arXiv subjects

Yang Chen

Publications and source records attributed to Yang Chen.

At least 289 records · Page 16Linked to original sources

arXiv2025

A Preliminary Study of Fixed Flaky Tests in Rust Projects on GitHub

Prior research has extensively studied flaky tests in various domains, such as web applications, mobile applications, and other open-source projects in a range of multiple programing languages, including Java, Javascript, Python, Ruby, and more. However, little attention has been given to flaky tests in Rust -- an emerging popular language known for its safety features relative to C/C++. Rust incorporates interesting features that make it easy to detect some flaky tests, e.g., the Rust standard randomizes the order of elements in hash tables, effectively exposing implementation-dependent flakiness. However, Rust still has several sources of nondeterminism that can lead to flaky tests. We present our work-in-progress on studying flaky tests in Rust projects on GitHub. Searching through the closed Github issues and pull requests. We focus on flaky tests that are fixed, not just reported, as the fixes can offer valuable information on root causes, manifestation characteristics, and strategies of fixes. By far, we have inspected 53 tests. Our initial findings indicate that the predominant root causes include asynchronous wait (33.9%), concurrency issues (24.5%), logic errors (9.4%). and network-related problems (9.4%).

cs.SE↗

arXiv2025

Universally optimizable strategy for magnetic gaps towards high-temperature quantum anomalous Hall states via magnetic-insulator/topological-insulator building-blocks

Optimizing the magnetic Zeeman-splitting term, specifically the magnetic gap of the topological surface states (TSSs), is a crucial issue and central challenge in advancing higher-temperature quantum anomalous Hall (QAH) states. In this work, we demonstrate a counterintuitive, nonmonotonic relationship between the magnetic gap and the hybridization strength in ferromagnetic-insulator (FMI)/topological-insulator (TI) sandwich structures. Concretely, insufficient hybridization strength fails to induce a substantial magnetic gap; while excessive hybridization incandesces the competition between kinetic and Coulomb exchange interactions, thereby reducing the gap. Strong hybridization strength also spatially delocalizes the TSSs, diminishing the effective orbital coupling between TSS-based p and magnetic d orbitals, which further weakens kinetic and Coulomb exchange interaction strength. Moreover, modifying the stacking order offers an experimentally viable approach to optimizing magnetic gaps, enabling the tunability of Chern numbers, chirality and maximizing global gaps. These findings unveil a universal guiding principle for optimizing magnetic gaps in FMI-TI proximity-based QAH systems, offering valuable insights for advancing experimental implementations in this field.

cond-mat.mtrl-sci↗

arXiv2025

Evolution of X-ray Gas in SN 1987A from 2007 to 2021: Ring Fading and Ejecta Brightening Unveiled through Differential Emission Measure Analysis

As the nearest supernova (SN) observed since Kepler's SN of 1604, SN 1987A provides an unprecedented opportunity to study in detail the early evolution of supernova remnants (SNRs). Despite extensive studies through both observations and simulations, there is still an urgent need for a more effective approach to integrate the results from two sides. In this study, we conducted a detailed differential emission measure (DEM) analysis on the XMM-Newton observations taken in 2007 to 2021 to characterize the continuous temperature structure of SN 1987A, which can be better compared with simulations. The X-ray plasma exhibit a temperature distribution with a major peak at $\sim0.5$-$1$ keV and a high-temperature tail extending to $\gtrsim5$ keV. The emission measure (EM) of the major peak started to decline around 2014, while the EM of the tail continued increasing and appears to have formed a secondary peak at $\sim3$-$5$ keV in recent years. Our DEM results consistent well with simulations, which help to further identify the major peak as originating from the equatorial ring and the secondary peak as arising from the newly shocked ejecta. Together with the simulations, our DEM analysis reveals recent fading of the ring and brightening of the ejecta in X-rays from SN 1987A. Additionally, we observed a recent decrease in the centroid energy of Fe K line, providing further evidence of newly shocked ejecta.

