Determining causal effects of temporal multi-intervention assists decision-making. Restricted by time-varying bias, selection bias, and interactions of multiple interventions, the disentanglement and estimation of multiple treatment effects from individual temporal data is still rare. To tackle these challenges, we propose a comprehensive framework of temporal counterfactual forecasting from an individual multiple treatment perspective (TCFimt). TCFimt constructs adversarial tasks in a seq2seq framework to alleviate selection and time-varying bias and designs a contrastive learning-based block to decouple a mixed treatment effect into separated main treatment effects and causal interactions which further improves estimation accuracy. Through implementing experiments on two real-world datasets from distinct fields, the proposed method shows satisfactory performance in predicting future outcomes with specific treatments and in choosing optimal treatment type and timing than state-of-the-art methods.

肺癌是最致命的癌症之一，部分诊断和治疗取决于肿瘤的准确描绘。目前是最常见的方法的人以人为本的分割，须遵守观察者间变异性，并且考虑到专家只能提供注释的事实，也是耗时的。最近展示了有前途的结果，自动和半自动肿瘤分割方法。然而，随着不同的研究人员使用各种数据集和性能指标验证了其算法，可靠地评估这些方法仍然是一个开放的挑战。通过2018年IEEE视频和图像处理（VIP）杯竞赛创建的计算机断层摄影扫描（LOTUS）基准测试的肺起源肿瘤分割的目标是提供唯一的数据集和预定义的指标，因此不同的研究人员可以开发和以统一的方式评估他们的方法。 2018年VIP杯始于42个国家的全球参与，以获得竞争数据。在注册阶段，有129名成员组成了来自10个国家的28个团队，其中9个团队将其达到最后阶段，6队成功完成了所有必要的任务。简而言之，竞争期间提出的所有算法都是基于深度学习模型与假阳性降低技术相结合。三种决赛选手开发的方法表明，有希望的肿瘤细分导致导致越来越大的努力应降低假阳性率。本次竞争稿件概述了VIP-Cup挑战，以及所提出的算法和结果。

仪器变量（IVS），治疗随机化的来源，条件无关的结果，在因果推理中发挥着重要作用，不观察到的混乱。然而，现有的基于IV的反事实预测方法需要良好预定义的IVS，而它是一种艺术而不是科学，可以在许多现实世界场景中找到有效的IV。此外，通过违反有效IVS的条件，预定的手工制作的IV可能是弱或错误的。这些棘手的事实阻碍了基于IV的反事实预测方法的应用。在本文中，我们提出了一种新颖的自动仪器可变分解（AUTOV）算法，以自动生成从观察到的变量（IV候选）的IVS角色的表示。具体地，我们让学习的IV表示通过相互信息最大化和最小化限制的结果，通过互动和排除条件满足相关性条件。我们还通过鼓励他们与治疗和结果相关，学习混乱的陈述。 IV和混淆器表示竞争其在对抗性游戏中的限制的信息，这使我们能够获得基于IV的反事实预测的有效IV表示。广泛的实验表明，我们的方法为基于准确的IV的反事实预测生成有效的IV表示。

We propose LiDAL, a novel active learning method for 3D LiDAR semantic segmentation by exploiting inter-frame uncertainty among LiDAR frames. Our core idea is that a well-trained model should generate robust results irrespective of viewpoints for scene scanning and thus the inconsistencies in model predictions across frames provide a very reliable measure of uncertainty for active sample selection. To implement this uncertainty measure, we introduce new inter-frame divergence and entropy formulations, which serve as the metrics for active selection. Moreover, we demonstrate additional performance gains by predicting and incorporating pseudo-labels, which are also selected using the proposed inter-frame uncertainty measure. Experimental results validate the effectiveness of LiDAL: we achieve 95% of the performance of fully supervised learning with less than 5% of annotations on the SemanticKITTI and nuScenes datasets, outperforming state-of-the-art active learning methods. Code release: https://github.com/hzykent/LiDAL.

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.

Single-cell technologies are revolutionizing the entire field of biology. The large volumes of data generated by single-cell technologies are high-dimensional, sparse, heterogeneous, and have complicated dependency structures, making analyses using conventional machine learning approaches challenging and impractical. In tackling these challenges, deep learning often demonstrates superior performance compared to traditional machine learning methods. In this work, we give a comprehensive survey on deep learning in single-cell analysis. We first introduce background on single-cell technologies and their development, as well as fundamental concepts of deep learning including the most popular deep architectures. We present an overview of the single-cell analytic pipeline pursued in research applications while noting divergences due to data sources or specific applications. We then review seven popular tasks spanning through different stages of the single-cell analysis pipeline, including multimodal integration, imputation, clustering, spatial domain identification, cell-type deconvolution, cell segmentation, and cell-type annotation. Under each task, we describe the most recent developments in classical and deep learning methods and discuss their advantages and disadvantages. Deep learning tools and benchmark datasets are also summarized for each task. Finally, we discuss the future directions and the most recent challenges. This survey will serve as a reference for biologists and computer scientists, encouraging collaborations.

