Parallel Search Research Articles

A Systematic Review and Meta-Analysis of the Effective Contribution of Indian Periodontists to Global Periodontal Research Advancement.

Developed nations put a lot of emphasis on scientific research and produce an enormous volume of avant-garde papers in impactful journals. These publications could serve as the foundation for different policies or other designs. The researchers in this study aimed to use a quantitative bibliometric strategy to analyze the development of Indian periodontists' publishing patterns in the PubMed database up to October 31, 2022. Publications that could be accessed through the PubMed database as of the end of October 2022 were included in the bibliometric study. By using certain search terms on the PubMed search engine, studies were found. Dental, periodontal, gingival, gingivitis, periodontal, periodontitis, and dental implants were among the terms used to find this article. To evaluate articles that are specifically about India, a parallel search was conducted with the following phrases together with "India." Selected parameters were examined in all papers, whether they had or lacked abstracts. Seven studies were selected which were in accordance with the inclusion and exclusion criteria of this study. According to the keyword search, India contributes an average of 5.65% of each keyword category to the PubMed database, since the total number of search results on PubMed for the seven keywords we entered was 1,037,584, and the same keywords when searched by adding the keyword "India" to the keyword generated a total of 58,624. Since the beginning of the last decade, India has recorded tremendous growth in all spheres of scientific literature publication, and the field of periodontics is no exception. Through the PubMed database, Indian periodontists have made a significant contribution to world literature, especially over the past 10 years, with the number of publications increasing nearly exponentially with each passing year.

Parallelized integrated nested Laplace approximations for fast Bayesian inference

There is a growing demand for performing larger-scale Bayesian inference tasks, arising from greater data availability and higher-dimensional model parameter spaces. In this work we present parallelization strategies for the methodology of integrated nested Laplace approximations (INLA), a popular framework for performing approximate Bayesian inference on the class of Latent Gaussian models. Our approach makes use of nested thread-level parallelism, a parallel line search procedure using robust regression in INLA’s optimization phase and the state-of-the-art sparse linear solver PARDISO. We leverage mutually independent function evaluations in the algorithm as well as advanced sparse linear algebra techniques. This way we can flexibly utilize the power of today’s multi-core architectures. We demonstrate the performance of our new parallelization scheme on a number of different real-world applications. The introduction of parallelism leads to speedups of a factor 10 and more for all larger models. Our work is already integrated in the current version of the open-source R-INLA package, making its improved performance conveniently available to all users.

Self-optimizing multi-domain auxiliary fusion deep complex convolution recurrent network for speech enhancement

A self-optimizing multi-domain auxiliary fusion deep complex convolution recurrent network for speech enhancement (AMDCCRN) was developed to further improve the feature completeness, correlation expression, and global optimization efficiency of the deep speech enhancement model for speech signals. By constructing a multi-domain auxiliary fusion deep complex convolution recurrent network (MDCCRN), the correlation representation of speech features and features in different spatial domains was enriched. The sparrow search algorithm (PGSSA), which is based on a game theory parallel and global optimization strategy, was introduced to improve parallel search capability and to further augment the efficiency and performance of the model's hyperparameter adaptive search. At the same time, PGSSA was used for adaptive optimization of six key model parameters required for AMDCCRN construction. Finally, a speech enhancement model suitable for multi-domain auxiliary fusion with self-learning ability was constructed. Test results on two common speech corpus data sets, THCHS-30 and WSJ0, showed that the proposed method achieved a better speech enhancement effect than other existing methods and that the validity and generalization of the proposed method had been verified.

