arXiv subjects

Min Wang

Publications and source records attributed to Min Wang.

At least 55 records · Page 3Linked to original sources

arXiv2025

Simultaneous generation of Raman-assisted Soliton Microcombs and Tunable Multi-chromatic Raman Microlasers in Single Monolithic Thin-film Lithium Niobate Microrings

High-performance integrated broadband coherent light sources are essential for advanced applications in high-bandwidth data processing and chip-scale metrology, yet remain challenging. In this study, we demonstrate a monolithic Z-cut lithium niobate on insulator (LNOI) microring platform that enables simultaneous generation of tunable multi-chromatic microlasers and Raman-assisted soliton microcombs. Exploiting the strong Raman activity and high second-order nonlinearity of LNOI, we engineered a dispersion-optimized microring with a loaded Q factor of 3.86X10^6, facilitating on-chip efficient broadband coherent light source. A novel phase-matching configuration with all the waves of the same ordinary polarization was realized for the first time in this platform, feasibly enabling modal-phase matched Raman-quadratic nonlinear processes that extend lasing signals into the visible spectrum. Under continuous-wave laser pumping at 3.73 mW in the telecom band, we achieved a Raman-assisted soliton comb centered at 1624.49 nm with record-low pump threshold on the LNOI platform. Concurrently, multi-chromatic Raman lasing outputs were observed at ~1700, ~813, and ~535 nm within the same microring. The system exhibited efficient wavelength tuning of these multi-chromatic laser signals through a 5 nm shift in pump wavelength. This work represents a significant advance in integrated photonics for versatile optical signal generation.

physics.optics↗

arXiv2025

From Code to Correctness: Closing the Last Mile of Code Generation with Hierarchical Debugging

While large language models have made significant strides in code generation, the pass rate of the generated code is bottlenecked on subtle errors, often requiring human intervention to pass tests, especially for complex problems. Existing LLM-based debugging systems treat generated programs as monolithic units, failing to address bugs at multiple levels of granularity, from low-level syntax errors to high-level algorithmic flaws. In this paper, we introduce Multi-Granularity Debugger (MGDebugger), a hierarchical code debugger by isolating, identifying, and resolving bugs at various levels of granularity. MGDebugger decomposes problematic code into a hierarchical tree structure of subfunctions, with each level representing a particular granularity of error. During debugging, it analyzes each subfunction and iteratively resolves bugs in a bottom-up manner. To effectively test each subfunction, we propose an LLM-simulated Python executor, which traces code execution and tracks important variable states to pinpoint errors accurately. Extensive experiments demonstrate that MGDebugger outperforms existing debugging systems, achieving an 18.9% improvement in accuracy over seed generations in HumanEval and a 97.6% repair success rate in HumanEvalFix. Furthermore, MGDebugger effectively fixes bugs across different categories and difficulty levels, demonstrating its robustness and effectiveness.

cs.CL↗

arXiv2025

Quantum Semantic Communication Beyond the Shannon-Wyner Channel Capacity

Quantum Secure Direct Communication (QSDC), a paradigm-shifting breakthrough in quantum communication, exploits quantum states for unmediated information transmission. Rooted in the inviolable fundamental laws of quantum mechanics, QSDC enables ultrasensitive detection of even the faintest eavesdropping attempts, guaranteeing true communication security solely when no interference exists. If eavesdropping or intrusion is detected mid-transmission, the system instantly alerts users and severs data flow, shielding them from unauthorized tracking and mitigating hacker threats. Over two decades, QSDC has seen extraordinary advancements, currently attaining kilobit-per-second transmission over 100 km of commercial optical fiber. However, its practical scalability remains constrained by insufficient transmission rates, a critical bottleneck. Semantic communication, which drastically boosts transmission efficiency by extracting core information features, nevertheless stays vulnerable to malicious intrusions. Integrating these paradigms promises to simultaneously enhance equivalent data rate and security. Herein, we propose and experimentally validate a quantum semantic communication scheme, applying it to 3D point clouds. It achieves a 46.30-fold efficiency gain over direct transmission, surpassing both Wyner and Shannon capacity limits. This breakthrough not only clears the path for large-scale QSDC deployment but also marks a pivotal milestone in quantum information science.

