Search

A new sightline geometry for the fast-ion D-alpha (FIDA) diagnostic on the Large Helical Device (LHD) has been confirmed to measure signals for high-energy fast ions produced by negative-ion neutral beam injection. The newly installed sightline uses a 180 keV tangential negative-ion neutral beamline as the active source. Due to the small angle between the beamline and FIDA sightline, the relative velocity between fast ions and injected neutrals is small. This allows for high-energy fast ions just below the beam injection energy to produce measurable Doppler-shifted FIDA emission. Experiments were conducted at LHD in order to compare the new sightline, which views a high-energy negative-ion tangential beamline, and the old sightline, which views a low-energy perpendicular positive-ion neutral beamline. The measured FIDA signal is validated against predictions from the synthetic fast-ion diagnostic code FIDASIM with a distribution function modelled by the 5D transport code GNET. The results of the experiment confirm that reducing the viewing angle with a tangential active beam allows FIDA diagnostic to view high-energy fast ions with a sufficient signal-to-noise ratio., Comment: 12 pages, 6 figures, 1 table

Promises made by politicians, corporate leaders, and public figures have a significant impact on public perception, trust, and institutional reputation. However, the complexity and volume of such commitments, coupled with difficulties in verifying their fulfillment, necessitate innovative methods for assessing their credibility. This paper introduces the concept of Promise Verification, a systematic approach involving steps such as promise identification, evidence assessment, and the evaluation of timing for verification. We propose the first multilingual dataset, ML-Promise, which includes English, French, Chinese, Japanese, and Korean, aimed at facilitating in-depth verification of promises, particularly in the context of Environmental, Social, and Governance (ESG) reports. Given the growing emphasis on corporate environmental contributions, this dataset addresses the challenge of evaluating corporate promises, especially in light of practices like greenwashing. Our findings also explore textual and image-based baselines, with promising results from retrieval-augmented generation (RAG) approaches. This work aims to foster further discourse on the accountability of public commitments across multiple languages and domains., Comment: 6 pages

The $(k,l)$-G\"{o}bel sequences defined by Ibstedt remain integers for the first (in some cases, many) terms, but for selected values of $(k,l)$, computations show that the terms eventually stop being integers. It is still unresolved whether the integrality of these sequences breaks down for all $k, l\geq 2$. In this article, we prove the non-integrality for a specific class of $(k,l)$ values., Comment: 19 pages, 5 figures

We investigate the performance of penalty-based quantum annealing (PQA) and constrained quantum annealing (CQA) in solving the graph partitioning problem under various noise models, including depolarizing, bit-flip, and phase-flip noise. We found that even in the absence of noise, the relative superiority of PQA or CQA is highly problem-dependent. PQA generally demonstrates relatively stable success probabilities, while CQA's performance varies significantly across problem instances. Notably, CQA experiences leakage from the constraint-satisfying subspace under most noise models, with the exception of phase-flip noise, where no leakage occurs exactly. The dependence of success probability on noise strength reveals that PQA consistently outperforms CQA in the weak noise regime, whereas CQA achieves higher success probabilities under strong phase-flip noise. However, even in this case, the overall success probability remains low, suggesting that CQA is not yet practical for noisy quantum annealers without the development of error-corrected quantum annealing and autonomous error correction techniques to address leakage from the constraint-satisfying subspace. To confine the system within the constraint subspace in CQA, error correction methods and cat code encoding will likely be required, both of which can suppress bit-flip errors., Comment: 8 pages, 5 figures

This paper presents an initialization method that can approximate a given approximate Ising model with a high degree of accuracy using the Factorization Machine (FM), a machine learning model. The construction of Ising models using FM is applied to the combinatorial optimization problem using the factorization machine with quantum annealing. It is anticipated that the optimization performance of FMQA will be enhanced through the implementation of the warm-start method. Nevertheless, the optimal initialization method for leveraging the warm-start approach in FMQA remains undetermined. Consequently, the present study compares a number of initialization methods and identifies the most appropriate for use with a warm-start in FMQA through numerical experimentation. Furthermore, the properties of the proposed FM initialization method are analyzed using random matrix theory, demonstrating that the approximation accuracy of the proposed method is not significantly influenced by the specific Ising model under consideration. The findings of this study will facilitate the advancement of combinatorial optimization problem-solving through the use of Ising machines., Comment: 25 pages, 5 figures

