arXiv subjects

Qian Cao

Publications and source records attributed to Qian Cao.

At least 37 records · Page 2Linked to original sources

arXiv2025

From Heuristics to Data: Quantifying Site Planning Layout Indicators with Deep Learning and Multi-Modal Data

The spatial layout of urban sites shapes land-use efficiency and spatial organization. Traditional site planning often relies on experiential judgment and single-source data, limiting systematic quantification of multifunctional layouts. We propose a Site Planning Layout Indicator (SPLI) system, a data-driven framework integrating empirical knowledge with heterogeneous multi-source data to produce structured urban spatial information. The SPLI supports multimodal spatial data systems for analytics, inference, and retrieval by combining OpenStreetMap (OSM), Points of Interest (POI), building morphology, land use, and satellite imagery. It extends conventional metrics through five dimensions: (1) Hierarchical Building Function Classification, refining empirical systems into clear hierarchies; (2) Spatial Organization, quantifying seven layout patterns (e.g., symmetrical, concentric, axial-oriented); (3) Functional Diversity, transforming qualitative assessments into measurable indicators using Functional Ratio (FR) and Simpson Index (SI); (4) Accessibility to Essential Services, integrating facility distribution and transport networks for comprehensive accessibility metrics; and (5) Land Use Intensity, using Floor Area Ratio (FAR) and Building Coverage Ratio (BCR) to assess utilization efficiency. Data gaps are addressed through deep learning, including Relational Graph Neural Networks (RGNN) and Graph Neural Networks (GNN). Experiments show the SPLI improves functional classification accuracy and provides a standardized basis for automated, data-driven urban spatial analytics.

cs.LG↗

arXiv2025

Spatiotemporal coupled Airy-Airy wavepacket and its propagation dynamics

Airy beams, celebrated for their self-acceleration, diffraction-free propagation, and self-healing properties, have garnered significant interest in optics and photonics, with applications spanning ultrafast optics, laser processing, nonlinear optics, and optical communications. Recent research primarily aims at independent control of Airy beams in both spatial and spatiotemporal domains. In a pioneering approach, we have successfully generated and controlled a spatiotemporal coupled (STc) Airy-Airy wavepacket, achieving its rotation while preserving vertical distribution in the spatiotemporal domain. Furthermore, we have investigated the self-acceleration and self-healing properties of the STc Airy-Airy wavepacket in this domain, noting that its dynamically adjustable rotation and spatiotemporal coupling capability provide a novel strategy for managing ultrafast lasers, with potential advancements in optical micromanipulation and time-domain coding communication.

physics.optics↗

arXiv2025

DiffVLA: Vision-Language Guided Diffusion Planning for Autonomous Driving

Research interest in end-to-end autonomous driving has surged owing to its fully differentiable design integrating modular tasks, i.e. perception, prediction and planing, which enables optimization in pursuit of the ultimate goal. Despite the great potential of the end-to-end paradigm, existing methods suffer from several aspects including expensive BEV (bird's eye view) computation, action diversity, and sub-optimal decision in complex real-world scenarios. To address these challenges, we propose a novel hybrid sparse-dense diffusion policy, empowered by a Vision-Language Model (VLM), called Diff-VLA. We explore the sparse diffusion representation for efficient multi-modal driving behavior. Moreover, we rethink the effectiveness of VLM driving decision and improve the trajectory generation guidance through deep interaction across agent, map instances and VLM output. Our method shows superior performance in Autonomous Grand Challenge 2025 which contains challenging real and reactive synthetic scenarios. Our methods achieves 45.0 PDMS.

cs.AI↗

arXiv2025

Propagation Dynamics of Photonic Toroidal Vortices Mediated by Orbital Angular Momenta

