Efficient Graph-based Algorithm Research Articles

Variational integrators and graph-based solvers for multibody dynamics in maximal coordinates

Multibody dynamics simulators are an important tool in many fields, including learning and control in robotics. However, many existing dynamics simulators suffer from inaccuracies when dealing with constrained mechanical systems due to unsuitable integrators with bad energy behavior and problematic constraint violations, for example in contact interactions. Variational integrators are numerical discretization methods that can reduce physical inaccuracies when simulating mechanical systems, and formulating the dynamics in maximal coordinates allows for easy and numerically robust incorporation of constraints such as kinematic loops or contacts. Therefore, this article derives a variational integrator for mechanical systems with equality and inequality constraints in maximal coordinates. Additionally, efficient graph-based sparsity-exploiting algorithms for solving the integrator are provided and implemented as an open-source simulator. The evaluation of the simulator shows improved physical accuracy due to the variational integrator and the advantages of the sparse solvers. Comparisons to minimal-coordinate algorithms show improved numerical robustness, and application examples of a walking robot and an exoskeleton with explicit constraints demonstrate the necessity and capabilities of maximal coordinates.

Mechanisms by which water-mediated hydrogen-bond networks govern pH-dependent reactions at membrane interfaces.

pH-dependent reactions at membrane interfaces are used by cells to maintain pH homeostasis and to ensure normal cell function. Membrane-bound proteins and peptide systems that sense pH typically bind protons using a few titratable sidechains that communicate with the bulk. The identity of these pH-sensing sidechains is central to hypotheses about protein reaction mechanisms and for the rational design of proton detectors. But, as protein groups hypothesized to sense pH in proteins and peptide systems are part of dynamic water-mediated networks, evaluating their pH-sensing functionality may prove difficult. We developed efficient graph-based algorithms and methodologies to evaluate dynamic hydrogen-bond networks of protein and peptide systems known to have a pH-sensing functionality, and to identify sites of hydrogen-bond networks where protons are likely to bind. We find that the assembly of dynamic water-mediated hydrogen-bond networks that include potential proton-sensing groups is a key event along the reaction coordinates of pH-dependent membrane proteins and membrane-bound peptides, and implement graph-based algorithms to characterize the dynamics of these water networks. We further find that polar and basic protein sidechains largely govern the dynamics of the water-mediated hydrogen-bond networks of proton-binding groups, and help maintain acidic sidechains within clusters that collectively can bind and store protons. Taken together, the ensemble of hydrogen-bond network computations we performed for pH-coupled proteins and peptide systems provide a framework to decipher the roles of dynamic water-mediated hydrogen-bond networks in proton binding at membrane interfaces. Research was supported in part by the European Union's Horizon 2020 Research and Innovation Program under the Marie Sklodowska-Curie grant agreement No 860592, Innovative Training Network ‘Proton and proton-coupled transport’, and by computing time from the Physics Department of the Freie Universität Berlin and from the Forschungszentrum Jülich (JURECA-DC, allocation PHDPORES).

An Effective Fuzzy Clustering of Crime Reports Embedded by a Universal Sentence Encoder Model

Crime reports clustering is crucial for identifying and preventing criminal activities that frequently happened in society. In the proposed work, named entities in a report are recognized to extract the crime-related phrases and subsequently, the phrases are preprocessed by applying stopword removal and lemmatization operations. Next, the module of the universal encoder model, called the transformer, is applied to extract phrases of the report to get a sentence embedding for each associated sentence, aggregation of which finally provides the vector representation of that report. An innovative and efficient graph-based clustering algorithm consisting of splitting and merging operations has been proposed to get the cluster of crime reports. The proposed clustering algorithm generates overlapping clusters, which indicates the existence of reports of multiple crime types. The fuzzy theory has been used to provide a score to the report for expressing its membership into different clusters, and accordingly, the reports are labelled by multiple categories. The efficiency of the proposed method has been assessed by taking into account different datasets and comparing them with other state-of-the-art approaches with the help of various performance measure metrics.

Dynamics of the Hénon Map in the Digital Domain

Nonlinear maps are usually implemented using finite-precision floating-point formats both in practical applications and in theoretical investigations. In the digital domain, the size of the state space is finite and every trajectory after a finite number of iterations reaches a cycle. It is therefore important to study the influence of rounding errors and the finiteness of the state space on properties of nonlinear maps such as the number of cycles, their periods, sizes of basins of attraction of cycles, and average convergence times. In this work, a thorough analysis of the dynamics of finite-precision implementations of the Hénon map is carried out. Six computational formulas and three popular finite-precision floating-point formats are considered. An exhaustive search is performed to find all cycles existing for single-precision floating-point implementations. Interval methods are used to reduce the number of initial conditions that must be considered. An efficient graph-based algorithm is designed to find basins of attraction. For the double-precision and extended-precision implementations, statistical methods are utilized to find cycles and to estimate sizes of their basins of attraction. Properties of observed cycles and corresponding dynamical phenomena are thoroughly discussed.