quant-ph↗

arXiv2025

On linkage bias-correction for estimators using iterated bootstraps

By amalgamating data from disparate sources, the resulting integrated dataset becomes a valuable resource for statistical analysis. In probabilistic record linkage, the effectiveness of such integration relies on the availability of linkage variables free from errors. Where this is lacking, the linked data set would suffer from linkage errors and the resultant analyses, linkage bias. This paper proposes a methodology leveraging the bootstrap technique to devise linkage bias-corrected estimators. Additionally, it introduces a test to assess whether increasing the number of bootstrap iterations meaningfully reduces linkage bias or merely inflates variance without further improving accuracy. An application of these methodologies is demonstrated through the analysis of a simulated dataset featuring hormone information, along with a dataset obtained from linking two data sets from the Australian Bureau of Statistics' labour mobility surveys.

stat.ME↗

arXiv2025

Wavelet Predictive Representations for Non-Stationary Reinforcement Learning

The real world is inherently non-stationary, with ever-changing factors, such as weather conditions and traffic flows, making it challenging for agents to adapt to varying environmental dynamics. Non-Stationary Reinforcement Learning (NSRL) addresses this challenge by training agents to adapt rapidly to sequences of distinct Markov Decision Processes (MDPs). However, existing NSRL approaches often focus on tasks with regularly evolving patterns, leading to limited adaptability in highly dynamic settings. Inspired by the success of Wavelet analysis in time series modeling, specifically its ability to capture signal trends at multiple scales, we propose WISDOM to leverage wavelet-domain predictive task representations to enhance NSRL. WISDOM captures these multi-scale features in evolving MDP sequences by transforming task representation sequences into the wavelet domain, where wavelet coefficients represent both global trends and fine-grained variations of non-stationary changes. In addition to the auto-regressive modeling commonly employed in time series forecasting, we devise a wavelet temporal difference (TD) update operator to enhance tracking and prediction of MDP evolution. We theoretically prove the convergence of this operator and demonstrate policy improvement with wavelet task representations. Experiments on diverse benchmarks show that WISDOM significantly outperforms existing baselines in both sample efficiency and asymptotic performance, demonstrating its remarkable adaptability in complex environments characterized by non-stationary and stochastically evolving tasks.

cs.LG↗

arXiv2025

Reconfigurable Physical Unclonable Function based on SOT-MRAM Chips

Hardware-based security primitives have become critical to enhancing information security in the Internet of Things (IoT) era. Physical unclonable functions (PUFs) utilize the inherent variations in the manufacturing process to generate cryptographic keys unique to a device. Reconfigurable PUFs (rPUFs) can update cryptographic keys for enhanced security in dynamic operational scenarios involving huge amounts of data, which makes them suitable for implementation in CMOS-integrated spin-orbit torque magnetic random access memory (SOT-MRAM) chips. However, a key challenge is achieving real-time reconfiguration independent of the environmental conditions, particularly the operating temperature. We propose a dual-pulse reconfiguration strategy for rPUFs in CMOS-integrated SOT-MRAM chips that effectively widens the operating window and achieves resilience across a wide range of operating temperatures without the need for dynamic feedback that overly complicates circuit design. The proposed strategy lays a solid foundation for the next generation of hardware-based security primitives to protect IoT architectures.

cs.CR↗

arXiv2025

WiFiSim: Simulating WiFi Probe Requests via AOSP Analysis and Device Behavior Modeling

WiFi probe request (PR) frames encode fine-grained device interactions and serve as a critical basis for mobility and crowd analytics. However, pervasive MAC address randomization and the scarcity of labeled datasets hinder progress in PR-based studies. We introduce WiFiSim, a simulation framework that reconstructs PR generation through Android Open Source Project (AOSP) protocol analysis and finite-state device behavior modeling. WiFiSim identifies the key determinants of PR structure and timing while capturing realistic user-driven state transitions. Experiments show that WiFiSim achieves less than 5% deviation from real measurements in both distributional and temporal dynamics, scales to large-scale dataset synthesis, and enables reliable evaluation of downstream applications. Source code and sample datasets are publicly released to foster reproducible research.

