AWEncoder: Adversarial Watermarking Pre-trained Encoders in Contrastive Learning

As a self-supervised learning paradigm, contrastive learning has been widely used to pre-train a powerful encoder as an effective feature extractor for various downstream tasks. This process requires numerous unlabeled training data and computational resources, which makes the pre-trained encoder become valuable intellectual property of the owner. However, the lack of a priori knowledge of downstream tasks makes it non-trivial to protect the intellectual property of the pre-trained encoder by applying conventional watermarking methods. To deal with this problem, in this paper, we introduce AWEncoder, an adversarial method for watermarking the pre-trained encoder in contrastive learning. First, as an adversarial perturbation, the watermark is generated by enforcing the training samples to be marked to deviate respective location and surround a randomly selected key image in the embedding space. Then, the watermark is embedded into the pre-trained encoder by further optimizing a joint loss function. As a result, the watermarked encoder not only performs very well for downstream tasks, but also enables us to verify its ownership by analyzing the discrepancy of output provided using the encoder as the backbone under both white-box and black-box conditions. Extensive experiments demonstrate that the proposed work enjoys pretty good effectiveness and robustness on different contrastive learning algorithms and downstream tasks, which has verified the superiority and applicability of the proposed work.Comment: https://scholar.google.com/citations?user=IdiF7M0AAAAJ&hl=e

Sulfated seaweed polysaccharides as multifunctional materials in drug delivery applications

In the last decades, the discovery of metabolites from marine resources showing biological activity has increased significantly. Among marine resources, seaweed is a valuable source of structurally diverse bioactive compounds. The cell walls of marine algae are rich in sulfated polysaccharides, including carrageenan in red algae, ulvan in green algae and fucoidan in brown algae. Sulfated polysaccharides have been increasingly studied over the years in the pharmaceutical field, given their potential usefulness in applications such as the design of drug delivery systems. The purpose of this review is to discuss potential applications of these polymers in drug delivery systems, with a focus on carrageenan, ulvan and fucoidan. General information regarding structure, extraction process and physicochemical properties is presented, along with a brief reference to reported biological activities. For each material, specific applications under the scope of drug delivery are described, addressing in privileged manner particulate carriers, as well as hydrogels and beads. A final section approaches the application of sulfated polysaccharides in targeted drug delivery, focusing with particular interest the capacity for macrophage targeting

Marketing Clues on the Label Raise the Purchase Intention of Genetically Modified Food

As more and more genetically modified foods (GMFs) must be labeled, adding more information to increase the willingness to buy genetically modified food has become the focus of scholars and enterprises. Most current studies have confirmed that the consumer attitudes and purchase intention toward GMFs are not good. This study aims to match consumers’ different information-processing mechanisms by adding marketing information clues and regulating their purchase intentions by contradictory attitudes towards GMFs. According to the interest demands of GMFs, the marketing clue information was divided into functional information and environmental information. Through two studies, we find that consumers are more inclined to environmental information than heuristic. Functional information is more attractive to males, and the young generation prefers ecological information. Consumers with high ambivalence towards genetically modified foods are more inclined to choose environmental attribute information

Robust Face Recognition in Low Resolution and Blurred Image Using Joint Information in Space and Frequency

Part 13: UMASInternational audienceRecognizing faces in low resolution and blurred images is common yet challenging task. Local Frequency Descriptor (LFD) has been proved to be effective for this problem and is extracted from a spatial neighborhood of each pixel of a frequency plane regardless of correlations between frequencies. To explore the frequency correlations and preserve low resolution and blur insensitive simultaneously, we propose Enhanced LFD (ELFD) in which information in space and frequency is jointly utilized so as to be more descriptive and discriminative than LFD. The selection of window size of short-term of Fourier transform adaptive to the testing image is also analyzed. In addition, linear weighting fusion of recognition results given by magnitude and phase is proposed. The experiments conducted on Yale and FERET databases demonstrate that promising results have been achieved by the proposed ELFD, adaptive window size selection and fusion scheme

Research on Sharing Intention Formation Mechanism Based on the Burden of Ownership and Fashion Consciousness

The sharing economy has become an important business model for China to promote energy conservation and emission reduction, improve the utilization efficiency of social resources, promote green and sustainable development, and achieve high-quality economic development. How to improve the willingness of individuals to share underutilized resources with others is becoming an urgent problem for enterprises and academia. Although current research on the sharing economy provides insights into users’ perspectives, little attention has been given to the comprehensive investigation of the sharing intention of individual service providers. Based on the motivation—opportunity—abilities (MOA) theory, we analyze the influencing factors and boundary conditions of individual resource sharing intention by taking new consumer groups as research samples. The results reveal that the sharing intention of individual service providers depends on their customized service capability, economic motivation, and perceived ease of use. Furthermore, the burden of ownership and fashion consciousness will further influence the sharing intention of individual service providers. This discovery provides theoretical basis for the development of an enterprise sharing economy and government guidance, and enriches the theoretical research on the sharing economy

A Coarse-to-Fine Registration Strategy for Multi-Sensor Images with Large Resolution Differences

