In this paper, we explore the feasibility of utilizing a mmWave radar sensor installed on a UAV to reconstruct the 3D shapes of multiple objects in a space. The UAV hovers at various locations in the space, and its onboard radar senor collects raw radar data via scanning the space with Synthetic Aperture Radar (SAR) operation. The radar data is sent to a deep neural network model, which outputs the point cloud reconstruction of the multiple objects in the space. We evaluate two different models. Model 1 is our recently proposed 3DRIMR/R2P model, and Model 2 is formed by adding a segmentation stage in the processing pipeline of Model 1. Our experiments have demonstrated that both models are promising in solving the multiple object reconstruction problem. We also show that Model 2, despite producing denser and smoother point clouds, can lead to higher reconstruction loss or even loss of objects. In addition, we find that both models are robust to the highly noisy radar data obtained by unstable SAR operation due to the instability or vibration of a small UAV hovering at its intended scanning point. Our exploratory study has shown a promising direction of applying mmWave radar sensing in 3D object reconstruction.

最近的研究表明，MMWave雷达感测在低可见性环境中对象检测的有效性，这使其成为自主导航系统中的理想技术。在本文中，我们将雷达介绍给点云（R2P），这是一个深度学习模型，该模型基于具有不正确点的粗糙和稀疏点云，生成具有精细几何细节的3D对象的平滑，密集且高度准确的点云表示。来自mmwave雷达。这些输入点云是从由原始MMWave雷达传感器数据生成的2D深度图像转换的，其特征是不一致，方向和形状误差。 R2P利用两个顺序的深度学习编码器块的体系结构在从多个角度观察到对象的基于雷达的输入点云的基本特征，并确保生成的输出点云及其准确的内部一致性和原始对象的详细形状重建。我们实施R2P来替换我们最近提出的3DRIMR（通过MMWave Radar）系统的第2阶段。我们的实验证明了R2P在流行的现有方法（例如PointNet，PCN和原始3DRIMR设计）上的显着性能提高。

近年来，在广泛的机器学习应用程序中，在梯度增强决策树（GBDT）方面取得了重大成功。通常，关于GBDT训练算法的共识是梯度，统计数据是根据高精度浮点计算的。在本文中，我们调查了一个本质上重要的问题，该问题在先前的文献中在很大程度上被忽略了：代表培训GBDT的梯度需要多少位？为了解决这个谜团，我们建议在GBDT的培训算法中以非常简单但有效的方式量化所有高精度梯度。令人惊讶的是，我们的理论分析和实证研究都表明，梯度的必要精度而不伤害任何性能可能很低，例如2或3位。对于低精度梯度，GBDT培训中的大多数算术操作可以用8、16或32位的整数操作代替。有希望的是，这些发现可能为从几个方面对GBDT进行更有效训练的方式铺平了道路：（1）加速直方图中梯度统计的计算； （2）在分布式培训期间压缩高精度统计信息的通信成本； （3）使用和开发硬件体系结构的灵感，这些架构很好地支持了用于GBDT培训的低精确计算。与大量数据集中的SOTA GBDT系统相比，我们在CPU，GPU和分布式集群上进行了基准测试，最多可容纳我们简单量化策略的速度，这表明了GBDT低表演培训的有效性和潜力。该代码将发布给LightGBM的官方存储库。

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.