Efficient graph-based algorithms for solving the team formation problem in production environment

This research investigates the pair formation problem described as forming pairs of people to achieve certain objectives. This problem is a subcategory of the well-known grouping problem that is cl...

Efficient graph-based algorithms for solving the team formation problem in production environment

This research investigates the pair formation problem described as forming pairs of people to achieve certain objectives. This problem is a subcategory of the well-known grouping problem that is classified as NP-complete. The necessity of pairing people in teams is frequent in many real-life fields such as education, social life, and production environments. To solve the problem, a mathematical formulation and a weighted graph-based representation are proposed. Pairing people is usually affected by psychological and productivity factors such as expertise and managers' opinion. These factors are summarised to produce quantitative scores representing the fitness relationship of each person towards others. Four algorithms are proposed to maximise the total weight of the formed pairs. The proposed algorithms are implemented and benchmarked using data instances of various sizes. The performance of the algorithms is evaluated against two proposed upper bounds. The results showed that the edge-based first algorithm (EPFA) outperforms other algorithms.

Application of an Efficient Graph-Based Partitioning Algorithm for Extended Target Tracking Using GM-PHD Filter

One of the main tasks involving the extended target tracking is how to partition the measurement set accurately and efficiently. In this article, an efficient graph-based partitioning algorithm is introduced for extended target tracking. To reduce the computational load and the interference of clutter on the measurement set partition, a measurement set preprocessing method based on density-based clustering algorithm is presented. An intuitive directed $k$ -nearest neighbor ( $k$ NN) graph model based on graph theory is established to represent the relationship between different measurements in the measurement set that needs to be segmented. In the framework of directed $k$ NN graph, a novel similarity metric based on shared nearest neighbor (SNN) is used, and a pairwise similarity that integrates the number of elements in the set of SNN and the closeness of data points is constructed. The spectral clustering algorithm is used to process the multiway cut in the directed $k$ NN graph. The graph-based partitioning algorithm is applied to the extended target Gaussian mixture probability hypothesis density filter. Simulation results illustrate the advantages of our proposed graph-based partitioning algorithm in performance and computational efficiency.

Epigraph: A Vaccine Design Tool Applied to an HIV Therapeutic Vaccine and a Pan-Filovirus Vaccine.

Epigraph is an efficient graph-based algorithm for designing vaccine antigens to optimize potential T-cell epitope (PTE) coverage. Epigraph vaccine antigens are functionally similar to Mosaic vaccines, which have demonstrated effectiveness in preliminary HIV non-human primate studies. In contrast to the Mosaic algorithm, Epigraph is substantially faster, and in restricted cases, provides a mathematically optimal solution. Epigraph furthermore has new features that enable enhanced vaccine design flexibility. These features include the ability to exclude rare epitopes from a design, to optimize population coverage based on inexact epitope matches, and to apply the code to both aligned and unaligned input sequences. Epigraph was developed to provide practical design solutions for two outstanding vaccine problems. The first of these is a personalized approach to a therapeutic T-cell HIV vaccine that would provide antigens with an excellent match to an individual’s infecting strain, intended to contain or clear a chronic infection. The second is a pan-filovirus vaccine, with the potential to protect against all known viruses in the Filoviradae family, including ebolaviruses. A web-based interface to run the Epigraph tool suite is available (http://www.hiv.lanl.gov/content/sequence/EPIGRAPH/epigraph.html).

Region-based colour modelling for joint crop and maize tassel segmentation

Crop segmentation is a frequently occurring problem for computer vision applications in agriculture. Meanwhile, the fine-grained shape extraction of maize tassels is also an essential step in the detasselling and field-based phenotyping research. However, existing methods are usually dependent on category, which is hard to transfer to other cultivars with different colours. To address this, the goal of this study is to develop a general method that can process different colours simultaneously and that has good flexibility and expandability. Targeting maize, we proposed to segment jointly the crop and maize tassel. In particular, a novel joint segmentation dataset regarding crop and maize tassel (323 images with corresponding manually-annotated ground-truth images) is constructed, hoping that it can serve as a benchmark to facilitate related studies. Technically, a region-based approach that leverages the efficient graph-based segmentation algorithm and simple linear iterative clustering (SLIC) is developed to generate region proposals. Also, we proposed to model colours with ensemble neural networks specific to each intensity, aiming to achieve robustness to illumination. In addition, two simple but effective strategies are devised to accelerate the colour statistics extraction and ensemble model prediction. The effectiveness and efficiency of our method are demonstrated on the two segmentation tasks, respectively. Results show that our method significantly outperforms other state-of-the-art approaches on tassel segmentation, with average precision of 74.3%, and achieves comparable performance of 77.8% on the traditional crop segmentation even with the naivest colour feature. The dataset and source code are made available online.

