arXiv subjects

Yi Cui

Publications and source records attributed to Yi Cui.

At least 19 recordsLinked to original sources

arXiv2026

From Events to Enhancement: A Survey on Event-Based Imaging Technologies

Event cameras offering high dynamic range and low latency have emerged as disruptive technologies in imaging. Despite growing research on leveraging these benefits for different imaging tasks, a comprehensive study of recently advances and challenges are still lacking. This limits the broader understanding of how to utilize events in universal imaging applications. In this survey, we first introduce a physical model and the characteristics of different event sensors as the foundation. Following this, we highlight the advancement and interaction of image/video enhancement tasks with events. Additionally, we explore advanced tasks, which capture richer light information with events, \eg~light field estimation, multi-view generation, and photometric. Finally, we discuss new challenges and open questions offering a perspective for this rapidly evolving field. More continuously updated resources are at this link: https://github.com/yunfanLu/Awesome-Event-Imaging

cs.CV↗

arXiv2026

Deploying RDMA for Distributed Inference and Training At Scale

RDMA can reduce communication overhead in distributed machine learning systems, but realizing these benefits in production requires more than replacing TCP. Variable-size messages, concurrent connections, bursty incasts, and service failures introduce challenges that hardware offload alone cannot address. This paper reports our experience deploying ByteExpress, a software communication layer over hardware RDMA, in ByteDance's parameter-server-based inference and training systems. We present three lessons from the deployment: efficient RDMA requires buffer management across application and transport layers; scalable communication requires coordinated management of connections, receive capacity, and outstanding data; and service availability requires recovery above the transport layer. ByteExpress combines an RDMA-aware allocator, zero-copy transfers, QP multiplexing, receive-credit flow control, a software send window, and RPC-assisted recovery. In production inference, ByteExpress reduces mean and P99 model parameter fetch latency by 35.7% and 43.7%, respectively, and total CPU usage by 10.69% compared with kernel TCP. In training, ByteExpress reduces P99 model parameter push latency by up to 74.0% compared with TCP, with no PFC pauses observed at runtime.

cs.LG↗

arXiv2026

NeuroDyn-EEG: An Interpretable Pre-trained Model for EEG Based on Neural Dynamics

Clinical scalp electroencephalography (EEG) offers a noninvasive window into neural dynamics of neuropsychiatric disorders. However, discriminative deep models often lack anatomically indexed physiological interpretability. We propose NeuroDyn-EEG, a pretraining framework integrating generative priors from neural dynamics. It couples an extended Jansen-Rit neural mass model, leadfield-based source projection, and simulation-based parameter inversion. Trained on synthetic parameter-EEG pairs within physiological ranges, NeuroDyn-EEG estimates 11 regional parameter families across 90 AAL regions plus one global parameter from standard 19-channel EEG, using only ~2.43M trainable parameters. We evaluate the framework across three levels. First, controlled simulations demonstrate robust parameter recovery under diverse noise conditions, while real resting-state EEG evaluations confirm spectral and phase consistency in an inverse-forward closed loop. Second, on four clinical benchmarks (AD65, PD31, Figshare MDD, and TUAB), NeuroDyn-EEG achieves competitive classification performance, securing the highest BACC, AUROC, and AUCPR on PD31 and MDD, and highest BACC on AD65. Third, post hoc regional analyses reveal disease-specific alterations: local synaptic connectivity C_1 involves the most altered regions in AD65, whereas the firing threshold theta ranks first in MDD, offering testable mechanistic hypotheses. Overall, NeuroDyn-EEG maps scalp EEG to anatomically indexed dynamical parameters, bridging representation learning and mechanistic neurophysiology. Code: https://github.com/Gnosis-Neurodynamics/NeuroDyn-EEG.

