arXiv subjects

Mingyu Fan

Publications and source records attributed to Mingyu Fan.

At least 19 recordsLinked to original sources

arXiv2026

Limit on the nuclear Schiff moment of europium-153

The Schiff moment of a nucleus is a symmetry-violating nuclear moment that indicates new physics beyond the Standard Model. We place the limit, $|\mathscr{S}({}^{153}$Eu)$| < 1.7 \times 10^{-8}$ $e\,$fm$^3$ (95\% confidence), on the Schiff moment of the $^{153}$Eu nucleus, using nuclear spin resonances in two ensembles of oppositely-polarized $^{153}$Eu$^{3+}$ ions in a Y${}_2$SiO${}_5$ crystal. This measurement using octupolar nuclei in a mm-scale crystal constrains new physics at the TeV energy-scale.

physics.atom-ph↗

arXiv2026

Multi-View Hierarchical Graph Neural Network for Sketch-Based 3D Shape Retrieval

Sketch-based 3D shape retrieval (SBSR) aims to retrieve 3D shapes that are consistent with the category of the input hand-drawn sketch. The core challenge of this task lies in two aspects: existing methods typically employ simplified aggregation strategies for independently encoded 3D multi-view features, which ignore the geometric relationships between views and multi-level details, resulting in weak 3D representation. Simultaneously, traditional SBSR methods are constrained by visible category limitations, leading to poor performance in zero-shot scenarios. To address these challenges, we propose Multi-View Hierarchical Graph Neural Network (MV-HGNN), a novel framework for SBSR. Specifically, we construct a view-level graph and capture adjacent geometric dependencies and cross-view message passing via local graph convolution and global attention. A view selector is further introduced to perform hierarchical graph coarsening, enabling a progressively larger receptive field for graph convolution and mitigating the interference of redundant views, which leads to more discriminate discriminative hierarchical 3D representation. To enable category agnostic alignment and mitigate overfitting to seen classes, we leverage CLIP text embeddings as semantic prototypes and project both sketch and 3D features into a shared semantic space. We use a two-stage training strategy for category-level retrieval and a one-stage strategy for zero-shot retrieval under the same model architecture. Under both category-level and zero-shot settings, extensive experiments on two public benchmarks demonstrate that MV-HGNN outperforms state-of-the-art methods.

cs.CV↗

arXiv2026

Sensing T-violating nuclear moments of paramagnetic ions in crystals

Precision measurements of time-reversal (T) symmetry violating moments probe physics beyond the Standard Model. We show that precision spectroscopy of paramagnetic lanthanide and actinide ions doped into noncentrosymmetric crystals offers a promising platform for extending the sensitivity of searches for T-violation in nuclear physics. The unpaired valence electron in these ions allows the engineering of highly-coherent hyperfine transitions that are insensitive to magnetic fields, yet highly sensitive to new physics. These systems also provide other advantages for new physics searches, including large numbers of ions that can be measured in doped crystals, strong electric polarization of the ions by the crystal fields, enhancement of T-violating nuclear moments in nonspherical nuclei, and accurate comagnetometers generated by crystal symmetry. We estimate the new physics sensitivity of these proposed systems to be two orders of magnitude better than existing constraints.

physics.atom-ph↗

arXiv2026

Recover to Predict: Progressive Retrospective Learning for Variable-Length Trajectory Prediction

Trajectory prediction is critical for autonomous driving, enabling safe and efficient planning in dense, dynamic traffic. Most existing methods optimize prediction accuracy under fixed-length observations. However, real-world driving often yields variable-length, incomplete observations, posing a challenge to these methods. A common strategy is to directly map features from incomplete observations to those from complete ones. This one-shot mapping, however, struggles to learn accurate representations for short trajectories due to significant information gaps. To address this issue, we propose a Progressive Retrospective Framework (PRF), which gradually aligns features from incomplete observations with those from complete ones via a cascade of retrospective units. Each unit consists of a Retrospective Distillation Module (RDM) and a Retrospective Prediction Module (RPM), where RDM distills features and RPM recovers previous timesteps using the distilled features. Moreover, we propose a Rolling-Start Training Strategy (RSTS) that enhances data efficiency during PRF training. PRF is plug-and-play with existing methods. Extensive experiments on datasets Argoverse 2 and Argoverse 1 demonstrate the effectiveness of PRF. Code is available at https://github.com/zhouhao94/PRF.

