arXiv subjects

Daisuke Inoue

Publications and source records attributed to Daisuke Inoue.

At least 19 recordsLinked to original sources

arXiv2026

NLLog: Lightweight, Explainable SOC Anomaly Detection via Log-to-Language Rewriting

System-generated logs underpin security monitoring, yet their rigid template-based format hinders both automated analysis and human comprehension. We present NLLog (Natural-Language Log), a lightweight pipeline that deterministically rewrites parsed templates into WHO-WHAT-SEVERITY sentences, pools them with term-frequency-inverse-document-frequency weighting, classifies sessions with tree ensembles, and back-projects evidence with TreeSHAP for analyst review. On Hadoop Distributed File System (HDFS) and Blue Gene/L (BGL) corpora, NLLog exceeds two reproduced matched-protocol baselines; across HDFS, BGL, and the AIT Alert Data Set, it sustains low false-positive rates with commodity-hardware latency suitable for security operations center triage. Coverage, sparse-versus-dense, faithfulness, and adversarial ablations show that fallback sufficiency is corpus-dependent, that an enrollment-time coverage check can surface refinement requirements before deployment, and that an auditable deterministic rewrite combined with lightweight dense encoding provides a measurable representation layer for log-anomaly detection and triage.

cs.CR↗

arXiv2026

Nonlocal Mean Field Schr\"{o}dinger Bridge with Learned Interactions

The Schr\"odinger Bridge Problem connects an initial distribution to a terminal one along a minimum-energy stochastic process. Its mean-field extension, the Mean-Field Schr\"odinger Bridge, governs interacting populations whose dynamics and costs depend on the collective distribution. When these interactions are nonlocal, their direct evaluation scales quadratically with the population size, making large ensembles intractable within FBSDE-based solvers. We replace these terms with neural surrogates in state and time, trained on empirical interaction values along sampled trajectories and embedded in a four-stage alternating scheme that updates the forward and backward potentials and the surrogates in turn, while preserving forward--backward consistency and the prescribed endpoint marginals. We derive Gr\"onwall-type stability bounds quantifying how surrogate errors propagate to the generated trajectories under a small-gain condition. On crowd-navigation and high-dimensional opinion-dynamics benchmarks, the surrogates reproduce the trajectories obtained with exact evaluation at reduced training cost. The advantage is most significant when the interaction is a nonlinear functional of the measure, such as the normalized bounded-confidence drift, for which random-batch subsampling is biased and unstable whereas the learned surrogate remains accurate.

math.OC↗

arXiv2026

PACT: Reducing Alert Fatigue in Low-Prevalence SOC Streams with Triggered Active Learning

Security operations centers face persistent alert fatigue: in low-prevalence streams, even low false-positive rates generate substantial investigation load, while aggregate F1 scores obscure analyst burden. We introduce PACT, a Pareto-aware controller for triggered active learning, which wraps an already-deployed frozen XGBoost-Focal screener with an adaptive windowing score-shift trigger and a hybrid acquisition rule combining threshold-relative uncertainty with high-score sampling. On two public low-prevalence benchmarks, AIT-ADS (AIT Alert Data Set), and BOTSv1 (Boss of the SOC version 1), PACT attains the lowest benign-normalized false-positive (FP) burden among the adaptive methods tested. It reduces burden by 43% and 21%, respectively, relative to a frozen baseline, while using 3.8x and 5.2x fewer analyst queries than periodic uniform-random updating. A matched-trigger ablation controls trigger timing and shows that acquisition contributes beyond timing alone, at the cost of approximately ten percentage points of positive-window recall under free-running triggers. A frozen threshold-only baseline pushes FP lower still but collapses BOTSv1 recall by 55 percentage points. Under the evaluated workload assumptions, pure FP minimization trades unacceptable recall for that lower burden.

cs.CR↗

arXiv2026

Training-Induced Escape from Token Clustering in a Mean-Field Formulation of Transformers

