arXiv subjects

Jingjing Yang

Publications and source records attributed to Jingjing Yang.

17 recordsLinked to original sources

arXiv2026

Partition Functions of Hermitian and PT-Symmetric Oscillators from Integrable Models

We develop an ODE/IM-based formulation for the thermal partition function and the spectral zeta function of the homogeneous Hermitian and PT-symmetric oscillators. For both classes of systems, the quantization condition can be expressed using the counting function $a(E)$, which can be solved via the Destri-de Vega equation of the integrable model. We then express the partition function and spectral zeta function as contour integrals involving the counting function, thereby providing a direct bridge between quantum spectral functions and integrable models.

hep-th↗

arXiv2026

M3Net: A Macro-to-Meso-to-Micro Clinical-inspired Hierarchical 3D Network for Pulmonary Nodule Classification

The accurate classification of benign and malignant pulmonary nodules in CT scans is critical for early lung cancer screening, yet remains challenging due to the multi-scale and heterogeneous nature of pulmonary nodules. While deep learning offers potential for auxiliary diagnosis, most existing models act as "black boxes", lacking the transparency and explainability required for trustworthy clinical integration. To address this issue, we propose M3Net, a novel 3D network for pulmonary nodule classification inspired by the hierarchical diagnostic workflow of radiologists, which integrates multi-scale contextual information from fine-grained structures to global anatomical relationships. Our framework constructs a progressive multi-scale input, from fine-grained nodule structures to local semantics and global spatial relationships. M3Net employs scale-specific encoders and ensures cross-scale semantic consistency through latent space projection and mutual information maximization. Extensive experiments on the public LIDC-IDRI dataset and a self-collected clinical dataset (USTC-FHLN) demonstrate that our method achieves state-of-the-art performance, with accuracies of 86.96% and 84.24% respectively, outperforming the best baseline by 3.26% and 2.17%. The results validate that M3Net provides a more robust and clinically relevant solution for pulmonary nodule classification. The code is available at https://github.com/jylEcho/M3-Net.

cs.CV↗

arXiv2025

JT-DA: Enhancing Data Analysis with Tool-Integrated Table Reasoning Large Language Models

In this work, we present JT-DA-8B (JiuTian Data Analyst 8B), a specialized large language model designed for complex table reasoning tasks across diverse real-world scenarios. To address the lack of high-quality supervision in tabular reasoning scenarios, we construct a comprehensive and diverse training corpus with 34 well-defined table reasoning tasks, by aggregating 29 public table QA datasets and 3 million tables. An automatic pipeline is proposed to generate realistic multi-step analytical tasks involving reasoning patterns. The model is trained upon open-source JT-Coder-8B model, an 8B-parameter decoder-only foundation model trained from scratch. In the training stage, we leverage LLM-based scoring and workflow-aligned filtering to distill high-quality, table-centric data. Both supervised fine-tuning (SFT) and Reinforcement learning (RL) are adopted to optimize our model. Afterwards, a four-stage table reasoning workflow is proposed, including table preprocessing, table sensing, tool-integrated reasoning, and prompt engineering, to improve model interpretability and execution accuracy. Experimental results show that JT-DA-8B achieves strong performance in various table reasoning tasks, demonstrating the effectiveness of data-centric generation and workflow-driven optimization.

cs.AI↗

arXiv2025

A simulation approach including under-resolved scales for multi-component fluid flows in multi-scale porous structures

In this study, we develop computational models and methodology for accurate multi-component-flow simulation in under-resolved multi-scale porous structures. It is generally impractical to fully resolve the flow in porous structures with large length-scale difference due to tremendously high computational expense. The flow contributions from under-resolved scales need to be accounted for with proper physics modeling as well as simulation processes. Using pre-computed physical properties such as the absolute permeability, K0, the capillary-pressure-saturation curve, and the relative permeability, Kr, in typically resolved porous structures, local fluid force is conjectured and applied to simulation in the under-resolved regions that are represented by porous media. By doing so, accurate simulation of flow in multi-scale porous structures becomes feasible. In order to check the accuracy and robustness of this method, a set of benchmark test cases are performed for both single-component and multi-component flows in artificially constructed multi-scale porous structures, and simulation results are compared with analytic solutions and/or results with much finer resolution resolving the porous structures. Quantitatively consistent results are obtained with proper input of K0, capillary pressure, and Kr in all tested cases. Specifically, imbibition patterns, entry pressure, residual component patterns, and the absolute and relative permeability are accurately captured with this approach.

