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Xiaolin Liu

Publications and source records attributed to Xiaolin Liu.

At least 19 recordsLinked to original sources

Elastic properties of amorphous LiTaCl$_6$ solid-state electrolyte

Amorphous solid-state electrolytes are attractive candidates for safe, high-energy-density all-solid-state batteries, yet their mechanical properties remain poorly understood from a computational perspective. Here, we investigate the elastic behavior of the recently discovered amorphous superionic Li-ion conductor LiTaCl$_6$ using density-functional-theory (DFT)-based methods, including unrelaxed static, relaxed static, and strain fluctuations from molecular dynamics (MD) simulations in the isobaric--isothermal (NPT) ensemble with DFT-trained machine-learning force fields (MLFFs). While the unrelaxed static method predicts a Young's modulus an order of magnitude higher than the experiment, the relaxed static method---commonly applied to crystalline electrolytes---still overestimates the modulus by more than 170%. In contrast, the MD approach using MLFFs yields a Young's modulus of $2.84 \pm 0.26$ GPa, which quantitatively agrees with the experimental value of $2.91 \pm 0.32$ GPa. Using the MLFF-MD approach, we further predict bulk modulus (4.44 GPa), shear modulus (1.02 GPa), and Poisson's ratio (0.39) for amorphous LiTaCl$_6$ and conclude that elastically it behaves like a soft polymer or gel. These results demonstrate that amorphous superionic materials possess some unique elastic properties and that, among the methods examined, only the MLFF-MD approach yields quantitative agreement with experiment, highlighting the necessity of a dynamical treatment to simulate their elastic response, consistent with recent findings for crystalline superionic conductors.

cond-mat.mtrl-sci

TR-GS: High-Fidelity Sparse-View CT Volumetric Rendering via t-Distribution Gaussian Splatting and Ray-Confidence Modeling

High-fidelity 3D medical visualization supports applications such as clinical assessment and surgical planning. Sparse-view computed tomography (CT) can reduce projection requirements and associated radiation exposure, but limited observations may introduce structural artifacts and reconstruction uncertainty. Although 3D Gaussian Splatting (3DGS) provides an efficient explicit representation for volumetric rendering, existing CT methods based on standard Gaussian primitives may be sensitive to unreliable observations under sparse-view acquisition. We present TR-GS, a Gaussian-splatting framework for sparse view CT volumetric rendering. TR-GS replaces standard Gaussian primitives with projectable Student's t-distribution primitives and introduces a ray-confidence model that regulates their degrees of freedom according to local ray observability. Confidence-guided 3D wavelet regularization is further used to balance high-frequency detail preservation and noise suppression. This work is licensed under a Creative Commons Attribution 4.0 International License. Experiments on synthetic and real-world datasets show that TR-GS improves over representative baselines in most evaluated settings and remains competitive in the remaining cases. The resulting volumetric representations may support downstream medical multimedia applications, including XR-based visualization and interactive clinical rendering.

cs.CV

A Multimodal Agentic Pathology Co-pilot via Evidence Grounded Reasoning

Pathology is the cornerstone of modern medicine, where accurate decision-making relies heavily on evidence-based practices. While artificial intelligence (AI) has the potential to transform clinical workflows, the intersection of AI and evidence-based medicine remains under-explored, with primitive attempts restricted to text-only general medicine. In this work, we present PathPocket, a multimodal AI agentic co-pilot designed specifically for evidence grounded pathology. We construct the most comprehensive pathology evidence corpus to date, encompassing approximately 110,472 public and authorized documents structured across a rigorous hierarchy of evidence from clinical guideline to expert opinion. From this meticulously graded foundation, we build a large-scale multimodal pathology hypergraph containing over 4.55 million entities and 7.10 million relations. Serving as a robust knowledge engine, this hypergraph provides traceable evidence for a collaborative multi-agent reasoning framework integrating input understanding, evidence retrieval, filtering, and diagnosis generation. This enables PathPocket to seamlessly resolve a wide spectrum of clinical tasks, ranging from text-only queries to complex multimodal diagnostics involving region-of-interest (ROI) and gigapixel whole-slide images (WSIs). We rigorously evaluate the system on a multidimensional benchmark of over 200,000 real-world cases, where it significantly outperforms existing state-of-the-arts. Crucially, extensive user studies demonstrate that PathPocket substantially improves the diagnostic accuracy and confidence of pathologists. By directly grounding pathology interpretations in verifiable literature, PathPocket offers a practical and scalable solution for the future of evidence grounded computational pathology.

