EFormer: Enhanced Transformer towards Semantic-Contour Features of Foreground for Portraits Matting

The portrait matting task aims to extract an alpha matte with complete semantics and finely-detailed contours. In comparison to CNN-based approaches, transformers with self-attention allow a larger receptive field, enabling it to better capture long-range dependencies and low-frequency semantic information of a portrait. However, the recent research shows that self-attention mechanism struggle with modeling high-frequency information and capturing fine contour details, which can lead to bias while predicting the portrait's contours. To address the problem, we propose EFormer to enhance the model's attention towards semantic and contour features. Especially the latter, which is surrounded by a large amount of high-frequency details. We build a semantic and contour detector (SCD) to accurately capture the distribution of semantic and contour features. And we further design contour-edge extraction branch and semantic extraction branch for refining contour features and complete semantic information. Finally, we fuse the two kinds of features and leverage the segmentation head to generate the predicted portrait matte. Remarkably, EFormer is an end-to-end trimap-free method and boasts a simple structure. Experiments conducted on VideoMatte240K-JPEGSD and AIM datasets demonstrate that EFormer outperforms previous portrait matte methods.Comment: 17 pages, 6 figure

Powdery Mildews Are Characterized by Contracted Carbohydrate Metabolism and Diverse Effectors to Adapt to Obligate Biotrophic Lifestyle

Powdery mildew is a widespread plant disease caused by obligate biotrophic fungal pathogens involving species-specific interactions between host and parasite. To gain genomic insights into the underlying obligate biotrophic mechanisms, we analyzed 15 microbial genomes covering powdery and downy mildews and rusts. We observed a genome-wide, massive contraction of multiple gene families in powdery mildews, such as enzymes in the carbohydrate metabolism pathway, when compared with ascomycete phytopathogens, while the fatty acid metabolism pathway maintained its integrity. We also observed significant differences in candidate secreted effector protein (CSEP) families between monocot and dicot powdery mildews, perhaps due to different selection forces. While CSEPs in monocot mildews are likely subject to positive selection causing rapid expansion, CSEP families in dicot mildews are shrinking under strong purifying selection. Our results not only illustrate obligate biotrophic mechanisms of powdery mildews driven by gene family evolution in nutrient metabolism, but also demonstrate how the divergence of CSEPs between monocot and dicot lineages might contribute to species-specific adaption

Neuron Learning Machine for Representation Learning

This paper presents a novel neuron learning machine (NLM) which can extract hierarchical features from data. We focus on the single-layer neural network architecture and propose to model the network based on the Hebbian learning rule. Hebbian learning rule describes how synaptic weight changes with the activations of presynaptic and postsynaptic neurons. We model the learning rule as the objective function by considering the simplicity of the network and stability of solutions. We make a hypothesis and introduce a correlation based constraint according to the hypothesis. We find that this biologically inspired model has the ability of learning useful features from the perspectives of retaining abstract information. NLM can also be stacked to learn hierarchical features and reformulated into convolutional version to extract features from 2-dimensional data

Computer vision: CCF Chinese conference, CCCV 2015, Xi'an, China, September 18-20, 2015, proceedings, part I

EI54

Osiris: A Malware Behavior Capturing System Implemented at Virtual Machine Monitor Layer

To perform behavior based malware analysis, behavior capturing is an important prerequisite. In this paper, we present Osiris system which is a tool to capture behaviors of executable files in Windows system. It collects API calls invoked not only by main process of the analysis file, but also API calls invoked by child processes which are created by main process, injected processes if process injection happens, and service processes if the main process creates services. By modifying the source code of Qemu, Osiris is implemented at the virtual machine monitor layer and has the following advantages. First, it does not rewrite the binary code of analysis file or interfere with its normal execution, so that behavior data are obtained more stealthily and transparently. Second, it employs a multi-virtual machine framework to simulate the network environment for malware analysis, so that network behaviors of a malware are stimulated to a large extend. Third, besides network environment, it also simulates most common host events to stimulate potential malicious behaviors of a malware. The experimental results show that Osiris automates the malware analysis process and provides good behavior data for the following detection algorithm

Computational Intelligence in Remote Sensing

With the development of Earth observation techniques, vast amounts of remote sensing data with a high spectral–spatial–temporal resolution are captured all the time, and remote sensing data processing and analysis have been successfully used in numerous fields, including geography, environmental monitoring, land survey, disaster management, mineral exploration and more [...