cs.NE↗

arXiv2026

LLM-Based Generative Retrieval for Snapchat Content Recommendation

Pretrained large language models (LLMs) are promising retrieval engines because they combine rich semantic priors, strong sequence modeling capabilities, and favorable scaling behavior. However, turning a pretrained LLM into a generative retriever in production deployment raises several challenges: the model must learn an internal item vocabulary that was absent from pretraining, and generate valid item identifiers under strict latency and cost constraints. We address these challenges through the design and launch of SnapLGR, an LLM-based generative retrieval system for short-video recommendation at Snapchat. The system is built around three main designs. First, we construct semantic identifiers (SIDs) from multimodal item embeddings and enhance them with Personalized PageRank (PPR)-based co-engagement contrastive learning, resulting in improved codebook utilization, reduced collisions, and infused collaborative signal. Second, we use continued pretraining (CPT) to ground the introduced SID tokens before supervised fine-tuning (SFT) on user interaction sequences. Third, we make SnapLGR serving practical through TensorRT-LLM CUDA-backed beam search and a decentralized worker-loop architecture. In a live A/B test, the launched system increased View Time by 0.37%, Time Spent by 0.09%, Deep Sessions by 0.18%, and Deep Sessions Unique User by 0.11% relative to the existing TIGER-style generative retrieval baseline. We then decompose this offline gap under a fixed tokenizer and quantify the gains due to model architecture, scaling, and pretraining. Overall, our deployment shows that successful production SnapLGR requires joint design across representation learning, vocabulary grounding, and efficient training and serving.

cs.IR↗

arXiv2026

Freestanding Antiferromagnetic Oxide Membranes: Synthesis and Characterization of Cr$_2$O$_3$

Antiferromagnetic (AFM) insulators stand out as a promising class of materials for fast switching, energy-efficient magnon-based technology, but our understanding of how to tune these properties with the lattice remains limited. As an AFM insulator with strong magnetoelectric response, Cr$_2$O$_3$ membranes stand out as a promising platform to explore multi-modal tunability via strain. However, fabricating these membranes is challenging due to the scarcity of etchable sacrificial layers with compatible lattice constants and sufficient surface quality. We address this issue by utilizing La$_{0.7}$Sr$_{0.3}$MnO$_3$ (LSMO) as a sacrificial layer, enabling successful fabrication of millimeter-scale Cr$_2$O$_3$ membranes. Substrate-free characterization reveals that strain due to the lattice mismatch during growth is released by the formation of small polycrystalline domains, preserving single-crystalline order over ~90% of the membrane area. Bulk-like structural properties are confirmed by transmission electron microscopy, X-ray diffraction, Raman spectroscopy, and second-harmonic generation spectroscopy. Platinum Hall measurements reveal an above-room-temperature N$é$el transition. These results establish Cr$_2$O$_3$ membranes as a viable platform for strain-tuning antiferromagnetism and magnetoelectricity.

cond-mat.mtrl-sci↗

arXiv2026

NMR evidence of pressure-induced structural transition and enhanced spin fluctuations up to 14~GPa in SrCu$_2$(BO$_3$)$_2$

The Shastry-Sutherland compound SrCu$_2$(BO$_3$)$_2$ has attracted considerable interest as a platform for exploring quantum phases and quantum phase transitions driven by magnetic frustration. The pressure-induced structural and magnetic phase transitions in SrCu$_2$(BO$_3$)$_2$, however, remain controversial. To address this issue, we performed high-pressure $^{11}$B nuclear magnetic resonance (NMR) measurements on SrCu$_2$(BO$_3$)$_2$ up to 14~GPa. The NMR spectra reveal two pressure-induced monoclinic phases. With pressure above 4~GPa and with temperature below 10~K, the rapid broadening of the NMR spectrum and the power-law behavior in the spin-lattice relaxation rate $1/T_1$ provide clear evidence for a gapless 3D antiferromagnetic (AFM) phase in the monoclinic phase. At an intermediate temperature range around 20~K, the emergence of the field-dependent NMR line splits resolves a two-dimensional, short-range ordered AFM phase; at temperature above 30~K, the sublinear power-law behavior of $1/T_1$ identifies an extended correlated paramagnetic regime.

