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Xinyang Yu

Publications and source records attributed to Xinyang Yu.

15 recordsLinked to original sources
arXiv2026

Dynamic Networks with Node Heterogeneity and Homophily

The goal of this paper is to model node heterogeneity and link homophily for dynamic networks. The proposed framework brings new insights on how networks evolve over time. It also provides more sophisticated tools for the prediction of future networks with statistical guarantees. The new model accounts for the link homophily associated with both observed traits and latent traits. The joint modeling of node heterogeneity and both observed and latent homophily effects also poses the significant challenge in statistical inference, resulted from the large number of confounding parameters in the model. To overcome this, we propose a novel normalized squared loss, paving the way for efficient and stable estimation of parameters in a high-dimensional setting. We provide a rigorous theoretical analysis of the estimation method, and demonstrate its effectiveness through extensive simulations and the illustration with some real-world network data.

math.ST↗
arXiv2026

Fault-tolerant quantum computing with a microwave Cat Bus

The scalability of fault-tolerant neutral-atom quantum computers is constrained by the latency of shuttling with optical tweezers, imposing a stringent trade-off between qubit overhead and circuit depth in quantum algorithm compilation. Here we propose a hardware-efficient, shuttling-free architecture that achieves all-to-all connectivity. Remote Rydberg atoms are resonantly entangled through a microwave ``Cat Bus''---a cavity mode autonomously stabilized in a bosonic cat state. The Cat Bus natively supports the highly parallelized execution of one-to-many $\mathrm{CZ}^n$ gates with exponentially suppressed crosstalk. We derive the resulting cat--atom error channel from the underlying interactions and physical constraints. For fault-tolerant operation, we develop a hardware-aware scheduling scheme that exploits the native cat--atom $\mathrm{CZ}^{n}$ gate to construct a syndrome-extraction circuit with minimum depth. We benchmark the architecture using hypergraph-product (HGP) codes and estimate a 180-fold reduction in syndrome-extraction cycle time at $N=10^5$ data qubits compared with an atom-rearrangement-based architecture. Under matched two-qubit depolarizing noise, the corresponding error threshold increases from $0.55\%$ to $0.72\%$. Under the hardware-derived error model, we obtain a threshold of $0.80\%$, corresponding to a threshold cooperativity of $C_{\mathrm{th}}=7.8 \times 10^4$, compatible with experimentally accessible parameters for Rydberg-coupled microwave-cavity systems. By avoiding atom transport, the Cat Bus provides a route towards high-speed, fault-tolerant neutral-atom quantum computation.

quant-ph↗
arXiv2026

Tools Are Not Islands: Set-Level Tool Retrieval for LLM Agents via Query-Conditioned Hyperedge Prediction

Large language model (LLM) agents increasingly rely on invoking external tools to complete real-world tasks. Tool retrieval, which selects a small task-relevant subset from a library of thousands of tools before the agent acts, has therefore become a critical component of LLM agent pipelines. However, existing retrievers either score each tool in isolation or assemble the tool set sequentially, so the joint utility of a candidate set is never evaluated as a whole. In this paper, we propose HYSET, short for HYperedge-based SEt-level Tool retrieval. Our contributions are threefold: (i) we formulate tool retrieval as query-conditioned hyperedge prediction on a tool co-invocation hypergraph, under which the tool set itself becomes the unit of scoring and most existing retrieval paradigms reduce to restricted instances; (ii) we capture size-dependent tool compatibility through cardinality-specific interactions; and (iii) we design HYSET as a pre-selection module requiring no modification to the downstream agent. Experiments on ToolBench demonstrate that HYSET consistently outperforms state-of-the-art baselines in both tool retrieval performance and end-to-end task success. Beyond the in-domain setting, HYSET further supports zero-shot/few-shot transfer, generalizing to held-out tools/categories and unseen domains with minimal supervision.

cs.LG↗
arXiv2026

A unified resource-pool architecture for high-dimensional direct-detection optical communication

