arXiv subjects

Yang Chen

Publications and source records attributed to Yang Chen.

At least 361 records · Page 20Linked to original sources

arXiv2024

Fine-Grained Side Information Guided Dual-Prompts for Zero-Shot Skeleton Action Recognition

Skeleton-based zero-shot action recognition aims to recognize unknown human actions based on the learned priors of the known skeleton-based actions and a semantic descriptor space shared by both known and unknown categories. However, previous works focus on establishing the bridges between the known skeleton representation space and semantic descriptions space at the coarse-grained level for recognizing unknown action categories, ignoring the fine-grained alignment of these two spaces, resulting in suboptimal performance in distinguishing high-similarity action categories. To address these challenges, we propose a novel method via Side information and dual-prompts learning for skeleton-based zero-shot action recognition (STAR) at the fine-grained level. Specifically, 1) we decompose the skeleton into several parts based on its topology structure and introduce the side information concerning multi-part descriptions of human body movements for alignment between the skeleton and the semantic space at the fine-grained level; 2) we design the visual-attribute and semantic-part prompts to improve the intra-class compactness within the skeleton space and inter-class separability within the semantic space, respectively, to distinguish the high-similarity actions. Extensive experiments show that our method achieves state-of-the-art performance in ZSL and GZSL settings on NTU RGB+D, NTU RGB+D 120, and PKU-MMD datasets.

cs.CV↗

arXiv2024

A Generic Approach to Fix Test Flakiness in Real-World Projects

Test flakiness, a non-deterministic behavior of builds irrelevant to code changes, is a major and continuing impediment to delivering reliable software. The very few techniques for the automated repair of test flakiness are specifically crafted to repair either Order-Dependent (OD) or Implementation-Dependent (ID) flakiness. They are also all symbolic approaches, i.e., leverage program analysis to detect and repair known test flakiness patterns and root causes, failing to generalize. To bridge the gap, we propose FlakyDoctor, a neuro-symbolic technique that combines the power of LLMs-generalizability-and program analysis-soundness-to fix different types of test flakiness. Our extensive evaluation using 873 confirmed flaky tests (332 OD and 541 ID) from 243 real-world projects demonstrates the ability of FlakyDoctor in repairing flakiness, achieving 57% (OD) and 59% (ID) success rate. Comparing to three alternative flakiness repair approaches, FlakyDoctor can repair 8% more ID tests than DexFix, 12% more OD flaky tests than ODRepair, and 17% more OD flaky tests than iFixFlakies. Regardless of underlying LLM, the non-LLM components of FlakyDoctor contribute to 12-31% of the overall performance, i.e., while part of the FlakyDoctor power is from using LLMs, they are not good enough to repair flaky tests in real-world projects alone. What makes the proposed technique superior to related research on test flakiness mitigation specifically and program repair, in general, is repairing 79 previously unfixed flaky tests in real-world projects. We opened pull requests for all cases with corresponding patches; 19 of them were accepted and merged at the time of submission.

cs.SE↗

arXiv2024

Interplay between electronic dephasing and localization in finite-sized Chern insulator

Anderson localization is anticipated to play a pivotal role in the manifestation of the quantum anomalous Hall effect, akin to its role in conventional quantum Hall effects. The significance of Anderson localization is particularly pronounced in elucidating the reasons behind the fragility of the observed quantum anomalous Hall state in the intrinsic magnetic topological insulator MnBi2Te4 with a large predicted magnetic gap. Here, employing varying sized MnBi2Te4 micro/nano-structures fabricated from a single molecular-beam-epitaxy-grown thin film, we have carried out a systematic size- and temperature-dependent study on the transport properties of the films regarding the quantum anomalous Hall states. The low-temperature transport properties of the finite-sized MnBi2Te4 samples can be quantitatively understood through Anderson localization, which plays an indispensable role in stabilizing the ground states. At higher temperatures, the failure of electron localization induced by an excessively short electronic dephasing length is identified as the cause of deviation from quantization. The work reveals that electronic dephasing and localization are non-negligible factors in designing high-temperature quantum anomalous Hall systems.

cond-mat.mes-hall↗

arXiv2024

A Monte-Carlo Simulation on Resonant Scattering of X-ray Line Emission in Supernova Remnants

