Efficient Algorithm Research Articles

StrainNet-LD: Large Displacement digital image correlation based on deep learning and displacement-field decomposition

First, a displacement-field decomposition strategy is proposed that effectively enhances the accuracy of digital image correlation (DIC) algorithms under large displacement or deformation conditions. The implementation methodologies of displacement-field decomposition under both Lagrangian and Eulerian descriptions are derived and presented. Second, displacement-field decomposition is applied to speckle image correlation algorithms based on deep learning, resolving the contradiction between registration capability of large displacement and the accuracy. A CUDA-accelerated and residual learning-based strain fitting algorithm is proposed for high-precision and real-time strain computation. This method is named “StrainNet for Large Displacement” (StrainNet-LD). StrainNet-LD achieves a calculation speed of 768 × 768 × 6 fps (768 × 768 × 3 fps with strain calculated simultaneously) on GPU with displacement MAE < 0.05 pixels and strain MAE < 0.002ε. Finally, rubber tensile tests, 3D motion measurement of composite shells, and morphology reconstruction experiments of speckle-free composite repair structures are conducted to validate the algorithm's robustness and efficiency. Some vital discussions of displacement-field decomposition are given in the Appendixes. The code and dataset are available at https://github.com/GW-Wang-thu/StrainNet-LD.

Optimal configuration of hydrogen storage capacity of hybrid microgrid considering peak regulation and frequency modulation requirements

This study proposes an innovative hydrogen storage capacity optimization configuration method that considers multiple demand factors, addressing the issue that traditional methods for optimizing hydrogen storage capacity in hybrid microgrids cannot simultaneously balance economic efficiency and grid connection quality. This method breaks through the traditional optimization framework and adopts a double-layer optimization model, combining the peak shaving operation cost of the hybrid microgrid with the interactive power deviation and power fluctuation penalty terms of the interconnection line during the frequency regulation stage, achieving dual optimization of economy and grid connection quality. Through precise mathematical modeling and efficient optimization algorithms, we have successfully obtained the minimum operating cost corresponding to the minimum power fluctuation, the minimum interconnection line interactive power deviation penalty term, and the optimal hydrogen storage capacity configuration scheme. Identify two typical days through cluster analysis. Option 1 (considering peak shaving, frequency regulation, and hydrogen electrolysis) has the lowest net load and a total operating cost of 3.0331 million yuan, which is better than Option 2 (3.0973 million yuan) and Option 3 (5.0398 million yuan). This plan effectively reduces wind and solar power waste, shortens the operating time of thermal power units, and demonstrates the rationality and economy of optimizing hydrogen storage capacity configuration.

Synthesis and evaluation of isothiazolo[4,5-b]pyridines as cyclin G-associated kinase (GAK) inhibitors.

Isothiazolo[4,3-b]pyridines have been extensively explored as inhibitors of cyclin G-associated kinase (GAK). In order to expand the structure-activity relationship study and to discover other chemotypes that act as GAK inhibitors, the closely related isothiazolo[4,5-b]pyridine scaffold was explored. An easy and efficient synthetic procedure to access 3,5- and 3,6-dihalogenated isothiazolo[4,5-b]pyridines as key building blocks was developed. Regioselective functionalization with various substituents was performed. None of the newly synthesized isothiazolo[4,5-b]pyridines were active as GAK inhibitors. Molecular modeling was applied to rationalise their inactivity as GAK binders.

An expandable and cost-effective data center network

With the rapid growth of data volume, the escalating complexity of data businesses, and the increasing reliance on the Internet for daily life and production, the scale of data centers is constantly expanding. The data center network (DCN) is a bridge connecting large-scale servers in data centers for large-scale distributed computing. How to build a DCN structure that is flexible and cost-effective, while maintaining its topological properties unchanged during network expansion has become a challenging issue. In this paper, we propose an expandable and cost-effective DCN, namely HHCube, which is based on the half hypercube structure. Further, we analyze some characteristics of HHCube, including connectivity, diameter, and bisection bandwidth of the HHCube. We also design an efficient algorithm to find the shortest path between any two distinct nodes and present a fault-tolerant routing scheme to obtain a fault-tolerant path between any two distinct fault-free nodes in HHCube. Meanwhile, we present two local diagnosis algorithms to determine the status of nodes in HHCube under the PMC model and MM* model, respectively. Our results demonstrate that despite the presence of up to 25% faulty nodes in HHCube, both algorithms achieve a correct diagnosis rate exceeding 90%. Finally, we compare HHCube with state-of-the-art DCNs including Fat-Tree, DCell, BCube, Ficonn, and HSDC, and the experimental results indicate that the HHCube is an excellent candidate for constructing expandable and cost-effective DCNs.