astro-ph.HE↗

arXiv2025

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning

Conditional dependency present one of the trickiest problems in Compositional Zero-Shot Learning, leading to significant property variations of the same state (object) across different objects (states). To address this problem, existing approaches often adopt either all-to-one or one-to-one representation paradigms. However, these extremes create an imbalance in the seesaw between transferability and discriminability, favoring one at the expense of the other. Comparatively, humans are adept at analogizing and reasoning in a hierarchical clustering manner, intuitively grouping categories with similar properties to form cohesive concepts. Motivated by this, we propose Homogeneous Group Representation Learning (HGRL), a new perspective formulates state (object) representation learning as multiple homogeneous sub-group representation learning. HGRL seeks to achieve a balance between semantic transferability and discriminability by adaptively discovering and aggregating categories with shared properties, learning distributed group centers that retain group-specific discriminative features. Our method integrates three core components designed to simultaneously enhance both the visual and prompt representation capabilities of the model. Extensive experiments on three benchmark datasets validate the effectiveness of our method.

cs.CV↗

arXiv2025

Assessing and Enhancing the Robustness of Large Language Models with Task Structure Variations for Logical Reasoning

Large language models (LLMs), such as LLaMA, Alpaca, Vicuna, GPT-3.5 and GPT-4, have advanced the performance of AI systems on various natural language processing tasks to human-like levels. However, their generalisation and robustness when performing logical reasoning has not been sufficiently assessed. To comprehensively evaluate this ability, we develop three new logical reasoning datasets named "ReClor-plus", "LogiQA-plus" and "LogiQAv2-plus" that extend standard logical reasoning datasets to evaluate the robustness of the LLM's reasoning. For each, we create three subsets: the first with randomly shuffled options, the second with the correct choices replaced by "none of the other options is correct", and the third with a combination of shuffling and substitution. Experiments on these datasets show that these simple augmentations greatly hinder the models' performance. Despite their high performance on the original publicly available datasets, we find that all models perform poorly on these newly constructed datasets. We also demonstrate that introducing task variations into the training set can markedly improve the model's performance on both the original and our developed datasets. Finally, we show that applying logic-driven data augmentation for fine-tuning and prompting can enhance generalisation in both discriminative and generative models, offering a path to improving their robustness for tasks involving logical reasoning. Source code and data are made publicly available at https://github.com/Strong-AI-Lab/Logical-and-abstract-reasoning.

cs.CL↗

arXiv2025

AceMath: Advancing Frontier Math Reasoning with Post-Training and Reward Modeling

In this paper, we introduce AceMath, a suite of frontier math models that excel in solving complex math problems, along with highly effective reward models capable of evaluating generated solutions and reliably identifying the correct ones. To develop the instruction-tuned math models, we propose a supervised fine-tuning (SFT) process that first achieves competitive performance across general domains, followed by targeted fine-tuning for the math domain using a carefully curated set of prompts and synthetically generated responses. The resulting model, AceMath-72B-Instruct greatly outperforms Qwen2.5-Math-72B-Instruct, GPT-4o and Claude-3.5 Sonnet. To develop math-specialized reward model, we first construct AceMath-RewardBench, a comprehensive and robust benchmark for evaluating math reward models across diverse problems and difficulty levels. After that, we present a systematic approach to build our math reward models. The resulting model, AceMath-72B-RM, consistently outperforms state-of-the-art reward models. Furthermore, when combining AceMath-72B-Instruct with AceMath-72B-RM, we achieve the highest average rm@8 score across the math reasoning benchmarks. We release model weights, training data, and evaluation benchmarks at: https://research.nvidia.com/labs/adlr/acemath

cs.CL↗

arXiv2025

On the optimal prediction of extreme events in heavy-tailed time series with applications to solar flare forecasting

