arXiv subjects

Cheng Su

Publications and source records attributed to Cheng Su.

At least 19 recordsLinked to original sources

arXiv2026

Stripe antiferromagnetic order and spin dynamics in the Shastry-Sutherland Ising compound Er2Be2GeO7

In rare-earth compounds, the interplay between geometric frustration and unusual single-ion anisotropy caused by the crystal electric field often leads to exotic magnetic phases. Here, we investigate the magnetic properties of the rare-earth Shastry-Sutherland compound Er$_2$Be$_2$GeO$_7$ using bulk measurements and neutron scattering down to sub-Kelvin temperatures. A $k=(0,0,0)$ stripe-like antiferromagnetic order with locally tilted Er$^{3+}$ moments is revealed by time-of-flight neutron powder diffraction, accompanied with a $λ$-shape specific-heat anomaly at $T_N=0.84$ K. The temperature dependence of the magnetic peak intensity corresponding to the ordered moment of Er$^{3+}$ approximates to the two-dimensional Ising universality class. Thermal- and cold-neutron inelastic scattering experiments demonstrate that the strong Ising anisotropy originates from the local crystal electric field, and reveal gapped magnetic excitations with weak dispersion in the ordered state. Mean-field random-phase approximation with inequivalent nearest-neighbor couplings accounts well for the collective excitations, while classical Monte Carlo simulations yield the fractional $1/4$ and $1/2$ magnetization plateaus. Our work provides a comprehensive description of the spin correlations in Er$_2$Be$_2$GeO$_7$ with a particular focus on the classical origin of its fractional magnetization plateaus.

cond-mat.str-el↗

arXiv2026

Ray Data: A dynamic and scalable system for heterogeneous data processing

While ML model training and inference are both GPU-intensive, CPU-based data processing is often the bottleneck. Existing distributed data processing systems assume homogeneous resource requirements. They excel at CPU-based computation but either under-utilize heterogeneous resources or impose high overheads on failure and reconfiguration. We introduce Ray Data, a scalable system for heterogeneous workloads spanning batch inference and ML training. The key idea is to combine dynamic task creation for memory efficiency with dynamic task placement for operator elasticity. Making both decisions dynamically allows the system to adapt to heterogeneous pipelines where the memory and compute requirements per operator are unknown and variable over time. Ray Data addresses two challenges: (1) leveraging dynamic task creation for memory-efficient pipelining, and (2) designing a task placement policy that jointly allocates heterogeneous compute and memory resources to maximize throughput while limiting memory usage. Ray Data improves throughput on heterogeneous batch inference pipelines by 2.6-12$\times$ compared to traditional data processing systems. By leveraging heterogeneous clusters, Ray Data improves end-to-end training time for multimodal models such as Stable Diffusion by 31% compared to popular ML data loaders.

cs.DC↗

arXiv2026

Contrasting Crystal-Field and Dipolar Anisotropies in the Triangular Antiferromagnet NaBaEr(BO$_3$)$_2$

The magnetic ground states of rare-earth frustrated magnets can be governed by competing energy scales arising from crystal-field anisotropy, exchange interactions, and long-range dipolar interactions. Here we investigate this interplay in the triangular-lattice antiferromagnet NaBaEr(BO$_3$)$_2$ by combining magnetization, specific heat, and neutron scattering measurements. Neutron powder diffraction resolves rotations of the ErO$_6$ octahedra and supports the lower-symmetry $R\bar{3}$ structure over the previously proposed $R\bar{3}m$ model. Magnetization measurements and inelastic neutron scattering establish an effective spin-1/2 description of its low-temperature magnetism with pronounced easy-axis anisotropy, arising from a well-isolated Kramers ground-state doublet separated from the first excited crystal-electric-field level by approximately 4 meV. Below $T_{\rm N}=0.1$ K, single-crystal neutron diffraction reveals A-type antiferromagnetic order with the ordered moment predominantly oriented within the $ab$ plane, despite the pronounced paramagnetic easy-axis anisotropy. The estimated dipolar interaction scale is comparable to $T_{\rm N}$, and classical dipolar-energy calculations for the observed stacking pattern favor an in-plane moment orientation. These results reveal contrasting crystal-field and dipolar anisotropies in determining the magnetic ground state of NaBaEr(BO$_3$)$_2$.

