Real-time light detection and ranging (LiDAR) perceptions, e.g., 3D object
detection and simultaneous localization and mapping are computationally
intensive to mobile devices of limited resources and often offloaded on the
edge. Offloading LiDAR perceptions requires compressing the raw sensor data,
and lossy compression is used for efficiently reducing the data volume. Lossy
compression degrades the quality of LiDAR point clouds, and the perception
performance is decreased consequently. In this work, we present an
interpolation algorithm improving the quality of a LiDAR point cloud to
mitigate the perception performance loss due to lossy compression. The
algorithm targets the range image (RI) representation of a point cloud and
interpolates points at the RI based on depth gradients. Compared to existing
image interpolation algorithms, our algorithm shows a better qualitative result
when the point cloud is reconstructed from the interpolated RI. With the
preliminary results, we also describe the next steps of the current work.

我们提出了一种基于深度梯度的插值算法，用于改善 LiDAR 点云的质量，以减轻由于有损压缩导致的感知性能损失。与现有图像插值算法相比，我们的算法在从插值后的范围图像重建点云时显示出更好的定性结果。

基于边缘辅助的 LiDAR 感知在有损点云压缩中的鲁棒性提升

Poster: Making Edge-assisted LiDAR Perceptions Robust to Lossy Point  Cloud Compression

Neural controlled differential equations (NCDEs), which are continuous
analogues to recurrent neural networks (RNNs), are a specialized model in
(irregular) time-series processing. In comparison with similar models, e.g.,
neural ordinary differential equations (NODEs), the key distinctive
characteristics of NCDEs are i) the adoption of the continuous path created by
an interpolation algorithm from each raw discrete time-series sample and ii)
the adoption of the Riemann--Stieltjes integral. It is the continuous path
which makes NCDEs be analogues to continuous RNNs. However, NCDEs use existing
interpolation algorithms to create the path, which is unclear whether they can
create an optimal path. To this end, we present a method to generate another
latent path (rather than relying on existing interpolation algorithms), which
is identical to learning an appropriate interpolation method. We design an
encoder-decoder module based on NCDEs and NODEs, and a special training method
for it. Our method shows the best performance in both time-series
classification and forecasting.

本文研究神经控制微分方程的连续解法，采用编码 - 解码模块，通过插值算法生成连续路径，达到更好的时间序列分类和预测表现。

神经控制微分方程中的可学习路径

Learnable Path in Neural Controlled Differential Equations

Most previous work on the recently developed language-modeling approach to
information retrieval focuses on document-specific characteristics, and
therefore does not take into account the structure of the surrounding corpus.
We propose a novel algorithmic framework in which information provided by
document-based language models is enhanced by the incorporation of information
drawn from clusters of similar documents. Using this framework, we develop a
suite of new algorithms. Even the simplest typically outperforms the standard
language-modeling approach in precision and recall, and our new interpolation
algorithm posts statistically significant improvements for both metrics over
all three corpora tested.

本文提出了一种基于语言模型的信息检索算法框架的新方法，并将类似文档群的信息整合进去以提高检索效果；实验结果表明，即使是此新方法中的较简单算法也能优于标准语言模型方法，并且我们的新插值算法在所有测试语料库中均能显著提高精度和召回率表现。