Multivariate time series forecasting is essential in domains such as finance, transportation, climate, and energy. However, existing patch-based methods typically adopt fixed-length segmentation, overlooking the heterogeneity of local temporal dynamics and the decoding heterogeneity of forecasting. Such designs lose details in information-dense regions, introduce redundancy in stable segments, and fail to capture the distinct complexities of short-term and long-term horizons. We propose TimeMosaic, a forecasting framework that aims to address temporal heterogeneity. TimeMosaic employs adaptive patch embedding to dynamically adjust granularity according to local information density, balancing motif reuse with structural clarity while preserving temporal continuity. In addition, it introduces segment-wise decoding that treats each prediction horizon as a related subtask and adapts to horizon-specific difficulty and information requirements, rather than applying a single uniform decoder. Extensive evaluations on benchmark datasets demonstrate that TimeMosaic delivers consistent improvements over existing methods, and our model trained on the large-scale corpus with 321 billion observations achieves performance competitive with state-of-the-art TSFMs.

Localization of magnetically actuated capsule endoscope (MCE) is essential for accurate actuation. Despite extensive progress in pose estimation using internal magnetic field sensors and external magnetic sources, it remains challenging to achieve localization when a time-varying internal magnetic field (IMF) exists. This study presents a compound sensing method for the magnetic ultrasound capsule endoscope (MUSCE) based on an internal magnetic field sensor array and an external permanent magnet source, achieving simultaneous 6-degree-of-freedom (DOF) localization for magnetic navigation and real-time ultrasound (US) beam scanning angle detection for distortion-free US imaging reconstruction. Firstly, a MUSCE consisting of an internal magnet, a US transducer, and hall sensors is designed, enabling simultaneous spiral structure-based locomotion and high-quality endoluminal US imaging. Then, a compound sensing strategy is presented, realizing the separation of time-varying IMF and external magnetic field (EMF), allowing synchronous 6-DOF MUSCE localization and US beam scanning angle detection. Finally, the effectiveness of the presented method is validated by tests. The demonstrated static localization error is 4.08 ± 1.91 mm in position norm and 2.46 ± 1.31°in orientation, in a workspace shared with the robotic manipulator. Also, the scanning angle detection can rectify distortion in US image, showing potential clinical applications.

In this work, we study the optimal parameter design and microrobotic navigation control of the parallel-mobile-coil system (PMCS) that consists of three mobile electromagnetic coils. With motion driven by a parallel mechanism, the three coils can move in 3D large space and keep as close as possible to the controlled microrobot for magnetic actuation. Although promising for microrobotic applications, how to design such a type of system for a specific workspace requirement is untackled. Regarding this issue, we propose a computational design method, by which one can calculate the structural parameters of a PMCS starting from a required cylindrical workspace. With the derived performance metrics for motion actuation and magnetic actuation of the PMCS, the system actuation performance (composed of motion and magnetic actuation) is optimized. Utilizing the design method, we optimally construct a prototype PMCS for microrobotic navigation. We then conduct experiments to validate the demanding field/force generation capability of the PMCS and demonstrate the navigation control of different types of magnetic microrobots. In particular, we design closed-loop motion controllers for both torque and force-driven microrobots, using which automated large-workspace and high-accuracy trajectory tracking is realized. Note to Practitioners—This work is motivated by the recent wide interest in magnetic microrobots. Driven by external magnetic fields, magnetic microrobots can navigate in a wireless manner for targeted delivery/therapy. To promote microrobot applications to the human body, a magnetic actuation system with large workspace is desirable. However, due to the fast decay of magnetic field, the commonly used stationary coil-based magnetic actuation systems have the workspace scalability problem. Thus, several mobile-coil-based systems have been designed. In this work, we propose an optimal design method for the PMCS, using which one can design a PMCS starting from a cylindrical workspace with performance being optimized. We construct a PMCS prototype with a workspace of$\Phi 230 \times 100$mm3, and we then study the automated microrobotic navigation control methods for the PMCS. Controllers are designed for different types of magnetic microrobots, and experiments show that, using the controllers, the PMCS can perform automated large-workspace microrobotic navigation control with high accuracy.

