arXiv subjects

Feifei Kou

Publications and source records attributed to Feifei Kou.

15 recordsLinked to original sources

arXiv2026

Mining and searching association relation of scientific papers based on deep learning

There is a complex correlation among the data of scientific papers. The phenomenon reveals the data characteristics, laws, and correlations contained in the data of scientific and technological papers in specific fields, which can realize the analysis of scientific and technological big data and help to design applications to serve scientific researchers. Therefore, the research on mining and searching the association relationship of scientific papers based on deep learning has far-reaching practical significance.

cs.DL↗

arXiv2026

Research on Domain Information Mining and Theme Evolution of Scientific Papers

In recent years, with the increase of social investment in scientific research, the number of research results in various fields has increased significantly. Cross-disciplinary research results have gradually become an emerging frontier research direction. There is a certain dependence between a large number of research results. It is difficult to effectively analyze today's scientific research results when looking at a single research field in isolation. How to effectively use the huge number of scientific papers to help researchers becomes a challenge. This paper introduces the research status at home and abroad in terms of domain information mining and topic evolution law of scientific and technological papers from three aspects: the semantic feature representation learning of scientific and technological papers, the field information mining of scientific and technological papers, and the mining and prediction of research topic evolution rules of scientific and technological papers.

cs.DL↗

arXiv2026

Adaptive Spectrum-Aware Feature Disentangled Network for Small Object Detection

Small Object Detection (SOD) is a fundamental yet challenging problem in computer vision due to its limited spatial resolution and weak visual cues. Although recent approaches have achieved remarkable advances, the background distractors in different frequency spectra still degrade the performance. In this paper, we propose a novel small object detection framework termed SFDNet, which is capable of detecting small objects via efficient spectrum-aware feature disentanglement. Specifically, we propose an Adaptive Spectrum Disentanglement (ASD) module that decomposes backbone features into multiple complementary spectral components, aiming to construct discriminative object-relevant representations by discarding the background distractors for each component. Afterwards, to strengthen the semantic consistency of the similar objects in the same class, we propose a Class-Wise Prototype Distillation (CPD) procedure, which establishes class prototypes for the object instances and enforces the compact representation by efficient prototype distillation. Extensive experiments on multiple challenging benchmarks show that SFDNet outperforms existing state-of-the-art methods by a large margin. Our code is available at https://github.com/ManOfStory/SFDNet.

cs.CV↗

arXiv2025

FAST Observations of the Microstructure in Interpulse Pulsars

In this study, we investigate the microstructure properties of four pulsars (PSRs J0953+0755 (B0950+08), J0627+0706, J0826+2637 (B0823+26) and J1946+1805 (B1944+17)) using the Five-hundred-meter Aperture Spherical radio Telescope (FAST), with particular emphasis on identifying microstructure within interpulse (IP). Through the application of autocorrelation function (ACF) analysis and fast Fourier transform (FFT) techniques, we have systematically examined the periodicity of microstructure in these pulsars. Our findings represent the first successful detection of microstructure within IP. Furthermore, we conducted a comprehensive statistical analysis comparing the characteristic timescales ($τ_μ$) and the characteristic periods $P_μ$ of quasi-periodic microstructure between the main pulse (MP) and IP, and our results indicate that the $τ_μ$ and $P_μ$ of microstructure across components appear consistent within measurement errors for PSR J0627+0706, but microstructure in IP are relatively smaller than those in MP for PSR J0953+0755. Furthermore, the relationship between $P_μ$ of microstructure and the rotation period in neutron star populations was reconfirmed: $P_μ(\text{ms})=(1.337\pm0.114)\times P(\text{s})^{(1.063\pm0.038)}$.

astro-ph.HE↗

arXiv2025

Leveraging Multimodal Data and Side Users for Diffusion Cross-Domain Recommendation