The dynamics of vortex rings in fluids have long captivated researchers due to the intriguing complexity of their behavior, despite the apparent simplicity of their structure. In optics, photonic toroidal vortices constitute a novel class of three-dimensional, space-time nonseparable structured light fields that carry transverse orbital angular momentum. However, as solutions to the dispersive form of Maxwell's equations, these wavepackets do not survive upon nondispersive propagation, and their dynamics remain elusive. In this article, the dynamics of photonic toroidal vortices under various dispersion regimes, mediated by both transverse and longitudinal orbital angular momentum, are investigated through simulations and experiments. The results reveal that the motion of a toroidal vortex is strongly affected by the presence of longitudinal orbital angular momentum. The swirling flow destabilizes the toroidal structure under dispersion conditions and induces topological transformations in the vortex line characterized by its annihilation and subsequent reformation in vacuum. Remarkably, the renascent toroidal vortex exhibits robust propagation in vacuum while maintaining its toroidal structure. These findings are supported by experimental validation and highlight the potential of photonic toroidal vortices as controllable channels for directional energy and information transfer.

physics.optics↗

arXiv2025

Frequency super-resolution with quantum environment engineering in a weakly coupled nuclear-spin system

Optical super-resolution has been widely employed to beat spatial diffraction limit, which is often stated by Abbe-Rayleigh criterion. Analogously, we propose a frequency super-resolution method, which beats conventional spectral resolution limit often approximated by full width half maximum of the spectral peak, Γ. This method utilizes recently developed quantum environment engineering technique. With numerical simulations and experiments, we demonstrate the frequency super-resolution method in a three-nuclear-spin system (Trifluoroiodoethylene), by successfully decomposing a thermal state spectrum of the spin F3 into four peaks of engineered pseudo-pure states of the quantum environment. The ultimate frequency resolution reaches {\sim} 0.005 Γ. This method is potentially useful in spectral decomposition of weakly coupled nuclear spin systems and might be improved further to acquire finer frequency super-resolution by employing more advanced quantum techniques..

quant-ph↗

arXiv2025

Full space-time abrupt autofocusing spherical Airy wavepacket

The ability to precisely focus optical beams is crucial for numerous applications, yet conventional Gaussian beams exhibit slow intensity transitions near the focal point, limiting their effectiveness in scenarios requiring sharp focusing. In this work, the spherical Airy wavepacket, a three dimensional light field with an Airy function distribution in the radial direction in the full space time domain, is introduced and experimentally demonstrated. Leveraging the recently developed spatiotemporal hologram technique and an exponential polar coordinate transformation, spherical Airy wavepacket is sculpted to exhibit ultrafast autofocusing with a dramatically reduced depth of focus compared to conventional Gaussian beams and circular Airy beams. Experimental measurements confirm its nonlinear intensity surge and tight spatiotemporal confinement.

physics.optics↗

arXiv2025

Three-dimensional abruptly autofocusing by counter-propagating Airy pulses with radial Airy beam profile

We report the experimental observation of a three-dimensional abruptly autofocusing effect by synthesizing a radially distributed Airy beam with two counter-propagating Airy pulses in time. As the wave packet propagates in a dispersive medium, the radially distributed Airy beam converges inward to the center point. Two Airy pulses counter-propagate toward each other to merge to form a high peak power pulse. As the result, the high intensity emerges abruptly as the wave packet achieves three-dimensional focusing. This autofocusing effect is believed to have potential applications such as material modification, plasma physics, nanoparticle manipulations, etc.

physics.optics↗

arXiv2025

Spatiotemporal Airy rings wavepackets

Airy waves, known for their non-diffracting and self-accelerating properties, have been extensively studied in spatial and temporal domains, but their spatiotemporal (ST) counterparts remain largely unexplored. We report the first experimental realization of a spatiotemporal Airy rings wavepacket, which exhibits an Airy function distribution in the radial dimension of the ST domain. The wavepacket demonstrates abrupt autofocusing under the combined effects of diffraction and dispersion, achieving a 110 um spatial and 320 fs temporal focus with a sharp intensity contrast along the propagation direction - ideal for nonlinear microscopy and multiphoton 3D printing. Notably, the wavepacket retains its autofocusing capability even after spatial obstruction, showcasing robust self-healing. Furthermore, by embedding a vortex phase, we create an ST-Airy vortex wavepacket that confines transverse orbital angular momentum (t-OAM) within a compact ST volume, enabling new avenues for studying light-matter interactions with t-OAM. Our findings advance the fundamental understanding of ST Airy waves and highlight their potential for transformative applications in ultrafast optics, structured light, and precision laser processing.

