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Local linear spatial quantile regression

Marc Hallin, Zudi Lu, Keming Yu
2009 Bernoulli  
Let {(Y i , X i ), i ∈ Z N } be a stationary real-valued (d + 1)-dimensional spatial processes. Denote by x → q p (x), p ∈ (0, 1), x ∈ R d , the spatial quantile regression function of order p, characterized by P{Y i ≤ q p (x)|X i = x} = p. Assume that the process has been observed over an N-dimensional rectangular domain of the form I n : We propose a local linear estimator of q p . That estimator extends to random fields with unspecified and possibly highly complex spatial dependence
more » ... , the quantile regression methods considered in the context of independent samples or time series. Under mild regularity assumptions, we obtain a Bahadur representation for the estimators of q p and its first-order derivatives, from which we establish consistency and asymptotic normality. The spatial process is assumed to satisfy general mixing conditions, generalizing classical time series mixing concepts. The size of the rectangular domain I n is allowed to tend to infinity at different rates depending on the direction in Z N (non-isotropic asymptotics). The method provides much richer information than the mean regression approach considered in most spatial modelling techniques.
doi:10.3150/08-bej168 fatcat:26xgconfrvbtlkbm3ctmtlkyuq

Quantile regression: applications and current research areas

Keming Yu, Zudi Lu, Julian Stander
2003 Statistician (London. Print)  
Quantile regression offers a more complete statistical model than mean regression and now has widespread applications. Consequently, we provide a review of this technique. We begin with an introduction to and motivation for quantile regression. We then discuss some typical application areas. Next we outline various approaches to estimation. We finish by briefly summarizing some recent research areas.
doi:10.1111/1467-9884.00363 fatcat:fwjns3eqprea3g7l5mvsqj5qcy

Nondestructive Classification and Recognition of Litchi Varieties Using Bionic Electronic Nose

Sai Xu, Huazhong Lu, Enli Lu, Keming Hou
2016 Advance Journal of Food Science and Technology  
In order to apply the bionic electronic nose in classifying the litchi into different classes, there were five different litchi varieties tested by the proposed methods in this study. Firstly, Physical differences of the 5 litchi varieties were compared in this study. Secondly, the response curves from the electronic nose (PEN3) were recorded for all the samples of the five litchi varieties. Variance Analysis (VA) was used for best characteristic value selection. Finally, via different pattern
more » ... ecognition techniques, including the Principal Component Analysis (PCA), the Linear Discrimination Analysis (LDA), the Probabilistic Neural Network (PNN), the Support Vector Machine (SVM) and the loading analysis (Loadings), it is found that PCA and LDA have a poor performance in classifying litchi varieties. The classification accuracy of the PNN model with training set and test set were 100 and 84%, respectively. As to the SVM model, the classification accuracy of training set and test set were 100 and 92%, respectively. According to the Loadings results, the sensors R3, R5, R8 and R1 can be chosen for developing special and simple instruments for the detection of litchi volatiles. The test results has demonstrated the feasibility and effectiveness of using bionic electronic nose for discriminating and classifying litchi varieties, which provides a new method for rapid and nondestructive classification of litchi varieties.
doi:10.19026/ajfst.12.2970 fatcat:lq7mpxj4vzblbcngukrurb33ii

High-throughput relation extraction algorithm development associating knowledge articles and electronic health records [article]

Yucong Lin, Keming Lu, Yulin Chen, Chuan Hong, Sheng Yu
2020 arXiv   pre-print
Objective: Medical relations are the core components of medical knowledge graphs that are needed for healthcare artificial intelligence. However, the requirement of expert annotation by conventional algorithm development processes creates a major bottleneck for mining new relations. In this paper, we present Hi-RES, a framework for high-throughput relation extraction algorithm development. We also show that combining knowledge articles with electronic health records (EHRs) significantly
more » ... s the classification accuracy. Methods: We use relation triplets obtained from structured databases and semistructured webpages to label sentences from target corpora as positive training samples. Two methods are also provided for creating improved negative samples by combining positive samples with na\"ive negative samples. We propose a common model that summarizes sentence information using large-scale pretrained language models and multi-instance attention, which then joins with the concept embeddings trained from the EHRs for relation prediction. Results: We apply the Hi-RES framework to develop classification algorithms for disorder-disorder relations and disorder-location relations. Millions of sentences are created as training data. Using pretrained language models and EHR-based embeddings individually provides considerable accuracy increases over those of previous models. Joining them together further tremendously increases the accuracy to 0.947 and 0.998 for the two sets of relations, respectively, which are 10-17 percentage points higher than those of previous models. Conclusion: Hi-RES is an efficient framework for achieving high-throughput and accurate relation extraction algorithm development.
arXiv:2009.03506v1 fatcat:xwvlyzdj4na4vowenevodmsc4m