Purpose: In radiology, large language models (LLMs), including ChatGPT, have recently gained attention, and their utility is being rapidly evaluated. However, concerns have emerged regarding their reliability in clinical applications due to limitations such as hallucinations and insufficient referencing. To address these issues, we focus on the latest technology, retrieval-augmented generation (RAG), which enables LLMs to reference reliable external knowledge (REK). Specifically, this study examines the utility and reliability of a recently released RAG-equipped LLM (RAG-LLM), NotebookLM, for staging lung cancer. Materials and methods: We summarized the current lung cancer staging guideline in Japan and provided this as REK to NotebookLM. We then tasked NotebookLM with staging 100 fictional lung cancer cases based on CT findings and evaluated its accuracy. For comparison, we performed the same task using a gold-standard LLM, GPT-4 Omni (GPT-4o), both with and without the REK. Results: NotebookLM achieved 86% diagnostic accuracy in the lung cancer staging experiment, outperforming GPT-4o, which recorded 39% accuracy with the REK and 25% without it. Moreover, NotebookLM demonstrated 95% accuracy in searching reference locations within the REK. Conclusion: NotebookLM successfully performed lung cancer staging by utilizing the REK, demonstrating superior performance compared to GPT-4o. Additionally, it provided highly accurate reference locations within the REK, allowing radiologists to efficiently evaluate the reliability of NotebookLM's responses and detect possible hallucinations. Overall, this study highlights the potential of NotebookLM, a RAG-LLM, in image diagnosis., Comment: 9 pages, 5 figures, 1 table, 3 ancillary files

We study the decidability and complexity of non-cooperative rational synthesis problem (abbreviated as NCRSP) for some classes of probabilistic strategies. We show that NCRSP for stationary strategies and Muller objectives is in 3-EXPTIME, and if we restrict the strategies of environment players to be positional, NCRSP becomes NEXPSPACE solvable. On the other hand, NCRSP_>, which is a variant of NCRSP, is shown to be undecidable even for pure finite-state strategies and terminal reachability objectives. Finally, we show that NCRSP becomes EXPTIME solvable if we restrict the memory of a strategy to be the most recently visited t vertices where t is linear in the size of the game.

Realizability asks whether there exists a program satisfying its specification. In this problem, we assume that each agent has her own objective and behaves rationally to satisfy her objective. Traditionally, the rationality of agents is modeled by a Nash equilibrium (NE), where each agent has no incentive to change her strategy because she cannot satisfy her objective by changing her strategy alone. However, an NE is not always an appropriate notion for the rationality of agents because the condition of an NE is too strong; each agent is assumed to know strategies of the other agents completely. In this paper, we use an epistemic model to define common knowledge of rationality of all agents (CKR). We define the verification problem as a variant of the realizability problem, based on CKR, instead of NE. We then analyze the complexity of the verification problems for the class of positional strategies.

In transition-metal compounds, the transition-metal d electrons play an important role in their physical properties; however, the effects of the electron correlation between the ligand p electrons have not been clear yet. In this Letter, the Ru 4d and O 2p partial density of states (PDOS) in transition-metal oxide SrRuO$_3$ involving Weyl fermions are investigated by resonant photoemission spectroscopy. The observations demonstrate that the O 2p PDOS is distorted from that predicted by first-principles calculations than the Ru 4d PDOS. The results indicate that the electron correlation in the ligand orbitals will be important to understand the electronic structure of the p-d hybridized state in strongly correlated electron systems, even with topological states., Comment: 24 pages, 7 figures

