尖峰神经网络（SNN）是一种具有生物学知识的模型，具有高计算能力和低功耗的优势。虽然对深SNN的培训仍然是一个空旷的问题，但它限制了深SNN的现实应用。在这里，我们提出了一个名为Spiking SiamFC ++的深SNN架构，用于对象跟踪，并通过端到端直接培训。具体而言，Alexnet网络在时间域中扩展以提取该功能，并采用替代梯度功能来实现对深SNN的直接监督培训。为了检查尖峰SiAMFC ++的性能，考虑了几种跟踪基准测试，包括OTB2013，OTB2015，Dot2015，Dot2016和UAV123。发现与原始的siAMFC ++相比，精度损失很小。与现有的基于SNN的目标跟踪器相比，例如暹罗（Siamsnn），提议的Spiking SiamFC ++的精度（连续）达到了85.24％（64.37％），远高于52.78％（44.32％）的精度（64.37％）。 。据我们所知，Spiking SiamFC ++的性能优于基于SNN的对象跟踪中现有的最新方法，该方法为目标跟踪领域中的SNN应用提供了新的路径。这项工作可能会进一步促进SNN算法和神经形态芯片的发展。

分布式学习已成为缩放机器学习并解决数据隐私需求不断增长的积分工具。虽然对网络拓扑的更强大，但分散的学习计划没有获得与其集中式同行相同的人气水平，因为它们具有较低的竞争性能。在这项工作中，我们将此问题归因于分散的学习工人之间缺乏同步，在经验和理论上表现出来，收敛速度与工人之间的同步水平相关联。我们认为，基于非线性漫步（非政府组织）的新型分散式学习框架，享有有吸引力的有限时间共识性，以实现更好的同步。我们对其收敛性提供了仔细分析，并讨论了现代分布式优化应用的优点，如深神经网络。我们对通信延迟和随机聊天如何影响学习的分析进一步实现了适应异步和随机通信的实际变体的推导。为了验证我们提案的有效性，我们通过广泛的测试，我们通过广泛的测试来利用竞争解决方案，令人鼓舞的结果报告。

最近推出的热集成技术已经了解并改善变推理（VI），提供了一个新的框架。在这项工作中，我们提出了热力学变目标（TVO）的仔细分析，弥合现有的变分目标和脱落的新见解，以推动该领域的差距。特别是，我们阐明了如何将TVO自然连接三个关键变方案，即重要性加权VI，仁义-VI，和MCMC-VI，它包含了最VI目标在实践中采用。为了解释理论和实践之间的性能差距，我们揭示热力学曲线的病理几何形状是如何产生负面影响TVO。通过推广加权平均持有人从几何平均值的整合路径，我们扩展TVO的理论和发现提高VI新的机遇。这促使我们的新VI的目标，命名为持有人的边界，这拼合热力学曲线和承诺，以实现精确的边缘数似然的一步逼近。提供对数字估计的选择的全面讨论。我们目前的合成和真实世界的数据集强有力的实证证据来支持我们的要求。

处理严重的级别不平衡对现实世界的应用构成了重大挑战，特别是当少数群体课程的准确分类和泛化是主要兴趣时。在计算机视觉中，从长尾数据集学习是一种重复主题，特别是对于自然图像数据集。虽然现有解决方案主要吸引采样或加权调整，以缓解病理不平衡，或强加归纳偏差，以优先考虑非杂散关联，以提高基于因果关系的不耐性原则的示例效率和模型泛化。我们的提议介绍了元分布式场景，其中数据生成机制在标签条件特征分布上不变。即使相应的特征分布表现出明显的差异，这种因果假设能够从主导类到所代价的对应物中的高效知识转移。这使我们能够利用因果关系程序来扩大少数阶级的代表性。我们的开发与现有的极端分类技术正交，因此可以无缝集成。我们提案的效用以广泛的综合性和现实世界计算机视觉任务验证，防止了SOTA解决方案。

As one of the most important psychic stress reactions, micro-expressions (MEs), are spontaneous and transient facial expressions that can reveal the genuine emotions of human beings. Thus, recognizing MEs (MER) automatically is becoming increasingly crucial in the field of affective computing, and provides essential technical support in lie detection, psychological analysis and other areas. However, the lack of abundant ME data seriously restricts the development of cutting-edge data-driven MER models. Despite the recent efforts of several spontaneous ME datasets to alleviate this problem, it is still a tiny amount of work. To solve the problem of ME data hunger, we construct a dynamic spontaneous ME dataset with the largest current ME data scale, called DFME (Dynamic Facial Micro-expressions), which includes 7,526 well-labeled ME videos induced by 671 participants and annotated by more than 20 annotators throughout three years. Afterwards, we adopt four classical spatiotemporal feature learning models on DFME to perform MER experiments to objectively verify the validity of DFME dataset. In addition, we explore different solutions to the class imbalance and key-frame sequence sampling problems in dynamic MER respectively on DFME, so as to provide a valuable reference for future research. The comprehensive experimental results show that our DFME dataset can facilitate the research of automatic MER, and provide a new benchmark for MER. DFME will be published via https://mea-lab-421.github.io.

