arXiv subjects

Yuting Sun

Publications and source records attributed to Yuting Sun.

16 recordsLinked to original sources

arXiv2026

DevBench: A Realistic, Developer-Informed Benchmark for Code Generation Models

DevBench is a telemetry-driven benchmark designed to evaluate Large Language Models (LLMs) on realistic code completion tasks. It includes 1,800 evaluation instances across six programming languages and six task categories derived from real developer telemetry and synthesized using generator models from multiple provider families to mitigate single-source bias. Unlike prior benchmarks, it emphasizes ecological validity, avoids training data contamination, and enables detailed diagnostics. The evaluation combines functional correctness, similarity-based metrics, and LLM-judge assessments focused on usefulness and contextual relevance. 9 state-of-the-art models were assessed, with the strongest achieving only 43.5% Pass@1, confirming the benchmark remains challenging and revealing differences in syntactic precision, semantic reasoning, and practical utility. Our benchmark provides actionable insights to guide model selection and improvement, detail that is often missing from other benchmarks but is essential for both practical deployment and targeted model development.

cs.LG↗

arXiv2025

Fabrication of airbridges with gradient exposure

In superconducting quantum circuits, airbridges are critical for eliminating parasitic slotline modes of coplanar waveguide circuits and reducing crosstalks between direct current magnetic flux biases. Here, we present a technique for fabricating superconducting airbridges. With this technique, a single layer of photoresist is employed, and the gradient exposure process is used to define the profile of airbridges. In order to properly obtain the bridge profile, we design exposure dosage based on residual photoresist thickness and laser power calibrations. Compared with other airbridge fabrication techniques, the gradient exposure fabrication technique provides the ability to produce lossless superconducting airbridges with flexible size and, thus, is more suitable for large-scale superconducting quantum circuits. Furthermore, this method reduces the complexity of the fabrication process and provides a high fabrication yield.

quant-ph↗

arXiv2025

Broadband merged-element Josephson parametric amplifier

Broadband quantum-limited amplifiers are essential for quantum information processing, yet challenges in design and fabrication continue to hinder their widespread applications. Here, we introduce the broadband merged-element Josephson parametric amplifier in which the discrete parallel capacitor is directly integrated with the Josephson junctions. This merged-element design eliminates the shortcomings of discrete capacitors, simplifying the fabrication process, reducing the need for high-precision lithography tools, and ensuring compatibility with standard superconducting qubit fabrication procedures. Experimental results demonstrate a gain of 15 dB over a 500 MHz bandwidth, a mean saturation power of -116 dBm and near-quantum-limited noise performance. This robust readily implemented parametric amplifier holds significant promise for broader applications in superconducting quantum information and the advancement of quantum computation.

quant-ph↗

arXiv2025

Time Series Analysis in Frequency Domain: A Survey of Open Challenges, Opportunities and Benchmarks

Frequency-domain analysis has emerged as a powerful paradigm for time series analysis, offering unique advantages over traditional time-domain approaches while introducing new theoretical and practical challenges. This survey provides a comprehensive examination of spectral methods from classical Fourier analysis to modern neural operators, systematically summarizing three open challenges in current research: (1) causal structure preservation during spectral transformations, (2) uncertainty quantification in learned frequency representations, and (3) topology-aware analysis for non-Euclidean data structures. Through rigorous reviewing of over 100 studies, we develop a unified taxonomy that bridges conventional spectral techniques with cutting-edge machine learning approaches, while establishing standardized benchmarks for performance evaluation. Our work identifies key knowledge gaps in the field, particularly in geometric deep learning and quantum-enhanced spectral analysis. The survey offers practitioners a systematic framework for method selection and implementation, while charting promising directions for future research in this rapidly evolving domain.

