Multisource Semisupervised Adversarial Domain Generalization Network for Cross-Scene Sea-Land Clutter Classification

Deep learning (DL)-based sea\textendash land clutter classification for sky-wave over-the-horizon-radar (OTHR) has become a novel research topic. In engineering applications, real-time predictions of sea\textendash land clutter with existing distribution discrepancies are crucial. To solve this problem, this article proposes a novel Multisource Semisupervised Adversarial Domain Generalization Network (MSADGN) for cross-scene sea\textendash land clutter classification. MSADGN can extract domain-invariant and domain-specific features from one labeled source domain and multiple unlabeled source domains, and then generalize these features to an arbitrary unseen target domain for real-time prediction of sea\textendash land clutter. Specifically, MSADGN consists of three modules: domain-related pseudolabeling module, domain-invariant module, and domain-specific module. The first module introduces an improved pseudolabel method called domain-related pseudolabel, which is designed to generate reliable pseudolabels to fully exploit unlabeled source domains. The second module utilizes a generative adversarial network (GAN) with a multidiscriminator to extract domain-invariant features, to enhance the model's transferability in the target domain. The third module employs a parallel multiclassifier branch to extract domain-specific features, to enhance the model's discriminability in the target domain. The effectiveness of our method is validated in twelve domain generalizations (DG) scenarios. Meanwhile, we selected 10 state-of-the-art DG methods for comparison. The experimental results demonstrate the superiority of our method.Comment: 15 pages, 8 figures, 4 table

Tongxinluo Prevents Endothelial Dysfunction Induced by Homocysteine Thiolactone In Vivo

Aim. To explore whether Chinese traditional medicine, tongxinluo (TXL), exerts beneficial effects on endothelial dysfunction induced by homocysteine thiolactone (HTL) and to investigate the potential mechanisms. Methods and Results. Incubation of cultured human umbilical vein endothelial cells with HTL (1 mM) for 24 hours significantly reduced cell viabilities assayed by MTT, and enhanced productions of reactive oxygen species. Pretreatment of cells with TXL (100, 200, and 400 μg/mL) for 1 hour reversed these effects induced by HTL. Further, coincubation with GW9662 (0.01, 0.1 mM) abolished the protective effects of TXL on HTL-treated cells. In ex vivo experiments, exposure of isolated aortic rings from rats to HTL (1 mM) for 1 hour dramatically impaired acetylcholine-induced endothelium-dependent relaxation, reduced SOD activity, and increased malondialdehyde content in aortic tissues. Preincubation of aortic rings with TXL (100, 200, and 400 μg/mL) normalized the disorders induced by HTL. Importantly, all effects induced by TXL were reversed by GW9662. In vivo analysis indicated that the administration of TXL (1.0 g/kg/d) remarkably suppressed oxidative stress and prevented endothelial dysfunction in rats fed with HTL (50 mg/kg/d) for 8 weeks. Conclusions. TXL improves endothelial functions in rats fed with HTL, which is related to PPARγ-dependent suppression of oxidative stress

Data Augmentation and Classification of Sea-Land Clutter for Over-the-Horizon Radar Using AC-VAEGAN

In the sea-land clutter classification of sky-wave over-the-horizon-radar (OTHR), the imbalanced and scarce data leads to a poor performance of the deep learning-based classification model. To solve this problem, this paper proposes an improved auxiliary classifier generative adversarial network~(AC-GAN) architecture, namely auxiliary classifier variational autoencoder generative adversarial network (AC-VAEGAN). AC-VAEGAN can synthesize higher quality sea-land clutter samples than AC-GAN and serve as an effective tool for data augmentation. Specifically, a 1-dimensional convolutional AC-VAEGAN architecture is designed to synthesize sea-land clutter samples. Additionally, an evaluation method combining both traditional evaluation of GAN domain and statistical evaluation of signal domain is proposed to evaluate the quality of synthetic samples. Using a dataset of OTHR sea-land clutter, both the quality of the synthetic samples and the performance of data augmentation of AC-VAEGAN are verified. Further, the effect of AC-VAEGAN as a data augmentation method on the classification performance of imbalanced and scarce sea-land clutter samples is validated. The experiment results show that the quality of samples synthesized by AC-VAEGAN is better than that of AC-GAN, and the data augmentation method with AC-VAEGAN is able to improve the classification performance in the case of imbalanced and scarce sea-land clutter samples.Comment: 13 pages, 16 figure