The prediction of extreme events in time series is a fundamental problem arising in many financial, scientific, engineering, and other applications. We begin by establishing a general Neyman-Pearson-type characterization of optimal extreme event predictors in terms of density ratios. This yields new insights and several closed-form optimal extreme event predictors for additive models. These results naturally extend to time series, where we study optimal extreme event prediction for both light- and heavy-tailed autoregressive and moving average models. Using a uniform law of large numbers for ergodic time series, we establish the asymptotic optimality of an empirical version of the optimal predictor for autoregressive models. Using multivariate regular variation, we obtain an expression for the optimal extremal precision in heavy-tailed infinite moving averages, which provides theoretical bounds on the ability to predict extremes in this general class of models. We address the important problem of predicting solar flares by applying our theory and methodology to a state-of-the-art time series consisting of solar soft X-ray flux measurements. Our results demonstrate the success and limitations in solar flare forecasting of long-memory autoregressive models and long-range-dependent, heavy-tailed FARIMA models.

math.ST↗

arXiv2025

Modeling the Saddle-like GeV-TeV Spectrum of HESS J1809-193: $γ$-Rays Arising from Reverse-Shocked Pulsar Wind Nebula?

Evolution of pulsar wind nebulae (PWNe) could be expected to leave imprints in $γ$-rays. We suggest that intriguing GeV-TeV spectral energy distribution (SED) of HESS J1809$-$193 and Fermi-LAT source J1810.3$-$1925e is very likely to be the $γ$-ray signature of PWN J1809$-$193 in light of the scenario that the PWN was struck by the reverse shock of the parent supernova remnant. Based on evolutionary theory of PWNe, we consider that, when the PWN was disrupted during collision by the reverse shock, some very high-energy electrons escaped impulsively. The remaining electrons stayed in the relic PWN, which was displaced from the pulsar. The very high-energy part of the remaining electrons were depleted by the strong magnetic field that was enhanced by the reverse shock compression in the reverberation stage, leaving the other part of them generating GeV emission. The particles injected from the pulsar after the disruption enter the relic PWN through the newly formed tunnel called the cocoon. The $γ$-ray emission from the escaped electrons can account for the TeV spectrum of component A of HESS J1809$-$193 or the TeV halo, while the electrons remaining after disruption can account for the GeV spectrum of J1810.3$-$1925e. Thus, combination of contributions from these two populations of electrons naturally reproduces the saddle-like SED of HESS J1809$-$193 and J1810.3$-$1925e from 5 GeV to 30 TeV, together with the spectral hardening around 100 GeV. We also show that the post-disruption injection of electrons can explain the spectrum of the relatively faint $γ$-ray emission of component B of HESS J1809$-$193.

astro-ph.HE↗

arXiv2025

Expanding Molecular Shell and Possible γ-ray Source Associated with Supernova Remnant Kesteven 67

We investigate the molecular environment of the supernova remnant (SNR) Kesteven 67 (G18.8+0.3) using observations in $^{12}$CO, $^{13}$CO, HCO$^+$,and HCN lines and possible associated $γ$-ray emission using 16-yr Fermi-LAT observation. We find that the SNR is closely surrounded by a molecular belt in the southeastern boundary, with the both recessed in the band-like molecular gas structure along the Galactic plane. The asymmetric molecular line profiles are widely present in the surrounding gas around local-standard-of-rest velocity +20 km s$^{-1}$. The secondary components centered at $\sim$+16km s$^{-1}$ in the belt and $\sim$+26 km s$^{-1}$ in the northern clump can be ascribed to the motion of a wind-blown molecular shell. This explanation is supported by the position-velocity diagram along a line cutting across the remnant, which shows an arc-like pattern, suggesting an expanding gas structure. With the simulation of chemical effects of shock propagation, the abundance ratios $N$(HCO$^+$)/$N$($^{12}$CO) $\sim2.6\times 10^{-5}$--$3.6\times 10^{-4}$ obtained in the belt can be more naturally interpreted by the wind-driven bubble shock than by the SNR shock. The belt and northern clump are very likely to be parts of an incomplete molecular shell of bubble driven by O-type progenitor star's wind. The analysis of 0.2--500 GeV $γ$-ray emission uncovers a possible point source (`Source~A') about 6.5$σ$ located in the north of the SNR, which essentially corresponds to northern molecular clump. Our spectral fit of the emission indicates that a hadronic origin is favored by the measured Galactic number ratio between CR electrons and protons $\sim0.01$.

