Paper explores physics-informed deep learning for system reliability assessment.
problem Limited study on deep learning for system reliability assessment.
method Physics-informed deep learning approach for system reliability assessment.
result Physics-informed deep learning can alleviate computational challenges and combine measurement data and mathematical models.
In many instances, information on engineering systems can be obtained through measurements, monitoring or direct observations of system performances and can be used to update the system reliability estimate. In structural reliability analysis, such information is expressed either by inequalities (e.g. for the observati…
A new hierarchy quantifies agency in systems based on information processing.
problem Lack of a measurable, universal definition for agency in intelligent systems.
method Developed a bottom-up framework based on information processing hierarchy.
result Identified three orders of information processing (I, II, III) as necessary for agency.
Paper proposes a method to verify PINN fidelity using Fisher information from dynamical systems.
problem Quantifying PINN fidelity beyond simple trajectory prediction.
method Employing Fisher information for differentiable dynamical systems to compare PINN's learned equations with analytical models.
result PINN fidelity is verified by matching Fisher information landscapes of learned equations and analytical models.
Physics-informed ESNs improve chaotic system prediction accuracy.
problem Predicting chaotic systems while adhering to physical laws.
method Integrates physics constraints into ESN training through an additional loss function.
result Physics-informed ESNs predict chaotic systems with a 2 Lyapunov time improvement.
Paper explores how to use mixed types of side information for better recommendations.
problem Challenges in using heterogeneous side information for recommender systems.
method Proposes a framework to jointly capture flat and hierarchical side information.
result Demonstrates significant performance gains over state-of-the-art methods.
Extends IPC to time-variant systems, revealing information processing in diverse dynamics.
problem Limited applicability of IPC to time-invariant systems and specific input distributions.
method Established connection between IPC and PC expansion, extended orthogonal bases to time and input history.
result IPC equivalent to squared norm of PC expansion coefficients, applicable to arbitrary input distributions.
Controller seeks informative system observations to predict nonlinear dynamics.
problem Predicting nonlinear dynamics with uncertain parameters.
method Expected free energy minimization for balancing goal state and informative observations.
result Controller improves performance in uncertain parameter scenarios.
VIBI interprets black-box systems by selecting key features that are both brief and comprehensive.
problem Lack of concise and comprehensive explanations for black-box decision systems.
method VIBI uses the information bottleneck principle to select key features that are maximally compressed and informative.
result VIBI provides more concise and comprehensive explanations compared to existing methods.
Greedy policy maximizes information in unknown linear systems.
problem Exploration in unknown linear dynamical systems.
method Online greedy policy maximizing information.
result Competitive performance compared to gradient-based methods.
Proposes a new method to enhance neural learning by maximizing information gain.
problem Improving neural learning by selecting key variables to maximize information gain.
method Adaptive Ensemble Kalman Filter to quantify uncertainty and maximize information gain.
result The proposed method enables the neural network to learn more effectively from stochastic systems.
New method estimates spin system mutual information using neural networks.
problem Estimating mutual information in spin systems.
method Monte Carlo sampling enhanced by autoregressive neural networks.
result Area law satisfied for temperatures away from critical temperature.
QAInfomax improves reading comprehension by maximizing mutual information, achieving state-of-the-art performance.
problem Distractor sentences in question answering datasets are hard to distinguish from relevant ones.
method QAInfomax regularizes reading comprehension models to learn mutual information among passages, questions, and answers.
result QAInfomax achieves state-of-the-art performance on Adversarial-SQuAD dataset.
Model election dynamics and disinformation impact using information flow.
problem Understanding the unpredictable evolution of election polls and disinformation effects.
method Model election dynamics by specifying the flow of relevant information, deriving poll statistics as outputs.
result Derived outputs (poll statistics) reveal insights into prediction, disinformation impact, and optimal information management strategies.
Complete criterion for VoI in multi-decision influence diagrams established.
problem Analyzing safety and fairness properties of AI systems using influence diagrams.
method Introduced ID homomorphisms and Tree of Systems to prove properties of multi-decision influence diagrams.
result First complete graphical criterion for VoI in influence diagrams with multiple decisions.
We consider a multivariate default system where random environmental information is available. We study the dynamics of the system in a general setting and adopt the point of view of change of probability measures. We also make a link with the density approach in the credit risk modelling. In the particular case where …
PULSE learns representations from physiological time series.
problem Lack of effective pretraining objectives for physiological time series.
method Proposes a pretraining framework exploiting dynamical systems generative model.
result PULSE learns representations that distinguish semantic classes and improve transfer learning.
Paper formulates mutual information optimal control for discrete-time systems.
problem Optimal control of discrete-time linear systems with mutual information.
method Formulates MIOCP as an extension of MEOCP, derives optimal policy and prior, proposes alternating minimization algorithm.
result Proposes an alternating minimization algorithm for MIOCP.
In this paper are presented methods of impact analysis on informatics system security accidents, qualitative and quantitative methods, starting with risk and informational system security definitions. It is presented the relationship between the risks of exploiting vulnerabilities of security system, security level of …
The behaviour of many real-world phenomena can be modelled by nonlinear dynamical systems whereby a latent system state is observed through a filter. We are interested in interacting subsystems of this form, which we model by a set of coupled maps as a synchronous update graph dynamical systems. Specifically, we study …
Redundancy improves learning stability and generalization in structured systems.
problem Understanding redundancy in structured systems for learning and generalization.
method Developed a theoretical framework that redefines redundancy as a geometric principle unifying various measures.
result Redundancy balances structure and coupling, leading to optimal stability and generalization.
