对心理健康支持的需求不断增长，强调了对话代理在全球和中国作为人类支持者的重要性。这些代理可以增加可用性并降低心理健康支持的相对成本。提供的支持可以分为两种主要类型：认知和情感支持。关于该主题的现有工作主要集中在采用认知行为疗法（CBT）原理的构造药物上。此类代理根据预定义的模板和练习来运行，以提供认知支持。但是，使用此类药物对情绪支持的研究是有限的。此外，大多数建设的代理商都以英语运作，强调了在中国进行此类研究的重要性。在这项研究中，我们分析了表情符疾病在减少精神痛苦症状方面的有效性。 Emohaa是一种对话剂，通过基于CBT的练习和指导性对话提供认知支持。它还通过使用户能够发泄所需的情绪问题来支持情感上的支持。该研究包括134名参与者，分为三组：Emohaa（基于CBT），Emohaa（Full）和控制。实验结果表明，与对照组相比，使用Emohaa的参与者在精神困扰症状方面的改善得到了更大的改善。我们还发现，添加情感支持剂对这种改善，主要是抑郁和失眠有互补的影响。根据获得的结果和参与者对平台的满意，我们得出结论，Emohaa是减少精神困扰的实用和有效工具。

基于草图的3D形状检索（SBSR）是一项重要但艰巨的任务，近年来引起了越来越多的关注。现有方法在限制设置中解决了该问题，而无需适当模拟真实的应用程序方案。为了模仿现实的设置，在此曲目中，我们采用了不同级别的绘图技能的业余爱好者以及各种3D形状的大规模草图，不仅包括CAD型号，而且还可以从真实对象扫描的模型。我们定义了两个SBSR任务，并构建了两个基准，包括46,000多个CAD型号，1,700个现实型号和145,000个草图。四个团队参加了这一轨道，并为这两个任务提交了15次跑步，由7个常用指标评估。我们希望，基准，比较结果和开源评估法会在3D对象检索社区中促进未来的研究。

随着Covid-19的爆发，近年来已经出现了大量相关研究。我们提出了一个基于肺CT扫描图像的自动COVID-19诊断框架，即PVT-COV19D。为了适应图像输入的不同维度，我们首先使用变压器模型对图像进行了分类，然后根据正常分布对数据集中进行采样，并将采样结果馈送到修改的PVTV2模型中以进行训练。COV19-CT-DB数据集上的大量实验证明了该方法的有效性。

目的是对临床文本去识别的自然语言处理（NLP）模型的评估取决于临床注释的可用性，临床注释通常由于隐私问题而受到限制。 NLP沙盒是一种通过采用联合模型到数据的方法来减轻NLP模型缺乏数据和评估框架的方法。这使得无偏见的联合模型评估无需共享多个机构的敏感数据。材料和方法我们利用Synapse协作框架，容器化软件和OpenAPI Generator来构建NLP沙盒（NLPSANDBOX.IO）。我们使用来自三个机构的数据评估了两个最先进的NLP去识别注释模型Philter和Neuroner。我们使用来自外部验证站点的数据进一步验证了模型性能。结果我们通过去识别临床模型评估证明了NLP沙箱的有用性。外部开发人员能够将其模型纳入NLP沙盒模板中，并提供用户体验反馈。讨论我们证明了使用NLP沙箱对临床文本去识别模型进行多站点评估的可行性，而无需共享数据。标准化模型和数据模式可以使模型传输和实现平稳。为了概括NLP沙箱，数据所有者和模型开发人员需要进行工作，以开发合适和标准化的模式，并调整其数据或模型以适合模式。结论NLP沙箱降低了利用临床数据进行NLP模型评估的障碍，并促进了联合会的NLP模型的联合，多站点，无偏见的评估。