cond-mat.str-el↗

arXiv2026

SafeBuild-Bench: A Temporal-Robust Construction Safety Benchmark with Graph-Enhanced Data Mining

Construction-safety models must handle concrete deployment risks, such as a worker standing near a scaffold edge without guardrails, rather than only recognize common objects in curated images. Yet real inspection archives are redundant, long-tailed, and collected across changing sites and months. We introduce SafeBuild-Bench, a metadata-driven benchmark for evaluating multimodal large language models on construction safety under realistic temporal and site variation. It is mined from 100K+ industrial image-text records and contains 3,314 task instances from over 3,000 expert-verified images, covering multiple-choice hazard identification and free-form hazard description. To make expert verification scalable, we develop GEMS, a graph-enhanced multimodal selection pipeline that combines a proxy-model confusion signal with graph-based diversity to identify informative candidates from redundant streams. On public instruction-tuning data, GEMS-selected subsets preserve robustness-oriented performance under small data budgets. On SafeBuild-Bench, current MLLMs remain far from reliable construction-safety understanding, with the best overall score near 60. We release the benchmark, evaluation scripts, and GEMS codebase at https://github.com/safebuild/gems.

cs.CV↗

arXiv2026

Spiral spin liquid resilient to quantization in the frustrated honeycomb antiferromagnet GdZnPO

Frustrated magnets host strong quantum fluctuations that can suppress conventional magnetic order and give rise to exotic quantum phases such as spin liquids. In some cases, however, quantum fluctuations lift classical degeneracies and stabilize ordered states via an order-by-quantum-disorder mechanism. The spin-7/2 honeycomb antiferromagnet GdZnPO has recently been proposed as a spiral spin-liquid candidate arising from cooperative fluctuations among a subextensively degenerate manifold of spiral states. Here, we investigate the local magnetization and spin dynamics in GdZnPO using nuclear magnetic resonance. In an intermediate field regime between $\sim$3 T and the saturation field ($\sim$12 T), we observe a spatially uniform magnetization and persistent low-energy spin dynamics down to 0.033 K, with no detectable symmetry breaking, providing spectroscopic evidence for a spin-liquid state. At lower fields below $\sim$3 T, a weak stripe order emerges below $\sim$0.25 K; however, strong fluctuations persist, as indicated by a nearly temperature-independent and unusually large spin-lattice relaxation rate in the low-temperature limit. Our results demonstrate that spin-7/2 quantization weakly lifts the spiral degeneracy, stabilizing subtle magnetic order while preserving robust dynamics and spin-liquid phenomenology. These findings establish GdZnPO as a promising platform for exploring spin liquids in high-spin frustrated magnets down to the lowest accessible temperatures.

cond-mat.str-el↗

arXiv2026

Metasurface Holography on a Relative-Phase Manifold for Stable and High Fidelity Tweezer-Array Generation

We present a new holographic approach for generating large scale, polarization resolved optical tweezer arrays. By analyzing the ideal Jones fields that realize a target pattern, we identify that the fundamental degrees of freedom are the relative phases of the individual tweezers, rather than the full spatial phase profile. Leveraging this insight, we formulate a reverse projection optimization that adjusts only a small set of phase parameters to approximate the ideal operator within the physical constraints of a metasurface. This produces significantly higher fidelity and robustness than Gerchberg_Saxton type algorithms. Experimentally, we demonstrate H, V, L, and R polarized tweezer arrays using a single layer metasurface. A key advantage of our method is its phase stability, yielding strong resistance to optical aberrations and enabling coherent global phase modulation such as forming vortex tweezer lattice, without degrading trap quality. This framework provides a conceptually clear and experimentally powerful route for scalable optical field synthesis.

