arXiv subjects

Jiawei Yin

Publications and source records attributed to Jiawei Yin.

3 recordsLinked to original sources

arXiv2026

High-performance silicon-metal laser welding resisting extreme conditions

Reliable material joining is essential for countless industrial applications. While femtosecond laser welding provides a route beyond conventional bonding methods, demonstrations of silicon-metal joints are rare due to nonlinear propagation effects and have so far been limited to shear joining strengths of a few MPa. Here, we demonstrate high-strength silicon-Kovar laser welding using sub-nanosecond pulses. By optimizing the focal position, the welding pattern, the laser polarization, and the metal roughness, remarkable shear joining strengths up to 15.8 MPa are achieved. The silicon-metal joints withstand temperatures of up to 500 {\deg}C and are hermetically sealed. Together with the remarkable strength values, the resistance to harsh environments underpins the applicability of silicon-metal welding in various fields including aerospace, nuclear science, and metallurgy.

physics.optics↗

arXiv2025

xLLM Technical Report

We introduce xLLM, an intelligent and efficient Large Language Model (LLM) inference framework designed for high-performance, large-scale enterprise-grade serving, with deep optimizations for diverse AI accelerators. To address these challenges, xLLM builds a novel decoupled service-engine architecture. At the service layer, xLLM-Service features an intelligent scheduling module that efficiently processes multimodal requests and co-locates online and offline tasks through unified elastic scheduling to maximize cluster utilization. This module also relies on a workload-adaptive dynamic Prefill-Decode (PD) disaggregation policy and a novel Encode-Prefill-Decode (EPD) disaggregation policy designed for multimodal inputs. Furthermore, it incorporates a distributed architecture to provide global KV Cache management and robust fault-tolerant capabilities for high availability. At the engine layer, xLLM-Engine co-optimizes system and algorithm designs to fully saturate computing resources. This is achieved through comprehensive multi-layer execution pipeline optimizations, an adaptive graph mode and an xTensor memory management. xLLM-Engine also further integrates algorithmic enhancements such as optimized speculative decoding and dynamic EPLB, collectively serving to substantially boost throughput and inference efficiency. Extensive evaluations demonstrate that xLLM delivers significantly superior performance and resource efficiency. Under identical TPOT constraints, xLLM achieves throughput up to 1.7x that of MindIE and 2.2x that of vLLM-Ascend with Qwen-series models, while maintaining an average throughput of 1.7x that of MindIE with Deepseek-series models. xLLM framework is publicly available at https://github.com/jd-opensource/xllm and https://github.com/jd-opensource/xllm-service.

cs.DC↗

arXiv2019

Rapid Circadian Entrainment in Models of Circadian Genes Regulation

The light-based minimum-time circadian entrainment problem for mammals, Neurospora, and Drosophila is studied based on the mathematical models of their circadian gene regulation. These models contain high order nonlinear differential equations. Two model simplification methods are applied to these high-order models: the phase response curves (PRC) and the Principal Orthogonal Decomposition (POD). The variational calculus and a gradient descent algorithm are applied for solving the optimal light input in the high-order models. As the results of the gradient descent algorithm rely heavily on the initial guesses, we use the optimal control of the PRC and the simplified model to initialize the gradient descent algorithm. In this paper, we present: (1) the application of PRC and direct shooting algorithm on high-order nonlinear models; (2) a general process for solving the minimum-time optimal control problem on high-order models; (3) the impacts of minimum-time optimal light on circadian gene transcription and protein synthesis.

q-bio.MN↗