arXiv subjects

Naoki Nishimura

Publications and source records attributed to Naoki Nishimura.

17 recordsLinked to original sources

arXiv2026

Microscopic derivation of the microstretch theory for carbon nanotubes

Twisted carbon nanotubes support phonons involving not only torsion, naturally associated with microrotation, but also radial breathing, which requires a scalar stretch degree of freedom. We derive an effective microstretch theory for these modes starting from nonlinear elasticity on a cylindrical surface. By linearizing the equation of motion around a uniformly twisted equilibrium configuration, we obtain the dynamical matrix for the twisting, longitudinal, and radial-breathing modes. This matrix coincides with that of a one-dimensional microstretch theory, and the corresponding elastic constants are expressed in terms of the Lam\'e constants, the nanotube radius, and the twist rate. The twist generates chiral couplings in the effective theory, which hybridize the three modes and open an anticrossing in the phonon dispersion. These results provide a microscopic basis for the microstretch description of phonons in twisted carbon nanotubes and clarify how structural chirality enters the effective couplings.

cond-mat.mtrl-sci↗

arXiv2026

Generalized continuum theory of phonon angular momentum in crystals

We formulate a generalized continuum theory of phonon angular momentum in crystals by introducing a local SO(3) material frame in addition to the macroscopic displacement field. The local frame represents rotational optical degrees of freedom of the unit cell and brings acoustic displacement modes and optical rotational modes into a common long-wavelength continuum description. In the linearized limit, the co-rotated deformation gradient and the rotational gradient associated with the local material frame recover the Eringen microdeformation and wryness tensors; isotropic micropolar elasticity then appears as a special case. Rotational symmetry and Noether's theorem determine the continuum phonon angular-momentum density, including both the displacement-polarization contribution and the intrinsic microrotation contribution. The theory further identifies the locking limit in which microrotation reduces to lattice vorticity and the improper-symmetry-breaking terms responsible for chiral phonon splitting.

cond-mat.mtrl-sci↗

arXiv2026

Interpretable clustering via optimal multi-way decision trees

Clustering is a fundamental unsupervised learning technique for uncovering data structures to facilitate knowledge discovery and decision-making. While clustering accuracy is crucial, interpretability significantly impacts the practical value of clustering results, particularly in high-risk decision-making contexts. Although decision-tree-based clustering methods offer high interpretability through explicit splitting rules, existing approaches often rely on local greedy search or require expensive computational costs limited to binary splits, resulting in deeper, less interpretable trees. To overcome these limitations, we establish a high-performance computational framework named Interpretable Clustering via Optimal Multi-way Trees (ICOMT). We make three primary contributions. First, we propose a new discretization method for numerical features using one-dimensional K-means clustering to capture data distributions. Second, we formulate a binary linear optimization (BLO) problem to guarantee tree optimality. Third, extensive validation on four public datasets demonstrates that our ICOMT method outperforms existing baselines, achieving superior clustering accuracy while maintaining shallow, concise tree structures.

cs.LG↗

arXiv2025

Subset Selection for Stratified Sampling in Online Controlled Experiments

Online controlled experiments, also known as A/B testing, are the digital equivalent of randomized controlled trials for estimating the impact of marketing campaigns on website visitors. Stratified sampling is a traditional technique for variance reduction to improve the sensitivity (or statistical power) of controlled experiments; this technique first divides the population into strata (homogeneous subgroups) based on stratification variables and then draws samples from each stratum to avoid sampling bias. To enhance the estimation accuracy of stratified sampling, we focus on the problem of selecting a subset of stratification variables that are effective in variance reduction. We design an efficient algorithm that selects stratification variables one by one by simulating a series of stratified sampling processes. We also estimate the computational complexity of our subset selection algorithm. Computational experiments using synthetic and real-world datasets demonstrate that our method can outperform other variance reduction techniques especially when multiple variables have a certain correlation with the outcome variable. Our subset selection method for stratified sampling can improve the sensitivity of online controlled experiments, thus enabling more reliable marketing decisions.

