arXiv subjects

Keishu Utimula

Publications and source records attributed to Keishu Utimula.

12 recordsLinked to original sources

arXiv2026

LOGOS-CA: A Cellular Automaton Using Natural Language as State and Rule

Large Language Models (LLMs), trained solely on massive text data, have achieved high performance on the Winograd Schema Challenge (WSC), a benchmark proposed to measure commonsense knowledge and reasoning abilities about the real world. This suggests that the language produced by humanity describes a significant portion of the world with considerable nuance. In this study, we attempt to harness the high expressive power of language within cellular automata. Specifically, we express cell states and rules in natural language and delegate their updates to an LLM. Through this approach, cellular automata can transcend the constraints of merely numerical states and fixed rules, providing us with a richer platform for simulation. Here, we propose LOGOS-CA (Language Oriented Grid Of Statements - Cellular Automaton) as a natural framework to achieve this and examine its capabilities. We confirmed that LOGOS-CA successfully performs simple forest fire simulations and also serves as an intriguing subject for investigation from an Artificial Life (ALife) perspective. In this paper, we report the results of these experiments and discuss directions for future research using LOGOS-CA.

nlin.CG↗

arXiv2025

A Model of Silence, or the Probability of "Un Ange Passe"

In French, the phrase "Un ange passe" ("An angel passes") refers to the sudden silence that falls over a co-present group -- that is, a group of people sharing the same physical space. As evidenced by the presence of similar expressions across languages and cultures, this phenomenon represents a universal feature of human conversation. At the same time, the meaning attributed to silence can differ greatly across national, cultural, and interpersonal contexts. Consequently, a wide range of studies have focused on the impact of silence on organizational productivity, its relationship to ideas and creativity, and its potential effectiveness in medical settings. Despite the important role that silence plays, very few studies have attempted to characterize its features using mathematical modeling. In this study, we propose a Markov chain model to describe the dynamics of silence in a co-present group and attempt to analyze its behavior. Our results reveal a phase-transition-like phenomenon, where the probability of silence abruptly drops to zero once individuals' awareness of the surrounding conversation falls below a critical threshold. In other words, such silence can emerge only when individuals retain a minimal degree of mutual awareness of those around them. The model proposed in this study not only offers a deeper understanding of conversational dynamics, but also holds potential for contributing to intercultural communication, organizational productivity, and medical practice.

cs.SI↗

arXiv2023

Using reinforcement learning to autonomously identify sources of error for agents in group missions

When agents swarm to execute a mission, some of them frequently exhibit sudden failure, as observed from the command base. It is generally difficult to determine whether a failure is caused by actuators (hypothesis, $h_a$) or sensors (hypothesis, $h_s$) by solely relying on the communication between the command base and concerning agent. However, by instigating collusion between the agents, the cause of failure can be identified; in other words, we expect to detect corresponding displacements for $h_a$ but not for $h_s$. In this study, we considered the question as to whether artificial intelligence can autonomously generate an action plan $\boldsymbol{g}$ to pinpoint the cause as aforedescribed. Because the expected response to $\boldsymbol{g}$ generally depends upon the adopted hypothesis [let the difference be denoted by $D(\boldsymbol{g})$], a formulation that uses $D\left(\boldsymbol{g}\right)$ to pinpoint the cause can be made. Although a $\boldsymbol{g}^*$ that maximizes $D(\boldsymbol{g})$ would be a suitable action plan for this task, such an optimization is difficult to achieve using the conventional gradient method, as $D(\boldsymbol{g})$ becomes nonzero in rare events such as collisions with other agents, and most swarm actions $\boldsymbol{g}$ give $D(\boldsymbol{g})=0$. In other words, throughout almost the entire space of $\boldsymbol{g}$, $D(\boldsymbol{g})$ has zero gradient, and the gradient method is not applicable. To overcome this problem, we formulated an action plan using Q-table reinforcement learning. Surprisingly, the optimal action plan generated via reinforcement learning presented a human-like solution to pinpoint the problem by colliding other agents with the failed agent. Using this simple prototype, we demonstrated the potential of applying Q-table reinforcement learning methods to plan autonomous actions to pinpoint the causes of failure.

