165 research outputs found
Domain Adaptive Faster R-CNN for Object Detection in the Wild
Object detection typically assumes that training and test data are drawn from
an identical distribution, which, however, does not always hold in practice.
Such a distribution mismatch will lead to a significant performance drop. In
this work, we aim to improve the cross-domain robustness of object detection.
We tackle the domain shift on two levels: 1) the image-level shift, such as
image style, illumination, etc, and 2) the instance-level shift, such as object
appearance, size, etc. We build our approach based on the recent
state-of-the-art Faster R-CNN model, and design two domain adaptation
components, on image level and instance level, to reduce the domain
discrepancy. The two domain adaptation components are based on H-divergence
theory, and are implemented by learning a domain classifier in adversarial
training manner. The domain classifiers on different levels are further
reinforced with a consistency regularization to learn a domain-invariant region
proposal network (RPN) in the Faster R-CNN model. We evaluate our newly
proposed approach using multiple datasets including Cityscapes, KITTI, SIM10K,
etc. The results demonstrate the effectiveness of our proposed approach for
robust object detection in various domain shift scenarios.Comment: Accepted to CVPR 201
Dual Auction Mechanism for Transaction Forwarding and Validation in Complex Wireless Blockchain Network
In traditional blockchain networks, transaction fees are only allocated to
full nodes (i.e., miners) regardless of the contribution of forwarding
behaviors of light nodes. However, the lack of forwarding incentive reduces the
willingness of light nodes to relay transactions, especially in the
energy-constrained Mobile Ad Hoc Network (MANET). This paper proposes a novel
dual auction mechanism to allocate transaction fees for forwarding and
validation behaviors in the wireless blockchain network. The dual auction
mechanism consists of two auction models: the forwarding auction and the
validation auction. In the forwarding auction, forwarding nodes use Generalized
First Price (GFP) auction to choose transactions to forward. Besides,
forwarding nodes adjust the forwarding probability through a no-regret
algorithm to improve efficiency. In the validation auction, full nodes select
transactions using Vickrey-Clarke-Grove (VCG) mechanism to construct the block.
We prove that the designed dual auction mechanism is Incentive Compatibility
(IC), Individual Rationality (IR), and Computational Efficiency (CE).
Especially, we derive the upper bound of the social welfare difference between
the social optimal auction and our proposed one. Extensive simulation results
demonstrate that the proposed dual auction mechanism decreases energy and
spectrum resource consumption and effectively improves social welfare without
sacrificing the throughput and the security of the wireless blockchain network
Integrating Overlapping Structures and Background Information of Words Significantly Improves Biological Sequence Comparison
Word-based models have achieved promising results in sequence comparison. However, as the important statistical properties of words in biological sequence, how to use the overlapping structures and background information of the words to improve sequence comparison is still a problem. This paper proposed a new statistical method that integrates the overlapping structures and the background information of the words in biological sequences. To assess the effectiveness of this integration for sequence comparison, two sets of evaluation experiments were taken to test the proposed model. The first one, performed via receiver operating curve analysis, is the application of proposed method in discrimination between functionally related regulatory sequences and unrelated sequences, intron and exon. The second experiment is to evaluate the performance of the proposed method with f-measure for clustering Hepatitis E virus genotypes. It was demonstrated that the proposed method integrating the overlapping structures and the background information of words significantly improves biological sequence comparison and outperforms the existing models
Compositional Mining of Multiple Object API Protocols through State Abstraction
API protocols specify correct sequences of method invocations. Despite their usefulness, API protocols are often unavailable in practice because writing them is cumbersome and error prone. Multiple object API protocols are more expressive than single object API protocols. However, the huge number of objects of typical object-oriented programs poses a major challenge to the automatic mining of multiple object API protocols: besides maintaining scalability, it is important to capture various object interactions. Current approaches utilize various heuristics to focus on small sets of methods. In this paper, we present a general, scalable, multiple object API protocols mining approach that can capture all object interactions. Our approach uses abstract field values to label object states during the mining process. We first mine single object typestates as finite state automata whose transitions are annotated with states of interacting objects before and after the execution of the corresponding method and then construct multiple object API protocols by composing these annotated single object typestates. We implement our approach for Java and evaluate it through a series of experiments
Protective Effect of Anthocyanin on Neurovascular Unit in Cerebral Ischemia/Reperfusion Injury in Rats
Treating cerebral ischemia continues to be a clinical challenge. Studies have shown that the neurovascular unit (NVU), as the central structural basis, plays a key role in cerebral ischemia. Here, we report that anthocyanin, a safe and natural antioxidant, could inhibit apoptosis and inflammation to protect NVU in rats impaired by middle cerebral artery occlusion/reperfusion (MCAO/R). Administration of anthocyanin significantly reduced infarct volume and neurological scores in MCAO/R rats. Anthocyanin could also markedly ameliorate cerebral edema and reduce the concentration of Evans blue (EB) by inhibiting MMP-9. Moreover, anthocyanin alleviated apoptotic injury resulting from MCAO/R through the regulation of Bcl-2 family proteins. The levels of inflammation-related molecules including tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), and interleukin-6 (IL-6), which were over-expressed with MCAO/R, were decreased by anthocyanin. In addition, Nuclear factor-kappa B (NF-κB) and the NLRP3 inflammasome pathway might be involved in the anti-inflammatory effect of anthocyanin. In conclusion, anthocyanin could protect the NVU through multiple pathways, and play a protective role in cerebral ischemia/reperfusion injury
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