physics.optics↗

arXiv2026

Correlation visibility and generalized Siegert relation for random light beams

Phase difference is central to classical coherence theory. With the advancement of various light-field modulation techniques, artificially generated pseudo-thermal light sources or random light beams can exhibit exotic wavefront correlation properties. However, such spatial wavefront correlations cannot be fully characterized using the phase difference alone. For instance, for a pair of conjugate pseudo-thermal beams, the spatial wavefronts exhibit a significant anti-correlation, meaning that the sum of their wavefronts tends to be constant. In this work, we propose the concept of degree of wavefront correlation $p^{(1)}$, ranging symmetrically from $-1$ to $+1$, for numerically calculating the wavefront correlation properties among various pseudo-thermal light sources, and the sign (positive or negative) can be used to determine the tendency-whether it leans toward wavefront-difference or wavefront-sum correlation. Numerical results demonstrate that the classical Siegert relation does not apply to pseudo-thermal light sources that exhibit wavefront-sum correlation properties. To address this, we propose a generalization valid for all Gaussian pseudo-thermal light. Experimentally, we introduce the measurable quantities of correlation visibility $\mathcal{V}_g$ and correlation background $μ_g$, which form a two-dimensional classification framework $\{μ_g,\mathcal{V}_g\}$ that enables the experimental characterization of diverse Gaussian pseudo-thermal light using a common-path interferometer and intensity correlation measurement. Furthermore, the correlation visibility $\mathcal{V}_g$ can serve as an observable criterion for a zero-mean, non-circularly symmetric, and jointly Gaussian distribution.

physics.optics↗

arXiv2026

Single-crystal growth, structural characterization, and physical properties of a decorated square-kagome antiferromagnet KCu$_7$TeO$_4$(SO$_4$)$_5$Cl

The square-kagome lattice, composed of two-dimensional corner-sharing triangles, provides a novel platform for studying frustrated magnetism. However, material realizations of the square-kagome lattice remain scarce. Here, we report the single-crystal growth, structural characterization, magnetic and electric properties of KCu$_7$TeO$_4$(SO$_4$)$_5$Cl, a nabokoite compound featuring a distorted and decorated square-kagome lattice. Weak anomalies near 4 K are observed in both magnetization and specific heat, indicating the onset of a magnetic transition.The formation of a long-range antiferromagnetic state below 4.5 K is further confirmed by $^{35}$Cl nuclear magnetic resonance (NMR) measurements. Magnetic susceptibility data reveal nearly isotropic Curie-Weiss temperatures ($\sim-145$ K) and $g$-factors ($\sim2.4$) for both in-plane and out-of-plane magnetic fields. Moreover, we observe two successive ferroelectric transitions at $T_\mathrm{FE1}\sim30$ K and $T_\mathrm{FE2}\sim27$ K, driven by inversion-symmetry breaking, most likely associated with distortions in the Cu2O$_4$Cl$_1$ pyramids and the adjacent SO$_4$ tetrahedra. These results suggest that a three-dimensional model incorporating interlayer couplings via decorating Cu2 sites is essential for capturing the magnetic and electric behaviors in KCu$_7$TeO$_4$(SO$_4$)$_5$Cl.

cond-mat.str-el↗

arXiv2026

Plasmonic Photocatalysis Enables Selective Oxidative Coupling of Methane with Nitrous Oxide under Ambient Conditions

Methane (CH4) and nitrous oxide (N2O) are potent greenhouse gases that represent substantial chemical energy. Conversion of these abundant waste gases to high-value chemicals typically requires high temperatures up to 1000 C, producing substantial CO2 emissions and limited selectivity toward desirable multi-carbon products. Here we demonstrate a plasmonic photocatalyst that enables CH4 and N2O conversion under ambient conditions to form C2 and C3 hydrocarbons. By systematically tuning AuPd alloys on TiO2, we identify an optimal composition (AuPd0.05) where Au enhances light harvesting and Pd enables selective C-H activation and C-C coupling. Under visible-light illumination, this catalyst produces C2H4, C2H6, C3H6, and C3H8 with ~80% selectivity while suppressing CO2 formation. In-situ spectroscopy and hot-carrier calculations show that plasmon-generated carriers redistribute interfacial hydroxyl intermediates, shifting the hydrophilic center to suppress overoxidation. Ab-initio calculations further reveal the reduction in C-C coupling barriers from 2.7 eV to 0.7 eV under illumination. Our work illustrates how engineering interfacial electronic and adsorbate dynamics enables selective multicarbon formation.

