Local Partition Research Articles

Generalized multiview sequential three-way decisions based on local partition order product space

Generalized multiview sequential three-way decisions based on local partition order product space

A study on the numerical solution of the Sobolev equation with a Burgers-type nonlinearity on two-dimensional irregular domains using the local RBF partition of unity method

A study on the numerical solution of the Sobolev equation with a Burgers-type nonlinearity on two-dimensional irregular domains using the local RBF partition of unity method

Space–time adaptive ADER-DG finite element method with LST-DG predictor and a posteriori sub-cell ADER-WENO finite-volume limiting for multidimensional detonation waves simulation

The space–time adaptive ADER–DG finite element method with LST–DG predictor and a posteriori sub–cell ADER–WENO finite–volume limiting was used for simulation of multidimensional reacting flows with detonation waves. The presented numerical method does not use any ideas of splitting or fractional time steps methods. The modification of the LST–DG predictor has been developed, based on a local partition of the time step in cells in which strong reactivity of the medium is observed. This approach made it possible to obtain solutions to classical problems of flows with detonation waves and strong stiffness, without significantly decreasing the time step. The results obtained show the very high applicability and efficiency of using the ADER–DG–PN method with a posteriori sub–cell limiting for simulating reactive flows with detonation waves. The numerical solution shows the correct formation and propagation of ZND detonation waves. The structure of detonation waves is resolved by this numerical method with subcell resolution even on coarse spatial meshes. The smooth components of the numerical solution are correctly and very accurately reproduced by the numerical method. Non–physical artifacts of the numerical solution, typical for problems with detonation waves, such as the propagation of non–physical shock waves and weak detonation fronts ahead of the main detonation front, did not arise in the results obtained. The results of simulating rather complex problems associated with the propagation of detonation waves in significantly inhomogeneous domains are presented, which show that all the main features of detonation flows are correctly reproduced by this numerical method. It can be concluded that the space–time adaptive ADER–DG–PN method with LST–DG predictor and a posteriori sub–cell ADER–WENO finite–volume limiting is perfectly applicable to simulating fairly complex reacting flows with detonation waves.

Local cochlear mechanical responses revealed through outer hair cell receptor potential measurements

Sensory hair cells, including the sensorimotor outer hair cells, which enable the sensitive, sharply tuned responses of the mammalian cochlea, are excited by radial shear between the organ of Corti and the overlying tectorial membrane. It is not currently possible to measure directly in vivo mechanical responses in the narrow cleft between the tectorial membrane and organ of Corti over a wide range of stimulus frequencies and intensities. The mechanical responses can, however, be derived by measuring hair cell receptor potentials. We demonstrate that the seemingly complex frequency- and intensity-dependent behavior of outer hair cell receptor potentials could be qualitatively explained by a two degrees of freedom system with local cochlear partition and tectorial membrane resonances strongly coupled by the outer hair cell stereocilia. A local minimum in the receptor potential below the characteristic frequency should always be observed at a frequency where the tectorial membrane mechanical impedance is minimal, i.e., at the presumed tectorial membrane resonance frequency. The tectorial membrane resonance frequency might, however, shift with stimulus intensity in accordance with a shift in the maximum of the tectorial membrane radial mechanical responses to lower frequencies, as observed in experiments.

PointAttN: You Only Need Attention for Point Cloud Completion

Point cloud completion referring to completing 3D shapes from partial 3D point clouds is a fundamental problem for 3D point cloud analysis tasks. Benefiting from the development of deep neural networks, researches on point cloud completion have made great progress in recent years. However, the explicit local region partition like kNNs involved in existing methods makes them sensitive to the density distribution of point clouds. Moreover, it serves limited receptive fields that prevent capturing features from long-range context information. To solve the problems, we leverage the cross-attention and self-attention mechanisms to design novel neural network for point cloud completion with implicit local region partition. Two basic units Geometric Details Perception (GDP) and Self-Feature Augment (SFA) are proposed to establish the structural relationships directly among points in a simple yet effective way via attention mechanism. Then based on GDP and SFA, we construct a new framework with popular encoder-decoder architecture for point cloud completion. The proposed framework, namely PointAttN, is simple, neat and effective, which can precisely capture the structural information of 3D shapes and predict complete point clouds with detailed geometry. Experimental results demonstrate that our PointAttN outperforms state-of-the-art methods on multiple challenging benchmarks. Code is available at: https://github.com/ohhhyeahhh/PointAttN

