With the success of neural volume rendering in novel view synthesis, neural implicit reconstruction with volume rendering has become popular. However, most methods optimize per-scene functions and are unable to generalize to novel scenes. We introduce VolRecon, a generalizable implicit reconstruction method with Signed Ray Distance Function (SRDF). To reconstruct with fine details and little noise, we combine projection features, aggregated from multi-view features with a view transformer, and volume features interpolated from a coarse global feature volume. A ray transformer computes SRDF values of all the samples along a ray to estimate the surface location, which are used for volume rendering of color and depth. Extensive experiments on DTU and ETH3D demonstrate the effectiveness and generalization ability of our method. On DTU, our method outperforms SparseNeuS by about 30% in sparse view reconstruction and achieves comparable quality as MVSNet in full view reconstruction. Besides, our method shows good generalization ability on the large-scale ETH3D benchmark. Project page: https://fangjinhuawang.github.io/VolRecon.

Unit commitment (UC) are essential tools to transmission system operators for finding the most economical and feasible generation schedules and dispatch signals. Constraint screening has been receiving attention as it holds the promise for reducing a number of inactive or redundant constraints in the UC problem, so that the solution process of large scale UC problem can be accelerated by considering the reduced optimization problem. Standard constraint screening approach relies on optimizing over load and generations to find binding line flow constraints, yet the screening is conservative with a large percentage of constraints still reserved for the UC problem. In this paper, we propose a novel machine learning (ML) model to predict the most economical costs given load inputs. Such ML model bridges the cost perspectives of UC decisions to the optimization-based constraint screening model, and can screen out higher proportion of operational constraints. We verify the proposed method's performance on both sample-aware and sample-agnostic setting, and illustrate the proposed scheme can further reduce the computation time on a variety of setup for UC problems.

许多现实世界中普遍存在的应用程序，例如停车建议和空气污染监测，都能从准确的长期时空预测（LSTF）中受益匪浅。 LSTF利用了空间和时间域，上下文信息和数据中固有模式之间的长期依赖性。最近的研究揭示了多画望神经网络（MGNN）提高预测性能的潜力。但是，由于几个问题，现有的MGNN方法不能直接应用于LSTF：一般性低，不充分使用上下文信息以及不平衡的图形融合方法。为了解决这些问题，我们构建了新的图形模型，以表示每个节点的上下文信息和长期时空数据依赖性结构。为了融合跨多个图形的信息，我们提出了一个新的动态多绘图融合模块，以通过空间注意力和图形注意机制来表征图中节点和跨图的节点的相关性。此外，我们引入了可训练的重量张量，以指示不同图中每个节点的重要性。在两个大规模数据集上进行的广泛实验表明，我们提出的方法显着改善了LSTF预测任务中现有图形神经网络模型的性能。

有限的GPU记忆资源阻碍了深度神经网络的进一步发展。因此，高度要求GPU内存资源的优化。通常应用交换和重新计算，以更好地利用GPU记忆。但是，作为一个新兴领域，仍然存在一些挑战：1）静态和动态方法的重新计算效率受到限制。 2）交换需要手动卸载参数，这会产生巨大的时间成本。 3）没有这种动态和细粒的方法，涉及张量与当今的张量重新组件一起交换。为了纠正上述问题，我们提出了一个名为Delta（动态张量卸载和重新组件）的新型调度程序经理。据我们所知，我们是第一个在没有用户监督的情况下进行张量交换和张量重新组合的合理的动态运行时间调度程序。在Delta中，我们提出了一种过滤器算法，以选择要从GPU内存中释放出来的最佳张量，并提出导演算法，以选择每个张量的适当动作。此外，故意考虑预取和重叠以克服交换和重新计算张量引起的时间成本。实验结果表明，DELTA不仅节省了40％-70％的GPU记忆，从而超过了最新方法，而且还获得了可比的收敛结果，并获得了可接受的时间延迟。此外，与基准相比，当训练Resnet-101训练Resnet-101时，Delta在训练Resnet-50和2.25 $ \ times $时获得2.04 $ \ times $最大批量。此外，我们实验中的交换成本和重新计算成本之间的比较表明，在张量交换和张量重新计算上制定合理的动态调度程序的重要性，这在某些相关工作中反驳了交换应该是第一个也是最好的选择。

培训文本到图像生成模型中的主要挑战之一是需要大量的高质量图像文本对。虽然图像样本通常很容易接近，但相关的文本描述通常需要仔细的人类标题，这是特别的 - 耗时和成本耗费。在本文中，我们提出了第一项工作来培训没有任何文本数据的文本到图像生成模型。我们的方法利用了强大的预训练剪辑模型的良好对齐的多模态语义空间：通过从图像特征生成文本特征，无缝地减轻了文本调节的要求。进行广泛的实验以说明所提出的方法的有效性。我们在标准的文本到图像生成任务中获得最先进的结果。重要的是，拟议的无语模型优于具有完整图像文本对训练的大多数现有型号。此外，我们的方法可以应用于微调预先训练的模型，它可以节省培训文本到图像生成模型的培训时间和成本。我们预先接受的模型在MS-Coco DataSet上获得竞争激烈的结果，在零拍摄的图像集中在MS-Coco DataSet上产生竞争结果，但距离最近提出的大型Dall-E型号的模型大小和培训数据大小约为1％。

