We present SLATE, a sequence labeling approach for extracting tasks from free-form content such as digitally handwritten (or "inked") notes on a virtual whiteboard. Our approach allows us to create a single, low-latency model to simultaneously perform sentence segmentation and classification of these sentences into task/non-task sentences. SLATE greatly outperforms a baseline two-model (sentence segmentation followed by classification model) approach, achieving a task F1 score of 84.4\%, a sentence segmentation (boundary similarity) score of 88.4% and three times lower latency compared to the baseline. Furthermore, we provide insights into tackling challenges of performing NLP on the inking domain. We release both our code and dataset for this novel task.

在皮肤病学诊断中，移动皮肤病学助理收集的私人数据存在于患者的分布式移动设备上。联合学习（FL）可以使用分散数据来训练模型，同时保持数据本地化。现有的FL方法假设所有数据都有标签。但是，由于高标签成本，医疗数据通常没有完整的标签。自我监督的学习（SSL）方法，对比度学习（CL）和蒙版自动编码器（MAE）可以利用未标记的数据来预先培训模型，然后用有限的标签进行微调。但是，组合SSL和FL有独特的挑战。例如，CL需要不同的数据，但每个设备仅具有有限的数据。对于MAE而言，尽管基于视觉变压器（VIT）的MAE在集中学习中具有更高的准确性，但尚未研究MAE在未标记数据的FL中的性能。此外，服务器和客户端之间的VIT同步与传统CNN不同。因此，需要设计特殊的同步方法。在这项工作中，我们提出了两个联邦自制的学习框架，用于具有有限标签的皮肤病学诊断。第一个具有较低的计算成本，适用于移动设备。第二个具有高精度，适合高性能服务器。根据CL，我们提出了与功能共享（FedClf）的联合对比度学习。共享功能可用于不同的对比信息，而无需共享原始数据以获得隐私。根据MAE，我们提出了Fedmae。知识拆分将所学的全球知识与每个客户分开。只有全球知识才能汇总为更高的概括性能。关于皮肤病学数据集的实验表明，所提出的框架的精度优于最先进的框架。

动机：癌症是异质的，影响了个性化治疗的精确方法。准确的亚型可以导致癌症患者的生存率更好。高通量技术为癌症亚型提供了多个OMIC数据。但是，由于OMICS数据的大量和高维度，精确的癌症亚型仍然具有挑战性。结果：这项研究提出了基于MLP和变压器块的深度学习方法拟议的亚型形式，以提取多摩学数据的低维表示。 K-均值和共识聚类也用于获得准确的亚型结果。我们比较了TCGA 10癌症类型的其他最先进的亚型方法。我们发现，基于生存分析，亚型形式可以在5000多个肿瘤的基准数据集上表现更好。此外，亚型形式还取得了泛滥亚型的出色结果，这可以帮助分析分子水平上各种癌症类型的共同点和差异。最后，我们将亚型格式应用于TCGA 10类型的癌症。我们确定了50种基本生物标志物，可用于研究靶向癌症药物并促进精密医学时代的癌症治疗。

基于无监督的域适应性（UDA），由于目标情景的表现有希望的表现，面部抗散热器（FAS）方法引起了人们的注意。大多数现有的UDA FAS方法通常通过对齐语义高级功能的分布来拟合受过训练的模型。但是，对未标记的目标域的监督不足，低水平特征对齐降低了现有方法的性能。为了解决这些问题，我们提出了UDA FAS的新颖观点，该视角将目标数据直接适合于模型，即，通过图像翻译将目标数据风格化为源域样式，并进一步将风格化的数据提供给训练有素的数据分类的源模型。提出的生成域适应（GDA）框架结合了两个精心设计的一致性约束：1）域间神经统计量的一致性指导发生器缩小域间间隙。 2）双层语义一致性确保了风格化图像的语义质量。此外，我们提出了域内频谱混合物，以进一步扩大目标数据分布，以确保概括并减少域内间隙。广泛的实验和可视化证明了我们方法对最新方法的有效性。

深层神经网络能够轻松地使用软磁横层（CE）丢失来记住嘈杂的标签。先前的研究试图解决此问题的重点是将噪声损失函数纳入CE损失。但是，记忆问题得到了缓解，但仍然由于非持鲁棒的损失而造成的。为了解决这个问题，我们专注于学习可靠的对比度表示数据，分类器很难记住CE损失下的标签噪声。我们提出了一种新颖的对比正则化函数，以通过标签噪声不主导表示表示的嘈杂数据来学习此类表示。通过理论上研究由提议的正则化功能引起的表示形式，我们揭示了学识渊博的表示形式将信息保留与真实标签和丢弃与损坏标签相关的信息有关的信息。此外，我们的理论结果还表明，学到的表示形式对标签噪声是可靠的。通过基准数据集的实验证明了该方法的有效性。

结构化修剪是一种常用的技术，用于将深神经网络（DNN）部署到资源受限的设备上。但是，现有的修剪方法通常是启发式，任务指定的，并且需要额外的微调过程。为了克服这些限制，我们提出了一个框架，将DNN压缩成纤薄的架构，具有竞争性表现，并且仅通过列车 - 一次（OTO）减少重大拖车。 OTO包含两个键：（i）我们将DNN的参数分区为零不变组，使我们能够修剪零组而不影响输出; （ii）促进零群，我们制定了结构性稀疏优化问题，提出了一种新颖的优化算法，半空间随机投影梯度（HSPG），以解决它，这优于组稀疏性探索的标准近端方法和保持可比的收敛性。为了展示OTO的有效性，我们从划痕上同时培训和压缩全模型，而无需微调推理加速和参数减少，并且在CIFAR10的VGG16实现最先进的结果，为CIFAR10和Squad的BERT为BERT竞争结果在resnet50上为想象成。源代码可在https://github.com/tianyic/only_train_once上获得。

