We present a sequential transfer learning framework for transformers on
functional Magnetic Resonance Imaging (fMRI) data and demonstrate its
significant benefits for decoding musical timbre. In the first of two phases,
we pre-train our stacked-encoder transformer architecture on Next Thought
Prediction, a self-supervised task of predicting whether or not one sequence of
fMRI data follows another. This phase imparts a general understanding of the
temporal and spatial dynamics of neural activity, and can be applied to any
fMRI dataset. In the second phase, we fine-tune the pre-trained models and
train additional fresh models on the supervised task of predicting whether or
not two sequences of fMRI data were recorded while listening to the same
musical timbre. The fine-tuned models achieve significantly higher accuracy
with shorter training times than the fresh models, demonstrating the efficacy
of our framework for facilitating transfer learning on fMRI data. Additionally,
our fine-tuning task achieves a level of classification granularity beyond
standard methods. This work contributes to the growing literature on
transformer architectures for sequential transfer learning on fMRI data, and
provides evidence that our framework is an improvement over current methods for
decoding timbre.

使用顺序迁移学习框架的转换器，可以有效地解码音乐音色，形成一种比现有方法更优的 fMRI 数据序列转换框架，用于预测不同 fMRI 数据序列是否记录了相同的音频音色。

基于顺序迁移学习的 fMRI 数据解码听到和想象中的音色

Sequential Transfer Learning to Decode Heard and Imagined Timbre from  fMRI Data

The knowledge graph (KG) is an essential form of knowledge representation
that has grown in prominence in recent years. Because it concentrates on
nominal entities and their relationships, traditional knowledge graphs are
static and encyclopedic in nature. On this basis, event knowledge graph (Event
KG) models the temporal and spatial dynamics by text processing to facilitate
downstream applications, such as question-answering, recommendation and
intelligent search. Existing KG research, on the other hand, mostly focuses on
text processing and static facts, ignoring the vast quantity of dynamic
behavioral information included in photos, movies, and pre-trained neural
networks. In addition, no effort has been done to include behavioral
intelligence information into the knowledge graph for deep reinforcement
learning (DRL) and robot learning. In this paper, we propose a novel dynamic
knowledge and skill graph (KSG), and then we develop a basic and specific KSG
based on CN-DBpedia. The nodes are divided into entity and attribute nodes,
with entity nodes containing the agent, environment, and skill (DRL policy or
policy representation), and attribute nodes containing the entity description,
pre-train network, and offline dataset. KSG can search for different agents'
skills in various environments and provide transferable information for
acquiring new skills. This is the first study that we are aware of that looks
into dynamic KSG for skill retrieval and learning. Extensive experimental
results on new skill learning show that KSG boosts new skill learning
efficiency.

提出了一种基于动态技能知识图谱（KSG）的新型技能检索和学习方法，可以在各种环境中搜索不同代理的技能并提供可转移的信息来获取新技能，从而提高了新技能学习的效率。