Search Efficient Binary Network Embedding

Traditional network embedding primarily focuses on learning a dense vector representation for each node, which encodes network structure and/or node content information, such that off-the-shelf machine learning algorithms can be easily applied to the vector-format node representations for network analysis. However, the learned dense vector representations are inefficient for large-scale similarity search, which requires to find the nearest neighbor measured by Euclidean distance in a continuous vector space. In this paper, we propose a search efficient binary network embedding algorithm called BinaryNE to learn a sparse binary code for each node, by simultaneously modeling node context relations and node attribute relations through a three-layer neural network. BinaryNE learns binary node representations efficiently through a stochastic gradient descent based online learning algorithm. The learned binary encoding not only reduces memory usage to represent each node, but also allows fast bit-wise comparisons to support much quicker network node search compared to Euclidean distance or other distance measures. Our experiments and comparisons show that BinaryNE not only delivers more than 23 times faster search speed, but also provides comparable or better search quality than traditional continuous vector based network embedding methods

Session Communication and Integration

The scenario-based specification of a large distributed system is usually naturally decomposed into various modules. The integration of specification modules contrasts to the parallel composition of program components, and includes various ways such as scenario concatenation, choice, and nesting. The recent development of multiparty session types for process calculi provides useful techniques to accommodate the protocol modularisation, by encoding fragments of communication protocols in the usage of private channels for a class of agents. In this paper, we extend forgoing session type theories by enhancing the session integration mechanism. More specifically, we propose a novel synchronous multiparty session type theory, in which sessions are separated into the communicating and integrating levels. Communicating sessions record the message-based communications between multiple agents, whilst integrating sessions describe the integration of communicating ones. A two-level session type system is developed for pi-calculus with syntactic primitives for session establishment, and several key properties of the type system are studied. Applying the theory to system description, we show that a channel safety property and a session conformance property can be analysed. Also, to improve the utility of the theory, a process slicing method is used to help identify the violated sessions in the type checking.Comment: A short version of this paper is submitted for revie

Wetting of Bio-Inspired Complexly-Shaped Fibers and Channels

This Dissertation is centered on studying the wetting of complexly-shaped fibers and channels which is inspired by the Lepidopteran proboscis. From materials science and engineering standpoint, the Lepidopteran proboscis is a multifunctional microfluidic device. The unique materials organization, morphology, structure, and surface properties of the proboscis allows the Lepidopterans to feed on various food sources from highly viscous to very thin liquids while keeping its surface clean. Thus, the study on the proboscis wetting phenomena has drawn great interests of materials scientists and engineers. The shape of the Lepidopteran proboscis has a very special design combining complexly-shaped fibers and channels. However, it remains unknown how this unique shape benefits the multiple functions of the proboscis. In this Dissertation, we investigate the effect of shape on the wetting properties of proboscis by separately studying the wetting of complexly-shaped fibers and channels, and then, applying the gained knowledge to explain various wetting phenomena observed on the Lepidopteran proboscis. In Chapter I, the definition of wetting is introduced and the fundamental studies on wetting of fibers and channels are reviewed. Then the structure and function of Lepidopteran proboscis is introduced, and the motivation for conducting the research in this Dissertation is explained. In Chapter II, several wetting phenomena on the ribbon-like fiber, e.g. the morphological transitions of droplet configurations, stability of coating films, capillary rise of menisci on ribbon-like fiber, and wetting of the ribbon rail are studied experimentally and theoretically. This study sets up the foundation for investigating the wetting phenomena of other complexly-shaped fibers and channels. The developed experimental and theoretical methods are actively used throughout this Dissertation. In Chapter III, the instability of a thin coating film on the internal and external walls of a straight hollow elliptical fiber is studied, and the mechanisms of drop formation from the coating films and the droplet morphology is briefly discussed. Then the study is expanded to the ring made of a curved elliptical tube to cover a broad range of wetting phenomena associated with such complexly-shaped fibers by discussing the effect of ring radius of curvature and the cross-sectional ellipticity. In Chapter IV, a new method for studying the wetting of complexly-shaped channels is developed based on the Princen theory, and examined with the V-shaped channel. Then, the wetting/dewetting of C-shaped channel is systematically studied both experimentally and theoretically. In Chapter V, several wetting phenomena associated with the Lepidopteran proboscises, e.g. the food uptake from a pool of liquid or from a limited volume of liquid, the stability of liquid films deposited on proboscis after dipping it into a nectar source, and self-assembly of proboscis after the insect emerges from the pupa, are discussed based on the study of wetting of complexly-shaped fibers and channels. All the results are summarized in Chapter VI