Bayesian rock-physics inversion using a localized ensemble-based approach — With an application to the Alvheim field

We describe, implement, and show the results of a localized ensemble-based approach for seismic amplitude-variation-with-offset (AVO) inversion with uncertainty quantification. Ensembles are simulated from prior probability distributions for fluid saturations and clay content. Starting with continuous saturations and clay content variables, we use depth-varying models for cementation and grain contact theory, Gassmann fluid substitution with mixed saturations, and approximations to the Zoeppritz equations for the AVO attributes at the top-reservoir. The local conditioning to seismic AVO observations relies on (1) the misfit between ensemble simulated seismic AVO data and the field observations in a local partition of the grid/local patch, of inlines/crosslines around the locations where we aim to predict, (2) correlations between the simulated reservoir properties and the data in local patches, and (3) local assessment to avoid unrealistic updates based on spurious correlations in the ensembles. Data from the Alvheim field in the North Sea are used to demonstrate the approach. The influence of the prior information from the well logs in combination with the seismic reflection data indicates the presence of higher oil and gas saturation in the lobe structures of the field and increased clay content at their edges.

A Novel Quintic B-Spline Technique for Numerical Solutions of the Fourth-Order Singular Singularly-Perturbed Problems

Singular singularly-perturbed problems (SSPPs) are a powerful mathematical tool for modelling a variety of real phenomena, such as nuclear reactions, heat explosions, mechanics, and hydrodynamics. In this paper, the numerical solutions to fourth-order singular singularly-perturbed boundary and initial value problems are presented using a novel quintic B-spline (QBS) approximation approach. This method uses a quasi-linearization approach to solve SSPNL initial/boundary value problems. And the non-linear problems are transformed into a sequence of linear problems by applying the quasi-linearization approach. The QBS functions produce more accurate results when compared to other existing approaches because of their local support, symmetry, and partition of unity features. This method can be applied to immediately solve the SSPPs without reducing the order in which they are presented. It has been demonstrated that the suggested numerical approach converges uniformly over the whole domain. The proposed approach is implemented on a few problems to validate the scheme. The computational results are compared, and they illustrate that the proposed approach performs better.

Local Partition Dimension of Grid Graph and Its Application to the Coordinates of Potential Disaster Areas in Jember Regency

Partition dimension was introduced as a part of interesting topic in graph theory. It was focus to observe about distance. The local partition dimension is an expansion of the partition dimension by adding certain conditions to the representation of the vertices 𝑣 to which is Π denoted by 𝑟(𝑣| Π). One of the conditions that must be met for 𝑟(𝑣| Π) is discussed. The minimum value of k so that there is a local distinguishing partition from V (G) is the local partition dimension of G or it can can be said that the distance of each neighbor is different. The local partition dimension of a graph G is denoted 〖pd〗_l (G). In this study, we used an axiomatic deductive methods and pattern recognition. In order to construct the discriminating set on the metric dimension (dim) and the discriminating partition on the partition dimension (pd), the pattern detection method looks for patterns in which the coordinate values are minimum and different. By all observations, the local partition dimensions of P_n×P_m Grid graph has two condition about the results of resolving partition. The Result of local partition dimension of a Grid graph 〖〖pd〗_l (P〗_n×P_m)=2, where n≥2 dan m≥2. In addition, it will decide how to convert the coordinates of areas in the Jember district that are prone to flooding and landslides into metric dimensions. It was about Coordinates of Flood and Landslide Disaster Locations in Jember Regency. The number of local and minimal partition sets generated for flood-prone areas in Jember Regency is 〖pd〗_l (G_Jember)=3.