Resonant scattering (RS) of X-ray line emission in supernova remnants (SNRs) may modify the observed line profiles and fluxes and has potential impact on estimating the physical properties of the hot gas and hence on understanding the SNR physics, but has not been theoretically modeled ever. Here we present our Monte-Carlo simulation of RS effect on X-ray resonant-line emission, typified by O VII He$α$ r line, from SNRs. We employ the physical conditions characterized by the Sedov-Taylor solution and some basic parameters similar to those in Cygnus Loop. We show that the impact of RS effect is most significant near the edge of the remnant. The line profiles are predicted to be asymmetric because of different temperatures and photon production efficiencies of the expanding gas at different radii. We also predict the surface brightness of the line emission would decrease in the outer projected region but is slightly enhanced in the inner. The G-ratio of the OVII He$α$ triplet can be effectively elevated by RS in the outer region. We show that RS effect of the O VII He$α$ r line in the southwestern boundary region of Cygnus Loop is non-negligible. The observed OVII G-ratio $\sim$1.8 of the region could be achieved with RS taken into account for properly elevated O abundance from the previous estimates. Additional simulation performed for the SNRs in ejecta-dominated phase like Cas A shows that RS in the shocked ejecta may have some apparently effects on the observational properties of oxygen resonant lines.

astro-ph.HE↗

arXiv2024

Seamlessly merging radar ranging/imaging, wireless communications, and spectrum sensing, for 6G empowered by microwave photonics

Integration of radar, wireless communications, and spectrum sensing is being investigated for 6G with an increased spectral efficiency. Microwave photonics (MWP), a technique that combines microwave engineering and photonic technology to take advantage of the wide bandwidth offered by photonics for microwave signal generation and processing is considered an effective solution for the implementation of the integration. In this paper, an MWP-assisted joint radar, wireless communications, and spectrum sensing (JRCSS) system that enables precise perception of the surrounding physical and electromagnetic environments while maintaining high-speed data communication is proposed and demonstrated. Communication signals and frequency-sweep signals are merged in the optical domain to achieve high-speed radar ranging and imaging, high-data-rate wireless communications, and wideband spectrum sensing. In an experimental demonstration, a JRCSS system supporting radar ranging with a measurement error within $\pm$ 4 cm, two-dimensional imaging with a resolution of 25 $\times$ 24.7 mm, wireless communications with a data rate of 2 Gbaud, and spectrum sensing with a frequency measurement error within $\pm$ 10 MHz in a 6-GHz bandwidth, is demonstrated.

eess.SP↗

arXiv2024

A multi-stage semi-supervised learning for ankle fracture classification on CT images

Because of the complicated mechanism of ankle injury, it is very difficult to diagnose ankle fracture in clinic. In order to simplify the process of fracture diagnosis, an automatic diagnosis model of ankle fracture was proposed. Firstly, a tibia-fibula segmentation network is proposed for the joint tibiofibular region of the ankle joint, and the corresponding segmentation dataset is established on the basis of fracture data. Secondly, the image registration method is used to register the bone segmentation mask with the normal bone mask. Finally, a semi-supervised classifier is constructed to make full use of a large number of unlabeled data to classify ankle fractures. Experiments show that the proposed method can segment fractures with fracture lines accurately and has better performance than the general method. At the same time, this method is superior to classification network in several indexes.

eess.IV↗

arXiv2024

VP3D: Unleashing 2D Visual Prompt for Text-to-3D Generation

Recent innovations on text-to-3D generation have featured Score Distillation Sampling (SDS), which enables the zero-shot learning of implicit 3D models (NeRF) by directly distilling prior knowledge from 2D diffusion models. However, current SDS-based models still struggle with intricate text prompts and commonly result in distorted 3D models with unrealistic textures or cross-view inconsistency issues. In this work, we introduce a novel Visual Prompt-guided text-to-3D diffusion model (VP3D) that explicitly unleashes the visual appearance knowledge in 2D visual prompt to boost text-to-3D generation. Instead of solely supervising SDS with text prompt, VP3D first capitalizes on 2D diffusion model to generate a high-quality image from input text, which subsequently acts as visual prompt to strengthen SDS optimization with explicit visual appearance. Meanwhile, we couple the SDS optimization with additional differentiable reward function that encourages rendering images of 3D models to better visually align with 2D visual prompt and semantically match with text prompt. Through extensive experiments, we show that the 2D Visual Prompt in our VP3D significantly eases the learning of visual appearance of 3D models and thus leads to higher visual fidelity with more detailed textures. It is also appealing in view that when replacing the self-generating visual prompt with a given reference image, VP3D is able to trigger a new task of stylized text-to-3D generation. Our project page is available at https://vp3d-cvpr24.github.io.

cs.CV↗

arXiv2024

SynerMix: Synergistic Mixup Solution for Enhanced Intra-Class Cohesion and Inter-Class Separability in Image Classification

