Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network

Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge bases, and then auxiliary diagnosis is done by rule matching. In this study, we present a clinical intelligent decision approach based on Convolutional Neural Networks(CNN), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. We use collected 18,590 copies of the real-world clinical electronic medical records to train and test the proposed model. Experimental results show that the proposed model can achieve 98.67\% accuracy and 96.02\% recall, which strongly supports that using convolutional neural network to automatically learn high-level semantic features of electronic medical records and then conduct assist diagnosis is feasible and effective.Comment: 9 pages, 4 figures, Accepted by Scientific Report

Towards Fully Decoupled End-to-End Person Search

End-to-end person search aims to jointly detect and re-identify a target person in raw scene images with a unified model. The detection task unifies all persons while the re-id task discriminates different identities, resulting in conflict optimal objectives. Existing works proposed to decouple end-to-end person search to alleviate such conflict. Yet these methods are still sub-optimal on one or two of the sub-tasks due to their partially decoupled models, which limits the overall person search performance. In this paper, we propose to fully decouple person search towards optimal person search. A task-incremental person search network is proposed to incrementally construct an end-to-end model for the detection and re-id sub-task, which decouples the model architecture for the two sub-tasks. The proposed task-incremental network allows task-incremental training for the two conflicting tasks. This enables independent learning for different objectives thus fully decoupled the model for persons earch. Comprehensive experimental evaluations demonstrate the effectiveness of the proposed fully decoupled models for end-to-end person search.Comment: DICTA 202

Multi-Sem Fusion: Multimodal Semantic Fusion for 3D Object Detection

LiDAR-based 3D Object detectors have achieved impressive performances in many benchmarks, however, multisensors fusion-based techniques are promising to further improve the results. PointPainting, as a recently proposed framework, can add the semantic information from the 2D image into the 3D LiDAR point by the painting operation to boost the detection performance. However, due to the limited resolution of 2D feature maps, severe boundary-blurring effect happens during re-projection of 2D semantic segmentation into the 3D point clouds. To well handle this limitation, a general multimodal fusion framework MSF has been proposed to fuse the semantic information from both the 2D image and 3D points scene parsing results. Specifically, MSF includes three main modules. First, SOTA off-the-shelf 2D/3D semantic segmentation approaches are employed to generate the parsing results for 2D images and 3D point clouds. The 2D semantic information is further re-projected into the 3D point clouds with calibrated parameters. To handle the misalignment between the 2D and 3D parsing results, an AAF module is proposed to fuse them by learning an adaptive fusion score. Then the point cloud with the fused semantic label is sent to the following 3D object detectors. Furthermore, we propose a DFF module to aggregate deep features in different levels to boost the final detection performance. The effectiveness of the framework has been verified on two public large-scale 3D object detection benchmarks by comparing with different baselines. The experimental results show that the proposed fusion strategies can significantly improve the detection performance compared to the methods using only point clouds and the methods using only 2D semantic information. Most importantly, the proposed approach significantly outperforms other approaches and sets new SOTA results on the nuScenes testing benchmark.Comment: Submitted to T-ITS Journa

Fast Bounded Online Gradient Descent Algorithms for Scalable Kernel-Based Online Learning

Kernel-based online learning has often shown state-of-the-art performance for many online learning tasks. It, however, suffers from a major shortcoming, that is, the unbounded number of support vectors, making it non-scalable and unsuitable for applications with large-scale datasets. In this work, we study the problem of bounded kernel-based online learning that aims to constrain the number of support vectors by a predefined budget. Although several algorithms have been proposed in literature, they are neither computationally efficient due to their intensive budget maintenance strategy nor effective due to the use of simple Perceptron algorithm. To overcome these limitations, we propose a framework for bounded kernel-based online learning based on an online gradient descent approach. We propose two efficient algorithms of bounded online gradient descent (BOGD) for scalable kernel-based online learning: (i) BOGD by maintaining support vectors using uniform sampling, and (ii) BOGD++ by maintaining support vectors using non-uniform sampling. We present theoretical analysis of regret bound for both algorithms, and found promising empirical performance in terms of both efficacy and efficiency by comparing them to several well-known algorithms for bounded kernel-based online learning on large-scale datasets.Comment: ICML201

Design and Implementation of Novel Fractional-Order Controllers for Stabilized Platforms

As a servo system to isolate disturbance or track trajectory, stabilized platform requires high-quality control. However, conventional PID control fails to meet that requirement.In this paper, a new controller design scheme is proposed for stabilized platform based on fractional calculus. The designed controller is called fractional-order PID (FOPID) controller, which has two extra parameters compared to conventional PID controller. On one hand, it enables people have more degrees of freedom to design FOPID controller, on the other hand, its differential order and integral order provides more flexibility to tune the controller performance. Therefore, a design method of FOPID controller based on dynamic software modeling is presented. To obtain the idea controller’s parameters, the particle swarm optimization (PSO) bionic algorithm is used to optimize an objective function.In addition, software simulation platform and hardware experiment platform are built to design and test the FOPID controller. Finally, simulations and experimental results are included to show the effectiveness of the new control method

CA1-projecting subiculum neurons facilitate object-place learning.

