arXiv subjects

Liming Yang

Publications and source records attributed to Liming Yang.

At least 19 recordsLinked to original sources

arXiv2026

A Stable and General Quantum Fractional-Step Lattice Boltzmann Method for Incompressible Flows

Quantum computing shows substantial potential in accelerating simulations and alleviating memory bottlenecks in computational fluid dynamics (CFD), owing to its inherent properties of superposition and entanglement. The lattice Boltzmann method (LBM), being largely algebraic in nature, has inspired the development of various quantum LBMs. However, most existing approaches fix the relaxation time at $\tau$ = 1, thereby confining a given mesh resolution to simulations at a single Reynolds number. Although our earlier quantum lattice kinetic scheme (LKS) lifted this restriction, it suffers from instability at high Reynolds numbers. To address this challenge, we propose a quantum fractional-step LBM (FS-LBM). In this framework, the predictor step is implemented on a quantum circuit using the standard LBM formulation, while the corrector step is performed classically. The relaxation time is retained at $\tau$ = 1 to ensure seamless compatibility with existing quantum LBMs. Benchmark simulations of representative two- and three-dimensional incompressible isothermal and thermal flows demonstrate that the quantum FS-LBM achieves accuracy and convergence orders consistent with its classical counterpart, while significantly outperforming the quantum LKS in both precision and stability. Notably, this work presents the first quantum LBM simulation of three-dimensional incompressible thermal flows.

quant-ph↗

arXiv2025

BAMAS: Structuring Budget-Aware Multi-Agent Systems

Large language model (LLM)-based multi-agent systems have emerged as a powerful paradigm for enabling autonomous agents to solve complex tasks. As these systems scale in complexity, cost becomes an important consideration for practical deployment. However, existing work rarely addresses how to structure multi-agent systems under explicit budget constraints. In this paper, we propose BAMAS, a novel approach for building multi-agent systems with budget awareness. BAMAS first selects an optimal set of LLMs by formulating and solving an Integer Linear Programming problem that balances performance and cost. It then determines how these LLMs should collaborate by leveraging a reinforcement learning-based method to select the interaction topology. Finally, the system is instantiated and executed based on the selected agents and their collaboration topology. We evaluate BAMAS on three representative tasks and compare it with state-of-the-art agent construction methods. Results show that BAMAS achieves comparable performance while reducing cost by up to 86%.

cs.MA↗

arXiv2025

Freezing dynamics of the ferrofluid droplet in a uniform magnetic field using the lattice Boltzmann flux solver

In this study, an enthalpy-based lattice Boltzmann flux solver is developed to simulate the freezing dynamics of a ferrofluid droplet under a uniform magnetic field. The accuracy and robustness of the solver are first validated through three benchmark tests: conductive freezing, static droplet freezing, and ferrofluid droplet deformation. The solver is then employed to investigate the influence of a uniform magnetic field on the freezing behavior of ferrofluid droplets, with particular emphasis on the overall freezing process, heat transfer characteristics, and freezing duration. The results reveal that the uniform magnetic field affects the freezing dynamics primarily by altering the droplet morphology. Under a vertically oriented magnetic field, the droplet elongates along the field direction, which increases the thermal resistance and consequently prolongs the freezing time. Conversely, a horizontally uniform magnetic field flattens the droplet, reducing the thermal resistance and thus shortening the freezing time. These findings provide new physical insight into magnetic-field-induced modulation of the freezing process in ferrofluid systems.

physics.flu-dyn↗

arXiv2025

Video-rate gigapixel ptychography via space-time neural field representations

Achieving gigapixel space-bandwidth products (SBP) at video rates represents a fundamental challenge in imaging science. Here we demonstrate video-rate ptychography that overcomes this barrier by exploiting spatiotemporal correlations through neural field representations. Our approach factorizes the space-time volume into low-rank spatial and temporal features, transforming SBP scaling from sequential measurements to efficient correlation extraction. The architecture employs dual networks for decoding real and imaginary field components, avoiding phase-wrapping discontinuities plagued in amplitude-phase representations. A gradient-domain loss on spatial derivatives ensures robust convergence. We demonstrate video-rate gigapixel imaging with centimeter-scale coverage while resolving 308-nm linewidths. Validations span from monitoring sample dynamics of crystals, bacteria, stem cells, microneedle to characterizing time-varying probes in extreme ultraviolet experiments, demonstrating versatility across wavelengths. By transforming temporal variations from a constraint into exploitable correlations, we establish that gigapixel video is tractable with single-sensor measurements, making ptychography a high-throughput sensing tool for monitoring mesoscale dynamics without lenses.

