View-shuffled clustering via the modified Hungarian algorithm.

In the majority of existing multi-view clustering methods, the prerequisite is that the data have the correct cross-view correspondence. However, this strong assumption may not always hold in real-world applications, giving rise to the so-called View-shuffled Problem (VsP). To address this challenge, we propose a novel multi-view clustering method, namely View-shuffled Clustering via the Modified Hungarian Algorithm (VsC-mH). Specifically, we first establish the cross-view correspondence of the shuffled data utilizing strategies of the global alignment and modified Hungarian algorithm (mH) based intra-category alignment. Subsequently, we generate the partition of the aligned data employing matrix factorization. The fusion of these two processes facilitates the interaction of information, resulting in improved quality of both data alignment and partition. VsC-mH is capable of handling the data with alignment ratios ranging from 0 to 100%. Both experimental and theoretical evidence guarantees the convergence of the proposed optimization algorithm. Extensive experimental results obtained on six practical datasets demonstrate the effectiveness and merits of the proposed method.

Continuous symmetry and chirality measures: approximate algorithms for large molecular structures.

Quantifying imperfect symmetry of molecules can help explore the sources, roles and extent of structural distortion. Based on the established methodology of continuous symmetry and chirality measures, we develop a set of three-dimensional molecular descriptors to estimate distortion of large structures. These three-dimensional geometrical descriptors quantify the gap between the desirable symmetry (or chirality) and the actual one. They are global parameters of the molecular geometry, intuitively defined, and have the ability to detect even minute structural changes of a given molecule across chemistry, including organic, inorganic, and biochemical systems. Application of these methods to large structures is challenging due to countless permutations that are involved in the symmetry operations and have to be accounted for. Our approach focuses on iteratively finding the approximate direction of the symmetry element in the three-dimensional space, and the relevant permutation. Major algorithmic improvements over previous versions are described, showing increased accuracy, reliability and structure preservation. The new algorithms are tested for three sets of molecular structures including pillar[5]arene complexes with Li+, C100 fullerenes, and large unit cells of metal organic frameworks. These developments complement our recent algorithms for calculating continuous symmetry and chirality measures for small molecules as well as protein homomers, and simplify the usage of the full set of measures for various research goals, in molecular modeling, QSAR and cheminformatics.

Maximizing Resource Efficiency in Wireless Networks through Virtualization and Opportunistic Channel Allocation.

Wireless cellular networks have become increasingly important in providing data access to cellular users via a grid of cells. Many applications are considered to read data from smart meters for potable water, gas, or electricity. This paper proposes a novel algorithm to assign paired channels for intelligent metering through wireless connectivity, which is particularly relevant due to the commercial advantages that a virtual operator currently provides. The algorithm considers the behavior of secondary spectrum channels assigned to smart metering in a cellular network. It explores spectrum reuse in a virtual mobile operator to optimize dynamic channel assignment. The proposed algorithm exploits the white holes in the cognitive radio spectrum and considers the coexistence of different uplink channels, resulting in improved efficiency and reliability for smart metering. The work also defines the average user transmission throughput and total smart meter cell throughput as metrics to measure performance, providing insights into the effects of the chosen values on the overall performance of the proposed algorithm.

Multi-Arm Global Cooperative Coal Gangue Sorting Method Based on Improved Hungarian Algorithm.

The existing multi-manipulator sorting method for gangue that utilizes a multi-task allocation strategy is not satisfactory. The single manipulator working space is fixed, lowering the cooperation degree between the manipulators and leading to a low sorting rate. Therefore, this paper proposes a multi-manipulator cooperative sorting method that can work globally. First, a benefit function based on the sorting time and quality of the gangue is constructed by combining the gangue flow information and the manipulator state. The time parameter is obtained via the manipulator's dynamic target tracking trajectory planning algorithm based on PID control. Secondly, the benefits matrix is standardized and updated many times to improve the Hungarian algorithm to achieve task allocation, and the initial solution with priority is obtained. Finally, the solutions are analyzed and processed cooperatively in order of priority. The conflicts between multiple robotic arms are eliminated through task cooperation and trajectory cooperation until the sorting task that the robot arm can execute is obtained from the allocation results. Experiments involving different sorting methods were completed on a multi-arm coal and gangue sorting experimental robot platform. The experimental results show that the sorting efficiency of the proposed method is about 10% and 20% higher than that of the fixed space dynamic and designated space fixed points methods, respectively, under different belt speeds. This method can guarantee system benefits, effectively implements cooperative control of multi-manipulator operations in the whole area, and improves the efficiency of coal gangue sorting.