Recent anatomical evidence suggests a functionally significant back-projection pathway from the subiculum to the CA1. Here we show that the afferent circuitry of CA1-projecting subicular neurons is biased by inputs from CA1 inhibitory neurons and the visual cortex, but lacks input from the entorhinal cortex. Efferents of the CA1-projecting subiculum neurons also target the perirhinal cortex, an area strongly implicated in object-place learning. We identify a critical role for CA1-projecting subicular neurons in object-location learning and memory, and show that this projection modulates place-specific activity of CA1 neurons and their responses to displaced objects. Together, these experiments reveal a novel pathway by which cortical inputs, particularly those from the visual cortex, reach the hippocampal output region CA1. Our findings also implicate this circuitry in the formation of complex spatial representations and learning of object-place associations

A Novel Dual-Shorting Point PIFA (GSM850 to IMT-A) for Mobile Handsets

A novel planar inverted-F antenna (PIFA) with dual-shorting points is proposed for multiband mobile handsets. The antenna comprises a meandered strip, a feeding point, two shorting points, and a slotted ground plane. For bandwidth enhancement of DCS/PCS/UMTS/WLAN 11.b/LTE2300/2500 and IMT-Advanced (International Mobile Telecommunications-Advanced), the antenna applies a dual-shorting points design, which generates a multimode between 1707 and 2815 MHz. The proposed antenna has good impedance matching characteristics for GSM (824–960 MHz)/DCS (1710–1880 MHz)/PCS (1850–1990 MHz)/UMTS (1920–2170 MHz)/LTE (2300–2400 MHz, 2500–2690 MHz)/WLAN 11.b (2400–2480 MHz) and IMT-A (4200–4800 MHz). The measured radiation efficiencies of the proposed antenna were all higher than 60% in GSM850/900, DCS/PCS, UMTS, LTE2300/2500, and WLAN 802.11 b, and it is up to 86% in IMT-A

A cross-sectional and longitudinal study of how two intervention methods affect the anxiety, sleep quality, and physical activity of junior high school students under quarantine

PurposeThis study investigated levels of anxiety and sleep quality and their association with physical activity in junior high school students under quarantine during the COVID-19 pandemic. It also tests the effectiveness of physical activity and psychological nursing interventions in alleviating anxiety ‘and improving sleep quality.MethodsIn July 2021, 14,000 home-quarantined junior high school students in Yangzhou City (China) were selected by random cluster sampling to complete an online survey. We then selected 95 junior high school students for an 8-week longitudinal experiment exploring whether the two types of intervention made positive contributions to students' anxiety, sleep quality, and physical activity.ResultsThe cross-sectional study revealed that physical activity was significantly related to anxiety and sleep quality. In the longitudinal study, students who underwent the exercise intervention or the psychological nursing intervention experienced significant improvement in their anxiety levels. The exercise intervention also promoted improved sleep quality. Overall, the exercise intervention was more effective than the psychological nursing intervention in reducing levels of anxiety and sleep disorders.ConclusionDuring the epidemic period, junior high school students should be encouraged to spend more time engaging in physical activity, and their sleep quality and anxiety shouldbe focused on

Arsenic-induced changes in the gene expression of lung epithelial L2 cells: implications in carcinogenesis

Background: Arsenic is a carcinogen that is known to induce cell transformation and tumor formation. Although studies have been performed to examine the modulation of signaling molecules caused by arsenic exposure, the molecular mechanisms by which arsenic causes cancer are still unclear. We hypothesized that arsenic alters gene expression leading to carcinogenesis in the lung.Results: In this study, we examined global gene expression in response to 0.75 uM arsenic treatment for 1 - 7 days in a rat lung epithelial cell line (L2) using an in-house 10 k rat DNA microarray. One hundred thirty one genes were identified using the one-class statistical analysis of microarray (SAM) test. Of them, 33 genes had a fold change of >/= 2 between at least two time points. These genes were then clustered into 5 groups using K-means cluster analysis based on their expression patterns. Seven selected genes, all associated with cancer, were confirmed by real-time PCR. These genes have functions directly or indirectly related to metabolism, glycolysis, cell proliferation and differentiation, and regulation of transcription.Conclusion: Our findings provide important insight for the future studies of arsenic-mediated lung cancer.Peer reviewedPhysiological Science

Effect of Li-deficiency impurities on the electron-overdoped LiFeAs superconductor

We use transport, inelastic neutron scattering, and angle resolved photoemission experiments to demonstrate that the stoichiometric LiFeAs is an intrinsically electron-overdoped superconductor similar to those of the electron-overdoped NaFe1-xTxAs and BaFe2-xTxAs2 (T = Co,Ni). Furthermore, we show that although transport properties of the stoichiometric superconducting LiFeAs and Li-deficient nonsuperconducting Li1-xFeAs are different, their electronic and magnetic properties are rather similar. Therefore, the nonsuperconducting Li1-xFeAs is also in the electron overdoped regime, where small Li deficiencies near the FeAs octahedra can dramatically suppress superconductivity through the impurity scattering effect.Comment: 5 figures,5 page