arXiv subjects

Victoria Leong

Publications and source records attributed to Victoria Leong.

4 recordsLinked to original sources

arXiv2026

Thermoinformational State Construction: Generative Energies, Entropies, and H-Theorem Consistency

We introduce a constructive framework for assigning thermodynamic structure to an arbitrary data system from its measured microstates. Starting from an empirical distribution over configurations, we first infer a data-driven energy function by fitting a Boltzmann-type model to the observed statistics, thereby defining an energy axis that is intrinsic to the system. We then push the empirical distribution onto this energy coordinate and pose an inverse maximum-entropy problem: we learn a strictly concave trace-form entropy functional whose maximizer, under a small set of constraints extracted from the data, reproduces the observed energy-space histogram. With energy and entropy defined in this coupled, system-specific manner, macroscopic variables such as internal energy, an entropy-energy relation S(U), and a thermoinformational temperature T^(-1)= dS/dU follow consistently along admissible families of states. We demonstrate the construction on canonical unimodal and multimodal examples, including a harmonic well (recovering the classical equilibrium limit up to gauge) and a bistable double-well where global-constraint MaxEnt surrogates can obscure barrier and coexistence structure. The resulting formulation provides a principled route from microstate data to thermodynamically consistent macroscopic descriptors, with an optimized entropy matched to the empirical system.

cond-mat.stat-mech↗

arXiv2025

A thermoinformational formulation for the description of neuropsychological systems

Complex systems produce high-dimensional signals that lack macroscopic variables analogous to entropy, temperature, or free energy. This work introduces a thermoinformational formulation that derives entropy, internal energy, temperature, and Helmholtz free energy directly from empirical microstate distributions of arbitrary datasets. The approach provides a data-driven description of how a system reorganizes, exchanges information, and moves between stable and unstable states. Applied to dual-EEG recordings from mother-infant dyads performing the A-not-B task, the formulation captures increases in informational heat during switches and errors, and reveals that correct choices arise from more stable, low-temperature states. In an independent optogenetic dam-pup experiment, the same variables separate stimulation conditions and trace coherent trajectories in thermodynamic state space. Across both human and rodent systems, this thermoinformational formulation yields compact and physically interpretable macroscopic variables that generalize across species, modalities, and experimental paradigms.

q-bio.NC↗

arXiv2025

Decoding Covert Speech from EEG Using a Functional Areas Spatio-Temporal Transformer

Covert speech involves imagining speaking without audible sound or any movements. Decoding covert speech from electroencephalogram (EEG) is challenging due to a limited understanding of neural pronunciation mapping and the low signal-to-noise ratio of the signal. In this study, we developed a large-scale multi-utterance speech EEG dataset from 57 right-handed native English-speaking subjects, each performing covert and overt speech tasks by repeating the same word in five utterances within a ten-second duration. Given the spatio-temporal nature of the neural activation process during speech pronunciation, we developed a Functional Areas Spatio-temporal Transformer (FAST), an effective framework for converting EEG signals into tokens and utilizing transformer architecture for sequence encoding. Our results reveal distinct and interpretable speech neural features by the visualization of FAST-generated activation maps across frontal and temporal brain regions with each word being covertly spoken, providing new insights into the discriminative features of the neural representation of covert speech. This is the first report of such a study, which provides interpretable evidence for speech decoding from EEG. The code for this work has been made public at https://github.com/Jiang-Muyun/FAST

eess.SP↗

arXiv2023

Learning multi-modal generative models with permutation-invariant encoders and tighter variational objectives

Devising deep latent variable models for multi-modal data has been a long-standing theme in machine learning research. Multi-modal Variational Autoencoders (VAEs) have been a popular generative model class that learns latent representations that jointly explain multiple modalities. Various objective functions for such models have been suggested, often motivated as lower bounds on the multi-modal data log-likelihood or from information-theoretic considerations. To encode latent variables from different modality subsets, Product-of-Experts (PoE) or Mixture-of-Experts (MoE) aggregation schemes have been routinely used and shown to yield different trade-offs, for instance, regarding their generative quality or consistency across multiple modalities. In this work, we consider a variational objective that can tightly approximate the data log-likelihood. We develop more flexible aggregation schemes that avoid the inductive biases in PoE or MoE approaches by combining encoded features from different modalities based on permutation-invariant neural networks. Our numerical experiments illustrate trade-offs for multi-modal variational objectives and various aggregation schemes. We show that our variational objective and more flexible aggregation models can become beneficial when one wants to approximate the true joint distribution over observed modalities and latent variables in identifiable models.

stat.ML↗