cs.NI↗

arXiv2025

LastingBench: Defend Benchmarks Against Knowledge Leakage

The increasing complexity of large language models (LLMs) raises concerns about their ability to "cheat" on standard Question Answering (QA) benchmarks by memorizing task-specific data. This undermines the validity of benchmark evaluations, as they no longer reflect genuine model capabilities but instead the effects of data leakage. While prior work has focused on detecting such leakage, little attention has been given to mitigating its impact and preserving the long-term utility of benchmarks. In this paper, we introduce LastingBench, a novel framework designed to continuously reinforce and safeguard existing benchmarks against knowledge leakage. LastingBench identifies leakage points in the context through perturbation, then rewrites the leakage points to counterfactual ones-disrupting memorization while preserving the benchmark's original evaluative intent. Evaluations of state-of-the-art QA benchmarks show significant performance gaps, highlighting the efficacy of LastingBench in reducing memorization effects. LastingBench offers a practical and scalable solution to ensure benchmark robustness over time, promoting fairer and more interpretable evaluations of LLMs.

cs.CL↗

arXiv2025

Meta-Policy Reflexion: Reusable Reflective Memory and Rule Admissibility for Resource-Efficient LLM Agent

Large language model (LLM) agents achieve impressive single-task performance but commonly exhibit repeated failures, inefficient exploration, and limited cross-task adaptability. Existing reflective strategies (e.g., Reflexion, ReAct) improve per-episode behavior but typically produce ephemeral, task-specific traces that are not reused across tasks. Reinforcement-learning based alternatives can produce transferable policies but require substantial parameter updates and compute. In this work we introduce Meta-Policy Reflexion (MPR): a hybrid framework that consolidates LLM-generated reflections into a structured, predicate-like Meta-Policy Memory (MPM) and applies that memory at inference time through two complementary mechanisms soft memory-guided decoding and hard rule admissibility checks(HAC). MPR (i) externalizes reusable corrective knowledge without model weight updates, (ii) enforces domain constraints to reduce unsafe or invalid actions, and (iii) retains the adaptability of language-based reflection. We formalize the MPM representation, present algorithms for update and decoding, and validate the approach in a text-based agent environment following the experimental protocol described in the provided implementation (AlfWorld-based). Empirical results reported in the supplied material indicate consistent gains in execution accuracy and robustness when compared to Reflexion baselines; rule admissibility further improves stability. We analyze mechanisms that explain these gains, discuss scalability and failure modes, and outline future directions for multimodal and multi-agent extensions.

cs.AI↗

arXiv2025

Temporal regularity for the stochastic heat equation with rough dependence in space

Consider the nonlinear stochastic heat equation $$ \frac{\partial u (t,x)}{\partial t}=\frac{\partial^2 u (t,x)}{\partial x^2}+ σ(u (t,x))\dot{W}(t,x),\quad t> 0,\, x\in \mathbb{R}, $$ where $\dot W$ is a Gaussian noise which is white in time and has the covariance of a fractional Brownian motion with Hurst parameter $H\in(\frac 14,\frac 12)$ in the space variable. When $σ(0)=0$, the well-posedness of the solution and its Hölder continuity have been proved by Hu et al. \cite{HHLNT2017}. In this paper, we study the asymptotic properties of the temporal gradient $u(t+\varepsilon, x)-u(t, x)$ at any fixed $t \ge 0$ and $x\in \mathbb R$, as $\varepsilon\downarrow 0$. As applications, we deduce Khintchine's law of iterated logarithm, Chung's law of iterated logarithm, and a result on the $q$-variations of the temporal process $\{u(t, x)\}_{t \ge 0}$, where $x\in \mathbb R$ is fixed.

math.PR↗

arXiv2025

Monolithic low-noise erbium-doped thin-film lithium niobate waveguide amplifier with 18 dB fiber to fiber net gain

Erbium-doped waveguide amplifiers have captured great attentions in recent years due to the rapid advance of photonic integration materials and fabrication techniques. In this work, a compact erbium-doped thin-film lithium niobate waveguide amplifier integrated with high-efficiency edge-couplers on the small footprint of 2 mm{\cross}25 mm, achieving >18 dB fiber-to-fiber (off-chip) net gain with bidirectional pumping by ~1480 nm laser diodes, is fabricated by the photolithography assisted chemo-mechanical etching technique. The fiber-to-fiber noise figures of the amplifier are also characterized to be around 5 dB, and the maximum amplified signal powers at the output fiber are above 13 dBm. Theoretical amplifier modeling resolving the erbium absorption and emission spectra predicts the efficient gain scaling with waveguide length for most of the telecom C-band wavelengths. The demonstrated high-external-gain erbium-doped waveguide amplifier will benefit various applications from optical communication and metrology to integrated photonic computing and artificial intelligence

