arXiv subjects

Jie Yan

Publications and source records attributed to Jie Yan.

At least 19 recordsLinked to original sources

arXiv2026

A micromechanical frequency reference with parts-per-trillion holdover stability

Microelectromechanical (MEMS) resonators are widely used in timekeeping applications, and recent advances in fabrication, materials, and encapsulation technology have advanced their potential as high stability frequency references. However, for holdover applications that require the highest levels of long-term frequency stability, compact vapor atomic clocks remain dominant. In this work, we demonstrate a 268 MHz MEMS clock that achieves record fractional frequency stability of ~8 parts-per-trillion at an averaging time of 8 hours, competitive with chip-scale atomic clocks. We achieved this using a single-crystal silicon electrostatic resonator that has no currently known intrinsic drift mechanism and is protected from the environment with a wafer-level encapsulation. We specifically identify gain variations in the sustaining electronics as the dominant limitation in conventional phase-locked oscillator architectures -- originating from temperature sensitivity and drifts in the electronic components -- and overcome this by implementing a frequency-locked loop architecture based on dual-frequency resonance tracking (DFRT). This novel approach removes the specific gain of the supporting electronics as a frequency determining variable in the oscillator. When combined with dual-mode tracking and ratiometric temperature stabilization of the resonator, this approach enables a dramatic enhancement to long-term frequency stability and establishes gain-insensitive DFRT locking as a general paradigm for high-stability MEMS clocks.

physics.app-ph↗

arXiv2026

Small Object Detection in Complex Backgrounds with Multi-Scale Attention and Global Relation Modeling

Small object detection under complex backgrounds remains a challenging task due to severe feature degradation, weak semantic representation, and inaccurate localization caused by downsampling operations and background interference. Existing detection frameworks are mainly designed for general objects and often fail to explicitly address the unique characteristics of small objects, such as limited structural cues and strong sensitivity to localization errors. In this paper, we propose a multi-level feature enhancement and global relation modeling framework tailored for small object detection. Specifically, a Residual Haar Wavelet Downsampling module is introduced to preserve fine-grained structural details by jointly exploiting spatial-domain convolutional features and frequency-domain representations. To enhance global semantic awareness and suppress background noise, a Global Relation Modeling module is employed to capture long-range dependencies at high-level feature stages. Furthermore, a Cross-Scale Hybrid Attention module is designed to establish sparse and aligned interactions across multi-scale features, enabling effective fusion of high-resolution details and high-level semantic information with reduced computational overhead. Finally, a Center-Assisted Loss is incorporated to stabilize training and improve localization accuracy for small objects. Extensive experiments conducted on the large-scale RGBT-Tiny benchmark demonstrate that the proposed method consistently outperforms existing state-of-the-art detectors under both IoU-based and scale-adaptive evaluation metrics. These results validate the effectiveness and robustness of the proposed framework for small object detection in complex environments.

cs.CV↗

arXiv2025

SocialTrack: Multi-Object Tracking in Complex Urban Traffic Scenes Inspired by Social Behavior

As a key research direction in the field of multi-object tracking (MOT), UAV-based multi-object tracking has significant application value in the analysis and understanding of urban intelligent transportation systems. However, in complex UAV perspectives, challenges such as small target scale variations, occlusions, nonlinear crossing motions, and motion blur severely hinder the stability of multi-object tracking. To address these challenges, this paper proposes a novel multi-object tracking framework, SocialTrack, aimed at enhancing the tracking accuracy and robustness of small targets in complex urban traffic environments. The specialized small-target detector enhances the detection performance by employing a multi-scale feature enhancement mechanism. The Velocity Adaptive Cubature Kalman Filter (VACKF) improves the accuracy of trajectory prediction by incorporating a velocity dynamic modeling mechanism. The Group Motion Compensation Strategy (GMCS) models social group motion priors to provide stable state update references for low-quality tracks, significantly improving the target association accuracy in complex dynamic environments. Furthermore, the Spatio-Temporal Memory Prediction (STMP) leverages historical trajectory information to predict the future state of low-quality tracks, effectively mitigating identity switching issues. Extensive experiments on the UAVDT and MOT17 datasets demonstrate that SocialTrack outperforms existing state-of-the-art (SOTA) methods across several key metrics. Significant improvements in MOTA and IDF1, among other core performance indicators, highlight its superior robustness and adaptability. Additionally, SocialTrack is highly modular and compatible, allowing for seamless integration with existing trackers to further enhance performance.

