Contrastive Trajectory Learning for Tour Recommendation

The main objective of Personalized Tour Recommendation (PTR) is to generate a sequence of point-of-interest (POIs) for a particular tourist, according to the user-specific constraints such as duration time, start and end points, the number of attractions planned to visit, and so on. Previous PTR solutions are based on either heuristics for solving the orienteering problem to maximize a global reward with a specified budget or approaches attempting to learn user visiting preferences and transition patterns with the stochastic process or recurrent neural networks. However, existing learning methodologies rely on historical trips to train the model and use the next visited POI as the supervised signal, which may not fully capture the coherence of preferences and thus recommend similar trips to different users, primarily due to the data sparsity problem and long-tailed distribution of POI popularity. This work presents a novel tour recommendation model by distilling knowledge and supervision signals from the trips in a self-supervised manner. We propose Contrastive Trajectory Learning for Tour Recommendation (CTLTR), which utilizes the intrinsic POI dependencies and traveling intent to discover extra knowledge and augments the sparse data via pre-training auxiliary self-supervised objectives. CTLTR provides a principled way to characterize the inherent data correlations while tackling the implicit feedback and weak supervision problems by learning robust representations applicable for tour planning. We introduce a hierarchical recurrent encoder-decoder to identify tourists’ intentions and use the contrastive loss to discover subsequence semantics and their sequential patterns through maximizing the mutual information. Additionally, we observe that a data augmentation step as the preliminary of contrastive learning can solve the overfitting issue resulting from data sparsity. We conduct extensive experiments on a range of real-world datasets and demonstrate that our model can significantly improve the recommendation performance over the state-of-the-art baselines in terms of both recommendation accuracy and visiting orders.

EpiScanpy: integrated single-cell epigenomic analysis

EpiScanpy is a toolkit for the analysis of single-cell epigenomic data, namely single-cell DNA methylation and single-cell ATAC-seq data. To address the modality specific challenges from epigenomics data, epiScanpy quantifies the epigenome using multiple feature space constructions and builds a nearest neighbour graph using epigenomic distance between cells. EpiScanpy makes the many existing scRNA-seq workflows from scanpy available to large-scale single-cell data from other -omics modalities, including methods for common clustering, dimension reduction, cell type identification and trajectory learning techniques, as well as an atlas integration tool for scATAC-seq datasets. The toolkit also features numerous useful downstream functions, such as differential methylation and differential openness calling, mapping epigenomic features of interest to their nearest gene, or constructing gene activity matrices using chromatin openness. We successfully benchmark epiScanpy against other scATAC-seq analysis tools and show its outperformance at discriminating cell types.

Autonomous Robots for Space: Trajectory Learning and Adaptation Using Imitation

This paper adds on to the on-going efforts to provide more autonomy to space robots and introduces the concept of programming by demonstration or imitation learning for trajectory planning of manipulators on free-floating spacecraft. A redundant 7-DoF robotic arm is mounted on small spacecraft dedicated for debris removal, on-orbit servicing and assembly, autonomous and rendezvous docking. The motion of robot (or manipulator) arm induces reaction forces on the spacecraft and hence its attitude changes prompting the Attitude Determination and Control System (ADCS) to take large corrective action. The method introduced here is capable of finding the trajectory that minimizes the attitudinal changes thereby reducing the load on ADCS. One of the critical elements in spacecraft trajectory planning and control is the power consumption. The approach introduced in this work carry out trajectory learning offline by collecting data from demonstrations and encoding it as a probabilistic distribution of trajectories. The learned trajectory distribution can be used for planning in previously unseen situations by conditioning the probabilistic distribution. Hence almost no power is required for computations after deployment. Sampling from a conditioned distribution provides several possible trajectories from the same start to goal state. To determine the trajectory that minimizes attitudinal changes, a cost term is defined and the trajectory which minimizes this cost is considered the optimal one.

Iterative learning-based path control for robot-assisted upper-limb rehabilitation

In robot-assisted rehabilitation, the performance of robotic assistance is dependent on the human user’s dynamics, which are subject to uncertainties. In order to enhance the rehabilitation performance and in particular to provide a constant level of assistance, we separate the task space into two subspaces where a combined scheme of adaptive impedance control and trajectory learning is developed. Human movement speed can vary from person to person and it cannot be predefined for the robot. Therefore, in the direction of human movement, an iterative trajectory learning approach is developed to update the robot reference according to human movement and to achieve the desired interaction force between the robot and the human user. In the direction normal to the task trajectory, human’s unintentional force may deteriorate the trajectory tracking performance. Therefore, an impedance adaptation method is utilized to compensate for unknown human force and prevent the human user drifting away from the updated robot reference trajectory. The proposed scheme was tested in experiments that emulated three upper-limb rehabilitation modes: zero interaction force, assistive and resistive. Experimental results showed that the desired assistance level could be achieved, despite uncertain human dynamics.

A Survey on Trajectory Data Management, Analytics, and Learning

Recent advances in sensor and mobile devices have enabled an unprecedented increase in the availability and collection of urban trajectory data, thus increasing the demand for more efficient ways to manage and analyze the data being produced. In this survey, we comprehensively review recent research trends in trajectory data management, ranging from trajectory pre-processing, storage, common trajectory analytic tools, such as querying spatial-only and spatial-textual trajectory data, and trajectory clustering. We also explore four closely related analytical tasks commonly used with trajectory data in interactive or real-time processing. Deep trajectory learning is also reviewed for the first time. Finally, we outline the essential qualities that a trajectory data management system should possess to maximize flexibility.