General GAN-generated image detection by data augmentation in fingerprint domain

In this work, we investigate improving the generalizability of GAN-generated image detectors by performing data augmentation in the fingerprint domain. Specifically, we first separate the fingerprints and contents of the GAN-generated images using an autoencoder based GAN fingerprint extractor, followed by random perturbations of the fingerprints. Then the original fingerprints are substituted with the perturbed fingerprints and added to the original contents, to produce images that are visually invariant but with distinct fingerprints. The perturbed images can successfully imitate images generated by different GANs to improve the generalization of the detectors, which is demonstrated by the spectra visualization. To our knowledge, we are the first to conduct data augmentation in the fingerprint domain. Our work explores a novel prospect that is distinct from previous works on spatial and frequency domain augmentation. Extensive cross-GAN experiments demonstrate the effectiveness of our method compared to the state-of-the-art methods in detecting fake images generated by unknown GANs

Corrosion behavior of Mg-3Gd-1Zn-0.4Zr alloy with and without stacking faults

To develop biodegradable magnesium alloy with desirable corrosion properties, a low Gd-containing Mg–3Gd–1Zn–0.4Zr (wt%, GZ31K) alloy was prepared. The as-cast ingot was solution treated and then hot extruded. Microstructures were characterized by scanning electron microscopy (SEM). Corrosion behavior of the alloy under each condition was studied by hydrogen evolution and quasi in-situ corrosion methods. It has been found that the as-cast alloy is composed of α-Mg, stacking faults (SFs) at the outer edge of the matrix grains, and eutectic phase along the grain boundaries. After solution treatment, the SFs disappear and precipitates rich in Zn and Zr elements form in the grain interior and boundaries. The microstructure is significantly refined after extrusion. Hydrogen evolution tests show that the as-cast alloy exhibits the best corrosion resistance, and the solution-treated alloy has the worst corrosion resistance. Corrosion rate of the alloy under each condition decreases first and then increases with prolonging immersion time. Corrosion experiments demonstrate that α-Mg was corroded preferentially, the eutectic phase and precipitates exhibit better corrosion resistance. The as-extruded alloy demonstrates uniform corrosion due to fine and homogeneous microstructure.This project was supported by the Natural Science Foundation of Jiangsu Province for Outstanding Youth (BK20160081), the Natural Science Foundation of Higher Education Institutions of Jiangsu Province – Key Project (18KJA430008), the Jiangsu Government Scholarship for Overseas Studies, the “333 Project” of Jiangsu Province (BRA2018338), the National Natural Science Foundation of China (51701093), and the Outstanding Scientific and Technological Innovation Team in Colleges and Universities of Jiangsu Province

Learning Second Order Local Anomaly for General Face Forgery Detection

In this work, we propose a novel method to improve the generalization ability of CNN-based face forgery detectors. Our method considers the feature anomalies of forged faces caused by the prevalent blending operations in face forgery algorithms. Specifically, we propose a weakly supervised Second Order Local Anomaly (SOLA) learning module to mine anomalies in local regions using deep feature maps. SOLA first decomposes the neighborhood of local features by different directions and distances and then calculates the first and second order local anomaly maps which provide more general forgery traces for the classifier. We also propose a Local Enhancement Module (LEM) to improve the discrimination between local features of real and forged regions, so as to ensure accuracy in calculating anomalies. Besides, an improved Adaptive Spatial Rich Model (ASRM) is introduced to help mine subtle noise features via learnable high pass filters. With neither pixel level annotations nor external synthetic data, our method using a simple ResNet18 backbone achieves competitive performances compared with state-of-the-art works when evaluated on unseen forgeries