LPulse: An efficient algorithm for service function chain placement and routing with delay guarantee

Modern network services increasingly depend on the effective orchestration of the Service Function Chain (SFC) with stringent end-to-end delay guarantees. To achieve this, the Delay-Constrained Service Function Chain Placement and Routing (DC-SFCPR) problem must be addressed. This problem involves the optimal selection of nodes for placing network functions and routes that adhere to a specific sequence of service functions, to minimize network bandwidth and CPU costs while strictly adhering to stringent end-to-end delay constraints. The DC-SFCPR problem is NP-hard and existing algorithms either fail to guarantee strict delay constraints or are computationally expensive, making them unsuitable for expanding network topologies. We propose the LPulse algorithm, designed to efficiently solve the DC-SFCPR problem. This algorithm utilizes a layered graph to embed the requirements of service functions, transforming the DC-SFCPR problem into a Delay-Constrained Shortest Path (DCSP) problem. The LPulse algorithm then applies Pulse, a depth-first search framework enhanced with efficient pruning strategies, and incorporates two novel acceleration strategies to solve the DCSP problem. We prove that LPulse ensures the optimality of solutions. Evaluations conducted across various topologies, with node scales ranging from 22 to 10,000, show that LPulse surpasses existing algorithms in both solution quality and speed. For instance, the number of cases meeting strict delay constraints with LPulse is 1.9× that of those solved by deep reinforcement learning algorithms; furthermore, its solving efficiency is 4.9× that of the highest-performing existing optimal algorithm, the LagrangianKsp algorithm.

On the connectedness of arithmetic hyperplanes

Discrete geometry is a geometry specific to computers that studies Zd structures. It appears naturally in image analysis or 3D printing. Our goal is to find efficient algorithms to characterise these geometric structures and their properties.We are interested in a fundamental structure of discrete geometry, the arithmetic hyperplanes, and more particularly in their connectedness. Many works have studied a connectedness defined from the neighbourhood by faces and have allowed to observe a percolation phenomenon. These studies have also allowed to decide the connectedness of a plane in an efficient way. We propose an extension of these results in the case of connectivity defined from general neighbourhoods.Beyond the new concepts that this extension requires, the main contribution of the paper lies in the use of analysis to solve this arithmetic problem and in the design of an algorithm that decides the general connectedness problem. The study of the thickness of connectedness as a function reveals discontinuities at each rational point. However, a much more regular underlying structure appears in the irrational case. Thus, the consideration of irrational vectors allows a simpler approach to the connectedness of arithmetic hyperplanes.

An Efficient Algorithm to Compute the Toughness in Graphs with Bounded Treewidth

Let t be a positive real number. A graph is called t-tough if the removal of any vertex set S that disconnects the graph leaves at most |S|/t components. The toughness of a graph is the largest t for which the graph is t-tough. We prove that toughness is fixed-parameter tractable parameterized with the treewidth. More precisely, we give an algorithm to compute the toughness of a graph G with running time O(|V(G)|3·tw(G)2tw(G))\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {O}}(|V(G)|^3\\cdot \ extrm{tw}(G)^{2\ extrm{tw}(G)})$$\\end{document} where tw(G)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{tw}(G)$$\\end{document} is the treewidth. If the treewidth is bounded by a constant, then this is a polynomial algorithm.

A Distribution-Free Method for Change Point Detection in Non-Sparse High Dimensional Data

We propose a distribution-free distance-based method for high dimensional change points that can address challenging situations when the sample size is very small compared to the dimension as in the so-called HDLSS data or when non-sparse changes may occur due to change in many variables but with small significant magnitudes. Our method can detect changes in mean or variance of high dimensional observations as well as other distributional changes. We present efficient algorithms that can detect single and multiple high dimensional change points. We use nonparametric metrics, including a new dissimilarity measure and some new distance and difference distance matrices, to develop a procedure to estimate change point locations. We also introduce a nonparametric test to determine the significance of estimated change points. We provide theoretical guaranties for our method and demonstrate its empirical performance in comparison with some of the recent methods for high dimensional change points. An R package called HDDchangepoint is developed to implement the proposed method.

