Towards Robust Curve Text Detection with Conditional Spatial Expansion

It is challenging to detect curve texts due to their irregular shapes and varying sizes. In this paper, we first investigate the deficiency of the existing curve detection methods and then propose a novel Conditional Spatial Expansion (CSE) mechanism to improve the performance of curve text detection. Instead of regarding the curve text detection as a polygon regression or a segmentation problem, we treat it as a region expansion process. Our CSE starts with a seed arbitrarily initialized within a text region and progressively merges neighborhood regions based on the extracted local features by a CNN and contextual information of merged regions. The CSE is highly parameterized and can be seamlessly integrated into existing object detection frameworks. Enhanced by the data-dependent CSE mechanism, our curve text detection system provides robust instance-level text region extraction with minimal post-processing. The analysis experiment shows that our CSE can handle texts with various shapes, sizes, and orientations, and can effectively suppress the false-positives coming from text-like textures or unexpected texts included in the same RoI. Compared with the existing curve text detection algorithms, our method is more robust and enjoys a simpler processing flow. It also creates a new state-of-art performance on curve text benchmarks with F-score of up to 78.4$\%$.Comment: This paper has been accepted by IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2019

Distributed Multi-Area Optimal Power Flow via Rotated Coordinate Descent Critical Region Exploration

We consider the problem of distributed optimal power flow (OPF) for multi-area electric power systems. A novel distributed algorithm is proposed, referred to as the rotated coordinate descent critical region exploration (RCDCRE). It allows each entity to independently update its boundary information and optimally solve its local OPF in an asynchronous fashion. RCDCRE method stitches coordinate descent and parametric programming using coordinate system rotation to reduce coordination, keep privacy and ensure convergence. The solution process does not require warm starts and can iterate from infeasible initial points using penalty-based formulations. The effectiveness of RCDCRE is verified based on IEEE 2-area 44-bus and 4-area 472-bus systems

Lightweight Salient Object Detection in Optical Remote-Sensing Images via Semantic Matching and Edge Alignment

Recently, relying on convolutional neural networks (CNNs), many methods for salient object detection in optical remote sensing images (ORSI-SOD) are proposed. However, most methods ignore the huge parameters and computational cost brought by CNNs, and only a few pay attention to the portability and mobility. To facilitate practical applications, in this paper, we propose a novel lightweight network for ORSI-SOD based on semantic matching and edge alignment, termed SeaNet. Specifically, SeaNet includes a lightweight MobileNet-V2 for feature extraction, a dynamic semantic matching module (DSMM) for high-level features, an edge self-alignment module (ESAM) for low-level features, and a portable decoder for inference. First, the high-level features are compressed into semantic kernels. Then, semantic kernels are used to activate salient object locations in two groups of high-level features through dynamic convolution operations in DSMM. Meanwhile, in ESAM, cross-scale edge information extracted from two groups of low-level features is self-aligned through L2 loss and used for detail enhancement. Finally, starting from the highest-level features, the decoder infers salient objects based on the accurate locations and fine details contained in the outputs of the two modules. Extensive experiments on two public datasets demonstrate that our lightweight SeaNet not only outperforms most state-of-the-art lightweight methods but also yields comparable accuracy with state-of-the-art conventional methods, while having only 2.76M parameters and running with 1.7G FLOPs for 288x288 inputs. Our code and results are available at https://github.com/MathLee/SeaNet.Comment: 11 pages, 4 figures, Accepted by IEEE Transactions on Geoscience and Remote Sensing 202

IBVC: Interpolation-driven B-frame Video Compression

Learned B-frame video compression aims to adopt bi-directional motion estimation and motion compensation (MEMC) coding for middle frame reconstruction. However, previous learned approaches often directly extend neural P-frame codecs to B-frame relying on bi-directional optical-flow estimation or video frame interpolation. They suffer from inaccurate quantized motions and inefficient motion compensation. To address these issues, we propose a simple yet effective structure called Interpolation-driven B-frame Video Compression (IBVC). Our approach only involves two major operations: video frame interpolation and artifact reduction compression. IBVC introduces a bit-rate free MEMC based on interpolation, which avoids optical-flow quantization and additional compression distortions. Later, to reduce duplicate bit-rate consumption and focus on unaligned artifacts, a residual guided masking encoder is deployed to adaptively select the meaningful contexts with interpolated multi-scale dependencies. In addition, a conditional spatio-temporal decoder is proposed to eliminate location errors and artifacts instead of using MEMC coding in other methods. The experimental results on B-frame coding demonstrate that IBVC has significant improvements compared to the relevant state-of-the-art methods. Meanwhile, our approach can save bit rates compared with the random access (RA) configuration of H.266 (VTM). The code will be available at https://github.com/ruhig6/IBVC.Comment: Submitted to IEEE TCSV