cs.CE↗

arXiv2025

Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model

Reinforcement learning from human feedback (RLHF) has been widely adopted to align language models (LMs) with human preference. Prior RLHF works typically take a bandit formulation, which, though intuitive, ignores the sequential nature of LM generation and can suffer from the sparse reward issue. While recent works propose dense token-level RLHF, treating each token as an action may be oversubtle to proper reward assignment. In this paper, we seek to get the best of both by training and utilizing a segment-level reward model, which assigns a reward to each semantically complete text segment that spans over a short sequence of tokens. For reward learning, our method allows dynamic text segmentation and compatibility with standard sequence-preference datasets. For effective RL-based LM training against segment reward, we generalize the classical scalar bandit reward normalizers into location-aware normalizer functions and interpolate the segment reward for further densification. With these designs, our method performs competitively on three popular RLHF benchmarks for LM policy: AlpacaEval 2.0, Arena-Hard, and MT-Bench. Ablation studies are conducted to further demonstrate our method.

cs.CL↗

arXiv2024

An Effective Descriptor for Predicting and Designing High-Temperature Ambient-Pressure Superconductors

Searching for ambient-pressure conventional superconductors with critical temperatures (TC) higher than 40 K is a key challenge in the field of high-temperature superconductivity, mainly due to lack of efficient and effective models to estimate TC of potential systems. In this work, we propose a simplified model to estimate the dimensionless electron-phonon coupling (EPC) strength {\lambda} by separately treating the EPC matrix elements which evaluate the pairing strength and the phonon-assisted nesting function P({\omega}) which evaluates the matching of electron bands and phonon spectra for forming potential electron pairs via phonons. Our model illuminates the critical role of P({\omega}) and its spectral integral P in determining {\lambda}, i.e., high P is a necessary condition leading to large {\lambda} and thus high TC, which is further demonstrated by showing that the reported high-TC traditional superconductors in literatures all have high P. As an easily quantifiable parameter, P({\omega}) and P provide an efficient and effective descriptor for accelerating the discovery and rational design of high-TC superconductors. By applying the model to screen over the Computational 2D Materials Database (C2DB), we successfully identify several high-TC superconducting systems as confirmed by accurate first-principles calculations. Our model opens new avenues for exploring high-TC systems.

cond-mat.supr-con↗

arXiv2024

Modeling for Non-exponential Production Systems Using Parts Flow Data: Model Parameter Estimation and Performance Analysis

Mathematical modeling of production systems is the foundation of all model-based approaches for production system analysis, design, improvement, and control. To construct such a model for the stochastic process of the production system more efficiently, a new modeling approach has been proposed that reversely identifies the model parameters using system performance metrics (e.g., production rate, work-in-process, etc.) derived from the parts flow data. This paper extends this performance metrics-based modeling approach to non-exponential serial production lines. Since no analytical expressions of performance metrics are available for non-exponential systems, we use neural network surrogate models to calculate those performance metrics as functions in terms of the system parameters. Then, based on the surrogate models and given performance metrics, the machine parameters are estimated by solving a constrained optimization problem that minimizes the mean square error of the performance metrics resulting from the estimated parameters compared to the true ones. With the designed multi-start particle swarm optimization algorithm, we find that multiple non-unique combinations of machine parameters can lead to practically the same system performance metrics and a linear relationship of the reliability parameters from these obtained estimations is observed. Besides, model sensitivity analysis is implemented to verify the robustness of the different combinations of machine parameters even under the potential improvement scenarios.

eess.SY↗

arXiv2023

Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution

The ongoing challenges in time series anomaly detection (TSAD), notably the scarcity of anomaly labels and the variability in anomaly lengths and shapes, have led to the need for a more efficient solution. As limited anomaly labels hinder traditional supervised models in TSAD, various SOTA deep learning techniques, such as self-supervised learning, have been introduced to tackle this issue. However, they encounter difficulties handling variations in anomaly lengths and shapes, limiting their adaptability to diverse anomalies. Additionally, many benchmark datasets suffer from the problem of having explicit anomalies that even random functions can detect. This problem is exacerbated by ill-posed evaluation metrics, known as point adjustment (PA), which can result in inflated model performance. In this context, we propose a novel self-supervised learning based Tri-domain Anomaly Detector (TriAD), which addresses these challenges by modeling features across three data domains - temporal, frequency, and residual domains - without relying on anomaly labels. Unlike traditional contrastive learning methods, TriAD employs both inter-domain and intra-domain contrastive loss to learn common attributes among normal data and differentiate them from anomalies. Additionally, our approach can detect anomalies of varying lengths by integrating with a discord discovery algorithm. It is worth noting that this study is the first to reevaluate the deep learning potential in TSAD, utilizing both rigorously designed datasets (i.e., UCR Archive) and evaluation metrics (i.e., PA%K and affiliation). Through experimental results on the UCR dataset, TriAD achieves an impressive three-fold increase in PA%K based F1 scores over SOTA deep learning models, and 50% increase of accuracy as compared to SOTA discord discovery algorithms.