cs.RO↗

arXiv2022

Artificial Life using a Book and Bookmarker

Reproduction, development, and individual interactions are essential topics in artificial life. The cellular automata, which can handle these in a composite way, is highly restricted in its form and behavior because it represents life as a pattern of cells. In contrast, the virtual creatures proposed by Karl Sims have a very high degree of freedom in terms of morphology and behavior. However, they have limited expressive capacity in terms of those viewpoints. This study carefully extracts the characteristics of the cellular automata and Sims models to propose a new artificial life model that can simulate reproduction, development, and individual interactions while exhibiting high expressive power for morphology and behavior. The simulation was performed by sequentially reading a book with genetic information and repeatedly executing four actions: expansion, connection, disconnection, and transition. The virtual creatures in the proposed model exhibit unique survival strategies and lifestyles and acquire interesting properties in reproduction, development, and individual interactions while having freedom in morphology and behavior.

cs.NE↗

arXiv2021

Application of canonical augmentation to the atomic substitution problem

A common approach for studying a solid solution or disordered system within a periodic ab-initio framework is to create a supercell in which a certain amount of target elements is substituted with other ones. The key to generating supercells is determining how to eliminate symmetry-equivalent structures from the large number of substitution patterns. Although the total number of substitutions is on the order of trillions, only symmetry-inequivalent atomic substitution patterns need to be identified, and their number is far smaller than the total. A straightforward solution would be to classify them after determining all possible patterns, but it is redundant and practically unfeasible. Therefore, to alleviate this drawback, we developed a new formalism based on the {\it canonical augmentation}, and successfully applied it to the atomic substitution problem. Our developed \verb|python| software package, which is called \textsc{SHRY} (\underline{S}uite for \underline{H}igh-th\underline{r}oughput generation of models with atomic substitutions implemented by p\underline{y}thon), enables us to pick up only symmetry-inequivalent structures from the vast number of candidates very efficiently. We demonstrate that the computational time required by our algorithm to find $N$ symmetry-inequivalent structures scales {\it linearly} with $N$ up to $\sim 10^9$. This is the best scaling for such problems.

cond-mat.mtrl-sci↗

arXiv2021

Intrinsic Electronic Defect States of Anatase using Density Functional Theory

In this work an overall electronic structure including the position and formation energies of various intrinsic defects are computed for anatase using Density Functional Theory aided by Hubbard correction (DFT+U). The intrinsic point defects considered here are, oxygen vacancy ($V_O$), oxygen interstitial ($O_i$), titanium vacancy ($V_{Ti}$) and titanium interstitial ($Ti_i$). Out of all the intrinsic defects considered here, $V_{Ti}$ and $Ti_i$ are found to be most stable under equilibrium condition. Whereas, conduction band in anatase is consisted of mainly Ti 3d with a minor component of O 2p states, valence band is found to be mainly composed of O 2p with a minor contribution from Ti 3d states. $V_O$ and $Ti_i$ are found to form localized states in the band gap. Moreover, anisotropy in the effective mass is seen. Finally, an alignment of band diagrams for all the intrinsic defect states is performed using vacuum potential from slab-supercell calculation as reference. This first principle study would help in the understanding of defect-induced insulating to conducting transition in anatase, which would have significant impact in the photocatalytic and optoelectronic area.

cond-mat.mtrl-sci↗

arXiv2020

Stochastic estimations of a total number of classes for the clusterings with too enormous samples to be accommodate into a clustering engine

We considered the problem how to handle the exploding number of possibilities to be sorted into irreducible classes by using a clustering tool when its input capacity cannot accommodate the total number of the possibility. Concrete situations are explained taking examples of atomic substitutions in the supercell modeling of alloys. The number of the possibility sometimes amounts to $\sim$ trillion being too large to be accommodate. It is hence not practically feasible to identify how many irreducible classes exist by straightforward manner even though there are several tools available to perform the clustering. We have developed a stochastic framework to avoid the shortage of capacity, providing a method to estimate the total number of irreducible classes (the order of the classes) as a statistical estimate. A prominent conclusion derived here is that the statistical variation of the number of classes at each sampling trial is working as a promising measure to estimate the order.

physics.comp-ph↗

arXiv2020

Quantum annealing approach to Ionic Diffusion in Solid

We have developed a framework for using quantum annealing computation to evaluate a key quantity in ionic diffusion in solids, the correlation factor. Existing methods can only calculate the correlation factor analytically in the case of physically unrealistic models, making it difficult to relate microstructural information about diffusion path networks obtainable by current ${ab\ initio}$ techniques to macroscopic quantities such as diffusion coefficients. We have mapped the problem into a quantum spin system described by the Ising Hamiltonian. By applying our framework in combination with ab initio technique, it is possible to understand how diffusion coefficients are controlled by temperatures, pressures, atomic substitutions, and other factors.We have calculated the correlation factor in a simple case with a known exact result by a variety of computational methods, including simulated quantum annealing on the spin models, the classical random walk, the matrix description, and quantum annealing on D-Wave with hybrid solver. This comparison shows that all the evaluations give consistent results with each other, but that many of the conventional approaches require infeasible computational costs. Quantum annealing is also currently infeasible because of the cost and scarcity of Q-bits, but we argue that when technological advances alter this situation, quantum annealing will easily outperform all existing methods.