Complexity of High-Dimensional Identity Testing with Coordinate Conditional Sampling

We study the identity testing problem for high-dimensional distributions. Given as input an explicit distribution \(\mu\) , an \(\varepsilon \gt 0\) , and access to sampling oracle(s) for a hidden distribution \(\pi\) , the goal in identity testing is to distinguish whether the two distributions \(\mu\) and \(\pi\) are identical or are at least \(\varepsilon\) -far apart. When there is only access to full samples from the hidden distribution \(\pi\) , it is known that exponentially many samples (in the dimension) may be needed for identity testing, and hence previous works have studied identity testing with additional access to various “conditional” sampling oracles. We consider a significantly weaker conditional sampling oracle, which we call the \(\mathsf{Coordinate\ Oracle}\) , and provide a computational and statistical characterization of the identity testing problem in this new model. We prove that if an analytic property known as approximate tensorization of entropy holds for an \(n\) -dimensional visible distribution \(\mu\) , then there is an efficient identity testing algorithm for any hidden distribution \(\pi\) using \(\widetilde{O}(n/\varepsilon)\) queries to the \(\mathsf{Coordinate\ Oracle}\) . Approximate tensorization of entropy is a pertinent condition as recent works have established it for a large class of high-dimensional distributions. We also prove a computational phase transition: for a well-studied class of \(n\) -dimensional distributions, specifically sparse antiferromagnetic Ising models over \(\{+1,-1\}^{n}\) , we show that in the regime where approximate tensorization of entropy fails, there is no efficient identity testing algorithm unless \(\mathsf{RP}=\mathsf{NP}\) . We complement our results with a matching \(\Omega(n/\varepsilon)\) statistical lower bound for the sample complexity of identity testing in the \(\mathsf{Coordinate\ Oracle}\) model.

Pruning and Validation Techniques Enhanced Genetic Algorithm for Energy Efficiency in Wireless Sensor Networks

Pruning and Validation Techniques Enhanced Genetic Algorithm for Energy Efficiency in Wireless Sensor Networks

Model-Based Co-Clustering in Customer Targeting Utilizing Large-Scale Online Product Rating Networks

Given the widely available online customer ratings on products, the individual-level rating prediction and clustering of customers and products are increasingly important for sellers to create targeting strategies for expanding the customer base and improving product ratings. However, the massive missing data problem is a significant challenge for modeling online product ratings. To address this issue, we propose a new co-clustering methodology based on a bipartite network modeling of large-scale ordinal product ratings. Our method extends existing co-clustering methods by incorporating covariates and ordinal ratings in the model-based co-clustering of a weighted bipartite network. We devise an efficient variational EM algorithm for model estimation. A simulation study demonstrates that our methodology is scalable for modeling large datasets and provides accurate estimation and clustering results. We further show that our model can successfully identify different groups of customers and products with meaningful interpretations and achieve promising predictive performance in a real application for customer targeting.

Topology optimization with graph neural network enabled regularized thresholding

Topology optimization algorithms often employ a smooth density function to implicitly represent geometries in a discretized domain. While this implicit representation offers great flexibility to parametrize the optimized geometry, it also leads to a transition region. Previous approaches, such as the Solid Isotropic Material Penalty (SIMP) method, have been proposed to modify the objective function aiming to converge toward integer density values and eliminate this non-physical transition region. However, the iterative nature of topology optimization renders this process computationally demanding, emphasizing the importance of achieving fast convergence. Accelerating convergence without significantly compromising the final solution can be challenging. In this work, we introduce a machine learning approach that leverages the message-passing Graph Neural Network (GNN) to eliminate the non-physical transition zone for the topology optimization problems. By representing the optimized structures as weighted graphs, we introduce a generalized filtering algorithm based on the topology of the spatial discretization. As such, the resultant algorithm can be applied to two- and three-dimensional space for both Cartesian (structured grid) and non-Cartesian discretizations (e.g. polygon finite element). The numerical experiments indicate that applying this filter throughout the optimization process may avoid excessive iterations and enable a more efficient optimization procedure.

Robust low-rank matrix completion via sparsity-inducing regularizer

This paper proposes a sparsity-inducing regularizer associated with the Welsch function. We theoretically show that the regularizer is quasiconvex and the corresponding Moreau envelope is convex. Moreover, the closed-form solution to its Moreau envelope, namely, the proximity operator, is derived. Unlike conventional nonconvex regularizers like the ℓp-norm with 0<p<1 that generally needs iterations to obtain the corresponding proximity operator, the developed regularizer has a closed-form proximity operator. We utilize our regularizer to penalize the singular values as well as sparse outliers of the distorted data, and develop an efficient algorithm for robust matrix completion. Convergence of the suggested method is analyzed and we prove that any accumulation point is a stationary point. Finally, experimental results demonstrate that our algorithm is superior to the competing techniques in terms of restoration performance. MATALB codes are available at https://github.com/bestzywang/RMC-NNSR.

