Unified framework detects changes in complex system models.
problem Accurate identification of dynamic changes in simulation models.
method Combines machine learning and process-driven simulation modeling.
result Significantly improves change point detection accuracy.
Develops a method to detect changes in linear systems with temporal correlations.
problem Detect abrupt changes in time series data with temporal correlations.
method Data-dependent threshold for online change point detection in linear dynamical systems.
result Achieves a pre-specified upper bound on the probability of false alarms and provides a finite-sample-based bound for detection probability.
Extends Neural ODEs to model discrete changes in continuous systems.
problem Lack of explicit termination time in existing Neural ODE formulations.
method Introduces neural event functions to implicitly define termination criteria.
result Models discrete changes in continuous systems without prior knowledge.
Paper extends SI method for detecting CPs in complex systems' frequency domain.
problem Identifying change points in complex systems' frequency domain.
method Extends SI framework to frequency domain using DFT properties and develops valid p-values.
result Reliable detection of genuine CPs with strong statistical guarantees.
New framework detects policy changes in black-box DMS over time.
problem Lack of transparency in black-box decision making systems.
method Proposes temporal transparency, maps to time series changepoint detection, develops framework.
result Reveals policy changes in real-world DMS, including announced and unannounced.
Change-point analysis is a flexible and computationally tractable tool for the analysis of times series data from systems that transition between discrete states and whose observables are corrupted by noise. The change-point algorithm is used to identify the time indices (change points) at which the system transitions …
Directly learns sparse changes in Markov networks without modeling individual structures.
problem Learning sparse structural changes in Markov networks.
method Directly learns sparse changes without modeling individual dense networks.
result Direct learning method provides insights into system changes.
Unified framework detects change-points and estimates parameters in nonlinear systems with regime switching.
problem Detecting change-points and estimating parameters in nonlinear dynamical systems with regime transitions.
method Residual-loss anomaly analysis of physics-informed neural networks, two-stage strategy.
result The method outperforms traditional approaches in change-point localization and parameter estimation accuracy.
System tracks regulatory changes for compliance officers.
problem Struggling to keep up with regulatory changes.
method Fetch announcements, classify importance and applicability.
result Simple hierarchical classification works best.
The stability analysis of socioeconomic systems has been centered on answering whether small perturbations when a system is in a given quantitative state will push the system permanently to a different quantitative state. However, typically the quantitative state of socioeconomic systems is subject to constant change. …
Dividing deep learning models for consistent anomaly detection in changing log data.
problem Anomaly detection methods fail when log data types change, leading to false negatives.
method Divide deep learning models based on log data correlation and extract correlations.
result Continues anomaly detection accuracy even when log data changes.
Meta-causal states group equivalent qualitative causal dynamics, useful for analyzing system changes.
problem Qualitative changes in causal relationships due to agent actions or environmental tipping points.
method Propose meta-causal states to group causal models based on equivalent qualitative behavior and parameterize specific mechanisms.
result Meta-causal states can be inferred from observed agent behavior and disentangled from unlabeled data.
Research explores unsupervised methods for detecting vessel behavior changes in real-time data streams.
problem Detecting shifts in vessel behavior for maritime traffic monitoring.
method Investigates unsupervised and semi-supervised change detection methods.
result Identifies shifts in vessel behavior for unusual events detection.
RAID algorithm detects anomalies in real-time IoT systems.
problem Anomaly detection limitations in multivariate dynamic processes.
method Adapts to non-stationary effects and handles data drift.
result Improved detection accuracy and root cause isolation.
Paper studies existence of Toda systems with sign-changing functions.
problem Existence of Toda systems with prescribed sign-changing functions.
method Variational method and blowup analysis.
result Blowup can only occur at points where h1 is positive. Bayesian method detects change points in time series data.
problem Detecting significant regime shifts in time series data.
method Bayesian autoregressive model with time-varying parameters.
result Enhanced estimate accuracy and forecasting power.
