1 citations · 2 across the 16 of their papers we have counts for
16 papers
Cross-Spectral Witness for Hidden Nonequilibrium Beyond the Scalar Ceiling
Yuda Bi, Vince D Calhoun
Partial observation is a pervasive obstacle in nonequilibrium physics: coarse graining may absorb hidden forcing into an apparently equilibrium-like reduced description, so a drive…
Timescale Coalescence Makes Hidden Persistent Forcing Spectrally Dark
Yuda Bi, Chenyu Zhang, Vince D Calhoun
Under coarse observation, unresolved slow forcing can remain dynamically active yet locally invisible to reduced spectral inference. For a solvable driven AR benchmark, the lo…
A Bell Experiment in an Entangled Universe
Pablo Tejerina-Pérez, Daniele Bertacca, Raul Jimenez +1
We propose a possible quantum signature of the early Universe that could lead to observational imprints of the quantum nature of the inflationary period. Graviton production from t…
Grokking as a Falsifiable Finite-Size Transition
Yuda Bi, Chenyu Zhang, Qiheng Wang +1
Grokking -- the delayed onset of generalization after early memorization -- is often described with phase-transition language, but that claim has lacked falsifiable finite-size inp…
Conditioning on a Volatility Proxy Compresses the Apparent Timescale of Collective Market Correlation
Yuda Bi, Vince D Calhoun
We address the attribution problem for apparent slow collective dynamics: is the observed persistence intrinsic, or inherited from a persistent driver? For the leading eigenvalue f…
Reservoir Subspace Injection for Online ICA under Top-n Whitening
Wenjun Xiao, Yuda Bi, Vince D Calhoun
Reservoir expansion can improve online independent component analysis (ICA) under nonlinear mixing, yet top- whitening may discard injected features. We formalize this bottlenec…
A 1/R Law for Kurtosis Contrast in Balanced Mixtures
Yuda Bi, Wenjun Xiao, Linhao Bai +1
Kurtosis-based Independent Component Analysis (ICA) weakens in wide, balanced mixtures. We prove a sharp redundancy law: for a standardized projection with effective width $R_{\mat…
Redundancy as a Structural Information Principle for Learning and Generalization
Yuda Bi, Ying Zhu, Vince D Calhoun
We present a theoretical framework that extends classical information theory to finite and structured systems by redefining redundancy as a fundamental property of information orga…
Scaling Laws are Redundancy Laws
Yuda Bi, Vince D Calhoun
Scaling laws, a defining feature of deep learning, reveal a striking power-law improvement in model performance with increasing dataset and model size. Yet, their mathematical orig…
The Financial Connectome: A Brain-Inspired Framework for Modeling Latent Market Dynamics
Yuda Bi, Vince D Calhoun
We propose the Financial Connectome, a new scientific discipline that models financial markets through the lens of brain functional architecture. Inspired by the foundational work…
MultiViT2: A Data-augmented Multimodal Neuroimaging Prediction Framework via Latent Diffusion Model
Bi Yuda, Jia Sihan, Gao Yutong +3
Multimodal medical imaging integrates diverse data types, such as structural and functional neuroimaging, to provide complementary insights that enhance deep learning predictions a…
GM-LDM: Latent Diffusion Model for Brain Biomarker Identification through Functional Data-Driven Gray Matter Synthesis
Hu Xu, Yang Jingling, Jia Sihan +2
Generative models based on deep learning have shown significant potential in medical imaging, particularly for modality transformation and multimodal fusion in MRI-based brain imag…
Cross-Modal Synthesis of Structural MRI and Functional Connectivity Networks via Conditional ViT-GANs
Yuda Bi, Anees Abrol, Jing Sui +1
The cross-modal synthesis between structural magnetic resonance imaging (sMRI) and functional network connectivity (FNC) is a relatively unexplored area in medical imaging, especia…
Exploring the Power of Generative Deep Learning for Image-to-Image Translation and MRI Reconstruction: A Cross-Domain Review
Yuda Bi
Deep learning has become a prominent computational modeling tool in the areas of computer vision and image processing in recent years. This research comprehensively analyzes the di…
MultiCrossViT: Multimodal Vision Transformer for Schizophrenia Prediction using Structural MRI and Functional Network Connectivity Data
Yuda Bi, Anees Abrol, Zening Fu +1
Vision Transformer (ViT) is a pioneering deep learning framework that can address real-world computer vision issues, such as image classification and object recognition. Importantl…
Prediction of Gender from Longitudinal MRI data via Deep Learning on Adolescent Data Reveals Unique Patterns Associated with Brain Structure and Change over a Two-year Period
Yuda Bi, Anees Abrol, Zening Fu +3
Deep learning algorithms for predicting neuroimaging data have shown considerable promise in various applications. Prior work has demonstrated that deep learning models that take a…