arXiv subjects

Tao Han

Publications and source records attributed to Tao Han.

At least 73 records · Page 4Linked to original sources

arXiv2025

Quantum Tomography at Colliders: With or Without Decays

The interpretation of groups of particle spins at colliders as quantum states has opened up the possibility of using colliders for quantum information. While most efforts have focused on utilizing the decays of the particles to infer their spins to reconstruct the quantum density matrix, we show that the production kinematics of the particles provides sufficient information about the spins to establish quantum tomography without using the decays. We perform a comparative study, highlighting the advantages and disadvantages of using this "kinematic approach" relative to the usual "decay approach." Since the kinematic approach leverages the simplicity of scattering kinematics, this approach promises to achieve the optimal statistical results for quantum tomography at colliders.

hep-ph↗

arXiv2025

Optimizing Case-Based Reasoning System for Functional Test Script Generation with Large Language Models

In this work, we explore the potential of large language models (LLMs) for generating functional test scripts, which necessitates understanding the dynamically evolving code structure of the target software. To achieve this, we propose a case-based reasoning (CBR) system utilizing a 4R cycle (i.e., retrieve, reuse, revise, and retain), which maintains and leverages a case bank of test intent descriptions and corresponding test scripts to facilitate LLMs for test script generation. To improve user experience further, we introduce Re4, an optimization method for the CBR system, comprising reranking-based retrieval finetuning and reinforced reuse finetuning. Specifically, we first identify positive examples with high semantic and script similarity, providing reliable pseudo-labels for finetuning the retriever model without costly labeling. Then, we apply supervised finetuning, followed by a reinforcement learning finetuning stage, to align LLMs with our production scenarios, ensuring the faithful reuse of retrieved cases. Extensive experimental results on two product development units from Huawei Datacom demonstrate the superiority of the proposed CBR+Re4. Notably, we also show that the proposed Re4 method can help alleviate the repetitive generation issues with LLMs.

cs.SE↗

arXiv2025

Model Predictive Online Monitoring of Dynamical Systems for Nested Signal Temporal Logic Specifications

This paper investigates the online monitoring problem for cyber-physical systems under signal temporal logic (STL) specifications. The objective is to design an online monitor that evaluates system correctness at runtime based on partial signal observations up to the current time so that alarms can be issued whenever the specification is violated or will inevitably be violated in the future. We consider a model-predictive setting where the system's dynamic model is available and can be leveraged to enhance monitoring accuracy. However, existing approaches are limited to a restricted class of STL formulae, permitting only a single application of temporal operators. This work addresses the challenge of nested temporal operators in the design of model-predictive monitors. Our method utilizes syntax tree structures to resolve dependencies between temporal operators and introduces the concept of basic satisfaction vectors. A new model-predictive monitoring algorithm is proposed by recursively updating these vectors online while incorporating pre-computed satisfaction regions derived from offline model analysis. We prove that the proposed approach is both sound and complete, ensuring no false alarms or missed alarms. Case studies are provided to demonstrate the effectiveness of our method.

math.OC↗

arXiv2025

FreeMesh: Boosting Mesh Generation with Coordinates Merging

The next-coordinate prediction paradigm has emerged as the de facto standard in current auto-regressive mesh generation methods. Despite their effectiveness, there is no efficient measurement for the various tokenizers that serialize meshes into sequences. In this paper, we introduce a new metric Per-Token-Mesh-Entropy (PTME) to evaluate the existing mesh tokenizers theoretically without any training. Building upon PTME, we propose a plug-and-play tokenization technique called coordinate merging. It further improves the compression ratios of existing tokenizers by rearranging and merging the most frequent patterns of coordinates. Through experiments on various tokenization methods like MeshXL, MeshAnything V2, and Edgerunner, we further validate the performance of our method. We hope that the proposed PTME and coordinate merging can enhance the existing mesh tokenizers and guide the further development of native mesh generation.

cs.GR↗

arXiv2025

Event-Based Eye Tracking. 2025 Event-based Vision Workshop

This survey serves as a review for the 2025 Event-Based Eye Tracking Challenge organized as part of the 2025 CVPR event-based vision workshop. This challenge focuses on the task of predicting the pupil center by processing event camera recorded eye movement. We review and summarize the innovative methods from teams rank the top in the challenge to advance future event-based eye tracking research. In each method, accuracy, model size, and number of operations are reported. In this survey, we also discuss event-based eye tracking from the perspective of hardware design.

