arXiv subjects

Qing Xia

Publications and source records attributed to Qing Xia.

At least 37 records · Page 2Linked to original sources

arXiv2026

Label-free Imaging of Single-Biomolecule Structure and Interaction by Stimulated Raman Photothermal Encoded Scattering

Current single molecule methods either rely on fluorescence or lack chemical information. Here we report stimulated Raman photothermal encoded scattering (SRPSCAT) microscopy for quantitative bond-selective imaging of single-biomolecule structures and interactions in native environments. In this approach, scattering of the target molecule is modulated by the deposited energy from stimulated Raman gain and loss processes, thereby encoding vibrational spectroscopic information. Leveraging single-molecule sensitivity of interferometric scattering, SRPSCAT can map single proteins with chemical specificity, determine their mass, and distinguish protein secondary structures based on their Raman fingerprints. Furthermore, single protein binding kinetics are quantified and the conformational dynamics of single de novo designed allosteric proteins are observed. Together, these results highlight the potential of SRPSCAT for label-free structural, functional and dynamic analysis at the single-molecule level.

physics.bio-ph↗

arXiv2026

Hierarchical Reinforcement Learning for Next Generation of Multi-AP Coordinated Spatial Reuse

In next generation of Wi-Fi networks Multiple Access Point Coordination (MAPC) is poised to significantly enhance the network performance by enabling a set of Access Points (APs) to coordinate with each other through advanced coordinating schemes so that to reduce inter-AP contention and congestion. This paper focuses on defining a framework to facilitate the coordination across multi-APs when these employ Coordinated Spatial Reuse (C-SR). In this case, the coordinating APs may need to reciprocally adjust their scheduling strategy, power control and link adaptation to meet specific Quality of Service (QoS) requirements, which by using classical approaches leads to high overhead due to negotiations needed across APs, and requires complex solutions in order to properly optimize the network across all the parameters in play. In this matter, a two layer Multi-Armed Bandit (MAB) algorithm has been proposed to optimize such a network while preserving the fair use of resources across all nodes. The validity of this holistic approach is confirmed by system level simulations, which show that the proposed algorithm not only improves the network in terms of sum-throughput, but also allows to enhance fairness, making this a robust solution for next-generation of Wi-Fi networks.

cs.NI↗

arXiv2026

Mid-wave infrared photothermal microscopy for molecular and metabolic imaging in deep tissues and spheroids

High-resolution chemical imaging within deep tissues and intact spheroids remains a grand challenge. Here, we introduce mid-wave infrared photothermal (MWIP) microscopy operating in the underexplored 2000-2500 nm spectral window for submicron-resolution molecular and metabolic imaging in intact tumor spheroids and deep tissues. A dark-field photothermal detection scheme significantly suppresses water background and enhances contrast. By accessing strong carbon-hydrogen combination absorptions, a detection limit of 0.12% for dimethyl sulfoxide is achieved, comparable to stimulated Raman scattering microscopy. Depth-resolved imaging of endogenous biomolecules up to 500 micrometers in excised mouse skin and brain tissues is demonstrated. MWIP further enables depth-resolved tracking of transdermal drug transport via carbon-deuterium overtone absorption. Using deuterium metabolic probes, fatty-acid metabolism is imaged at 200 micrometers deep within intact tumor spheroids through carbon-deuterium overtone and combination bands. Collectively, MWIP offers a platform for functional imaging of 3D biological systems in their native environments.

physics.optics↗

arXiv2025

Synthetic Fluency and Epistemic Offloading in Undergraduate Mathematics in the Age of AI

