arXiv subjects

Peipei Wang

Publications and source records attributed to Peipei Wang.

At least 19 recordsLinked to original sources

arXiv2026

Imaging Exploration of Molecular Subtypes in Tongue Squamous Cell Carcinoma

Tongue squamous cell carcinoma (TSCC) is an aggressive malignancy with marked biological heterogeneity and variable clinical outcomes. Although molecular profiling has improved understanding of TSCC heterogeneity, its clinical use remains constrained by invasive tissue sampling and limited representation of whole-tumor spatial complexity. Meanwhile, most radiomics studies in TSCC have focused on downstream clinical endpoints, and whether imaging can non-invasively reflect intrinsic molecular subtypes remains unclear. In this study, an integrated transcriptomic-radiomics framework was used to investigate the relationship between preoperative imaging phenotypes and molecular subtypes in TSCC. Transcriptomic data from 60 TSCC cases in The Cancer Genome Atlas were analyzed using unsupervised consensus clustering, followed by differential expression and functional enrichment analyses. Matched preoperative imaging data from The Cancer Imaging Archive were manually annotated for primary tumor regions, and radiomic features were extracted using PyRadiomics; group differences were assessed with the U-test. Two stable molecular subtypes, C1 and C2, were identified. Their biological differences were mainly associated with squamous epithelial differentiation, inflammatory signaling, and lipid metabolism, with C2 showing greater enrichment of immune-related pathways. In addition, 10 radiomic features differed significantly between the two subtypes, mainly wavelet-derived texture features from gray-level size zone, dependence, co-occurrence, and run length matrices (P=0.00202-0.0162). These findings support the potential of radiomics as a non-invasive approach for characterizing molecular heterogeneity in TSCC and provide an initial radiogenomic framework for biologically informed preoperative assessment.

q-bio.GN↗

arXiv2026

Evaluating the spatial intra-pixel sensitivity variations and influence based on space observation

Intra-pixel sensitivity variations (IPSVs) in charge-coupled devices (CCDs) and complementary metal-oxide-semiconductor (CMOS) detectors constitute a significant source of astrometric error for undersampled stellar observations. Since laboratory-based IPSV measurements suffer from limited applicability, we propose a computational method to directly infer IPSV from stellar images and validate it with simulated data. By minimizing the flux residuals between theoretical and observed stellar models through least-squares fitting, we can successfully recover the IPSV, which is treated as nearly identical across pixels. Simulations demonstrate that the reconstructed IPSV achieves high accuracy, and the instrumental point spread function (IPSF) restored using this IPSV improves stellar centroiding by nearly 30$\times$, effectively eliminating periodic pixel-phase errors. The method remains robust under different morphologies of IPSV and varying sampling conditions. Additionally, the framework can be extended to an iterative IPSF-IPSV closed-loop scheme that updates both components simultaneously, providing a practical pathway for continuous detector calibration in future space-based astronomical surveys.

astro-ph.IM↗

arXiv2026

CSST-PSFNet: A Point Spread Function Reconstruction Model for the CSST Based on Deep Learning

This paper presents CSST-PSFNet, a deep learning method for high-fidelity point spread function (PSF) reconstruction developed for the Chinese Space Station Survey Telescope (CSST). The model integrates a residual neural network, a lightweight Transformer architecture, and a variational latent representation to address key challenges in CSST imaging, including severe PSF undersampling, inter-band variability, and smooth spatial variation across the focal plane. Trained and validated on high-resolution star-PSF pairs generated by the CSST Main Survey Simulator, CSST-PSFNet achieves improved pixel-level accuracy and more precise recovery of shape parameters relevant to weak lensing compared to widely used PSFEx. On both the standard test dataset and a blurred dataset representing the upper bound of expected on-orbit PSF degradation, the model achieves a size residual precision below 0.005 and an ellipticity residual precision below 0.002. A weak-label adaptation experiment further shows that the model can recover PSFEx-level performance when the true PSF is unknown, demonstrating robustness in controlled degradation scenarios and weak-label adaptation experiments. These results indicate that CSST-PSFNet provides a flexible and extensible framework for future on-orbit PSF calibration in large-scale CSST surveys, with potential applications in weak-lensing cosmology and precision astrophysical measurements.