Leader population learning rate schedule

The successful application of modern deep neural network models is deeply influenced by the choice of hyperparameters. As one of the most important hyperparameters, the Learning Rate (LR) needs to be fine-tuned. In order to train models that can perform better, finding a suitable LR schedule quickly has become a pressing problem. Current research often relies on manual use of parallel search methods such as grid search, or serial methods such as Bayesian. However, the variety and number of models and datasets result in a very large LR space, and these methods consume significant resources to find only sub-optimal static LR schedules within a given time frame. It was found that in the currently widely used Distributed Data Parallel (DDP) deep learning, where multiple nodes themselves constitute the population, it is possible to use population algorithms to optimise the searched LR schedule dynamically throughout the training process with little additional training time. To maximise the potential of both the searched LR schedule and the population optimisation of the participating multi-nodes, this paper proposes a Leader Population Learning Rate Schedule (LPLRS) for the DDP deep learning environment, which continuously explores for better learning rates in the neighbourhood of the searched LR schedule and guides the subsequent training process. LPLRS achieves higher model classification accuracy on the Cifar 10&100 test datasets on the state-of-the-art Wide Residual Network with Sharpness-Aware Minimization (WRN(SAM)) model compared to the latest SGDR, CLR, and StepLR learning rate schedules.

Performance optimization of lead-free MASnBr3 based perovskite solar cells by SCAPS-1D device simulation

Perovskites are at the forefront of research into potential alternatives for bulky and costly silicon-based solar cells. In recent years, lead-based organic and inorganic perovskite solar cells have broken efficiency records. However, these have stability issues and may pose health risks in the long-term. Hence, there has been ideally inorganic perovskite solar cells and parallel search for lead-free to match and eventually surpass the achievements of lead perovskite analogues. This study reports a modelling-guided device optimization process to design highly efficient lead-free n-i-p methyl ammonium tin bromide (MASnBr3) perovskite solar cells. We have studied the effect of the various hole and electron transport layers on the performance of MASnBr3 devices. The influence of different parameters, such as doping concentration of optimized HTLs/ETLs, the thickness of the perovskite layer, NA/ND of the absorption layer, and the defect density, is thoroughly investigated using numerical simulations. An optimized device FTO/SnO2/MASnBr3/NiO/Au is proposed here with an open circuit voltage of 1.1214 V, a short circuit current density of 34.8654 mA/cm2, fill factors of 88.30%, a theoretical power conversion efficiencies of 34.52%, and quantum efficiencies of 98%. This work reveals the potential of the MASnBr3 material as a perovskite for toxicity-free renewable energy.

Determining approximate solution of partial differential equations using radial basis function model trained with parallel symbiotic organisms search algorithm

SummaryPartial differential equations (PDEs) find extensive applications in geophysics (weather and climate modeling), astrophysics, and quantum mechanics. Solving PDEs by the conventional methods at times becomes difficult due to the complexity involved in these real‐world problems. Thus instead of an exact solution determining an approximate solution is also helpful for solving the problems. Here a model based on radial basis function (RBF) trained with parallel symbiotic organism search (PSOS) optimization is proposed to find out an approximate solution. The PSOS is a nature‐inspired optimization algorithm that ensures better accuracy due to the potential exploration of agents in the search space. Simultaneously, the computational complexity of this algorithm is low due to inbuilt parallelism. Simulation studies are reported for five real applications of PDEs: 2D modified Helmholtz equation, Poisson problem with Dirichlet boundary condition, elliptic PDE equation, and two convection‐diffusion equations. Comparison is carried out with the same RBF model parameters trained with parallel social spider optimization, original symbiotic organisms search algorithm, real coded genetic algorithm, and particle swarm optimization. The proposed model achieves minimum root mean square errors in most of the cases and achieves it with lower run time compared to the existing algorithms.

Generic Exact Combinatorial Search at HPC Scale

Exact combinatorial search is essential to a wide range of important applications, and there are many large problems that need to be solved quickly. Searches are extremely challenging to parallelise due to a combination of factors, e.g. searches are non-deterministic, dynamic pruning changes the workload, and search tasks have very different runtimes. YewPar is a C++/HPX framework that generalises parallel search by providing a range of sophisticated search skeletons.This paper demonstrates generic high performance combinatorial search, i.e. that a variety of exact combinatorial searches can be easily parallelised for HPC using YewPar. We present a new mechanism for profiling key aspects of YewPar parallel combinatorial search, and demonstrate its value. We exhibit, for the first time, generic exact combinatorial searches at HPC scale. We baseline YewPar against state-of-the-art sequential C++ and C++/OpenMP implementations. We demonstrate that deploying YewPar on an HPC system can dramatically reduce the runtime of large problems, e.g. from days to just 100s. The maximum relative speedups we achieve for an enumeration search are near-linear up to 195(6825) compute-nodes(workers), super-linear for an optimisation search on up to 128(4480) (pruning reduces the workload), and sub-linear for decision searches on up to 64(2240) compute-nodes(workers).