While deep learning has recently achieved great success on multi-view stereo (MVS), limited training data makes the trained model hard to be generalized to unseen scenarios. Compared with other computer vision tasks, it is rather difficult to collect a large-scale MVS dataset as it requires expensive active scanners and labor-intensive process to obtain ground truth 3D structures. In this paper, we introduce BlendedMVS, a novel large-scale dataset, to provide sufficient training ground truth for learning-based MVS. To create the dataset, we apply a 3D reconstruction pipeline to recover high-quality textured meshes from images of well-selected scenes. Then, we render these mesh models to color images and depth maps. To introduce the ambient lighting information during training, the rendered color images are further blended with the input images to generate the training input. Our dataset contains over 17k high-resolution images covering a variety of scenes, including cities, architectures, sculptures and small objects. Extensive experiments demonstrate that BlendedMVS endows the trained model with significantly better generalization ability compared with other MVS datasets. The dataset and pretrained models are available at https: //github.com/YoYo000/BlendedMVS.

Existing measures and representations for trajectories have two longstanding fundamental shortcomings, i.e., they are computationally expensive and they can not guarantee the `uniqueness' property of a distance function: dist(X,Y) = 0 if and only if X=Y, where $X$ and $Y$ are two trajectories. This paper proposes a simple yet powerful way to represent trajectories and measure the similarity between two trajectories using a distributional kernel to address these shortcomings. It is a principled approach based on kernel mean embedding which has a strong theoretical underpinning. It has three distinctive features in comparison with existing approaches. (1) A distributional kernel is used for the very first time for trajectory representation and similarity measurement. (2) It does not rely on point-to-point distances which are used in most existing distances for trajectories. (3) It requires no learning, unlike existing learning and deep learning approaches. We show the generality of this new approach in three applications: (a) trajectory anomaly detection, (b) anomalous sub-trajectory detection, and (c) trajectory pattern mining. We identify that the distributional kernel has (i) a unique data-dependent property and the above uniqueness property which are the key factors that lead to its superior task-specific performance; and (ii) runtime orders of magnitude faster than existing distance measures.

3D pose estimation is a challenging problem in computer vision. Most of the existing neural-network-based approaches address color or depth images through convolution networks (CNNs). In this paper, we study the task of 3D human pose estimation from depth images. Different from the existing CNN-based human pose estimation method, we propose a deep human pose network for 3D pose estimation by taking the point cloud data as input data to model the surface of complex human structures. We first cast the 3D human pose estimation from 2D depth images to 3D point clouds and directly predict the 3D joint position. Our experiments on two public datasets show that our approach achieves higher accuracy than previous state-of-art methods. The reported results on both ITOP and EVAL datasets demonstrate the effectiveness of our method on the targeted tasks.

Harvesting question-answer (QA) pairs from customer service chatlog in the wild is an efficient way to enrich the knowledge base for customer service chatbots in the cold start or continuous integration scenarios. Prior work attempts to obtain 1-to-1 QA pairs from growing customer service chatlog, which fails to integrate the incomplete utterances from the dialog context for composite QA retrieval. In this paper, we propose N-to-N QA extraction task in which the derived questions and corresponding answers might be separated across different utterances. We introduce a suite of generative/discriminative tagging based methods with end-to-end and two-stage variants that perform well on 5 customer service datasets and for the first time setup a benchmark for N-to-N DialogQAE with utterance and session level evaluation metrics. With a deep dive into extracted QA pairs, we find that the relations between and inside the QA pairs can be indicators to analyze the dialogue structure, e.g. information seeking, clarification, barge-in and elaboration. We also show that the proposed models can adapt to different domains and languages, and reduce the labor cost of knowledge accumulation in the real-world product dialogue platform.

We present DualNER, a simple and effective framework to make full use of both annotated source language corpus and unlabeled target language text for zero-shot cross-lingual named entity recognition (NER). In particular, we combine two complementary learning paradigms of NER, i.e., sequence labeling and span prediction, into a unified multi-task framework. After obtaining a sufficient NER model trained on the source data, we further train it on the target data in a {\it dual-teaching} manner, in which the pseudo-labels for one task are constructed from the prediction of the other task. Moreover, based on the span prediction, an entity-aware regularization is proposed to enhance the intrinsic cross-lingual alignment between the same entities in different languages. Experiments and analysis demonstrate the effectiveness of our DualNER. Code is available at https://github.com/lemon0830/dualNER.