Laser-induced breakdown spectroscopy （LIBS） has become a powerful technology in geological applications. The correct identification of rocks and soils is critical to many geological projects. In this study, LIBS dat...Laser-induced breakdown spectroscopy （LIBS） has become a powerful technology in geological applications. The correct identification of rocks and soils is critical to many geological projects. In this study, LIBS database software with a user-friendly and intuitive interface is developed based on Windows, consisting of a database module and a sample identification module. The database module includes a basic database containing LIBS persistent lines for elements and a dedicated geological database containing LIBS emission lines for several rock and soil reference standards. The module allows easy use of the data. A sample identification module based on partial least squares discriminant analysis （PLS-DA） or support vector machine （SVM） algorithms enables users to classify groups of unknown spectra. The developed system was used to classify rock and soil data sets in a dedicated database and the results demonstrate that the system is capable of fast and accurate classification of rocks and soils, and is thus useful for the detection of geological materials.展开更多
Speckle effects on classification results can be sup- pressed to some extent by introducing the contextual information. An unsupervised classification algorithm is proposed for polarimetric synthetic aperture radar （...Speckle effects on classification results can be sup- pressed to some extent by introducing the contextual information. An unsupervised classification algorithm is proposed for polarimetric synthetic aperture radar （POLSAR） images based on the mean shift （MS） segmentation and Markov random field （MRF）. First, polarimetdc features are exacted by target decomposition for MS segmentation. An initial classification is executed by using the target decomposition and the agglomerative hierarchical clus- tering algorithm. Thereafter, a classification step based on MRF is performed by using the mean coherence matrices obtained for each segment. Under the MRF framework, the smoothness term is defined according to the distance between neighboring areas. By using POLSAR images acquired by the German Aerospace Centre and National Aeronautics and Space Administration/Jet Propulsion Laboratory, the experimental results confirm that the proposed method has higher accuracy and better regional connectivity than other classification methods.展开更多
The difference between stages I and ]]I of gastric gas- trointestinal stromal tumor depends principally on the number of mitosis. According with TNM classification, the presence in the tumor of high mitotic rate deter...The difference between stages I and ]]I of gastric gas- trointestinal stromal tumor depends principally on the number of mitosis. According with TNM classification, the presence in the tumor of high mitotic rate deter- mines the upgrading. Many studies exposed different count techniques in evaluating the number of mitosis. An international standardized method to assess mitotic rate is needed.展开更多
With recent breakthroughs in camera and image processing technologies single-particle electron cryo-microscopy （CryoEM） has suddenly gained the attention of structural biologists as a powerful tool able to solve the...With recent breakthroughs in camera and image processing technologies single-particle electron cryo-microscopy （CryoEM） has suddenly gained the attention of structural biologists as a powerful tool able to solve the atomic structures of biological complexes and assemblies. Compared with x-ray crystallography, CryoEM can be applied to partially flexible structures in solution and without the necessity of crystallization, which is especially important for large complexes and assemblies. This review briefly explains several key bottlenecks for atomic resolution CryoEM, and describes the corre- sponding solutions for these bottlenecks based on the recent technical advancements. The review also aims to provide an overview about the technical differences between its applications in biology and those in material science.展开更多
In recent decades, the problem of drying out of conifers has become a subject of significant importance due to the widespread mortality of trees caused by stem pest’s damage. Early detection of areas affected by inse...In recent decades, the problem of drying out of conifers has become a subject of significant importance due to the widespread mortality of trees caused by stem pest’s damage. Early detection of areas affected by insect outbreaks is of great relevance for preventing the further spread of pests. Forests of Belarus are largely affected by conifers dieback caused by the bark beetle. The aim of the study was to identify drying out conifers using a TripleSat satellite multispectral image of a woodland area in Belarus based on preliminary airborne measurements. Spectrometers operating in a spectral range of 400</span><span style="font-family:""> </span><span style="font-family:Verdana;">-</span><span style="font-family:""> </span><span style="font-family:Verdana;">900 nm were used in airborne measurements, resulting in distinguishing various drying out stages with an accuracy of 27</span><span style="font-family:Verdana;">%</span><span style="font-family:Verdana;"> - 74% for aerial data. In this study, a supervised classification of the TripleSat image based on the method of linear discriminant analysis (LDA) was performed. The input data for LDA algorithm is a set of remote sensing vegetation indices. Results of the study demonstrate that about 90% of the test site is at the green-attack stage that is confirmed by ground surveys of this area.展开更多
