电子设计自动化（EDA）社区一直在积极探索非常大规模的计算机辅助设计（VLSI CAD）的机器学习。许多研究探索了基于学习的技术，用于设计流中的跨阶段预测任务，以实现更快的设计收敛。尽管建筑机器学习（ML）模型通常需要大量数据，但由于缺乏大型公共数据集，大多数研究只能生成小型内部数据集进行验证。在本文中，我们介绍了第一个用于机器学习任务的开源数据集，称为CircuitNet。该数据集由基于6种开源RISC-V设计的商业设计工具的多功能运行中提取的10K以上样品组成。

医学对话生成是一项重要但具有挑战性的任务。以前的大多数作品都依赖于注意力机制和大规模预处理的语言模型。但是，这些方法通常无法从长时间的对话历史中获取关键信息，从而产生准确和信息丰富的响应，因为医疗实体通常散布在多种话语中以及它们之间的复杂关系。为了减轻此问题，我们提出了一个具有关键信息召回（Medpir）的医疗响应生成模型，该模型建立在两个组件上，即知识吸引的对话图形编码器和召回增强的生成器。知识吸引的对话图编码器通过利用话语中的实体之间的知识关系，并使用图形注意力网络对话图来构建对话图。然后，召回增强的发电机通过在产生实际响应之前生成对话的摘要来增强这些关键信息的使用。两个大型医学对话数据集的实验结果表明，Medpir在BLEU分数和医疗实体F1度量中的表现优于强大的基准。

预先接受的语言模型实现了最先进的导致各种自然语言处理（NLP）任务。 GPT-3表明，缩放预先训练的语言模型可以进一步利用它们的巨大潜力。最近提出了一个名为Ernie 3.0的统一框架，以预先培训大型知识增强型号，并培训了具有10亿参数的模型。 Ernie 3.0在各种NLP任务上表现出最先进的模型。为了探讨缩放的表现，我们培养了百卢比的3.0泰坦参数型号，在PaddlePaddle平台上有高达260亿参数的泰坦。此外，我们设计了一种自我监督的对抗性损失和可控语言建模损失，以使ERNIE 3.0 TITAN产生可信和可控的文本。为了减少计算开销和碳排放，我们向Ernie 3.0泰坦提出了一个在线蒸馏框架，教师模型将同时教授学生和培训。埃塞尼3.0泰坦是迄今为止最大的中国密集预训练模型。经验结果表明，Ernie 3.0泰坦在68个NLP数据集中优于最先进的模型。

Dose verification based on proton-induced positron emitters is a promising quality assurance tool and may leverage the strength of artificial intelligence. To move a step closer towards practical application, the sensitivity analysis of two factors needs to be performed: biological washout and depth selection. selection. A bi-directional recurrent neural network (RNN) model was developed. The training dataset was generated based upon a CT image-based phantom (abdomen region) and multiple beam energies/pathways, using Monte-Carlo simulation (1 mm spatial resolution, no biological washout). For the modeling of biological washout, a simplified analytical model was applied to change raw activity profiles over a period of 5 minutes, incorporating both physical decay and biological washout. For the study of depth selection (a challenge linked to multi field/angle irradiation), truncations were applied at different window lengths (100, 125, 150 mm) to raw activity profiles. Finally, the performance of a worst-case scenario was examined by combining both factors (depth selection: 125 mm, biological washout: 5 mins). The accuracy was quantitatively evaluated in terms of range uncertainty, mean absolute error (MAE) and mean relative errors (MRE). Our proposed AI framework shows good immunity to the perturbation associated with two factors. The detection of proton-induced positron emitters, combined with machine learning, has great potential to implement online patient-specific verification in proton therapy.

Objects in a scene are not always related. The execution efficiency of the one-stage scene graph generation approaches are quite high, which infer the effective relation between entity pairs using sparse proposal sets and a few queries. However, they only focus on the relation between subject and object in triplet set subject entity, predicate entity, object entity, ignoring the relation between subject and predicate or predicate and object, and the model lacks self-reasoning ability. In addition, linguistic modality has been neglected in the one-stage method. It is necessary to mine linguistic modality knowledge to improve model reasoning ability. To address the above-mentioned shortcomings, a Self-reasoning Transformer with Visual-linguistic Knowledge (SrTR) is proposed to add flexible self-reasoning ability to the model. An encoder-decoder architecture is adopted in SrTR, and a self-reasoning decoder is developed to complete three inferences of the triplet set, s+o-p, s+p-o and p+o-s. Inspired by the large-scale pre-training image-text foundation models, visual-linguistic prior knowledge is introduced and a visual-linguistic alignment strategy is designed to project visual representations into semantic spaces with prior knowledge to aid relational reasoning. Experiments on the Visual Genome dataset demonstrate the superiority and fast inference ability of the proposed method.