Use of XTFEM based on the consecutive interpolation procedure of quadrilateral element to calculate J-integral and SIFs of an FGM plate

An extended twice - interpolation finite element method (XTFEM) based on the consecutive interpolation procedure (CIP) of quadrilateral element in two-dimensional (2D) with continuous nodal stress for stress fields near material interface and geometry boundary such as inclusion and hole is presented. In contrast to the traditional approaches, the approximation shape functions constructed based on the CIP involve both nodal values and averaged nodal gradients as interpolation conditions. The CIP for quadrilateral element is developed recently by Tinh Quoc Bui, et al., 2014.In this research, we exhibit a pioneering extension of CIP – based consecutive-interpolation quadrilateral element enhanced by enrichment in terms of local partition of unity method to precisely model of a functionally graded material (FGM) plate had a single-edge crack a, and the influence of holes and inclusions near the crack was investigated in 2D structure to calculate the stress intensity factors (SIFs), an important parameter in predicting the direction of the crack even when the crack is no longer growing, taking advantages of the strengths and making use all the desirable features of both techniques, the CIP and the local enriched partition of unity method. Using MATLAB codes combined with XTFEM based on CIP of quadrilateral element. The accuracy and performance of the proposed XTFEM are compared to standard finite element method (FEM) (ANSYS). The comparison revealed the following analytical results: SIFs of the FGM isotropic plate had a crack a with an average error of 2.55% and the J-integral of 2.86%. The XTFEM gave better errors than FEM compared to the Exact and Experimental methods.

Attribute‐guided transformer for robust person re‐identification

AbstractRecent studies reveal the crucial role of local features in learning robust and discriminative representations for person re‐identification (Re‐ID). Existing approaches typically rely on external tasks, for example, semantic segmentation, or pose estimation, to locate identifiable parts of given images. However, they heuristically utilise the predictions from off‐the‐shelf models, which may be sub‐optimal in terms of both local partition and computational efficiency. They also ignore the mutual information with other inputs, which weakens the representation capabilities of local features. In this study, the authors put forward a novel Attribute‐guided Transformer (AiT), which explicitly exploits pedestrian attributes as semantic priors for discriminative representation learning. Specifically, the authors first introduce an attribute learning process, which generates a set of attention maps highlighting the informative parts of pedestrian images. Then, the authors design a Feature Diffusion Module (FDM) to iteratively inject attribute information into global feature maps, aiming at suppressing unnecessary noise and inferring attribute‐aware representations. Last, the authors propose a Feature Aggregation Module (FAM) to exploit mutual information for aggregating attribute characteristics from different images, enhancing the representation capabilities of feature embedding. Extensive experiments demonstrate the superiority of our AiT in learning robust and discriminative representations. As a result, the authors achieve competitive performance with state‐of‐the‐art methods on several challenging benchmarks without any bells and whistles.

A Two-Branch Neural Network for Short-Axis PET Image Quality Enhancement.

The axial field of view (FOV) is a key factor that affects the quality of PET images. Due to hardware FOV restrictions, conventional short-axis PET scanners with FOVs of 20 to 35 cm can acquire only low-quality PET (LQ-PET) images in fast scanning times (2-3 minutes). To overcome hardware restrictions and improve PET image quality for better clinical diagnoses, several deep learning-based algorithms have been proposed. However, these approaches use simple convolution layers with residual learning and local attention, which insufficiently extract and fuse long-range contextual information. To this end, we propose a novel two-branch network architecture with swin transformer units and graph convolution operation, namely SW-GCN. The proposed SW-GCN provides additional spatial- and channel-wise flexibility to handle different types of input information flow. Specifically, considering the high computational cost of calculating self-attention weights in full-size PET images, in our designed spatial adaptive branch, we take the self-attention mechanism within each local partition window and introduce global information interactions between nonoverlapping windows by shifting operations to prevent the aforementioned problem. In addition, the convolutional network structure tends to consider the information in each channel equally during the feature extraction process. In our designed channel adaptive branch, we use a Watts Strogatz topology structure to connect each feature map to its most relevant features in each graph convolutional layer, substantially reducing information redundancy. Moreover, ensemble learning is adopted in our SW-GCN for mapping distinct features from the two well-designed branches to the enhanced PET images. We carried out extensive experiments on three single-bed position scans for 386 patients. The test results demonstrate that our proposed SW-GCN approach outperforms state-of-the-art methods in both quantitative and qualitative evaluations.