Corrects technical error in change of measure for HTB models.
problem Technical error in change of measure for HTB models.
method Provided supplement conditions to correct the error.
result Corrected technical error in change of measure for HTB models.
We investigate the dynamics of growth models in terms of dynamical system theory. We analyse some forms of knowledge and its influence on economic growth. We assume that the rate of change of knowledge depends on both the rate of change of physical and human capital. First, we study model with constant savings. The mod…
Paper proposes a method for learning and planning in time-varying environments.
problem Learning and planning in unknown, time-varying environments.
method Computes the maximally likely model of the environment using maximum likelihood estimation.
result Generalizes learning algorithms for time-invariant Markov decision processes to time-varying ones.
Transfer learning improves performance modeling by reducing model construction cost.
problem Reducing the cost of constructing performance models for configurable systems.
method Empirical study on four software systems, varying configurations and environmental conditions.
result Transfer learning is beneficial for small environmental changes but only reduces sampling efficiency for severe changes.
Considering a Hamiltonian Dynamical System describing the motion of charged particle in a Tokamak or a Stellarator, we build a change of coordinates to reduce its dimension. This change of coordinates is in fact an intricate succession of mappings that are built using Hyperbolic Partial Differential Equations, Differen…
BRPC online Bayesian calibration handles gradual and abrupt system changes.
problem Aligning model outputs with field observations in evolving systems.
method Bayesian Recursive Projected Calibration (BRPC) for streaming data under simulator mismatch and nonstationarity.
result Improves calibration accuracy under gradual changes and robustness under abrupt regime shifts.
Method distinguishes between failures and domain shifts in industrial data streams.
problem Confusing domain shifts with failures in industrial data.
method Modified Page-Hinkley changepoint detector and supervised domain-adaptation-based anomaly detection.
result Allows differentiation between failures and domain shifts.
Detect changes in noisy dynamical systems using empirical approximations and finite-sample bounds.
problem Change detection in noisy dynamical systems
method Partition-based empirical approximations and finite-state stationary distribution stability
result Finite-sample bound for empirical stationary density
Extends Toda system existence results to negative functions.
problem Existence of solutions to Toda systems with sign-changing functions.
method Improved Moser-Trudinger inequality, Brezis-Merle type analyses, Pohozaev identities.
result Sufficient conditions for Toda system solutions remain valid with negative functions.
Study analyzes online student behavior patterns using log data.
problem Understanding and optimizing student learning in online educational systems.
method Non-negative matrix factorization techniques for soft clustering.
result Behavioral changes of individual students and the system over time.
A novel OC-SVM calibration method improves change point detection in time series.
problem Identifying change points in system health status using time series data.
method Heuristic search method to find optimal input data and hyperparameters for OC-SVM.
result OC-SVM can detect change points in time series with fewer training data, achieving satisfactory accuracy.
The classical Sturm-Hurwitz-Kellogg theorem asserts that a function, orthogonal to an n-dimensional Chebyshev system on a circle, has at least n+1 sign changes. We prove the converse: given an n-dimensional Chebyshev system on a circle and a function with at least n+1 sign changes, there exists an orientation preservin…
Counterfactual approach explains AI decisions using causal data inputs.
problem Explain AI decisions made by data-driven models.
method Define explanations as causal data inputs that drive decisions and are irreducible.
result Counterfactual explanations better communicate decision-making than importance weights.
Paper explains AI's vulnerability to small changes and proposes a defense.
problem Adversarial attacks on machine learning systems.
method Information-theoretic approach, drawing on communication theory.
result The proposed defense method detects classifier errors caused by small perturbations.
Study shows regional banking uncertainty spillovers over 10 years.
problem Understanding uncertainty spillovers between regional banking systems.
method Improved Diebold-Yilmaz method using daily returns and ridge regularization.
result Regional uncertainty is significantly causally related, with North America influencing EU and ASEAN.
We use a method, inspired by Pohozeav's work, to study asymptotic behaviors of non-variational elliptic systems in dimension n greater than two. The results apply to changing sign solutions.
