arXiv subjects

Li Chen

Publications and source records attributed to Li Chen.

At least 217 records · Page 12Linked to original sources

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↗

arXiv2025

Formation and evolution of new primordial open cluster groups: Feedback-driven star formation

The formation mechanisms of open cluster (OCs) groups remain unclear due to limited sample sizes and data precision. Recent advancements in Gaia astrometric data provide an unprecedented opportunity to study OC groups in greater detail. This study aims to extend the sample of OC groups and investigate their formation and evolution mechanisms, with a focus on the role of stellar feedback in triggering star formation. We identify four new OC groups based on Gaia data, whose member OCs are spatially proximate and kinematically coherent. Their age spreads are consistent with the timescale of continuous star formation, suggesting that their member OCs formed sequentially from the same molecular cloud. N-body simulation results further reveal that these groups will gradually disperse, evolving into independent OCs. By analyzing the correlation between OC ages and their separation from potential SN explosion sites, we predict SN explosion regions around the birthplaces of OC groups. The strong correlation between OC ages and predicted SN explosion sites supports a supernova-triggered star formation scenario. Additionally, we trace pulsar (PSR) orbits to examine their association with these regions. We detected three PSRs near Group 1 and 26 PSRs near Group 2, whose birthplaces align with the predicted SN explosions regions. The presence of PSRs associated with OC groups provides additional observational evidence for SN explosions in this region, further supporting a supernova-triggered star formation scenario for G1 and G2. We propose that multiple SN explosions in a short period triggered the formation of Group 1 and Group 2, reinforcing the hierarchical star formation model. These results highlight the multi-scale interactions driving star and OC formation and provide new insights into the role of stellar feedback in shaping OC groups.

astro-ph.GA↗

arXiv2025

ATOMS: ALMA Three-millimeter Observations of Massive Star-forming regions -XXI. A Large-sample Observational Study of Ethanol and Dimethyl Ether in Hot Cores

Hot cores, as a stage of massive star formation, exhibit abundant line emissions of COMs. We present a deep line survey of two isomers of C$_2$H$_6$O: ethanol (C$_2$H$_5$OH; EA), and dimethyl ether (CH$_3$OCH$_3$; DE) as well as their possible precursor CH$_3$OH towards 60 hot cores by using the ALMA 3 mm line observations. EA is detected in 40 hot cores and DE is detected in 59 hot cores. Of these, EA and DE are simultaneously detected in 39 hot cores. We calculate rotation temperatures and column densities of EA and DE by using the XCLASS software. The average rotation temperature of EA is higher than that of DE, whereas the average column density of EA is lower than that of DE. Combined with previous studies of hot cores and hot corinos, we find strong column density correlations among EA and DE ($ρ$ = 0.92), EA and CH$_3$OH ($ρ$ = 0.82), as well as DE and CH$_3$OH ($ρ$ = 0.80). The column density ratios of EA/DE versus the column densities of CH$_3$OH remain nearly constant with values within ~ 1 order of magnitude. These strong correlations and the stable ratios, suggest that EA, DE, and CH$_3$OH could be chemically linked, with CH$_3$OH potentially serving as a precursor for EA and DE. Compared with chemical models, the three different warm-up timescale models result in the systematic overproduction of EA and the systematic underproduction of DE. Therefore, our large sample observations can provide crucial constraints on chemical models.

astro-ph.GA↗

arXiv2025

ALMA observations of CH3COCH3 and the related species CH3CHO, CH3OH, and C2H5CN in line-rich molecular cores

Context. Acetone (CH3COCH3) is a carbonyl-bearing complex organic molecule, yet interstellar observations of acetone remain limited. Studying the formation and distribution of CH3COCH3 in the interstellar medium can provide valuable insights into prebiotic chemistry and the evolution of interstellar molecules. Aims. We explore the spatial distribution of CH3COCH3 and its correlation with the O-bearing molecules acetaldehyde (CH3CHO) and methanol (CH3OH), as well as the N-bearing molecule ethyl cyanide (C2H5CN), in massive protostellar clumps. Methods. We observed 11 massive protostellar clumps using ALMA at 345 GHz, with an angular resolution of 0.7''-1.0''. Spectral line transitions were identified using the eXtended CASA Line Analysis Software Suite. We constructed integrated intensity maps of CH3COCH3, CH3CHO, CH3OH, and C2H5CN and derived their rotation temperatures, column densities, and abundances under the assumption of local thermodynamic equilibrium. Results. CH3COCH3 is detected in 16 line-rich cores from 9 massive protostellar clumps: 12 high-mass cores, 3 intermediate-mass cores, and 1 low-mass core. CH3CHO and CH3OH are also detected in all 16 cores, while C2H5CN is detected in 15. The integrated intensity maps reveal similar spatial distributions for CH3COCH3, CH3CHO, CH3OH, and C2H5CN. The line emission peaks of all four molecules coincide with the continuum emission peaks in regions without ultracompact HII regions. Significant correlations are observed in the abundances of these molecules, which also exhibit similar average temperatures. Conclusions. Our observational results, supported by chemical models, suggest that CH3COCH3, CH3CHO, and CH3OH originate from the same gas. The observed temperatures and abundances of CH3COCH3 are consistent with model predictions involving grain surface chemistry.