physics.optics↗

arXiv2025

Exploiting Scale-Variant Attention for Segmenting Small Medical Objects

Early detection and accurate diagnosis can predict the risk of malignant disease transformation, thereby increasing the probability of effective treatment. Identifying mild syndrome with small pathological regions serves as an ominous warning and is fundamental in the early diagnosis of diseases. While deep learning algorithms, particularly convolutional neural networks (CNNs), have shown promise in segmenting medical objects, analyzing small areas in medical images remains challenging. This difficulty arises due to information losses and compression defects from convolution and pooling operations in CNNs, which become more pronounced as the network deepens, especially for small medical objects. To address these challenges, we propose a novel scale-variant attention-based network (SvANet) for accurately segmenting small-scale objects in medical images. The SvANet consists of scale-variant attention, cross-scale guidance, Monte Carlo attention, and vision transformer, which incorporates cross-scale features and alleviates compression artifacts for enhancing the discrimination of small medical objects. Quantitative experimental results demonstrate the superior performance of SvANet, achieving 96.12%, 96.11%, 89.79%, 84.15%, 80.25%, 73.05%, and 72.58% in mean Dice coefficient for segmenting kidney tumors, skin lesions, hepatic tumors, polyps, surgical excision cells, retinal vasculatures, and sperms, which occupy less than 1% of the image areas in KiTS23, ISIC 2018, ATLAS, PolypGen, TissueNet, FIVES, and SpermHealth datasets, respectively.

eess.IV↗

arXiv2025

Monolithically Integrated Optical Convolutional Processors on Thin Film Lithium Niobate

Photonic neural networks (PNNs) of sufficiently large physical dimensions and high operation accuracies are envisaged as an ideal candidate for breaking the major bottlenecks in the current artificial intelligence architectures in terms of latency, energy efficiency and computational power. To achieve this vision, it is of vital importance to scale up the PNNs and in the meantime reduce the high demand on the dimensions required by the PNNs. The underlying cause of this strategy is the enormous gap between the scales of photonic and electronic integrated circuits. Here, we demonstrate monolithically integrated optical convolutional processors on thin film lithium niobate (TFLN) to enable large-scale programmable convolution kernels and in turn greatly reduce the dimensions required by the subsequent fully connected layers. Experimental validation achieves high classification accuracies of 96%/86% on the MNIST/Fashion-MNIST datasets and 84.6% on the AG News dataset, while dramatically reducing the required subsequent fully connected layer dimensions to 196x10 (from 784x10) and 175x4 (from 800x4), respectively. Furthermore, our devices can be driven by commercial field-programmable gate array (FPGA) systems, a unique advantage in addition to their scalable channel number and kernel size, our architecture provides a solution to build practical machine learning photonic devices.

physics.optics↗

arXiv2025

Power-efficient ultra-broadband soliton microcombs in resonantly-coupled microresonators

The drive to miniaturize optical frequency combs for practical deployment has spotlighted microresonator solitons as a promising chip-scale candidate. However, these soliton microcombs could be very power-hungry when their span increases, especially with fine comb spacings. As a result, realizing an octave-spanning comb at microwave repetition rates for direct optical-microwave linkage is considered not possible for photonic integration due to the high power requirements. Here, we introduce the concept of resonant-coupling to soliton microcombs to reduce pump consumption significantly. Compared to conventional waveguide-coupled designs, we demonstrate (i) a threefold increase in spectral span for high-power combs and (ii) up to a tenfold reduction in repetition frequency for octave-spanning operation. This configuration is compatible with laser integration and yields reliable, turnkey soliton generation. By eliminating the long-standing pump-power bottleneck, microcombs will soon become readily available for portable optical clocks, massively parallel data links, and field-deployable spectrometers.

