arXiv subjects

Kai Zhu

Publications and source records attributed to Kai Zhu.

At least 55 records · Page 3Linked to original sources

arXiv2025

3D-ICE 4.0: Accurate and efficient thermal modeling for 2.5D/3D heterogeneous chiplet systems

The increasing power densities and intricate heat dissipation paths in advanced 2.5D/3D chiplet systems necessitate thermal modeling frameworks that deliver detailed thermal maps with high computational efficiency. Traditional compact thermal models (CTMs) often struggle to scale with the complexity and heterogeneity of modern architectures. This work introduces 3D-ICE 4.0, designed for heterogeneous chip-based systems. Key innovations include: (i) preservation of material heterogeneity and anisotropy directly from industrial layouts, integrated with OpenMP and SuperLU MT-based parallel solvers for scalable performance, (ii) adaptive vertical layer partitioning to accurately model vertical heat conduction, and (iii) temperature-aware non-uniform grid generation. The results with different benchmarks demonstrate that 3D-ICE 4.0 achieves speedups ranging from 3.61x-6.46x over state-of-the-art tools, while reducing grid complexity by more than 23.3% without compromising accuracy. Compared to the commercial software COMSOL, 3D-ICE 4.0 effectively captures both lateral and vertical heat flows, validating its precision and robustness. These advances demonstrate that 3D-ICE 4.0 is an efficient solution for thermal modeling in emerging heterogeneous 2.5D/3D integrated systems.

eess.SY↗

arXiv2025

PWACG: Partial Wave Analysis Code Generator supporting Newton-conjugate gradient method

This paper introduces a novel Partial Wave Analysis Code Generator (PWACG) that automatically generates high-performance partial wave analysis codes. This is achieved by leveraging the JAX automatic differentiation library and the jinja2 template engine. The resulting code is constructed using the high-performance API of JAX, and includes support for the Newton's Conjugate Gradient optimization method, as well as the full utilization of parallel computing capabilities offered by GPUs. By harnessing these advanced computing techniques, PWACG demonstrates a significant advantage in efficiently identifying global optimal points compared to conventional partial wave analysis software packages.

physics.comp-ph↗

arXiv2025

Pinpointing Physical Solutions in Y(4230) Decays

To resolve ambiguities from multiple solutions in experimental measurements, we construct a $χ^2$ function incorporating constraints such as isospin conservation and amplitude relations. By minimizing the global $χ^2$, we identify physical solutions for seven hidden-charm decay channels of $Y(4230)$. Crucially, the physical solution for $Y(4230) \to π^{+}π^{-} J/ψ$ corresponds to the largest among four experimental solutions, potentially modifying inputs for theoretical calculations. Additionally, we predict $Γ_{ee} B(Y(4230) \to π^{+}π^{-} ψ(2S))$ based on our results, acknowledging substantial uncertainties in current measurements.

hep-ph↗

arXiv2025

From ID-based to ID-free: Rethinking ID Effectiveness in Multimodal Collaborative Filtering Recommendation

Most existing multimodal collaborative filtering recommendation (MCFRec) methods rely heavily on ID features and multimodal content to enhance recommendation performance. However, this paper reveals that ID features are effective but have limited benefits in multimodal collaborative filtering recommendation. Therefore, this paper systematically deconstruct the pros and cons of ID features: (i) they provide initial embedding but lack semantic richness, (ii) they provide a unique identifier for each user and item but hinder generalization to untrained data, and (iii) they assist in aligning and fusing multimodal features but may lead to representation shift. Based on these insights, this paper proposes IDFREE, an ID-free multimodal collaborative Filtering REcommEndation baseline. IDFREE replaces ID features with multimodal features and positional encodings to generate semantically meaningful ID-free embeddings. For ID-free multimodal collaborative filtering, it further proposes an adaptive similarity graph module to construct dynamic user-user and item-item graphs based on multimodal features. Then, an augmented user-item graph encoder is proposed to construct more effective user and item encoding. Finally, IDFREE achieves inter-multimodal alignment based on the contrastive learning and uses Softmax loss as recommendation loss. Basic experiments on three public datasets demonstrate that IDFREE outperforms existing ID-based MCFRec methods, achieving an average performance gain of 72.24% across standard metrics (Recall@5, 10, 20, 50 and NDCG@5, 10, 20, 50). Exploratory and extended experiments further validate our findings on the limitations of ID features in MCFRec. The code is released at https://github.com/G-H-Li/IDFREE.

cs.IR↗

arXiv2025

State Space Prompting via Gathering and Spreading Spatio-Temporal Information for Video Understanding

