arXiv subjects

Wen Zhou

Publications and source records attributed to Wen Zhou.

At least 19 recordsLinked to original sources

arXiv2026

Fusing Earth observation embeddings and spaceborne terrain with meteorological forcing for flood peak depth prediction from high-water marks

Flood peak depth on normally dry land governs damage and risk to life, but is observed only sparsely, by post-event high-water mark (HWM) surveys, and depth derived from satellite flood extent is produced after the event and inherits the vertical error of the digital elevation model (DEM) it is computed from. Deep-learning flood models have in turn been trained on hydrodynamic simulations rather than on observed depths. We ask whether flood peak depth can be learned from field observations and predicted before the peak from spaceborne terrain, geospatial foundation-model embeddings and antecedent meteorological forcing. We assembled 5689 USGS HWM surveys from 39 events (2011-2025) in 24 U.S. states and Puerto Rico, assigned each a peak time from nearby sensors, paired each with a rain-free window at the same site, and linked them to Copernicus GLO-30 terrain, AlphaEarth embeddings and 144 h of hourly forcing masked to the lead time. A cross-modal fusion network trained on these observations predicts depth at the surveyed marks with a mean absolute error of 0.39 m 6 h ahead of the peak. Supervision by surveyed depth corrects the DEM error implicitly: differencing the measured peak water level with the same DEM gives 0.93 m at the same marks, 2.4 times larger, although it uses the measured water level and the model does not. Forcing accounts for 77% of the attributed reduction in depth error; terrain and the embeddings are statistically equivalent to each other and contribute the remaining 23%. On eleven held-out events the model still separates flooded from rain-free conditions but no longer resolves depth at the marks (R2 = 0.03), so the skill reported here is confined to the events and regions sampled. Field-surveyed depths can thus supervise flood depth prediction and correct terrain error that extent-based estimation cannot, while transfer to unseen storms remains the open problem.

cs.LG↗

arXiv2026

On the Consistency of Spectral Clustering under Generalized Gaussian Mixture Models

Spectral clustering is a widely used clustering method with strong empirical performance and favorable computational scalability. Existing theory has established its consistency and risk guarantees under Gaussian and related mixture models, primarily in settings with relatively simple latent structure. In this paper, we study spectral clustering under a semiparametric Gaussian mixture model, where the observed data become Gaussian-mixture distributed only after unknown monotone coordinate-wise transformations. We derive non-asymptotic bounds for its cluster recovery error. As a byproduct of the theory, we show that spectral clustering provides a theoretically justified initialization for a broad class of high-dimensional mixture-based learning procedures.

stat.ME↗

arXiv2026

A Duality of the F Distribution

The F distribution arises naturally in linear regression or analysis of variance. Elementary textbooks state the result in terms of the standard normal random vector and fixed projection matrices. More advanced textbooks and research articles have extended the result to spherically symmetric random vectors. We complement the literature with a dual result that the F distribution arises with a fixed vector and random projection matrices generated by a left-spherical random matrix.

math.ST↗

arXiv2026

Estimating the concurrence for quantum states via symmetric measurements

Concurrence is one of the most well-known entanglement measures. We show that, for arbitrary bipartite states, the lower bound of concurrence induced by symmetric measurements is always tighter than the one obtain from realignment. Besides, we derive an extended version of lower bound of concurrence induced by symmetric measurements, which can also be experimentally identified without state tomography, and further present a corresponding lower bound of genuine tripartite entanglement concurrence.

quant-ph↗

arXiv2026

VirusCascade: Hijacking Collaborative Reflection in LLM-Powered Recommender Agents

