Adaptive Partitioning Method Research Articles

The structural and electronic split: Boron vs aluminum hydrides

We systematically investigated the structural evolution of boron (B) and aluminum (Al) hydrides using various DFT and ab initio methods, aiming to reveal the similarities and differences in their geometric and electronic structures. While B hydrides have been extensively studied both experimentally and theoretically, less is known about its group 13 heavier congener, Al. Extensive global minimum searches of the B2Hx (Al2Hx) and B3Hy (Al3Hy) hydrides (x = [0–6], y = [0–9]) were performed to identify the most stable geometric structures for each stoichiometry. In most of the series, B and Al hydrides exhibit qualitatively different structures, except for the most saturated X2H5 and X2H6 stoichiometries. Chemical bonding analyses employing adaptive natural density partitioning and electron localization function methods identified notable differences between B and Al hydrides in most of the compositions. B hydrides predominantly possess two-center (2c) and three-center (3c) bonding elements, suggesting a relatively balanced electron distribution. On the contrary, Al hydrides tend to retain unpaired electrons or lone pairs on Al atoms, forming a large number of closely lying isomers with various combinations of 1c, 2c, 3c, and 4c bonding elements. Thermodynamic stability analyses revealed that all studied clusters demonstrated stability toward various H/H2 dissociation pathways, with Al hydrides being less stable than B counterparts.

Benders Adaptive-Cuts Method for Two-Stage Stochastic Programs

Benders decomposition is one of the most applied methods to solve two-stage stochastic problems (TSSP) with a large number of scenarios. The main idea behind the Benders decomposition is to solve a large problem by replacing the values of the second-stage subproblems with individual variables and progressively forcing those variables to reach the optimal value of the subproblems, dynamically inserting additional valid constraints, known as Benders cuts. Most traditional implementations add a cut for each scenario (multicut) or a single cut that includes all scenarios. In this paper, we present a novel Benders adaptive-cuts method, where the Benders cuts are aggregated according to a partition of the scenarios, which is dynamically refined using the linear program-dual information of the subproblems. This scenario aggregation/disaggregation is based on the Generalized Adaptive Partitioning Method (GAPM), which has been successfully applied to TSSPs. We formalize this hybridization of Benders decomposition and the GAPM by providing sufficient conditions under which an optimal solution of the deterministic equivalent can be obtained in a finite number of iterations. Our new method can be interpreted as a compromise between the Benders single-cuts and multicuts methods, drawing on the advantages of both sides, by rendering the initial iterations faster (as for the single-cuts Benders) and ensuring the overall faster convergence (as for the multicuts Benders). Computational experiments on three TSSPs [the Stochastic Electricity Planning, Stochastic Multi-Commodity Flow, and conditional value-at-risk (CVaR) Facility Location] validate these statements, showing that the new method outperforms the other implementations of Benders methods, as well as other standard methods for solving TSSPs, in particular when the number of scenarios is very large. Moreover, our study demonstrates that the method is not only effective for the risk-neutral decision makers, but also that it can be used in combination with the risk-averse CVaR objective. Funding: Financial support from Agencia Nacional de Investigación y Desarrollo - Chile [FONDECYT 1200809] and STIC-Amsud [STIC19007] is gratefully acknowledged.

Spatial-temporal adaptive network partitioning for urban traffic signal control

In response to rapidly growing and diversifying traffic demand, it is necessary to develop a network partitioning method that could achieve real-time global optimal performance and adapt to traffic network evolution. In this paper, an adaptive partitioning method is presented, which achieves optimal partitions at runtime and determines appropriate Time-of-Day breakpoints to update partition results simultaneously. For each time interval, partitioning schemes are firstly assessed in terms of modularity by taking roadway geometry, real-time traffic flow information, and signal timing into account. Two values are attained from the assessment: the maximum modularity of the optimal partition and the modularity obtained from the existing partition. Then the existing partition is updated, provided that the relative deviation of these two values exceeds a given threshold for a certain number of successive time intervals. Experimental results show that the above-mentioned partitioning scheme outperforms some notable traffic control techniques in modularity in the spatial aspect. In the temporal aspect, the updating scheme can well respond to varying traffic conditions and yield significantly higher average modularity.