cs.AI

Fractional calculus via variable-transform-based spectral approximations

We present a novel and unifying framework for constructing spectral approximations to fractional integral operators. These spectral approximations are based on transplanted Chebyshev polynomials, which are obtained by composing Chebyshev polynomials with a variable transform. When an algebraic transform is used, the framework produces spectral approximations based on Jacobi fractional polynomials. When an exponential transform is used, it yields a versatile spectral approximation that is applicable to a much broader class of fractional calculus problems. The construction of such spectral approximations is both numerically stable and optimal in terms of complexity. These spectral approximations lead to stable and fast spectral methods for fractional calculus. The spectral approximation based on the double-exponential transform is demonstrated through extensive numerical examples that are intractable for existing spectral methods.

math.NA

Moment-Video: Diagnosing Temporal Fidelity of Video MLLMs on Momentary Visual Events

Video multimodal large language models (MLLMs) have made rapid progress on general and long-form video understanding, yet their ability to preserve brief answer-critical visual evidence remains underexplored. Many practical questions are determined by momentary visual events: localized actions or state transitions that may last only a few frames. Such evidence can be skipped by sparse frame sampling, suppressed by visual-token compression, or diluted by coarse temporal aggregation, causing failures that language-side reasoning cannot reliably recover. We introduce Moment-Video, a benchmark for diagnosing the temporal fidelity of video MLLMs through momentary visual event understanding. Each question is grounded in a localized, visually observable, and sampling-sensitive event, requiring models to notice, count, describe, or reason about transient evidence rather than rely on persistent objects, global scene context, or language priors. Moment-Video contains 1,000 human-verified video-QA pairs across 7 domains and 25 fine-grained subcategories, covering four task types: Temporal Occurrence, Temporal Counting, Action Description, and Temporal Reasoning. We evaluate 33 proprietary and open-source MLLMs on Moment-Video. The best-performing model, Seed-2.0-Pro, achieves only 39.6% overall accuracy, while most open-source models remain below 25%, revealing a substantial gap in momentary visual event understanding. Diagnostic analyses show that denser frame sampling improves some models but does not eliminate the bottleneck, and longer videos introduce stronger temporal-localization challenges. These findings suggest that current video MLLMs still lack temporally faithful representations for capturing, preserving, and using brief but decisive visual evidence.

cs.CV

Large-Eccentricity Asymptotics and Fast Analytic Approximation for Fourier modes of Post-Newtonian Eccentric Waveforms

In this work, we develop analytic asymptotic methods for computing the Fourier modes of gravitational waves from post-Newtonian binary systems in the quasi-Keplerian parametrization in the high eccentricity regime. We also derive the large-eccentricity asymptotic expansion of the eccentricity enhancement function appearing in the tail contributions to the radiation. Furthermore, based on these results, we construct an endpoint-constrained analytic approximation that significantly accelerates the computation of the Fourier modes at large eccentricity. The overall error of this analytic approximation is controlled within $10^{-3}$, and it remains valid for Fourier modes with $p\le200$ and $e\le 0.9$. This approach provides analytic building blocks for modeling frequency-domain gravitational waves from highly eccentric binaries.

gr-qc

Revisiting observational constraints on coupled exponential quintessence with energy and momentum transfers: degeneracy with massive neutrinos

We investigate the impact of massive neutrinos on cosmological models in which dark energy, described by a quintessence scalar field $\phi$ with an exponential potential, interacts with dark matter through both energy and momentum transfers. Previous analyses have shown that the inclusion of low-redshift data tends to favour the detection of a pure momentum transfer between the dark sectors, consistent with the fact that such a transfer generically suppresses the growth of cosmic structures. Since massive neutrinos also reduce matter clustering, a potential degeneracy between the interaction parameters and the neutrino mass may arise. After updating the observational constraints on the model parameters obtained in earlier studies, we investigate the effect of allowing the neutrino mass to vary. We find that the detection of momentum transfer degrades once massive neutrinos are included. This occurs because a new degeneracy emerges between the neutrino mass and the parameter governing the energy exchange between dark energy and dark matter. Our findings differ from previous results in the literature, where the detection of momentum transfer was reported to be robust against varying neutrino masses. This suggests that the robustness of such detections depends on the underlying model and should therefore be carefully reassessed for each specific interacting scenario.

astro-ph.CO

Codesigning Ripplet: an LLM-Assisted Assessment Authoring System Grounded in a Conceptual Model of Teachers' Workflows

Assessments are critical in education, but creating them can be difficult. To address this challenge in a grounded way, we partnered with 13 teachers in a seven-month codesign process. We developed a conceptual model that characterizes the iterative dual process where teachers develop assessments while simultaneously refining requirements. To enact this model in practice, we built Ripplet, a web-based tool with multilevel reusable interactions to support assessment authoring. The extended codesign revealed that Ripplet enabled teachers to create formative assessments they would not have otherwise made, shifted their practices from generation to curation, and helped them reflect more on assessment quality. In a user study with 15 additional teachers, compared to their current practices, teachers felt the results were more worth their effort and that assessment quality improved.

