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。

组合来自多视图图像的信息对于提高自动化方法的疾病诊断方法的性能和鲁棒性至关重要。但是，由于多视图图像的非对齐特性，跨视图的构建相关性和数据融合在很大程度上仍然是一个开放的问题。在这项研究中，我们提出了输血，这是一种基于变压器的体系结构，可使用卷积层和强大的注意机制合并不同的多视图成像信息。特别是，针对丰富的跨视图上下文建模和语义依赖性挖掘，提出了发散的融合注意（DIFA）模块，以解决从不同图像视图中捕获未对齐数据之间的长期相关性的关键问题。我们进一步提出了多尺度注意（MSA），以收集多尺度特征表示的全局对应关系。我们评估了心脏MRI（M \＆MS-2）挑战队列中多疾病，多视图\＆多中心右心室分段的输血。输血表明了针对最先进方法的领先绩效，并为多视图成像集成的新观点打开了稳健的医学图像分割。

自上而下的实例分割框架与自下而上的框架相比，它在对象检测方面表现出了优越性。虽然它有效地解决了过度细分，但自上而下的实例分割却遭受了过度处理问题。然而，完整的分割掩模对于生物图像分析至关重要，因为它具有重要的形态特性，例如形状和体积。在本文中，我们提出了一个区域建议纠正（RPR）模块，以解决这个具有挑战性的分割问题。特别是，我们提供了一个渐进式皇家模块，以逐渐将邻居信息引入一系列ROI。 ROI功能被馈入专门的进料网络（FFN）以进行提案框回归。有了其他邻居信息，提出的RPR模块显示了区域建议位置的校正显着改善，因此与最先进的基线方法相比，在三个生物图像数据集上表现出有利的实例分割性能。实验结果表明，所提出的RPR模块在基于锚固的和无锚的自上而下实例分割方法中有效，这表明该方法可以应用于生物学图像的一般自上而下实例分割。代码可用。

通常对端到端自动语音识别（ASR）模型进行训练，以优化整个令牌序列的损失，同时忽略了明确的音素粒度监督。这可能导致由于相似的混淆或音素减少而导致的识别错误。为了减轻这个问题，我们提出了一个基于监督对比学习（Scala）的新框架，以增强端到端ASR系统的音素表示学习。具体而言，我们将自我监督的掩盖对比预测编码（MCPC）扩展到完全监督的设置，在此设置以下方式应用监督。首先，Scala掩盖了可变长度编码器特征，根据音素边界，从预先训练的声学模型中提取的音素强制对齐；然后，它通过对比度学习预测了蒙版的特征。强制对齐可以提供音素标签，以减轻自我监督的MCPC中正阴对引入的噪声。关于阅读和自发语音数据集的实验表明，与基线相比，我们提出的方法分别达到了2.8和1.4点字符错误率（CER）绝对降低。

Designing experiments often requires balancing between learning about the true treatment effects and earning from allocating more samples to the superior treatment. While optimal algorithms for the Multi-Armed Bandit Problem (MABP) provide allocation policies that optimally balance learning and earning, they tend to be computationally expensive. The Gittins Index (GI) is a solution to the MABP that can simultaneously attain optimality and computationally efficiency goals, and it has been recently used in experiments with Bernoulli and Gaussian rewards. For the first time, we present a modification of the GI rule that can be used in experiments with exponentially-distributed rewards. We report its performance in simulated 2- armed and 3-armed experiments. Compared to traditional non-adaptive designs, our novel GI modified design shows operating characteristics comparable in learning (e.g. statistical power) but substantially better in earning (e.g. direct benefits). This illustrates the potential that designs using a GI approach to allocate participants have to improve participant benefits, increase efficiencies, and reduce experimental costs in adaptive multi-armed experiments with exponential rewards.

Transformer has achieved impressive successes for various computer vision tasks. However, most of existing studies require to pretrain the Transformer backbone on a large-scale labeled dataset (e.g., ImageNet) for achieving satisfactory performance, which is usually unavailable for medical images. Additionally, due to the gap between medical and natural images, the improvement generated by the ImageNet pretrained weights significantly degrades while transferring the weights to medical image processing tasks. In this paper, we propose Bootstrap Own Latent of Transformer (BOLT), a self-supervised learning approach specifically for medical image classification with the Transformer backbone. Our BOLT consists of two networks, namely online and target branches, for self-supervised representation learning. Concretely, the online network is trained to predict the target network representation of the same patch embedding tokens with a different perturbation. To maximally excavate the impact of Transformer from limited medical data, we propose an auxiliary difficulty ranking task. The Transformer is enforced to identify which branch (i.e., online/target) is processing the more difficult perturbed tokens. Overall, the Transformer endeavours itself to distill the transformation-invariant features from the perturbed tokens to simultaneously achieve difficulty measurement and maintain the consistency of self-supervised representations. The proposed BOLT is evaluated on three medical image processing tasks, i.e., skin lesion classification, knee fatigue fracture grading and diabetic retinopathy grading. The experimental results validate the superiority of our BOLT for medical image classification, compared to ImageNet pretrained weights and state-of-the-art self-supervised learning approaches.