physics.optics↗

arXiv2025

Spatiotemporal Photonic Emulator of Potential-free Schrödinger Equation

Photonic quantum emulator utilizes photons to emulate the quantum physical behavior of a complex quantum system. Recent study in spatiotemporal optics has enriched the toolbox for designing and manipulating complex spatiotemporal optical wavepackets, bringing new opportunities in building such quantum emulators. In this work, we demonstrate a new type of photonic quantum emulator enabled by spatiotemporal localized wavepackets with spherical harmonic symmetry. The spatiotemporal field distribution of these wavepackets has the same distributions of the wavefunction solutions to the potential-free Schrödinger equation with two controllable quantum numbers. A series of such localized wavepackets are experimentally generated with their localized feature verified. These localized wavepackets can propagate invariantly in space-time like particles, forming a new type of photonic quantum emulator that may provide new insight in studying quantum physics and open up new applications in studying light-matter interactions and quantum optics.

physics.optics↗

arXiv2025

Construction of Optical Spatiotemporal Skyrmions

The creation and manipulation of photonic skyrmions provide a novel degree of freedom for light-matter interactions, optical communication and nanometrology. Since the localized vortex within skyrmions arises from the twist and curl of the phase structure, the orbital angular momentum of light is essential for their construction. While numerous skyrmionic textures have been proposed, they are formed within the spatial domain and induced by the longitudinal orbital angular momentum. Here we theoretically propose and experimentally observe spatiotemporal skyrmions within a picosecond pulse wavepacket, generated through vectorial sculpturing of spatiotemporal wavepackets. The skyrmionic textures emerge within the spatiotemporal distribution of a vector field encompass all possible polarization states. Constructed upon the transverse orbital angular momentum, spatiotemporal skyrmions, in contrast to spatial skyrmions, exhibit no helical twisting perpendicular to the skyrmion plane, demonstrating enhanced stability to deformations or perturbations. These results expand the skyrmion family and offer new insights into optical quasiparticles, potentially leading to advanced applications in optical metrology, sensing, and data storage.

physics.optics↗

arXiv2025

Perfect Spatiotemporal Optical Vortices

Recently, spatiotemporal optical vortices (STOVs) with transverse orbital angular momentum have emerged as a significant research topic. While various STOV fields have been explored, they often suffer from a critical limitation: the spatial and temporal dimentions of the STOV wavepacket are strongly correlated with the topological charge. This dependence hinders the simultaneous achievement of high spatial accuracy and high topological charge. To address this limitation, we theoretically and experimentally investigate a new class of STOV wavepackets generated through the spatiotemporal Fourier transform of polychromatic Bessel-Gaussian beams, which we term as perfect spatiotemporal optical vortices. Unlike conventional STOVs, perfect STOVs exhibit spatial and temporal diameters that are independent of the topological charge. Furthermore, we demonstrate the generation of spatiotemporal optical vortex lattices by colliding perfect STOV wavepackets, enabling flexible manipulation of the number and sign of sub-vortices.

