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Researcher profile

Yu He

Yu He contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computer Visioncond-mat.str-elcond-mat.mtrl-sciphysics.opticsquant-phcond-mat.mes-hallcond-mat.supr-conGraphicsArtificial Intelligenceastro-ph.HEcond-mat.dis-nneess.IVInformation RetrievalMachine Learningphysics.atom-phphysics.data-an

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Published work

16 published item(s)

preprint2026arXiv

Fashion130K: An E-commerce Fashion Dataset for Outfit Generation with Unified Multi-modal Condition

Recent research work on fashion outfit generation focuses on promoting visual consistency of garments by leveraging key information from reference image and text prompt. However, the potential of outfit generation remains underexplored, requiring comprehensive e-commercial dataset and elaborative utilization of multi-modal condition. In this paper, we propose a brand-new e-commerce dataset, named Fashion130k, with various occasions, models, and garment types. For the consistent generation of garment, we design a framework with Unified Multi-modal Condition (UMC) to align and integrate the text and visual prompts into generation model. Specifically, we explore an embedding refiner to extract the unified embeddings of multi-modal prompts, within which a Fusion Transformer is proposed to align the multi-modal embeddings by adjusting the modality gap between text and image. Based on unified embeddings, the attention in generation model is redesigned to emphasis the correlations between prompts and noise image, inducing that the noise image can select the pivotal tokens of prompts for consistent outfit generation. Our dataset and proposed framework offer a general and nuanced exploration of multi-modal prompts for generation models. Extensive experiments on real-world applications and benchmark demonstrate the effectiveness of UMC in visual consistency, achieving promising result than that of SoTA methods.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

HyperDiT: Hyper-Connected Transformers for High-Fidelity Pixel-Space Diffusion

Pixel-space diffusion models bypass the reconstruction bottleneck of Variational Autoencoders (VAEs) but face a fundamental "granularity dilemma": capturing global semantics favors large patch scales, while generating high-fidelity details demands fine-grained inputs. To address this issue, we propose HyperDiT, a unified framework establishing Hyper-Connected Cross-Scale Interactions to bridge the semantic and pixel manifold. Diverging from injecting semantics by AdaLN, HyperDiT utilizes Cross-Attention mechanisms, enabling fine-grained tokens to query multi-level semantic anchors globally. To resolve the spatial mismatch during multi-scale interactions, we introduce Scale-Aware Rotary Position Embedding (SA-RoPE) to ensure precise geometric alignment among tokens of varying patch sizes. Furthermore, we incorporate Registers to learn the dense semantics from a pretrained Visual Foundation Model (VFM), effectively reducing generation hallucination and artifacts. Extensive experiments demonstrate that HyperDiT achieves state-of-the-art (SoTA) FID of $\mathbf{1.56}$ on ImageNet $256\times256$ directly within the pixel space. By combining the fine-grained stream with semantic guidance, HyperDiT offers a superior paradigm for high-fidelity pixel generation.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

LiWi: Layering in the Wild

Recent advances in generative models have empowered impressive layered image generation, yet their success is largely confined to graphic design domains. The layering of in-the-wild images remains an underexplored problem, limiting fine-grained editing and applications of images in real-world scenarios. Specifically, challenges remain in scalable layered data and the modeling of object interaction in natural images, such as illumination effects and structural boundary. To address these bottlenecks, we propose a novel framework for high-fidelity natural image decomposition. First, we introduce an Agent-driven Data Decomposition (ADD) pipeline that orchestrates agents and tools to synthesize layered data without manual intervention. Utilizing this pipeline, we construct a large-scale dataset, named LiWi-100k, with over 100,000 high-quality layered in-the-wild images. Second, we present a novel framework that jointly improves photometric fidelity and alpha boundary accuracy. Specifically, shadow-guided learning explicitly models the illumination effects, and degradation-restoration objective provides boundary-correction supervision by recovering clean foreground image from degraded one. Extensive experiments demonstrate that our framework achieves state-of-the-art (SoTA) performance in natural image decomposition, outperforming existing models in RGB L1 and Alpha IoU metrics. We will soon release our code and dataset.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Ultrahigh-Energy Gamma-ray Emission Associated with Black Hole-Jet Systems