Simulating nonequilibirum dynamics of a quantum many-body system is one of the promising applications of quantum computing. We simulate the time evolution of one-dimensional ${\bf Z}_2$ lattice gauge theory on IBM's superconducting 156-qubit device ibm\_fez. We consider the Floquet circuit made of the Trotter decomposition of Hamiltonian evolution and focus on its dynamics toward thermalization. Quantum simulation with the help of error mitigation is successful in running the Floquet circuit made of $38$ and $116$ qubits up to $10$ Trotter steps in the best case. This is enough to reach the early stage of prethermalization. Our work would be a benchmark for the potential power of quantum computing for high-energy physics problems., Comment: 11 pages, 8 figures

Quantum walks, mathematical models referred to as the quantum counterparts of random walks, have garnered significant attention in recent years with the advancement of quantum computing. The weak limit theorem for quantum walks, analogous to the central limit theorem for random walks, is one of the most important theorems in this field. In this study, we investigated the weak limit theorem for a two-state discrete-time quantum walk on a two-dimensional square lattice. As a result, we derived a two-dimensional probability distribution whose support is the intersection of two ellipses. The probability distribution we obtained resembles the distribution of one-dimensional quantum walks while possessing a unique form specific to two dimensions., Comment: 14 Pages, 8 figures

We recently presented a quantitative model to explain the particle-size dependence of photoluminescence (PL) quantum yields and revealed that exciton quenching is not diffusion controlled, but limited by surface reactions. However, the exciton decay kinetics has not yet been analyzed using our theoretical model. Here, we study kinetic aspects of the model and show that it should be extended to take into account subdiffusion rather than normal diffusion to maintain consistency with the observed complex decay kinetics; we also show that the PL decay kinetics is nonexponential even when the PL quenching is limited by surface reactions under subdiffusion. Our theoretical analysis of the PL quantum yield and the PL decay kinetics provides a comprehensive picture of mobile charge carriers, immobile polarons, and self-trapped excitons., Comment: 6 figures

This paper presents SaSLaW, a spontaneous dialogue speech corpus containing synchronous recordings of what speakers speak, listen to, and watch. Humans consider the diverse environmental factors and then control the features of their utterances in face-to-face voice communications. Spoken dialogue systems capable of this adaptation to these audio environments enable natural and seamless communications. SaSLaW was developed to model human-speech adjustment for audio environments via first-person audio-visual perceptions in spontaneous dialogues. We propose the construction methodology of SaSLaW and display the analysis result of the corpus. We additionally conducted an experiment to develop text-to-speech models using SaSLaW and evaluate their performance of adaptations to audio environments. The results indicate that models incorporating hearing-audio data output more plausible speech tailored to diverse audio environments than the vanilla text-to-speech model., Comment: 5 pages, accepted for INTERSPEECH 2024

This study investigates the application of Factorization Machines with Quantum Annealing (FMQA) to address the crystal structure problem (CSP) in materials science. FMQA is a black-box optimization algorithm that combines machine learning with annealing machines to find samples to a black-box function that minimize a given loss. The CSP involves determining the optimal arrangement of atoms in a material based on its chemical composition, a critical challenge in materials science. We explore FMQA's ability to efficiently sample optimal crystal configurations by setting the loss function to the energy of the crystal configuration as given by a predefined interatomic potential. Further we investigate how well the energies of the various metastable configurations, or local minima of the potential, are learned by the algorithm. Our investigation reveals FMQA's potential in quick ground state sampling and in recovering relational order between local minima., Comment: 9 pages, 3 tables, 3 figures

The transition probability for a one-dimensional discrete symmetric random walk under a reflecting barrier was once given by the method of images. [S. Chandrasekhar, Rev. Mod. Phys. 15, 1 (1943).] However, several inconsistencies have been reported when the method of images is applied in cases where a reflecting barrier is considered, even after the exact solution has been obtained. Here, we explicitly show that the method of images becomes applicable if the image position is shifted., Comment: 7 pages: 1 figure

