33 research outputs found
Deep Residual Learning for Image Recognition
Deeper neural networks are more difficult to train. We present a residual
learning framework to ease the training of networks that are substantially
deeper than those used previously. We explicitly reformulate the layers as
learning residual functions with reference to the layer inputs, instead of
learning unreferenced functions. We provide comprehensive empirical evidence
showing that these residual networks are easier to optimize, and can gain
accuracy from considerably increased depth. On the ImageNet dataset we evaluate
residual nets with a depth of up to 152 layers---8x deeper than VGG nets but
still having lower complexity. An ensemble of these residual nets achieves
3.57% error on the ImageNet test set. This result won the 1st place on the
ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100
and 1000 layers.
The depth of representations is of central importance for many visual
recognition tasks. Solely due to our extremely deep representations, we obtain
a 28% relative improvement on the COCO object detection dataset. Deep residual
nets are foundations of our submissions to ILSVRC & COCO 2015 competitions,
where we also won the 1st places on the tasks of ImageNet detection, ImageNet
localization, COCO detection, and COCO segmentation.Comment: Tech repor
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
State-of-the-art object detection networks depend on region proposal
algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN
have reduced the running time of these detection networks, exposing region
proposal computation as a bottleneck. In this work, we introduce a Region
Proposal Network (RPN) that shares full-image convolutional features with the
detection network, thus enabling nearly cost-free region proposals. An RPN is a
fully convolutional network that simultaneously predicts object bounds and
objectness scores at each position. The RPN is trained end-to-end to generate
high-quality region proposals, which are used by Fast R-CNN for detection. We
further merge RPN and Fast R-CNN into a single network by sharing their
convolutional features---using the recently popular terminology of neural
networks with 'attention' mechanisms, the RPN component tells the unified
network where to look. For the very deep VGG-16 model, our detection system has
a frame rate of 5fps (including all steps) on a GPU, while achieving
state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS
COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015
competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning
entries in several tracks. Code has been made publicly available.Comment: Extended tech repor
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
Existing deep convolutional neural networks (CNNs) require a fixed-size
(e.g., 224x224) input image. This requirement is "artificial" and may reduce
the recognition accuracy for the images or sub-images of an arbitrary
size/scale. In this work, we equip the networks with another pooling strategy,
"spatial pyramid pooling", to eliminate the above requirement. The new network
structure, called SPP-net, can generate a fixed-length representation
regardless of image size/scale. Pyramid pooling is also robust to object
deformations. With these advantages, SPP-net should in general improve all
CNN-based image classification methods. On the ImageNet 2012 dataset, we
demonstrate that SPP-net boosts the accuracy of a variety of CNN architectures
despite their different designs. On the Pascal VOC 2007 and Caltech101
datasets, SPP-net achieves state-of-the-art classification results using a
single full-image representation and no fine-tuning.
The power of SPP-net is also significant in object detection. Using SPP-net,
we compute the feature maps from the entire image only once, and then pool
features in arbitrary regions (sub-images) to generate fixed-length
representations for training the detectors. This method avoids repeatedly
computing the convolutional features. In processing test images, our method is
24-102x faster than the R-CNN method, while achieving better or comparable
accuracy on Pascal VOC 2007.
In ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2014, our
methods rank #2 in object detection and #3 in image classification among all 38
teams. This manuscript also introduces the improvement made for this
competition.Comment: This manuscript is the accepted version for IEEE Transactions on
Pattern Analysis and Machine Intelligence (TPAMI) 2015. See Changelo
Using datamining approaches to selectacupoints in acupuncture and Moxibustion for knee osteoarthritis
Background: Acupuncture and moxibustion are traditional Chinese medicine therapies commonly used to treat knee osteoarthritis (KOA). Although acupoint selection affects the effectiveness of acupuncture and moxibustion, the basic rules of acupoint selection are little understood and there is a lack of guidelines regarding prescription. In this study, we used data mining approaches to investigate the principles of acupoint selection and provide a framework for formulation prescription in acupuncture and moxibustion for clinical treatment of KOA.Materials and Methods: PubMed, Cochrane Library, Science Citation Index, Wanfang database, VIP database, and China National Knowledge Infrastructure were searched for randomized controlled clinical trials published in English or Chinese from January 1, 2009 to October 1, 2015 evaluating the effect of acupuncture and moxibustion on KOA. Databases were established. Frequency statistics and association rule were used to extract and analyze the data.Results: A total of 876 acupuncture prescriptions and 122 acupoints were included in the analysis. Acupoints were concentrated in acupoints of fourteen meridians. The most frequently used acupoints were Dubi (ST35), Neixiyan (EX-LE4), Yanglingquan (GB34), Xuehai (SP10), Liangqiu (ST34), Zusanli (ST36), Yinlingquan (SP9), and Ashi point. The most frequently used meridian was Stomach Meridian of Foot-Yangming. Acupoints were concentrated mainly in the lower limbs. 42 acupoint pairs occurred frequently, and the top acupoint pairing was Dubi (ST35) and Neixiyan (EX-LE4).Conclusion: Acupoint selection and formulation prescription should focus on locally affected areas, and follow the theory of meridians, which helps establish guidelines for acupuncture and moxibustion in KOA patients.Key words: acupuncture and moxibustion, knee osteoarthritis, acupoint, data mining technolog
USING DATA MINING APPROACHES TO SELECT ACUPOINTS IN ACUPUNCTURE AND MOXIBUSTION FOR KNEE OSTEOARTHRITIS
Background: Acupuncture and moxibustion are traditional Chinese medicine therapies commonly used to treat knee osteoarthritis
(KOA). Although acupoint selection affects the effectiveness of acupuncture and moxibustion, the basic rules of acupoint selection
are little understood and there is a lack of guidelines regarding prescription. In this study, we used data mining approaches to
investigate the principles of acupoint selection and provide a framework for formulation prescription in acupuncture and
moxibustion for clinical treatment of KOA.
Materials and Methods: PubMed, Cochrane Library, Science Citation Index, Wanfang database, VIP database, and China National
Knowledge Infrastructure were searched for randomized controlled clinical trials published in English or Chinese from January 1,
2009 to October 1, 2015 evaluating the effect of acupuncture and moxibustion on KOA. Databases were established.
Frequency statistics and association rule were used to extract and analyze the data.
Results: A total of 876 acupuncture prescriptions and 122 acupoints were included in the analysis. Acupoints were concentrated in
acupoints of fourteen meridians. The most frequently used acupoints were Dubi (ST35), Neixiyan (EX-LE4), Yanglingquan (GB34),
Xuehai (SP10), Liangqiu (ST34), Zusanli (ST36), Yinlingquan (SP9), and Ashi point. The most frequently used meridian was
Stomach Meridian of Foot-Yangming. Acupoints were concentrated mainly in the lower limbs. 42 acupoint pairs occurred frequently,
and the top acupoint pairing was Dubi (ST35) and Neixiyan (EX-LE4).
Conclusion: Acupoint selection and formulation prescription should focus on locally affected areas, and follow the theory of
meridians, which helps establish guidelines for acupuncture and moxibustion in KOA patients
Genetic and Physio-Biochemical Characterization of a Novel Premature Senescence Leaf Mutant in Rice (Oryza sativa L.)
Premature senescence greatly affects the yield production and the grain quality in plants, although the molecular mechanisms are largely unknown. Here, we identified a novel rice premature senescence leaf 85 (psl85) mutant from ethyl methane sulfonate (EMS) mutagenesis of cultivar Zhongjian100 (the wild-type, WT). The psl85 mutant presented a distinct dwarfism and premature senescence leaf phenotype, starting from the seedling stage to the mature stage, with decreasing level of chlorophyll and degradation of chloroplast, declined photosynthetic capacity, increased content of malonaldehyde (MDA), upregulated expression of senescence-associated genes, and disrupted reactive oxygen species (ROS) scavenging system. Moreover, endogenous abscisic acid (ABA) level was significantly increased in psl85 at the late aging phase, and the detached leaves of psl85 showed more rapid chlorophyll deterioration than that of WT under ABA treatment, indicating that PSL85 was involved in ABA-induced leaf senescence. Genetic analysis revealed that the premature senescence leaf phenotype was controlled by a single recessive nuclear gene which was finally mapped in a 47 kb region on the short arm of chromosome 7, covering eight candidate open reading frames (ORFs). No similar genes controlling a premature senescence leaf phenotype have been identified in the region, and cloning and functional analysis of the gene is currently underway