This research aims to accelerate the inference speed of large language models
(LLMs) with billions of parameters. We propose \textbf{S}mart \textbf{P}arallel
\textbf{A}uto-\textbf{C}orrect d\textbf{E}coding (SPACE), an innovative
approach designed for achieving lossless acceleration of LLMs. By integrating
semi-autoregressive inference and speculative decoding capabilities, SPACE
uniquely enables autoregressive LLMs to parallelize token generation and
verification. This is realized through a specialized semi-autoregressive
supervised fine-tuning process that equips existing LLMs with the ability to
simultaneously predict multiple tokens. Additionally, an auto-correct decoding
algorithm facilitates the simultaneous generation and verification of token
sequences within a single model invocation. Through extensive experiments on a
range of LLMs, SPACE has demonstrated inference speedup ranging from 2.7x-4.0x
on HumanEval-X while maintaining output quality.

本研究旨在加快具有数十亿参数的大型语言模型的推理速度。我们提出了一个名为 SPACE（Smart Parallel Auto-Correct decoding）的创新方法，用于实现 LLMs 的无损加速。通过集成半自回归推理和猜测解码功能，SPACE 使得自回归 LLMs 能够并行生成和验证令牌。此外，一种自动纠正解码算法在单个模型调用中便利地实现了令牌序列的同时生成和验证。通过对多个 LLMs 进行广泛实验，SPACE 在保持输出质量的同时，实现了 2.7 倍至 4.0 倍的推理加速。

生成遇见验证：智能并行自动校正解码加速大型语言模型推理

Generation Meets Verification: Accelerating Large Language Model  Inference with Smart Parallel Auto-Correct Decoding

A novel approach is presented for discovering PDEs that govern the motion of
satellites in space. The method is based on SINDy, a data-driven technique
capable of identifying the underlying dynamics of complex physical systems from
time series data. SINDy is utilized to uncover PDEs that describe the laws of
physics in space, which are non-deterministic and influenced by various factors
such as drag or the reference area (related to the attitude of the satellite).
In contrast to prior works, the physically interpretable coordinate system is
maintained, and no dimensionality reduction technique is applied to the data.
By training the model with multiple representative trajectories of LEO -
encompassing various inclinations, eccentricities, and altitudes - and testing
it with unseen orbital motion patterns, a mean error of around 140 km for the
positions and 0.12 km/s for the velocities is achieved. The method offers the
advantage of delivering interpretable, accurate, and complex models of orbital
motion that can be employed for propagation or as inputs to predictive models
for other variables of interest, such as atmospheric drag or the probability of
collision in an encounter with a spacecraft or space objects. In conclusion,
the work demonstrates the promising potential of using SINDy to discover the
equations governing the behaviour of satellites in space. The technique has
been successfully applied to uncover PDEs describing the motion of satellites
in LEO with high accuracy. The method possesses several advantages over
traditional models, including the ability to provide physically interpretable,
accurate, and complex models of orbital motion derived from high-entropy
datasets. These models can be utilised for propagation or as inputs to
predictive models for other variables of interest.

通过基于 SINDy 的方法，该研究发现了描述卫星运动的偏微分方程，对包括轨道倾角、偏心率和高度在内的多个代表性轨道模式进行训练与测试，取得了很高的准确性，为描述卫星在轨运动提供了具有物理解释性、准确性和复杂性的模型。

从数据中寻找现实世界的轨道运动定律

Finding Real-World Orbital Motion Laws from Data

6D pose estimation in space poses unique challenges that are not commonly
encountered in the terrestrial setting. One of the most striking differences is
the lack of atmospheric scattering, allowing objects to be visible from a great
distance while complicating illumination conditions. Currently available
benchmark datasets do not place a sufficient emphasis on this aspect and mostly
depict the target in close proximity.
Prior work tackling pose estimation under large scale variations relies on a
two-stage approach to first estimate scale, followed by pose estimation on a
resized image patch. We instead propose a single-stage hierarchical end-to-end
trainable network that is more robust to scale variations. We demonstrate that
it outperforms existing approaches not only on images synthesized to resemble
images taken in space but also on standard benchmarks.

本论文提出了一种单阶段的分层端对端可训练网络，可以更好地解决大规模空间场景中的 6D 姿态估计问题，相比现有方法在合成的太空图像和标准数据集上表现更优秀。关键词包括 6D 姿态估计、空间、大气散射、基准数据集和端对端可训练网络