physics.optics↗

arXiv2025

TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learning

Spatial representation learning (SRL) aims at learning general-purpose neural network representations from various types of spatial data (e.g., points, polylines, polygons, networks, images, etc.) in their native formats. Learning good spatial representations is a fundamental problem for various downstream applications such as species distribution modeling, weather forecasting, trajectory generation, geographic question answering, etc. Even though SRL has become the foundation of almost all geospatial artificial intelligence (GeoAI) research, we have not yet seen significant efforts to develop an extensive deep learning framework and benchmark to support SRL model development and evaluation. To fill this gap, we propose TorchSpatial, a learning framework and benchmark for location (point) encoding, which is one of the most fundamental data types of spatial representation learning. TorchSpatial contains three key components: 1) a unified location encoding framework that consolidates 15 commonly recognized location encoders, ensuring scalability and reproducibility of the implementations; 2) the LocBench benchmark tasks encompassing 7 geo-aware image classification and 10 geo-aware image regression datasets; 3) a comprehensive suite of evaluation metrics to quantify geo-aware model's overall performance as well as their geographic bias, with a novel Geo-Bias Score metric. Finally, we provide a detailed analysis and insights into the model performance and geographic bias of different location encoders. We believe TorchSpatial will foster future advancement of spatial representation learning and spatial fairness in GeoAI research. The TorchSpatial model framework and LocBench benchmark are available at https://github.com/seai-lab/TorchSpatial, and the Geo-Bias Score evaluation framework is available at https://github.com/seai-lab/PyGBS.

cs.CV↗

arXiv2024

Feature Interaction Fusion Self-Distillation Network For CTR Prediction

Click-Through Rate (CTR) prediction plays a vital role in recommender systems, online advertising, and search engines. Most of the current approaches model feature interactions through stacked or parallel structures, with some employing knowledge distillation for model compression. However, we observe some limitations with these approaches: (1) In parallel structure models, the explicit and implicit components are executed independently and simultaneously, which leads to insufficient information sharing within the feature set. (2) The introduction of knowledge distillation technology brings about the problems of complex teacher-student framework design and low knowledge transfer efficiency. (3) The dataset and the process of constructing high-order feature interactions contain significant noise, which limits the model's effectiveness. To address these limitations, we propose FSDNet, a CTR prediction framework incorporating a plug-and-play fusion self-distillation module. Specifically, FSDNet forms connections between explicit and implicit feature interactions at each layer, enhancing the sharing of information between different features. The deepest fusion layer is then used as the teacher model, utilizing self-distillation to guide the training of shallow layers. Empirical evaluation across four benchmark datasets validates the framework's efficacy and generalization capabilities. The code is available on https://anonymous.4open.science/r/FSDNet.

cs.IR↗

arXiv2024

BSharedRAG: Backbone Shared Retrieval-Augmented Generation for the E-commerce Domain

Retrieval Augmented Generation (RAG) system is important in domains such as e-commerce, which has many long-tail entities and frequently updated information. Most existing works adopt separate modules for retrieval and generation, which may be suboptimal since the retrieval task and the generation task cannot benefit from each other to improve performance. We propose a novel Backbone Shared RAG framework (BSharedRAG). It first uses a domain-specific corpus to continually pre-train a base model as a domain-specific backbone model and then trains two plug-and-play Low-Rank Adaptation (LoRA) modules based on the shared backbone to minimize retrieval and generation losses respectively. Experimental results indicate that our proposed BSharedRAG outperforms baseline models by 5% and 13% in Hit@3 upon two datasets in retrieval evaluation and by 23% in terms of BLEU-3 in generation evaluation. Our codes, models, and dataset are available at https://bsharedrag.github.io.

cs.CL↗

arXiv2024

Multi-Modal Experience Inspired AI Creation

AI creation, such as poem or lyrics generation, has attracted increasing attention from both industry and academic communities, with many promising models proposed in the past few years. Existing methods usually estimate the outputs based on single and independent visual or textual information. However, in reality, humans usually make creations according to their experiences, which may involve different modalities and be sequentially correlated. To model such human capabilities, in this paper, we define and solve a novel AI creation problem based on human experiences. More specifically, we study how to generate texts based on sequential multi-modal information. Compared with the previous works, this task is much more difficult because the designed model has to well understand and adapt the semantics among different modalities and effectively convert them into the output in a sequential manner. To alleviate these difficulties, we firstly design a multi-channel sequence-to-sequence architecture equipped with a multi-modal attention network. For more effective optimization, we then propose a curriculum negative sampling strategy tailored for the sequential inputs. To benchmark this problem and demonstrate the effectiveness of our model, we manually labeled a new multi-modal experience dataset. With this dataset, we conduct extensive experiments by comparing our model with a series of representative baselines, where we can demonstrate significant improvements in our model based on both automatic and human-centered metrics. The code and data are available at: \url{https://github.com/Aman-4-Real/MMTG}.