Change detection (CD) is to decouple object changes (i.e., object missing or appearing) from background changes (i.e., environment variations) like light and season variations in two images captured in the same scene over a long time span, presenting critical applications in disaster management, urban development, etc. In particular, the endless patterns of background changes require detectors to have a high generalization against unseen environment variations, making this task significantly challenging. Recent deep learning-based methods develop novel network architectures or optimization strategies with paired-training examples, which do not handle the generalization issue explicitly and require huge manual pixel-level annotation efforts. In this work, for the first attempt in the CD community, we study the generalization issue of CD from the perspective of data augmentation and develop a novel weakly supervised training algorithm that only needs image-level labels. Different from general augmentation techniques for classification, we propose the background-mixed augmentation that is specifically designed for change detection by augmenting examples under the guidance of a set of background-changing images and letting deep CD models see diverse environment variations. Moreover, we propose the augmented & real data consistency loss that encourages the generalization increase significantly. Our method as a general framework can enhance a wide range of existing deep learning-based detectors. We conduct extensive experiments in two public datasets and enhance four state-of-the-art methods, demonstrating the advantages of our method. We release the code at https://github.com/tsingqguo/bgmix.

Large-scale pre-trained language models (PLMs) bring new opportunities to challenge problems, especially those that need high-level intelligence, such as the math word problem (MWPs). However, directly applying existing PLMs to MWPs can fail as the generation process lacks sufficient supervision and thus lacks fast adaptivity as humans. We notice that human reasoning has a dual reasoning framework that consists of an immediate reaction system (system 1) and a delicate reasoning system (system 2), where the entire reasoning is determined by their interaction. This inspires us to develop a cooperative reasoning-induced PLM for solving MWPs, called Cooperative Reasoning (CoRe), resulting in a human-like reasoning architecture with system 1 as the generator and system 2 as the verifier. In our approach, the generator is responsible for generating reasoning paths, and the verifiers are used to supervise the evaluation in order to obtain reliable feedback for the generator. We evaluate our CoRe framework on several mathematical reasoning datasets and achieve decent improvement over state-of-the-art methods, up to 9.8% increase over best baselines.

道路网络的图结构对于自动驾驶系统的下游任务，例如全球计划，运动预测和控制至关重要。过去，公路网络图通常由人类专家手动注释，这是耗时且劳动力密集的。为了获得更好的有效性和效率的道路网络图，需要进行自动的路网图检测方法。先前的作品要么是后处理的语义分割图，要么提出基于图的算法以直接预测道路网络图。但是，以前的作品遭受了硬编码的启发式处理算法和劣质最终性能。为了增强先前的SOTA（最新方法）方法RNGDET，我们添加了一个实例分割头，以更好地监督模型培训，并使模型能够利用骨干网络的多尺度功能。由于新提出的方法从RNGDET改进，因此命名为RNGDET ++。所有方法均在大型公开数据集上进行评估。 RNGDET ++在几乎所有度量分数上都优于基线模型。它将拓扑正确性APL（平均路径长度相似性）提高了3 \％。演示视频和补充材料可在我们的项目页面\ url {https://tonyxuqaq.github.io/projects/rngdetplusplus/}中获得。

随着自动驾驶汽车的快速发展，目击者对高清地图（HD地图）的需求蓬勃发展，这些地图（HD地图）在自主驾驶场景中提供了可靠且强大的静态环境信息。作为高清图中的主要高级元素之一，道路车道中心线对于下游任务（例如预测和计划）至关重要。人类注释器手动注释车道中心线高清图是劳动密集型，昂贵且效率低下的，严重限制了自动驾驶系统的广泛应用和快速部署。以前的工作很少探索中心线高清图映射问题，这是由于拓扑复杂和道路中心线的严重重叠问题。在本文中，我们提出了一种名为CenterLinedet的新方法，以自动创建Lane Centrine HD地图。通过模仿学习对CenterLinedet进行训练，并可以通过使用车辆安装的传感器进行迭代有效地检测到车道中心线的图。由于应用了类似DITR的变压器网络，CenterLinedet可以处理复杂的图形拓扑，例如车道相交。在大型公开数据集Nuscenes上评估了所提出的方法，并通过比较结果很好地证明了CenterLinedet的优势。本文附有一个演示视频和一个补充文档，可在\ url {https://tonyxuqaq.github.io/projects/centerlinedet/}中获得。

物流运营商最近提出了一项技术，可以帮助降低城市货运分销中的交通拥堵和运营成本，最近提出了移动包裹储物柜（MPLS）。鉴于他们能够在整个部署领域搬迁，因此他们具有提高客户可访问性和便利性的潜力。在这项研究中，我们制定了移动包裹储物柜问题（MPLP），这是位置路由问题（LRP）的特殊情况，该案例确定了整天MPL的最佳中途停留位置以及计划相应的交付路线。开发了基于混合Q学习网络的方法（HQM），以解决所得大问题实例的计算复杂性，同时逃脱了本地Optima。此外，HQM与全球和局部搜索机制集成在一起，以解决经典强化学习（RL）方法所面临的探索和剥削困境。我们检查了HQM在不同问题大小（最多200个节点）下的性能，并根据遗传算法（GA）进行了基准测试。我们的结果表明，HQM获得的平均奖励比GA高1.96倍，这表明HQM具有更好的优化能力。最后，我们确定有助于车队规模要求，旅行距离和服务延迟的关键因素。我们的发现概述了MPL的效率主要取决于时间窗口的长度和MPL中断的部署。