cs.CV↗

arXiv2025

Exploring Micro Accidents and Driver Responses in Automated Driving: Insights from Real-world Videos

Automated driving in level 3 autonomy has been adopted by multiple companies such as Tesla and BMW, alleviating the burden on drivers while unveiling new complexities. This article focused on the under-explored territory of micro accidents during automated driving, characterized as not fatal but abnormal aberrations such as abrupt deceleration and snake driving. These micro accidents are basic yet pervasive events that might results in more severe accidents. Through collecting a comprehensive dataset of user generated video recording such micro accidents in natural driving scenarios, this article locates key variables pertaining to environments and autonomous agents using machine learning methods. Subsequently, crowdsourcing method provides insights into human risk perceptions and reactions to these micro accidents. This article thus describes features of safety critical scenarios other than crashes and fatal accidents, informing and potentially advancing the design of automated driving systems.

cs.HC↗

arXiv2025

Driver Assistant: Persuading Drivers to Adjust Secondary Tasks Using Large Language Models

Level 3 automated driving systems allows drivers to engage in secondary tasks while diminishing their perception of risk. In the event of an emergency necessitating driver intervention, the system will alert the driver with a limited window for reaction and imposing a substantial cognitive burden. To address this challenge, this study employs a Large Language Model (LLM) to assist drivers in maintaining an appropriate attention on road conditions through a "humanized" persuasive advice. Our tool leverages the road conditions encountered by Level 3 systems as triggers, proactively steering driver behavior via both visual and auditory routes. Empirical study indicates that our tool is effective in sustaining driver attention with reduced cognitive load and coordinating secondary tasks with takeover behavior. Our work provides insights into the potential of using LLMs to support drivers during multi-task automated driving.

cs.HC↗

arXiv2025

StepFun-Formalizer: Unlocking the Autoformalization Potential of LLMs through Knowledge-Reasoning Fusion

Autoformalization aims to translate natural-language mathematical statements into a formal language. While LLMs have accelerated progress in this area, existing methods still suffer from low accuracy. We identify two key abilities for effective autoformalization: comprehensive mastery of formal-language domain knowledge, and reasoning capability of natural language problem understanding and informal-formal alignment. Without the former, a model cannot identify the correct formal objects; without the latter, it struggles to interpret real-world contexts and map them precisely into formal expressions. To address these gaps, we introduce ThinkingF, a data synthesis and training pipeline that improves both abilities. First, we construct two datasets: one by distilling and selecting large-scale examples rich in formal knowledge, and another by generating informal-to-formal reasoning trajectories guided by expert-designed templates. We then apply SFT and RLVR with these datasets to further fuse and refine the two abilities. The resulting 7B and 32B models exhibit both comprehensive formal knowledge and strong informal-to-formal reasoning. Notably, StepFun-Formalizer-32B achieves SOTA BEq@1 scores of 40.5% on FormalMATH-Lite and 26.7% on ProverBench, surpassing all prior general-purpose and specialized models.

cs.CL↗

arXiv2025

StepFun-Prover Preview: Let's Think and Verify Step by Step

We present StepFun-Prover Preview, a large language model designed for formal theorem proving through tool-integrated reasoning. Using a reinforcement learning pipeline that incorporates tool-based interactions, StepFun-Prover can achieve strong performance in generating Lean 4 proofs with minimal sampling. Our approach enables the model to emulate human-like problem-solving strategies by iteratively refining proofs based on real-time environment feedback. On the miniF2F-test benchmark, StepFun-Prover achieves a pass@1 success rate of $70.0\%$. Beyond advancing benchmark performance, we introduce an end-to-end training framework for developing tool-integrated reasoning models, offering a promising direction for automated theorem proving and Math AI assistant.

cs.AI↗

arXiv2025

Step-3 is Large yet Affordable: Model-system Co-design for Cost-effective Decoding

