最近已显示ASR最近取得了出色的性能。但是，他们中的大多数都依靠大量的配对数据，这对于全球低资源语言是不可行的。本文研究了如何直接从未配对的电话序列和语音话语中学习。我们设计了两个阶段的迭代框架。 GAN培训在第一阶段被采用，以找到未配对的语音和电话序列之间的映射关系。在第二阶段，引入了另一个HMM模型以从发电机的输出中训练，从而提高了性能，并为下一次迭代提供了更好的细分。在实验中，我们首先研究模型设计的不同选择。然后，我们将框架与不同类型的基准进行比较：（i）受监督的方法（ii）基于声学单元发现的方法（III）方法从未配对的数据中学习。我们的框架的表现始终如一，比所有基于TIMIT数据集从未配对数据中学习的所有声学单元发现方法和以前的方法更好。

本文研究了一个可转移的音素嵌入框架，旨在在几次设置下处理跨语义的文本到语音（TTS）问题。转移学习是一种常见的方法，因为从头开始训练几乎没有训练数据，但必将过度合适。尽管如此，我们发现幼稚的转移学习方法在极少数的设置下未能适应看不见的语言，那里提供了不到8分钟的数据。我们通过提出一个由基于音素的TTS模型和一个代码簿模块组成的框架来解决问题，以将不同语言的音素投射到学习的潜在空间中。此外，通过利用音素级别的自我监督的学习特征，我们有效地提高了综合语音的质量。实验表明，使用4秒的数据使用4个话语，足以在使用我们的框架适应不见语的语言时综合可理解的语音。

个性化语音合成系统是一个非常期望的应用程序，其中系统可以使用罕见的登记录制与用户的语音产生语音。最近有两种主要方法可以在近期建立这样的系统：扬声器适配和扬声器编码。一方面，扬声器适配方法微调训练有素的多扬声器文本到语音（TTS）模型，只有少数注册样本。然而，它们需要至少有数千个微调步骤以进行高质量适应，使其难以在设备上施加。另一方面，扬声器编码方法将注册话语编码为扬声器嵌入。训练的TTS模型可以在相应的扬声器嵌入上综合用户的语音。然而，扬声器编码器遭受了所看到和看不见的扬声器之间的泛化差距。在本文中，我们建议将元学习算法应用于扬声器适应方法。更具体地说，我们使用模型不可知的元学习（MAML）作为多扬声器TTS模型的训练算法，其旨在找到一个很好的元初始化，以便快速地将模型调整到任何几次扬声器适应任务。因此，我们还可以将元训练的TTS模型调整为有效地解除扬声器。我们的实验比较了两个基线的提出方法（Meta-TTS）：扬声器适配方法基线和扬声器编码方法基线。评估结果表明，Meta-TTS可以从扬声器适应基线的少量适应步骤中综合高扬声器相似性语音，而不是扬声器适配基线，并且在相同的训练方案下优于扬声器编码基线。当基线的扬声器编码器用额外的8371个扬声器进行预先培训时，Meta-TTS仍然可以越优于库特布特数据集的基线，并在VCTK数据集上实现可比结果。

This paper focuses on designing efficient models with low parameters and FLOPs for dense predictions. Even though CNN-based lightweight methods have achieved stunning results after years of research, trading-off model accuracy and constrained resources still need further improvements. This work rethinks the essential unity of efficient Inverted Residual Block in MobileNetv2 and effective Transformer in ViT, inductively abstracting a general concept of Meta-Mobile Block, and we argue that the specific instantiation is very important to model performance though sharing the same framework. Motivated by this phenomenon, we deduce a simple yet efficient modern \textbf{I}nverted \textbf{R}esidual \textbf{M}obile \textbf{B}lock (iRMB) for mobile applications, which absorbs CNN-like efficiency to model short-distance dependency and Transformer-like dynamic modeling capability to learn long-distance interactions. Furthermore, we design a ResNet-like 4-phase \textbf{E}fficient \textbf{MO}del (EMO) based only on a series of iRMBs for dense applications. Massive experiments on ImageNet-1K, COCO2017, and ADE20K benchmarks demonstrate the superiority of our EMO over state-of-the-art methods, \eg, our EMO-1M/2M/5M achieve 71.5, 75.1, and 78.4 Top-1 that surpass \textbf{SoTA} CNN-/Transformer-based models, while trading-off the model accuracy and efficiency well.

