Multi-index evaluation learning-based computation offloading optimization for power internet of things

Cloud–edge-device collaborative computation offloading can provide flexible and real-time data processing services for massive resource-constrained devices in power internet of things (PIoT). However, the computation offloading optimization in PIoT still faces several challenges such as high computation offloading delay caused by uncertain information, coupling between task offloading and computation resource allocation, and degraded optimization performance due to the lack of multi-index consideration. To address the above challenges, we formulate a joint optimization problem of task offloading and computation resource allocation to minimize the average computation offloading delay. Specifically, a multi-index evaluation learning-based two-stage computation offloading (MINCO) algorithm is proposed to decouple the joint optimization problem into two-stage subproblems and solve them with evaluation and learning of multiple indexes including data flow characteristic, service priority, empirical average computation offloading delay, and empirical arm selection times. Simulation results show that compared with the baseline 1 and baseline 2 algorithms, MINCO improves the average computation offloading delay by 14.67% and 30.71%. Moreover, MINCO can evaluate different service priorities and data flow characteristics to meet different requirements of computation offloading delay.     

一种改进的光学层析图像重建方法

Alexander D．Klose将联合差分方法用于光学层析图像重建的梯度计算中,但给出的对光学参量的求导算法有局限,他的算法只能实现对边界点光学参量的导数计算,而无法实现对内部点光学参量导数的计算,会导致图像重建失败。在联合差分算法的基础上,研究了针对内部点光学参量的求导方法,给出了一种基于树形结构的对内部点光学参量求导的策略。具体实现时,为了降低计算复杂度,采用近似梯度计算方法。算法的仿真实验结果表明：该方法可以有效地实现对内部点光学参量的导数计算,提出的近似计算方法可降低梯度计算复杂度,提高运算速度,并可得到良好的图像重建质量。     

基于对偶数理论的资料同化新方法

针对变分资料同化中目标泛函梯度计算精度不高且复杂等问题, 提出了一种基于对偶数理论的资料同化新方法, 主要优点是: 能避免复杂的伴随模式开发及其逆向积分, 只需在对偶数空间通过正向积分就能同时计算出目标泛函和梯度向量的值. 首先利用对偶数理论把梯度分析过程转换为对偶数空间中目标泛函计算过程, 简单、高效和高精度地获得梯度向量值; 其次结合典型的最优化方法, 给出了非线性物理系统资料同化问题的新求解算法; 最后对Lorenz 63混沌系统、包含开关的不可微物理模型和抛物型偏微分方程分别进行了资料同化数值实验, 结果表明: 新方法能有效和准确地估计出预报模式的初始条件或物理参数值.     

Perturbation Monte Carlo methods to solve inverse photon migration problems in heterogeneous tissues

We introduce a novel and efficient method to provide solutions to inverse photon migration problems in heterogeneous turbid media. The method extracts derivative information from a single Monte Carlo simulation to permit the rapid determination of rates of change in the detected photon signal with respect to perturbations in background tissue optical properties. We then feed this derivative information to a nonlinear optimization algorithm to determine the optical properties of the tissue heterogeneity under examination. We demonstrate the use of this approach to solve rapidly a two-region inverse problem of photon migration in the transport regime, for which diffusion-approximation-based approaches are not applicable.     

基于微分图像自相关的离焦模糊图像盲复原

针对离焦模糊图像的盲复原算法的研究具有重要的实际意义和实用价值.根据光学离焦成像模型,研究提出了一种基于微分图像自相关的离焦模糊图像超分辨力盲复原算法,即首先采用拉普拉斯算子对离焦模糊图像进行二阶微分并求微分图像的自相关,然后从自相关结果所包含的信息中确定离焦模糊半径,最后以离焦模糊模型结合MPMAP超分辨力复原算法对离焦模糊图像进行肓复原.实验证明:算法能够以较高的精度估计出离焦模糊半径并实现离焦模糊图像的盲复原,该算法较其它同类算法在减少计算过程中需要考虑的各类因素的同时也减少了计算量,提高了结果精度,依靠超分辨力复原算法获取更多的复原图像信息,已在实际刑侦和物证鉴定的离焦模糊图像判读和鉴定中获得成功应用.     

Efficient Markets and Contingent Claims Valuation: An Information Theoretic Approach

This research article shows how the pricing of derivative securities can be seen from the context of stochastic optimal control theory and information theory. The financial market is seen as an information processing system, which optimizes an information functional. An optimization problem is constructed, for which the linearized Hamilton–Jacobi–Bellman equation is the Black–Scholes pricing equation for financial derivatives. The model suggests that one can define a reasonable Hamiltonian for the financial market, which results in an optimal transport equation for the market drift. It is shown that in such a framework, which supports Black–Scholes pricing, the market drift obeys a backwards Burgers equation and that the market reaches a thermodynamical equilibrium, which minimizes the free energy and maximizes entropy.     

