6A identification algorithms.Moreover,the results of cross-species prediction indicate that the integration of sequential and structural dependencies allows CR-NSSD to capture general features of m6A modification sites among different species,thus improving the accuracy of cross-species identification.

Opportunistic maintenance for multi-component systems considering structural dependence and economic dependence

Although opportunistic maintenance strategies are widely used for multi-component systems, all opportunistic mainte- nance strategies only consider economic dependence and do not take structural dependence into account. An opportunistic main- tenance strategy is presented for a multi-component system that considers both structural dependence and economic dependence. The cost relation and time relation among components based on structural dependence are developed. The maintenance strategy for each component of a multi-component system involves one of five maintenance actions, namely, no-maintenance, a minimal maintenance action, an imperfect maintenance action, a perfect maintenance action, and a replacement action. The maintenance action is determined by the virtual age of the component, the life expectancy of the component, and the age threshold values. Monte Carlo simulation is designed to obtain the optimal oppor- tunistic maintenance strategy of the system over its lifetime. The simulation result reveals that the minimum maintenance cost with a strategy that considers structural dependence is less than that with a strategy that does not consider structural dependence. The availability with a strategy that considers structural dependence is greater than that with a strategy that does not consider structural dependence under the same conditions.

Copula-EVT Based Tail Dependence Structure of Financial Markets in China

Tail dependence structure model based on Copula theory and extreme value theory （EVT） is constructed to picture the tail correlation of financial time series more exact. The empirical research results show that the Gumbel Copula can fit the upper and lower tail dependence structures of Shanghai A share index and Shenzhen A share index, and correlation of upper tails of both indices is stronger than that of lower-tails.

Structural Dependence of Creep/Fatigue Behaviour of Single Crystal Ni-base Superalloy

The effect of different initial microstructures deftned by γ' precipitate morphology has been investigated at the creep/fatigue conditions of 900℃ and 500 MPa. The wave form of stress as a function of time for cyclic load was of trapezoidal shape with a hold time of 10s at the upper stress level. The TEM was employed to examine the deformation process in strengthened γ' matrix in dependence of γ' precipitate morphology. The fracture lifetime and cycle number up to fracture were the criteria to evaluate the additional cyclic component efFect on the course of deformation

Quasi-40-Day Oscillation and Its Teleconnection Structure together with the Possible Dependence on Conversion of Barotropic Unstable Energy of Temporal Mean Flow

A study is made of the distribution of the diagnostic quantity vector E and the teleconnection structure of 30-50 (quasi-40) day oscillation, together with the dependence on the conversion of barotropic unstable energy of mean flow in terms of ECWMF daily 500 hPa grid data in winter, indicating that the energy transportation is closely associated with the westerly jet position, with zonal (meridional) propagation in the strong (weak) wind region, that considerable conversion of barotropic energy occurs at the jet exit region where low-frequency oscillation gains energy from the mean flow, leading to maximum kinetic energy for the oscillation observed there, which is marked by evident barotropy in striking contrast to the baroclinicity at low latitudes and that the teleconnection core is related to the center of action in the atmosphere and bound up with the pattern of the west wind.

Dependence of Structural and Optoelectrical Properties on the Composition of Electron Beam Evaporated Zn_xCd_(1-x)S Thin Films

Thin films of ZnxCd1-xS have been prepared by electron beam evaporation of a mixture of ZnS & CdS powders. The films are deposited onto sodalime glass slides under similar conditions.The composition of the films is varied from CdS to ZnS (x=0 to 1). The films show a regular change in color from toner red to orange yellow as Zn concentration increases to maximum.These films are characterized for their optical, electricaI and structural properties. The bandgap value of ZnxCd1-xS films is found to vary linearIy from 2.20 eV to 3.44 eV with change in the x value from 0 to 1. The resistivity of these films is in the range of 171.0 Ωcm to 5.5× 106Ωcm for x=0~0.6. All the samples show cubic structure after annealing in air at 250℃ for 40 min.The lattice constant ao varies from 0.5884 nm to 0.54109 nm linearly.

Co Content Dependence of Crystal Structure and Specific Magnetization of Fe_(1-x)Co_x-SiO_2 Granules Prepared by Sol-Gel Method

The Co content dependence of crystal structure and specific magnetization of Fe1-xCox-SiO2granular solid prepared by the sol-gel method have been studied. It is found that the crystal structure, Iattice parameter and specific magnetization of the FeCo alloy particles depend on the Co content.

Structure Dependence of Excitonic Effects in Chiral Graphene Nanoribbons

We explore the excitonic effects in chiral graphene nanoribbons (cGNRs), whose edges are composed alternatively of armchair-edged and zigzag-edged segments. For cGNRs dominated by armchair edges, their energy gaps and exciton energies decrease with increasing chirality angles, and they, as functions of widths, oscillate with the period of three, while the exciton binding energies do not have such distinct oscillation. On the other hand, for cGNRs dominated by zigzag edges, all the energy gaps, exciton energies, and exciton binding energies show oscillation properties with their widths, due to the interactions between the edge states localized at the opposite zigzag edges. In addition, the triplet excitons are energy degenerate when the electrons are spin-unpolarized, while the degeneracy split when the electrons are spin-polarized. All the studied cGNRs show strong excitonic effects with the exciton binding energies of hundreds of meV.

