arXiv subjects

Zhenyang Wang

Publications and source records attributed to Zhenyang Wang.

7 recordsLinked to original sources

arXiv2026

DataVista: Diagnosing Multimodal LLMs on Data Video Understanding

Data video is a media form that integrates data visualization with video narrative, widely adopted in news reporting and business analysis. Compared with general video understanding, data video understanding places greater emphasis on accurately reading data from animated charts, integrating evidence across charts and time, and understanding how narrative organization and visual design communicate information. Yet existing benchmarks target either general videos or static charts, and data video understanding has not been systematically evaluated. We present DataVista, the first benchmark for data video understanding, containing 961 real-world data videos and 6,775 evaluation questions organized under a three-level progressive capability framework (data perception, temporal reasoning, narrative understanding) with 10 fine-grained question types across five topic domains. Systematic evaluation of 19 mainstream MLLMs shows that the best-performing model, Gemini-3.1-Pro, achieves 70.0% overall accuracy, still far below human expert performance, with models performing worst on Causal Reasoning and Narrative Structure. Increasing frame counts and adding subtitles mainly benefit data perception and temporal reasoning, with limited gains in narrative understanding. Further analysis of model responses identifies typical failure modes in chart reading, evidence judgment, and instruction understanding. The benchmark is available at https://github.com/HKUSTDial/DataVista.

cs.CV↗

arXiv2026

DataMagic: Authoring Data Videos through Declarative Multi-Agent Orchestration

Data videos communicate data insights through dynamic charts, voice narration, and synchronized animations, and have become a widely adopted form of data storytelling. However, producing them requires expertise in data analysis, narrative design, and video editing. Static visualization tools lack narrative and animation capabilities; authoring tools rely on pre-prepared charts rather than raw data; and pixel-level models generate videos end-to-end but cannot guarantee data accuracy or provenance. End-to-end automatic generation faces two core challenges: how to uniformly represent charts, narration, and animations together with their temporal relationships, and how to efficiently search a vast design space for narrative-coherent compositions. We present DataMagic, which authors data videos from raw tabular data through declarative multi-agent orchestration. First, the declarative specification DVSpec unifies charts, narration, and animations with data-bound references and declarative synchronization, ensuring data provenance and automatic audio-visual alignment. Second, a "Generate-then-Orchestrate" multi-agent strategy generates candidate scenes in parallel and then optimizes narrative coherence through global orchestration. DVSpec provides a shared state for three complementary interaction modes, bridging full automation with fine-grained human control. Evaluations on 109 real-world samples show that even the most advanced LLM (e.g., GPT-5) achieves only 2.13/5 with execution success rates between 48.62% and 86.24%; DataMagic improves quality to 3.89 (+83%) with success rates above 95%, with the most significant gains in animation and narrative dimensions. A user study shows that, compared to a conversational LLM workflow, DataMagic improves creation efficiency (79.7% reduction in task time) and reduces perceived cognitive load. Project page: https://github.com/HKUSTDial/DataMagic.

cs.HC↗

arXiv2026

DataMagic: Transforming Tabular Data into Data Insight Video

Data videos integrate dynamic charts, voice narration, and synchronized animations to communicate data insights as temporal narratives, making them an effective medium for improving data consumption efficiency in the data management lifecycle. However, producing high-quality data videos requires expertise spanning data analysis, narrative design, and video production. Existing approaches fall short: static visualization tools (e.g., BI dashboards) lack narrative logic and animation; authoring tools require users to pre-prepare visualizations rather than working from raw data; pixel-level video generation models cannot guarantee data fidelity or provenance. We demonstrate DataMagic, an end-to-end interactive system that transforms raw tabular data and natural language queries into narrative data-insight videos. To ensure data fidelity, DataMagic introduces the declarative specification DVSpec, which binds visual and animation elements to underlying data fields through data-driven semantic references. To address the combinatorial explosion of the design space, DataMagic adopts a Generate-then-Orchestrate multi-agent architecture that generates candidate scenes in parallel and then optimizes narrative coherence through global orchestration. Leveraging DVSpec's decoupling of logic and rendering, the system further supports three interaction modes and structured provenance-based data Q&A, transforming one-way videos into explorable interactive data interfaces. Evaluation on 109 real-world samples validates the effectiveness of the DataMagic. Homepage: https://datamagic-home.github.io/

cs.HC↗

arXiv2025

Catalogue of chiral phonon materials

Chiral phonons, circularly polarized lattice vibrations carrying intrinsic angular momentum, offer unprecedented opportunities for controlling heat flow, manipulating quantum states through spin-phonon coupling, and realizing exotic transport phenomena. Despite their fundamental importance, a universal framework for identifying and classifying these elusive excitations has remained out of reach. Here, we address this challenge by establishing a comprehensive symmetry-based theory that systematically classifies the helicity and the velocity-angular momentum tensor underlying phonon magnetization in thermal transport across all 230 crystallographic space groups. Our approach, grounded in fundamental representations of phononic angular momentum, reveals three distinct classes of crystals: achiral crystals with vanishing angular momentum, chiral crystals with s-wave helicity, and achiral crystals exhibiting higher-order helicity patterns beyond the s-wave. By performing high-throughput computations and symmetry analysis of the dynamical matrices for 11614 crystalline compounds, we identified 2738 materials exhibiting chiral phonon modes and shortlisted the 170 most promising candidates for future experimental investigation. These results are compiled into an open-access Chiral Phonon Materials Database website, enabling rapid screening for materials with desired chiral phonon properties. Our theoretical framework transcends phonons--it provides a universal paradigm for classifying chiral excitations in crystalline lattices, from magnons to electronic quasiparticles.