cs.HC

Analyzing intermittent stochastic gravitational wave background I:Effect of detector response

With the growing number of gravitational-wave detections, particularly from binary black hole mergers, there is increasing anticipation that an astrophysical background, formed by an ensemble of faint, high-redshift events, will be observed in the near future by the ground-based detector network. This background is anticipated to exhibit non-Gaussian statistical properties. To develop a robust method for detecting such a non-Gaussian gravitational-wave background, we revisit optimal detection strategies based on the Gaussian-mixture likelihood model. In this work, we demonstrate that properly accounting for the detector antenna pattern is essential. Current approaches typically rely on the overlap reduction function averaged over the sky. Through simulations, we show that using such an averaged response introduces significant biases in parameter estimation. In addition, we propose a computationally feasible method that incorporates second-order corrections as an approximation of the full integral over the source distribution. Our results indicate that this approach effectively eliminates these biases. We also show that our method remains robust even when considering anisotropic backgrounds.

gr-qc

General Fourier expansion of post-Newtonian binary dynamics based on a quasi-Keplerian framework

We have introduced a new method for computing gravitational-wave emission from nonspinning binaries which systematically unifies the various integrals arising in the Fourier expansions of post-Newtonian dynamics, providing a simple, practical scheme for calculations at arbitrary precision. Using this approach, we derived the full set of 3PN dynamical quantities and gravitational-wave Fourier modes and have released the corresponding numerical code as open source. Furthermore, when radiation-reaction effects is not included, we found that the tail contribution to the energy and angular momentum fluxes can be resummed into an exceptionally compact expression with the help of the new method. These advances pave the way for more convenient and accurate frequency-domain waveform modeling in the future.

gr-qc

Spectral Approximation to Fractional Integral Operators

We propose a fast and stable method for constructing matrix approximations to fractional integral operators applied to series in the Chebyshev fractional polynomials. This method utilizes a recurrence relation satisfied by the fractional integrals of mapped Chebyshev polynomials and significantly outperforms existing methods. Through numerical examples, we highlight the broad applicability of these matrix approximations, including the solution of boundary value problems for fractional integral and differential equations. Additional applications include fractional differential equation initial value problems and fractional eigenvalue problems.

math.NA

RESCUE: Crowd Evacuation Simulation via Controlling SDM-United Characters

Crowd evacuation simulation is critical for enhancing public safety, and demanded for realistic virtual environments. Current mainstream evacuation models overlook the complex human behaviors that occur during evacuation, such as pedestrian collisions, interpersonal interactions, and variations in behavior influenced by terrain types or individual body shapes. This results in the failure to accurately simulate the escape of people in the real world. In this paper, aligned with the sensory-decision-motor (SDM) flow of the human brain, we propose a real-time 3D crowd evacuation simulation framework that integrates a 3D-adaptive SFM (Social Force Model) Decision Mechanism and a Personalized Gait Control Motor. This framework allows multiple agents to move in parallel and is suitable for various scenarios, with dynamic crowd awareness. Additionally, we introduce Part-level Force Visualization to assist in evacuation analysis. Experimental results demonstrate that our framework supports dynamic trajectory planning and personalized behavior for each agent throughout the evacuation process, and is compatible with uneven terrain. Visually, our method generates evacuation results that are more realistic and plausible, providing enhanced insights for crowd simulation. The code is available at http://cic.tju.edu.cn/faculty/likun/projects/RESCUE.

cs.CV

A continuous approach to computing the pseudospectra of linear operators

We propose a continuous approach to computing the pseudospectra of linear operators with compact or compact-plus-scalar resolvent, following a 'solve-then-discretize' strategy. Instead of taking a finite section approach or using a finite-dimensional matrix to approximate the operator of interest, the new method employs an operator analogue of the Lanczos process to work with operators and functions directly. The method is shown to be free of spectral pollution and spectral invisibility, fully adaptive, and nearly optimal in accuracy. The advantages of the method are demonstrated by extensive numerical examples and comparison with the traditional method.

math.NA

Effect of kick velocity on gravitational wave detection of binary black holes with space- and ground-based detectors

During the coalescence of binary black holes (BBHs), asymmetric gravitational wave (GW) emission imparts a kick velocity to the remnant black hole, affecting observed waveforms and parameter estimation. In this study, we investigate the impact of this effect on GW observations using space- and ground-based detectors. By applying Lorentz transformations, we analyze waveform modifications due to kick velocities. For space-based detectors, nearly 50% of detected signals require corrections, while for ground-based detectors, this fraction is below one-third. For Q3d population model, space-based detectors could observe kick effects in over 60% of massive BBH mergers, while in pop3 model, this fraction could drop to 3$\sim$4%. Third-generation ground-based detectors may detect kick effects in up to 16% of stellar-mass BBH mergers. Our findings highlight the importance of incorporating kick velocity effects into waveform modeling, enhancing GW signal interpretation and our understanding of BBH dynamics and astrophysical implications.

