arXiv subjects

Li Chen

Publications and source records attributed to Li Chen.

At least 199 records · Page 11Linked to original sources

arXiv2025

On Chip Quantum States Generation by Incoherent Light

On-chip quantum sources based on nonlinear processes are pivotal components in integrated photonics, driving significant advancements in quantum information technologies over recent decades. Usually, the pump coherence has been considered to be crucial for ensuring the quality of generated states, therefore incoherent light is rarely used in quantum information processing. In this work, we explore and reveal the constructive influence of pumped temporal incoherence on the quantum properties of photon sources. Taking silicon waveguides as nonlinear media, we theoretically show that temporal incoherence of light can improve pumping utilization efficiency, resulting in higher source brightness in a spontaneous four-wave mixing process, and the spectrally uncorrelated nature of incoherent light is transferred to the generated photon source, allowing high-purity state preparation. Experimentally, we obtain a higher photon pair generation rate and the lower heralded second-order autocorrelation with an Amplified Spontaneous Emission source. Additionally, we successfully generate a polarization-entangled state with Bell inequality violation of S = 2.64 and a fidelity of 95.7%. Our study reveals the mechanism behind incoherently pumped quantum states and presents a method for generating photon sources using an easily accessible incoherent light.

quant-ph↗

arXiv2025

Rapid passage to ordered states in Rydberg atom arrays

Given a finite lifetime, a ubiquitous challenge in quantum systems is how to prepare a target state in the shortest possible time. This issue is particularly relevant for Rydberg atom arrays in optical tweezers where the dephasing time is typically restricted to a few microseconds. In this work, we develop a rapid passage to ordered many-body states in a Rydberg atomic chain, which allows the transition of the system to various ordered phases in the phase diagram, such as the Z$_2$-, Z$_3$-, and Z$_4$-ordered antiferromagnetic states. Our scheme ramps the parameter in a "non-adiabatic to quasi-adiabatic to non-adiabatic (NQN)" manner. The NQN configuration significantly reduces the time cost required for the state preparation using entirely adiabatic methods, and is generally appliable to sizable number of atoms. Moreover, we experimentally validate the NQN scheme on the neutral-atom quantum cloud computer Aquila.

quant-ph↗

arXiv2025

Riesz transform, function spaces and their applications on infinite dimensional compact groups

On a compact connected group $G$, consider the infinitesimal generator $-L$ of a central symmetric Gaussian convolution semigroup $(μ_t)_{t>0}$. We establish several regularity results of the solution to the Poisson equation $LU=F$, both in strong and weak senses. To this end, we introduce two classes of Lipschitz spaces for $1\le p\le \infty$: $Λ_θ^p$, defined via the associated Markov semigroup, and $\mathrm L_θ^p$, defined via the intrinsic distance. In the strong sense, we prove a priori Sobolev regularity and Lipschitz regularity in the class of $Λ_θ^p$ space. In the distributional sense, we further show local regularity in the class of $\mathrm L_θ^{\infty}$ space. These results require some strong assumptions on $-L$. Our main techniques build on the differentiability of the associated semigroup, explicit dimension-free $L^p$ ($1<p<\infty$) boundedness of first and second order Riesz transforms, and a comparison between the two Lipschitz norms.

math.AP↗

arXiv2025

Optimal Control for Kuramoto Model: from Many-Particle Liouville Equation to Diffusive Mean-Field Problem

In this paper, we investigate the mean-field optimal control problem of a swarm of Kuramoto oscillators. Using the notion of wrapped distribution, we explain the connection between the stochastic particle system and the mean-field PDE on the periodic domain. In the limit of an infinite number of oscillators the collective dynamics of the agents' density is described by a diffusive mean-field model in the form of a non-local PDE, where the non-locality arises from the synchronization mechanism. We prove the existence of the optimal control of the mean-field model by using $Γ$-convergence strategy of the cost functional corresponding to the Liouville equation on the particle level. In the discussion of propagation of chaos for fixed control functions we complete the relative entropy estimate by using large deviation estimate given by \cite{MR3858403}.

math.AP↗

arXiv2025

The Digital Cybersecurity Expert: How Far Have We Come?