XNgNSi (X = HCC, F; Ng = Kr, Xe, Rn): A New Class of Metastable Insertion Compounds Containing Ng-C/F and Ng-N Bonds and Possible Isomerization therein.

Recently, astronomically important silaisocyanoacetylene (HCCNSi) possessing a large dipole moment has been detected for the first time with the help of crossed molecular beam experiments. Quantum chemical computations at higher levels of theory have also been performed to characterize the transient species. In this study, we have analyzed the equilibrium geometry, stability, reactivity, and energetics as well as the nature of bonding in the noble gas (Ng) inserted HCCNSi compound. We have also considered its F analogue to understand the influence of the most electronegative atom in the compound. Metastable behavior of the XNgNSi compounds (X = HCC, F; Ng = Kr-Rn) is examined by calculating thermochemical parameters like free energy change (ΔG) and zero-point-energy-corrected dissociation energy (D0) at 298 K for all possible two-body (2B) and three-body (3B) (both neutral as well as ionic) dissociation channels using coupled-cluster theory [CCSD(T)] in addition to density functional theory (DFT) as well as second order Møller-Plesset perturbation theory (MP2). The set of predicted compounds is found to be endergonic in nature, having high positive free energy change suggesting the thermochemical stability of the compounds except for the 2B Ng-release paths. Though thermodynamically feasible, they are kinetically protected with very high activation free energy barriers. Interestingly, the release of Ng from the parent moiety XNgNSi produces the XSiN isomer, by 180° flipping of the NSi moiety. This can also be seen in the dynamical simulation carried out with the help of atom-centered density matrix propagation (ADMP) technique at 2000K for 1 ps. The bonding in Ng-C, Ng-F, and Ng-N bonds of the studied compounds is analyzed and described with the aid of natural bond orbital (NBO), topological parameters computed using atoms-in-molecules theory (AIM), energy decomposition analysis (EDA), and adaptive natural density partitioning (AdNDP) methods. The natural charge distribution on the constituent atoms suggests that the compounds can be partitioned into both ways of representations, viz., neutral radical as well as ionic fragments. Lastly, the reactivity of the compounds is scrutinized using certain reactivity descriptors calculated within the domain of conceptual density functional theory (CDFT).

A novel tunnel-lining crack recognition system based on digital image technology

Structural health monitoring (SHM) combined with digital image technology has been widely applied to infrastructure operation management. However, the linear illumination of the tunnel and the various lining diseases limit the quality of lining-crack recognition. In this article, a novel tunnel-lining crack recognition system is established. The system involves three main procedures: image preprocessing and enhancement, feature extraction, and crack characterization. To meet tunnel environmental conditions, mature image enhancement and morphological algorithms are packaged into the system; meanwhile, this paper proposes differentiated noise filtering and an improved segmenting method combining adaptive partitioning, edge detection and threshold method to improve the recognition accuracy. A self-regulating calibration method that uses parallel projection is also applied to crack characterization, achieving real-time size calibration. The results of experiments to compare the effects of the proposed system and field application tests confirm the stability and reliability of the system. A further deviation factor analysis provides reasonable suggestions for system improvement.

TpSpMV: A two-phase large-scale sparse matrix-vector multiplication kernel for manycore architectures

Sparse matrix-vector multiplication (SpMV) is one of the important subroutines in numerical linear algebras widely used in lots of large-scale applications. Accelerating SpMV on multicore and manycore architectures based on Compressed Sparse Row (CSR) format via row-wise parallelization is one of the most popular directions. However, there are three main challenges in optimizing parallel CSR-based SpMV: (a) limited local memory of each computing unit can be overwhelmed by assignments to long rows of large-scale sparse matrices; (b) irregular accesses to the input vector result in expensive memory access latency; (c) sparse data structure leads to low bandwidth usage. This paper proposes a two-phase large-scale SpMV, called tpSpMV, based on the memory structure and computing architecture of multicore and manycore architectures to alleviate the three main difficulties. First, we propose the two-phase parallel execution technique for tpSpMV that performs parallel CSR-based SpMV into two separate phases to overcome the computational scale limitation. Second, we respectively propose the adaptive partitioning methods and parallelization designs using the local memory caching technique for the two phases to exploit the architectural advantages of the high-performance computing platforms and alleviate the problem of high memory access latency. Third, we design several optimizations, such as data reduction, aligned memory accessing, and pipeline technique, to improve bandwidth usage and optimize tpSpMV’s performance. Experimental results on SW26010 CPUs of the Sunway TaihuLight supercomputer prove that tpSpMV achieves up to 28.61 speedups and yields the performance improvement of 13.16% over the state-of-the-art work on average.

