We present MAV3D (Make-A-Video3D), a method for generating three-dimensional
dynamic scenes from text descriptions. Our approach uses a 4D dynamic Neural
Radiance Field (NeRF), which is optimized for scene appearance, density, and
motion consistency by querying a Text-to-Video (T2V) diffusion-based model. The
dynamic video output generated from the provided text can be viewed from any
camera location and angle, and can be composited into any 3D environment. MAV3D
does not require any 3D or 4D data and the T2V model is trained only on
Text-Image pairs and unlabeled videos. We demonstrate the effectiveness of our
approach using comprehensive quantitative and qualitative experiments and show
an improvement over previously established internal baselines. To the best of
our knowledge, our method is the first to generate 3D dynamic scenes given a
text description.

MAV3D 使用 4D 神经辐射场和 T2V 模型从文本描述中生成 3D 动态场景，无需 3D 或 4D 数据，通过综合的定量和定性实验证明其有效性，并显示比已建立的内部基线更好的性能。

基于文本的 4D 动态场景生成

Text-To-4D Dynamic Scene Generation

Understanding the connections between unstructured text and semi-structured
table is an important yet neglected problem in natural language processing. In
this work, we focus on content-based table retrieval. Given a query, the task
is to find the most relevant table from a collection of tables. Further
progress towards improving this area requires powerful models of semantic
matching and richer training and evaluation resources. To remedy this, we
present a ranking based approach, and implement both carefully designed
features and neural network architectures to measure the relevance between a
query and the content of a table. Furthermore, we release an open-domain
dataset that includes 21,113 web queries for 273,816 tables. We conduct
comprehensive experiments on both real world and synthetic datasets. Results
verify the effectiveness of our approach and present the challenges for this
task.

本研究旨在解决自然语言处理中未结构化文本与半结构化表之间的关系问题，通过提出基于内容的表的检索方法，应用精心设计的特征和神经网络结构实现查找中最相关表格的目标，并发布了一个包含 21,113 个网络查询和 273,816 个表格的开放领域数据集，验证了该方法的有效性并提出了该任务的挑战。

基于内容的 Web 查询表格检索

Content-Based Table Retrieval for Web Queries

All solutions SAT (AllSAT for short) is a variant of propositional
satisfiability problem. Despite its significance, AllSAT has been relatively
unexplored compared to other variants. We thus survey and discuss major
techniques of AllSAT solvers. We faithfully implement them and conduct
comprehensive experiments using a large number of instances and various types
of solvers including one of the few public softwares. The experiments reveal
solver's characteristics. Our implemented solvers are made publicly available
so that other researchers can easily develop their solver by modifying our
codes and compare it with existing methods.

本文介绍 AllSAT 问题及其重要意义，探讨解决方案，实施大量实验并公开开发的求解器以及该问题的特征。