An Improved Crucible Spatial Bubble Detection Based on YOLOv5 Fusion Target Tracking.

A three-dimensional spatial bubble counting method is proposed to solve the problem of the existing crucible bubble detection only being able to perform two-dimensional statistics. First, spatial video images of the transparent layer of the crucible are acquired by a digital microscope, and a quartz crucible bubble dataset is constructed independently. Secondly, to address the problems of poor real-time and the insufficient small-target detection capability of existing methods for quartz crucible bubble detection, rich detailed feature information is retained by reducing the depth of down-sampling in the YOLOv5 network structure. In the neck, the dilated convolution algorithm is used to increase the feature map perceptual field to achieve the extraction of global semantic features; in front of the detection layer, an effective channel attention network (ECA-Net) mechanism is added to improve the capability of expressing significant channel characteristics. Furthermore, a tracking algorithm based on Kalman filtering and Hungarian matching is presented for bubble counting in crucible space. The experimental results demonstrate that the detector algorithm presented in this paper can effectively reduce the missed detection rate of tiny bubbles and increase the average detection precision from 96.27% to 98.76% while reducing weight by half and reaching a speed of 82 FPS. The excellent detector performance improves the tracker's accuracy significantly, allowing for real-time and high-precision counting of bubbles in quartz crucibles. It is an effective method for detecting crucible spatial bubbles.

A time series representation of protein sequences for similarity comparison.

Based on the physicochemical indexes of 20 amino acids and the Hungarian algorithm, each amino acid was mapped into a vector. And, the protein sequence can be represented as time series in eleven-dimensional space. In addition, the DTW algorithm was applied to calculate the distance between two time series to compare the similarities of protein sequences. The validity and accuracy of this method was illustrated by similarity comparison of ND5 proteins of nine species. Furthermore, homology analysis of eleven ACE2 proteins, which included human, Malayan pangolin and six species of bats, confirmed that the human had shorter evolutionary distance from the pangolin than those bats. The phylogenetic tree of spike protein sequences of 36 coronaviruses, which were divided into five groups, Class I, Class II, Class III, SARS-CoVs and COVID-19, was constructed.

Making Sense of Algorithms in Discrete Mathematics.

Network analysis is a topic in secondary mathematics education of growing importance because it offers students an opportunity to understand how to model and solve many authentic technology and engineering problems. However, very little is known about how students make sense of the algorithms typically used in network analysis. In this study, I used the Hungarian algorithm to explore how students make sense of a network algorithm and how it can be used to solve assignment problems. I report the results of a design-based research project in which eight Year 12 students participated in a teaching experiment that spanned four 60-min lessons. A hypothetical learning trajectory was developed in which students were introduced to the steps of the Hungarian algorithm incrementally. The results suggest that students made sense of the intermediate steps of the algorithm, the results of those steps, and how the algorithm works to solve assignment problems. The difficulties that students encountered are also discussed. Supplementary Information: The online version contains supplementary material available at 10.1007/s10763-021-10180-3.

A fast protein binding site comparison algorithm for proteome-wide protein function prediction and drug repurposing.

The expansion of three-dimensional protein structures and enhanced computing power have significantly facilitated our understanding of protein sequence/structure/function relationships. A challenge in structural genomics is to predict the function of uncharacterized proteins. Protein function deconvolution based on global sequence or structural homology is impracticable when a protein relates to no other proteins with known function, and in such cases, functional relationships can be established by detecting their local ligand binding site similarity. Here, we introduce a sequence order-independent comparison algorithm, PocketShape, for structural proteome-wide exploration of protein functional site by fully considering the geometry of the backbones, orientation of the sidechains, and physiochemical properties of the pocket-lining residues. PocketShape is efficient in distinguishing similar from dissimilar ligand binding site pairs by retrieving 99.3% of the similar pairs while rejecting 100% of the dissimilar pairs on a dataset containing 1538 binding site pairs. This method successfully classifies 83 enzyme structures with diverse functions into 12 clusters, which is highly in accordance with the actual structural classification of proteins classification. PocketShape also achieves superior performances than other methods in protein profiling based on experimental data. Potential new applications for representative SARS-CoV-2 drugs Remdesivir and 11a are predicted. The high accuracy and time-efficient characteristics of PocketShape will undoubtedly make it a promising complementary tool for proteome-wide protein function inference and drug repurposing study.