physics.optics↗

arXiv2025

Deep-ultraviolet ptychographic pocket-scope (DART): mesoscale lensless molecular imaging with label-free spectroscopic contrast

The mesoscale characterization of biological specimens has traditionally required compromises between resolution, field-of-view, depth-of-field, and molecular specificity, with most approaches relying on external labels. Here we present the Deep-ultrAviolet ptychogRaphic pockeT-scope (DART), a handheld platform that transforms label-free molecular imaging through intrinsic deep-ultraviolet spectroscopic contrast. By leveraging biomolecules' natural absorption fingerprints and combining them with lensless ptychographic microscopy, DART resolves down to 308-nm linewidths across centimeter-scale areas while maintaining millimeter-scale depth-of-field. The system's virtual error-bin methodology effectively eliminates artifacts from limited temporal coherence and other optical imperfections, enabling high-fidelity molecular imaging without lenses. Through differential spectroscopic imaging at deep-ultraviolet wavelengths, DART quantitatively maps nucleic acid and protein distributions with femtogram sensitivity, providing an intrinsic basis for explainable virtual staining. We demonstrate DART's capabilities through molecular imaging of tissue sections, cytopathology specimens, blood cells, and neural populations, revealing detailed molecular contrast without external labels. The combination of high-resolution molecular mapping and broad mesoscale imaging in a portable platform opens new possibilities from rapid clinical diagnostics, tissue analysis, to biological characterization in space exploration.

physics.optics↗

arXiv2025

An Enthalpy-Based Unified Lattice Boltzmann Flux Solver for Liquid Solidification

An enthalpy-based uniform lattice Boltzmann flux solver (EULBFS) is proposed in this paper for simulating liquid solidification, incorporating the effects of volume expansion and shrinkage caused by density differences between liquid and solid phases. The proposed solver first establishes the relationships between the macroscopic governing equations and mesoscopic formal equations that describe the temperature, flow, and phase fields. The macroscopic governing equations are then discretized by the finite volume method (FVM), with the corresponding fluxes calculated based on the established relationships. In this way, it enables a unified and coherent solution framework for all fields. In contrast to the conventional lattice Boltzmann methods, the present approach handles additional terms directly via finite volume discretization, offering a more straightforward and flexible formulation. Furthermore, the use of the total enthalpy equation to couple the temperature field with the phase fraction allows for efficient modeling of phase change processes, significantly reducing the computational complexity associated with interface tracking. The accuracy and robustness of the proposed solver are demonstrated by a series of benchmark tests, including the conductive freezing problem, the three-phase Stefan problem, the freezing of a liquid film in a two-dimensional container, the solidification of a static droplet on a cold surface, and the freezing of a droplet upon impact with a cold surface.

hep-lat↗

arXiv2025

FinMME: Benchmark Dataset for Financial Multi-Modal Reasoning Evaluation

Multimodal Large Language Models (MLLMs) have experienced rapid development in recent years. However, in the financial domain, there is a notable lack of effective and specialized multimodal evaluation datasets. To advance the development of MLLMs in the finance domain, we introduce FinMME, encompassing more than 11,000 high-quality financial research samples across 18 financial domains and 6 asset classes, featuring 10 major chart types and 21 subtypes. We ensure data quality through 20 annotators and carefully designed validation mechanisms. Additionally, we develop FinScore, an evaluation system incorporating hallucination penalties and multi-dimensional capability assessment to provide an unbiased evaluation. Extensive experimental results demonstrate that even state-of-the-art models like GPT-4o exhibit unsatisfactory performance on FinMME, highlighting its challenging nature. The benchmark exhibits high robustness with prediction variations under different prompts remaining below 1%, demonstrating superior reliability compared to existing datasets. Our dataset and evaluation protocol are available at https://huggingface.co/datasets/luojunyu/FinMME and https://github.com/luo-junyu/FinMME.

cs.CL↗

arXiv2025

Quantum Lattice Kinetic Scheme for Solving Two-dimensional and Three-dimensional Incompressible Flows

