Augmentation-based edge differentially private path publishing in networks

Paths in a given network represent the occurrence sequences of nodes in many real world applications, such as disease transmission chains, object trajectories and data access sequences. In this paper, we address the problem of publishing edge-privacy preserved path information for a single path such that legitimate users with the full knowledge of the network can reconstruct the path with the published information, but not adversaries, even if they have the maximum background knowledge of all the vertices and all edges but one (on the path) of the network. Existing studies on edge privacy against inference attacks focus on publishing either differential privacy (DP) noise injected graph statistics or DP edge perturbed graph topology to achieve edge differential privacy preservation. However, none of them provides an assurance on both edge privacy and data utility. To effectively protect edge privacy and maintain data utility, we propose a novel scheme of DP augmentation instead of DP perturbation as did in existing work, that publishes a simple-topology graph containing an augmented path with fake edges and vertices applying differential privacy to protect the actual path, such that only the legitimate users are able to reconstruct the actual path with high probability. We theoretically analyse the performance of our algorithm in terms of output quality on differential privacy and utility, and execution efficiency. We also conduct extensive experimental evaluations on a high-performance cluster system to validate our analytical results

Creativity Evaluation Method for Procedural Content Generated Game Items via Machine Learning

Procedural Content Generation via Machine Learning (PCGML) refers to methods that apply machine learning algorithms to generate game content. In particular, the generation of game item descriptions requires techniques to evaluate the similarity between items, and consequently their creativity. This paper improves the BLEU2vec text similarity evaluation technique by integrating it with Byte Pair Encoding (BPE) to capture the relevance of compound words in generated game item descriptions. This novel technique, called Split BLEU2vec, splits compound words into sub-words enabling their similarity evaluation. Our results show that when compared to BLEU2vec baseline, Split BLEu2vec is able to account for semantic embedding of compound words in item descriptions of the game Legend of Zelda.</p

Control with Markov sensors/actuators assignment

This note is concerned with the stability analysis and controller design for linear systems involving a network of sensors and actuators, which are triggered in groups by random events. These events are modeled by two independent Markov chains. A novel stability criterion is obtained by considering transmission delays in the measurement and control signals. Based on the stability criterion, the controller gain is designed. A numerical example is given to show the effectiveness of the proposed method

Creativity Evaluation Method for Procedural Content Generated Game Items via Machine Learning

Procedural Content Generation via Machine Learning (PCGML) refers to methods that apply machine learning algorithms to generate game content. In particular, the generation of game item descriptions requires techniques to evaluate the similarity between items, and consequently their creativity. This paper improves the BLEU2vec text similarity evaluation technique by integrating it with Byte Pair Encoding (BPE) to capture the relevance of compound words in generated game item descriptions. This novel technique, called Split BLEU2vec, splits compound words into sub-words enabling their similarity evaluation. Our results show that when compared to BLEU2vec baseline, Split BLEu2vec is able to account for semantic embedding of compound words in item descriptions of the game Legend of Zelda.</p

Nanofiber-based colorimetric platform for point-of-care detection of E. coli

Accurate and rapid detection of bacteria in complex environmental samples using simple and portable devices is still a major challenge. This research presents a simple nanofiber-based platform for highly sensitive colorimetric/fluorometric detection of Escherichia coli (E. coli). Nanofiber membranes (NFM) were loaded with target molecules (fluorescent and chromogenic substrate) via chemical modification to prepare functional NFM (NFM-MUG and NFM-XG). The β-glucuronidase secreted by E. coli during the metabolic process triggered the functionalized NFM to produce biological signals and color change. The intensity of bio-signals and color was shown to enable quantitative and qualitative detection of E. coli. The nanofiber-based platforms exhibited high stability and a wide detection range (102-107 CFU/mL). The limit of detection (LOD) of NFM-MUG and NFM-XG sensors for E. coli were 26 and 69 CFU mL−1, respectively. The sensing time required for NFM-MUG and NFM-XG was 15 mins and 30 mins respectively. The nanofiber-based platforms also exhibited high specificity and low interference from ionic compounds and pH changes. Notably, this assay was easily combined with a smartphone app as a portable device for on-site detection of E. coli, showing a great promise in the field of environmental and food safety testing

pH-sensitive alginate hydrogel for synergistic anti-infection

This work designed a pH-sensitive sodium alginate hydrogel for combating bacterial infection caused by tissue damage. The antibacterial hydrogels were prepared using sodium alginate, citric acid, and vancomycin by one-step in situ method. Vancomycin (Van) was loaded into hydrogels via reversible imine bonds for controlled drug delivery. The morphology, swelling properties, and antibacterial activity of hydrogel were characterized. The hydrogel shown strong water absorbent behavior and pH-dependent performance. The result under weak acid conditions, the drug release rate of van-loaded gel was faster than neutral and alkaline conditions and followed zero-order kinetic release model, and the cumulative release amount could reach 86.7 % over 320 min. The van-loaded gel had highly effective antibacterial activity in a weak acid environment, the combination of citric acid and vancomycin had a synergistic therapeutic effect for acute infection. The drug-loaded hydrogel shows good biocompatibility. Compared with gauze, the drug-loaded hydrogel exhibited good coagulation properties, high platelet adhesion, high fluid absorption capacity, and proper balance of fluid on the wound bed. This work proposed this simple alginate-based drug delivery system has potential applications in the field of clinical treatment of infections