An efficient algorithm for solving multilevel multi-objective linear fractional optimization problem with neutrosophic parameters

This work demonstrates the modeling of the multilevel-multiobjective linear fractional optimization (ML-MOLFO) problem in a neutrosophic decision environment. The primary driving force behind this work is to determine how to handle a ML-MOLFO problem while taking into account a real-life decision-making scenario. There is a hierarchy of decision-makers and many decision-making processes, such as agree, not-sure, and disagree, in a real-life decision-making setting. By taking into account such genuine situations, all resource, coefficient of objective, and constraint functions are represented as single-valued trapezoidal neutrosophic numbers. As far as we can tell, there is no established technique for resolving the ML-MOLFO problem with neutrosophic parameters in the literature review. Therefore, by outlining goal programming methods in the intuitionistic fuzzy environment, we create our new intuitionistic fuzzy Chebyshev goal programming (IFCGP) method to address the introduced problem. In the proposed method, first we used a modified ranking function to transform the problem into an analogous crisp ML-MOLFO problem, and then it was converted into a multilevel multiobjective linear optimization problem through linearized techniques. The linearization procedure in the proposed method, unlike the existing one, does not require derivatives, additional variables, or inequality constraints, which makes it simpler. To illustrate the adequacy and performance of the proposed new method, we considered numerical tests and application problems. We also compared the performance of the resulting optimal solution with existing approaches using the distance metric, which indicates our method is non-dominated.

Fast Bayesian Inference for Spatial Mean-Parameterized Conway–Maxwell–Poisson Models

Count data with complex features arise in many disciplines, including ecology, agriculture, criminology, medicine, and public health. Zero inflation, spatial dependence, and non-equidispersion are common features in count data. There are currently two classes of models that allow for these features—the mode-parameterized Conway–Maxwell–Poisson (COMP) distribution and the generalized Poisson model. However both require the use of either constraints on the parameter space or a parameterization that leads to challenges in interpretability. We propose spatial mean-parameterized COMP models that retain the flexibility of these models while resolving the above issues. We use a Bayesian spatial filtering approach in order to efficiently handle high-dimensional spatial data and we use reversible-jump MCMC to automatically choose the basis vectors for spatial filtering. The COMP distribution poses two additional computational challenges—an intractable normalizing function in the likelihood and no closed-form expression for the mean. We propose a fast computational approach that addresses these challenges by, respectively, introducing an efficient auxiliary variable algorithm and pre-computing key approximations for fast likelihood evaluation. We illustrate the application of our methodology to simulated and real datasets, including Texas HPV-cancer data and US vaccine refusal data. Supplementary materials for this article are available online.

Intelligent detection of office occupancy using hybrid data-mining

The importance of office occupancy detection is evident, due to its critical role in workplace management and facility optimization. Recent studies have recommended the use of data-mining techniques for this purpose. In this research, a powerful hybrid model is developed upon artificial neural network (ANN) coupled with shuffled complex evolution (SCE) algorithm for the binary prediction of occupancy in an office room based on several characteristics such as light, temperature, humidity, and CO2. The trained model is tested via two datasets (considering the opened/closed office door while occupied) and according to the results, it achieved an excellent prediction. In detail, the values of area under the curve (AUC) were above 0.98 along with mean square errors below 0.03; indicating the superiority of the proposed SCE algorithm compared to three baseline techniques, namely Archimedes optimization algorithm (AOA), incomprehensible but Intelligible-in-time logics (ILA), and gazelle optimization algorithm (GOA) in this study, as well as some others in earlier literature. Moreover, the SCE-ANN emerged as the most efficient (i.e., the quickest and least complex) algorithm. In the end, the dataset is exposed to a well-known statistical method called principal component analysis (PCA) to evaluate the importance of the considered input factors and identify the most crucial ones. Altogether, the findings of this study can provide valuable environmental and economic contributions to related sectors such as building energy management.

A comprehensive numerical approach to coil placement in cerebral aneurysms: mathematical modeling and in silico occlusion classification.