Dependence of Nitrogen/Argon Reaction Gas Amount on Structural,Mechanical and Optical Properties of Thin WNx Films

WNxfilms are deposited by reactive chemical vapor deposition at different amounts of nitrogen in gas mixtures.Experimental data demonstrate that nitrogen amount has a strong effect on microstructure, phase formation,texture morphology, mechanical and optical properties of the WNxfilms. With increasing nitrogen a phase transition from a single WNxphase with low crystallinity structure to a well-mixed crystallized hexagonal WNxand face-centered-cubic W2N phases appears. Relatively smooth morphology at lower N2concentration changes to a really smooth morphology and then granular with coarse surface at higher N2concentration. The SEM observation clearly shows a columnar structure at lower N2concentration and a dense nanoplates one for higher nitrogen content. The hardness of WNxthin films mainly depends on the film microstructure. The absorbance peak position shifts to shorter wavelength continuously with increasing nitrogen amount and decreasing particle size.

Uniform asymptotics for finite-time ruin probability in some dependent compound risk models with constant interest rate

Consider two dependent renewal risk models with constant interest rate. By using some methods in the risk theory, uniform asymptotics for finite-time ruin probability is derived in a non-compound risk model, where claim sizes are upper tail asymptotically independent random variables with dominatedly varying tails, claim inter-arrival times follow the widely lower orthant dependent structure, and the total amount of premiums is a nonnegative stochastic process. Based on the obtained result, using the method of analysis for the tail probability of random sums, a similar result in a more complex and reasonable compound risk model is also obtained, where individual claim sizes are specialized to be extended negatively dependent and accident inter-arrival times are still widely lower orthant dependent, and both the claim sizes and the claim number have dominatedly varying tails.

Existence and Uniqueness of Positive Solutions for a System of Multi-order Fractional Differential Equations

In this paper, we study a class of ruin problems, in which premiums and claims are dependent. Under the assumption that premium income is a stochastic process, we raise the model that premiums and claims are dependent, give its numerical characteristics and the ruin probability of the individual risk model in the surplus process. In addition, we promote the number of insurance policies to a Poisson process with parameter λ, using martingale methods to obtain the upper bound of the ultimate ruin probability.

The ICM method with objective function transformed by variable discrete condition for continuum structure

ICM （Independent Continuous Mapping） method can solve topological optimization problems with the minimized weight as the objective and subjected to displacement constraints. To get a clearer topological configuration, by introducing the discrete condition of topological variables and integrating with the original objective, an optimal model with multi-objectives is formulated to make the topological variables approach 0 or 1 as near as possible, and the model reduces the effect of deleting rate on the result. The image-filtering method is employed to eliminate the checkerboard patterns and mesh dependence that occurred in the topology optimization of a continuum structure. The computational efficiency is enhanced through selecting quasi-active displacement constraints and a design region. Numerical examples indicate that this algorithm is robust and practicable, though the number of iterations is slightly increased with respect to the original algorithm.

Research on Ruin Probability of Risk Model Based on AR(1)Time Series

The insurance industry typically exploits ruin theory on collected data to gain more profits.However,state-of-art approaches fail to consider the dependency of the intensity of claim numbers,resulting in the loss of accuracy.In this work,we establish a new risk model based on traditional AR(1)time series,and propose a fine-gained insurance model which has a dependent data structure.We leverage Newton iteration method to figure out the adjustment coefficient and evaluate the exponential upper bound of the ruin probability.We claim that our model significantly improves the precision of insurance model and explores an interesting direction for future research.

Evolution of domain structure in Fe_(3)GeTe_(2)

Two-dimensional(2D)magnets provide an ideal platform to explore new physical phenomena in fundamental magnetism and to realize the miniaturization of magnetic devices.The study on its domain structure evolution with thickness is of great significance for better understanding the 2D magnetism.Here,we investigate the magnetization reversal and domain structure evolution in 2D ferromagnet Fe_(3)GeTe_(2)(FGT)with a thickness range of 11.2-112 nm.Three types of domain structures and their corresponding hysteresis loops can be obtained.The magnetic domain varies from a circular domain via a dendritic domain to a labyrinthian domain with increasing FGT thickness,which is accompanied by a transition from squared to slanted hysteresis loops with reduced coercive fields.These features can be ascribed to the total energy changes from exchange interaction-dominated to dipolar interaction-dominated with increasing FGT thickness.Our finding not only enriches the fundamental magnetism,but also paves a way towards spintronics based on 2D magnet.