Increasing optical communication capacity without proportionally increasing receiver complexity remains a key challenge for direct-detection links. Conventional systems typically assign wavelength, polarization and intensity to fixed, separately recovered functions, so that alphabet expansion is accompanied by additional demultiplexing, polarization handling, receiver branches and electronic processing. Here we introduce a unified resource-pool architecture for high-dimensional direct-detection optical communication, in which wavelength, polarization and intensity are jointly organized as a composite optical symbol space and recovered through optical-domain joint projection rather than dimension-by-dimension separation. The receiver is implemented with an integrated disordered photonic processor that transforms each composite optical state into a reproducible multi-output electrical fingerprint for single-shot direct recovery. In a dual-wavelength transmission experiment, the system resolves 4096 composite symbols, corresponding to 12 bits per symbol slot, with a bit error rate of 4.25e-4 after 10 km standard-fiber transmission. Additional experiments demonstrate dense polarization alphabets, wavelength-indexed state-space expansion and high-launch-power operation over hollow-core fiber. These results establish disorder-enabled joint projection in an integrated photonic processor as a route to hardware-efficient high-dimensional direct-detection communication beyond conventional dimension-partitioned receiver architecture.

physics.optics↗
arXiv2026

Near-UV Single-Pixel Imaging with All-Inorganic Lead-Free Perovskite

Single-pixel imaging (SPI) is a powerful computational imaging technology that reconstructs spatial information from sequentially encoded optoelectrical signals without pixelated detector arrays. Solution-processible metal halide perovskites are promising photoactive candidates for SPI, but the toxicity of lead-based compositions remains a critical barrier to practical development. Here, we demonstrate one-step fabrication of low-dimensional, lead-free K$_2$CuBr$_3$ thin film as near-UV photoactive channels for single-pixel imaging. By systematic antisolvent engineering, compact and uniform K2CuBr3 films are obtained and integrated into planar photoconductors devices. The resulting photodetectors exhibit stale photoswitching under 405 nm illumination, low dark current on the order of $10^{-10}$ A, with fast response and recovery time 38.82 and 61.94 $\mu$s, respectively. Integrated into an SPI configuration, the K2CuBr3 photoconductor successfully reconstructs near-UV images, with the signal-to-noise ratio improving from 16.4 to 31.7 dB as the illumination irradiance increases. This work highlights solution-processed lead-free copper halides as promising photoactive materials for compact, non-toxic and cost-effective UV computational imaging systems.

physics.optics↗
arXiv2026

Harnessing Non-Boltzmann Steady States in Lanthanide Nanocrystals for Mid-Infrared Optoelectronics

Converting mid-infrared (MIR) radiation to visible or near-infrared wavelengths is essential for imaging and sensing, yet achieving sensitive, low-power, and scalable detection remains challenging. Lanthanide nanocrystals provide an alternative through ratiometric luminescence but are typically constrained by Boltzmann statistics, which tie population distributions to lattice temperature and limit signal contrast. Here we show that MIR irradiation rebalances dissipative relaxation pathways, driving lanthanide emitters into a non-Boltzmann steady state that enables non-thermal control of population distributions. This allows emission behaviors inaccessible under thermal equilibrium. We exploit this regime to achieve linear MIR detection with respect to MIR power across 6.8 to 8.6 micrometers. The ratiometric response is intrinsically independent of the pump power, enabling operation at an ultralow excitation power of 10 uW, several orders of magnitude lower than conventional approaches. Using standard silicon photodetectors, we then demonstrate room-temperature MIR imaging with detection limits approaching 4 nW um-2. Our results establish lanthanide nanoparticles as an efficient platform for MIR conversion and sensing in nanophotonic systems.

physics.optics↗
arXiv2026

Mid-Infrared Modulation of Quantum Emitters in Hexagonal Boron Nitride

Single photon emitters (SPEs) are promising building blocks for practical devices in quantum technologies. Traditionally, these systems are excited using off-resonant visible light through their phonon transitions, yet this process remains poorly understood. Here, we explore the interaction of mid-infrared (MIR) excitation on the properties of SPEs in hexagonal boron nitride. Notably, we present a reversible, non-destructive method to enhance emission from blue SPEs using MIR co-excitation. By resonantly driving defect-localized in-plane infrared-active optical phonon modes near 7.3 um, the MIR field modulates carrier dynamics through a phonon-assisted recombination. This unique feature, not observed previously for defects in solids, is a promising reservoir in a growing toolkit to modulate quantum emitters at room temperature for their use in practical quantum technologies.

physics.optics↗
arXiv2026

First Thin-Film Lithium Tantalate Polarization Controller Enabling Reset-Free Mrad/s Tracking for Optical Interconnects

