Exploit Structure Information Research Articles

Structure Enhanced Path Reasoning for Knowledge Graph Completion

Knowledge graphs are crucial foundations for building intelligent systems, such as question answering and recommendation. However, their performance is hampered by the incompleteness of KGs, so the knowledge graph completion arises to infer whether a triple of the form (head entity, relation, tail entity) is a missing fact. The path-based approach that encodes paths from the head entity to the tail entity for reasoning achieves good performance. Previous work suggests that entity type is beneficial for learning path representations. Nevertheless, the semantics of entities are not captured accurately, as many entities are not typed or loosely typed. In addition, previous methods tend to model paths only from the forward direction but fail to capture new path patterns from the reverse direction (i.e., tail entity to head entity). In this paper, we introduce a structure enhanced path reasoning (SPR) framework to address the above-given problems. First, the model uilizes the structure of entities, i.e., their relational contexts (the relations linked from the given entity), to obtain a reliable path representation that captures correct entity semantics. This information is accessible to all nonisolated entities in all KGs, so that it can compensate the semantics for entities or KGs that have no type available. Second, we leverage the structure of paths to derive their reverse paths, so as to enhance the path representation by additionally encoding the new patterns embedded in them through a dual path encoding method. In order to verify the effectiveness of the proposed methods, we design different architectures based on LSTM and Transformer, respectively. Experimental results on two benchmark datasets, WN18RR, and FB15k-237, show that our approach apparently outperforms state-of-the-art methods on fact prediction task and relation prediction task. Furthermore, extensive experiments illustrate the benefits of enhancing path reasoning by exploiting structure information from entity relational contexts and the dual path encoding method.

Multi-Interface Channel Allocation in Fog Computing Systems Using Thompson Sampling

In fog computing systems, each fog node often maintains multiple interfaces to achieve simultaneous communications with end devices. To maximize the utilization of network capacities and avoid interference, a critical mission for each fog node is to allocate distinct channels to its interfaces, also known as multi-interface channel allocation, to maximize the total throughput by successful transmissions. However, the effective allocation scheme design is challenging because the full knowledge of channel state dynamics is often hard to attain in practice. Faced with such uncertainties, online learning is needed to cooperate with online decision making. In this article, we devise an integrated design to conduct such multi-interface channel allocation in fog computing systems. Specifically, by formulating the channel allocation problem in the settings of multiarmed bandit with multiple plays and leveraging Thompson sampling techniques, we propose a multi-interface channel allocation with binary feedback (MICA-B) scheme, which makes online channel allocation decisions through effective learning from binary transmission feedback. Our theoretical analysis shows that MICA-B achieves a sublinear O(logT) regret bound on the performance loss (also known as regret) over a finite time horizon T. Based on MICA-B, we further exploit structure information of channel characteristics and design constrained MICA-B (CoMICA-B) to improve learning efficiency. Further, we propose multi-interface channel allocation with multilevel feedback (MICA-M) which extends MICA to handle more general cases with multilevel feedback information. Our simulation results verify the effectiveness and robustness of MICA-B, CoMICA-B, and MICA-M in terms of regret reduction.

Deep Fusion Clustering Network

Deep clustering is a fundamental yet challenging task for data analysis. Recently we witness a strong tendency of combining autoencoder and graph neural networks to exploit structure information for clustering performance enhancement. However, we observe that existing literature 1) lacks a dynamic fusion mechanism to selectively integrate and refine the information of graph structure and node attributes for consensus representation learning; 2) fails to extract information from both sides for robust target distribution (i.e., “groundtruth” soft labels) generation. To tackle the above issues, we propose a Deep Fusion Clustering Network (DFCN). Specifically, in our network, an interdependency learning-based Structure and Attribute Information Fusion (SAIF) module is proposed to explicitly merge the representations learned by an autoencoder and a graph autoencoder for consensus representation learning. Also, a reliable target distribution generation measure and a triplet self-supervision strategy, which facilitate cross-modality information exploitation, are designed for network training. Extensive experiments on six benchmark datasets have demonstrated that the proposed DFCN consistently outperforms the state-of-the-art deep clustering methods.

An online multiple object tracker based on structure keeper net

We propose a novel online multiple object tracker taking structure information into account. State-of-the-art multi-object tracking (MOT) approaches commonly focus on discriminative appearance features, while neglect in different levels structure information and the core of data association. Addressing this, we design a new tracker fully exploiting structure information and encoding such information into the cost function of the graph matching model. Firstly, a new measurement is proposed to compare the structure similarity of two graphs whose nodes are equal. With this measurement, we define a complete matching which performs association in high efficiency. Secondly, for incomplete matching scenarios, a structure keeper net (SKnet) is designed to adaptively establish the graph for matching. Finally, we conduct extensive experiments on benchmarks including MOT2015 and MOT17. The results demonstrate the competitiveness and practicability of our tracker.

An efficient and globally optimal solution to perspective-n-line problem

This research develops an accurate and efficient method for the Perspective-n-Line (PnL) problem. The developed method addresses and solves PnL via exploiting the problem’s geometry in a non-linear least squares fashion. Specifically, by representing the rotation matrix with a novel quaternion parameterization, the PnL problem is first decomposed into four independent subproblems. Then, each subproblem is reformulated as an unconstrained minimization problem, in which the Kronecker product is adopted to write the cost function in a more compact form. Finally, the Gröbner basis technique is used to solve the polynomial system derived from the first-order optimality conditions of the cost function. Moreover, a novel strategy is presented to improve the efficiency of the algorithm. It is improved by exploiting structure information embedded in the rotation parameterization to accelerate the computing of coefficient matrix of a cost function. Experiments on synthetic data and real images show that the developed method is comparable to or better than state-of-the-art methods in accuracy, but with reduced computational requirements.

