Inheritance and Development of Chinese Ancient Figure Painting and Modern Fashion Drawing

Nowadays, with the rapid development of economy, people have growing requirements for clothing fashion, which have driven the development of clothing industry in China. However, opportunities and challenges usually coexist. The art of fashion drawing, which has the function of advertising and artistic appreciation etc., also faces great challenges because of this. Especially, with the rapid development of science and technology, it is an important problem worthy of further research and thinking to inherit and develop the Chinese ancient figure painting and modern fashion drawing and effectively combines the drawing skills of traditional Chinese painting with those of modern fashion drawing

Predicting Changes of Rainfall Erosivity and Hillslope Erosion Risk Across Greater Sydney Region, Australia

Rainfall changes have significant effect on rainfall erosivity and hillslope erosion, but the magnitude of the impact is not well quantified because of the lack of high resolution rainfall data. Recently, the 2-km rainfall projections from regional climate models have become available for the Greater Sydney Region (GSR) at daily time step for the current (1990-2009) and future (2040-2059) periods. These climate projections allow predicting of rainfall erosivity changes and the associated hillslope erosion risk for climate change assessment and mitigation. In this study, we developed a daily rainfall erosivity model for GSR to predict rainfall erosivity from the current and future daily rainfall data. We produced time-series hillslope erosion risk maps using the revised universal soil loss equations on monthly and annual bases for the two contrasting periods. These products were spatially interpolated to a fine resolution (100 m) useful for climate impact assessment and erosion risk mitigation. The spatial variation was assessed based on the state plan regions and the temporal variation on monthly and annual bases. These processes have been implemented in a geographic information system so that they are automated, fast, and repeatable. Our prediction shows relatively good correlation with point-based Pluviograph calculation on rainfall erosivity and the previous study (both R2 and Ec \u3e 0.70). The results indicate that hillslope erosion risk is likely to increase 10-60% in the GSR within the next 50 years, and changes are greater in the coastal and the Blue Mountains, particularly in late summer (January and February). The methodology developed in this study is being extended to south-east Australia

Probably Approximately Correct Federated Learning

Federated learning (FL) is a new distributed learning paradigm, with privacy, utility, and efficiency as its primary pillars. Existing research indicates that it is unlikely to simultaneously attain infinitesimal privacy leakage, utility loss, and efficiency. Therefore, how to find an optimal trade-off solution is the key consideration when designing the FL algorithm. One common way is to cast the trade-off problem as a multi-objective optimization problem, i.e., the goal is to minimize the utility loss and efficiency reduction while constraining the privacy leakage not exceeding a predefined value. However, existing multi-objective optimization frameworks are very time-consuming, and do not guarantee the existence of the Pareto frontier, this motivates us to seek a solution to transform the multi-objective problem into a single-objective problem because it is more efficient and easier to be solved. To this end, we propose FedPAC, a unified framework that leverages PAC learning to quantify multiple objectives in terms of sample complexity, such quantification allows us to constrain the solution space of multiple objectives to a shared dimension, so that it can be solved with the help of a single-objective optimization algorithm. Specifically, we provide the results and detailed analyses of how to quantify the utility loss, privacy leakage, privacy-utility-efficiency trade-off, as well as the cost of the attacker from the PAC learning perspective

Trading Off Privacy, Utility and Efficiency in Federated Learning

Federated learning (FL) enables participating parties to collaboratively build a global model with boosted utility without disclosing private data information. Appropriate protection mechanisms have to be adopted to fulfill the opposing requirements in preserving \textit{privacy} and maintaining high model \textit{utility}. In addition, it is a mandate for a federated learning system to achieve high \textit{efficiency} in order to enable large-scale model training and deployment. We propose a unified federated learning framework that reconciles horizontal and vertical federated learning. Based on this framework, we formulate and quantify the trade-offs between privacy leakage, utility loss, and efficiency reduction, which leads us to the No-Free-Lunch (NFL) theorem for the federated learning system. NFL indicates that it is unrealistic to expect an FL algorithm to simultaneously provide excellent privacy, utility, and efficiency in certain scenarios. We then analyze the lower bounds for the privacy leakage, utility loss and efficiency reduction for several widely-adopted protection mechanisms including \textit{Randomization}, \textit{Homomorphic Encryption}, \textit{Secret Sharing} and \textit{Compression}. Our analysis could serve as a guide for selecting protection parameters to meet particular requirements