stat.CO↗

arXiv2025

Theory of spin Seebeck effect activated by acoustic chiral phonons

We theoretically explore the generation of spin current driven by a temperature gradient in a junction between a chiral insulator and a normal metal. Based on the gyromagnetic response induced by microscopic acoustic-phonon-mediated lattice rotation, we derive a formula for the spin current when a finite temperature difference is imposed between two ends of the sample. We clarify how the phonon-mediated spin current depends on the sample geometry, the thermal conductivity, the heat conductance at the interface, and the average temperature. Our formulation provides a microscopic foundation for the chiral-phonon-activated spin Seebeck effect without relying on magnetism or spin-orbit interactions.

cond-mat.mes-hall↗

arXiv2024

Robust personalized pricing under uncertainty of purchase probabilities

This paper is concerned with personalized pricing models aimed at maximizing the expected revenues or profits for a single item. While it is essential for personalized pricing to predict the purchase probabilities for each consumer, these predicted values are inherently subject to unavoidable errors that can negatively impact the realized revenues and profits. To address this issue, we focus on robust optimization techniques that yield reliable solutions to optimization problems under uncertainty. Specifically, we propose a robust optimization model for personalized pricing that accounts for the uncertainty of predicted purchase probabilities. This model can be formulated as a mixed-integer linear optimization problem, which can be solved exactly using mathematical optimization solvers. We also develop a Lagrangian decomposition algorithm combined with line search to efficiently find high-quality solutions for large-scale optimization problems. Experimental results demonstrate the effectiveness of our robust optimization model and highlight the utility of our Lagrangian decomposition algorithm in terms of both computational efficiency and solution quality.

math.OC↗

arXiv2024

Strategic Coupon Allocation for Increasing Providers' Sales Experiences in Two-sided Marketplaces

In a two-sided marketplace, network effects are crucial for competitiveness, and platforms need to retain users through advanced customer relationship management as much as possible. Maintaining numerous providers' stable and active presence on the platform is highly important to enhance the marketplace's scale and diversity. The strongest motivation for providers to continue using the platform is to realize actual profits through sales. Then, we propose a personalized promotion to increase the number of successful providers with sales experiences on the platform. The main contributions of our research are twofold. First, we introduce a new perspective in provider management with the distribution of successful sales experiences. Second, we propose a personalized promotion optimization method to maximize the number of providers' sales experiences. By utilizing this approach, we ensure equal opportunities for providers to experience sales without being monopolized by a few providers. Through experiments using actual data on coupon distribution, we confirm that our method enables the implementation of coupon allocation strategies that significantly increase the total number of providers having sales experiences.

cs.IR↗

arXiv2024

Closed-loop measurements in an atom interferometer gyroscope with velocity-dependent phase-dispersion compensation

Atom interferometer-based gyroscopes are expected to have a wide range of applications due to their high sensitivity. However, their dynamic range is limited by dephasing caused by velocity-dependent Sagnac phase shift in combination with the longitudinal velocity distribution of the atoms, restricting measurements of large angular velocities. In this study, we present a method for restoring the contrast deterioration in angular velocity measurements with interferometer gyroscopes using atomic beams. Our findings show that by introducing the pseudo-rotation effect with appropriate two-photon detunings for Raman lights in the interferometer, it is possible to effectively cancel the Sagnac phase shift for all atoms in the velocity distribution of the beam. Consequently, the contrast is unaffected by the rotation. Furthermore, we applied this method to an interferometer gyroscope with counter-propagating atomic beams sharing the same Raman lights. It is found that the angular velocity of the system can be estimated through the detuning point where the phase difference between the two interferometers is zero. This approach ensures that the scale factor of the atom interferometer gyroscope is independent of the change in the longitudinal velocity distribution of the atomic beam. We demonstrate our technique using the interferometer gyroscope of thermal atomic beams of rubidium-87, achieving a measurement of angular velocity of $\mathrm{{1.0}^{\circ}/s}$ even with an acceleration of 0.68$\mathrm{m/s^2}$ on a three-axis rotation table. This simple and robust dispersion compensation method with Raman light detuning benefits dynamic angular velocity measurements in field applications such as the inertial navigation of vehicles.

physics.atom-ph↗

arXiv2024

Balancing Immediate Revenue and Future Off-Policy Evaluation in Coupon Allocation

