arXiv subjects

Tamiki Komatsuzaki

Publications and source records attributed to Tamiki Komatsuzaki.

18 recordsLinked to original sources

arXiv2026

SAVER: Stochastic Adaptive Variance-Driven Exploration and Reconstruction for Low-Dose Computed Tomography

Computed Tomography (CT) is indispensable in clinical diagnostics, yet minimizing radiation dose without compromising image quality remains a critical challenge. Conventional low-dose protocols often rely on fixed, uniform angular sampling, independent of the underlying structural complexity of organs of individual patients. We propose ``Stochastic Adaptive Variance-Driven Exploration and Reconstruction'' (SAVER), an adaptive data acquisition framework that selects projection angles in real-time based on the statistical variance of acquired data. Utilizing a Softmax-based stochastic scheduling scheme with simulated annealing, SAVER prioritizes directions with high structural information while maintaining necessary exploration. Numerical experiments across 8 diverse phantoms demonstrate that SAVER achieves consistently higher reconstruction fidelity than conventional random sampling, particularly for objects with high structural anisotropy. Furthermore, the proposed method exhibits robust performance under significant measurement noise. By dynamically reallocating radiation dose to the most informative projections, SAVER provides a mathematically-grounded approach to maximize diagnostic quality per unit of radiation dose, marking a shift toward sample-dependent, data-driven CT acquisition.

cs.LG↗

arXiv2025

Enumeration algorithms for combinatorial problems using Ising machines: When should we stop exploring energy landscapes?

Combinatorial problems such as combinatorial optimization and constraint satisfaction problems arise in decision-making across various fields of science and technology. In real-world applications, when multiple optimal or constraint-satisfying solutions exist, enumerating all these solutions is often desirable, as it provides flexibility in decision-making. However, combinatorial problems and their enumeration versions pose significant computational challenges due to combinatorial explosion. To address these challenges, we propose enumeration algorithms for combinatorial optimization and constraint satisfaction problems using Ising machines. Ising machines are specialized devices designed to efficiently solve combinatorial problems by exploring the energy landscape of an Ising model. Ising machines typically sample lower-energy solutions with higher probability. Our enumeration algorithms repeatedly perform such sampling to collect all desirable solutions. The crux of the proposed algorithms lies in their stopping criteria for sampling-based energy landscape exploration, which are derived from probability theory. In particular, the proposed algorithms have theoretical guarantees that the failure probability of enumeration is bounded above by a user-specified value, provided that lower-cost solutions are sampled more frequently and equal-cost solutions are sampled with equal probability. Many physics-based Ising machines are expected to (approximately) satisfy these conditions. As a demonstration, we applied our algorithm using simulated annealing to maximum clique enumeration on random graphs. We found that our algorithm enumerates all maximum cliques in large, dense graphs faster than a conventional branch-and-bound algorithm specifically designed for maximum clique enumeration. These findings underscore the effectiveness and potential of our proposed approach.

cs.DS↗

arXiv2025

Risk-Averse Best Arm Set Identification with Fixed Budget and Fixed Confidence

Decision making under uncertain environments in the maximization of expected reward while minimizing its risk is one of the ubiquitous problems in many subjects. Here, we introduce a novel problem setting in stochastic bandit optimization that jointly addresses two critical aspects of decision-making: maximizing expected reward and minimizing associated uncertainty, quantified via the mean-variance(MV) criterion. Unlike traditional bandit formulations that focus solely on expected returns, our objective is to efficiently and accurately identify the Pareto-optimal set of arms that strikes the best trade-off between expected performance and risk. We propose a unified meta-algorithmic framework capable of operating under both fixed-confidence and fixed-budget regimes, achieved through adaptive design of confidence intervals tailored to each scenario using the same sample exploration strategy. We provide theoretical guarantees on the correctness of the returned solutions in both settings. To complement this theoretical analysis, we conduct extensive empirical evaluations across synthetic benchmarks, demonstrating that our approach outperforms existing methods in terms of both accuracy and sample efficiency, highlighting its broad applicability to risk-aware decision-making tasks in uncertain environments.

