Learning Segmentation Masks with the Independence Prior

An instance with a bad mask might make a composite image that uses it look fake. This encourages us to learn segmentation by generating realistic composite images. To achieve this, we propose a novel framework that exploits a new proposed prior called the independence prior based on Generative Adversarial Networks (GANs). The generator produces an image with multiple category-specific instance providers, a layout module and a composition module. Firstly, each provider independently outputs a category-specific instance image with a soft mask. Then the provided instances' poses are corrected by the layout module. Lastly, the composition module combines these instances into a final image. Training with adversarial loss and penalty for mask area, each provider learns a mask that is as small as possible but enough to cover a complete category-specific instance. Weakly supervised semantic segmentation methods widely use grouping cues modeling the association between image parts, which are either artificially designed or learned with costly segmentation labels or only modeled on local pairs. Unlike them, our method automatically models the dependence between any parts and learns instance segmentation. We apply our framework in two cases: (1) Foreground segmentation on category-specific images with box-level annotation. (2) Unsupervised learning of instance appearances and masks with only one image of homogeneous object cluster (HOC). We get appealing results in both tasks, which shows the independence prior is useful for instance segmentation and it is possible to unsupervisedly learn instance masks with only one image.Comment: 7+5 pages, 13 figures, Accepted to AAAI 201

Development of the Abnormal Tension Pattern Recognition Module for Twisted Yarn Based on Deep Learning Edge Computing

This study aims to develop an artificial intelligence module for recognizing abnormal tension in textile weaving, The module can be used to address the time-consuming and inaccurate issues associated with traditional manual methods. Long short-term memory (LSTM) recurrent neural networks as the algorithm for identifying different types of abnormal tension are employed in this module. This study focuses on training and validating the model using five common patterns. Additionally, an approach involving the integration of plug-in modules and edge computing in deep learning is employed to achieve the research objectives without altering the original system architecture. Multiple experiments were conducted to search for the optimal model parameters. According to the experimental results, the average recognition rate for abnormal tension is 97.12%, with an average computation time of 46.2 milliseconds per sample. The results indicate that the recognition accuracy and computation time meet the practical performance requirements of the system

Property Analysis of Composable Web services

Web services are basic components constructing a flexible business process software. By composing multiple Web services, a complicated business process across organization and departments can be formed. This paper present a formal model of composable Web services: composed service process nets (CSPNs). Properties of Composable Web services are analyzed based on CSPNs. The relation between a CSPN and its corresponding Web service process subnets are discussed. The property analysis methods of CSPNs are come up with. A dynamic property of CSPNs can be determined based on static net structures by means of the proposed methods

Multiple metallic-shell nanocylinders for surface-enhanced spectroscopes

The optical properties of multiple dielectric-core-gold-shell nanocylinder pairs are investigated by two-dimensional finite difference time domain method. The core-shell cylinders are assumed to be of the same dimension and composition. For normal incidence, the diffraction spectra of multiple cylinder pairs contain the lightning-rod plasmon mode, and the electric field intensity is concentrated in the gap between the nanocylinder pairs in the infrared region. The resonance wavelength and local field enhancement of this plasmon mode can be tuned by varying the pair-distance between the pairs, the gap-distance between the pairs, and the optical constants of the dielectric-core and the surrounding medium. The results show that the multiple core-shell nanocylinder pair contains the plasmon mode same as that of the solid metallic cylinder pairs at the long wavelength part of the spectrum. The large electric field intensity in the infrared region at long wavelength makes multiple core-shell cylinders as ideal candidates for surface-enhanced spectroscopes

The P Protein of Spring Viremia of Carp Virus Negatively Regulates the Fish Interferon Response by Inhibiting the Kinase Activity of TANK-Binding Kinase 1

Spring viremia of carp virus (SVCV) is an efficient pathogen causing high mortality in the common carp. Fish interferon (IFN) is a powerful cytokine enabling host cells to establish an antiviral response; therefore, the strategies that SVCV uses to avoid the cellular IFN response were investigated. Here, we report that the SVCV P protein is phosphorylated by cellular TANK-binding kinase 1 (TBK1), which decreases IFN regulatory factor 3 (IRF3) phosphorylation and suppresses IFN production. First, overexpression of P protein inhibited the IFN promoter activation induced by SVCV and the IFN activity activated by the mitochondrial antiviral signaling protein (MAVS) although TBK1 activity was not blocked by P protein. Second, P protein colocalized and interacted with TBK1. Dominant negative experiments suggested that the TBK1 N-terminal kinase domain interacted with P protein and was essential for P protein and IRF3 phosphorylation. Finally, P protein overexpression reduced the IRF3 phosphorylation activated by TBK1 and reduced host cellular ifn transcription. Collectively, our data demonstrated that the SVCV P protein is a decoy substrate for the host phosphokinase TBK1, preventing IFN production and facilitating SVCV replication. IMPORTANCE TBK1 is a pivotal phosphokinase that activates host IFN production to defend against viral infection; thus, it is a potential target for viruses to negatively regulate IFN response and facilitate viral evasion. We report that the SVCV P protein functions as a decoy substrate for cellular TBK1, leading to the reduction of IRF3 phosphorylation and suppression of IFN expression. These findings reveal a novel immune evasion mechanism of SVCV.</p