Energy-Efficient Swarming Flight Formation Transitions Using the Improved Fair Hungarian Algorithm.

Recently, drone shows have impressed many people through a convergence of technology and art. However, these demonstrations have limited operating hours based on the battery life. Thus, it is important to minimize the unnecessary transition time between scenes without collision to increase operating time. This paper proposes a fast and energy-efficient scene transition algorithm that minimizes the transition times between scenes. This algorithm reduces the maximum drone movement distance to increase the operating time and exploits a multilayer method to avoid collisions between drones. In addition, a swarming flight system including robust communication and position estimation is presented as a concrete experimental system. The proposed algorithm was verified using the swarming flight system at a drone show performed with 100 drones.

A Two-Stage Service Migration Algorithm in Parked Vehicle Edge Computing for Internet of Things.

Parked vehicle edge computing (PVEC) utilizes both idle resources in parked vehicles (PVs) and roadside units (RSUs) as service providers (SPs) to improve the performance of vehicular internet of things (IoT). However, it is difficult to make optimal service migration decisions in PVEC networks due to the uncertain parking duration and resources heterogeneity of PVs. In this paper, we formulate the service migration of all the vehicles as an optimization problem with the objective of minimizing the average latency. We propose a two-stage service migration algorithm for PVEC networks, which divides the original problem into the service migration between SPs and the serving PV selection in parking lots. The service migration between SPs is transformed to an online problem based on Lyapunov optimization, where the expected parking duration of PVs is utilized. A modified Hungarian algorithm is proposed to select the PVs for migration. A series of simulation experiments based on the real-world vehicle traces are conducted to verify the superior performance of the proposed two-stage service migration (SEA) algorithm as compared with the state-of- art solutions.

Emergency Communications Based on Throughput-Aware D2D Multicasting in 5G Public Safety Networks.

Emergency communications need to meet the developing demand of equipment and the complex scenarios of network in public safety networks (PSNs). Heterogeneous Cloud Radio Access Network (H-CRAN), an important technology of the 5th generation wireless systems (5G), plays an important role in PSN. H-CRAN has the features of resource sharing and centralized allocation which can make up for resource shortage in emergency communications. Therefore, an emergency communications strategy based on Device-to-device (D2D) multicast is proposed to make PSN more flexible and rapid. Nearby users can communicate directly without a base station through D2D. This strategy may guarantee high speed data transmission and stable continuous real-time communications. It is divided into three steps. Firstly, according to the distance between users, the alternative cluster head is divided. Secondly, two kinds of cluster head user selection schemes are developed. One is based on terminal power and the other is based on the number of extended users. Last but not least, the Hungarian Algorithm based on throughput-aware is used to channel multiplexing. The numerical results show that the proposed scheme can effectively extend the coverage of PSN and optimize the utilization of resources.

Output-Only Damage Detection of Shear Building Structures Using an Autoregressive Model-Enhanced Optimal Subpattern Assignment Metric.

This paper proposes a novel output-only structural damage indicator by incorporating the pole-based optimal subpattern assignment distance with autoregressive models to localize and relatively assess the severity of damages for sheared structures. Autoregressive models can model dynamic systems well, while their model poles can represent the state of the dynamic systems. Structural damage generally causes changes in the dynamic characteristics (especially the natural frequency, mode shapes and damping ratio) of structures. Since the poles of the autoregressive models can solve the modal parameters of the structure, the poles have a close relationship with the modal parameters so that the changes in the poles of its autoregressive model reflect structural damages. Therefore, we can identify the damage by tracking the shifts in the dynamic system poles. The optimal subpattern assignment distance, which is the performance evaluator in multi-target tracking algorithms to measure the metric between true and estimated tracks, enables the construction of damage sensitive indicator from system poles using the Hungarian algorithm. The proposed approach has been validated with a five-story shear-building using numerical simulations and experimental verifications, which are subjected to excitations of white noise, El Centro earthquake and sinusoidal wave with frequencies sweeping, respectively; the results indicate that this approach can localize and quantify structural damages effectively in an output-only and data-driven way.

