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A bstract The Liouville theorem states that classical time evolution is an incompressible flow in phase space. We investigate two formulations of classical mechanics in which this property is manifested. First, the traditional Hamilton-Jacobi theory provides an in-out formalism. Second, a recent idea employing an exponential representation of time evolution provides an in-in formalism. Through explicit examples, it is demonstrated that the on-shell action in the former and the exponential generator in the latter are disparate objects. Still, a concrete relation between the two is identified in terms of a matching calculation. A strictly classical derivation and formulation of classical scattering theory is provided.

A bstract We study the (ambi-)twistor model for spinning particles interacting via electromagnetic field, as a toy model for studying classical dynamics of gravitating bodies including effects of both spins to all orders. We compute the momentum kick and spin kick up to one-loop order and show precisely how they are encoded in the classical eikonal. The all-orders-in-spin effects are encoded as a dynamical implementation of the Newman-Janis shift, and we find that the expansion in both spins can be resummed to simple expressions in special kinematic configurations, at least up to one-loop order. We confirm that the classical eikonal can be understood as the generator of canonical transformations that map the in-states of a scattering process to the out-states. We also remark that cut contributions for converting worldline propagators from time-symmetric to retarded amount to the iterated action of the leading eikonal at one-loop order.

A bstract In a classical scattering problem, the classical eikonal is defined as the generator of the canonical transformation that maps in-states to out-states. It can be regarded as the classical limit of the log of the quantum S -matrix. In a classical analog of the Born approximation in quantum mechanics, the classical eikonal admits an expansion in oriented tree graphs, where oriented edges denote retarded/advanced worldline propagators. The Magnus expansion, which takes the log of a time-ordered exponential integral, offers an efficient method to compute the coefficients of the tree graphs to all orders. We exploit a Hopf algebra structure behind the Magnus expansion to develop a fast algorithm which can compute the tree coefficients up to the 12th order (over half a million trees) in less than an hour. In a relativistic setting, our methods can be applied to the post-Minkowskian (PM) expansion for gravitational binaries in the worldline formalism. We demonstrate the methods by computing the 3PM eikonal and find agreement with previous results based on amplitude methods. Importantly, the Magnus expansion yields a finite eikonal, while the naïve eikonal based on the time-symmetric propagator is infrared-divergent from 3PM on.

Large-scale single-cell analyses have become increasingly important given the role of cellular heterogeneity in complex biological systems. However, no techniques at present enable optical imaging of uniquely tagged individual cells. Fluorescence-based approaches can distinguish only a small number of distinct cells or cell groups at a time because of spectral crosstalk between conventional fluorophores. Here we investigate large-scale cell tracking using intracellular laser particles as imaging probes that emit coherent laser light with a characteristic wavelength. Made of silica-coated semiconductor microcavities, these laser particles have single-mode emission over a broad range from 1,170 nm to 1,580 nm with sub-nanometre linewidths, enabling massive spectral multiplexing. We explore the stability and biocompatibility of these probes in vitro and their utility for wavelength-multiplexed cell tagging and imaging. We demonstrate real-time tracking of thousands of individual cells in a three-dimensional tumour model over several days, showing different behavioural phenotypes.

The clinical phenotypes of nonalcoholic fatty liver disease (NAFLD) encompass from simple steatosis to nonalcoholic steatohepatitis (NASH) with varying degrees of fibrosis or cirrhosis. Liver biopsy has been the standard to diagnose NASH. However, there has been strong need for precise and accurate noninvasive tests because of invasiveness and sampling variability of biopsy. Metabolomics has drawn attention as a promising diagnostic methodology in the field of NAFLD, particularly to unravel metabolic alterations which plays relevant roles in the progression of NASH. There have been numerous metabolomics researches to find new biomarker of NASH in the last decade, fueled by the recent advances in the metabolomics methodology. This review briefly covers recent research advances on the lipidomics, amino acids and bile acid metabolomics regarding continuing attempts to discover relevant biomarkers for NASH.

Organic memristors are promising candidates for the flexible synaptic components of wearable intelligent systems. With heightened concerns for the environment, considerable effort has been made to develop organic transient memristors to realize eco‐friendly flexible neural networks. However, in the transient neural networks, achieving flexible memristors with biorealistic synaptic plasticity for energy efficient learning processes is still challenging. Herein, a biodegradable and flexible polymer‐based memristor, suitable for the spike‐dependent learning process, is demonstrated. An electrochemical metallization phenomenon for the conductive nanofilament growth in a polymer medium of poly (vinyl alcohol) (PVA) is analyzed and a PVA‐based transient and flexible artificial synapse is developed. The developed device exhibits superior biodegradability and stable mechanical flexibility due to the high water solubility and excellent tensile strength of the PVA film, respectively. In addition, the developed flexible memristor is operated as a reliable synaptic device with optimized synaptic plasticity, which is ideal for artificial neural networks with the spike‐dependent operations. The developed device is found to be effectively served as a reliable synaptic component with high energy efficiency in practical neural networks. This novel strategy for developing transient and flexible artificial synapses can be a fundamental platform for realizing eco‐friendly wearable intelligent systems. An interactive preprint version of the article can be found here: https://doi.org/10.22541/au.166603245.58711630/v1. A biodegradable and flexible polymer‐based memristor for eco‐friendly artificial synapses is demonstrated. The developed device exhibits superior biodegradability and mechanical flexibility due to the high water solubility and excellent tensile strength of the polymer, respectively. Moreover, the memristors are operated as reliable synaptic cells with optimized synaptic plasticity, which is ideal for artificial neural networks with high energy efficiency.

