Time-series datasets are central in numerous fields of science and
engineering, such as biomedicine, Earth observation, and network analysis.
Extensive research exists on state-space models (SSMs), which are powerful
mathematical tools that allow for probabilistic and interpretable learning on
time series. Estimating the model parameters in SSMs is arguably one of the
most complicated tasks, and the inclusion of prior knowledge is known to both
ease the interpretation but also to complicate the inferential tasks. Very
recent works have attempted to incorporate a graphical perspective on some of
those model parameters, but they present notable limitations that this work
addresses. More generally, existing graphical modeling tools are designed to
incorporate either static information, focusing on statistical dependencies
among independent random variables (e.g., graphical Lasso approach), or dynamic
information, emphasizing causal relationships among time series samples (e.g.,
graphical Granger approaches). However, there are no joint approaches combining
static and dynamic graphical modeling within the context of SSMs. This work
proposes a novel approach to fill this gap by introducing a joint graphical
modeling framework that bridges the static graphical Lasso model and a
causal-based graphical approach for the linear-Gaussian SSM. We present DGLASSO
(Dynamic Graphical Lasso), a new inference method within this framework that
implements an efficient block alternating majorization-minimization algorithm.
The algorithm's convergence is established by departing from modern tools from
nonlinear analysis. Experimental validation on synthetic and real weather
variability data showcases the effectiveness of the proposed model and
inference algorithm.

该研究论文提出了一种新的联合图模型框架来填补现有的状态空间模型中静态图模型和基于因果关系的图模型之间的缺失。作者通过引入一个高效的块交替主要化极小化算法实现了该框架，并通过对合成和真实气象变化数据的实验验证了所提出模型和推断算法的有效性。

稀疏图线性动态系统

Sparse Graphical Linear Dynamical Systems

Reasoning about the effect of interventions and counterfactuals is a
fundamental task found throughout the data sciences. A collection of
principles, algorithms, and tools has been developed for performing such tasks
in the last decades (Pearl, 2000). One of the pervasive requirements found
throughout this literature is the articulation of assumptions, which commonly
appear in the form of causal diagrams. Despite the power of this approach,
there are significant settings where the knowledge necessary to specify a
causal diagram over all variables is not available, particularly in complex,
high-dimensional domains. In this paper, we introduce a new graphical modeling
tool called cluster DAGs (for short, C-DAGs) that allows for the partial
specification of relationships among variables based on limited prior
knowledge, alleviating the stringent requirement of specifying a full causal
diagram. A C-DAG specifies relationships between clusters of variables, while
the relationships between the variables within a cluster are left unspecified,
and can be seen as a graphical representation of an equivalence class of causal
diagrams that share the relationships among the clusters. We develop the
foundations and machinery for valid inferences over C-DAGs about the clusters
of variables at each layer of Pearl's Causal Hierarchy (Pearl and Mackenzie
2018; Bareinboim et al. 2020) - L1 (probabilistic), L2 (interventional), and L3
(counterfactual). In particular, we prove the soundness and completeness of
d-separation for probabilistic inference in C-DAGs. Further, we demonstrate the
validity of Pearl's do-calculus rules over C-DAGs and show that the standard ID
identification algorithm is sound and complete to systematically compute causal
effects from observational data given a C-DAG. Finally, we show that C-DAGs are
valid for performing counterfactual inferences about clusters of variables.

该研究介绍了一种叫做 “集群有向无环图（Cluster DAGs）” 的新型图形建模工具，其可以基于有限的先验知识提供变量之间关系的部分规范，从而缓解了在复杂、高维度领域中指定完全因果图的严格要求。在该图形模型下，本研究还开发了基于 “Pearl's Causal Hierarchy” 的各层级的变量集聚进行推理的方法，并验证了 C-DAGs 的有效性。

聚类 DAG 中的因果效应识别

Causal Effect Identification in Cluster DAGs

Point cloud registration for 3D objects is a challenging task due to sparse
and noisy measurements, incomplete observations and large transformations. In
this work, we propose \textbf{G}raph \textbf{M}atching \textbf{C}onsensus
\textbf{Net}work (\textbf{GMCNet}), which estimates pose-invariant
correspondences for full-range Partial-to-Partial point cloud Registration
(PPR) in the object-level registration scenario. To encode robust point
descriptors, \textbf{1)} we first comprehensively investigate
transformation-robustness and noise-resilience of various geometric features.
\textbf{2)} Then, we employ a novel {T}ransformation-robust {P}oint
{T}ransformer (\textbf{TPT}) module to adaptively aggregate local features
regarding the structural relations, which takes advantage from both handcrafted
rotation-invariant ({\textit{RI}}) features and noise-resilient spatial
coordinates. \textbf{3)} Based on a synergy of hierarchical graph networks and
graphical modeling, we propose the {H}ierarchical {G}raphical {M}odeling
(\textbf{HGM}) architecture to encode robust descriptors consisting of i) a
unary term learned from {\textit{RI}} features; and ii) multiple smoothness
terms encoded from neighboring point relations at different scales through our
TPT modules. Moreover, we construct a challenging PPR dataset (\textbf{MVP-RG})
based on the recent MVP dataset that features high-quality scans. Extensive
experiments show that GMCNet outperforms previous state-of-the-art methods for
PPR. Notably, GMCNet encodes point descriptors for each point cloud
individually without using cross-contextual information, or ground truth
correspondences for training. Our code and datasets are available at:
this https URL

GMCNet 使用包括几何特征、自适应局部特征和基于图表征的统计建模等多种策略实现了全范围的点云配准，在点云配准方面超过了以往的最先进方法。

完整范围内的健壮部分点云对齐

Robust Partial-to-Partial Point Cloud Registration in a Full Range

Often we wish to predict a large number of variables that depend on each
other as well as on other observed variables. Structured prediction methods are
essentially a combination of classification and graphical modeling, combining
the ability of graphical models to compactly model multivariate data with the
ability of classification methods to perform prediction using large sets of
input features. This tutorial describes conditional random fields, a popular
probabilistic method for structured prediction. CRFs have seen wide application
in natural language processing, computer vision, and bioinformatics. We
describe methods for inference and parameter estimation for CRFs, including
practical issues for implementing large scale CRFs. We do not assume previous
knowledge of graphical modeling, so this tutorial is intended to be useful to
practitioners in a wide variety of fields.

该论文介绍结构化预测方法 —— 条件随机场，它是分类方法和图形建模的结合，可用于自然语言处理、计算机视觉和生物信息学等领域，该论文还描述了 CRFs 的推断和参数估计方法，以及实现大规模 CRFs 的实际问题。