arXiv subjects

Mohammad Abbasi

Publications and source records attributed to Mohammad Abbasi.

5 recordsLinked to original sources

arXiv2026

Fermi-Point Topology Determines Emergent Conformal Criticality in Extended Quantum Spin Chains

Quantum criticality in one-dimensional quantum systems is characterized by emergent conformal field theories (CFTs), whose central charge counts independent gapless degrees of freedom. Establishing a microscopic connection between this universal conformal structure and the momentum-space topology of the underlying quasiparticle spectrum remains challenging. Here, we uncover a direct correspondence between Fermi-point topology, conformal criticality, and quantum entanglement in an extended quantum spin chain with competing cluster interactions, exchange anisotropy, and a transverse magnetic field. We show that interaction- and field-driven Lifshitz transitions generate conformal critical phases with effective central charges $c_{\rm eff}=1/2$, $1$, $3/2$, $2$, and $3$, including a multicritical point where Ising and Luttinger-liquid sectors coexist. Importantly, the central charge is not determined simply by the number of lattice gap closings or Fermi points, but by the number and conformal content of independent low-energy continuum sectors after accounting for lattice symmetries, reciprocal-lattice identifications, and mode equivalences. Thus, Lifshitz transitions may leave the conformal anomaly unchanged or modify the central charge depending on whether spectral reconstruction generates new independent continuum sectors. Real- and momentum-space entanglement spectra provide complementary microscopic signatures, revealing both the conformal content and momentum-space organization of critical modes. Our results establish a microscopic framework linking Fermi-point topology to emergent CFTs and show how interaction-driven spectral reconstruction can generate higher-central-charge criticality and unconventional multicritical behavior.

cond-mat.str-el↗

arXiv2026

Ground-State Phase Diagram, Higher-Winding Topology, and Lifshitz Criticality in an Anisotropic Four-Spin XX Chain

We investigate the quantum critical and topological properties of a spin-$1/2$ XX chain with anisotropic four-spin cluster interactions in a transverse magnetic field. The interplay of exchange anisotropy, cluster interactions, and the external field produces a rich phase diagram with multiple gapped topological phases and distinct quantum critical boundaries. We identify several Lifshitz-type transitions arising from reconstructions of the low-energy Fermi-point structure, including an unconventional multicritical point where distinct critical branches intersect and the momentum-space topology undergoes a singular reorganization. In the isotropic limit, a conventional Lifshitz transition emerges through the merging and annihilation of Fermi points, revealing distinct mechanisms of Fermi-point reconstruction within the same model. The gapped phases are characterized by quantized winding numbers $\nu=0,\pm1,-2,$ and $\pm3$, with the higher-winding phases induced by the extended cluster interactions. The $\nu=\pm3$ phases exhibit a richer topological structure than conventional short-range Kitaev-type chains and are separated from other sectors by field- and interaction-driven gap closings. These topological distinctions are further reflected in characteristic degeneracy patterns of the lowest levels of the bulk entanglement spectrum. Our results demonstrate that anisotropic multispin interactions provide a versatile route to realizing higher-winding topological phases and unconventional multicriticality in one-dimensional quantum systems.

cond-mat.str-el↗

arXiv2026

Diffusion MRI Transformer with a Diffusion Space Rotary Positional Embedding (D-RoPE)

Diffusion Magnetic Resonance Imaging (dMRI) plays a critical role in studying microstructural changes in the brain. It is, therefore, widely used in clinical practice; yet progress in learning general-purpose representations from dMRI has been limited. A key challenge is that existing deep learning approaches are not well-suited to capture the unique properties of diffusion signals. Brain dMRI is normally composed of several brain volumes, each with different attenuation characteristics dependent on the direction and strength of the diffusion-sensitized gradients. Thus, there is a need to jointly model spatial, diffusion-weighting, and directional dependencies in dMRI. Furthermore, varying acquisition protocols (e.g., differing numbers of directions) further limit traditional models. To address these gaps, we introduce a diffusion space rotatory positional embedding (D-RoPE) plugged into our dMRI transformer to capture both the spatial structure and directional characteristics of diffusion data, enabling robust and transferable representations across diverse acquisition settings and an arbitrary number of diffusion directions. After self-supervised masked autoencoding pretraining, tests on several downstream tasks show that the learned representations and the pretrained model can provide competitive or superior performance compared to several baselines in these downstream tasks (even compared to a fully trained baseline); the finetuned features from our pretrained encoder resulted in a 6% higher accuracy in classifying mild cognitive impairment and a 0.05 increase in the correlation coefficient when predicting cognitive scores. Code is available at: github.com/gustavochau/D-RoPE.

cs.CV↗

arXiv2026

Interpretable Cross-Network Attention for Resting-State fMRI Representation Learning

Understanding how large-scale functional brain networks reorganize during cognitive decline remains a central challenge in neuroimaging. While recent self-supervised models have shown promise for learning representations from resting-state fMRI, their internal mechanisms are difficult to interpret, limiting mechanistic insight. We propose BrainInterNet, a network-aware self-supervised framework based on masked reconstruction with cross-attention that explicitly models inter-network dependencies in rs-fMRI. By selectively masking predefined functional networks and reconstructing them from remaining context, our approach enables direct quantification of network predictability and interpretable analysis of cross-network interactions. We train BrainInterNet on multi-cohort fMRI data (from the ABCD, HCP Development, HCP Young Adults, and HCP Aging datasets) and evaluate on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, in total comprising 5,582 recordings. Our method reveals systematic alterations in the brain's network interactions under AD, including in the default mode, limbic, and attention networks. In parallel, the learned representations support accurate Alzheimer's-spectrum classification and yield a compact summary marker that tracks disease severity longitudinally. Together, these results demonstrate that network-guided masked modeling with cross-attention provides an interpretable and effective framework for characterizing functional reorganization in neurodegeneration.

cs.LG↗

arXiv2025

Generating Novel Brain Morphology by Deforming Learned Templates

Designing generative models for 3D structural brain MRI that synthesize morphologically-plausible and attribute-specific (e.g., age, sex, disease state) samples is an active area of research. Existing approaches based on frameworks like GANs or diffusion models synthesize the image directly, which may limit their ability to capture intricate morphological details. In this work, we propose a 3D brain MRI generation method based on state-of-the-art latent diffusion models (LDMs), called MorphLDM, that generates novel images by applying synthesized deformation fields to a learned template. Instead of using a reconstruction-based autoencoder (as in a typical LDM), our encoder outputs a latent embedding derived from both an image and a learned template that is itself the output of a template decoder; this latent is passed to a deformation field decoder, whose output is applied to the learned template. A registration loss is minimized between the original image and the deformed template with respect to the encoder and both decoders. Empirically, our approach outperforms generative baselines on metrics spanning image diversity, adherence with respect to input conditions, and voxel-based morphometry. Our code is available at https://github.com/alanqrwang/morphldm.

eess.IV↗