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We propose an innovative approach to the concurrent exploration of gravitational waves originating from Galactic binaries through the development of a new Local Maxima Particle Swarm Optimization (LMPSO) algorithm. Our methodology employs strategic Create Voids (CV) to streamline parameter space, maximizing the identification of local maxima for the $\mathcal{F}$-statistic even in the overlapped signals case. Subsequently, a "find-real-$\mathcal{F}$-statistic-analysis", which implements the astrophysical models and properties of $\mathcal{F}$-statistic in parameter space, is conducted to reveal Galactic binary gravitational wave signals within the dataset. Our new approach eliminates inaccuracies associated with signal subtraction contamination, which is a challenge for traditional iterative-subtraction method when addressing low signal-to-noise ratio signals (e.g., SNR $<$ 15). We also consider the effect of overlapping signals and degeneracy noise. To demonstrate the efficacy of our approach, we utilize the residuals from the LISA mock data challenge (LDC1-4), where 10982 injection sources with SNR $\ge$ 15 have been eliminated. For the remaining low SNR sources (SNR $<$ 15), the LMPSO-CV method efficiently identifies 8995 signals with a 52.3\% detection rate or 3463 signals with a 73.1\% detection rate when the correlation coefficient threshold $R_t$ is set to 0.8.

Modern society's reliance on power systems is at risk from the escalating effects of wind-related climate change. Yet, failure to identify the intricate relationship between wind-related climate risks and power systems could lead to serious short- and long-term issues, including partial or complete blackouts. Here, we develop a comprehensive framework to assess China's power system resilience across various climate change scenarios, enabling a holistic evaluation of the repercussions induced by wind-related climate change. Our findings indicate that China's current wind projects and planning strategies could be jeopardized by wind-related climate change, with up to a 12\% decline in regional wind power availability. Moreover, our results underscore a pronounced vulnerability of power system resilience amidst the rigors of hastened climate change, unveiling a potential amplification of resilience deterioration, even approaching fourfold by 2060 under the most severe scenario, relative to the 2020 benchmark. This work advocates for strategic financial deployment within the power sector aimed at climate adaptation, enhancing power system resilience to avert profound losses from long-term, wind-influenced climatic fluctuations., Comment: Not suitable for publication

Resolving individual gravitational waves from tens of millions of double white dwarf (DWD) binaries in the Milky Way is a challenge for future space-based gravitational wave detection programs. By using previous data to define the priors for the next search, we propose an accelerated approach of searching the DWD binaries and demonstrate its efficiency based on the GBSIEVER detection pipeline. Compared to the traditional GBSIEVER method, our method can obtain $\sim 50\%$ of sources with 2.5\% of the searching time for LDC1-4 data. In addition, we find that both methods have a similar ability to detect the Milky Way structure by their confirmed sources. The relative error of distance and chirp mass is about 20\% for DWD binaries whose gravitational wave frequency is higher than $4\times10^{-3}$ Hz, even if they are close to the Galactic center. Finally, we propose a signal-to-noise ratio (SNR) threshold for LISA to confirm the detection of DWD binaries. The threshold should be 16 when the gravitational wave frequency is lower than $4\times10^{-3}$ Hz and 9 when the frequency range is from $4\times10^{-3}$ Hz to $1.5\times10^{-2}$ Hz., Comment: 16 pages, 19 figures

Real-world node embedding applications often contain hundreds of billions of edges with high-dimension node features. Scaling node embedding systems to efficiently support these applications remains a challenging problem. In this paper we present a high-performance multi-GPU node embedding system. It uses model parallelism to split node embeddings onto each GPU's local parameter server, and data parallelism to train these embeddings on different edge samples in parallel. We propose a hierarchical data partitioning strategy and an embedding training pipeline to optimize both communication and memory usage on a GPU cluster. With the decoupled design of CPU tasks (random walk) and GPU tasks (embedding training), our system is highly flexible and can fully utilize all computing resources on a GPU cluster. Comparing with the current state-of-the-art multi-GPU single-node embedding system, our system achieves 5.9x-14.4x speedup on average with competitive or better accuracy on open datasets. Using 40 NVIDIA V100 GPUs on a network with almost three hundred billion edges and more than one billion nodes, our implementation requires only 3 minutes to finish one training epoch., Comment: Accepted by Scalable Deep Learning over Parallel And Distributed Infrastructures, An IPDPS 2021 Workshop