arXiv subjects

Jing Guo

Publications and source records attributed to Jing Guo.

At least 55 records · Page 3Linked to original sources

arXiv2025

A Giant Peanut-shaped Ultra-High-Energy Gamma-Ray Emitter Off the Galactic Plane

Ultra-high-energy (UHE), exceeding 100 TeV (10^12 electronvolts), γ-rays manifests extreme particle acceleration in astrophysical sources. Recent observations by γ-ray telescopes, particularly by the Large High Altitude Air Shower Observatory (LHAASO), have revealed a few tens of UHE sources, indicating numerous Galactic sources capable of accelerating particles to PeV (10^15 electronvolts) energies. However, discerning the dominant acceleration mechanisms (leptonic versus hadronic), the relative contributions of specific source classes, and the role of particle transport in shaping their observed emission are central goals of modern UHE astrophysics. Here we report the discovery of a giant UHE γ-ray emitter at -17.5° off the Galactic plane - a region where UHE γ-ray sources are rarely found. The emitter exhibits a distinctive asymmetric shape, resembling a giant "Peanut" spanning 0.45° \times 4.6°, indicative of anisotropic particle distribution over a large area. A highly aged millisecond pulsar (MSP) J0218+4232 is the sole candidate accelerator positionally coincident with the Peanut region. Its association with UHE γ-rays extending to 0.7 PeV, if confirmed, would provide the first evidence of a millisecond pulsar powering PeV particles. Such a finding challenges prevailing models, which posit that millisecond pulsars cannot sustain acceleration to PeV energies. The detection reveals fundamental gaps in understanding particle acceleration, cosmic-ray transport, and interstellar magnetic field effects, potentially revealing new PeV accelerator (PeVatron) classes.

astro-ph.HE↗

arXiv2025

Adaptive Nonlinear Model Predictive Control of Monoclonal Antibody Glycosylation in CHO Cell Culture

N-glycosylation is a critical quality attribute of monoclonal antibodies (mAbs), the dominant class of biopharmaceuticals. Controlling glycosylation remains difficult due to intrinsic pathway complexity, limited online measurements, and a lack of tailored control strategies. This work applies an adaptive nonlinear model predictive control (ANMPC) framework to a fed-batch mAb production process, using a multiscale model that links extracellular conditions to intracellular Golgi reactions to predict glycan profiles. Model parameters are updated online as new measurements arrive, after which a shrinking-horizon optimization computes the control inputs; only the first control move is implemented each cycle. Case studies show that, with a minimal day-1 galactose excitation, ANMPC mitigates model-plant mismatch and achieves up to 130% and 96% higher performance than open-loop optimization and state NMPC, respectively. Under more realistic conditions (partial measurement availability and longer preparation time), ANMPC maintains comparable performance, indicating robustness to practical limitations. Overall, the results demonstrate that ANMPC can actively shape glycan distributions in silico and offers a viable path toward closed-loop control of mAb glycosylation.

math.OC↗

arXiv2025

Spec-Driven AI for Science: The ARIA Framework for Automated and Reproducible Data Analysis

The rapid expansion of scientific data has widened the gap between analytical capability and research intent. Existing AI-based analysis tools, ranging from AutoML frameworks to agentic research assistants, either favor automation over transparency or depend on manual scripting that hinders scalability and reproducibility. We present ARIA (Automated Research Intelligence Assistant), a spec-driven, human-in-the-loop framework for automated and interpretable data analysis. ARIA integrates six interoperable layers, namely Command, Context, Code, Data, Orchestration, and AI Module, within a document-centric workflow that unifies human reasoning and machine execution. Through natural-language specifications, researchers define analytical goals while ARIA autonomously generates executable code, validates computations, and produces transparent documentation. Beyond achieving high predictive accuracy, ARIA can rapidly identify optimal feature sets and select suitable models, minimizing redundant tuning and repetitive experimentation. In the Boston Housing case, ARIA discovered 25 key features and determined XGBoost as the best performing model (R square = 0.93) with minimal overfitting. Evaluations across heterogeneous domains demonstrate ARIA's strong performance, interpretability, and efficiency compared with state-of-the-art systems. By combining AI for research and AI for science principles within a spec-driven architecture, ARIA establishes a new paradigm for transparent, collaborative, and reproducible scientific discovery.

