arXiv subjects

Jiayang Wang

Publications and source records attributed to Jiayang Wang.

11 recordsLinked to original sources

arXiv2026

Beyond Tables: Doc2DB-Bench for Relationally Faithful Document-to-Database Construction

Practical AI systems increasingly need to turn long, heterogeneous documents into queryable relational databases, not isolated spreadsheets. In domains such as finance, healthcare, education, transportation, and enterprise operations, downstream workflows rely on normalized schemas, entity identities, keys, cross-table relationships, and integrity constraints for analytics, compliance, auditing, and SQL-backed decision making. Existing Document-to-Table benchmarks are insufficient for this setting: flattening evidence into single tables can duplicate entities, obscure many-to-many relationships, create sparse records, and avoid testing whether extracted facts form a valid database instance. This creates an urgent need to evaluate document understanding as database construction rather than field extraction. We introduce Doc2DB-Bench, a benchmark for Document-to-Database construction, containing 203 long-document instances across 42 schemas and seven domain groups, with 117 entity tables, 132 relationship tables, 7,341 rows, and 41,935 cells. Built through a controllable DB-to-Doc synthesis pipeline and organized by a taxonomy of intra-table extraction and inter-table reasoning, the generated documents undergo authenticity verification, proving indistinguishable from real-world references. Doc2DB-Bench thus provides a testbed for reliable, auditable, and relationally faithful LLM-based data systems. The benchmark is publicly available at https://github.com/SetonLiang/Doc2DB-Bench.

cs.CL↗

arXiv2026

Lattice genome: representation and analysis of heterogeneous crystalline microstructures

Inspired by the concept of a generalized materials genome, we introduce the notions of lattice gene and lattice genome for crystalline materials. A lattice gene is a compact representation of the local crystalline structure obtained by encoding the Kikuchi diffraction patterns with a variational autoencoder. We show that this representation satisfies key criteria for a materials gene: compactness, experimental accessibility, existence of a distance metric reflecting structural similarity, and sufficient information content for reconstructing the original diffraction patterns. The lattice genome is the spatially resolved collection of lattice genes across a representative area mapped by electron backscatter diffraction (EBSD), which captures mesoscale heterogeneity that ultimately controls properties. We demonstrate three applications of the lattice genome: (i) latent component maps that visualize grain-scale and intra-grain heterogeneities, (ii) domain segmentation based on distance and angle metrics in the latent space, and (iii) kernel and domain latent vector spreads that quantify intragranular heterogeneity as high-dimensional analogs of kernel average misorientation and grain orientation spread. All three tools are validated on microstructures of additively manufactured and wrought Ni-base superalloys in as-built and recrystallized conditions.

cond-mat.mtrl-sci↗

arXiv2026

Matrix representations and distance metrics for unlabeled ranked phylogenetic networks

Phylogenetic networks are graphs inferred from molecular sequence data that represent ancestral histories shaped by reticulate processes such as recombination, hybridization, and horizontal gene transfer. We introduce a family of distance metrics for rooted, ranked, unlabeled phylogenetic networks, extending a previously developed distance for ranked trees. Our approach relies on a bijective triangular matrix representation of phylogenetic networks that captures the temporal order of internal events, speciations, and hybridizations. Our metrics, defined as standard matrix norms, allow efficient quantitative comparisons of network topologies, timed networks and networks with differing numbers of hybridizations. Our distance can be used for both isochronous networks where all tips are sampled at one time point, and heterochronous networks where tips are allowed to be sampled at different time points. We show that our metrics capture biologically meaningful differences among evolutionary histories in both simulations and empirical posterior distributions of viral phylogenetic networks. These tools fill a methodological gap, enabling principled comparisons of ranked, unlabeled phylogenetic networks, including ancestral recombination graphs.

stat.ME↗

arXiv2026

Thermodynamic assessment of the Ba-La-S and Ga-La-S systems

This paper presents the first thermodynamic assessment of binary and pseudo-binary phase diagrams in the Ba--La--S and Ga--La--S systems by means of the CALPHAD method. Experimental phase diagram equilibrium data and thermodynamic properties available from the literature were critically reviewed and assessed using thermodynamic models for the Gibbs energies of individual phases. The associated solution model was used to describe the short-range ordering behavior of the liquid phases. To supplement the limited experimental data reported in the literature, ab initio molecular dynamics calculations were employed to derive mixing enthalpies of the liquid phases in the binary subsystems. The resulting phase diagrams and calculated thermodynamic properties show good agreement with available literature within the investigated compositional ranges of binary and pseudo-binary systems.