cs.RO↗

arXiv2026

TaPD: Temporal-adaptive Progressive Distillation for Observation-Adaptive Trajectory Forecasting in Autonomous Driving

Trajectory prediction is essential for autonomous driving, enabling vehicles to anticipate the motion of surrounding agents to support safe planning. However, most existing predictors assume fixed-length histories and suffer substantial performance degradation when observations are variable or extremely short in real-world settings (e.g., due to occlusion or a limited sensing range). We propose TaPD (Temporal-adaptive Progressive Distillation), a unified plug-and-play framework for observation-adaptive trajectory forecasting under variable history lengths. TaPD comprises two cooperative modules: an Observation-Adaptive Forecaster (OAF) for future prediction and a Temporal Backfilling Module (TBM) for explicit reconstruction of the past. OAF is built on progressive knowledge distillation (PKD), which transfers motion pattern knowledge from long-horizon "teachers" to short-horizon "students" via hierarchical feature regression, enabling short observations to recover richer motion context. We further introduce a cosine-annealed distillation weighting scheme to balance forecasting supervision and feature alignment, improving optimization stability and cross-length consistency. For extremely short histories where implicit alignment is insufficient, TBM backfills missing historical segments conditioned on scene evolution, producing context-rich trajectories that strengthen PKD and thereby improve OAF. We employ a decoupled pretrain-reconstruct-finetune protocol to preserve real-motion priors while adapting to backfilled inputs. Extensive experiments on Argoverse 1 and Argoverse 2 show that TaPD consistently outperforms strong baselines across all observation lengths, delivers especially large gains under very short inputs, and improves other predictors (e.g., HiVT) in a plug-and-play manner. Code will be available at https://github.com/zhouhao94/TaPD.

cs.CV↗

arXiv2025

Manifold-Aware Point Cloud Completion via Geodesic-Attentive Hierarchical Feature Learning

Point cloud completion seeks to recover geometrically consistent shapes from partial or sparse 3D observations. Although recent methods have achieved reasonable global shape reconstruction, they often rely on Euclidean proximity and overlook the intrinsic nonlinear geometric structure of point clouds, resulting in suboptimal geometric consistency and semantic ambiguity. In this paper, we present a manifold-aware point cloud completion framework that explicitly incorporates nonlinear geometry information throughout the feature learning pipeline. Our approach introduces two key modules: a Geodesic Distance Approximator (GDA), which estimates geodesic distances between points to capture the latent manifold topology, and a Manifold-Aware Feature Extractor (MAFE), which utilizes geodesic-based $k$-NN groupings and a geodesic-relational attention mechanism to guide the hierarchical feature extraction process. By integrating geodesic-aware relational attention, our method promotes semantic coherence and structural fidelity in the reconstructed point clouds. Extensive experiments on benchmark datasets demonstrate that our approach consistently outperforms state-of-the-art methods in reconstruction quality.

cs.CV↗

arXiv2025

Memory-Guided Point Cloud Completion for Dental Reconstruction

Partial dental point clouds often suffer from large missing regions caused by occlusion and limited scanning views, which bias encoder-only global features and force decoders to hallucinate structures. We propose a retrieval-augmented framework for tooth completion that integrates a prototype memory into standard encoder--decoder pipelines. After encoding a partial input into a global descriptor, the model retrieves the nearest manifold prototype from a learnable memory and fuses it with the query feature through confidence-gated weighting before decoding. The memory is optimized end-to-end and self-organizes into reusable tooth-shape prototypes without requiring tooth-position labels, thereby providing structural priors that stabilize missing-region inference and free decoder capacity for detail recovery. The module is plug-and-play and compatible with common completion backbones, while keeping the same training losses. Experiments on a self-processed Teeth3DS benchmark demonstrate consistent improvements in Chamfer Distance, with visualizations showing sharper cusps, ridges, and interproximal transitions. Our approach provides a simple yet effective way to exploit cross-sample regularities for more accurate and faithful dental point-cloud completion.

