204 research outputs found
Study on the Wald-W Method of Uncertain Decision-making
AbstractUncertain decision-making is one of important research areas in the decision-making theory. For a long time five decision standards such as optimism decision standard, pessimism decision standard, compromised decision standard, equality decision standard and regret decision standard have been regarded as a model in all the available literatures. This article put forwards a new type of uncertainty decision-making method, and makes a more systematic study of Wald-W method through the way of solving matrix game
M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network
Feature pyramids are widely exploited by both the state-of-the-art one-stage
object detectors (e.g., DSSD, RetinaNet, RefineDet) and the two-stage object
detectors (e.g., Mask R-CNN, DetNet) to alleviate the problem arising from
scale variation across object instances. Although these object detectors with
feature pyramids achieve encouraging results, they have some limitations due to
that they only simply construct the feature pyramid according to the inherent
multi-scale, pyramidal architecture of the backbones which are actually
designed for object classification task. Newly, in this work, we present a
method called Multi-Level Feature Pyramid Network (MLFPN) to construct more
effective feature pyramids for detecting objects of different scales. First, we
fuse multi-level features (i.e. multiple layers) extracted by backbone as the
base feature. Second, we feed the base feature into a block of alternating
joint Thinned U-shape Modules and Feature Fusion Modules and exploit the
decoder layers of each u-shape module as the features for detecting objects.
Finally, we gather up the decoder layers with equivalent scales (sizes) to
develop a feature pyramid for object detection, in which every feature map
consists of the layers (features) from multiple levels. To evaluate the
effectiveness of the proposed MLFPN, we design and train a powerful end-to-end
one-stage object detector we call M2Det by integrating it into the architecture
of SSD, which gets better detection performance than state-of-the-art one-stage
detectors. Specifically, on MS-COCO benchmark, M2Det achieves AP of 41.0 at
speed of 11.8 FPS with single-scale inference strategy and AP of 44.2 with
multi-scale inference strategy, which is the new state-of-the-art results among
one-stage detectors. The code will be made available on
\url{https://github.com/qijiezhao/M2Det.Comment: AAAI1
CBNet: A Novel Composite Backbone Network Architecture for Object Detection
In existing CNN based detectors, the backbone network is a very important
component for basic feature extraction, and the performance of the detectors
highly depends on it. In this paper, we aim to achieve better detection
performance by building a more powerful backbone from existing backbones like
ResNet and ResNeXt. Specifically, we propose a novel strategy for assembling
multiple identical backbones by composite connections between the adjacent
backbones, to form a more powerful backbone named Composite Backbone Network
(CBNet). In this way, CBNet iteratively feeds the output features of the
previous backbone, namely high-level features, as part of input features to the
succeeding backbone, in a stage-by-stage fashion, and finally the feature maps
of the last backbone (named Lead Backbone) are used for object detection. We
show that CBNet can be very easily integrated into most state-of-the-art
detectors and significantly improve their performances. For example, it boosts
the mAP of FPN, Mask R-CNN and Cascade R-CNN on the COCO dataset by about 1.5
to 3.0 percent. Meanwhile, experimental results show that the instance
segmentation results can also be improved. Specially, by simply integrating the
proposed CBNet into the baseline detector Cascade Mask R-CNN, we achieve a new
state-of-the-art result on COCO dataset (mAP of 53.3) with single model, which
demonstrates great effectiveness of the proposed CBNet architecture. Code will
be made available on https://github.com/PKUbahuangliuhe/CBNet.Comment: 7 pages,6 figure
Electrically pumped semiconductor laser with low spatial coherence and directional emission
We design and fabricate an on-chip laser source that produces a directional
beam with low spatial coherence. The lasing modes are based on the axial orbit
in a stable cavity and have good directionality. To reduce the spatial
coherence of emission, the number of transverse lasing modes is maximized by
fine-tuning the cavity geometry. Decoherence is reached in a few nanoseconds.
Such rapid decoherence will facilitate applications in ultrafast speckle-free
full-field imaging
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Deformation induced structural evolution in bulk metallic glasses
The structural behavior of binary Cu50Zr50 and ternary Cu50Zr45Ti5 bulk metallic glasses (BMGs) under applied stress was investigated by means of in-situ high energy X-ray synchrotron diffraction. The components of the strain tensors were determined from the shifts of the maxima of the atomic pair correlation functions (PDF) in real space. The anisotropic atomic reorientation in the first-nearest-neighbor shell versus stress suggests structural rearrangements in short-range order. Within the plastic deformation range the overall strain of the metallic glass is equal to the yield strain. After unloading, the atomic structure returns to the stress-free state, and the short-range order is identical to that of the undeformed state. Plastic deformation, however, leads to localized shear bands whose contribution to the volume averaged diffraction pattern is too weak to be detected. A concordant region evidenced by the anisotropic component is activated to counterbalance the stress change due to the atomic bond reorientation in the first-nearest-neighbor shell. The size of the concordant region is an important factor dominating the yield strength and the plastic strain ability of the BMGs
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