Lattice Boltzmann method (LBM) is particularly well-suited for implementation on quantum circuits owing to its simple algebraic operations and natural parallelism. However, most quantum LBMs fix $\tau$ = 1 to avoid nonlinear collision, which restricts the simulation to a fixed mesh size for a given Reynolds number. To preserve the simplicity of setting $\tau$ = 1 while enhancing flexibility, we propose a quantum lattice kinetic scheme (LKS) by introducing a constant parameter $A$ into the equilibrium distribution function (EDF), enabling independent adjustment of the fluid's viscosity. This modification removes the constraint on mesh size, making it possible to simulate flows with arbitrary Reynolds numbers. The Chapman-Enskog analysis confirms the modified EDF still recovers the Navier-Stokes equations without compromising collision accuracy. We evaluate the method on 2D and 3D Taylor-Green vortex and lid-driven cavity flows, demonstrating that quantum LKS attains the same accuracy and convergence order as classical LKS. The first application of quantum LBM to 3D incompressible flows represents a significant step forward in large-scale fluid dynamics simulation.

quant-ph↗

arXiv2025

A Novel Quantum Fourier Ordinary Differential Equation Solver for Solving Linear and Nonlinear Partial Differential Equations

In this work, a novel quantum Fourier ordinary differential equation (ODE) solver is proposed to solve both linear and nonlinear partial differential equations (PDEs). Traditional quantum ODE solvers transform a PDE into an ODE system via spatial discretization and then integrate it, thereby converting the task of solving the PDE into computing the integral for the driving function $f(x)$. These solvers rely on the quantum amplitude estimation algorithm, which requires the driving function $f(x)$ to be within the range of [0, 1] and necessitates the construction of a quantum circuit for the oracle R that encodes $f(x)$. This construction can be highly complex, even for simple functions like $f(x) = x$. An important exception arises for the specific case of $f(x) = sin^2(mx+c)$, which can be encoded more efficiently using a set of $Ry$ rotation gates. To address these challenges, we expand the driving function $f(x)$ as a Fourier series and propose the Quantum Fourier ODE Solver. This approach not only simplifies the construction of the oracle R but also removes the restriction that $f(x)$ must lie within [0,1]. The proposed method was evaluated by solving several representative linear and nonlinear PDEs, including the Navier-Stokes (N-S) equations. The results show that the quantum Fourier ODE solver produces results that closely match both analytical and reference solutions.

quant-ph↗

arXiv2025

ReLearn: Unlearning via Learning for Large Language Models

Current unlearning methods for large language models usually rely on reverse optimization to reduce target token probabilities. However, this paradigm disrupts the subsequent tokens prediction, degrading model performance and linguistic coherence. Moreover, existing evaluation metrics overemphasize contextual forgetting while inadequately assessing response fluency and relevance. To address these challenges, we propose ReLearn, a data augmentation and fine-tuning pipeline for effective unlearning, along with a comprehensive evaluation framework. This framework introduces Knowledge Forgetting Rate (KFR) and Knowledge Retention Rate (KRR) to measure knowledge-level preservation, and Linguistic Score (LS) to evaluate generation quality. Our experiments show that ReLearn successfully achieves targeted forgetting while preserving high-quality output. Through mechanistic analysis, we further demonstrate how reverse optimization disrupts coherent text generation, while ReLearn preserves this essential capability. Code is available at https://github.com/zjunlp/unlearn.

cs.CL↗

arXiv2024

Fast On-device LLM Inference with NPUs

On-device inference for Large Language Models (LLMs), driven by increasing privacy concerns and advancements of mobile-sized models, has gained significant interest. However, even mobile-sized LLMs (e.g., Gemma-2B) encounter unacceptably high inference latency, often bottlenecked by the prefill stage in tasks like screen UI understanding. We present llm.npu, the first LLM inference system utilizing on-device Neural Processing Unit (NPU) offloading to reduce prefill latency. llm.npu enhances NPU offloading efficiency by re-constructing the prompt and model in three levels: (1) At prompt level, it divides variable-length prompts into multiple fixed-sized chunks while maintaining data dependencies; (2) At tensor level, it identifies and extracts significant outliers to run on the CPU/GPU in parallel with minimal overhead; (3) At block level, it schedules Transformer blocks in an out-of-order manner to the CPU/GPU and NPU based on their hardware affinity and sensitivity to accuracy. Compared to competitive baselines, llm.npu achieves 22.4x faster prefill speed and 30.7$\times$ energy savings on average, and up to 32.8x speedup in an end-to-end real-world application. For the first time, llm.npu achieves more than 1,000 tokens/sec prefilling for a billion-sized model.

