用于预测神经影像数据的深度学习算法在各种应用中显示出巨大的希望。先前的工作表明，利用数据的3D结构的深度学习模型可以在几个学习任务上胜过标准机器学习。但是，该领域的大多数先前研究都集中在成年人的神经影像学数据上。在一项大型纵向发展研究的青少年大脑和认知发展（ABCD）数据集中，我们检查了结构性MRI数据，以预测性别并确定与性别相关的大脑结构变化。结果表明，性别预测准确性异常高（> 97％），训练时期> 200，并且这种准确性随着年龄的增长而增加。大脑区域被确定为研究的任务中最歧视性的，包括主要的额叶区域和颞叶。当评估年龄增加两年的性别预测变化时，揭示了一组更广泛的视觉，扣带和孤立区域。我们的发现表明，即使在较小的年龄范围内，也显示出与性别相关的结构变化模式。这表明，通过查看这些变化与不同的行为和环境因素如何相关，可以研究青春期大脑如何变化。

Image restoration tasks have achieved tremendous performance improvements with the rapid advancement of deep neural networks. However, most prevalent deep learning models perform inference statically, ignoring that different images have varying restoration difficulties and lightly degraded images can be well restored by slimmer subnetworks. To this end, we propose a new solution pipeline dubbed ClassPruning that utilizes networks with different capabilities to process images with varying restoration difficulties. In particular, we use a lightweight classifier to identify the image restoration difficulty, and then the sparse subnetworks with different capabilities can be sampled based on predicted difficulty by performing dynamic N:M fine-grained structured pruning on base restoration networks. We further propose a novel training strategy along with two additional loss terms to stabilize training and improve performance. Experiments demonstrate that ClassPruning can help existing methods save approximately 40% FLOPs while maintaining performance.

放映摄像头（UDC）为全屏智能手机提供了优雅的解决方案。但是，由于传感器位于显示屏下，UDC捕获的图像遭受了严重的降解。尽管可以通过图像恢复网络解决此问题，但这些网络需要大规模的图像对进行培训。为此，我们提出了一个模块化网络，称为MPGNET，该网络使用生成对抗网络（GAN）框架来模拟UDC成像。具体而言，我们注意到UDC成像降解过程包含亮度衰减，模糊和噪声损坏。因此，我们将每个降解与特征相关的模块化网络建模，并将所有模块化网络级联成型以形成生成器。加上像素的歧视器和受监督的损失，我们可以训练发电机以模拟UDC成像降解过程。此外，我们提出了一个用于UDC图像恢复的Dwformer的变压器式网络。出于实际目的，我们使用深度卷积而不是多头自我注意力来汇总本地空间信息。此外，我们提出了一个新型的渠道注意模块来汇总全局信息，这对于亮度恢复至关重要。我们对UDC基准进行了评估，我们的方法在P-Oled轨道上超过了先前的最新模型和T-Oled轨道上的0.71 dB。

Dimage Dehazing是低级视觉中的一个活跃主题，并且随着深度学习的快速发展，已经提出了许多图像去悬式网络。尽管这些网络的管道效果很好，但改善图像飞行性能的关键机制尚不清楚。因此，我们不针对带有精美模块的飞行网络。相反，我们对流行的U-NET进行了最小的修改，以获得紧凑的飞行网络。具体而言，我们将U-NET中的卷积块与门控机构，使用选择性内核进行融合，并跳过连接，并调用所得的U-NET变体Gunet。结果，由于开销大大减少，Gunet优于多个图像脱掩的数据集上的最新方法。最后，我们通过广泛的消融研究来验证这些关键设计为图像去除网络的性能增益。

主要的图像到图像翻译方法基于完全卷积的网络，该网络提取和翻译图像的特征，然后重建图像。但是，在使用高分辨率图像时，它们的计算成本不可接受。为此，我们介绍了多曲线翻译器（MCT），它不仅可以预测相应的输入像素的翻译像素，还可以预测其相邻像素的翻译像素。而且，如果将高分辨率图像删除到其低分辨率版本中，则丢失的像素是其余像素的相邻像素。因此，MCT可以使网络仅馈入倒数采样的图像以执行全分辨率图像的映射，从而大大降低计算成本。此外，MCT是一种使用现有基本型号的插件方法，仅需要更换其输出层。实验表明，MCT变体可以实时处理4K图像，并比各种逼真的图像到图像翻译任务上的基本模型实现可比甚至更好的性能。