cs.CV↗

arXiv2025

SDGraph: Multi-Level Sketch Representation Learning by Sparse-Dense Graph Architecture

Freehand sketches exhibit unique sparsity and abstraction, necessitating learning pipelines distinct from those designed for images. For sketch learning methods, the central objective is to fully exploit the effective information embedded in sketches. However, there is limited research on what constitutes effective sketch information, which in turn constrains the performance of existing approaches. To tackle this issue, we first proposed the Multi-Level Sketch Representation Scheme to identify the effective information. The scheme organizes sketch representation into three levels: sketch-level, stroke-level, and point-level. This design is based on the granularity of analytical elements, from coarse (sketch-level) to fine (point-level), thereby ensuring more comprehensive coverage of the sketch information. For each level, we conducted theoretical analyses and experimental evaluations to identify and validate the effective information. Building on the above studies, we developed SDGraph, a deep learning architecture designed to exploit the identified effective information across the three levels. SDGraph comprises two complementary modules: a Sparse Graph that treats strokes as nodes for sketch-level and stroke-level representation learning, and a Dense Graph that treats points as nodes for sketch-level and point-level representation learning. Both modules employ graph convolution along with down-sampling and up-sampling operations, enabling them to function as both encoder and decoder. Besides that, an information fusion module bridges the two graphs to further enhance feature extraction. SDGraph supports a wide range of sketch-related downstream tasks, achieving accuracy improvements of 1.15\% and 2.30\% over the state-of-the-art in classification and retrieval, respectively, and 32.93\% improvement in vector sketch generation quality.

cs.GR↗

arXiv2025

NVP-HRI: Zero Shot Natural Voice and Posture-based Human-Robot Interaction via Large Language Model

Effective Human-Robot Interaction (HRI) is crucial for future service robots in aging societies. Existing solutions are biased toward only well-trained objects, creating a gap when dealing with new objects. Currently, HRI systems using predefined gestures or language tokens for pretrained objects pose challenges for all individuals, especially elderly ones. These challenges include difficulties in recalling commands, memorizing hand gestures, and learning new names. This paper introduces NVP-HRI, an intuitive multi-modal HRI paradigm that combines voice commands and deictic posture. NVP-HRI utilizes the Segment Anything Model (SAM) to analyze visual cues and depth data, enabling precise structural object representation. Through a pre-trained SAM network, NVP-HRI allows interaction with new objects via zero-shot prediction, even without prior knowledge. NVP-HRI also integrates with a large language model (LLM) for multimodal commands, coordinating them with object selection and scene distribution in real time for collision-free trajectory solutions. We also regulate the action sequence with the essential control syntax to reduce LLM hallucination risks. The evaluation of diverse real-world tasks using a Universal Robot showcased up to 59.2\% efficiency improvement over traditional gesture control, as illustrated in the video https://youtu.be/EbC7al2wiAc. Our code and design will be openly available at https://github.com/laiyuzhi/NVP-HRI.git.

cs.RO↗

arXiv2025

Lifetimes of the Metastable $6\mathrm{d}\, ^{2}\mathrm{D}_{5/2}$ and $6\mathrm{d}\, ^{2}\mathrm{D}_{3/2}$ States of Ra$^+$

We report lifetime measurements of the metastable $6\mathrm{d}\, ^{2}\mathrm{D}_{5/2}$ and $6\mathrm{d}\, ^{2}\mathrm{D}_{3/2}$ states of Ra$^+$. The measured lifetimes, $\tau_{5} = $ 303.8(1.5) ms and $\tau_{3} = $ 642(9) ms, are important for optical frequency standards and for benchmarking high-precision relativistic atomic theory. Independent of the reported measurements, the D state lifetimes were calculated using the coupled-cluster single double triple method, in which the coupled-cluster equations for both core and valence triple excitations were solved iteratively. The method was designed for precise prediction of atomic properties, especially for heavy elements, where relativistic and correlation corrections become large, making their treatment more challenging. This Letter presents the first tests of the method for transition properties. Our prediction agrees with experimental values within the uncertainties. The ability to accurately predict the atomic properties of heavy elements is important for many applications, from tests of fundamental symmetries to the development of optical clocks.

physics.atom-ph↗

arXiv2025

Natural Multimodal Fusion-Based Human-Robot Interaction: Application With Voice and Deictic Posture via Large Language Model

