SPARQL CONSTRUCT queries allow for the specification of data processing
pipelines that transform given input graphs into new output graphs. It is now
common to constrain graphs through SHACL shapes allowing users to understand
which data they can expect and which not. However, it becomes challenging to
understand what graph data can be expected at the end of a data processing
pipeline without knowing the particular input data: Shape constraints on the
input graph may affect the output graph, but may no longer apply literally, and
new shapes may be imposed by the query template. In this paper, we study the
derivation of shape constraints that hold on all possible output graphs of a
given SPARQL CONSTRUCT query. We assume that the SPARQL CONSTRUCT query is
fixed, e.g., being part of a program, whereas the input graphs adhere to input
shape constraints but may otherwise vary over time and, thus, are mostly
unknown. We study a fragment of SPARQL CONSTRUCT queries (SCCQ) and a fragment
of SHACL (Simple SHACL). We formally define the problem of deriving the most
restrictive set of Simple SHACL shapes that constrain the results from
evaluating a SCCQ over any input graph restricted by a given set of Simple
SHACL shapes. We propose and implement an algorithm that statically analyses
input SHACL shapes and CONSTRUCT queries and prove its soundness and
complexity.

本文研究了如何推导 SPARQL CONSTRUCT 查询的所有可能输出图形的形状约束集合，同时使用给定的输入 SHACL 形状约束限制了 SCCQ 的评估结果。

从形状到形状：推测 SPARQL CONSTRUCT 查询结果的 SHACL 形状（扩展版）

From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL  CONSTRUCT Queries (Extended Version)

This paper presents a data processing pipeline designed to extract
information from the hyperspectral signature of unknown space objects. The
methodology proposed in this paper determines the material composition of space
objects from single pixel images. Two techniques are used for material
identification and classification: one based on machine learning and the other
based on a least square match with a library of known spectra. From this
information, a supervised machine learning algorithm is used to classify the
object into one of several categories based on the detection of materials on
the object. The behaviour of the material classification methods is
investigated under non-ideal circumstances, to determine the effect of
weathered materials, and the behaviour when the training library is missing a
material that is present in the object being observed. Finally the paper will
present some preliminary results on the identification and classification of
space objects.

该研究论文提出了一种数据处理流水线，旨在从未知太空物体的高光谱特征中提取信息，通过单像素图像确定太空物体的物质组成，并使用基于机器学习和最小二乘匹配的技术对物质进行识别和分类，研究了在非理想情况下的物质分类方法的行为，并初步展示了太空物体的鉴定和分类的一些初步结果。

高光谱物质分析下的空间目标识别和分类

Space Object Identification and Classification from Hyperspectral  Material Analysis

We describe a method for 3D human pose estimation from transient images
(i.e., a 3D spatio-temporal histogram of photons) acquired by an optical
non-line-of-sight (NLOS) imaging system. Our method can perceive 3D human pose
by `looking around corners' through the use of light indirectly reflected by
the environment. We bring together a diverse set of technologies from NLOS
imaging, human pose estimation and deep reinforcement learning to construct an
end-to-end data processing pipeline that converts a raw stream of photon
measurements into a full 3D human pose sequence estimate. Our contributions are
the design of data representation process which includes (1) a learnable
inverse point spread function (PSF) to convert raw transient images into a deep
feature vector; (2) a neural humanoid control policy conditioned on the
transient image feature and learned from interactions with a physics simulator;
and (3) a data synthesis and augmentation strategy based on depth data that can
be transferred to a real-world NLOS imaging system. Our preliminary experiments
suggest that our method is able to generalize to real-world NLOS measurement to
estimate physically-valid 3D human poses.

本文通过光学非视线成像系统获取的瞬态图像描述了一种实现 3D 人体姿势估计的方法，通过使用间接反射的光线，实现了从街角窥视的功能；该方法将非视线成像、人体姿势估计和深度强化学习等多个领域的技术结合起来，通过端对端的数据处理流程，将源源不断的光子测量转换为整个 3D 人体姿势序列的估计，并通过数据综合和增强策略实现靠近现实的 NLOS 成像系统数据的预测。初步实验结果表明，本文所提出的方法可以推广至真实世界的 NLOS 测量，从而实现估算物理上有效的 3D 人体姿势。