cs.CV↗

arXiv2025

Edge Approximation Text Detector

Pursuing efficient text shape representations helps scene text detection models focus on compact foreground regions and optimize the contour reconstruction steps to simplify the whole detection pipeline. Current approaches either represent irregular shapes via box-to-polygon strategy or decomposing a contour into pieces for fitting gradually, the deficiency of coarse contours or complex pipelines always exists in these models. Considering the above issues, we introduce EdgeText to fit text contours compactly while alleviating excessive contour rebuilding processes. Concretely, it is observed that the two long edges of texts can be regarded as smooth curves. It allows us to build contours via continuous and smooth edges that cover text regions tightly instead of fitting piecewise, which helps avoid the two limitations in current models. Inspired by this observation, EdgeText formulates the text representation as the edge approximation problem via parameterized curve fitting functions. In the inference stage, our model starts with locating text centers, and then creating curve functions for approximating text edges relying on the points. Meanwhile, truncation points are determined based on the location features. In the end, extracting curve segments from curve functions by using the pixel coordinate information brought by truncation points to reconstruct text contours. Furthermore, considering the deep dependency of EdgeText on text edges, a bilateral enhanced perception (BEP) module is designed. It encourages our model to pay attention to the recognition of edge features. Additionally, to accelerate the learning of the curve function parameters, we introduce a proportional integral loss (PI-loss) to force the proposed model to focus on the curve distribution and avoid being disturbed by text scales.

cs.CV↗

arXiv2025

TGP: Two-modal occupancy prediction with 3D Gaussian and sparse points for 3D Environment Awareness

3D semantic occupancy has rapidly become a research focus in the fields of robotics and autonomous driving environment perception due to its ability to provide more realistic geometric perception and its closer integration with downstream tasks. By performing occupancy prediction of the 3D space in the environment, the ability and robustness of scene understanding can be effectively improved. However, existing occupancy prediction tasks are primarily modeled using voxel or point cloud-based approaches: voxel-based network structures often suffer from the loss of spatial information due to the voxelization process, while point cloud-based methods, although better at retaining spatial location information, face limitations in representing volumetric structural details. To address this issue, we propose a dual-modal prediction method based on 3D Gaussian sets and sparse points, which balances both spatial location and volumetric structural information, achieving higher accuracy in semantic occupancy prediction. Specifically, our method adopts a Transformer-based architecture, taking 3D Gaussian sets, sparse points, and queries as inputs. Through the multi-layer structure of the Transformer, the enhanced queries and 3D Gaussian sets jointly contribute to the semantic occupancy prediction, and an adaptive fusion mechanism integrates the semantic outputs of both modalities to generate the final prediction results. Additionally, to further improve accuracy, we dynamically refine the point cloud at each layer, allowing for more precise location information during occupancy prediction. We conducted experiments on the Occ3DnuScenes dataset, and the experimental results demonstrate superior performance of the proposed method on IoU based metrics.

cs.CV↗

arXiv2025

Mixture of Experts based Multi-task Supervise Learning from Crowds

Existing truth inference methods in crowdsourcing aim to map redundant labels and items to the ground truth. They treat the ground truth as hidden variables and use statistical or deep learning-based worker behavior models to infer the ground truth. However, worker behavior models that rely on ground truth hidden variables overlook workers' behavior at the item feature level, leading to imprecise characterizations and negatively impacting the quality of truth inference. This paper proposes a new paradigm of multi-task supervised learning from crowds, which eliminates the need for modeling of items's ground truth in worker behavior models. Within this paradigm, we propose a worker behavior model at the item feature level called Mixture of Experts based Multi-task Supervised Learning from Crowds (MMLC). Two truth inference strategies are proposed within MMLC. The first strategy, named MMLC-owf, utilizes clustering methods in the worker spectral space to identify the projection vector of the oracle worker. Subsequently, the labels generated based on this vector are considered as the inferred truth. The second strategy, called MMLC-df, employs the MMLC model to fill the crowdsourced data, which can enhance the effectiveness of existing truth inference methods. Experimental results demonstrate that MMLC-owf outperforms state-of-the-art methods and MMLC-df enhances the quality of existing truth inference methods.