Strong Gravitational Lensing of Gravitational Waves with TianQin

When gravitational waves pass by a massive object on its way to the Earth, strong gravitational lensing effect will happen. Thus the GW signal will be amplified, deflected, and delayed in time. Through analysing the lensed GW waveform, physical properties of the lens can be inferred. On the other hand, neglecting lensing effects in the analysis of GW data may induce systematic errors in the estimating of source parameters. As a space-borne GW detector, TianQin will be launched in the 2030s. It is expected to detect dozens of MBHBs merger as far as z = 15, and thus will have high probability to detect at least one lensed event during the mission lifetime. In this article, we discuss the capability of TianQin to detect lensed MBHBs signals. Three lens models are considered in this work: the point mass model, the SIS model, and the NFW model. The sensitive frequency band for space-borne GW detectors is around milli-hertz, and the corresponding GW wavelength could be comparable to the lens gravitational length scale, which requires us to account for wave diffraction effects. In calculating lensed waveforms, we adopt the approximation of geometric optics at high frequencies to accelerate computation, while precisely evaluate the diffraction integral at low frequencies. Through a Fisher analysis, we analyse the accuracy to estimate the lens parameters. We find that the accuracy can reach to the level of 10^-3 for the mass of point mass and SIS lens, and to the level of 10^-5 for the density of NFW lens. We also assess the impact on the accurate of estimating the source parameters, and find that the improvement of the accuracy is dominated by the increasing of SNR.Comment: 12 pages, 8 figure

Stability of SARS-CoV-2 in cold-chain transportation environments and the efficacy of disinfection measures

BackgroundLow temperature is conducive to the survival of COVID-19. Some studies suggest that cold-chain environment may prolong the survival of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and increase the risk of transmission. However, the effect of cold-chain environmental factors and packaging materials on SARS-CoV-2 stability remains unclear.MethodsThis study aimed to reveal cold-chain environmental factors that preserve the stability of SARS-CoV-2 and further explore effective disinfection measures for SARS-CoV-2 in the cold-chain environment. The decay rate of SARS-CoV-2 pseudovirus in the cold-chain environment, on various types of packaging material surfaces, i.e., polyethylene plastic, stainless steel, Teflon and cardboard, and in frozen seawater was investigated. The influence of visible light (wavelength 450 nm-780 nm) and airflow on the stability of SARS-CoV-2 pseudovirus at -18°C was subsequently assessed.ResultsExperimental data show that SARS-CoV-2 pseudovirus decayed more rapidly on porous cardboard surfaces than on nonporous surfaces, including polyethylene (PE) plastic, stainless steel, and Teflon. Compared with that at 25°C, the decay rate of SARS-CoV-2 pseudovirus was significantly lower at low temperatures. Seawater preserved viral stability both at -18°C and with repeated freeze−thaw cycles compared with that in deionized water. Visible light from light-emitting diode (LED) illumination and airflow at -18°C reduced SARS-CoV-2 pseudovirus stability.ConclusionOur studies indicate that temperature and seawater in the cold chain are risk factors for SARS-CoV-2 transmission, and LED visible light irradiation and increased airflow may be used as disinfection measures for SARS-CoV-2 in the cold-chain environment

Efficient Self-supervised Vision Transformers for Representation Learning

This paper investigates two techniques for developing efficient self-supervised vision transformers (EsViT) for visual representation learning. First, we show through a comprehensive empirical study that multi-stage architectures with sparse self-attentions can significantly reduce modeling complexity but with a cost of losing the ability to capture fine-grained correspondences between image regions. Second, we propose a new pre-training task of region matching which allows the model to capture fine-grained region dependencies and as a result significantly improves the quality of the learned vision representations. Our results show that combining the two techniques, EsViT achieves 81.3% top-1 on the ImageNet linear probe evaluation, outperforming prior arts with around an order magnitude of higher throughput. When transferring to downstream linear classification tasks, EsViT outperforms its supervised counterpart on 17 out of 18 datasets. The code and models will be publicly available.Comment: 24 pages, 12 figures, file size 13.6M

LACMA: Language-Aligning Contrastive Learning with Meta-Actions for Embodied Instruction Following

End-to-end Transformers have demonstrated an impressive success rate for Embodied Instruction Following when the environment has been seen in training. However, they tend to struggle when deployed in an unseen environment. This lack of generalizability is due to the agent's insensitivity to subtle changes in natural language instructions. To mitigate this issue, we propose explicitly aligning the agent's hidden states with the instructions via contrastive learning. Nevertheless, the semantic gap between high-level language instructions and the agent's low-level action space remains an obstacle. Therefore, we further introduce a novel concept of meta-actions to bridge the gap. Meta-actions are ubiquitous action patterns that can be parsed from the original action sequence. These patterns represent higher-level semantics that are intuitively aligned closer to the instructions. When meta-actions are applied as additional training signals, the agent generalizes better to unseen environments. Compared to a strong multi-modal Transformer baseline, we achieve a significant 4.5% absolute gain in success rate in unseen environments of ALFRED Embodied Instruction Following. Additional analysis shows that the contrastive objective and meta-actions are complementary in achieving the best results, and the resulting agent better aligns its states with corresponding instructions, making it more suitable for real-world embodied agents. The code is available at: https://github.com/joeyy5588/LACMA.Comment: EMNLP 202

