JINSP(Jiutian Intelligence Network Simulation Platform) describes a series of
basic emulators and their combinations, such as the simulation of the protocol
stack for dynamic users in a real environment, which is composed of user
behavior simulation, base station simulation, and terminal simulation. It is
applied in specific business scenarios, such as multi-target antenna
optimization, compression feedback, and so on. This paper provides detailed
descriptions of each emulator and its combination based on this foundation,
including the implementation process of the emulator, integration with the
platform, experimental results, and other aspects.

JINSP 描述了一系列基本仿真器及其组合，如在真实环境中模拟动态用户的协议栈仿真，其中包括用户行为仿真、基站仿真和终端仿真。它在多目标天线优化、压缩反馈等特定业务场景中应用，在该基础上提供了每个仿真器及其组合的详细描述，包括仿真器的实现过程、与平台的集成、实验结果等方面。

JINSP 中的仿真解析器

Emulators in JINSP

Instruction tuning has recently been recognized as an effective way of
aligning Large Language Models (LLMs) to enhance their generalization ability
across various tasks. However, when tuning publicly accessible, centralized
LLMs with private instruction data, privacy concerns are inevitable. While
direct transfer of parameterized modules between models is a plausible approach
to address this, its implications and effectiveness need further exploration.
This paper focuses on Offsite-Tuning (OFT), a representative technique that
transfers transformer blocks between centralized LLMs and downstream emulators.
Given the limited understanding of the underlying mechanism of OFT, we perform
an empirical analysis on LLMs from the perspectives of representation and
functional similarity. Interestingly, our findings reveal a unique modular
structure within the layers of LLMs that appears to emerge as the model size
expands. Simultaneously, we note subtle but potentially significant changes in
representation and intermediate predictions across the layers. Inspired by
these observations, we propose CRaSh, involving Clustering, Removing, and
Sharing, a training-free strategy to derive improved emulators from LLMs. CRaSh
significantly boosts performance of OFT with billions of parameters.
Furthermore, we investigate the optimal solutions yielded by fine-tuning with
and without full model through the lens of loss landscape. Our findings
demonstrate a linear connectivity among these optima falling over the same
basin, thereby highlighting the effectiveness of CRaSh and OFT. The source code
is publicly available at this https URL

借助 CRaSh 方法，通过探索 Large Language Models (LLMs) 的模块化结构和表示相似性，本文研究了 Offsite-Tuning (OFT) 技术及其与后端模拟器之间的转换，提高其性能并解决隐私问题。研究发现模型大小会导致 LLMs 内层面的唯一模块结构的出现，并注意到隐含的表示和中间预测方面的微妙变化。实验结果证明 CRaSh 和 OFT 的有效性，且细调尝试的结构优化解具有线性连通性。

CRaSh：聚类、剪枝和共享增强细调，无需完整大型语言模型

CRaSh: Clustering, Removing, and Sharing Enhance Fine-tuning without  Full Large Language Model

Many fields of science and engineering rely on running simulations with
complex and computationally expensive models to understand the involved
processes in the system of interest. Nevertheless, the high cost involved
hamper reliable and exhaustive simulations. Very often such codes incorporate
heuristics that ironically make them less tractable and transparent. This paper
introduces an active learning methodology for adaptively constructing surrogate
models, i.e. emulators, of such costly computer codes in a multi-output
setting. The proposed technique is sequential and adaptive, and is based on the
optimization of a suitable acquisition function. It aims to achieve accurate
approximations, model tractability, as well as compact and expressive simulated
datasets. In order to achieve this, the proposed Active Multi-Output Gaussian
Process Emulator (AMOGAPE) combines the predictive capacity of Gaussian
Processes (GPs) with the design of an acquisition function that favors sampling
in low density and fluctuating regions of the approximation functions.
Comparing different acquisition functions, we illustrate the promising
performance of the method for the construction of emulators with toy examples,
as well as for a widely used remote sensing transfer code.

提出一种基于高斯过程的主动学习方法，用于自适应构建模拟代理模型以提高模拟效率和透明度，并通过在近似函数的低密度和波动区域采样来设计采集函数，以实现精度、可分析性和模拟能力的平衡。

高斯过程的计算机代码主动仿真 -- 远程感应应用

Active emulation of computer codes with Gaussian processes --  Application to remote sensing

Artificial neural-networks have the potential to emulate cloud processes with
higher accuracy than the semi-empirical emulators currently used in climate
models. However, neural-network models do not intrinsically conserve energy and
mass, which is an obstacle to using them for long-term climate predictions.
Here, we propose two methods to enforce linear conservation laws in
neural-network emulators of physical models: Constraining (1) the loss function
or (2) the architecture of the network itself. Applied to the emulation of
explicitly-resolved cloud processes in a prototype multi-scale climate model,
we show that architecture constraints can enforce conservation laws to
satisfactory numerical precision, while all constraints help the neural-network
better generalize to conditions outside of its training set, such as global
warming.

两种方法被提出在物理模型神经网络仿真中使用以保持线性守恒定律有助于更好地推广到其训练数据之外的条件，例如全球变暖。