我们提出了一种新颖的方法来重新定位或放置识别，这是许多机器人技术，自动化和AR应用中要解决的基本问题。我们不依靠通常不稳定的外观信息，而是考虑以局部对象形式给出参考图的情况。我们的本地化框架依赖于3D语义对象检测，然后与地图中的对象关联。可能的配对关联集是基于评估空间兼容性的合并度量的层次聚类而生长的。后者特别使用有关​​相对对象配置的信息，该信息相对于全局转换是不变的。随着相机逐步探索环境并检测更多对象，关联集将进行更新和扩展。我们在几种具有挑战性的情况下测试我们的算法，包括动态场景，大型视图变化以及具有重复实例的场景。我们的实验表明，我们的方法在鲁棒性和准确性方面都优于先前的艺术。

近年来，尤其是在户外环境中，自我监督的单眼深度估计已取得了重大进展。但是，在大多数现有数据被手持设备捕获的室内场景中，深度预测结果无法满足。与室外环境相比，使用自我监督的方法估算室内环境的单眼视频深度，导致了两个额外的挑战：（i）室内视频序列的深度范围在不同的框架上有很大变化，使深度很难进行。网络以促进培训的一致深度线索； （ii）用手持设备记录的室内序列通常包含更多的旋转运动，这使姿势网络难以预测准确的相对摄像头姿势。在这项工作中，我们通过对这些挑战进行特殊考虑并巩固了一系列良好实践，以提高自我监督的单眼深度估计室内环境的表现，从而提出了一种新颖的框架单声道++。首先，提出了具有基于变压器的比例回归网络的深度分解模块，以明确估算全局深度尺度因子，预测的比例因子可以指示最大深度值。其次，我们不像以前的方法那样使用单阶段的姿势估计策略，而是建议利用残留姿势估计模块来估计相对摄像机在连续迭代的跨帧中构成。第三，为了为我们的残留姿势估计模块纳入广泛的坐标指南，我们建议直接在输入上执行坐标卷积编码，以实现姿势网络。提出的方法在各种基准室内数据集（即Euroc Mav，Nyuv2，扫描仪和7片）上进行了验证，证明了最先进的性能。

事件摄像机是受到生物启发的视觉传感器，异步代表像素级亮度随着事件流而变化。基于事件的单眼多视图立体声（EMV）是一种利用事件流以估算具有已知轨迹的半密度3D结构的技术。对于基于事件的单眼大满贯，这是一项关键任务。但是，所需的密集计算工作负载使其对于嵌入式平台上的实时部署而具有挑战性。在本文中，通过实现最关键和最耗时的阶段，包括事件反向预测和FPGA上的体积射线计数，提出Eventor作为快速有效的EMV加速器。高度平行且完全管道的处理元素是通过FPGA专门设计的，并与嵌入式臂集成为异质系统，以改善吞吐量并减少记忆足迹。同时，通过重新安排，近似计算和混合数据量化，将EMVS算法重新制定为更硬件的方式。戴维斯数据集的评估结果表明，与英特尔i5 CPU平台相比，Eventor的能源效率最高可提高$ 24 \ times $。

场景文本识别（str）是图像和文本之间的重要桥梁，吸引了丰富的研究关注。虽然卷积神经网络（CNNS）在此任务中取得了显着的进展，但大多数现有工作都需要额外的模块（上下文建模模块）来帮助CNN捕获全局依赖项来解决归纳偏差并加强文本特征之间的关系。最近，该变压器已被提出作为通过自我关注机制的全球背景建模的有希望的网络，但在应用于识别时主要缺点是效率。我们提出了一个1-D拆分来解决复杂性的挑战，并用变压器编码器替换CNN，以减少对上下文建模模块的需求。此外，最近的方法使用冻结的初始嵌入来指导解码器对文本进行解码，导致精度损失。我们建议使用从变压器编码器中学到的学习学习的可读初始嵌入，使其自适应不同的输入图像。最重要的是，我们介绍了一个新颖的文本识别架构，名为基于变压器的文本识别器，其中包含三个阶段（转换，特征提取和预测）组成的初始嵌入指导（TRIG）。广泛的实验表明，我们的方法可以在文本识别基准上实现最先进的。