Assessing the impacts of phosphorus inactive clay on phosphorus release control and phytoplankton community structure in eutrophic lakes

Addressing the challenge that phosphorus is the key factor and cause for eutrophication, we evaluated the phosphorus release control performance of a new phosphorus inactive clay (PIC) and compared with Phoslock(®). Meanwhile, the impacts of PIC and Phoslock(®) on phytoplankton abundance and community structure in eutrophic water were also discussed. With the dosage of 40 mg/L, PIC effectively removed 97.7% of total phosphorus (TP) and 98.3% of soluble reactive phosphorus (SRP) in eutrophic waters. In sediments, Fe/Al-phosphorus and organic phosphorus remained stable whereas Ca-phosphorus had a significant increase of 13.1%. The results indicated that PIC may form the active overlay at water-sediment interface and decrease the bioavailability of phosphorus. The phytoplankton abundance was significantly reduced by PIC and decreased from (1.0-2.4) × 10(7) cells/L to (1.3-4.3) × 10(6) cells/L after 15 d simultaneous experiment. The phytoplankton community structure was also altered, where Cyanobacteria and Bacillariophyceae were the most inhibited and less dominant due to their sensitivity to phosphorus. After PIC treatment, the residual lanthanum concentration in water was 1.44-3.79 μg/L, and the residual aluminium concentration was low as 101.26-103.72 μg/L, which was much less than the recommended concentration of 200 μg/L. This study suggests that PIC is an appropriate material for phosphorus inactivation and algal bloom control, meaning its huge potential application in eutrophication restoration and management

Cross-Modality High-Frequency Transformer for MR Image Super-Resolution

Improving the resolution of magnetic resonance (MR) image data is critical to computer-aided diagnosis and brain function analysis. Higher resolution helps to capture more detailed content, but typically induces to lower signal-to-noise ratio and longer scanning time. To this end, MR image super-resolution has become a widely-interested topic in recent times. Existing works establish extensive deep models with the conventional architectures based on convolutional neural networks (CNN). In this work, to further advance this research field, we make an early effort to build a Transformer-based MR image super-resolution framework, with careful designs on exploring valuable domain prior knowledge. Specifically, we consider two-fold domain priors including the high-frequency structure prior and the inter-modality context prior, and establish a novel Transformer architecture, called Cross-modality high-frequency Transformer (Cohf-T), to introduce such priors into super-resolving the low-resolution (LR) MR images. Comprehensive experiments on two datasets indicate that Cohf-T achieves new state-of-the-art performance

WMFormer++: Nested Transformer for Visible Watermark Removal via Implict Joint Learning

Watermarking serves as a widely adopted approach to safeguard media copyright. In parallel, the research focus has extended to watermark removal techniques, offering an adversarial means to enhance watermark robustness and foster advancements in the watermarking field. Existing watermark removal methods mainly rely on UNet with task-specific decoder branches--one for watermark localization and the other for background image restoration. However, watermark localization and background restoration are not isolated tasks; precise watermark localization inherently implies regions necessitating restoration, and the background restoration process contributes to more accurate watermark localization. To holistically integrate information from both branches, we introduce an implicit joint learning paradigm. This empowers the network to autonomously navigate the flow of information between implicit branches through a gate mechanism. Furthermore, we employ cross-channel attention to facilitate local detail restoration and holistic structural comprehension, while harnessing nested structures to integrate multi-scale information. Extensive experiments are conducted on various challenging benchmarks to validate the effectiveness of our proposed method. The results demonstrate our approach's remarkable superiority, surpassing existing state-of-the-art methods by a large margin

CALICO: Self-Supervised Camera-LiDAR Contrastive Pre-training for BEV Perception

Perception is crucial in the realm of autonomous driving systems, where bird's eye view (BEV)-based architectures have recently reached state-of-the-art performance. The desirability of self-supervised representation learning stems from the expensive and laborious process of annotating 2D and 3D data. Although previous research has investigated pretraining methods for both LiDAR and camera-based 3D object detection, a unified pretraining framework for multimodal BEV perception is missing. In this study, we introduce CALICO, a novel framework that applies contrastive objectives to both LiDAR and camera backbones. Specifically, CALICO incorporates two stages: point-region contrast (PRC) and region-aware distillation (RAD). PRC better balances the region- and scene-level representation learning on the LiDAR modality and offers significant performance improvement compared to existing methods. RAD effectively achieves contrastive distillation on our self-trained teacher model. CALICO's efficacy is substantiated by extensive evaluations on 3D object detection and BEV map segmentation tasks, where it delivers significant performance improvements. Notably, CALICO outperforms the baseline method by 10.5% and 8.6% on NDS and mAP. Moreover, CALICO boosts the robustness of multimodal 3D object detection against adversarial attacks and corruption. Additionally, our framework can be tailored to different backbones and heads, positioning it as a promising approach for multimodal BEV perception