Nature is infinitely resolution-free. In the context of this reality,
existing diffusion models, such as Diffusion Transformers, often face
challenges when processing image resolutions outside of their trained domain.
To overcome this limitation, we present the Flexible Vision Transformer (FiT),
a transformer architecture specifically designed for generating images with
unrestricted resolutions and aspect ratios. Unlike traditional methods that
perceive images as static-resolution grids, FiT conceptualizes images as
sequences of dynamically-sized tokens. This perspective enables a flexible
training strategy that effortlessly adapts to diverse aspect ratios during both
training and inference phases, thus promoting resolution generalization and
eliminating biases induced by image cropping. Enhanced by a meticulously
adjusted network structure and the integration of training-free extrapolation
techniques, FiT exhibits remarkable flexibility in resolution extrapolation
generation. Comprehensive experiments demonstrate the exceptional performance
of FiT across a broad range of resolutions, showcasing its effectiveness both
within and beyond its training resolution distribution. Repository available at
this https URL

为了克服传统图像处理方法中目标域以外的图像分辨率挑战，本文引入一种名为 Flexible Vision Transformer (FiT) 的变压器架构，它专门用于生成具有无限制分辨率和宽高比的图像。与传统的静态分辨率网格方式不同，FiT 将图像构想为动态尺寸令牌序列，从而实现了灵活的训练策略，适应了不同宽高比的图像。经过精心调整的网络结构和训练无关的外推技术的集成增强了 FiT 在分辨率外推生成方面的灵活性。综合实验证明 FiT 在广泛分辨率范围内表现出色，展示了它在训练分辨率分布内外的有效性。

FiT: 弹性视觉 Transformer 用于扩散模型

FiT: Flexible Vision Transformer for Diffusion Model

Nature is an amorphous concept. Yet, it is essential for the planet's
well-being to understand how the economy interacts with it. To address the
growing demand for information on corporate nature disclosure, we provide
datasets and classifiers to detect nature communication by companies. We ground
our approach in the guidelines of the Taskforce on Nature-related Financial
Disclosures (TNFD). Particularly, we focus on the specific dimensions of water,
forest, and biodiversity. For each dimension, we create an expert-annotated
dataset with 2,200 text samples and train classifier models. Furthermore, we
show that nature communication is more prevalent in hotspot areas and directly
effected industries like agriculture and utilities. Our approach is the first
to respond to calls to assess corporate nature communication on a large scale.

通过使用 TNFD 指南，我们提供了数据集和分类器，以检测企业对自然界的沟通。我们专注于水、森林和生物多样性等特定方面，建立了每个方面的专家标注数据集，并训练了分类器模型。同时，我们发现自然界的沟通在热点地区和农业、公共事业等受影响行业中更为常见。这是首次针对大规模评估企业自然界沟通做出的回应。

探索自然：用于分析与自然相关披露的数据集和模型

Exploring Nature: Datasets and Models for Analyzing Nature-Related  Disclosures

Recent publications explore AI biases in detecting objects and people in the
environment. However, there is no research tackling how AI examines nature.
This case study presents a pioneering exploration into the AI attitudes
(ecocentric, anthropocentric and antipathetic) toward nature. Experiments with
a Large Language Model (LLM) and an image captioning algorithm demonstrate the
presence of anthropocentric biases in AI. Moreover, to delve deeper into these
biases and Human-Nature-AI interaction, we conducted a real-life experiment in
which participants underwent an immersive de-anthropocentric experience in a
forest and subsequently engaged with ChatGPT to co-create narratives. By
creating fictional AI chatbot characters with ecocentric attributes, emotions
and views, we successfully amplified ecocentric exchanges. We encountered some
difficulties, mainly that participants deviated from narrative co-creation to
short dialogues and questions and answers, possibly due to the novelty of
interacting with LLMs. To solve this problem, we recommend providing
preliminary guidelines on interacting with LLMs and allowing participants to
get familiar with the technology. We plan to repeat this experiment in various
countries and forests to expand our corpus of ecocentric materials.

AI biases in detecting objects and people in the environment have been explored, but this case study pioneers an exploration into the attitudes of AI towards nature, revealing anthropocentric biases; through experiments and real-life interactions, participants engaged with ChatGPT to co-create narratives with ecocentric attributes, emotions, and views, resulting in amplified ecocentric exchanges and the plan to expand ecocentric materials.

Synocene, 人 - 自然 - 人工智能交互之外的超人类世纪

Synocene, Beyond the Anthropocene: De-Anthropocentralising  Human-Nature-AI Interaction

This is an updated version of supplementary information to accompany "Quantum
supremacy using a programmable superconducting processor", an article published
in the October 24, 2019 issue of Nature. The main article is freely available
at this https URL Summary of changes since
arXiv:1910.11333v1 (submitted 23 Oct 2019): added URL for qFlex source code;
added Erratum section; added Figure S41 comparing statistical and total
uncertainty for log and linear XEB; new References [1,65]; miscellaneous
updates for clarity and style consistency; miscellaneous typographical and
formatting corrections.

更新版本的补充信息，是 2019 年 10 月 24 日发表在 Nature 杂志上的 “使用可编程超导处理器实现量子霸权” 的文章的附加内容，主要更新了与 qFlex 源代码的 URL、勘误部分、比较 log 和 linear XEB 的统计和总不确定度的图 S41、参考文献和风格等方面的细节。