This paper introduces a novel framework for reactive control in dual-arm cooperative robotic systems, addressing the significant challenges posed by high-dimensional, non-convex optimization demands, intricate kinematic, multi-modal distribution, the need for precise, and synchronized coordination. The core of our approach is a two-stage sampling-based model predictive control, which integrates k-means, dual quaternion, and null space into a cohesive system. This integration enhances the system's ability to manage complex coordination tasks, such as obstacle avoidance and holding a water cup, while mitigating risks associated with local optima and reducing control jitter. Our framework not only improves performance and reliability, but also overcomes the traditional computational bottlenecks inherent in dual-arm coordination. These advancements are validated through extensive simulations and experiments, demonstrating the robustness and efficiency of our proposed methodology.

The 4D millimeter wave radar has gained increasing attention in autonomous driving due to its robustness against environmental interference compared to other perception devices such as cameras or LiDAR. However, the practical deployment of radar remains challenging due to the noise and high sparsity of radar point clouds, as well as the resource limitations of edge computing. To address these issues, we propose Radar-Mamba, a lightweight and efficient radar enhancement approach for boosting radar perception. The proposed approach contains three main components: cross-modal alignment, radar enhancement architecture based on the Mamba model, and Doppler feature fusion. Specifically, the point clouds of radar and LiDAR are first refined and aligned to build more dense and rich occupancies. The aligned 4D radar data is then enhanced by capturing both local and global spatial-temporal features, while integrating radar-specific velocity and elevation information for further denoising. Experiments on two open-source datasets demonstrate that our method achieves state-of-the-art performance and generates high-quality 4D point clouds with a density comparable to LiDAR while maintaining a low parameter count friendly for practical deployment.

Recently around half of the world’s current population resides in urban areas and benefit from rich services in the smart city. The majority of smart city services are recommendation-related services, and with the development of Internet, most recommendation services in smart economy are online recommendations. Online travel platforms (OTPs) (like Booking, Airbnb, Ctrip, and Fliggy) provide people sufficient resources and convenient approaches to plan and enjoy their trips in smart city. Hotel recommendation is essential for the success of OTPs. However, it is more challenging compared to item recommendation in typical E-commerce scenarios (e.g., Taobao, Jd, and YouTube). The in-nature characteristics of low-frequency and high unit-price lead to more severe sparse and long-tail data distributions. Moreover, for enhancing user experience and business returns, the recommender system seeks to improve both click-through rate (CTR) and conversion rate (CVR) where the seesaw phenomenon may occur. In order to address the aforementioned shortages in hotel recommendation, a multitask learning (MTL) method with a novel flexible multilevel extraction network [denoted as flexible MTL (FMTL)] is proposed. Particularly, FMTL takes MTL into consideration in a unified representation learning framework and is divided into feature encoding and task prediction. In the feature encoding phase, we introduce a novel multirepresentation extractor with temperature-adjusted gating mechanism (T-MRE) for each task, producing more flexible representations for sparse and long-tail data. Moreover, we fuse different representations for each task with three strategies during the prediction phase and empirically demonstrate that the simple concatenation strategy is superior than other relatively complex gating approaches. Offline and live experiments with regard to both overall metrics and user group analysis based on the scarcity of user behaviors illustrate that without significantly increasing model parameters, our FMTL model outperforms substantially over several state-of-the-art models.

MOTIVATION: Cancer is well known to be the end result of somatic mutations that disrupt normal cell division. The number of such mutations that have to be accumulated in a cell before cancer develops depends on the type of cancer. The waiting time T(m) until the appearance of m mutations in a cell is thus an important quantity in population genetics models of carcinogenesis. Such models are often difficult to analyze theoretically because of the complex interactions of mutation, drift and selection. They are also computationally expensive to simulate because of the large number of cells and the low mutation rate. RESULTS: We develop an efficient algorithm for simulating the waiting time T(m) until m mutations under a population genetics model of cancer development. We use an exact algorithm to simulate evolution of small cell populations and coarse-grained τ-leaping approximation to handle large populations. We compared our hybrid simulation algorithm with the exact algorithm in small populations and with available asymptotic results for large populations. The comparison suggested that our algorithm is accurate and computationally efficient. We used the algorithm to study the waiting time for up to 20 mutations under a Moran model with variable population sizes. Our new algorithm may be useful for studying realistic models of carcinogenesis, which incorporates variable mutation rates and fitness effects.