Peat, a crucial component in whisky production, imparts distinctive and
irreplaceable flavours to the final product. However, the extraction of peat
disrupts ancient ecosystems and releases significant amounts of carbon,
contributing to climate change. This paper aims to address this issue by
conducting a feasibility study on enhancing peat use efficiency in whisky
manufacturing through non-destructive analysis using hyperspectral imaging.
Results show that shot-wave infrared (SWIR) data is more effective for
analyzing peat samples and predicting total phenol levels, with accuracies up
to 99.81%.

通过使用高光谱成像技术进行非破坏性分析，本文旨在解决威士忌酿造中保持泥炭利用效率的问题。结果表明，短波红外数据对于分析泥炭样本和预测总酚水平更为有效，准确率高达 99.81%。

利用高光谱成像和机器学习进行非破坏性泥炭分析

Non-Destructive Peat Analysis using Hyperspectral Imaging and Machine  Learning

The emergence of foundation models, including language and vision models, has
reshaped AI's landscape, offering capabilities across various applications.
Deploying and fine-tuning these large models, like GPT-3 and BERT, presents
challenges, especially in the current foundation model era. We introduce
Emulator-Assisted Tuning (EAT) combined with Parameter-Efficient Fine-Tuning
(PEFT) to form Parameter-Efficient Emulator-Assisted Tuning (PEAT). Further, we
expand this into federated learning as Federated PEAT (FedPEAT). FedPEAT uses
adapters, emulators, and PEFT for federated model tuning, enhancing model
privacy and memory efficiency. Adapters adjust pre-trained models, while
emulators give a compact representation of original models, addressing both
privacy and efficiency. Adaptable to various neural networks, our approach also
uses deep reinforcement learning for hyper-parameter optimization. We tested
FedPEAT in a unique scenario with a server participating in collaborative
federated tuning, showcasing its potential in tackling foundation model
challenges.

在当前的基础模型时代中，我们介绍了 Emulator-Assisted Tuning (EAT) 和 Parameter-Efficient Fine-Tuning (PEFT) 结合形成的 Parameter-Efficient Emulator-Assisted Tuning (PEAT)，进一步将其扩展为联邦学习的 FedPEAT。FedPEAT 使用适配器、仿真器和 PEFT 进行联邦模型调优，提高了模型的隐私性和内存效率。同时，我们的方法适用于各种神经网络，并利用深度增强学习进行超参数优化。我们在一种特殊的场景中对 FedPEAT 进行了测试，通过服务器参与协作联邦调优的方式展示了其在解决基础模型挑战方面的潜力。

FedPEAT：移动边缘计算中的联邦学习、参数高效微调和仿真器辅助调整在人工智能基础模型上的融合

FedPEAT: Convergence of Federated Learning, Parameter-Efficient Fine  Tuning, and Emulator Assisted Tuning for Artificial Intelligence Foundation  Models with Mobile Edge Computing