cond-mat.str-el↗

arXiv2026

First-passage reliability sensitivity analysis of linear systems subjected to non-Gaussian wind excitations by surface decomposition method

This contribution develops a surface decomposition method for first-passage dynamic reliability sensitivity analysis of linear systems exposed to non-Gaussian wind excitations. The first-passage failure probability sensitivity is formulated as a system surface integral over a highly non-smooth and high-dimensional hypersurface. This complex integral is first decomposed into a collection of component surface integrals over the truncated smooth quadratic hypersurfaces. The dominant components are then identified based on the relative magnitudes of the first-order approximations of the component failure probabilities. A nested sampling algorithm is constructed to efficiently estimate the sum of these component surface integrals, in which the number of system limit-state function evaluations equals the number of outer-level samples while remaining independent of the inner-level sample size. A key advantage of the present approach is that the function evaluation results can be reused across different design parameters. Two numerical examples are explored to demonstrate the effectiveness of the proposed method. The results indicate that the number of function evaluations required is typically below 100 to achieve a target coefficient of variation of 0.1.

stat.ME↗

arXiv2026

From Foundation to Application: Improving VLA Models in Practice

Despite recent progress of VLA foundation models, the disparity between laboratory conditions and real-world applications continues to impede their practical implementation. To bridge this gap, we present LingBot-VLA 2.0, which advances LingBot-VLA through improvements in three functional domains. (1) Generalization across tasks and embodiments. Compared to the previous version, we revamp the data processing pipeline and curate around 60,000 hours of data for pretraining, including 50,000 hours of robot trajectories spanning 20 robot configurations and 10,000 hours of egocentric human videos. (2) Expanded action space in addition to dual-arm hardware platforms. In particular, our system accommodates degrees of freedom for the heads, waists, mobile bases, and dexterous hands, thereby empowering the robots to tackle more complex tasks in practical scenarios. (3) Predictive dynamics modeling for improved temporal reasoning. Specifically, we formulate future prediction as a proxy task, facilitated by a video representation model for semantic priors and a depth estimation model for geometric cues. Evaluations on the GM-100 benchmark, conducted in a generalist setting, validate the beneficial impact of these proposed modifications. Furthermore, benefiting from the expanded pretraining data that covers whole-body degrees of freedom, LingBot-VLA-2.0 demonstrates strong cross-embodiment long-horizon mobile manipulation capability across the two robotic platforms.

cs.RO↗

arXiv2026

Surface decomposition method for sensitivity analysis of first-passage dynamic reliability of linear systems

This work presents a novel surface decomposition method for the sensitivity analysis of first-passage dynamic reliability of linear systems subjected to Gaussian random excitations. The method decomposes the sensitivity of first-passage failure probability into a sum of surface integrals over the constrained component limit-state hypersurfaces. The evaluation of these surface integrals can be accomplished, owing to the availability of closed-form linear expressions of both the component limit-state functions and their sensitivities for linear systems. An importance sampling strategy is introduced to further enhance the efficiency for estimating the sum of these surface integrals. The number of function evaluations required for the reliability sensitivity analysis is typically on the order of 10^2 to 10^3. The approach is particularly advantageous when a large number of design parameters are considered, as the results of function evaluations can be reused across different parameters. Three numerical examples are investigated to demonstrate the effectiveness of the proposed method.

stat.ME↗

arXiv2025

Universal Magnetocaloric Effect near Quantum Critical Point of Magnon Bose-Einstein Condensation

