Degeneration-Tuning: Using Scrambled Grid shield Unwanted Concepts from Stable Diffusion

Owing to the unrestricted nature of the content in the training data, large text-to-image diffusion models, such as Stable Diffusion (SD), are capable of generating images with potentially copyrighted or dangerous content based on corresponding textual concepts information. This includes specific intellectual property (IP), human faces, and various artistic styles. However, Negative Prompt, a widely used method for content removal, frequently fails to conceal this content due to inherent limitations in its inference logic. In this work, we propose a novel strategy named \textbf{Degeneration-Tuning (DT)} to shield contents of unwanted concepts from SD weights. By utilizing Scrambled Grid to reconstruct the correlation between undesired concepts and their corresponding image domain, we guide SD to generate meaningless content when such textual concepts are provided as input. As this adaptation occurs at the level of the model's weights, the SD, after DT, can be grafted onto other conditional diffusion frameworks like ControlNet to shield unwanted concepts. In addition to qualitatively showcasing the effectiveness of our DT method in protecting various types of concepts, a quantitative comparison of the SD before and after DT indicates that the DT method does not significantly impact the generative quality of other contents. The FID and IS scores of the model on COCO-30K exhibit only minor changes after DT, shifting from 12.61 and 39.20 to 13.04 and 38.25, respectively, which clearly outperforms the previous methods

A Retrodirective Array Enabled by CMOS Chips for Two-Way Wireless Communication with Automatic Beam Tracking

This article proposes and demonstrates a retrodirective array (RDA) for two-way wireless communication with automatic beam tracking. The proposed RDA is enabled by specifically designed chips made using a domestic complementary metal-oxide semiconductor (CMOS) process. The highly integrated CMOS chip includes a receiving (Rx) chain, a transmitting (Tx) chain, and a unique tracking phase-locked loop (PLL) for the crucial conjugated phase recovery in the RDA. This article also proposes a method to reduce the beam pointing error (BPE) in a conventional RDA. To validate the above ideas simply yet without loss of generality, a 2.4 GHz RDA is demonstrated through two-way communication links between the Rx and Tx chains, and an on-chip quadrature coupler is designed to achieve a non-retrodirective signal suppression of 23 dBc. The experimental results demonstrate that the proposed RDA, which incorporates domestically manufactured low-cost 0.18 μm CMOS chips, is capable of automatically tracking beams covering ±40° with a reduced BPE. Each CMOS chip in the RDA has a compact size of 4.62 mm2 and a low power consumption of 0.15 W. To the best of the authors’ knowledge, this is the first research to demonstrate an RDA with a fully customized CMOS chip for wireless communication with automatic beam tracking

Modeling oil-paper insulation frequency domain spectroscopy based on its microscopic dielectric processes

The present study sets out to devise a universal function model to explain the characteristic curve obtained in the frequency domain spectroscopy (FDS) test on oil-paper insulation, based on its microscopic conduction and relaxation processes, and thus to enhance the accuracy and applicability of the test. First, from the analysis of the relationship between the real and imaginary parts of the dielectric's complex permittivity, it is demonstrated that a relaxation peak co-exists with the conduction process in the low-frequency band of an FDS curve obtained for oil-impregnated paper sample. Second, values for polarization barrier heights, essential to the determination of the microscopic polarization mechanisms, are presented as results of FDS and thermally stimulated depolarization current experiments carried out on oil-impregnated paper samples. The polarization peaks obtained in the imaginary permittivity frequency spectrum are determined as, respectively, space charge polarization and interface polarization. Finally, a function model in good agreement with experiment data is proposed, which quantitatively describes the FDS curve in oil-impregnated sample, including two relaxation processes and one conduction process

Data Loss Reconstruction Method for a Bridge Weigh-in-Motion System Using Generative Adversarial Networks

In the application of a bridge weigh-in-motion (WIM) system, the collected data may be temporarily or permanently lost due to sensor failure or system transmission failure. The high data loss rate weakens the distribution characteristics of the collected data and the ability of the monitoring system to conduct assessments on bridge condition. A deep learning-based model, or generative adversarial network (GAN), is proposed to reconstruct the missing data in the bridge WIM systems. The proposed GAN in this study can model the collected dataset and predict the missing data. Firstly, the data from stable measurements before the data loss are provided, and then the generator is trained to extract the retained features from the dataset and the data lost in the process are collected by using only the responses of the remaining functional sensors. The discriminator feeds back the recognition results to the generator in order to improve its reconstruction accuracy. In the model training, two loss functions, generation loss and confrontation loss, are used, and the general outline and potential distribution characteristics of the signal are well processed by the model. Finally, by applying the engineering data of the Hangzhou Jiangdong Bridge to the GAN model, this paper verifies the effectiveness of the proposed method. The results show that the final reconstructed dataset is in good agreement with the actual dataset in terms of total vehicle weight and axle weight. Furthermore, the approximate contour and potential distribution characteristics of the original dataset are reproduced. It is suggested that the proposed method can be used in real-life applications. This research can provide a promising method for the data reconstruction of bridge monitoring systems

An Edge-assisted, Object-oriented Random Forest Approach for Refined Extraction of Tea Plantations Using Multi-temporal Sentinel-2 and High-resolution Gaofen-2 Imagery