Recently, pre-trained state space models have shown great potential for video classification, which sequentially compresses visual tokens in videos with linear complexity, thereby improving the processing efficiency of video data while maintaining high performance. To apply powerful pre-trained models to downstream tasks, prompt learning is proposed to achieve efficient downstream task adaptation with only a small number of fine-tuned parameters. However, the sequentially compressed visual prompt tokens fail to capture the spatial and temporal contextual information in the video, thus limiting the effective propagation of spatial information within a video frame and temporal information between frames in the state compression model and the extraction of discriminative information. To tackle the above issue, we proposed a State Space Prompting (SSP) method for video understanding, which combines intra-frame and inter-frame prompts to aggregate and propagate key spatiotemporal information in the video. Specifically, an Intra-Frame Gathering (IFG) module is designed to aggregate spatial key information within each frame. Besides, an Inter-Frame Spreading (IFS) module is designed to spread discriminative spatio-temporal information across different frames. By adaptively balancing and compressing key spatio-temporal information within and between frames, our SSP effectively propagates discriminative information in videos in a complementary manner. Extensive experiments on four video benchmark datasets verify that our SSP significantly outperforms existing SOTA methods by 2.76% on average while reducing the overhead of fine-tuning parameters.

cs.CV↗

arXiv2025

MaNGA DynPop. VII. A Unified Bulge-Disk-Halo Model for Explaining Diversity in Circular Velocity Curves of 6000 Spiral and Early-Type Galaxies

We derive circular velocity curves (CVCs) from stellar dynamical models for $\sim6000$ nearby galaxies in the final data release of the Sloan Digital Sky Survey-IV MaNGA survey with integral-field spectroscopy, exploring connections between the inner gravitational potential (traced by CVC amplitude/shape) and galaxy properties. The maximum circular velocity ($V_{\rm circ}^{\rm max}$) and circular velocity at the half-light radius ($V_{\rm circ}(R_{\rm e}^{\rm maj})$) both scale linearly with the stellar second velocity moment $σ_{\rm e}^2\equiv\langle V^2+σ^2\rangle$ within the half-light isophote, following $V_{\rm circ}^{\rm max} \approx 1.72σ_{\rm e}$ (7$\%$ error) and $V_{\rm circ}(R_{\rm e}^{\rm maj}) \approx 1.62σ_{\rm e}$ (7$\%$ error). CVC shapes (rising, flat, declining) correlate strongly with structural and stellar population properties: declining curves dominate in massive, early-type, bulge-dominated galaxies with old, metal-rich stars and early quenching, while rising CVCs prevail in disk-dominated systems with younger stellar populations and ongoing star formation. Using a unified bulge-disk-halo model, we predict CVC shapes with minimal bias, identifying three governing parameters: bulge-to-total mass ratio ($B/T$), dark matter fraction within $R_{\rm e}$, and bulge Sersic index. The distribution of CVC shapes across the mass-size plane reflects evolutionary pathways driven by (i) in situ star formation (spurring bulge growth) and (ii) dry mergers. This establishes CVC morphology as a diagnostic for galaxy evolution, linking dynamical signatures to structural and stellar population histories.

astro-ph.GA↗

arXiv2025

Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications

Molecular dynamics simulations hold great promise for providing insight into the microscopic behavior of complex molecular systems. However, their effectiveness is often constrained by long timescales associated with rare events. Enhanced sampling methods have been developed to address these challenges, and recent years have seen a growing integration with machine learning techniques. This review provides a comprehensive overview of how they are reshaping the field, with a particular focus on the data-driven construction of collective variables. Furthermore, these techniques have also improved biasing schemes and unlocked novel strategies via reinforcement learning and generative approaches. In addition to methodological advances, we highlight applications spanning different areas such as biomolecular processes, ligand binding, catalytic reactions, and phase transitions. We conclude by outlining future directions aimed at enabling more automated strategies for rare-event sampling.

physics.comp-ph↗

arXiv2025

Optical+NIR analysis of a Newly Confirmed Einstein ring at z$\sim$1 from the Kilo-Degree Survey: Dark matter fraction, total and dark matter density slope and IMF

