Urban spatio-temporal prediction is crucial for informed decision-making,
such as transportation management, resource optimization, and urban planning.
Although pretrained foundation models for natural languages have experienced
remarkable breakthroughs, wherein one general-purpose model can tackle multiple
tasks across various domains, urban spatio-temporal modeling lags behind.
Existing approaches for urban prediction are usually tailored for specific
spatio-temporal scenarios, requiring task-specific model designs and extensive
in-domain training data. In this work, we propose a universal model, UniST, for
urban spatio-temporal prediction. Drawing inspiration from large language
models, UniST achieves success through: (i) flexibility towards diverse
spatio-temporal data characteristics, (ii) effective generative pre-training
with elaborated masking strategies to capture complex spatio-temporal
relationships, (iii) spatio-temporal knowledge-guided prompts that align and
leverage intrinsic and shared knowledge across scenarios. These designs
together unlock the potential of a one-for-all model for spatio-temporal
prediction with powerful generalization capability. Extensive experiments on 15
cities and 6 domains demonstrate the universality of UniST in advancing
state-of-the-art prediction performance, especially in few-shot and zero-shot
scenarios.

UniST 是一个通用模型，通过对多样的时空数据特征的灵活性、精心设计的掩模策略以及时空知识引导的提示，实现对城市时空预测的强大泛化能力，15 个城市和 6 个领域的广泛实验证明了 UniST 在提升最先进预测性能方面的普适性，特别是在少样本和零样本情况下。

UniST：用于城市时空预测的提示增强通用模型

UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal  Prediction

Current arbitrary style transfer models are limited to either image or video
domains. In order to achieve satisfying image and video style transfers, two
different models are inevitably required with separate training processes on
image and video domains, respectively. In this paper, we show that this can be
precluded by introducing UniST, a Unified Style Transfer framework for both
images and videos. At the core of UniST is a domain interaction transformer
(DIT), which first explores context information within the specific domain and
then interacts contextualized domain information for joint learning. In
particular, DIT enables exploration of temporal information from videos for the
image style transfer task and meanwhile allows rich appearance texture from
images for video style transfer, thus leading to mutual benefits. Considering
heavy computation of traditional multi-head self-attention, we present a simple
yet effective axial multi-head self-attention (AMSA) for DIT, which improves
computational efficiency while maintains style transfer performance. To verify
the effectiveness of UniST, we conduct extensive experiments on both image and
video style transfer tasks and show that UniST performs favorably against
state-of-the-art approaches on both tasks. Our code and results will be
released.

本文介绍了一种基于统一风格转移框架 UnST 和领域交互变压器（DIT）的方法，使得同时能实现对图像和视频完成风格转移任务。实验表明 UniST 在图像和视频领域中与现有方法相比表现更佳，通过简单而有效的轴向多头自我关注（AMSA）获得更高的计算效率和风格转移表现。

一石二鸟：图像和视频风格转换联合学习的统一框架

Two Birds, One Stone: A Unified Framework for Joint Learning of Image  and Video Style Transfers

Semantic typing aims at classifying tokens or spans of interest in a textual
context into semantic categories such as relations, entity types, and event
types. The inferred labels of semantic categories meaningfully interpret how
machines understand components of text. In this paper, we present UniST, a
unified framework for semantic typing that captures label semantics by
projecting both inputs and labels into a joint semantic embedding space. To
formulate different lexical and relational semantic typing tasks as a unified
task, we incorporate task descriptions to be jointly encoded with the input,
allowing UniST to be adapted to different tasks without introducing
task-specific model components. UniST optimizes a margin ranking loss such that
the semantic relatedness of the input and labels is reflected from their
embedding similarity. Our experiments demonstrate that UniST achieves strong
performance across three semantic typing tasks: entity typing, relation
classification and event typing. Meanwhile, UniST effectively transfers
semantic knowledge of labels and substantially improves generalizability on
inferring rarely seen and unseen types. In addition, multiple semantic typing
tasks can be jointly trained within the unified framework, leading to a single
compact multi-tasking model that performs comparably to dedicated single-task
models, while offering even better transferability.

本文提出 UniST 框架以实现语义打标，该框架通过将输入和标签投影到共同的语义嵌入空间来捕捉标签的语义。同时，多种语义打标任务可以在该框架中联合训练，从而获得性能相当于单独任务模型的单个紧凑多任务模型，同时也提高了转移能力。