A Realistic Interpretation of Quantum Wavefunctions as Temperature Dependent Vacuum Polarization Waves

We discuss in this paper a novel interpretation of Born rule as an approximated thermodynamic law which emerges from the interaction of a quantum system with a non-stationary thermal bath associated to vacuum fluctuations induced by external environment radiation. In particular we assume that vacuum polarization is a real non relativistic phenomena caused by hidden vacuum charge oscillations which diffuses heat energy in a dispersive and dissipative dielectric medium with a temperature dependent speed of propagation. We propose a model which couples vacuum wavefunctions to vacuum charge fluctuations and we deduce a temperature dependent running fine structure constant function proportional, at first approximation, to the squared of the effective electron charge and compatible with known experimental data. We interpret the vacuum symmetry breaking energy fluctuations induced in scattering experiments of particle physics and in laser assisted nuclear reactions as thermal quasi-monochromatic beams produced by the decay of hidden non equilibrium massive photons propagating with a variable light speed. We suggest, exploiting an old analogy between plasmons and pseudo Goldstone bosons, to interpret heat diffusion of this non relativistic polarized vacuum as a real De Broglie electromagnetic scalar wave associated to the radiation emitted by the hidden massive photons with acceleration proportional to vacuum Unruh like temperature. We predict a temperature dependent deviation from Coulomb law and a generalized dispersive law of these hidden unstable photons that could be revealed as not stationary coloured noise in experiments on anomalous heat diffusions associated to the decay of unstable accelerated pairs produced in nuclear physics experiments. We discuss then how our proposal of a temperature dependent non relativistic vacuum polarization might be applied to deduce a dynamic generalization of Born rule based on a realistic interpretation of quantum wavefunctions as averaged electromagnetic waves of hidden massive photons. Finally we suggest to test our time asymmetric model looking for very fast oscillating polarization thermal waves emitted during the not instantaneous wavefunction collapse and revealed as not stationary bulk heating effects in experiments on accelerated conductors and nanoconductors.

Screening-Assisted Dynamic Multiple Testing with False Discovery Rate Control

In the era of big data,high-dimensional data always arrive in streams,making timely and accurate decision necessary.It has become particularly important to rapidly and sequentially identify individuals whose behavior deviates from the norm.Aiming at identifying as many irregular behavioral patterns as possible,the authors develop a large-scale dynamic testing system in the framework of false discovery rate(FDR)control.By fully exploiting the sequential feature of datastreams,the authors propose a screening-assisted procedure that filters streams and then only tests streams that pass the filter at each time point.A data-driven optimal screening threshold is derived,giving the new method an edge over existing methods.Under some mild conditions on the dependence structure of datastreams,the FDR is shown to be strongly controlled and the suggested approach for determining screening thresholds is asymptotically optimal.Simulation studies show that the proposed method is both accurate and powerful,and a real-data example is used for illustrative purpose.

The Finite-time Ruin Probability of a Discrete-time Risk Model with Subexponential and Dependent Insurance and Financial Risks

Consider a discrete-time risk model with insurance and financial risks in a stochastic economic environment. Assume that the insurance and financial risks form a sequence of independent and identically distributed random vectors with a generic random vector following a wide type of dependence structure. An asymptotic formula for the finite-time ruin probability with subexponential insurance risks is derived. In doing so, the subexponentiality of the product of two dependent random variables is investigated simultaneously.

pyvine:The Python Package for Regular Vine Copula Modeling,Sampling and Testing

Regular vine copula provides rich models for dependence structure modeling.It combines vine structures and families of bivariate copulas to construct a number of multivariate distributions that can model a wide range dependence patterns with different tail dependence for different pairs.Two special cases of regular vine copulas,C-vine and D-vine copulas,have been extensively investigated in the literature.We propose the Python package,pyvine,for modeling,sampling and testing a more generalized regular vine copula(R-vine for short).R-vine modeling algorithm searches for the R-vine structure which maximizes the vine tree dependence in a sequential way.The maximum likelihood estimation algorithm takes the sequential estimations as initial values and uses L-BFGS-B algorithm for the likelihood value optimization.R-vine sampling algorithm traverses all edges of the vine structure from the last tree in a recursive way and generates the marginal samples on each edge according to some nested conditions.Goodness-of-fit testing algorithm first generates Rosenblatt’s transformed data E and then tests the hypothesis H^(∗)_(0):E∼C_(⊥)by using Anderson–Darling statistic,where C_(⊥)is the independence copula.Bootstrap method is used to compute an adjusted p-value of the empirical distribution of replications of Anderson–Darling statistic.The computing of related functions of copulas such as cumulative distribution functions,Hfunctions and inverse H-functions often meets with the problem of overflow.We solve this problem by reinvestigating the following six families of bivariate copulas:Normal,Student t,Clayton,Gumbel,Frank and Joe’s copulas.Approximations of the above related functions of copulas are given when the overflow occurs in the computation.All these are implemented in a subpackage bvcopula,in which subroutines are written in Fortran and wrapped into Python and,hence,good performance is guaranteed.