Macro placement is a crucial subproblem of chip design, focusing on determining the locations of numerous macros while minimizing multiple metrics. In recent years, reinforcement learning (RL) has gained traction as a favorable technique to improve placement performance. However, existing RL-based placers ignore the orientation of macros, resulting in the state space constrained to two-dimensional discrete coordinates and greatly restricting the exploration opportunities. To address this issue, we propose a novel macro placement method, RSPlace, which guides the bidirectional expansion of the global search tree to offer the RL agent more exploration opportunities, incorporating rotation into the RL-based macro placement solution for the first time. RSPlace intelligently determines the optimal rotation angle to maximize placement benefits by leveraging rotation sensing and placement perturbations. Extensive experiments demonstrate that taking the macro orientation into account substantially broadens the feasible locations and effectively reduces the half-perimeter wirelength (HPWL), thus ensuring that our approach significantly improves the optimization effect compared to the state-of-the-art method.

MOTIVATION: The modeling of bio-molecular system across molecular scales remains a central challenge in scientific research. Large language models (LLMs) are increasingly applied to bio-molecular discovery, yet systematic evaluation across multi-scale biological problems and rigorous assessment of their tool-augmented capabilities remain limited. RESULTS: We reveal a systematic gap between LLM performance and mechanistic understanding through the proposed cross-scale bio-molecular benchmark: BioMol-LLM-Bench, a unified framework comprising 26 downstream tasks that covers 4 distinct difficulty levels, and computational tools are integrated for a more comprehensive evaluation. Evaluation on 13 representative models reveals 4 benchmark-specific observations: chain-of-thought-style training does not consistently improve performance on the evaluated biological tasks; the evaluated hybrid mamba-attention model shows strong performance on long bio-molecular sequence tasks; supervised fine-tuned models show task-specific specialization with reduced performance in some general settings; and current LLMs perform better on classification tasks than on challenging regression tasks under this benchmark setting. AVAILABILITY: Source code is available at https://github.com/AI-HPC-Research-Team/BioMol-LLM-Bench.

Privacy concerns hinder the sharing and utilization of trajectory data collected from Internet of Things (IoT) devices. While local differential privacy mitigates leakage risks by perturbing data on mobile devices, existing methods primarily focus on enhancing global-level statistical usability of perturbed trajectories (such as spatial distributions and mobility transition patterns), neglecting individual-level semantic usability (such as road network consistency and traffic rule compliance). The resulting trajectories frequently contain unrealistic behaviors that violate traffic rules, severely limiting their applicability. To overcome this challenge, we propose TPIS, a trajectory protection approach with individual semantic utility under local differential privacy. First, we design a semantic-enhanced hierarchical modeling method (SEHM) that leverages traffic rule semantic information and geospatial information to model the road network as a weighted graph, supported by an R-tree index for efficient coarse-grained and fine-grained trajectory matching. Second, we propose a cascaded perturbation method combining stochastic sampling guided by the hierarchical graph model (SHG) and multi-feature fusion perturbation mechanism based on δ-location set (MFPD). SHG strategically constrains the perturbation space to preserve global-level statistical usability while ensuring rigorous privacy, and MFPD incorporates a comprehensive trajectory difference metric and δ -location set definition to generate perturbed trajectories that maintain individual-level structural integrity of real-world movement patterns. TPIS efficiently balances privacy and utility and ensures high computational efficiency. Theoretical analysis confirms its differential privacy guarantee and upper bound on perturbation error. Experiments conducted on four real-world datasets demonstrate significant improvements over existing methods, with up to 8× higher utility and 14× greater computational efficiency.

