Recent studies have shown that using an external Language Model (LM) benefits the end-to-end Automatic Speech Recognition (ASR). However, predicting tokens that appear less frequently in the training set is still quite challenging. The long-tail prediction problems have been widely studied in many applications, but only been addressed by a few studies for ASR and LMs. In this paper, we propose a new memory augmented lookup dictionary based Transformer architecture for LM. The newly introduced lookup dictionary incorporates rich contextual information in training set, which is vital to correctly predict long-tail tokens. With intensive experiments on Chinese and English data sets, our proposed method is proved to outperform the baseline Transformer LM by a great margin on both word/character error rate and tail tokens error rate. This is achieved without impact on the decoding efficiency. Overall, we demonstrate the effectiveness of our proposed method in boosting the ASR decoding performance, especially for long-tail tokens.

Accurate airway extraction from computed tomography (CT) images is a critical step for planning navigation bronchoscopy and quantitative assessment of airway-related chronic obstructive pulmonary disease (COPD). The existing methods are challenging to sufficiently segment the airway, especially the high-generation airway, with the constraint of the limited label and cannot meet the clinical use in COPD. We propose a novel two-stage 3D contextual transformer-based U-Net for airway segmentation using CT images. The method consists of two stages, performing initial and refined airway segmentation. The two-stage model shares the same subnetwork with different airway masks as input. Contextual transformer block is performed both in the encoder and decoder path of the subnetwork to finish high-quality airway segmentation effectively. In the first stage, the total airway mask and CT images are provided to the subnetwork, and the intrapulmonary airway mask and corresponding CT scans to the subnetwork in the second stage. Then the predictions of the two-stage method are merged as the final prediction. Extensive experiments were performed on in-house and multiple public datasets. Quantitative and qualitative analysis demonstrate that our proposed method extracted much more branches and lengths of the tree while accomplishing state-of-the-art airway segmentation performance. The code is available at https://github.com/zhaozsq/airway_segmentation.

Anomaly analytics is a popular and vital task in various research contexts, which has been studied for several decades. At the same time, deep learning has shown its capacity in solving many graph-based tasks like, node classification, link prediction, and graph classification. Recently, many studies are extending graph learning models for solving anomaly analytics problems, resulting in beneficial advances in graph-based anomaly analytics techniques. In this survey, we provide a comprehensive overview of graph learning methods for anomaly analytics tasks. We classify them into four categories based on their model architectures, namely graph convolutional network (GCN), graph attention network (GAT), graph autoencoder (GAE), and other graph learning models. The differences between these methods are also compared in a systematic manner. Furthermore, we outline several graph-based anomaly analytics applications across various domains in the real world. Finally, we discuss five potential future research directions in this rapidly growing field.

Virtual reality (VR) over wireless is expected to be one of the killer applications in next-generation communication networks. Nevertheless, the huge data volume along with stringent requirements on latency and reliability under limited bandwidth resources makes untethered wireless VR delivery increasingly challenging. Such bottlenecks, therefore, motivate this work to seek the potential of using semantic communication, a new paradigm that promises to significantly ease the resource pressure, for efficient VR delivery. To this end, we propose a novel framework, namely WIreless SEmantic deliveRy for VR (WiserVR), for delivering consecutive 360{\deg} video frames to VR users. Specifically, deep learning-based multiple modules are well-devised for the transceiver in WiserVR to realize high-performance feature extraction and semantic recovery. Among them, we dedicatedly develop a concept of semantic location graph and leverage the joint-semantic-channel-coding method with knowledge sharing to not only substantially reduce communication latency, but also to guarantee adequate transmission reliability and resilience under various channel states. Moreover, implementation of WiserVR is presented, followed by corresponding initial simulations for performance evaluation compared with benchmarks. Finally, we discuss several open issues and offer feasible solutions to unlock the full potential of WiserVR.

Despite high global prevalence of hepatic steatosis, no automated diagnostics demonstrated generalizability in detecting steatosis on multiple international datasets. Traditionally, hepatic steatosis detection relies on clinicians selecting the region of interest (ROI) on computed tomography (CT) to measure liver attenuation. ROI selection demands time and expertise, and therefore is not routinely performed in populations. To automate the process, we validated an existing artificial intelligence (AI) system for 3D liver segmentation and used it to purpose a novel method: AI-ROI, which could automatically select the ROI for attenuation measurements. AI segmentation and AI-ROI method were evaluated on 1,014 non-contrast enhanced chest CT images from eight international datasets: LIDC-IDRI, NSCLC-Lung1, RIDER, VESSEL12, RICORD-1A, RICORD-1B, COVID-19-Italy, and COVID-19-China. AI segmentation achieved a mean dice coefficient of 0.957. Attenuations measured by AI-ROI showed no significant differences (p = 0.545) and a reduction of 71% time compared to expert measurements. The area under the curve (AUC) of the steatosis classification of AI-ROI is 0.921 (95% CI: 0.883 - 0.959). If performed as a routine screening method, our AI protocol could potentially allow early non-invasive, non-pharmacological preventative interventions for hepatic steatosis. 1,014 expert-annotated liver segmentations of patients with hepatic steatosis annotations can be downloaded here: https://drive.google.com/drive/folders/1-g_zJeAaZXYXGqL1OeF6pUjr6KB0igJX.