We report the spectroscopic confirmation of a bright blue Einstein ring in the Kilo Degree Survey (KiDS) footprint: the Einstein ``blue eye''. Spectroscopic data from X-Shooter at the Very Large Telescope (VLT) show that the lens is a typical early-type galaxy (ETG) at $z_l=0.9906$, while the background source is a Ly$α$ emitter at $z_s=2.823$. The reference lens modeling was performed on a high-resolution $Y-$band adaptive-optics image from HAWK-I at VLT. Assuming a singular isothermal ellipsoid (SIE) total mass density profile, we inferred an Einstein radius $R_{Ein}=10.47 \pm 0.06$ kpc. The average slope of the total mass density inside the Einstein radius, as determined by a joint analysis of lensing and isotropic Jeans equations is $γ_{tot}=2.14^{+0.06}_{-0.07}$, showing no systematic deviation from the slopes of lower redshift galaxies, This can be the evidence of ETGs developing through dry mergers plus moderate dissipationless accretion. Stellar population analysis with 8-band ($gri$ZYJHK$s$) photometries from KiDS and VIKING shows that the total stellar mass of the lens is $M*=(3.95\pm 0.35)\times 10^{11} M_\odot$ (Salpeter Initial Mass Function, IMF), implying a dark matter fraction inside the effective radius to be $f_{\rm DM}=0.307\pm 0.151$. We finally explored the dark matter halo slope and found a strong degeneracy with the dynamic stellar mass. Dark matter adiabatic contraction is needed to explain the posterior distribution of the slope unless IMF heavier than Salpeter is assumed.

astro-ph.GA↗

arXiv2025

A 20-Year Retrospective on Power and Thermal Modeling and Management

As processor performance advances, increasing power densities and complex thermal behaviors threaten both energy efficiency and system reliability. This survey covers more than two decades of research on power and thermal modeling and management in modern processors. We start by comparing analytical, regression-based, and neural network-based techniques for power estimation, then review thermal modeling methods, including finite element, finite difference, and data-driven approaches. Next, we categorize dynamic runtime management strategies that balance performance, power consumption, and reliability. Finally, we conclude with a discussion of emerging challenges and promising research directions.

eess.SY↗

arXiv2025

A Scalable and Quantum-Accurate Foundation Model for Biomolecular Force Field via Linearly Tensorized Quadrangle Attention

Accurate atomistic biomolecular simulations are vital for disease mechanism understanding, drug discovery, and biomaterial design, but existing simulation methods exhibit significant limitations. Classical force fields are efficient but lack accuracy for transition states and fine conformational details critical in many chemical and biological processes. Quantum Mechanics (QM) methods are highly accurate but computationally infeasible for large-scale or long-time simulations. AI-based force fields (AIFFs) aim to achieve QM-level accuracy with efficiency but struggle to balance many-body modeling complexity, accuracy, and speed, often constrained by limited training data and insufficient validation for generalizability. To overcome these challenges, we introduce LiTEN, a novel equivariant neural network with Tensorized Quadrangle Attention (TQA). TQA efficiently models three- and four-body interactions with linear complexity by reparameterizing high-order tensor features via vector operations, avoiding costly spherical harmonics. Building on LiTEN, LiTEN-FF is a robust AIFF foundation model, pre-trained on the extensive nablaDFT dataset for broad chemical generalization and fine-tuned on SPICE for accurate solvated system simulations. LiTEN achieves state-of-the-art (SOTA) performance across most evaluation subsets of rMD17, MD22, and Chignolin, outperforming leading models such as MACE, NequIP, and EquiFormer. LiTEN-FF enables the most comprehensive suite of downstream biomolecular modeling tasks to date, including QM-level conformer searches, geometry optimization, and free energy surface construction, while offering 10x faster inference than MACE-OFF for large biomolecules (~1000 atoms). In summary, we present a physically grounded, highly efficient framework that advances complex biomolecular modeling, providing a versatile foundation for drug discovery and related applications.

physics.chem-ph↗

arXiv2025

ViewPoint: Panoramic Video Generation with Pretrained Diffusion Models

Panoramic video generation aims to synthesize 360-degree immersive videos, holding significant importance in the fields of VR, world models, and spatial intelligence. Existing works fail to synthesize high-quality panoramic videos due to the inherent modality gap between panoramic data and perspective data, which constitutes the majority of the training data for modern diffusion models. In this paper, we propose a novel framework utilizing pretrained perspective video models for generating panoramic videos. Specifically, we design a novel panorama representation named ViewPoint map, which possesses global spatial continuity and fine-grained visual details simultaneously. With our proposed Pano-Perspective attention mechanism, the model benefits from pretrained perspective priors and captures the panoramic spatial correlations of the ViewPoint map effectively. Extensive experiments demonstrate that our method can synthesize highly dynamic and spatially consistent panoramic videos, achieving state-of-the-art performance and surpassing previous methods.

cs.CV↗

arXiv2025

Validating remotely sensed biomass estimates with forest inventory data in the western US