cs.LG↗

arXiv2024

Beyond the Nucleus: Cytoplasmic Dominance in Follicular Thyroid Carcinoma Detection Using Single-Cell Raman Imaging Across Multiple Devices

Cytological diagnosis of follicular thyroid carcinoma (FTC) is one of major challenges in the field of endocrine oncology due to absence of evident morphological indicators. Morphological abnormalities in the nucleus are typically key indicators in cancer cytopathology and are attributed to a range of biochemical alterations in nuclear components. Consequently, Raman spectroscopy has been widely used to detect cancer in various cytological samples, often identifying biochemical changes prior to observable morphological alterations. However, in the case of FTC, cytoplasmic features such as carotenoids, cytochromes, and lipid droplets have shown greater diagnostic relevance compared to nuclear features. This study leverages single-cell Raman imaging to explore the spatial origin of diagnostic signals in FTC and normal thyroid (NT) cells, assessing the contributions of the nucleus and cytoplasm independently. Our results demonstrate that Raman spectra from the cytoplasmic region can distinguish between FTC and NT cells with an accuracy of 84% under co-culture conditions, maintaining robustness across multiple devices. In contrast, classification based on nuclear spectra achieved only 53% accuracy, suggesting that biochemical alterations in the cytoplasm play a more significant role in FTC detection than those in the nucleus. Our work elevates the promise of Raman-based cytopathology by providing complementary organelle-dependent information to traditional diagnostic methods and demonstrating transferability across different devices.

q-bio.QM↗

arXiv2024

A Quantum Algorithm for Dynamic Mode Decomposition Integrated with a Quantum Differential Equation Solver

We present a quantum algorithm that analyzes time series data simulated by a quantum differential equation solver. The proposed algorithm is a quantum version of the dynamic mode decomposition algorithm used in diverse fields such as fluid dynamics and epidemiology. Our quantum algorithm can also compute matrix eigenvalues and eigenvectors by analyzing the corresponding linear dynamical system. Our algorithm handles a broad range of matrices, in particular those with complex eigenvalues. The complexity of our quantum algorithm is $O(\operatorname{poly}\log N)$ for an $N$-dimensional system. This is an exponential speedup over known classical algorithms with at least $O(N)$ complexity. Thus, our quantum algorithm is expected to enable high-dimensional dynamical systems analysis and large matrix eigenvalue decomposition, intractable for classical computers.

quant-ph↗

arXiv2024

Low-Dimensional Projection of Reactive Islands in Chemical Reaction Dynamics Using a Supervised Dimensionality Reduction Method

Transition state theory is a standard framework for predicting the rate of a chemical reaction. Although the transition state theory has been successfully applied to numerous chemical reaction analyses, many experimental and theoretical studies have reported chemical reactions with a reactivity which cannot be explained by the transition state theory due to dynamic effects. Dynamical systems theory provides a theoretical framework for elucidating dynamical mechanisms of such chemical reactions. In particular, reactive islands are essential phase space structures revealing dynamical reaction patterns. However, the numerical computation of reactive islands in a reaction system of many degrees of freedom involves an intrinsic challenge -- the curse of dimensionality. In this paper, we propose a dimensionality reduction algorithm for computing reactive islands in a reaction system of many degrees of freedom. Using the supervised principal component analysis, the proposed algorithm projects reactive islands into a low-dimensional phase space with preserving the dynamical information on reactivity as much as possible. The effectiveness of the proposed algorithm is examined by numerical experiments for Hénon-Heiles systems extended to many degrees of freedom. The numerical results indicate that our proposed algorithm is effective in terms of the quality of reactivity prediction and the clearness of the boundaries of projected reactive islands. The proposed algorithm is a promising elemental technology for practical applications of dynamical systems analysis to real chemical systems.