astro-ph.HE↗

arXiv2025

A Fast Path-Planning Method for Continuous Harvesting of Table-Top Grown Strawberries

Continuous harvesting and storage of multiple fruits in a single operation allow robots to significantly reduce the travel distance required for repetitive back-and-forth movements. Traditional collision-free path planning algorithms, such as Rapidly-Exploring Random Tree (RRT) and A-star (A), often fail to meet the demands of efficient continuous fruit harvesting due to their low search efficiency and the generation of excessive redundant points. This paper presents the Interactive Local Minima Search Algorithm (ILMSA), a fast path-planning method designed for the continuous harvesting of table-top grown strawberries. The algorithm featured an interactive node expansion strategy that iteratively extended and refined collision-free path segments based on local minima points. To enable the algorithm to function in 3D, the 3D environment was projected onto multiple 2D planes, generating optimal paths on each plane. The best path was then selected, followed by integrating and smoothing the 3D path segments. Simulations demonstrated that ILMSA outperformed existing methods, reducing path length by 21.5% and planning time by 97.1% compared to 3D-RRT, while achieving 11.6% shorter paths and 25.4% fewer nodes than the Lowest Point of the Strawberry (LPS) algorithm in 3D environments. In 2D, ILMSA achieved path lengths 16.2% shorter than A, 23.4% shorter than RRT, and 20.9% shorter than RRT-Connect, while being over 96% faster and generating significantly fewer nodes. Field tests confirmed ILMSA's suitability for complex agricultural tasks, having a combined planning and execution time and an average path length that were approximately 58% and 69%, respectively, of those achieved by the LPS algorithm.

cs.RO↗

arXiv2024

Uncertainty-Aware Out-of-Distribution Detection with Gaussian Processes

Deep neural networks (DNNs) are often constructed under the closed-world assumption, which may fail to generalize to the out-of-distribution (OOD) data. This leads to DNNs producing overconfident wrong predictions and can result in disastrous consequences in safety-critical applications. Existing OOD detection methods mainly rely on curating a set of OOD data for model training or hyper-parameter tuning to distinguish OOD data from training data (also known as in-distribution data or InD data). However, OOD samples are not always available during the training phase in real-world applications, hindering the OOD detection accuracy. To overcome this limitation, we propose a Gaussian-process-based OOD detection method to establish a decision boundary based on InD data only. The basic idea is to perform uncertainty quantification of the unconstrained softmax scores of a DNN via a multi-class Gaussian process (GP), and then define a score function to separate InD and potential OOD data based on their fundamental differences in the posterior predictive distribution from the GP. Two case studies on conventional image classification datasets and real-world image datasets are conducted to demonstrate that the proposed method outperforms the state-of-the-art OOD detection methods when OOD samples are not observed in the training phase.

stat.ML↗

arXiv2024

Reconstructing Quantitative Cerebral Perfusion Images Directly From Measured Sinogram Data Acquired Using C-arm Cone-Beam CT

To shorten the door-to-puncture time for better treating patients with acute ischemic stroke, it is highly desired to obtain quantitative cerebral perfusion images using C-arm cone-beam computed tomography (CBCT) equipped in the interventional suite. However, limited by the slow gantry rotation speed, the temporal resolution and temporal sampling density of typical C-arm CBCT are much poorer than those of multi-detector-row CT in the diagnostic imaging suite. The current quantitative perfusion imaging includes two cascaded steps: time-resolved image reconstruction and perfusion parametric estimation. For time-resolved image reconstruction, the technical challenge imposed by poor temporal resolution and poor sampling density causes inaccurate quantification of the temporal variation of cerebral artery and tissue attenuation values. For perfusion parametric estimation, it remains a technical challenge to appropriately design the handcrafted regularization for better solving the associated deconvolution problem. These two challenges together prevent obtaining quantitatively accurate perfusion images using C-arm CBCT. The purpose of this work is to simultaneously address these two challenges by combining the two cascaded steps into a single joint optimization problem and reconstructing quantitative perfusion images directly from the measured sinogram data. In the developed direct cerebral perfusion parametric image reconstruction technique, TRAINER in short, the quantitative perfusion images have been represented as a subject-specific conditional generative model trained under the constraint of the time-resolved CT forward model, perfusion convolutional model, and the subject's own measured sinogram data. Results shown in this paper demonstrated that using TRAINER, quantitative cerebral perfusion images can be accurately obtained using C-arm CBCT in the interventional suite.