physics.flu-dyn↗

arXiv2025

Thermodynamic Bethe ansatz and wall crossing for deformed supersymmetric quantum mechanics

We study the deformed supersymmetric quantum mechanics with a polynomial superpotential with $\hbar$ correction. In the minimal chamber, where all turning points are real and distinct, it was shown that the exact Wentzel--Kramers--Brillouin periods obey the ${\mathbb Z}_4$-extended thermodynamic Bethe ansatz (TBA) equations of the undeformed potential. By changing the energy parameter above/below the critical points, the turning points become complex, and the moduli are outside of the minimal chamber. We study the wall crossing of the ${\mathbb Z}_4$-extended TBA equations by this change of moduli and show that the ${\mathbb Z}_4$ structure is preserved after the wall crossing. In particular, the TBA equations for the cubic superpotential are studied in detail, where there are two chambers (minimal and maximal). At the maximally symmetric point in the maximal chamber, the TBA system becomes the two sets of the $D_3$-type TBA equations, which are regarded as the ${\mathbb Z}_4$ extension of the $A_3/{\mathbb Z}_2$-type TBA equation.

hep-th↗

arXiv2025

The impact on health system expenditure in Australia and OECD countries from accelerated NCD mortality decline through prevention or treatment strategies to achieve Sustainable Development Goal Target 3.4

Background: It is unclear what the relative impacts of prevention or treatment of NCDs are on future health system expenditure. First, we estimated expenditure in Australia for prevention vs treatment pathways to achieve SDG target 3.4. Second, we applied the method to 34 other OECD countries. Methods: We used GBD data to estimate average annual percentage changes in disease incidence, remission, and CFRs from 1990-2021, and projected to 2030 to estimate business-as-usual (BAU) reductions in NCD mortality risk (40q30). For countries not on track to meet SDG3.4 under BAU, we modelled two intervention scenarios commencing in 2022 to achieve SDG3.4: (1) prevention via accelerated incidence reduction; (2) treatment via accelerated increases in remission and decreases in CFRs. Australian disease expenditure data were input into a PMSLT model to estimate expenditure changes from 2022 to 2040. Assuming similar expenditure patterns, the method was applied across OECD countries. Findings: In Australia, current trends project a 25% reduction in 40q30 by 2030, short of the 33.3% SDG3.4 target. Achieving this requires a 2.53 percentage point (pp) annual acceleration in incidence decline (prevention) or 1.56pp acceleration in CFR reduction and remission increase (treatment). Prevention reduces disease expenditure by 0.72%-3.17% by 2030 and 2040; treatment initially increase expenditure by 0.16%, before reducing it by 0.98%. A treatment scenario reducing only CFRs increased expenditure initially; increasing remission alone achieved savings similar to prevention. Only Sweden, Ireland, and South Korea were on track to meet SDG3.4. Other OECD countries showed similar expenditure impacts to Australia. Interpretation: Whether reducing NCD mortality saves money depends on pathway taken (prevention or treatment). Care is needed when linking NCD mortality reduction to health system savings.

econ.GN↗

arXiv2025

Simultaneous Polysomnography and Cardiotocography Reveal Temporal Correlation Between Maternal Obstructive Sleep Apnea and Fetal Hypoxia