The increasing deployment of large language models (LLMs) in the cybersecurity domain underscores the need for effective model selection and evaluation. However, traditional evaluation methods often overlook specific cybersecurity knowledge gaps that contribute to performance limitations. To address this, we develop CSEBenchmark, a fine-grained cybersecurity evaluation framework based on 345 knowledge points expected of cybersecurity experts. Drawing from cognitive science, these points are categorized into factual, conceptual, and procedural types, enabling the design of 11,050 tailored multiple-choice questions. We evaluate 12 popular LLMs on CSEBenchmark and find that even the best-performing model achieves only 85.42% overall accuracy, with particular knowledge gaps in the use of specialized tools and uncommon commands. Different LLMs have unique knowledge gaps. Even large models from the same family may perform poorly on knowledge points where smaller models excel. By identifying and addressing specific knowledge gaps in each LLM, we achieve up to an 84% improvement in correcting previously incorrect predictions across three existing benchmarks for two cybersecurity tasks. Furthermore, our assessment of each LLM's knowledge alignment with specific cybersecurity roles reveals that different models align better with different roles, such as GPT-4o for the Google Senior Intelligence Analyst and Deepseek-V3 for the Amazon Privacy Engineer. These findings underscore the importance of aligning LLM selection with the specific knowledge requirements of different cybersecurity roles for optimal performance.

cs.CR↗

arXiv2025

LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving

Existing learning-based autonomous driving (AD) systems face challenges in comprehending high-level information, generalizing to rare events, and providing interpretability. To address these problems, this work employs Large Language Models (LLMs) as a decision-making component for complex AD scenarios that require human commonsense understanding. We devise cognitive pathways to enable comprehensive reasoning with LLMs, and develop algorithms for translating LLM decisions into actionable driving commands. Through this approach, LLM decisions are seamlessly integrated with low-level controllers by guided parameter matrix adaptation. Extensive experiments demonstrate that our proposed method not only consistently surpasses baseline approaches in single-vehicle tasks, but also helps handle complex driving behaviors even multi-vehicle coordination, thanks to the commonsense reasoning capabilities of LLMs. This paper presents an initial step toward leveraging LLMs as effective decision-makers for intricate AD scenarios in terms of safety, efficiency, generalizability, and interoperability. We aspire for it to serve as inspiration for future research in this field. Project page: https://sites.google.com/view/llm-mpc

cs.RO↗

arXiv2025

Co-optimizing Physical Reconfiguration Parameters and Controllers for an Origami-inspired Reconfigurable Manipulator

Reconfigurable robots that can change their physical configuration post-fabrication have demonstrate their potential in adapting to different environments or tasks. However, it is challenging to determine how to optimally adjust reconfigurable parameters for a given task, especially when the controller depends on the robot's configuration. In this paper, we address this problem using a tendon-driven reconfigurable manipulator composed of multiple serially connected origami-inspired modules as an example. Under tendon actuation, these modules can achieve different shapes and motions, governed by joint stiffnesses (reconfiguration parameters) and the tendon displacements (control inputs). We leverage recent advances in co-optimization of design and control for robotic system to treat reconfiguration parameters as design variables and optimize them using reinforcement learning techniques. We first establish a forward model based on the minimum potential energy method to predict the shape of the manipulator under tendon actuations. Using the forward model as the environment dynamics, we then co-optimize the control policy (on the tendon displacements) and joint stiffnesses of the modules for goal reaching tasks while ensuring collision avoidance. Through co-optimization, we obtain optimized joint stiffness and the corresponding optimal control policy to enable the manipulator to accomplish the task that would be infeasible with fixed reconfiguration parameters (i.e., fixed joint stiffness). We envision the co-optimization framework can be extended to other reconfigurable robotic systems, enabling them to optimally adapt their configuration and behavior for diverse tasks and environments.

cs.RO↗

arXiv2025

The ALMA-ATOMS survey: A sample of weak hot core candidates identified through line stacking