Cross-domain recommendation (CDR) aims to address the persistent cold-start problem in Recommender Systems. Current CDR research concentrates on transferring cold-start users' information from the auxiliary domain to the target domain. However, these systems face two main issues: the underutilization of multimodal data, which hinders effective cross-domain alignment, and the neglect of side users who interact solely within the target domain, leading to inadequate learning of the target domain's vector space distribution. To address these issues, we propose a model leveraging Multimodal data and Side users for diffusion Cross-domain recommendation (MuSiC). We first employ a multimodal large language model to extract item multimodal features and leverage a large language model to uncover user features using prompt learning without fine-tuning. Secondly, we propose the cross-domain diffusion module to learn the generation of feature vectors in the target domain. This approach involves learning feature distribution from side users and understanding the patterns in cross-domain transformation through overlapping users. Subsequently, the trained diffusion module is used to generate feature vectors for cold-start users in the target domain, enabling the completion of cross-domain recommendation tasks. Finally, our experimental evaluation of the Amazon dataset confirms that MuSiC achieves state-of-the-art performance, significantly outperforming all selected baselines. Our code is available: https://anonymous.4open.science/r/MuSiC-310A/.

cs.IR↗

arXiv2025

The Frequency-dependent Modulation Features of PSR J1948+3540

Using observations from GMRT and FAST, we conducted multi-wavelength studies on PSR J1948+3540 and analyzed its intensity modulation characteristics in detail. We found that the intensity modulation of this pulsar exhibits broad low-frequency modulation features. The modulation frequency/period is time-dependent, but the dominant modulation component varies with the observing frequency. Specifically, at low frequencies, the modulation is dominated by the first half of the middle component, while at high frequencies, it is dominated by the second half of the middle component. Spectral analysis revealed that the intensities of the leading and trailing components vary with the observing frequency, but the middle component does not change significantly. Besides, the polarization analyses reveal that the peak of the radiation intensity is located in the latter half of the middle component, whereas the linear polarization is dominant in the former half. However, due to the low degree of linear polarization, the change of the dominant modulation component with the observed frequency is not caused by the variation in linear polarization. The phenomenon of the dominant modulation component varying with observing frequency has not been reported before and remains difficult to understand within the current theoretical framework.

astro-ph.HE↗

arXiv2025

The Timing and Polarization of PSR J0002+6216

The combined timing analysis of data from the Five-hundred-meter Aperture Spherical Radio Telescope (FAST) and the Fermi Large Area Telescope (Fermi-LAT) confirmed that PSR J0002+6216 is not a hyper-velocity (exceeding 1000 km s$^{-1}$) pulsar. From this analysis, we determined the total proper motion of PSR J0002+6216 to be $μ_{\rm tot}=39.05\pm15.79$ mas yr$^{-1}$, which is consistent with Very Long Baseline Interferometry (VLBI) measurements to within 0.24$σ$. Moreover, two glitches were detected for the first time, which occurred on MJD 58850(17) and MJD 60421(6), respectively. The second glitch exhibited an exponential recovery process, with $Q = 0.0090(3)$ and $τ_{\rm d} = 45(3)$ days. Additionally, with FAST high-sensitivity observations, we measured the interstellar rotation measure (RM) and the three-dimensional (3D) orientation of the spin axis for the first time, and updated the dispersion measure (DM) of PSR J0002+6216. Currently, no variations in RM or DM have been detected. By combining the measured RM with the observed position angle of the spin axis, we determined that the intrinsic position angle of the pulsar's spin axis is $ψ_{0}(\text{intrinsic}) = 89.9^{\circ} \pm 4.6^{\circ}$. When we compared this with the proper motion position angle obtained from VLBI, we found a misalignment of approximately 23$^{\circ}$ between the spin and velocity angles of PSR J0002+6216. At present, pulsars with 2D spin-velocity angle measurements are unable to fully test the Janka et al.(2022) model. However, with more high-precision observational data in the future, we will be able to further test models related to pulsar birth.

astro-ph.HE↗

arXiv2024

Scientific and Technological Information Oriented Semantics-adversarial and Media-adversarial Cross-media Retrieval