The rapid adoption of generative artificial intelligence (AI) tools in higher education is transforming how students engage with undergraduate mathematics, raising concerns about learning and assessment validity. This study examines the impact of AI accessibility across a two-semester, multi-course dataset including Business Calculus, Linear Algebra, and Calculus III. By comparing unproctored homework and proctored exam performance, we analyze how student learning behaviors shift in AI-accessible environments, particularly through epistemic off-loading of mathematical work. Guided by a sociocognitive framework, we employ complementary measures -- performance gaps, homework-exam correlations, and Wasserstein distance -- to characterize divergence between practice and mastery. Results reveal a growing integrity gap as course content shifts from procedural to conceptual and spatially intensive mathematics. In both Business Calculus and Linear Algebra, differences in homework format (online versus hand-written, TA-graded) do not yield substantively different performance patterns, indicating that paper-based homework is not inherently more resistant to AI-mediated offloading. While homework retains partial predictive validity in procedural courses, upper-division courses exhibit a collapse in alignment between homework and exams, indicating that unproctored assessments increasingly reflect synthetic fluency rather than internalized understanding. These findings highlight the need to rethink assessment practices in the AI era.

math.HO↗

arXiv2025

Unfitted Lattice Green's Function Method for Exterior Scattering in Complex Geometry

This paper develops a finite-difference analogue of the boundary integral/element method for the numerical solution of two-dimensional exterior scattering from scatterers of arbitrary shapes. The discrete fundamental solution, known as the lattice Green's function (LGF), for the Helmholtz equation on an infinite lattice is derived and employed to construct boundary algebraic equations through the discrete potentials framework. Unlike the continuous fundamental solution used in boundary integral methods, the LGF introduces no singularity, which simplifies numerical implementation. Boundary conditions are incorporated through local Lagrange interpolation on unfitted cut cells. The resulting method retains key advantages of boundary integral approaches-including dimension reduction and the absence of artificial boundary conditions--while enabling finite differences for complex geometries. Numerical results demonstrate the accuracy and robustness of the method for various scatterers, including circular, triangular, and multiple-body configurations.

math.NA↗

arXiv2025

A geometrically robust unfitted boundary algebraic equation method based on discrete potentials and local basis functions

We present an unfitted boundary algebraic equation (BAE) method for solving elliptic partial differential equations in complex geometries. The method employs lattice Green's functions on infinite regular grids combined with discrete potential theory to construct single and double layer potentials, which is a discrete analog to boundary integral method. Local basis functions on cut cells accommodate arbitrary boundary conditions and seamlessly integrate with the boundary algebraic equations. The difference potentials framework enables efficient treatment of nonhomogeneous terms and fast computation of layer potentials via FFT-based solvers. We establish theoretical stability and convergence through a novel interpolation operator framework. Key advantages of the developed method include: dimension reduction, geometric flexibility, mesh-independent conditioning, small-cut stability, and uniform treatment of smooth and non-smooth geometries. Numerical experiments validate accuracy and robustness across ellipses and diamonds with varying aspect ratios and sharp corners, and an application of potential flows in unbounded domains.

math.NA↗

arXiv2025

Anomalous size effects with fixed criticality in bistable flexible mechanical metamaterials

When the structure deformation is dominated by the low-energy deformation mode, the structure hardens with the increase in the size (number of units) at small sizes. This anomalous behavior will eventually disappear with the decay length of the finite structure converging to a size-independent characteristic quantity, but the specific critical point at which the anomalous behavior disappears still cannot be accurately and concisely described. Here, under two steady states of the bistable chain, we observed anomalous size effects with constant and oscillating criticality (the proportion of inhomogeneous deformation), two criticalities exactly separate the increasing and decreasing intervals of stiffness variation. They are interrelated due to the implied symmetries between the two steady states. On the other hand, they are distinguished because of the opposite superposition modes under the two steady states. Specifically, the constant criticality corresponds to the anomalous size effect achieved by the competition mechanism, while the oscillating criticality reveals an anomalous size effect achieved by the new mechanism (cancellation mechanism). In the anomalous size effect achieved by the cancellation mechanism, the singular characteristics generated by the completely cancelled deformation make it very robust. This robustness reflects in that the anomalous effect is no longer limited to linear small deformation, but it can still be observed stably in nonlinear large deformation. Our study reinterprets the anomalous size effect at a quantitative level, and the proposed cancellation mechanism expands the possible application range of this anomalous effect.

physics.app-ph↗

arXiv2025

Unfitted boundary algebraic equation method based on difference potentials and lattice Green's function in 3D