Determination of coupling patterns by parallel searches for μ− → e+ and μ− → e− in muonic atoms

We investigate a possibility that the μ−→e+ conversion is discovered prior to the μ−→e− conversion, and its implications to the new physics search. We focus on the specific model including the mixing of the SU(2)L doublet- and singlet-type scalar leptoquarks, which induces not only the lepton flavor violation but also the lepton number violation. Such a structure is motivated by R-parity violating (RPV) supersymmetric models, where a sbottom mediates the conversion processes. We formulate the μ−→e+ rate in analogy with the muon capture in a muonic atom, and numerically evaluate it using several target nuclei. The lepton flavor universality test of pion decay directly limits the μ−→e+ rate, and the maximally allowed μ−→e+ branching ratio is ∼10−18 under the various bounds on RPV parameters. We show that either μ−→e− or μ−→e+ signals can be discovered in near future experiments. This indicates that parallel searches for these conversions will give us significant information on the pattern of coupling constants.

SkinnerMT

SkinnerMT is an adaptive query processing engine, specialized for multi-core platforms. SkinnerMT features different strategies for parallel processing that allow users to trade between average run time and performance robustness.First, SkinnerMT supports execution strategies that execute multiple query plans in parallel, thereby reducing the risk to find near-optimal plans late and improving robustness. Second, SkinnerMT supports data-parallel processing strategies. Its parallel multi-way join algorithm is sensitive to the assignment from tuples to threads. Here, SkinnerMT uses a cost-based optimization strategy, based on runtime feedback. Finally, SkinnerMT supports hybrid processing methods, mixing parallel search with data-parallel processing.The experiments show that parallel search increases robustness while parallel processing increases average-case performance. The hybrid approach combines advantages from both. Compared to traditional database systems, SkinnerMT is preferable for benchmarks where query optimization is hard. Compared to prior adaptive processing baselines, SkinnerMT exploits parallelism better.

A high dynamic weak spread spectrum signal acquisition strategy based on the transfer model

To solve the problem of low Earth orbit (Leo) satellite constellation link spread spectrum signal acquisition in the high dynamic weak environment, this paper proposes a acquisition strategy based on transfer model. In this paper, we analyze the dynamic characteristics of link signal. An improved method of parallel code phase search acquisition based on transfer model acquisition strategy is introduced, the improvement of the proposed acquisition strategy to the traditional acquisition algorithm is illustrated through theoretical analysis. Simulation results verify the superiority of the acquisition strategy based on the transfer model for high dynamic weak signal acquisition.

Parallel predictive entropy search for multi-objective Bayesian optimization with constraints applied to the tuning of machine learning algorithms

Real-world problems often involve the optimization of several objectives under multiple constraints. An example is the hyper-parameter tuning problem of machine learning algorithms. For example, minimizing both an estimate of the generalization error of a deep neural network and its prediction time. We may also consider, as a constraint, that the deep neural network must be implemented in a chip with an area below some size. Here, both the objectives and the constraint are black boxes, i.e., functions whose analytical expressions are unknown and are expensive to evaluate. Bayesian optimization (BO) methods have shown state-of-the-art results in these tasks. For this, they evaluate iteratively, at carefully chosen locations, the objectives and the constraints with the goal of solving the optimization problem in a small number of iterations. Nevertheless, most BO methods are sequential and perform evaluations at just one input location, at each iteration. Sometimes, however, we may evaluate several configurations in parallel. If this is the case, as when a cluster of computers is available, sequential evaluations result in a waste of resources. To avoid this, one has to choose which locations to evaluate in parallel, at each iteration. This article introduces PPESMOC, Parallel Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints, an information-based batch method for the simultaneous optimization of multiple expensive-to-evaluate black-box functions under the presence of several constraints. Iteratively, PPESMOC selects a batch of input locations at which to evaluate the black-boxes in parallel so as to maximally reduce the entropy of the Pareto set of the optimization problem. To our knowledge, this is the first information-based batch method for constrained multi-objective BO. We present empirical evidence in the form of several optimization problems that illustrate the effectiveness of PPESMOC. Moreover, we also show in several experiments the utility of the proposed method to tune the hyper-parameters of machine learning algorithms.