cs.AI↗

arXiv2024

Multiplication Operators on Hilbert Spaces

Let $S$ be a subnormal operator on a separable complex Hilbert space $\mathcal H$ and let $\mu$ be the scalar-valued spectral measure for the minimal normal extension $N$ of $S.$ Let $R^\infty (\sigma(S),\mu)$ be the weak-star closure in $L^\infty (\mu)$ of rational functions with poles off $\sigma(S),$ the spectrum of $S.$ The multiplier algebra $M(S)$ consists of functions $f\in L^\infty(\mu)$ such that $f(N)\mathcal H \subset \mathcal H.$ The multiplication operator $M_{S,f}$ of $f\in M(S)$ is defined $M_{S,f} = f(N) |_{\mathcal H}.$ We show that for $f\in R^\infty (\sigma(S),\mu),$ (1) $M_{S,f}$ is invertible iff $f$ is invertible in $M(S)$ and (2) $M_{S,f}$ is Fredholm iff there exists $f_0\in R^\infty (\sigma(S),\mu)$ and a polynomial $p$ such that $f=pf_0,$ $f_0$ is invertible in $M(S),$ and $p$ has only zeros in $\sigma (S) \setminus \sigma_e (S),$ where $\sigma_e (S)$ denotes the essential spectrum of $S.$ Consequently, we characterize $\sigma(M_{S,f})$ and $\sigma_e(M_{S,f})$ in terms of some cluster subsets of $f.$ Moreover, we show that if $S$ is an irreducible subnormal operator and $f \in R^\infty (\sigma(S),\mu),$ then $M_{S,f}$ is invertible iff $f$ is invertible in $R^\infty (\sigma(S),\mu).$ The results answer the second open question raised by J. Dudziak in 1984.

math.FA↗

arXiv2024

Metasurface spectrometers beyond resolution-sensitivity constraints

Optical spectroscopy plays an essential role across scientific research and industry for non-contact materials analysis1-3, increasingly through in-situ or portable platforms4-6. However, when considering low-light-level applications, conventional spectrometer designs necessitate a compromise between their resolution and sensitivity7,8, especially as device and detector dimensions are scaled down. Here, we report on a miniaturizable spectrometer platform where light throughput onto the detector is instead enhanced as the resolution is increased. This planar, CMOS-compatible platform is based around metasurface encoders designed to exhibit photonic bound states in the continuum9, where operational range can be altered or extended simply through adjusting geometric parameters. This system can enhance photon collection efficiency by up to two orders of magnitude versus conventional designs; we demonstrate this sensitivity advantage through ultra-low-intensity fluorescent and astrophotonic spectroscopy. This work represents a step forward for the practical utility of spectrometers, affording a route to integrated, chip-based devices that maintain high resolution and SNR without requiring prohibitively long integration times.

physics.optics↗

arXiv2023

History Matters: Temporal Knowledge Editing in Large Language Model

The imperative task of revising or updating the knowledge stored within large language models arises from two distinct sources: intrinsic errors inherent in the model which should be corrected and outdated knowledge due to external shifts in the real world which should be updated. Prevailing efforts in model editing conflate these two distinct categories of edits arising from distinct reasons and directly modify the original knowledge in models into new knowledge. However, we argue that preserving the model's original knowledge remains pertinent. Specifically, if a model's knowledge becomes outdated due to evolving worldly dynamics, it should retain recollection of the historical knowledge while integrating the newfound knowledge. In this work, we introduce the task of Temporal Knowledge Editing (TKE) and establish a benchmark AToKe (Assessment of TempOral Knowledge Editing) to evaluate current model editing methods. We find that while existing model editing methods are effective at making models remember new knowledge, the edited model catastrophically forgets historical knowledge. To address this gap, we propose a simple and general framework termed Multi-Editing with Time Objective (METO) for enhancing existing editing models, which edits both historical and new knowledge concurrently and optimizes the model's prediction for the time of each fact. Our assessments demonstrate that while AToKe is still difficult, METO maintains the effectiveness of learning new knowledge and meanwhile substantially improves the performance of edited models on utilizing historical knowledge.

cs.CL↗

arXiv2023

ArcMMLU: A Library and Information Science Benchmark for Large Language Models

In light of the rapidly evolving capabilities of large language models (LLMs), it becomes imperative to develop rigorous domain-specific evaluation benchmarks to accurately assess their capabilities. In response to this need, this paper introduces ArcMMLU, a specialized benchmark tailored for the Library & Information Science (LIS) domain in Chinese. This benchmark aims to measure the knowledge and reasoning capability of LLMs within four key sub-domains: Archival Science, Data Science, Library Science, and Information Science. Following the format of MMLU/CMMLU, we collected over 6,000 high-quality questions for the compilation of ArcMMLU. This extensive compilation can reflect the diverse nature of the LIS domain and offer a robust foundation for LLM evaluation. Our comprehensive evaluation reveals that while most mainstream LLMs achieve an average accuracy rate above 50% on ArcMMLU, there remains a notable performance gap, suggesting substantial headroom for refinement in LLM capabilities within the LIS domain. Further analysis explores the effectiveness of few-shot examples on model performance and highlights challenging questions where models consistently underperform, providing valuable insights for targeted improvements. ArcMMLU fills a critical gap in LLM evaluations within the Chinese LIS domain and paves the way for future development of LLMs tailored to this specialized area.