There is a growing interest in using pulsed high magnetic field as a controlling parameter of physical phenomena in various scientific disciplines, such as condensed matter physics, particle physics, plasma physics, chemistry and biological studies. We devised a concise and portable pulsed magnetic field generator that produces a 40 T field with a pulse duration of 2 ms. It is assembled using only off-the-shelf components and a homemade coil that leverages small computers, Raspberry Pi, and Python codes. It allows for straightforward modification for general purposes. As working examples, we show representative applications in condensed matter experiments of magnetoresistance, magnetization, and magnetostriction measurements for graphite, NdNi$_{2}$P$_{2}$, and NdCo$_{2}$P$_{2}$, respectively, with the maximum magnetic field of 41 T and the lowest temperature of 4.2 K., Comment: 5 pages (Main body) + 19 pages (Supplement)

Determining the number of clusters is a fundamental issue in data clustering. Several algorithms have been proposed, including centroid-based algorithms using the Euclidean distance and model-based algorithms using a mixture of probability distributions. Among these, greedy algorithms for searching the number of clusters by repeatedly splitting or merging clusters have advantages in terms of computation time for problems with large sample sizes. However, studies comparing these methods in systematic evaluation experiments still need to be included. This study examines centroid- and model-based cluster search algorithms in various cases that Gaussian mixture models (GMMs) can generate. The cases are generated by combining five factors: dimensionality, sample size, the number of clusters, cluster overlap, and covariance type. The results show that some cluster-splitting criteria based on Euclidean distance make unreasonable decisions when clusters overlap. The results also show that model-based algorithms are insensitive to covariance type and cluster overlap compared to the centroid-based method if the sample size is sufficient. Our cluster search implementation codes are available at https://github.com/lipryou/searchClustK

Spoken dialogue plays a crucial role in human-AI interactions, necessitating dialogue-oriented spoken language models (SLMs). To develop versatile SLMs, large-scale and diverse speech datasets are essential. Additionally, to ensure hiqh-quality speech generation, the data must be spontaneous like in-wild data and must be acoustically clean with noise removed. Despite the critical need, no open-source corpus meeting all these criteria has been available. This study addresses this gap by constructing and releasing a large-scale spoken dialogue corpus, named Japanese Corpus for Human-AI Talks (J-CHAT), which is publicly accessible. Furthermore, this paper presents a language-independent method for corpus construction and describes experiments on dialogue generation using SLMs trained on J-CHAT. Experimental results indicate that the collected data from multiple domains by our method improve the naturalness and meaningfulness of dialogue generation., Comment: 8 pages, 6 figures

Spotforming is a target-speaker extraction technique that uses multiple microphone arrays. This method applies beamforming (BF) to each microphone array, and the common components among the BF outputs are estimated as the target source. This study proposes a new common component extraction method based on nonnegative tensor factorization (NTF) for higher model interpretability and more robust spotforming against hyperparameters. Moreover, attractor-based regularization was introduced to facilitate the automatic selection of optimal target bases in the NTF. Experimental results show that the proposed method performs better than conventional methods in spotforming performance and also shows some characteristics suitable for practical use., Comment: Accepted at EUSIPCO2024

The influence of annealing on the superconducting properties of eutectic high-entropy alloy NbScTiZr was examined by measuring magnetization, electrical resistivity, and zero-field specific heat. Additionally, the extent of lattice strain was assessed via the analysis of lattice parameters and Vickers microhardness. The greatest lattice strain was inferred in the sample annealed at 400 $^{\circ}$C. Field-dependent magnetization datasets indicate enhanced flux pinning in the as-cast, 400 $^{\circ}$C annealed, and 600 $^{\circ}$C annealed samples. The superconducting parameters such as upper critical field, Ginzburg-Landau coherence length, and magnetic penetration depth do not strongly depend on the annealing temperature. However, the Maki parameter shows a peak at 400 $^{\circ}$C annealing temperature and correlates with the magnitude of lattice strain. This suggests that greater lattice strain may enhance the Maki parameter by altering the orbital-limited field through the modification of flux pinning strength. Although the influence of lattice strain is less discernible in zero-field specific heat measurements, the superconducting parameters deduced from specific heat data suggest a potential for strong-coupled superconductivity in samples heat-treated above 600 $^{\circ}$C. Our investigation underscores the substantial impact of lattice strain on the Maki parameter of eutectic HEA superconductors, primarily through the modulation of flux pinning strength., Comment: to be published in Mater. Today Commun