To address the issues of MixUp and its variants (e.g., Manifold MixUp) in image classification tasks-namely, their neglect of mixing within the same class (intra-class mixup) and their inadequacy in enhancing intra-class cohesion through their mixing operations-we propose a novel mixup method named SynerMix-Intra and, building upon this, introduce a synergistic mixup solution named SynerMix. SynerMix-Intra specifically targets intra-class mixup to bolster intra-class cohesion, a feature not addressed by current mixup methods. For each mini-batch, it leverages feature representations of unaugmented original images from each class to generate a synthesized feature representation through random linear interpolation. All synthesized representations are then fed into the classification and loss layers to calculate an average classification loss that significantly enhances intra-class cohesion. Furthermore, SynerMix combines SynerMix-Intra with an existing mixup approach (e.g., MixUp, Manifold MixUp), which primarily focuses on inter-class mixup and has the benefit of enhancing inter-class separability. In doing so, it integrates both inter- and intra-class mixup in a balanced way while concurrently improving intra-class cohesion and inter-class separability. Experimental results on six datasets show that SynerMix achieves a 0.1% to 3.43% higher accuracy than the best of either MixUp or SynerMix-Intra alone, averaging a 1.16% gain. It also surpasses the top-performer of either Manifold MixUp or SynerMix-Intra by 0.12% to 5.16%, with an average gain of 1.11%. Given that SynerMix is model-agnostic, it holds significant potential for application in other domains where mixup methods have shown promise, such as speech and text classification. Our code is publicly available at: https://github.com/wxitxy/synermix.git.

cs.CV↗

arXiv2024

Shock and Cosmic Ray Chemistry Associated with the Supernova Remnant W28

Supernova remnants (SNRs) exert strong influence on the physics and chemistry of the nearby molecular clouds (MCs) through shock waves and the cosmic rays (CRs) they accelerate. To investigate the SNR-cloud interaction in the prototype interacting SNR W28 (G6.4$-$0.1), we present new observations of $\rm HCO^+$, HCN and HNC $J=1\text{--}0$ lines, supplemented by archival data of CO isotopes, $\rm N_2H^+$ and $\rm H^{13}CO^+$. We compare the spatial distribution and spectral line profiles of different molecular species. Using local thermodynatic equilibrium (LTE) assumption, we obtain an abundance ratio $N({\rm HCO^+})/N({\rm CO})\sim10^{-4}$ in the northeastern shocked cloud, which is higher by an order of magnitude than the values in unshocked clouds. This can be accounted for by the chemistry jointly induced by shock and CRs, with the physical parameters previously obtained from observations: preshock density $n_{\rm H}\sim 2\times 10^{5}\rm \ cm^{-3}$, CR ionization rate $ζ=2.5\times 10^{-15} \rm \ s^{-1}$ and shock velocity $V_{\rm s}=15\text{--}20\rm \ km\ s^{-1}$. Towards a point outside the northeastern boundary of W28 with known high CR ionization rate, we estimate the abundance ratio $ N({\rm HCO^+})/N({\rm N_2H^+}) \approx 0.6\text{--}3.3$, which can be reproduced by a chemical simulation if a high density $n_{\rm H}\sim 2\times 10^5 \ \rm cm^{-3}$ is adopted.

astro-ph.GA↗

arXiv2024

Search for GeV gamma-ray emission from the possible TeV-bright red dwarfs with Fermi-LAT

Red dwarfs have been suggested to be among the possible astrophysical species accelerating particles and emitting TeV $γ$-rays. As an effort to search for the GeV $γ$-ray counterparts of the suggested TeV emission from eight red dwarfs, we analyse the 0.2--500 GeV $γ$-ray emission of the regions covering them exploiting the $\sim$13.6 yr Pass 8 data of the Fermi Large Area Telescope. A GeV $γ$-ray emission excess with significance of 3.8$σ$ is detected in the direction of the red dwarf V962 Tau. This emission contains V962 Tau in 1$σ$ error radius and is independent of the catalog source. However, the stellar flare scenario can hardly explain the total energy and lightcurve derived from the $γ$-ray emission in view of the spectral analysis. We also analyse the lightcurves in the positions of the eight red dwarfs and no time bin with significance $>$5$σ$ is found. Therefore, no significant emission from the red dwarfs could be concluded to be detected by Fermi-LAT.