Benchmark-Driven Configuration of a Parallel Model-Based Optimization Algorithm

This article introduces a benchmarking framework that allows rigorous evaluation of parallel model-based optimizers for expensive functions. The framework establishes a relationship between estimated costs of parallel function evaluations (on real-world problems) to known sets of test functions. Such real-world problems are not always readily available (e.g., confidentiality and proprietary software). Therefore, new test problems are created by Gaussian process simulation. The proposed framework is applied in an extensive benchmark study to compare multiple state-of-the-art parallel optimizers with a novel model-based algorithm, which combines ideas of an explorative search for global model quality with parallel local searches to increase function exploitation. The benchmarking framework is used to configure good batch size setups for parallel algorithms systematically based on landscape properties. Furthermore, we introduce a proof of concept for a novel automatic batch size configuration. The predictive quality of the batch size configuration is evaluated on a large set of test functions and the functions generated by Gaussian process simulation. The introduced algorithm outperforms multiple state-of-the-art optimizers, especially on multimodal problems. Additionally, it proves to be particularly robust over various problem landscapes, and performs well with all tested batch sizes. Consequently, this makes it well suited for black-box kinds of problems.

Fast Algorithms for Denial Constraint Discovery

Denial constraints (DCs) are an integrity constraint formalism widely used to detect inconsistencies in data. Several algorithms have been devised to discover DCs from data, as manually specifying them is burdensome and, worse yet, error-prone. The existing algorithms follow two basic steps: building an intermediate data structure from records, then enumerating the DCs from that intermediate. However, current algorithms are often inefficient in computing these intermediates. Also, it is still unclear which enumeration algorithm performs best since some of the available algorithms have not yet been compared to each other. In response, we present a set of new algorithms with improved design choices. We introduce a parallel pipeline for rapidly computing the intermediate using custom data representations, algorithms, and indexes. For DC enumeration, we propose an inverted index, pruning, and parallel search strategies. We present hybrid approaches that integrate our techniques with previous enumeration algorithms, improving their performance in many scenarios. Our experimental study shows that the proposed DC discovery algorithms are consistently much faster (up to an order of magnitude) than the current state-of-the-art.

Differential Evolution Algorithm-Aided Time-Varying Carrier Frequency Offset Estimation for OFDM Underwater Acoustic Communication

Orthogonal frequency division multiplexing (OFDM) is the preferred scheme for high-speed communication in the field of underwater acoustic communication. However, it is very sensitive to the carrier frequency offset (CFO). This study used a time-varying CFO estimation method aided by the differential evolution (DE) algorithm to accurately estimate the CFO of an OFDM system. This method was based on the principle that the received OFDM signal with inter-carrier interference could be considered by a Multi Carrier-code division multiple access (MC-CDMA) system on the receiver side because MC-CDMA is a technology that combines OFDM and code division multiple access (CMDA). Because it is suitable for solving problems where there are dependencies between adjacent variables, the DE algorithm was used to capture the varying CFO values on the adjacent blocks. The spreading code of the MC-CDMA was obtained based on the estimated CFO values, which were elements in the DE solutions. Then the received signal was reconstructed. The Root-Mean-Square Error between the reconstructed and actual received signals was used as the cost function, and the CFO was estimated using the DE algorithm because of its powerful parallel search capability. The simulation results showed that the proposed method had a high estimation accuracy. Compared with other intelligent optimization algorithms such as the genetic algorithm and simulated annealing mutated-genetic algorithm, the time-varying CFO estimation performance of the DE algorithm was better because of its unique ability to solve problems with dependencies between adjacent variables. Specifically, under the condition of a high signal-to-noise ratio, the improvement of estimation accuracy reaches 36.13%, and the Bit Error Rate of demodulation is thus reduced by 75%, compared with the reference algorithms. In addition, the proposed method also has good applicability to modulation methods. For phase-shift keying and quadrature amplitude modulation, in particular, the proposed method not only achieved high-precision time-varying CFO estimation values, but also reduced the demodulation deterioration caused by noise.