Advancing beyond traditional static scoring models, LLM-powered agentic recommender systems (LLM-ARS) instantiate users and items as autonomous agents, whose semantic states are dynamically refined through a recurrent process known as collaborative reflection. While this mechanism improves recommendation quality, it simultaneously introduces a systemic vulnerability: adversarial evidence injected into a single agent can be rationalised into a legitimate preference narrative, written back into memory, and propagated to other agents through interaction contexts. We term the local rationalisation process reflection laundering, and its system-wide escalation through collaborative reflection collaborative-reflection hijacking. Existing attacks on recommender systems, whether based on interaction-level data poisoning or text-level adversarial perturbations, assume static pipelines and thus cannot exploit this recurrent, multi-agent amplification pathway. To bridge this gap, we first conduct a controlled vulnerability analysis that establishes two exploitable properties underlying collaborative-reflection hijacking: reflective persistence and cross-agent propagation. Then building on these findings, we propose VirusCascade, the first black-box targeted promotion attack that jointly shapes semantic and structural attack surfaces: the former ensures the target item is naturally rationalised as satisfying broad user preferences, the latter positions it for system-wide propagation. Extensive experiments on four real-world datasets across diverse LLM-ARS architectures demonstrate that VirusCascade consistently achieves state-of-the-art targeted exposure under evaluated stealth constraints, reaching a mean E@20 of 0.384 and surpassing the strongest baseline by an absolute margin of +0.185.

cs.CR↗

arXiv2026

Realistic modeling of Photonic Terahertz Communication based on a real-world 30 km 30 Gbps experimental demonstration

Experimental studies have demonstrated that THz links can support multi-kilometer, gigabit-per-second wireless transmission. However, few studies have effectively validated channel models through high-capacity, long-distance experiments. In this work, we experimentally demonstrate a 30 km ultra-long-haul photonic THz wireless communication system. 16 Gbaud quadrature phase shift keying (QPSK) signals are transmitted and tested, achieving a maximum data rate of 30 Gbps. For the first time, we validate two widely used THz channel models using a fully implemented 30 km experimental setup, providing a systematic and thorough analysis that integrates theoretical modeling with experimental results. We also perform an analysis of power consumption in photonic THz communication systems and provide a detailed comparison between simulated and experimental power consumption results. Finally, a detailed performance comparison between the simulation and experimental results, including signal-to-noise ratio (SNR), bit error rate (BER) and data rate, are provided. Our work provides essential insights for the design and energy-efficient deployment of future high-capacity THz networks, establishing a benchmark for subsequent research in channel modeling, experimental verification, and system-level energy efficiency.

eess.SP↗

arXiv2026

Applying foundation model embeddings towards urban livability evaluation

While accurate measurement of socioeconomic indicators remains challenging in data-scarce regions, which limits policy interventions and resource allocation, high-resolution geospatial data is widely available and can contain information on various livability statistics. We investigate which physical features are encoded within foundation model embeddings, such as AlphaEarth, AnySat, and TerraMind, and provide a systematic framework for identifying the most predictive geospatial indicators. By analyzing how different types of geospatial data influence urban livability predictions, our approach enables researchers to prioritize the most informative features for their specific applications. Additionally, we demonstrate how to leverage foundation model embeddings to enhance prediction performance for these outcomes. This work contributes a principled methodology for extracting actionable information from satellite imagery while accounting for complex spatial dependencies, with applications in predicting urban livability in regions with limited observation data.

cs.LG↗

arXiv2026

HOMIE: Human-object Centric Video Personalization via Multimodal Intelligent Enhancement

Human-object centric video personalization (HOCVP) is a core task within subject-driven video generation. However, existing methods suffer from two key limitations. First, most approaches focusing on inter-subject personalization still struggle to strike a balance between high subject fidelity and accurate interaction patterns between humans and diverse objects, especially when objects represent abstract concepts such as logos. Second, while intra-subject references (e.g., OCR maps, multi-view inputs) are expected to enhance subject fidelity, most existing works lack mechanisms to understand such latent correspondence. To address both challenges, we propose HOMIE, an HOCVP framework that tackles both inter- and intra-subject input settings in a unified manner. Compared to previous approaches, HOMIE proposes a better MLLM integration strategy to extract knowledge of reference-level relationships without compromising the controllability of text encoders or incurring costly re-alignment. Specifically, we introduce global multimodal guidance within self-attention to better align MLLM-derived semantic features with VAE tokens. Furthermore, we propose modality-reference embedding to differentiate tokens from MLLM features and VAE tokens and associate intra-subject reference image tokens. Extensive experiments validate that our method achieves state-of-the-art performance across various HOCVP tasks. Project Page: https://yiyangcai.github.io/homie-page.github.io/

cs.CV↗

arXiv2026

Conformal Network Link Prediction with False Discovery Rate Control under Unstructured Missingness

