arXiv subjects

Yang Chen

Publications and source records attributed to Yang Chen.

At least 379 records · Page 21Linked to original sources

arXiv2024

Discovery and timing of pulsar J2016$+$3711 in supernova remnant CTB 87 with FAST

We report on our discovery of the radio pulsar, PSR J2016$+$3711, in supernova remnant (SNR) CTB 87, with a $\sim10.8σ$ significance of pulses, which confirms the compact nature of the X-ray point source in CTB 87. It is the first pulsar discovered in SNRs using Five-hundred-meter Aperture Spherical radio Telescope (FAST). Its integrated radio pulse profile can be well described by a single component, with a width at 50% of the peak flux density of about 28.1$^\circ$ and an effective width of about 32.2$^\circ$. The mean flux density at 1.25 GHz is estimated to be about 15.5$μ$Jy. Combined with the non-detection of the radio pulse at lower frequencies, the radio spectral index of the pulsar is constrained to be $\lesssim 2.3$. We also present the timing solution based on 28 follow-up FAST observations. Our results reveal a period of 50.81 ms, period derivative of $7.2\times 10^{-14}$ s s$^{-1}$, and dispersion measure of 428 pc cm$^{-3}$. The strength of the equatorial surface magnetic dipole magnetic field is inferred to be about $1.9\times10^{12}$ G. Using the ephemeris obtained from the radio observations, we searched Fermi-LAT data for gamma-ray pulsations but detected no pulsed signal. We also searched for radio pulses with FAST toward the X-ray counterpart of the gamma-ray binary HESS J1832$-$093 proximate to SNR G22.7$-$00.2 but found no signal.

astro-ph.HE↗

arXiv2024

A Fermi-LAT Study of Globular Cluster Dynamical Evolution in the Milky Way: Millisecond Pulsars as the Probe

Using archival {\it Fermi}-LAT data with a time span of $\sim12$ years, we study the population of Millisecond Pulsars (MSPs) in Globular Clusters (GlCs) and investigate their dependence on cluster dynamical evolution in the Milky Way Galaxy. We show that the $γ$-ray luminosity ($L_γ$) and emissivity ($ε_γ=L_γ/M$) are good indicators of the population and abundance of MSPs in GlCs, and they are highly dependent on the dynamical evolution history of the host clusters. Specifically speaking, the dynamically older GlCs with more compact structures are more likely to have larger $L_γ$ and $ε_γ$, and these trends can be summarized as strong correlations with cluster stellar encounter rate $Γ$ and the specific encounter rate ($Λ=Γ/M$), with $L_γ\propto Γ^{0.70\pm0.11}$ and $ε_γ\propto Λ^{0.73\pm0.13}$ for dynamically normal GlCs. However, as GlCs evolve into deep core collapse, these trends are found to be reversed, implying that strong encounters may have lead to the ejection of MSPs from core-collapsed Systems. Besides, the GlCs are found to exhibit larger $ε_γ$ with increasing stellar mass function slope, decreasing tidal radius and distances from the Galactic Center (GC). These correlations indicate that, as GlCs losing kinetic energy and spiral in towards GC, tidal stripping and mass segregation have a preference in leading to the loss of normal stars from GlCs, while MSPs are more likely to concentrate to cluster center and be deposited into the GC. Moreover, we gauge $ε_γ$ of GlCs is $\sim10-1000$ times larger than the Galactic bulge, the latter is thought to reside thousands of unresolved MSPs and may responsible for the GC $γ$-ray excess, which support that GlCs are generous contributors to the population of MSPs in the GC.