Magnetic field is a favorable power source for actuation and control of micro-/nanorobots. To overcome the fast decay of magnetic field for large-workspace microrobotic actuation, mobile field source-based systems have been proposed. In this work, we report a new mobile-coil system, i.e., QuadMag. It consists of four electromagnetic coils, whose motion is actuated by a parallel mechanism. Compared to previous systems with three mobile coils, e.g., DeltaMag, the additional coil in the QuadMag increases the degree-of-freedom (DoF) for magnetic control. However, to control QuadMag, new control methods should be developed for the over-constrained parallel mechanism and for the field/force of the four coils. We derive the Jacobian matrix for the differential motion of the parallel mechanism and then formulate the field, force and simultaneous field and force control methods for magnetic actuation. Comparative experiments validate the enhanced actuation efficiency when controlling torque-driven helical microrobots. Moreover, the magnetic actuation dexterity is also enhanced by the additional coil. We conduct simulated navigation experiments and prove the actuation capability of QuadMag for 3D force-driven microrobot navigation with controlled robot orientation.

Yang Feng, Shuhao Gu, Dengji Guo, Zhengxin Yang, Chenze Shao. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Mechanical rubbing of blood clot using miniature magnetic helical robots is a potential way for thrombolysis. In this paper, we report a new strategy for this issue based on mobile coils. Previously, we proposed the concept of magnetic actuation with parallel mobile coils, in which multiple coils can move in 3D space. Enabled by mobility of the coils, additional degree-of-freedom (DOF) could be utilized for actuation performance optimization. Besides the primary helical propulsion by rotating magnetic fields, our strategy aims to optimize the coil motion to make the magnetic force contributes the most to the helical robot forward motion. For this goal, modeling of the magnetic field and force of multiple mobile coils are presented, based on which an optimization algorithm is formulated to output the best coil motion. For validation, an enhanced mobile coil system having a workspace of Φ500 mm ×150 mm is constructed based on the parallel mobile coil concept. Simulations show the effectiveness of the proposed strategy, whose effective workspace for a specific task can also be obtained. After implementing the proposed strategy, preliminary experiments using clot analog demonstrate that the removal speed is accelerated over 50% compared to that without coil motion optimization.

Large workspace localization of magnetic robots is important for medical applications. This paper presents a novel localization strategy to achieve simultaneous localization and actuation of magnetic robots using hall-effect sensors. We integrate 25 sensors into a sensing probe and mount it on to the mobile-coil system, which realizes accurate sensing and actuation of magnetic devices within a cylindrical workspace of ϕ500 mm×150 mm. Simulation results show the average localization error using the proposed method is 1.7 mm. A verification experiment is conducted to prove the design advantages; Another two experiments are conducted to demonstrate the simultaneous actuation and localization of a torque-driven robot and a force-driven floating robot respectively. For the force-driven floating robot, the average variation between the localization results and the desired trajectory is less than 2 mm.

Neural machine translation models usually adopt the teacher forcing strategy for training which requires the predicted sequence matches ground truth word by word and forces the probability of each prediction to approach a 0-1 distribution. However, the strategy casts all the portion of the distribution to the ground truth word and ignores other words in the target vocabulary even when the ground truth word cannot dominate the distribution. To address the problem of teacher forcing, we propose a method to introduce an evaluation module to guide the distribution of the prediction. The evaluation module accesses each prediction from the perspectives of fluency and faithfulness to encourage the model to generate the word which has a fluent connection with its past and future translation and meanwhile tends to form a translation equivalent in meaning to the source. The experiments on multiple translation tasks show that our method can achieve significant improvements over strong baselines.

Magnetic helical microswimmers can be propelled by rotating magnetic field and are adept at passing through narrow space. To date, various magnetic actuation systems and control methods have been developed to drive these microswimmers. However, steering their spacial movement in a large workspace is still challenging, which could be significant for potential medical applications. In this regard, this paper designs an eye-in-hand stereo-vision module and corresponding refraction-rectified location algorithm. Combined with the motor module and the coil module, the mobile-coil system is capable of generating dynamic magnetic fields in a large 3D workspace. Based on the system, a robust triple-loop stereo visual servoing strategy is proposed that operates simultaneous tracking, locating, and steering, through which the helical swimmer is able to follow a long-distance 3D path. A scaled-up magnetic helical swimmer is employed in the path following experiment. Our prototype system reaches a cylindrical workspace with a diameter more than 200 mm, and the mean error of path tracking is less than 2 mm.

Zhengxin Yang, Jinchao Zhang, Fandong Meng, Shuhao Gu, Yang Feng, Jie Zhou. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.