We propose a new method for predicting multiple missing links in partially observed networks while controlling the false discovery rate (FDR), a largely unresolved challenge in network analysis. The main difficulty lies in handling complex dependencies and unknown missing patterns. We introduce conformal link prediction, a distribution-free procedure grounded in the exchangeability structure of weighted graphon models. Our approach constructs conformal p-values via a novel multi-splitting strategy that restores exchangeability within local test sets, thereby ensuring valid row-wise FDR control, even under unknown missing mechanisms. To achieve FDR control across all missing links, we further develop a new aggregation scheme based on e-values, which accommodates arbitrary dependence across network predictions. Our method requires no assumptions on the missing rates, applies to weighted, unweighted, undirected, and bipartite networks, and enjoys finite-sample theoretical guarantees. Extensive simulations and real-world data study confirm the effectiveness and robustness of the proposed approach.

stat.ME↗

arXiv2026

DomainShuttle: Freeform Open Domain Subject-driven Text-to-video Generation

Open domain subject-driven text-to-video (S2V) generation has drawn significant interest in academia and industry. Open domain S2V mainly involves two scenarios: in-domain, which requires retaining the reference subject features as much as possible, and cross-domain, which preserves the intrinsic features of the subject while allowing subject-irrelevant properties to vary flexibly according to the text prompt. Existing methods primarily focus on maximizing subject fidelity in in-domain scenarios, which limits their editability and adaptability in cross-domain scenarios, such as novel styles, semantic combinations, or domain attributes. In this study, we propose that an ideal S2V method should flexibly shuttle between different domains, achieving strong performance in both in-domain and cross-domain scenarios. To this end, we propose DomainShuttle, which could achieve high fidelity and generative flexibility for open domain video personalization. Specifically, we introduce Domain-MoT, which decouples videos and reference features and introduces the domain-aware AdaLN for domain-specific modeling of reference images. We then introduce the Video-Reference DualRoPE scheme, which places reference image tokens and video tokens in separate RoPE spaces to enable precise subject-level spatial modeling, and Cross-Pair Consistent Loss, which aims to extract intrinsic subject features unaffected by irrelevant features. Extensive experiments demonstrate that DomainShuttle achieves significant performance improvements over existing methods, exhibiting high subject fidelity and generative flexibility across diverse open domain application scenarios.

cs.CV↗

arXiv2026

SEED: Targeted Data Selection by Weighted Independent Set

Data selection seeks to identify a compact yet informative subset from large-scale training corpora, balancing sample quality against collection diversity. We formulate this problem as a Weighted Independent Set (WIS) on a similarity graph, where nodes represent data samples weighted by influence, and edges connect semantically redundant pairs. This formulation naturally yields subsets that are simultaneously high-quality and diverse. However, two challenges arise in practice: naive node weights fail to distinguish informative signals from gradient noise, and edge construction under heterogeneous domain distributions produces structurally imbalanced graphs that bias selection toward sparse regions. To address these issues, we introduce two principled refinements from a unified graph perspective: (1) \textit{node value calibration} that restricts influence estimation to the bilateral salient subspace to ground node importance in task-relevant signals rather than surface-level statistics; (2) \textit{local scale normalization} that adapts edge thresholds to local neighborhood density, mitigating graph imbalance induced by cross-domain distribution shifts. Together, these components yield a robust and scalable data selection pipeline dubbed SEED. We further construct \texttt{Honeybee-Remake-SEED-200K}, a compact multimodal dataset curated by SEED. Extensive experiments show that SEED consistently outperforms state-of-the-art methods on instruction tuning, visual instruction tuning, and semantic segmentation across diverse model families.