cs.AI↗

arXiv2025

The minimum degree of minimal 2-extendable claw-free graphs

A connected graph $G$ with a perfect matching is said to be $k$-extendable for integers $k$, $1 \leq k\leq \frac{|V(G)|}{2}-1$, if any matching in $G$ of size $k$ is contained in a perfect matching of $G$. A $k$-extendable graph is minimal if the deletion of any edge results in a graph that is not $k$-extendable. In 1994, Plummer proved that every $k$-extendable claw-free graph has minimum degree at least $2k$. Recently, He et al. showed that every minimal 1-extendable graph has minimum degree 2 or 3. In this paper, we prove that the minimum degree of a minimal 2-extendable claw-free graph is either $4$ or $5$.

math.CO↗

arXiv2025

In Vivo Wideband MR Elastography for Assessing Age-Related Viscoelastic Changes of the Human Brain

Magnetic Resonance Elastography (MRE) noninvasively maps brain biomechanics and is highly sensitive to alterations associated with aging and neurodegenerative disease. Most implementations use a single frequency or a narrow frequency band, limiting the analysis of frequency-dependent viscoelastic parameters. We developed a dual-actuator wideband MRE (5-50 Hz) protocol and acquired wavefields at 13 frequencies in 24 healthy adults (young: 23-39 years; older: 50-63 years). Shear wave speed (SWS) maps were generated as a proxy for stiffness, and SWS dispersion was modeled using Newtonian, Kelvin-Voigt, and power-law rheological models. Whole-brain stiffness declined with age, with the strongest effect observed at low frequencies (5-16 Hz: -0.24%/year; p=0.019) compared with mid (20-35 Hz: -0.12%/year; p=0.030) and high frequencies (40-50 Hz: -0.10%/year; p=0.165). Compared to older brains, younger adults showed 14.3% higher baseline stiffness in the power-law model (p=0.001) and 8.5-9.0% higher viscosity according to the Newtonian and Kelvin-Voigt model (p<0.05). White and cortical gray matter exhibited similar age-related decreases, while deep gray matter showed an increase in the power-law exponent (+0.001/year; p=0.036), suggesting a transition toward more fluid-like properties associated with aging. Wideband MRE revealed frequency-dependent and region-specific biomechanical alterations with aging, with the strongest effects observed at low frequencies. Extending brain MRE into the low frequency regime potentially enhances sensitivity to solid-fluid interactions. Therefore, low frequency MRE may serve as an early biomechanical marker of microstructural brain changes due to aging and neurodegeneration.

physics.med-ph↗

arXiv2025

Variational Quantum Linear Solver for Simulating Quantum Transport in Nanoscale Semiconductor Devices

This work develops simulation methods that enable the application of the variational quantum linear solver (VQLS) to simulate quantum transport in nanoscale semiconductor devices. Most previous work on VQLS applications in semiconductor device simulations focuses on solving the Poisson equation, where the coefficient matrix of the sparse linear system is real and symmetric. Solving the quantum transport equation, however, leads to coefficient matrices that are complex and non-symmetric. This work addresses the challenges of applying VQLS to quantum transport simulations. We propose new forms of cost functions to solve complex and non-symmetric linear systems with faster computing speed. We further develop efficient decomposition methods for cost function evaluation, which target reducing the quantum circuit complexity and improving noise robustness when solving the quantum transport equation using the non-equilibrium Green's function method. While classical computation faces the challenge of the "curse of dimensionality" as the spatial-energy numerical grid dimensions grow, the proposed quantum-computing-based method scales logarithmically with the grid size, which offers a promising opportunity for addressing the computational challenges of solving quantum transport in semiconductor devices.