Transformers perform inference by iteratively transforming token representations across layers. This layerwise computation has been studied empirically, and recent mean-field theories of Transformer dynamics explain how attention can drive token distributions toward clustering. However, existing mean-field analyses largely treat model parameters as prescribed, leaving open how training reshapes this clustering picture. We study this question in a noisy mean-field Transformer in which only a parameter-linear FFN is trained under $L^2$ regularization. We find and analyze a training-induced phase in the dynamics: after initially following attention-driven clustering, the token distribution can leave the clustered regime near the final layers. Our mathematical analysis is based on an entropy-regularized interaction energy that captures the clustering bias of attention. More broadly, our results point toward a training-aware mean-field theory of Transformer dynamics, in which training and inference dynamics are treated together.

cs.LG↗

arXiv2026

AI-Driven Security Alert Screening and Alert Fatigue Mitigation in Security Operations Centers: A Survey

Security alert screening is the downstream task of filtering, prioritizing, correlating, and contextualizing alerts for analyst attention in Security Operations Centers. This survey reviews artificial-intelligence-driven alert screening and alert-fatigue mitigation from 2015 to 2026. We synthesize 119 records, including 87 core studies, into a four-stage workflow taxonomy covering filtering, triage, correlation, and generative augmentation. We find persistent gaps in operational validation, adversarial robustness, cross-environment generalization, and evaluation practice. The survey concludes with a research agenda toward trustworthy Cognitive Security Operations Centers.

cs.CR↗

arXiv2025

Reservoir Predictive Path Integral Control for Unknown Nonlinear Dynamics

Neural networks have found extensive application in data-driven control of nonlinear dynamical systems, yet fast online identification and control of unknown dynamics remain central challenges. To meet these challenges, this paper integrates echo-state networks (ESNs)--reservoir computing models implemented with recurrent neural networks--and model predictive path integral (MPPI) control--sampling-based variants of model predictive control. The proposed reservoir predictive path integral (RPPI) enables fast learning of nonlinear dynamics with ESNs and exploits the learned nonlinearities directly in MPPI control computation without linearization approximations. This framework is further extended to uncertainty-aware RPPI (URPPI), which achieves robust stochastic control by treating ESN output weights as random variables and minimizing an expected cost over their distribution to account for identification errors. Experiments on controlling a Duffing oscillator and a four-tank system demonstrate that URPPI improves control performance, reducing control costs by up to 60% compared to traditional quadratic programming-based model predictive control methods.

eess.SY↗

arXiv2025

Impact of multiband effects on non-Fermi-liquid transport phenomena in bilayer nickelates

Recently discovered high-$T_c$ superconductivity in thin-film bilayer nickelates La$_3$Ni$_2$O$_7$ under ambient pressure has attracted great interest. Non-Fermi-liquid transport behaviors, such as $T$-linear resistivity and a positive Hall coefficient that increases at low temperatures, have been reported in this system. In this study, we analyze the non-Fermi-liquid transport phenomena in the thin-film bilayer nickelate La$_3$Ni$_2$O$_7$ using a multiorbital tight-binding model. In La$_3$Ni$_2$O$_7$, the cold spots composed of Ni $d_{x^2-y^2}$ orbital emerge, since the spin fluctuations cause stronger quasiparticle damping $\gamma$ in the Ni $d_{z^2}$ orbital. Notably, in the present study, we derive a rigorous formula for the Hall coefficient $R_H$ incorporating the $\gamma$ in the quasi-quantum metric (qQM) term. We find that the $T$ dependence of $\gamma$ in the qQM term is important in determining $R_H$. In La$_3$Ni$_2$O$_7$, the $T$ dependence of $R_H$ becomes pronounced due to the competition between the positive contribution from the hole band and the negative contribution from the electron band. Moreover, the qQM term plays an important role in describing the Nernst coefficient and other transport phenomena involving the second derivative velocity $v^{\mu\nu}$.

cond-mat.str-el↗

arXiv2025

Unified mechanism of charge-density-wave and high-$T_c$ superconductivity protected from oxygen vacancies in bilayer nickelates