Is computation reversible?

Recent studies have suggested that computation is essentially reversible, provided no information is lost. This is a consequence of Landauer’s principle which only requires energy expenditure and entropy increase for information deletion. In this paper we propose to treat information as being intrinsic to points of non-analyticity, so that the movement of information is always associated with the dissipation of heat. This allows us to construct a theory consistent with causality, and the second law of thermodynamics. Since computation requires the movement of information bits through finite volume gates, energy is dissipated even when information is not destroyed, thus indicating that computation is fundamentally non-reversible.     

Zeta function and regularized determinant on a disc and on a cone

We give formulas for the analytic extension of the zeta function of the induced Laplacian L on a disc and on a cone. This allows the explicit computation of the value of the zeta function and of its derivative at the origin, and hence we get a formula for the regularized determinant of L.     

薄膜椭偏测量的自适应混合反演算法

基于梯度的线性反演方法计算效率高,基于随机扰动的模拟退火方法寻找最优解能力强针对薄膜椭偏测量的多极值问题,综合两者的优点,提出一种求解薄膜椭偏测量问题的混合反演算法.模型每次扰动采用线性寻优方法搜寻局部最优解,叠代过程中采用均匀设计的模拟退火方法随机搜寻模型,使该算法有跳出局部最优解的能力,可以在较少的叠代次数内搜寻到全局最优解,从而提高求解薄膜椭偏测量非线性反演方法的计算效率.对反演过程控制参数进行讨论,该算法具有自适应的特点.计算表明,该算法可有效求解薄膜椭偏测量的多极值问题.     

Partial Information Decomposition and the Information Delta: A Geometric Unification Disentangling Non-Pairwise Information

Information theory provides robust measures of multivariable interdependence, but classically does little to characterize the multivariable relationships it detects. The Partial Information Decomposition (PID) characterizes the mutual information between variables by decomposing it into unique, redundant, and synergistic components. This has been usefully applied, particularly in neuroscience, but there is currently no generally accepted method for its computation. Independently, the Information Delta framework characterizes non-pairwise dependencies in genetic datasets. This framework has developed an intuitive geometric interpretation for how discrete functions encode information, but lacks some important generalizations. This paper shows that the PID and Delta frameworks are largely equivalent. We equate their key expressions, allowing for results in one framework to apply towards open questions in the other. For example, we find that the approach of Bertschinger et al. is useful for the open Information Delta question of how to deal with linkage disequilibrium. We also show how PID solutions can be mapped onto the space of delta measures. Using Bertschinger et al. as an example solution, we identify a specific plane in delta-space on which this approach’s optimization is constrained, and compute it for all possible three-variable discrete functions of a three-letter alphabet. This yields a clear geometric picture of how a given solution decomposes information.     

Separation of components from impulses in reassigned spectrograms

Two computational methods for pruning a reassigned spectrogram to show only quasisinusoidal components, or only impulses, or both, are presented mathematically and provided with step-by-step algorithms. Both methods compute the second-order mixed partial derivative of the short-time Fourier transform phase, and rely on the conditions that components and impulses are each well-represented by reassigned spectrographic points possessing particular values of this derivative. This use of the mixed second-order derivative was introduced by Nelson [J. Acoust. Soc. Am. 110, 2575-2592 (2001)] but here our goals are to completely describe the computation of this derivative in a way that highlights the relations to the two most influential methods of computing a reassigned spectrogram, and also to demonstrate the utility of this technique for plotting spectrograms showing line components or impulses while excluding most other points. When applied to speech signals, vocal tract resonances (formants) or glottal pulsations can be effectively isolated in expanded views of the phonation process.     

Determination of Initial Variable Derivative Increments Using Genetic Algorithm in Damped Least-Squares Automatic Lens Design Problem

An important factor in performing effective optimization with the damped least-squares method is to establish appropriate initial values for the variable derivative increments prior to starting the optimization process. It is shown first that the determination of these increments can be treated as a combinatorial problem. Then, a novel method of determining optimum variable derivative increments is developed using a genetic algorithm and the characteristics of the eigenvalues of the Jacobian matrix. Some numerical experiments to show the effectiveness of this method are also presented. The proposed method reduces the number of optimization reiterations required to reach a stationary point.     

Exponential speedup of quantum newton optimization algorithm for general polynomials

Quantum optimization algorithms can outperform their classical counterpart and are key in modern technology. The second-order optimization algorithm(the Newton algorithm) is a critical optimization method, speeding up the convergence by employing the second-order derivative of loss functions in addition to their first derivative. Here, we propose a new quantum second-order optimization algorithm for general polynomials with a computational complexity of O(poly(log d)). We use this algorithm to solve the nonlinear equation and learning parameter problems in factorization machines. Numerical simulations show that our new algorithm is faster than its classical counterpart and the first-order quantum gradient descent algorithm. While existing quantum Newton optimization algorithms apply only to homogeneous polynomials, our new algorithm can be used in the case of general polynomials, which are more widely present in real applications.     