RaTrack: Moving Object Detection and Tracking with 4D Radar Point Cloud

Mobile autonomy relies on the precise perception of dynamic environments. Robustly tracking moving objects in 3D world thus plays a pivotal role for applications like trajectory prediction, obstacle avoidance, and path planning. While most current methods utilize LiDARs or cameras for Multiple Object Tracking (MOT), the capabilities of 4D imaging radars remain largely unexplored. Recognizing the challenges posed by radar noise and point sparsity in 4D radar data, we introduce RaTrack, an innovative solution tailored for radar-based tracking. Bypassing the typical reliance on specific object types and 3D bounding boxes, our method focuses on motion segmentation and clustering, enriched by a motion estimation module. Evaluated on the View-of-Delft dataset, RaTrack showcases superior tracking precision of moving objects, largely surpassing the performance of the state of the art. We release our code and model at https://github.com/LJacksonPan/RaTrack.Comment: Accepted to ICRA 2024. 8 pages, 4 figures. Co-first authorship for Zhijun Pan, Fangqiang Ding and Hantao Zhong, listed randomly. See demo vide at: https://www.youtube.com/watch?v=_uSpbxOlLG

SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models

Recent advances in large language models (LLMs) have demonstrated notable progress on many mathematical benchmarks. However, most of these benchmarks only feature problems grounded in junior and senior high school subjects, contain only multiple-choice questions, and are confined to a limited scope of elementary arithmetic operations. To address these issues, this paper introduces an expansive benchmark suite SciBench that aims to systematically examine the reasoning capabilities required for complex scientific problem solving. SciBench contains two carefully curated datasets: an open set featuring a range of collegiate-level scientific problems drawn from mathematics, chemistry, and physics textbooks, and a closed set comprising problems from undergraduate-level exams in computer science and mathematics. Based on the two datasets, we conduct an in-depth benchmark study of two representative LLMs with various prompting strategies. The results reveal that current LLMs fall short of delivering satisfactory performance, with an overall score of merely 35.80%. Furthermore, through a detailed user study, we categorize the errors made by LLMs into ten problem-solving abilities. Our analysis indicates that no single prompting strategy significantly outperforms others and some strategies that demonstrate improvements in certain problem-solving skills result in declines in other skills. We envision that SciBench will catalyze further developments in the reasoning abilities of LLMs, thereby ultimately contributing to scientific research and discovery.Comment: Work in progress, 18 page

Estuarine plastisphere as an overlooked source of N2O production

“Plastisphere”, microbial communities colonizing plastic debris, has sparked global concern for marine ecosystems. Microbiome inhabiting this novel human-made niche has been increasingly characterized; however, whether the plastisphere holds crucial roles in biogeochemical cycling remains largely unknown. Here we evaluate the potential of plastisphere in biotic and abiotic denitrification and nitrous oxide (N2O) production in estuaries. Biofilm formation provides anoxic conditions favoring denitrifiers. Comparing with surrounding bulk water, plastisphere exhibits a higher denitrifying activity and N2O production, suggesting an overlooked N2O source. Regardless of plastisphere and bulk water, bacterial and fungal denitrifications are the main regulators for N2O production instead of chemodenitrification. However, the contributions of bacteria and fungi in the plastisphere are different from those in bulk water, indicating a distinct N2O production pattern in the plastisphere. These findings pinpoint plastisphere as a N2O source, and provide insights into roles of the new biotope in biogeochemical cycling in the Anthropocene