Human-designed algorithms have long been fundamental in solving a variety of
scientific and engineering challenges. Recently, data-driven deep learning
methods have also risen to prominence, offering innovative solutions across
numerous scientific fields. While traditional algorithms excel in capturing the
core aspects of specific problems, they often lack the flexibility needed for
varying problem conditions due to the absence of specific data. Conversely,
while data-driven approaches utilize vast datasets, they frequently fall short
in domain-specific knowledge. To bridge these gaps, we introduce \textbf{FMint}
(Foundation Model based on Initialization), a generative pre-trained model that
synergizes the precision of human-designed algorithms with the adaptability of
data-driven methods. This model is specifically engineered for high-accuracy
simulation of dynamical systems. Starting from initial trajectories provided by
conventional methods, FMint quickly delivers highly accurate solutions. It
incorporates in-context learning and has been pre-trained on a diverse corpus
of 500,000 dynamical systems, showcasing exceptional generalization across a
broad spectrum of real-world applications. By effectively combining algorithmic
rigor with data-driven flexibility, FMint sets the stage for the next
generation of scientific foundation models, tackling complex problems with both
efficiency and high accuracy.

FMint 是一个基于初始条件的基础模型，通过在传统方法提供的初始轨迹上进行快速学习，将人为设计的算法与数据驱动方法的灵活性相结合，为高准确性的动力系统模拟提供了创新解决方案。

FMint: 桥接人工设计与数据预训练模型的微分方程基础模型

FMint: Bridging Human Designed and Data Pretrained Models for  Differential Equation Foundation Model

In this work, we propose GPT-FL, a generative pre-trained model-assisted
federated learning (FL) framework. At its core, GPT-FL leverages generative
pre-trained models to generate diversified synthetic data. These generated data
are used to train a downstream model on the server, which is then fine-tuned
with private client data under the standard FL framework. We show that GPT-FL
consistently outperforms state-of-the-art FL methods in terms of model test
accuracy, communication efficiency, and client sampling efficiency. Through
comprehensive ablation analysis, we discover that the downstream model
generated by synthetic data plays a crucial role in controlling the direction
of gradient diversity during FL training, which enhances convergence speed and
contributes to the notable accuracy boost observed with GPT-FL. Also,
regardless of whether the target data falls within or outside the domain of the
pre-trained generative model, GPT-FL consistently achieves significant
performance gains, surpassing the results obtained by models trained solely
with FL or synthetic data.

本文提出了一个基于生成式预训练模型辅助联邦学习框架的 GPT-FL 方法，在提高通信效率和客户抽样效率的同时，优化梯度多样性来增强模型收敛速度和提高模型测试准确性。

GPT-FL: 基于生成式预训练模型的联邦学习

GPT-FL: Generative Pre-trained Model-Assisted Federated Learning

Modeling human personality is important for several AI challenges, from the
engineering of artificial psychotherapists to the design of persona bots.
However, the field of computational personality analysis heavily relies on
labeled data, which may be expensive, difficult or impossible to get. This
problem is amplified when dealing with rare personality types or disorders
(e.g., the anti-social psychopathic personality disorder). In this context, we
developed a text-based data augmentation approach for human personality
(PEDANT). PEDANT doesn't rely on the common type of labeled data but on the
generative pre-trained model (GPT) combined with domain expertise. Testing the
methodology on three different datasets, provides results that support the
quality of the generated data.

本文介绍了一种基于自然语言处理及人工智能的方法 ——PEDANT，可以更轻松、经济地收集稀缺个性类型的语料库，以提升计算机自动分析人格的能力。