arXiv subjects

Liping Wang

Publications and source records attributed to Liping Wang.

At least 19 recordsLinked to original sources

arXiv2026

An Adaptive Decentralized Quasi-Newton Method with Stepsizes Independent of the Local-Update Budget

This paper proposes a novel Adaptive Decentralized Quasi-Newton (AdaDQN) method for solving smooth nonconvex optimization problems over undirected networks. While standard decentralized algorithms with multiple fixed local updates typically admit a convergence stepsize inversely proportional to the number of local updates, we show that this scaling is worst-case tight for the typical unscaled fixed-local-update scheme. We revisit a class of gradient-tracking methods with this scheme from a surrogate-based perspective and establish a Robust Inexact Algorithm (RIA) framework. Inspired by this framework, AdaDQN integrates a safeguarded consensus-aware termination criterion, a standard event-triggered communication protocol, and a scalable memoryless BFGS update. We establish an $\mathcal{O}(1/T)$ best-iterate rate for first-order stationarity. For a squared stationarity tolerance $\delta$, the guaranteed gradient complexity of the fixed scheme is $\mathcal{O}(nK_g/\delta)$, whereas AdaDQN attains $\mathcal{O}(n/\delta+n\alpha\tilde\varepsilon^{-2})$, independent of the maximum local-update budget ($K_g$). Numerical experiments demonstrate that AdaDQN achieves a superior computation-communication tradeoff, outperforming state-of-the-art decentralized methods across various performance metrics.

math.OC↗

arXiv2026

Towards Accurate Emotion-Attributed Video Captioning via Fine-grained Emotion-Cause Pair Extraction

Emotional Video Captioning (EVC) is a challenging task that aims to generate factually accurate and emotionally rich descriptions for videos. Existing EVC methods leverage holistic visual features to mine global emotional cues, and then aggregate multimodal features to guide the emotional caption generation, which ignores the critical characteristic of the EVC task. Visual emotions are evoked by specific motivational causes, which are usually only implied in core video segments. The holistic mining brings significant information redundancy and inaccurate emotional cues. Thus, fine-grained visual cause extraction has a facilitative effect on both emotion perception and emotion-attributed caption generation. To this end, we propose a fine-grained emotion-cause pair extraction framework for emotion-attributed video captioning. Specifically, we learn pair-wise emotion and cause features in two rounds: 1) We propose a Concept-aware Visual Semantic Decomposition module to augment visual features by exploring scene, object, and motion concepts. Besides, to enhance emotional features, we propose a Visual-guided Emotion Interpretable Learning module, which guides emotion refinement with visual temporal dynamics, and augments the interpretable refinement process by reliable VAD-vector constraints. 2) We achieve emotion-cause pair extraction by cross-coupling the visual and emotional features before and after refinement, and leverage contrastive loss to achieve semantic forced alignment. Overall, our approach optimizes complex semantic understanding and emotion perception of videos, leading to a promising performance in emotional captioning. Extensive experiments on three challenging datasets demonstrate the superiority of our approach and each proposed module, e.g., achieving the best performances with +4.4% and +5.4% w.r.t. BLEU-2 and ROUGE-L, respectively, on the EVC-MSVD dataset.

cs.CV↗

arXiv2026

Prototype-Guided Classification Sub-Task Decoupling Framework: Enhancing Generalization and Interpretability for Multivariate Time Series

Time Series Classification (TSC) is a long-standing research problem that has gained increasing attention in recent years with the rapid growth of large-scale temporal data. Despite substantial progress enabled by deep learning, designing TSC models that are both accurate and interpretable remains a challenging task. Many existing approaches adopt a direct feature-to-label classification paradigm, by collapsing high-dimensional temporal embeddings into class logits via a single linear projection (often after global pooling), the paradigm conflates feature extraction and decision logic into an inseparable mapping. To address these limitations, we propose PDFTime, a prototype-guided framework that reformulates time series classification as a multi-stage decision process. Instead of direct feature-to-label mapping, PDFTime leverages learned prototypes to approximate class-conditional feature distributions in the latent space, enabling progressive discrimination through classification sub-tasks of varying granularity. To our knowledge, PDFTime is the first framework to reformulate time series classification as a decoupled, multi-stage similarity-based reasoning process, breaking the long-standing paradigm of direct, black-box feature-to-label mapping. Extensive evaluations demonstrate that PDFTime achieves state-of-the-art (SOTA) performance across UEA and UCR benchmarks. Notably, it secures the top-$1$ accuracy on 80 out of 128 datasets in the UCR archive, significantly outperforming recent strong baselines in both consistency and generalization.