Bose-Einstein condensation (BEC), a macroscopic quantum phenomenon arising from phase coherence and bosonic statistics, has been realized in quantum magnets. Here, we report the observation of a universal magnetocaloric effect (MCE) near a BEC quantum critical point (QCP) in copper sulfate crystal ($CuSO_4 \cdot 5H_2O$). By conducting magnetocaloric and nuclear magnetic resonance measurements, we uncover a field-driven BEC QCP, evidenced by the universal scaling law $T_c \propto (B_c - B)^{2/3}$ and the perfect data collapse of the magnetic Grüneisen ratio. Thermal excitation triggers a dimensional crossover to a 1D quantum-critical regime, where the MCE scaling strictly matches the universality class of 1D Fermi gases. Notably, the quantum-critical MCE enables cooling down to 12.8 mK without helium-3, with very fast thermal relaxation rate that is critical for high cooling power. This work demonstrates the universal MCE in magnon BEC systems, using a common copper sulfate compound as a paradigmatic example, and paves the way for next-generation sub-Kelvin cooling.

cond-mat.str-el↗

arXiv2025

HybridRAG-based LLM Agents for Low-Carbon Optimization in Low-Altitude Economy Networks

Low-Altitude Economy Networks (LAENets) are emerging as a promising paradigm to support various low-altitude services through integrated air-ground infrastructure. To satisfy low-latency and high-computation demands, the integration of Unmanned Aerial Vehicles (UAVs) with Mobile Edge Computing (MEC) systems plays a vital role, which offloads computing tasks from terminal devices to nearby UAVs, enabling flexible and resilient service provisions for ground users. To promote the development of LAENets, it is significant to achieve low-carbon multi-UAV-assisted MEC networks. However, several challenges hinder this implementation, including the complexity of multi-dimensional UAV modeling and the difficulty of multi-objective coupled optimization. To this end, this paper proposes a novel Retrieval Augmented Generation (RAG)-based Large Language Model (LLM) agent framework for model formulation. Specifically, we develop HybridRAG by combining KeywordRAG, VectorRAG, and GraphRAG, empowering LLM agents to efficiently retrieve structural information from expert databases and generate more accurate optimization problems compared with traditional RAG-based LLM agents. After customizing carbon emission optimization problems for multi-UAV-assisted MEC networks, we propose a Double Regularization Diffusion-enhanced Soft Actor-Critic (R\textsuperscript{2}DSAC) algorithm to solve the formulated multi-objective optimization problem. The R\textsuperscript{2}DSAC algorithm incorporates diffusion entropy regularization and action entropy regularization to improve the performance of the diffusion policy. Furthermore, we dynamically mask unimportant neurons in the actor network to reduce the carbon emissions associated with model training. Simulation results demonstrate the effectiveness and reliability of the proposed HybridRAG-based LLM agent framework and the R\textsuperscript{2}DSAC algorithm.

cs.NI↗

arXiv2025

Robustness of ferromagnetism in van der Waals magnet Fe$_3$GeTe$_2$ to hydrostatic pressure

Two-dimensional van der Waals ferromagnet Fe$_3$GeTe$_2$ (FGT) holds a great potential for applications in spintronic devices, due to its high Curie temperature, easy tunability, and excellent structural stability in air. Theoretical studies have shown that pressure, as an external parameter, significantly affects its ferromagnetic properties. In this study, we have performed comprehensive high-pressure neutron powder diffraction (NPD) experiments on FGT up to 5 GPa, to investigate the evolution of its structural and magnetic properties with hydrostatic pressure. The NPD data clearly reveal the robustness of the ferromagnetism in FGT, despite of an apparent suppression by hydrostatic pressure. As the pressure increases from 0 to 5 GPa, the Curie temperature is found to decrease monotonically from 225(5) K to 175(5) K, together with a dramatically suppressed ordered moment of Fe, which is well supported by the first-principles calculations. Although no pressure-driven structural phase transition is observed up to 5 GPa, quantitative analysis on the changes of bond lengths and bond angles indicate a significant modification of the exchange interactions, which accounts for the pressure-induced suppression of the ferromagnetism in FGT.

cond-mat.mtrl-sci↗

arXiv2024

Hybrid RAG-empowered Multi-modal LLM for Secure Data Management in Internet of Medical Things: A Diffusion-based Contract Approach