eess.IV↗

arXiv2024

On the recurrence coefficients for the $q$-Laguerre weight and discrete Painlevé equations

We study the dependence of recurrence coefficients in the three-term recurrence relation for orthogonal polynomials with a certain deformation of the $q$-Laguerre weight on the degree parameter $n$. We show that this dependence is described by a discrete Painlevé equation on the family of $A_{5}^{(1)}$ Sakai surfaces, but this equation is different from the standard examples of discrete Painlevé equations of this type and instead is a composition of two such. This case study is a good illustration of the effectiveness of a recently proposed geometric identification scheme for discrete Painlevé equations.

nlin.SI↗

arXiv2024

Molecular chemistry induced by J-shock toward supernova remnant W51C

Shock waves from supernova remnants (SNRs) have strong influence on the physical and chemical properties of molecular clouds (MCs). Shocks propagating into magnetized MCs can be classified into "jump" J-shock and "continuous" C-shock. The molecular chemistry in the re-formed molecular gas behind J-shock is still not well understood, which will provide a comprehensive view of the chemical feedback of SNRs and the chemical effects of J-shock. We conducted a W-band (71.4-89.7 GHz) observation toward a re-formed molecular clump behind a J-shock induced by SNR W51C with the Yebes 40 m radio telescope to study the molecular chemistry in the re-formed molecular gas. Based on the local thermodynamic equilibrium (LTE) assumption, we estimate the column densities of HCO+, HCN, C2H and o-c-C3H2, and derive the maps of their abundance ratios with CO. The gas density is constrained by non-LTE analysis of the HCO+ J=1-0 line. We obtain the following abundance ratios: $N({\rm HCO^+})/N({\rm CO})\sim (1.0\text{--}4.0)\times 10^{-4}$, $N({\rm HCN})/N({\rm CO})\sim (1.8\text{--}5.3)\times 10^{-4}$, $N({\rm C_2H})/N({\rm CO})\sim (1.6\text{--}5.0)\times 10^{-3}$, and $N({o\text{-}c\text{-}{\rm C_3H_2}})/N({\rm CO})\sim (1.2\text{--}7.9)\times 10^{-4}$. The non-LTE analysis suggests that the gas density is $n_{\rm H_2}\gtrsim 10^4\rm \ cm^{-3}$. We find that the N(C2H)/N(CO) and N(o-c-C3H2)/N(CO) are higher than typical values in quiescent MCs and shocked MCs by 1-2 orders of magnitude, which can be qualitatively attributed to the abundant C+ and C at the earliest phase of molecular gas re-formation. The Paris-Durham shock code can reproduce, although not perfectly, the observed abundance ratios, especially the enhanced N(C2H)/N(CO) and N(o-c-C3H2)/N(CO), with J-shocks propagating in to both non-irradiated and irradiated molecular gas with a preshock density of $n_{\rm H}=2\times 10^3\rm \ cm^{-3}$.

astro-ph.GA↗

arXiv2024

Operator learning regularization for macroscopic permeability prediction in dual-scale flow problem

Liquid composites moulding is an important manufacturing technology for fibre reinforced composites, due to its cost-effectiveness. Challenges lie in the optimisation of the process due to the lack of understanding of key characteristic of textile fabrics - permeability. The problem of computing the permeability coefficient can be modelled as the well-known Stokes-Brinkman equation, which introduces a heterogeneous parameter $β$ distinguishing macropore regions and fibre-bundle regions. In the present work, we train a Fourier neural operator to learn the nonlinear map from the heterogeneous coefficient $β$ to the velocity field $u$, and recover the corresponding macroscopic permeability $K$. This is a challenging inverse problem since both the input and output fields span several order of magnitudes, we introduce different regularization techniques for the loss function and perform a quantitative comparison between them.