Coupon allocation drives customer purchases and boosts revenue. However, it presents a fundamental trade-off between exploiting the current optimal policy to maximize immediate revenue and exploring alternative policies to collect data for future policy improvement via off-policy evaluation (OPE). To balance this trade-off, we propose a novel approach that combines a model-based revenue maximization policy and a randomized exploration policy for data collection. Our framework enables flexible adjustment of the mixture ratio between these two policies to optimize the balance between short-term revenue and future policy improvement. We formulate the problem of determining the optimal mixture ratio as multi-objective optimization, enabling quantitative evaluation of this trade-off. We empirically verified the effectiveness of the proposed mixed policy using synthetic data. Our main contributions are: (1) Demonstrating a mixed policy combining deterministic and probabilistic policies, flexibly adjusting the data collection vs. revenue trade-off. (2) Formulating the optimal mixture ratio problem as multi-objective optimization, enabling quantitative evaluation of this trade-off.

cs.LG↗

arXiv2024

Fast solution to the fair ranking problem using the Sinkhorn algorithm

In two-sided marketplaces such as online flea markets, recommender systems for providing consumers with personalized item rankings play a key role in promoting transactions between providers and consumers. Meanwhile, two-sided marketplaces face the problem of balancing consumer satisfaction and fairness among items to stimulate activity of item providers. Saito and Joachims (2022) devised an impact-based fair ranking method for maximizing the Nash social welfare based on fair division; however, this method, which requires solving a large-scale constrained nonlinear optimization problem, is very difficult to apply to practical-scale recommender systems. We thus propose a fast solution to the impact-based fair ranking problem. We first transform the fair ranking problem into an unconstrained optimization problem and then design a gradient ascent method that repeatedly executes the Sinkhorn algorithm. Experimental results demonstrate that our algorithm provides fair rankings of high quality and is about 1000 times faster than application of commercial optimization software.

cs.IR↗

arXiv2024

Interpretable Price Bounds Estimation with Shape Constraints in Price Optimization

This study addresses the interpretable estimation of price bounds in the context of price optimization. In recent years, price-optimization methods have become indispensable for maximizing revenue and profits. However, effective application of these methods to real-world pricing operations remains a significant challenge. It is crucial for operators responsible for setting prices to utilize reasonable price bounds that are not only interpretable but also acceptable. Despite this necessity, most studies assume that price bounds are given constant values, and few have explored reasonable determinations of these bounds. Therefore, we propose a comprehensive framework for determining price bounds that includes both the estimation and adjustment of these bounds. Specifically, we first estimate price bounds using three distinct approaches based on historical pricing data. Then, we adjust the estimated price bounds by solving an optimization problem that incorporates shape constraints. This method allows the implementation of price optimization under practical and reasonable price bounds suitable for real-world applications. We report the effectiveness of our proposed method through numerical experiments using historical pricing data from actual services.

cs.GT↗

arXiv2024

Robust portfolio optimization model for electronic coupon allocation

Currently, many e-commerce websites issue online/electronic coupons as an effective tool for promoting sales of various products and services. We focus on the problem of optimally allocating coupons to customers subject to a budget constraint on an e-commerce website. We apply a robust portfolio optimization model based on customer segmentation to the coupon allocation problem. We also validate the efficacy of our method through numerical experiments using actual data from randomly distributed coupons. Main contributions of our research are twofold. First, we handle six types of coupons, thereby making it extremely difficult to accurately estimate the difference in the effects of various coupons. Second, we demonstrate from detailed numerical results that the robust optimization model achieved larger uplifts of sales than did the commonly-used multiple-choice knapsack model and the conventional mean-variance optimization model. Our results open up great potential for robust portfolio optimization as an effective tool for practical coupon allocation.

cs.IR↗

arXiv2024

Covariate Ordered Systematic Sampling as an Improvement to Randomized Controlled Trials

The Randomized Controlled Trial (RCT) or A/B testing is considered the gold standard method for estimating causal effects. Fisher famously advocated randomly allocating experiment units into treatment and control groups to preclude systematic biases. We propose a variant of systematic sampling called Covariate Ordered Systematic Sampling (COSS). In COSS, we order experimental units using a pre-experiment covariate and allocate them alternately into treatment and control groups. Using theoretical proofs, experiments on simulated data, and hundreds of A/B tests conducted within 3 real-world marketing campaigns, we show how our method achieves better sensitivity gains than commonly used variance reduction techniques like CUPED while retaining the simplicity of RCTs.