cond-mat.mtrl-sci↗

arXiv2024

3D Printable Plasmonic Titanium Nitride Nanoparticles Enhanced Thermoplastic Polyurethane Composite for Improved Photothermal De-Icing and Infrared Labeling

Plasmonic nanomaterials offer a direct and effective approach to harnessing solar energy. Specifically, plasmonic semiconductors enable a highly efficient light-to-heat conversion process, outperforming noble metals in stability, cost-effectiveness, and accessibility. In this study, a composite 3D printing filament (T-TPU), composed of titanium nitride (TiN) and thermoplastic polyurethane (TPU), was synthesized using a combined extrusion process involving a twin-screw extruder and a single-screw extruder. The resulting T-TPU filament could be used with fused deposition modeling (FDM) 3D printing to produce custom-designed patterns. Notably, these printed patterns exhibited superior photothermal performance, with potential applications in photothermal de-icing and infrared labeling. Additionally, the wavelength-dependent plasmonic and photothermal responses of the printed patterns were experimentally investigated and supported by finite elemental method (FEM) simulations, revealing a temperature increase of approximately 2.5 under IR LED light when compared to commercial black thermoplastic polyurethane (C-TPU), that was more obvious than a difference less than 1 under UV or visible LED light sources. Finally, the mechanical properties of T-TPU, altered by the inclusion of TiN nanoparticles, were assessed, showing a slight enhancement in modulus and friction coefficient relative to neat TPU (N-TPU). Molecular dynamics (MD) simulations indicated that the TiN nanoparticles promoted strong interactions between polymer chains and TiN particles, enhancing the modulus of elasticity and contributing to the improved mechanical properties of T-TPU. These findings suggest improved abrasion resistance, demonstrating the stability and durability of the composite material.

physics.optics↗

arXiv2024

Anisotropic Thermal Conductivity of 3D Printed Graphene Enhanced Thermoplastic Polyurethanes Structure toward Photothermal Conversion

Solar photothermal conversion is one of the most straightforward methods to utilize solar energy. In this manuscript, a novel double-layer structure constructed of graphene enhanced thermoplastic polyurethanes (G-TPU) and neat thermoplastic polyurethanes (N-TPU) was developed via fused deposition modelling (FDM) 3D printing process. The developed G-TPU-N-TPU double-layer structure exhibited anisotropic thermal conductivity that simultaneously satisfied high in-plane (IP) thermal conductivity and low through-plane (TP) thermal conductivity. The top G-TPU layer essentially offered a high IP thermal conductivity of 4.54 W(mK) that lead to overall structure anisotropic thermal conductivity ratio (TCIP-TCTP) of 8. And the low thermal conductivity in the TP direction led to the heat retention effects for thermal storage. Nonetheless, the exceptional photothermal conversion effect of graphene flakes guaranteed the superior photothermal performance that was promising in the photothermal de-icing and infrared labels applications. Finally, the graphene flake enhancement in the mechanical properties of the G-TPU-N-TPU double layer structure was also evaluated that contributed to excellent impact resistance with a puncture energy reaching 12.86 J, and extraordinary wear resistance with a small friction coefficient of 0.1 over 1000 cycles, which ensured the structure suitable for applications at harsh environment.

cond-mat.mtrl-sci↗

arXiv2022

Investigation of plasmonic enhanced solar photothermal effect of Au NR@PVDF micro/nano-film

Gold nanospheres (Au NSs) and gold nanorods (Au NRs) are traditional noble metal plasmonic nanomaterials. Particularly, Au NRs with tunable longitudinal plasmon resonance from visible to the near infrared (NIR) range were suitable for high efficient photothermal applications due to extended light receiving range. In this work, we synthesized Au NRs and Au NSs of similar volume, and subsequently developed them into Au NR/PVDF and Au NS/PVDF nanofilm, both of which exhibited excellent solar photothermal performance evaluated by solar photothermal experiments. We found that Au NR/PVDF nanofilm showed higher solar photothermal performance than Au NS/PVDF nanofilm. Through detailed analysis, such as morphological characterization, optical measurement, and finite element method (FEM) modeling, we found that the plasmonic coupling effects inside the aggregated Au NRs nanoclusters contributed to the spectral blue-shifts and intensified photothermal performance. As compared to Au NS/PVDF nanofilms, Au NR/PVDF nanofilm exhibited higher efficient light-to-heat conversion rate, because of the extended light receiving range and high absorbance, as the result of strong plasmonic interactions inside nanoclusters, which was further validated by monochromatic laser photothermal experiments and FEM simulations. Our work proved that the Au NRs have huge potential for plasmonic solar photothermal applications, and are envisioned for novel plasmonic applications.

physics.optics↗