Thermal Stability and Properties of Deformation-Processed Cu-Fe In Situ Composites

Keming Liu, Zhengyi Jiang, Jingwei Zhao, Jin Zou, Lei Lu, Deping Lu
2015 Metallurgical and Materials Transactions. A  
Thermal Stability and Properties of Deformation-Processed Cu-Fe In Situ Composites KEMING LIU, ZHENGYI JIANG, JINGWEI ZHAO, JIN ZOU, LEI LU, and DEPING LU This paper investigated the thermal stability,  ... 
doi:10.1007/s11661-015-2791-x fatcat:tbfcl3inrnaeffqrvvnse2vk4i

Immunophenotypes Based on the Tumor Immune Microenvironment Allow for Unsupervised Penile Cancer Patient Stratification

Chengbiao Chu, Kai Yao, Jiangli Lu, Yijun Zhang, Keming Chen, Jiabin Lu, Chris Zhiyi Zhang, Yun Cao
2020 Cancers  
All the results were evaluated and checked by two pathologists (Chu CB and Lu JL) who were blinded to the patients' clinical data.  ... 
doi:10.3390/cancers12071796 pmid:32635549 fatcat:k2wbzkfgtbadvf4w57azr4rduy

An Appraisal of Lung Nodules Automatic Classification Algorithms for CT Images

Xinqi Wang, Keming Mao, Lizhe Wang, Peiyi Yang, Duo Lu, Ping He
2019 Sensors  
Dey and Lu [124] achieved an accuracy of 90.40%. The deep features can be combined with traditional features to obtain better representation of lung nodules.  ... 
doi:10.3390/s19010194 fatcat:l22h2tvy6fhkxmjidrvci3dq3i

Semi-supervised change detection via Gaussian processes

Keming Chen, Chunlei Huo, Zhixin Zhou, Hanqing Lu, Jian Cheng
2009 2009 IEEE International Geoscience and Remote Sensing Symposium  
Change detection is one of the most important applications of the remote sensing technology. Compared with the supervised change detection approaches and the unsupervised ones, semi-supervised change detection methods which are more feasible at the operating level have received a great attention from the remote sensing community in recent years [1, 2] . The semi-supervised change detection method is based on a more realistic assumption that ground-truth information is available for at least a
more » ... rtion of the detected images. It involves a learning process which can take profit from the abundant cheep unlabeled data. Recently, kernel-based methods, such as inductive support vector machines (ISVMs) and transductive support vector machines (TSVMs), have been applied promisingly to remote sensing image classification and change detection [1, 2] . Differing from the ISVMs, TSVMs introduces test data in training process and considers minimizing errors produced by test dataset instead of only minimizing classification errors of training data. [1] showed substantial improvement obtained with the TSVMs over the ISVMs, especially for ill-posed classification problems caused by the limited quantity and quality of the training samples. Gaussian process (GP) classifier which was originally developed for regression is another interesting kernel-based classification approach. By contrast to SVM classifiers, GP classifiers have not yet been investigated in the context of remote sensing. Similar to the spirit of the TSVMs that the label errors occur near the decision boundary, Lawrence and Jordan [3] proposed a Gaussian process approach, which can be viewed as a Bayesian counterpart to the TSVMs. This approach involves a novel transductive learning under a probabilistic framework to learn a GP classifier. The main difference of this approach from the standard GP is that it introduces a null category region which is equivalent to the traditional notion "margin" in TSVMs and thus the overall noise model is termed a null category noise model (NCNM). Unlike the traditional machine learning classification approaches, the NCNM maps a latent process variable f into three categories instead of traditionally two categories. The idea behind this approach is to use the unlabeled data to steer the decision boundary to the region of low data density. Specifically, they propose to modify the likelihood such that the model imitates the role of SVM hinge loss by penalizing the decision boundaries that lie in a high density region and favors the decision boundary with large margins. In this paper, we propose to investigate the capabilities of NCNM for remote sensing image change detection. Regarding the change detection problem as a particular case of classification one, our aim is to detecting the changes or differences between the two multitemporal images. Given the input features i
doi:10.1109/igarss.2009.5418269 dblp:conf/igarss/ChenHZLC09 fatcat:2wfrgmkjezgrxd7sjf5i43buay

