Soft continuum robots have broad application prospects due to their superior flexibility, compliance and safety. However, the high mechanical complexity addresses a major challenge in precise pose control modeling. Deep Deterministic Policy Gradient (DDPG) algorithm, as a foundational modelfree, off-policy method, is well-suited for such tasks. However, DDPG is known for its inherent instabilities, particularly its susceptibility to Q-value overestimation and challenges in sparse reward environments, which can hinder performance and efficiency. To address these limitations, this paper proposes a Hybrid Reward-Shaped DDPG and PSO fusion algorithm(HR-DP). This method integrates a multi-component reward function with the PSO algorithm, aiming to guide the agent's exploration more effectively and higher exploration accuracy. The target point tracking experiment by the two-segment soft continuum robot in simulation was performed. The results demonstrate that the robot controlled by HR-DP can achieve significantly faster convergence, higher cumulative rewards, higher accuracy and greater stability than that by the standard DDPG baseline and other state-of-theart methods. This work provides a practical and effective HR-DP algorithm enhanced from DDPG, which can benefit pose control of the multi-segment soft continuum robot in complex, real-world environment.

This paper investigates the dynamic characteristics of an H40 CNC machine tool worktable via operational modal analysis (OMA) to provide insights for enhancing the performance of high-speed, high-precision CNC machines. A modal testing scheme was established using 16 measurement points, and the modal parameters were identified from batch measurements using LMS TestLab software. The results were validated using the Modal Assurance Criterion (MAC), confirming the rationality of the analysis. This study provides a valuable reference for the dynamic analysis and optimization of similar machine tool components.

To address the flexible operation requirements of narrow workspace in minimally invasive surgery(MIS), this paper proposes a continuum robot system featuring cooperative traction-torsion actuation via flexible shafts. The flexible sections realize Omni-bendable movements by traction of the flexible shafts, while the rigid section achieves fine positioning through gear transmission driven by the rotary of the flexible shafts. In order to formulate the relation between the distal position of the continuum robot and the traction of the flexible shafts, a dynamics model is established in Adams to generate simulation data. A nonlinear mapping between the end-effector position and multi-flexible-shaft inputs is constructed by training the simulated data in a back-propagation(BP) neural network. O-shaped and Sshaped trajectory-tracking and pose-comparison tests are conducted in Adams-Matlab/Simulink co-simulation environment. The results demonstrates that the end effector of the continuum robot can achieve high positioning accuracy and stable tracking, validating the feasibility and potential application in MIS.

The confine workspace in minimally invasive surgery and the difficulty of installing force sensor at the distal of surgical instrument make it necessary for surgeons to rely on clinical experience to assess the safety of the surgical site. This paper proposes a force-sensor-free method for perceiving gripping force in the opening-and-closing mechanism of a surgical manipulator driven by a flexible shaft. By constructing a dataset between the rotational torque of the flexible shaft and the clamping force of the opening-and-closing mechanism, an end-toend mapping model between flexible shaft torque and clamping force is established by incorporating the dataset into a multi-layer perceptron(MLP) neural network model,. This enables the acquisition of the clamping force exerted by the surgical instrument on the target tissue without relying on pressure sensors at the instrument tip, thereby enhancing the immersion and safety of surgical procedures.

Time series pre-training has recently garnered wide attention for its potential to reduce labeling expenses and benefit various downstream tasks. Prior methods are mainly based on pre-training techniques well-acknowledged in vision or language, such as masked modeling and contrastive learning. However, randomly masking time series or calculating series-wise similarity will distort or neglect inherent temporal correlations crucial in time series data. To emphasize temporal correlation modeling, this paper proposes TimeSiam as a simple but effective self-supervised pre-training framework for Time series based on Siamese networks. Concretely, TimeSiam pre-trains Siamese encoders to capture intrinsic temporal correlations between randomly sampled past and current subseries. With a simple data augmentation method (e.g. masking), TimeSiam can benefit from diverse augmented subseries and learn internal time-dependent representations through a past-to-current reconstruction. Moreover, learnable lineage embeddings are also introduced to distinguish temporal distance between sampled series and further foster the learning of diverse temporal correlations. TimeSiam consistently outperforms extensive advanced pre-training baselines, demonstrating superior forecasting and classification capabilities across 13 standard benchmarks in both intra- and cross-domain scenarios.

Deep models have demonstrated remarkable performance in time series forecasting. However, due to the partially-observed nature of real-world applications, solely focusing on the target of interest, so-called endogenous variables, is usually insufficient to guarantee accurate forecasting. Notably, a system is often recorded into multiple variables, where the exogenous variables can provide valuable external information for endogenous variables. Thus, unlike well-established multivariate or univariate forecasting paradigms that either treat all the variables equally or ignore exogenous information, this paper focuses on a more practical setting: time series forecasting with exogenous variables. We propose a novel approach, TimeXer, to ingest external information to enhance the forecasting of endogenous variables. With deftly designed embedding layers, TimeXer empowers the canonical Transformer with the ability to reconcile endogenous and exogenous information, where patch-wise self-attention and variate-wise cross-attention are used simultaneously. Moreover, global endogenous tokens are learned to effectively bridge the causal information underlying exogenous series into endogenous temporal patches. Experimentally, TimeXer achieves consistent state-of-the-art performance on twelve real-world forecasting benchmarks and exhibits notable generality and scalability. Code is available at this repository: https://github.com/thuml/TimeXer.

