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

Semiconductor Defect Pattern Classification by Self-Proliferation-and-Attention Neural Network

Semiconductor manufacturing is on the cusp of a revolution: the Internet of Things (IoT). With IoT we can connect all the equipment and feed information back to the factory so that quality issues can be detected. In this situation, more and more edge devices are used in wafer inspection equipment. This edge device must have the ability to quickly detect defects. Therefore, how to develop a high-efficiency architecture for automatic defect classification to be suitable for edge devices is the primary task. In this paper, we present a novel architecture that can perform defect classification in a more efficient way. The first function is self-proliferation, using a series of linear transformations to generate more feature maps at a cheaper cost. The second function is self-attention, capturing the long-range dependencies of feature map by the channel-wise and spatial-wise attention mechanism. We named this method as self-proliferation-and-attention neural network. This method has been successfully applied to various defect pattern classification tasks. Compared with other latest methods, SP&A-Net has higher accuracy and lower computation cost in many defect inspection tasks

Neutrophil Spontaneous Death Is Mediated by Down-Regulation of Autocrine Signaling through GPCR, PI3Kγ\gamma, ROS, and actin

Neutrophil spontaneous apoptosis plays a crucial role in neutrophil homeostasis and the resolution of inflammation. We previously established Akt deactivation as a key mediator of this tightly regulated cellular death program. Nevertheless, the molecular mechanisms governing the diminished Akt activation were not characterized. Here, we report that Akt deactivation during the course of neutrophil spontaneous death was a result of reduced PtdIns(3,4,5)P3 level. The phosphatidylinositol lipid kinase activity of $PI3K\gamma$, but not class IA PI3Ks, was significantly reduced during neutrophil death. The production of PtdIns(3,4,5)P3 in apoptotic neutrophils was mainly maintained by autocrinely released chemokines that elicited $PI3K\gamma$ activation via G protein–coupled receptors. Unlike in other cell types, serum-derived growth factors did not provide any survival advantage in neutrophils. $PI3K\gamma$, but not class IA PI3Ks, was negatively regulated by gradually accumulated ROS in apoptotic neutrophils, which suppressed $PI3K\gamma$ activity by inhibiting an actin-mediated positive feedback loop. Taken together, these results provide insight into the mechanism of neutrophil spontaneous death and reveal a cellular pathway that regulates PtdIns(3,4,5)P3/Akt in neutrophils

Uniformly Strong Persistence for a Delayed Predator-Prey Model

An asymptotically periodic predator-prey model with time delay is investigated. Some sufficient conditions for the uniformly strong persistence of the system are obtained. Our result is an important complementarity to the earlier results

An Impulsive Three-Species Model with Square Root Functional Response and Mutual Interference of Predator

An impulsive two-prey and one-predator model with square root functional responses, mutual interference, and integrated pest management is constructed. By using techniques of impulsive perturbations, comparison theorem, and Floquet theory, the existence and global asymptotic stability of prey-eradication periodic solution are investigated. We use some methods and sufficient conditions to prove the permanence of the system which involve multiple Lyapunov functions and differential comparison theorem. Numerical simulations are given to portray the complex behaviors of this system. Finally, we analyze the biological meanings of these results and give some suggestions for feasible control strategies