cs.LG↗

arXiv2026

Towards a Foundation Model for the Martian Atmosphere

The martian atmosphere hosts dynamical phenomena ranging from planet-encircling dust storms to mesoscale orographic clouds and nocturnal low-level jets. General circulation model show capability to simulate these phenomena, but is computationally expensive at resolution needed to resolve mesoscale features. While assimilation of satellite remote sensing observation enable forecasting capabilities using such models, observation record is often sparse, short and fragmented across instrument generators. These constraints motivate the development of a data-driven foundation model for the Martian atmosphere. Foundation models live in a complex design landscape. There is an interplay between the available data, the physics of the underlying processes and corresponding developments in AI. Even though the idea of a foundation model is to address multiple use cases in a data- and compute-efficient manner, it is important to have a clear picture what applications can sensibly addressed by a single model. The purpose of this paper is to elucidate this design landscape. We discuss available data ranging from atmospheric retrievals to reanalysis datasets as well as existing physical models. Moreover, we identify a wide range of candidate downstream applications. Finally, we consider relevant recent developments in artificial intelligence (AI) that can be leveraged in this context. Here, we put a particular emphasis on AI models for atmospheric physics, data-driven approaches to data assimilation as well as methods to work in a limited data setting.

astro-ph.EP↗

arXiv2026

A Multi-Agent Framework with Structured Reasoning and Reflective Refinement for Multimodal Empathetic Response Generation

Multimodal empathetic response generation (MERG) aims to generate emotionally engaging and empathetic responses based on users' multimodal contexts. Existing approaches usually rely on an implicit one-pass generation paradigm from multimodal context to the final response, which overlooks two intrinsic characteristics of MERG: (1) Human perception of emotional cues is inherently structured rather than a direct mapping. The conventional paradigm neglects the hierarchical progression of emotion perception, leading to distorted emotional judgments. (2) Given the inherent complexity and ambiguity of human emotions, the conventional paradigm is prone to significant emotional biases, ultimately resulting in suboptimal empathy. In this paper, we propose a multi-agent framework for MERG, which enhances empathy through structured reasoning and reflective refinement. Specifically, we first introduce a structured empathetic reasoning-to-generation module that explicitly decomposes response generation via multimodal perception, consistency-aware emotion forecasting, pragmatic strategy planning, and strategy-guided response generation, providing a clearer intermediate path from multimodal evidence to response realization. Besides, we develop a global reflection and refinement module, in which a global reflection agent performs step-wise auditing over intermediate states and the generated response, eliminating existing emotional biases and empathy errors, and triggering targeted regeneration. Overall, such a closed-loop framework enables our model to gradually improve the accuracy of emotion perception and eliminate emotion biases during the iteration process. Experiments on several benchmarks, e.g., IEMOCAP and MELD, demonstrate that our model has superior empathic response generation capabilities compared to state-of-the-art methods.

cs.CV↗

arXiv2026

PDE foundation models are skillful AI weather emulators for the Martian atmosphere

We show that AI foundation models that are pretrained on numerical solutions to a diverse corpus of partial differential equations can be adapted and fine-tuned to obtain skillful predictive weather emulators for the Martian atmosphere. We base our work on the Poseidon PDE foundation model for two-dimensional systems. We develop a method to extend Poseidon from two to three dimensions while keeping the pretraining information. Moreover, we investigate the performance of the model in the presence of sparse initial conditions. Our results make use of four Martian years (approx.~34 GB) of training data and a median compute budget of 13 GPU hours. We find that the combination of pretraining and model extension yields a performance increase of 34.4\% on a held-out year. This shows that PDEs-FMs can not only approximate solutions to (other) PDEs but also anchor models for real-world problems with complex interactions that lack a sufficient amount of training data or a suitable compute budget.