Unconventional charge and spin density-wave states are commonly observed in bilayer nickelates, drawing considerable attention due to their proximity to high-$T_c$ superconductivity in various phase diagrams. However, the nature and mechanisms of charge and spin density-waves (DWs) in nickelates remain poorly understood. Numerous experiments have reported that the charge-density-wave (CDW) transition temperature $T_{ cdw}$ and the spin-density-wave (SDW) transition temperature $T_{sdw}$ are closely related but distinct. However, in contrast to these experiments, previous mean-field-type analyses have yielded only a simple SDW phase. To resolve this key problem, this paper demonstrates that sizable CDW instabilities emerge in proportion to the SDW instability in La$3$Ni$2$O$7$.This behavior is driven by the paramagnon-interference (PMI) mechanism, which captures important electron correlations beyond mean-field theory. Therefore, (i) experimental CDW + SDW coexisting state is naturally explained. In addition, (ii) the CDW + SDW fluctuations cooperatively drive high-$T_c$ superconductivity. Notably, the predicted $s$-wave SC state is robust against the inner apical O vacancies. Furthermore, (iii) the CDW instability is highly sensitive to the size of the $d_{z^2}$-orbital hole pocket, allowing for the realization of CDW quantum criticality through carrier-doping and pressure application. We find that the coexistence of charge and spin fluctuations is essential in bilayer nickelates, with both playing a cooperative role in mediating high-$T_c$ superconductivity.

cond-mat.supr-con↗

arXiv2024

Traffic signal optimization in large-scale urban road networks: an adaptive-predictive controller using Ising models

Realizing smooth traffic flow is important for achieving carbon neutrality. Adaptive traffic signal control, which considers traffic conditions, has thus attracted attention. However, it is difficult to ensure optimal vehicle flow throughout a large city using existing control methods because of their heavy computational load. Here, we propose a control method called AMPIC (Adaptive Model Predictive Ising Controller) that guarantees both scalability and optimality. The proposed method employs model predictive control to solve an optimal control problem at each control interval with explicit consideration of a predictive model of vehicle flow. This optimal control problem is transformed into a combinatorial optimization problem with binary variables that is equivalent to the so-called Ising problem. This transformation allows us to use an Ising solver, which has been widely studied and is expected to have fast and efficient optimization performance. We performed numerical experiments using a microscopic traffic simulator for a realistic city road network. The results show that AMPIC enables faster vehicle cruising speed with less waiting time than that achieved by classical control methods, resulting in lower CO2 emissions. The model predictive approach with a long prediction horizon thus effectively improves control performance. Systematic parametric studies on model cities indicate that the proposed method realizes smoother traffic flows for large city road networks. Among Ising solvers, D-Wave's quantum annealing is shown to find near-optimal solutions at a reasonable computational cost.

math.OC↗

arXiv2024

Predicting unobserved climate time series data at distant areas via spatial correlation using reservoir computing

Collecting time series data spatially distributed in many locations is often important for analyzing climate change and its impacts on ecosystems. However, comprehensive spatial data collection is not always feasible, requiring us to predict climate variables at some locations. This study focuses on a prediction of climatic elements, specifically near-surface temperature and pressure, at a target location apart from a data observation point. Our approach uses two prediction methods: reservoir computing (RC), known as a machine learning framework with low computational requirements, and vector autoregression models (VAR), recognized as a statistical method for analyzing time series data. Our results show that the accuracy of the predictions degrades with the distance between the observation and target locations. We quantitatively estimate the distance in which effective predictions are possible. We also find that in the context of climate data, a geographical distance is associated with data correlation, and a strong data correlation significantly improves the prediction accuracy with RC. In particular, RC outperforms VAR in predicting highly correlated data within the predictive range. These findings suggest that machine learning-based methods can be used more effectively to predict climatic elements in remote locations by assessing the distance to them from the data observation point in advance. Our study on low-cost and accurate prediction of climate variables has significant value for climate change strategies.