cs.LG↗

arXiv2023

TinyAD: Memory-efficient anomaly detection for time series data in Industrial IoT

Monitoring and detecting abnormal events in cyber-physical systems is crucial to industrial production. With the prevalent deployment of the Industrial Internet of Things (IIoT), an enormous amount of time series data is collected to facilitate machine learning models for anomaly detection, and it is of the utmost importance to directly deploy the trained models on the IIoT devices. However, it is most challenging to deploy complex deep learning models such as Convolutional Neural Networks (CNNs) on these memory-constrained IIoT devices embedded with microcontrollers (MCUs). To alleviate the memory constraints of MCUs, we propose a novel framework named Tiny Anomaly Detection (TinyAD) to efficiently facilitate onboard inference of CNNs for real-time anomaly detection. First, we conduct a comprehensive analysis of depthwise separable CNNs and regular CNNs for anomaly detection and find that the depthwise separable convolution operation can reduce the model size by 50-90% compared with the traditional CNNs. Then, to reduce the peak memory consumption of CNNs, we explore two complementary strategies, in-place, and patch-by-patch memory rescheduling, and integrate them into a unified framework. The in-place method decreases the peak memory of the depthwise convolution by sparing a temporary buffer to transfer the activation results, while the patch-by-patch method further reduces the peak memory of layer-wise execution by slicing the input data into corresponding receptive fields and executing in order. Furthermore, by adjusting the dimension of convolution filters, these strategies apply to both univariate time series and multidomain time series features. Extensive experiments on real-world industrial datasets show that our framework can reduce peak memory consumption by 2-5x with negligible computation overhead.

cs.LG↗

arXiv2022

Spatial-Temporal Meta-path Guided Explainable Crime Prediction

Exposure to crime and violence can harm individuals' quality of life and the economic growth of communities. In light of the rapid development in machine learning, there is a rise in the need to explore automated solutions to prevent crimes. With the increasing availability of both fine-grained urban and public service data, there is a recent surge in fusing such cross-domain information to facilitate crime prediction. By capturing the information about social structure, environment, and crime trends, existing machine learning predictive models have explored the dynamic crime patterns from different views. However, these approaches mostly convert such multi-source knowledge into implicit and latent representations (e.g., learned embeddings of districts), making it still a challenge to investigate the impacts of explicit factors for the occurrences of crimes behind the scenes. In this paper, we present a Spatial-Temporal Metapath guided Explainable Crime prediction (STMEC) framework to capture dynamic patterns of crime behaviours and explicitly characterize how the environmental and social factors mutually interact to produce the forecasts. Extensive experiments show the superiority of STMEC compared with other advanced spatiotemporal models, especially in predicting felonies (e.g., robberies and assaults with dangerous weapons).

cs.LG↗

arXiv2022

Exceptionally high phonon-limited carrier mobility in BX (X = P, As, Sb) monolayers

Ideal two-dimensional (2D) semiconductors with high mobility comparable to three-dimensional (3D) Si or GaAs are still lacking, hindering the development of high-performance 2D devices. Here in this work, using first-principles calculations and considering all the electron-phonon couplings, we show that monolayer BX (X = P, As, Sb) with honeycomb lattices have intrinsic phonon-limited carrier mobility reaching record-high values of 1200-14000 $cm^2V^{-1}s^{-1}$ at room temperature. Despite being polar and the band edges located at the K point with multiple valleys, these three systems unusually have small carrier scattering rates. Detailed analysis shows that, both the intravalley scattering and the intervalley scattering between two equivalent K points are weak, which can be understood from the large mismatch between the electron bands and phonon spectrum and suppressed electron-phonon coupling strength. Furthermore, we reveal the general trend of mobility increase from BP to BAs and to BSb and conclude that: smaller effective masses, larger sound velocities, higher optical phonon energies, heavy atomic masses, and out-of-plane orbitals tend to result in small match between the electron and phonon bands, small electron-phonon coupling strengths, and thus high mobility. Our work demonstrates that 2D semiconductors can achieve comparable carrier mobility to 3D GaAs, thus opening doors to 2D high-performance electronic devices.

