AAANE: Attention-based Adversarial Autoencoder for Multi-scale Network Embedding

Network embedding represents nodes in a continuous vector space and preserves structure information from the Network. Existing methods usually adopt a "one-size-fits-all" approach when concerning multi-scale structure information, such as first- and second-order proximity of nodes, ignoring the fact that different scales play different roles in the embedding learning. In this paper, we propose an Attention-based Adversarial Autoencoder Network Embedding(AAANE) framework, which promotes the collaboration of different scales and lets them vote for robust representations. The proposed AAANE consists of two components: 1) Attention-based autoencoder effectively capture the highly non-linear network structure, which can de-emphasize irrelevant scales during training. 2) An adversarial regularization guides the autoencoder learn robust representations by matching the posterior distribution of the latent embeddings to given prior distribution. This is the first attempt to introduce attention mechanisms to multi-scale network embedding. Experimental results on real-world networks show that our learned attention parameters are different for every network and the proposed approach outperforms existing state-of-the-art approaches for network embedding.Comment: 8 pages, 5 figure

Precise Request Tracing and Performance Debugging for Multi-tier Services of Black Boxes

As more and more multi-tier services are developed from commercial components or heterogeneous middleware without the source code available, both developers and administrators need a precise request tracing tool to help understand and debug performance problems of large concurrent services of black boxes. Previous work fails to resolve this issue in several ways: they either accept the imprecision of probabilistic correlation methods, or rely on knowledge of protocols to isolate requests in pursuit of tracing accuracy. This paper introduces a tool named PreciseTracer to help debug performance problems of multi-tier services of black boxes. Our contributions are two-fold: first, we propose a precise request tracing algorithm for multi-tier services of black boxes, which only uses application-independent knowledge; secondly, we present a component activity graph abstraction to represent causal paths of requests and facilitate end-to-end performance debugging. The low overhead and tolerance of noise make PreciseTracer a promising tracing tool for using on production systems

Graph Representation Learning-Based Recommender Systems

University of Technology Sydney. Faculty of Engineering and Information Technology.Personalized recommendation has been applied to many online services such as E-commerce and adverting. It facilitates users to discover a small set of relevant items, which meet their personalized interests, from many choices. Nowadays, various auxiliary information on users and items become increasingly available in online platforms, such as user demographics, social relations, and item knowledge. More recent evidences suggests that incorporating such auxiliary data with collaborative filtering can better capture the underlying and complex user-item relationships, and further achieve higher recommendation quality. In this thesis, we focus on auxiliary data with graph structure, such as social networks and knowledge graphs (KG). For example, we can improve recommendation performance by mining social relationships between users, and also by using knowledge graphs to enhance the semantics of recommended items. Network representation learning aims to represent each vertex in a network (graph) as a low-dimensional vector while still preserving its structural information. Due to the availability of massive graph data in recommender systems, it is a promising approach to combine network representation learning with recommendation. Applying the learned graph features to recommender systems will effectively enhance the learning ability of the recommender systems and improve the accuracy and user satisfaction of the recommender systems. For network representation learning and its application in recommendation systems, the major contributions of this thesis are as follows: (1) Attention-based Adversarial Autoencoder for Multi-scale Network Embedding. Existing Network representation methods usually adopt a one-size-fits-all approach when concerning multi-scale structure information, such as first- and second-order proximity of nodes, ignoring the fact that different scales play different roles in embedding learning. We propose an Attention-based Adversarial Autoencoder Network Embedding (AAANE) framework, which promotes the collaboration of different scales and lets them vote for robust representations. (2) Multi-modal Multi-view Bayesian Semantic Embedding for Community Question Answering: Semantic embedding has demonstrated its value in latent representation learning of data, and can be effectively adopted for many applications. However, it is difficult to propose a joint learning framework for semantic embedding in Community Question Answer (CQA), because CQA data have multi-view and sparse properties. In this thesis, we propose a generic Multi-modal Multi-view Semantic Embedding (MMSE) framework via a Bayesian model for question answering. (3) Context-Dependent Propagating-based Video Recommendation in Multi-modal Heterogeneous Information Networks. Conventional approaches to video recommendation primarily focus on exploiting content features or simple user-video interactions to model the users’ preferences. However these methods fail to model the complex video context interdependency, which is obscure/hidden in heterogeneous auxiliary data. In this paper, we propose a Context-Dependent Propagating Recommendation network (CDPRec) to obtain accurate video embedding and capture global context cues among videos in HINs. The CDPRec can iteratively propagate the contexts of a video along links in a graph-structured HIN and explore multiple types of dependencies among the surrounding video nodes. (4) Knowledge Graph Enhanced Neural Collaborative Filtering. Existing neural collaborative filtering (NCF) recommendation methods suffer from severe sparsity problem. Knowledge Graph (KG), which commonly consists of fruitful connected facts about items, presents an unprecedented opportunity to alleviate the sparsity problem. However, NCF only methods can hardly model the high-order connectivity in KG, and ignores complex pairwise correlations between user/item embedding dimensions. To address these issues, we propose a novel Knowledge graph enhanced Neural Collaborative Recommendation (K-NCR) framework, which effectively combines user-item interaction information and auxiliary knowledge information for recommendation

