Following the earlier formalism of the categorical representation learning
(arXiv:2103.14770) by the first two authors, we discuss the construction of the
"RG-flow based categorifier". Borrowing ideas from theory of renormalization
group flows (RG) in quantum field theory, holographic duality, and hyperbolic
geometry, and mixing them with neural ODE's, we construct a new algorithmic
natural language processing (NLP) architecture, called the RG-flow categorifier
or for short the RG categorifier, which is capable of data classification and
generation in all layers. We apply our algorithmic platform to biomedical data
sets and show its performance in the field of sequence-to-function mapping. In
particular we apply the RG categorifier to particular genomic sequences of flu
viruses and show how our technology is capable of extracting the information
from given genomic sequences, find their hidden symmetries and dominant
features, classify them and use the trained data to make stochastic prediction
of new plausible generated sequences associated with new set of viruses which
could avoid the human immune system. The content of the current article is part
of the recent US patent application submitted by first two authors (U.S. Patent
Application No.: 63/313.504).

本文讨论了一种名为 RG-flow categorifier 的新型算法自然语言处理体系结构，该算法可用于数据分类和生成，特别应用于生物医学数据集，用于序列到功能的映射，并针对流感病毒基因组序列的隐含对称性和显性特征进行分类和预测。

算法分类器的分类表征学习和 RG 流算符

Categorical Representation Learning and RG flow operators for  algorithmic classifiers

Unlike widely used Named Entity Recognition (NER) data sets in generic
domains, biomedical NER data sets often contain mentions consisting of
discontinuous spans. Conventional sequence tagging techniques encode Markov
assumptions that are efficient but preclude recovery of these mentions. We
propose a simple, effective transition-based model with generic neural encoding
for discontinuous NER. Through extensive experiments on three biomedical data
sets, we show that our model can effectively recognize discontinuous mentions
without sacrificing the accuracy on continuous mentions.

提出了一种基于转换和使用通用神经编码的简单有效的模型，能够识别医学命名实体识别中存在的连续和不连续的提及，同时在三个生物医学数据集上实验表明，该模型能够有效识别不连续提及，不会牺牲连续提及的准确性。