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关键词： Deep learning   Hyperspectral images   Limited labeled samples   Locality preserving convolutional network   HYPERSPECTRAL CLASSIFICATION   SELECTION  

摘要： One major challenge of designing deep learning systems for hyperspectral data classification is the lack of labeled training samples. Inspired by recent manifold learning researches, this paper presents a novel Locality Preserving Convolutional Network to address this challenge. The proposed method invents a semi-supervised locality-preserving regularization operation, and inserts a new layer in the three-dimensional convolutional neural network for end-to-end spatial-spectral classification. The benefits are three-fold. First, by using unlabeled training samples which are more easily available, the proposed method reduces the number of labeled samples required for training a deep learning model; Second, the proposed method incorporates the intrinsic geographical correlation among nearby samples into the extracted features, which prevents it from losing accuracy when only limited labeled samples are available; Third, with a three-dimensional architecture, the proposed method can extract the spatial and spectral features simultaneously from the hyperspectral data for classification. A gradient-decent based approach is deployed to train the whole network in a unified way. Experiments over different benchmarks show that, the proposed method relieves the Hughes phenomenon for deep learning, and achieves competitively high classification accuracy compared to other state-of-the-art approaches. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Stroke   Classification   Aetiology   S-C-O   CAUSATIVE CLASSIFICATION   ISCHEMIC-STROKE   CLINICAL-TRIAL   PHASE-III   INTERRATER RELIABILITY   SUBTYPE CLASSIFICATION   OUTCOME MEASURES   BARTHEL INDEX   AGREEMENT  

摘要： BackgroundInter-observer variability in stroke aetiological classification may have an effect on trial power and estimation of treatment effect. We modelled the effect of misclassification on required sample size in a hypothetical cardioembolic (CE) stroke *** performed a systematic review to quantify the reliability (inter-observer variability) of various stroke aetiological classification systems. We then modelled the effect of this misclassification in a hypothetical trial of anticoagulant in CE stroke contaminated by patients with non-cardioembolic (non-CE) stroke aetiology. Rates of misclassification were based on the summary reliability estimates from our systematic review. We randomly sampled data from previous acute trials in CE and non-CE participants, using the Virtual International Stroke Trials Archive. We used bootstrapping to model the effect of varying misclassification rates on sample size required to detect a between-group treatment effect across 5000 permutations. We described outcomes in terms of survival and stroke recurrence censored at *** 4655 titles, we found 14 articles describing three stroke classification systems. The inter-observer reliability of the classification systems varied from fair' to very good' and suggested misclassification rates of 5% and 20% for our modelling. The hypothetical trial, with 80% power and alpha 0.05, was able to show a difference in survival between anticoagulant and antiplatelet in CE with a sample size of 198 in both trial arms. Contamination of both arms with 5% misclassified participants inflated the required sample size to 237 and with 20% misclassification inflated the required sample size to 352, for equivalent trial power. For an outcome of stroke recurrence using the same data, base-case estimated sample size for 80% power and alpha 0.05 was n=502 in each arm, increasing to 605 at 5% contamination and 973 at 20% *** aetiological classification sys

关键词： Small-sample image classification   Few-shot   Ensemble learning   Deep convolutional neural network   SCENE  

摘要： Small-sample classification is a challenging problem in computer vision. In this work, we show how to efficiently and effectively utilize semantic information of the annotations to improve the performance of small-sample classification. First, we propose an image-text dual neural network to improve the classification performance on small-sample datasets. The proposed model consists of two sub-models, an image classification model and a text classification model. After training the sub-models separately, we design a novel method to fuse the two sub-models rather than simply combine their results. Our image-text dual neural network aims to utilize the text information to overcome the training problem of deep models on small-sample datasets. Then, we propose to incorporate a decision strategy into the image-text dual neural network to further improve the performance of our original model on few-shot datasets. To demonstrate the effectiveness of the proposed models, we conduct experiments on the LabelMe and UIUC-Sports datasets. Experimental results show that our method is superior to other models. (C) 2018 Elsevier B.V. All rights reserved.

关键词： HUMAN BLOOD-PLASMA   NMR-SPECTRA   QUANTIFICATION   DECONVOLUTION   STRENGTHS  

摘要： Nuclear magnetic resonance (NMR) spectroscopy is a principal analytical technique in metabolomics. Extracting metabolic information from NMR spectra is complex due to the fact that an immense amount of detail on the chemical composition of a biological sample is expressed through a single spectrum. The simplest approach to quantify the signal is through spectral binning which involves subdividing the spectra into regions along the chemical shift axis and integrating the peaks within each region. However, due to overlapping resonance signals, the integration values do not always correspond to the concentrations of specific metabolites. An alternate, more advanced statistical approach is spectral deconvolution. BATMAN (Bayesian AuTomated Metabolite Analyser for NMR data) performs spectral deconvolution using prior information on the spectral signatures of metabolites. In this way, BATMAN estimates relative metabolic concentrations. In this study, both spectral binning and spectral deconvolution using BATMAN were applied to 400 MHz and 900 MHz NMR spectra of blood plasma samples from lung cancer patients and control subjects. The relative concentrations estimated by BATMAN were compared with the binning integration values in terms of their ability to discriminate between lung cancer patients and controls. For the 400 MHz data, the spectral binning approach provided greater discriminatory power. However, for the 900 MHz data, the relative metabolic concentrations obtained by using BATMAN provided greater predictive power. While spectral binning is computationally advantageous and less laborious, complementary models developed using BATMAN-estimated features can add complementary information regarding the biological interpretation of the data and therefore are clinically useful.

关键词： 化工分析   化工检验   化工生产  

摘要： 在我国经济不断快速提升的大环境下,当前我国人民对于化工生产的商品需求量越来越多,对商品的质量要求也越来越高,而保证化工生产商品质量的重要条件是能够在化工生产中进行化工分析和化工检验两项内容。只有保证这两项内容能够顺利进行,才能有效提高化工生产商品质量以及生产技术水平。所以在化工生产中必须要进行化学分析和化学检验,本文通过研究我国现阶段化工生产分析与检验工作中存在的主要问题,并提出合理的解决策略,从而提高我国化工分析与化工检验工作效率保证化工产品质量。

关键词： 合同形式   价值功能   形式自由的限定  

摘要： 以比较法视角,对于合同形式立法沿革进行梳理,结合我国立法现状,得出合适的立法模式:在坚持意思自治和合同形式自由的原则的基础上,对于部分类型的合同形式进行一定的限制。