Homotopy reservoir computing: Harnessing chaos for computation.

Reservoir computing (RC) has traditionally relied on tuning systems toward the edge of chaos to optimize their computational capability. In contrast, we propose a novel method that starts from a fully chaotic system and systematically tames it into a trainable reservoir using homotopy. Our approach constructs adaptive reservoirs whose internal dynamics evolve in real time with the input, yielding a new class of computational models: Homotopy Reservoir Computing (Homotopy RC).

DCNN models with post-hoc interpretability for the automated detection of glossitis and OSCC on the tongue.

This study aimed to develop and evaluate deep convolutional neural network (DCNN) models with Grad-CAM visualization for the automated classification with interpretability of tongue conditions-specifically glossitis and oral squamous cell carcinoma (OSCC)-using clinical tongue photographs, with a focus on their potential for early detection and telemedicine-based diagnostics. A total of 652 tongue images were categorized into normal control (n = 294), glossitis (n = 340), and OSCC (n = 17). Four pretrained DCNN architectures (VGG16, VGG19, ResNet50, ResNet152) were fine-tuned using transfer learning.

Signal-noise separation using unsupervised reservoir computing.

Removing noise from a signal without knowing the characteristics of the noise is a challenging task. This paper introduces a signal-noise separation method based on time-series prediction. We use Reservoir Computing (RC) to extract the maximum portion of "predictable information" from a given signal.

Power-efficiency trade-off for the finite-time quantum harmonic Otto heat engine via a phase-space approach.

Thermodynamic constraints impose a trade-off between power and efficiency in heat engines, preventing the simultaneous achievement of high power and high efficiency. For classical microscopic engines, explicit inequalities have been discovered, demonstrating the inherent inevitability of this power-efficiency trade-off. However, extensions of these results to quantum engines have so far been limited to cases of slow operation.

BaGeP and BaGeAs pnictides as promising ferroelectric semiconductors for thin film solar cell.

Ferroelectric semiconductors offer a unique route to enhance photovoltaic (PV) performance through spontaneous polarization-assisted separation of e-h pairs. In this study, we investigate BaGePn (Pn = P or As) as promising candidates for thin-film solar cells using density functional theory with a hybrid functional. These pnictides exhibit spontaneous polarization, suitable optical band gaps near the Shockley-Queisser limit, and strong visible-light absorption.

Master Stability for Traveling Waves on Networks.

We present a framework for determining effectively the spectrum and stability of traveling waves on networks with symmetries, such as rings and lattices, by computing master stability curves (MSCs). Unlike traditional methods, MSCs are independent of system size and can be readily used to assess wave destabilization and multistability in small and large networks.

Supervised learning of the Jaynes-Cummings Hamiltonian.

We investigate the utility of deep neural networks (DNNs) in estimating the Jaynes-Cummings Hamiltonian's parameters from its energy spectrum alone. We assume that the energy spectrum may or may not be corrupted by noise. In the noiseless case, we use the vanilla DNN (vDNN) model and find that the error tends to decrease as the number of input nodes increases.

Exploring the Reaction Network of Acetic Acid in Supercritical Water via Machine Learning Interatomic Potential.

Supercritical water oxidation offers promising solutions for waste treatment, but understanding its complex molecular reaction mechanisms remains challenging due to extreme experimental conditions. We compare two computational approaches, a machine learning potential (NequIP) and a reactive force field (ReaxFF), to model acetic acid oxidation in supercritical water, a key industrial process. While ReaxFF predicts the apparent activation barrier closer to experimental measurements, NequIP more accurately reproduces the observed product distributions and reaction pathways.

Pseudochaotic many-body dynamics as a pseudorandom state generator.

Quantum chaos is central to understanding quantum dynamics and is crucial for generating random quantum states, a key resource for quantum information tasks. In this work, we introduce a new class of quantum many-body dynamics, termed pseudochaotic dynamics. Although distinct from chaotic dynamics, out-of-time-ordered correlators, the key indicators of quantum chaos, fail to distinguish them.