Monitoring aboveground biomass (AGB) and its density (AGBD) at high resolution is essential for carbon accounting and ecosystem management. While NASA's spaceborne Global Ecosystem Dynamics Investigation (GEDI) LiDAR mission provides globally distributed reference measurements for AGBD estimation, the majority of commercial remote sensing products based on GEDI remain without rigorous or independent validation. Here, we present an independent regional validation of an AGBD dataset offered by terraPulse, Inc., based on independent reference data from the US Forest Service Forest Inventory and Analysis (FIA) program. Aggregated to 64,000-hectare hexagons and US counties across the US states of Utah, Nevada, and Washington, we found very strong agreement between terraPulse and FIA estimates. At the hexagon scale, we report R2 = 0.88, RMSE = 26.68 Mg/ha, and a correlation coefficient (r) of 0.94. At the county scale, agreement improves to R2 = 0.90, RMSE =32.62 Mg/ha, slope = 1.07, and r = 0.95. Spatial and statistical analyses indicated that terraPulse AGBD values tended to exceed FIA estimates in non-forest areas, likely due to FIA's limited sampling of non-forest vegetation. The terraPulse AGBD estimates also exhibited lower values in high-biomass forests, likely due to saturation effects in its optical remote-sensing covariates. This study advances operational carbon monitoring by delivering a scalable framework for comprehensive AGBD validation using independent FIA data, as well as a benchmark validation of a new commercial dataset for global biomass monitoring.

stat.AP↗

arXiv2025

Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems

We introduce an encoder-only approach to learn the evolution operators of large-scale non-linear dynamical systems, such as those describing complex natural phenomena. Evolution operators are particularly well-suited for analyzing systems that exhibit complex spatio-temporal patterns and have become a key analytical tool across various scientific communities. As terabyte-scale weather datasets and simulation tools capable of running millions of molecular dynamics steps per day are becoming commodities, our approach provides an effective tool to make sense of them from a data-driven perspective. The core of it lies in a remarkable connection between self-supervised representation learning methods and the recently established learning theory of evolution operators. To show the usefulness of the proposed method, we test it across multiple scientific domains: explaining the folding dynamics of small proteins, the binding process of drug-like molecules in host sites, and autonomously finding patterns in climate data. Code and data to reproduce the experiments are made available open source.

cs.LG↗

arXiv2025

Semantically-Aware Game Image Quality Assessment

Assessing the visual quality of video game graphics presents unique challenges due to the absence of reference images and the distinct types of distortions, such as aliasing, texture blur, and geometry level of detail (LOD) issues, which differ from those in natural images or user-generated content. Existing no-reference image and video quality assessment (NR-IQA/VQA) methods fail to generalize to gaming environments as they are primarily designed for distortions like compression artifacts. This study introduces a semantically-aware NR-IQA model tailored to gaming. The model employs a knowledge-distilled Game distortion feature extractor (GDFE) to detect and quantify game-specific distortions, while integrating semantic gating via CLIP embeddings to dynamically weight feature importance based on scene content. Training on gameplay data recorded across graphical quality presets enables the model to produce quality scores that align with human perception. Our results demonstrate that the GDFE, trained through knowledge distillation from binary classifiers, generalizes effectively to intermediate distortion levels unseen during training. Semantic gating further improves contextual relevance and reduces prediction variance. In the absence of in-domain NR-IQA baselines, our model outperforms out-of-domain methods and exhibits robust, monotonic quality trends across unseen games in the same genre. This work establishes a foundation for automated graphical quality assessment in gaming, advancing NR-IQA methods in this domain.

cs.CV↗

arXiv2025

Wan: Open and Advanced Large-Scale Video Generative Models

This report presents Wan, a comprehensive and open suite of video foundation models designed to push the boundaries of video generation. Built upon the mainstream diffusion transformer paradigm, Wan achieves significant advancements in generative capabilities through a series of innovations, including our novel VAE, scalable pre-training strategies, large-scale data curation, and automated evaluation metrics. These contributions collectively enhance the model's performance and versatility. Specifically, Wan is characterized by four key features: Leading Performance: The 14B model of Wan, trained on a vast dataset comprising billions of images and videos, demonstrates the scaling laws of video generation with respect to both data and model size. It consistently outperforms the existing open-source models as well as state-of-the-art commercial solutions across multiple internal and external benchmarks, demonstrating a clear and significant performance superiority. Comprehensiveness: Wan offers two capable models, i.e., 1.3B and 14B parameters, for efficiency and effectiveness respectively. It also covers multiple downstream applications, including image-to-video, instruction-guided video editing, and personal video generation, encompassing up to eight tasks. Consumer-Grade Efficiency: The 1.3B model demonstrates exceptional resource efficiency, requiring only 8.19 GB VRAM, making it compatible with a wide range of consumer-grade GPUs. Openness: We open-source the entire series of Wan, including source code and all models, with the goal of fostering the growth of the video generation community. This openness seeks to significantly expand the creative possibilities of video production in the industry and provide academia with high-quality video foundation models. All the code and models are available at https://github.com/Wan-Video/Wan2.1.