cs.LG↗

arXiv2026

BladeSDF : Unconditional and Conditional Generative Modeling of Representative Blade Geometries Using Signed Distance Functions

Generative AI has emerged as a transformative paradigm in engineering design, enabling automated synthesis and reconstruction of complex 3D geometries while preserving feasibility and performance relevance. This paper introduces a domain-specific implicit generative framework for turbine blade geometry using DeepSDF, addressing critical gaps in performance-aware modeling and manufacturable design generation. The proposed method leverages a continuous signed distance function (SDF) representation to reconstruct and generate smooth, watertight geometries with quantified accuracy. It establishes an interpretable, near-Gaussian latent space that aligns with blade-relevant parameters, such as taper and chord ratios, enabling controlled exploration and unconditional synthesis through interpolation and Gaussian sampling. In addition, a compact neural network maps engineering descriptors, such as maximum directional strains, to latent codes, facilitating the generation of performance-informed geometry. The framework achieves high reconstruction fidelity, with surface distance errors concentrated within $1\%$ of the maximum blade dimension, and demonstrates robust generalization to unseen designs. By integrating constraints, objectives, and performance metrics, this approach advances beyond traditional 2D-guided or unconstrained 3D pipelines, offering a practical and interpretable solution for data-driven turbine blade modeling and concept generation.

cs.LG↗

arXiv2026

Multi-task Modeling for Engineering Applications with Sparse Data

Modern engineering and scientific workflows often require simultaneous predictions across related tasks and fidelity levels, where high-fidelity data is scarce and expensive, while low-fidelity data is more abundant. This paper introduces an Multi-Task Gaussian Processes (MTGP) framework tailored for engineering systems characterized by multi-source, multi-fidelity data, addressing challenges of data sparsity and varying task correlations. The proposed framework leverages inter-task relationships across outputs and fidelity levels to improve predictive performance and reduce computational costs. The framework is validated across three representative scenarios: Forrester function benchmark, 3D ellipsoidal void modeling, and friction-stir welding. By quantifying and leveraging inter-task relationships, the proposed MTGP framework offers a robust and scalable solution for predictive modeling in domains with significant computational and experimental costs, supporting informed decision-making and efficient resource utilization.

stat.ML↗

arXiv2025

A Unified Algorithm for Nonconvex Decentralized Nonlinear Optimization

In this paper, we study the decentralized optimization problem of minimizing a finite sum of continuously differentiable and possibly nonconvex functions over a fixed-connected undirected network. We propose a unified decentralized nonconvex algorithmic framework that includes many existing state-of-the-art gradient tracking and quasi-Newton algorithms. A general framework for the convergence analysis of our unified algorithm is presented under both nonconvex and the Kurdyka-{\L}ojasiewicz condition settings. In particular, some new quasi-Newton algorithms under this framework are proposed. Our numerical results show that these newly developed algorithms are very efficient compared with other state-of-the-art algorithms for solving decentralized nonconvex nonlinear optimization.

math.OC↗

arXiv2025

Reducing Temperature Swing and Rectifying Radiative Heat Transfer for Passive Dynamic Space Thermal Control with Variable-Emittance Coatings

