Object detection using convolutional networks with adaptively adjusting receptive field of convolutional filter

The receptive field size of a convolutional filter in a deep convolutional network is a crucial issue for object detection task, as the output must response to a suitable size of area in the image to capture proper information. Receptive field size of convolutional filter is fixed due to the inherently fixed geometric structure in its building module. However, objects of interest vary significantly in size within the images for object detection. Different locations of images correspond to objects with different scales, and high level convolutional layers encode semantic features over spatial positions, thus adaptive determination of receptive field size of convolutional filter is desirable for object detection. The authors propose a new module to adaptively determine the receptive field size of convolutional filter, named adaptive convolution. It is based on the idea of dilating the convolutional filter with multiple dilation values and choosing the maximum activation as output, without adding any other parameters. The plain counterparts in existing convolutional neural networks can be easily replaced by adaptive convolution, giving rise to adaptive convolutional networks. Adequate experiments have proven the effectiveness of authors’ method

Robust fault recovery strategy for multi-source flexibly interconnected distribution networks in extreme disaster scenarios

To enhance the resilience of power distribution networks against extreme natural disasters, this article introduces a robust fault recovery strategy for multi-source, flexible interconnected power distribution networks, particularly under scenarios of extreme disasters. Initially, the comprehensive risk of system failure due to ice load on distribution lines and poles is fully considered, and a model for the overall failure rate of lines is constructed. This model addresses the diverse failure scenarios triggered by various meteorological conditions. Through the use of information entropy, typical extreme disaster failure scenarios are identified, and lines with high failure rates under these scenarios are determined. Subsequently, a box-type interval model is developed to represent the uncertainty in the output of distributed generation (DG), and on this basis, a robust fault recovery model for multi-source power distribution networks interconnected through soft open points (SOPs) is established, and use the Column and Constraint Generation (C&CG) algorithm to solve the problem. Finally, the fault recovery model and strategy proposed are validated through an illustrative example based on a modified IEEE 33-node interconnected system

Apple Valsa canker: insights into pathogenesis and disease control

Abstract Apple Valsa canker (AVC) has caused significant losses worldwide, especially in East Asia. Various fungal species from the genus Cytospora/Valsa can infect tree bark and cause tissue rot, and Valsa mali (Vm) is responsible for the most severe tree branch deaths and yield losses. Since AVC was first reported in Japan in 1903, the pathogen species, biological characteristics, infection and pathogenesis, spore dissemination, and disease cycle have been intensively investigated. Based on the new cognition of the disease dynamics, the disease control strategy has shifted from scraping diseased tissue to protecting the bark from infection. In this review, we summarize new knowledge of the Vm infection process mediated by various kinds of virulence factors, including cell wall degrading enzymes, toxins, effectors, microRNA-like RNAs, and pathogenic signaling regulators. We also introduce progress in evaluating germplasm resources and identifying disease response-related genes in apples. In addition, we elaborate current understanding of spore dissemination and disease cycles in orchards and disease prevention techniques. Finally, we provide recommendations for developing more cost-effective strategies for controlling AVC by applying genetic resistance and biological fungicides

DC Characteristics Optimization of a Double G-Shield 50 V RF LDMOS

An N-type 50 V RF LDMOS with a RESURF (reduced surface field) structure of dual field plates (grounded shield, or G-shield) was investigated. The effect of the two field plates and N-drift region, including the junction depth and dopant concentration, on the DC characteristics was analyzed by employing the Taurus TCAD device simulator. A high BV (breakdown voltage) can be achieved while keeping a low RDSON (on-resistance). The simulation results show that the N-drift region dopant concentration has an obvious effect on the BV and RDSON and the junction depth affected these values less. There is an optimized length for the second field plate for a given dopant concentration of the N-drift region. Both factors should be optimized together to determine the best DC characteristics. Meanwhile, the effect of the first field plate on the BV and RDSON can be ignored. According to the simulation results, 50 V RF LDMOS with an optimized RESURF structure of a double G-shield was fabricated using 0.35 µm technologies. The measurement data show the same trend as the TCAD simulation, where a BV of 118 V and RDSON of 26 ohm·mm were achieved

Differentially expressed protein-coding genes and lncRNAs, and PCA of PSI values.

<p>Venn diagram of common differentially expressed protein-coding genes (A) and lncRNAs (B) in five developmental stages. (C) Dynamic expression profiles of <i>CP</i> and TU78568. (D) Two-way PCA plot of protein-coding genes based on PSI values.</p

Time-series modules and co-expression network of lncRNAs and protein-coding genes.

<p>(A) Time-series modules of protein-coding genes and lncRNAs. The top panel shows protein-coding genes and the second panel shows lncRNAs. Numbers in the top left corner indicate module number. Numbers in lower left corners indicate numbers of protein-coding genes or lncRNAs in each module. The same color was used to represent each cluster. Functional categories of genes in green (B) and red modules (C). Benjamini adjusted <i>P</i> values were transformed by ‒log₁₀. (D) Heat map showing the largest two co-expression networks of protein-coding genes. Values represent log₂(FPKM+1) of each gene in each sample minus the mean value of each gene across all samples.</p

Expression profile and PCA of protein-coding genes.

<p>(A) Heat map showing the expression profile of protein-coding genes. The top panel is the tree constructed by Pearson correlation. (B) Two-way PCA plot of protein-coding genes based on expression profile.</p

Temporal expression profiles of protein-coding genes and lncRNAs.

<p>(A) Dynamic changes in expression profiles of protein-coding genes and lncRNAs. The top panel shows protein-coding genes and the bottom panel shows lncRNAs. Values represent the pairwise Pearson correlation. Correlation between every two samples was calculated by log₂-transformed (FPKM+1) gene expression values. Three main expression patterns can be distinguished. (B) Distributions of Shannon entropy-based temporal specificity scores were calculated for distinct classes of lncRNAs and protein-coding genes.</p