cond-mat.mtrl-sci↗

arXiv2026

Eutectic and peritectic equilibria in coherent binary alloys

This work extends the Cahn--Larché thermodynamic framework to binary alloys in which two coherent solid phases coexist with an incoherent liquid and investigates how coherency strain energy modifies classical eutectic and peritectic equilibria. We derive equilibrium conditions for three-phase coexistence that include an elastic energy term dependent on the molar fractions of the solid phases and apply them to model binary eutectic and peritectic systems. We find that coherency stress transforms the eutectic point into a finite three-phase equilibrium field spanning a continuous range of compositions and temperatures. In contrast, coherency stress in peritectic systems progressively destabilizes the two-solid equilibrium without generating a stable three-phase field and can suppress the peritectic reaction entirely. This asymmetry is governed by the geometric relationship between the stress-free compositions of the phases: when the liquid composition lies between those of the two solids (eutectic configuration), the liquid serves as a thermodynamic buffer against the coherency penalty on the solid--solid pair; when it lies outside (peritectic configuration), no such mechanism is available. These results demonstrate that coherency stress can fundamentally alter three-phase equilibria involving a liquid and suggest that such effects may be significant in systems with large coherent misfits.

cond-mat.mtrl-sci↗

arXiv2026

Hearing the forest for the trees: machine learning and topological acoustics for remote sensing with seismic noise

Monitoring remote forests is a global challenge central to climate mitigation and biodiversity conservation, yet satellite observations are frequently limited by weather, dense canopies, and solar dependency. Here we show that passive seismic sensing offers a persistent, all-weather alternative for autonomous ecosystem monitoring by capturing characteristic learnable signatures of trees within the ambient wavefield. Using seismic data from Alaska, we demonstrate that cross-correlations between stations provide a physical basis for forest detection by approximating the empirical Green's function of the medium. Supervised machine learning models applied to these data achieve a classification accuracy of 86%, identifying key discriminating frequencies (35 to 60 Hz) consistent with known forest-wave interactions. A topological acoustics analysis of the geometric phase change independently confirms the physical origin of these data-driven classifications. Together, these results provide the first demonstration that subtle forest-wave interactions manifest in ambient seismic noise and can be harnessed as a scalable tool for continuous vegetation monitoring, offering a robust solution for tracking environmental change challenging regions.

physics.geo-ph↗

arXiv2025

Well-Designed k-Space Coverage is Important for Good MRI Denoising

Object: Modern computational MRI denoising approaches are often designed assuming fixed k-space coverage. This contrasts with earlier acquisition-design literature that leveraged k-space coverage modifications (e.g., reducing spatial resolution) to improve SNR. This work investigates whether the performance of modern computational denoising methods can be further enhanced by k-space coverage modifications. Materials and Methods: Using realistic simulations of noisy data, k-space coverage and averaging patterns were optimized for two advanced image denoising/reconstruction approaches: parallel imaging with total variation regularization and a U-Net neural network. For reference, comparisons against classical linear filtering/apodization methods were also performed. Performance was quantified using normalized root-mean-squared error (NRMSE) and structural similarity (SSIM) metrics. Results: Advanced computational denoising methods can be substantially enhanced, both quantitatively and qualitatively, by reducing the spatial resolution of the acquisition to improve SNR. Indeed, even simple linear filtering/apodization with optimized k-space coverage can rival advanced methods using naive higher-resolution coverage. Discussion: Classical acquisition design principles that allow spatial resolution to be traded for SNR enhancement are still very relevant for modern computational denoising techniques. However, the optimization of k-space coverage and denoising/reconstruction methods can also be somewhat confounded because the NRMSE and SSIM metrics have low sensitivity to spatial resolution.

eess.SP↗

arXiv2024

Quasi-van der Waals Epitaxial Growth of γ'-GaSe Thin Films on GaAs(111)B Substrates

