Partitioning Algorithm Research Articles

Effective anti-submarine decision support system based on heuristic rank-based Dijkstra and adaptive threshold partitioning mechanism

Submarines possess strong covert striking capabilities, making anti-submarine warfare (ASW) a global naval priority. The Hidden Markov Anti-Submarine Model (HMASM) finds crucial applications in dynamic and uncertain ASW. The model delineates ASW into two phases: partitioning and search path planning. Current partitioning algorithms often include cells leading to redundant and competitive search, determined empirically. Additionally, in HMASM, the path planning algorithms based on genetic algorithm (GA) are time-consuming and exhibits unstable performance. This paper proposes a novel solution to these issues. For partitioning, a Reassigned k-Nearest Neighbour (RKNN) algorithm is introduced, identifying and reallocating units causing repeated and competing searches. For search path planning, heuristic rules for Dijkstra's cost function and goal point selection transform the NP-hard problem of maximizing the expected number of detections (ED) into a deterministic algorithm-compatible form. A heuristic rank-based selection model, Rank-Based Dijkstra under the Hidden Markov Model (HMM-R-Dijkstra), considering distance and probability, is added to Dijkstra. Furthermore, an Adaptive Threshold Partitioning (ATP) dynamically monitors searcher exploration by setting variables and thresholds to determine optimal partitioning timing, preventing untimely and excessive partitioning. Combining RKNN, HMM-R-Dijkstra, and ATP forms R-HRD-ATP, optimizing all parameters using parallel structures. Through three comparative experiments, R-HRD-ATP's performance steadily improves. Experiments comparing R-HRD-ATP to GA, Sparrow Search Algorithm (SSA)-GA, and Ant Colony Optimization (ACO)-GA reveal performance enhancements of 25–70.99% and time savings of 56–295 times for our model. Importantly, no parameter adjustments are required. The success of R-HRD-ATP indicates that its heuristic rules can support the establishment of robust applications of deterministic path planning algorithms in HMASM.

Dynamic stream partitioning for time-triggered traffic in Time-Sensitive Networking

Time-Sensitive Networking (TSN) is a promising network technology that can ensure bounded latency and jitter for industrial real-time scenarios. It establishes the IEEE 802.1 Qbv standard to precisely control the periodic transmission time of the time-triggered (TT) traffic. However, it is still a challenge to efficiently schedule the TT traffic in large-scale networks with high transmission performance. In this paper, we propose a novel method called dynamic stream partitioning (DSP) to solve this problem. It satisfies the IEEE 802.1 Qbv standard and can significantly reduce the scheduling time by reducing the number of constraints. Based on the DSP method, we analyze the impact of stream partitioning on the transmission performance of the TT traffic, and propose an indicator called Normalized Degradation of Performance (NDOP) to quantify it dynamically. Furthermore, we design a partitioning-aware dynamic routing (PADR) to expand the scheduling space. Integrated with NDOP and PADR, we propose a joint routing and dynamic stream partitioning (JR/DSP) algorithm. Extensive simulation experiments verify that the proposed JR/DSP algorithm has higher scalability, transmission performance, and schedulability compared to the static stream partitioning algorithms.

GNNGL-PPI: multi-category prediction of protein-protein interactions using graph neural networks based on global graphs and local subgraphs

Most proteins exert their functions by interacting with other proteins, making the identification of protein-protein interactions (PPI) crucial for understanding biological activities, pathological mechanisms, and clinical therapies. Developing effective and reliable computational methods for predicting PPI can significantly reduce the time-consuming and labor-intensive associated traditional biological experiments. However, accurately identifying the specific categories of protein-protein interactions and improving the prediction accuracy of the computational methods remain dual challenges. To tackle these challenges, we proposed a novel graph neural network method called GNNGL-PPI for multi-category prediction of PPI based on global graphs and local subgraphs. GNNGL-PPI consisted of two main components: using Graph Isomorphism Network (GIN) to extract global graph features from PPI network graph, and employing GIN As Kernel (GIN-AK) to extract local subgraph features from the subgraphs of protein vertices. Additionally, considering the imbalanced distribution of samples in each category within the benchmark datasets, we introduced an Asymmetric Loss (ASL) function to further enhance the predictive performance of the method. Through evaluations on six benchmark test sets formed by three different dataset partitioning algorithms (Random, BFS, DFS), GNNGL-PPI outperformed the state-of-the-art multi-category prediction methods of PPI, as measured by the comprehensive performance evaluation metric F1-measure. Furthermore, interpretability analysis confirmed the effectiveness of GNNGL-PPI as a reliable multi-category prediction method for predicting protein-protein interactions.