Active Player Detection in Handball Scenes Based on Activity Measures.

In team sports training scenes, it is common to have many players on the court, each with his own ball performing different actions. Our goal is to detect all players in the handball court and determine the most active player who performs the given handball technique. This is a very challenging task, for which, apart from an accurate object detector, which is able to deal with complex cluttered scenes, additional information is needed to determine the active player. We propose an active player detection method that combines the Yolo object detector, activity measures, and tracking methods to detect and track active players in time. Different ways of computing player activity were considered and three activity measures are proposed based on optical flow, spatiotemporal interest points, and convolutional neural networks. For tracking, we consider the use of the Hungarian assignment algorithm and the more complex Deep SORT tracker that uses additional visual appearance features to assist the assignment process. We have proposed the evaluation measure to evaluate the performance of the proposed active player detection method. The method is successfully tested on a custom handball video dataset that was acquired in the wild and on basketball video sequences. The results are commented on and some of the typical cases and issues are shown.

Clustering analysis of microRNA and mRNA expression data from TCGA using maximum edge-weighted matching algorithms.

BACKGROUND: microRNA (miRNA) is a short RNA (~ 22 nt) that regulates gene expression at the posttranscriptional level. Aberration of miRNA expressions could affect their targeting mRNAs involved in cancer-related signaling pathways. We conduct clustering analysis of miRNA and mRNA using expression data from the Cancer Genome Atlas (TCGA). We combine the Hungarian algorithm and blossom algorithm in graph theory. Data analysis is done using programming language R and Python. METHODS: We first quantify edge-weights of the miRNA-mRNA pairs by combining their expression correlation coefficient in tumor (T_CC) and correlation coefficient in normal (N_CC). We thereby introduce a bipartite graph partition procedure to identify cluster candidates. Specifically, we propose six weight formulas to quantify the change of miRNA-mRNA expression T_CC relative to N_CC, and apply the traditional hierarchical clustering to subjectively evaluate the different weight formulas of miRNA-mRNA pairs. Among these six different weight formulas, we choose the optimal one, which we define as the integrated mean value weights, to represent the connections between miRNA and mRNAs. Then the Hungarian algorithm and the blossom algorithm are employed on the miRNA-mRNA bipartite graph to passively determine the clusters. The combination of Hungarian and the blossom algorithms is dubbed maximum weighted merger method (MWMM). RESULTS: MWMM identifies clusters of different sizes that meet the mathematical criterion that internal connections inside a cluster are relatively denser than external connections outside the cluster and biological criterion that the intra-cluster Gene Ontology (GO) term similarities are larger than the inter-cluster GO term similarities. MWMM is developed using breast invasive carcinoma (BRCA) as training data set, but can also applies to other cancer type data sets. MWMM shows advantage in GO term similarity in most cancer types, when compared to other algorithms. CONCLUSIONS: miRNAs and mRNAs that are likely to be affected by common underlying causal factors in cancer can be clustered by MWMM approach and potentially be used as candidate biomarkers for different cancer types and provide clues for targets of precision medicine in cancer treatment.

Improved algorithms for quantifying the near symmetry of proteins: complete side chains analysis.

Symmetry of proteins, an important source of their elegant structure and unique functions, is not as perfect as it may seem. In the framework of continuous symmetry, in which symmetry is no longer a binary yes/no property, such imperfections can be quantified and used as a global descriptor of the three-dimensional structure. We present an improved algorithm for calculating the continuous symmetry measure for proteins that takes into account their complete set of atoms including all side chains. Our method takes advantage of the protein sequence and the division into peptides in order to improve the accuracy and efficiency of the calculation over previous methods. The Hungarian algorithm is applied to solve the assignment problem and find the permutation that defines the symmetry operation. Analysis of the symmetry of several sets of protein homomers, with various degrees of rotational symmetry is presented. The new methodology lays the foundations for accurate, efficient and reliable large scale symmetry analysis of protein structure and can be used as a collective variable that describes changes of the protein geometry along various processes, both at the backbone level and for the complete protein structure.