astro-ph.GA↗

arXiv2025

Creating multi-beam interference from two-beam interference with assistant of harmonics generation

Linear optics-based multi-beam interference (MBI), like the Fabry-Perot interferometer, plays an important role in precision optical metrology applications such as laser stabilization in optical clocks, precision spectroscopy, and gravitational wave detection. Here, we propose and experimentally verify a nonlinear optics-based MBI principle with the assistance of cascading and recycling harmonics generation of two-beam interference. By cascading and recycling the harmonics processes, in combining with optical power amplification (OPA) to compensate for power losses arising from limited nonlinear conversion efficiency, a total 16th harmonic is achieved, and the observed interference fringes gradually evolve from a sinusoidal curve to a Lorentz-like curve. In principle, there is no limitation on the number of cascading and recycling nonlinear processes with the assistance of OPAs and sharp interference fringes, analogous to those in a high-finesse cavity, can be obtained. The nonlinear optics-based MBI mechanism revealed here will find promising applications in precision optical metrology.

physics.optics↗

arXiv2025

SEAFL: Enhancing Efficiency in Semi-Asynchronous Federated Learning through Adaptive Aggregation and Selective Training

Federated Learning (FL) is a promising distributed machine learning framework that allows collaborative learning of a global model across decentralized devices without uploading their local data. However, in real-world FL scenarios, the conventional synchronous FL mechanism suffers from inefficient training caused by slow-speed devices, commonly known as stragglers, especially in heterogeneous communication environments. Though asynchronous FL effectively tackles the efficiency challenge, it induces substantial system overheads and model degradation. Striking for a balance, semi-asynchronous FL has gained increasing attention, while still suffering from the open challenge of stale models, where newly arrived updates are calculated based on outdated weights that easily hurt the convergence of the global model. In this paper, we present {\em SEAFL}, a novel FL framework designed to mitigate both the straggler and the stale model challenges in semi-asynchronous FL. {\em SEAFL} dynamically assigns weights to uploaded models during aggregation based on their staleness and importance to the current global model. We theoretically analyze the convergence rate of {\em SEAFL} and further enhance the training efficiency with an extended variant that allows partial training on slower devices, enabling them to contribute to global aggregation while reducing excessive waiting times. We evaluate the effectiveness of {\em SEAFL} through extensive experiments on three benchmark datasets. The experimental results demonstrate that {\em SEAFL} outperforms its closest counterpart by up to $\sim$22\% in terms of the wall-clock training time required to achieve target accuracy.

cs.DC↗

arXiv2025

Optimal coordination of resources: A solution from reinforcement learning

Efficient allocation is important in nature and human society, where individuals frequently compete for limited resources. The Minority Game (MG) is perhaps the simplest toy model to address this issue. However, most previous solutions assume that the strategies are provided a priori and static, failing to capture their adaptive nature. Here, we introduce the reinforcement learning (RL) paradigm to MG, where individuals adjust decisions based on accumulated experience and expected rewards dynamically. We find that this RL framework achieves optimal resource coordination when individuals balance the exploitation of experience with random exploration. Yet, the imbalanced strategies of the two lead to suboptimal partial coordination or even anti-coordination. Our mechanistic analysis reveals a symmetry-breaking in action preferences at the optimum, offering a fresh solution to the MG and new insights into the resource allocation problem.

physics.soc-ph↗

arXiv2025

Evolution of cooperation with Q-learning: the impact of information perception

The inherent complexity of human beings manifests in a remarkable diversity of responses to intricate environments, enabling us to approach problems from varied perspectives. However, in the study of cooperation, existing research within the reinforcement learning framework often assumes that individuals have access to identical information when making decisions, which contrasts with the reality that individuals frequently perceive information differently. In this study, we employ the Q-learning algorithm to explore the impact of information perception on the evolution of cooperation in a two-person Prisoner's Dilemma game. We demonstrate that the evolutionary processes differ significantly across three distinct information perception scenarios, highlighting the critical role of information structure in the emergence of cooperation. Notably, the asymmetric information scenario reveals a complex dynamical process, including the emergence, breakdown, and reconstruction of cooperation, mirroring psychological shifts observed in human behavior. Our findings underscore the importance of information structure in fostering cooperation, offering new insights into the establishment of stable cooperative relationships among humans.