Dynamic radiative thermal control is crucial for normal operation and energy saving of spacecraft that copes with changing thermal environment involving heat dissipation to cold deep space, external heating from the Sun and nearby planet, and internal heating from onboard electronics. Variable-emittance coatings, whose infrared emittance can be tuned passively by temperature or actively by external stimuli, could provide a viable solution. In this work, we experimentally demonstrate self-adaptive dynamic radiative heat transfer with variable-emittance coating based on thermochromic VO2 in space-like thermal environment with a coldfinger and a custom-made sample mount inside a vacuum cryostat. Black Actar and highly reflective tungsten mirror are used to calibrate the parasitic head load and heat flux sensor sensitivity, while multiple static-emittance samples made of silicon wafers with different doping levels are measured for validation of the experimental method and for direct comparison with the variable-emittance VO2 coating. With the coldfinger at 80 K to mimic external radiative scenarios in space, the tunable coating exhibits 6-fold enhancement in radiative thermal conductance upon VO2 phase transition for promoted heat dissipation, in addition to reduced temperature swing by almost 20degC compared to the static emitters. With the coldfinger at 25degC as internal radiative scenarios in space, similar 6-fold heat dissipation from the variable-emittance coating is also observed, while radiative heat transfer is much suppressed with a constant radiative thermal conductance when the coldfinger is hotter than the tunable coating at 25degC, leading to a thermal rectification factor of 1.8 experimentally achieved.

physics.app-ph↗

arXiv2025

Tunable Coatings on Various Substrates for Self-Adaptive Energy Harvesting with Daytime Solar Heating and Nighttime Radiative Cooling

In this work, tunable vanadium dioxide (VO2) metafilms on different substrate materials fabricated via low-oxygen furnace oxidation are demonstrated for self-adaptive daytime solar heating and nighttime radiative cooling. Because of its thermally-driven insulator-to-metal phase transition behavior, the VO2 metafilms work as spectrally-selective solar absorber with a high solar absorptance of 0.86 and a low infrared emissivity of ~0.2 at daytime, while they behave as selective cooler at nighttime to dissipate heat effectively through the atmospheric transparency window with a high emissivity of ~0.76 to cold outer space. From the outdoor vacuum tests, a significant temperature rise up to 169 K upon solar heating and a temperature drop of 17 K at night are experimentally observed from these tunable VO2 metafilms. With the atmosphere temperature fitted in-situ, the accurate heat transfer model shows excellent agreement with the stagnation temperature measurement, and indicates a high heating power of ~400 W/m2 at 80{\deg}C sample temperature in the middle of the day, and a cooling power of ~60 W/m2 at 30{\deg}C in equilibrium with ambient at night. This work would facilitate the development of self-adaptive coatings with cost-effective and scalable fabrication approaches for all-day energy harvesting.

cond-mat.mtrl-sci↗

arXiv2025

High-quality Tungsten-doped Vanadium Dioxide Thin Films Fabricated in an Extremely Low-oxygen Furnace Environment

This work reports the fabrication and characterization of high-quality tungsten-doped vanadium dioxide (WxV1-xO2, x = 0~3 at. %) by thermal oxidation of sputtered tungsten-vanadium alloyed thin films with different atomic percentages and high-temperature annealing in an extremely low oxygen atmosphere (5 to 20 ppm) along with reduction of surface over-oxides in high vacuum (1 mPa). Oxidation parameters such as temperature, time and nitrogen purging rate are first optimized for obtaining high quality undoped VO2 thin film. Insulator-to-metal (IMT) phase transition behavior of VO2 thin films fabricated in a low-O2 environment is characterized with temperature dependent spectral infrared transmittance and electrical resistivity measurements, where there is 15% higher infrared transmittance change and additional 1 order change in resistivity in comparison with VO2 thin films fabricated in a O2-rich environment. Grazing angle X-ray diffraction scan confirms no presence of higher oxides in the VO2 oxidized in low-O2 environment, which improves its quality significantly. Comprehensive studies on thermal annealing and vacuum reduction for tungsten doped VO2 thin films are also carried out to find the optimal fabrication conditions. With the tungsten at. % measured by X-ray photoelectron spectroscopy, the optimal WVO2 thin films fabricated through this streamlined oxidation, annealing and reduction processes in extremely low-O2 furnace environment exhibit lowered IMT temperature at -23{\deg}C per at.% of tungsten dopants from 68{\deg}C without doping. This low-cost and scalable fabrication method could facilitate the wide development of tunable WVO2 coatings in thermal and energy applications.