An adaptive key selection method for the multilevel index model for effective service management in the cloud

SUMMARYThe growing number of services processed and stored in the cloud has led to difficulties in managing and discovering the required services efficiently. Multilevel index model is an efficient method to manage and retrieve services in service repositories. When adding a new service to a multilevel index model, a key needs to be selected for the service, but existing key selection methods cannot adapt to the situation that hot services change over time. To address this problem, this article proposes an adaptive key selection method to improve the efficiency of service retrieval. However, the service addition operation of the adaptive key selection method is inefficient in the multilevel index model. For this reason, this article improves the multilevel index model by introducing local equivalence partition. This indexing model improves the service addition efficiency of the adaptive key selection method without affecting the service retrieval efficiency. It is experimentally demonstrated that the retrieval and addition efficiencies of the adaptive key selection method are close to the ideal state optimum under the multilevel index model with local equivalence partitioning.

A localized meshfree technique for solving fractional Benjamin–Ono equation describing long internal waves in deep stratified fluids

This article proposes a local radial basis function partition of unity method to find the numerical solution of the time-fractional Benjamin–Ono equation. The proposed method is based on partitioning the original domain into several overlapping subdomains and employing the radial basis function approximation on each local subdomain. First, the time-discrete scheme of this equation is obtained by utilizing a finite difference approach. Then, spatial discretization is established by using the local radial basis function partition of unity method. Furthermore, the convergence and stability analysis of the proposed time-discrete scheme are investigated thoroughly. Also, numerical experiments are executed through some illustrated problems, and the results are compared with those acquired by the tanh approach and Kudryashov approach solutions to show the high accuracy and plausibility of the proposed method. Moreover, to demonstrate the physical significance, the conservation constants known as mass, momentum, and energy for this equation are also calculated by the proposed method.

Distributed Graph Embedding with Information-Oriented Random Walks

Graph embedding maps graph nodes to low-dimensional vectors, and is widely adopted in machine learning tasks. The increasing availability of billion-edge graphs underscores the importance of learning efficient and effective embeddings on large graphs, such as link prediction on Twitter with over one billion edges. Most existing graph embedding methods fall short of reaching high data scalability. In this paper, we present a general-purpose, distributed, information-centric random walk-based graph embedding framework, DistGER, which can scale to embed billion-edge graphs. DistGER incrementally computes information-centric random walks. It further leverages a multi-proximity-aware, streaming, parallel graph partitioning strategy, simultaneously achieving high local partition quality and excellent workload balancing across machines. DistGER also improves the distributed Skip-Gram learning model to generate node embeddings by optimizing the access locality, CPU throughput, and synchronization efficiency. Experiments on real-world graphs demonstrate that compared to state-of-the-art distributed graph embedding frameworks, including KnightKing, DistDGL, and Pytorch-BigGraph, DistGER exhibits 2.33×--129× acceleration, 45% reduction in cross-machines communication, and >10% effectiveness improvement in downstream tasks.

Absolute phase measurement method based onbidirectional coding patterns.

The stair-phase-coding patterns have been widely used to determine the fringe order for phase unwrapping of the wrapped phase in three-dimensional shape measurement. Although the special coding sequence algorithm can achieve with a large number of codewords, it needs the current codeword and its adjacent codewords to jointly determine the fringe order. If any codeword of the grouped adjacent codewords is incorrectly recognized, it will result in many false fringe orders. It increases the probability of fringe order error in the decoding process. And it is challenging to significantly increase the number of codewords. To solve this problem, we propose an absolute phase measurement method based on bidirectional coding patterns. The wrapped phase of the object is obtained by four-step phase-shifting patterns, and the fringe order is obtained by bidirectional coding patterns. When generating the bidirectional coding patterns, we code two groups of stair phase with different frequencies along the horizontal direction, which respectively represent local fringe order and partition information. Then, we alternately repeat the two groups of stair phase along the vertical direction in the whole pattern to obtain the bidirectional coding patterns. Each local fringe order information and the corresponding partition information in a small region jointly determine the fringe order of pixels in this small region. Fringe order errors in a small region do not affect other regions. To verify the effectiveness of our method, we performed simulations and experiments. Simulation and experimental results show that our method is effective for objects with different sizes and isolated objects.

