Accelerated genetic algorithm based on search-space decomposition for change detection in remote sensing images

Detecting change areas among two or more remote sensing images is a key technique in remote sensing. It usually consists of generating and analyzing a difference image thus to produce a change map. Analyzing the difference image to obtain the change map is essentially a binary classification problem, and can be solved by optimization algorithms. This paper proposes an accelerated genetic algorithm based on search-space decomposition (SD-aGA) for change detection in remote sensing images. Firstly, the BM3D algorithm is used to preprocess the remote sensing image to enhance useful information and suppress noises. The difference image is then obtained using the logarithmic ratio method. Secondly, after saliency detection, fuzzy c-means algorithm is conducted on the salient region detected in the difference image to identify the changed, unchanged and undetermined pixels. Only those undetermined pixels are considered by the optimization algorithm, which reduces the search space significantly. Inspired by the idea of the divide-and-conquer strategy, the difference image is decomposed into sub-blocks with a method similar to down-sampling, where only those undetermined pixels are analyzed and optimized by SD-aGA in parallel. The category labels of the undetermined pixels in each sub-block are optimized according to an improved objective function with neighborhood information. Finally the decision results of the category labels of all the pixels in the sub-blocks are remapped to their original positions in the difference image and then merged globally. Decision fusion is conducted on each pixel based on the decision results in the local neighborhood to produce the final change map. The proposed method is tested on six diverse remote sensing image benchmark datasets and compared against six state-of-the-art methods. Segmentations on the synthetic image and natural image corrupted by different noise are also carried out for comparison. Results demonstrate the excellent performance of the proposed SD-aGA on handling noises and detecting the changed areas accurately. In particular, compared with the traditional genetic algorithm, SD-aGA can obtain a much higher degree of detection accuracy with much less computational time

Hybrid Graph Neural Networks for Crowd Counting

Crowd counting is an important yet challenging task due to the large scale and density variation. Recent investigations have shown that distilling rich relations among multi-scale features and exploiting useful information from the auxiliary task, i.e., localization, are vital for this task. Nevertheless, how to comprehensively leverage these relations within a unified network architecture is still a challenging problem. In this paper, we present a novel network structure called Hybrid Graph Neural Network (HyGnn) which targets to relieve the problem by interweaving the multi-scale features for crowd density as well as its auxiliary task (localization) together and performing joint reasoning over a graph. Specifically, HyGnn integrates a hybrid graph to jointly represent the task-specific feature maps of different scales as nodes, and two types of relations as edges:(i) multi-scale relations for capturing the feature dependencies across scales and (ii) mutual beneficial relations building bridges for the cooperation between counting and localization. Thus, through message passing, HyGnn can distill rich relations between the nodes to obtain more powerful representations, leading to robust and accurate results. Our HyGnn performs significantly well on four challenging datasets: ShanghaiTech Part A, ShanghaiTech Part B, UCF_CC_50 and UCF_QNRF, outperforming the state-of-the-art approaches by a large margin.Comment: To appear in AAAI 202

Quantum phase transition of light in a 1-D photon-hopping-controllable resonator array

We give a concrete experimental scheme for engineering the insulator-superfluid transition of light in a one-dimensional (1-D) array of coupled superconducting stripline resonators. In our proposed architecture, the on-site interaction and the photon hopping rate can be tuned independently by adjusting the transition frequencies of the charge qubits inside the resonators and at the resonator junctions, respectively, which permits us to systematically study the quantum phase transition of light in a complete parameter space. By combining the techniques of photon-number-dependent qubit transition and fast read-out of the qubit state using a separate low-Q resonator mode, the statistical property of the excitations in each resonator can be obtained with a high efficiency. An analysis of the various decoherence sources and disorders shows that our scheme can serve as a guide to coming experiments involving a small number of coupled resonators.Comment: 7 pages, 4 figure

Pharmacokinetics, tissue distribution, and metabolites of a polyvinylpyrrolidone-coated norcantharidin chitosan nanoparticle formulation in rats and mice, using LC-MS/MS