q-bio.PE↗

arXiv2025

Evolution of cooperation in a bimodal mixture of conditional cooperators

Extensive behavioral experiments reveal that conditional cooperation is a prevalent phenomenon. Previous game-theoretical studies have predominantly relied on hard-manner models, where cooperation is triggered only upon reaching a specific threshold. However, this approach contrasts with the observed flexibility of human behaviors, where individuals adapt their strategies dynamically based on their surroundings. To capture this adaptability, we introduce a soft form of conditional cooperation by integrating the Q-learning algorithm from reinforcement learning. In this form, players not only reciprocate mutual cooperation but may also defect in highly cooperative environments or cooperate in less cooperative settings to maximize rewards. To explore the effects of hard and soft conditional cooperators, we examine their interactions in two scenarios: structural mixture (SM) and probabilistic mixture (PM), where the two behavioral modes are fixed and probabilistically adopted, respectively. In SM, hard conditional cooperators enhance cooperation when the threshold is low but hinder it otherwise. Surprisingly, in PM, the cooperation prevalence exhibits two first-order phase transitions as the probability is varied, leading to high, low, and vanishing levels of cooperation. Analysis of Q-tables offers insights into the "psychological shifts" of soft conditional cooperators and the overall evolutionary dynamics. Model extensions confirm the robustness of our findings. These results highlight the novel complexities arising from the diversity of conditional cooperators.

q-bio.PE↗

arXiv2025

Regional Weather Variable Predictions by Machine Learning with Near-Surface Observational and Atmospheric Numerical Data

Accurate and timely regional weather prediction is vital for sectors dependent on weather-related decisions. Traditional prediction methods, based on atmospheric equations, often struggle with coarse temporal resolutions and inaccuracies. This paper presents a novel machine learning (ML) model, called MiMa (short for Micro-Macro), that integrates both near-surface observational data from Kentucky Mesonet stations (collected every five minutes, known as Micro data) and hourly atmospheric numerical outputs (termed as Macro data) for fine-resolution weather forecasting. The MiMa model employs an encoder-decoder transformer structure, with two encoders for processing multivariate data from both datasets and a decoder for forecasting weather variables over short time horizons. Each instance of the MiMa model, called a modelet, predicts the values of a specific weather parameter at an individual Mesonet station. The approach is extended with Re-MiMa modelets, which are designed to predict weather variables at ungauged locations by training on multivariate data from a few representative stations in a region, tagged with their elevations. Re-MiMa (short for Regional-MiMa) can provide highly accurate predictions across an entire region, even in areas without observational stations. Experimental results show that MiMa significantly outperforms current models, with Re-MiMa offering precise short-term forecasts for ungauged locations, marking a significant advancement in weather forecasting accuracy and applicability.

physics.ao-ph↗

arXiv2025

Towards Synergistic, Generalized, and Efficient Dual-System for Robotic Manipulation

The increasing demand for versatile robotic systems to operate in diverse and dynamic environments has emphasized the importance of a generalist policy, which leverages a large cross-embodiment data corpus to facilitate broad adaptability and high-level reasoning. However, the generalist would struggle with inefficient inference and cost-expensive training. The specialist policy, instead, is curated for specific domain data and excels at task-level precision with efficiency. Yet, it lacks the generalization capacity for a wide range of applications. Inspired by these observations, we introduce RoboDual, a synergistic dual-system that supplements the merits of both generalist and specialist policy. A diffusion transformer-based specialist is devised for multi-step action rollouts, exquisitely conditioned on the high-level task understanding and discretized action output of a vision-language-action (VLA) based generalist. Compared to OpenVLA, RoboDual achieves 26.7% improvement in real-world setting and 12% gain on CALVIN by introducing a specialist policy with merely 20M trainable parameters. It maintains strong performance with 5% of demonstration data only, and enables a 3.8 times higher control frequency in real-world deployment. Code would be made publicly available. Our project page is hosted at: https://opendrivelab.com/RoboDual/

cs.RO↗

arXiv2025

QCS: Feature Refining from Quadruplet Cross Similarity for Facial Expression Recognition

Facial expression recognition faces challenges where labeled significant features in datasets are mixed with unlabeled redundant ones. In this paper, we introduce Cross Similarity Attention (CSA) to mine richer intrinsic information from image pairs, overcoming a limitation when the Scaled Dot-Product Attention of ViT is directly applied to calculate the similarity between two different images. Based on CSA, we simultaneously minimize intra-class differences and maximize inter-class differences at the fine-grained feature level through interactions among multiple branches. Contrastive residual distillation is utilized to transfer the information learned in the cross module back to the base network. We ingeniously design a four-branch centrally symmetric network, named Quadruplet Cross Similarity (QCS), which alleviates gradient conflicts arising from the cross module and achieves balanced and stable training. It can adaptively extract discriminative features while isolating redundant ones. The cross-attention modules exist during training, and only one base branch is retained during inference, resulting in no increase in inference time. Extensive experiments show that our proposed method achieves state-of-the-art performance on several FER datasets.