Building roof segmentation from aerial images using a lineand region-based watershed segmentation technique.

In this paper, we present a novel strategy for roof segmentation from aerial images (orthophotoplans) based on the cooperation of edge- and region-based segmentation methods. The proposed strategy is composed of three major steps. The first one, called the pre-processing step, consists of simplifying the acquired image with an appropriate couple of invariant and gradient, optimized for the application, in order to limit illumination changes (shadows, brightness, etc.) affecting the images. The second step is composed of two main parallel treatments: on the one hand, the simplified image is segmented by watershed regions. Even if the first segmentation of this step provides good results in general, the image is often over-segmented. To alleviate this problem, an efficient region merging strategy adapted to the orthophotoplan particularities, with a 2D modeling of roof ridges technique, is applied. On the other hand, the simplified image is segmented by watershed lines. The third step consists of integrating both watershed segmentation strategies into a single cooperative segmentation scheme in order to achieve satisfactory segmentation results. Tests have been performed on orthophotoplans containing 100 roofs with varying complexity, and the results are evaluated with the VINETcriterion using ground-truth image segmentation. A comparison with five popular segmentation techniques of the literature demonstrates the effectiveness and the reliability of the proposed approach. Indeed, we obtain a good segmentation rate of 96% with the proposed method compared to 87.5% with statistical region merging (SRM), 84% with mean shift, 82% with color structure code (CSC), 80% with efficient graph-based segmentation algorithm (EGBIS) and 71% with JSEG.

Pruning Rogue Taxa Improves Phylogenetic Accuracy: An Efficient Algorithm and Webservice

The presence of rogue taxa (rogues) in a set of trees can frequently have a negative impact on the results of a bootstrap analysis (e.g., the overall support in consensus trees). We introduce an efficient graph-based algorithm for rogue taxon identification as well as an interactive webservice implementing this algorithm. Compared with our previous method, the new algorithm is up to 4 orders of magnitude faster, while returning qualitatively identical results. Because of this significant improvement in scalability, the new algorithm can now identify substantially more complex and compute-intensive rogue taxon constellations. On a large and diverse collection of real-world data sets, we show that our method yields better supported reduced/pruned consensus trees than any competing rogue taxon identification method. Using the parallel version of our open-source code, we successfully identified rogue taxa in a set of 100 trees with 116 334 taxa each. For simulated data sets, we show that when removing/pruning rogue taxa with our method from a tree set, we consistently obtain bootstrap consensus trees as well as maximum-likelihood trees that are topologically closer to the respective true trees.

Natural and Remote Sensing Image Segmentation Using Memetic Computing

In order to solve the image segmentation problem which assigns a label to every pixel in an image such that pixels with the same label share certain visual characteristics more effectively, a novel approach based on memetic algorithm (MISA) is proposed. Watershed segmentation is applied to segment original images into non-overlap small regions before performing the portioning process by MISA. MISA adopts a straightforward representation method to find the optimal combination of watershed regions under the criteria of interclass variance in feature space. After implementing cluster-based crossover and mutation, an individual learning procedure moves exocentric regions in current cluster to the one they should belong to according to the distance between these regions and cluster centers in feature space. In order to evaluate the new algorithm, six texture images, three remote sensing images and three natural images are employed in experiments. The experimental results show that MISA outperforms its genetic version, the Fuzzy c-means algorithm, and K-means algorithm in partitioning most of the test problems, and is an effective approach when compared with two state-ofthe-art image segmentation algorithms including an efficient graph-based algorithm and a spectral clustering ensemble-based algorithm.

Fast max-margin clustering for unsupervised word sense disambiguation in biomedical texts

BackgroundWe aim to solve the problem of determining word senses for ambiguous biomedical terms with minimal human effort.MethodsWe build a fully automated system for Word Sense Disambiguation by designing a system that does not require manually-constructed external resources or manually-labeled training examples except for a single ambiguous word. The system uses a novel and efficient graph-based algorithm to cluster words into groups that have the same meaning. Our algorithm follows the principle of finding a maximum margin between clusters, determining a split of the data that maximizes the minimum distance between pairs of data points belonging to two different clusters.ResultsOn a test set of 21 ambiguous keywords from PubMed abstracts, our system has an average accuracy of 78%, outperforming a state-of-the-art unsupervised system by 2% and a baseline technique by 23%. On a standard data set from the National Library of Medicine, our system outperforms the baseline by 6% and comes within 5% of the accuracy of a supervised system.ConclusionOur system is a novel, state-of-the-art technique for efficiently finding word sense clusters, and does not require training data or human effort for each new word to be disambiguated.