Optimizing service design for the intercity demand responsive transit system: Model, algorithm, and comparative analysis

Intercity Demand Responsive Transit (IDRT) systems, providing on-demand “door-to-door” services for intercity travelers, have been recently implemented in a few cities and gained popularity. To devise a theoretical tool for designing efficient IDRT systems, this study proposes a continuous approximation (CA) model to describe the operation metrics of the IDRT. In a two-city system, an optimization model is proposed to study the zoning strategies in the main city, the service area in the satellite city, and headways. The objective is to minimize the average system cost, including the costs of both the operators and the passengers. A two-stage partition algorithm is proposed to generate a near-optimal solution by combining the greedy algorithm and sensitivity-based coordination optimization method. Subsequently, we develop a direct solution algorithm to establish a lower bound for the optimal solution, facilitating a comparison with the near-optimal solution. Throughout all sensitivity analyses, the gaps between the near-optimal solution and the lower-bound solution remain below 3 %. The comparison results demonstrate that the computation time and solution quality of the two-stage partition algorithm are considered acceptable. Simulation is performed to test the validity of the CA model, and the errors in the average system costs are less than 1.16 %. Sensitivity analyses are carried out to evaluate the impacts of several key parameters (e.g., demand densities and line-haul distances) on the optimization results. By comparison with Intercity Fixed Route Transit (IFRT), it can be seen that the choice of IDRT depends on passengers' preference for door-to-door transportation. This study may serve as a valuable reference for the practical implementation of IDRT.

Research on Gas Multi-indicator Warning Method of Coal Mine Working Face Based on MOA-Transformer.

The gas emission from the coal mine working face is influenced by multiple factors, resulting in the real-time value, fluctuation, and trend changes of gas concentration being relatively independent and interrelated. This paper establishes a gas multi-indicator warning method that can comprehensively warn the status of real-time value, fluctuation, and trend changes of gas concentration from the working face. This paper proposes six basic indicators and includes two main research contents: intelligent threshold partition and gas multi-indicator warning. First, this paper proposes an intelligent threshold partition algorithm based on GF-KMeans (genetic fixed-centered K-means), which combines a genetic algorithm (GA) and an FC-KMeans (fixed-centered K-means) algorithm to dynamically partition the threshold range corresponding to the gas warning level. The GA solves the local optimal problem in the traditional K-Means algorithm, enhancing its stability and predictability. The FC-KMeans algorithm achieves a more precise control in the initial clustering center selection. Second, this paper researches a gas multi-indicator warning method based on a multihead optimal attention (MOA)-Transformer. By using the multihead optimization attention mechanism to represent classification features and utilizing Transformer's encoder structure to classify gas warning. The experimental result shows that the accuracy of the MOA-Transformer method is 86.17%, which is 3.45% higher than that of the Transformer method. The precision of the MOA-Transformer method is 88.78%, which is 3.75% higher than that of the Transformer method. The recall of the MOA-Transformer method is 85.23%, which is 4.70% higher than that of the Transformer method. The macro-F1 of the MOA-Transformer method is 86.96%, which is 4.39% higher than that of the Transformer method. The results fully demonstrate the superiority of the MOA-Transformer method in gas warning tasks.