共享工作空间中无线轨迹的生成对于大多数多机器人应用程序至关重要。但是，许多基于模型预测控制（MPC）的广泛使用的方法缺乏基础优化的可行性的理论保证。此外，当以分布式的方式应用无中央协调员时，僵局通常会无限期地互相阻挡。尽管存在诸如引入随机扰动之类的启发式方法，但没有进行深入的分析来验证这些措施。为此，我们提出了一种系统的方法，称为Infinite-Horizo​​n模型预测性控制，并通过死锁解决。 MPC用警告范围对拟议的修改后的Voronoi进行了配方，作为凸优化。基于此公式，对僵局的状况进行了正式分析，并证明与力平衡相似。提出了一个检测分辨率方案，该方案可以在甚至在发生之前有效地在网上检测到僵局，并且一旦检测到，便利用自适应分辨率方案来解决僵局，并在绩效上进行理论保证。此外，所提出的计划算法可确保在输入和模型约束下每个时间步骤的基础优化的递归可行性，对于所有机器人都是并发的，并且只需要本地通信。全面的模拟和实验研究是通过大规模多机器人系统进行的。与其他最先进的方法相比，尤其是在拥挤和高速场景中，成功率的显着提高了成功率。

Recent advances in neural radiance fields have enabled the high-fidelity 3D reconstruction of complex scenes for novel view synthesis. However, it remains underexplored how the appearance of such representations can be efficiently edited while maintaining photorealism. In this work, we present PaletteNeRF, a novel method for photorealistic appearance editing of neural radiance fields (NeRF) based on 3D color decomposition. Our method decomposes the appearance of each 3D point into a linear combination of palette-based bases (i.e., 3D segmentations defined by a group of NeRF-type functions) that are shared across the scene. While our palette-based bases are view-independent, we also predict a view-dependent function to capture the color residual (e.g., specular shading). During training, we jointly optimize the basis functions and the color palettes, and we also introduce novel regularizers to encourage the spatial coherence of the decomposition. Our method allows users to efficiently edit the appearance of the 3D scene by modifying the color palettes. We also extend our framework with compressed semantic features for semantic-aware appearance editing. We demonstrate that our technique is superior to baseline methods both quantitatively and qualitatively for appearance editing of complex real-world scenes.

Explaining the black-box predictions of NLP models naturally and accurately is an important open problem in natural language generation. These free-text explanations are expected to contain sufficient and carefully-selected evidence to form supportive arguments for predictions. Due to the superior generative capacity of large pretrained language models, recent work built on prompt engineering enables explanation generation without specific training. However, explanation generated through single-pass prompting often lacks sufficiency and conciseness. To address this problem, we develop an information bottleneck method EIB to produce refined explanations that are sufficient and concise. Our approach regenerates the free-text explanation by polishing the single-pass output from the pretrained language model but retaining the information that supports the contents being explained. Experiments on two out-of-domain tasks verify the effectiveness of EIB through automatic evaluation and thoroughly-conducted human evaluation.

Coverage path planning is a major application for mobile robots, which requires robots to move along a planned path to cover the entire map. For large-scale tasks, coverage path planning benefits greatly from multiple robots. In this paper, we describe Turn-minimizing Multirobot Spanning Tree Coverage Star(TMSTC*), an improved multirobot coverage path planning (mCPP) algorithm based on the MSTC*. Our algorithm partitions the map into minimum bricks as tree's branches and thereby transforms the problem into finding the maximum independent set of bipartite graph. We then connect bricks with greedy strategy to form a tree, aiming to reduce the number of turns of corresponding circumnavigating coverage path. Our experimental results show that our approach enables multiple robots to make fewer turns and thus complete terrain coverage tasks faster than other popular algorithms.

In the scope of "AI for Science", solving inverse problems is a longstanding challenge in materials and drug discovery, where the goal is to determine the hidden structures given a set of desirable properties. Deep generative models are recently proposed to solve inverse problems, but these currently use expensive forward operators and struggle in precisely localizing the exact solutions and fully exploring the parameter spaces without missing solutions. In this work, we propose a novel approach (called iPage) to accelerate the inverse learning process by leveraging probabilistic inference from deep invertible models and deterministic optimization via fast gradient descent. Given a target property, the learned invertible model provides a posterior over the parameter space; we identify these posterior samples as an intelligent prior initialization which enables us to narrow down the search space. We then perform gradient descent to calibrate the inverse solutions within a local region. Meanwhile, a space-filling sampling is imposed on the latent space to better explore and capture all possible solutions. We evaluate our approach on three benchmark tasks and two created datasets with real-world applications from quantum chemistry and additive manufacturing, and find our method achieves superior performance compared to several state-of-the-art baseline methods. The iPage code is available at https://github.com/jxzhangjhu/MatDesINNe.