Black holes (BH), one of the most intriguing objects in the universe, can manifest themselves through electromagnetic radiation initiated by the accretion flow. Some stellar-mass BHs drive relativistic jets when accreting matter from their companion stars, forming microquasars. Non-thermal emission from the radio to tera-electronvolt (TeV) gamma-ray band has been observed from microquasars, indicating the acceleration of relativistic particles. Here we report detection of four microquasars (SS 433, V4641 Sgr, GRS 1915+105, MAXI J1820+070) of spectrum extending to the ultrahigh-energy (UHE; photon energy $E>100$ TeV) band and one microquasar (Cygnus X-1) of spectrum approaching 100 TeV, using the Large High Altitude Air Shower Observatory (LHAASO). Notably, the total emission associated with SS 433 cannot be interpreted with a single leptonic component. In the UHE band, its emission is in spatial coincidence with a giant atomic cloud, which is consistent with a hadronic origin. An elongated source is discovered from V4641 Sgr with the spectrum continuing up to 800 TeV. The detection of UHE gamma rays demonstrates that accreting BHs and their environments can operate as extremely efficient accelerators of particles out of 1 peta-electronvolt (PeV), suggesting microquasars to be important contributors to Galactic cosmic rays especially around the `knee' region.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Antiferromagnetic order in Co-doped Fe$_5$GeTe$_2$ probed by resonant magnetic x-ray scattering

The quasi-two-dimensional van der Waals magnet Fe$_{5-δ}$GeTe$_2$ has emerged as a promising platform for electronic and spintronic functionalities at room temperature, owing to its large ferromagnetic ordering temperature $T_{\text{C}}$ $\sim$ 315 K. Interestingly, by cobalt (Co) substitution of iron in F5GT, $i.e.$ $({\text{Fe}}_{1-x}{\text{Co}}_x)_{5-δ}{\text{GeTe}}_2$ (Co-F5GT), not only can its magnetic transition temperature be further enhanced, but the magnetic and structural ground states can also be tuned. Specifically, an antiferromagnetic (AFM) order is induced beyond the Co doping level $x \ge 0.4$. Here, we investigate the magnetic properties of a Co-F5GT single crystal at $x = 0.45(1)$, by utilizing the element specific, resonant magnetic x-ray scattering technique. Our study reveals an A-type, Ising-like AFM ground state, with a transition temperature $T_{\text{N}}$ $\sim$ 340 K. In addition, our work unveils an important contribution from Co magnetic moments to the magnetic order. The application of the in-plane magnetic fields gradually polarize the spin moments along the field direction, but without inducing incommensurate spin texture(s).

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

NeRFReN: Neural Radiance Fields with Reflections

Neural Radiance Fields (NeRF) has achieved unprecedented view synthesis quality using coordinate-based neural scene representations. However, NeRF's view dependency can only handle simple reflections like highlights but cannot deal with complex reflections such as those from glass and mirrors. In these scenarios, NeRF models the virtual image as real geometries which leads to inaccurate depth estimation, and produces blurry renderings when the multi-view consistency is violated as the reflected objects may only be seen under some of the viewpoints. To overcome these issues, we introduce NeRFReN, which is built upon NeRF to model scenes with reflections. Specifically, we propose to split a scene into transmitted and reflected components, and model the two components with separate neural radiance fields. Considering that this decomposition is highly under-constrained, we exploit geometric priors and apply carefully-designed training strategies to achieve reasonable decomposition results. Experiments on various self-captured scenes show that our method achieves high-quality novel view synthesis and physically sound depth estimation results while enabling scene editing applications.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Persistent homology analysis of a generalized Aubry-André-Harper model

Observing critical phases in lattice models is challenging due to the need to analyze the finite time or size scaling of observables. We study how the computational topology technique of persistent homology can be used to characterize phases of a generalized Aubry-André-Harper model. The persistent entropy and mean squared lifetime of features obtained using persistent homology behave similarly to conventional measures (Shannon entropy and inverse participation ratio) and can distinguish localized, extended, and crticial phases. However, we find that the persistent entropy also clearly distinguishes ordered from disordered regimes of the model. The persistent homology approach can be applied to both the energy eigenstates and the wavepacket propagation dynamics.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Pervasive beyond room-temperature ferromagnetism in a doped van der Waals magnet: Ni doped Fe$_5$GeTe$_2$ with $T_{\text{C}}$ up to 478 K