physics.chem-ph↗

arXiv2023

Finding Optimal Pathways in Chemical Reaction Networks Using Ising Machines

Finding optimal pathways in chemical reaction networks is essential for elucidating and designing chemical processes, with significant applications such as synthesis planning and metabolic pathway analysis. Such a chemical pathway-finding problem can be formulated as a constrained combinatorial optimization problem, aiming to find an optimal combination of chemical reactions connecting starting materials to target materials in a given network. Due to combinatorial explosion, the computation time required to find an optimal pathway increases exponentially with the network size. Ising machines, including quantum and simulated annealing devices, are promising novel computers dedicated to such hard combinatorial optimization. However, to the best of our knowledge, there has yet to be an attempt to apply Ising machines to chemical pathway-finding problems. In this article, we present the first Ising/quantum computing application for chemical pathway-finding problems. The Ising model, translated from a chemical pathway-finding problem, involves several types of penalty terms for violating constraints. It is not obvious how to set appropriate penalty strengths of different types. To address this challenge, we employ Bayesian optimization for parameter tuning. Furthermore, we introduce a novel technique that enhances tuning performance by grouping penalty terms according to the underlying problem structure. The performance evaluation and analysis of the proposed algorithm were conducted using a D-Wave Advantage system and simulated annealing. The benchmark results reveal challenges in finding exact optimal pathways. Concurrently, the results indicate the feasibility of finding approximate optimal pathways, provided that a certain degree of relative error in cost value is acceptable.

quant-ph↗

arXiv2022

Gaussian Process Classification Bandits

Classification bandits are multi-armed bandit problems whose task is to classify a given set of arms into either positive or negative class depending on whether the rate of the arms with the expected reward of at least h is not less than w for given thresholds h and w. We study a special classification bandit problem in which arms correspond to points x in d-dimensional real space with expected rewards f(x) which are generated according to a Gaussian process prior. We develop a framework algorithm for the problem using various arm selection policies and propose policies called FCB and FTSV. We show a smaller sample complexity upper bound for FCB than that for the existing algorithm of the level set estimation, in which whether f(x) is at least h or not must be decided for every arm's x. Arm selection policies depending on an estimated rate of arms with rewards of at least h are also proposed and shown to improve empirical sample complexity. According to our experimental results, the rate-estimation versions of FCB and FTSV, together with that of the popular active learning policy that selects the point with the maximum variance, outperform other policies for synthetic functions, and the version of FTSV is also the best performer for our real-world dataset.

cs.LG↗

arXiv2021

Transfer entropy dependent on distance among agents in quantifying leader-follower relationships

Synchronized movement of (both unicellular and multicellular) systems can be observed almost everywhere. Understanding of how organisms are regulated to synchronized behavior is one of the challenging issues in the field of collective motion. It is hypothesized that one or a few agents in a group regulate(s) the dynamics of the whole collective, known as leader(s). The identification of the leader (influential) agent(s) is very crucial. This article reviews different mathematical models that represent different types of leadership. We focus on the improvement of the leader-follower classification problem. It was found using a simulation model that the use of interaction domain information significantly improves the leader-follower classification ability using both linear schemes and information-theoretic schemes for quantifying influence. This article also reviews different schemes that can be used to identify the interaction domain using the motion data of agents.

nlin.AO↗

arXiv2021

A Note on Enumeration by Fair Sampling

This note describes an algorithm for enumerating all the elements in a finite set based on uniformly random sampling from the set. This algorithm can be used for enumeration by fair sampling with quantum annealing. Our algorithm is based on a lemma of the coupon collector's problem and is an improved version of the algorithm described in arXiv:2007.08487 (2020). We provide a mathematical analysis and a numerical demonstration of our algorithm.

