The past decade has witnessed the rapid development of ML and DL
methodologies in agricultural systems, showcased by great successes in variety
of agricultural applications. However, these conventional ML/DL models have
certain limitations: They heavily rely on large, costly-to-acquire labeled
datasets for training, require specialized expertise for development and
maintenance, and are mostly tailored for specific tasks, thus lacking
generalizability. Recently, foundation models have demonstrated remarkable
successes in language and vision tasks across various domains. These models are
trained on a vast amount of data from multiple domains and modalities. Once
trained, they can accomplish versatile tasks with just minor fine-tuning and
minimal task-specific labeled data. Despite their proven effectiveness and huge
potential, there has been little exploration of applying FMs to agriculture
fields. Therefore, this study aims to explore the potential of FMs in the field
of smart agriculture. In particular, we present conceptual tools and technical
background to facilitate the understanding of the problem space and uncover new
research directions in this field. To this end, we first review recent FMs in
the general computer science domain and categorize them into four categories:
language FMs, vision FMs, multimodal FMs, and reinforcement learning FMs.
Subsequently, we outline the process of developing agriculture FMs and discuss
their potential applications in smart agriculture. We also discuss the unique
challenges associated with developing AFMs, including model training,
validation, and deployment. Through this study, we contribute to the
advancement of AI in agriculture by introducing AFMs as a promising paradigm
that can significantly mitigate the reliance on extensive labeled datasets and
enhance the efficiency, effectiveness, and generalization of agricultural AI
systems.

本研究旨在探索基于基础模型（foundation models）在智能农业领域的潜力，通过回顾计算机科学领域的最新研究，并将其归类为语言基础模型、视觉基础模型、多模态基础模型和强化学习基础模型，本文提出了农业基础模型的发展过程和潜在应用及其在模型训练、验证和部署过程中可能面临的挑战，旨在通过引入农业基础模型这一有前景的范式，显著减少对大规模标记数据集的依赖，同时提高农业人工智能系统的效率、效果和泛化能力。

智能农业中的基础模型：基础、机遇和挑战

Foundation Models in Smart Agriculture: Basics, Opportunities, and  Challenges

This paper study provides a novel contribution to the field of signal
processing and DL for ECG signal analysis by introducing a new feature
representation method for ECG signals. The proposed method is based on
transforming time frequency 1D vectors into 2D images using Gramian Angular
Field transform. Moving on, the classification of the transformed ECG signals
is performed using Convolutional Neural Networks (CNN). The obtained results
show a classification accuracy of 97.47% and 98.65% for anomaly detection.
Accordingly, in addition to improving the classification performance compared
to the state-of-the-art, the feature representation helps identify and
visualize temporal patterns in the ECG signal, such as changes in heart rate,
rhythm, and morphology, which may not be apparent in the original signal. This
has significant implications in the diagnosis and treatment of cardiovascular
diseases and detection of anomalies.

该研究提出了一种新的特征表示方法，将时间频率 1D 向量转换为 2D 图像，并利用卷积神经网络对转换后的心电信号进行分类，从而提高了分类性能并帮助识别和可视化心电信号的时间模式，具有诊断和治疗心血管疾病以及检测异常的重要意义。

基于深度卷积神经网络和格里曼角场的心电图分类

ECG classification using Deep CNN and Gramian Angular Field

An electroencephalogram (EEG) signal is currently accepted as a standard for
automatic sleep staging. Lately, Near-human accuracy in automated sleep staging
has been achievable by Deep Learning (DL) based approaches, enabling multi-fold
progress in this area. However, An extensive and expensive clinical setup is
required for EEG based sleep staging. Additionally, the EEG setup being
obtrusive in nature and requiring an expert for setup adds to the inconvenience
of the subject under study, making it adverse in the point of care setting. An
unobtrusive and more suitable alternative to EEG is Electrocardiogram (ECG).
Unsurprisingly, compared to EEG in sleep staging, its performance remains
sub-par. In order to take advantage of both the modalities, transferring
knowledge from EEG to ECG is a reasonable approach, ultimately boosting the
performance of ECG based sleep staging. Knowledge Distillation (KD) is a
promising notion in DL that shares knowledge from a superior performing but
usually more complex teacher model to an inferior but compact student model.
Building upon this concept, a cross-modality KD framework assisting features
learned through models trained on EEG to improve ECG-based sleep staging
performance is proposed. Additionally, to better understand the distillation
approach, extensive experimentation on the independent modules of the proposed
model was conducted. Montreal Archive of Sleep Studies (MASS) dataset
consisting of 200 subjects was utilized for this study. The results from the
proposed model for weighted-F1-score in 3-class and 4-class sleep staging
showed a 13.40 \% and 14.30 \% improvement, respectively. This study
demonstrates the feasibility of KD for single-channel ECG based sleep staging's
performance enhancement in 3-class (W-R-N) and 4-class (W-R-L-D)
classification.

利用知识蒸馏从 EEG 到 ECG 的跨模态知识传递，提高了 ECG 睡眠分期的分类性能。

利用 EEG 辅助深度知识蒸馏提高基于单导联心电图的睡眠分期

EEG aided boosting of single-lead ECG based sleep staging with Deep Knowledge Distillation

We address the problem of compiling defeasible theories to Datalog$^\neg$
programs. We prove the correctness of this compilation, for the defeasible
logic $DL(\partial_{||})$, but the techniques we use apply to many other
defeasible logics. Structural properties of $DL(\partial_{||})$ are identified
that support efficient implementation and/or approximation of the conclusions
of defeasible theories in the logic, compared with other defeasible logics. We
also use previously well-studied structural properties of logic programs to
adapt to incomplete Datalog$^\neg$ implementations.

通过将可防御理论编译成 Datalog 程序，为 DL（||）中的可防御逻辑证明了编译的正确性，并确定了 DL（||）的结构属性，从而支持与其他可防御逻辑相比，在逻辑中高效实现和 / 或近似可防御理论的结论，并利用逻辑程序的已知结构属性来适应不完整的 Datalog 实现。