quant-ph↗

arXiv2025

ShizhenGPT: Towards Multimodal LLMs for Traditional Chinese Medicine

Despite the success of large language models (LLMs) in various domains, their potential in Traditional Chinese Medicine (TCM) remains largely underexplored due to two critical barriers: (1) the scarcity of high-quality TCM data and (2) the inherently multimodal nature of TCM diagnostics, which involve looking, listening, smelling, and pulse-taking. These sensory-rich modalities are beyond the scope of conventional LLMs. To address these challenges, we present ShizhenGPT, the first multimodal LLM tailored for TCM. To overcome data scarcity, we curate the largest TCM dataset to date, comprising 100GB+ of text and 200GB+ of multimodal data, including 1.2M images, 200 hours of audio, and physiological signals. ShizhenGPT is pretrained and instruction-tuned to achieve deep TCM knowledge and multimodal reasoning. For evaluation, we collect recent national TCM qualification exams and build a visual benchmark for Medicinal Recognition and Visual Diagnosis. Experiments demonstrate that ShizhenGPT outperforms comparable-scale LLMs and competes with larger proprietary models. Moreover, it leads in TCM visual understanding among existing multimodal LLMs and demonstrates unified perception across modalities like sound, pulse, smell, and vision, paving the way toward holistic multimodal perception and diagnosis in TCM. Datasets, models, and code are publicly available. We hope this work will inspire further exploration in this field.

cs.CL↗

arXiv2025

Efficient and stable diffusion generated methods for ground state computation in Bose--Einstein condensates

This paper investigates numerical methods for approximating the ground state of Bose--Einstein condensates (BECs) by introducing two relaxed formulations of the Gross--Pitaevskii energy functional. These formulations achieve first- and second-order accuracy with respect to the relaxation parameter $ τ$, and are shown to converge to the original energy functional as $ τ\to 0 $. A key feature of the relaxed functionals is their concavity, which ensures that local minima lie on the boundary of the concave hull. This property prevents energy increases during constraint normalization and enables the development of energy-dissipative algorithms. Numerical methods based on sequential linear programming are proposed, accompanied by rigorous analysis of their stability with respect to the relaxed energy. To enhance computational efficiency, an adaptive strategy is introduced, dynamically refining solutions obtained with larger relaxation parameters to achieve higher accuracy with smaller ones. Numerical experiments demonstrate the stability, convergence, and energy dissipation of the proposed methods, while showcasing the adaptive strategy's effectiveness in improving computational performance.

math.NA↗

arXiv2025

On the averaging theorems for stochastic perturbation of conservative linear systems

For stochastic perturbations of linear systems with non-zero pure imaginary spectrum we discuss the averaging theorems in terms of the slow-fast action-angle variables and in the sense of Krylov-Bogoliubov. Then we show that if the diffusion matrix of the perturbation is uniformly elliptic, then in all cases the averaged dynamics does not depend on a hamiltonian part of the perturbation.

math.DS↗

arXiv2025

Superconducting susceptibility signal captured in a record wide pressure range

In recent years, the resistance signature of the high temperature superconductivity above 250 K in highly compressed hydrides (more than 100 GPa) has garnered significant attention within the condensed matter physics community. This has sparked renewed optimism for achieving superconductivity under room-temperature conditions. However, the superconducting diamagnetism, another crucial property for confirming the superconductivity, has yet to be conclusively observed. The primary challenge arises from the weak diamagnetic signals detected from the small samples compressed in diamond anvil cells. Therefore, the reported results of superconducting diamagnetism in hydrides have sparked intense debate, highlighting the urgent need for workable methodology to assess the validity of the experimental results. Here, we are the first to report the ultrahigh pressure measurements of the superconducting diamagnetism on Nb0.44Ti0.56, a commercial superconducting alloy, in a record-wide pressure range from 5 GPa to 160 GPa. We present detailed results on factors such as sample size, the diamagnetic signal intensity, the signal-to-noise ratio and the superconducting transition temperature across various pressures and different pressure transmitting media. These comprehensive results clearly demonstrate that this alloy is an ideal reference sample for evaluating superconductivity in compressed hydrides,validating the credibility of the experimental systems and superconducting diamagnetic results, as well as determining the nature of the superconductivity of the investigated sample. In addition, these results also provide a valuable benchmark for studying the pressure-induced superconductivity in other material families.

