We introduce Xmodel-LM, a compact and efficient 1.1B language model
pre-trained on over 2 trillion tokens. Trained on our self-built dataset
(Xdata), which balances Chinese and English corpora based on downstream task
optimization, Xmodel-LM exhibits remarkable performance despite its smaller
size. It notably surpasses existing open-source language models of similar
scale. Our model checkpoints and code are publicly accessible on GitHub at
this https URL

我们介绍了 Xmodel-LM，这是一个在超过 2 万亿个标记上进行预训练的紧凑高效的 1.1B 语言模型，通过在我们自建的数据集 (Xdata) 上进行训练，该数据集在下游任务优化的基础上平衡了中文和英文文本，尽管模型规模较小，但 Xmodel-LM 表现出色，显著超过了类似规模的现有开源语言模型。我们的模型检查点和代码在 GitHub 上公开可访问。

Xmodel-LM 技术报告

Xmodel-LM Technical Report

The rise of large-scale multimodal models has paved the pathway for
groundbreaking advances in generative modeling and reasoning, unlocking
transformative applications in a variety of complex tasks. However, a pressing
question that remains is their genuine capability for stronger forms of
generalization, which has been largely underexplored in the multimodal setting.
Our study aims to address this by examining sequential compositional
generalization using \textsc{CompAct} (\underline{Comp}ositional
\underline{Act}ivities)\footnote{Project Page:
http://cyberiada.github.io/CompAct}, a carefully constructed,
perceptually grounded dataset set within a rich backdrop of egocentric kitchen
activity videos. Each instance in our dataset is represented with a combination
of raw video footage, naturally occurring sound, and crowd-sourced step-by-step
descriptions. More importantly, our setup ensures that the individual concepts
are consistently distributed across training and evaluation sets, while their
compositions are novel in the evaluation set. We conduct a comprehensive
assessment of several unimodal and multimodal models. Our findings reveal that
bi-modal and tri-modal models exhibit a clear edge over their text-only
counterparts. This highlights the importance of multimodality while charting a
trajectory for future research in this domain.

本研究通过研究多模态设置下的顺序组合泛化，检验了使用 CompAct（组合活动）数据集在丰富的自我中心厨房活动视频背景中。研究发现，双模态和三模态模型明显优于仅文本的对应模型，强调了多模态在未来研究中的重要性。

多模态模型中的顺序组合泛化

Sequential Compositional Generalization in Multimodal Models

Medical image compression is a widely studied field of data processing due to
its prevalence in modern digital databases. This domain requires a high color
depth of 12 bits per pixel component for accurate analysis by physicians,
primarily in the DICOM format. Standard raster-based compression of images via
filtering is well-known; however, it remains suboptimal in the medical domain
due to non-specialized implementations. This study proposes a lossless medical
image compression algorithm, CompaCT, that aims to target spatial features and
patterns of pixel concentration for dynamically enhanced data processing. The
algorithm employs fractal pixel traversal coupled with a novel approach of
segmentation and meshing between pixel blocks for preprocessing. Furthermore,
delta and entropy coding are applied to this concept for a complete compression
pipeline. The proposal demonstrates that the data compression achieved via
fractal segmentation preprocessing yields enhanced image compression results
while remaining lossless in its reconstruction accuracy. CompaCT is evaluated
in its compression ratios on 3954 high-color CT scans against the efficiency of
industry-standard compression techniques (i.e., JPEG2000, RLE, ZIP, PNG). Its
reconstruction performance is assessed with error metrics to verify lossless
image recovery after decompression. The results demonstrate that CompaCT can
compress and losslessly reconstruct medical images, being 37% more
space-efficient than industry-standard compression systems.

该研究提出了一种名为 CompaCT 的无损医学图像压缩算法，通过使用分形分割预处理，以及数据压缩的动态增强，实现了对医学图像的高效压缩和无损重建，其压缩比相比行业标准系统提高了 37%。