Structure alignment of attributes and visual features for cross-dataset person re-identification

In cross-dataset person re-identification, it is challenging to address the problem of domain shift between training and test data. Although unsupervised domain adaptation methods have been developed, the performance is still much weaker compared with that of supervised methods because these models cannot follow a supervised optimization in unlabeled target domains. To address this problem, a transductive structure alignment-based self-reconstruction dictionary learning approach is proposed in this paper for cross-dataset person re-identification (PRID). Specifically, visual-attribute embedding is first learned to achieve knowledge transfer from the source domain to the target domain. In this process, visual-attribute structures are aligned via class prototype dictionaries to promote the discrimination of predicted semantic attributes by exploiting structure information between the visual feature and class prototype. Moreover, to mitigate domain shift, domain-invariant visual-attribute self-reconstruction is integrated into our dictionary learning framework. An identifier is then constructed by integrating the discriminativeness of attribute and compatibility matrix shared both source domain and target domain. Finally, the pre-learned model is tuned by selecting samples from the target domain which are not labeled but assigned pseudo-labels. Extensive experimental results on benchmark datasets show that our approach outperforms several state-of-the-art approaches.

Partial Multi-Label Learning

It is expensive and difficult to precisely annotate objects with multiple labels. Instead, in many real tasks, annotators may roughly assign each object with a set of candidate labels. The candidate set contains at least one but unknown number of ground-truth labels, and is usually adulterated with some irrelevant labels. In this paper, we formalize such problems as a new learning framework called partial multi-label learning (PML). To solve the PML problem, a confidence value is maintained for each candidate label to estimate how likely it is a ground-truth label of the instance. On one hand, the relevance ordering of labels on each instance is optimized by minimizing a rank loss weighted by the confidences; on the other hand, the confidence values are optimized by further exploiting structure information in feature and label spaces.Experimental results on various datasets show that the proposed approach is effective for solving PML problems.

Discriminative sparse representation for face recognition

Recently Sparse Representation (or coding) based Classification (SRC) has gained great success in face recognition. In SRC, the testing image is expected to be best represented as a sparse linear combination of training images from the same class, and the representation fidelity is measured by the l2-norm or l1-norm of the coding residual. However, SRC emphasizes the sparsity too much and overlooks the spatial information during local feature encoding process which has been demonstrated to be critical in real-world face recognition problems. Besides, some work considers the spatial information but overlooks the different discriminative ability in different face regions. In this paper, we propose to weight spatial locations based on their discriminative abilities in sparse coding for robust face recognition. Specifically, we learn the weights at face locations according to the information entropy in each face region, so as to highlight locations in face images that are important for classification. Furthermore, in order to construct a robust weights to fully exploit structure information of each face region, we employed external data to learn the weights, which can cover all possible face image variants of different persons, so the robustness of obtained weights can be guaranteed. Finally, we consider the group structure of training images (i.e. those from the same subject) and added an l2,1-norm (group Lasso) constraint upon the formulation, which enforcing the sparsity at the group level. Extensive experiments on three benchmark face datasets demonstrate that our proposed method is much more robust and effective than baseline methods in dealing with face occlusion, corruption, lighting and expression changes, etc.

Joint representation and pattern learning for robust face recognition

Image feature is a significant factor for the success of robust face recognition. Recently sparse representation based classifier (SRC) has been widely applied to robust face recognition by using sparse representation residuals to tolerate disturbed image features (e.g., occluded pixels). In order to deal with more complicated image variations, robust representation based classifier, which estimates feature weights (e.g., low weight values are given to the pixels with big representation residuals), has attracted much attention in recent work. Although these methods have achieved improved performance by estimating feature weights independently, structured information and prior knowledge of image features are ignored in these works, resulting in unsatisfactory performance in some challenging cases. Thus how to better learn image feature weight to fully exploit structure information and prior knowledge is still an open question in robust face recognition. In this paper, we proposed a novel joint representation and pattern learning (JRPL) model, in which the feature pattern weight is simultaneously learned with the representation of query image. Especially a feature pattern dictionary, which captures structured information and prior knowledge of image features, are constructed to represent the unknown feature pattern weight of a query image. An efficient algorithm to solve JRPL was also presented in this paper. The experiments of face recognition with various variations and occlusions on several benchmark datasets clearly show the advantage of the proposed JRPL in accuracy and efficiency.

Followee recommendation in microblog using matrix factorization model with structural regularization.

Microblog that provides us a new communication and information sharing platform has been growing exponentially since it emerged just a few years ago. To microblog users, recommending followees who can serve as high quality information sources is a competitive service. To address this problem, in this paper we propose a matrix factorization model with structural regularization to improve the accuracy of followee recommendation in microblog. More specifically, we adapt the matrix factorization model in traditional item recommender systems to followee recommendation in microblog and use structural regularization to exploit structure information of social network to constrain matrix factorization model. The experimental analysis on a real-world dataset shows that our proposed model is promising.

Raptorx: Exploiting structure information for protein alignment by statistical inference

This work presents RaptorX, a statistical method for template-based protein modeling that improves alignment accuracy by exploiting structural information in a single or multiple templates. RaptorX consists of three major components: single-template threading, alignment quality prediction, and multiple-template threading. This work summarizes the methods used by RaptorX and presents its CASP9 result analysis, aiming to identify major bottlenecks with RaptorX and template-based modeling and hopefully directions for further study. Our results show that template structural information helps a lot with both single-template and multiple-template protein threading especially when closely-related templates are unavailable, and there is still large room for improvement in both alignment and template selection. The RaptorX web server is available at http://raptorx.uchicago.edu.