GaSe is an important member of the post-transition metal chalcogenide family and is an emerging two-dimensional (2D) semiconductor material. Because it is a van der Waals material, it can be fabricated into atomic-scale ultrathin films, making it suitable for the preparation of compact, heterostructure devices. In addition, GaSe possesses unusual optical and electronic properties, such as a shift from an indirect-bandgap single-layer film to a direct-bandgap bulk material, rare intrinsic p-type conduction, and nonlinear optical behaviors. These properties make GaSe an appealing candidate for the fabrication of field-effect transistors, photodetectors, and photovoltaics. However, the wafer-scale production of pure GaSe single crystal thin films remains challenging. This study develops an approach for the direct growth of nanometer-thick GaSe films on GaAs substrates using molecular beam epitaxy. It yields smooth thin GaSe films with the rare γ'-polymorph. We analyze the formation mechanism of γ'-GaSe using density functional theory and speculate that it is stabilized by Ga vacancies since the formation enthalpy of γ'-GaSe tends to become lower than that of other polymorphs when the Ga vacancy concentration increases. Finally, we investigate the growth conditions of GaSe, providing valuable insights for exploring 2D/3D quasi-van der Waals epitaxial growth.

cond-mat.mtrl-sci↗

arXiv2024

Treatment and Aging Studies of GaAs(111)B Substrates for van der Waals Chalcogenide Film Growth

GaAs(111)B is a semiconductor substrate widely used in research and commercial fields due to its low cost, mature synthesis technology, and excellent properties for manufacturing electronic devices. It is not only used to grow three-dimensional (3D) strongly-bonded materials, but has also been used as a substrate for layered, van der Waals (vdW)-bonded chalcogenide film growth. However, GaAs(111)B wafers cannot be directly used for growing epitaxial vdW chalcogenide films for two reasons: (1) the GaAs surface has a substantial number of dangling bonds that need to be passivated for vdW layers growth; (2) the substrate surface is covered with a thin epi-ready oxide layer which must be removed before film growth. In this paper, we optimize the method for deoxidizing GaAs(111)B substrates under a Se overpressure and successfully create a smooth, deoxidized, and passivated substrate for subsequent growth of vdW chalcogenide materials. We demonstrate the benefits of this method for the growth of vdW chalcogenide thin films using GaSe as a representative of vdW chalcogenides. In addition, we find that severely aged substrates have difficulty maintaining a smooth surface during the deoxidation and passivation process and cause GaSe crystals to nucleate in random shapes and orientations. We describe a method using water droplet testing to determine the age of the substrate. Finally, X-ray photoelectron spectroscopy (XPS) characterization reveals that the natural aging of GaAs(111)B in the air results in an increase in surface oxides, Ga2O3 and As2O3, while exposure to ultraviolet (UV)-ozone not only enhances the contents of these two oxides but also generates a new oxide, As2O5. Our research contributes to expanding the compatibility of GaAs(111)B with diverse growth materials and the production of high-quality heterostructure devices.

cond-mat.mtrl-sci↗

arXiv2021

FamDroid: Learning-Based Android Malware Family Classification Using Static Analysis

Android is currently the most extensively used smartphone platform in the world. Due to its popularity and open source nature, Android malware has been rapidly growing in recent years, and bringing great risks to users' privacy. The malware applications in a malware family may have common features and similar behaviors, which are beneficial for malware detection and inspection. Thus, classifying Android malware into their corresponding families is an important task in malware analysis. At present, the main problem of existing research works on Android malware family classification lies in that the extracted features are inadequate to represent the common behavior characteristics of the malware in malicious families, and leveraging a single classifier or a static ensemble classifier is restricted to further improve the accuracy of classification. In this paper, we propose FamDroid, a learning-based Android malware family classification scheme using static analysis technology. In FamDroid, the explicit features including permissions, hardware components, app components, intent filters are extracted from the apk files of a malware application. Besides, a hidden feature generated from the extracted APIs is used to represents the API call relationship in the application. Then, we design an adaptive weighted ensemble classifier, which considers the adaptability of the sample to each base classifier, to carry out accurate malware family classification. We conducted experiments on the Drebin dataset which contains 5560 Android malicious applications. The superiority of FamDroid is demonstrated through comparing it with 5 traditional machine learning models and 4 state-of-the-art reference schemes. FamDroid can correctly classify 98.92% of malware samples into their families and achieve 99.12% F1-Score.

cs.CR↗

arXiv2005

A Study for the Feature Core of Dynamic Reduct

To the reduct problems of decision system, the paper proposes the notion of dynamic core according to the dynamic reduct model. It describes various formal definitions of dynamic core, and discusses some properties about dynamic core. All of these show that dynamic core possesses the essential characters of the feature core.

cs.AI↗