Macro placement is a crucial sub-stage of chip design that focuses on determining the locations of numerous macros while optimizing multiple metrics. In recent years, evolutionary algorithms (EAs) have emerged as a promising technique for enhancing placement performance. However, the solution space of the previous methods is often limited by two-dimensional grids due to insufficient consideration of macro orientation during placement, leading to missed opportunities for further improvement. To address this issue, we propose EA-Rotation for macro placement, which incorporates rotation into the mutation operator to perturb the placement by adjusting the orientation of macros. Utilizing this efficient operator in conjunction with a greedy algorithm, EA-Rotation achieves superior placement solutions. Additionally, EA-Rotation serves as a fine-tuning technique for optimizing both the locations and orientation of macros, significantly improving the placement solutions of existing methods. Comprehensive experiments demonstrate that EA-Rotation outperforms state-of-the-art methods and is effectively integrated with other mutation operators.

Kechen Jiao, Zhirui Fang, Jiahao Liu, Bei Li, Qifan Wang, Xinyu Liu, Junhao Ruan, Zhongjian Qiao, Yifan Zhu, Yaxin Xu, Jingang Wang, Xiu Li. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Human-Computer Interaction (HRI) is crucial in robotics. This article introduces a new method for human-machine interaction on robotic arms using a drag teaching approach. By analyzing the kinematics and dynamics of the Xarm7 robotic arm, the method reduces positioning difficulties. The F/T sensor on the arm detects force and torque. Direct human guidance is matched with robot response in real-time to control the arm's position and direction. The research also records and reproduces motion trajectories using cubic interpolation for precision. A mechanical claw on the arm detects and compares sensor data to position and grasp objects. Experimental results show similar shapes between the dragging and reproduced trajectories, with reduced motion errors.

Single-cell RNA sequencing (scRNA-seq) technology is one of the most cost-effective and efficacious methods for revealing cellular heterogeneity and diversity. Precise identification of cell types is essential for establishing a robust foundation for downstream analyses and is a prerequisite for understanding heterogeneous mechanisms. However, the accuracy of existing methods warrants improvement, and highly accurate methods often impose stringent equipment requirements. Moreover, most unsupervised learning-based approaches are constrained by the need to input the number of cell types a prior, which limits their widespread application. In this paper, we propose a novel algorithm framework named WLGG. Initially, to capture the underlying nonlinear information, we introduce a weighted distance penalty term utilizing the Gaussian kernel function, which maps data from a low-dimensional nonlinear space to a high-dimensional linear space. We subsequently impose a Lasso constraint on the regularized Gaussian graphical model to enhance its ability to capture linear data characteristics. Additionally, we utilize the Eigengap strategy to predict the number of cell types and obtain predicted labels via spectral clustering. The experimental results on 14 test datasets demonstrate the superior clustering accuracy of the WLGG algorithm over 16 alternative methods. Furthermore, downstream analysis, including marker gene identification, pseudotime inference, and functional enrichment analysis based on the similarity matrix and predicted labels from the WLGG algorithm, substantiates the reliability of WLGG and offers valuable insights into biological dynamic biological processes and regulatory mechanisms.

The widespreadPomacea canaliculataandPomacea maculatain North America and Asia have caused significant adverse effects on the local ecological environment and residents’ health. Timely knowledge of the distribution of eggs from the twoPomaceaspp. in a certain region can effectively reduce the cost of treatment and improve prevention effectiveness. Most of the existing methods are only able to identify eggs from the twoPomaceaspp. or detect them in specific but not natural environments while they cannot achieve good results in the face of a complex real-world scene. This letter proposes a model for detecting eggs from the twoPomaceaspp. based on dynamic convolution and multiscale feature fusion. The model can identify and locate the eggs of the twoPomaceaspp. effectively. At the same time, we combined the proposed model with scale invariant feature transform (SIFT) algorithm to design a system for counting eggs of the twoPomaceaspp., which can automatically identify the eggs in the actual natural environment and alleviate duplicate counting caused by image acquisition. Besides, we also built a dataset of 20 000 images ofPomacea canaliculataeggs andPomacea maculataeggs from unmanned aerial vehicle (UAV) aerial photography. Experimental results showed that the proposed deep learning model has a better performance than others, and the proposed computer vision system can be successfully applied to supportPomaceaspp. disease management.