arXiv subjects

Can Liao

Publications and source records attributed to Can Liao.

9 recordsLinked to original sources

arXiv2026

General Symmetry-Based Potential Energy Surface Grid Reduction in Normal Coordinates

The construction of a grid-based potential energy surface (PES) can be prohibitively expensive as the number of grid points grows exponentially with molecular size. Molecular symmetry can reduce this cost by eliminating symmetry-equivalent points. We present an algebraic symmetry-based grid reduction method, ASyBGR, that is capable of handling non-Abelian and higher-order cyclic symmetry groups. Non-coordinate-mixing operations are encoded into a vector space bit-string, where Gaussian elimination and closure can identify all symmetry-valid sign change patterns and the coordinate axes whose grids can be halved about the origin. In the presence of degenerate subspaces, we optimize the coordinate basis to maximize reduction. The method was validated by comparing vibrational configuration interaction (VCI) energies calculated using the full and symmetry-reduced fourth-order HDMR PESs for molecules spanning a broad range of symmetries. The number of grid points required to construct the PES was reduced by as much as 81\%, while VCI energies deviated below 1 1/cm for all test cases on average. The relative root-mean-square deviation (RRMSD) between the full and symmetry-reduced potential energy and dipole moment surfaces were at most on the order of 10$^{-3}$ and 10$^{-2}$, respectively.

physics.chem-ph↗

arXiv2026

PeakFlow: Peak-Guided Coarse-to-Refined Modeling for EEG-Based Dynamic Affective Trajectory Prediction

Most existing EEG-based emotion recognition studies formulate affective decoding as static category prediction, although emotions elicited by continuous stimulation evolve over time, accumulate, reach peak intensity, and then recover. This motivates EEG-based dynamic affective trajectory prediction, which estimates continuous affective intensity curves from sequential EEG observations. Existing temporal regression models can capture coarse intensity trends but often fail to preserve peak-centered structure, leading to inaccurate peak timing and terminal-peak bias, where the predicted maximum is shifted toward the end of a trial. To address this issue, we propose PeakFlow, a peak-guided coarse-to-refined framework for EEG-based dynamic affective trajectory prediction. PeakFlow first learns a coarse affective flow through EEG temporal tokenization and masked temporal modeling, then applies a lightweight residual refiner for peak-guided bounded calibration. The refiner uses trajectory-aware cues and a peak-centered objective combining global trajectory consistency, peak-zone emphasis, peak-probability localization, terminal suppression, and residual regularization. This design preserves the global affective trend while correcting peak misalignment, peak-value deviation, and false-terminal predictions. Leave-one-subject-out experiments on SEED-VII show that PeakFlow improves both global trajectory fitting and peak-centered temporal reliability over strong dynamic modeling baselines. Auxiliary evaluation on FIRMED further suggests its potential for sparse peak-centered ordinal intensity analysis. These results highlight the importance of peak-aware modeling for temporally faithful EEG-based dynamic emotion prediction. Code is available at https://github.com/jukebox333/PeakFlow.

cs.HC↗

arXiv2026

Tau-induced atrophy drives functional connectivity disruption in Alzheimer's disease

Alzheimer's disease involves progressive tau accumulation and spread, leading to regional brain atrophy and disruption of large-scale functional networks. While tau propagation and tissue degeneration have been widely modeled, how atrophy dynamics translate into functional connectivity (FC) degradation remains unclear. Here, we develop a multiphysics framework integrating anisotropic tau reaction-diffusion, finite-deformation biomechanics, and network modeling to link tau-driven atrophy with FC changes. Model fidelity is evaluated by quantitatively comparing simulated atrophy patterns with imaging-derived measurements. Using longitudinal structural and functional MRI, we identify an approximately linear relationship between regional atrophy rates and FC change. We then construct an atrophy-informed structural network degradation matrix from model-predicted region-specific atrophy rates and embed it into a neural oscillation model to predict FC disruption. Our results show that (i) the coupled reaction-diffusion-biomechanical model reproduces observed regional atrophy, (ii) regional atrophy rates parsimoniously predict longitudinal FC changes, and (iii) the atrophy-informed degradation matrix captures the direction and relative magnitude of regional FC disruption. By converting tau-driven atrophy into predictive FC trajectories, the proposed framework offers a clinically interpretable avenue for forecasting disease progression and informing trial design.

physics.med-ph↗

arXiv2025

FIRMED: A Peak-Centered Multimodal Dataset with Fine-Grained Annotation for Emotion Recognition

Traditional video-induced physiological datasets usually rely on whole-trial labels, which introduce temporal label noise in dynamic emotion recognition. We present FIRMED, a peak-centered multimodal dataset based on an immediate-recall annotation paradigm, with synchronized EEG, ECG, GSR, PPG, and facial recordings from 35 participants. FIRMED provides event-centered timestamps, emotion labels, and intensity annotations, and its annotation quality is supported by subjective and physiological validation. Benchmark experiments show that FIRMED consistently outperforms whole-trial labeling, yielding an average gain of 3.8 percentage points across eight EEG-based classifiers, with further improvements under multimodal fusion. FIRMED provides a practical benchmark for temporally localized supervision in multimodal affective computing.

