The annotation burden and extensive labor for gathering a large medical
dataset with images and corresponding labels are rarely cost-effective and
highly intimidating. This results in a lack of abundant training data that
undermines downstream tasks and partially contributes to the challenge image
analysis faces in the medical field. As a workaround, given the recent success
of generative neural models, it is now possible to synthesize image datasets at
a high fidelity guided by external constraints. This paper explores this
possibility and presents \textbf{GuideGen}: a pipeline that jointly generates
CT images and tissue masks for abdominal organs and colorectal cancer
conditioned on a text prompt. Firstly, we introduce Volumetric Mask Sampler to
fit the discrete distribution of mask labels and generate low-resolution 3D
tissue masks. Secondly, our Conditional Image Generator autoregressively
generates CT slices conditioned on a corresponding mask slice to incorporate
both style information and anatomical guidance. This pipeline guarantees high
fidelity and variability as well as exact alignment between generated CT
volumes and tissue masks. Both qualitative and quantitative experiments on 3D
abdominal CTs demonstrate a high performance of our proposed pipeline, thereby
proving our method can serve as a dataset generator and provide potential
benefits to downstream tasks. It is hoped that our work will offer a promising
solution on the multimodality generation of CT and its anatomical mask. Our
source code is publicly available at
this https URL

本研究提出了一种生成医学图像数据集的管道，通过生成神经模型生成 CT 图像和组织掩码，从而为下游任务提供潜在益处。

GuideGen: 用于联合 CT 体积和解剖结构生成的文本引导框架

GuideGen: A Text-guided Framework for Joint CT Volume and Anatomical  structure Generation

Generative neural models hold great promise in enhancing programming
education by synthesizing new content for students. We seek to design neural
models that can automatically generate programming tasks for a given
specification in the context of visual programming domains. Despite the recent
successes of large generative models like GPT-4, our initial results show that
these models are ineffective in synthesizing visual programming tasks and
struggle with logical and spatial reasoning. We propose a novel neuro-symbolic
technique, NeurTaskSyn, that can synthesize programming tasks for a
specification given in the form of desired programming concepts exercised by
its solution code and constraints on the visual task. NeurTaskSyn has two
components: the first component is trained via imitation learning procedure to
generate possible solution codes, and the second component is trained via
reinforcement learning procedure to guide an underlying symbolic execution
engine that generates visual tasks for these codes. We demonstrate the
effectiveness of NeurTaskSyn through an extensive empirical evaluation and a
qualitative study on reference tasks taken from the Hour of Code: Classic Maze
challenge by Code-dot-org and the Intro to Programming with Karel course by
CodeHS-dot-com.

使用神经符号技术 NeurTaskSyn, 可以为给定的编程要求自动综合编程任务，可提高编程教育的质量。

视觉编程的神经任务合成

Neural Task Synthesis for Visual Programming

Generative neural models have recently achieved state-of-the-art results for
constituency parsing. However, without a feasible search procedure, their use
has so far been limited to reranking the output of external parsers in which
decoding is more tractable. We describe an alternative to the conventional
action-level beam search used for discriminative neural models that enables us
to decode directly in these generative models. We then show that by improving
our basic candidate selection strategy and using a coarse pruning function, we
can improve accuracy while exploring significantly less of the search space.
Applied to the model of Choe and Charniak (2016), our inference procedure
obtains 92.56 F1 on section 23 of the Penn Treebank, surpassing prior
state-of-the-art results for single-model systems.

通过改进候选选择和使用粗略的修剪功能，我们描述了一种可行的搜索过程，及其用于 Choe 和 Charniak（2016）模型的推理过程在 Penn Treebank 的 23 节上能够获得 92.56 F1，超过以前的最先进结果。

生成神经语法分析的有效推论

Effective Inference for Generative Neural Parsing

Recent work has proposed several generative neural models for constituency
parsing that achieve state-of-the-art results. Since direct search in these
generative models is difficult, they have primarily been used to rescore
candidate outputs from base parsers in which decoding is more straightforward.
We first present an algorithm for direct search in these generative models. We
then demonstrate that the rescoring results are at least partly due to implicit
model combination rather than reranking effects. Finally, we show that explicit
model combination can improve performance even further, resulting in new
state-of-the-art numbers on the PTB of 94.25 F1 when training only on gold data
and 94.66 F1 when using external data.

本文提出了一种直接搜索联合生成神经模型的算法，并证明重新评分结果部分是由于隐式模型组合，而不是重新排序效果所致，最后表明显式模型组合可以进一步改善性能，可以在只训练黄金数据时获得 94.25 F1，在使用外部数据时可获得 94.66 F1，从而实现了新的最先进水平。