cs.LG↗

arXiv2026

Multivariate Inference of Network Moments by Subsampling

Network moments--rescaled counts of motifs such as stars and triangles--are fundamental summaries of network structure, widely used in goodness-of-fit testing, model selection, and network comparison. While the univariate distribution of a single network moment can be approximated by subsampling, the consistency of subsampling for their {\it joint} distribution has remained unestablished. In this paper, we prove that node subsampling provides an asymptotically accurate approximation of the joint distribution of multiple network moments under a general sparse graphon model. The theoretical analysis requires a careful characterization of the dependence structure among network moments and the corresponding multivariate asymptotic convergence, going substantially beyond existing univariate results. Building on this foundation, we address a practically important open problem: two-sample testing between unmatchable networks with unequal edge densities. We propose a novel subsampling-based procedure that combines {\it sparsification} with a {\it sample-splitting} strategy. This yields the first subsampling-based inferential procedure valid for this setting, to our knowledge. We demonstrate the utility of multivariate subsampling inference through simulation studies and a real data application comparing coexpression networks of core and non-core genes in a study of parallel adaptation in Trinidadian guppies, where joint and conditional moment distributions reveal a structural difference that no marginal test can detect.

stat.ME↗

arXiv2026

Multiscale simulations guided advances for all-optical phase-change waveguides

Photonic computing using chalcogenide phase-change materials (PCMs) is under active development for energy-efficient artificial intelligence (AI) applications. A key requirement is to enable as many optically programmable levels per device as possible, while maintaining relatively low optical loss. In this work, we carry out multiscale simulations using density functional theory and finite-difference time-domain methods, proposing a "the shorter the better" strategy to optimize the performance of Sb2Te photonic waveguide devices. Our subsequent experimental characterizations of Sb2Te thin films and optical device measurements fully verify our theoretical predictions. In particular, we reveal the unconventional optical properties of metastable crystalline Sb2Te, and utilize these features for device design, yielding a simultaneous improvement in both the programming window and the optical loss. Overall, an optical programming precision exceeding 7-bit is achieved using a single waveguide cell, setting a new record for all-optical phase-change memory devices. Our work serves as a compelling example of computational material design, which demonstrates the predictive power of multiscale simulations in guiding the design of phase-change photonic devices for enhanced performance.

cond-mat.mtrl-sci↗

arXiv2026

Low-loss phase-change material based programmable mode converter for photonic computing

Phase-change materials (PCMs)-based integrated photonic memory offers a viable pathway for the development of neuromorphic computing chip. The sizable optical contrast in the telecom band between amorphous and crystalline phases of PCM, in particular, Ge2Sb2Te5 (GST), is used for multilevel programming. However, the high extinction coefficient k of crystalline GST leads to high optical loss, posing a serious challenge for scaling up the device array for practical use. In this work, we focus on the atomic understanding and application of the so-called low-loss PCM, Sb2Se3, through multiscale simulations. First, we elucidate the bonding origin of the wavelength dependent optical properties of amorphous and crystalline Sb2Se3 via ab initio calculations. Given the suppressed k in the telecom band, we design a programable mode converter (PMC) waveguide device that utilizes only the contrast in refractive index n between amorphous and crystalline Sb2Se3 to encode multiple optical levels per waveguide device. The finite-difference time-domain simulations show that a single PMC device can achieve 5-bit programming precision (32 levels) via direct laser writing, and the photonic tensor core formed by the PMC array could possibly be scaled to 128*128. Finally, a thorough comparison between low-loss PCM and conventional PCM is provided.