This work presents an unfitted boundary algebraic equation (BAE) method for solving three-dimensional elliptic partial differential equations on complex geometries using finite difference on structured meshes. We demonstrate that replacing finite auxiliary domains with free-space LGFs streamlines the computation of difference potentials, enabling matrix-free implementations and significant cost reductions. We establish theoretical foundations by showing the equivalence between direct formulations in difference potentials framework and indirect single/double layer formulations and analyzing their spectral properties. The spectral analysis demonstrates that discrete double layer formulations provide better-conditioned systems for iterative solvers, similarly as in boundary integral method. The method is validated through matrix-free numerical experiments on both Poisson and modified Helmholtz equations in 3D implicitly defined geometries, showing optimal convergence rates and computational efficiency. This framework naturally extends to unbounded domains and provides a foundation for applications to more complex systems like Helmholtz and Stokes equations.

math.NA↗

arXiv2025

First Constraint on the Diffuse Supernova Neutrino Background through the CE$ν$NS process from the LZ experiment

We report the limits on the diffuse supernova neutrino background (DSNB) flux and the fundamental DSNB parameters measured from the first science run of the LUX-ZEPLIN (LZ) experiment, a dual-phase xenon detector located at the Sanford Underground Research Facility in Lead, South Dakota, USA. This is the first time the DSNB limit is measured through the process of the coherent elastic neutrino-nucleus scattering (CE$ν$NS). Using an exposure of 60~live days and a fiducial mass of 5.5~t, the upper limit on the DSNB $ν_x$ (each of $ν_μ$, $ν_τ$, $\barν_μ$, $\barν_τ$) flux is $686-826$~cm$^{-2}$s$^{-1}$ at the 90\% confidence level for neutrino energies E$>$19.3~MeV, assuming the flux for each $ν_x$ flavor is the same. The interval accounts for the uncertainty in existing DSNB models. The present result is comparable to the existing best limit and further improvements are expected after collecting data from an estimated 1,000-day exposure in the future.

hep-ex↗

arXiv2024

A Novel Combined Data-Driven Approach for Electricity Theft Detection

The two-way flow of information and energy is an important feature of the Energy Internet. Data analytics is a powerful tool in the information flow that aims to solve practical problems using data mining techniques. As the problem of electricity thefts via tampering with smart meters continues to increase, the abnormal behaviors of thefts become more diversified and more difficult to detect. Thus, a data analytics method for detecting various types of electricity thefts is required. However, the existing methods either require a labeled dataset or additional system information which is difficult to obtain in reality or have poor detection accuracy. In this paper, we combine two novel data mining techniques to solve the problem. One technique is the Maximum Information Coefficient (MIC), which can find the correlations between the non-technical loss (NTL) and a certain electricity behavior of the consumer. MIC can be used to precisely detect thefts that appear normal in shapes. The other technique is the clustering technique by fast search and find of density peaks (CFSFDP). CFSFDP finds the abnormal users among thousands of load profiles, making it quite suitable for detecting electricity thefts with arbitrary shapes. Next, a framework for combining the advantages of the two techniques is proposed. Numerical experiments on the Irish smart meter dataset are conducted to show the good performance of the combined method.

eess.SY↗

arXiv2024

Sharing-Based Channel Access Procedure For Next Generation of Wireless LAN

This paper proposes a new channel access procedure to mitigate the channel access contention in next generation of Wireless Local-Area Networks (WLANs) by allowing cooperation among devices belonging to same network, while maintaining high flexibility in terms of how each device may contend the medium. After introducing the details of the proposed procedure, which is here referred to as sharing-based protocol, an analytical analysis is provided to compare it with the two state-of-art protocols currently adopted in IEEE 802.11 standard, i.e, Enhanced Distributed Channel Access (EDCA)-based and trigger-based protocol. In this regards, closed form expressions are derived to evaluate the success probability of channel access for each protocol. In order to show the merit of the proposed procedure, a comprehensive system level analysis is also provided, which highlights that the proposed procedure outperforms the two state-of-art protocols in terms of mitigating the End-to-End (E2E) delay and allowing a better spectrum utilization by reducing the overall congestion in the system.