cond-mat.supr-con↗

arXiv2025

Superconducting-insulating phase transition in pressurized Ba1-xKxBiO3

We report the first observation of a pressure-induced transition from a superconducting (SC) to an insulating (I) phase in single-crystal Ba1-xKxBiO3 (x = 0.4, 0.43, 0.52, and 0.58) superconductors. X-ray diffraction measurements conducted at 20 K reveal a direct relationship between this SC-I transition and a pressure-induced distortion of crystal structure. With increasing pressure, the lattice parameters a and c of the ambient-pressure superconducting tetragonal (T) phase are compressed continuously below a critical pressure (Pc1), wherein the pressure (P) dependence of superconducting transition temperature (Tc) displays a small variation. However, upon further compression, the lattice of the compressed T phase displays an anisotropic change, and Tc shows a monotonous decrease. When the pressure reaches Pc2 (Pc2 > Pc1), the compressed T phase collapses along the c axis, followed by the disappearance of superconductivity and the appearance of the insulating phase. This SC-I transition is fully reversible, with the critical pressure increasing alongside K doping concentration. These findings are strikingly similar to the SC-I transition observed in hole-doped high-Tc cuprate superconductors under pressure. Identifying their commonalities could deepen our understanding of the mechanisms that underlie high- Tc superconductivity in these two oxide superconductors with a perovskite structure.

cond-mat.supr-con↗

arXiv2025

Investigations of key issues on the reproducibility of high-Tc superconductivity emerging from compressed La3Ni2O7

Recently, the signatures of superconductivity near 80 K have been discovered in the single crystal of La3Ni2O7 under pressure, which makes it a new candidate of the high-temperature superconductors dominated by 3d transition elements after the cuprate and iron-pnictide superconductors. However, there are several critical questions that have been perplexing the scientific community. These questions include (1) what factors contribute to the inconsistent reproducibility of the experimental results? (2) what is the fundamental nature of pressure-induced superconductivity: bulk or non-bulk (filamentary-like)? (3) where does the superconducting phase locate within the sample if it is filamentary-like? (4) is the oxygen content important for developing and stabilizing its superconductivity? In this study, we employ comprehensive high-pressure techniques to address these crucial issues. Through our modulated ac susceptibility measurements, we are the first to find that the superconductivity in this nickelate is filamentary-like. Our scanning transmission electron microscopy (STEM) investigations suggest that the filamentary-like superconductivity most likely emerges at the interface between the La3Ni2O7 and La4Ni3O10 phases. By tuning the oxygen content on the polycrystalline La3Ni2O7, we also find that the oxygen content plays vital role for developing and stabilizing its superconductivity. The upper and lower bounds of the oxygen content are 7.35 and 6.89, respectively. Our results provide not only new insights into understanding the puzzling issues in this material, but also significant information for achieving a better understanding on the superconductivity of this material.

cond-mat.supr-con↗

arXiv2025

SyncAnimation: A Real-Time End-to-End Framework for Audio-Driven Human Pose and Talking Head Animation

Generating talking avatar driven by audio remains a significant challenge. Existing methods typically require high computational costs and often lack sufficient facial detail and realism, making them unsuitable for applications that demand high real-time performance and visual quality. Additionally, while some methods can synchronize lip movement, they still face issues with consistency between facial expressions and upper body movement, particularly during silent periods. In this paper, we introduce SyncAnimation, the first NeRF-based method that achieves audio-driven, stable, and real-time generation of speaking avatar by combining generalized audio-to-pose matching and audio-to-expression synchronization. By integrating AudioPose Syncer and AudioEmotion Syncer, SyncAnimation achieves high-precision poses and expression generation, progressively producing audio-synchronized upper body, head, and lip shapes. Furthermore, the High-Synchronization Human Renderer ensures seamless integration of the head and upper body, and achieves audio-sync lip. The project page can be found at https://syncanimation.github.io

cs.CV↗

arXiv2025

Environmental large language model Evaluation (ELLE) dataset: A Benchmark for Evaluating Generative AI applications in Eco-environment Domain

Generative AI holds significant potential for ecological and environmental applications such as monitoring, data analysis, education, and policy support. However, its effectiveness is limited by the lack of a unified evaluation framework. To address this, we present the Environmental Large Language model Evaluation (ELLE) question answer (QA) dataset, the first benchmark designed to assess large language models and their applications in ecological and environmental sciences. The ELLE dataset includes 1,130 question answer pairs across 16 environmental topics, categorized by domain, difficulty, and type. This comprehensive dataset standardizes performance assessments in these fields, enabling consistent and objective comparisons of generative AI performance. By providing a dedicated evaluation tool, ELLE dataset promotes the development and application of generative AI technologies for sustainable environmental outcomes. The dataset and code are available at https://elle.ceeai.net/ and https://github.com/CEEAI/elle.

