We consider manipulations in the context of coalitional games, where a
coalition aims to increase the total payoff of its members. An allocation rule
is immune to coalitional manipulation if no coalition can benefit from internal
reallocation of worth on the level of its subcoalitions
(reallocation-proofness), and if no coalition benefits from a lower worth while
all else remains the same (weak coalitional monotonicity). Replacing additivity
in Shapley's original characterization by these requirements yields a new
foundation of the Shapley value, i.e., it is the unique efficient and symmetric
allocation rule that awards nothing to a null player and is immune to
coalitional manipulations. We further find that for efficient allocation rules,
reallocation-proofness is equivalent to constrained marginality, a weaker
variant of Young's marginality axiom. Our second characterization improves upon
Young's characterization by weakening the independence requirement intrinsic to
marginality.

在协作联盟游戏中，我们研究了协作联盟为了增加成员的总收益而进行的操纵行为。我们提出了一种新的 Shapley 值的基础，该值是唯一的高效和对联盟操纵免疫的分配规则，它将空置玩家分配为零，并满足弱协作单调性和次协作联盟再分配免疫性。此外，我们还发现对于高效的分配规则，次协作联盟再分配免疫性等价于约束边际性，这是 Young 的边际性公理的一种较弱变体。我们的第二个特征改进了 Young 的特征，从而减弱了边际性内在的独立性要求。

合作联盟的操纵和 Shapley 价值的免疫性

Coalitional Manipulations and Immunity of the Shapley Value

Radiance Fields (RFs) have shown great potential to represent scenes from
casually captured discrete views. Compositing parts or whole of multiple
captured scenes could greatly interest several XR applications. Prior works can
generate new views of such scenes by tracing each scene in parallel. This
increases the render times and memory requirements with the number of
components. In this work, we provide a method to create a single, compact,
fused RF representation for a scene composited using multiple RFs. The fused RF
has the same render times and memory utilizations as a single RF. Our method
distills information from multiple teacher RFs into a single student RF while
also facilitating further manipulations like addition and deletion into the
fused representation.

本文介绍基于 Radiance Fields 的方法，用于将多个场景的 RF 合成为单个压缩的 RF 表示形式，提高 XR 应用的渲染效率。

FusedRF：多辐射场融合

FusedRF: Fusing Multiple Radiance Fields

We offer a method for one-shot image synthesis that allows controlling
manipulations of a single image by inverting a quasi-robust classifier equipped
with strong regularizers. Our proposed method, entitled Magic, samples
structured gradients from a pre-trained quasi-robust classifier to better
preserve the input semantics while preserving its classification accuracy,
thereby guaranteeing credibility in the synthesis. Unlike current methods that
use complex primitives to supervise the process or use attention maps as a weak
supervisory signal, Magic aggregates gradients over the input, driven by a
guide binary mask that enforces a strong, spatial prior. Magic implements a
series of manipulations with a single framework achieving shape and location
control, intense non-rigid shape deformations, and copy/move operations in the
presence of repeating objects and gives users firm control over the synthesis
by requiring simply specifying binary guide masks. Our study and findings are
supported by various qualitative comparisons with the state-of-the-art on the
same images sampled from ImageNet and quantitative analysis using machine
perception along with a user survey of 100+ participants that endorse our
synthesis quality.

本研究提出了一种单张图像的综合方法，利用一个配备有强正则化器的准鲁棒分类器反演图像，从而实现对单张图像的操纵；该方法采样来自预训练的准鲁棒分类器的结构梯度，以更好地保留输入样本的语义，同时保证合成图像的分类准确性，实现形状、位置、非刚性形变等多种控制操作。

MAGIC: 通过反转准鲁棒分类器的掩模引导图像合成

MAGIC: Mask-Guided Image Synthesis by Inverting a Quasi-Robust Classifier

Explanation methods aim to make neural networks more trustworthy and
interpretable. In this paper, we demonstrate a property of explanation methods
which is disconcerting for both of these purposes. Namely, we show that
explanations can be manipulated arbitrarily by applying visually hardly
perceptible perturbations to the input that keep the network's output
approximately constant. We establish theoretically that this phenomenon can be
related to certain geometrical properties of neural networks. This allows us to
derive an upper bound on the susceptibility of explanations to manipulations.
Based on this result, we propose effective mechanisms to enhance the robustness
of explanations.

在这篇论文中，我们研究了解释方法的隐患，表明在输入数据上的无用微扰可以任意操纵神经网络的解释结果，同时提出了增强解释鲁棒性的有效机制。