cs.NI↗

arXiv2024

Bounding the diameter and eigenvalues of amply regular graphs via Lin-Lu-Yau curvature

An amply regular graph is a regular graph such that any two adjacent vertices have $α$ common neighbors and any two vertices with distance $2$ have $β$ common neighbors. We prove a sharp lower bound estimate for the Lin--Lu--Yau curvature of any amply regular graph with girth $3$ and $β>α$. The proof involves new ideas relating discrete Ricci curvature with local matching properties: This includes a novel construction of a regular bipartite graph from the local structure and related distance estimates. As a consequence, we obtain sharp diameter and eigenvalue bounds for amply regular graphs.

math.CO↗

arXiv2023

Single virus fingerprinting by widefield interferometric defocus-enhanced mid-infrared photothermal microscopy

Clinical identification and fundamental study of viruses rely on the detection of viral proteins or viral nucleic acids. Yet, amplification-based and antigen-based methods are not able to provide precise compositional information of individual virions due to small particle size and low-abundance chemical contents (e.g., ~ 5000 proteins in a vesicular stomatitis virus). Here, we report a widefield interferometric defocus-enhanced mid-infrared photothermal (WIDE-MIP) microscope for high-throughput fingerprinting of single viruses. With the identification of feature absorption peaks, WIDE-MIP reveals the contents of viral proteins and nucleic acids in single DNA vaccinia viruses and RNA vesicular stomatitis viruses. Different nucleic acids signatures of thymine and uracil residue vibrations are obtained to differentiate DNA and RNA viruses. WIDE-MIP imaging further reveals an enriched \b{eta} sheet components in DNA varicella-zoster virus proteins. Together, these advances open a new avenue for compositional analysis of viral vectors and elucidating protein function in an assembled virion.

physics.optics↗

arXiv2023

Voxel2Hemodynamics: An End-to-end Deep Learning Method for Predicting Coronary Artery Hemodynamics

Local hemodynamic forces play an important role in determining the functional significance of coronary arterial stenosis and understanding the mechanism of coronary disease progression. Computational fluid dynamics (CFD) have been widely performed to simulate hemodynamics non-invasively from coronary computed tomography angiography (CCTA) images. However, accurate computational analysis is still limited by the complex construction of patient-specific modeling and time-consuming computation. In this work, we proposed an end-to-end deep learning framework, which could predict the coronary artery hemodynamics from CCTA images. The model was trained on the hemodynamic data obtained from 3D simulations of synthetic and real datasets. Extensive experiments demonstrated that the predicted hemdynamic distributions by our method agreed well with the CFD-derived results. Quantitatively, the proposed method has the capability of predicting the fractional flow reserve with an average error of 0.5\% and 2.5\% for the synthetic dataset and real dataset, respectively. Particularly, our method achieved much better accuracy for the real dataset compared to PointNet++ with the point cloud input. This study demonstrates the feasibility and great potential of our end-to-end deep learning method as a fast and accurate approach for hemodynamic analysis.

cs.CV↗

arXiv2023

Segmentation and Vascular Vectorization for Coronary Artery by Geometry-based Cascaded Neural Network

Segmentation of the coronary artery is an important task for the quantitative analysis of coronary computed tomography angiography (CCTA) images and is being stimulated by the field of deep learning. However, the complex structures with tiny and narrow branches of the coronary artery bring it a great challenge. Coupled with the medical image limitations of low resolution and poor contrast, fragmentations of segmented vessels frequently occur in the prediction. Therefore, a geometry-based cascaded segmentation method is proposed for the coronary artery, which has the following innovations: 1) Integrating geometric deformation networks, we design a cascaded network for segmenting the coronary artery and vectorizing results. The generated meshes of the coronary artery are continuous and accurate for twisted and sophisticated coronary artery structures, without fragmentations. 2) Different from mesh annotations generated by the traditional marching cube method from voxel-based labels, a finer vectorized mesh of the coronary artery is reconstructed with the regularized morphology. The novel mesh annotation benefits the geometry-based segmentation network, avoiding bifurcation adhesion and point cloud dispersion in intricate branches. 3) A dataset named CCA-200 is collected, consisting of 200 CCTA images with coronary artery disease. The ground truths of 200 cases are coronary internal diameter annotations by professional radiologists. Extensive experiments verify our method on our collected dataset CCA-200 and public ASOCA dataset, with a Dice of 0.778 on CCA-200 and 0.895 on ASOCA, showing superior results. Especially, our geometry-based model generates an accurate, intact and smooth coronary artery, devoid of any fragmentations of segmented vessels.

