Cyclic peptides, characterized by geometric constraints absent in linear peptides, offer enhanced biochemical properties, presenting new opportunities to address unmet medical needs. However, designing target-specific cyclic peptides remains underexplored due to limited training data. To bridge the gap, we propose CP-Composer, a novel generative framework that enables zero-shot cyclic peptide generation via composable geometric constraints. Our approach decomposes complex cyclization patterns into unit constraints, which are incorporated into a diffusion model through geometric conditioning on nodes and edges. During training, the model learns from unit constraints and their random combinations in linear peptides, while at inference, novel constraint combinations required for cyclization are imposed as input. Experiments show that our model, despite trained with linear peptides, is capable of generating diverse target-binding cyclic peptides, reaching success rates from 38% to 84% on different cyclization strategies.

Retention Network (RetNet) is a novel neural network model, with a inference complexity of O(1), considered to be the successor of the Transformer model. This work presents an inference accelerator supporting RetNet and DNN algorithms focus on dataflow optimizing, model quantization for improved parallelism and reusability, and addressing K-V cache issues in Transformers. To meet model inference storage bandwidth requirements, we used output block stationary (OBS) dataflow to explore data reusability in time and space. A hardware accelerator for the position encoding module is designed, reducing storage bandwidth and on-chip storage during Extrapolatable Position Embedding (XPOS). Compared to Transformer models of the same size architecture, this work has better inference accuracy, achieving energy efficiency ratio of 77.79 GOPs/W, a 12.5x speedup, and 2.13x energy efficiency compared to GPU implementations, which is 1.33x better than the transformer work baseline. Meanwhile, the inference process of Muti-Scale-Retention(MSR) is completed with the smallest on-chip RAM of 350KB, thereby enabling a more efficient ASIC implementation of hardware accelerators.

Nowadays, vast amounts of multimedia data can be obtained across different collections (or domains). Therefore, it poses significant challenges for the utilization of those cross-collection data, for examples, the summarization of similarities and differences of data across different domains (e.g., CNN and NYT), as well as finding visually similar images across different visual domains (e.g., photos, paintings and hand-drawn sketches). In this paper, a supervised cross-collection Latent Dirichlet Allocation (scLDA) approach is proposed to utilize the data across different collections. As a natural extension of traditional Latent Dirichlet Allocation (LDA), scLDA not only takes the structural priors of different collections into consideration, but also exploits the category information. The strength of this work lies in integrating topic modeling, cross-domain learning and supervised learning together. We conduct scLDA for comparative text mining as well as classification of news articles and images from different collections. The results suggest that our proposed scLDA can generate meaningful collection-specific topics and achieves better retrieval accuracy than other related topic models.