Novel free paclitaxel-loaded poly(L-γ-glutamylglutamine)–paclitaxel nanoparticles

The purpose of this study was to develop a novel formulation of paclitaxel (PTX) that would improve its therapeutic index. Here, we combined a concept of polymer–PTX drug conjugate with a concept of polymeric micelle drug delivery to form novel free PTX-loaded poly(L-γ-glutamylglutamine) (PGG)–PTX conjugate nanoparticles. The significance of this drug formulation emphasizes the simplicity, novelty, and flexibility of the method of forming nanoparticles that contain free PTX and conjugated PTX in the same drug delivery system. The results of effectively inhibiting tumor growth in mouse models demonstrated the feasibility of the nanoparticle formulation. The versatility and potential of this dual PTX drug delivery system can be explored with different drugs for different indications. Novel and simple formulations of PTX-loaded PGG–PTX nanoparticles could have important implications in translational medicines

Enrichment of Polychlorinated Biphenyls from Aqueous Solutions Using Fe3O4 Grafted Multiwalled Carbon Nanotubes with Poly Dimethyl Diallyl Ammonium Chloride

In this paper, Fe3O4 nanoparticles (Fe3O4 NPs) grafted carboxyl groups of multiwalled carbon nanotubes with cationic polyelectrolyte poly (dimethyldiallylammonium chloride) (PDDA) (MWCNTs-COO−/PDDA@Fe3O4), are successfully synthesized and used for the extraction of six kinds of major toxic polychorinated biphenyls (PCBs) from a large volume of water solution. The hydrophilicity of the PDDA cage can enhance the dispersibility of sorbents in water samples, and the superparamagnetism of the Fe3O4 NPs facilitate magnetic separation which directly led to the simplification of the extraction procedure. With the magnetic solid-phase extraction (MSPE) technique based on the MWCNTs-COO−/PDDA@Fe3O4 sorbents, it requires only 30 min to extract trace levels of PCBs from 500 mL water samples. When the eluate condensed to 1.0 mL, concentration factors for PCBs became over 500. The spiked recoveries of several real water samples for PCBs were in the range of 73.3–98.9% with relative standard deviations varying from 3.8% to 9.4%, reflecting good accuracy of the method. Therefore, preconcentration of trace level of PCBs by using this MWCNTs-COO−/PDDA@Fe3O4 sorbent, which are stable for multiple reuses, from water solution can be performed

Weighted Evidence Combination Rule Based on Evidence Distance and Uncertainty Measure: An Application in Fault Diagnosis

Conflict management in Dempster-Shafer theory (D-S theory) is a hot topic in information fusion. In this paper, a novel weighted evidence combination rule based on evidence distance and uncertainty measure is proposed. The proposed approach consists of two steps. First, the weight is determined based on the evidence distance. Then, the weight value obtained in first step is modified by taking advantage of uncertainty. Our proposed method can efficiently handle high conflicting evidences with better performance of convergence. A numerical example and an application based on sensor fusion in fault diagnosis are given to demonstrate the efficiency of our proposed method