Secure data management and effective data sharing have become paramount in the rapidly evolving healthcare landscape, especially with the growing integration of the Internet of Medical Things (IoMT). The rise of generative artificial intelligence has further elevated Multi-modal Large Language Models (MLLMs) as essential tools for managing and optimizing healthcare data in IoMT. MLLMs can support multi-modal inputs and generate diverse types of content by leveraging large-scale training on vast amounts of multi-modal data. However, critical challenges persist in developing medical MLLMs, including security and freshness issues of healthcare data, affecting the output quality of MLLMs. To this end, in this paper, we propose a hybrid Retrieval-Augmented Generation (RAG)-empowered medical MLLM framework for healthcare data management. This framework leverages a hierarchical cross-chain architecture to facilitate secure data training. Moreover, it enhances the output quality of MLLMs through hybrid RAG, which employs multi-modal metrics to filter various unimodal RAG results and incorporates these retrieval results as additional inputs to MLLMs. Additionally, we employ age of information to indirectly evaluate the data freshness impact of MLLMs and utilize contract theory to incentivize healthcare data holders to share their fresh data, mitigating information asymmetry during data sharing. Finally, we utilize a generative diffusion model-based deep reinforcement learning algorithm to identify the optimal contract for efficient data sharing. Numerical results demonstrate the effectiveness of the proposed schemes, which achieve secure and efficient healthcare data management.

cs.AI↗

arXiv2024

Successive magnetic transitions in the spin-5/2 easy-axis triangular-lattice antiferromagnet Na$_2$BaMn(PO$_4$)$_2$: A neutron diffraction study

Motivated by the recent observations of various exotic quantum states in the equilateral triangular-lattice phosphates Na$_2$BaCo(PO$_4$)$_2$ with $J\rm_{eff}$ = 1/2 and Na$_2$BaNi(PO$_4$)$_2$ with $S$ = 1, the magnetic properties of spin-5/2 antiferromagnet Na$_2$BaMn(PO$_4$)$_2$, their classical counterpart, are comprehensively investigated experimentally. DC magnetization and specific heat measurements on polycrystalline samples indicate two successive magnetic transitions at $T\rm_{N1}$ $\approx$ 1.13 K and $T\rm_{N2}$ $\approx$ 1.28 K, respectively. Zero-field neutron powder diffraction measurement at 67 mK reveals a Y-like spin configuration as its ground-state magnetic structure, with both the $ab$-plane and $c$-axis components of the Mn$^{2+}$ moments long-range ordered. The incommensurate magnetic propagation vector $k$ shows a dramatic change for the intermediate phase between $T\rm_{N1}$ and $T\rm_{N2}$, in which the spin state is speculated to change into a collinear structure with only the $c$-axis moments ordered, as stabilized by thermal fluctuations. The successive magnetic transitions observed in Na$_2$BaMn(PO$_4$)$_2$ are in line with the expectation for a triangle-lattice antiferromagnet with an easy-axis magnetic anisotropy.

cond-mat.str-el↗

arXiv2024

Privacy-preserving Pseudonym Schemes for Personalized 3D Avatars in Mobile Social Metaverses

The emergence of mobile social metaverses, a novel paradigm bridging physical and virtual realms, has led to the widespread adoption of avatars as digital representations for Social Metaverse Users (SMUs) within virtual spaces. Equipped with immersive devices, SMUs leverage Edge Servers (ESs) to deploy their avatars and engage with other SMUs in virtual spaces. To enhance immersion, SMUs incline to opt for 3D avatars for social interactions. However, existing 3D avatars are typically generated through scanning the real faces of SMUs, which can raise concerns regarding information privacy and security, such as profile identity leakages. To tackle this, we introduce a new framework for personalized 3D avatar construction, leveraging a two-layer network model that provides SMUs with the option to customize their personal avatars for privacy preservation. Specifically, our approach introduces avatar pseudonyms to jointly safeguard the profile and digital identity privacy of the generated avatars. Then, we design a novel metric named Privacy of Personalized Avatars (PoPA), to evaluate effectiveness of the avatar pseudonyms. To optimize pseudonym resource, we model the pseudonym distribution process as a Stackelberg game and employ Deep Reinforcement Learning (DRL) to learn equilibrium strategies under incomplete information. Simulation results validate the efficacy and feasibility of our proposed schemes for mobile social metaverses.