cs.AI↗

arXiv2025

Transforming Weather Data from Pixel to Latent Space

The increasing impact of climate change and extreme weather events has spurred growing interest in deep learning for weather research. However, existing studies often rely on weather data in pixel space, which presents several challenges such as smooth outputs in model outputs, limited applicability to a single pressure-variable subset (PVS), and high data storage and computational costs. To address these challenges, we propose a novel Weather Latent Autoencoder (WLA) that transforms weather data from pixel space to latent space, enabling efficient weather task modeling. By decoupling weather reconstruction from downstream tasks, WLA improves the accuracy and sharpness of weather task model results. The incorporated Pressure-Variable Unified Module transforms multiple PVS into a unified representation, enhancing the adaptability of the model in multiple weather scenarios. Furthermore, weather tasks can be performed in a low-storage latent space of WLA rather than a high-storage pixel space, thus significantly reducing data storage and computational costs. Through extensive experimentation, we demonstrate its superior compression and reconstruction performance, enabling the creation of the ERA5-latent dataset with unified representations of multiple PVS from ERA5 data. The compressed full PVS in the ERA5-latent dataset reduces the original 244.34 TB of data to 0.43 TB. The downstream task further demonstrates that task models can apply to multiple PVS with low data costs in latent space and achieve superior performance compared to models in pixel space. Code, ERA5-latent data, and pre-trained models are available at https://anonymous.4open.science/r/Weather-Latent-Autoencoder-8467.

cs.CV↗

arXiv2025

Revisiting holographic dark energy from the perspective of multi-messenger gravitational wave astronomy: future joint observations with short gamma-ray bursts

The advent of third-generation (3G) gravitational-wave (GW) detectors opens new opportunities for multi-messenger observations of binary neutron star merger events, holding significant potential for probing the history of cosmic expansion. In this paper, we investigate the holographic dark energy (HDE) model by using the future GW standard siren data observed from the 3G GW detectors and the short $γ$-ray burst THESEUS-like detector joint observations. We find that GW data alone can achieve a relatively precise estimation of the Hubble constant, with precision of $0.2\%$-$0.6\%$, but its ability to constrain other cosmological parameters remains limited. Nonetheless, since the GW data can break parameter degeneracies generated by the mainstream EM observations, CMB + BAO + SN (CBS), GW standard sirens play a crucial role in enhancing the accuracy of parameter estimation. With the addition of GW data to CBS, the constraints on cosmological parameters $H_0$, $c$ and $Ω_{\rm{m}}$ can be improved by $63\%$-$88\%$, $27\%$-$44\%$ and $55\%$-$70\%$. In summary, observations of GW standard sirens from 3G GW detectors could be pivotal in helping solve the Hubble tension and probe the fundamental nature of dark energy.

astro-ph.CO↗

arXiv2025

Measuring Quantum Discord at the LHC

There has been an increasing interest in exploring quantities associated with quantum information at colliders. We perform a detailed analysis describing how to measure the quantum discord in the top anti-top quantum state at the Large Hadron Collider (LHC). While for pure states, quantum discord, entanglement, and Bell nonlocality all probe the same correlations, for mixed states they probe different aspects of quantum correlations. The quantum discord, in particular, is interesting because it aims to encapsulate all correlations between systems that cannot have a classical origin. We employ two complementary approaches for the study of the top anti-top system, namely the decay method and the kinematic method. We highlight subtleties associated with measuring discord for reconstructed quantum states at colliders. Usually quantum discord is difficult to compute due to an extremization that must be performed. We show, however, that for the $t\bar{t}$ system this extremization can be performed analytically and we provide closed-form formulas for the quantum discord. We demonstrate that at the high luminosity LHC, discord is projected to be measurable with a precision of approximately 5% using the decay method and sub-percent levels using the kinematic method. Even with current LHC datasets, discord can be measured with 1-2% precision with the kinematic method. By systematically investigating quantum discord for the first time through a detailed collider analysis, this work expands the toolkit for quantum information studies in particle physics and lays the groundwork for deeper insights into the quantum properties in high-energy collisions.

hep-ph↗

arXiv2025

Symmetry-Broken Kondo Screening and Zero-Energy Mode in the Kagome Superconductor CsV3Sb5