Large language models (LLMs) face low hardware efficiency during decoding, especially for long-context reasoning tasks. This paper introduces Step-3, a 321B-parameter VLM with hardware-aware model-system co-design optimized for minimizing decoding costs. Step-3 innovates in two key dimensions: (1) A novel Multi-Matrix Factorization Attention (MFA) mechanism that significantly reduces both KV cache size and computation while maintaining high attention expressiveness, and (2) Attention-FFN Disaggregation (AFD), a distributed inference system that decouples attention and Feed-Forward Network (FFN) layers into specialized subsystems. This co-design achieves unprecedented cost efficiency: Step-3 significantly reduces theoretical decoding costs compared with models like DeepSeek-V3 and Qwen3 MoE 235B, with the gains widening at longer context. Step-3 achieves low cost while activating 38B parameters per token (more than DeepSeek-V3 and Qwen3 MoE 235B), demonstrating that hardware-aligned attention arithmetic intensity, MoE sparsity, and AFD are critical to cost-effectiveness. We perform a head-to-head comparison with DeepSeek-V3 in its favorable scenarios. Our implementation on Hopper GPUs achieves a decoding throughput of up to 4,039 tokens per second per GPU under 50ms TPOT SLA (4K context, FP8, no MTP). It is higher than DeepSeek-V3's 2,324 in the same setup and sets a new Pareto frontier for LLM decoding.

cs.LG↗

arXiv2025

Highly reliable, ultra-wideband, isolator-free quantum-dot mode-locked frequency combs for optical interconnects beyond 3.2Tb/s

Quantum dot mode-locked laser-based optical frequency combs are emerging as a critical solution for achieving low-cost, high-efficiency, and large-capacity optical interconnects. The practical implementation of wavelength division multiplexing interconnects necessitates a temperature-stable OFC source with a minimum 100 GHz channel spacing to enable high-bandwidth modulation while mitigating the complexity of optical filtering and detection. By leveraging the advanced co-doping technique and a colliding pulse mode-locking scheme, here, we report a compact, ultra-wideband, highly reliable, isolator-free 100 GHz-spacing InAs/GaAs QD OFC source operating up to a record temperature of 140 {\deg}C. The comb source delivers a record 3 dB optical bandwidth of 14.312 nm, containing flat-top comb lines, each supporting 128 Gb/s PAM-4 modulation, which results in a total throughput of 3.328 Tb/s with an extremely low power consumption of 0.394 pJ/bit at 25{\deg}C. Performance remains stable at 85 {\deg}C, with negligible degradation of device critical metrics. Remarkably, accelerated aging tests under harsh conditions (85 {\deg}C with 8x threshold current injection) revealed a mean time to failure of approximately 207 years. The QD OFC source demonstrated in this work, for the first time, establishes a concrete link between fundamental research on comb sources and their practical deployment in next-generation, high-density optical interconnect systems.

cond-mat.mes-hall↗

arXiv2025

OmniFC: Rethinking Federated Clustering via Lossless and Secure Distance Reconstruction

Federated clustering (FC) aims to discover global cluster structures across decentralized clients without sharing raw data, making privacy preservation a fundamental requirement. There are two critical challenges: (1) privacy leakage during collaboration, and (2) robustness degradation due to aggregation of proxy information from non-independent and identically distributed (Non-IID) local data, leading to inaccurate or inconsistent global clustering. Existing solutions typically rely on model-specific local proxies, which are sensitive to data heterogeneity and inherit inductive biases from their centralized counterparts, thus limiting robustness and generality. We propose Omni Federated Clustering (OmniFC), a unified and model-agnostic framework. Leveraging Lagrange coded computing, our method enables clients to share only encoded data, allowing exact reconstruction of the global distance matrix--a fundamental representation of sample relationships--without leaking private information, even under client collusion. This construction is naturally resilient to Non-IID data distributions. This approach decouples FC from model-specific proxies, providing a unified extension mechanism applicable to diverse centralized clustering methods. Theoretical analysis confirms both reconstruction fidelity and privacy guarantees, while comprehensive experiments demonstrate OmniFC's superior robustness, effectiveness, and generality across various benchmarks compared to state-of-the-art methods. Code will be released.