Joint Optimization of Run-Length Coding, Huffman Coding, and Quantization Table With Complete Baseline JPEG Decoder Compatibility

To maximize rate distortion performance while remaining faithful to the JPEG syntax, the joint optimization of the Huffman tables, quantization step sizes, and DCT indices of a JPEG encoder is investigated. Given Huffman tables and quantization step sizes, an efficient graph-based algorithm is first proposed to find the optimal DCT indices in the form of run-size pairs. Based on this graph-based algorithm, an iterative algorithm is then presented to jointly optimize run-length coding, Huffman coding, and quantization table selection. The proposed iterative algorithm not only results in a compressed bitstream completely compatible with existing JPEG and MPEG decoders, but is also computationally efficient. Furthermore, when tested over standard test images, it achieves the best JPEG compression results, to the extent that its own JPEG compression performance even exceeds the quoted PSNR results of some state-of-the-art wavelet-based image coders such as Shapiro's embedded zerotree wavelet algorithm at the common bit rates under comparison. Both the graph-based algorithm and the iterative algorithm can be applied to application areas such as web image acceleration, digital camera image compression, MPEG frame optimization, and transcoding, etc.

Toward Objective Evaluation of Image Segmentation Algorithms

Unsupervised image segmentation is an important component in many image understanding algorithms and practical vision systems. However, evaluation of segmentation algorithms thus far has been largely subjective, leaving a system designer to judge the effectiveness of a technique based only on intuition and results in the form of a few example segmented images. This is largely due to image segmentation being an ill-defined problem-there is no unique ground-truth segmentation of an image against which the output of an algorithm may be compared. This paper demonstrates how a recently proposed measure of similarity, the Normalized Probabilistic Rand (NPR) index, can be used to perform a quantitative comparison between image segmentation algorithms using a hand-labeled set of ground-truth segmentations. We show that the measure allows principled comparisons between segmentations created by different algorithms, as well as segmentations on different images. We outline a procedure for algorithm evaluation through an example evaluation of some familiar algorithms-the mean-shift-based algorithm, an efficient graph-based segmentation algorithm, a hybrid algorithm that combines the strengths of both methods, and expectation maximization. Results are presented on the 300 images in the publicly available Berkeley Segmentation Data Set.

Mining interesting association rules from customer databases and transaction databases

In this paper, we examine a new data mining issue of mining association rules from customer databases and transaction databases. The problem is decomposed into two subproblems: identifying all the large itemsets from the transaction database and mining association rules from the customer database and the large itemsets identified. For the first subproblem, we propose an efficient algorithm to discover all the large itemsets from the transaction database. Experimental results show that by our approach, the total execution time can be reduced significantly. For the second subproblem, a relationship graph is constructed according to the identified large itemsets from the transaction database and the priorities of condition attributes from the customer database. Based on the relationship graph, we present an efficient graph-based algorithm to discover interesting association rules embedded in the transaction database and the customer database.

Efficient Graph-Based Algorithms for Discovering and Maintaining Association Rules in Large Databases

In this paper, we study the issues of mining and maintaining association rules in a large database of customer transactions. The problem of mining association rules can be mapped into the problems of finding large itemsets which are sets of items brought together in a sufficient number of transactions. We revise a graph-based algorithm to further speed up the process of itemset generation. In addition, we extend our revised algorithm to maintain discovered association rules when incremental or decremental updates are made to the databases. Experimental results show the efficiency of our algorithms. The revised algorithm is a significant improvement over the original one on mining association rules. The algorithms for maintaining association rules are more efficient than re-running the mining algorithms for the whole updated database and outperform previously proposed algorithms that need multiple passes over the database.

A delay budgeting algorithm ensuring maximum flexibility in placement

In this paper, we present a new, general approach to the problem of computing upper bounds on net delays. The upper bounds on net delays are computed so that timing constraints between input and output signals are satisfied. The set of delay upper bounds is called a delay budget. The objective of this work is to compute a delay budget that will lead to timing feasible circuit placement and routing. In our formulation, we find a delay budget so that the placement phase has "maximum flexibility." We formulate this problem as a convex programming problem and prove that it has a special structure. We utilize the special structure of the problem to propose an efficient graph-based algorithm. We present experimental results for our algorithms with the MCNC placement benchmarks. Our experiments use budgeting results as net length constraints for the TimberWolf placement program, which we use to evaluate the budgeting algorithms. We obtain an average of 50% reduction in net length constraint violations over the well-known zero-slack algorithm (ZSA). We also study different delay budgeting objective functions, which yield 2/spl times/ performance improvements without loss of solution quality. Our results and graph-based formulation show that our proposed algorithm is suitable for modern large-scale budgeting problems.