Translating human intent into robot commands is crucial for the future of service robots in an aging society. Existing Human-Robot Interaction (HRI) systems relying on gestures or verbal commands are impractical for the elderly due to difficulties with complex syntax or sign language. To address the challenge, this paper introduces a multi-modal interaction framework that combines voice and deictic posture information to create a more natural HRI system. The visual cues are first processed by the object detection model to gain a global understanding of the environment, and then bounding boxes are estimated based on depth information. By using a large language model (LLM) with voice-to-text commands and temporally aligned selected bounding boxes, robot action sequences can be generated, while key control syntax constraints are applied to avoid potential LLM hallucination issues. The system is evaluated on real-world tasks with varying levels of complexity using a Universal Robots UR3e manipulator. Our method demonstrates significantly better performance in HRI in terms of accuracy and robustness. To benefit the research community and the general public, we will make our code and design open-source.

cs.RO↗

arXiv2024

Comagnetometry using mirror-symmetric ions in a crystal

Searches for physics beyond the Standard Model using spin sensors are susceptible to spurious frequency shifts and noise due to magnetic fields. Therefore a comagnetometer -- an auxiliary sensor that allows mundane magnetic field effects to be differentiated from new physics -- is an essential feature of many precision searches. Here we demonstrate the operation of a novel type of comagnetometer using nuclear spins of dopant ions in a crystal, comparing four different sub-ensembles of ions. We demonstrate rejection of magnetic-field-induced shifts to better than 1 part in 10$^5$ using this system, laying the groundwork for improved searches of time-reversal symmetry violation using solid-state systems.

physics.atom-ph↗

arXiv2024

Wideband Search for Axionlike Dark Matter Using Octupolar Nuclei in a Crystal

Most of the matter in the Universe is in the form of dark matter, which has evaded detection so far. Ultralight axionlike particles (ALPs) are a class of dark matter candidates that produce measurable signatures in the form of oscillating violations of discrete symmetries in nuclei. We report results from a search for an oscillating parity-odd time-reversal-odd nuclear Schiff moment of $^{153}$Eu ions in a crystal, which leads to constraints on ALP-gluon coupling strength across a wide band spanning eight decades in ALP mass.

physics.atom-ph↗

arXiv2024

Spectroscopy of electric dipole and quadrupole transitions in $^{224}$Ra$^+$

We report on spectroscopy of the low-lying electronic transitions in $^{224}$Ra$^+$. The ion's low charge to mass ratio and convenient wavelengths make $^{224}$Ra$^+$ a promising optical clock candidate. We measured the frequencies of the the $^2{S}_{1/2} \ $$\leftrightarrow$$\ ^2{P}_{1/2}$ cooling transition, the $^2{S}_{1/2}\ $$\leftrightarrow$$\ ^2{D}_{5/2}$ clock transition, the $^2{D}_{3/2} \ $$\leftrightarrow$$\ ^2{P}_{3/2}$ electric dipole transition, and the $^2{D}_{5/2} \ $$\leftrightarrow$$\ ^2{P}_{3/2}$ cleanout transition. From these measurements we calculate the frequencies of the $^2{D}_{3/2}\ $$\leftrightarrow$$\ ^2{P}_{1/2}$ repump transition, the $^2{S}_{1/2} \ $$\leftrightarrow$$\ ^2{D}_{3/2}$ electric quadrupole transition, and the $^2{S}_{1/2} \ $$\leftrightarrow$$\ ^2{P}_{3/2}$ electric dipole transition.