astro-ph.HE↗

arXiv2024

Efficient size-prescribed $k$-core search

$k$-core is a subgraph where every node has at least $k$ neighbors within the subgraph. The $k$-core subgraphs has been employed in large platforms like Network Repository to comprehend the underlying structures and dynamics of the network. Existing studies have primarily focused on finding $k$-core groups without considering their size, despite the relevance of solution sizes in many real-world scenarios. This paper addresses this gap by introducing the size-prescribed $k$-core search (SPCS) problem, where the goal is to find a subgraph of a specified size that has the highest possible core number. We propose two algorithms, namely the {\it TSizeKcore-BU} and the {\it TSizeKcore-TD}, to identify cohesive subgraphs that satisfy both the $k$-core requirement and the size constraint. Our experimental results demonstrate the superiority of our approach in terms of solution quality and efficiency. The {\it TSizeKcore-BU} algorithm proves to be highly efficient in finding size-prescribed $k$-core subgraphs on large datasets, making it a favorable choice for such scenarios. On the other hand, the {\it TSizeKcore-TD} algorithm is better suited for small datasets where running time is less critical.

cs.DS↗

arXiv2024

SEMRes-DDPM: Residual Network Based Diffusion Modelling Applied to Imbalanced Data

In the field of data mining and machine learning, commonly used classification models cannot effectively learn in unbalanced data. In order to balance the data distribution before model training, oversampling methods are often used to generate data for a small number of classes to solve the problem of classifying unbalanced data. Most of the classical oversampling methods are based on the SMOTE technique, which only focuses on the local information of the data, and therefore the generated data may have the problem of not being realistic enough. In the current oversampling methods based on generative networks, the methods based on GANs can capture the true distribution of data, but there is the problem of pattern collapse and training instability in training; in the oversampling methods based on denoising diffusion probability models, the neural network of the inverse diffusion process using the U-Net is not applicable to tabular data, and although the MLP can be used to replace the U-Net, the problem exists due to the simplicity of the structure and the poor effect of removing noise. problem of poor noise removal. In order to overcome the above problems, we propose a novel oversampling method SEMRes-DDPM.In the SEMRes-DDPM backward diffusion process, a new neural network structure SEMST-ResNet is used, which is suitable for tabular data and has good noise removal effect, and it can generate tabular data with higher quality. Experiments show that the SEMResNet network removes noise better than MLP; SEMRes-DDPM generates data distributions that are closer to the real data distributions than TabDDPM with CWGAN-GP; on 20 real unbalanced tabular datasets with 9 classification models, SEMRes-DDPM improves the quality of the generated tabular data in terms of three evaluation metrics (F1, G-mean, AUC) with better classification performance than other SOTA oversampling methods.

cs.LG↗

arXiv2024

FFAD: A Novel Metric for Assessing Generated Time Series Data Utilizing Fourier Transform and Auto-encoder

The success of deep learning-based generative models in producing realistic images, videos, and audios has led to a crucial consideration: how to effectively assess the quality of synthetic samples. While the Fréchet Inception Distance (FID) serves as the standard metric for evaluating generative models in image synthesis, a comparable metric for time series data is notably absent. This gap in assessment capabilities stems from the absence of a widely accepted feature vector extractor pre-trained on benchmark time series datasets. In addressing these challenges related to assessing the quality of time series, particularly in the context of Fréchet Distance, this work proposes a novel solution leveraging the Fourier transform and Auto-encoder, termed the Fréchet Fourier-transform Auto-encoder Distance (FFAD). Through our experimental results, we showcase the potential of FFAD for effectively distinguishing samples from different classes. This novel metric emerges as a fundamental tool for the evaluation of generative time series data, contributing to the ongoing efforts of enhancing assessment methodologies in the realm of deep learning-based generative models.

cs.LG↗

arXiv2024

Discovery of an old supernova remnant candidate through carbon monoxide line emission

Most old supernova remnants (SNRs) in the Milky Way have not yet been identified. Considering their large potential number and the sufficient momentum-energy transfer to the interstellar medium (ISM), they are a key part of our understanding of the overall role of SNRs in the ISM. Here we report our discovery of an expanding molecular shell identified by CO line observations, namely G16.11-0.51. It covers a known SNR, specifically G16.0-0.5, and is larger in size, i.e. 0.56 deg over 0.20 deg. Based on its spatial and kinematic structures, weak nonthermal radio-continuum emission, and derived physical properties, we suggest that it is an old SNR. At a systemic velocity of +41.3 km s^-1, the best estimated kinematic distance of G16.11-0.51 is ~3.2 kpc, implying its radius of about 15.6 pc. The age of G16.11-0.51 is estimated to be greater than ~10^5 yr, and, in a dense molecular environment, it has formed dense and thin shell layers. The kinetic energy of the expanding molecular gas of G16.11-0.51 is about 6.4x10^49 erg, accounting for approximately six percent of the initial SN explosion energy. Although old SNRs have essentially become cold and hard to detect, our discovery suggests that they can be found by searching for CO line emissions.