Optimal control of a black-box system based on surrogate models by spatial adaptive partitioning method

When the dynamic model of a classical optimal control problem is explicit, we can transform this problem into a nonlinear programming problem and solve it by employing a traditional method. However, in some cases, no mathematical model of state equations is provided explicitly except for input–output data obtained from a simulation model. The hybrid model composed of functional mockup unit blocks generated in multiple platforms is a typical example. In this work, we regard these blocks as black-box models and use hierarchical neural network model to surrogate right-hand-side derivative functions of state equations. Specifically, to obtain highly accurate hierarchical neural network model, we explore a spatial adaptive partitioning criterion combining global sensitivity indices and interval length of local spaces based on the input–output data. Compared with models trained by several other partition criteria, numerical results verify that surrogate models obtained by the spatial adaptive partitioning method have higher accuracy. A mathematical example and a trajectory optimization problem of the black-box industrial robot Manutec r3 indicate the effectiveness of our proposed strategy.

Adaptive Dynamic Grid Partitioning for Reactive-Power/Voltage Control Based on Secondary Voltage Control

Reactive power voltage partition control is an important part of AVC secondary voltage control. Aiming at the current status of secondary voltage control, based on the voltage/reactive sensitivity and modularity function, an adaptive dynamic partitioning method is proposed, which changes with the operating conditions of the grid. With each reactive power source partition center, the load nodes are mapped to the corresponding reactive power sources according to the voltage/reactive sensitivity, and the regional merging automatically forms the optimal partition by using the modularity function as a measure. Finally, the proposed method is verified by the IEEE-39 system.

A Pedestrian Dead-Reckoning System for Walking and Marking Time Mixed Movement Using an SHSs Scheme and a Foot-Mounted IMU

A pedestrian dead-reckoning (PDR) system based on micro-inertial technology usually uses a sensor integrated with a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer and is attached to the person’s body (usually on the foot) to acquire the trajectory of the pedestrian. Although a variety of PDR system solutions have been proposed, the PDR solution for marking time and walking hybrid motions has not been formally proposed and solved. Therefore, in this paper, we propose the PDR system using a step-and-heading system scheme for marking time and walking hybrid motion. The main contributions include: 1) an adaptive gait partitioning method for walking and marking time hybrid motions is proposed; 2) a motion classification algorithm that uses a multi-layer perceptron to classify whether each step is walking or marking time is proposed; and 3) a new step length estimator based on several currently proposed models is proposed. Finally, an extended Kalman filtering algorithm combining heuristic heading reduction, flat-ground hypothesis, and cardinal heading aided inertial navigation techniques is used for heading estimation. We evaluate the effectiveness of the proposed algorithm through experiments, and the experimental results show that the average position error of the two groups of test experiments based on marking time and walking hybrid motion is 0.42% and 0.6%, respectively.

Probing the structural and electronic properties of zirconium doped boron clusters: Zr distorted B12 ligand framework.

As an extension of boron based materials, transition-metal doped boron clusters deserve interest in controlling size-dependent structural and electronic properties. Herein, using the Crystal structure AnaLYsis by Particle Swarm Optimization (CALYPSO) method and density functional theory (DFT) calculations, we have performed a global search for the lowest-energy structures of ZrBQn (Q = 0, -1) clusters with n = 10-20. The results show that the ground-state structures of the obtained clusters feature a distinctive structural evolution pattern, from half-sandwich bowl to distorted drum-like and then to Zr-centered distorted tubular motifs. For the sake of validating the current ground-state structures, photoelectron spectra are predicted from time-dependent DFT calculations. More interestingly, the neutral and anionic ZrB12 clusters are found to possess enhanced stability in the size regime studied here. The stability of the closed shell half-sandwich ZrB12 cluster is analyzed by intrinsic bond orbital (IBO) and Adaptive Natural Density Partitioning (AdNDP) methods, which indicates that the stability mechanism is caused by the dopant Zr atom breaking the boron bowl's triangle B3 unit to form a quasi-linear B3 unit in B12 and strengthen both the interaction of the B-B σ-bonds and the Zr-B π-bonds.

