相同地形的不同卫星图像的相对辐射归一化（RRN）对于改变检测，对象分类/分割和映射任务是必要的。但是，传统的RRN模型不强大，通过对象变化扰乱，并且RRN模型精确考虑对象变化无法鲁布布地获取无更改集。本文提出了通过潜在变化噪声建模的自动稳健的相对辐射归一化方法。它们利用先验知识，即在相对辐射尺度化下没有变化点具有小尺度噪声，并且在辐射归一化之后，变化点具有大规模的辐射噪声，组合随机期望最大化方法快速且强大地提取No-Change集以学习相对辐射归一化映射映射函数。这使我们的模型在理论上就是关于概率理论和数学扣除的基础。具体地，当我们选择直方图匹配作为与高斯噪声（HM-RRN-RRN-RRN-MOG）混合的相对辐射算法学习方案（HM-RRN-MOG）的相对辐射归一化学习方案，HM-RRN-MOG模型实现了最佳性能。我们的模型具有强大地反对云/雾气/变化的能力。我们的方法自然地为RRN生成一个强大的评估指示器，即No-Change Set Totor Square error。我们将HM-RRN-MOG模型应用于后一种植被/水变化检测任务，这减少了无辐射对比度和NDVI / NDWI对无变化集的差异，产生了一致和可比的结果。我们利用No-Change集合到建筑物变更检测任务中，有效地减少了伪变化并提高了精度。

人工智能（AI）系统在许多领域越来越受欢迎。尽管如此，AI技术仍在开发阶段，并且需要解决许多问题。其中，需要对AI系统进行展示的可靠性，以便AI系统可以充满信心地由公众信任使用。在本文中，我们提供了AI系统可靠性的统计视角。与其他因素不同，AI系统的可靠性专注于时间尺寸。也就是说，系统可以针对预期时段执行其设计的功能。我们为AI可靠性研究引入了所谓的智能统计框架，包括五个组件：系统结构，可靠性度量，故障原因分析，可靠性评估和测试规划。我们审查了可靠性数据分析和软件可靠性的传统方法，并讨论如何为可靠性建模和AI系统进行评估来转换现有方法。我们还描述了最近的建模和分析AI可靠性和概述统计研究挑战的发展，包括分销检测，训练集，对抗攻击，模型准确性和不确定性量化的影响，以及讨论这些主题可以与AI可靠性有关，具有说明性示例。最后，我们讨论了AI可靠性评估的数据收集和测试计划以及如何提高系统设计，以获得更高的AI可靠性。本文结束了一些结论备注。

精确分割牙齿并识别牙科网格模型上的相应解剖标签在计算机辅助性正畸治疗中是必不可少的。手动执行这两个任务是耗时，繁琐的，更重要的是，由于患者牙齿的异常和大规模差异，高度依赖于矫正者的经验。一些基于机器学习的方法已经设计和应用于正畸场，以自动分割牙科网格（例如，口腔扫描）。相比之下，牙齿地标定位的研究数量仍然有限。本文提出了一种基于网格深度学习（称为TS-MDL）的两级框架，用于联合牙齿标签和原始内部扫描的地标识别。我们的TS-MDL首先采用端到端\ EMPH {i} MeshsegNet方法（即，现有网格孔的变体，具有改进的精度和效率），以在下采样扫描上标记每个牙齿。由分割输出引导，我们的TS-MDL进一步选择原始网格上的每个牙齿的感兴趣区域（ROI），以构造开头的光重变量（即PINTNET-REG），用于回归相应的地标热插块。我们的TS-MDL在实际的数据集上进行了评估，显示了有希望的细分和本地化性能。具体而言，TS-MDL的第一阶段中的\ EMPH {i} Meshsegnet达到了0.964 \ PM0.054 $ 0.964 \ PM0.054 $的平均骰子相似度系数（DSC），显着优于原始的Meshsegnet。在第二阶段，PointNet-Reg实现了0.597 \ PM0.761 \，预测和地面真理之间的平均绝对误差（MAE），以66美元的地标，与地标检测的其他网络相比，比较优越。所有这些结果表明我们在临床实践中的TS-MDL潜在使用。

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

An increasing number of public datasets have shown a marked clinical impact on assessing anatomical structures. However, each of the datasets is small, partially labeled, and rarely investigates severe tumor subjects. Moreover, current models are limited to segmenting specific organs/tumors, which can not be extended to novel domains and classes. To tackle these limitations, we introduce embedding learned from Contrastive Language-Image Pre-training (CLIP) to segmentation models, dubbed the CLIP-Driven Universal Model. The Universal Model can better segment 25 organs and 6 types of tumors by exploiting the semantic relationship between abdominal structures. The model is developed from an assembly of 14 datasets with 3,410 CT scans and evaluated on 6,162 external CT scans from 3 datasets. We rank first on the public leaderboard of the Medical Segmentation Decathlon (MSD) and achieve the state-of-the-art results on Beyond The Cranial Vault (BTCV). Compared with dataset-specific models, the Universal Model is computationally more efficient (6x faster), generalizes better to CT scans from varying sites, and shows stronger transfer learning performance on novel tasks. The design of CLIP embedding enables the Universal Model to be easily extended to new classes without catastrophically forgetting the previously learned classes.