astro-ph.IM↗

arXiv2025

SpeedLLM: An FPGA Co-design of Large Language Model Inference Accelerator

This paper introduces SpeedLLM, a neural network accelerator designed on the Xilinx Alevo U280 platform and optimized for the Tinyllama framework to enhance edge computing performance. Key innovations include data stream parallelism, a memory reuse strategy, and Llama2 operator fusion, which collectively reduce latency and energy consumption. SpeedLLM's data pipeline architecture optimizes the read-compute-write cycle, while the memory strategy minimizes FPGA resource demands. The operator fusion boosts computational density and throughput. Results show SpeedLLM outperforms traditional Tinyllama implementations, achieving up to 4.8* faster performance and 1.18* lower energy consumption, offering improvements in edge devices.

cs.AR↗

arXiv2025

Enhanced anomalous Hall effect in the topological Kagome metal Cs(V$_{1-x}$Mn$_x$)$_3$Sb$_5$

As a fundamental physical phenomenon, achieving and controlling a large anomalous Hall effect (AHE) is crucial for advancing the understanding of topological physics and for developing applied technologies in spintronics. The recently discovered topological Kagome metal $A$V$_3$Sb$_5$ ($A =$ K, Rb, Cs)exhibits a significant AHE along with charge density wave (CDW) and superconductivity, providing an ideal platform to study the interactions between nontrivial band topology, CDW, and superconductivity. In this study, we systematically investigated the evolution of CDW, superconductivity, and AHE in electron (Mn)-doped Cs(V$_{1-x}$Mn$_x$)$_3$Sb$_5$ single crystals. The experimental results show that electron doping rapidly suppresses superconductivity, while the CDW order remains relatively robust. Meanwhile, a significantly enhanced AHE, with a maximum anomalous Hall conductivity (AHC) of ~25331 \Ohm ^{-1}\cm^{-1} and an anomalous Hall angle of 6.66% occurs at a relatively low doping level of $x = 0.03$. Based on the Tian-Ye-Jin (TYJ) scaling model, such a significant enhancement AHC is mainly dominated by the skew scattering. We speculated enhanced skew scattering between electrons and Mn originating from the strengthened spin-orbital coupling. Our finding provides important guidance for the design and development of transverse transport properties in topological Kagome materials.

cond-mat.str-el↗

arXiv2024

A Survey on Bundle Recommendation: Methods, Applications, and Challenges

In recent years, bundle recommendation systems have gained significant attention in both academia and industry due to their ability to enhance user experience and increase sales by recommending a set of items as a bundle rather than individual items. This survey provides a comprehensive review on bundle recommendation, beginning by a taxonomy for exploring product bundling. We classify it into two categories based on bundling strategy from various application domains, i.e., discriminative and generative bundle recommendation. Then we formulate the corresponding tasks of the two categories and systematically review their methods: 1) representation learning from bundle and item levels and interaction modeling for discriminative bundle recommendation; 2) representation learning from item level and bundle generation for generative bundle recommendation. Subsequently, we survey the resources of bundle recommendation including datasets and evaluation metrics, and conduct reproducibility experiments on mainstream models. Lastly, we discuss the main challenges and highlight the promising future directions in the field of bundle recommendation, aiming to serve as a useful resource for researchers and practitioners. Our code and datasets are publicly available at https://github.com/WUT-IDEA/bundle-recommendation-survey.

cs.IR↗

arXiv2023

Perpendicular in-plane negative magnetoresistance in ZrTe5

The unique band structure in topological materials frequently results in unusual magneto-transport phenomena, one of which is in-plane longitudinal negative magnetoresistance (NMR) with the magnetic field aligned parallel to the electrical current direction. This NMR is widely considered as a hallmark of chiral anomaly in topological materials. Here we report the observation of in-plane NMR in the topological material ZrTe5 when the in-plane magnetic field is both parallel and perpendicular to the current direction, revealing an unusual case of quantum transport beyond the chiral anomaly. We find that a general theoretical model, which considers the combined effect of Berry curvature and orbital moment, can quantitatively explain this in-plane NMR. Our results provide new insights into the understanding of in-plane NMR in topological materials.

