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.

We introduce anchored radial observations (ARO), a novel shape encoding for learning neural field representation of shapes that is category-agnostic and generalizable amid significant shape variations. The main idea behind our work is to reason about shapes through partial observations from a set of viewpoints, called anchors. We develop a general and unified shape representation by employing a fixed set of anchors, via Fibonacci sampling, and designing a coordinate-based deep neural network to predict the occupancy value of a query point in space. Differently from prior neural implicit models, that use global shape feature, our shape encoder operates on contextual, query-specific features. To predict point occupancy, locally observed shape information from the perspective of the anchors surrounding the input query point are encoded and aggregated through an attention module, before implicit decoding is performed. We demonstrate the quality and generality of our network, coined ARO-Net, on surface reconstruction from sparse point clouds, with tests on novel and unseen object categories, "one-shape" training, and comparisons to state-of-the-art neural and classical methods for reconstruction and tessellation.

In time series forecasting, decomposition-based algorithms break aggregate data into meaningful components and are therefore appreciated for their particular advantages in interpretability. Recent algorithms often combine machine learning (hereafter ML) methodology with decomposition to improve prediction accuracy. However, incorporating ML is generally considered to sacrifice interpretability inevitably. In addition, existing hybrid algorithms usually rely on theoretical models with statistical assumptions and focus only on the accuracy of aggregate predictions, and thus suffer from accuracy problems, especially in component estimates. In response to the above issues, this research explores the possibility of improving accuracy without losing interpretability in time series forecasting. We first quantitatively define interpretability for data-driven forecasts and systematically review the existing forecasting algorithms from the perspective of interpretability. Accordingly, we propose the W-R algorithm, a hybrid algorithm that combines decomposition and ML from a novel perspective. Specifically, the W-R algorithm replaces the standard additive combination function with a weighted variant and uses ML to modify the estimates of all components simultaneously. We mathematically analyze the theoretical basis of the algorithm and validate its performance through extensive numerical experiments. In general, the W-R algorithm outperforms all decomposition-based and ML benchmarks. Based on P50_QL, the algorithm relatively improves by 8.76% in accuracy on the practical sales forecasts of JD.com and 77.99% on a public dataset of electricity loads. This research offers an innovative perspective to combine the statistical and ML algorithms, and JD.com has implemented the W-R algorithm to make accurate sales predictions and guide its marketing activities.

Offline reinforcement learning (RL) enables the agent to effectively learn from logged data, which significantly extends the applicability of RL algorithms in real-world scenarios where exploration can be expensive or unsafe. Previous works have shown that extracting primitive skills from the recurring and temporally extended structures in the logged data yields better learning. However, these methods suffer greatly when the primitives have limited representation ability to recover the original policy space, especially in offline settings. In this paper, we give a quantitative characterization of the performance of offline hierarchical learning and highlight the importance of learning lossless primitives. To this end, we propose to use a \emph{flow}-based structure as the representation for low-level policies. This allows us to represent the behaviors in the dataset faithfully while keeping the expression ability to recover the whole policy space. We show that such lossless primitives can drastically improve the performance of hierarchical policies. The experimental results and extensive ablation studies on the standard D4RL benchmark show that our method has a good representation ability for policies and achieves superior performance in most tasks.

Conceptual knowledge is fundamental to human cognition and knowledge bases. However, existing knowledge probing works only focus on evaluating factual knowledge of pre-trained language models (PLMs) and ignore conceptual knowledge. Since conceptual knowledge often appears as implicit commonsense behind texts, designing probes for conceptual knowledge is hard. Inspired by knowledge representation schemata, we comprehensively evaluate conceptual knowledge of PLMs by designing three tasks to probe whether PLMs organize entities by conceptual similarities, learn conceptual properties, and conceptualize entities in contexts, respectively. For the tasks, we collect and annotate 24k data instances covering 393 concepts, which is COPEN, a COnceptual knowledge Probing bENchmark. Extensive experiments on different sizes and types of PLMs show that existing PLMs systematically lack conceptual knowledge and suffer from various spurious correlations. We believe this is a critical bottleneck for realizing human-like cognition in PLMs. COPEN and our codes are publicly released at https://github.com/THU-KEG/COPEN.