诸如FastSpeech之类的非自动回归文本（TTS）模型可以比以前具有可比性的自回归模型合成语音的速度要快得多。 FastSpeech模型的培训依赖于持续时间预测的自回归教师模型（提供更多信息作为输入）和知识蒸馏（以简化输出中的数据分布），这可以缓解一对多的映射问题（即多个多个映射问题语音变化对应于TTS中的同一文本）。但是，FastSpeech有几个缺点：1）教师学生的蒸馏管线很复杂且耗时，2）从教师模型中提取的持续时间不够准确，并且从教师模型中提取的目标MEL光谱图会遭受信息损失的影响。由于数据的简化，两者都限制了语音质量。在本文中，我们提出了FastSpeech 2，它解决了FastSpeech中的问题，并更好地解决了TTS中的一对一映射问题1）直接用地面实现目标直接训练该模型，而不是教师的简化输出，以及2 ）作为条件输入，引入更多语音信息（例如，音高，能量和更准确的持续时间）。具体而言，我们从语音波形中提取持续时间，音高和能量，并将其直接作为训练中的条件输入，并在推理中使用预测的值。我们进一步设计了FastSpeech 2s，这是首次尝试从文本中直接生成语音波形的尝试，从而享受完全端到端推断的好处。实验结果表明，1）FastSpeech 2在FastSpeech上实现了3倍的训练，而FastSpeech 2s的推理速度甚至更快； 2）FastSpeech 2和2S的语音质量优于FastSpeech，而FastSpeech 2甚至可以超越自回归型号。音频样本可在https://speechresearch.github.io/fastspeech2/上找到。

A further understanding of cause and effect within observational data is critical across many domains, such as economics, health care, public policy, web mining, online advertising, and marketing campaigns. Although significant advances have been made to overcome the challenges in causal effect estimation with observational data, such as missing counterfactual outcomes and selection bias between treatment and control groups, the existing methods mainly focus on source-specific and stationary observational data. Such learning strategies assume that all observational data are already available during the training phase and from only one source. This practical concern of accessibility is ubiquitous in various academic and industrial applications. That's what it boiled down to: in the era of big data, we face new challenges in causal inference with observational data, i.e., the extensibility for incrementally available observational data, the adaptability for extra domain adaptation problem except for the imbalance between treatment and control groups, and the accessibility for an enormous amount of data. In this position paper, we formally define the problem of continual treatment effect estimation, describe its research challenges, and then present possible solutions to this problem. Moreover, we will discuss future research directions on this topic.

The growing interest in intelligent services and privacy protection for mobile devices has given rise to the widespread application of federated learning in Multi-access Edge Computing (MEC). Diverse user behaviors call for personalized services with heterogeneous Machine Learning (ML) models on different devices. Federated Multi-task Learning (FMTL) is proposed to train related but personalized ML models for different devices, whereas previous works suffer from excessive communication overhead during training and neglect the model heterogeneity among devices in MEC. Introducing knowledge distillation into FMTL can simultaneously enable efficient communication and model heterogeneity among clients, whereas existing methods rely on a public dataset, which is impractical in reality. To tackle this dilemma, Federated MultI-task Distillation for Multi-access Edge CompuTing (FedICT) is proposed. FedICT direct local-global knowledge aloof during bi-directional distillation processes between clients and the server, aiming to enable multi-task clients while alleviating client drift derived from divergent optimization directions of client-side local models. Specifically, FedICT includes Federated Prior Knowledge Distillation (FPKD) and Local Knowledge Adjustment (LKA). FPKD is proposed to reinforce the clients' fitting of local data by introducing prior knowledge of local data distributions. Moreover, LKA is proposed to correct the distillation loss of the server, making the transferred local knowledge better match the generalized representation. Experiments on three datasets show that FedICT significantly outperforms all compared benchmarks in various data heterogeneous and model architecture settings, achieving improved accuracy with less than 1.2% training communication overhead compared with FedAvg and no more than 75% training communication round compared with FedGKT.

Reinforcement learning (RL) is one of the most important branches of AI. Due to its capacity for self-adaption and decision-making in dynamic environments, reinforcement learning has been widely applied in multiple areas, such as healthcare, data markets, autonomous driving, and robotics. However, some of these applications and systems have been shown to be vulnerable to security or privacy attacks, resulting in unreliable or unstable services. A large number of studies have focused on these security and privacy problems in reinforcement learning. However, few surveys have provided a systematic review and comparison of existing problems and state-of-the-art solutions to keep up with the pace of emerging threats. Accordingly, we herein present such a comprehensive review to explain and summarize the challenges associated with security and privacy in reinforcement learning from a new perspective, namely that of the Markov Decision Process (MDP). In this survey, we first introduce the key concepts related to this area. Next, we cover the security and privacy issues linked to the state, action, environment, and reward function of the MDP process, respectively. We further highlight the special characteristics of security and privacy methodologies related to reinforcement learning. Finally, we discuss the possible future research directions within this area.