The existence of long range magnetic order in low dimensional magnetic systems, such as the quasi-two-dimensional (2D) van der Waals (vdW) magnets, has attracted intensive studies of new physical phenomena. The vdW Fe$_N$GeTe$_2$ ($N$ = 3, 4, 5; FGT) family is exceptional owing to its vast tunability of magnetic properties. Particularly, a ferromagnetic ordering temperature ($T_{\text{C}}$) above room temperature at $N$ = 5 (F5GT) is observed. Here, our study shows that, by nickel (Ni) substitution of iron (Fe) in F5GT, a record high $T_{\text{C}}$ = 478(6) K is achieved. Importantly, pervasive, beyond-room-temperature ferromagnetism exists in almost the entire doping range of the phase diagram of Ni-F5GT. We argue that this striking observation in Ni-F5GT can be possibly due to several contributing factors, in which the structural alteration enhanced 3D magnetic couplings might be critical for enhancing the ferromagnetic order.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Reflection, transmission and surface susceptibility tensor in two-dimensional materials

In a recent experiment, the out-of-plane surface susceptibility of a single-layer two-dimensional atom crystal in the visible spectrum has been measured. This susceptibility gives a measurable contribution to the reflectivity of two-dimensional materials. Here we provide a complete theoretical description of the reflective properties, considering incoming s and p polarized plane waves at any angle of incidence on the crystal, computing local, reflected and transmitted electromagnetic fields. We finally connect the microscopic polarizability to both the in-plane and the out-of-plane macroscopic surface susceptibilities.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Topological spin texture of chiral edge states in photonic two-dimensional quantum walks

Topological insulators host topology-linked boundary states, whose spin and charge degrees of freedom could be exploited to design topological devices with enhanced functionality. We experimentally observe that dissipationless chiral edge states in a spin-orbit coupled anomalous Floquet topological phase exhibit topological spin texture on boundaries, realized via a two-dimensional quantum walk. Our experiment shows that, for a walker traveling around a closed loop along the boundary in real space, its spin evolves and winds through a great circle on the Bloch sphere, which implies that edge-spin texture has nontrivial winding. This winding is linked to the bulk Dirac Hamiltonian around the energy-gap opening point. Our experiment confirms that two-dimensional anomalous Floquet topological systems exhibit topological spin texture on the boundary, which could inspire novel topology-based spintronic phenomena and devices.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

ZOOMER: Boosting Retrieval on Web-scale Graphs by Regions of Interest

We introduce ZOOMER, a system deployed at Taobao, the largest e-commerce platform in China, for training and serving GNN-based recommendations over web-scale graphs. ZOOMER is designed for tackling two challenges presented by the massive user data at Taobao: low training/serving efficiency due to the huge scale of the graphs, and low recommendation quality due to the information overload which distracts the recommendation model from specific user intentions. ZOOMER achieves this by introducing a key concept, Region of Interests (ROI) in GNNs for recommendations, i.e., a neighborhood region in the graph with significant relevance to a strong user intention. ZOOMER narrows the focus from the whole graph and "zooms in" on the more relevant ROIs, thereby reducing the training/serving cost and mitigating the information overload at the same time. With carefully designed mechanisms, ZOOMER identifies the interest expressed by each recommendation request, constructs an ROI subgraph by sampling with respect to the interest, and guides the GNN to reweigh different parts of the ROI towards the interest by a multi-level attention module. Deployed as a large-scale distributed system, ZOOMER supports graphs with billions of nodes for training and thousands of requests per second for serving. ZOOMER achieves up to 14x speedup when downsizing sampling scales with comparable (even better) AUC performance than baseline methods. Besides, both the offline evaluation and online A/B test demonstrate the effectiveness of ZOOMER.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Metallic surface states in a correlated d-electron topological Kondo insulator candidate FeSb2

