arXiv subjects

Weihan Zhang

Publications and source records attributed to Weihan Zhang.

14 recordsLinked to original sources

arXiv2026

Cubic-Root Gaussian Approximation under Unrestricted Covariance

For Gaussian approximation over high-dimensional rectangles under unrestricted covariance, Chernozhukov et al. (2023b) conjectured that the $n^{-1/4}$ rate, up to logarithmic factors, is near-optimal. We show that, under the coordinatewise subexponential condition with scale $B_n$ and the marginal variance lower bound condition with constant $b$ in Chernozhukov et al. (2023b), the approximation error in dimension $d$ is bounded by \begin{align*} C_b\min\left\{ 1,\, \left(\frac{B_n^2}{n}\right)^{1/3}\{\log(2dn)\}^{7/3} + \frac{B_n}{\sqrt n}\{\log(2dn)\}^{5/2} \right\}. \end{align*} In particular, for bounded $B_n$ and polynomial dimension, the new bound is $n^{-1/3}$ and therefore falsifies the polynomial-dimensional $n^{-1/4}$ near-optimality conjecture. The proof uses a two-stage interpolation and a rank-free matrix-weighted Gaussian surface bound, which may be of independent interest. The initial proof attempt was generated by ChatGPT 5.6 Pro (OpenAI) and subsequently corrected and rewritten by the authors. The machine-checked Lean formalization of the proof can be found at the GitHub repository (https://github.com/WeihanZhang2001/cubic-root-gaussian-approximation-under-unrestricted-covariance).

math.ST↗

arXiv2026

From Scalars to Time Series: Rethinking Implicit Neural Representations for Time-Varying Volumetric Data

Implicit neural representations (INRs) for time-varying volumetric data are typically trained using dense sampling over spatiotemporal coordinates, where each observation corresponds to a single point in space and time. This coordinate-wise formulation requires extensive sampling during optimization, leading to high computational cost and inefficient use of temporal structure. In this work, we revisit this design choice and show that dense spatiotemporal sampling is not necessary for learning time-varying fields. Instead, we represent the data as a collection of spatially indexed time series and train INRs using sequence-level supervision over each spatial location, rather than coordinate-wise scalar samples. This reformulation eliminates the need for dense spatiotemporal sampling and instead learns each spatial location from its full temporal evolution in a structured manner. We demonstrate that this representation is compatible with a range of existing INR architectures and consistently improves reconstruction quality, while significantly reducing training cost. Furthermore, we show that this formulation can be combined with mixture-of-experts architectures, and that our MoE instantiation further improves reconstruction quality compared to both the base reformulation and existing MoE-based INR methods, providing a stronger capacity allocation under heterogeneous temporal dynamics.

cs.AI↗

arXiv2026

Open-source segmentation and biometry dataset using spectrally-multiplexed whole-eye optical coherence tomography

Whole-eye optical coherence tomography (WEOCT) has emerged as a transformative imaging modality capable of simultaneously capturing the anterior and posterior segments of the human eye. WEOCT enables comprehensive ocular biometry, which is critical for a wide range of clinical and research applications-from intraocular lens power calculation, myopia progression monitoring, and refractive surgery planning to the precise measurement of the visual and optical axes and the generation of personalized eye models for eye tracking in virtual, augmented and mixed reality(VR/AR/MR). However, existing WEOCT systems often face trade-offs between signal-to-noise ratio, imaging speed, and the ability to capture dynamic processes without motion artifacts. To address these limitations, we present a novel spectrally-multiplexed WEOCT system that utilizes two synchronized 200 kHz swept sources at 1310 nm and 1060 nm. Coupled with an automated end-to-end processing pipeline involving deep learning-based surface segmentation, 3D distortion correction, surface fitting and ray-tracing refraction correction, our system enables anatomically accurate 3D reconstruction of the segmented ocular layers. Through a 300+ participant user study and comprehensive phantom studies, we demonstrate that our system can provide simultaneous accurate measurements of cornea topography and 3D pupil center. While labeled retinal OCT data is abundantly available in open-source repositories, labeled B-scan or volumetric anterior segment data remains significantly limited. Consequently, research groups working in related domains must often acquire their own data using custom imaging systems. To help bridge this gap, we are releasing as open-source a comprehensive dataset comprising 6,621 processed volumes from 276 unique participants with corresponding segmentation and calibrated 3D anterior point clouds.