physics.flu-dyn↗

arXiv2024

Dual-functional microwave photonic system for concurrent radar and secure communication via radar signal masking

Integrating functions such as radar and communication into a single system is of great significance for the miniaturization and functional integration of future electronic warfare and 6G systems. Here, we show a dual-functional microwave photonic system for concurrent radar and secure communication. The scheme utilizes microwave photonic frequency multiplying and frequency conversion techniques to shift both the intermediate frequency radar and communication signals to the same frequency band, enabling radar and communication operations at the same time and frequency. The high-power radar signal is also used to mask the communication signal, increasing the difficulty of signal interception and thus enhancing security. By employing de-chirping at the radar receiver and self-interference cancelation at the communication receiver, the radar function can be implemented and the communication signal can also be correctly demodulated after removing the radar masking. An experiment is performed. A 0.3-GHz bandwidth linearly frequency-modulated signal is quadrupled and superimposed with two up-converted 0.5-Gbaud orthogonal frequency-division multiplexing signals. A communication data rate of 2 Gbit/s, a radar ranging measurement error of less than $\pm$ 0.3 cm, and a radar inverse synthetic aperture radar imaging resolution of 12.5$\times$10.2 cm are achieved.

physics.optics↗

arXiv2024

Neuron: Learning Context-Aware Evolving Representations for Zero-Shot Skeleton Action Recognition

Zero-shot skeleton action recognition is a non-trivial task that requires robust unseen generalization with prior knowledge from only seen classes and shared semantics. Existing methods typically build the skeleton-semantics interactions by uncontrollable mappings and conspicuous representations, thereby can hardly capture the intricate and fine-grained relationship for effective cross-modal transferability. To address these issues, we propose a novel dyNamically Evolving dUal skeleton-semantic syneRgistic framework with the guidance of cOntext-aware side informatioN (dubbed Neuron), to explore more fine-grained cross-modal correspondence from micro to macro perspectives at both spatial and temporal levels, respectively. Concretely, 1) we first construct the spatial-temporal evolving micro-prototypes and integrate dynamic context-aware side information to capture the intricate and synergistic skeleton-semantic correlations step-by-step, progressively refining cross-model alignment; and 2) we introduce the spatial compression and temporal memory mechanisms to guide the growth of spatial-temporal micro-prototypes, enabling them to absorb structure-related spatial representations and regularity-dependent temporal patterns. Notably, such processes are analogous to the learning and growth of neurons, equipping the framework with the capacity to generalize to novel unseen action categories. Extensive experiments on various benchmark datasets demonstrated the superiority of the proposed method.

cs.CV↗

arXiv2024

Empirical color correction to MIST and PARSEC isochrones on Gaia BR-RP and G-RP with benchmark open clusters

Recent literature reports a color deviation between observed Gaia color-magnitude diagrams (CMDs) and theoretical model isochrone predictions, particularly in the very low-mass regime. To assess its impact on cluster age determination via isochrone fitting, we quantified the color deviations for three benchmark clusters, Hyades, Pleiades, and Praesepe, both for the Gaia color (BP-RP) and (G-RP). In general, the (G-RP) color deviations are smaller than the (BP-RP) ones. Empirical color correction functions based on these benchmarks are derived for the currently available MIST and PARSEC 1.2S isochrone models. Applying the correction functions to 31 additional open clusters and 3 moving groups results in a significantly improved alignment between the isochrones and observed CMDs. With our empirical corrections, isochrones provide age estimates consistent with literature values obtained through the spectral Lithium Depletion Boundary method, validating the effectiveness of our approach. The corresponding metallicities with PARSEC 1.2S also show a good agreement with the spectroscopic results. The empirical color correction function we present in this work offers a tool for a consistent age determination within the full mass range of stellar clusters using the isochrone fitting method.

astro-ph.SR↗