Inhibition of return as a foraging facilitator in visual search: Evidence from long-term training.

Inhibition of return (IOR) discourages visual attention from returning to previously attended locations, and has been theorized as a mechanism to facilitate foraging in visual search by inhibitory tagging of inspected items. Previous studies using visual search and probe-detection tasks (i.e., the probe-following-search paradigm) found longer reaction times (RTs) for probes appearing at the searched locations than probes appearing at novel locations. This IOR effect was stronger in serial than parallel search, favoring the foraging facilitator hypothesis. However, evidence for this hypothesis was still lacking because no attempt was made to study how IOR would change when search efficiency gradually improves. The current study employed the probe-following-search paradigm and long-term training to examine how IOR varied following search efficiency improvements across training days. According to the foraging facilitator hypothesis, inhibitory tagging is an after-effect of attentional engagement. Therefore, when attentional engagement in a visual search task is reduced via long-term training, the strength of inhibitory tagging decreases, thus predicting a reduced IOR effect. Consistent with this prediction, two experiments consistently showed that IOR decreased while search efficiency improved through training, although IOR reached the floor more quickly than search efficiency. These findings support the notion that IOR facilitates search performance via stronger inhibitory tagging in more difficult visual search.

Event-Triggered Cooperative Formation Control for Autonomous Surface Vehicles Under the Maritime Search Operation

To improve the autonomy of maritime search and rescue (SAR) operation, this paper concentrates on the formation control problem for autonomous surface vehicles (ASVs) with the limited communication resource. A novel parallel search guidance, considering the maneuvering characteristics of ASVs, is developed to guide the formation to execute the automatic SAR operation. That can guarantee that the corresponding guidance law is highly efficient, self-driving and suitable for the large-scale formation. Combined with the guidance principle, a formation control algorithm is proposed by fuse of the event-triggered control and neural networks (NNs). In the proposed scheme, the gain uncertainty of actuators is effectively compensated requiring no prior information around the model structure. Unlike the existing results, the proposed event-triggered mechanism can activate synchronously both the controller and the NNs weight estimator. Considerable effort has been made to guarantee the semi-global uniform ultimate bounded (SGUUB) stability. Finally, two examples are illustrated to verify the effectiveness of the algorithm.

Distribution Network Reactive Power Optimization Method Based on Filter Coordinated Quantum State Transfer Algorithm

To solve the problem of mixed integer nonlinear programming in dynamic reactive power optimization of distribution networks, a two-stage dynamic reactive power optimization model is used to reduce the size of the reactive power optimization calculation.The first stage is nonlinear planning, with the objective of minimizing the combined system network loss and node voltage offset. The second stage is a mixed-integer planning, with the objective of minimizing the incremental power system network loss on the basis of satisfying the constraints such as the number of equipment regulation throughout the day. In view of the fact that the conventional penalty function method to deal with the constraints will result in improper selection of penalty factors, an improved quantum state transfer algorithm with mixed filter technique is proposed. The algorithm adopts a two-population parallel search approach, and the individual adaptation degree and constraint violation degree are constituted as filter pairs as evaluation metrics to replace the penalty functions. Finally, the proposed algorithm, the traditional state transfer algorithm and Colony Algorithm are solved separately for the model using the IEEE 14 system as examples, and the correctness of the proposed model and algorithm is verified by comparing and analyzing the simulation results.