One symbol blind synchronization in SIMO molecular communication systems

Molecular communication offers new possibilities in the micro-and nano-scale application environments. Similar to other communication paradigms, molecular communication also requires clock synchronization between the transmitter and the receiver nanomachine in many time-and control-sensitive applications. This letter presents a novel high-efficiency blind clock synchronization mechanism. Without knowing the channel parameters of the diffusion coefficient and the transmitter-receiver distance, the receiver only requires one symbol to achieve synchronization. The samples are used to estimate the propagation delay by least square method and achieve clock synchronization. Single-input multiple-output (SIMO) diversity design is then proposed to mitigate channel noise and therefore to improve the synchronization accuracy. The simulation results show that the proposed clock synchronization mechanism has a good performance and may help chronopharmaceutical drug delivery applications

Attitudes, practices and information needs regarding novel influenza A (H7N9) among employees of food production and operation in Guangzhou, Southern China: a cross-sectional study

BACKGROUND: As of 30 May 2013, 132 human infections with avian influenza A (H7N9) had been reported in 10 Chinese cities. On 17 May 2013, because a chicken infection with H7 subtype avian influenza virus was detected in Guanzhou, Guangzhou became the 11th city to conduct emergency response operations. The goal of this study was to identify attitudes, practices and information needs among employees of food production and operation in Guangzhou. METHODS: A cross-sectional survey of face-to-face interviews was used during 17–24 June 2013. All adults seeking health examination in Guangzhou Center for Disease Control and Prevention who had lived in Guangzhou for at least 3 months, were engaged in food production and operation, and agreed to participate were interviewed. RESULTS: Of 1,450 participants, 69.72% worried about being infected with the A/H7N9 and 74.41% stated that they had searched for information about A/H7N9. The internet (76.92%), television (67.56%), and newspapers (56.26%) were the main methods of obtaining information; the use of these methods differed significantly by various demographic variables (P < 0.05). More than one-fifth of participants complained that the information was not timely enough (20.28%) and was intentionally concealed by the government (20.76%). Nearly one-third (32.35%) did not believe that the government could control the A/H7N9 epidemic. Most participants (80.76%) reported washing hands more frequently than before, while over one-third (37.17%) stated no longer buying poultry. A total of 84.00% indicated a willingness to receive an A/H7N9 vaccine, and the primary reason for not being willing was concern about safety (58.19%). A history of influenza vaccination and worry about being infected with the A/H7N9 were significantly associated with intention to receive an A/H7N9 vaccine (P < 0.05). CONCLUSIONS: Our findings provide insight into the attitudes and practices of employees of food production and operation 3 months after the first human A/H7N9 case reported in China, and 1 month after infected chickens were identified in Guangzhou. Distrust in the health department should be addressed, and more effort should be made to improve compliance of proper preventive measures to reduce panic among the public. The information needs should be taken into account in the next step of health education

Shortening the standard testing time for residual biogas potential (RBP) tests using biogas yield models and substrate physicochemical characteristics

The residual biogas potential (RBP) test is a procedure to ensure the anaerobic digestion process performance and digestate stability. Standard protocols for RBP require a significant time for sample preparation, characterisation and testing of the rig setup followed by batch experiments of a minimum of 28 days. To reduce the experimental time to obtain the RBP result, four biogas kinetic models were evaluated for their strength of fit for biogas production data from RBP tests. It was found that the pseudo-parallel first-order model and the first-order autoregressive (AR (1)) model provide a high strength of fit and can predict the RBP result with good accuracy (absolute percentage errors < 10%) using experimental biogas production data of 15 days. Multivariate regression with decision trees (DTs) was adopted in this study to predict model parameters for the AR (1) model from substrate physicochemical parameters. The mean absolute percentage error (MAPE) of the predicted AR (1) model coefficients, the constants and the RBP test results at day 28 across DTs with 20 training set samples are 4.76%, 72.04% and 52.13%, respectively. Using five additional data points to perform the leave-one-out cross-validation method, the MAPEs decreased to 4.31%, 59.29% and 45.62%. This indicates that the prediction accuracy of DTs can be further improved with a larger training dataset. A Gaussian Process Regressor was guided by the DT-predicted AR (1) model to provide probability distribution information for the biogas yield prediction

Ant-behavior inspired intelligent nanonet for targeted drug delivery in cancer therapy

Targeted drug delivery system is believed as one of the most promising solutions for cancer treatment due to its low-dose requirement and less side effects. However, both passive targeting and active targeting rely on systemic blood circulation and diffusion, which is actually not the real “active” drug delivery. In this paper, an ant-behavior inspired nanonetwork composing of intelligent nanomachines is proposed. A big intelligent nanomachine take small intelligent nanomachines and drugs to the vicinity of of the tumor area. The small intelligent nanomachines can coordinate with each other to find the most effective path to the tumor cell for drug transportation. The framework and mechanism of this cooperative network are proposed. The route finding algorithm is presented. The convergence performance is analytically analyzed where the influence of the factors such as molecule degradation rate, home-destination distance, number of small nanomachines to the convergence is presented. Finally the simulation results validate the effectiveness of the proposed mechanism and analytical analysi