cs.CL↗

arXiv2024

The minimum degree of minimal $k$-factor-critical claw-free graphs*

A graph $G$ of order $n$ is said to be $k$-factor-critical for integers $1\leq k< n$, if the removal of any $k$ vertices results in a graph with a perfect matching. A $k$-factor-critical graph is minimal if for every edge, the deletion of it results in a graph that is not $k$-factor-critical. In 1998, O. Favaron and M. Shi conjectured that every minimal $k$-factor-critical graph has minimum degree $k+1$. In this paper, we confirm the conjecture for minimal $k$-factor-critical claw-free graphs. Moreover, we show that every minimal $k$-factor-critical claw-free graph $G$ has at least $\frac{k-1}{2k}|V(G)|$ vertices of degree $k+1$ in the case of $(k+1)$-connected, yielding further evidence for S. Norine and R. Thomas' conjecture on the minimum degree of minimal bricks when $k=2$.

math.CO↗

arXiv2024

Low-temperature mean valence of nickel ions in pressurized La$_3$Ni$_2$O$_7$

The discovery of high critical temperature (Tc) superconductivity in pressurized La$_3$Ni$_2$O$_7$ has ignited renewed excitement in the search of novel high-Tc superconducting compounds with 3d transition metals. Compared to other ambient-pressure superconductors, such as copper-oxide and iron-oxypnictides, unraveling the mechanisms of the pressure-induced superconductivity poses significant and unique challenges. A critical factor in this phenomenon seems to be related to the electronic configuration of 3d orbitals, which may play a fundamental role in driving high-Tc superconductivity. However, the pressure effects on the mixed-valence states of 3d-orbital cations and their influence on the emergence of high-Tc superconductivity remain poorly understood. Here, we use high-pressure (P) and low-temperature synchrotron X-ray absorption spectroscopy to investigate the influence of pressure on the mean valence change of Ni ions in La$_3$Ni$_2$O$_7$. Our results demonstrate that at a low-temperature of 20 K, the mean valence remains relatively stable across the pressures range from 1 atm to 40 GPa. Based on analyzing the absorption data, we find that, at a critical pressure, the ambient-pressure ordered phases disappear and both the structural and the superconducting phase transition occur. The pressure-induced structural phase transition revealed by our absorption results is consistent with that determined by X-ray diffraction, offering new information for a comprehensive understanding on the pressure-induced superconductivity in La$_3$Ni$_2$O$_7$.

cond-mat.supr-con↗

arXiv2024

Model-free stochastic linear quadratic design by semidefinite programming

In this article, we study a model-free design approach for stochastic linear quadratic (SLQ) controllers. Based on the convexity of the SLQ dual problem and the Karush-Kuhn-Tucker (KKT) conditions, we find the relationship between the optimal point of the dual problem and the Q-function, which can be used to develop a novel model-free semidefinite programming (SDP) algorithm for deriving optimal control gain. This study provides a new optimization perspective for understanding Q-learning algorithms and lays a theoretical foundation for effective reinforcement learning (RL) algorithms. Finally, the effectiveness of the proposed model-free SDP algorithm is demonstrated by two case simulations.

math.OC↗

arXiv2024

Learning Quadrupedal Robot Locomotion for Narrow Pipe Inspection

Various pipes are extensively used in both industrial settings and daily life, but the pipe inspection especially those with narrow sizes are still very challenging with tremendous time and manufacturing consumed. Quadrupedal robots, inspired from patrol dogs, can be a substitution of traditional solutions but always suffer from navigation and locomotion difficulties. In this paper, we introduce a Reinforcement Learning (RL) based method to train a policy enabling the quadrupedal robots to cross narrow pipes adaptively. A new privileged visual information and a new reward function are defined to tackle the problems. Experiments on both simulation and real world scenarios were completed, demonstrated that the proposed method can achieve the pipe-crossing task even with unexpected obstacles inside.

cs.RO↗