cond-mat.mes-hall↗

arXiv2023

Gate-tuned ambipolar superconductivity with strong pairing interaction in intrinsic gapped monolayer 1T'-MoTe2

Gate tunable two-dimensional (2D) superconductors offer significant advantages when studying superconducting phase transitions. Here, we address superconductivity in exfoliated 1T'-MoTe2 monolayers with an intrinsic band gap of ~7.3 meV using electrostatic doping. Despite large differences in the dispersion of the conduction and the valence bands, superconductivity can be achieved easily for both electrons and holes. The onset of superconductivity occurs near 7-8K for both charge carrier types. This temperature is much higher than in bulk samples. Also the in-plane upper critical field is strongly enhanced and exceeds the BCS Pauli limit in both cases. Gap information is extracted using point-contact spectroscopy. The gap ratio exceeds multiple times the value expected for BCS weak-coupling. All these observations suggest a strong enhancement of the pairing interaction.

cond-mat.supr-con↗

arXiv2022

GGViT:Multistream Vision Transformer Network in Face2Face Facial Reenactment Detection

Detecting manipulated facial images and videos on social networks has been an urgent problem to be solved. The compression of videos on social media has destroyed some pixel details that could be used to detect forgeries. Hence, it is crucial to detect manipulated faces in videos of different quality. We propose a new multi-stream network architecture named GGViT, which utilizes global information to improve the generalization of the model. The embedding of the whole face extracted by ViT will guide each stream network. Through a large number of experiments, we have proved that our proposed model achieves state-of-the-art classification accuracy on FF++ dataset, and has been greatly improved on scenarios of different compression rates. The accuracy of Raw/C23, Raw/C40 and C23/C40 was increased by 24.34%, 15.08% and 10.14% respectively.

cs.CV↗

arXiv2022

Thermal transport properties and some hydrodynamic-like behavior in 3D topological semimetal ZrTe5

Hydrodynamic fluidity in condensed matter physics has been experimentally demonstrated only in a limited number of compounds due to the stringent conditions that must be met. Herein, we performed thermal and electrical transport experiments in three-dimensional topological semimetal ZrTe5. By measuring the thermal properties in a wide temperature range, two representative experimental evidences of the hydrodynamics are observed in temperature window between the ballistic and diffusive regimes: a faster evolution of the thermal conductivity than in the ballistic regime and the non-monotonic temperature-dependent effective quasiparticle mean-free-path. In addition, magneto-thermal conductivity results indicate that charged quasiparticles, as well as phonons, may also play an important role in this hydrodynamic-like flow in ZrTe5.

cond-mat.str-el↗

arXiv2022

Strain-tuned topological phase transition and unconventional Zeeman effect in ZrTe5 microcrystals

The geometric phase (Berry phase) of an electronic wave function is the fundamental basis of the topological properties in solids. Modulating band structure provides a tuning knob for the Berry phase, and in the extreme case drives a topological phase transition. Despite the significant developments in topological materials study, it remains a challenge to tune between different topological phases while tracing the impact of the Berry phase on quantum charge transport, in the same material. Here we report both in a magnetotransport study of ZrTe5. By tuning the band structure with uniaxial strain, we directly map a weak- to strong- topological phase transition through a gapless Dirac semimetal phase via quantum oscillations. Moreover, we demonstrate the impact of the strain-tunable spin-dependent Berry phase on the Zeeman effect through the amplitude of the quantum oscillations. We show that such a spin-dependent Berry phase, largely neglected in solid-state systems, is critical in modeling quantum oscillations in Dirac bands in topological materials.

cond-mat.mtrl-sci↗

arXiv2021

Crossover behavior in the magnetoresistance of thin flakes of the topological material ZrTe5

