Guoyuan Yang | Searcharxiv

arXiv2026

Realization and manipulation of spiral charge density waves in a two-dimensional metal

Nearly degenerate charge-density-wave (CDW) states play a central role in the competition among collective phenomena. In real materials, however, these states are often intertwined by disorder, hindering their disentanglement and control. Here we show that strain can lift this near-degeneracy and spatially separate distinct CDW states in NbSe2. Using van der Waals (vdW) interactions, we stabilize a micron-scale strain network that produces spatially inhomogeneous strain fields. Within this landscape, the intrinsic 3 * 3 CDW superlattice of pristine NbSe2 transforms into an isolated unidirectional 4 * 1 order under 1D-confined compression, and into a 2 * 2 order under biaxial tension. The 4 * 1 CDW has a multiband origin and exhibits markedly enhanced thermal stability, persisting up to 70 K. At strain-network nodes, it further develops into chiral spiral textures, which can be melted by voltage pulses. These results establish strain as a powerful approach to disentangle, stabilize and manipulate competing electronic orders.

cond-mat.mes-hall↗

arXiv2025

Sparse Autoencoders Bridge The Deep Learning Model and The Brain

We present SAE-BrainMap, a novel framework that directly aligns deep learning visual model representations with voxel-level fMRI responses using sparse autoencoders (SAEs). First, we train layer-wise SAEs on model activations and compute the correlations between SAE unit activations and cortical fMRI signals elicited by the same natural image stimuli with cosine similarity, revealing strong activation correspondence (maximum similarity up to 0.76). Depending on this alignment, we construct a voxel dictionary by optimally assigning the most similar SAE feature to each voxel, demonstrating that SAE units preserve the functional structure of predefined regions of interest (ROIs) and exhibit ROI-consistent selectivity. Finally, we establish fine-grained hierarchical mapping between model layers and the human ventral visual pathway, also by projecting voxel dictionary activations onto individual cortical surfaces, we visualize the dynamic transformation of the visual information in deep learning models. It is found that ViT-B/16$_{CLIP}$ tends to utilize low-level information to generate high-level semantic information in the early layers and reconstructs the low-dimension information later. Our results establish a direct, downstream-task-free bridge between deep neural networks and human visual cortex, offering new insights into model interpretability.

q-bio.NC↗