格上两方一轮身份认证协议

在量子技术对传统密码冲击下,格上的口令认证密钥交换PAKE协议研究成为关注的热点。但是,当前格上PAKE协议大多通过两轮或三轮完成,且存在传输负载过重现象。而目前格上一轮协议多适用于一对一通信且不具有身份认证功能。针对以上问题提出的基于密钥共识的格上一轮身份认证协议,通过结合格上公钥加密、ASPH函数和密钥共识,实现了高效、安全的一轮通信中的身份认证与密钥协商。这一解决方案有望改善传统PAKE协议的问题,对于应对量子技术对密码学的挑战提供了一种思路。

PROJECTION METHODS AND APPROXIMATIONS FOR ORDINARY DIFFERENTIAL EQUATIONS

A formulation of a differential equation as projection and fixed point pi-Mem alloivs approximations using general piecnvise functions. We prone existence and uniqueness of the up proximate solution* convergence in the L2 norm and nodal supercnnvergence. These results generalize those obtained earlier by Hulme for continuous piecevjise polynomials and by Delfour-Dubeau for discontinuous pieceuiise polynomials. A duality relationship for the two types of approximations is also given.

Actor-Critic Reinforcement Learning and Application in Developing Computer-Vision-Based Interface Tracking

This paper synchronizes control theory with computer vision by formalizing object tracking as a sequential decision-making process.A reinforcement learning(RL)agent successfully tracks an interface between two liquids,which is often a critical variable to track in many chemical,petrochemical,metallurgical,and oil industries.This method utilizes less than 100 images for creating an environment,from which the agent generates its own data without the need for expert knowledge.Unlike supervised learning(SL)methods that rely on a huge number of parameters,this approach requires far fewer parameters,which naturally reduces its maintenance cost.Besides its frugal nature,the agent is robust to environmental uncertainties such as occlusion,intensity changes,and excessive noise.From a closed-loop control context,an interface location-based deviation is chosen as the optimization goal during training.The methodology showcases RL for real-time object-tracking applications in the oil sands industry.Along with a presentation of the interface tracking problem,this paper provides a detailed review of one of the most effective RL methodologies:actor–critic policy.

Low-rank spectral estimation algorithm of learning Markov model

This paper proposes a low-rank spectral estimation algorithm of learning Markov model.First,an approximate projection algorithm for the rank-constrained frequency matrix set is proposed,and thereafter its local Lipschitzian error bound established.Then,we propose a low-rank spectral estimation algorithm for estimating the state transition frequency matrix and the probability matrix of Markov model by applying the approximate projection algorithm to correct the maximum likelihood estimation of the frequency matrix,and prove that there is only a multiplying constant difference in estimation errors between the low-rank spectral estimation and the maximum likelihood estimation under appropriate conditions.Finally,numerical comparisons with the prevailing maximum likelihood estimation,spectral estimation,and rank-constrained maxi-mum likelihood estimation show that the low-rank spectral estimation algorithm is effective.

Spatio-temporal dynamics of phytohormones in the tomato graft healing process

Graft healing involves a series of cytological and molecular events including wound responses, callus formation and vascular bundle remodelling. Hormones are important signalling molecules regulating plant development and responses to environmental stimuli. However,the detailed dynamics of phytohormones in graft healing remain elusive. In this research, internodes above and below the graft site were harvested from 0 to 168 h after grafting(HAG), and liquid chromatography tandem mass spectrometry(LC-MS/MS) was used to determinate jasmonic acid, auxin, cytokinin, ethylene, salicylic acid, abscisic acid and gibberellin levels during the graft healing process. Uniform manifold approximation and projection(UMAP) and k-means analyses were performed to explore hormone spatio-temporal dynamics. We found the stage-specific and asymmetric accumulation of phytohormones in the tomato graft healing process. At the early healing stage(before vascular bundle reconnection), IAA, cZ, ABA, JA and SA mainly accumulated above the graft site, while tZ and ACC mainly accumulated below the graft site. MEIAA, ICAld and IP mainly accumulated at the later stage. Comminated with the healing process, we suggested that JA is mainly involved in wound responses, IAA is beneficial to the formation of callus and vascular cell development, tZ promotes cell division, and IP is linked to vascular bundle remodelling. In addition, expression of JA-related genes SlMYC2 and SlJAZ2, IAA-related gene SlIAA1, tZ-related genes SlHP2 and SlRR8, and IP-related gene SlRR9 correlated with hormone accumulation. The findings provide important information about the hormones and genes involved in the tomato graft healing process.