The rapid escalation of computing power driven by large-scale artificial intelligence is placing unprecedented demands on the bandwidth, latency, and energy efficiency of data-center interconnects (DCIs). Self-homodyne coherent (SHC) transmission is a promising architecture because it preserves the spectral efficiency of coherent detection while greatly simplifying digital signal processing, but its practical deployment is critically limited by random and often ultrafast state-of-polarization (SOP) fluctuations that induce carrier fading and destabilize coherent reception. Here we report the first integrated polarization controller based on thin-film lithium tantalate (TFLT), enabling reset-free polarization tracking at Mrad/s speeds. The four-stage electro-optic device exhibits polarization-dependent loss (PDL) below 0.3 dB, a half-wave voltage below 2.5 V, high modulation bandwidth, and negligible DC drift. To accommodate the finite tuning range of integrated phase shifters, we develop a finite-boundary gradient-descent (FBGD) control algorithm that ensures reset-free SOP evolution with no phase jump. The implemented adaptive polarization controller (APC) is validated through both standalone polarization-tracking measurements and a dual-polarization 16-QAM SHC 400-Gbps transmission system. Transient polarization disturbances can be tracked at speeds up to 2 Mrad/s, while stable reset-free operation under continuous polarization disturbances is maintained up to 1 Mrad/s. This reset-free performance represents more than doubling the state of the art, while the pre-FEC bit-error rates remain below the HD-FEC threshold under realistic DCI conditions and lightning-scale polarization disturbances. These results establish TFLT as a new platform for ultrafast, low-power, reset-free, and drift-free polarization control in coherent optical interconnects and beyond.

physics.optics↗
arXiv2024

LocRef-Diffusion:Tuning-Free Layout and Appearance-Guided Generation

Recently, text-to-image models based on diffusion have achieved remarkable success in generating high-quality images. However, the challenge of personalized, controllable generation of instances within these images remains an area in need of further development. In this paper, we present LocRef-Diffusion, a novel, tuning-free model capable of personalized customization of multiple instances' appearance and position within an image. To enhance the precision of instance placement, we introduce a Layout-net, which controls instance generation locations by leveraging both explicit instance layout information and an instance region cross-attention module. To improve the appearance fidelity to reference images, we employ an appearance-net that extracts instance appearance features and integrates them into the diffusion model through cross-attention mechanisms. We conducted extensive experiments on the COCO and OpenImages datasets, and the results demonstrate that our proposed method achieves state-of-the-art performance in layout and appearance guided generation.

cs.CV↗
arXiv2024

Squeezing atomic $p$-orbital condensates for detecting gravitational waves

Detecting the faint signal of continuous gravitational waves (CWs) stands as a major frontier in gravitational-wave astronomy, pushing the need for detectors whose sensitivity exceeds the standard quantum limit (SQL). Here, we propose an orbital optomechanical (OOM) sensor that exploits the sensitive coupling of an orbitally squeezed $p$-orbital Bose-Einstein condensate to spacetime distortions, enabling the detection of interferometer phase shifts induced by CWs. This sensor achieves a theoretical quantum-noise-limited sensitivity 16 dB below the SQL while reducing the required laser power by five orders of magnitude. The performance arises from a novel noise trade-off: a counter-propagating readout scheme suppresses photonic shot noise, while orbital squeezing minimizes the remaining atomic projection noise. By leveraging quantum control over atomic orbital degrees of freedom, this approach establishes a new framework for interferometric sensing with direct applications to the search for CWs and ultralight dark matter.

cond-mat.quant-gas↗
arXiv2024

AquilaMoE: Efficient Training for MoE Models with Scale-Up and Scale-Out Strategies

In recent years, with the rapid application of large language models across various fields, the scale of these models has gradually increased, and the resources required for their pre-training have grown exponentially. Training an LLM from scratch will cost a lot of computation resources while scaling up from a smaller model is a more efficient approach and has thus attracted significant attention. In this paper, we present AquilaMoE, a cutting-edge bilingual 8*16B Mixture of Experts (MoE) language model that has 8 experts with 16 billion parameters each and is developed using an innovative training methodology called EfficientScale. This approach optimizes performance while minimizing data requirements through a two-stage process. The first stage, termed Scale-Up, initializes the larger model with weights from a pre-trained smaller model, enabling substantial knowledge transfer and continuous pretraining with significantly less data. The second stage, Scale-Out, uses a pre-trained dense model to initialize the MoE experts, further enhancing knowledge transfer and performance. Extensive validation experiments on 1.8B and 7B models compared various initialization schemes, achieving models that maintain and reduce loss during continuous pretraining. Utilizing the optimal scheme, we successfully trained a 16B model and subsequently the 8*16B AquilaMoE model, demonstrating significant improvements in performance and training efficiency.

cs.CL↗
arXiv2023

Adiabatically compressing chiral p-wave Bose-Einstein condensates into the lowest landau level