Endovascular coil embolization is one of the primary treatment techniques for cerebral aneurysms. Although it is a well-established and minimally invasive method, it bears the risk of suboptimal coil placement which can lead to incomplete occlusion of the aneurysm possibly causing recurrence. One of the key features of coils is that they have an imprinted natural shape supporting the fixation within the aneurysm. For the spatial discretization, our mathematical coil model is based on the discrete elastic rod model which results in a dimension-reduced 1D system of differential equations. We include bending and twisting responses to account for the coils natural curvature and allow for the placement of several coils having different material parameters. Collisions between coil segments and the aneurysm wall are handled by an efficient contact algorithm that relies on an octree based collision detection. In time, we use a standard symplectic semi-implicit Euler time stepping method. Our model can be easily incorporated into blood flow simulations of embolized aneurysms. In order to differentiate optimal from suboptimal placements, we employ a suitable in silico Raymond-Roy-type occlusion classification and measure the local packing density in the aneurysm at its neck, wall region and core. We investigate the impact of uncertainties in the coil parameters and embolization procedure. To this end, we vary the position and the angle of insertion of the micro-catheter, and approximate the local packing density distributions by evaluating sample statistics.

Neural secret key enabled secure cloud storage with efficient packet checker algorithm

Cloud computing technology is utilized for the commercial purpose of implementing virtualization for the storage of data, which is then controlled and made accessible to users via remote servers. With the increased use of various communication devices and direct access to web servers, there is a favorable opportunity to orchestrate attacks and exploit vulnerabilities in a system. Distributed Denial of Service (DDoS) is a commonly used technique employed by attackers in cloud environments. This study introduces a security layer incorporating a Packet Checker Algorithm (PCA) to detect and eliminate counterfeit packets. The algorithm takes into account transmission delay time as well as minimum and maximum thresholds, thereby enhancing the response time of the Intrusion Detection Process (IDP) in a cloud environment that utilizes neural encryption. Neural encryption is used to synchronize neural networks and create new public channel protocols for a secure cloud storage paradigm. This study greatly expands upon the hop count inspection and filtering method. It incorporates the time slot filtering function and implements a unique key set to differentiate between genuine packets and falsified packets. This novel methodology has the capability to detect Distributed Denial of Service (DDoS) attacks and related anomalies during the initial phases of data transmission. The technique considers Time-to-Live (TTL), Hope count, and Transmission-Delay-Time as crucial components.

Acquisition of Data on Kinematic Responses to Unpredictable Gait Perturbations: Collection and Quality Assurance of Data for Use in Machine Learning Algorithms for (Near-)Fall Detection.

Slip, trip, and fall (STF) accidents cause high rates of absence from work in many companies. During the 2022 reporting period, the German Social Accident Insurance recorded 165,420 STF accidents, of which 12 were fatal and 2485 led to disability pensions. Particularly in the traffic, transport and logistics sector, STF accidents are the most frequently reported occupational accidents. Therefore, an accurate detection of near-falls is critical to improve worker safety. Efficient detection algorithms are essential for this, but their performance heavily depends on large, well-curated datasets. However, there are drawbacks to current datasets, including small sample sizes, an emphasis on older demographics, and a reliance on simulated rather than real data. In this paper we report the collection of a standardised kinematic STF dataset from real-world STF events affecting parcel delivery workers and steelworkers. We further discuss the use of the data to evaluate dynamic stability control during locomotion for machine learning and build a standardised database. We present the data collection, discuss the classification of the data, present the totality of the data statistically, and compare it with existing databases. A significant research gap is the limited number of participants and focus on older populations in previous studies, as well as the reliance on simulated rather than real-world data. Our study addresses these gaps by providing a larger dataset of real-world STF events from a working population with physically demanding jobs. The population studied included 110 participants, consisting of 55 parcel delivery drivers and 55 steelworkers, both male and female, aged between 19 and 63 years. This diverse participant base allows for a more comprehensive understanding of STF incidents in different working environments.

The constrained permutation flowshop problem: An effective two-stage iterated greedy algorithm to minimize weighted tardiness

In the domain of just-in-time permutation flowshop scheduling, most studies typically assume that all jobs either have their own soft due date or none of them do. However, in practice, scheduling a combination of hard and soft due date jobs, particularly with the context of emergency order insertion, remains a significant research topic. This paper addresses a constrained permutation flowshop scheduling problem with a mix of hard and soft due date jobs under total weighted tardiness criterion (CPFSP-TWT). We establish a mathematical model and propose an effective Two-Stage Iterated Greedy (ETSIG) algorithm tailored to the problem's characteristics, incorporating a two-stage constructive heuristic to generate a high-quality initial solution. We introduce problem-specific acceleration mechanisms based on position-bound considerations to enhance operational efficiency. We propose three knowledge-based repair strategies for handling infeasible solutions, along with a dynamic self-adjustment mechanism. Additionally, three efficient local search procedures integrate several specific perturbation operators to balance algorithmic exploitation and exploration abilities. Experimental evaluations affirm ETSIG's superiority over five state-of-the-art metaheuristics from closely related literature, establishing its efficacy in addressing CPFSP-TWT.