quant-ph↗

arXiv2021

Modes of Information Flow in Collective Cohesion

Pairwise interactions between individuals are taken as fundamental drivers of collective behavior responsible for group cohesion and decision-making. While an individual directly influences only a few neighbors, over time indirect influences penetrate a much larger group. The abiding question is how this spread of influence comes to affect the collective. One or a few individuals are often identified as leaders, being more influential than others. Transfer entropy and time-delayed mutual information are used to identify underlying asymmetric interactions, such as leader-follower classification in aggregated individuals--cells, birds, fish, and animals. However, these conflate distinct functional modes of information flow between individuals. Computing information measures conditioning on multiple agents requires the proper sampling of a probability distribution whose dimension grows exponentially with the number of agents being conditioned on. Employing simple models of interacting self-propelled particles, we examine the pitfalls of using time-delayed mutual information and transfer entropy to quantify the strength of influence from a leader to a follower. Surprisingly, one must be wary of these pitfalls even for two interacting particles. As an alternative we decompose transfer entropy and time-delayed mutual information into intrinsic, shared, and synergistic modes of information flow. The result not only properly reveals the underlying effective interactions, but also facilitates a more detailed diagnosis of how individual interactions lead to collective behavior. This exposes the role of individual and group memory in collective behaviors. In addition, we demonstrate in a multi-agent system how knowledge of the decomposed information modes between a single pair of agents reveals the nature of many-body interactions without conditioning on additional agents.

nlin.AO↗

arXiv2020

An information-theoretic approach to infer the underlying interaction domain among elements from finite length trajectories in a noisy environment

Transfer entropy in information theory was recently demonstrated [Phys. Rev. E 102, 012404 (2020)] to enable us to elucidate the interaction domain among interacting elements solely from an ensemble of trajectories. There, only pairs of elements whose distances are shorter than some distance variable, termed cutoff distance, are taken into account in the computation of transfer entropies. The prediction performance in capturing the underlying interaction domain is subject to noise level exerted on the elements and the sufficiency of statistics of the interaction events. In this paper, the dependence of the prediction performance is scrutinized systematically on noise level and the length of trajectories by using a modified Vicsek model. The larger the noise level and the shorter the time length of trajectories, the more the derivative of average transfer entropy fluctuates, which makes it difficult to identify the interaction domain in terms of the position of global minimum of the derivative of average transfer entropy. A measure to quantify the degree of strong convexity at coarse-grained level is proposed. It is shown that the convexity score scheme can identify the interaction distance fairly well even while the position of global minimum of the derivative of average transfer entropy does not. We also derive an analytical model to explain the relationship between the interaction domain and the change of transfer entropy that supports our cutoff distance technique to elucidate the underlying interaction domain from trajectories.

nlin.AO↗

arXiv2020

Minor-embedding heuristics for large-scale annealing processors with sparse hardware graphs of up to 102,400 nodes

Minor embedding heuristics have become an indispensable tool for compiling problems in quadratically unconstrained binary optimization (QUBO) into the hardware graphs of quantum and CMOS annealing processors. While recent embedding heuristics have been developed for annealers of moderate size (about 2000 nodes) the size of the latest CMOS annealing processor (with 102,400 nodes) poses entirely new demands on the embedding heuristic. This raises the question, if recent embedding heuristics can maintain meaningful embedding performance on hardware graphs of increasing size. Here, we develop an improved version of the probabilistic-swap-shift-annealing (PSSA) embedding heuristic [which has recently been demonstrated to outperform the standard embedding heuristic by D-Wave Systems (Cai et al., 2014)] and evaluate its embedding performance on hardware graphs of increasing size. For random-cubic and Barabasi-Albert graphs we find the embedding performance of improved PSSA to consistently exceed the threshold of the best known complete graph embedding by a factor of 3.2 and 2.8, respectively, up to hardware graphs with 102,400 nodes. On the other hand, for random graphs with constant edge density not even improved PSSA can overcome the deterministic threshold guaranteed by the existence of the best known complete graph embedding. Finally, we prove a new upper bound on the maximal embeddable size of complete graphs into hardware graphs of CMOS annealers and show that the embedding performance of its currently best known complete graph embedding has optimal order for hardware graphs with fixed coordination number.