astro-ph.GA↗

arXiv2024

DIGIC: Domain Generalizable Imitation Learning by Causal Discovery

Causality has been combined with machine learning to produce robust representations for domain generalization. Most existing methods of this type require massive data from multiple domains to identify causal features by cross-domain variations, which can be expensive or even infeasible and may lead to misidentification in some cases. In this work, we make a different attempt by leveraging the demonstration data distribution to discover the causal features for a domain generalizable policy. We design a novel framework, called DIGIC, to identify the causal features by finding the direct cause of the expert action from the demonstration data distribution via causal discovery. Our framework can achieve domain generalizable imitation learning with only single-domain data and serve as a complement for cross-domain variation-based methods under non-structural assumptions on the underlying causal models. Our empirical study in various control tasks shows that the proposed framework evidently improves the domain generalization performance and has comparable performance to the expert in the original domain simultaneously.

cs.LG↗

arXiv2024

Upper Limits of $^{44}$Ti Decay Emission in Four Nearby Thermonuclear Supernova Remnants

To identify progenitors and investigate evidence of He burning, we searched for decay radiation of freshly synthesized $^{44}$Ti in four young nearby thermonuclear supernova remnants: Kepler, SN 1885, G1.9+0.3 and SN 1006, by analysing the up-to-date NuSTAR archival data. No apparent flux excess from the 68 and 78 keV line emissions accompanying decay was detected above the power law continuum applied for the remnants and the absorbed stray light. By comparing the inferred upper limits of the line flux and the initial $^{44}$Ti masses with a wide variety of supernova nucleosynthesis models, we placed constraints on the supernova progenitors. We derived the first NuSTAR line flux upper limit for Kepler and ruled out most of the double-detonation scenarios with a thick He layer under low density. We estimated, for the first time, the upper limit for SN 1885, which is high because of the large distance yet still remains consistent with the He shell detonation. The new flux and mass limit of G1.9+0.3 derived from a longer total exposure is lower than the results from previous studies and evidently excludes explosive burning of He-rich matter. The relatively advanced age and the large spatial extent of SN 1006 have prevented meaningful constraints.

astro-ph.HE↗

arXiv2024

Upper limits on the radio pulses from magnetars and a central compact object with FAST

Magnetars and central compact objects (CCOs) are subgroups of neutron stars that show a number of properties distinguished from canonical radio pulsars. We performed radio observations of three magnetars SGR 0418+5729, 1E 2259+586, 4U 0142+61, and a CCO PSR J1852+0040 with the Fivehundred-meter Aperture Spherical radio Telescope (FAST) at 1.25 GHz, aiming to search for radio pulsations in their quiescent states. During two observation epochs, no radio pulses have been detected towards any target above a significance of signal-to-noise ratio (S/N) = 7 from either the direct folding or blind search. We provided the most stringent upper limit of radio flux (<~ 2 -- 4 $μ$Jy) for the magnetars and the CCO. For the magnetars with long periods, the real upper limits are likely an order of magnitude larger due to the red noise. The deep radio observations suggest that these magnetars and the CCO are indeed radio-quiet sources or unfavorably beamed.

astro-ph.HE↗

arXiv2024

Constrained Decoding for Cross-lingual Label Projection

Zero-shot cross-lingual transfer utilizing multilingual LLMs has become a popular learning paradigm for low-resource languages with no labeled training data. However, for NLP tasks that involve fine-grained predictions on words and phrases, the performance of zero-shot cross-lingual transfer learning lags far behind supervised fine-tuning methods. Therefore, it is common to exploit translation and label projection to further improve the performance by (1) translating training data that is available in a high-resource language (e.g., English) together with the gold labels into low-resource languages, and/or (2) translating test data in low-resource languages to a high-source language to run inference on, then projecting the predicted span-level labels back onto the original test data. However, state-of-the-art marker-based label projection methods suffer from translation quality degradation due to the extra label markers injected in the input to the translation model. In this work, we explore a new direction that leverages constrained decoding for label projection to overcome the aforementioned issues. Our new method not only can preserve the quality of translated texts but also has the versatility of being applicable to both translating training and translating test data strategies. This versatility is crucial as our experiments reveal that translating test data can lead to a considerable boost in performance compared to translating only training data. We evaluate on two cross-lingual transfer tasks, namely Named Entity Recognition and Event Argument Extraction, spanning 20 languages. The results demonstrate that our approach outperforms the state-of-the-art marker-based method by a large margin and also shows better performance than other label projection methods that rely on external word alignment.

cs.CL↗