astro-ph.GA↗

arXiv2024

Uncovering Heterogeneity of Solar Flare Mechanism With Mixture Models

The physics of solar flares occurring on the Sun is highly complex and far from fully understood. However, observations show that solar eruptions are associated with the intense kilogauss fields of active regions, where free energies are stored with field-aligned electric currents. With the advent of high-quality data sources such as the Geostationary Operational Environmental Satellites (GOES) and Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI), recent works on solar flare forecasting have been focusing on data-driven methods. In particular, black box machine learning and deep learning models are increasingly adopted in which underlying data structures are not modeled explicitly. If the active regions indeed follow the same laws of physics, there should be similar patterns shared among them, reflected by the observations. Yet, these black box models currently used in the literature do not explicitly characterize the heterogeneous nature of the solar flare data, within and between active regions. In this paper, we propose two finite mixture models designed to capture the heterogeneous patterns of active regions and their associated solar flare events. With extensive numerical studies, we demonstrate the usefulness of our proposed method for both resolving the sample imbalance issue and modeling the heterogeneity for rare energetic solar flare events.

astro-ph.SR↗

arXiv2024

Correlation between magnetic domain structures and quantum anomalous Hall effect in epitaxial MnBi2Te4 thin films

We use magnetic force microscopy (MFM) to study spatial uniformity of magnetization of epitaxially grown MnBi2Te4 thin films. Compared to films which exhibit no quantum anomalous Hall effect (QAH), films with QAH are observed to have more spatial uniformity of magnetization with larger domain size. The domain evolution upon magnetic field sweeping indicates that the magnetic domains or the spatial nonuniformity of magnetization originates from the strong pinning of the inherent sample inhomogeneity. A direct correlation between the Hall resistivity and the domain size has been established by analyzing a series of thin films with and without QAH. Our observation shows that one has to suppress the spatial nonuniformity of magnetization to allow the Hall resistivity to be quantized. The fact that a sizable longitudinal resistivity remains even for the QAH sample suggests a quantized Hall insulator scenario. Our work provides important insights to the understanding of the quantization mechanism and the dissipation of the QAH state in MnBi2Te4 system.

cond-mat.mes-hall↗

arXiv2024

Reentrant quantum anomalous Hall effect in molecular beam epitaxy-grown MnBi2Te4 thin films

In this study, we investigate intrinsic magnetic topological insulator MnBi2Te4 thin films grown by molecular beam epitaxy. We observe a reentrant quantum anomalous Hall effect when the Fermi energy enters the valance band and magnetic field equals zero, indicating the emergence of the Chern Anderson insulator state. The discovery opens a new avenue for realizing the QAH effect and underscores the fundamental role of both Berry curvature and Anderson localization.

cond-mat.mes-hall↗

arXiv2024

Analytical approximations for generalized quantum Rabi models

The quantum Rabi model is essential for understanding interacting quantum systems. It serves as the simplest non-integrable yet solvable model describing the interaction between a two-level system and a single mode of a bosonic field. In this study, we delve into the exploration of the generalized quantum Rabi model, wherein the bosonic mode of the field undergoes squeezing. Utilizing the Segal-Bargmann representation of the infinite-dimensional Hilbert space, we demonstrate that the energy spectrum of the generalized quantum Rabi model, when both the Rabi coupling strength and the squeezing strength are not significantly large compared to the field mode frequency, can be analytically determined by a bi-confluent Fuchsian equation with two regular singularities at 0 and 1 and an irregular singularity of rank two at infinity.

quant-ph↗

arXiv2024

PeVatron Candidate SNR G106.3+2.7 in a Low-density Cavity: a Multiwavelength Test

In this paper, we constrain the density of the interstellar medium (ISM) around the hadronic PeVatron candidate, supernova remnant (SNR) G106.3+2.7, based on X-ray and $γ$-ray observations. The purpose of this investigation is to understand the influence of the gaseous environment on this SNR as a proton PeVatron candidate. By modelling the self-regulated propagation of the CRs injected from the SNR, we calculate the $γ$-ray emission of CRs via the hadronuclear interactions with the molecular cloud and the ISM, and use the measured $γ$-ray flux to constrain the ISM density around the SNR. Our results support the picture that the SNR is expanding into a low-density ($n<0.05 cm^{-3}$) cavity, enabling the SNR to be a potential proton PeVatron despite that it presently is not in the very early phase.

