Relation Structure-Aware Heterogeneous Information Network Embedding

Heterogeneous information network (HIN) embedding aims to embed multiple types of nodes into a low-dimensional space. Although most existing HIN embedding methods consider heterogeneous relations in HINs, they usually employ one single model for all relations without distinction, which inevitably restricts the capability of network embedding. In this paper, we take the structural characteristics of heterogeneous relations into consideration and propose a novel Relation structure-aware Heterogeneous Information Network Embedding model (RHINE). By exploring the real-world networks with thorough mathematical analysis, we present two structure-related measures which can consistently distinguish heterogeneous relations into two categories: Affiliation Relations (ARs) and Interaction Relations (IRs). To respect the distinctive characteristics of relations, in our RHINE, we propose different models specifically tailored to handle ARs and IRs, which can better capture the structures and semantics of the networks. At last, we combine and optimize these models in a unified and elegant manner. Extensive experiments on three real-world datasets demonstrate that our model significantly outperforms the state-of-the-art methods in various tasks, including node clustering, link prediction, and node classification

Block Belief Propagation for Parameter Learning in Markov Random Fields

Traditional learning methods for training Markov random fields require doing inference over all variables to compute the likelihood gradient. The iteration complexity for those methods therefore scales with the size of the graphical models. In this paper, we propose \emph{block belief propagation learning} (BBPL), which uses block-coordinate updates of approximate marginals to compute approximate gradients, removing the need to compute inference on the entire graphical model. Thus, the iteration complexity of BBPL does not scale with the size of the graphs. We prove that the method converges to the same solution as that obtained by using full inference per iteration, despite these approximations, and we empirically demonstrate its scalability improvements over standard training methods.Comment: Accepted to AAAI 201

Brownian Motion and Entropic Torque Driven Motion of Domain-Wall in Antiferromagnets

We study the spin dynamics in antiferromagnetic nanowire under an applied temperature gradient using micromagnetic simulations on a classical spin model with a uniaxial anisotropy. The entropic torque driven domain-wall motion and the Brownian motion are discussed in detail, and their competition determines the antiferromagnetic wall motion towards the hotter or colder region. Furthermore, the spin dynamics in an antiferromagnet can be well tuned by the anisotropy and the temperature gradient. Thus, this paper not only strengthens the main conclusions obtained in earlier works [Kim et al., Phys. Rev. B 92, 020402(R) (2015); Selzer et al., Phys. Rev. Lett. 117, 107201 (2016)], but more importantly gives the concrete conditions under which these conclusions apply, respectively. Our results may provide useful information on the antiferromagnetic spintronics for future experiments and storage device design.Comment: 6 pages, 7 figures, published in Physical Review

An Ontology-Based Artificial Intelligence Model for Medicine Side-Effect Prediction: Taking Traditional Chinese Medicine as An Example

In this work, an ontology-based model for AI-assisted medicine side-effect (SE) prediction is developed, where three main components, including the drug model, the treatment model, and the AI-assisted prediction model, of proposed model are presented. To validate the proposed model, an ANN structure is established and trained by two hundred and forty-two TCM prescriptions. These data are gathered and classified from the most famous ancient TCM book and more than one thousand SE reports, in which two ontology-based attributions, hot and cold, are introduced to evaluate whether the prescription will cause SE or not. The results preliminarily reveal that it is a relationship between the ontology-based attributions and the corresponding predicted indicator that can be learnt by AI for predicting the SE, which suggests the proposed model has a potential in AI-assisted SE prediction. However, it should be noted that, the proposed model highly depends on the sufficient clinic data, and hereby, much deeper exploration is important for enhancing the accuracy of the prediction

An Event-Based Neurobiological Recognition System with Orientation Detector for Objects in Multiple Orientations

A new multiple orientation event-based neurobiological recognition system is proposed by integrating recognition and tracking function in this paper, which is used for asynchronous address-event representation (AER) image sensors. The characteristic of this system has been enriched to recognize the objects in multiple orientations with only training samples moving in a single orientation. The system extracts multi-scale and multi-orientation line features inspired by models of the primate visual cortex. An orientation detector based on modified Gaussian blob tracking algorithm is introduced for object tracking and orientation detection. The orientation detector and feature extraction block work in simultaneous mode, without any increase in categorization time. An addresses lookup table (addresses LUT) is also presented to adjust the feature maps by addresses mapping and reordering, and they are categorized in the trained spiking neural network. This recognition system is evaluated with the MNIST dataset which have played important roles in the development of computer vision, and the accuracy is increase owing to the use of both ON and OFF events. AER data acquired by a DVS are also tested on the system, such as moving digits, pokers, and vehicles. The experimental results show that the proposed system can realize event-based multi-orientation recognition.The work presented in this paper makes a number of contributions to the event-based vision processing system for multi-orientation object recognition. It develops a new tracking-recognition architecture to feedforward categorization system and an address reorder approach to classify multi-orientation objects using event-based data. It provides a new way to recognize multiple orientation objects with only samples in single orientation