An Image Enhancement Method for Side-Scan Sonar Image Based on Multi-Stage Repairing Image Fusion

The noise interference of side-scan sonar images is stronger than that of optical images, and the gray level is uneven. To solve this problem, we propose a side-scan sonar image enhancement method based on multi-stage repairing image fusion. Firstly, to remove the environmental noise in the sonar image, we perform adaptive Gaussian smoothing on the original image and the weighted average grayscale image. Then, the smoothed images are all processed through multi-stage image repair. The multi-stage repair network consists of three stages. The first two stages consist of a novel encoder–decoder architecture to extract multi-scale contextual features, and the third stage uses a network based on the resolution of the original inputs to generate spatially accurate outputs. Each phase is not a simple stack. Between each phase, the supervised attention module (SAM) improves the repair results of the previous phase and passes them to the next phase. At the same time, the multi-scale cross-stage feature fusion mechanism (MCFF) is used to complete the information lost in the repair process. Finally, to correct the gray level, we propose a pixel-weighted fusion method based on the unsupervised color correction method (UCM), which performs weighted pixel fusion between the RGB image processed by the UCM algorithm and the gray-level image. Compared with the algorithm with the SOTA methods on datasets, our method shows that the peak signal-to-noise ratio (PSNR) is increased by 26.58%, the structural similarity (SSIM) is increased by 0.68%, and the mean square error (MSE) is decreased by 65.02% on average. In addition, the processed image is balanced in terms of image chromaticity, image contrast, and saturation, and the grayscale is balanced to match human visual perception.</p

An Image Enhancement Method for Side-Scan Sonar Image Based on Multi-Stage Repairing Image Fusion

The noise interference of side-scan sonar images is stronger than that of optical images, and the gray level is uneven. To solve this problem, we propose a side-scan sonar image enhancement method based on multi-stage repairing image fusion. Firstly, to remove the environmental noise in the sonar image, we perform adaptive Gaussian smoothing on the original image and the weighted average grayscale image. Then, the smoothed images are all processed through multi-stage image repair. The multi-stage repair network consists of three stages. The first two stages consist of a novel encoder–decoder architecture to extract multi-scale contextual features, and the third stage uses a network based on the resolution of the original inputs to generate spatially accurate outputs. Each phase is not a simple stack. Between each phase, the supervised attention module (SAM) improves the repair results of the previous phase and passes them to the next phase. At the same time, the multi-scale cross-stage feature fusion mechanism (MCFF) is used to complete the information lost in the repair process. Finally, to correct the gray level, we propose a pixel-weighted fusion method based on the unsupervised color correction method (UCM), which performs weighted pixel fusion between the RGB image processed by the UCM algorithm and the gray-level image. Compared with the algorithm with the SOTA methods on datasets, our method shows that the peak signal-to-noise ratio (PSNR) is increased by 26.58%, the structural similarity (SSIM) is increased by 0.68%, and the mean square error (MSE) is decreased by 65.02% on average. In addition, the processed image is balanced in terms of image chromaticity, image contrast, and saturation, and the grayscale is balanced to match human visual perception.</p

New Advances in Turnover Intention of General Practitioners at Home and Abroad

Reducing the turnover intention among general practitioners(GPs) and strengthening the construction of GPs workforce are the key links to improve the development of primary healthcare system and hierarchical medical system to maintain and improve people's health, and achieve the goals of Healthy China. Considering global shortage of primary healthcare and China's increasing emphasis on both general medicine and GPs, we discussed the role of GPs in primary healthcare, and systematically analyzed the significance of reducing GPs turnover intention, and comprehensive summarized the key factors associated with turnover intention among GPs, including demographic characteristics, job satisfaction, organizational identity, salary and work enthusiasm, then put forward some advice on dealing with turnover intention among domestic GPs, which may provide effective evidence for related departments to formulate policies and measures to stabilize GPs team, and to improve the attractiveness of GPs as an occupation.</p

A general and transferable deep learning framework for predicting phase formation in materials

Machine learning has been widely exploited in developing new materials. However, challenges still exist: small dataset is common for most tasks; new datasets, special descriptors and specific models need to be built from scratch when facing a new task; knowledge cannot be readily transferred between independent models. In this paper we propose a general and transferable deep learning (GTDL) framework for predicting phase formation in materials. The proposed GTDL framework maps raw data to pseudo-images with some special 2-D structure, e.g., periodic table, automatically extracts features and gains knowledge through convolutional neural network, and then transfers knowledge by sharing features extractors between models. Application of the GTDL framework in case studies on glass-forming ability and high-entropy alloys show that the GTDL framework for glass-forming ability outperformed previous models and can correctly predicted the newly reported amorphous alloy systems; for high-entropy alloys the GTDL framework can discriminate five types phases (BCC, FCC, HCP, amorphous, mixture) with accuracy and recall above 94% in fivefold cross-validation. In addition, periodic table knowledge embedded in data representations and knowledge shared between models is beneficial for tasks with small dataset. This method can be easily applied to new materials development with small dataset by reusing well-trained models for related materials