Quantum entanglement and extractable work for Gaussian states.

The study of quantum thermodynamics aims to elucidate the role played by quantum principles in the emergent features of quantum thermodynamic processes. Specifically, it is of fundamental importance to understand how quantum correlation among different parties enables thermodynamic features distinguishable from those arising in classical thermodynamics. In this work, we investigate the relation between extractable work and quantum correlations for two-mode Gaussian states.

Activated cancer-associated fibroblasts correlate with poor survival and decreased lymphocyte infiltration in infiltrative type distal cholangiocarcinoma.

Cancer-associated fibroblasts promote tumor progression through growth facilitation, invasion, and immune evasion. This study investigated the impact of activated cancer-associated fibroblasts (aCAFs) on survival outcomes, immune response, and molecular pathways in distal bile duct (DBD) cancer. We analyzed 469 patients (418 from our cohort and 51 from The Cancer Genome Atlas) with DBD adenocarcinoma.

Thermodynamic uncertainty relation for systems with active Ornstein-Uhlenbeck particles.

Thermodynamic uncertainty relations (TURs) delineate tradeoff relations between the thermodynamic cost and the magnitude of an observable's fluctuation. While TURs have been established for various nonequilibrium systems, their applicability to systems influenced by active noise remains largely unexplored. Here, we present an explicit expression of TUR for systems with active Ornstein-Uhlenbeck particles (AOUPs).

Benchmarking First-Principles Approaches for the Band Gap Prediction of Nanoporous Materials.

We present a comprehensive benchmarking study of first-principles calculation methods, based on density functional theory (DFT) and its extensions, to evaluate the fundamental and optical band gaps of nanoporous materials, including metal-organic frameworks (MOFs), covalent-organic frameworks (COFs), and a zeolite. We find that a hybrid approach using the HSE06 functional generally underestimates the fundamental band gaps compared to the nonself-consistent GW (GW) approximation, and a DFT approach incorporating self-consistent extended Hubbard interactions shows varying agreement with GW results depending on the electronic characteristics of materials. Using the Bethe-Salpeter equation (BSE) on top of GW calculations (GW+BSE) and time-dependent DFT (TDDFT) with the PBE functional, we compute optical band gaps and absorption spectra that are in good agreement with experiments.

Controlling 2D Nanoparticle Self-Assembly Mesophases via Symmetry Breaking Driven by Single Bottlebrush Polymer Conjugation.

Polymer grafting density critically influences the self-assembly of polymer-grafted nanoparticles, yet the low grafting density regime remains underexplored. Here, we investigate the thin-film self-assembly of bottlebrush polymer-grafted core/shell nanoparticles (BPGNPs) under quasi-2D confinement at near-zero grafting densities through coarse-grained molecular dynamics (CGMD). The NP core is modeled using a hard-core/soft-shoulder (HCSS) potential, and it is compared against Weeks-Chandler-Andersen (WCA) potential.

Spatiotemporally-controlled droplet merging reveals 2D diffusion dynamics of multi-component surfactants.

Investigating the two-dimensional (2D) diffusion dynamics of multi-component surfactant systems is challenging due to the difficulty in establishing well-defined initial boundaries both temporally and spatially. To overcome this challenge, we employed two key strategies. First, we utilized a merging droplet technique which provides controlled spatiotemporal initial conditions, enabling in-situ measurement of multi-component surfactant diffusion.

Competition between group interactions and nonlinearity in voter dynamics on hypergraphs.

Social dynamics are often driven by both pairwise (i.e., dyadic) relationships and higher-order (i.

Gauss-Legendre-spherical-t (GLST) cubature-based factorization of long-range electrostatics in simulations.

We develop a highly parallelizable algorithm to calculate long-range electrostatic interactions named the Gauss-Legendre-Spherical-t (GLST) cubature method. Motivated by our recent spherical grid and treecode method, we utilize the Gauss-Legendre quadrature for integration over a finite range and spherical t-design for integration over a unit sphere. The resulting GLST cubature breaks the long-range interaction term into a sum of terms that can be calculated in parallel with minimal inter-processor communication.