由于遮挡引起的严重观察，基于手动对象相互作用的单个基于手动对象相互作用的重建具有挑战性。本文提出了一种基于物理的方法，以更好地解决重建中的歧义。它首先提出了一个基于力的动力学模型，该模型不仅恢复了未观察到的触点，而且还解决了合理的接触力。接下来，提出了一种基于置信的幻灯片预防方案，该方案将运动学上的信心和接触力都结合在一起，共同模拟静态和滑动接触运动。定性和定量实验表明，该提出的技术在物理上可行，更准确的手动相互作用，并使用单个RGBD传感器实时估计可见的接触力。

我们介绍了第一个基于学习的可重建性预测指标，以改善使用无人机的大规模3D城市场景获取的视图和路径计划。与以前的启发式方法相反，我们的方法学习了一个模型，该模型明确预测了从一组观点重建3D城市场景的能力。为了使这种模型可训练并同时适用于无人机路径计划，我们在培训期间模拟了基于代理的3D场景重建以设置预测。具体而言，我们设计的神经网络经过训练，可以预测场景的重构性，这是代理几何学的函数，一组观点，以及在飞行中获得的一系列场景图像。为了重建一个新的城市场景，我们首先构建了3D场景代理，然后依靠我们网络的预测重建质量和不确定性度量，基于代理几何形状，以指导无人机路径计划。我们证明，与先前的启发式措施相比，我们的数据驱动的可重建性预测与真实的重建质量更加紧密相关。此外，我们学到的预测变量可以轻松地集成到现有的路径计划中，以产生改进。最后，我们根据学习的可重建性设计了一个新的迭代视图计划框架，并在重建合成场景和真实场景时展示新计划者的卓越性能。

事件提取（EE）是信息提取的重要任务，该任务旨在从非结构化文本中提取结构化事件信息。大多数先前的工作都专注于提取平坦的事件，同时忽略重叠或嵌套的事件。多个重叠和嵌套EE的模型包括几个连续的阶段来提取事件触发器和参数，这些阶段患有错误传播。因此，我们设计了一种简单而有效的标记方案和模型，以将EE作为单词关系识别，称为oneee。触发器或参数单词之间的关系在一个阶段同时识别出并行网格标记，从而产生非常快的事件提取速度。该模型配备了自适应事件融合模块，以生成事件感知表示表示和距离感知的预测指标，以整合单词关系识别的相对距离信息，从经验上证明这是有效的机制。对3个重叠和嵌套的EE基准测试的实验，即少数FC，GENIA11和GENIA13，表明Oneee实现了最新的（SOTA）结果。此外，ONEEE的推理速度比相同条件下的基线的推理速度快，并且由于它支持平行推断，因此可以进一步改善。

最近，深度学习方法已经在许多医学图像分割任务中实现了最先进的表现。其中许多是基于卷积神经网络（CNN）。对于这种方法，编码器是从输入图像中提取全局和局部信息的关键部分。然后将提取的特征传递给解码器以预测分割。相比之下，最近的几部作品显示了使用变压器的卓越性能，可以更好地对远程空间依赖性进行建模并捕获低级细节。但是，对于某些任务无法有效替换基于卷积的编码器的某些任务，变形金刚作为唯一的编码器表现不佳。在本文中，我们提出了一个带有双重编码器的模型，用于3D生物医学图像分割。我们的模型是带有独立变压器编码器的U形CNN。我们融合了卷积编码器和变压器的信息，并将其传递给解码器以获得结果。我们从三个不同的挑战中评估了三个公共数据集上的方法：BTCV，MODA和DECHANLON。与在每个任务上有和没有变压器的最先进模型相比，我们提出的方法在整个方面都获得了更高的骰子分数。

由于字体之类的文本属性是文档格式和页面样式的核心设计元素，因此自动属性识别有利于全面的实用应用。现有方法在区分不同属性方面已经产生令人满意的性能，但是它们仍然在区分类似属性的情况下只有微妙的差异。此外，在现实世界中出现意外和明显的成像扭曲的现实情况下，他们的性能严重下降。在本文中，我们旨在通过提出炸玉米饼来解决这些问题，炸玉米饼是针对最常见文档场景量身定制的文本属性识别的对比框架。具体而言，炸玉米饼利用对比学习来消除由模糊和开放式属性引起的歧义陷阱。为了实现这一目标，我们从三个角度设计了学习范式：1）生成属性视图，2）提取微妙但至关重要的细节，以及3）利用有价值的视图对学习，以充分解锁预训练潜力。广泛的实验表明，Taco超过了受监督的对应物，并在多个属性识别任务上取得了最新的进步。将提供炸玉米饼的在线服务。