Novel system for modulated lidar parameter optimization

We present a novel system for parameter design and optimization of modulated lidar. The system is realized by combining software simulation with hardware circuit. This method is more reliable and flexible for lidar parameter optimization compared with theoretical computation or fiber-simulated system. Experiments confirm that the system is capable of optimizing parameters for modulated lidar. Key parameters are analyzed as well. The optimal filter bandwidth is 200 MHz and the optimal modulation depth is 0.5 under typical application environment.     

Gauge Covariance and Momentum Space Asymptotically Non-logarithmic Total Derivatives

We focus on a new gauge symmetry keeping regularization scheme for momentum integration and point out that dropping out momentum space asymptotic non-logarithmic total derivative divergent integrations in quantum field theory is a simple and natural way to keep the computation program gauge-covariant.     

Bottleneck Problems: An Information and Estimation-Theoretic View

Information bottleneck (IB) and privacy funnel (PF) are two closely related optimization problems which have found applications in machine learning, design of privacy algorithms, capacity problems (e.g., Mrs. Gerber’s Lemma), and strong data processing inequalities, among others. In this work, we first investigate the functional properties of IB and PF through a unified theoretical framework. We then connect them to three information-theoretic coding problems, namely hypothesis testing against independence, noisy source coding, and dependence dilution. Leveraging these connections, we prove a new cardinality bound on the auxiliary variable in IB, making its computation more tractable for discrete random variables. In the second part, we introduce a general family of optimization problems, termed “bottleneck problems”, by replacing mutual information in IB and PF with other notions of mutual information, namely f-information and Arimoto’s mutual information. We then argue that, unlike IB and PF, these problems lead to easily interpretable guarantees in a variety of inference tasks with statistical constraints on accuracy and privacy. While the underlying optimization problems are non-convex, we develop a technique to evaluate bottleneck problems in closed form by equivalently expressing them in terms of lower convex or upper concave envelope of certain functions. By applying this technique to a binary case, we derive closed form expressions for several bottleneck problems.     

连续变量纠缠增强对实验参量的依赖关系

具有正交振幅和正交相位分量量子关联的连续变量量子纠缠态光场是进行量子信息和量子计算研究的最基本的资源。随着量子信息和量子计算研究的深入开展,为了实现高质量的信息传递和高效率的量子计算,必须尽可能提高所利用的纠缠态光场的纠缠度。基于光学参变过程量子纠缠增强是提高连续变量纠缠态光场纠缠度的一种有效方法,详细讨论了连续变量纠缠增强与非简并光学参变放大器各实验参量的关系,讨论了这些参量对纠缠增强的影响。计算结果将为优化利用非简并光学参变放大器构建的纠缠增强系统,进一步提高量子纠缠增强效率提供参考。     

Fractional diffusion-wave problem in cylindrical coordinates

In this Letter, we present analytical and numerical solutions for an axis-symmetric diffusion-wave equation. For problem formulation, the fractional time derivative is described in the sense of Riemann-Liouville. The analytical solution of the problem is determined by using the method of separation of variables. Eigenfunctions whose linear combination constitute the closed form of the solution are obtained. For numerical computation, the fractional derivative is approximated using the Grünwald-Letnikov scheme. Simulation results are given for different values of order of fractional derivative. We indicate the effectiveness of numerical scheme by comparing the numerical and the analytical results for α=1 which represents the order of derivative.     

Elastic Properties of Lithium and Sodium Amides

Russian Physics Journal - This paper presents the optimization of the structural parameters of tetragonal and orthorhombic crystals α-LiNH2 and α-NaNH2 and computation of elastic...     

Engineering complex topological memories from simple Abelian models

In three spatial dimensions, particles are limited to either bosonic or fermionic statistics. Two-dimensional systems, on the other hand, can support anyonic quasiparticles exhibiting richer statistical behaviors. An exciting proposal for quantum computation is to employ anyonic statistics to manipulate information. Since such statistical evolutions depend only on topological characteristics, the resulting computation is intrinsically resilient to errors. The so-called non-Abelian anyons are most promising for quantum computation, but their physical realization may prove to be complex. Abelian anyons, however, are easier to understand theoretically and realize experimentally. Here we show that complex topological memories inspired by non-Abelian anyons can be engineered in Abelian models. We explicitly demonstrate the control procedures for the encoding and manipulation of quantum information in specific lattice models that can be implemented in the laboratory. This bridges the gap between requirements for anyonic quantum computation and the potential of state-of-the-art technology.