BGS: Accelerate GNN training on multiple GPUs

Emerging Graph Neural Networks (GNNs) have made significant progress in processing graph-structured data, yet existing GNN frameworks face scalability issues when training large-scale graph data using multiple GPUs. Frequent feature data transfers between CPUs and GPUs are a major bottleneck, and current caching schemes have not fully considered the characteristics of multi-GPU environments, leading to inefficient feature extraction. To address these challenges, we propose BGS, an auxiliary framework designed to accelerate GNN training from a data perspective in multi-GPU environments. Firstly, we introduce a novel training set partition algorithm, assigning independent training subsets to each GPU to enhance the spatial locality of node access, thus optimizing the efficiency of the feature caching strategy. Secondly, considering that GPUs can communicate at high speeds via NVLink connections, we designed a feature caching placement strategy suitable for multi-GPU environments. This strategy aims to improve the overall hit rate by setting reasonable redundant caches on each GPU. Evaluations on two representative GNN models, GCN and GraphSAGE, show that BGS significantly improves the hit rate of feature caching strategies in multi-GPU environments and substantially reduces the time overhead of data loading, achieving a performance improvement of 1.5 to 6.2 times compared to the baseline.

Fast Coding Unit Partitioning Algorithm for Video Coding Standard Based on Block Segmentation and Block Connection Structure and CNN

The recently introduced Video Coding Standard, VVC, presents a novel Quadtree plus Nested Multi-Type Tree (QTMTT) block structure. This structure enables a more flexible block partition and demonstrates enhanced compression performance compared to its predecessor, HEVC. However, The introduction of the new structure has led to a more complex partition search process, resulting in a considerable increase in time complexity. The QTMTT structure yields diverse Coding Unit (CU) block sizes, posing challenges for CNN model inference. In this study, we propose a representation structure termed Block Segmentation and Block Connection (BSC), rooted in texture features. This ensures that partial CU blocks are uniformly represented in size. To address different-sized CUs, various levels of CNN models are designed for prediction. Moreover, we introduce a post-processing method and a multi-thresholding scheme to further mitigate errors introduced by CNNs. This allows for flexible and adjustable acceleration, achieving a trade-off between coding time complexity and performance. Experimental results indicate that, in comparison to VTM-10.0, our “Fast” scheme reduces the average complexity by 57.14% with a 1.86% increase in BDBR. Meanwhile, the “Moderate” scheme reduces average complexity by 50.14% with only a 1.39% increase in BDBR.

Research on WSN clustering algorithm of firefly algorithm to optimize k-means

Aiming at the problem that uneven clustering of wireless sensor networks leads to large energy consumption differences between clusters and short network lifetime, this paper proposes a clustering routing algorithm of WSN based on the Firefly algorithm to optimize k-means (FAKMC). This paper mainly studies the clustering stage of the network, adopts the k-means algorithm for node partition, and uses the improved FA to optimize the sensitive problem of the initial clustering centre of k-means. Simulation results show that, compared to LEACH and k-means, the clustering result proposed in this paper is more uniform and can extend the network life cycle.

The International Association for the Study of Lung Cancer Pleural Mesothelioma Staging Project: Proposal for Revision of the TNM Stage Groupings in the Forthcoming (Ninth) Edition of the TNM Classification for Pleural Mesothelioma

IntroductionThe eighth edition of the TNM classification of pleural mesothelioma (PM) saw substantial changes in T and N components and stage groupings. The International Association for the Study of Lung Cancer collected data into a multinational database to further refine this classification. This ninth edition proposal incorporates changes proposed in the clinical (c)T component but not the pathologic T component, to include size criteria, and further refines TNM stage groupings for PM. MethodsData were submitted through electronic data capture or batch transfer from institutional databases. Survival was measured from diagnosis date. Candidate stage groups were developed using a recursive partitioning and amalgamation algorithm applied to all cM0 cases for clinical stage and subsequently for pathologic stage. Cox models were developed to estimate survival for each stage group. ResultsOf 3598 submitted cases, 2192 were analyzable for overall clinical stage and 445 for overall pathologic stage. Recursive partitioning and amalgamation generated survival tree on overall survival outcomes restricted to cM0, with newly proposed (ninth edition) cT and cN component-derived optimal stage groupings of stage I (T1N0), II (T1N1; T2N0), IIIA (T1N2; T2N1/2; any T3), IIIB (any T4), and IV (any M1). Although cT and pathologic T descriptors are different in the ninth edition, aligning pathologic stage groupings with clinical stage produced better discrimination than did retaining eighth edition pathologic stage groupings. ConclusionsTo our knowledge, this revision of the clinical TNM classification for PM is the first to incorporate the measurement-based proposed changes in cT category. The pathologic TNM aligns with clinical TNM.