Due to their ability to offer more comprehensive information than data from a single view, multi-view (multi-source, multi-modal, multi-perspective, etc.) data are being used more frequently in remote sensing tasks. However, as the number of views grows, the issue of data quality becomes more apparent, limiting the potential benefits of multi-view data. Although recent deep neural network (DNN) based models can learn the weight of data adaptively, a lack of research on explicitly quantifying the data quality of each view when fusing them renders these models inexplicable, performing unsatisfactorily and inflexible in downstream remote sensing tasks. To fill this gap, in this paper, evidential deep learning is introduced to the task of aerial-ground dual-view remote sensing scene classification to model the credibility of each view. Specifically, the theory of evidence is used to calculate an uncertainty value which describes the decision-making risk of each view. Based on this uncertainty, a novel decision-level fusion strategy is proposed to ensure that the view with lower risk obtains more weight, making the classification more credible. On two well-known, publicly available datasets of aerial-ground dual-view remote sensing images, the proposed approach achieves state-of-the-art results, demonstrating its effectiveness. The code and datasets of this article are available at the following address: https://github.com/gaopiaoliang/Evidential.

In this tutorial paper, we look into the evolution and prospect of network architecture and propose a novel conceptual architecture for the 6th generation (6G) networks. The proposed architecture has two key elements, i.e., holistic network virtualization and pervasive artificial intelligence (AI). The holistic network virtualization consists of network slicing and digital twin, from the aspects of service provision and service demand, respectively, to incorporate service-centric and user-centric networking. The pervasive network intelligence integrates AI into future networks from the perspectives of networking for AI and AI for networking, respectively. Building on holistic network virtualization and pervasive network intelligence, the proposed architecture can facilitate three types of interplay, i.e., the interplay between digital twin and network slicing paradigms, between model-driven and data-driven methods for network management, and between virtualization and AI, to maximize the flexibility, scalability, adaptivity, and intelligence for 6G networks. We also identify challenges and open issues related to the proposed architecture. By providing our vision, we aim to inspire further discussions and developments on the potential architecture of 6G.

As natural language processing (NLP) for gender bias becomes a significant interdisciplinary topic, the prevalent data-driven techniques such as large-scale language models suffer from data inadequacy and biased corpus, especially for languages with insufficient resources such as Chinese. To this end, we propose a Chinese cOrpus foR Gender bIas Probing and Mitigation CORGI-PM, which contains 32.9k sentences with high-quality labels derived by following an annotation scheme specifically developed for gender bias in the Chinese context. Moreover, we address three challenges for automatic textual gender bias mitigation, which requires the models to detect, classify, and mitigate textual gender bias. We also conduct experiments with state-of-the-art language models to provide baselines. To our best knowledge, CORGI-PM is the first sentence-level Chinese corpus for gender bias probing and mitigation.

Deploying reliable deep learning techniques in interdisciplinary applications needs learned models to output accurate and ({even more importantly}) explainable predictions. Existing approaches typically explicate network outputs in a post-hoc fashion, under an implicit assumption that faithful explanations come from accurate predictions/classifications. We have an opposite claim that explanations boost (or even determine) classification. That is, end-to-end learning of explanation factors to augment discriminative representation extraction could be a more intuitive strategy to inversely assure fine-grained explainability, e.g., in those neuroimaging and neuroscience studies with high-dimensional data containing noisy, redundant, and task-irrelevant information. In this paper, we propose such an explainable geometric deep network dubbed as NeuroExplainer, with applications to uncover altered infant cortical development patterns associated with preterm birth. Given fundamental cortical attributes as network input, our NeuroExplainer adopts a hierarchical attention-decoding framework to learn fine-grained attentions and respective discriminative representations to accurately recognize preterm infants from term-born infants at term-equivalent age. NeuroExplainer learns the hierarchical attention-decoding modules under subject-level weak supervision coupled with targeted regularizers deduced from domain knowledge regarding brain development. These prior-guided constraints implicitly maximizes the explainability metrics (i.e., fidelity, sparsity, and stability) in network training, driving the learned network to output detailed explanations and accurate classifications. Experimental results on the public dHCP benchmark suggest that NeuroExplainer led to quantitatively reliable explanation results that are qualitatively consistent with representative neuroimaging studies.

Considering the computation complexity, we propose a Guided Hybrid Quantization with One-to-one Self-Teaching (GHOST}) framework. More concretely, we first design a structure called guided quantization self-distillation (GQSD), which is an innovative idea for realizing lightweight through the synergy of quantization and distillation. The training process of the quantization model is guided by its full-precision model, which is time-saving and cost-saving without preparing a huge pre-trained model in advance. Second, we put forward a hybrid quantization (HQ) module to obtain the optimal bit width automatically under a constrained condition where a threshold for distribution distance between the center and samples is applied in the weight value search space. Third, in order to improve information transformation, we propose a one-to-one self-teaching (OST) module to give the student network a ability of self-judgment. A switch control machine (SCM) builds a bridge between the student network and teacher network in the same location to help the teacher to reduce wrong guidance and impart vital knowledge to the student. This distillation method allows a model to learn from itself and gain substantial improvement without any additional supervision. Extensive experiments on a multimodal dataset (VEDAI) and single-modality datasets (DOTA, NWPU, and DIOR) show that object detection based on GHOST outperforms the existing detectors. The tiny parameters (<9.7 MB) and Bit-Operations (BOPs) (<2158 G) compared with any remote sensing-based, lightweight or distillation-based algorithms demonstrate the superiority in the lightweight design domain. Our code and model will be released at https://github.com/icey-zhang/GHOST.