Hot cores represent critical astrophysical environments for high-mass star formation, distinguished by their rich spectra of organic molecular emission lines. We aim to utilize high-angular resolution molecular line data from ALMA to identify hot cores, with a particular focus on weak-emission candidates, and to provide one of the largest samples of hot core candidates. We propose to use spectral stacking and imaging techniques of complex organic molecules (COMs) in the ALMA-ATOMS survey, including line identification & weights, segmentation of line datacubes, resampling, stacking and normalization, moment 0 maps, and data analysis, to search for hot core candidates. We classify cores with dense emission of CH3OH and at least one molecule from the other six molecules as hot core candidates. In addition to the existing sample of 60 strong hot cores from the ALMA-ATOMS survey, we have detected 40 new weak candidates through stacking. All hot core candidates display compact emission from at least one of the other six COM species. For the strong sample, the stacking method provides molecular column density estimates that are consistent with previous fitting results. For the newly identified weak candidates, all species except CH3CHO show compact emission in the stacked image, which cannot be fully resolved spatially. These weak candidates exhibit column densities of COMs that are approximately one order of magnitude lower than those of the strong sample. The entire hot core sample, including the weak candidates, reveals tight correlations between the compact emission of CH3OH and other COM species, suggesting they may share a similar chemical environment for COMs, with CH3OH potentially acting as a precursor for other COMs. The molecular line stacking technique is used to identify hot core candidates in this work, leading to the identification of 40 new hot core candidates.

astro-ph.GA↗

arXiv2025

An all optical broadband tunable quantum frequency shifter

A frequency shifter of the photon is a key component for frequency-multiplexed high-capacity quantum communications and frequency-encoded quantum computation. Existed methods for shifting the frequency of a photon based on electro-optical, or acousto-optical effect, however, suffer the limited frequency shift up to a few hundreds of GHz, furthermore, high-quality micro-wave electronics are required. The frequency of a photon can also be shifted with the frequency difference equal to the frequency of pump laser by using an all optical-wave-mixing approach, which is usually about tens of THz. So, there is a big frequency shifting gap between these methods. Here, we propose a new scheme of a quantum frequency shifter based on an all-optical wave-mixing process, which can theoretically achieve a frequency shift ranging from GHz to a few THz, therefore bridging the gap. As a principle of poof, by using two pump beams in a three-wave mixing cascading process, a heralded single photon is frequency-shifted more than 400GHz, and the shift can be tuned continuously over broadband by changing the frequency difference between two pump lasers. Besides, high coincidence to accidence ratio between the shifted photons and the heralded photon indicates the preserve of quantum properties. The present quantum frequency shifter is in analog to an electro-optical based shifter, but with much broader tuning ability. Our all-optical quantum frequency shifter will become a fundamental building block for high-speed quantum communication networks and frequency domain photonic quantum computation.

quant-ph↗

arXiv2025

MLLM-as-a-Judge for Image Safety without Human Labeling

Image content safety has become a significant challenge with the rise of visual media on online platforms. Meanwhile, in the age of AI-generated content (AIGC), many image generation models are capable of producing harmful content, such as images containing sexual or violent material. Thus, it becomes crucial to identify such unsafe images based on established safety rules. Pre-trained Multimodal Large Language Models (MLLMs) offer potential in this regard, given their strong pattern recognition abilities. Existing approaches typically fine-tune MLLMs with human-labeled datasets, which however brings a series of drawbacks. First, relying on human annotators to label data following intricate and detailed guidelines is both expensive and labor-intensive. Furthermore, users of safety judgment systems may need to frequently update safety rules, making fine-tuning on human-based annotation more challenging. This raises the research question: Can we detect unsafe images by querying MLLMs in a zero-shot setting using a predefined safety constitution (a set of safety rules)? Our research showed that simply querying pre-trained MLLMs does not yield satisfactory results. This lack of effectiveness stems from factors such as the subjectivity of safety rules, the complexity of lengthy constitutions, and the inherent biases in the models. To address these challenges, we propose a MLLM-based method includes objectifying safety rules, assessing the relevance between rules and images, making quick judgments based on debiased token probabilities with logically complete yet simplified precondition chains for safety rules, and conducting more in-depth reasoning with cascaded chain-of-thought processes if necessary. Experiment results demonstrate that our method is highly effective for zero-shot image safety judgment tasks.

cs.CV↗

arXiv2025

Enhancing Causal Effect Estimation with Diffusion-Generated Data

Estimating causal effects from observational data is inherently challenging due to the lack of observable counterfactual outcomes and even the presence of unmeasured confounding. Traditional methods often rely on restrictive, untestable assumptions or necessitate valid instrumental variables, significantly limiting their applicability and robustness. In this paper, we introduce Augmented Causal Effect Estimation (ACEE), an innovative approach that utilizes synthetic data generated by a diffusion model to enhance causal effect estimation. By fine-tuning pre-trained generative models, ACEE simulates counterfactual scenarios that are otherwise unobservable, facilitating accurate estimation of individual and average treatment effects even under unmeasured confounding. Unlike conventional methods, ACEE relaxes the stringent unconfoundedness assumption, relying instead on an empirically checkable condition. Additionally, a bias-correction mechanism is introduced to mitigate synthetic data inaccuracies. We provide theoretical guarantees demonstrating the consistency and efficiency of the ACEE estimator, alongside comprehensive empirical validation through simulation studies and benchmark datasets. Results confirm that ACEE significantly improves causal estimation accuracy, particularly in complex settings characterized by nonlinear relationships and heteroscedastic noise.