Adaptive Memory Controller for High-performance Multi-channel Memory

As the number of CPU/GPU cores and IPs in SOC increases and applications require explosive memory bandwidth, simultaneously achieving good throughput and fairness in the memory system among interfering applications is very challenging. Recent works proposed priority-based thread scheduling and channel partitioning to improve throughput and fairness. However, combining these different approaches leads to performance and fairness degradation. In this paper, we analyze the problems incurred when combining priority-based scheduling and channel partitioning and propose dynamic priority thread scheduling and adaptive channel partitioning method. In addition, we propose dynamic address mapping to further optimize the proposed scheme. Combining proposed methods could enhance weighted speedup and fairness for memory intensive applications by 4.2% and 10.2% over TCM or by 19.7% and 19.9% over FR-FCFS on average whereas the proposed scheme requires space less than TCM by 8%.

Mapping Epigenetic Landscapes of Gene Regulatory Networks by Adaptive Weighted Ensemble Sampling

Nonlinear interactions in gene regulatory networks (GRNs) give rise to multiple steady states, where attractor states represent stable gene expression profiles, or cell phenotypes. Phenotypic changes such as differentiation, reprogramming, and carcinogenesis have been described as dynamic processes occurring over a rugged landscape. Computational methods that predict how GRN features govern state-structure and dynamics over the global epigenetic landscape can potentially inform strategies for cellular reprogramming. Stochastic simulations of reaction network dynamics provide a flexible approach, because they are not inherently limited to small networks and can treat events that are single-molecule in nature, such as epigenetic control of gene activity. However, brute-force stochastic simulations have limited efficiency for mapping multi-stable dynamic landscapes due to trapping in local attractors. Rare event sampling methods provide a means of sampling global distributions without sacrificing molecular mechanistic detail. We demonstrate the use of adaptive Weighted Ensemble (WE) sampling methods for efficiently computing the global quasi-potential landscape for complex multi-gene networks underlying stem cell differentiation. The WE method, combined with adaptive partitioning methods for efficient discretization of state space, provides computational speedups over brute force stochastic simulations in parameter regimes where spontaneous transitioning between states is rare. Our simulations reveal how network parameters governing kinetics of regulatory interactions at promoters can significantly alter the structure of the global landscape.

The Spatiotemporal Correlation Analysis of Urban Traffic Characteristics Using an Adaptive Partitioning Method

Non-stationary and nonlinear features in the dynamic trajectories of traffic parameters may stop rich and hidden multi-dimensional knowledge from being extracted, contributing a correlation result with false judgments or ignorable significance. As the fundamental improvement for a dynamic congestion analyzing method, the paper decomposes a time series of traffic data into 2 components based on a polynomial approximation method, called the “trend” and “detail” components. In order to simplify the selection of the fitting function to obtain the trend component, the paper proposes an adaptive partitioning method based on the sensitivity search technology. The given examples prove the effectiveness of the proposed methods for a spatiotemporal traffic congestion study.

Straight line extraction via phase-grouping method based on adaptive partitioning

Concerning the poor performance in anti-noise and the limitations of the fixed partition of the phase-grouping method,an adaptive phase partitioning method was proposed.Firstly,this paper used the adaptive smoothing and Wallis filtering for image pre-processing and adopted the Canny method for edge detection;secondly,selected the optimal partitioning of the image based on the adaptive phase partitioning method to create the line supporting area;finally,fitted and merged the lines by the least squares method.The experimental results show that the proposed method can effectively solve the line breakage and mistaken extraction caused by the noise and fixed partition,and this method extracts line completely and precisely.