cond-mat.mtrl-sci↗

arXiv2020

Example-Based Named Entity Recognition

We present a novel approach to named entity recognition (NER) in the presence of scarce data that we call example-based NER. Our train-free few-shot learning approach takes inspiration from question-answering to identify entity spans in a new and unseen domain. In comparison with the current state-of-the-art, the proposed method performs significantly better, especially when using a low number of support examples.

cs.CL↗

arXiv2017

Order parameter analysis of synchronization transitions on star networks

Collective behaviors of populations of coupled oscillators have attracted much attention in recent years. In this paper, an order parameter approach is proposed to study the low-dimensional dynam- ical mechanism of collective synchronizations by adopting the star-topology of coupled oscillators as a prototype system. The order parameter equation of star-linked phase oscillators can be obtained in terms of the Watanabe-Strogatz transformation, Ott-Antonsen ansatz, and the ensemble order parameter approach. Different solutions of the order parameter equation correspond to diverse col- lective states, and different bifurcations reveal various transitions among these collective states. The properties of various transitions are revealed in the star-network model by using tools of nonlinear dynamics such as time reversibility analysis and linear stability analysis.

nlin.AO↗

arXiv2015

Synchronization of phase oscillators with frequency-weighted coupling

Recently, the first-order synchronization transition has been studied in systems of coupled phase oscillators. In this paper, we propose a framework to investigate the synchronization in the frequency-weighted Kuramoto model with all-to-all couplings. A rigorous mean-field analysis is implemented to predict the possible steady states. Furthermore, a detailed linear stability analysis proves that the incoherent state is only neutrally stable below the synchronization threshold. Nevertheless, interestingly, the amplitude of the order parameter decays exponentially (at least for short time) in this regime, resembling the Landau damping effect in plasma physics. Moreover, the explicit expression for the critical coupling strength is determined by both the mean-field method and linear operator theory. The mechanism of bifurcation for the incoherent state near the critical point is further revealed by the amplitude expansion theory, which shows that the oscillating standing wave state could also occur in this model for certain frequency distributions. Our theoretical analysis and numerical results are consistent with each other, which can help us understand the synchronization transition in general networks with heterogenous couplings.

nlin.AO↗

arXiv2015

Order parameter analysis for low-dimensional behaviors of coupled phase-oscillators

Coupled phase-oscillators are important models related to synchronization. Recently, Ott-Antonsen(OA) ansatz is developed and used to get low-dimensional collective behaviors in coupled oscillator systems. In this paper, we develop a simple and concise approach based on the equations of order parameters, namely, order parameter analysis, with which we point out that the OA ansatz is rooted in the dynamical symmetry of the order parameters. With our approach the scope of the OA ansatz is identified as two conditions, i.e., infinite size of the system and only three nonzero Fourier coefficients of the coupling function. Coinciding with each of the conditions, a distinctive system out of the scope is taken into account and discussed with the order parameter analysis. Two approximation methods are introduced respectively, namely the ensemble approach and the dominating-term assumption.

nlin.AO↗

arXiv2014

Explosive or Continuous: Incoherent state determines the route to synchronization

Collective behaviors of coupled oscillators have attracted much attention. In this Letter, we propose an ensemble order parameter(EOP) equation that enables us to grasp the essential low-dimensional dynamical mechanism of the explosive synchronization in heterogeneous networks. Dif- ferent solutions of the EOP equation build correspondences with diverse collective states, and different bifurcations reveal various transitions among these collective states. The structural relationship between the incoherent state and synchronous state leads to different routes of transitions to synchronization, either continuous or discontinuous. The explosive synchronization is determined by the bistable state where the measure of each state and the critical points are obtained analytically by using the EOP equation. Our method and results hold for heterogeneous networks with star graph motifs such as scale-free networks, and hence, provide an effective approach in understanding the routes to synchronization in more general complex networks.

nlin.AO↗