There has been much recent progress in controlling $p$-orbital degrees of freedom in optical lattices, for example with lattice shaking, sublattice swapping, and lattice potential programming. Here, we present a protocol of preparing lowest Landau level (LLL) states of cold atoms by adiabatically compressing $p$-orbital Bose-Einstein condensates confined in two-dimensional optical lattices. The system starts from a chiral $p+ip$ Bose-Einstein condensate (BEC) state, which acquires finite angular momentum by spontaneous symmetry breaking. Such chiral BEC states have been achieved in recent optical lattice experiments for cold atoms loaded in the $p$-bands. Through an adiabatic adjustment of the lattice potential, we compress the three-dimensional BEC into a two-dimensional system, in which the orbital degrees of freedom continuously morph into LLL states. This process is enforced by the discrete rotation symmetry of the lattice potential. The final quantum state inherits large angular momentum from the original chiral $p+ip$ state, with one quantized unit per particle. We investigate the quantum many-body ground state of interacting bosons in the LLL considering contact repulsion. This leads to an exotic gapped BEC state. Our theory can be readily tested in experiments for the required techniques are all accessible to the current optical lattice experiments.

cond-mat.quant-gas↗
arXiv2023

Video Infringement Detection via Feature Disentanglement and Mutual Information Maximization

The self-media era provides us tremendous high quality videos. Unfortunately, frequent video copyright infringements are now seriously damaging the interests and enthusiasm of video creators. Identifying infringing videos is therefore a compelling task. Current state-of-the-art methods tend to simply feed high-dimensional mixed video features into deep neural networks and count on the networks to extract useful representations. Despite its simplicity, this paradigm heavily relies on the original entangled features and lacks constraints guaranteeing that useful task-relevant semantics are extracted from the features. In this paper, we seek to tackle the above challenges from two aspects: (1) We propose to disentangle an original high-dimensional feature into multiple sub-features, explicitly disentangling the feature into exclusive lower-dimensional components. We expect the sub-features to encode non-overlapping semantics of the original feature and remove redundant information. (2) On top of the disentangled sub-features, we further learn an auxiliary feature to enhance the sub-features. We theoretically analyzed the mutual information between the label and the disentangled features, arriving at a loss that maximizes the extraction of task-relevant information from the original feature. Extensive experiments on two large-scale benchmark datasets (i.e., SVD and VCSL) demonstrate that our method achieves 90.1% TOP-100 mAP on the large-scale SVD dataset and also sets the new state-of-the-art on the VCSL benchmark dataset. Our code and model have been released at https://github.com/yyyooooo/DMI/, hoping to contribute to the community.

cs.CV↗
arXiv2023

A two-way heterogeneity model for dynamic networks

Dynamic network data analysis requires joint modelling individual snapshots and time dynamics. This paper proposes a new two-way heterogeneity model towards this goal. The new model equips each node of the network with two heterogeneity parameters, one to characterize the propensity of forming ties with other nodes and the other to differentiate the tendency of retaining existing ties over time. Though the negative log-likelihood function is non-convex, it is locally convex in a neighbourhood of the true value of the parameter vector. By using a novel method of moments estimator as the initial value, the consistent local maximum likelihood estimator (MLE) can be obtained by a gradient descent algorithm. To establish the upper bound for the estimation error of the MLE, we derive a new uniform deviation bound, which is of independent interest. The usefulness of the model and the associated theory are further supported by extensive simulation and the analysis of some real network data sets.

stat.ME↗
arXiv2021

Linear Discriminant Analysis with High-dimensional Mixed Variables

Datasets containing both categorical and continuous variables are frequently encountered in many areas, and with the rapid development of modern measurement technologies, the dimensions of these variables can be very high. Despite the recent progress made in modelling high-dimensional data for continuous variables, there is a scarcity of methods that can deal with a mixed set of variables. To fill this gap, this paper develops a novel approach for classifying high-dimensional observations with mixed variables. Our framework builds on a location model, in which the distributions of the continuous variables conditional on categorical ones are assumed Gaussian. We overcome the challenge of having to split data into exponentially many cells, or combinations of the categorical variables, by kernel smoothing, and provide new perspectives for its bandwidth choice to ensure an analogue of Bochner's Lemma, which is different to the usual bias-variance tradeoff. We show that the two sets of parameters in our model can be separately estimated and provide penalized likelihood for their estimation. Results on the estimation accuracy and the misclassification rates are established, and the competitive performance of the proposed classifier is illustrated by extensive simulation and real data studies.

stat.ME↗