Emoticons have been widely employed to express different types of moods, emotions, and feelings in microblog environments. They are therefore regarded as one of the most important signals for microblog sentiment analy...Emoticons have been widely employed to express different types of moods, emotions, and feelings in microblog environments. They are therefore regarded as one of the most important signals for microblog sentiment analysis. Most existing studies use several emoticons that convey clear emotional meanings as noisy sentiment labels or similar sentiment indicators. However, in practical microblog environments, tens or even hundreds of emoticons are frequently adopted and all emoticons have their own unique emotional meanings. Besides, a considerable number of emoticons do not have clear emotional meanings. An improved sentiment analysis model should not overlook these phenomena. Instead of manually assigning sentiment labels to several emoticons that convey relatively clear meanings, we propose the emoticon space model （ESM） that leverages more emotieons to construct word representations from a massive amount of unlabeled data. By projecting words and microblog posts into an emoticon space, the proposed model helps identify subjectivity, polarity, and emotion in microblog environments. The experimental results for a public microblog benchmark corpus （NLP＆CC 2013） indicate that ESM effectively leverages emoticon signals best runs. and outperforms previous state-of-the-art strategies and benchmark展开更多
Time series classification is related to many dif- ferent domains, such as health informatics, finance, and bioinformatics. Due to its broad applications, researchers have developed many algorithms for this kind of ta...Time series classification is related to many dif- ferent domains, such as health informatics, finance, and bioinformatics. Due to its broad applications, researchers have developed many algorithms for this kind of tasks, e.g., multivariate time series classification. Among the classifi- cation algorithms, k-nearest neighbor （k-NN） classification （particularly 1-NN） combined with dynamic time warping （DTW） achieves the state of the art performance. The defi- ciency is that when the data set grows large, the time con- sumption of 1-NN with DTW will be very expensive. In con- trast to 1-NN with DTW, it is more efficient but less ef- fective for feature-based classification methods since their performance usually depends on the quality of hand-crafted features. In this paper, we aim to improve the performance of traditional feature-based approaches through the feature learning techniques. Specifically, we propose a novel deep learning framework, multi-channels deep convolutional neu- ral networks （MC-DCNN）, for multivariate time series classi- fication. This model first learns features from individual uni- variate time series in each channel, and combines information from all channels as feature representation at the final layer. Then, the learnt features are applied into a multilayer percep- tron （MLP） for classification. Finally, the extensive experi- ments on real-world data sets show that our model is not only more efficient than the state of the art but also competitive in accuracy. This study implies that feature learning is worth to be investigated for the problem of time series classification.展开更多
For many supervised learning applications, addi- tional information, besides the labels, is often available dur- ing training, but not available during testing. Such additional information, referred to the privileged ...For many supervised learning applications, addi- tional information, besides the labels, is often available dur- ing training, but not available during testing. Such additional information, referred to the privileged information, can be exploited during training to construct a better classifier. In this paper, we propose a Bayesian network （BN） approach for learning with privileged information. We propose to in- corporate the privileged information through a three-node BN. We further mathematically evaluate different topologies of the three-node BN and identify those structures, through which the privileged information can benefit the classifica- tion. Experimental results on handwritten digit recognition, spontaneous versus posed expression recognition, and gender recognition demonstrate the effectiveness of our approach.展开更多
基金supported by National Major Scientific Instruments and Equipment Development Special Funds,China(No.2011YQ030113)
文摘Laser-induced breakdown spectroscopy （LIBS） has become a powerful technology in geological applications. The correct identification of rocks and soils is critical to many geological projects. In this study, LIBS database software with a user-friendly and intuitive interface is developed based on Windows, consisting of a database module and a sample identification module. The database module includes a basic database containing LIBS persistent lines for elements and a dedicated geological database containing LIBS emission lines for several rock and soil reference standards. The module allows easy use of the data. A sample identification module based on partial least squares discriminant analysis （PLS-DA） or support vector machine （SVM） algorithms enables users to classify groups of unknown spectra. The developed system was used to classify rock and soil data sets in a dedicated database and the results demonstrate that the system is capable of fast and accurate classification of rocks and soils, and is thus useful for the detection of geological materials.
基金supported by the National Natural Science Foundation of China(6100118741001256+1 种基金40971219)the National High Technology Research and Development Program of China(863 Program)(2013 AA122301)
文摘Speckle effects on classification results can be sup- pressed to some extent by introducing the contextual information. An unsupervised classification algorithm is proposed for polarimetric synthetic aperture radar （POLSAR） images based on the mean shift （MS） segmentation and Markov random field （MRF）. First, polarimetdc features are exacted by target decomposition for MS segmentation. An initial classification is executed by using the target decomposition and the agglomerative hierarchical clus- tering algorithm. Thereafter, a classification step based on MRF is performed by using the mean coherence matrices obtained for each segment. Under the MRF framework, the smoothness term is defined according to the distance between neighboring areas. By using POLSAR images acquired by the German Aerospace Centre and National Aeronautics and Space Administration/Jet Propulsion Laboratory, the experimental results confirm that the proposed method has higher accuracy and better regional connectivity than other classification methods.
文摘The difference between stages I and ]]I of gastric gas- trointestinal stromal tumor depends principally on the number of mitosis. According with TNM classification, the presence in the tumor of high mitotic rate deter- mines the upgrading. Many studies exposed different count techniques in evaluating the number of mitosis. An international standardized method to assess mitotic rate is needed.