cs.NI↗

arXiv2023

Type-II antiferromagnetic ordering in double perovskite oxide Sr$_2$NiWO$_6$

Magnetic double perovskite compounds provide a fertile playground to explore interesting electronic and magnetic properties. By complementary macroscopic characterizations, neutron powder diffraction measurements and first-principles calculations, we have performed comprehensive studies on the magnetic ordering in the double perovskite compound Sr$_2$NiWO$_6$. It is found by neutron diffraction to order magnetically in a collinear type-II antiferromagnetic structure in a tetragonal lattice with $k$ = (0.5, 0, 0.5) below $T\rm_N$ = 56 K. In the ground state, the ordered moment of the spin-1 Ni$^{2+}$ ions is determined to be 1.9(2) $μ\rm_{B}$, indicating a significant quenching of the orbital moment. The Ni$^{2+}$ moments in Sr$_2$NiWO$_6$ are revealed to cant off the $c$ axis by 29.2$^{\circ}$, which is well supported by the first-principles magnetic anisotropy energy calculations. Furthermore, the in-plane and out-of-plane next-nearest-neighbor superexchange couplings ($J\rm_2$ and $J\rm_{2c}$) are found to play a dominant role in the spin Hamiltonian of Sr$_2$NiWO$_6$, which accounts for the stabilization of the type-II AFM structure as its magnetic ground state.

cond-mat.str-el↗

arXiv2023

Dipolar Spin Liquid Ending with Quantum Critical Point in a Gd-based Triangular Magnet

By performing experiment and model studies on a triangular-lattice dipolar magnet KBaGd(BO$_3$)$_2$ (KBGB), we find the highly frustrated magnet with a planar anisotropy hosts a strongly fluctuating dipolar spin liquid (DSL), which originates from the intriguing interplay between dipolar and Heisenberg interactions. The DSL constitutes an extended regime in the field-temperature phase diagram, which gets lowered in temperature as field increases and eventually ends with an unconventional quantum critical point (QCP) at $B_c\simeq 0.75$~T. Based on dipolar Heisenberg model calculations, we identify the DSL as a Berezinskii-Kosterlitz-Thouless (BKT) phase with emergent U(1) symmetry. Due to the tremendous entropy accumulation that can be related to the strong BKT and quantum fluctuations, unprecedented magnetic cooling effects are observed in the DSL regime and particularly near the QCP, making KBGB a superior dipolar coolant to commercial Gd-based refrigerants. We establish the phase diagram for triangular-lattice dipolar quantum magnets where emergent symmetry plays an essential role, and provide a basis and opens an avenue for their applications in sub-Kelvin refrigeration.

cond-mat.str-el↗

arXiv2022

Zigzag magnetic order in a novel tellurate compound Na$_{4-δ}$NiTeO$_{6}$ with $\mathit{S}$ = 1 chains

Na$_{4-δ}$NiTeO$_{6}$ is a rare example in the transition-metal tellurate family of realizing an $S$ = 1 spin-chain structure. By performing neutron powder diffraction measurements, the ground-state magnetic structure of Na$_{4-δ}$NiTeO$_{6}$ is determined. These measurements reveal that below $T\rm_{N}$ ${\sim}$ 6.8(2) K, the Ni$^{2+}$ moments form a screwed ferromagnetic (FM) spin-chain structure running along the crystallographic $a$ axis but these FM spin chains are coupled antiferromagnetically along the $b$ and $c$ directions, giving rise to a magnetic propagation vector of $k$ = (0, 1/2, 1/2). This zigzag magnetic order is well supported by first-principles calculations. The moment size of Ni$^{2+}$ spins is determined to be 2.1(1) $μ$$\rm_{B}$ at 3 K, suggesting a significant quenching of the orbital moment due to the crystalline electric field (CEF) effect. The previously reported metamagnetic transition near $H\rm_{C}$ ${\sim}$ 0.1 T can be understood as a field-induced spin-flip transition. The relatively easy tunability of the dimensionality of its magnetism by external parameters makes Na$_{4-δ}$NiTeO$_{6}$ a promising candidate for further exploring various types of novel spin-chain physics.