stat.ME↗

arXiv2025

Beyond Traditional Coherence Time: An Electromagnetic Perspective for Mobile Channels

Channel coherence time has been widely regarded as a critical parameter in the design of mobile systems. However, a prominent challenge lies in integrating electromagnetic (EM) polarization effects into the derivation of the channel coherence time. In this paper, we develop a framework to analyze the impact of polarization mismatch on the channel coherence time. Specifically, we first establish an EM channel model to capture the essence of EM wave propagation. Based on this model, we then derive the EM temporal correlation function, incorporating the effects of polarization mismatch and beam misalignment. Further, considering the random orientation of the mobile user equipment (UE), we derive a closed-form solution for the EM coherence time in the turning scenario. When the trajectory degenerates into a straight line, we also provide a closed-form lower bound on the EM coherence time. The simulation results validate our theoretical analysis and reveal that neglecting the EM polarization effects leads to overly optimistic estimates of the EM coherence time.

eess.SP↗

arXiv2025

Accelerated Approximate Optimization of Multi-Commodity Flows on Directed Graphs

We provide $m^{1+o(1)}kε^{-1}$-time algorithms for computing multiplicative $(1 - ε)$-approximate solutions to multi-commodity flow problems with $k$-commodities on $m$-edge directed graphs, including concurrent multi-commodity flow and maximum multi-commodity flow. To obtain our results, we provide new optimization tools of potential independent interest. First, we provide an improved optimization method for solving $\ell_{q, p}$-regression problems to high accuracy. This method makes $\tilde{O}_{q, p}(k)$ queries to a high accuracy convex minimization oracle for an individual block, where $\tilde{O}_{q, p}(\cdot)$ hides factors depending only on $q$, $p$, or $\mathrm{poly}(\log m)$, improving upon the $\tilde{O}_{q, p}(k^2)$ bound of [Chen-Ye, ICALP 2024]. As a result, we obtain the first almost-linear time algorithm that solves $\ell_{q, p}$ flows on directed graphs to high accuracy. Second, we present optimization tools to reduce approximately solving composite $\ell_{1, \infty}$-regression problems to solving $m^{o(1)}ε^{-1}$ instances of composite $\ell_{q, p}$-regression problem. The method builds upon recent advances in solving box-simplex games [Jambulapati-Tian, NeurIPS 2023] and the area convex regularizer introduced in [Sherman, STOC 2017] to obtain faster rates for constrained versions of the problem. Carefully combining these techniques yields our directed multi-commodity flow algorithm.

cs.DS↗

arXiv2025

Dual Phonon Universe in a Spinor Bose Gas

We show the possibility of simulating a dual universe in a pseudospin-1/2 Bose-Einstein condensate (BEC), wherein two phononic modes experience distinct curved spacetime metrics. Through ramping the interspecies interaction of the BEC, we observe that one universe expands, and in the mean time, the other contracts. These findings can be directly verified in existing cold-atom experimental setups by detecting the correlators of the scaled density contrast (SDC): The sizes of the expanded/contracted universes can be deduced from the SDC's oscillating frequencies, while the expanding/contracting histories are reflected in the SDC's amplitude. Our study demonstrates the great potential of spinor BECs for exploring multiverse-related physics in laboratory settings.

cond-mat.quant-gas↗

arXiv2025

Comprehensive Manuscript Assessment with Text Summarization Using 69707 articles

Rapid and efficient assessment of the future impact of research articles is a significant concern for both authors and reviewers. The most common standard for measuring the impact of academic papers is the number of citations. In recent years, numerous efforts have been undertaken to predict citation counts within various citation windows. However, most of these studies focus solely on a specific academic field or require early citation counts for prediction, rendering them impractical for the early-stage evaluation of papers. In this work, we harness Scopus to curate a significantly comprehensive and large-scale dataset of information from 69707 scientific articles sourced from 99 journals spanning multiple disciplines. We propose a deep learning methodology for the impact-based classification tasks, which leverages semantic features extracted from the manuscripts and paper metadata. To summarize the semantic features, such as titles and abstracts, we employ a Transformer-based language model to encode semantic features and design a text fusion layer to capture shared information between titles and abstracts. We specifically focus on the following impact-based prediction tasks using information of scientific manuscripts in pre-publication stage: (1) The impact of journals in which the manuscripts will be published. (2) The future impact of manuscripts themselves. Extensive experiments on our datasets demonstrate the superiority of our proposed model for impact-based prediction tasks. We also demonstrate potentials in generating manuscript's feedback and improvement suggestions.