Cross-media retrieval of scientific and technological information is one of the important tasks in the cross-media study. Cross-media scientific and technological information retrieval obtain target information from massive multi-source and heterogeneous scientific and technological resources, which helps to design applications that meet users' needs, including scientific and technological information recommendation, personalized scientific and technological information retrieval, etc. The core of cross-media retrieval is to learn a common subspace, so that data from different media can be directly compared with each other after being mapped into this subspace. In subspace learning, existing methods often focus on modeling the discrimination of intra-media data and the invariance of inter-media data after mapping; however, they ignore the semantic consistency of inter-media data before and after mapping and media discrimination of intra-semantics data, which limit the result of cross-media retrieval. In light of this, we propose a scientific and technological information oriented Semantics-adversarial and Media-adversarial Cross-media Retrieval method (SMCR) to find an effective common subspace. Specifically, SMCR minimizes the loss of inter-media semantic consistency in addition to modeling intra-media semantic discrimination, to preserve semantic similarity before and after mapping. Furthermore, SMCR constructs a basic feature mapping network and a refined feature mapping network to jointly minimize the media discriminative loss within semantics, so as to enhance the feature mapping network's ability to confuse the media discriminant network. Experimental results on two datasets demonstrate that the proposed SMCR outperforms state-of-the-art methods in cross-media retrieval.

cs.IR↗

arXiv2024

The Study of Mode Switching behavior of PSR J0614+2229 Using the Parkes Ultra-wideband Receiver Observations

In this paper, we presented a detailed single pulse and polarization study of PSR J0614+2229 based on the archived data observed on 2019 August 15 (MJD 58710) and September 12 (MJD 58738) using the Ultra-wideband Low-frequency Receiver on the Parkes radio telescope. The single-pulse sequences show that this pulsar switches between two emission states, in which the emission of state A occurs earlier than that of state B in pulse longitude. We found that the variation in relative brightness between the two states is related to time and both states follow a simple power law very well. Based on the phase-aligned multi-frequency profiles, we found that there is a significant difference in the distributions of spectral index across the emission regions of the two states. Furthermore, we obtained the emission height evolution for the two emission states and found that, at a fixed frequency, the emission height of state A is higher than that of state B. What is even more interesting is that the emission heights of both states A and B have not changed with frequency. Our results suggest that the mode switching of this pulsar is possibly caused by changes in the emission heights that alter the distributions of spectral index across the emission regions of states A and B resulting in the frequency-dependent behaviors, i.e., intensity and pulse width.

astro-ph.HE↗

arXiv2023

SimCGNN: Simple Contrastive Graph Neural Network for Session-based Recommendation

Session-based recommendation (SBR) problem, which focuses on next-item prediction for anonymous users, has received increasingly more attention from researchers. Existing graph-based SBR methods all lack the ability to differentiate between sessions with the same last item, and suffer from severe popularity bias. Inspired by nowadays emerging contrastive learning methods, this paper presents a Simple Contrastive Graph Neural Network for Session-based Recommendation (SimCGNN). In SimCGNN, we first obtain normalized session embeddings on constructed session graphs. We next construct positive and negative samples of the sessions by two forward propagation and a novel negative sample selection strategy, and then calculate the constructive loss. Finally, session embeddings are used to give prediction. Extensive experiments conducted on two real-word datasets show our SimCGNN achieves a significant improvement over state-of-the-art methods.

cs.IR↗

arXiv2022

Research topic trend prediction of scientific papers based on spatial enhancement and dynamic graph convolution network

