Real-world graphs generally have only one kind of tendency in their
connections. These connections are either homophily-prone or heterophily-prone.
While graphs with homophily-prone edges tend to connect nodes with the same
class (i.e., intra-class nodes), heterophily-prone edges tend to build
relationships between nodes with different classes (i.e., inter-class nodes).
Existing GNNs only take the original graph during training. The problem with
this approach is that it forgets to take into consideration the ``missing-half"
structural information, that is, heterophily-prone topology for homophily-prone
graphs and homophily-prone topology for heterophily-prone graphs. In our paper,
we introduce Graph cOmplementAry Learning, namely GOAL, which consists of two
components: graph complementation and complemented graph convolution. The first
component finds the missing-half structural information for a given graph to
complement it. The complemented graph has two sets of graphs including both
homophily- and heterophily-prone topology. In the latter component, to handle
complemented graphs, we design a new graph convolution from the perspective of
optimisation. The experiment results show that GOAL consistently outperforms
all baselines in eight real-world datasets.

本文提出了 Graph cOmplementAry Learning（GOAL）方法，包括两个组件：图互补和互补图卷积，以解决现有 GNN 仅在训练时考虑原始图的问题，实现了对同质和异质性度量进行无差别的图卷积，该方法在八个真实数据集上表现出色。

寻找缺失的一半：图补充学习用于同质和异质图

Finding the Missing-half: Graph Complementary Learning for  Homophily-prone and Heterophily-prone Graphs

Generating digital humans that move realistically has many applications and
is widely studied, but existing methods focus on the major limbs of the body,
ignoring the hands and head. Hands have been separately studied, but the focus
has been on generating realistic static grasps of objects. To synthesize
virtual characters that interact with the world, we need to generate full-body
motions and realistic hand grasps simultaneously. Both sub-problems are
challenging on their own and, together, the state-space of poses is
significantly larger, the scales of hand and body motions differ, and the
whole-body posture and the hand grasp must agree, satisfy physical constraints,
and be plausible. Additionally, the head is involved because the avatar must
look at the object to interact with it. For the first time, we address the
problem of generating full-body, hand and head motions of an avatar grasping an
unknown object. As input, our method, called GOAL, takes a 3D object, its
position, and a starting 3D body pose and shape. GOAL outputs a sequence of
whole-body poses using two novel networks. First, GNet generates a goal
whole-body grasp with a realistic body, head, arm, and hand pose, as well as
hand-object contact. Second, MNet generates the motion between the starting and
goal pose. This is challenging, as it requires the avatar to walk towards the
object with foot-ground contact, orient the head towards it, reach out, and
grasp it with a realistic hand pose and hand-object contact. To achieve this,
the networks exploit a representation that combines SMPL-X body parameters and
3D vertex offsets. We train and evaluate GOAL, both qualitatively and
quantitatively, on the GRAB dataset. Results show that GOAL generalizes well to
unseen objects, outperforming baselines. GOAL takes a step towards synthesizing
realistic full-body object grasping.

本文介绍了一种生成数字人物的方法，使其可以同时进行全身运动和手部抓握以及头部朝向，并且在物理约束和合理性方面都具有逼真和可行性。

生成用于手 - 物抓取的四维全身动作

GOAL: Generating 4D Whole-Body Motion for Hand-Object Grasping

We study goal misgeneralization, a type of out-of-distribution generalization
failure in reinforcement learning (RL). Goal misgeneralization failures occur
when an RL agent retains its capabilities out-of-distribution yet pursues the
wrong goal. For instance, an agent might continue to competently avoid
obstacles, but navigate to the wrong place. In contrast, previous works have
typically focused on capability generalization failures, where an agent fails
to do anything sensible at test time. We formalize this distinction between
capability and goal generalization, provide the first empirical demonstrations
of goal misgeneralization, and present a partial characterization of its
causes.

本文研究强化学习中的一种广义泛化失败 —— 目标错误泛化。在此类失败中，强化学习代理在越出分布的情况下保留其能力，但追求错误的目标。我们阐明了能力和目标泛化之间的差别，提供了目标错误泛化的第一次经验演示，并对其原因进行了部分表征。