原始的“七个图案”阐述了科学计算领域的基本方法的路线图，其中图案是一种捕获计算和数据移动模式的算法方法。我们介绍了“仿真智力的九个主题”，是一种开发和整合的路线图，以合并科学计算，科学模拟和人工智能所必需的基本算法。我们称之为合并模拟智能（SI），短暂。我们认为模拟智能的主题是相互连接的和相互依存的，很像操作系统层中的组件一样。使用这种隐喻，我们探讨了模拟智能操作系统堆栈（Si-Stack）和其中图案的各层的性质：（1）多种物理和多尺度建模; （2）替代建模和仿真; （3）基于仿真的推理; （4）因果建模和推理; （5）基于代理的建模; （6）概率编程; （7）可微分的编程; （8）开放式优化; （9）机器编程。我们相信图案之间的协调努力提供了加速科学发现的巨大机会，从综合生物和气候科学中解决逆问题，指导核能实验，并预测社会经济环境中的紧急行为。我们详细说明了Si-stack的每层，详细说明了最先进的方法，提出了示例以突出挑战和机遇，并倡导具体的方法来推进主题和与其组合的协同作用。推进和整合这些技术可以实现稳健且有效的假设仿真 - 分析类型的科学方法，我们用几种使用案例为人机组合和自动化学介绍。

Masked image modeling (MIM) performs strongly in pre-training large vision Transformers (ViTs). However, small models that are critical for real-world applications cannot or only marginally benefit from this pre-training approach. In this paper, we explore distillation techniques to transfer the success of large MIM-based pre-trained models to smaller ones. We systematically study different options in the distillation framework, including distilling targets, losses, input, network regularization, sequential distillation, etc, revealing that: 1) Distilling token relations is more effective than CLS token- and feature-based distillation; 2) An intermediate layer of the teacher network as target perform better than that using the last layer when the depth of the student mismatches that of the teacher; 3) Weak regularization is preferred; etc. With these findings, we achieve significant fine-tuning accuracy improvements over the scratch MIM pre-training on ImageNet-1K classification, using all the ViT-Tiny, ViT-Small, and ViT-base models, with +4.2%/+2.4%/+1.4% gains, respectively. Our TinyMIM model of base size achieves 52.2 mIoU in AE20K semantic segmentation, which is +4.1 higher than the MAE baseline. Our TinyMIM model of tiny size achieves 79.6% top-1 accuracy on ImageNet-1K image classification, which sets a new record for small vision models of the same size and computation budget. This strong performance suggests an alternative way for developing small vision Transformer models, that is, by exploring better training methods rather than introducing inductive biases into architectures as in most previous works. Code is available at https://github.com/OliverRensu/TinyMIM.

Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. The security risks stemming from them have not been explored. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.

Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recently. However, we observe that two drawbacks of the positive and negative sample construction mechanisms limit the performance of existing algorithms from further improvement. 1) The quality of positive samples heavily depends on the carefully designed data augmentations, while inappropriate data augmentations would easily lead to the semantic drift and indiscriminative positive samples. 2) The constructed negative samples are not reliable for ignoring important clustering information. To solve these problems, we propose a Cluster-guided Contrastive deep Graph Clustering network (CCGC) by mining the intrinsic supervision information in the high-confidence clustering results. Specifically, instead of conducting complex node or edge perturbation, we construct two views of the graph by designing special Siamese encoders whose weights are not shared between the sibling sub-networks. Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views. Moreover, to construct semantic meaningful negative sample pairs, we regard the centers of different high-confidence clusters as negative samples, thus improving the discriminative capability and reliability of the constructed sample pairs. Lastly, we design an objective function to pull close the samples from the same cluster while pushing away those from other clusters by maximizing and minimizing the cross-view cosine similarity between positive and negative samples. Extensive experimental results on six datasets demonstrate the effectiveness of CCGC compared with the existing state-of-the-art algorithms.

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.

Compressed videos often exhibit visually annoying artifacts, known as Perceivable Encoding Artifacts (PEAs), which dramatically degrade video visual quality. Subjective and objective measures capable of identifying and quantifying various types of PEAs are critical in improving visual quality. In this paper, we investigate the influence of four spatial PEAs (i.e. blurring, blocking, bleeding, and ringing) and two temporal PEAs (i.e. flickering and floating) on video quality. For spatial artifacts, we propose a visual saliency model with a low computational cost and higher consistency with human visual perception. In terms of temporal artifacts, self-attention based TimeSFormer is improved to detect temporal artifacts. Based on the six types of PEAs, a quality metric called Saliency-Aware Spatio-Temporal Artifacts Measurement (SSTAM) is proposed. Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics. We believe that SSTAM will be beneficial for optimizing video coding techniques.