cs.AI↗

arXiv2024

See or Guess: Counterfactually Regularized Image Captioning

Image captioning, which generates natural language descriptions of the visual information in an image, is a crucial task in vision-language research. Previous models have typically addressed this task by aligning the generative capabilities of machines with human intelligence through statistical fitting of existing datasets. While effective for normal images, they may struggle to accurately describe those where certain parts of the image are obscured or edited, unlike humans who excel in such cases. These weaknesses they exhibit, including hallucinations and limited interpretability, often hinder performance in scenarios with shifted association patterns. In this paper, we present a generic image captioning framework that employs causal inference to make existing models more capable of interventional tasks, and counterfactually explainable. Our approach includes two variants leveraging either total effect or natural direct effect. Integrating them into the training process enables models to handle counterfactual scenarios, increasing their generalizability. Extensive experiments on various datasets show that our method effectively reduces hallucinations and improves the model's faithfulness to images, demonstrating high portability across both small-scale and large-scale image-to-text models. The code is available at https://github.com/Aman-4-Real/See-or-Guess.

cs.CV↗

arXiv2024

Ultrafast bursts of tailored spatiotemporal vortex pulses

Orbital angular momentums (OAMs) of light can be categorized into longitudinal OAM (L-OAM) and transverse OAM (T-OAM). Light carrying time-varying L-OAM, known as self-torqued light, was recently discovered during harmonic generation and has been extensively developed within the context of optical frequency combs (OFCs). Meanwhile, ultrafast bursts of optical pulses, analogous to OFCs, are sought for various light-matter interaction, spectroscopic and nonlinear applications. However, achieving transiently switchable T-OAM of light on request, namely spatiotemporal vortex pulse bursts, with independently controlled spatiotemporal profile of each comb tooth, remain unrealized thus far. In this work, the experimental generation of spatiotemporal vortex bursts featured with controllable time-dependent characteristics is reported. The resultant bursts comprised of spatiotemporal optical vortex comb teeth have picosecond timescale switchable T-OAMs with defined arrangement, manifesting as spatiotemporal torquing of light. We also show ultrafast control of T-OAM chirality, yielding pulse bursts with staggered azimuthal local momentum density, resembling Kármán vortex streets. This approach enables the tailoring of more intricate spatiotemporal wavepacket bursts, such as high-purity modes variation in both radial and azimuthal quantum numbers of spatiotemporal Laguerre-Gaussian wavepackets over time, which may facilitate a host of novel applications in ultrafast light-mater interactions, high-dimensional quantum entanglements, space-time photonic topologies as well as spatiotemporal metrology and photography.

physics.optics↗

arXiv2024

YuLan: An Open-source Large Language Model

Large language models (LLMs) have become the foundation of many applications, leveraging their extensive capabilities in processing and understanding natural language. While many open-source LLMs have been released with technical reports, the lack of training details hinders further research and development. This paper presents the development of YuLan, a series of open-source LLMs with $12$ billion parameters. The base model of YuLan is pre-trained on approximately $1.7$T tokens derived from a diverse corpus, including massive English, Chinese, and multilingual texts. We design a three-stage pre-training method to enhance YuLan's overall capabilities. Subsequent phases of training incorporate instruction-tuning and human alignment, employing a substantial volume of high-quality synthesized data. To facilitate the learning of complex and long-tail knowledge, we devise a curriculum-learning framework throughout across these stages, which helps LLMs learn knowledge in an easy-to-hard manner. YuLan's training is finished on Jan, 2024 and has achieved performance on par with state-of-the-art LLMs across various English and Chinese benchmarks. This paper outlines a comprehensive technical roadmap for developing LLMs from scratch. Our model and codes are available at https://github.com/RUC-GSAI/YuLan-Chat.

cs.CL↗