This paper proposes a new task called spatial voice conversion, which aims to convert a target voice while preserving spatial information and non-target signals. Traditional voice conversion methods focus on single-channel waveforms, ignoring the stereo listening experience inherent in human hearing. Our baseline approach addresses this gap by integrating blind source separation (BSS), voice conversion (VC), and spatial mixing to handle multi-channel waveforms. Through experimental evaluations, we organize and identify the key challenges inherent in this task, such as maintaining audio quality and accurately preserving spatial information. Our results highlight the fundamental difficulties in balancing these aspects, providing a benchmark for future research in spatial voice conversion. The proposed method's code is publicly available to encourage further exploration in this domain., Comment: Accepted to Interspeech 2024

RemixIT and Remixed2Remixed are domain adaptation-based speech enhancement (DASE) methods that use a teacher model trained in full supervision to generate pseudo-paired data by remixing the outputs of the teacher model. The student model for enhancing real-world recorded signals is trained using the pseudo-paired data without ground truth. Since the noisy signals are recorded in natural environments, the dataset inevitably suffers data imbalance in some acoustic properties, leading to subpar performance for the underrepresented data. The signal-to-noise ratio (SNR), inherently balanced in supervised learning, is a prime example. In this paper, we provide empirical evidence that the SNR of pseudo data has a significant impact on model performance using the dataset of the CHiME-7 UDASE task, highlighting the importance of balanced SNR in DASE. Furthermore, we propose adopting curriculum learning to encompass a broad range of SNRs to boost performance for underrepresented data., Comment: Accepted at Interspeech2024

Register automaton (RA) is an extension of finite automaton for dealing with data values in an infinite domain. In the previous work, we proposed disjunctive mu$^\downarrow$-calculus, which is a subclass of modal mu-calculus with the freeze quantifier, and showed that it has the same expressive power as RA. However, disjunctive mu$^\downarrow$-calculus is defined as a logic on finite words, whereas temporal specifications in model checking are usually given in terms of infinite words. In this paper, we re-define the syntax and semantics of disjunctive mu$^\downarrow$-calculus to be suitable for infinite words and prove that the obtained temporal logic has the same expressive power as Buchi RA., Comment: 6 pages, 1 figure, this is a longer version of a short paper submitted to IEICE Transactions on Information and Systems

We propose noise-robust voice conversion (VC) which takes into account the recording quality and environment of noisy source speech. Conventional denoising training improves the noise robustness of a VC model by learning noisy-to-clean VC process. However, the naturalness of the converted speech is limited when the noise of the source speech is unseen during the training. To this end, our proposed training conditions a VC model on two latent variables representing the recording quality and environment of the source speech. These latent variables are derived from deep neural networks pre-trained on recording quality assessment and acoustic scene classification and calculated in an utterance-wise or frame-wise manner. As a result, the trained VC model can explicitly learn information about speech degradation during the training. Objective and subjective evaluations show that our training improves the quality of the converted speech compared to the conventional training., Comment: 5 pages, accepted for INTERSPEECH 2024, audio samples: http://y-saito.sakura.ne.jp/sython/Corpus/SRC4VC/IS2024_CDT_supplementary/demo_cdt.html