Supervised Question Answering systems (QA systems) rely on domain-specific human-labeled data for training. Unsupervised QA systems generate their own question-answer training pairs, typically using secondary knowledge sources to achieve this outcome. Our approach (called PIE-QG) uses Open Information Extraction (OpenIE) to generate synthetic training questions from paraphrased passages and uses the question-answer pairs as training data for a language model for a state-of-the-art QA system based on BERT. Triples in the form of <subject, predicate, object> are extracted from each passage, and questions are formed with subjects (or objects) and predicates while objects (or subjects) are considered as answers. Experimenting on five extractive QA datasets demonstrates that our technique achieves on-par performance with existing state-of-the-art QA systems with the benefit of being trained on an order of magnitude fewer documents and without any recourse to external reference data sources.

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.

Knowledge graph embedding (KGE), which maps entities and relations in a knowledge graph into continuous vector spaces, has achieved great success in predicting missing links in knowledge graphs. However, knowledge graphs often contain incomplete triples that are difficult to inductively infer by KGEs. To address this challenge, we resort to analogical inference and propose a novel and general self-supervised framework AnKGE to enhance KGE models with analogical inference capability. We propose an analogical object retriever that retrieves appropriate analogical objects from entity-level, relation-level, and triple-level. And in AnKGE, we train an analogy function for each level of analogical inference with the original element embedding from a well-trained KGE model as input, which outputs the analogical object embedding. In order to combine inductive inference capability from the original KGE model and analogical inference capability enhanced by AnKGE, we interpolate the analogy score with the base model score and introduce the adaptive weights in the score function for prediction. Through extensive experiments on FB15k-237 and WN18RR datasets, we show that AnKGE achieves competitive results on link prediction task and well performs analogical inference.

Digital engineering transformation is a crucial process for the engineering paradigm shifts in the fourth industrial revolution (4IR), and artificial intelligence (AI) is a critical enabling technology in digital engineering transformation. This article discusses the following research questions: What are the fundamental changes in the 4IR? More specifically, what are the fundamental changes in engineering? What is digital engineering? What are the main uncertainties there? What is trustworthy AI? Why is it important today? What are emerging engineering paradigm shifts in the 4IR? What is the relationship between the data-intensive paradigm and digital engineering transformation? What should we do for digitalization? From investigating the pattern of industrial revolutions, this article argues that ubiquitous machine intelligence (uMI) is the defining power brought by the 4IR. Digitalization is a condition to leverage ubiquitous machine intelligence. Digital engineering transformation towards Industry 4.0 has three essential building blocks: digitalization of engineering, leveraging ubiquitous machine intelligence, and building digital trust and security. The engineering design community at large is facing an excellent opportunity to bring the new capabilities of ubiquitous machine intelligence and trustworthy AI principles, as well as digital trust, together in various engineering systems design to ensure the trustworthiness of systems in Industry 4.0.

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/

Learning the underlying distribution of molecular graphs and generating high-fidelity samples is a fundamental research problem in drug discovery and material science. However, accurately modeling distribution and rapidly generating novel molecular graphs remain crucial and challenging goals. To accomplish these goals, we propose a novel Conditional Diffusion model based on discrete Graph Structures (CDGS) for molecular graph generation. Specifically, we construct a forward graph diffusion process on both graph structures and inherent features through stochastic differential equations (SDE) and derive discrete graph structures as the condition for reverse generative processes. We present a specialized hybrid graph noise prediction model that extracts the global context and the local node-edge dependency from intermediate graph states. We further utilize ordinary differential equation (ODE) solvers for efficient graph sampling, based on the semi-linear structure of the probability flow ODE. Experiments on diverse datasets validate the effectiveness of our framework. Particularly, the proposed method still generates high-quality molecular graphs in a limited number of steps.