ZrTe5 is a layered material that exhibits intricate topological effects. Intensive theoretically and experimental efforts have been devoted to try to understand the physics in this materials. In this paper the temperature dependent magneto-transport properties of ZrTe5 thin flakes are investigated. A characteristic temperature T* is observed in the temperature dependence of three different types of magnetoresistance simultaneously, which are the saturated Hall anomaly, the chiral anomaly and the longitudinal magnetoresistance. Furthermore, the value of T* decreases monotonically from 200K to 160K with increasing thickness of the ZrTe5 thin flakes from 42nm to 89nm. Temperature induced topological phase transitions are attributed to the cause of such anomaly in the three types of magnetoresistance at T*. Our findings provide a multi-parameter indicator for the emergence of topological phase transition in ZrTe5 and could be extended to the study of other topological materials. The temperature dependence of the three types of magnetoresistance also shed light on the role of anomalous Hall Effect in the transport properties of ZrTe5.

cond-mat.mtrl-sci↗

arXiv2021

Stacking faults in $α$-RuCl$_3$ revealed by local electric polarization

We present out-of-plane dielectric and magnetodielectric measurements of single crystallines $α$-RuCl$_3$ with various degrees of stack faults. A frequency dependent, but field independent, dielectric anomaly appears at $T_{A}\:(f=100\:\mathrm{kHz})\sim$ 4 K once both magnetic transitions at $T_{N1}\sim$ 7 K and $T_{N2}\sim$ 14 K set in. The observed dielectric anomaly is attributed to the emergency of possible local electric polarizations whose inversion symmetry is broken by inhomogeneously distributed stacking faults. A field-induced intermediate phase is only observed when a magnetic field is applied perpendicular to the Ru-Ru bonds for samples with minimal stacking faults. Less pronounced in-plane anisotropy is found in samples with sizable contribution from stacking imperfections. Our findings suggest that dielectric measurement is a sensitive probe in detecting the structural and magnetic properties, which may be a promising tool especially in studying $α$-RuCl$_3$ thin film devices. Moreover, the stacking details of RuCl$_3$ layers strongly affect the ground state both in the magnetic and electric channels. Such a fragile ground state against stacking faults needs to be overcome for realistic applications utilizing the magnetic and/or electric properties of Kitaev based physics in $α$-RuCl$_3$.

cond-mat.str-el↗

arXiv2021

Demystifying Regular Expression Bugs: A comprehensive study on regular expression bug causes, fixes, and testing

Regular expressions cause string-related bugs and open security vulnerabilities for DOS attacks. However, beyond ReDoS (Regular expression Denial of Service), little is known about the extent to which regular expression issues affect software development and how these issues are addressed in practice. We conduct an empirical study of 356 merged regex-related pull request bugs from Apache, Mozilla, Facebook, and Google GitHub repositories. We identify and classify the nature of the regular expression problems, the fixes, and the related changes in the test code. The most important findings in this paper are as follows: 1) incorrect regular expression behavior is the dominant root cause of regular expression bugs (165/356, 46.3%). The remaining root causes are incorrect API usage (9.3%) and other code issues that require regular expression changes in the fix (29.5%), 2) fixing regular expression bugs is nontrivial as it takes more time and more lines of code to fix them compared to the general pull requests, 3) most (51%) of the regex-related pull requests do not contain test code changes. Certain regex bug types (e.g., compile error, performance issues, regex representation) are less likely to include test code changes than others, and 4) the dominant type of test code changes in regex-related pull requests is test case addition (75%). The results of this study contribute to a broader understanding of the practical problems faced by developers when using, fixing, and testing regular expressions.

cs.SE↗

arXiv2021

Quantum transport properties of beta-Bi4I4 near and well beyond the extreme quantum limit

We have investigated the magneto-transport properties of beta-Bi4I4 bulk crystal, which was recently theoretically proposed and experimentally demonstrated to be a topological insulator. At low temperature T and magnetic field B, a series of Shubnikov-De Haas(SdH) oscillations are observed on the magnetoresistivity (MR). The detailed analysis reveals a light cyclotron mass of 0.1 me, and the field angle dependence of MR reveals that the SdH oscillations originate from a convex Fermi surface. In the extreme quantum limit (EQL) region, there is a metal-insulator transition occurring soon after the EQL. We perform the scaling analysis, and all the isotherms fall onto a universal scaling with a fitted critical exponent of 6.5. The enormous value of critical exponent implies this insulating quantum phase originated from strong electron-electron interactions in high fields. However, in the far end of EQL, both the longitudinal and Hall resistivity increase exponentially with B, and the temperature dependence of the MR reveals an energy gap induced by the high magnetic field, signifying a magnetic freeze-out effect. Our findings indicate that bulk beta-Bi4I4 is an excellent candidate for a 3D topological system for exploring EQL physics and relevant exotic quantum phases.