cs.CL↗

arXiv2023

Spatially-coded Fourier ptychography: flexible and detachable coded thin films for quantitative phase imaging with uniform phase transfer characteristics

Fourier ptychography (FP) is an enabling imaging technique that produces high-resolution complex-valued images with extended field coverages. However, when FP images a phase object with any specific spatial frequency, the captured images contain only constant values, rendering the recovery of the corresponding linear phase ramp impossible. This challenge is not unique to FP but also affects other common microscopy techniques -- a rather counterintuitive outcome given their widespread use in phase imaging. The underlying issue originates from the non-uniform phase transfer characteristic inherent in microscope systems, which impedes the conversion of object wavefields into discernible intensity variations. To address this challenge, we present spatially-coded Fourier ptychography (scFP), a new method that synergizes FP with spatial-domain coded detection for true quantitative phase imaging. In scFP, a flexible and detachable coded thin film is attached atop the image sensor in a regular FP setup. The spatial modulation of this thin film ensures a uniform phase response across the entire synthetic bandwidth. It improves reconstruction quality and corrects refractive index underestimation issues prevalent in conventional FP and related tomographic implementations. The inclusion of the coded thin film further adds a new dimension of measurement diversity in the spatial domain. The development of scFP is expected to catalyse new research directions and applications for phase imaging, emphasizing the need for true quantitative accuracy with uniform frequency response.

physics.optics↗

arXiv2023

A Note on Spectral Mapping Theorems for Subnormal Operators

For a compact subset $K\subset \mathbb C$ and a positive finite Borel measure $\mu$ supported on $K,$ let $\text{Rat}(K)$ denote the space of rational functions with poles off $K,$ let $R^\infty (K,\mu)$ be the weak-star closure of $\text{Rat}(K)$ in $L^\infty (\mu),$ and let $R^2 (K,\mu)$ be the closure of $\text{Rat}(K)$ in $L^2(\mu).$ We show that there exists a compact subset $K\subset \mathbb C,$ a positive finite Borel measure $\mu$ supported on $K,$ and a function $f\in R^\infty (K,\mu)$ such that $R^\infty (K,\mu)$ has no non-trivial direct $L^\infty$ summands, $f$ is invertible in $R^2 (K,\mu)\cap L^\infty(\mu),$ and $f$ is not invertible in $R^\infty (K,\mu).$ The result answers an open question concerning spectral mapping theorems for subnormal operators raised by J. Dudziak in 1984.

math.FA↗

arXiv2023

The Commutant of Multiplication by z on the Closure of Rational Functions in $L^t(\mu)$

For a compact set $K\subset \mathbb C,$ a finite positive Borel measure $\mu$ on $K,$ and $1 \le t < \i,$ let $\text{Rat}(K)$ be the set of rational functions with poles off $K$ and let $R^t(K, \mu)$ be the closure of $\text{Rat}(K)$ in $L^t(\mu).$ For a bounded Borel subset $\mathcal D\subset \mathbb C,$ let $\area_{\mathcal D}$ denote the area (Lebesgue) measure restricted to $\mathcal D$ and let $H^\i (\mathcal D)$ be the weak-star closed sub-algebra of $L^\i(\area_{\mathcal D})$ spanned by $f,$ bounded and analytic on $\mathbb C\setminus E_f$ for some compact subset $E_f \subset \mathbb C\setminus \mathcal D.$ We show that if $R^t(K, \mu)$ contains no non-trivial direct $L^t$ summands, then there exists a Borel subset $\mathcal R \subset K$ whose closure contains the support of $\mu$ and there exists an isometric isomorphism and a weak-star homeomorphism $\rho$ from $R^t(K, \mu) \cap L^\infty(\mu)$ onto $H^\infty(\mathcal R)$ such that $\rho(r) = r$ for all $r\in\text{Rat}(K).$ Consequently, we obtain some structural decomposition theorems for $\rtkmu$.

math.FA↗