We present SRC4VC, a new corpus containing 11 hours of speech recorded on smartphones by 100 Japanese speakers. Although high-quality multi-speaker corpora can advance voice conversion (VC) technologies, they are not always suitable for testing VC when low-quality speech recording is given as the input. To this end, we first asked 100 crowdworkers to record their voice samples using smartphones. Then, we annotated the recorded samples with speaker-wise recording-quality scores and utterance-wise perceived emotion labels. We also benchmark SRC4VC on any-to-any VC, in which we trained a multi-speaker VC model on high-quality speech and used the SRC4VC speakers' voice samples as the source in VC. The results show that the recording quality mismatch between the training and evaluation data significantly degrades the VC performance, which can be improved by applying speech enhancement to the low-quality source speech samples., Comment: Accepted for INTERSPEECH 2024, corpus project page: https://y-saito.sakura.ne.jp/sython/Corpus/SRC4VC/index.html

The two stable surfaces of GdRu2Si2 are studied using spin-polarized scanning tunneling microscopy (SP-STM). Depending on the applied magnetic field different magnetic phases have been found and the presented measurements are in agreement with the respective previously proposed multi-Q spin textures. In particular the multi-Q nature of the zero magnetic field state, for which previous experiments could not rule out the coexistence of single-Q states, can be confirmed by our spin-resolved measurements on the Si-terminated surface. The surfaces of GdRu2Si2 exhibit strong magnetism-induced modulations of the spin-averaged density of states. We find that while the magnetic contribution to the tunnel signal can be clearly identified for the Si-terminated surface this proves to be much more difficult for the Gd-terminated surface. However, the magnetic field dependent spatial modulations on the Gd-terminated surface demonstrate that additional magnetic phase transitions occur for the surface layer compared to those identified for bulk GdRu2Si2 and possible spin textures are presented., Comment: 7 pages, 5 figures

Complex quantum many-body dynamics spread initially localized quantum information across the entire system. Information scrambling refers to such a process, whose simulation is one of the promising applications of quantum computing. We demonstrate the Hayden-Preskill recovery protocol and the interferometric protocol for calculating out-of-time-ordered correlators to study the scrambling property of a one-dimensional kicked-Ising model on 20-qubit trapped-ion quantum processors. The simulated quantum circuits have a geometrically local structure that exhibits the ballistic growth of entanglement, resulting in the circuit depth being linear in the number of qubits for the entire state to be scrambled. We experimentally confirm the growth of signals in the Hayden-Preskill recovery protocol and the decay of out-of-time-ordered correlators at late times. As an application of the created scrambling circuits, we also experimentally demonstrate the calculation of the microcanonical expectation values of local operators adopting the idea of thermal pure quantum states., Comment: 26 pages, 16 figures

A pioneering study conducted by Egbers and Rath [Acta Mech. 111 pp. 125--140 (1995)] experimentally captured spiral waves to elucidate the transition in the wide-gap spherical Couette flow. However, the physical field quantities of the spiral waves corresponding to light patterns of various intensities, as obtained in the experiment, remain unclear, and we have yet to move beyond the understanding that the reflected light from shear-sensitive flake tracers responds to a flow that appears at the transition. In this study, the experiment to visualize spiral waves using aluminum flakes, as performed by Egbers and Rath, was numerically reproduced by solving the translational and rotational motions of the particles in a spiral wave. First, the spiral wave in a spherical Couette flow with an aspect ratio $\eta=1/2$ was numerically calculated using the Newton--Raphson method. Subsequently, the image that was numerically reproduced from the spiral wave was compared with an experimentally visualized image. The torque acting on the inner sphere and the phase angular velocity of the spiral waves with various wavenumbers were provided. Attempts have been made to determine the instantaneous physical quantity to which the light and dark patterns obtained in the visualization corresponded, and the orientation motion of the flakes developed in the advective history of the flow is essential to yield favorable results. Exploring the correlation between flow visualization results and shear structures may provide a new avenue for quantitatively estimating spatial structures and time scales in complex and quickly time-varying flow fields, such as turbulence.