gr-qc

Building Machine Learning Challenges for Anomaly Detection in Science

Scientific discoveries are often made by finding a pattern or object that was not predicted by the known rules of science. Oftentimes, these anomalous events or objects that do not conform to the norms are an indication that the rules of science governing the data are incomplete, and something new needs to be present to explain these unexpected outliers. The challenge of finding anomalies can be confounding since it requires codifying a complete knowledge of the known scientific behaviors and then projecting these known behaviors on the data to look for deviations. When utilizing machine learning, this presents a particular challenge since we require that the model not only understands scientific data perfectly but also recognizes when the data is inconsistent and out of the scope of its trained behavior. In this paper, we present three datasets aimed at developing machine learning-based anomaly detection for disparate scientific domains covering astrophysics, genomics, and polar science. We present the different datasets along with a scheme to make machine learning challenges around the three datasets findable, accessible, interoperable, and reusable (FAIR). Furthermore, we present an approach that generalizes to future machine learning challenges, enabling the possibility of large, more compute-intensive challenges that can ultimately lead to scientific discovery.

cs.LG

A complete waveform comparison of post-Newtonian and numerical relativity in eccentric orbits

This study presents a thorough comparative analysis between post-Newtonian (PN) and numerically relativistic (NR) waveforms in eccentric orbits, covering nonspinning and spin-aligned configurations. The comparison examines frequency, amplitude, and phase characteristics of various harmonic modes, such as 22, 21, 33, 32, 44, 43, and 55 modes. The study utilizes eccentric PN waveforms based on 3PN quasi-Keplerian parameterization with 3PN radiative reaction, surpassing Newtonian quadrupole moment with higher-order moments. NR waveforms from RIT and SXS catalogs span mass ratios from 1/4 to 1, eccentricities up to 0.45, and durations exceeding $17000M$ across nonspinning and spin-aligned configurations. Focusing on the 22 mode, frequency comparisons between quadrupole and higher-order moments of $Ψ_4^{22}$ and $h^{22}$ were conducted. Amplitude comparisons revealed superior accuracy in quadrupole moments of $Ψ_4^{22}$. Analysis of total 180 sets of eccentric waveforms showed increasing fitting residuals with rising eccentricity, correlating with smaller mass ratios. Comparisons of initial eccentricity from PN fitting, 3PN quasi-Keplerian parameterization, and RIT/SXS catalogs revealed alignment discrepancies. Frequency, phase, and amplitude comparisons of 22 modes showed consistent inspiral behavior between PN and NR, with divergences near merger for nonspinning PN and pre-200M for spin-aligned PN.

gr-qc

Characterizing the effect of eccentricity on the dynamics of binary black hole mergers in numerical relativity

Many articles have partially studied the configuration of eccentric orbital binary black hole (BBH) mergers. However, there is a scarcity of systematic and comprehensive research on the effect of eccentricity on BBH dynamics. Thanks to the rich and numerous numerical relativistic simulations of eccentric orbital BBH mergers from RIT catalog, this paper aims to investigate the impact of initial eccentricity $e_0$ on various dynamic quantities such as merger time $T_{\text{merger}}$, peak luminosity $L_{\text{peak}}$ of gravitational waves, recoil velocity $V_f$, mass $M_f$, and spin $α_f$ of merger remnants. We cover configurations of no spin, spin alignment, and spin precession, as well as a broad parameter space of mass ratio ranging from 1/32 to 1 and initial eccentricity from 0 to 1. For non-spinning BBH with an initial coordinate separation of $11.3M$ ($M$ is the total mass of BBH), we make the first discovery of a ubiquitous oscillation in the relationship between dynamic quantities $L_{\text{peak}}$, $V_f$, $M_f$, $α_f$, and initial eccentricity $e_0$. Additionally, at $24.6M$, we observe the same oscillation phenomenon in the case of mass ratio $q=1$, but do not see it in other mass ratios, suggesting that this oscillation will be evident in numerical simulations with sufficiently dense initial eccentricity. abbreviated

gr-qc

Spectral approximation of convolution operators of Fredholm type

We have developed a method for constructing spectral approximations for convolution operators of Fredholm type. The algorithm we propose is numerically stable and takes advantage of the recurrence relations satisfied by the entries of such a matrix approximation. When used for computing the Fredholm convolution of two given functions, such approximations produce the convolution more rapidly than the state-of-the-art methods. The proposed approximation also leads to a spectral method for solving the Fredholm convolution integral equations and enables the computation of eigenvalues and pseudospectra of Fredholm convolution operators, which is otherwise intractable with existing techniques.

math.NA