Magnetic resonance imaging of extraocular rectus muscles abnormalities in acute acquired concomitant esotropia

AIM:To investigate the difference of medial rectus(MR)and lateral rectus(LR)between acute acquired concomitant esotropia(AACE)and the healthy controls(HCs)detected by magnetic resonance imaging(MRI).METHODS:A case-control study.Eighteen subjects with AACE and eighteen HCs were enrolled.MRI scanning data were conducted in target-controlled central gaze with a 3-Tesla magnetic resonance scanner.Extraocular muscles(EOMs)were scanned in contiguous image planes 2-mm thick spanning the EOM origins to the globe equator.To form posterior partial volumes(PPVs),the LR and MR cross-sections in the image planes 8,10,12,and 14 mm posterior to the globe were summed and multiplied by the 2-mm slice thickness.The data were classified according to the right eye,left eye,dominant eye,and non-dominant eye,and the differences in mean cross-sectional area,maximum cross-sectional area,and PPVs of the MR and LR muscle in the AACE group and HCs group were compared under the above classifications respectively.RESULTS:There were no significant differences between the two groups of demographic characteristics.The mean cross-sectional area of the LR muscle was significantly greater in the AACE group than that in the HCs group in the non-dominant eyes(P=0.028).The maximum cross-sectional area of the LR muscle both in the dominant and non-dominant eye of the AACE group was significantly greater than the HCs group(P=0.009,P=0.016).For the dominant eye,the PPVs of the LR muscle were significantly greater in the AACE than that in the HCs group(P=0.013),but not in the MR muscle(P=0.698).CONCLUSION:The size and volume of muscles dominant eyes of AACE subjects change significantly to overcome binocular diplopia.The LR muscle become larger to compensate for the enhanced convergence in the AACE.

Energy Management of Price-maker Community Energy Storage by Stochastic Dynamic Programming

In this paper,we propose an analytical stochastic dynamic programming(SDP)algorithm to address the optimal management problem of price-maker community energy storage.As a price-maker,energy storage smooths price differences,thus decreasing energy arbitrage value.However,this price-smoothing effect can result in significant external welfare changes by reduc-ing consumer costs and producer revenues,which is not negligible for the community with energy storage systems.As such,we formulate community storage management as an SDP that aims to maximize both energy arbitrage and community welfare.To incorporate market interaction into the SDP format,we propose a framework that derives partial but sufficient market information to approximate impact of storage operations on market prices.Then we present an analytical SDP algorithm that does not require state discretization.Apart from computational efficiency,another advantage of the analytical algorithm is to guide energy storage to charge/discharge by directly comparing its current marginal value with expected future marginal value.Case studies indicate community-owned energy storage that maximizes both arbitrage and welfare value gains more benefits than storage that maximizes only arbitrage.The proposed algorithm ensures optimality and largely reduces the computational complexity of the standard SDP.Index Terms-Analytical stochastic dynamic programming,energy management,energy storage,price-maker,social welfare.

On the effective inversion by imposing a priori information for retrieval of land surface parameters

The anisotropy of the land surface can be best described by the bidirectional reflectance distribution function (BRDF). As the field of multiangular remote sensing advances, it is increasingly probable that BRDF models can be inverted to estimate the important biological or climatological parameters of the earth surface such as leaf area index and albedo. The state-of-the-art of BRDF is the use of the linear kernel-driven models, mathematically described as the linear combination of the isotropic kernel, volume scattering kernel and geometric optics kernel. The computational stability is characterized by the algebraic operator spectrum of the kernel-matrix and the observation errors. Therefore, the retrieval of the model coefficients is of great importance for computation of the land surface albedos. We first consider the smoothing solution method of the kernel-driven BRDF models for retrieval of land surface albedos. This is known as an ill-posed inverse problem. The ill-posedness arises from that the linear kernel driven BRDF model is usually underdetermined if there are too few looks or poor directional ranges, or the observations are highly dependent. For example, a single angular observation may lead to an under-determined system whose solution is infinite (the null space of the kernel operator contains nonzero vectors) or no solution (the rank of the coefficient matrix is not equal to the augmented matrix). Therefore, some smoothing or regularization technique should be applied to suppress the ill-posedness. So far, least squares error methods with a priori knowledge, QR decomposition method for inversion of the BRDF model and regularization theories for ill-posed inversion were developed. In this paper, we emphasize on imposing a priori information in different spaces. We first propose a general a priori imposed regularization model problem, and then address two forms of regularization scheme. The first one is a regularized singular value decomposition method, and then we propose a retrieval method in I 1 space. We show that the proposed method is suitable for solving land surface parameter retrieval problem if the sampling data are poor. Numerical experiments are also given to show the efficiency of the proposed methods.