cond-mat.mtrl-sci↗

arXiv2025

Three-Dimensional Continuous Multi-Walled Carbon Nanotubes Network-Toughened Diamond Composite

Enhancing the fracture toughness of diamond while preserving its hardness is a significant challenge. Traditional toughening strategies have primarily focused on modulating the internal microstructural units of diamonds, including adjustments to stacking sequences, faults, nanotwinning, and the incorporation of amorphous phases, collectively referred to as intrinsic toughening. Here, we introduce an extrinsic toughening strategy to develop an unparalleled tough diamond composite with complex and abundant sp2-sp3 bonding interfaces, by incorporating highly dispersed multi-walled carbon nanotubes (MWCNTs) into the gaps of diamond grains to create a three-dimensional (3D) continuous MWCTNs network-toughen heterogeneous structure. The resultant composite exhibits a hardness of approximately 91.6 GPa and a fracture toughness of roughly 36.4 MPa.m1/2, which is six times higher than that of synthetic diamond and even surpasses that of tungsten alloys, surpassing the benefits achievable through intrinsic toughening alone. The remarkable toughening behavior can be attributed to the formation of numerous mixed sp2-sp3 bonding interactions at the 3D continuous network MWCNTs/diamond interfaces, which facilitate efficient energy dissipation. Our 3D continuous network heterogeneous structure design provides an effective approach for enhancing the fracture toughness of superhard materials, offering a new paradigm for the advanced composite ceramics.

cond-mat.other↗

arXiv2025

GTRSS: Graph-based Top-$k$ Representative Similar Subtrajectory Query

Trajectory mining has attracted significant attention. This paper addresses the Top-k Representative Similar Subtrajectory Query (TRSSQ) problem, which aims to find the k most representative subtrajectories similar to a query. Existing methods rely on costly filtering-validation frameworks, resulting in slow response times. Addressing this, we propose GTRSS, a novel Graph-based Top-k Representative Similar Subtrajectory Query framework. During the offline phase, GTRSS builds a dual-layer graph index that clusters trajectories containing similar representative subtrajectories. In the online phase, it efficiently retrieves results by navigating the graph toward query-relevant clusters, bypassing full-dataset scanning and heavy computation. To support this, we introduce the Data Trajectory Similarity Metric (DTSM) to measure the most similar subtrajectory pair. We further combine R-tree and grid filtering with DTSM pruning rules to speed up index building. To the best of our knowledge, GTRSS is the first graph-based solution for top-k subtrajectory search. Experiments on real datasets demonstrate that GTRSS significantly enhances both efficiency and accuracy, achieving a retrieval accuracy of over 90 percent and up to two orders of magnitude speedup in query performance.

cs.DB↗

arXiv2025

Maximizing Infrared Transmission Contrast Upon Phase Transition of Thermally Grown Vanadium Dioxide Thin Films by Rapid Thermal Processing

Pristine vanadium dioxide (VO2), an insulator-to-metal transition (IMT) material, is grown via furnace oxidation followed by rapid thermal annealing with forming gas (5%H2/95%N2) which reduces surface over-oxides such as V2O5 formed during the oxidation. The evolutional IMT behaviors of the thermochromic film and vanadium oxide states over different reduction time are systematically studied with temperature-dependent infrared spectrometry, electrical resistivity, and X-ray diffraction measurements. After optimally reducing surface over-oxides to VO2, infrared transmission contrast upon phase transition is enhanced to 46% (at 9 um wavelength) compared to 23% from fully oxidation without any reduction. Moreover, pristine VO2 thin film obtained from thermal oxidation and optimal reduction processes exhibits sharp phase transition and narrow thermal hysteresis within 2~4{\deg}C in both infrared transmission and electrical resistivity, which are comparable to the VO2 of best quality prepared by other sophisticated fabrication techniques. The thermally grown method presented here would facilitate the scalable fabrication of high-quality VO2 thin films and tunable radiative coatings for high-performance thermal control applications.

cond-mat.mtrl-sci↗

arXiv2025

Lowering Insulator-to-Metal Transition Temperature of Vanadium Dioxide Thin Films via Co-Sputtering, Furnace Oxidation and Thermal Annealing