Recently, metasurfaces composed of artificially fabricated subwavelength structures have shown remarkable potential for the manipulation of light with unprecedented functionality. Here, we first demonstrate a metasurface application to realize a compact near-eye display system for augmented reality with a wide field of view. A key component is a see-through metalens with an anisotropic response, a high numerical aperture with a large aperture, and broadband characteristics. By virtue of these high-performance features, the metalens can overcome the existing bottleneck imposed by the narrow field of view and bulkiness of current systems, which hinders their usability and further development. Experimental demonstrations with a nanoimprinted large-area see-through metalens are reported, showing full-color imaging with a wide field of view and feasibility of mass production. This work on novel metasurface applications shows great potential for the development of optical display systems for future consumer electronics and computer vision applications. Here, the authors demonstrate a metasurface application to realize a compact near-eye display system for augmented reality with a wide field of view, full-color imaging, high resolution and a sufficiently large eyebox.

This paper proposes a hybrid deep learning emotion classification system (HDECS), a hybrid multimodal deep learning system designed for emotion classification in a specific national language. Emotion classification is important in diverse fields, including tailored corporate services, AI advancement, and more. Additionally, most sentiment classification techniques in speaking situations are based on a single modality: voice, conversational text, vital signs, etc. However, analyzing these data presents challenges because of the variations in vocal intonation, text structures, and the impact of external stimuli on physiological signals. Korean poses challenges in natural language processing, including subject omission and spacing issues. To overcome these challenges and enhance emotion classification performance, this paper presents a case study using Korean multimodal data. The case study model involves retraining two pretrained models, LSTM and CNN, until their predictions on the entire dataset reach an agreement rate exceeding 0.75. Predictions are used to generate emotional sentences appended to script data, which are further processed using BERT for final emotion prediction. The research result is evaluated by using categorical cross-entropy (CCE) to measure the difference between the model’s predictions and actual labels, F1 score, and accuracy. According to the evaluation, the case model outperforms the existing KLUE/roBERTa model with improvements of 0.5 in CCE, 0.09 in accuracy, and 0.11 in F1 score. As a result, the HDECS is expected to perform well not only on Korean multimodal datasets but also on sentiment classification considering the speech characteristics of various languages and regions.

This study presents findings that demonstrate the possibility of simplifying neural networks by inducing multifunctionality through separate manipulation within a single material. Herein, two‐terminal memristor W/ZnTe/W devices implemented a multifunctional memristor comprising a selector, synapse, and a neuron using an ovonic threshold switching material. By setting the low‐current level (µA) in the forming process, a stable memory‐switching operation is achieved, and the capacity to implement a synapse is demonstrated based on paired‐pulse facilitation/depression, potentiation/depression, spike‐amplitude‐dependent plasticity, and spike‐number‐dependent plasticity outcomes. Based on synaptic behavior, the Modified National Institute of Standards and Technology database image classification accuracy is up to 90%. Conversely, by setting the high‐current level (mA) in the forming process, the stable bipolar threshold switching operation and good selector characteristics (300 ns switching speed, free‐drift, recovery properties) are demonstrated. In addition, a stochastic neuron is implemented using the stochastic switching response in the positive voltage region. Utilizing stochastic neurons, it is possible to create a generative restricted Boltzmann machine model. An Ovonic threshold switching material based multifunctional memristor device is presented. The two‐terminal multifunctional memristor is expected to function as a selector in memory arrays, a synaptic device and a stochastic neuron in neuromorphic systems by setting the appropriate operating current level in the forming process. Utilizing the stochastic neurons, a restricted Boltzmann machine model is created.

Most previous studies have modeled resistive switching based on physical principles. While physics-driven models provide valuable physical insight, their complexity and device specificity limit scalability and often result in high computational costs in large-scale circuit simulations. To address these limitations, we propose a generic compact modeling methodology in LTspice. This generalized approach constructs compact models for diverse resistive memory technologies using a minimal set of common circuit elements. It integrates nonlinear resistors, capacitors, and switches to reproduce key switching phenomena—including filament formation, polarization-modulated tunneling, and spin-dependent resistance—thereby capturing the essential behaviors of RRAM, TS, FTJ, and MRAM. This generalized framework enables flexible parameter tuning to reflect each device’s distinct switching dynamics. Validation using experimental and reference data confirms that the resulting models accurately reproduce the key switching characteristics across all four device classes. Monte Carlo simulations further demonstrate the framework’s ability to capture variability arising from parameter fluctuations and cycling effects. The proposed framework scales seamlessly from single-device operation to array-level simulation, enabling performance evaluation of SNN- and BNN-based inference systems and supporting accurate estimation of power consumption. Collectively, these results demonstrate the framework’s broad applicability and scalability for large-scale neuromorphic and in-memory computing architectures.