cs.LG↗

arXiv2025

Spatial-Wavelength Multiplexing Reliable Photonic Integrated General-Purpose Analog Computing System

In the "post-Moore era", the growing challenges in traditional computing have driven renewed interest in analog computing, leading to various proposals for the development of general-purpose analog computing (GPAC) systems. In this work, we present a GPAC prototype featuring a silicon photonic chip designed for fully optical analog computation. This system leverages on-chip multi-channel architectures to enable parallel processing and utilizes wavelength-division multiplexing to significantly enhance computational capacity. In addition, we have developed an error-correction algorithm to monitor processing operations in real time, ensuring the reliability of computational results. Experimentally, we demonstrate the system's capability to solve ordinary differential equations and its applications in communications, microwave photonics, and image processing. The chip's energy efficiency is evaluated to reach up to 227 tera-operations per second per watt. Through this research, we provide a novel hardware framework and innovative directions for analog photonic computing.

physics.optics↗

arXiv2025

Fully Integrated Vacuum-based Quantum Random Number Generator

Quantum random number generators play a crucial role in securing high-demand information contexts by producing true random numbers. Nevertheless, the large volume and high-cost limit their widespread use. Here, we propose a system on chip that fully leverages the advantages of different photonic integrated platforms, where the interference optical paths and photodiodes are integrated on a standard silicon process, while the laser source on-chip is realized on a III-V platform. Using micro-lens coupling package technology, which contributes to a topnotch coupling loss lower than 2dB, the components on different platforms are combined and packaged with the amplifier circuits in a 42mm* 24mm footprint in a butterfly form. This complete miniaturized and cost-effective entropy source enables outputting a vacuum noise signal with a 3dB bandwidth of over 500MHz. After sampling and post-processing, a random number generation rate of up to 6.57Gbps is achieved. The results show a feasible way of overcoming the laser integration problem with silicon-based integrated quantum photonics. Foreseeable, commercial applications on a large scale are significantly promoted.

physics.optics↗

arXiv2024

CCFC++: Enhancing Federated Clustering through Feature Decorrelation

In federated clustering, multiple data-holding clients collaboratively group data without exchanging raw data. This field has seen notable advancements through its marriage with contrastive learning, exemplified by Cluster-Contrastive Federated Clustering (CCFC). However, CCFC suffers from heterogeneous data across clients, leading to poor and unrobust performance. Our study conducts both empirical and theoretical analyses to understand the impact of heterogeneous data on CCFC. Findings indicate that increased data heterogeneity exacerbates dimensional collapse in CCFC, evidenced by increased correlations across multiple dimensions of the learned representations. To address this, we introduce a decorrelation regularizer to CCFC. Benefiting from the regularizer, the improved method effectively mitigates the detrimental effects of data heterogeneity, and achieves superior performance, as evidenced by a marked increase in NMI scores, with the gain reaching as high as 0.32 in the most pronounced case.

cs.LG↗

arXiv2024

CCFC: Bridging Federated Clustering and Contrastive Learning

Federated clustering, an essential extension of centralized clustering for federated scenarios, enables multiple data-holding clients to collaboratively group data while keeping their data locally. In centralized scenarios, clustering driven by representation learning has made significant advancements in handling high-dimensional complex data. However, the combination of federated clustering and representation learning remains underexplored. To bridge this, we first tailor a cluster-contrastive model for learning clustering-friendly representations. Then, we harness this model as the foundation for proposing a new federated clustering method, named cluster-contrastive federated clustering (CCFC). Benefiting from representation learning, the clustering performance of CCFC even double those of the best baseline methods in some cases. Compared to the most related baseline, the benefit results in substantial NMI score improvements of up to 0.4155 on the most conspicuous case. Moreover, CCFC also shows superior performance in handling device failures from a practical viewpoint.

cs.LG↗

arXiv2023

ClusterDDPM: An EM clustering framework with Denoising Diffusion Probabilistic Models