physics.atom-ph↗

arXiv2024

Laser Cooling and Hyperfine Measurements of Radium-225 Ions

$^{225}$Ra$^+$ ions (nuclear spin $I=1/2$) have transitions that are first-order insensitive to magnetic field noise, which is advantageous for optical clocks and quantum information science. We report on laser cooling and trapping of $^{225}$Ra$^+$ ions and hyperfine splitting measurements of the ion's $\mathrm{7s}$ $^2\mathrm{S}_{1/2}$, $\mathrm{7p}$ $^2\mathrm{P}_{1/2}$, and $\mathrm{6d}$ $^2\mathrm{D}_{3/2}$ states. We measured the ground state hyperfine constant, $A(\mathrm{S}_{1/2}) =$ $-27.684511052(5)$ $\mathrm{GHz}$, and the quadratic Zeeman coefficient, $C_2 =$ $142.3(1.0)$ $\mathrm{Hz\ G}^{-2}$, of the $^2\mathrm{S}_{1/2} (F=0, m_F = 0) \leftrightarrow~^2\mathrm{S}_{1/2} (F=1, m_{F} = 0)$ transition. Our result addresses a discrepancy in the literature for the ground state hyperfine splitting. We measured the hyperfine constants of the $^2\mathrm{P}_{1/2}$ state, $A(\mathrm{P}_{1/2}) =$ $-5.447(4)$ $\mathrm{GHz}$, and the $^2\mathrm{D}_{3/2}$ state, $A(\mathrm{D}_{3/2}) =$ $-619.7(1.1)$ $\mathrm{MHz}$. We also performed state preparation and measurement using the ground state hyperfine levels and realized a fidelity of $0.9951(9)$.

physics.atom-ph↗

arXiv2023

Opportunities for Fundamental Physics Research with Radioactive Molecules

Molecules containing short-lived, radioactive nuclei are uniquely positioned to enable a wide range of scientific discoveries in the areas of fundamental symmetries, astrophysics, nuclear structure, and chemistry. Recent advances in the ability to create, cool, and control complex molecules down to the quantum level, along with recent and upcoming advances in radioactive species production at several facilities around the world, create a compelling opportunity to coordinate and combine these efforts to bring precision measurement and control to molecules containing extreme nuclei. In this manuscript, we review the scientific case for studying radioactive molecules, discuss recent atomic, molecular, nuclear, astrophysical, and chemical advances which provide the foundation for their study, describe the facilities where these species are and will be produced, and provide an outlook for the future of this nascent field.

nucl-ex↗

arXiv2022

Coulomb gas sum rules for vortex-pair fluctuations in 2D superfluids

Vortex fluctuations above and below the critical Kosterlitz-Thouless (KT) transition temperature are characterized using simulations of the 2D XY model. The net winding number of vortices at a given temperature in a circle of radius $R$ is computed as a function of $R$. The average squared winding number is found to vary linearly with the perimeter of the circle at all temperatures above and below $T_{KT}$, and the slope with $R$ displays a sharp peak near the specific heat peak, decreasing then to a value at infinite temperature that is in agreement with an early theory by Dhar. We have also computed the vortex-vortex distribution functions, finding an asymptotic power-law variation in the vortex separation distance at all temperatures. In conjunction with a Coulomb-gas sum rule on the perimeter fluctuations, these can be used to successfully model the start of the perimeter-slope peak in the region below $T_{KT}$.

cond-mat.stat-mech↗

arXiv2021

Sliding Sequential CVAE with Time Variant Socially-aware Rethinking for Trajectory Prediction

Pedestrian trajectory prediction is a key technology in many applications such as video surveillance, social robot navigation, and autonomous driving, and significant progress has been made in this research topic. However, there remain two limitations of previous studies. First, with the continuation of time, the prediction error at each time step increases significantly, causing the final displacement error to be impossible to ignore. Second, the prediction results of multiple pedestrians might be impractical in the prediction horizon, i.e., the predicted trajectories might collide with each other. To overcome these limitations, this work proposes a novel trajectory prediction method called CSR, which consists of a cascaded conditional variational autoencoder (CVAE) module and a socially-aware regression module. The cascaded CVAE module first estimates the future trajectories in a sequential pattern. Specifically, each CVAE concatenates the past trajectories and the predicted points so far as the input and predicts the location at the following time step. Then, the socially-aware regression module generates offsets from the estimated future trajectories to produce the socially compliant final predictions, which are more reasonable and accurate results than the estimated trajectories. Moreover, considering the large model parameters of the cascaded CVAE module, a slide CVAE module is further exploited to improve the model efficiency using one shared CVAE, in a slidable manner. Experiments results demonstrate that the proposed method exhibits improvements over state-of-the-art method on the Stanford Drone Dataset (SDD) and ETH/UCY of approximately 38.0% and 22.2%, respectively.

cs.CV↗