cs.LG↗

arXiv2024

An Uncertainty-aware, Mesh-free Numerical Method for Kolmogorov PDEs

This study introduces an uncertainty-aware, mesh-free numerical method for solving Kolmogorov PDEs. In the proposed method, we use Gaussian process regression (GPR) to smoothly interpolate pointwise solutions that are obtained by Monte Carlo methods based on the Feynman-Kac formula. The proposed method has two main advantages: 1. uncertainty assessment, which is facilitated by the probabilistic nature of GPR, and 2. mesh-free computation, which allows efficient handling of high-dimensional PDEs. The quality of the solution is improved by adjusting the kernel function and incorporating noise information from the Monte Carlo samples into the GPR noise model. The performance of the method is rigorously analyzed based on a theoretical lower bound on the posterior variance, which serves as a measure of the error between the numerical and true solutions. Extensive tests on three representative PDEs demonstrate the high accuracy and robustness of the method compared to existing methods.

math.NA↗

arXiv2023

Robust $T$-Linear Resistivity due to SU(4) Valley + Spin Fluctuation Mechanism in Magic Angle Twisted Bilayer Graphene

In the magic angle twisted bilayer graphene (MATBG), non-Fermi liquid like transport phenomena are universally observed. To understand their origin, we perform the self-consistent analysis of the self-energy due to SU(4) valley + spin fluctuations induced by the electron-electron correlation. In the SU(4) fluctuation mechanism, the fifteen channels of fluctuations contribute additively to the self-energy. Therefore, the SU(4) fluctuation mechanism gives much higher electrical resistance than the spin fluctuation mechanism. By the same reason, SU(4) fluctuations of intermediate strength provide $T$-linear resistivity down to $\sim1$K. Interestingly, the $T$-linear resistivity is robustly realizedfor wide range of electron filling, even away from the van-Hove filling. This study provides a strong evidence for the importance of electron-electron correlation in MATBG.

cond-mat.str-el↗

arXiv2023

How WEIRD is Usable Privacy and Security Research? (Extended Version)

In human factor fields such as human-computer interaction (HCI) and psychology, researchers have been concerned that participants mostly come from WEIRD (Western, Educated, Industrialized, Rich, and Democratic) countries. This WEIRD skew may hinder understanding of diverse populations and their cultural differences. The usable privacy and security (UPS) field has inherited many research methodologies from research on human factor fields. We conducted a literature review to understand the extent to which participant samples in UPS papers were from WEIRD countries and the characteristics of the methodologies and research topics in each user study recruiting Western or non-Western participants. We found that the skew toward WEIRD countries in UPS is greater than that in HCI. Geographic and linguistic barriers in the study methods and recruitment methods may cause researchers to conduct user studies locally. In addition, many papers did not report participant demographics, which could hinder the replication of the reported studies, leading to low reproducibility. To improve geographic diversity, we provide the suggestions including facilitate replication studies, address geographic and linguistic issues of study/recruitment methods, and facilitate research on the topics for non-WEIRD populations.

cs.CR↗

arXiv2023

Convergence Analysis of the Upwind Difference Methods for Hamilton-Jacobi-Bellman Equations

This paper investigates the convergence properties of the upwind difference scheme for the Hamilton--Jacobi--Bellman (HJB) equation, a central partial differential equation in optimal control theory. First, assuming the existence of a classical solution, we show that the numerical solution converges to the true solution with a first-order rate with respect to the time step. This result complements the square-root rate established in previous studies for viscosity solutions. Second, by exploiting the correspondence between HJB equations and conservation laws, we prove the convergence of the optimal control input. This analysis is crucial for practical applications where the control input is the primary quantity of interest, yet it has rarely been addressed in previous studies. Finally, we confirm the validity of our theoretical results through numerical experiments on typical control problems.

math.NA↗

arXiv2022

Dynamical cooperation model for mitigating the segregation phase in Schelling's model