astro-ph.HE↗

arXiv2024

tf.data service: A Case for Disaggregating ML Input Data Processing

Machine learning (ML) computations commonly execute on expensive specialized hardware, such as GPUs and TPUs, which provide high FLOPs and performance-per-watt. For cost efficiency, it is essential to keep these accelerators highly utilized. This requires preprocessing input data at the rate at which the accelerators can ingest and perform ML computations on the data. To avoid data stalls, the host CPU and RAM required for input data processing per accelerator core used for ML computations varies across jobs. Hence, the traditional approach of processing input data on ML accelerator hosts with a fixed hardware ratio leads to either under-utilizing the accelerators or the host CPU and RAM. In this paper, we address these concerns by building a disaggregated ML data processing system. We present tf.data service, an open-source disaggregated input data processing service built on top of tf.data in TensorFlow. We show that disaggregating data preprocessing has three key advantages for large-scale ML training jobs. First, the service can horizontally scale-out to right-size CPU/RAM host resources for data processing in each job, saving 32x training time and 26x cost, on average. Second, the service can share ephemeral preprocessed data results across jobs, to optimize CPU usage and reduce redundant computations. Finally, the service supports coordinated reads, a technique that avoids stragglers due to different input sizes in distributed training, reducing training time by 2.2x, on average. Our design is inspired by lessons learned from deploying tf.data service in production, including relaxing data visitation guarantees without impacting model accuracy.

cs.LG↗

arXiv2023

Behaviour Modelling of Social Animals via Causal Structure Discovery and Graph Neural Networks

Better understanding the natural world is a crucial task with a wide range of applications. In environments with close proximity between humans and animals, such as zoos, it is essential to better understand the causes behind animal behaviour and what interventions are responsible for changes in their behaviours. This can help to predict unusual behaviours, mitigate detrimental effects and increase the well-being of animals. There has been work on modelling the dynamics behind swarms of birds and insects but the complex social behaviours of mammalian groups remain less explored. In this work, we propose a method to build behavioural models using causal structure discovery and graph neural networks for time series. We apply this method to a mob of meerkats in a zoo environment and study its ability to predict future actions and model the behaviour distribution at an individual-level and at a group level. We show that our method can match and outperform standard deep learning architectures and generate more realistic data, while using fewer parameters and providing increased interpretability.

cs.MA↗

arXiv2023

Point Cloud Part Editing: Segmentation, Generation, Assembly, and Selection

Ideal part editing should guarantee the diversity of edited parts, the fidelity to the remaining parts, and the quality of the results. However, previous methods do not disentangle each part completely, which means the edited parts will affect the others, resulting in poor diversity and fidelity. In addition, some methods lack constraints between parts, which need manual selections of edited results to ensure quality. Therefore, we propose a four-stage process for point cloud part editing: Segmentation, Generation, Assembly, and Selection. Based on this process, we introduce SGAS, a model for part editing that employs two strategies: feature disentanglement and constraint. By independently fitting part-level feature distributions, we realize the feature disentanglement. By explicitly modeling the transformation from object-level distribution to part-level distributions, we realize the feature constraint. Considerable experiments on different datasets demonstrate the efficiency and effectiveness of SGAS on point cloud part editing. In addition, SGAS can be pruned to realize unsupervised part-aware point cloud generation and achieves state-of-the-art results.

cs.CV↗

arXiv2023

UniIR: Training and Benchmarking Universal Multimodal Information Retrievers

Existing information retrieval (IR) models often assume a homogeneous format, limiting their applicability to diverse user needs, such as searching for images with text descriptions, searching for a news article with a headline image, or finding a similar photo with a query image. To approach such different information-seeking demands, we introduce UniIR, a unified instruction-guided multimodal retriever capable of handling eight distinct retrieval tasks across modalities. UniIR, a single retrieval system jointly trained on ten diverse multimodal-IR datasets, interprets user instructions to execute various retrieval tasks, demonstrating robust performance across existing datasets and zero-shot generalization to new tasks. Our experiments highlight that multi-task training and instruction tuning are keys to UniIR's generalization ability. Additionally, we construct the M-BEIR, a multimodal retrieval benchmark with comprehensive results, to standardize the evaluation of universal multimodal information retrieval.