physics.optics↗

arXiv2026

Efficient Community Search on Attributed Public-Private Graphs

Public-private graph, where a public network is visible to everyone and every user is also associated with its own small private graph accessed by itself only, widely exists in real-world applications of social networks and financial networks. Most existing work on community search, finding a query-dependent community containing a given query, only studies on a public graph, neglecting the privacy issues in public-private networks. However, considering both the public and private attributes of users enables community search to be more accurate, comprehensive, and personalized to discover hidden patterns. In this paper, we study a novel problem of attributed community search in public-private graphs (ACS-PP), aiming to find a connected k-core community that shares the most keywords with the query node. This problem uncovers structurally cohesive communities, such as interest-based user groups or core teams in collaborative networks. To optimize search efficiency, we propose an integrated scheme of constructing a public global graph index and a private personalized graph index. For the private index, we developed a compact structure of the PP-FP-tree index. The PP-FP-tree is constructed based on the public and private neighbors of the query node in the public-private graph, serving as an efficient index to mine frequent node sets that share the most common attributes with the query node. Extensive experiments on real public-private graph datasets validate both the efficiency and quality of our proposed PP-FP search algorithm against existing competitors. The case study on public-private collaboration networks provides insights into the discovery of public-private communities.

cs.DB↗

arXiv2026

Estimation and Inference for Causal Explainability

Understanding how much each variable contributes to an outcome is a central question across disciplines. A causal view of explainability is favorable for its ability in uncovering underlying mechanisms and generalizing to new contexts. Based on a family of causal explainability quantities, we develop methods for their estimation and inference. In particular, we construct a one-step correction estimator using semi-parametric efficiency theory, which explicitly leverages the independence structure of variables to reduce the asymptotic variance. For a null hypothesis on the boundary, i.e., zero explainability, we show its equivalence to Fisher's sharp null, which motivates a randomization-based inference procedure. Finally, we illustrate the empirical efficacy of our approach through simulations as well as an immigration experiment dataset, where we investigate how features and their interactions shape public opinion toward admitting immigrants.

stat.ME↗

arXiv2025

Kinetically accessible 1D magnetic chains of transition-metal chalcogenides and halides on van der Waals surfaces

One-dimensional (1D) chains offer unique opportunities for nanoelectronics and spintronics, yet their experimental realization remains challenging because 1D motifs are often thermodynamically disfavored relative to higher-dimensional phases. Here we present a high-throughput first-principles exploration of 1D single-atomic transition-metal chalcogenide and halide chains, screening 6,832 candidates constructed from binary combinations of 28 metals and 8 non-metals. To assess kinetic accessibility, we compare the formation energetics of 1D chains with competing two-dimensional polymorphs at the nucleation stage across relevant chemical-potential windows, using nucleation-stage thermodynamic selectivity as a proxy. This workflow identifies 183 kinetically accessible 1D chains. Interpretable machine-learning analysis reveals two simple stability descriptors as key drivers of 1D stabilization. The accessible chains exhibit diverse magnetic configurations with different magnetic characters. We further uncover their pronounced magnetoelastic couplings, exemplified by CrTe with giant magnetostriction reaching 5.93%. Finally, we show that selected metallic ferromagnetic chains retain robust edge magnetism on superconducting substrates, laying the groundwork for proximity-induced topological superconductivity and Majorana zero modes.

cond-mat.mtrl-sci↗

arXiv2025

Electric-Field-Tunable Luttinger compensated antiferromagnetism in double CrCl2 chains

Luttinger compensated antiferromagnets (LcAFMs), combining spin polarization with vanishing net magnetization, offering distinct advantages for next-generation spintronic applications. Using first-principles calculations, we demonstrate that conventional antiferromagnetic CrCl2 double chains can be transformed into one-dimensional LcAFMs under an external electric field, exhibiting pronounced isotropic spin splitting. The magnitude of the splitting, as well as the band gap, can be effectively tuned by both in-plane and out-of-plane fields, thereby providing greater controllability than in two-dimensional counterparts. To further enhance the tunability, we design a nearly lattice-matched CrCl2/MoTe2 heterostructure and uncover that interfacial charge transfer generates a built-in electric field, inducing spin splitting comparable to that driven by external fields. These results establish interfacial engineering as a highly efficient route to realize and manipulate LcAFM states in low-dimensional magnets, expanding the design principles for spintronic functionalities at the nanoscale.