Thermochromic vanadium dioxide thin films have attracted much attention recently for constructing variable-emittance coatings upon its insulator-metal phase transition for dynamic thermal control. However, fabrication of high-quality vanadium dioxide thin films in a cost-effective way is still a challenge. In addition, the phase transition temperature of vanadium dioxide is around 68{\deg}C, which is higher than most of terrestrial and extraterrestrial applications. In this study, we report the fabrication and characterization of tungsten-doped vanadium dioxide thin films with lowered phase transition temperatures via co-sputtering, furnace oxidation and thermal annealing processes for wider application needs. The doping is achieved by co-sputtering of tungsten and vanadium targets while the doping level is varied by carefully controlling the sputtering power for tungsten. Doped thin film samples of 30-nm thick with different tungsten atomic concentrations are prepared by co-sputtering onto undoped silicon wafers. Optimal oxidation time of 4 hours is determined to reach full oxidation in an oxygen-rich furnace environment at 300{\deg}C. Systematic thermal annealing study is carried out to find the optimal annealing temperature and time. By using an optical cryostat coupled to an infrared spectrometer, the temperature-dependent infrared transmittance of fully annealed tungsten-doped vanadium dioxide thin films are measured in a wide temperature range from -60{\deg}C to 100{\deg}C. The phase transition temperature is found to decrease at 24.5{\deg}C per at.% of tungsten doping, and the thermal hysteresis between heating and cooling shrinks at 5.5{\deg}C per at.% from the fabricated vanadium dioxide thin films with tungsten doping up to 4.1 at.%.

cond-mat.mtrl-sci↗

arXiv2025

Efficient Multiple Temporal Network Kernel Density Estimation

Kernel density estimation (KDE) has become a popular method for visual analysis in various fields, such as financial risk forecasting, crime clustering, and traffic monitoring. KDE can identify high-density areas from discrete datasets. However, most existing works only consider planar distance and spatial data. In this paper, we introduce a new model, called TN-KDE, that applies KDE-based techniques to road networks with temporal data. Specifically, we introduce a novel solution, Range Forest Solution (RFS), which can efficiently compute KDE values on spatiotemporal road networks. To support the insertion operation, we present a dynamic version, called Dynamic Range Forest Solution (DRFS). We also propose an optimization called Lixel Sharing (LS) to share similar KDE values between two adjacent lixels. Furthermore, our solutions support many non-polynomial kernel functions and still report exact values. Experimental results show that our solutions achieve up to 6 times faster than the state-of-the-art method.

cs.DB↗

arXiv2024

GradStop: Exploring Training Dynamics in Unsupervised Outlier Detection through Gradient

Unsupervised Outlier Detection (UOD) is a critical task in data mining and machine learning, aiming to identify instances that significantly deviate from the majority. Without any label, deep UOD methods struggle with the misalignment between the model's direct optimization goal and the final performance goal of Outlier Detection (OD) task. Through the perspective of training dynamics, this paper proposes an early stopping algorithm to optimize the training of deep UOD models, ensuring they perform optimally in OD rather than overfitting the entire contaminated dataset. Inspired by UOD mechanism and inlier priority phenomenon, where intuitively models fit inliers more quickly than outliers, we propose GradStop, a sampling-based label-free algorithm to estimate model's real-time performance during training. First, a sampling method generates two sets: one likely containing more outliers and the other more inliers, then a metric based on gradient cohesion is applied to probe into current training dynamics, which reflects model's performance on OD task. Experimental results on 4 deep UOD algorithms and 47 real-world datasets and theoretical proofs demonstrate the effectiveness of our proposed early stopping algorithm in enhancing the performance of deep UOD models. Auto Encoder (AE) enhanced by GradStop achieves better performance than itself, other SOTA UOD methods, and even ensemble AEs. Our method provides a robust and effective solution to the problem of performance degradation during training, enabling deep UOD models to achieve better potential in anomaly detection tasks.

cs.LG↗