FragQC: An efficient quantum error reduction technique using quantum circuit fragmentation

Quantum computers must meet extremely stringent qualitative and quantitative requirements on their qubits in order to solve real-life problems. Quantum circuit fragmentation techniques divide a large quantum circuit into a number of sub-circuits that can be executed on the smaller noisy quantum hardware available. However, the process of quantum circuit fragmentation involves finding an ideal cut that has exponential time complexity and also the classical post-processing required to reconstruct the output. In this paper, we represent a quantum circuit using a weighted graph and propose a novel classical graph partitioning algorithm for selecting an efficient fragmentation that reduces the entanglement between the sub-circuits along with balancing the estimated error in each sub-circuit. We also demonstrate a comparative study of different classical and quantum approaches to graph partitioning for finding such a cut. We present FragQC, a software tool that cuts a quantum circuit into sub-circuits when its error probability exceeds a certain threshold. With this proposed approach, we achieve an increase in fidelity of 13.38% compared to direct execution without cutting the circuit, and 7.88% over the state-of-the-art ILP-based method for the benchmark circuits.The code for FragQC is available at https://github.com/arnavdas88/FragQC.

A graph based named entity disambiguation using clique partitioning and semantic relatedness

Disambiguating name mentions in texts is a crucial task in Natural Language Processing, especially in entity linking. The credibility and efficiency of such systems depend largely on this task. For a given name entity mention in a text, there are many potential candidate entities that may refer to it in the knowledge base. Therefore, it is very difficult to assign the correct candidate from the whole set of candidate entities of this mention. To solve this problem, collective entity disambiguation is a prominent approach. In this paper, we present a novel algorithm called CPSR for collective entity disambiguation, which is based on a graph approach and semantic relatedness. A clique partitioning algorithm is used to find the best clique that contains a set of candidate entities. These candidate entities provide the answers to the corresponding mentions in the disambiguation process. To evaluate our algorithm, we carried out a series of experiments on seven well-known datasets, namely, AIDA/CoNLL2003-TestB, IITB, MSNBC, AQUAINT, ACE2004, Cweb, and Wiki. The Kensho Derived Wikimedia Dataset (KDWD) is used as the knowledge base for our system. From the experimental results, our CPSR algorithm outperforms both the baselines and other well-known state-of-the-art approaches.

Fast CU partition algorithm based on swin-transformer for depth intra coding in 3D-HEVC

Fast CU partition algorithm based on swin-transformer for depth intra coding in 3D-HEVC

Cluster analysis applied to obtaining reference models for building thermal performance studies

Cluster analysis has often been used to obtain reference models. However, using different methods in each analysis step may yield different results. Furthermore, the validation of the results is often neglected. This study aims to develop a cluster analysis method to obtain reference models for use in thermal performance studies of buildings from different clustering configurations. The procedure proposed is composed of six steps: (1) initial database composition, (2) data matrices formation, (3) cluster analysis, (4) determination of reference models, (5) methods validation and (6) results interpretation. Different data treatments, similarity measures and partitioning algorithms were combined in the cluster analysis to define the method with the best cluster formation. The weighting factor was the data treatment that most contributed to obtaining suitable clustering solutions. City-block, Euclidean Distance and Squared Euclidean Distance similarity measures resulted in suitable formations as well as Complete Linkage, Ward and K-means algorithms. Two clusters were obtained from the cluster methodology, for which two reference models were determined. Hypothesis tests showed that clusters differ for most performance indicators. It was concluded that the procedure proposed could identify the combination of methods that results in the best application of cluster analysis. The main contribution of this paper is to introduce a procedure for validating clusters. This procedure is an applicable technique to obtain reference models.