On the Volume Conservation of the Immersed Boundary Method

The immersed boundary(IB)method is an approach to problems of fluid-structure interaction in which an elastic structure is immersed in a viscous incompressible fluid.The IB formulation of such problems uses a Lagrangian description of the structure and an Eulerian description of the fluid.It is well known that some versions of the IB method can suffer from poor volume conservation.Methods have been introduced to improve the volume-conservation properties of the IB method,but they either have been fairly specialized,or have used complex,nonstandard Eulerian finite-difference discretizations.In this paper,we use quasi-static and dynamic benchmark problems to investigate the effect of the choice of Eulerian discretization on the volume-conservation properties of a formally second-order accurate IB method.We consider both collocated and staggered-grid discretization methods.For the tests considered herein,the staggered-grid IB scheme generally yields at least a modest improvement in volume conservation when compared to cell-centered methods,and in many cases considered in this work,the spurious volume changes exhibited by the staggered-grid IB method are more than an order of magnitude smaller than those of the collocated schemes.We also compare the performance of cell-centered schemes that use either exact or approximate projection methods.We find that the volumeconservation properties of approximate projection IB methods depend strongly on the formulation of the projection method.When used with the IB method,we find that pressure-free approximate projection methods can yield extremely poor volume conservation,whereas pressure-increment approximate projection methods yield volume conservation that is nearly identical to that of a cell-centered exact projection method.

Stationary flow fields prediction of variable physical domain based on proper orthogonal decomposition and kriging surrogate model

In this paper a new flow field prediction method which is independent of the governing equations, is developed to predict stationary flow fields of variable physical domain. Predicted flow fields come from linear superposition of selected basis modes generated by proper orthogonal decomposition（POD）. Instead of traditional projection methods, kriging surrogate model is used to calculate the superposition coefficients through building approximate function relationships between profile geometry parameters of physical domain and these coefficients. In this context,the problem which troubles the traditional POD-projection method due to viscosity and compressibility has been avoided in the whole process. Moreover, there are no constraints for the inner product form, so two forms of simple ones are applied to improving computational efficiency and cope with variable physical domain problem. An iterative algorithm is developed to determine how many basis modes ranking front should be used in the prediction. Testing results prove the feasibility of this new method for subsonic flow field, but also prove that it is not proper for transonic flow field because of the poor predicted shock waves.

A Trust Region Affine Scaling Method for Bound Constrained Optimization

We study a new trust region affine scaling method for general bound constrained optimiza- tion problems. At each iteration, we compute two trial steps. We compute one along some direction obtained by solving an appropriate quadratic model in an ellipsoidal region. This region is defined by an affine scaling technique. It depends on both the distances of current iterate to boundaries and the trust region radius. For convergence and avoiding iterations trapped around nonstationary points, an auxiliary step is defined along some newly defined approximate projected gradient. By choosing the one which achieves more reduction of the quadratic model from the two above steps as the trial step to generate next iterate, we prove that the iterates generated by the new algorithm are not bounded away from stationary points. And also assuming that the second-order sufficient condition holds at some nondegenerate stationary point, we prove the Q-linear convergence of the objective function values. Preliminary numerical experience for problems with bound constraints from the CUTEr collection is also reported.