As a consumed and influential natural plant beverage, tea is widely planted in subtropical and tropical areas all over the world. Affected by (sub) tropical climate characteristics, the underlying surface of the tea distribution area is extremely complex, with a variety of vegetation types. In addition, tea distribution is scattered and fragmentized in most of China. Therefore, it is difficult to obtain accurate tea information based on coarse resolution remote sensing data and existing feature extraction methods. This study proposed a boundary-enhanced, object-oriented random forest method on the basis of high-resolution GF-2 and multi-temporal Sentinel-2 data. This method uses multispectral indexes, textures, vegetable indices, and variation characteristics of time-series NDVI from the multi-temporal Sentinel-2 imageries to obtain abundant features related to the growth of tea plantations. To reduce feature redundancy and computation time, the feature elimination algorithm based on Mean Decrease Accuracy (MDA) was used to generate the optimal feature set. Considering the serious boundary inconsistency problem caused by the complex and fragmented land cover types, high resolution GF-2 image was segmented based on the MultiResolution Segmentation (MRS) algorithm to assist the segmentation of Sentinel-2, which contributes to delineating meaningful objects and enhancing the reliability of the boundary for tea plantations. Finally, the object-oriented random forest method was utilized to extract the tea information based on the optimal feature combination in the Jingmai Mountain, Yunnan Province. The resulting tea plantation map had high accuracy, with a 95.38% overall accuracy and 0.91 kappa coefficient. We conclude that the proposed method is effective for mapping tea plantations in high heterogeneity mountainous areas and has the potential for mapping tea plantations in large areas

Evaluation of Three Long-Term Remotely Sensed Precipitation Estimates for Meteorological Drought Monitoring over China

Remotely sensed precipitation estimates (RSPEs) play an essential role in monitoring drought, especially in ungauged or sparsely gauged areas. In this study, we evaluated the ability of three popular long-term RSPEs (PERSIANN, CHIRPS, and MSWEP) in capturing the meteorological drought variations over the 10 first-level water resource basins of China, based on the standardized precipitation index (SPI). Drought events were identified by run theory, and the drought characteristics (i.e., duration, severity, and intensity) were also evaluated and compared with a gridded in situ observational precipitation dataset (CMA). The results showed that the three RSPEs could generally capture the spatial patterns and trends of the CMA and showed better performance in the wetter basins. MSWEP had the best performance for the categorical skill of POD, followed by CHIRPS and PERSIANN for the four timescales. SPI6 was the optimal timescale for identifying meteorological drought events. There were large skill divergences in the 10 first-level basins for capturing the drought characteristics. CHIRPS can efficiently reproduce the spatial distribution of drought characteristics, with similar metrics of MDS, MDI, and MDP, followed by MSWEP and PERSIANN. Overall, no single product always outperformed the other products in capturing drought characteristics, underscoring the necessity of multiproduct ensemble applications. Our study’s findings may provide useful information for drought monitoring in areas with complex terrain and sparse rain-gauge networks

ERCC1 rs11615 polymorphism and chemosensitivity to platinum drugs in patients with ovarian cancer: a systematic review and meta-analysis

Abstract Objective To explore the relationship between ERCC1 rs11615 polymorphism and chemosensitivity to platinum drugs in ovarian cancer by the method of meta-analysis. Methods Pubmed, Web of Science, EMBASE, Cochrane Library, China National Knowledge Infrastructure (CNKI), and China Wanfang databases were comprehensively searched up to September 2020, to identify the relationship between ERCC1 rs11615 polymorphism and chemosensitivity of ovarian cancer. The data was analyzed by Stata 15.0 statistic software. Results A total of 10 published papers were included, including 1866 patients with ovarian cancer. The results showed that compared allele C at ERCC1 rs11615 locus with allele T, the pooled OR was 0.92 (95%CI:0.68 ~ 1.24, P > 0.05). There were no significant differences in recessive, dominant, homozygous, and heterozygous models. In accordance with a subgroup analysis of Ethnicity, all genotypes were statistically significant in the Asian population. In the allelic, dominant, recessive, homozygous and heterozygous models, the OR was 0.70 (95%CI:0.51 ~ 0.95), 0.20 (95%CI:0.07 ~ 0.56), 0.79 (95%CI:0.63 ~ 1.00), 0.21 (95%CI:0.07 ~ 0.59), 0.19 (95%CI:0.07 ~ 0.54), respectively, while in the Caucasian population, no statistically significant genotype was found. Conclusion The ERCC1 rs11615 polymorphism is associated with chemosensitivity in patients with ovarian cancer, especially in the Asian population, but not in the Caucasian population

Enhancing the Enantioselectivity and Catalytic Efficiency of Esterase from Bacillus subtilis for Kinetic Resolution of l‑Menthol through Semirational Design

Enzymatic kinetic resolution is a promising way to produce l-menthol. However, the properties of the reported biocatalysts are still unsatisfactory and far from being ready for industrial application. Herein, a para-nitrobenzylesterase (pnbA) gene from Bacillus subtilis was cloned and expressed to produce l-menthol from d,l-menthyl acetate. The highest enantiomeric excess (ee) value of the product generated by pnbA was only approximately 80%, with a high conversion rate (47.8%) of d,l-menthyl acetate with the help of a cosolvent, indicating high catalytic activity but low enantioselectivity (E = 19.95). To enhance the enantioselectivity and catalytic efficiency of pnbA to d,l-menthyl acetate in an organic solvent-free system, site-directed mutagenesis was performed based on the results of molecular docking. The F314E/F315T mutant showed the best catalytic properties (E = 36.25) for d,l-menthyl acetate, with 92.11% ee and 30.58% conversion of d,l-menthyl acetate. To further improve the properties of pnbA, additional mutants were constructed based on the structure-guided triple-code saturation mutagenesis strategy. Finally, four mutants were screened for the best enantioselectivity (ee > 99%, E > 300) and catalytic efficiency at a high substrate concentration (200 g/L) without a cosolvent. This work provides several generally applicable biocatalysts for the industrial production of l-menthol