Diffusion models have significantly advanced the field of generative modeling. However, training a diffusion model is computationally expensive, creating a pressing need to adapt off-the-shelf diffusion models for downstream generation tasks. Current fine-tuning methods focus on parameter-efficient transfer learning but overlook the fundamental transfer characteristics of diffusion models. In this paper, we investigate the transferability of diffusion models and observe a monotonous chain of forgetting trend of transferability along the reverse process. Based on this observation and novel theoretical insights, we present Diff-Tuning, a frustratingly simple transfer approach that leverages the chain of forgetting tendency. Diff-Tuning encourages the fine-tuned model to retain the pre-trained knowledge at the end of the denoising chain close to the generated data while discarding the other noise side. We conduct comprehensive experiments to evaluate Diff-Tuning, including the transfer of pre-trained Diffusion Transformer models to eight downstream generations and the adaptation of Stable Diffusion to five control conditions with ControlNet. Diff-Tuning achieves a 24.6% improvement over standard fine-tuning and enhances the convergence speed of ControlNet by 24%. Notably, parameter-efficient transfer learning techniques for diffusion models can also benefit from Diff-Tuning. Code is available at this repository: https://github.com/thuml/Diffusion-Tuning.

Deep object detection has achieved state-of-the-art performance but highly relies on large-scale labeled datasets. Active Object Detection (AOD) presents criteria to select the most informative samples to annotate, thereby maximizing the performance with limited labeled data. But current AOD methods mostly aggregate criterion values calculated from every single box without the MultiBox prediction structure of object detection, making them easily overwhelmed by uninformative boxes (e.g. boundary and background boxes). This paper presents a MultiBox Sample Selection (MSS) criterion composed of MultiBox Uncertainty (MBU) and MultiBox Committee (MBC). MBU eliminates the influence of uninformative boxes and selects the boxes that best reflect the prediction uncertainty of the whole image. MBC consists of a committee measurement, which is based on the fact that certain but incorrect box predictions are usually inconsistent among nearby boxes. Experimental results on in-domain and cross-domain object detection datasets show the highest performance under the same labeling budget.

Time series analysis is widely used in extensive areas. Recently, to reduce labeling expenses and benefit various tasks, self-supervised pre-training has attracted immense interest. One mainstream paradigm is masked modeling, which successfully pre-trains deep models by learning to reconstruct the masked content based on the unmasked part. However, since the semantic information of time series is mainly contained in temporal variations, the standard way of randomly masking a portion of time points will seriously ruin vital temporal variations of time series, making the reconstruction task too difficult to guide representation learning. We thus present SimMTM, a Simple pre-training framework for Masked Time-series Modeling. By relating masked modeling to manifold learning, SimMTM proposes to recover masked time points by the weighted aggregation of multiple neighbors outside the manifold, which eases the reconstruction task by assembling ruined but complementary temporal variations from multiple masked series. SimMTM further learns to uncover the local structure of the manifold, which is helpful for masked modeling. Experimentally, SimMTM achieves state-of-the-art fine-tuning performance compared to the most advanced time series pre-training methods in two canonical time series analysis tasks: forecasting and classification, covering both in- and cross-domain settings.

General visualization recommendation systems typically make design decisions for the dataset automatically. However, most of them can only prune meaningless visualizations but fail to recommend targeted results. This paper contributes TaskVis, a task-oriented visualization recommendation system that allows users to select their tasks precisely on the interface. We first summarize a task base with 18 classical analytic tasks by a survey both in academia and industry. On this basis, we maintain a rule base, which extends empirical wisdom with our targeted modeling of the analytic tasks. Then, our rule-based approach enumerates all the candidate visualizations through answer set programming. After that, the generated charts can be ranked by four ranking schemes. Furthermore, we introduce a task-based combination recommendation strategy, leveraging a set of visualizations to give a brief view of the dataset collaboratively. Finally, we evaluate TaskVis through a series of use cases and a user study.

In cloud storage systems, the use of erasure coding results in high read latency and long recovery time when drive or node failure happens. In this paper, we design a parity independent array codes (PIT), a variation of STAR code, which is triple fault tolerant and nearly space-optimal, and also propose an efficient single-failure recovery scheme (PITR) for them to mitigate the problem. In addition, we present a "shortened" version of PIT (SPIT) to further reduce the recovery cost. In this way, less disk I/O and network resources are used, thereby reducing the recovery time and achieving a high system reliability and availability.