Background: Obstructive sleep apnea syndrome (OSAS) during pregnancy is common and can negatively affect fetal outcomes. However, studies on the immediate effects of maternal hypoxia on fetal heart rate (FHR) changes are lacking. Methods: We used time-synchronized polysomnography (PSG) and cardiotocography (CTG) data from two cohorts to analyze the correlation between maternal hypoxia and FHR changes (accelerations or decelerations). Maternal hypoxic event characteristics were analyzed using generalized linear modeling (GLM) to assess their associations with different FHR changes. Results: A total of 118 pregnant women participated. FHR changes were significantly associated with maternal hypoxia, primarily characterized by accelerations. A longer hypoxic duration correlated with more significant FHR accelerations (P < 0.05), while prolonged hypoxia and greater SpO2 drop were linked to FHR decelerations (P < 0.05). Both cohorts showed a transient increase in FHR during maternal hypoxia, which returned to baseline after the event resolved. Conclusion: Maternal hypoxia significantly affects FHR, suggesting that maternal OSAS may contribute to fetal hypoxia. These findings highlight the importance of maternal-fetal interactions and provide insights for future interventions.

eess.SP↗

arXiv2025

Track and Trace: Automatically Uncovering Cross-chain Transactions in the Multi-blockchain Ecosystems

Cross-chain technology enables seamless asset transfer and message-passing within decentralized finance (DeFi) ecosystems, facilitating multi-chain coexistence in the current blockchain environment. However, this development also raises security concerns, as malicious actors exploit cross-chain asset flows to conceal the provenance and destination of assets, thereby facilitating illegal activities such as money laundering. Consequently, the need for cross-chain transaction traceability has become increasingly urgent. Prior research on transaction traceability has predominantly focused on single-chain and centralized finance (CeFi) cross-chain scenarios, overlooking DeFispecific considerations. This paper proposes ABCTRACER, an automated, bi-directional cross-chain transaction tracing tool, specifically designed for DeFi ecosystems. By harnessing transaction event log mining and named entity recognition techniques, ABCTRACER automatically extracts explicit cross-chain cues. These cues are then combined with information retrieval techniques to encode implicit cues. ABCTRACER facilitates the autonomous learning of latent associated information and achieves bidirectional, generalized cross-chain transaction tracing. Our experiments on 12 mainstream cross-chain bridges demonstrate that ABCTRACER attains 91.75% bi-directional traceability (F1 metrics) with self-adaptive capability. Furthermore, we apply ABCTRACER to real-world cross-chain attack transactions and money laundering traceability, thereby bolstering the traceability and blockchain ecological security of DeFi bridging applications.

cs.SE↗

arXiv2024

TBA equations and quantum periods for D-type Argyres-Douglas theories

We construct TBA equations for D-type Argyres-Douglas theories with an SU(2) flavor symmetry based on their spectral networks. We show that the solutions of these TBA equations agree with the quantum periods of the corresponding quantum Seiberg-Witten curves defined in the Nekrasov-Shatashvili limit of the Omega background, including a centrifugal correction. We study the variety of TBA systems across the Coulomb branch moduli space and find that they correspond to the Dynkin diagrams of $D_n$ Lie algebras in the minimal chamber, and reproduce the TBA equations for reflectionless D scattering theories at the maximally symmetric point. Numerical computations demonstrate that the quantum periods obtained from the Borel-Pad\'e resummation and their WKB expansions are in agreement with the solutions of the TBA equations.

hep-th↗

arXiv2024

MIPI 2024 Challenge on Few-shot RAW Image Denoising: Methods and Results

The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems. However, the scarcity of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). Building on the achievements of the previous MIPI Workshops held at ECCV 2022 and CVPR 2023, we introduce our third MIPI challenge including three tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Few-shot RAW Image Denoising track on MIPI 2024. In total, 165 participants were successfully registered, and 7 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art erformance on Few-shot RAW Image Denoising. More details of this challenge and the link to the dataset can be found at https://mipichallenge.org/MIPI2024.

cs.CV↗

arXiv2024

With Trail to Follow: Measurements of Real-world Non-fungible Token Phishing Attacks on Ethereum