Extended Kalman Filter-Based Vehicle Tracking Using Uniform Planar Array for Vehicle Platoon Systems.

We develop an extended Kalman filter-based vehicle tracking algorithm, specifically designed for uniform planar array layouts and vehicle platoon scenarios. We first propose an antenna placement strategy to design the optimal antenna array configuration for precise vehicle tracking in vehicle-to-infrastructure networks. Furthermore, a vehicle tracking algorithm is proposed to improve the position estimation performance by specifically considering the characteristics of the state evolution model for vehicles in the platoon. The proposed algorithm enables the sharing of corrected error transition vectors among platoon vehicles, for the purpose of enhancing the tracking performance for vehicles in unfavorable positions. Lastly, we propose an array partitioning algorithm that effectively divides the entire antenna array into sub-arrays for vehicles in the platoon, aiming to maximize the average tracking performance. Numerical studies verify that the proposed tracking and array partitioning algorithms improve the position estimation performance.

A multi-node attack scheme based on community partitioning in large scale infrastructure networks

Node attack plays a vital role in evaluating the reliability of large scale infrastructure networks, which has attracted intensive research interests. Unfortunately, most of them focus on single node attack. Multi-node attack based on MBA (Module based Adaptive) is a promising research vector with great potential in improving efficiency. However, MBA-based node attack typically needs to repetitively consider community structures of large networks during the entire node attack process, which brings significant loss in network reliability and causes computational overheads. In light of this, we propose a novel scheme based on MBA to implement multi-node attack, which adopts the Multilevel community partitioning algorithm to overcome the above limitations. We evaluate our scheme on two artificial synthetic networks and four real-world networks with comparison to four widely used node attack schemes, and demonstrate the non-negligible superiority of our scheme over all benchmarks. Especially in the US Powergrid, our scheme can always achieve robustness around 10 times that of other schemes when deleting the same percentage of nodes. To the best of our knowledge, we are the first to achieve an MBA-based node attack scheme that only needs to execute the community partitioning algorithm once during the entire node deletion process.

Characterizing multimorbidity in ALIVE: comparing single and ensemble clustering methods.

Multimorbidity, defined as having 2 or more chronic conditions, is a growing public health concern, but research in this area is complicated by the fact that multimorbidity is a highly heterogenous outcome. Individuals in a sample may have a differing number and varied combinations of conditions. Clustering methods, such as unsupervised machine learning algorithms, may allow us to tease out the unique multimorbidity phenotypes. However, many clustering methods exist, and choosing which to use is challenging because we do not know the true underlying clusters. Here, we demonstrate the use of 3 individual algorithms (partition around medoids, hierarchical clustering, and probabilistic clustering) and a clustering ensemble approach (which pools different clustering approaches) to identify multimorbidity clusters in the AIDS Linked to the Intravenous Experience cohort study. We show how the clusters can be compared based on cluster quality, interpretability, and predictive ability. In practice, it is critical to compare the clustering results from multiple algorithms and to choose the approach that performs best in the domain(s) that aligns with plans to use the clusters in future analyses.

PICO: Pipeline Inference Framework for Versatile CNNs on Diverse Mobile Devices

Distributing the inference of convolutional neural network (CNN) to multiple mobile devices has been studied in recent years to achieve real-time inference without losing accuracy. However, how to map CNN to devices remains a challenge. On the one hand, scheduling the workload of state-of-the-art CNNs with multiple devices is NP-Hard because the structures of CNNs are directed acyclic graphs (DAG) rather than simple chains. On the other hand, distributing the inference workload suffers from expensive communication and unbalanced computation due to the wireless environment and heterogeneous devices. This paper presents PICO, a pipeline cooperation framework to accelerate the inference of versatile CNNs on diverse mobile devices. At its core, PICO features: (1) a generic graph partition algorithm that considers the characteristics of any given CNN and orchestrates it into a list of model pieces with suitable granularity, and (2) a many-to-many mapping algorithm that produces the best pipeline configuration for heterogeneous devices. In our experiment with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2 \sim 8$</tex-math></inline-formula> Raspberry-Pi devices, the throughput can be improved by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1.8 \sim 6.8 \times$</tex-math></inline-formula> under different CPU frequencies.

