Exploitation of symmetry in periodic Self-Consistent-Field ab initio calculations: application to large three-dimensional compounds

Symmetry can dramatically reduce the computational cost (running time and memory allocation) of Self-Consistent-Field ab initio calculations for crystalline systems. Crucial for running time is use of symmetry in the evaluation of one- and two-electron integrals, diagonalization of the Fock matrix at selected points in reciprocal space, reconstruction of the density matrix. As regards memory allocation, full square matrices (overlap, Fock and density) in the Atomic Orbital (AO) basis are avoided and a direct transformation from the packed AO to the SACO (Symmetry Adapted Crystalline Orbital) basis is performed, so that the largest matrix to be handled has the size of the largest sub-block in the latter basis. We here illustrate the effectiveness of this scheme, following recent advancements in the CRYSTAL code, concerning memory allocation and direct basis set transformation. Quantitative examples are given for large unit cell systems, such as zeolites (all-silica faujasite and silicalite MFI) and garnets (pyrope). It is shown that the full SCF of 3D systems containing up to 576 atoms and 11136 Atomic Orbitals in the cell can be run with a hybrid functional on a single core PC with 500 MB RAM in about 8 h.     

ATTITUDE CONTROL TECHNOLOGY FOR MASS MOMENT NANO-SATELLITE IN LOW EARTH ORBIT 1)

针对气动力矩严重影响低轨纳卫星姿态控制效果的问题,创新性地提出了利用质量矩技术将气动干扰转化为控制力矩的解决方法.由于气动力矩矢量垂直于大气来流速度方向,因而采用质量矩与磁力矩相结合的方式三轴全驱动控制卫星姿态,从而避免系统欠驱动. 建立双执行机构控制方式的姿态动力学模型,并根据各干扰项的影响简化了控制方程.针对气动力不确定、星体参数误差、未知环境影响等复杂干扰,设计了针对理想控制力矩基于干扰观测器的滑模控制器. 为减小滑块附加干扰力矩,研究了理想控制力矩的最优分配策略. 最后, 为双执行机构搭建了半物理仿真平台,结果表明: 姿态机动过程中, 与滑块加速度相关的附加惯性力矩远大于其他干扰项,最优力矩分配策略能够大幅减小快时变的附加干扰, 优化效果明显; 姿态保持过程中,干扰观测器能有效观测系统慢时变干扰, 提高滑模控制律的姿态控制精度,姿态角收敛误差小于$\pm $0.1$^\circ$.最终验证了在低轨纳卫星上利用质量矩技术控制姿态的可行性.     

Approximation algorithms for scheduling on multi-core processor with shared speedup resources

We consider a joint resource partition and scheduling problem. We are given $m$ identical cores and discrete resources of total size $k$. We need to partition the resources among these cores. A set of jobs must be processed non-preemptively on these cores after the resource partition. The processing time of a job on a core depends on the size of resources allocated to that corresponding core. The resource allocation scheme is static, i.e., we cannot change the amount of resources that was allocated to a core during the whole scheduling. Hassidim et al. (2013) investigated this problem with a general processing time function, i.e., the processing time of a job is an arbitrary function of the level of resources allocated to that core. They provided an algorithm with approximation ratio of 36. In this paper, we improve the approximation ratio to 8 by presenting a new resource partition scheme. Next, we consider a special model where the core’s speed is proportional to its allocated resource, then we present two algorithms with improved approximation ratios.     

The Brownian plane with minimal neck baby universe

For each , let be a uniform rooted quadrangulation, endowed with an appropriate measure, of size n conditioned to have r(n) vertices in its root block. We prove that for a suitable function r(n), after rescaling graph distance by converges to a random pointed non‐compact metric measure space , in the local Gromov‐Hausdorff‐Prokhorov topology. The space is built by identifying a uniform point of the Brownian map with the distinguished point of the Brownian plane. © 2017 Wiley Periodicals, Inc. Random Struct. Alg., 51, 729–752, 2017     

Optimizing project selection: a perspective from resource constraints

In reality, projects usually consume complex resources. Making good use of the various resources is vital for optimal project selection and maximum profit earning. This paper proposes a new project selection model from the perspective of complex resource constraints. In the model, the resources are divided into non-renewable and renewable categories, and some resources of the two categories can both be shared by different projects. In addition, the paper considers the situation where the company has resources in stock and can purchase them in the marketplace if they are out of stock. The paper proves that the proposed model which considers renewable resource and resource sharing produces higher profit than the ones that do not consider renewable resource and resource sharing. To solve the complex model problem, an improved genetic algorithm is presented. For the sake of illustration, a case study is provided.     

