Fused Text Segmentation Networks for Multi-oriented Scene Text Detection

In this paper, we introduce a novel end-end framework for multi-oriented scene text detection from an instance-aware semantic segmentation perspective. We present Fused Text Segmentation Networks, which combine multi-level features during the feature extracting as text instance may rely on finer feature expression compared to general objects. It detects and segments the text instance jointly and simultaneously, leveraging merits from both semantic segmentation task and region proposal based object detection task. Not involving any extra pipelines, our approach surpasses the current state of the art on multi-oriented scene text detection benchmarks: ICDAR2015 Incidental Scene Text and MSRA-TD500 reaching Hmean 84.1% and 82.0% respectively. Morever, we report a baseline on total-text containing curved text which suggests effectiveness of the proposed approach.Comment: Accepted by ICPR201

Design and Performance of Inductive Electrostatic Sprayer

Abstract: In order to improve spraying results, an inductive electrostatic sprayer was designed. The performance of the sprayer was then tested. The test result shows that the charge-to-mass ratio can reach 0.951 mc/Kg when electrostatic voltage is 20 KV and working pressure is 0.25 to 0.4 MPa. The particle size distribution of charged droplets are more concentrated than that of uncharged droplets, the axial velocity of charged droplets is faster than that of uncharged droplets, and the velocity distribution uniformity is also improved. The average deposition rate under charging conditions is 14% higher than that in uncharged conditions. Moreover, the deposit rate of the back of the leaf is evident

CARE: A Large Scale CT Image Dataset and Clinical Applicable Benchmark Model for Rectal Cancer Segmentation

Rectal cancer segmentation of CT image plays a crucial role in timely clinical diagnosis, radiotherapy treatment, and follow-up. Although current segmentation methods have shown promise in delineating cancerous tissues, they still encounter challenges in achieving high segmentation precision. These obstacles arise from the intricate anatomical structures of the rectum and the difficulties in performing differential diagnosis of rectal cancer. Additionally, a major obstacle is the lack of a large-scale, finely annotated CT image dataset for rectal cancer segmentation. To address these issues, this work introduces a novel large scale rectal cancer CT image dataset CARE with pixel-level annotations for both normal and cancerous rectum, which serves as a valuable resource for algorithm research and clinical application development. Moreover, we propose a novel medical cancer lesion segmentation benchmark model named U-SAM. The model is specifically designed to tackle the challenges posed by the intricate anatomical structures of abdominal organs by incorporating prompt information. U-SAM contains three key components: promptable information (e.g., points) to aid in target area localization, a convolution module for capturing low-level lesion details, and skip-connections to preserve and recover spatial information during the encoding-decoding process. To evaluate the effectiveness of U-SAM, we systematically compare its performance with several popular segmentation methods on the CARE dataset. The generalization of the model is further verified on the WORD dataset. Extensive experiments demonstrate that the proposed U-SAM outperforms state-of-the-art methods on these two datasets. These experiments can serve as the baseline for future research and clinical application development.Comment: 8 page

Loss of FKBP5 Affects Neuron Synaptic Plasticity: An Electrophysiology Insight

FKBP5 (FKBP51) is a glucocorticoid receptor (GR) binding protein, which acts as a co-chaperone of heat shock protein 90 (HSP90) and negatively regulates GR. Its association with mental disorders has been identified, but its function in disease development is largely unknown. Long-term potentiation (LTP) is a functional measurement of neuronal connection and communication, and is considered one of the major cellular mechanisms that underlies learning and memory, and is disrupted in many mental diseases. In this study, a reduction in LTP in Fkbp5 knockout (KO) mice was observed when compared to WT mice, which correlated with changes to the glutamatergic and GABAergic signaling pathways. The frequency of mEPSCs was decreased in KO hippocampus, indicating a decrease in excitatory synaptic activity. While no differences were found in levels of glutamate between KO and WT, a reduction was observed in the expression of excitatory glutamate receptors (NMDAR1, NMDAR2B and AMPAR), which initiate and maintain LTP. The expression of the inhibitory neurotransmitter GABA was found to be enhanced in Fkbp5 KO hippocampus. Further investigation suggested that increased expression of GAD65, but not GAD67, accounted for this increase. Additionally, a functional GABAergic alteration was observed in the form of increased mIPSC frequency in the KO hippocampus, indicating an increase in presynaptic GABA release. Our findings uncover a novel role for Fkbp5 in neuronal synaptic plasticity and highlight the value of Fkbp5 KO as a model for studying its role in neurological function and disease development

When graph convolution meets double attention: online privacy disclosure detection with multi-label text classification

With the rise of Web 2.0 platforms such as online social media, people’s private information, such as their location, occupation and even family information, is often inadvertently disclosed through online discussions. Therefore, it is important to detect such unwanted privacy disclosures to help alert people affected and the online platform. In this paper, privacy disclosure detection is modeled as a multi-label text classification (MLTC) problem, and a new privacy disclosure detection model is proposed to construct an MLTC classifier for detecting online privacy disclosures. This classifier takes an online post as the input and outputs multiple labels, each reflecting a possible privacy disclosure. The proposed presentation method combines three different sources of information, the input text itself, the label-to-text correlation and the label-to-label correlation. A double-attention mechanism is used to combine the first two sources of information, and a graph convolutional network is employed to extract the third source of information that is then used to help fuse features extracted from the first two sources of information. Our extensive experimental results, obtained on a public dataset of privacy-disclosing posts on Twitter, demonstrated that our proposed privacy disclosure detection method significantly and consistently outperformed other state-of-the-art methods in terms of all key performance indicators