Transportation mode classification, the process of predicting the class labels of moving objects transportation modes, has been widely applied to a variety of real world applications, such as traffic management, urban computing, and behavior study. However, existing studies of transportation mode classification typically extract the explicit features of trajectory data but fail to capture the implicit features that affect the classification performance. In addition, most of the existing studies also prefer to apply RNN-based models to embed trajectories, which is only suitable for classifying small-scale data. To tackle the above challenges, we propose an effective and scalable framework for transportation mode classification over GPS trajectories, abbreviated Estimator. Estimator is established on a developed CNN-TCN architecture, which is capable of leveraging the spatial and temporal hidden features of trajectories to achieve high effectiveness and efficiency. Estimator partitions the entire traffic space into disjointed spatial regions according to traffic conditions, which enhances the scalability significantly and thus enables parallel transportation classification. Extensive experiments using eight public real-life datasets offer evidence that Estimator i) achieves superior model effectiveness (i.e., 99% Accuracy and 0.98 F1-score), which outperforms state-of-the-arts substantially; ii) exhibits prominent model efficiency, and obtains 7-40x speedups up over state-of-the-arts learning-based methods; and iii) shows high model scalability and robustness that enables large-scale classification analytics.

人和车辆轨迹体现了运输基础设施的重要信息，轨迹相似性计算是许多涉及轨迹数据分析的现实世界应用中的功能。最近，基于深度学习的轨迹相似性技术使得能够提高传统相似性技术提高效率和适应性。然而，现有的轨迹相似度学习提案强调了时间相似性的空间相似性，使得它们次开用于时光分析。为此，我们提出了ST2VEC，这是一种基于轨迹表示的学习架构，其考虑了道路网络中的时空相似度学习的对轨迹对之间的细粒度的空间和时间相关性。据我们所知，这是第一个用于时空轨迹相似性分析的深学习建议。具体而言，ST2VEC包含三个阶段：（i）培训选择代表性培训样本的数据准备; （ii）设计轨迹的空间和时间建模，其中设计了通用时间建模模块（TMM）的轨迹的空间和时间特征; （iii）时空共关节融合（STCF），其中开发了统一的融合（UF）方法，以帮助产生统一的时空轨迹嵌入，以捕获轨迹之间的时空相似关系。此外，由课程概念启发，ST2VEC采用课程学习进行模型优化，以提高融合和有效性。实验研究提供了证据表明，ST2VEC显着胜过了所有最先进的竞争对手，在有效性，效率和可扩展性方面，同时显示出低参数敏感性和良好的模型稳健性。

随着大数据的快速增长，分布式机器学习（ML）已广泛应用于培训大型模型。随机梯度下降（SGD）可以说是ML的Workhorse算法。 SGD培训的分布式ML型号涉及大量的梯度通信，这限制了分布式ML的可扩展性。因此，压缩梯度以减少通信是重要的。在本文中，我们提出了FastSGD，一种用于分布式ML的快速压缩的SGD框架。为了以低成本实现高压缩比，FastSGD表示梯度作为键值对，并在线性时间复杂度压缩梯度键和值。对于梯度值压缩，FASTSGD首先使用互焦数映射器将原始值转换为互焦值，然后，它利用对数量化来进一步将互焦值减少到小整数。最后，FastSGD通过给定阈值过滤减少梯度整数。对于渐变键压缩，FastSGD提供了一种自适应细粒度的Δ编码方法，用于存储具有更少位的渐变键。实际ML模型和数据集的广泛实验证明，与最先进的方法相比，FastSGD实现了高达4个级别的压缩比，并加速了高达8倍的收敛时间。

随着大数据的爆炸性增加，培训机器学习（ML）模型成为计算密集型工作量，需要几天甚至几周。因此，重用已经训练的模型受到了受关注的，称为转移学习。转移学习避免通过将知识从源任务转移到目标任务来避免从头开始培训新模型。现有的传输学习方法主要专注于如何通过特定源模型提高目标任务的性能，并假设给出了源模型。虽然有许多源模型可用，但数据科学家难以手动选择目标任务的最佳源模型。因此，如何在模型数据库中有效地选择合适的源模型进行模型重用是一个有趣但未解决的问题。在本文中，我们提出了SMS，有效，高效，灵活的源模型选择框架。即使源数据集具有明显不同的数据标签，SMS也是有效的，并且灵活地支持具有任何类型的结构的源模型，并且有效地避免任何培训过程。对于每个源模型，SMS首先将目标数据集中的样本加速到软标签中，通过直接将该模型直接应用于目标数据集，然后使用高斯分布适合软标签的集群，最后测量源模型使用的显着能力高斯混合的公制。此外，我们提出了一种改进的SMS（I-SMS），其降低了源模型的输出数量。 I-SMS可以显着降低选择时间，同时保留SMS的选择性能。关于一系列实用模型重用工作负载的广泛实验证明了SMS的有效性和效率。

In this paper, we study the problem of knowledge-intensive text-to-SQL, in which domain knowledge is necessary to parse expert questions into SQL queries over domain-specific tables. We formalize this scenario by building a new Chinese benchmark KnowSQL consisting of domain-specific questions covering various domains. We then address this problem by presenting formulaic knowledge, rather than by annotating additional data examples. More concretely, we construct a formulaic knowledge bank as a domain knowledge base and propose a framework (ReGrouP) to leverage this formulaic knowledge during parsing. Experiments using ReGrouP demonstrate a significant 28.2% improvement overall on KnowSQL.