A parallel CUDA implementation of the Gauss-Legendre-spherical-t method for electrostatic interactions.

Computing electrostatic interactions remains the bottleneck of molecular dynamics (MD) simulations despite more than a century of effort in developing methods to accelerate the calculation. Previously, we have developed the spherical grids and treecode and Gauss-Legendre-spherical-t (GLST) algorithms for electrostatic interactions. Here, we explain the computational details and discuss the performance of GLST.

Variations of the Depletion Zones around Inclusions Explain the Complexity of Brush-Induced Depletion Interactions.

Depletion forces are relevant in a variety of contexts such as the phase behavior of colloid-polymer or colloid-depletant mixtures and clustering of inclusions in mobile brushes. They arise from the tendency to minimize the volume of the depletion zone formed around colloidal particles or inclusions. In comparison to depletion interactions widely studied for colloidal particles or polymers in a suspension of spherical depletants, depletion interactions between nonspherical inclusions in mobile polymer brushes display complex behaviors.

Experimental demonstration of generalized quantum fluctuation theorems in the presence of coherence.

Fluctuation theorems have elevated the second law of thermodynamics to a statistical realm by establishing a connection between time-forward and time-reversal probabilities, providing invaluable insight into nonequilibrium dynamics. While well established in classical systems, their quantum generalization, incorporating coherence and the diversity of quantum noise, remains open. We report the experimental validation of a quantum fluctuation theorem (QFT) in a photonic system, applicable to general quantum processes with nonclassical characteristics, including quasi-probabilistic descriptions of entropy production and multiple time-reversal processes.

Self-consistent gravity model for inferring node mass in flow networks.

The gravity model, inspired by Newton's law of universal gravitation, has been a cornerstone in the analysis of trade flows between countries. In this model, each country is assigned an economic mass, where greater economic masses lead to stronger trade interactions. Traditionally, proxy variables like gross domestic product or other economic indicators have been used to approximate this economic mass.

Data augmentation using diffusion models to enhance inverse Ising inference.

Identifying model parameters from observed configurations poses a fundamental challenge in data science, especially with limited data. Recently, diffusion models have emerged as a novel paradigm in generative machine learning, capable of producing new samples that closely mimic observed data. These models learn the gradient of model probabilities, bypassing the need for cumbersome calculations of partition functions across all possible configurations.

Vertical Cancer Transmission via Asexual Fragmentation and Associated Cancer Prevalence.

While sexual reproduction is a general feature of animals, fissiparity and budding are relatively uncommon modes of asexual reproduction by which a fragment from a parent becomes an independent organism. Unlike unitary development, tumor cells can be included in the detached fragment destined to become offspring. Although fragmentation facilitates the vertical transmission of parental tumor cells to nascent progeny, this process requires significantly fewer cell replications than development from a zygote.

Unconventional domain tessellations in moiré-of-moiré lattices.

Imposing incommensurable periodicity on the periodic atomic lattice can lead to complex structural phases consisting of locally periodic structure bounded by topological defects. Twisted trilayer graphene (TTG) is an ideal material platform to study the interplay between different atomic periodicities, which can be tuned by twist angles between the layers, leading to moiré-of-moiré lattices. Interlayer and intralayer interactions between two interfaces in TTG transform this moiré-of-moiré lattice into an intricate network of domain structures at small twist angles, which can harbour exotic electronic behaviours.

Scattering diagrams, tight gradings, and generalized positivity.

In 2013, Lee, Li, and Zelevinsky introduced combinatorial objects called compatible pairs to construct the greedy bases for rank-2 cluster algebras, consisting of indecomposable positive elements including the cluster monomials. Subsequently, Rupel extended this construction to the setting of generalized rank-2 cluster algebras by defining compatible gradings. We find a class of combinatorial objects which we call tight gradings.