The deformability of human red blood cells (RBCs), which comprise almost 99% of the cells in whole blood, is largely related not only to pathophysiological blood flow but also to the levels of intracellular compounds. Therefore, statistical estimates of the deformability of individual RBCs are of paramount importance in the clinical diagnosis of blood diseases. Although the micro-scale hydrodynamic interactions of individual RBCs lead to non-Newtonian blood rheology, there is no established method to estimate individual RBC deformability from the rheological data of RBC suspensions, and the possibility of this estimation has not been proven. To address this issue, we conducted an integrated analysis of a model of the rheology of RBC suspensions, coupled with macro-rheological data of human RBCs suspended in plasma. Assuming a non-linear curve of the relative viscosity of the suspensions as a function of the cell volume fraction, the statistical average of the membrane shear elasticity was estimated for individual intact RBCs or hardened RBCs. Both estimated values reproduced well the experimentally observed shear-thinning non-Newtonian behavior in these suspensions. We hereby conclude that our complementary approach makes it possible to estimate the statistical average of individual RBC deformability from macro-rheological data obtained with usual rheometric tests.

Medicine and deep learning-based artificial intelligence (AI) engineering represent two distinct fields each with decades of published history. With such history comes a set of terminology that has a specific way in which it is applied. However, when two distinct fields with overlapping terminology start to collaborate, miscommunication and misunderstandings can occur. This narrative review aims to give historical context for these terms, accentuate the importance of clarity when these terms are used in medical AI contexts, and offer solutions to mitigate misunderstandings by readers from either field. Through an examination of historical documents, including articles, writing guidelines, and textbooks, this review traces the divergent evolution of terms for data sets and their impact. Initially, the discordant interpretations of the word 'validation' in medical and AI contexts are explored. Then the data sets used for AI evaluation are classified, namely random splitting, cross-validation, temporal, geographic, internal, and external sets. The accurate and standardized description of these data sets is crucial for demonstrating the robustness and generalizability of AI applications in medicine. This review clarifies existing literature to provide a comprehensive understanding of these classifications and their implications in AI evaluation. This review then identifies often misunderstood terms and proposes pragmatic solutions to mitigate terminological confusion. Among these solutions are the use of standardized terminology such as 'training set,' 'validation (or tuning) set,' and 'test set,' and explicit definition of data set splitting terminologies in each medical AI research publication. This review aspires to enhance the precision of communication in medical AI, thereby fostering more effective and transparent research methodologies in this interdisciplinary field., Comment: 20 pages, 3 figures, 3 tables

Recently, Maesaka, Watanabe, and the third author discovered a phenomenon where the iterated integral expressions of multiple zeta values become discretized. In this paper, we extend their result to the case of multiple polylogarithms and provide two proofs. The first proof uses the method of connected sums, while the second employs induction based on the difference equations that discrete multiple polylogarithms satisfy. We also investigate several applications of our main result., Comment: 29 pages

The burgeoning fields of spintronics and topological electronics require materials possessing a unique combination of properties: ferromagnetism, metallicity, and chemical stability. SrRuO3 (SRO) stands out as a compelling candidate due to its exceptional combination of these attributes. However, understanding its behavior under tensile strain, especially its thickness-dependent changes, remains elusive. This study employs machine-learning-assisted molecular beam epitaxy to investigate SRO films with thicknesses from 1 to 10 nm. This work complements the existing focus on compressive-strained SRO, opening a new avenue for exploring its hitherto concealed potential. Using soft X-ray magnetic circular dichroism, we uncover an intriguing interplay between film thickness, electronic structure, and magnetic properties. Our key findings reveal an intensified localization of Ru 4d t2g-O 2p hybridized states at lower thicknesses, attributed to the weakened orbital hybridization. Furthermore, we find a progressive reduction of magnetic moments for both Ru and O ions as film thickness decreases. Notably, a non-ferromagnetic insulating state emerges at a critical thickness of 1 nm, marking a pivotal transition from the metallic ferromagnetic phase. These insights emphasize the importance of considering thickness-dependent properties when tailoring SRO for next-generation spintronic and topological electronic devices.