CoupleNet: Coupling Global Structure with Local Parts for Object Detection

The region-based Convolutional Neural Network (CNN) detectors such as Faster R-CNN or R-FCN have already shown promising results for object detection by combining the region proposal subnetwork and the classification subnetwork together. Although R-FCN has achieved higher detection speed while keeping the detection performance, the global structure information is ignored by the position-sensitive score maps. To fully explore the local and global properties, in this paper, we propose a novel fully convolutional network, named as CoupleNet, to couple the global structure with local parts for object detection. Specifically, the object proposals obtained by the Region Proposal Network (RPN) are fed into the the coupling module which consists of two branches. One branch adopts the position-sensitive RoI (PSRoI) pooling to capture the local part information of the object, while the other employs the RoI pooling to encode the global and context information. Next, we design different coupling strategies and normalization ways to make full use of the complementary advantages between the global and local branches. Extensive experiments demonstrate the effectiveness of our approach. We achieve state-of-the-art results on all three challenging datasets, i.e. a mAP of 82.7% on VOC07, 80.4% on VOC12, and 34.4% on COCO. Codes will be made publicly available.Comment: Accepted by ICCV 201

Channel prior convolutional attention for medical image segmentation

Characteristics such as low contrast and significant organ shape variations are often exhibited in medical images. The improvement of segmentation performance in medical imaging is limited by the generally insufficient adaptive capabilities of existing attention mechanisms. An efficient Channel Prior Convolutional Attention (CPCA) method is proposed in this paper, supporting the dynamic distribution of attention weights in both channel and spatial dimensions. Spatial relationships are effectively extracted while preserving the channel prior by employing a multi-scale depth-wise convolutional module. The ability to focus on informative channels and important regions is possessed by CPCA. A segmentation network called CPCANet for medical image segmentation is proposed based on CPCA. CPCANet is validated on two publicly available datasets. Improved segmentation performance is achieved by CPCANet while requiring fewer computational resources through comparisons with state-of-the-art algorithms. Our code is publicly available at \url{https://github.com/Cuthbert-Huang/CPCANet}

Tracking Control Based on Control Allocation with an Innovative Control Effector Aircraft Application

This paper proposes a control allocation method for the tracking control problem of a class of morphing aircraft with special actuators which are different from the conventional actuation surfaces. This design of actuators can bring about some potential advantages to the flight vehicles; however, due to the integral constraints, the desired control cannot be performed accurately; therefore, it leads to undesirable tracking errors, so influencing the performance of the system. Because the system could be control allocated, based on the designed cost function that describes the tracking errors, the cuckoo search algorithm (CSA) is introduced to search for the optimum solution within the calculated actuator execution commands that are equivalent to the desired commands. Several improvement measures are proposed for boosting the efficiency of the CSA and ensuring reasonable solutions. Simulation results show that the proposed control allocation method is necessary and effective, and the improvement measures are helpful in obtaining the optimum solution

Sample-efficient benchmarking of multi-photon interference on a boson sampler in the sparse regime

Verification of a quantum advantage in the presence of noise is a key open problem in the study of near-term quantum devices. In this work, we show how to assess the quality of photonic interference in a linear optical quantum device (boson sampler) by using a maximum likelihood method to measure the strength at which various noise sources are present in the experiment. This allows us to use a sparse set of samples to test whether a given boson sampling experiment meets known upper bounds on the level of noise permissible to demonstrate a quantum advantage. Furthermore, this method allows us monitor the evolution of noise in real time, creating a valuable diagnostic tool. Finally, we observe that sources of noise in the experiment compound, meaning that the observed value of the mutual photon indistinguishability, which is the main imperfection in our study, is an effective value taking into account all sources of error in the experiment

Enhanced Interfacial Electronic Transfer of BiVO4 Coupled with 2D g‐C3N4 for Visible‐light Photocatalytic Performance

A BiVO4/2D g‐C3N4 direct dual semiconductor photocatalytic system has been fabricated via electrostatic self‐assembly method of BiVO4 microparticle and g‐C3N4 nanosheet. According to experimental measurements and first‐principle calculations, the formation of built‐in electric field and the opposite band bending around the interface region in BiVO4/2D g‐C3N4 as well as the intimate contact between BiVO4 and 2D g‐C3N4 will lead to high separation efficiency of charge carriers. More importantly, the intensity of bulid‐in electric field is greatly enhanced due to the ultrathin nanosheet structure of 2D g‐C3N4. As a result, BiVO4/2D g‐C3N4 exhibits excellent photocatalytic performance with the 93.0% Rhodamine B (RhB) removal after 40 min visible light irradiation, and the photocatalytic reaction rate is about 22.7 and 10.3 times as high as that of BiVO4 and 2D g‐C3N4, respectively. In addition, BiVO4/2D g‐C3N4 also displays enhanced photocatalytic performance in the degradation of tetracycline (TC). It is expected that this work may provide insights into the understanding the significant role of built‐in electric field in heterostructure and fabricating highly efficient direct dual semiconductor systems