Designing protein sequences with specific biological functions and structural
stability is crucial in biology and chemistry. Generative models already
demonstrated their capabilities for reliable protein design. However, previous
models are limited to the unconditional generation of protein sequences and
lack the controllable generation ability that is vital to biological tasks. In
this work, we propose TaxDiff, a taxonomic-guided diffusion model for
controllable protein sequence generation that combines biological species
information with the generative capabilities of diffusion models to generate
structurally stable proteins within the sequence space. Specifically, taxonomic
control information is inserted into each layer of the transformer block to
achieve fine-grained control. The combination of global and local attention
ensures the sequence consistency and structural foldability of
taxonomic-specific proteins. Extensive experiments demonstrate that TaxDiff can
consistently achieve better performance on multiple protein sequence generation
benchmarks in both taxonomic-guided controllable generation and unconditional
generation. Remarkably, the sequences generated by TaxDiff even surpass those
produced by direct-structure-generation models in terms of confidence based on
predicted structures and require only a quarter of the time of models based on
the diffusion model. The code for generating proteins and training new versions
of TaxDiff is available at:this https URL

使用生物物种信息和扩散模型的生成能力相结合，提出了一种名为 TaxDiff 的生物蛋白序列生成模型，实现了精确控制生成结构稳定蛋白的能力，并在多个蛋白序列生成基准测试中表现出更好的性能。

TaxDiff: 蛋白质序列生成的分类指导扩散模型

TaxDiff: Taxonomic-Guided Diffusion Model for Protein Sequence  Generation

The past decades have witnessed an increasing interest in spiking neural
networks (SNNs) due to their great potential of modeling time-dependent data.
Many algorithms and techniques have been developed; however, theoretical
understandings of many aspects of spiking neural networks are still cloudy. A
recent work [Zhang et al. 2021] disclosed that typical SNNs could hardly
withstand both internal and external perturbations due to their bifurcation
dynamics and suggested that self-connection has to be added. In this paper, we
investigate the theoretical properties of SNNs with self-connection, and
develop an in-depth analysis on structural stability by specifying the lower
and upper bounds of the maximum number of bifurcation solutions. Numerical
experiments conducted on simulation and practical tasks demonstrate the
effectiveness of the proposed results.

论文探讨了自连接的脉冲神经网络的理论特性，分析了其结构稳定性，并在模拟和实际任务上进行了数值实验，证明了其有效性。

脉冲神经网络的结构稳定性

Structural Stability of Spiking Neural Networks

We present a unified framework for characterizing local Nash equilibria in
continuous games on either infinite-dimensional or finite-dimensional
non-convex strategy spaces. We provide intrinsic necessary and sufficient
first- and second-order conditions ensuring strategies constitute local Nash
equilibria. We term points satisfying the sufficient conditions differential
Nash equilibria. Further, we provide a sufficient condition (non-degeneracy)
guaranteeing differential Nash equilibria are isolated and show that such
equilibria are structurally stable. We present tutorial examples to illustrate
our results and highlight degeneracies that can arise in continuous games.

本文提出了一个统一的框架来表征在无限维或有限维的非凸策略空间上进行连续博弈的局部 Nash 平衡，给出了确保策略构成局部 Nash 平衡的本质必要和充分一阶和二阶条件，将满足充分条件的点称为微分 Nash 平衡，进一步提供了一个充分条件（非退化性）来保证微分 Nash 平衡是孤立的，并且表明这样的平衡是结构稳定的。文章用教程示例说明结果，并且突出连续博弈中可能出现的退化。