cond-mat.mtrl-sci↗

arXiv2026

Cascade of Spin Liquids in a Bilayer Triangular-lattice Antiferromagnet Rb_2Co_2(SeO_3)_3

In frustrated Ising magnets, classical spin liquids (CSLs) with macroscopic ground-state degeneracy can survive against conventional magnetic order, as exemplified by systems on triangular, kagome and pyrochlore lattices at zero field. Here we report the discovery of a high-field route toward spin liquids in a bilayer triangular lattice antiferromagnet, Rb$_2$Co$_2$(SeO$_3$)$_3$. We demonstrate that a cascade of CSLs -- characterized by doubly degenerate one-up-one-down local spin configurations and a residual entropy of 1/2(1-M/M_s)Rln2 per mole -- emerges through field-controlled dilution of Ising dimers. Owing to the interplay of intra- and inter-layer interactions, these CSLs are further stabilized by lattice symmetry breaking at fractional magnetization plateaus. Such field-induced spin liquids can be understood as a consequence of generalized ice rules, analogous to those governing in pyrochlore antiferromagnets. In particular, the 5/6-plateau state is a candidate quantum spin liquid. Our results thereby establish a new pathway for exploring diverse spin liquid states across both classical and quantum regimes.

cond-mat.str-el↗

arXiv2026

NMR evidence of spin supersolid and Pomeranchuk effect behaviors in the triangular-lattice antiferromagnet Rb$_2$Ni$_2$(SeO$_3$)$_3$

We performed $^{85}$Rb nuclear magnetic resonance (NMR) measurements on the $S$ = 1 bilayer triangular-lattice antiferromagnet Rb$_2$Ni$_2$(SeO$_3$)$_3$ in magnetic fields up to 26 T. In the field range from 3 T to 26 T, the NMR spectral lines split and their respective spectral weight ratios reveal the existence of the magnetic up-up-down (UUD) phase, although the 1/3-plateau phase is only reached at fields above 16 T. Two distinct gapless regimes are further identified: one at low fields and low temperatures, and the other at high fields and high temperatures, consistent with the spin supersolid Y and V phases. Notably, the UUD-V phase boundary exhibits a negative slope in $dT/dH$, where the supersolid phase is located at temperatures above the solid phase due to strong low-energy spin fluctuations.

cond-mat.str-el↗

arXiv2026

Beyond Boundaries: Leveraging Vision Foundation Models for Source-Free Object Detection

Source-Free Object Detection (SFOD) aims to adapt a source-pretrained object detector to a target domain without access to source data. However, existing SFOD methods predominantly rely on internal knowledge from the source model, which limits their capacity to generalize across domains and often results in biased pseudo-labels, thereby hindering both transferability and discriminability. In contrast, Vision Foundation Models (VFMs), pretrained on massive and diverse data, exhibit strong perception capabilities and broad generalization, yet their potential remains largely untapped in the SFOD setting. In this paper, we propose a novel SFOD framework that leverages VFMs as external knowledge sources to jointly enhance feature alignment and label quality. Specifically, we design three VFM-based modules: (1) Patch-weighted Global Feature Alignment (PGFA) distills global features from VFMs using patch-similarity-based weighting to enhance global feature transferability; (2) Prototype-based Instance Feature Alignment (PIFA) performs instance-level contrastive learning guided by momentum-updated VFM prototypes; and (3) Dual-source Enhanced Pseudo-label Fusion (DEPF) fuses predictions from detection VFMs and teacher models via an entropy-aware strategy to yield more reliable supervision. Extensive experiments on six benchmarks demonstrate that our method achieves state-of-the-art SFOD performance, validating the effectiveness of integrating VFMs to simultaneously improve transferability and discriminability.