stat.ME↗

arXiv2023

An IPW-based Unbiased Ranking Metric in Two-sided Markets

In modern recommendation systems, unbiased learning-to-rank (LTR) is crucial for prioritizing items from biased implicit user feedback, such as click data. Several techniques, such as Inverse Propensity Weighting (IPW), have been proposed for single-sided markets. However, less attention has been paid to two-sided markets, such as job platforms or dating services, where successful conversions require matching preferences from both users. This paper addresses the complex interaction of biases between users in two-sided markets and proposes a tailored LTR approach. We first present a formulation of feedback mechanisms in two-sided matching platforms and point out that their implicit feedback may include position bias from both user groups. On the basis of this observation, we extend the IPW estimator and propose a new estimator, named two-sided IPW, to address the position bases in two-sided markets. We prove that the proposed estimator satisfies the unbiasedness for the ground-truth ranking metric. We conducted numerical experiments on real-world two-sided platforms and demonstrated the effectiveness of our proposed method in terms of both precision and robustness. Our experiments showed that our method outperformed baselines especially when handling rare items, which are less frequently observed in the training data.

cs.IR↗

arXiv2020

Predicting Online Item-choice Behavior: A Shape-restricted Regression Perspective

This paper examines the relationship between user pageview (PV) histories and their item-choice behavior on an e-commerce website. We focus on PV sequences, which represent time series of the number of PVs for each user--item pair. We propose a shape-restricted optimization model that accurately estimates item-choice probabilities for all possible PV sequences. This model imposes monotonicity constraints on item-choice probabilities by exploiting partial orders for PV sequences, according to the recency and frequency of a user's previous PVs. To improve the computational efficiency of our optimization model, we devise efficient algorithms for eliminating all redundant constraints according to the transitivity of the partial orders. Experimental results using real-world clickstream data demonstrate that our method achieves higher prediction performance than that of a state-of-the-art optimization model and common machine learning methods.

cs.IR↗

arXiv2019

Item Listing Optimization for E-commerce Websites based on Diversity

For e-commerce websites, deciding the manner in which items are listed on webpages is an important issue because it can dramatically affect item sales. One of the simplest strategies of listing items to improve the overall sales is to do so in a descending order of sales or sales numbers. However, in lists generated using this strategy, items with high similarity are often placed consecutively. In other words, the generated item list might be biased toward a specific preference. Therefore, this study employs penalties for items with high similarity being placed next to each other in the list and transforms the item listing problem to a quadratic assignment problem (QAP). The QAP is well-known as an NP-hard problem that cannot be solved in polynomial time. To solve the QAP, we employ quantum annealing (QA), which exploits the quantum tunneling effect to efficiently solve an optimization problem. In addition, we propose a problem decomposition method based on the structure of the item listing problem because the quantum annealer we use (i.e., D-Wave 2000Q) has a limited number of quantum bits. Our experimental results indicate that we can create an item list that considers both sales and diversity. In addition, we observe that using the problem decomposition method based on a problem structure can lead to a better solution with the quantum annealer in comparison with the existing problem decomposition method.

quant-ph↗

arXiv2016

A Latent-class Model for Estimating Product-choice Probabilities from Clickstream Data

This paper analyzes customer product-choice behavior based on the recency and frequency of each customer's page views on e-commerce sites. Recently, we devised an optimization model for estimating product-choice probabilities that satisfy monotonicity, convexity, and concavity constraints with respect to recency and frequency. This shape-restricted model delivered high predictive performance even when there were few training samples. However, typical e-commerce sites deal in many different varieties of products, so the predictive performance of the model can be further improved by integration of such product heterogeneity. For this purpose, we develop a novel latent-class shape-restricted model for estimating product-choice probabilities for each latent class of products. We also give a tailored expectation-maximization algorithm for parameter estimation. Computational results demonstrate that higher predictive performance is achieved with our latent-class model than with the previous shape-restricted model and common latent-class logistic regression.

cs.AI↗