The quantum states of matter reorganize themselves in response to defects, giving rise to emergent local excitations that imprint unique characteristics of the host states. While magnetic impurities are known to generate Kondo screening in a Fermi liquid and Yu-Shiba-Rusinov (YSR) states in a conventional superconductor, it remains unclear whether they can evoke distinct phenomena in the kagome superconductor AV3Sb5 (where A is K, Rb or Cs), which may host an orbital-antiferromagnetic charge density wave (CDW) state and an unconventional superconducting state driven by the convergence of topology, geometric frustration and electron correlations. In this work, we visualize the local density of states induced near various types of impurities in both the CDW and superconducting phases of CsV3-xMxSb5 (M = Ta, Cr) using scanning tunneling microscopy. We observe Kondo resonance states near magnetic Cr dopants. Notably, unlike in any known metal or CDW compound, the spatial pattern of Kondo screening breaks all in-plane mirror symmetries of the kagome lattice, suggesting an electronic chirality due to putative orbital loop currents. While Cooper pairs show relative insensitivity to nonmagnetic impurities, native V vacancies with weak magnetic moments induce a pronounced zero-bias conductance peak (ZBCP). This ZBCP coexists with trivial YSR states within the superconducting gap and does not split in energy with increasing tunneling transmission, tending instead to saturate. This behavior is reminiscent of signature of Majorana zero modes, which could be trapped by a sign-change boundary in the superconducting order parameter near a V vacancy, consistent with a surface topological superconducting state. Our findings provide a new approach to exploring novel quantum states on kagome lattices.

cond-mat.supr-con↗

arXiv2025

Colorful Particle Production at High-Energy Muon Colliders

A high-energy $μ^+μ^-$ collider provides a wide variety of mechanisms for the production of new heavy particles. While the reach for such particles via the direct annihilation of $μ^+μ^-$ will approach the center-of-mass energy of the collider, the partonic fusions from gauge bosons, quarks, and gluons, originating from the incoming muon beams will open new channels for single production and pair production of particles with different quantum numbers. We present the production rates for a wide variety of colored states including color-triplets, color-sextets, color-octets, leptoquarks, and leptogluons. We find that pair production from the direct annihilation of $μ^+μ^-$ generally has a cross section of $0.1 - 1$ fb once above the production threshold. On the other hand, pair production through the quark and gluon content of the muon leads to a cross section of roughly $10^{-4}$ fb at the same particle mass. We perform simple estimations of the mass reach for each particle and find that a 10 TeV muon collider can extend the reach for color-triplets beyond what is possible at the high luminosity run of the Large Hadron Collider. Leptoquarks and leptogluons, with sensitivity driven by single production, can also be probed to higher masses at a muon collider than what the Large Hadron Collider can reach. A final example where a muon collider has superior reach is for color-octet scalars and vectors. Together, these cases illustrate the point that a muon collider is a competitive machine for searching for colored heavy particles, thus strengthening the motivation for such lepton colliders in the energy frontier. Although our study is focused on a muon collider, our results are largely applicable to high-energy $e^+e^-$ collisions as well.

hep-ph↗

arXiv2025

VQLTI: Long-Term Tropical Cyclone Intensity Forecasting with Physical Constraints

Tropical cyclone (TC) intensity forecasting is crucial for early disaster warning and emergency decision-making. Numerous researchers have explored deep-learning methods to address computational and post-processing issues in operational forecasting. Regrettably, they exhibit subpar long-term forecasting capabilities. We use two strategies to enhance long-term forecasting. (1) By enhancing the matching between TC intensity and spatial information, we can improve long-term forecasting performance. (2) Incorporating physical knowledge and physical constraints can help mitigate the accumulation of forecasting errors. To achieve the above strategies, we propose the VQLTI framework. VQLTI transfers the TC intensity information to a discrete latent space while retaining the spatial information differences, using large-scale spatial meteorological data as conditions. Furthermore, we leverage the forecast from the weather prediction model FengWu to provide additional physical knowledge for VQLTI. Additionally, we calculate the potential intensity (PI) to impose physical constraints on the latent variables. In the global long-term TC intensity forecasting, VQLTI achieves state-of-the-art results for the 24h to 120h, with the MSW (Maximum Sustained Wind) forecast error reduced by 35.65%-42.51% compared to ECMWF-IFS.