Paper presents neural network-based change-point detection methods.
problem Detecting change points in time series data.
method Online neural networks for change-point detection.
result Proposed methods outperform existing algorithms.
New method detects anomalies in systems influenced by their environment.
problem Detecting anomalies in systems under environmental influence.
method Adversarial learning and time series representation learning.
result Successfully addresses label sparsity and subjectivity in anomaly detection.
Software estimates inequality in random systems with changing communities.
problem Measuring inequality in systems with dynamic interactions and random attributes.
method Piecewise homogeneous Markov chain for changing points, copula function for multivariate distribution, Monte Carlo algorithm for entropy estimation.
result Estimates Random Theil's Entropy to measure inequality in random systems.
Paper monitors system state sequences to detect and assess deviations.
problem Detecting and evaluating deviations in dynamic systems.
method Data reduction, symbolic representation, anomaly detection, Markov Chains, generalized Jensen-Shannon Divergence.
result The approach detects and assesses system deviations probabilistically.
Meta-reinforcement learning improves fault-adaptive control efficiency.
problem Adaptive control under abrupt system faults with strict time constraints.
method Model-agnostic meta learning (MAML) with a fault library of prior policies.
result Improved sample efficiency and quick adaptation to new faults.
Spectral analysis detects structural changes in financial networks.
problem Detecting structural transitions in financial networks to assess systemic risk.
method Ensemble properties of spectral radius of random graph models calibrated on real-world evolving networks.
result The spectral deviation captures ongoing topological changes in financial networks.
New algorithm reduces control error in systems with changing dynamics.
problem Online control of systems with time-varying linear dynamics.
method Introduces adaptive regret metric and a novel meta-algorithm.
result First adaptive regret bound for online convex optimization with memory.
New bandit algorithm detects and adapts to seasonal changes in rewards.
problem Adapting to abrupt changes in user preferences during events.
method Detects and adapts to seasonal changes in reward function.
result Outperforms state-of-the-art algorithms for non-stationary environments.
Paper proposes a new method to model event sequences in information systems.
problem Analyzing event logs to understand system procedures and predict changes.
method Combines hidden semi-Markov model and classification trees learning.
result The proposed approach can identify frequent sequence patterns relevant to observable events.
Causal models help ensure fairness in systems with changing environments.
problem Ensuring fairness in systems with dynamic, long-term effects.
method Causal directed acyclic graphs (DAGs) to model fairness and manipulate causal assumptions.
result Causal assumptions enable simulation and off-policy estimation of interventions.
An active margin system for margin loans is proposed for Chinese margin lending market, which uses cash and randomly selected stock as collateral. The conditional probability of negative return(CPNR) after a forced sale of securities from under-margined account in a falling market is used to measure the risk faced by t…
New algorithm detects and adapts to changes in real-time data streams.
problem Adapting to fast-changing data in real-time systems.
method Concept drift detection followed by prototype-based adaptation.
result Stable and quick adjustments during model adaptation.
New methods detect changes in data with missing values.
problem Detecting changes in high-dimensional data with missing values.
method Proposes three imputation methods and adapts model selection for incomplete data.
result Methods improve change point detection in scenarios with missing values.
New bandit algorithm detects and adapts to changing user preferences.
problem Dynamic user preferences in recommender systems.
method Contextual bandit algorithm that detects changes and updates strategy.
result Upper regret bound analysis shows effectiveness in non-stationary environments.
A novel model arbitrates between planning and habitual control for efficient decision-making.
problem Balancing flexibility and efficiency in decision-making systems.
method Introduces an arbitrator that switches between planning and habitual control systems.
result The model learns kinematics quickly and adapts to changing environments.
Triadic-OCD detects changes in data streams robustly and optimally, even in asynchronous settings.
problem Online change detection in data streams with practical constraints.
method Triadic-OCD framework for asynchronous online change detection with provable robustness, optimality, and convergence.
result The proposed triadic-OCD algorithm achieves optimal performance and convergence in asynchronous settings.