physics.optics↗

arXiv2025

Integrated optomechanical ultrasonic sensors with nano-Pascal-level sensitivity

Ultrasonic sensors are widely used for object detection and localization in underwater and biological settings. The operational range and spatial resolution are inherently limited by sensor sensitivity, in which conventional piezoelectric transducers have been overwhelmed by advanced photonic sensors. Here, we demonstrate an optomechanical ultrasonic sensor integrated into a photonic platform, which comprises a suspended SiO2 membrane embedded with a high-Q Si3N4 microring resonator. By exploiting simultaneous optical and mechanical resonances, the sensor achieves a record low noise-equivalent pressure (NEP) of 218 nPa/Hz^1/2 at 289 kHz in air and 9.6 nPa/Hz^1/2 at 52 kHz in water. We demonstrate its versatility through photoacoustic gas spectroscopy in air and underwater ultrasound imaging, achieving a minimum detectable C2H2 concentration of 2.9 ppm (integration time 1 s) and an imaging resolution of 1.89 mm, respectively. Our work represents a significant advancement in compact CMOS-compatible ultrasound sensing, unlocking new possibilities in biomedical imaging, environmental monitoring, industrial testing, and underwater communications.

physics.optics↗

arXiv2025

Weak TransNet: A Petrov-Galerkin based neural network method for solving elliptic PDEs

While deep learning has achieved remarkable success in solving partial differential equations (PDEs), it still faces significant challenges, particularly when the PDE solutions have low regularity or singularities. To address these issues, we propose the Weak TransNet (WTN) method, based on a Petrov-Galerkin formulation, for solving elliptic PDEs in this work, though its framework may extend to other classes of equations. Specifically, the neural feature space defined by TransNet (Zhang et al., 2023) is used as the trial space, while the test space is composed of radial basis functions. Since the solution is expressed as a linear combination of trial functions, the coefficients can be determined by minimizing the weak PDE residual via least squares. Thus, this approach could help mitigate the challenges of non-convexity and ill-conditioning that often arise in neural network training. Furthermore, the WTN method is extended to handle problems whose solutions exhibit multiscale features or possess sharp variations. Several numerical experiments are presented to demonstrate the robustness and efficiency of the proposed methods.

math.NA↗

arXiv2025

The Chinese Pulsar Timing Array data release I. Single pulsar noise analysis

The Chinese Pulsar Timing Array (CPTA) has collected observations from 57 millisecond pulsars using the Five-hundred-meter Aperture Spherical Radio Telescope (FAST) for close to three years, for the purpose of searching for gravitational waves (GWs). To robustly search for ultra-low-frequency GWs, pulsar timing arrays (PTAs) need to use models to describe the noise from the individual pulsars. We report on the results from the single pulsar noise analysis of the CPTA data release I (DR1). Conventionally, power laws in the frequency domain are used to describe pulsar red noise and dispersion measurement (DM) variations over time. Employing Bayesian methods, we found the choice of number and range of frequency bins with the highest evidence for each pulsar individually. A comparison between a dataset using DM piecewise measured (DMX) values and a power-law Gaussian process to describe the DM variations shows strong Bayesian evidence in favour of the power-law model. Furthermore, we demonstrate that the constraints obtained from four independent software packages are very consistent with each other. The short time span of the CPTA DR1, paired with the large sensitivity of FAST, has proved to be a challenge for the conventional noise model using a power law. This mainly shows in the difficulty to separate different noise terms due to their covariances with each other. Nineteen pulsars are found to display covariances between the short-term white noise and long-term red and DM noise. With future CPTA datasets, we expect that the degeneracy can be broken. Finally, we compared the CPTA DR1 results against the noise properties found by other PTA collaborations. While we can see broad agreement, there is some tension between different PTA datasets for some of the overlapping pulsars. This could be due to the differences in the methods and frequency range compared to the other PTAs.

astro-ph.HE↗

arXiv2025

Order within disorder: spectral key generation and distribution in random lasers

In secure communication, highly random entropy sources are essential for information security. Random lasers (RLs), which arise from multiple scattering in disordered structures, are potentially ideal entropy sources. Traditionally, RLs are viewed as disordered and unpredictable. However, in this work, we present novel evidence that orderly patterns exist beneath the seemingly disordered outputs of RLs. Utilizing deep learning techniques, a variety of advanced neural network models are used to analyze the spectral data in multiple dimensions. The results show that the time series of RLs spectra are unpredictable, but spectral wavelength component intensities can be recovered due to inter-modal correlations. This finding not only breaks through the traditional perception that RLs are unpredictable, but also reveals for the first time that RLs have the dual characteristics of both randomness and determinism. Based on this new characteristic, we further expand the application field of RLs and innovatively design a new type of key generation and distribution scheme. In this scheme, the disordered property of RLs is used for key generation to ensure high randomness, while their ordered property is used for key distribution to guarantee accuracy and reliability. The scheme provides a new strategy for secure communication.

physics.optics↗