cs.CV↗

arXiv2025

Prospects for detecting cosmic filaments in Lyman-alpha emission across redshifts $z=2-5$

The standard $\rm Λ$CDM cosmological model predicts that a large amount of diffuse neutral hydrogen distributes in cosmic filaments, which could be mapped through Lyman-alpha (Ly$α$) emission observations. We use the hydrodynamical simulation Illustris-TNG50 to investigate the evolution of surface brightness and detectability of neutral hydrogen in cosmic filaments across redshifts $z=2-5$. While the HI column density of cosmic filaments decreases with redshift, due to the rising temperature with cosmic time in filaments, the surface brightness of Ly$α$ emission in filaments is brighter at lower redshifts, suggesting that the detection of cosmic filaments is more feasible at lower redshifts. However, most of the Ly$α$ emission from cosmic filaments is around $10^{-21}$ $\rm erg\ s^{-1}cm^{-2}arsec^{-2}$, making it extremely challenging to detect with current observational instruments. We further generate mock images using the Multi-Unit Spectroscopic Explorer (MUSE) spectrograph installed on the Very Large Telescope (VLT) and a MUSE-like spectrograph on the upcoming Extremely Large Telescope (ELT). Our finding indicates that while the VLT can only detect filamentary structures made of dense gas in galactic centers, the ELT is expected to reveal much finer filamentary structures from diffuse neutral hydrogen outside of galaxies. Compared to the VLT, both the number density and the longest length of filaments are greatly boosted with the ELT. Hence the forthcoming ELT is highly promising to provide a clearer view of cosmic filaments in Ly$α$ emission.

astro-ph.GA↗

arXiv2025

Exploring Reliable PPG Authentication on Smartwatches in Daily Scenarios

Photoplethysmography (PPG) Sensors, widely deployed in smartwatches, offer a simple and non-invasive authentication approach for daily use. However, PPG authentication faces reliability issues due to motion artifacts from physical activity and physiological variability over time. To address these challenges, we propose MTL-RAPID, an efficient and reliable PPG authentication model, that employs a multitask joint training strategy, simultaneously assessing signal quality and verifying user identity. The joint optimization of these two tasks in MTL-RAPID results in a structure that outperforms models trained on individual tasks separately, achieving stronger performance with fewer parameters. In our comprehensive user studies regarding motion artifacts (N = 30), time variations (N = 32), and user preferences (N = 16), MTL-RAPID achieves a best AUC of 99.2\% and an EER of 3.5\%, outperforming existing baselines. We opensource our PPG authentication dataset along with the MTL-RAPID model to facilitate future research on GitHub.

cs.CV↗

arXiv2025

BACON: Improving Clarity of Image Captions via Bag-of-Concept Graphs

Advancements in large Vision-Language Models have brought precise, accurate image captioning, vital for advancing multi-modal image understanding and processing. Yet these captions often carry lengthy, intertwined contexts that are difficult to parse and frequently overlook essential cues, posing a great barrier for models like GroundingDINO and SDXL, which lack the strong text encoding and syntax analysis needed to fully leverage dense captions. To address this, we propose BACON, a prompting method that breaks down VLM-generated captions into disentangled, structured elements such as objects, relationships, styles, and themes. This approach not only minimizes confusion from handling complex contexts but also allows for efficient transfer into a JSON dictionary, enabling models without linguistic processing capabilities to easily access key information. We annotated 100,000 image-caption pairs using BACON with GPT-4V and trained an LLaVA captioner on this dataset, enabling it to produce BACON-style captions without relying on costly GPT-4V. Evaluations of overall quality, precision, and recall-as well as user studies-demonstrate that the resulting caption model consistently outperforms other SOTA VLM models in generating high-quality captions. Besides, we show that BACON-style captions exhibit better clarity when applied to various models, enabling them to accomplish previously unattainable tasks or surpass existing SOTA solutions without training. For example, BACON-style captions help GroundingDINO achieve 1.51x higher recall scores on open-vocabulary object detection tasks compared to leading methods.

cs.CV↗