With the popularity of Non-Fungible Tokens (NFTs), NFTs have become a new target of phishing attacks, posing a significant threat to the NFT trading ecosystem. There has been growing anecdotal evidence that new means of NFT phishing attacks have emerged in Ethereum ecosystem. Most of the existing research focus on detecting phishing scam accounts for native cryptocurrency on the blockchain, but there is a lack of research in the area of phishing attacks of emerging NFTs. Although a few studies have recently started to focus on the analysis and detection of NFT phishing attacks, NFT phishing attack means are diverse and little has been done to understand these various types of NFT phishing attacks. To the best of our knowledge, we are the first to conduct case retrospective analysis and measurement study of real-world historical NFT phishing attacks on Ethereum. By manually analyzing the existing scams reported by Chainabuse, we classify NFT phishing attacks into four patterns. For each pattern, we further investigate the tricks and working principles of them. Based on 469 NFT phishing accounts collected up until October 2022 from multiple channels, we perform a measurement study of on-chain transaction data crawled from Etherscan to characterizing NFT phishing scams by analyzing the modus operandi and preferences of NFT phishing scammers, as well as economic impacts and whereabouts of stolen NFTs. We classify NFT phishing transactions into one of the four patterns by log parsing and transaction record parsing. We find these phishing accounts stole 19,514 NFTs for a total profit of 8,858.431 ETH (around 18.57 million dollars). We also observe that scammers remain highly active in the last two years and favor certain categories and series of NFTs, accompanied with signs of gang theft.

cs.CR↗

arXiv2023

Exact WKB Analysis and TBA Equations for the Stark Effect

We apply the exact WKB analysis to a couple of one-dimensional Schroedinger-type equations reduced from the Stark effect of hydrogen in a uniform electric field. By introducing Langer's modification and incorporating the Stokes graphs, we prove the exactness of the Bohr-Sommerfeld quantization conditions for the Borel-resummed quantum WKB periods in the specific parameter regions of the electric field intensity and magnetic quantum number. It is also found these quantization conditions get modified with an additional suppressed contribution when the parameters vary beyond the specific regions. We also present Thermodynamic Bethe Ansatz (TBA) equations governing the quantum periods in the absence of Langer's modification and discuss its wall-crossing and analytic continuation. Numerical calculations are conducted to compare the complex resonant frequencies from our quantization conditions against ones from the Riccati-Pade method, the TBA equations are also confirmed by comparing its expansions with all-order quantum periods.

hep-th↗

arXiv2022

Data Augmentation for Depression Detection Using Skeleton-Based Gait Information

In recent years, the incidence of depression is rising rapidly worldwide, but large-scale depression screening is still challenging. Gait analysis provides a non-contact, low-cost, and efficient early screening method for depression. However, the early screening of depression based on gait analysis lacks sufficient effective sample data. In this paper, we propose a skeleton data augmentation method for assessing the risk of depression. First, we propose five techniques to augment skeleton data and apply them to depression and emotion datasets. Then, we divide augmentation methods into two types (non-noise augmentation and noise augmentation) based on the mutual information and the classification accuracy. Finally, we explore which augmentation strategies can capture the characteristics of human skeleton data more effectively. Experimental results show that the augmented training data set that retains more of the raw skeleton data properties determines the performance of the detection model. Specifically, rotation augmentation and channel mask augmentation make the depression detection accuracy reach 92.15% and 91.34%, respectively.

cs.CV↗

arXiv2021

Radio Emission from Outflow-Cloud Interaction and Its Constraint on TDE Outflow

Tidal disruption event (TDE) can launch an ultrafast outflow. If the black hole is surrounded by large amounts of clouds, outflow-cloud interaction will generate bow shocks, accelerate electrons and produce radio emission. Here we investigate the interaction between a non-relativistic outflow and clouds in active galaxies, which is manifested as outflow-BLR (broad line region) interaction, and can be extended to outflow-torus interaction. This process can generate considerable radio emission, which may account for the radio flares appearing a few months later after TDE outbursts. Benefitting from efficient energy conversion from outflow to shocks and the strong magnetic field, outflow-cloud interaction may play a non-negligible, or even dominating role in generating radio flares in a cloudy circumnuclear environment if the CNM density is no more than 100 times the Sgr A*-like one. In this case, the evolution of radio spectra can be used to directly constrain the properties of outflows.