cs.CV↗

arXiv2023

Task-Robust Pre-Training for Worst-Case Downstream Adaptation

Pre-training has achieved remarkable success when transferred to downstream tasks. In machine learning, we care about not only the good performance of a model but also its behavior under reasonable shifts of condition. The same philosophy holds when pre-training a foundation model. However, the foundation model may not uniformly behave well for a series of related downstream tasks. This happens, for example, when conducting mask recovery regression where the recovery ability or the training instances diverge like pattern features are extracted dominantly on pre-training, but semantic features are also required on a downstream task. This paper considers pre-training a model that guarantees a uniformly good performance over the downstream tasks. We call this goal as $\textit{downstream-task robustness}$. Our method first separates the upstream task into several representative ones and applies a simple minimax loss for pre-training. We then design an efficient algorithm to solve the minimax loss and prove its convergence in the convex setting. In the experiments, we show both on large-scale natural language processing and computer vision datasets our method increases the metrics on worse-case downstream tasks. Additionally, some theoretical explanations for why our loss is beneficial are provided. Specifically, we show fewer samples are inherently required for the most challenging downstream task in some cases.

cs.CV↗

arXiv2023

Asymptotic Properties of Some Freud Polynomials

We study the asymptotic properties of monic orthogonal polynomials (OPs) with respect to some Freud weights when the degree of the polynomial tends to infinity, including the asymptotics of the recurrence coefficients, the nontrivial leading coefficients of the monic OPs, the associated Hankel determinants and the squares of $L^2$-norm of the monic OPs. These results are derived from the combination of the ladder operator approach, Dyson's Coulomb fluid approach and some recent results in the literature.

math.CA↗

arXiv2023

Automatic Identification of Driving Maneuver Patterns using a Robust Hidden Semi-Markov Models

There is an increase in interest to model driving maneuver patterns via the automatic unsupervised clustering of naturalistic sequential kinematic driving data. The patterns learned are often used in transportation research areas such as eco-driving, road safety, and intelligent vehicles. One such model capable of modeling these patterns is the Hierarchical Dirichlet Process Hidden Semi-Markov Model (HDP-HSMM), as it is often used to estimate data segmentation, state duration, and transition probabilities. While this model is a powerful tool for automatically clustering observed sequential data, the existing HDP-HSMM estimation suffers from an inherent tendency to overestimate the number of states. This can result in poor estimation, which can potentially impact impact transportation research through incorrect inference of driving patterns. In this paper, a new robust HDP-HSMM (rHDP-HSMM) method is proposed to reduce the number of redundant states and improve the consistency of the model's estimation. Both a simulation study and a case study using naturalistic driving data are presented to demonstrate the effectiveness of the proposed rHDP-HSMM in identifying and inference of driving maneuver patterns.

stat.ML↗

arXiv2023

Full-length-body CBCT imaging in upright position with robotic-arm system: a simulation study

Upright position CT scans make it possible for full-length-body imaging at conditions more relevant to daily situations, but the substantial weight of the upright CT scanners increases the risks to floor's stability and patients'safety. Robotic-arm CBCT systems are supposed to be a better solution for this task, but such systems still face challenges including long scanning time and low reconstruction quality. To address the above challenges, this paper proposes a novel method to calculate optimal scanning pitch based on data completeness analysis, which can complete the whole-body scan in the shortest time without a significant decline in image quality. Besides, an FDK-style reconstruction method based on normalized projections is proposed to obtain fast image reconstruction. Extensive experiments prove the effectiveness of the proposed optimal scanning trajectory. Qualitative and quantitative comparisons with FDK and iterative algorithms show that the proposed reconstruction method can obtain high imaging quality with reasonable computation costs. The method proposed in this paper is expected to promote the application of robotic-arm CBCT systems in orthopedic functional analysis.