Text clustering and topic extraction are two important tasks in text mining. Usually, these two tasks are performed separately. For topic extraction to facilitate clustering, we can first project texts into a topic space and then perform a clustering algorithm to obtain clusters. To promote topic extraction by clustering, we can first obtain clusters with a clustering algorithm and then extract cluster-specific topics. However, this naive strategy ignores the fact that text clustering and topic extraction are strongly correlated and follow a chicken-and-egg relationship. Performing them separately fails to make them mutually benefit each other to achieve the best overall performance. In this paper, we propose an unsupervised text clustering and topic extraction framework (ClusTop) which integrates text clustering and topic extraction into a unified framework and can achieve high-quality clustering result and extract topics from each cluster simultaneously. Our framework includes four components: enhanced language model training, dimensionality reduction, clustering and topic extraction, where the enhanced language model can be viewed as a bridge between clustering and topic extraction. On one hand, it provides text embeddings with a strong cluster structure which facilitates effective text clustering; on the other hand, it pays high attention on the topic related words for topic extraction because of its self-attention architecture. Moreover, the training of enhanced language model is unsupervised. Experiments on two datasets demonstrate the effectiveness of our framework and provide benchmarks for different model combinations in this framework.

This paper illustrates the technologies of user next intent prediction with a concept knowledge graph. The system has been deployed on the Web at Alipay, serving more than 100 million daily active users. Specifically, we propose AlipayKG to explicitly characterize user intent, which is an offline concept knowledge graph in the Life-Service domain modeling the historical behaviors of users, the rich content interacted by users and the relations between them. We further introduce a Transformer-based model which integrates expert rules from the knowledge graph to infer the online user's next intent. Experimental results demonstrate that the proposed system can effectively enhance the performance of the downstream tasks while retaining explainability.

Capturing feature information effectively is of great importance in vision tasks. With the development of convolutional neural networks (CNNs), concepts like residual connection and multiple scales promote continual performance gains on diverse deep learning vision tasks. However, the existing methods do not organically combined advantages of these valid ideas. In this paper, we propose a novel CNN architecture called GoogLe2Net, it consists of residual feature-reutilization inceptions (ResFRI) or split residual feature-reutilization inceptions (Split-ResFRI) which create transverse passages between adjacent groups of convolutional layers to enable features flow to latter processing branches and possess residual connections to better process information. Our GoogLe2Net is able to reutilize information captured by foregoing groups of convolutional layers and express multi-scale features at a fine-grained level, which improves performances in image classification. And the inception we proposed could be embedded into inception-like networks directly without any migration costs. Moreover, in experiments based on popular vision datasets, such as CIFAR10 (97.94%), CIFAR100 (85.91%) and Tiny Imagenet (70.54%), we obtain better results on image classification task compared with other modern models.

Despite some successful applications of goal-driven navigation, existing deep reinforcement learning-based approaches notoriously suffers from poor data efficiency issue. One of the reasons is that the goal information is decoupled from the perception module and directly introduced as a condition of decision-making, resulting in the goal-irrelevant features of the scene representation playing an adversary role during the learning process. In light of this, we present a novel Goal-guided Transformer-enabled reinforcement learning (GTRL) approach by considering the physical goal states as an input of the scene encoder for guiding the scene representation to couple with the goal information and realizing efficient autonomous navigation. More specifically, we propose a novel variant of the Vision Transformer as the backbone of the perception system, namely Goal-guided Transformer (GoT), and pre-train it with expert priors to boost the data efficiency. Subsequently, a reinforcement learning algorithm is instantiated for the decision-making system, taking the goal-oriented scene representation from the GoT as the input and generating decision commands. As a result, our approach motivates the scene representation to concentrate mainly on goal-relevant features, which substantially enhances the data efficiency of the DRL learning process, leading to superior navigation performance. Both simulation and real-world experimental results manifest the superiority of our approach in terms of data efficiency, performance, robustness, and sim-to-real generalization, compared with other state-of-art baselines. Demonstration videos are available at \colorb{https://youtu.be/93LGlGvaN0c.