Regeneration of Vascular Tissues in Broussonetia Papyrifera Stems After Removal of the Xylem

Cui Keming, Liu Qinghua, Lu Pengzhe, Li Zhengli
1989 IAWA Journal  
. , 1987;; Lu et at. 1987) , provided that the treatment is applied when many immature xylem cells are present and the exposed xylem is covered with plastic sheeting.  ... 
doi:10.1163/22941932-90000488 fatcat:qxxik2devveyho2j5nz2eq7c5a

Fast Object-Level Change Detection for VHR Images

Chunlei Huo, Zhixin Zhou, Hanqing Lu, Chunhong Pan, Keming Chen
2010 IEEE Geoscience and Remote Sensing Letters  
A novel approach is presented for change detection of very high resolution images, which is accomplished by fast objectlevel change feature extraction and progressive change feature classification. Object-level change feature is helpful for improving the discriminability between the changed class and the unchanged class. Progressive change feature classification helps improve the accuracy and the degree of automation, which is implemented by dynamically adjusting the training samples and
more » ... ly tuning the separating hyperplane. Experiments demonstrate the effectiveness of the proposed approach.
doi:10.1109/lgrs.2009.2028438 fatcat:4svxyqvygngjbigj4ixicn7i4i

Segmentation of the Longmen Mountains thrust belt, Western Sichuan Foreland Basin, SW China

Wenzheng Jin, Liangjie Tang, Keming Yang, Guimei Wan, Zhizhou Lü
2010 Tectonophysics  
For example, transmeridional segmentation is recognized in the Zagros orogenic belt in Iran, the Kuqa foreland basin of the Tarim Basin in China (Lu et al., 2000; Wang et al., 2004; Miao et al., 2004;  ... 
doi:10.1016/j.tecto.2009.12.007 fatcat:ufriz5gs65ah3lncp6wumwkyzy

An Improved Shuffled Frog-Leaping Algorithm for Flexible Job Shop Scheduling Problem

Kong Lu, Li Ting, Wang Keming, Zhu Hanbing, Takano Makoto, Yu Bin
2015 Algorithms  
The flexible job shop scheduling problem is a well-known combinatorial optimization problem. This paper proposes an improved shuffled frog-leaping algorithm to solve the flexible job shop scheduling problem. The algorithm possesses an adjustment sequence to design the strategy of local searching and an extremal optimization in information exchange. The computational result shows that the proposed algorithm has a powerful search capability in solving the flexible job shop scheduling problem
more » ... red with other heuristic algorithms, such as the genetic algorithm, tabu search and ant colony optimization. Moreover, the results also show that the improved strategies could improve the performance of the algorithm effectively. Keywords: shuffled frog-leaping algorithm; flexible job shop scheduling problem; local search; extremal optimization OPEN ACCESS Algorithms 2015, 8 20 1. Introduction The scheduling of operations has a vital role in the planning and managing of manufacturing processes. The job-shop scheduling problem (JSP) is one of the most popular scheduling models in practice. In the JSP, a set of jobs should be processed with a set of machines, and each job consists of a sequence of consecutive operations. Moreover, a machine can only process one operation at one time, and the operation cannot be interrupted. Additionally, JSP is aimed at minimizing the number of operations for these jobs under the above constraints. The flexible job-shop scheduling problem (FJSP) is an extension of the conventional JSP, in which operations are allowed to be processed on any one of the existed machines. FJSP is more complicated than JSP, because FJSP not only needs to identify the arrangement of all processes of all machines, but also needs to determine the sequence of processes on each machine. FJSP breaks through the uniqueness restriction of resources. Each process can be completed by many different machines, so that the job shop scheduling problem is closer to the real production process. JSP has been proven to be an NP-hard problem [1], and even for a simple instance with only ten operations and ten available machines for selection, it is still hard to search for a convincing result. As an extension of JSP, FJSP is more complicated to solve. A lot of the literature believes that adopting a heuristic method is a reasonable approach for solving this kind of complex problem [2-9]. Thus, there are many researchers who have used heuristic methods to solve FJSP. Brandimarte [10] attempted to use a hierarchical approach to solve the flexible job shop scheduling problem, and a tabu search is adopted to enhance the effectiveness of the approach. Fattahi et al. [11] proposed a mathematical model for the flexible job shop scheduling problem, and two heuristic approaches (tabu search and simulated annealing heuristics) are also introduced to solve the real size problems. Gao et al. [12] developed a hybrid genetic algorithm to solve the flexible job shop scheduling problem. In the algorithm, the two vectors are used to represent the solution, the individuals of GA are improved by a variable neighborhood descent and the advanced genetic operators are presented by a special chromosome structurer. Yao et al. [2] presented an improved ant colony optimization to solve FJSP, and an adaptive parameter, crossover operation and pheromone updating strategy are used to improve the performance of the algorithm. The shuffled frog-leaping algorithm (SFLA) was developed by Eusuff and Lansey [13] . It is a meta-heuristic optimization method, which combines the advantages of the genetic-based memetic algorithm (MA) and the social behavior-based PSO algorithm [14] . A meme is a kind of information body, which can be distributed, reproduced and exchanged by infecting the thoughts of human beings or animals. The most salient characteristic of the meme algorithm is that memes can share and exchange experience, knowledge and information along with relying on a local search method in the process of evolution. Therefore, with the meme algorithm allows an individual of the traditional genetic algorithm model to become more intelligent. The group of SFLA consists of the frog group in which the individual frogs can communicate with each other. Each frog can be seen as a meme carrier. Along with the communication among frogs, the meme evolution can be performed during the searching process of the algorithm. Due to its efficiency and practical value, SFLA has attracted more attention and has been successfully used in a number of classical combinatorial optimization problems [15] [16] [17] [18] [19] .
doi:10.3390/a8010019 fatcat:v3bzf7frevehvkgbcwwvuyeylq

