对接系统对于在线多人游戏中创建公平匹配至关重要，这直接影响玩家的满足感和游戏体验。大多数对接系统在很大程度上取决于对玩家游戏技能的精确估计来构建公平的游戏。但是，新手的技能等级通常是不准确的，因为当前的对接评级算法需要大量游戏才能学习新玩家的真正技能。在早期阶段使用这些不可靠的技能得分通常会导致团队绩效方面的差异，这会导致负面的游戏体验。这被称为对接评级算法的“冷启动”问题。为了克服这个难题，本文提出了QuickSkill，这是一个基于深度学习的新手技能估算框架，以快速探究在线多人游戏中新玩家的能力。 QuickSkill提取了玩家最初的几款游戏中的顺序性能功能，以通过专用的神经网络来预测他/她的未来技能评级，从而在玩家的早期游戏阶段进行准确的技能估计。通过使用Quickskill进行对接，可以在最初的冷门时期大大改善游戏公平性。我们在离线和在线场景中都在流行的移动多人游戏中进行实验。使用两个现实世界中的匿名游戏数据集获得的结果表明，提议的QuickSkill提供了对新手游戏技能的精确估计，从而导致团队技能差异明显降低和更好的玩家游戏体验。据我们所知，提议的Quickskill是第一个解决传统技能评级算法的冷门问题的框架。

Brain extraction and registration are important preprocessing steps in neuroimaging data analysis, where the goal is to extract the brain regions from MRI scans (i.e., extraction step) and align them with a target brain image (i.e., registration step). Conventional research mainly focuses on developing methods for the extraction and registration tasks separately under supervised settings. The performance of these methods highly depends on the amount of training samples and visual inspections performed by experts for error correction. However, in many medical studies, collecting voxel-level labels and conducting manual quality control in high-dimensional neuroimages (e.g., 3D MRI) are very expensive and time-consuming. Moreover, brain extraction and registration are highly related tasks in neuroimaging data and should be solved collectively. In this paper, we study the problem of unsupervised collective extraction and registration in neuroimaging data. We propose a unified end-to-end framework, called ERNet (Extraction-Registration Network), to jointly optimize the extraction and registration tasks, allowing feedback between them. Specifically, we use a pair of multi-stage extraction and registration modules to learn the extraction mask and transformation, where the extraction network improves the extraction accuracy incrementally and the registration network successively warps the extracted image until it is well-aligned with the target image. Experiment results on real-world datasets show that our proposed method can effectively improve the performance on extraction and registration tasks in neuroimaging data. Our code and data can be found at https://github.com/ERNetERNet/ERNet

Deformable image registration, i.e., the task of aligning multiple images into one coordinate system by non-linear transformation, serves as an essential preprocessing step for neuroimaging data. Recent research on deformable image registration is mainly focused on improving the registration accuracy using multi-stage alignment methods, where the source image is repeatedly deformed in stages by a same neural network until it is well-aligned with the target image. Conventional methods for multi-stage registration can often blur the source image as the pixel/voxel values are repeatedly interpolated from the image generated by the previous stage. However, maintaining image quality such as sharpness during image registration is crucial to medical data analysis. In this paper, we study the problem of anti-blur deformable image registration and propose a novel solution, called Anti-Blur Network (ABN), for multi-stage image registration. Specifically, we use a pair of short-term registration and long-term memory networks to learn the nonlinear deformations at each stage, where the short-term registration network learns how to improve the registration accuracy incrementally and the long-term memory network combines all the previous deformations to allow an interpolation to perform on the raw image directly and preserve image sharpness. Extensive experiments on both natural and medical image datasets demonstrated that ABN can accurately register images while preserving their sharpness. Our code and data can be found at https://github.com/anonymous3214/ABN

Recently, massive architectures based on Convolutional Neural Network (CNN) and self-attention mechanisms have become necessary for audio classification. While these techniques are state-of-the-art, these works' effectiveness can only be guaranteed with huge computational costs and parameters, large amounts of data augmentation, transfer from large datasets and some other tricks. By utilizing the lightweight nature of audio, we propose an efficient network structure called Paired Inverse Pyramid Structure (PIP) and a network called Paired Inverse Pyramid Structure MLP Network (PIPMN). The PIPMN reaches 96\% of Environmental Sound Classification (ESC) accuracy on the UrbanSound8K dataset and 93.2\% of Music Genre Classification (MGC) on the GTAZN dataset, with only 1 million parameters. Both of the results are achieved without data augmentation or model transfer. Public code is available at: https://github.com/JNAIC/PIPMN

Multi-view graph clustering (MGC) methods are increasingly being studied due to the explosion of multi-view data with graph structural information. The critical point of MGC is to better utilize the view-specific and view-common information in features and graphs of multiple views. However, existing works have an inherent limitation that they are unable to concurrently utilize the consensus graph information across multiple graphs and the view-specific feature information. To address this issue, we propose Variational Graph Generator for Multi-View Graph Clustering (VGMGC). Specifically, a novel variational graph generator is proposed to extract common information among multiple graphs. This generator infers a reliable variational consensus graph based on a priori assumption over multiple graphs. Then a simple yet effective graph encoder in conjunction with the multi-view clustering objective is presented to learn the desired graph embeddings for clustering, which embeds the inferred view-common graph and view-specific graphs together with features. Finally, theoretical results illustrate the rationality of VGMGC by analyzing the uncertainty of the inferred consensus graph with information bottleneck principle. Extensive experiments demonstrate the superior performance of our VGMGC over SOTAs.