Weakly-supervised object localization aims to indicate the category as well as the scope of an object in an image given only the image-level labels. Most of the existing works are based on Class Activation Mapping (CAM) and endeavor to enlarge the discriminative area inside the activation map to perceive the whole object, yet ignore the co-occurrence confounder of the object and context (e.g., fish and water), which makes the model inspection hard to distinguish object boundaries. Besides, the use of CAM also brings a dilemma problem that the classification and localization always suffer from a performance gap and can not reach their highest accuracy simultaneously. In this paper, we propose a casual knowledge distillation method, dubbed KD-CI-CAM, to address these two under-explored issues in one go. More specifically, we tackle the co-occurrence context confounder problem via causal intervention (CI), which explores the causalities among image features, contexts, and categories to eliminate the biased object-context entanglement in the class activation maps. Based on the de-biased object feature, we additionally propose a multi-teacher causal distillation framework to balance the absorption of classification knowledge and localization knowledge during model training. Extensive experiments on several benchmarks demonstrate the effectiveness of KD-CI-CAM in learning clear object boundaries from confounding contexts and addressing the dilemma problem between classification and localization performance.

Dynamic treatment regimes assign personalized treatments to patients sequentially over time based on their baseline information and time-varying covariates. In mobile health applications, these covariates are typically collected at different frequencies over a long time horizon. In this paper, we propose a deep spectral Q-learning algorithm, which integrates principal component analysis (PCA) with deep Q-learning to handle the mixed frequency data. In theory, we prove that the mean return under the estimated optimal policy converges to that under the optimal one and establish its rate of convergence. The usefulness of our proposal is further illustrated via simulations and an application to a diabetes dataset.

Nowadays, time-stamped web documents related to a general news query floods spread throughout the Internet, and timeline summarization targets concisely summarizing the evolution trajectory of events along the timeline. Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order. To tackle this challenge, in this paper, we propose a Unified Timeline Summarizer (UTS) that can generate abstractive and extractive timeline summaries in time order. Concretely, in the encoder part, we propose a graph-based event encoder that relates multiple events according to their content dependency and learns a global representation of each event. In the decoder part, to ensure the chronological order of the abstractive summary, we propose to extract the feature of event-level attention in its generation process with sequential information remained and use it to simulate the evolutionary attention of the ground truth summary. The event-level attention can also be used to assist in extracting summary, where the extracted summary also comes in time sequence. We augment the previous Chinese large-scale timeline summarization dataset and collect a new English timeline dataset. Extensive experiments conducted on these datasets and on the out-of-domain Timeline 17 dataset show that UTS achieves state-of-the-art performance in terms of both automatic and human evaluations.

Hybrid unmanned aerial vehicles (UAVs) integrate the efficient forward flight of fixed-wing and vertical takeoff and landing (VTOL) capabilities of multicopter UAVs. This paper presents the modeling, control and simulation of a new type of hybrid micro-small UAVs, coined as lifting-wing quadcopters. The airframe orientation of the lifting wing needs to tilt a specific angle often within $ 45$ degrees, neither nearly $ 90$ nor approximately $ 0$ degrees. Compared with some convertiplane and tail-sitter UAVs, the lifting-wing quadcopter has a highly reliable structure, robust wind resistance, low cruise speed and reliable transition flight, making it potential to work fully-autonomous outdoor or some confined airspace indoor. In the modeling part, forces and moments generated by both lifting wing and rotors are considered. Based on the established model, a unified controller for the full flight phase is designed. The controller has the capability of uniformly treating the hovering and forward flight, and enables a continuous transition between two modes, depending on the velocity command. What is more, by taking rotor thrust and aerodynamic force under consideration simultaneously, a control allocation based on optimization is utilized to realize cooperative control for energy saving. Finally, comprehensive Hardware-In-the-Loop (HIL) simulations are performed to verify the advantages of the designed aircraft and the proposed controller.

Due to their ability to offer more comprehensive information than data from a single view, multi-view (multi-source, multi-modal, multi-perspective, etc.) data are being used more frequently in remote sensing tasks. However, as the number of views grows, the issue of data quality becomes more apparent, limiting the potential benefits of multi-view data. Although recent deep neural network (DNN) based models can learn the weight of data adaptively, a lack of research on explicitly quantifying the data quality of each view when fusing them renders these models inexplicable, performing unsatisfactorily and inflexible in downstream remote sensing tasks. To fill this gap, in this paper, evidential deep learning is introduced to the task of aerial-ground dual-view remote sensing scene classification to model the credibility of each view. Specifically, the theory of evidence is used to calculate an uncertainty value which describes the decision-making risk of each view. Based on this uncertainty, a novel decision-level fusion strategy is proposed to ensure that the view with lower risk obtains more weight, making the classification more credible. On two well-known, publicly available datasets of aerial-ground dual-view remote sensing images, the proposed approach achieves state-of-the-art results, demonstrating its effectiveness. The code and datasets of this article are available at the following address: https://github.com/gaopiaoliang/Evidential.