Local generalization and bucketization technique for personalized privacy preservation

Anonymization technique has been extensively studied and widely applied for privacy-preserving data publishing. In most previous approaches, a microdata table consists of three categories of attributes, namely explicit-identifier, quasi-identifier (QI), and sensitive attribute. In general, individuals may have different views on the sensitivity of different attributes. Therefore, there is another type of attribute that contains both QI values and sensitive values, termed semi-sensitive attribute. In this paper, we propose a new anonymization technique, called Local Generalization and Bucketization, to prevent identity disclosure and protect the sensitive values on each semi-sensitive attribute and sensitive attribute. The rationale is to use local generalization and local bucketization to divide the tuples into local equivalence groups and partition the sensitive values into local buckets, respectively. The protections of local generalization and local bucketization are independent, so that they can be implemented by appropriate algorithms without weakening other protection. Besides, the protection of local bucketization for each semi-sensitive attribute and sensitive attribute is also independent. Consequently, local bucketization can comply with various principles in different attributes according to the actual requirements of anonymization. We conducted extensive experiments to illustrate the effectiveness of the proposed approach.

An efficient localized meshless collocation method for the two-dimensional Burgers-type equation arising in fluid turbulent flows

This paper focusses on the numerical technique based on a localized meshless collocation method for approximating the Burgers-type equation in two dimensions. The method uses two main stages to approximate the unknown solution. First, the time derivative is discretized by the backward Euler method and a semi-discrete approach is obtained. Meanwhile, the unconditional stability and convergence of the semi-discrete scheme are proved by virtue of the discrete energy method in an appropriate Sobolev space. Second, a local radial basis function partition of unity (LRBF-PU) method is applied to approximate the spatial direction and a fully-discrete scheme is established. The LRBF-PU is based on subdividing the original domain to several subdomains and uses the radial basis function interpolation on each local subdomain. Compared with the global collocation technique, the main advantages of this technique are that it can reduce the computational cost and obtain a well linear system. Three illustrative examples are presented to demonstrate the accuracy and efficiency of the proposed method.

Revealing the role of local shear strain partition of transformable particles in a TRIP-reinforced bulk metallic glass composite via digital image correlation

Revealing the role of local shear strain partition of transformable particles in a TRIP-reinforced bulk metallic glass composite via digital image correlation

Local and parallel partition of unity scheme for the mixed Navier-Stokes-Darcy problem

Local and parallel partition of unity scheme for the mixed Navier-Stokes-Darcy problem

Transactional Data Anonymization for Privacy and Information Preservation via Disassociation and Local Suppression

Ubiquitous devices in IoT-based environments create a large amount of transactional data on daily personal behaviors. Releasing these data across various platforms and applications for data mining can create tremendous opportunities for knowledge-based decision making. However, solid guarantees on the risk of re-identification are required to make these data broadly available. Disassociation is a popular method for transactional data anonymization against re-identification attacks in privacy-preserving data publishing. The anonymization algorithm of disassociation is performed in parallel, suitable for the asymmetric paralleled data process in IoT where the nodes have limited computation power and storage space. However, the anonymization algorithm of disassociation is based on the global recoding mode to achieve transactional data km -anonymization, which leads to a loss of combinations of items in transactional datasets, thus decreasing the data quality of the published transactions. To address the issue, we propose a novel vertical partition strategy in this paper. By employing local suppression and global partition, we first eliminate the itemsets which violate km-anonymity to construct the first km-anonymous record chunk. Then, by the processes of itemset creating and reducing, we recombine the globally partitioned items from the first record chunk to construct remaining km-anonymous record chunks. The experiments illustrate that our scheme can retain more association between items in the dataset, which improves the utility of published data.