Learning from Rich Semantics and Coarse Locations for Long-tailed Object Detection

Long-tailed object detection (LTOD) aims to handle the extreme data imbalance in real-world datasets, where many tail classes have scarce instances. One popular strategy is to explore extra data with image-level labels, yet it produces limited results due to (1) semantic ambiguity -- an image-level label only captures a salient part of the image, ignoring the remaining rich semantics within the image; and (2) location sensitivity -- the label highly depends on the locations and crops of the original image, which may change after data transformations like random cropping. To remedy this, we propose RichSem, a simple but effective method, which is robust to learn rich semantics from coarse locations without the need of accurate bounding boxes. RichSem leverages rich semantics from images, which are then served as additional soft supervision for training detectors. Specifically, we add a semantic branch to our detector to learn these soft semantics and enhance feature representations for long-tailed object detection. The semantic branch is only used for training and is removed during inference. RichSem achieves consistent improvements on both overall and rare-category of LVIS under different backbones and detectors. Our method achieves state-of-the-art performance without requiring complex training and testing procedures. Moreover, we show the effectiveness of our method on other long-tailed datasets with additional experiments. Code is available at \url{https://github.com/MengLcool/RichSem}.Comment: Accepted by NeurIPS202

A hydrated deep eutectic electrolyte with finely-tuned solvation chemistry for high-performance zinc-ion batteries

Despite their cost-effectiveness and intrinsic safety, aqueous zinc-ion batteries have faced challenges with poor reversibility originating from various active water-induced side reactions. After systematically scrutinizing the effects of water on the evolution of solvation structures, electrolyte properties, and electrochemical performances through experimental and theoretical approaches, a hydrated deep eutectic electrolyte with a water-deficient solvation structure ([Zn(H2O)2(eg)2(otf)2]) and reduced free water content in the bulk solution is proposed in this work. This electrolyte can dramatically suppress water-induced side reactions and provide high Zn2+ mass transfer kinetics, resulting in highly reversible Zn anodes (∼99.6% Coulombic efficiency over 1000 cycles and stable cycling over 4500 h) and high capacity Zn//NVO full cells (436 mA h g−1). This work will aid the understanding of electrolyte solvation structure–electrolyte property–electrochemical performance relationships of aqueous electrolytes in aqueous zinc-ion batteries

Clinical observation on the treatment of displaced distal radial and ulnar fractures in children by closed manipulation combined with splinting

ObjectiveThe aim of this study was to investigate the clinical efficacy of closed manipulation combined with splinting in the treatment of displaced distal radial and ulnar fractures in children.MethodsA total of 82 children with displaced fractures of the distal radial and ulnar segment who met the inclusion criteria and were treated as outpatients or inpatients in the orthopedic department of Guangzhou Orthopedic Hospital, from January 2016 to June 2022 were randomly divided into an observation group and a control group: 41 children in the observation group were treated with closed manipulation combined with splint fixation; 41 children in the control group were fixed with incisional repositioning elastic nails combined with internal plates. The Anderson efficacy grading, visual analog scale (VAS) score, fracture healing time, treatment cost, hospital days, and complications were observed and compared between the two groups.ResultThe efficacy was evaluated according to the Anderson forearm fracture efficacy evaluation criteria, and the results of statistical analysis showed no statistically significant differences between the two groups (P > 0.05). At 3 and 7 weeks after treatment, the VAS scores of children in both groups decreased (P < 0.05), and the VAS scores in the observation group were significantly lower than those in the control group (P < 0.05), indicating that the observation group had a significant advantage in the relief of pain after treatment. The fractures healed in both groups after treatment with the two different methods, and the difference in healing time between the two groups was not statistically significant (P > 0.05). The length of hospital stay, treatment cost, and complication ratio were significantly lower in the observation group than in the control group (P < 0.05).ConclusionIn children with displaced fractures of the distal radial and ulnar segments, treatment by manual repositioning with external splinting can achieve satisfactory results with simple operation, low cost, short hospital stay, and few complications, which is especially suitable to be promoted in primary hospitals and has good social benefits