cond-mat.mtrl-sci↗

arXiv2020

Observation of a thermoelectric Hall plateau in the extreme quantum limit

The thermoelectric Hall effect is the generation of a transverse heat current upon applying an electric field in the presence of a magnetic field. Here we demonstrate that the thermoelectric Hall conductivity $α_{xy}$ in the three-dimensional Dirac semimetal ZrTe$_5$ acquires a robust plateau in the extreme quantum limit of magnetic field. The plateau value is independent of the field strength, disorder strength, carrier concentration, or carrier sign. We explain this plateau theoretically and show that it is a unique signature of three-dimensional Dirac or Weyl electrons in the extreme quantum limit. We further find that other thermoelectric coefficients, such as the thermopower and Nernst coefficient, are greatly enhanced over their zero-field values even at relatively low fields.

cond-mat.mtrl-sci↗

arXiv2020

Approaching Three-Dimensional Quantum Hall effect in Bulk HfTe5

The discovery of quantum Hall effect in two-dimensional (2D) electronic systems inspired the topological classifications of electronic systems1,2. By stacking 2D quantum Hall effects with interlayer coupling much weaker than the Landau level spacing, quasi-2D quantum Hall effects have been experimentally observed3~7, due to the similar physical origin of the 2D counterpart. Recently, in a real 3D electronic gas system where the interlayer coupling is much stronger than the Landau level spacing, 3D quantum Hall effect has been observed in ZrTe58. In this Letter, we report the electronic transport features of its sister bulk material, i.e., HfTe5, under external magnetic field. We observe a series of plateaus in Hall resistance \r{ho}xy as magnetic field increases until it reaches the quantum limit at 1~2 Tesla. At the plateau regions, the longitudinal resistance \r{ho}xx exhibits local minima. Although \r{ho}xx is still nonzero, its value becomes much smaller than \r{ho}xy at the last few plateaus. By mapping the Fermi surface via measuring the Shubonikov-de Haas oscillation, we find that the strength of Hall plateau is proportional to the Fermi wavelength, suggesting that its formation may be attributed to the gap opening from the interaction driven Fermi surface instability. By comparing the bulk band structures of ZrTe5 and HfTe5, we find that there exists an extra pocket near the Fermi level of HfTe5, which may lead to the finite but nonzero longitudinal conductance.

cond-mat.mtrl-sci↗

arXiv2019

Social Influence-based Attentive Mavens Mining and Aggregative Representation Learning for Group Recommendation

Frequent group activities of human beings have become an indispensable part in their daily life. Group recommendation can recommend satisfactory activities to group members in the recommender systems, and the key issue is how to aggregate preferences in different group members. Most existing group recommendation employed the predefined static aggregation strategies to aggregate the preferences of different group members, but these static strategies cannot simulate the dynamic group decision-making. Meanwhile, most of these methods depend on intuitions or assumptions to analyze the influence of group members and lack of convincing theoretical support. We argue that the influence of group members plays a particularly important role in group decision-making and it can better assist group profile modeling and perform more accurate group recommendation. To tackle the issue of preference aggregation for group recommendation, we propose a novel attentive aggregation representation learning method based on sociological theory for group recommendation, namely SIAGR (short for "Social Influence-based Attentive Group Recommendation"), which takes attention mechanisms and the popular method (BERT) as the aggregation representation for group profile modeling. Specifically, we analyze the influence of group members based on social identity theory and two-step flow theory and exploit an attentive mavens mining method. In addition, we develop a BERT-based representation method to learn the interaction of group members. Lastly, we complete the group recommendation under the neural collaborative filtering framework and verify the effectiveness of the proposed method by experimenting.

cs.SI↗