arXiv subjects

Jie Ling

Publications and source records attributed to Jie Ling.

4 recordsLinked to original sources

arXiv2026

ConsistencyPlanner: Real-time Planning with Fast-Sampling Consistency Models

Closed-loop planning in complex, real-world driving scenarios presents a critical challenge for autonomous driving systems. While traditional rule-based methods are interpretable, their predefined heuristics lack the adaptability for dynamic traffic environments. Learning-based approaches have shown considerable promise. Conversely, learning-based approaches, despite their promise, struggle to balance the modeling diverse and multimodal driving behaviors and real-time planning, often leading to indecisive or unsafe actions. To address this limitation, we propose Consistency Planner, a real-time planning framework with fast-sampling consistency models. Our approach is built upon two key technical contributions. Efficient Multimodal Sampling: We employ fast-sampling consistency models to generate a diverse set of plausible future trajectories. This enables efficient, real-time exploration of multimodal actions, overcoming the computational bottlenecks of previous iterative generative methods. Heterogeneous Feature Fusion: We introduce an attention-enhanced decoder that dynamically integrates heterogeneous input features (including scene feature and action token) into a cohesive representation for robust planning. Extensive evaluation in the Waymax simulator demonstrates superior performance in safety metrics compared to existing methods, with particularly strong results in challenging dynamic scenarios.

cs.RO↗

arXiv2024

Joint Edge Optimization Deep Unfolding Network for Accelerated MRI Reconstruction

Magnetic Resonance Imaging (MRI) is a widely used imaging technique, however it has the limitation of long scanning time. Though previous model-based and learning-based MRI reconstruction methods have shown promising performance, most of them have not fully utilized the edge prior of MR images, and there is still much room for improvement. In this paper, we build a joint edge optimization model that not only incorporates individual regularizers specific to both the MR image and the edges, but also enforces a co-regularizer to effectively establish a stronger correlation between them. Specifically, the edge information is defined through a non-edge probability map to guide the image reconstruction during the optimization process. Meanwhile, the regularizers pertaining to images and edges are incorporated into a deep unfolding network to automatically learn their respective inherent a-priori information.Numerical experiments, consisting of multi-coil and single-coil MRI data with different sampling schemes at a variety of sampling factors, demonstrate that the proposed method outperforms other compared methods.

eess.IV↗

arXiv2020

Machine Learning based prediction of noncentrosymmetric crystal materials

Noncentrosymmetric materials play a critical role in many important applications such as laser technology, communication systems,quantum computing, cybersecurity, and etc. However, the experimental discovery of new noncentrosymmetric materials is extremely difficult. Here we present a machine learning model that could predict whether the composition of a potential crystalline structure would be centrosymmetric or not. By evaluating a diverse set of composition features calculated using matminer featurizer package coupled with different machine learning algorithms, we find that Random Forest Classifiers give the best performance for noncentrosymmetric material prediction, reaching an accuracy of 84.8% when evaluated with 10 fold cross-validation on the dataset with 82,506 samples extracted from Materials Project. A random forest model trained with materials with only 3 elements gives even higher accuracy of 86.9%. We apply our ML model to screen potential noncentrosymmetric materials from 2,000,000 hypothetical materials generated by our inverse design engine and report the top 20 candidate noncentrosymmetric materials with 2 to 4 elements and top 20 borate candidates

physics.comp-ph↗

arXiv2020

Performance of High-Mobility MIMO Communications with Doppler Diversity

A rapid change of channels in high-speed mobile communications will lead to difficulties in channel estimation and tracking but can also provide Doppler diversity. In this paper, the performance of a multiple-input multiple-output system with pilot-assisted repetition coding and spatial multiplexing is studied. With minimum mean square error (MMSE) channel estimation, an equivalent channel model and the corresponding system model are presented. Based on random matrix theory, asymptotic expressions of the normalized achievable sum rate of the linear receivers, such as the maximal ratio combining (MRC) receiver, MMSE receiver and MRC-like receiver, are derived. In addition, according to the symbol error rate of the MRC-like receiver, the maximum normalized Doppler diversity order and the minimum coding gain loss can be achieved when the repetition number and signal-to-noise ratio tend to infinity, and the corresponding conditions are derived. Based on the theoretical results, the impacts of different system configurations and channel parameters on the system performance are demonstrated.

cs.IT↗