Measuring Policy Performance in Online Pricing with Offline Data:Worst-case Perspective and Bayesian Perspective

The problems of online pricing with offline data,among other similar online decision making with offline data problems,aim at designing and evaluating online pricing policies in presence of a certain amount of existing offline data.To evaluate pricing policies when offline data are available,the decision maker can either position herself at the time point when the offline data are already observed and viewed as deterministic,or at the time point when the offline data are not yet generated and viewed as stochastic.We write a framework to discuss how and why these two different positions are relevant to online policy evaluations,from a worst-case perspective and from a Bayesian perspective.We then use a simple online pricing setting with offline data to illustrate the constructions of optimal policies for these two approaches and discuss their differences,especially whether we can decompose the searching for the optimal policy into independent subproblems and optimize separately,and whether there exists a deterministic optimal policy.

带先验约束的地表参数提取的有效反演方法

地球表面的各向异性特性可以用地表二向反射函数(BRDF)恰当地描述.BRDF的核心是利用线性核驱动模型,数学上表述为各向同性核、体散射核和几何光学核的线性组合.随着多角度遥感领域的发展,BRDF模型越来越被看作是可以反演重要的有关地表生物的或气候的参数,比如说叶面积指数和地表反照率.一个线性逼近的核驱动BRDF模型通常可以写成下述形式(Roujean等,1992):fiso+kvol(ti,tv,φ)fvol+kgeo(ti,tv,φ)fgeo=r(ti,tv,φ),其中r表示地表的二向反射;kvol和kgeo为通常所说的核函数,即为已知的入射和观测几何特性的函数,分别描述了体散射和几何散射(包括折射和反射);ti是太阳方向天顶角,tv是观测方向天顶角;φ表示太阳-观测方向的相对方位角;fiso,fvol和fgeo为未知的待反演参数,可以用来拟合观测.计算过程的稳定性是由核矩阵的代数算子特征谱和观测噪音/误差来刻画的.因此为了计算地表反照率,成功反演模型参数是至关重要的环节.我们首先考虑了为计算BRDF模型反演的光滑解方法.业已知道,这是一个不适定的反问题.不适定性是由线性核驱动BRDF模型的欠定性表征的,比如说观测严重不足或观测方向范围有限,或者是观测数据高度线性相关以及噪音的污染等.例如,一次单角度观测可以导致一个欠定的系统(核算子的零空间含有非零向量)或者系统无解(系数矩阵的秩不等于增广矩阵的秩).因此,光滑性或正则化技巧应当加以利用来压制不适定性.Li等(2001)应用先验知识把原始模型转换为一个超定的模型并求得最小二乘解.Pokrovsky等(2002)应用QR分解反演BRDF模型.Wang等(2007)考虑到了反演的正则化策略并提出了不适定地表参数反演的一个完整的正则化理论.在文中,强调从不同的空间添加先验信息于反演模型中.首先从数学物理的观点,第一次提出了一个用于反演的施加先验约束的一般的正则化模型,接着阐述了两种正则化策略.第一个是正则化的奇异值分解方法(Wang等,2007),接着提出了一个基于l1空间的反演方法.我们证明了新提出的方法对于有效观测数据不足情况下反演地表参数是可行的,并通过数值试验验证了新提出方法的反演有效性.

Digital twin-driven smart supply chain

Today’s supply chain is becoming complex and fragile.Hence,supply chain managers need to create and unlock the value of the smart supply chain.A smart supply chain requires connectivity,visibility,and agility,and it needs be integrated and intelligent.The digital twin(DT)concept satisfies these requirements.Therefore,we propose creating a DT-driven supply chain(DTSC)as an innovative and integrated solution for the smart supply chain.We provide background information to explain the DT concept and to demonstrate the method for building a DTSC by using the DT concept.We discuss three research opportunities in building a DTSC,including supply chain modeling,real-time supply chain optimization,and data usage in supply chain collaboration.Finally,we highlight a motivating case from JD.COM,China’s largest retailer by revenue,in applying the DTSC platform to address supply chain network reconfiguration challenges during the COVID-19 pandemic.

