Quantitative precipitation nowcasting (QPN) is a highly challenging task in weather forecasting. The ability to provide precise, immediate, and detailed QPN products is necessary for a variety of situations, including storm warnings, air travel, and large gatherings. To address this challenge, this paper proposes a new transformer LPT(Lightweight Physics-informed Transformer)-QPN for QPN tasks, utilizing vertical cumulative liquid water content (VIL) products. This model adopts novel transformer modules to model long-term evolution of precipitation and incorporates multi-head squared attention (MHSA) to model its highly non-linear relationships while reducing computational complexity. The results of experimental evaluations demonstrate the superiority of LPT-QPN when compared to existing state-of-the-art QPN models. In particular, the LPT-QPN model demonstrates greater accuracy for long lead time and in high-intensity areas, confirmed in both quantitative and qualitative evaluations. Additionally, through three customized fine-tuning schemes, we are able to further improve the predictability of the LPT-QPN model for specific precipitation events. By incorporating physical constraints of convection diffusion equation, our approach offers novel perspectives for future explorations that combine physical prior knowledge and deep learning techniques.

Underwater acoustic signal denoising is a challenging task due to the complexity of the underwater environment. Most of the existing methods cannot effectively cope with the problem of underwater acoustic signal (UWAS) denoising at low signal-to-noise ratios (SNRs). According to the characteristics of UWAS, a novel idea is proposed to simultaneously model latent features from both the time and frequency dimensions of complex-valued spectrum in a dual-branch self-attention network, namely DBSA-Net. In this model, both magnitude and phase information in the complex spectrum are enhanced from different dimensions by two branches. Specifically, DBSA-Net is an encoder-decoder based network with several global-local-self-attention (GL-SA) blocks distributed on dual branches between encoder and decoder. Each GL-SA block incorporates global self-attention and local self-attention to capture distant context and fine-grained local dependencies along the temporal and frequency dimensions. Moreover, we also design an information interaction module between two branches to exchange complementary information. This interaction module together with a merge block fuse features extracted from different dimensions, thus enhancing the capability of our model to learn the target signal features. Extensive experiments are conducted to evaluate our model on a publicly available dataset. Results of the ablation experiments show that the different modules of DBSA-Net play their respective roles in improving denoising performance and are empirically valid. In both the seen ships and unseen ships scenarios, the proposed DBSA-Net outperforms existing approaches by a large margin on various evaluation metrics.

Cloud computing has emerged as a promising computational infrastructure for cost-efficient workflow execution by provisioning on-demand resources in a pay-as-you-go manner. While scientific workflows require accessing community-wide resources, they usually need to be performed in collaborative cloud environments composed of multiple datacenters. Although such environments facilitate scientific collaboration, the movements of input and intermediate datasets across geographically distributed datacenters may cause intolerable latency that would hinder efficient execution of large-scale data-intensive scientific workflows. To address the problem, in this article we propose a novel multi-level K-cut graph partitioning algorithm to minimize the volume of data transfer across datacenters while satisfying load balancing and fixed data constraints. The algorithm first contracts the fixed input datasets in the same datacenter and their consuming tasks, and coarsens the contracted graph to a predefined scale in a level-by-level manner. Then, a K-cut algorithm is used to partition the resulted graph into K parts such that the cut size is minimized. After that, the partitioned graph is projected back to the original workflow graph, during which the load balancing constraint is maintained. We evaluate our algorithm using three real-world workflow applications and the results demonstrate that the proposed algorithm outperforms other state-of-the-art algorithms.

Underwater acoustic signal recognition systems face challenges in achieving high accuracy when processing complex data with low signal-to-noise ratio (SNR) in underwater environments, leading to limited noise robustness. Conventional approaches typically employ pre-trained denoising models for preprocessing noisy signals. However, due to disparate optimization goals between denoising and recognition models, denoising methods might introduce signal distortion, hampering effective enhancement of system accuracy. To address this issue, this paper proposes a novel joint training framework with cross-attention fusion for robust underwater acoustic signal recognition (UASR), called CAF-JT. CAF-JT consists of a denoising module, a recognition module, and the CAF module. It addresses the mismatch problem arising from different optimization directions by jointly training the denoising frontend and the recognition backend. Additionally, inspired by the multi-condition training (MCT) method, the CAF module is designed to fuse characteristics from both denoised and noisy audio, thus incorporating noise information. This fusion mechanism enables the model to better adapt to the characteristics of the noisy environment and enhance its noise robustness. Furthermore, to improve the performance of UASR, TF-Transformer blocks are incorporated into both the denoising module and the recognition module to capture the spatio-temporal distribution of spectral features. The proposed approach is evaluated on two open-source underwater acoustic signal datasets, namely ShipsEar and DeepShip. Extensive experimental demonstrate the superiority of CAF-JT over conventional joint training approaches, showcasing its improved noise robustness. Particularly in low SNR conditions, CAF-JT achieves the best average recognition rates of 94.84% and 93.61% on the two datasets, respectively.