Unsupervised Change Detection in SAR Image using Graph Cuts

Keming Chen, Chunlei Huo, Zhixin Zhou, Hanqing Lu
2008 IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium  
In this paper, we present an unsupervised change detection approach in temporal sets of SAR images. The change detection is represented as a task of energy minimization and the energy function is minimized using graph cuts. Neighboring pixels are taken into account in a priority sequence according to their distance from the center pixel, and the energy function is formed based on Markov Random Field (MRF) model. Graph cuts algorithm is employed for computing maximum a-posteriori (MAP) estimates
more » ... of the MRF. Experiments results obtained on a SAR data set confirm the effectiveness of the proposed approach. The comparisons between graph cuts algorithm and iterated conditional modes (ICM) algorithm about the quality of change map and running time of energy minimization illustrate that graph cuts algorithm is a huge improvement over ICM.
doi:10.1109/igarss.2008.4779562 dblp:conf/igarss/ChenHZL08 fatcat:vaa6axq24zhn5gxywqeo2n35nm

A Case Study on Attribute Recognition of Heated Metal Mark Image Using Deep Convolutional Neural Networks

Keming Mao, Duo Lu, Dazhi E, Zhenhua Tan
2018 Sensors  
Heated metal mark is an important trace to identify the cause of fire. However, traditional methods mainly focus on the knowledge of physics and chemistry for qualitative analysis and make it still a challenging problem. This paper presents a case study on attribute recognition of the heated metal mark image using computer vision and machine learning technologies. The proposed work is composed of three parts. Material is first generated. According to national standards, actual needs and
more » ... ity, seven attributes are selected for research. Data generation and organization are conducted, and a small size benchmark dataset is constructed. A recognition model is then implemented. Feature representation and classifier construction methods are introduced based on deep convolutional neural networks. Finally, the experimental evaluation is carried out. Multi-aspect testings are performed with various model structures, data augments, training modes, optimization methods and batch sizes. The influence of parameters, recognitio efficiency and execution time are also analyzed. The results show that with a fine-tuned model, the recognition rate of attributes metal type, heating mode, heating temperature, heating duration, cooling mode, placing duration and relative humidity are 0.925, 0.908, 0.835, 0.917, 0.928, 0.805 and 0.92, respectively. The proposed method recognizes the attribute of heated metal mark with preferable effect, and it can be used in practical application.
doi:10.3390/s18061871 pmid:29880774 pmcid:PMC6022075 fatcat:dt2xii23mjddzayrmccaegg5n4

Real time monitoring of the curing degree and the manufacturing process of fiber reinforced composites with a carbon nanotube buckypaper sensor

Shaowei Lu, Chenxu Zhao, Lu Zhang, Duo Chen, Dandan Chen, Xiaoqiang Wang, Keming Ma
2018 RSC Advances  
A flexible and highly sensitive carbon nanotube buckypaper (BP) as a sensing layer was embedded within composite for cure monitoring applications. BP sensor can monitor the resin phase transition and resin cure degree during composite manufacturing.
doi:10.1039/c8ra03445a pmid:35541750 pmcid:PMC9081186 fatcat:grlichf76zcehena7fdgbqulky
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