Big data analytics for Earth observation has been playing an increasingly important role in supporting environmental monitoring, disaster early warning, and sustainable development through timely analysis of massive multi-source data collected by satellites. With the growing need for such timely Big Data analysis, Space Edge Computing (SEC) networks have been proposed to provide in-orbit analytic services for users worldwide, by integrating computing capability through Low-Earth-Orbit (LEO) satellites. However, the monitoring frequency of LEO satellites over specific target areas remains limited due to orbital constraints, making it difficult to meet the high-frequency data acquisition demands of Big Data analytics. Satellite inclination maneuver is a promising method to cover a wide range of target areas and enhance monitoring frequency by adjusting orbital inclination of satellites. Although such maneuvering enables satellites to timely process datasets, reducing energy wastage caused by inter-satellite data transmission, it consumes propulsion fuel, which is limited and difficult to replenish in a timely manner. Therefore, balancing the energy consumed for data processing and the fuel consumed for maneuvering is essential for efficient and sustainable Big Data analytics in SEC networks. In this paper, we aim to optimize Big Data query evaluation problem with satellite maneuver in an SEC network, focusing on minimizing the weighted sum of energy consumed for processing and fuel consumed for maneuvering. Specifically, we consider that each Earth observation service needs to guarantee a certain level of monitoring frequency, which may not always be satisfied by the original orbital coverage of LEO satellites. In such cases, some satellites will be selected to perform inclination maneuvers for additional monitoring and processing to guarantee the quality of Earth observation services. To this end, we first propose an approximation algorithm with a provable approximation ratio for the offline query evaluation problem, which leverages a customized auxiliary graph to jointly minimize energy and fuel consumption. We then devise an online learning algorithm, referred to as the customized Lipschitz bandit learning algorithm, with a bounded regret for the online Big Data query evaluation problem in an SEC network. We finally evaluate the performance of the proposed algorithms in a real SEC network topology. Experiment results show that the performance of the proposed algorithms achieve 11% lower energy consumption and 10.6% lower fuel consumption than those of their comparison counterparts.

Contemporary advancements in large language models (LLMs), both proprietary and open-source, have demonstrated capabilities surpassing human performance across diverse general domains. Nevertheless, their efficacy in specialized professional fields, particularly medicine, remains suboptimal within the open-source ecosystem. To address these limitations, we introduce Domain Adaptative Medicine-LLM (DAMe-LLM). Our approach encompasses continued pre-training, supervised fine-tuning (SFT), and reinforcement learning from human feedback (RLHF) methodologies. In the development of DAMe-LLM, we have curated an extensive Chinese and English medical dataset for continued pre-training, complemented by a high-fidelity SFT dataset encompassing a broad spectrum of medical specialties. Our contribution provides the scientific community with valuable resources for further investigation and development in the domain of medicine artificial intelligence.

Extreme learning machine (ELM) is a learning algorithm for single-hidden layer feedforward neural networks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs. After the input weights and the hidden layer biases are chosen randomly, ELM can be simply considered a linear system. However, the learning time of ELM is mainly spent on calculating the Moore-Penrose inverse matrices of the hidden layer output matrix. This paper focuses on effective computation of the Moore-Penrose inverse matrices for ELM, several methods are proposed. They are the reduced QR factorization with column Pivoting and Geninv ELM (QRGeninv-ELM), tensor product matrix ELM (TPM-ELM). And we compare QRGeninv-ELM, TPM-ELM with the relational algorithm of Moore-Penrose inverse matrices for ELM, the relational algorithms are: Cholesky factorization of singular matrix ELM (Geninv-ELM), QR factorization and Ginv ELM (QRGinv-ELM), the conjugate Gram-Schmidt process ELM (CGS-ELM). The experimental results and the statistical analysis of the experimental results both demonstrate that QRGeninv-ELM, TPM-ELM and Geninv-ELM are faster than other kinds of ELM and can reach comparable generalization performance.