physics.med-ph↗

arXiv2023

Control3D: Towards Controllable Text-to-3D Generation

Recent remarkable advances in large-scale text-to-image diffusion models have inspired a significant breakthrough in text-to-3D generation, pursuing 3D content creation solely from a given text prompt. However, existing text-to-3D techniques lack a crucial ability in the creative process: interactively control and shape the synthetic 3D contents according to users' desired specifications (e.g., sketch). To alleviate this issue, we present the first attempt for text-to-3D generation conditioning on the additional hand-drawn sketch, namely Control3D, which enhances controllability for users. In particular, a 2D conditioned diffusion model (ControlNet) is remoulded to guide the learning of 3D scene parameterized as NeRF, encouraging each view of 3D scene aligned with the given text prompt and hand-drawn sketch. Moreover, we exploit a pre-trained differentiable photo-to-sketch model to directly estimate the sketch of the rendered image over synthetic 3D scene. Such estimated sketch along with each sampled view is further enforced to be geometrically consistent with the given sketch, pursuing better controllable text-to-3D generation. Through extensive experiments, we demonstrate that our proposal can generate accurate and faithful 3D scenes that align closely with the input text prompts and sketches.

cs.CV↗

arXiv2023

3DStyle-Diffusion: Pursuing Fine-grained Text-driven 3D Stylization with 2D Diffusion Models

3D content creation via text-driven stylization has played a fundamental challenge to multimedia and graphics community. Recent advances of cross-modal foundation models (e.g., CLIP) have made this problem feasible. Those approaches commonly leverage CLIP to align the holistic semantics of stylized mesh with the given text prompt. Nevertheless, it is not trivial to enable more controllable stylization of fine-grained details in 3D meshes solely based on such semantic-level cross-modal supervision. In this work, we propose a new 3DStyle-Diffusion model that triggers fine-grained stylization of 3D meshes with additional controllable appearance and geometric guidance from 2D Diffusion models. Technically, 3DStyle-Diffusion first parameterizes the texture of 3D mesh into reflectance properties and scene lighting using implicit MLP networks. Meanwhile, an accurate depth map of each sampled view is achieved conditioned on 3D mesh. Then, 3DStyle-Diffusion leverages a pre-trained controllable 2D Diffusion model to guide the learning of rendered images, encouraging the synthesized image of each view semantically aligned with text prompt and geometrically consistent with depth map. This way elegantly integrates both image rendering via implicit MLP networks and diffusion process of image synthesis in an end-to-end fashion, enabling a high-quality fine-grained stylization of 3D meshes. We also build a new dataset derived from Objaverse and the evaluation protocol for this task. Through both qualitative and quantitative experiments, we validate the capability of our 3DStyle-Diffusion. Source code and data are available at \url{https://github.com/yanghb22-fdu/3DStyle-Diffusion-Official}.

cs.CV↗

arXiv2023

xASTNN: Improved Code Representations for Industrial Practice

The application of deep learning techniques in software engineering becomes increasingly popular. One key problem is developing high-quality and easy-to-use source code representations for code-related tasks. The research community has acquired impressive results in recent years. However, due to the deployment difficulties and performance bottlenecks, seldom these approaches are applied to the industry. In this paper, we present xASTNN, an eXtreme Abstract Syntax Tree (AST)-based Neural Network for source code representation, aiming to push this technique to industrial practice. The proposed xASTNN has three advantages. First, xASTNN is completely based on widely-used ASTs and does not require complicated data pre-processing, making it applicable to various programming languages and practical scenarios. Second, three closely-related designs are proposed to guarantee the effectiveness of xASTNN, including statement subtree sequence for code naturalness, gated recursive unit for syntactical information, and gated recurrent unit for sequential information. Third, a dynamic batching algorithm is introduced to significantly reduce the time complexity of xASTNN. Two code comprehension downstream tasks, code classification and code clone detection, are adopted for evaluation. The results demonstrate that our xASTNN can improve the state-of-the-art while being faster than the baselines.

cs.SE↗