Automatic image registration for multi-sensors has always been an important task for remote sensing applications. However, registration for images with large resolution differences has not been fully considered. A coarse-to-fine registration strategy for images with large differences in resolution is presented. The strategy consists of three phases. First, the feature-base registration method is applied on the resampled sensed image and the reference image. Edge point features acquired from the edge strength map (ESM) of the images are used to pre-register two images quickly and robustly. Second, normalized mutual information-based registration is applied on the two images for more accurate transformation parameters. Third, the final transform parameters are acquired through direct registration between the original high- and low-resolution images. Ant colony optimization (ACO) for continuous domain is adopted to optimize the similarity metrics throughout the three phases. The proposed method has been tested on image pairs with different resolution ratios from different sensors, including satellite and aerial sensors. Control points (CPs) extracted from the images are used to calculate the registration accuracy of the proposed method and other state-of-the-art methods. The feature-based preregistration validation experiment shows that the proposed method effectively narrows the value range of registration parameters. The registration results indicate that the proposed method performs the best and achieves sub-pixel registration accuracy of images with resolution differences from 1 to 50 times

Silencing of SNHG6 induced cell autophagy by targeting miR-26a-5p/ULK1 signaling pathway in human osteosarcoma

Abstract Background lncRNAs have been proved to play crucial parts in various human cytopathology and cell physiology, including tumorigenesis. Down-regulated lncRNAs SNHG6 have shown great cell proliferation inhibitory effects in cancer development. Here we investigated how SNHG6 effected human osteosarcoma (OS) development and progression. Methods: Reverse transcription-quantitative PCR was performed to detect SNHG6 mRNA level in both OS tissues and cell lines. MTT and colony formation assays were used to determine the growth impact of SNHG6. Wound healing and trans-well assay were performed to measure the invasion effect of SNHG6. Western blotting were utilized to dissect molecular mechanisms. Results We identified SNHG6 as a lncRNAs that significantly up-regulated in OS tissues and cells, patients with high SNHG6 expression suffered more malignant metastasis and shorter survival times. Furthermore, silencing of SNHG6 in OS significantly inhibited OS cell growth, weakened cell invasion capacity, arrested cell cycle at G0/G1 phase, and induced cell apoptosis. Additionally, mechanism assays suggested that SNHG6 could competitively sponging miR-26a-5p thereby regulating ULK1, and induced cell apoptosis and autophagy by targeting caspase3 and ATF3. Conclusions: Our findings demonstrated that SNHG6 acted as an oncogene in osteosarcoma cells through regulating miR-26a-5p/ULK1 at a post-transcriptional level. SNHG6 might serve as a candidate prognostic biomarker and a target for novel therapies of osteosarcoma patients

A complex system health state assessment method with reference value optimization for interpretable BRB

Abstract Health condition assessment is the basis for formulating and optimizing maintenance strategies of complex systems, which is crucial for ensuring the safe and stable operation of these systems. In complex system health condition assessment, it is not only necessary for the model to handle various uncertainties to ensure the accuracy of assessment results, but also to have a transparent and reasonable assessment process and interpretable, traceable assessment results. belief rule base (BRB) has been widely used as an interpretable modeling method in health condition assessment. However, BRB-based models currently face two issues: (1) inaccuracies in expert-provided parameters that can affect the model's accuracy, and (2) after model optimization, interpretability may be reduced. Therefore, this paper proposes a new method for complex system health condition assessment called interpretable BRB with reference value optimization (I-BRB). Firstly, to address the issue of inaccurate reference values, a reference value optimization algorithm with interpretability constraints is designed, which optimizes the reference values without compromising expert knowledge. Secondly, the remaining parameters are optimized using the projection covariance matrix adaptation evolution strategy (P-CMA-ES) with interpretability constraints to improve the model's accuracy. Finally, a case study evaluating the bearing components of a flywheel system is conducted to validate the proposed method. Experimental results demonstrate that I-BRB achieves higher accuracy in health condition assessment

A Study of the Impact of Social Responsibility Cues on the Long-Term Effectiveness of Gamification Strategies: Insights from the Adverse Effects of Game Strategies

Mobile applications can integrate games or gamification elements to build a game metaverse, thus increasing use duration. Research on game metaverses is relatively scarce, mainly focusing on the positive effects of game elements. Few studies have considered the push-away power of game or gamification elements. In this paper, we explore the role of pro-environmental cues in mitigating the push-away power of game or gamification elements from the perspective of the adverse effects of game elements. A total of 250 participants were recruited to engage in two two-factor between-subject studies. Study 1 demonstrated that pro-environmental cues increased self-consciousness during the game and mitigated adverse outcomes after the game. The results of Study 2 further supported the findings of Study 1. The results showed that the perception of pleasure during the game reduced the effects of pro-environmental cues. The pro-environmental cues mitigated adverse outcomes after the game experience when perceiving lower or moderate enjoyment. In comparison, the effects of pro-environmental cues on mitigating negative consequences after the game experience were insignificant when experiencing higher enjoyment