Interview has been regarded as one of the most crucial step for recruitment. To fully prepare for the interview with the recruiters, job seekers usually practice with mock interviews between each other. However, such a mock interview with peers is generally far away from the real interview experience: the mock interviewers are not guaranteed to be professional and are not likely to behave like a real interviewer. Due to the rapid growth of online recruitment in recent years, recruiters tend to have online interviews, which makes it possible to collect real interview data from real interviewers. In this paper, we propose a novel application named EZInterviewer, which aims to learn from the online interview data and provides mock interview services to the job seekers. The task is challenging in two ways: (1) the interview data are now available but still of low-resource; (2) to generate meaningful and relevant interview dialogs requires thorough understanding of both resumes and job descriptions. To address the low-resource challenge, EZInterviewer is trained on a very small set of interview dialogs. The key idea is to reduce the number of parameters that rely on interview dialogs by disentangling the knowledge selector and dialog generator so that most parameters can be trained with ungrounded dialogs as well as the resume data that are not low-resource. Evaluation results on a real-world job interview dialog dataset indicate that we achieve promising results to generate mock interviews. With the help of EZInterviewer, we hope to make mock interview practice become easier for job seekers.

Panoptic Part Segmentation (PPS) unifies panoptic segmentation and part segmentation into one task. Previous works utilize separated approaches to handle thing, stuff, and part predictions without shared computation and task association. We aim to unify these tasks at the architectural level, designing the first end-to-end unified framework named Panoptic-PartFormer. Moreover, we find the previous metric PartPQ biases to PQ. To handle both issues, we make the following contributions: Firstly, we design a meta-architecture that decouples part feature and things/stuff feature, respectively. We model things, stuff, and parts as object queries and directly learn to optimize all three forms of prediction as a unified mask prediction and classification problem. We term our model as Panoptic-PartFormer. Secondly, we propose a new metric Part-Whole Quality (PWQ) to better measure such task from both pixel-region and part-whole perspectives. It can also decouple the error for part segmentation and panoptic segmentation. Thirdly, inspired by Mask2Former, based on our meta-architecture, we propose Panoptic-PartFormer++ and design a new part-whole cross attention scheme to further boost part segmentation qualities. We design a new part-whole interaction method using masked cross attention. Finally, the extensive ablation studies and analysis demonstrate the effectiveness of both Panoptic-PartFormer and Panoptic-PartFormer++. Compared with previous Panoptic-PartFormer, our Panoptic-PartFormer++ achieves 2% PartPQ and 3% PWQ improvements on the Cityscapes PPS dataset and 5% PartPQ on the Pascal Context PPS dataset. On both datasets, Panoptic-PartFormer++ achieves new state-of-the-art results with a significant cost drop of 70% on GFlops and 50% on parameters. Our models can serve as a strong baseline and aid future research in PPS. Code will be available.

In contrast to the control-theoretic methods, the lack of stability guarantee remains a significant problem for model-free reinforcement learning (RL) methods. Jointly learning a policy and a Lyapunov function has recently become a promising approach to ensuring the whole system with a stability guarantee. However, the classical Lyapunov constraints researchers introduced cannot stabilize the system during the sampling-based optimization. Therefore, we propose the Adaptive Stability Certification (ASC), making the system reach sampling-based stability. Because the ASC condition can search for the optimal policy heuristically, we design the Adaptive Lyapunov-based Actor-Critic (ALAC) algorithm based on the ASC condition. Meanwhile, our algorithm avoids the optimization problem that a variety of constraints are coupled into the objective in current approaches. When evaluated on ten robotic tasks, our method achieves lower accumulated cost and fewer stability constraint violations than previous studies.

Surgical robot automation has attracted increasing research interest over the past decade, expecting its huge potential to benefit surgeons, nurses and patients. Recently, the learning paradigm of embodied AI has demonstrated promising ability to learn good control policies for various complex tasks, where embodied AI simulators play an essential role to facilitate relevant researchers. However, existing open-sourced simulators for surgical robot are still not sufficiently supporting human interactions through physical input devices, which further limits effective investigations on how human demonstrations would affect policy learning. In this paper, we study human-in-the-loop embodied intelligence with a new interactive simulation platform for surgical robot learning. Specifically, we establish our platform based on our previously released SurRoL simulator with several new features co-developed to allow high-quality human interaction via an input device. With these, we further propose to collect human demonstrations and imitate the action patterns to achieve more effective policy learning. We showcase the improvement of our simulation environment with the designed new features and tasks, and validate state-of-the-art reinforcement learning algorithms using the interactive environment. Promising results are obtained, with which we hope to pave the way for future research on surgical embodied intelligence. Our platform is released and will be continuously updated in the website: https://med-air.github.io/SurRoL/

In this paper, a semantic communication framework for image transmission is developed. In the investigated framework, a set of servers cooperatively transmit images to a set of users utilizing semantic communication techniques. To evaluate the performance of studied semantic communication system, a multimodal metric is proposed to measure the correlation between the extracted semantic information and the original image. To meet the ISS requirement of each user, each server must jointly determine the semantic information to be transmitted and the resource blocks (RBs) used for semantic information transmission. We formulate this problem as an optimization problem aiming to minimize each server's transmission latency while reaching the ISS requirement. To solve this problem, a value decomposition based entropy-maximized multi-agent reinforcement learning (RL) is proposed, which enables servers to coordinate for training and execute RB allocation in a distributed manner to approach to a globally optimal performance with less training iterations. Compared to traditional multi-agent RL, the proposed RL improves the valuable action exploration of servers and the probability of finding a globally optimal RB allocation policy based on local observation. Simulation results show that the proposed algorithm can reduce the transmission delay by up to 16.1% compared to traditional multi-agent RL.