cond-mat.mtrl-sci↗

arXiv2025

Understanding the atomically precise evolution of the miscibility of newly prepared face-centered cubic W-Cu nanoalloys and its asymmetry

According to classical Miedema theory, reducing crystals to the order of nanometer sizes might greatly modulate the mixing enthalpy of elements, thus enabling the invention of a lot of new bulk-immiscible alloys. Although numerous alloys with higher mixing enthalpies remain unexplored, this strategy is approaching its limit, as reflected by the critical diameter of recent alloys of 1.8 nm, which corresponds to ~150 atoms and hardly provides a crystalline order. Future development requires not only even smaller atomic-scale control but also a new surface energy-saving mechanism. Here, we report the formation of W-Cu nanoalloys with a very large miscibility gap in the bulk via the use of an atomically size-selected cluster beam source as an example. The face-centered cubic (FCC) structure was demonstrated through electron diffraction, which indicated a lattice constant of 3.88Å for W0.85Cu0.15 nanoalloys (~2280 atoms). In this comprehensive study that covers a large parameter space of W/Cu compositions and numbers of atoms, an asymmetric miscibility nanophase diagram in which W-rich compositions favor mixing and the critical size is approximately 6000 atoms, which far exceeds the approximately tens of atoms predicted via classical theory, was obtained for the first time. Density functional theory (DFT) calculations revealed a mutual strain-induced mechanism that simultaneously lowers the surface energies while reducing the size to the atomic scale. This approach paves the way for the development of new high-performance nonequilibrium phase alloys.

cond-mat.mtrl-sci↗

arXiv2025

Automatic Semantic Alignment of Flow Pattern Representations for Exploration with Large Language Models

Explorative flow visualization allows domain experts to analyze complex flow structures by interactively investigating flow patterns. However, traditional visual interfaces often rely on specialized graphical representations and interactions, which require additional effort to learn and use. Natural language interaction offers a more intuitive alternative, but teaching machines to recognize diverse scientific concepts and extract corresponding structures from flow data poses a significant challenge. In this paper, we introduce an automated framework that aligns flow pattern representations with the semantic space of large language models (LLMs), eliminating the need for manual labeling. Our approach encodes streamline segments using a denoising autoencoder and maps the generated flow pattern representations to LLM embeddings via a projector layer. This alignment empowers semantic matching between textual embeddings and flow representations through an attention mechanism, enabling the extraction of corresponding flow patterns based on textual descriptions. To enhance accessibility, we develop an interactive interface that allows users to query and visualize flow structures using natural language. Through case studies, we demonstrate the effectiveness of our framework in enabling intuitive and intelligent flow exploration.

cs.HC↗

arXiv2025

Tunable altermagnetism via inter-chain engineering in parallelassembled atomic chains

Altermagnetism has recently drawn considerable attention in three- and twodimensional materials. Here, we extend this concept to quasi-one-dimensional (Q1D) monolayers assembled from single-atomic magnetic chains. Through systematically examining nine types of structures, two stacking orders, and intra-/inter-chain magnetic couplings, we identify four out of thirty promising structural prototypes for hosting altermagnetism, which yields 192 potential monolayer materials. We further confirm eight thermodynamically stable Q1D monolayers via high-throughput calculations. Using symmetry analysis and first-principles calculations, we find that the existence of altermagnetism is determined by the type of inter-chain magnetic coupling and predict three intrinsic altermagnets,$CrBr_3$,$VBr_3$,$MnBr_3$,due to their ferromagnetic inter-chain couplings and five extrinsic ones,$CrF_3$,$CrCl_3$,$CrI_3$,$FeCl_3$and $CoTe_3$, ascribed to their neglectable or antiferromagnetic inter-chain couplings. Moreover, the inter-chain magnetic coupling here is highly tunable by manipulating the inter-chain spacing, leading to experimentally feasible transitions between altermagnetic and nodal-line semiconducting states. In addition, applying external electric fields can further modulate the spin splitting. Our findings establish a highly tunable family of Q1D altermagnets, offering fundamental insights into the intricate relationship between geometry, electronic structure, and magnetism.These discoveries hold significant promises for experimental realization and future spintronic applications.

cond-mat.mtrl-sci↗

arXiv2025

High-Throughput Discovery of Kagome Materials in Transition Metal Oxide Monolayers