eess.IV↗

arXiv2023

Non-Iterative Solution for Coordinated Optimal Dispatch via Equivalent Projection-Part II: Method and Applications

This two-part paper develops a non-iterative coordinated optimal dispatch framework, i.e., free of iterative information exchange, via the innovation of the equivalent projection (EP) theory. The EP eliminates internal variables from technical and economic operation constraints of the subsystem and obtains an equivalent model with reduced scale, which is the key to the non-iterative coordinated optimization. In Part II of this paper, a novel projection algorithm with the explicit error guarantee measured by the Hausdorff distance is proposed, which characterizes the EP model by the convex hull of its vertices. This algorithm is proven to yield a conservative approximation within the prespecified error tolerance and can obtain the exact EP model if the error tolerance is set to zero, which provides flexibility to balance the computation accuracy and effort. Applications of the EP-based coordinated dispatch are demonstrated based on the multi-area coordination and transmission-distribution coordination. Case studies with a wide range of system scales verify the superiority of the proposed projection algorithm in terms of computational efficiency and scalability, and validate the effectiveness of the EP-based coordinated dispatch in comparison with the joint optimization.

math.OC↗

arXiv2023

Non-Iterative Solution for Coordinated Optimal Dispatch via Equivalent Projection-Part I: Theory

Coordinated optimal dispatch is of utmost importance for the efficient and secure operation of hierarchically structured power systems. Conventional coordinated optimization methods, such as the Lagrangian relaxation and Benders decomposition, require iterative information exchange among subsystems. Iterative coordination methods have drawbacks including slow convergence, risk of oscillation and divergence, and incapability of multi-level optimization problems. To this end, this paper aims at the non-iterative coordinated optimization method for hierarchical power systems. The theory of the equivalent projection (EP) is proposed, which makes external equivalence of the optimal dispatch model of the subsystem. Based on the EP theory, a coordinated optimization framework is developed, where each subsystem submits the EP model as a substitute for its original model to participate in the cross-system coordination. The proposed coordination framework is proven to guarantee the same optimality as the joint optimization, with additional benefits of avoiding iterative information exchange, protecting privacy, compatibility with practical dispatch scheme, and capability of multi-level problems.

math.OC↗

arXiv2022

Pixel is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detection

Although weakly-supervised techniques can reduce the labeling effort, it is unclear whether a saliency model trained with weakly-supervised data (e.g., point annotation) can achieve the equivalent performance of its fully-supervised version. This paper attempts to answer this unexplored question by proving a hypothesis: there is a point-labeled dataset where saliency models trained on it can achieve equivalent performance when trained on the densely annotated dataset. To prove this conjecture, we proposed a novel yet effective adversarial trajectory-ensemble active learning (ATAL). Our contributions are three-fold: 1) Our proposed adversarial attack triggering uncertainty can conquer the overconfidence of existing active learning methods and accurately locate these uncertain pixels. {2)} Our proposed trajectory-ensemble uncertainty estimation method maintains the advantages of the ensemble networks while significantly reducing the computational cost. {3)} Our proposed relationship-aware diversity sampling algorithm can conquer oversampling while boosting performance. Experimental results show that our ATAL can find such a point-labeled dataset, where a saliency model trained on it obtained $97\%$ -- $99\%$ performance of its fully-supervised version with only ten annotated points per image.

cs.CV↗