与大脑变化相关的阿尔茨海默氏病（AD）和轻度认知障碍（MCI）的评估仍然是一项艰巨的任务。最近的研究表明，多模式成像技术的组合可以更好地反映病理特征，并有助于更准确地诊断AD和MCI。在本文中，我们提出了一种新型的基于张量的多模式特征选择和回归方法，用于诊断和生物标志物对正常对照组的AD和MCI鉴定。具体而言，我们利用张量结构来利用多模式数据中固有的高级相关信息，并研究多线性回归模型中的张量级稀疏性。我们使用三种成像方式（VBM- MRI，FDG-PET和AV45-PET）具有疾病严重程度和认知评分的临床参数来分析ADNI数据的方法的实际优势。实验结果表明，我们提出的方法与疾病诊断的最新方法的优越性能以及疾病特异性区域和与模态相关的差异的鉴定。这项工作的代码可在https://github.com/junfish/bios22上公开获得。

知识驱动的对话世代最近取得了非凡的突破。与一般的对话系统相比，卓越的知识对话系统可以通过预先提供的知识产生更多信息和知识渊博的响应。但是，在实际应用中，对话系统无法事先提供相应的知识。为了解决该问题，我们设计了一个名为DRKQG的知识驱动的对话系统（\ emph {通过查询生成动态检索知识，以获取信息性对话响应}）。具体而言，系统可以分为两个模块：查询生成模块和对话生成模块。首先，利用时间感知机制来捕获上下文信息，并可以生成查询以检索知识。然后，我们集成了复制机制和变压器，该机制允许响应生成模块产生从上下文和检索知识中得出的响应。 LIC2022，语言和情报技术竞赛的实验结果表明，我们的模块在自动评估指标上的大幅度优于基线模型，而BAIDU语言学团队的人类评估表明，我们的系统在事实上取得了令人印象深刻的结果，实际上是正确的，知识渊博。

人的大脑位于复杂的神经生物学系统的核心，神经元，电路和子系统以神秘的方式相互作用。长期以来，了解大脑的结构和功能机制一直是神经科学研究和临床障碍疗法的引人入胜的追求。将人脑作为网络的连接映射是神经科学中最普遍的范例之一。图神经网络（GNN）最近已成为建模复杂网络数据的潜在方法。另一方面，深层模型的可解释性低，从而阻止了他们在医疗保健等决策环境中的使用。为了弥合这一差距，我们提出了一个可解释的框架，以分析特定的利益区域（ROI）和突出的联系。提出的框架由两个模块组成：疾病预测的面向脑网络的主链模型和全球共享的解释发生器，该模型突出了包括疾病特异性的生物标志物，包括显着的ROI和重要连接。我们在三个现实世界中的脑疾病数据集上进行实验。结果证明了我们的框架可以获得出色的性能并确定有意义的生物标志物。这项工作的所有代码均可在https://github.com/hennyjie/ibgnn.git上获得。

大脑网络将大脑区域之间的复杂连接性描述为图形结构，这为研究脑连接素提供了强大的手段。近年来，图形神经网络已成为使用结构化数据的普遍学习范式。但是，由于数据获取的成本相对较高，大多数大脑网络数据集的样本量受到限制，这阻碍了足够的培训中的深度学习模型。受元学习的启发，该论文以有限的培训示例快速学习新概念，研究了在跨数据库中分析脑连接组的数据有效培训策略。具体而言，我们建议在大型样本大小的数据集上进行元训练模型，并将知识转移到小数据集中。此外，我们还探索了两种面向脑网络的设计，包括Atlas转换和自适应任务重新启动。与其他训练前策略相比，我们的基于元学习的方法实现了更高和稳定的性能，这证明了我们提出的解决方案的有效性。该框架还能够以数据驱动的方式获得有关数据集和疾病之间相似之处的新见解。

Mapping the connectome of the human brain using structural or functional connectivity has become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph Neural Networks (GNNs) motivated from geometric deep learning have attracted broad interest due to their established power for modeling complex networked data. Despite their superior performance in many fields, there has not yet been a systematic study of how to design effective GNNs for brain network analysis. To bridge this gap, we present BrainGB, a benchmark for brain network analysis with GNNs. BrainGB standardizes the process by (1) summarizing brain network construction pipelines for both functional and structural neuroimaging modalities and (2) modularizing the implementation of GNN designs. We conduct extensive experiments on datasets across cohorts and modalities and recommend a set of general recipes for effective GNN designs on brain networks. To support open and reproducible research on GNN-based brain network analysis, we host the BrainGB website at https://braingb.us with models, tutorials, examples, as well as an out-of-box Python package. We hope that this work will provide useful empirical evidence and offer insights for future research in this novel and promising direction.