cond-mat.str-el↗

arXiv2022

Evolution from helical to collinear ferromagnetic order of the Eu$^{2+}$ spins in RbEu(Fe$_{1-x}$Ni$_{x}$)$_{4}$As$_{4}$

The ground-state magnetic structures of the Eu$^{2+}$ spins in recently discovered RbEu(Fe$_{1-x}$Ni$_{x}$)$_{4}$As$_{4}$ superconductors have been investigated by neutron powder diffraction measurements. It is found that as the superconductivity gets suppressed with the increase of Ni doping, the magnetic propagation vector of the Eu sublattice diminishes, corresponding to the decrease of the rotation angle between the moments in neighboring Eu layers. The ferromagnetic Eu layers are helically modulated along the $\mathit{c}$ axis with an incommensurate magnetic propagation vector in both the ferromagnetic superconductor RbEu(Fe$_{0.95}$Ni$_{0.05}$)$_{4}$As$_{4}$ and the superconducting ferromagnet RbEu(Fe$_{0.93}$Ni$_{0.07}$)$_{4}$As$_{4}$. Such a helical structure transforms into a purely collinear ferromagnetic structure for non-superconducting RbEu(Fe$_{0.91}$Ni$_{0.09}$)$_{4}$As$_{4}$, with all the Eu$^{2+}$ spins lying along the tetragonal (1 1 0) direction. The evolution from helical to collinear ferromagnetic order of the Eu$^{2+}$ spins with increasing Ni doping is supported by first-principles calculations. The variation of the rotation angle between adjacent Eu$^{2+}$ layers can be well explained by considering the change of magnetic exchange couplings mediated by the indirect Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction.

cond-mat.str-el↗

arXiv2021

Key-Point Interpolation: A Sparse Data Interpolation Algorithm based on B-splines

B-splines are widely used in the fields of reverse engineering and computer-aided design, due to their superior properties. Traditional B-spline surface interpolation algorithms usually assume regularity of the data distribution. In this paper, we introduce a novel B-spline surface interpolation algorithm: KPI, which can interpolate sparsely and non-uniformly distributed data points. As a two-stage algorithm, our method generates the dataset out of the sparse data using Kriging, and uses the proposed KPI (Key-Point Interpolation) method to generate the control points. Our algorithm can be extended to higher dimensional data interpolation, such as reconstructing dynamic surfaces. We apply the method to interpolating the temperature of Shanxi Province. The generated dynamic surface accurately interpolates the temperature data provided by the weather stations, and the preserved dynamic characteristics can be useful for meteorology studies.

cs.CG↗

arXiv2021

A novel feed rate scheduling method based on Sigmoid function with chord error and kinematics constraints

In high speed CNC (Compute Numerical Control) machining, the feed rate scheduling has played an important role to ensure machining quality and machining efficiency. In this paper, a novel feed rate scheduling method is proposed for generating smooth feed rate profile conveniently with the consideration of both geometric error and kinematic error. First, a relationship between feed rate value and chord error is applied to determine the feed rate curve. Then, breaking points, which can split whole curve into several blocks, can be found out using proposed two step screening method. For every block, a feed rate profile based on the Sigmoid function is generated. With the consideration of kinematic limitation and machining efficiency, a time-optimal feed rate adjustment algorithm is proposed to further adjust feed rate value at breaking points. After planning feed rate profile for each block, all blocks feed rate profile will be connected smoothly. The resulting feed rate profile is more concise compared with the polynomial profile and more efficient compared with the trigonometric profile. Finally, simulations with two free-form NURBS curves are conducted and comparison with the sine-curve method are carried out to verify the feasibility and applicability of the proposed method.

eess.SY↗