A Method for Identifying Gross Errors in Dam Monitoring Data

Real and effective monitoring data are crucial in assessing the structural safety of dams. Gross errors, resulting from manual mismeasurement, instrument failure, or other factors, can significantly impact the evaluation process. It is imperative to eliminate such anomalous data. However, existing methods for detecting gross errors in concrete dam deformation often focus on analyzing a single monitoring effect quantity. This can lead to sudden jumps in values of effect quantity caused by changes in environmental variables being mistakenly identified as gross error. Therefore, a method based on Fuzzy C-Means clustering algorithm (FCM) partitioning and density clustering algorithm (Ordering Points To Identify the Clustering Structure, OPTICS) combined with Local Outlier Factor (LOF) algorithm for gross error identification is proposed. Firstly, the FCM algorithm is used to achieve the division of measurement point areas. Then, the OPTICS and LOF algorithms are jointly utilized to determine the gross errors. Finally, the real gross errors are identified by comparing the time of occurrence of the gross errors at measurement points in the same area. Through the case study, the results indicate that the method can effectively identify spurious, gross errors in the monitoring effect quantity caused by environmental mutations. The accuracy of gross error detection is significantly improved, and the rate of misjudgment of gross errors is reduced.

Fast Decision-Tree-Based Series Partitioning and Mode Prediction Termination Algorithm for H.266/VVC

With the advancement of network technology, multimedia videos have emerged as a crucial channel for individuals to access external information, owing to their realistic and intuitive effects. In the presence of high frame rate and high dynamic range videos, the coding efficiency of high-efficiency video coding (HEVC) falls short of meeting the storage and transmission demands of the video content. Therefore, versatile video coding (VVC) introduces a nested quadtree plus multi-type tree (QTMT) segmentation structure based on the HEVC standard, while also expanding the intra-prediction modes from 35 to 67. While the new technology introduced by VVC has enhanced compression performance, it concurrently introduces a higher level of computational complexity. To enhance coding efficiency and diminish computational complexity, this paper explores two key aspects: coding unit (CU) partition decision-making and intra-frame mode selection. Firstly, to address the flexible partitioning structure of QTMT, we propose a decision-tree-based series partitioning decision algorithm for partitioning decisions. Through concatenating the quadtree (QT) partition division decision with the multi-type tree (MT) division decision, a strategy is implemented to determine whether to skip the MT division decision based on texture characteristics. If the MT partition decision is used, four decision tree classifiers are used to judge different partition types. Secondly, for intra-frame mode selection, this paper proposes an ensemble-learning-based algorithm for mode prediction termination. Through the reordering of complete candidate modes and the assessment of prediction accuracy, the termination of redundant candidate modes is accomplished. Experimental results show that compared with the VVC test model (VTM), the algorithm proposed in this paper achieves an average time saving of 54.74%, while the BDBR only increases by 1.61%.

Formal Logic Enabled Personalized Federated Learning through Property Inference

Recent advancements in federated learning (FL) have greatly facilitated the development of decentralized collaborative applications, particularly in the domain of Artificial Intelligence of Things (AIoT). However, a critical aspect missing from the current research landscape is the ability to enable data-driven client models with symbolic reasoning capabilities. Specifically, the inherent heterogeneity of participating client devices poses a significant challenge, as each client exhibits unique logic reasoning properties. Failing to consider these device-specific specifications can result in critical properties being missed in the client predictions, leading to suboptimal performance. In this work, we propose a new training paradigm that leverages temporal logic reasoning to address this issue. Our approach involves enhancing the training process by incorporating mechanically generated logic expressions for each FL client. Additionally, we introduce the concept of aggregation clusters and develop a partitioning algorithm to effectively group clients based on the alignment of their temporal reasoning properties. We evaluate the proposed method on two tasks: a real-world traffic volume prediction task consisting of sensory data from fifteen states and a smart city multi-task prediction utilizing synthetic data. The evaluation results exhibit clear improvements, with performance accuracy improved by up to 54% across all sequential prediction models.