Closed-form CRLB and LPI power allocation strategies for MIMO radars with unneglectable clutter

This paper studies clutter’s effect on MIMO radar performance through obtaining closed-form Cramer Rao lower bounds (CRLBs) for the range and velocity parameters versus the powers assigned to the transmit antennas which resulted in more calculation’ complexity. To verify extracted CRLBs, the maximum likelihood estimation’s error variance (MLEEV) of range and velocity were derived in attendance of clutter. Based on obtained closed-form CRLBs, four optimized power allocation (PA) strategies are proposed which consider measurement error statistics as the objective function or constraints to improve radar performance. So, the calculated closed-form CRLBs are simplified to the closed-forms versus assigned powers to the transmitters. Simulation results verify the proposed CRLBs’ accuracy, and improvements have been made by proposed power allocations.     

A new fog computing resource management (FRM) model based on hybrid load balancing and scheduling for critical healthcare applications

Critical healthcare application tasks require a real-time response because it affects patients’ life. Fog computing is the best solution to get a fast response and less energy consumption in healthcare. However, current solutions face difficulties in scheduling the tasks to the correct computing devices based on their priorities and capacity to meet the tasks’ deadlines and resource limitations with minimal latency. Furthermore, challenges of load balancing and prioritization are raised when dealing with inadequate computing resources and telecommunication networks while obtaining the best scheduling of emergency healthcare tasks. In this study, a fog computing resource management (FRM) model is proposed, which the proposed model has three main solutions. Firstly, resource availability is calculated according to the average execution time of each task. Secondly, load balancing is enhanced by proposing a hybrid approach that combines the multi-agent load balancing algorithm and the throttled load balancing algorithm. Thirdly, task scheduling is done based on priority, resource availability, and load balancing. The results have been acquired using the iFogSim toolkit. Two datasets are used in this study, the blood pressure dataset was acquired from the UTeM clinic, and the ECG dataset was acquired from the University of California at Irvine. Both datasets are integrated to enlarge the attributes and get accurate results. The results demonstrate the effectiveness of managing resources and optimizing task scheduling and balancing in a fog computing environment. In comparison with other research studies, the FRM model outperforms delay by 55%, response time by 72%, cost by 72%, and energy consumption by 70%.     

Enhancing the physical layer security of artificial noise-aided half-duplex cooperative NOMA systems

The objective of this paper is to propose techniques for enhancing the physical layer security (PLS) performance of half-duplex cooperative non-orthogonal multiple access (HD-CNOMA) network in the presence of an external passive eavesdropper. We propose an artificial noise (AN)-aided framework and derive approximate analytical expressions for the secrecy outage probabilities (SOPs) of the downlink users. It is demonstrated that the proposed AN-aided framework significantly reduces the SOPs of the users and completely resolves the zero-diversity order problem, which is prevalent in HD-CNOMA network without AN. To further enhance the PLS performance, we determine optimal power allocation coefficients (OPACs) for the downlink users at the base station (BS) that minimizes the system SOP (SSOP) of the AN-aided HD-CNOMA network. With the help of extensive numerical and simulation investigations, it is shown that the proposed OPAC leads to significant reduction of the SSOP, while lowering the SOPs of the users, compared to random/equal setting of the PACs.     

Chromatic scheduling polytopes coming from the bandwidth allocation problem in point-to-multipoint radio access systems

Point-to-Multipoint systems are a kind of radio systems supplying wireless access to voice/data communication networks. Such systems have to be run using a certain frequency spectrum, which typically causes capacity problems. Hence it is, on the one hand, necessary to reuse frequencies but, on the other hand, no interference must be caused thereby. This leads to a combinatorial optimization problem, the bandwidth allocation problem, a special case of so-called chromatic scheduling problems. Both problems are NP-hard and it is known that, for these problems, there exist no polynomial time algorithms with a fixed approximation ratio. Algorithms based on cutting planes have shown to be successful for many other combinatorial optimization problems. In order to apply such methods, knowledge on the associated polytopes is required. The present paper contributes to this issue, exploring basic properties of chromatic scheduling polytopes and several classes of facet-defining inequalities. J. L. Marenco: This work supported by UBACYT Grant X036, CONICET Grant 644/98 and ANPCYT Grant 11-09112. A. K. Wagler: This work supported by the Deutsche Forschungsgemeinschaft (Gr 883/9–1).