Improving Quality-of-Service in Cloud/Fog Computing through Efficient Resource Allocation.

Recently, a massive migration of enterprise applications to the cloud has been recorded in the IT world. One of the challenges of cloud computing is Quality-of-Service management, which includes the adoption of appropriate methods for allocating cloud-user applications to virtual resources, and virtual resources to the physical resources. The effective allocation of resources in cloud data centers is also one of the vital optimization problems in cloud computing, particularly when the cloud service infrastructures are built by lightweight computing devices. In this paper, we formulate and present the task allocation and virtual machine placement problems in a single cloud/fog computing environment, and propose a task allocation algorithmic solution and a Genetic Algorithm Based Virtual Machine Placement as solutions for the task allocation and virtual machine placement problem models. Finally, the experiments are carried out and the results show that the proposed solutions improve Quality-of-Service in the cloud/fog computing environment in terms of the allocation cost.

An evaluation of independent component analyses with an application to resting-state fMRI.

We examine differences between independent component analyses (ICAs) arising from different assumptions, measures of dependence, and starting points of the algorithms. ICA is a popular method with diverse applications including artifact removal in electrophysiology data, feature extraction in microarray data, and identifying brain networks in functional magnetic resonance imaging (fMRI). ICA can be viewed as a generalization of principal component analysis (PCA) that takes into account higher-order cross-correlations. Whereas the PCA solution is unique, there are many ICA methods-whose solutions may differ. Infomax, FastICA, and JADE are commonly applied to fMRI studies, with FastICA being arguably the most popular. Hastie and Tibshirani (2003) demonstrated that ProDenICA outperformed FastICA in simulations with two components. We introduce the application of ProDenICA to simulations with more components and to fMRI data. ProDenICA was more accurate in simulations, and we identified differences between biologically meaningful ICs from ProDenICA versus other methods in the fMRI analysis. ICA methods require nonconvex optimization, yet current practices do not recognize the importance of, nor adequately address sensitivity to, initial values. We found that local optima led to dramatically different estimates in both simulations and group ICA of fMRI, and we provide evidence that the global optimum from ProDenICA is the best estimate. We applied a modification of the Hungarian (Kuhn-Munkres) algorithm to match ICs from multiple estimates, thereby gaining novel insights into how brain networks vary in their sensitivity to initial values and ICA method.

GIRAF: a method for fast search and flexible alignment of ligand binding interfaces in proteins at atomic resolution.

Comparison and classification of protein structures are fundamental means to understand protein functions. Due to the computational difficulty and the ever-increasing amount of structural data, however, it is in general not feasible to perform exhaustive all-against-all structure comparisons necessary for comprehensive classifications. To efficiently handle such situations, we have previously proposed a method, now called GIRAF. We herein describe further improvements in the GIRAF protein structure search and alignment method. The GIRAF method achieves extremely efficient search of similar structures of ligand binding sites of proteins by exploiting database indexing of structural features of local coordinate frames. In addition, it produces refined atom-wise alignments by iterative applications of the Hungarian method to the bipartite graph defined for a pair of superimposed structures. By combining the refined alignments based on different local coordinate frames, it is made possible to align structures involving domain movements. We provide detailed accounts for the database design, the search and alignment algorithms as well as some benchmark results.

Similarity search for local protein structures at atomic resolution by exploiting a database management system.

A method to search for local structural similarities in proteins at atomic resolution is presented. It is demonstrated that a huge amount of structural data can be handled within a reasonable CPU time by using a conventional relational database management system with appropriate indexing of geometric data. This method, which we call geometric indexing, can enumerate ligand binding sites that are structurally similar to sub-structures of a query protein among more than 160,000 possible candidates within a few hours of CPU time on an ordinary desktop computer. After detecting a set of high scoring ligand binding sites by the geometric indexing search, structural alignments at atomic resolution are constructed by iteratively applying the Hungarian algorithm, and the statistical significance of the final score is estimated from an empirical model based on a gamma distribution. Applications of this method to several protein structures clearly shows that significant similarities can be detected between local structures of non-homologous as well as homologous proteins.