Building agent-based hybrid intelligent systems : a case study

Many complex problems (e.g., financial investment planning, foreign exchange trading, data mining from large/multiple databases) require hybrid intelligent systems that integrate many intelligent techniques (e.g., fuzzy logic, neural networks, and genetic algorithms). However, hybrid intelligent systems are difficult to develop because they have a large number of parts or components that have many interactions. On the other hand, agents offer a new and often more appropriate route to the development of complex systems, especially in open and dynamic environments. Thus, this paper discusses the development of an agent-based hybrid intelligent system for financial investment planning, in which a great number of heterogeneous computing techniques/packages are easily integrated into a unifying agent framework. This shows that agent technology can indeed facilitate the development of hybrid intelligent systems.<br /

An agent-based hybrid framework for database mining

While knowledge discovery in databases (KDD) is defined as an iterative sequence of the following steps: data pre-processing, data mining, and post data mining, a significant amount of research in data mining has been done, resulting in a variety of algorithms and techniques for each step. However, a single data-mining technique has not been proven appropriate for every domain and data set. Instead, several techniques may need to be integrated into hybrid systems and used cooperatively during a particular data-mining operation. That is, hybrid solutions are crucial for the success of data mining. This paper presents a hybrid framework for identifying patterns from databases or multi-databases. The framework integrates these techniques for mining tasks from an agent point of view. Based on the experiments conducted, putting different KDD techniques together into the agent-based architecture enables them to be used cooperatively when needed. The proposed framework provides a highly flexible and robust data-mining platform and the resulting systems demonstrate emergent behaviors although it does not improve the performance of individual KDD techniques. <br /

Tensorized Self-Attention: Efficiently Modeling Pairwise and Global Dependencies Together

Neural networks equipped with self-attention have parallelizable computation, light-weight structure, and the ability to capture both long-range and local dependencies. Further, their expressive power and performance can be boosted by using a vector to measure pairwise dependency, but this requires to expand the alignment matrix to a tensor, which results in memory and computation bottlenecks. In this paper, we propose a novel attention mechanism called "Multi-mask Tensorized Self-Attention" (MTSA), which is as fast and as memory-efficient as a CNN, but significantly outperforms previous CNN-/RNN-/attention-based models. MTSA 1) captures both pairwise (token2token) and global (source2token) dependencies by a novel compatibility function composed of dot-product and additive attentions, 2) uses a tensor to represent the feature-wise alignment scores for better expressive power but only requires parallelizable matrix multiplications, and 3) combines multi-head with multi-dimensional attentions, and applies a distinct positional mask to each head (subspace), so the memory and computation can be distributed to multiple heads, each with sequential information encoded independently. The experiments show that a CNN/RNN-free model based on MTSA achieves state-of-the-art or competitive performance on nine NLP benchmarks with compelling memory- and time-efficiency

An agent-based framework for petroleum information services from distributed heterogeneous data resources

For making good decisions in the area of petroleum production, it is becoming a big problem how to timely gather sufficient and correct information, which may be stored in databases, data files, or on the World Wide Web. In this paper, Gaia methodology and Open Agent Architecture were employed to contribute a framework to solve above problem. The framework consists of three levels, namely, role mode, agent type, and agent instance. The model with five roles is analyzed. Four agent types are designed Six agent instances are developed for constructing the system of petroleum information services. The experimental results show that all agents in the system can work cooperatively to organize and retrieve relevant petroleum information. The successful implementation of the framework shows that agent-based technology can significantly facilitate the construction of complex systems in distributed heterogeneous data resource environment.<br /