Theoretically Principled Federated Learning for Balancing Privacy and Utility

We propose a general learning framework for the protection mechanisms that protects privacy via distorting model parameters, which facilitates the trade-off between privacy and utility. The algorithm is applicable to arbitrary privacy measurements that maps from the distortion to a real value. It can achieve personalized utility-privacy trade-off for each model parameter, on each client, at each communication round in federated learning. Such adaptive and fine-grained protection can improve the effectiveness of privacy-preserved federated learning. Theoretically, we show that gap between the utility loss of the protection hyperparameter output by our algorithm and that of the optimal protection hyperparameter is sub-linear in the total number of iterations. The sublinearity of our algorithm indicates that the average gap between the performance of our algorithm and that of the optimal performance goes to zero when the number of iterations goes to infinity. Further, we provide the convergence rate of our proposed algorithm. We conduct empirical results on benchmark datasets to verify that our method achieves better utility than the baseline methods under the same privacy budget

A Meta-learning Framework for Tuning Parameters of Protection Mechanisms in Trustworthy Federated Learning

Trustworthy Federated Learning (TFL) typically leverages protection mechanisms to guarantee privacy. However, protection mechanisms inevitably introduce utility loss or efficiency reduction while protecting data privacy. Therefore, protection mechanisms and their parameters should be carefully chosen to strike an optimal tradeoff between \textit{privacy leakage}, \textit{utility loss}, and \textit{efficiency reduction}. To this end, federated learning practitioners need tools to measure the three factors and optimize the tradeoff between them to choose the protection mechanism that is most appropriate to the application at hand. Motivated by this requirement, we propose a framework that (1) formulates TFL as a problem of finding a protection mechanism to optimize the tradeoff between privacy leakage, utility loss, and efficiency reduction and (2) formally defines bounded measurements of the three factors. We then propose a meta-learning algorithm to approximate this optimization problem and find optimal protection parameters for representative protection mechanisms, including Randomization, Homomorphic Encryption, Secret Sharing, and Compression. We further design estimation algorithms to quantify these found optimal protection parameters in a practical horizontal federated learning setting and provide a theoretical analysis of the estimation error.Comment: arXiv admin note: text overlap with arXiv:2209.0023

Inhibition of microRNA-383 promotes apoptosis of human colon cancer cells by upregulation of caspase-2 gene expression

Purpose: To investigate microRNA-383 (miR-383) as a therapeutic target for the management of colon cancer.Methods: Total RNA was isolated using RNeasy RNA isolation kit according to the manufacturer’s instructions. cDNA was synthesized using RevertAid cDNA synthesis kit. Expression analysis was carried out by quantitative real-time polymerase chain reaction (RT-PCR). Cell proliferation was examined using CellTiter 96 AQueous One Solution Cell Proliferation Assay system, while apoptosis was detected by 4',6-diamidino-2-phenylindole (DAPI) and annexin V/PI double staining followed by flow cytometry. The miR-383 target was delimited using TargetScan software. Protein expression analysis was carried out by western blotting.Results: The results indicate that miR-383 was highly expressed in colon cancer cells. Down-regulation of miR-383 inhibited cancer cell proliferation, and promoted apoptosis and cell cycle arrest. Furthermore, in silico analysis revealed caspase-2 gene to be the downstream target of miR-383, a finding that was further confirmed by western blotting.Conclusion: The results reveal that miR-383 may be an important target to tackle the increasing incidence of colon cancer. Thus, drugs that target miR-383 and inhibit its expression can potentially be developed for the treatment of colon cancer.Keywords: MicroRNA, Colon cancer, Cell proliferation, Apoptosis, Protein expressio

EST analysis of gene expression in the tentacle of Cyanea capillata

AbstractJellyfish, Cyanea capillata, has an important position in head patterning and ion channel evolution, in addition to containing a rich source of toxins. In the present study, 2153 expressed sequence tags (ESTs) from the tentacle cDNA library of C. capillata were analyzed. The initial ESTs consisted of 198 clusters and 818 singletons, which revealed approximately 1016 unique genes in the data set. Among these sequences, we identified several genes related to head and foot patterning, voltage-dependent anion channel gene and genes related to biological activities of venom. Five kinds of proteinase inhibitor genes were found in jellyfish for the first time, and some of them were highly expressed with unknown functions

The effect in the film thickness reducing mechanism of functional groups in porous carbon sulfuric acid supercapacitor

In this paper, the different types and number of functional groups in porous carbon–carbon pore channels are discussed in the thinning mechanism of ionic solvent thin films, which has a significant impact on the absorption of H2SO4 electrolyte based Electric Double Layer Capacitors (EDLC). By exploring the binding energy of –OH, –COOH, –SO3H, –NO2 and other four functional groups with sulfuric acid and hexahydrate sulfuric acid of porous carbon channel and hexahydrate sulfuric acid, it was found that –OH had no repulsive effect on the cathode of the battery, and –COOH, –SO3H, –NO2 and other functional groups had obvious repulsive effect on the cathode of EDLC with the increase of the functional groups number, that is, there was an effect of increasing the capacitance of EDLC by increasing the number of sulfide molecular. This will excavate the potential electrode material in the practical application