cs.HC↗

arXiv2025

Numerically Exact Configuration Interaction at Quadrillion-Determinant Scale

The combinatorial scaling of configuration interaction (CI) has long restricted its applicability to only the simplest molecular systems. Here, we report the first numerically exact CI calculation exceeding one quadrillion ($10^{15}$) determinants, enabled by categorical compression within the small-tensor-product distributed active space (STP-DAS) framework. As a demonstration, we converged the relativistic complete active space CI (CASCI) ground state of HBrTe involving over $10^{15}$ complex-valued 2-spinor determinants in under 34.5 hours (time-to-completion) using 1000 nodes, representing the largest CASCI calculation reported to date. Additionally, we achieved $\boldsymbol{\sigma}$-build times of just 5 minutes for systems with approximately 150 billion complex-valued 2-spinor determinants using only a few compute nodes. Extensive benchmarks confirm that the method retains numerical exactness with drastically reduced resource demands. Compared to previous state-of-the-art CI calculations, this work represents a 3-orders-of-magnitude increase in CI space, a 6-orders-of-magnitude increase in FLOP count, and a 6-orders-of-magnitude improvement in computational speed. By introducing a numerically exact, categorically compressed representation of the CI expansion vectors and reformulating the $\boldsymbol{\sigma}$-build accordingly, we eliminate memory bottlenecks associated with storing excitation lists and CI vectors while significantly reducing computational cost. A compression-compatible preconditioner further enhances performance by generating compressed CI expansion vectors throughout Davidson iterations. This work establishes a new computational frontier for numerically exact CI methods, enabling chemically and physically accurate simulations of strongly correlated, spin-orbit coupled systems previously thought to be beyond reach.

physics.chem-ph↗

arXiv2025

Neuron Platonic Intrinsic Representation From Dynamics Using Contrastive Learning

The Platonic Representation Hypothesis suggests a universal, modality-independent reality representation behind different data modalities. Inspired by this, we view each neuron as a system and detect its multi-segment activity data under various peripheral conditions. We assume there's a time-invariant representation for the same neuron, reflecting its intrinsic properties like molecular profiles, location, and morphology. The goal of obtaining these intrinsic neuronal representations has two criteria: (I) segments from the same neuron should have more similar representations than those from different neurons; (II) the representations must generalize well to out-of-domain data. To meet these, we propose the NeurPIR (Neuron Platonic Intrinsic Representation) framework. It uses contrastive learning, with segments from the same neuron as positive pairs and those from different neurons as negative pairs. In implementation, we use VICReg, which focuses on positive pairs and separates dissimilar samples via regularization. We tested our method on Izhikevich model-simulated neuronal population dynamics data. The results accurately identified neuron types based on preset hyperparameters. We also applied it to two real-world neuron dynamics datasets with neuron type annotations from spatial transcriptomics and neuron locations. Our model's learned representations accurately predicted neuron types and locations and were robust on out-of-domain data (from unseen animals). This shows the potential of our approach for understanding neuronal systems and future neuroscience research.

q-bio.NC↗

arXiv2025

Molecule-dynamic-based Aging Clock and Aging Roadmap Forecast with Sundial

Addressing the unavoidable bias inherent in supervised aging clocks, we introduce Sundial, a novel framework that models molecular dynamics through a diffusion field, capturing both the population-level aging process and the individual-level relative aging order. Sundial enables unbiasedestimation of biological age and the forecast of aging roadmap. Fasteraging individuals from Sundial exhibit a higher disease risk compared to those identified from supervised aging clocks. This framework opens new avenues for exploring key topics, including age- and sex-specific aging dynamics and faster yet healthy aging paths.

q-bio.QM↗

arXiv2018

Muon Tracing and Image Reconstruction Algorithms for Cosmic Ray Muon Computed Tomography

Cosmic ray muon computed tomography (μCT) is a new imaging modality with unique characteristics that could be particularly important for diverse applications including nuclear nonproliferation, spent nuclear fuel monitoring, cargo scanning, and volcano imaging. The strong scattering dependence of muons on atomic number Z in combination with high penetration range could offer a significant advantage over existing techniques when dense, shielded containers must be imaged. However, μCT reconstruction using conventional filtered back-projection is limited due to the overly simple assumptions that do not take into account the effects of multiple Coulomb scattering prompting the need for more sophisticated approaches to be developed. In this paper, we argue that the use of improved muon tracing and scattering angle projection algorithms as well as use of an algebraic reconstruction technique should produce muon tomographic images with improved quality or require fewer muons to produce the same image quality compared to the case where conventional methods are used. We report on the development and assessment of three novel muon tracing methods and two new scattering angle projection methods for μCT. Simulated dry storage casks with single and partial missing fuel assemblies were used as numerical examples to assess and compare the proposed methods. The simulated images showed an expected improvement in image quality when compared with more conventional techniques, even without muon momentum information, which should lead to improved detection capability, even for partial defects.

physics.ins-det↗

arXiv2017

Detection of Missing Assemblies and Estimation of the Scattering Densities in a VSC-24 Dry Storage Cask with Cosmic-Ray-Muon-Based Computed Tomography

Highly energetic, cosmic-ray muons can easily penetrate a dry storage cask and yield information about the material inside it by making use of the physics of multiple Coulomb scattering. Work by others has shown this information may be used for verification of dry storage cask contents after continuity of knowledge has been lost. In our modeling and simulation approach, we use ideal planar radiation detectors to record the trajectories and momentum of both incident and exiting cosmic ray muons; this choice allows us to demonstrate the fundamental limit of the technology for a particular measurement and reconstruction method. In a method analogous to computed tomography with attenuation coefficient replaced by scattering density is we apply a filtered back projection algorithm in order to reconstruct both the geometry and material information in modeled scenarios for concrete-walled cask VSC-24. A scenario where one of the middle four spent nuclear fuel assemblies is missing undetectable with a simple PoCA-based approach is expected to be readily detectable with CT-based approach. Moreover, a trickier scenario where the one or more assemblies is replaced by dummy assembly is put forward. In this case, we expect that this dry storage cask should be found to be not as declared based on our simulation and reconstruction results. Furthermore, we show that the material composition can be estimated if the momentum of individual muons can be precisely measured.

physics.ins-det↗