Kagome materials are known for hosting exotic quantum states, including quantum spin liquids, charge density waves, and unconventional superconductivity. The search for kagome monolayers is driven by their ability to exhibit neat and well-defined kagome bands near the Fermi level, which are more easily realized in the absence of interlayer interactions. However, this absence also destabilizes the monolayer forms of many bulk kagome materials, posing significant challenges to their discovery. In this work, we propose a strategy to address this challenge by utilizing oxygen vacancies in transition metal oxides within a "1+3" design framework. Through high-throughput computational screening of 349 candidate materials, we identified 12 thermodynamically stable kagome monolayers with diverse electronic and magnetic properties. These materials were classified into three categories based on their lattice geometry, symmetry, band gaps, and magnetic configurations. Detailed analysis of three representative monolayers revealed kagome band features near their Fermi levels, with orbital contributions varying between oxygen 2p and transition metal d states. This study demonstrates the feasibility of the "1+3" strategy, offering a promising approach to uncovering low-dimensional kagome materials and advancing the exploration of their quantum phenomena.

cond-mat.mes-hall↗

arXiv2024

Versatile Ordering Network: An Attention-based Neural Network for Ordering Across Scales and Quality Metrics

Ordering has been extensively studied in many visualization applications, such as axis and matrix reordering, for the simple reason that the order will greatly impact the perceived pattern of data. Many quality metrics concerning data pattern, perception, and aesthetics are proposed, and respective optimization algorithms are developed. However, the optimization problems related to ordering are often difficult to solve (e.g., TSP is NP-complete), and developing specialized optimization algorithms is costly. In this paper, we propose Versatile Ordering Network (VON), which automatically learns the strategy to order given a quality metric. VON uses the quality metric to evaluate its solutions, and leverages reinforcement learning with a greedy rollout baseline to improve itself. This keeps the metric transparent and allows VON to optimize over different metrics. Additionally, VON uses the attention mechanism to collect information across scales and reposition the data points with respect to the current context. This allows VONs to deal with data points following different distributions. We examine the effectiveness of VON under different usage scenarios and metrics. The results demonstrate that VON can produce comparable results to specialized solvers. The code is available at https://github.com/sysuvis/VON.

cs.LG↗

arXiv2023

Intralayer Negative Poisson's Ratio in Two-Dimensional Black Arsenic by Strain Engineering

Negative Poisson's ratio as the anomalous characteristic generally exists in artificial architectures, such as re-entrant and honeycomb structures. The structures with negative Poisson's ratio have attracted intensive attention due to their unique auxetic effect and many promising applications in shear resistant and energy absorption fields. However, experimental observation of negative Poisson's ratio in natural materials barely happened, although various two-dimensional layered materials are predicted in theory. Herein, we report the anisotropic Raman response and the intrinsic intralayer negative Poisson's ratio of two-dimensional natural black arsenic (b-As) via strain engineering strategy. The results were evident by the detailed Raman spectrum of b-As under uniaxial strain together with density functional theory calculations. It is found that b-As was softer along the armchair than zigzag direction. The anisotropic mechanical features and van der Waals interactions play essential roles in strain-dependent Raman shifts and negative Poisson's ratio in the natural b-As along zigzag direction. This work may shed a light on the mechanical properties and potential applications of two-dimensional puckered materials.

cond-mat.mtrl-sci↗

arXiv2021

Classification of COVID-19 X-ray Images Using a Combination of Deep and Handcrafted Features

Coronavirus Disease 2019 (COVID-19) demonstrated the need for accurate and fast diagnosis methods for emergent viral diseases. Soon after the emergence of COVID-19, medical practitioners used X-ray and computed tomography (CT) images of patients' lungs to detect COVID-19. Machine learning methods are capable of improving the identification accuracy of COVID-19 in X-ray and CT images, delivering near real-time results, while alleviating the burden on medical practitioners. In this work, we demonstrate the efficacy of a support vector machine (SVM) classifier, trained with a combination of deep convolutional and handcrafted features extracted from X-ray chest scans. We use this combination of features to discriminate between healthy, common pneumonia, and COVID-19 patients. The performance of the combined feature approach is compared with a standard convolutional neural network (CNN) and the SVM trained with handcrafted features. We find that combining the features in our novel framework improves the performance of the classification task compared to the independent application of convolutional and handcrafted features. Specifically, we achieve an accuracy of 0.988 in the classification task with our combined approach compared to 0.963 and 0.983 accuracy for the handcrafted features with SVM and CNN respectively.

eess.IV↗