Automated Radiation Therapy Treatment Planning Using Meta-Optimization

<h3>Purpose/Objective(s)</h3> Radiation therapy treatment planning is a time-consuming process involving iterative adjustments of hyperparameters. To automate the treatment planning process, we propose a meta-optimization framework, called MetaPlanner (MP). <h3>Materials/Methods</h3> Our MP algorithm automates planning by performing meta-optimization of treatment planning hyperparameters. The algorithm uses a derivative-free method (i.e., parallel Nelder-Mead simplex search) to search for weight configurations that minimize a meta-scoring function. Meta-scoring is performed by constructing a tier list of the relevant considerations (e.g., dose homogeneity, conformity, spillage, and OAR sparing) to mimic the clinical decision-making process. Additionally, we have made our source code publicly available via github (https://github.com/chh105/MetaPlanner). <h3>Results</h3> The proposed MP method is evaluated on two datasets (21 prostate cases and 6 head and neck cases) collected as part of clinical workflow. MP is applied to both IMRT and VMAT planning and compared to a baseline of manual VMAT plans. MP in both IMRT and VMAT scenarios has comparable or better performance than manual VMAT planning for all evaluated metrics. <h3>Conclusion</h3> Our proposed MP provides a general framework for fully automated treatment planning that produces high quality treatment plans. Our MP method promises to substantially reduce the workload of treatment planners while maintaining or improving plan quality.

Black-box and surrogate optimization for tuning spiking neural models of striatum plasticity.

The basal ganglia (BG) is a brain structure that has long been proposed to play an essential role in action selection, and theoretical models of spiking neurons have tried to explain how the BG solves this problem. A recently proposed functional and biologically inspired network model of the striatum (an important nucleus of the BG) is based on spike-timing-dependent eligibility (STDE) and captured important experimental features of this nucleus. The model can recognize complex input patterns and consistently choose rewarded actions to respond to such sensory inputs. However, model tuning is challenging due to two main reasons. The first is the expert knowledge required, resulting in tedious and potentially biased trial-and-error procedures. The second is the computational cost of assessing model configurations (approximately 1.78 h per evaluation). This study addresses the model tuning problem through numerical optimization. Considering the cost of assessing solutions, the selected methods stand out due to their low requirements for solution evaluations and compatibility with high-performance computing. They are the SurrogateOpt solver of Matlab and the RBFOpt library, both based on radial basis function approximations, and DIRECT-GL, an enhanced version of the widespread black-box optimizer DIRECT. Besides, a parallel random search serves as a baseline reference of the outcome of opting for sophisticated methods. SurrogateOpt turns out to be the best option for tuning this kind of model. It outperforms, on average, the quality of the configuration found by an expert and works significantly faster and autonomously. RBFOpt and the random search share the second position, but their average results are below the option found by hand. Finally, DIRECT-GL follows this line becoming the worst-performing method.

Segmentation on remote sensing imagery for atmospheric air pollution using divergent differential evolution algorithm.

Air pollution is a global issue causing major health hazards. By proper monitoring of air quality, actions can be taken to control air pollution. Satellite remote sensing is an effective way to monitor global atmosphere. Various sensors and instruments fitted to satellites and airplanes are used to obtain the radar images. These images are quite complex with various wavelength differentiated by very close color differences. Clustering of such images based on its wavelengths can provide the much-needed relief in better understanding of these complex images. Such task related to image segmentation is a universal optimization issue that can be resolved with evolutionary techniques. Differential Evolution (DE) is a fairly fast and operative parallel search algorithm. Though classical DE algorithm is popular, there is a need for varying the mutation strategy for enhancing the performance for varied applications. Several alternatives of classical DE are considered by altering the trial vector and control parameter. In this work, a new alteration of DE technique labeled as DiDE (Divergent Differential Evolution Algorithm) is anticipated. The outcomes of this algorithm were tested and verified with the traditional DE techniques using fifteen benchmark functions. The new variant DiDE exhibited much superior outcomes compared to traditional approaches. The novel approach was then applied on remote sensing imagery collected form TEMIS, a web based service for atmospheric satellite images and the image was segmented. Fuzzy Tsallis entropy method of multi-level thresholding technique is applied over DiDE to develop image segmentation. The outcomes obtained were related with the segmented results using traditional DE and the outcome attained was found to be improved profoundly. Experimental results illustrate that by acquainting DiDE in multilevel thresholding, the computational delay was greatly condensed and the image quality was significantly improved.