1,244 research outputs found
Detection of imidacloprid and Bisphenol-S by Solid Phase Extraction (SPE) coupled with UV-VIS spectrometer and LC-MS
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
Transformation, Identification, and Inversion of Goldberg-Coxeter Fullerenes
It is difficult to identify a G-C fullerene directly from its dimensions as
its lattice is not proportional to that of its archetype in general, although
they have the same three-dimensional shape. In this paper, the area scale
factor of a G-C fullerene is proved to be an integer, which can be calculated
from its dimensions. All the G-C transformations are k-inflations that can be
easily identified and inversed, primary transformations whose area scale
factors are prime numbers, or composite transformations whose area scale
factors are the product of those of its sub-transformations. As the result, a
method to identify any G-C fullerenes according to the area scale factor was
presented.Comment: 6 pages, 3 figure
Trace Elements in Coal Gangue: A Review
Coal gangue is one of the largest industrial residues. It has high ash content, low carbonaceous content, and heating value. Meanwhile, it has some trace elements. Large quantities of coal gangue cause serious environmental problems by polluting the air, water, and soil as well as occupying a tremendous amount of land. Now, coal gangue utilization is a matter of great concern and has attracted wide interest. However, some toxic trace elements in coal gangue should be paid more attention during the utilization of coal gangue. In this article, the modes of occurrence and the leaching characters of trace elements in coal gangue were introduced according to the result of the sequential extraction method and the leaching method. The release character of trace elements during combustion of coal gangue and the environmental implication of trace elements in coal gangue were also discussed. The sulfide-bound trace elements are dominant form in coal gangue. Leaching behavior of trace elements from coal gangue is affected by many factors. Different trace elements presented different transformation behaviors. Trace elements in coal gangue could release out and produce environmental implication in various degrees, depending on the type of trace elements
- …