部分标签学习（PLL）是一项奇特的弱监督学习任务，其中训练样本通常与一组候选标签而不是单个地面真理相关联。尽管在该域中提出了各种标签歧义方法，但他们通常假设在许多现实世界应用中可能不存在类平衡的方案。从经验上讲，我们在面对长尾分布和部分标记的组合挑战时观察到了先前方法的退化性能。在这项工作中，我们首先确定先前工作失败的主要原因。随后，我们提出了一种新型的基于最佳运输的框架太阳能，它允许完善被歧义的标签，以匹配边缘级别的先验分布。太阳能还结合了一种新的系统机制，用于估计PLL设置下的长尾类先验分布。通过广泛的实验，与先前的最先进的PLL方法相比，太阳能在标准化基准方面表现出基本优势。代码和数据可在以下网址获得：https：//github.com/hbzju/solar。

作为一种概率建模技术，基于流的模型在无损压缩\ cite {idf，idf ++，lbb，ivpf，iflow}的领域表现出了巨大的潜力。与其他深层生成模型（例如自动回应，VAE）\ cite {bitswap，hilloc，pixelcnn ++，pixelsnail}，这些模型明确地模拟了数据分布概率，因此基于流的模型的性能更好，因为它们的出色概率密度估计和满意度的概率和满意度的概率。在基于流量的模型中，多尺度体系结构提供了从浅层到输出层的快捷方式，从而大大降低了计算复杂性并避免添加更多层时性能降解。这对于构建基于先进的基于流动的可学习射击映射至关重要。此外，实用压缩任务中模型设计的轻量级要求表明，具有多尺度体系结构的流量在编码复杂性和压缩效率之间取得了最佳的权衡。

嵌入学习是深度建议模型中的重要技术，可以将分类特征映射到密集的矢量。但是，嵌入表通常需要大量参数，这些参数成为存储和效率瓶颈。已经采用了分布式培训解决方案将嵌入表分配到多个设备中。但是，如果不仔细分区，则嵌入表很容易导致失衡。这是名为“嵌入桌碎片”的分布式系统的重大设计挑战，即，我们应该如何对嵌入表进行分配以平衡跨设备的成本，这是一项非平凡的任务，因为1）很难有效，精确地衡量成本，和2）已知分区问题是NP-HARD。在这项工作中，我们在Meta中介绍了新颖的实践，即Autoshard，该实践使用神经成本模型直接预测多桌成本和利用深度强化学习以解决分区问题。开源的大规模合成数据集和Meta生产数据集的实验结果证明了Autoshard的优越性优于启发式方法。此外，Autoshard的学习政策可以转移到具有不同数量的表和不同表格比率的碎片任务中，而无需进行任何微调。此外，Autoshard可以在几秒钟内有效地将数百张桌子碎片。 Autoshard的有效性，可转移性和效率使其适合生产使用。我们的算法已在元生产环境中部署。可以在https://github.com/daochenzha/autoshard上获得原型

反事实是一种新兴的模型解释类型，最近引起了行业和学术界的大量关注。与传统的基于特征的解释（例如，归因）不同，反事实是一系列假设样本，可以将模型决策翻转而对查询的扰动最小。鉴于有效的反事实，人类能够在``假设的情况''的情况下进行推理，以便更好地理解模型决策边界。但是，释放反事实可能是有害的，因为它可能无意间泄漏敏感信息给对手，这给模型安全性和数据隐私带来了更高的风险。为了弥合差距，在本文中，我们提出了一个新颖的框架，以生成不同的私人反事实（DPC），而无需触摸已部署的模型或解释集，在该集合中注入了噪音以进行保护，同时保持反事实的解释作用。特别是，我们使用功能机制训练自动编码器来构建嘈杂的类原型，然后根据差异隐私的后处理免疫从潜在原型中得出DPC。进一步的评估证明了拟议框架的有效性，表明DPC可以成功缓解提取和推理攻击的风险。

受益于医疗保健数据的数字化和计算能力的发展，机器学习方法越来越多地用于医疗领域。在医疗保健机器学习中已经确定了公平性问题，导致对有限医疗资源的不公平分配或某些群体的健康风险过多。因此，解决公平问题最近引起了医疗保健社区的越来越多的关注。然而，机器学习的机器学习与机器学习中的公平性的交集仍在研究中。在这篇综述中，我们通过暴露公平问题，总结可能的偏见，整理缓解方法并指出挑战以及未来的机会来建立桥梁。