Mixed Distillation Helps Smaller Language Model Better Reasoning

While large language models (LLMs) have demonstrated exceptional performance in recent natural language processing (NLP) tasks, their deployment poses substantial challenges due to high computational and memory demands in real-world applications. Recent studies have focused on enhancing smaller models through knowledge distillation from LLMs, yielding promising results. However, these models often struggle to match the performance of LLMs, especially in tasks that require reasoning. In this work, we introduce Mixed Distillation (MD) framework, which capitalizes on the strengths of Program of Thought (PoT) and Chain of Thought (CoT) capabilities within LLMs, combining multiple prompting techniques and distilling these capabilities into smaller models. Our experimental results show that MD significantly enhances the single-path and multi-path reasoning ability of smaller models in various tasks. In terms of accuracy and generality of reasoning tasks, the model generated by it exceeds the comprehensive performance of two individually distilled models. Notably, LLaMA2-7B and CodeLlama-7B using MD achieved remarkable improvements of (84.5%) and (85.5%), respectively, outperforming GPT-3.5-Turbo by (2.5%) and (3.5%), on the SVAMP benchmark.Comment: Working in Progress, 17 pages, 16 figure

Numerical simulation of rubber coating circular plates subjected to near-field underwater explosion

[Objectives] To study the coating effects on structures subjected to near-field underwater explosion, [Methods] the coupling Runge-Kutta Discontinuous Galerkin Method, Boundary Element Method and Finite Element Method are developed in this paper to solve a rubber coating circular plate subjected to near-field underwater explosion. The results are compared with experimentally obtained wet face pressure and bubble shape to verify the method, and the explosion bubble response to the flexible boundary is discussed. [Results] The results indicate that the rubber coating plate will shorten the pulse width of the shockwave, but the cavitation collapse will occur more easily. In addition, solid rubber coating has little effect on the deformation of the explosion bubble. [Conclusions] This research can provide a theoretical basis and technical support for the coating design and optimization of warships subjected to near-field underwater explosion

Shock mitigation properties of compound claddings subjected to underwater explosion loads

[Objectives] To improve the shock resistance ability of submarines, [Methods] an anti-shock cladding for submarines which consists of a compound coating with rubber and plastic foam is proposed on the basis of the shock environment of submarines. The numerical model is established by Abaqus/Explicit to analyze the dynamic response of anti-shock cladding under the combined loads of hydrostatic pressure and underwater explosion loads. [Results] The results indicate that the deformation of the cladding is negligible under hydrostatic pressure, and the shock load is largely mitigated when the yield stress of the cladding is higher than the hydrostatic pressure. Conversely, the deformation of the cladding is great under hydrostatic pressure and the shock mitigation effects of the coating are weakened when the yield stress of the cladding is lower than the hydrostatic pressure. As such, the yield stress of the cladding should be higher than the hydrostatic pressure, and densification should be avoided. [Conclusions] The research findings in this paper can provide guidance for the design of anti-shock cladding for submarines

The dynamic dysregulated network identifies stage-specific markers during lung adenocarcinoma malignant progression and metastasis

Brain metastasis occurs in approximately 30% of patients with lung adenocarcinoma (LUAD) and is closely associated with poor prognosis, recurrence, and death. However, dynamic gene regulation and molecular mechanism driving LUAD progression remain poorly understood. In this study, we performed a comprehensive single-cell transcriptome analysis using data from normal, early stage, advanced stage, and brain metastasis LUAD. Our single-cell-level analysis reveals the cellular composition heterogeneity at different stages during LUAD progression. We identified stage-specific risk genes that could contribute to LUAD progression and metastasis by reprogramming immune-related and metabolic-related functions. We constructed an early advanced metastatic dysregulated network and revealed the dynamic changes in gene regulations during LUAD progression. We identified 6 early advanced (HLA-DRB1, HLA-DQB1, SFTPB, SFTPC, PLA2G1B, and FOLR1), 8 advanced metastasis (RPS15, RPS11, RPL13A, RPS24, HLA-DRB5, LYPLA1, KCNJ15, and PSMA3), and 2 common risk genes in different stages (SFTPD and HLA-DRA) as prognostic markers in LUAD. Particularly, decreased expression of HLA-DRA, HLA-DRB1, HLA-DQB1, and HLA-DRB5 refer poor prognosis in LUAD by controlling antigen processing and presentation and T cell activation. Increased expression of PSMA3 and LYPLA1 refer poor prognosis by reprogramming fatty acid metabolism and RNA catabolic process. Our findings will help further understanding the pathobiology of brain metastases in LUAD