astro-ph.HE↗

arXiv2017

BFDA: A Matlab Toolbox for Bayesian Functional Data Analysis

We provide a MATLAB toolbox, BFDA, that implements a Bayesian hierarchical model to smooth multiple functional data with the assumptions of the same underlying Gaussian process distribution, a Gaussian process prior for the mean function, and an Inverse-Wishart process prior for the covariance function. This model-based approach can borrow strength from all functional data to increase the smoothing accuracy, as well as estimate the mean-covariance functions simultaneously. An option of approximating the Bayesian inference process using cubic B-spline basis functions is integrated in BFDA, which allows for efficiently dealing with high-dimensional functional data. Examples of using BFDA in various scenarios and conducting follow-up functional regression are provided. The advantages of BFDA include: (1) Simultaneously smooths multiple functional data and estimates the mean-covariance functions in a nonparametric way; (2) flexibly deals with sparse and high-dimensional functional data with stationary and nonstationary covariance functions, and without the requirement of common observation grids; (3) provides accurately smoothed functional data for follow-up analysis.

stat.OT↗

arXiv2016

Efficient Bayesian hierarchical functional data analysis with basis function approximations using Gaussian-Wishart processes

Functional data are defined as realizations of random functions (mostly smooth functions) varying over a continuum, which are usually collected with measurement errors on discretized grids. In order to accurately smooth noisy functional observations and deal with the issue of high-dimensional observation grids, we propose a novel Bayesian method based on the Bayesian hierarchical model with a Gaussian-Wishart process prior and basis function representations. We first derive an induced model for the basis-function coefficients of the functional data, and then use this model to conduct posterior inference through Markov chain Monte Carlo. Compared to the standard Bayesian inference that suffers serious computational burden and unstableness for analyzing high-dimensional functional data, our method greatly improves the computational scalability and stability, while inheriting the advantage of simultaneously smoothing raw observations and estimating the mean-covariance functions in a nonparametric way. In addition, our method can naturally handle functional data observed on random or uncommon grids. Simulation and real studies demonstrate that our method produces similar results as the standard Bayesian inference with low-dimensional common grids, while efficiently smoothing and estimating functional data with random and high-dimensional observation grids where the standard Bayesian inference fails. In conclusion, our method can efficiently smooth and estimate high-dimensional functional data, providing one way to resolve the curse of dimensionality for Bayesian functional data analysis with Gaussian-Wishart processes.

stat.ME↗

arXiv2015

Smoothing and mean-covariance estimation of functional data with a Bayesian hierarchical model

Functional data, with basic observational units being functions (e.g., curves, surfaces) varying over a continuum, are frequently encountered in various applications. While many statistical tools have been developed for functional data analysis, the issue of smoothing all functional observations simultaneously is less studied. Existing methods often focus on smoothing each individual function separately, at the risk of removing important systematic patterns common across functions. We propose a nonparametric Bayesian approach to smooth all functional observations simultaneously and nonparametrically. In the proposed approach, we assume that the functional observations are independent Gaussian processes subject to a common level of measurement errors, enabling the borrowing of strength across all observations. Unlike most Gaussian process regression models that rely on pre-specified structures for the covariance kernel, we adopt a hierarchical framework by assuming a Gaussian process prior for the mean function and an Inverse-Wishart process prior for the covariance function. These prior assumptions induce an automatic mean-covariance estimation in the posterior inference in addition to the simultaneous smoothing of all observations. Such a hierarchical framework is flexible enough to incorporate functional data with different characteristics, including data measured on either common or uncommon grids, and data with either stationary or nonstationary covariance structures. Simulations and real data analysis demonstrate that, in comparison with alternative methods, the proposed Bayesian approach achieves better smoothing accuracy and comparable mean-covariance estimation results. Furthermore, it can successfully retain the systematic patterns in the functional observations that are usually neglected by the existing functional data analyses based on individual-curve smoothing.

stat.ME↗