基金supported by Tsinghua–Peking Joint Center for Life Sciences,China
文摘With recent breakthroughs in camera and image processing technologies single-particle electron cryo-microscopy （CryoEM） has suddenly gained the attention of structural biologists as a powerful tool able to solve the atomic structures of biological complexes and assemblies. Compared with x-ray crystallography, CryoEM can be applied to partially flexible structures in solution and without the necessity of crystallization, which is especially important for large complexes and assemblies. This review briefly explains several key bottlenecks for atomic resolution CryoEM, and describes the corre- sponding solutions for these bottlenecks based on the recent technical advancements. The review also aims to provide an overview about the technical differences between its applications in biology and those in material science.
文摘In recent decades, the problem of drying out of conifers has become a subject of significant importance due to the widespread mortality of trees caused by stem pest’s damage. Early detection of areas affected by insect outbreaks is of great relevance for preventing the further spread of pests. Forests of Belarus are largely affected by conifers dieback caused by the bark beetle. The aim of the study was to identify drying out conifers using a TripleSat satellite multispectral image of a woodland area in Belarus based on preliminary airborne measurements. Spectrometers operating in a spectral range of 400</span><span style="font-family:""> </span><span style="font-family:Verdana;">-</span><span style="font-family:""> </span><span style="font-family:Verdana;">900 nm were used in airborne measurements, resulting in distinguishing various drying out stages with an accuracy of 27</span><span style="font-family:Verdana;">%</span><span style="font-family:Verdana;"> - 74% for aerial data. In this study, a supervised classification of the TripleSat image based on the method of linear discriminant analysis (LDA) was performed. The input data for LDA algorithm is a set of remote sensing vegetation indices. Results of the study demonstrate that about 90% of the test site is at the green-attack stage that is confirmed by ground surveys of this area.
基金Tsinghua-Samsung Joint Laboratory, the National Basic Research 973 Program of China under Grant No. 2015CB358700, and the National Natural Science Foundation of China under Grant Nos. 61472206, 61073071, and 61303075.
文摘Emoticons have been widely employed to express different types of moods, emotions, and feelings in microblog environments. They are therefore regarded as one of the most important signals for microblog sentiment analysis. Most existing studies use several emoticons that convey clear emotional meanings as noisy sentiment labels or similar sentiment indicators. However, in practical microblog environments, tens or even hundreds of emoticons are frequently adopted and all emoticons have their own unique emotional meanings. Besides, a considerable number of emoticons do not have clear emotional meanings. An improved sentiment analysis model should not overlook these phenomena. Instead of manually assigning sentiment labels to several emoticons that convey relatively clear meanings, we propose the emoticon space model （ESM） that leverages more emotieons to construct word representations from a massive amount of unlabeled data. By projecting words and microblog posts into an emoticon space, the proposed model helps identify subjectivity, polarity, and emotion in microblog environments. The experimental results for a public microblog benchmark corpus （NLP＆CC 2013） indicate that ESM effectively leverages emoticon signals best runs. and outperforms previous state-of-the-art strategies and benchmark
文摘Time series classification is related to many dif- ferent domains, such as health informatics, finance, and bioinformatics. Due to its broad applications, researchers have developed many algorithms for this kind of tasks, e.g., multivariate time series classification. Among the classifi- cation algorithms, k-nearest neighbor （k-NN） classification （particularly 1-NN） combined with dynamic time warping （DTW） achieves the state of the art performance. The defi- ciency is that when the data set grows large, the time con- sumption of 1-NN with DTW will be very expensive. In con- trast to 1-NN with DTW, it is more efficient but less ef- fective for feature-based classification methods since their performance usually depends on the quality of hand-crafted features. In this paper, we aim to improve the performance of traditional feature-based approaches through the feature learning techniques. Specifically, we propose a novel deep learning framework, multi-channels deep convolutional neu- ral networks （MC-DCNN）, for multivariate time series classi- fication. This model first learns features from individual uni- variate time series in each channel, and combines information from all channels as feature representation at the final layer. Then, the learnt features are applied into a multilayer percep- tron （MLP） for classification. Finally, the extensive experi- ments on real-world data sets show that our model is not only more efficient than the state of the art but also competitive in accuracy. This study implies that feature learning is worth to be investigated for the problem of time series classification.
基金This paper was supported by the National Natural Sci- ence Foundation of China （Gront Nos. 61175037, 6122831M）, US Natural Science Foundation CNS-1205664, Special Innovation Project on Speech of Anhui Province （11010202192）, Project from Anhui Science and Technol- ogy Agency （1106c0805008） and Youth Creative Project of University of Science and Technology of China.
文摘For many supervised learning applications, addi- tional information, besides the labels, is often available dur- ing training, but not available during testing. Such additional information, referred to the privileged information, can be exploited during training to construct a better classifier. In this paper, we propose a Bayesian network （BN） approach for learning with privileged information. We propose to in- corporate the privileged information through a three-node BN. We further mathematically evaluate different topologies of the three-node BN and identify those structures, through which the privileged information can benefit the classifica- tion. Experimental results on handwritten digit recognition, spontaneous versus posed expression recognition, and gender recognition demonstrate the effectiveness of our approach.