A novel feature selection approach for intrusion detection data classification

Intrusion Detection Systems (IDSs) play a significant role in monitoring and analyzing daily activities occurring in computer systems to detect occurrences of security threats. However, the routinely produced analytical data from computer networks are usually of very huge in size. This creates a major challenge to IDSs, which need to examine all features in the data to identify intrusive patterns. The objective of this study is to analyze and select the more discriminate input features for building computationally efficient and effective schemes for an IDS. For this, a hybrid feature selection algorithm in combination with wrapper and filter selection processes is designed in this paper. Two main phases are involved in this algorithm. The upper phase conducts a preliminary search for an optimal subset of features, in which the mutual information between the input features and the output class serves as a determinant criterion. The selected set of features from the previous phase is further refined in the lower phase in a wrapper manner, in which the Least Square Support Vector Machine (LSSVM) is used to guide the selection process and retain optimized set of features. The efficiency and effectiveness of our approach is demonstrated through building an IDS and a fair comparison with other state-of-the-art detection approaches. The experimental results show that our hybrid model is promising in detection compared to the\ud previously reported results

The morphological features of different Schatzker types of tibial plateau fractures: a three-dimensional computed tomography study

BACKGROUND: Tibial plateau fractures are of great challenge to treat with open reduction and internal fixation, because fractures vary from simple to complex, with little or extensive articular involvement. Hence, recognition and comprehension of the fracture features will help orthopedic surgeons understand the injury mechanism better and manage these fractures by planning optimal surgical procedures. This study aimed to evaluate the morphological characteristics of tibial plateau fractures based on the Schatzker classification. METHODS: A total of 186 patients with 188 tibial plateau fractures from 2010 to 2014 in our hospital were reviewed using a computed tomography scan and three-dimensional (3D) reconstruction. The main fracture line angles (FLA) of Schatzker types I, II, and IV were measured. For each fracture, depression depth was measured, and the depression zone was also located. Depression zones were overlapped to obtain a frequency diagram. RESULTS: Schatzker type I and II fractures had three subtypes: single anterolateral fracture, single posterolateral fracture, and complex fracture (the anterolateral and posterolateral parts). Schatzker type IV fractures were also divided into three subtypes: single posteromedial fracture, single anteromedial fracture, and the whole medial fracture. For various Schatzker types and subtypes of fracture, fracture depression clustered and occurred at different locations of the tibial plateau. A significant difference was observed in the depression depth among the different Schatzker types (P < 0.01, Kruskal-Wallis test), especially between Schatzker type III and other types (Nemenyi test). There was no difference in the depression depth among the subtypes of Schatzker type II, whereas the difference was significant between the two subtypes of Schatzker type IV. CONCLUSIONS: Schatzker type I, II, and IV fractures could be divided into three corresponding subtypes by FLA. Various Schatzker types of fractures differed in location and depth of depression. A proper operative approach should be made based on the morphological characteristics of individual types of tibial plateau fractures

MEMORY OPTIMIZATIONS FOR HIGH-THROUGHPUT COMPUTER SYSTEMS

The emergence of new non-volatile memory (NVM) technology and deep neural network (DNN) inferences bring challenges related to off-chip memory access. Ensuring crash consistency leads to additional memory operations and exposes memory update operations on the critical execution path. DNN inference execution on some accelerators suffers from intensive off-chip memory access. The focus of this dissertation is to tackle the issues related to off-chip memory in these high performance computing systems. The logging operations, required by the crash consistency, impose a significant performance overhead due to the extra memory access. To mitigate the persistence time of log requests, we introduce a load-aware log entry allocation scheme that allocates log requests to the address whose bank has the lightest workload. To address the problem of intra-record ordering, we propose to buffer log metadata in a non-volatile ADR buffer until the corresponding log can be removed. Moreover, the recently proposed LAD introduced unnecessary logging operations on multicore CPU. To reduce these unnecessary operations, we have devised two-stage transaction execution and virtual ADR buffers. To tackle the challenge of low response time and high computational intensity associated with DNN inferences, these computations are often executed on customized accelerators. However, data loading from off-chip memory typically takes longer than computing, thereby reducing performance in some scenarios, especially on edge devices. To address this issue, we propose an optimization of the widely adopted Weight Stationary dataflow to remove redundant accesses to IFMAP in off-chip memory by reordering the loops in the standard convolution operation. Furthermore, to enhance the off-chip memory throughput, we introduce the load-aware placement for data tiles on off-chip memory that reduces intra/inter contentions caused by concurrent accesses from multiple tiles and improves the off-chip memory device parallelism during access