Variational autoencoder (VAE) and generative adversarial networks (GAN) have found widespread applications in clustering and have achieved significant success. However, the potential of these approaches may be limited due to VAE's mediocre generation capability or GAN's well-known instability during adversarial training. In contrast, denoising diffusion probabilistic models (DDPMs) represent a new and promising class of generative models that may unlock fresh dimensions in clustering. In this study, we introduce an innovative expectation-maximization (EM) framework for clustering using DDPMs. In the E-step, we aim to derive a mixture of Gaussian priors for the subsequent M-step. In the M-step, our focus lies in learning clustering-friendly latent representations for the data by employing the conditional DDPM and matching the distribution of latent representations to the mixture of Gaussian priors. We present a rigorous theoretical analysis of the optimization process in the M-step, proving that the optimizations are equivalent to maximizing the lower bound of the Q function within the vanilla EM framework under certain constraints. Comprehensive experiments validate the advantages of the proposed framework, showcasing superior performance in clustering, unsupervised conditional generation and latent representation learning.

cs.LG↗

arXiv2023

Federated clustering with GAN-based data synthesis

Federated clustering (FC) is an extension of centralized clustering in federated settings. The key here is how to construct a global similarity measure without sharing private data, since the local similarity may be insufficient to group local data correctly and the similarity of samples across clients cannot be directly measured due to privacy constraints. Obviously, the most straightforward way to analyze FC is to employ the methods extended from centralized ones, such as K-means (KM) and fuzzy c-means (FCM). However, they are vulnerable to non independent-and-identically-distributed (non-IID) data among clients. To handle this, we propose a new federated clustering framework, named synthetic data aided federated clustering (SDA-FC). It trains generative adversarial network locally in each client and uploads the generated synthetic data to the server, where KM or FCM is performed on the synthetic data. The synthetic data can make the model immune to the non-IID problem and enable us to capture the global similarity characteristics more effectively without sharing private data. Comprehensive experiments reveals the advantages of SDA-FC, including superior performance in addressing the non-IID problem and the device failures.

cs.LG↗

arXiv2023

Privacy-Preserving Federated Deep Clustering based on GAN

Federated clustering (FC) is an essential extension of centralized clustering designed for the federated setting, wherein the challenge lies in constructing a global similarity measure without the need to share private data. Conventional approaches to FC typically adopt extensions of centralized methods, like K-means and fuzzy c-means. However, these methods are susceptible to non-independent-and-identically-distributed (non-IID) data among clients, leading to suboptimal performance, particularly with high-dimensional data. In this paper, we present a novel approach to address these limitations by proposing a Privacy-Preserving Federated Deep Clustering based on Generative Adversarial Networks (GANs). Each client trains a local generative adversarial network (GAN) locally and uploads the synthetic data to the server. The server applies a deep clustering network on the synthetic data to establish $k$ cluster centroids, which are then downloaded to the clients for cluster assignment. Theoretical analysis demonstrates that the GAN-generated samples, shared among clients, inherently uphold certain privacy guarantees, safeguarding the confidentiality of individual data. Furthermore, extensive experimental evaluations showcase the effectiveness and utility of our proposed method in achieving accurate and privacy-preserving federated clustering.

cs.LG↗

arXiv2023

The Ensemble Approach of Column Generation for Solving Cutting Stock Problems

This paper investigates the column generation (CG) for solving cutting stock problems (CSP). Traditional CG method, which repeatedly solves a restricted master problem (RMP), often suffers from two critical issues in practice -- the loss of solution quality introduced by linear relaxation of both feasible domain and objective and the high time cost of last iterations close to convergence. We empirically find that the first issue is common in ordinary CSPs with linear cutting constraints, while the second issue is especially severe in CSPs with nonlinear cutting constraints that are often generated by approximating chance constraints. We propose an alternative approach, ensembles of multiple column generation processes. In particular, we present two methods -- \mc (multi-column) which return multiple feasible columns in each RMP iteration, and \mt (multi-path) which restarts the RMP iterations from different initialized column sets once the iteration time exceeds a given time limit. The ideas behind are same: leverage the multiple column generation pathes to compensate the loss induced by relaxation, and add earlier sub-optimal columns to accelerate convergence of RMP iterations. Besides, we give theoretical analysis on performance improvement guarantees. Experiments on cutting stock problems demonstrate that compared to traditional CG, our method achieves significant run-time reduction on CSPs with nonlinear constraints, and dramatically improves the ratio of solve-to-optimal on CSPs with linear constraints.

math.OC↗