arXiv subjects

Dongli Wang

Publications and source records attributed to Dongli Wang.

4 recordsLinked to original sources

arXiv2025

Few to Big: Prototype Expansion Network via Diffusion Learner for Point Cloud Few-shot Semantic Segmentation

Few-shot 3D point cloud semantic segmentation aims to segment novel categories using a minimal number of annotated support samples. However, prototypes derived from the limited non-structural point cloud support set are often misaligned and have a small capacity, hindering effective gen eralization to novel categories. This stems from two core issues: i) the prototype possess limited representational capacity fails to cover the full intra-class diversity of a novel category, and ii) the prototypes suffer from misalignment with the query space due to the inter-set inconsistency between support and query sets. To address these issues, our work focuses on leveraging the few support samples to construct a well-aligned big-capacity prototype. Motivated by the powerful generative capabilities of diffusion models, we re-purpose its pre-trained conditional encoder to provide rich feature components for prototype ex pansion. Subsequently, a push-pull force aligns this expanded prototype towards the query feature space. Under this setup, we introduce the Prototype Expansion Network (PENet), a framework that constructs aligned big-capacity prototypes from two complementary feature sources. Specifically, PENet employs a dual-stream learner architecture: it retains a conventional fully supervised Intrinsic Learner (IL) to distill representative features, while introducing a novel Diffusion Learner (DL) to provide rich generalizable features. The resulting dual prototypes are then processed by a Prototype Assimilation Module (PAM), which adopts a push-pull attention block to align the prototypes with the query space. Furthermore, a Prototype Calibration Mechanism (PCM) regularizes the final big-capacity prototype to prevent semantic drift. Extensive experiments on the S3DIS and ScanNet datasets demonstrate that PENet outperforms state-of-the-art methods across various few-shot settings.

cs.CV↗

arXiv2022

Fast Compressive Channel Estimation for MmWave MIMO Hybrid Beamforming Systems

Given the high degree of computational complexity of the channel estimation technique based on the conventional one-dimensional (1-D) compressive sensing (CS) framework employed in the hybrid beamforming architecture, this study proposes two low-complexity channel estimation strategies. One is two-stage CS, which exploits row-group sparsity to estimate angle-of-arrival (AoA) first and uses the conventional 1-D CS method to obtain angle-of-departure (AoD). The other is two-dimensional (2-D) CS, which utilizes a 2-D dictionary to reconstruct the 2-D sparse signal. To conduct a meaningful comparison of the three CS frameworks, i.e., 1-D, two-stage and 2-D CS, the orthogonal match pursuit (OMP) algorithm is employed as the basic algorithm and is expanded to two variants for the proposed frameworks. Analysis and simulations demonstrate that when the 1-D CS method is compared, two-stage CS has somewhat lower performance but significantly lower computational complexity, while 2-D CS is not only the same as 1-D CS in terms of performance but also slightly lower in computational complexity than two-stage CS.

eess.SP↗

arXiv2021

Room-Temperature Anisotropic Plasma Mirror and Polarization-Controlled Optical Switch Based on Type-II Weyl Semimetal WP2

Anisotropy in electronic structures may ignite intriguing anisotropic optical responses, as has been well demonstrated in various systems including superconductors, semiconductors, and even topological Weyl semimetals. Meanwhile, it is well established in metal optics that the metal reflectance declines from one to zero when the photon frequency is above the plasma frequency ωp , behaving as a plasma mirror. However, the exploration of anisotropic plasma mirrors and corresponding applications remains elusive, especially at room temperature. Here, we discover a pronounced anisotropic plasma reflectance edge in the type-II Weyl semimetal WP2, with an anisotropy ratio of ωp up to 1.5. Such anisotropic plasma mirror behavior and its robustness against temperature promise optical device applications over a wide temperature range. For example, the high sensitivity of polarization-resolved plasma reflectance edge renders WP2 an inherent polarization detector. We further achieve a room-temperature WP2-based optical switch, effectively controlled by simply tuning the light polarization. These findings extend the frontiers of metal optics as a discipline and promise the design of multifunctional devices combining both topological and optical features.

cond-mat.mtrl-sci↗

arXiv2019

Moiré Engineering of Electronic Phenomena in Correlated Oxides

Moiré engineering has recently emerged as a capable approach to control quantum phenomena in condensed matter systems. In van der Waals heterostructures, moiré patterns can be formed by lattice misorientation between adjacent atomic layers, creating long range electronic order. To date, moiré engineering has been executed solely in stacked van der Waals multilayers. Herein, we describe our discovery of electronic moiré patterns in films of a prototypical magnetoresistive oxide La0.67Sr0.33MnO3 (LSMO) epitaxially grown on LaAlO3 (LAO) substrates. Using scanning probe nano-imaging, we observe microscopic moiré profiles attributed to the coexistence and interaction of two distinct incommensurate patterns of strain modulation within these films. The net effect is that both electronic conductivity and ferromagnetism of LSMO are modulated by periodic moiré textures extending over mesoscopic scales. Our work provides an entirely new route with potential to achieve spatially patterned electronic textures on demand in strained epitaxial materials.

cond-mat.mes-hall↗