We consider a Schelling-like segregation model, in which the behavior of individual agents is determined by a mixed individual and global utility. With a high ratio of global utility being incorporated, the agents are cooperative in order to realize a homogenized state, otherwise the agents are less cooperative, leading to an undesired Nash equilibrium with low utility. In the present study, we introduce a dynamically varying cooperation degree parameter to prevent the agents from falling into such a low-utility equilibrium state. More precisely, a large cooperation degree is assigned when the agents are in high-utility regions, whereas agents having low utility behave more individually. Simulation results show that homogenized phases with globally high utility are achieved with the present dynamical control, even for the case of a low mean value of cooperation degree. Since the cooperation degree represents the magnitude with which Pigouvian tax is enforced in the model of residential movement within a city, this result suggests the possibility of tax intervention to circumvent the undesired segregation of residents.

cond-mat.mes-hall↗

arXiv2022

Internet Service Providers' and Individuals' Attitudes, Barriers, and Incentives to Secure IoT

Internet Service Providers (ISPs) and individual users of Internet of Things (IoT) play a vital role in securing IoT. However, encouraging them to do so is hard. Our study investigates ISPs' and individuals' attitudes towards the security of IoT, the obstacles they face, and their incentives to keep IoT secure, drawing evidence from Japan. Due to the complex interactions of the stakeholders, we follow an iterative methodology where we present issues and potential solutions to our stakeholders in turn. For ISPs, we survey 27 ISPs in Japan, followed by a workshop with representatives from government and 5 ISPs. Based on the findings from this, we conduct semi-structured interviews with 20 participants followed by a more quantitative survey with 328 participants. We review these results in a second workshop with representatives from government and 7 ISPs. The appreciation of challenges by each party has lead to findings that are supported by all stakeholders. Securing IoT devices is neither users' nor ISPs' priority. Individuals are keen on more interventions both from the government as part of regulation and from ISPs in terms of filtering malicious traffic. Participants are willing to pay for enhanced monitoring and filtering. While ISPs do want to help users, there appears to be a lack of effective technology to aid them. ISPs would like to see more public recognition for their efforts, but internally they struggle with executive buy-in and effective means to communicate with their customers. The majority of barriers and incentives are external to ISPs and individuals, demonstrating the complexity of keeping IoT secure and emphasizing the need for relevant stakeholders in the IoT ecosystem to work in tandem.

cs.CY↗

arXiv2022

A fictitious-play finite-difference method for linearly solvable mean field games

An iterative finite difference scheme for mean field games (MFGs) is proposed. The target MFGs are derived from control problems for multidimensional systems with advection terms. For such MFGs, linearization using the Cole-Hopf transformation and iterative computation using fictitious play are introduced. This leads to an implementation-friendly algorithm that iteratively solves explicit schemes. The convergence properties of the proposed scheme are mathematically proved by tracking the error of the variable through iterations. Numerical calculations show that the proposed method works stably for both one- and two-dimensional control problems.

math.OC↗

arXiv2021

Electro-osmotic diode based on colloidal nano-valves between double membranes

The rectification of electro-osmotic flows is important in micro/nano fluidics applications such as micro-pumps and energy conversion devices. Here, we propose a simple electro-osmotic diode in which colloidal particles are contained between two parallel membranes with different pore densities. While the flow in the forward direction just pushes the colloidal particles toward the high-pore-density membrane, the backward flow is blocked by the particles near the low-pore-density membrane, which clog the pores. Nonequilibrium molecular dynamics simulations show a strong nonlinear dependence on the electric field for both the electric current and electro-osmotic flow, indicating diode characteristics. A mathematical model to reproduce the electro-osmotic diode behavior is constructed, introducing an effective pore diameter as a model for pores clogged by the colloidal particles. Good agreement is obtained between the proposed model with estimated parameter values and the results of direct molecular dynamics simulations. The proposed electro-osmotic diode has potential application in downsized microfluidic pumps, e.g., the pump induced under AC electric fields.

cond-mat.mes-hall↗