cs.LG↗

arXiv2025

Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling

Data-driven artificial intelligence (AI) models have made significant advancements in weather forecasting, particularly in medium-range and nowcasting. However, most data-driven weather forecasting models are black-box systems that focus on learning data mapping rather than fine-grained physical evolution in the time dimension. Consequently, the limitations in the temporal scale of datasets prevent these models from forecasting at finer time scales. This paper proposes a physics-AI hybrid model (i.e., WeatherGFT) which generalizes weather forecasts to finer-grained temporal scales beyond training dataset. Specifically, we employ a carefully designed PDE kernel to simulate physical evolution on a small time scale (e.g., 300 seconds) and use a parallel neural networks with a learnable router for bias correction. Furthermore, we introduce a lead time-aware training framework to promote the generalization of the model at different lead times. The weight analysis of physics-AI modules indicates that physics conducts major evolution while AI performs corrections adaptively. Extensive experiments show that WeatherGFT trained on an hourly dataset, effectively generalizes forecasts across multiple time scales, including 30-minute, which is even smaller than the dataset's temporal resolution.

cs.LG↗

arXiv2025

High Energy Leptonic Collisions and Electroweak Parton Distribution Functions

In high-energy leptonic collisions well above the electroweak scale, the collinear splitting mechanism of the electroweak gauge bosons becomes the dominant phenomena via the initial state radiation and the final state showering. We point out that at future high-energy lepton colliders, such as a multi-TeV muon collider, the electroweak parton distribution functions (EW PDFs) should be adopted as the proper description for partonic collisions of the initial states. The leptons and electroweak gauge bosons are the EW partons, that evolve according to the unbroken Standard Model (SM) gauge group and that effectively resum potentially large collinear logarithms. We present a formalism for the EW PDFs at the Next-to-Leading-Log (NLL) accuracy. We calculate semi-inclusive cross sections for some important SM processes at a future multi-TeV muon collider. We conclude that it is appropriate to adopt the EW PDF formalism for future high-energy lepton colliders.

hep-ph↗

arXiv2024

Efficient Circuit Cutting and Scheduling in a Multi-Node Quantum System with Dynamic EPR Pairs

Despite advancements, current quantum hardware faces significant challenges, including limited qubit counts and high susceptibility to noise, which hinder the execution of large, complex algorithms. To address these limitations, multi-node quantum systems and quantum circuit cutting techniques partition large circuits into smaller subcircuits that can be executed on individual quantum machines and then reconstructed using classical resources. However, these methods introduce new challenges, such as the large overhead from subcircuit reconstruction and additional noise from entangled EPR pairs, especially in multi-node quantum systems. In this paper, we propose the Efficient Circuit Cutting and Scheduling (EC2S) system, which integrates EPR pairs with circuit cutting to address these issues. EC2S improves system performance by transitioning from logical to physical EPR pairs and further reduces computational overhead by minimizing the number of subcircuits during the reconstruction phase. \sol~ is implemented using Qiskit and evaluated on both real quantum hardware and various emulators. Compared to the state-of-the-art Qiskit-Addon-Cut, EC2S achieves significant improvements in fidelity, up to 16.7\%, and reduces system-wide expenditure by up to 99.5\%.

cs.DC↗

arXiv2024

FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere

Seamless forecasting that produces warning information at continuum timescales based on only one system is a long-standing pursuit for weather-climate service. While the rapid advancement of deep learning has induced revolutionary changes in classical forecasting field, current efforts are still focused on building separate AI models for weather and climate forecasts. To explore the seamless forecasting ability based on one AI model, we propose FengWu-Weather to Subseasonal (FengWu-W2S), which builds on the FengWu global weather forecast model and incorporates an ocean-atmosphere-land coupling structure along with a diverse perturbation strategy. FengWu-W2S can generate 6-hourly atmosphere forecasts extending up to 42 days through an autoregressive and seamless manner. Our hindcast results demonstrate that FengWu-W2S reliably predicts atmospheric conditions out to 3-6 weeks ahead, enhancing predictive capabilities for global surface air temperature, precipitation, geopotential height and intraseasonal signals such as the Madden-Julian Oscillation (MJO) and North Atlantic Oscillation (NAO). Moreover, our ablation experiments on forecast error growth from daily to seasonal timescales reveal potential pathways for developing AI-based integrated system for seamless weather-climate forecasting in the future.

cs.LG↗