Real-Time, Adaptive, and Locality-Based Graph Partitioning Method for Video Scene Clustering

We propose in this paper an efficient, adaptive, and locality-based graph partitioning method for video scene clustering. First, a graph partitioning method is proposed to group video shots into scenes, and a peer-group filtering (PGF) scheme is used to identify all the shots similar to each particular shot based on Fisher's discriminant analysis. To work with computable shot similarity measures that have only limited discriminating power, we develop a graph partitioning scheme to cluster the shots by maximizing the likeness of shots within the same cluster and minimizing that between different clusters. Second, considering that video data are normally obtained and viewed sequentially, we propose to perform a locality-based PGF and graph partitioning on video segments with 50 shots, 100 shots, and so on. This proposed locality-based method has the advantage that the number of scene clusters is not required to be known a priori, and it can achieve performance comparable to that processing on the whole video sequence. Experimental results are presented to demonstrate the effectiveness and efficiency of the proposed method.

Network code assisted HARQ scheme in multi-user systems with cooperative relays

A network code assisted HARQ system considering uplink (UL) transmission with multiple mobile stations (MSs) cooperating with a relay-station (RS) is proposed. The RS is assumed to successfully monitor and decode the packets transmitted from the MSs to the base station (BS). The BS partitions the incorrectly decoded packets into groups based on their MS-to-BS channel conditions and feedbacks this partition to the RS. The RS employs a network-coded HARQ scheme within each group for subsequent retransmissions. We proposed a dynamic and adaptive partitioning method based on the BS channel state information and the corresponding HARQ retransmission schemes. Also, we derived a low-complexity joint demodulation and decoding structure. Simulations showed that our scheme improves the system throughput by 15% when compared with the relay assisted chase-combining HARQ system. We also explored suitable ranges of diversity orders for the RS-to-BS and MS-to-BS channels that provide significant system throughput improvements with practical implementation cost.

Fast Multidimensional Entropy Estimation by $k$-d Partitioning

We describe a nonparametric estimator for the differential entropy of a multidimensional distribution, given a limited set of data points, by a recursive rectilinear partitioning. The estimator uses an adaptive partitioning method and runs in Theta( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">log</sub> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> ) time, with low memory requirements. In experiments using known distributions, the estimator is several orders of magnitude faster than other estimators, with only modest increase in bias and variance.

Advanced Genetic Algorithms Based on Adaptive Partitioning Method

Genetic algorithms (GA) are well known and very popular stochastic optimization algorithm. Although, GA is very powerful method to find the global optimum, it has some drawbacks, for example, premature convergence to local optima, slow convergence speed to global optimum. To enhance the performance of the GA, this paper proposes an adaptive genetic algorithm based on partitioning method. The partitioning method, which enables a genetic algorithm to find a solution very effectively, adaptively divides the search space into promising sub-spaces to reduce the complexity of optimization. This partitioning method is more effective as the complexity of the search space is increasing. The validity of the proposed method is confirmed by applying it to several bench mark test function examples and a traveling salesman problem.

Adaptive Partitioning in Combined Quantum Mechanical and Molecular Mechanical Calculations of Potential Energy Functions for Multiscale Simulations

In many applications of multilevel/multiscale methods, an active zone must be modeled by a high-level electronic structure method, while a larger environmental zone can be safely modeled by a lower-level electronic structure method, molecular mechanics, or an analytic potential energy function. In some cases though, the active zone must be redefined as a function of simulation time. Examples include a reactive moiety diffusing through a liquid or solid, a dislocation propagating through a material, or solvent molecules in a second coordination sphere (which is environmental) exchanging with solvent molecules in an active first coordination shell. In this article, we present a procedure for combining the levels smoothly and efficiently in such systems in which atoms or groups of atoms move between high-level and low-level zones. The method dynamically partitions the system into the high-level and low-level zones and, unlike previous algorithms, removes all discontinuities in the potential energy and force whenever atoms or groups of atoms cross boundaries and change zones. The new adaptive partitioning (AP) method is compared to Rode's "hot spot" method and Morokuma's "ONIOM-XS" method that were designed for multilevel molecular dynamics (MD) simulations. MD simulations in the microcanonical ensemble show that the AP method conserves both total energy and momentum, while the ONIOM-XS method fails to conserve total energy and the hot spot method fails to conserve both total energy and momentum. Two versions of the AP method are presented, one scaling as O(2N) and one with linear scaling in N, where N is the number of groups in a buffer zone separating the active high-level zone from the environmental low-level zone. The AP method is also extended to systems with multiple high-level zones to allow, for example, the study of ions and counterions in solution using the multilevel approach.