cs.CV↗

arXiv2025

Channel-last gate-all-around nanosheet oxide semiconductor transistors

As we move beyond the era of transistor miniaturization, back-end-of-line-compatible transistors that can be stacked monolithically in the third dimension promise improved performance for low-power electronics. In advanced transistor architectures, such as gate-all-around nanosheets, the conventional channel-first process involves depositing dielectrics directly onto the channel. Atomic layer deposition of gate dielectrics on back-end-of-line compatible channel materials, such as amorphous oxide semiconductors, can induce defects or cause structural modifications that degrade electrical performance. While post-deposition annealing can partially repair this damage, it often degrades other device metrics. We report a novel channel-last concept that prevents such damage. Channel-last gate-all-around self-aligned transistors with amorphous oxide-semiconductor channels exhibit high on-state current ($>$ 1 mA/$μ$m) and low subthreshold swing (minimum of 63 mV/dec) without the need for post-deposition processing. This approach offers a general, scalable pathway for transistors with atomic layer deposited channel materials, enabling the future of low-power three-dimensional electronics.

cond-mat.mtrl-sci↗

arXiv2025

Single-Ion Sensing in Liquid Using Fluorescent h-BN Point Defects

Understanding the chemical state of individual ions in solutions is crucial for advancing knowledge of complex systems. However, sensing systems at the single-ion level in liquid environments remains a significant challenge. A strategy is introduced that leverages the optical emission properties of point defects in hexagonal boron nitride (h-BN) as single ion sensors. The interaction of optically active h-BN defects with ions in solution leads to distinct spectral shifts, enabling precise visualization and analyzing of individual ions. Using Li+ ions in organic electrolytes as a model, spectral shifts exceeding 10 nm were observed upon ion addition. Application of an external electric field further enhanced these shifts to over 40 nm, enabling real-time monitoring of electrical field induced local perturbations of Li+ ions. Through this approach, individual point defects were shown to spectroscopically distinguish ions of varying charges (e.g., Na+, Mg2+, and Al3+) based on their local electrical field, each producing a distinct spectral shift. This platform allows direct sensing of ions and their chemical states in liquid environments, providing insights into subtle interfacial changes at the single-ion level, with measurable spectral shifts detectable at millisecond temporal resolution and at concentrations down to 10 micromolar range. This capability presents potential applications in various fields involving ions in liquids that include battery technology and environmental science.

cond-mat.mes-hall↗

arXiv2025

Defect Engineered Hexagonal-Boron Nitride Enables Ionic Conduction for Lithium Metal Batteries

The practical implementation of lithium-metal anodes has been hindered by uncontrollable dendrite formation and interfacial instability. This study presents a defect-engineering approach of a chemically stable and electrically insulating interfacial layer of hexagonal boron nitride (h-BN) that markedly enhances ionic conductivity through argon ion irradiation. Initially, the electrochemical performance from commercially available, large-area chemical vapor deposition (CVD)-grown h-BN films with industrial-scale argon ion implantation motivated our subsequent detailed investigations using lab-scale exfoliated single-crystal h-BN flakes. Integration of these exfoliated flakes into a hybrid microfluidic-microelectronic chip provided direct evidence that controlled vacancy defects transform h-BN into an efficient lithium-ion conductor while preserving its intrinsic electrical insulation. Experimental validation confirmed improved lithium-metal anode stability, achieving dendrite-free cycling with Li plating/stripping Coulombic efficiencies exceeding 99.5% about 1000 cycles. Further assemble of irradiated h-BN in lithium-sulfur batteries effectively mitigates the polysulfide shuttle effect, sustaining over 97% specific capacity around 300 cycles. These results establish a robust, scalable interface-engineering route for next-generation lithium-metal batteries that combine high ionic transport with excellent electrical insulation.

cond-mat.mtrl-sci↗