cond-mat.mtrl-sci↗

arXiv2026

The UK and Ireland Geophysical Array -- Concept and Design

Scientific exploration of the UK and Ireland's subsurface has made important contributions to scholarship and prosperity for people and the planet, including economic growth, sustainable use of natural resources, storage of greenhouse gases, and inspiring curiosity about the Earth beneath our feet. This article outlines a vision for an array of seismological instruments spanning the UK and Ireland, UKI Array, augmented by other types of geophysical sensors, to maximise the value offered by existing equipment pools. The mission is to research natural phenomena and structure in the deep and shallow Earth, to solve problems concerning hazards and resources, to connect scientists to schools and the broader public, and thus to inspire a new generation to learn about geophysics. The vision was created through a community driven process of engagement and participation. This paper describes the concept and design of the UKI-Array; a companion paper discusses related opportunities and potential applications.

physics.geo-ph↗

arXiv2026

Model-Free Inference for Characterizing Protein Mutations through a Coevolutionary Lens

Multiple sequence alignment (MSA) data play a crucial role in the study of protein mutations, with contact prediction being a notable application. Existing methods are often model-based or algorithmic and typically do not incorporate statistical inference to quantify the uncertainty of the prediction outcomes. To address this, we propose a novel framework that transforms the task of contact prediction into a statistical testing problem. Our approach is motivated by the partial correlation for continuous random variables. With one-hot encoding of MSA data, we are able to construct a partial correlation graph for multivariate categorical variables. In this framework, two connected nodes in the graph indicate that the corresponding positions on the protein form a contact. A new spectrum-based test statistic is introduced to test whether two positions are partially correlated. Moreover, the new framework enables the identification of amino acid combinations that contribute to the correlation within the identified contacts, an important but largely unexplored aspect of protein mutations. Numerical experiments demonstrate that our proposed method is valid in terms of controlling Type I errors and powerful in general. Real data applications on various protein families further validate the practical utility of our approach in coevolution and mutation analysis.

stat.ME↗

arXiv2026

Effects of thermal annealing and film thickness on the structural and optical properties of indium-tin-oxide thin films

Indium-tin oxide (ITO) is a crucial functional layer for the optoelectronic applications, such as non-volatile color display thin films based on the ITO/phase-change material (PCM)/ITO/reflective metal multilayer structures on a silicon substrate. In addition to non-volatile color tuning by PCMs, thermally induced crystallization may alter the optical properties of ITO layers as well. But the potential change in color of the ITO layers is not considered so far. In this work, we investigate the structural and optical properties of ITO thin films via X-ray diffraction, spectroscopic ellipsometry and ultraviolet-visible spectrophotometry measurements. After thermal annealing at 250 °C, the ITO thin films of 15-100 nm get crystallized with strong changes in refractive index n and extinction coefficient k in the visible light range. However, for the 5-nm ITO thin film, crystallization is only observed after thermal annealing at 350 °C and the change in color is limited upon phase transition. We provide a colormap of the ITO/platinum/silicon structure in terms of the annealing temperature (150-350 °C) and ITO film thickness (5-100 nm). Our work suggests that the intrinsic change in colors of ITO layers should also be considered for the PCM-based reconfigurable display application.

physics.optics↗

arXiv2025

A new entanglement measure based on the total concurrence

Quantum entanglement is a crucial resource in quantum information processing, advancing quantum technologies. The greater the uncertainty in subsystems' pure states, the stronger the quantum entanglement between them. From the dual form of $q$-concurrence ($q\geq 2$) we introduce the total concurrence. A bona fide measure of quantum entanglement is introduced, the $\mathcal{C}^{t}_q$-concurrence ($q \geq 2$), which is based on the total concurrence. Analytical lower bounds for the $\mathcal{C}^{t}_q$-concurrence are derived. In addition, an analytical expression is derived for the $\mathcal{C}^{t}_q$-concurrence in the cases of isotropic and Werner states. Furthermore, the monogamy relations that the $\mathcal{C}^{t}_q$-concurrence satisfies for qubit systems are examined. Additionally, based on the parameterized $α$-concurrence and its complementary dual, the $\mathcal{C}^{t}_α$-concurrence $(0\leqα\leq\frac{1}{2})$ is also proposed.

quant-ph↗