cs.CV↗

arXiv2025

Derivation of Vlasov equations for multi-species fermions

We explore a system of two-species fermions with $N$ particles. Through this exploration, we establish a two-species Husimi measure and construct a two-species Bogoliubov-Born-Green-Kirkwood-Yvon (BBGKY) hierarchy with error terms. We provide proofs of the smallness of these errors in the semiclassical and mean-field limits, fixing $\hbar = N^{-1/3}$. After that, we rigorously establish the uniqueness of this hierarchy. This allows us to conclude that the two-species Husimi measure from the solution of the $N$-particle Schrödinger equation approximates the solution of the Vlasov equation for two-species systems.

math-ph↗

arXiv2025

DriveLM: Driving with Graph Visual Question Answering

We study how vision-language models (VLMs) trained on web-scale data can be integrated into end-to-end driving systems to boost generalization and enable interactivity with human users. While recent approaches adapt VLMs to driving via single-round visual question answering (VQA), human drivers reason about decisions in multiple steps. Starting from the localization of key objects, humans estimate object interactions before taking actions. The key insight is that with our proposed task, Graph VQA, where we model graph-structured reasoning through perception, prediction and planning question-answer pairs, we obtain a suitable proxy task to mimic the human reasoning process. We instantiate datasets (DriveLM-Data) built upon nuScenes and CARLA, and propose a VLM-based baseline approach (DriveLM-Agent) for jointly performing Graph VQA and end-to-end driving. The experiments demonstrate that Graph VQA provides a simple, principled framework for reasoning about a driving scene, and DriveLM-Data provides a challenging benchmark for this task. Our DriveLM-Agent baseline performs end-to-end autonomous driving competitively in comparison to state-of-the-art driving-specific architectures. Notably, its benefits are pronounced when it is evaluated zero-shot on unseen objects or sensor configurations. We hope this work can be the starting point to shed new light on how to apply VLMs for autonomous driving. To facilitate future research, all code, data, and models are available to the public.

cs.CV↗

arXiv2025

LAMOST medium-resolution spectroscopic survey of Galactic Open Clusters (LAMOST-MRS-O): An overview of survey plan and preliminary results

As part of the LAMOST medium-resolution spectroscopic survey, the LAMOST-MRS-O is a non-time domain survey that aims to perform medium-resolution spectral observations for member stars in the open cluster area. This survey plans to obtain the spectroscopic parameters such as radial velocity and metal abundances of member stars and provide data support for further study on the chemical and dynamical characteristics and evolution of open clusters in combination with Gaia data. We have completed the observations on ten open cluster fields and obtained 235184 medium-resolution spectra of 133792 stars. Based on the data analyzed of LAMOST DR11V1.1, for some clusters of particular concern, it is found that the sampling ratio of members stars with Gmag < 15 mag can reach 70%, which indicates that the LAMOST-MRS-O has reached our initial design goal.

astro-ph.GA↗

arXiv2025

The ALMA-ATOMS survey: Vibrationally excited HC$_3$N lines in hot cores

Interstellar molecules are excellent tools for studying the physical and chemical environments of massive star-forming regions. In particular, vibrationally excited HC$_3$N (HC$_3$N*) lines are the key tracers for probing hot cores environments. We present the Atacama Large Millimeter/submillimeter Array (ALMA) 3 mm observations of HC$_3$N* lines in 60 hot cores, aiming to investigate how physical conditions affect the excitation of HC$_3$N* transitions. We have used the XCLASS for line identification. Under the assumption of local thermodynamic equilibrium (LTE), we derived the rotation temperature and column density of HC$_3$N* transitions in hot cores. Additionally, we calculated the H$_2$ column density and number density, along with the abundance of HC$_3$N* relative to H$_2$, to enable a comparison of the physical properties of hot cores with different numbers of HC$_3$N* states. We have detected HC$_3$N* lines in 52 hot cores, in which 29 cores showing more than one vibrationally excited state. Hot cores with higher gas temperatures have more detections of these vibrationally excited lines. The excitation of HC$_3$N* requires dense environments, with its spatial distribution influenced by the presence of UC Hii regions. The observed column density of HC$_3$N* contributes to the number of HC$_3$N* states in hot core environments. After analyzing the various factors influencing HC$_3$N* excitation in hot cores, we conclude that the excitation of HC$_3$N* is mainly driven by mid-IR pumping, while collisional excitation is ineffective.

astro-ph.GA↗