quant-ph↗

arXiv2019

A Bad Arm Existence Checking Problem

We study a bad arm existing checking problem in which a player's task is to judge whether a positive arm exists or not among given K arms by drawing as small number of arms as possible. Here, an arm is positive if its expected loss suffered by drawing the arm is at least a given threshold. This problem is a formalization of diagnosis of disease or machine failure. An interesting structure of this problem is the asymmetry of positive and negative (non-positive) arms' roles; finding one positive arm is enough to judge existence while all the arms must be discriminated as negative to judge non-existence. We propose an algorithms with arm selection policy (policy to determine the next arm to draw) and stopping condition (condition to stop drawing arms) utilizing this asymmetric problem structure and prove its effectiveness theoretically and empirically.

cs.LG↗

arXiv2016

Single Molecule Data Analysis: An Introduction

We review methods of data analysis for biophysical data with a special emphasis on single molecule applications. Our review is intended for anyone, from student to established researcher. For someone just getting started, we focus on exposing the logic, strength and limitations of each method and cite, as appropriate, the relevant literature for implementation details. We review traditional frequentist and Bayesian parametric approaches to data analysis and subsequently extend our discussion to recent non-parametric and information theoretic methods.

physics.bio-ph↗

arXiv2016

Theory of Gas Phase Scattering and Reactivity for Astrochemistry

Because of the very peculiar conditions of chemistry in many astrophysical gases (low densities, mostly low temperatures, kinetics-dominated chemical evolution), great efforts have been devoted to study molecular signatures and chemical evolution. While experiments are being performed in many laboratories, it appears that the efforts directed towards theoretical works are not as strong. This report deals with the present status of chemical physics/physical chemistry theory, for the qualitative and quantitative understanding of kinetics of molecular scattering, being it reactive or inelastic. By gathering several types of expertise, from applied mathematics to physical chemistry, dialog is made possible, as a step towards new and more adapted theoretical frameworks, capable of meeting the theoretical, methodological and numerical challenges of kinetics-dominated gas phase chemistry in astrophysical environments. A state of the art panorama is presented, alongside present-day strengths and shortcomings. However, coverage is not complete, being limited in this report to actual attendance of the workshop. Some paths towards relevant progress are proposed.

astro-ph.IM↗

arXiv2013

Reactivity Boundaries to Separate the Fate of a Chemical Reaction Associated with Multiple Saddles

Reactivity boundaries that divide the origin and destination of trajectories are crucial of importance to reveal the mechanism of reactions, which was recently found to exist robustly even at high energies for index-one saddles [Phys. Rev. Lett. 105, 048304 (2010)]. Here we revisit the concept of the reactivity boundary and propose a more general definition that can involve a single reaction associated with a bottleneck made up of higher index saddles and/or several saddle points with different indices, where the normal form theory, based on expansion around a single stationary point, does not work. We numerically demonstrate the reactivity boundary by using a reduced model system of the $H^+_5$ cation where the proton exchange reaction takes place through a bottleneck made up of two index-two saddle points and two index-one saddle points. The cross section of the reactivity boundary in the reactant region of the phase space reveals which initial conditions are effective in making the reaction happen, and thus sheds light on the reaction mechanism.

physics.chem-ph↗

arXiv2013

Reactivity Boundaries to Separate the Fate of a Chemical Reaction Associated with an Index-two saddle

Reactivity boundaries that divide the destination and the origin of trajectories are of crucial importance to reveal the mechanism of reactions. We investigate whether such reactivity boundaries can be extracted for higher index saddles in terms of a nonlinear canonical transformation successful for index-one saddles by using a model system with an index-two saddle. It is found that the true reactivity boundaries do not coincide with those extracted by the transformation taking into account a nonlinearity in the region of the saddle even for small perturbations, and the discrepancy is more pronounced for the less repulsive direction of the index-two saddle system. The present result indicates an importance of the global properties of the phase space to identify the reactivity boundaries, relevant to the question of what reactant and product are in phase space, for saddles with index more than one.

physics.chem-ph↗