The resistance of a conventional insulator diverges as temperature approaches zero. The peculiar low temperature resistivity saturation in the 4f Kondo insulator (KI) SmB6 has spurred proposals of a correlation-driven topological Kondo insulator (TKI) with exotic ground states. However, the scarcity of model TKI material families leaves difficulties in disentangling key ingredients from irrelevant details. Here we use angle-resolved photoemission spectroscopy (ARPES) to study FeSb2, a correlated d-electron KI candidate that also exhibits a low temperature resistivity saturation. On the (010) surface, we find a rich assemblage of metallic states with two-dimensional dispersion. Measurements of the bulk band structure reveal band renormalization, a large temperature-dependent band shift, and flat spectral features along certain high symmetry directions, providing spectroscopic evidence for strong correlations. Our observations suggest that exotic insulating states resembling those in SmB6 and YbB12 may also exist in systems with d instead of f electrons.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Style-compatible Object Recommendation for Multi-room Indoor Scene Synthesis

Traditional indoor scene synthesis methods often take a two-step approach: object selection and object arrangement. Current state-of-the-art object selection approaches are based on convolutional neural networks (CNNs) and can produce realistic scenes for a single room. However, they cannot be directly extended to synthesize style-compatible scenes for multiple rooms with different functions. To address this issue, we treat the object selection problem as combinatorial optimization based on a Labeled LDA (L-LDA) model. We first calculate occurrence probability distribution of object categories according to a topic model, and then sample objects from each category considering their function diversity along with style compatibility, while regarding not only separate rooms, but also associations among rooms. User study shows that our method outperforms the baselines by incorporating multi-function and multi-room settings with style constraints, and sometimes even produces plausible scenes comparable to those produced by professional designers.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Super Resolution Convolutional Neural Network for Feature Extraction in Spectroscopic Data

Two dimensional (2D) peak finding is a common practice in data analysis for physics experiments, which is typically achieved by computing the local derivatives. However, this method is inherently unstable when the local landscape is complicated, or the signal-to-noise ratio of the data is low. In this work, we propose a new method in which the peak tracking task is formalized as an inverse problem, thus can be solved with a convolutional neural network (CNN). In addition, we show that the underlying physics principle of the experiments can be used to generate the training data. By generalizing the trained neural network on real experimental data, we show that the CNN method can achieve comparable or better results than traditional derivative based methods. This approach can be further generalized in different physics experiments when the physical process is known.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Inequivalence of Single-Particle and Population Lifetimes in a Cuprate Superconductor

We study optimally doped Bi-2212 ($T_\textrm{c}=96$~K) using femtosecond time- and angle-resolved photoelectron spectroscopy. Energy-resolved population lifetimes are extracted and compared with single-particle lifetimes measured by equilibrium photoemission. The population lifetimes deviate from the single-particle lifetimes in the low excitation limit by one to two orders of magnitude. Fundamental considerations of electron scattering unveil that these two lifetimes are in general distinct, yet for systems with only electron-phonon scattering they should converge in the low-temperature, low-fluence limit. The qualitative disparity in our data, even in this limit, suggests that scattering channels beyond electron-phonon interactions play a significant role in the electron dynamics of cuprate superconductors.

0 citations0 reviewsNo code linkedOpen access
preprint2010arXiv

Quantum interface between frequency-uncorrelated down-converted entanglement and atomic-ensemble quantum memory

Photonic entanglement source and quantum memory are two basic building blocks of linear-optical quantum computation and long-distance quantum communication. In the past decades, intensive researches have been carried out, and remarkable progress, particularly based on the spontaneous parametric down-converted (SPDC) entanglement source and atomic ensembles, has been achieved. Currently, an important task towards scalable quantum information processing (QIP) is to efficiently write and read entanglement generated from a SPDC source into and out of an atomic quantum memory. Here we report the first experimental realization of a quantum interface by building a 5 MHz frequency-uncorrelated SPDC source and reversibly mapping the generated entangled photons into and out of a remote optically thick cold atomic memory using electromagnetically induced transparency. The frequency correlation between the entangled photons is almost fully eliminated with a suitable pump pulse. The storage of a triggered single photon with arbitrary polarization is shown to reach an average fidelity of 92% for 200 ns storage time. Moreover, polarization-entangled photon pairs are prepared, and one of photons is stored in the atomic memory while the other keeps flying. The CHSH Bell's inequality is measured and violation is clearly observed for storage time up to 1 microsecond. This demonstrates the entanglement is stored and survives during the storage. Our work establishes a crucial element to implement scalable all-optical QIP, and thus presents a substantial progress in quantum information science.

0 citations0 reviewsNo code linkedOpen access