Supply chain and logistics innovations with the Belt and Road Initiative

The Belt and Road Initiative(BRI)is a massive,ambitious,long-term project initiated by the Chinese government,with participation from many other countries,to facilitate trade and improve logistics in an effort to promote global economic development.In this paper,we identified the supply chain and logistics innovations linked to the BRI.These innovations include new routes and modes for global trade,new supply chain design,reduction of cross-border logistics frictions,and entrepreneurial development.Examples of some of these innovations are emerging,while new ones are being developed.These innovations can enable businesses to improve their operational performances and create economic value.At the same time,to realize the full potentials of BRI,new work processes and technologies,incentive alignment,collaborations among businesses,and optimized plan-ning are needed.This provides great opportunities for researchers to explore how to overcome barriers and achieve the full values of BRI.

Synergy between Customer Segmentation and Personalization

Personalized pricing is on the rise in the retail industry,and it’s been used in many service platforms.Personalized pricing assumes that the platform has an idea of who the customer is,and allows the platform to incentivize the customers based on their characteristics and actions.In this work,we consider a personalized pricing problem with revisiting customers.We assume a customer’s utility depends on some feature information and some unobserved personal shock.For the case where customer utilities are unknown,we propose a heuristic that combines the idea of binary search and feature-based pricing,and show that it outperforms common benchmarks.We also demonstrate that if we have some information about either the customer utility or the dependence on the feature information,then we can learn the other much faster,and this is what we mean by the synergy between customer segmentation and personalization.This observation can help managers make better personalized decisions for customers that repeatedly interact with the platform.

Multi-Label Markov Random Fields as an Efficient and Effective Tool for Image Segmentation, Total Variations and Regularization

One of the classical optimization models for image segmentation is the well known Markov Random Fields(MRF)model.This model is a discrete optimization problem,which is shown here to formulate many continuous models used in image segmentation.In spite of the presence of MRF in the literature,the dominant perception has been that the model is not effective for image segmentation.We show here that the reason for the non-effectiveness is due to the lack of access to the optimal solution.Instead of solving optimally,heuristics have been engaged.Those heuristic methods cannot guarantee the quality of the solution nor the running time of the algorithm.Worse still,heuristics do not link directly the input functions and parameters to the output thus obscuring what would be ideal choices of parameters and functions which are to be selected by users in each particular application context.We describe here how MRF can model and solve efficiently several known continuous models for image segmentation and describe briefly a very efficient polynomial time algorithm,which is provably fastest possible,to solve optimally the MRF problem.The MRF algorithm is enhanced here compared to the algorithm in Hochbaum(2001)by allowing the set of assigned labels to be any discrete set.Other enhancements include dynamic features that permit adjustments to the input parameters and solves optimally for these changes with minimal computation time.Several new theoretical results on the properties of the algorithm are proved here and are demonstrated for images in the context of medical and biological imaging.An interactive implementation tool for MRF is described,and its performance and flexibility in practice are demonstrated via computational experiments.We conclude that many continuous models common in image segmentation have discrete analogs to various special cases of MRF and as such are solved optimally and efficiently,rather than with the use of continuous techniques,such as PDE methods,that restrict the type of functions used and furthermore,can only guarantee convergence to a local minimum.

Designing mechanically tough graphene oxide materials using deep reinforcement learning

Graphene oxide(GO)is playing an increasing role in many technologies.However,it remains unanswered how to strategically distribute the functional groups to further enhance performance.We utilize deep reinforcement learning(RL)to design mechanically tough GOs.The design task is formulated as a sequential decision process,and policy-gradient RL models are employed to maximize the toughness of GO.Results show that our approach can stably generate functional group distributions with a toughness value over two standard deviations above the mean of random GOs.In addition,our RL approach reaches optimized functional group distributions within only 5000 rollouts,while the simplest design task has 2×10^(11)possibilities.Finally,we show that our approach is scalable in terms of the functional group density and the GO size.The present research showcases the impact of functional group distribution on GO properties,and illustrates the effectiveness and data efficiency of the deep RL approach.