cs.CL↗

arXiv2025

Hardware-efficient quantum error correction via concatenated bosonic qubits

In order to solve problems of practical importance, quantum computers will likely need to incorporate quantum error correction, where a logical qubit is redundantly encoded in many noisy physical qubits. The large physical-qubit overhead typically associated with error correction motivates the search for more hardware-efficient approaches. Here, using a microfabricated superconducting quantum circuit, we realize a logical qubit memory formed from the concatenation of encoded bosonic cat qubits with an outer repetition code of distance $d=5$. The bosonic cat qubits are passively protected against bit flips using a stabilizing circuit. Cat-qubit phase-flip errors are corrected by the repetition code which uses ancilla transmons for syndrome measurement. We realize a noise-biased CX gate which ensures bit-flip error suppression is maintained during error correction. We study the performance and scaling of the logical qubit memory, finding that the phase-flip correcting repetition code operates below threshold, with logical phase-flip error decreasing with code distance from $d=3$ to $d=5$. Concurrently, the logical bit-flip error is suppressed with increasing cat-qubit mean photon number. The minimum measured logical error per cycle is on average $1.75(2)\%$ for the distance-3 code sections, and $1.65(3)\%$ for the longer distance-5 code, demonstrating the effectiveness of bit-flip error suppression throughout the error correction cycle. These results, where the intrinsic error suppression of the bosonic encodings allows us to use a hardware-efficient outer error correcting code, indicate that concatenated bosonic codes are a compelling paradigm for reaching fault-tolerant quantum computation.

quant-ph↗

arXiv2025

Spatiotemporal Learning with Context-aware Video Tubelets for Ultrasound Video Analysis

Computer-aided pathology detection algorithms for video-based imaging modalities must accurately interpret complex spatiotemporal information by integrating findings across multiple frames. Current state-of-the-art methods operate by classifying on video sub-volumes (tubelets), but they often lose global spatial context by focusing only on local regions within detection ROIs. Here we propose a lightweight framework for tubelet-based object detection and video classification that preserves both global spatial context and fine spatiotemporal features. To address the loss of global context, we embed tubelet location, size, and confidence as inputs to the classifier. Additionally, we use ROI-aligned feature maps from a pre-trained detection model, leveraging learned feature representations to increase the receptive field and reduce computational complexity. Our method is efficient, with the spatiotemporal tubelet classifier comprising only 0.4M parameters. We apply our approach to detect and classify lung consolidation and pleural effusion in ultrasound videos. Five-fold cross-validation on 14,804 videos from 828 patients shows our method outperforms previous tubelet-based approaches and is suited for real-time workflows.

cs.CV↗

arXiv2025

Exploiting Student Parallelism for Efficient GPU Inference of BERT-like Models in Online Services

Due to high accuracy, BERT-like models have been widely adopted by text mining and web searching. However, large BERT-like models suffer from inefficient online inference, facing the following two problems on GPUs: (1) their high accuracy relies on the large model depth, which linearly increases the sequential computation on GPUs; (2) stochastic and dynamic online workloads cause extra costs from batching and paddings. Therefore, we present \sys for the real-world setting of GPU inference on online workloads. At its core, \sys adopts stacking distillation and boosting ensemble, distilling the original deep model into a group of shallow but virtually stacked student models running in parallel. This enables \sys to achieve a lower model depth (e.g., two layers) than the others and the lowest inference latency while maintaining accuracy. In addition, adaptive student pruning realizes dynamic student numbers according to changing online workloads. Especially for occasional workload bursts, it can temporarily decrease the student number with minimal accuracy loss to improve system throughput. We conduct comprehensive experiments to verify the effectiveness, whose results show that \sys outperforms the baselines by $4.1\times\sim 1.6\times$ in latency while maintaining accuracy and achieves up to $22.27\times$ higher throughput for workload bursts.

cs.LG↗