arXiv subjects

Wen Zhou

Publications and source records attributed to Wen Zhou.

At least 19 recordsLinked to original sources

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

Estimating the concurrence for quantum states via symmetric measurements

We derive improved lower bounds of concurrence induced by symmetric measurements, which retains experimental feasibility without state tomography. More importantly, we resolve a related inequality conjecture, which implies that numerous previous results based on symmetric measurements are strictly stronger than the one based on realignment. In addition, we also present a lower bound of genuine tripartite entanglement concurrence based on symmetric measurements.

quant-ph↗

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

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↗

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 $\alpha$-concurrence and its complementary dual, the $\mathcal{C}^{t}_\alpha$-concurrence $(0\leq\alpha\leq\frac{1}{2})$ is also proposed.

quant-ph↗

arXiv2025

Atomistic understanding of two-dimensional monatomic phase-change material for non-volatile optical applications

Elemental antimony (Sb) is a promising material for phase-change memory, neuromorphic computing and nanophotonic applications, because its compositional simplicity can prevent phase segregation upon extensive programming. Scaling down the film thickness is a necessary step to prolong the lifetime of amorphous Sb, but the optical properties of Sb are also significantly altered as the thickness is reduced to a few nanometers, adding complexity to device optimization. In this work, we aim to provide atomistic understanding of the thickness-dependent optical responses in Sb thin films. As thickness decreases, both the extinction coefficient and optical contrast reduce in the near-infrared spectrum, consistent with previous optical measurements. Such thickness dependence gives rise to a bottom thickness limit of 2 nm in photonic applications, as predicted by coarse-grained device simulations. Further bonding analysis reveals a fundamentally different behavior for amorphous and crystalline Sb upon downscaling, resulting in the reduction of optical contrast. Thin film experiments are also carried out to validate our predictions. The thickness-dependent optical trend is fully demonstrated by our ellipsometric spectroscopy experiments, and the bottom thickness limit of 2 nm is confirmed by structural characterization experiments. Finally, we show that the greatly improved amorphous-phase stability of the 2 nm Sb thin film enables robust and reversible optical switching in a silicon-based waveguide device.

cond-mat.mtrl-sci↗

arXiv2025

Quantum Separability Criteria Based on Symmetric Measurements

We propose experimentally feasible separability criteria for bipartite systems based on local symmetric measurements. Through detailed examples, we demonstrate that our criteria can detect entanglement more effectively compared to existing counterparts. Furthermore,we demonstrate the potential for our results to be generalized to general multipartite systems.

quant-ph↗

arXiv2025

Generalized product-form monogamy relations in multi-qubit systems

Monogamy of entanglement essentially characterizes the entanglement distributions among the subsystems. Generally it is given by summation-form monogamy inequalities. In this paper, we present the product-form monogamy inequalities satisfied by the $\nu$-th ($\nu\geq2$) power of the concurrence. We show that they are tighter than the existing ones by detailed example. We then establish tighter product-form monogamy inequalities based on the negativity. We show that they are valid even for high dimensional states to which the well-known CKW inequality is violated.

quant-ph↗

arXiv2025

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 {\deg}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 {\deg}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 {\deg}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

Quantum-imaginarity-based quantum speed limit

The quantum speed limit sets a fundamental restriction on the evolution time of quantum systems. We explore the relationship between quantum imaginarity and the quantum speed limit by utilizing measures such as relative entropy, trace distance, and geometric imaginarity. These speed limits define the fundamental constraints on the minimum time necessary for quantum systems to evolve under various dynamical processes. As applications the dephasing dynamics and dissipative dynamics are analyzed in detail. The quantum speed limit in stochastic-approximate transformations is also investigated. Our quantum speed limits provide lower bounds on how fast a physical system evolves to attain or lose certain imaginarity, with potential applications in efficient quantum computation designs, quantum control and quantum sensing.

quant-ph↗

arXiv2025

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↗

arXiv2025

Modeling and prediction of mutation fitness on protein functionality with structural information using high-dimensional Potts model

Quantifying the effects of amino acid mutations in proteins presents a significant challenge due to the vast combinations of residue sites and amino acid types, making experimental approaches costly and time-consuming. The Potts model has been used to address this challenge, with parameters capturing evolutionary dependency between residue sites within a protein family. However, existing methods often use the mean-field approximation to reduce computational demands, which lacks provable guarantees and overlooks critical structural information for assessing mutation effects. We propose a new framework for analyzing protein sequences using the Potts model with node-wise high-dimensional multinomial regression. Our method identifies key residue interactions and important amino acids, quantifying mutation effects through evolutionary energy derived from model parameters. It encourages sparsity in both site-wise and amino acid-wise dependencies through element-wise and group sparsity. We have established, for the first time to our knowledge, the $\ell_2$ convergence rate for estimated parameters in the high-dimensional Potts model using sparse group Lasso, matching the existing minimax lower bound for high-dimensional linear models with a sparse group structure, up to a factor depending only on the multinomial nature of the Potts model. This theoretical guarantee enables accurate quantification of estimated energy changes. Additionally, we incorporate structural data into our model by applying penalty weights across site pairs. Our method outperforms others in predicting mutation fitness, as demonstrated by comparisons with high-throughput mutagenesis experiments across 12 protein families.

stat.ME↗

arXiv2025

Amorphous phase-change memory alloy with no resistance drift

Spontaneous structural relaxation is intrinsic to glassy materials due to their metastable nature. For phase-change materials (PCMs), the resultant temporal change in electrical resistance seriously hamper in-memory computing (IMC) applications. Here, we report an ab-initio-calculation-informed design of amorphous PCM composed of robust "molecule-like" motifs with minimal Peierls distortion, depriving the amorphous alloy of structural ingredients that would gradually evolve upon aging to entail resistance drift. We demonstrate amorphous CrTe3 thin films that display practically no resistance drift at any working temperature from -200 to 165 degree C. We achieve multilevel programming of CrTe3 through both step-wise crystallization and step-wise amorphization using a hybrid opto-electronic device at various temperatures. Moreover, the application potential of CrTe3 in neuromorphic computing is testified by its incorporation in a vehicle with automatic path-tracking function. Our work opens a new avenue to achieving IMC-requisite properties via judicious design of the composition and atomic-level structure of disordered PCM alloys.

cond-mat.mtrl-sci↗