In recent years, with the increase of social investment in scientific research, the number of research results in various fields has increased significantly. Accurately and effectively predicting the trends of future research topics can help researchers discover future research hotspots. However, due to the increasingly close correlation between various research themes, there is a certain dependency relationship between a large number of research themes. Viewing a single research theme in isolation and using traditional sequence problem processing methods cannot effectively explore the spatial dependencies between these research themes. To simultaneously capture the spatial dependencies and temporal changes between research topics, we propose a deep neural network-based research topic hotness prediction algorithm, a spatiotemporal convolutional network model. Our model combines a graph convolutional neural network (GCN) and Temporal Convolutional Network (TCN), specifically, GCNs are used to learn the spatial dependencies of research topics a and use space dependence to strengthen spatial characteristics. TCN is used to learn the dynamics of research topics' trends. Optimization is based on the calculation of weighted losses based on time distance. Compared with the current mainstream sequence prediction models and similar spatiotemporal models on the paper datasets, experiments show that, in research topic prediction tasks, our model can effectively capture spatiotemporal relationships and the predictions outperform state-of-art baselines.

cs.IR↗

arXiv2022

Cross-Media Scientific Research Achievements Retrieval Based on Deep Language Model

Science and technology big data contain a lot of cross-media information.There are images and texts in the scientific paper.The s ingle modal search method cannot well meet the needs of scientific researchers.This paper proposes a cross-media scientific research achievements retrieval method based on deep language model (CARDL).It achieves a unified cross-media semantic representation by learning the semantic association between different modal data, and is applied to the generation of text semantic vector of scientific research achievements, and then cross-media retrieval is realized through semantic similarity matching between different modal data.Experimental results show that the proposed CARDL method achieves better cross-modal retrieval performance than existing methods. Key words science and technology big data ; cross-media retrieval; cross-media semantic association learning; deep language model; semantic similarity

cs.IR↗

arXiv2022

The unusual emission from PSR B1859+07 with FAST

We present simultaneous broad-band radio observations on the abnormal emission mode from PSR B1859$+$07 using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). This pulsar shows peculiar emission phenomena, which are occasional shifts of emission to an early rotational phase and mode change of emission at the normal phase. We confirm all these three emission modes with our datasets, including the B (burst) and Q (quiet) modes of the non-shift pulses and the emission shift mode with a quasi-periodicity of 155 pulses. We also identify a new type of emission shift event, which has emission at the normal phase during the event. We studied polarisation properties of these emission modes in details, and found that they all have similar polarisation angle (PA) curve, indicating the emission of all these three modes are from the same emission height.

astro-ph.HE↗

arXiv2020

Discovery and timing of pulsars in the globular cluster M13 with FAST

We report the discovery of a binary millisecond pulsar (namely PSR J1641+3627F or M13F) in the globular cluster M13 (NGC 6205) and timing solutions of M13A to F using observations made with the Five-hundred-metre Aperture Spherical radio Telescope (FAST). PSR J1641+3627F has a spin period of 3.00 ms and an orbital period of 1.4 days. The most likely companion mass is 0.16 M$_{\odot}$. M13A to E all have short spin periods and small period derivatives. We also confirm that the binary millisecond pulsar PSR J1641$+$3627E (also M13E) is a black widow with a companion mass around 0.02 M$_{\odot}$. We find that all the binary systems have low eccentricities compared to those typical for globular cluster pulsars and that they decrease with distance from the cluster core. This is consistent with what is expected as this cluster has a very low encounter rate per binary.

astro-ph.HE↗

arXiv2019

Study of Three Rotating Radio Transients with FAST

Rotating radio transients (RRATs) are peculiar astronomical objects whose emission mechanism remains under investigation. In this paper, we present observations of three RRATs, J1538+2345, J1854+0306 and J1913+1330, observed with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Specifically, we analyze the mean pulse profiles and temporal flux density evolutions of the RRATs. Owing to the high sensitivity of FAST, the derived burst rates of the three RRATs are higher than those in previous reports. RRAT J1854+0306 exhibited a time-dynamic mean pulse profile, whereas RRAT J1913+1330 showed distinct radiation and nulling segments on its pulse intensity trains. The mean pulse profile variation with frequency is also studied for RRAT J1538+2345 and RRAT J1913+1330, and the profiles at different frequencies could be well fitted with a cone-core model and a conal-beam model, respectively.

astro-ph.HE↗