Similar to natural language models, pre-trained genome language models are
proposed to capture the underlying intricacies within genomes with unsupervised
sequence modeling. They have become essential tools for researchers and
practitioners in biology. However, the \textit{hand-crafted} tokenization
policies used in these models may not encode the most discriminative patterns
from the limited vocabulary of genomic data. In this paper, we introduce VQDNA,
a general-purpose framework that renovates genome tokenization from the
perspective of genome vocabulary learning. By leveraging vector-quantized
codebook as \textit{learnable} vocabulary, VQDNA can adaptively tokenize
genomes into \textit{pattern-aware} embeddings in an end-to-end manner. To
further push its limits, we propose Hierarchical Residual Quantization (HRQ),
where varying scales of codebooks are designed in a hierarchy to enrich the
genome vocabulary in a coarse-to-fine manner. Extensive experiments on 32
genome datasets demonstrate VQDNA's superiority and favorable parameter
efficiency compared to existing genome language models. Notably, empirical
analysis of SARS-CoV-2 mutations reveals the fine-grained pattern awareness and
biological significance of learned HRQ vocabulary, highlighting its untapped
potential for broader applications in genomics.

引入了 VQDNA，一个从基因词汇学习的角度改进基因标记化的通用框架；通过利用可学习的词汇的向量量化码书，VQDNA 可以自适应地将基因标记化为具有模式感知的嵌入结果；通过在层次结构中设计不同尺度的码书，提出了层次残差量化方法（HRQ）。在 32 个基因数据集上的广泛实验表明，与现有的基因语言模型相比，VQDNA 具有卓越的性能和有利的参数效率。值得注意的是，对 SARS-CoV-2 突变的经验分析揭示了学习 HRQ 词汇的细粒度模式感知和生物学意义，突显了其在基因组学中更广泛应用的潜力。

VQDNA: 多物种基因组序列建模的向量量化能力释放

VQDNA: Unleashing the Power of Vector Quantization for Multi-Species  Genomic Sequence Modeling

Night photography often struggles with challenges like low light and
blurring, stemming from dark environments and prolonged exposures. Current
methods either disregard priors and directly fitting end-to-end networks,
leading to inconsistent illumination, or rely on unreliable handcrafted priors
to constrain the network, thereby bringing the greater error to the final
result. We believe in the strength of data-driven high-quality priors and
strive to offer a reliable and consistent prior, circumventing the restrictions
of manual priors. In this paper, we propose Clearer Night Image Restoration
with Vector-Quantized Codebook (VQCNIR) to achieve remarkable and consistent
restoration outcomes on real-world and synthetic benchmarks. To ensure the
faithful restoration of details and illumination, we propose the incorporation
of two essential modules: the Adaptive Illumination Enhancement Module (AIEM)
and the Deformable Bi-directional Cross-Attention (DBCA) module. The AIEM
leverages the inter-channel correlation of features to dynamically maintain
illumination consistency between degraded features and high-quality codebook
features. Meanwhile, the DBCA module effectively integrates texture and
structural information through bi-directional cross-attention and deformable
convolution, resulting in enhanced fine-grained detail and structural fidelity
across parallel decoders. Extensive experiments validate the remarkable
benefits of VQCNIR in enhancing image quality under low-light conditions,
showcasing its state-of-the-art performance on both synthetic and real-world
datasets. The code is available at this https URL

提出了基于向量量化码本（VQCNIR）的清晰夜间图像恢复方法，通过自适应照明增强模块和可变形双向交叉注意力模块，在低光条件下实现了显著且可靠的图像恢复效果。