quant-ph↗

arXiv2020

Feature space of XRD patterns constructed by auto-encorder

It would be a natural expectation that only major peaks, not all of them, would make an important contribution to the characterization of the XRD pattern. We developed a scheme that can identify which peaks are relavant to what extent by using auto-encoder technique to construct a feature space for the XRD peak patterns. Individual XRD patterns are projected onto a single point in the two-dimensional feature space constructed using the method. If the point is significantly shifted when a peak of interest is masked, then we can say the peak is relevant for the characterization represented by the point on the space. In this way, we can formulate the relevancy quantitatively. By using this scheme, we actually found such a peak with a significant peak intensity but low relevancy in the characterization of the structure. The peak is not easily explained by the physical viewpoint such as the higher-order peaks from the same plane index, being a heuristic finding by the power of machine-learning.

physics.comp-ph↗

arXiv2019

First Principles Calculations of Superconducting Critical Temperature of ThCr$_2$Si$_2$-Type Structure

High critical temperature (T$_c$) superconductor has a great potential in many industrial applications. However, discovering a compound having high T$_c$ is still remaining a big challenge for experimental approach due to time-consuming and high cost. In this paper, we investigated the critical temperature (T$_c$) of several compounds of ThCr$_2$Si$_2$-type structure (space group I4/mmm) since some of them had already been investigated and trusted as the potential candidates for superconductivity. First principle calculation was performed to compute the critical temperature (T$_c$) based on allen-dynes equation modification of McMillian formula. In order to confirm our calculation scheme, we compared our result with compounds which had been experimentally determined obtained from $NIMS$ database. The result showed a very good agreement with experimental data. Based on this scheme, finally, we found ThCu$_2$Si$_2$ and ThAu$_2$Si$_2$ which were most likely to exhibit a superconductivity around 3.88 K and 4.27. $keywords$ : superconductivity, ThCr$_2$Si$_2$-type structure, ThCu$_2$Si$_2$, ThAu$_2$Si$_2$, critical temperature

cond-mat.supr-con↗

arXiv2019

Machine learning clustering technique applied to powder X-ray diffraction patterns to distinguish alloy substitutions

We applied the clustering technique using DTW (dynamic time wrapping) analysis to XRD (X-ray diffraction) spectrum patterns in order to identify the microscopic structures of substituents introduced in the main phase of magnetic alloys. The clustering is found to perform well to identify the concentrations of the substituents with successful rates (around 90%). The sufficient performance is attributed to the nature of DTW processing to filter out irrelevant informations such as the peak intensities (due to the incontrollability of diffraction conditions in polycrystalline samples) and the uniform shift of peak positions (due to the thermal expansions of lattices).

physics.comp-ph↗

arXiv2018

$\boldsymbol{Ab\ initio}$ search of polymer crystals with high thermal conductivity

Lattice thermal conductivities (LTC) for a subset of polymer crystals from the Polymer Genome Library were investigated to explore high LTC polymer systems. We employed a first-principles approach to evaluating phonon lifetimes within the third-order perturbation theory combined with density functional theory, and then solved the linearized Boltzmann transport equation with single-mode relaxation time approximated by the computed lifetime. Typical high LTC polymer systems, polyethylene (PE) crystal and fiber, were benchmarked, which is reasonably consistent with previous references, validating our approach. We then applied it to not only typical polymer crystals, but also some selected ones having structural similarities to PE. Among the latter crystals, we discovered that beta phase of Poly(vinylidenesurely fluoride) (PVF-$β$) crystal has higher LTC than PE at low temperature. Our detailed mode analysis revealed that the phonon lifetime of PVF-$β$ is more locally distributed around lower frequency modes and four-times larger than that of PE. It was also found from a simple data analysis that the LTC relatively correlates with curvature of energy-volume plot. The curvature would be used as a descriptor for further exploration of high LTC polymer crystals by means of a data-driven approach beyond human-based one.

cond-mat.mtrl-sci↗