A novel formulation containing polyvinylpyrrolidone (PVP) K30-coated norcantharidin (NCTD) chitosan nanoparticles (PVP–NCTD–NPs) was prepared by ionic gelation between chitosan and sodium tripolyphosphate. The average particle size of the PVP–NCTD–NPs produced was 140.03 ± 6.23 nm; entrapment efficiency was 56.33% ± 1.41%; and drug-loading efficiency was 8.38% ± 0.56%. The surface morphology of NCTD nanoparticles (NPs) coated with PVP K30 was characterized using various analytical techniques, including X-ray diffraction and atomic force microscopy. NCTD and its metabolites were analyzed using a sensitive and specific liquid chromatography-tandem mass spectrometry method with samples from mice and rats. The results indicated the importance of the PVP coating in controlling the shape and improving the entrapment efficiency of the NPs. Pharmacokinetic profiles of the NCTD group and PVP–NCTD–NP group, after oral and intravenous administration in rats, revealed that relative bioavailabilities were 173.3% and 325.5%, respectively. The elimination half-life increased, and there was an obvious decrease in clearance. The tissue distribution of NCTD in mice after the intravenous administration of both formulations was investigated. The drug was not quantifiable at 6 hours in all tissues except for the liver and kidneys. The distribution of the drug in the liver and bile was notably improved in the PVP–NCTD–NP group. The metabolites and excretion properties of NCTD were investigated by analyzing rat feces and urine samples, collected after oral administration. A prototype drug and two metabolites were found in the feces, and seven metabolites in the urine. The primary elimination route of NCTD was via the urine. The quantity of the parent drug eliminated in the feces of the PVP–NCTD–NP group, was 32 times greater than that of the NCTD group, indicating that the NPs dramatically increased the reduction quantity from liver to bile. We conclude that PVP–NCTD–NPs are an adequate formulation for enhancing the absorption of NCTD, and significantly improving therapeutic effects targeting the hepatic system. Decarboxylation and hydroxylation were the dominant metabolic pathways for NCTD. Metabolites were mainly excreted into rat kidney and finally into urine

Grooming of Dynamic Traffic in WDM Star and Tree Networks Using Genetic Algorithm

The advances in WDM technology lead to the great interest in traffic grooming problems. As traffic often changes from time to time, the problem of grooming dynamic traffic is of great practical value. In this paper, we discuss dynamic grooming of traffic in star and tree networks. A genetic algorithm (GA) based approach is proposed to support arbitrary dynamic traffic patterns, which minimizes the number of ADM's and wavelengths. To evaluate the algorithm, tighter bounds are derived. Computer simulation results show that our algorithm is efficient in reducing both the numbers of ADM's and wavelengths in tree and star networks.Comment: 15 page

Manometric Measurement of the Sphincter of Oddi in Patients with Common Bile Duct Stones: A Consecutive Study of the Han Population of China

Objective. Role of dysfunction of the sphincter of Oddi (SO) in choledocholithiasis is controversial. This study was to evaluate SO motor activity in patients with common bile duct (CBD) stones in the Han population of China. Patients and Methods. In this study, 76 patients with CBD stones were enrolled in a single tertiary endoscopy center. Data of SO motor activities was prospectively evaluated by endoscopic manometry. Mean basal SO pressure, amplitude, and frequency were collected and analyzed. Results. The mean basal SO pressure, amplitude, and frequency were 52.7±40.0 (1.60–171.1) mmHg, 39.9±19.7 (14.9–115.5) mmHg, and 5.7±3.2 (1.3–13.8)/min, respectively. The basal SO pressure was higher in patients with CBD stones < 10 mm in diameter than that in those with CBD stones larger than 10 mm in diameter (60.7±41.0 mmHg versus 36.8±29.4 mmHg, P=0.043). There was no significant difference in the basal SO pressure, amplitude, and frequency when compared with the CBD diameter, CBD stone number, prior cholecystectomy, periampullary diverticula, and symptoms. Levels of alanine aminotransferase, aspartate transaminase, γ-glutamyl transpeptidase, and alkaline phosphatase showed no significant difference in patients with normal or elevated basal SO pressure. Conclusion. These results identify that, in Chinese Han population, abnormalities of SO motor activity are associated with CBD stones