The article reviews how gradient flow systems on hypergraphs connect to information geometry and nonequilibrium physics.
problem Understanding the geometry of perturbed gradient flow systems on hypergraphs.
method Formulating modern nonequilibrium principles within the framework of perturbed gradient flow systems on hypergraphs.
result New concepts like moduli spaces and thermodynamical area are introduced to understand speed limits.
Researchers infer firm-level supply chain networks from sector-level data to assess systemic risk.
problem Estimating systemic risk in economic systems using firm-level data.
method Maximum-entropy algorithms applied to input-output tables and firm-level aggregate output data.
result The most realistic systemic risk content is retrieved by models incorporating disaggregated firm-specific inputs by sector.
Calculates local Granger causality for Gaussian and nonlinear systems.
problem Understanding causal influence in complex systems.
method Vector autoregression and information-theoretic approach.
result Local Granger causality offers a robust and fast method for time-directed information transfer.
The ability to integrate information in the brain is considered to be an essential property for cognition and consciousness. Integrated Information Theory (IIT) hypothesizes that the amount of integrated information (Φ) in the brain is related to the level of consciousness. IIT proposes that to quantify information i…
We address a fundamental problem that is systematically encountered when modeling complex systems: the limitedness of the information available. In the case of economic and financial networks, privacy issues severely limit the information that can be accessed and, as a consequence, the possibility of correctly estimati…
New theory improves understanding of ensemble learning systems.
problem Improving the effectiveness of ensemble learning systems.
method Generalized Fano's inequality to account for information loss in model combination.
result Reveals strengths and weaknesses of ensemble systems on accuracy and diversity.
Visualizes information flow in ML systems for better understanding and analysis.
problem Understanding the flow of information in complex ML systems.
method Proposes a visual approach using Sankey Diagrams to analyze flow of information.
result Demonstrates the effectiveness of the proposed technique in diagnosing model performance.
Physics-informed model reduces RBC simulation costs.
problem Computational infeasibility of direct numerical simulations for turbulent systems.
method Combines CNN and recurrent architecture, penalized with PDEs, uses conformal prediction.
result Significant reduction in computational cost for long-term simulations.
Paper proposes a method for weather-informed probabilistic forecasting and scenario generation in power systems.
problem Challenges of integrating renewable energy sources into power grids due to their stochasticity and uncertainty.
method Combines probabilistic forecasting and Gaussian copula for day-ahead prediction and scenario generation of load, wind, and solar power.
result Demonstrates superior performance of the proposed weather-informed Temporal Fusion Transformer (WI-TFT) model.
Meta-active learning optimizes control of safety-critical systems by efficiently learning dynamics and configurations.
problem Efficiently learning system dynamics and optimal configurations for safety-critical systems like deep brain stimulation.
method Meta-learning an acquisition function using LSTM, cast as meta-learning, with a mixed-integer linear program policy.
result Achieved a 46% increase in information gain and a 20% speedup in computation time over baselines.
This paper uses information theory to improve risk modeling in big data.
problem Insufficient application of information theory in actuarial science.
method Explores information theory to uncover performance limits of insurance big data systems.
result Guidance for risk modeling and actuarial pricing systems.
Confidentiality of patient information is an essential part of Electronic Health Record System. Patient information, if exposed, can cause a serious damage to the privacy of individuals receiving healthcare. Hence it is important to remove such details from physician notes. A system is proposed which consists of a deep…
EIDGM model estimates DE parameters from RCS data.
problem Estimating DE parameters from RCS data with heterogeneities.
method Physics-informed neural network emulator + Wasserstein GAN parameter generator.
result EIDGM accurately captures diverse parameter distributions.
The financial market entropy is modeled using open quantum systems.
problem Understanding entropy in financial market dynamics.
method Using Open Quantum Systems to model entropy gain in financial markets.
result Interesting non-classical results generated by relaxing assumptions.
Photovoltaic systems have been widely deployed in recent times to meet the increased electricity demand as an environmental-friendly energy source. The major challenge for integrating photovoltaic systems in power systems is the unpredictability of the solar power generated. In this paper, we analyze the impact of havi…
The paper uses information theory to find limits of feedback control systems.
problem Fundamental performance limitations of feedback control systems.
method Utilizes information theory to derive Lp bounds on control error. result Bounds are characterized by the conditional entropy of the disturbance.
New bandit algorithm maximizes information gain.
problem Optimizing decision-making in uncertain environments.
method Approximates information maximization using entropy and free energy principles.
result Asymptotic optimality proven for two-armed bandit problem.
For well over a quarter century, detection systems have been driven by models learned from input features collected from real or simulated environments. An artifact (e.g., network event, potential malware sample, suspicious email) is deemed malicious or non-malicious based on its similarity to the learned model at runt…
Enhances KWS in vehicles with multi-source fusion.
problem Improving precision and recall rates in vehicle keyword spotting.
method Integrates vehicle information into a DNN for speech classification and selects optimal sensitivity parameters.
result Significantly improved performance metrics (precision, recall, MSE) compared to baseline.
Proposes PEID for analyzing synergistic causation in complex systems.
problem Challenges in identifying and analyzing synergistic causation in complex systems.
method Partial Effective Information Decomposition (PEID) framework.
result Unified and computable characterization of synergistic causal relations.
A new method extracts features and reconstructs moments in dynamical systems using information geometry.
problem Reconstructing moments in dynamical systems efficiently and accurately.
method Information-geometric approach on spaces of probability measures.
result Moments can be expanded in eigenfunctions of a kernel integral operator, enabling nonparametric forecasting.
Machine learning approximates phase transitions using Fisher information.
problem Understanding phase transitions from data using machine learning.
method Information geometry and Fisher information.
result Machine learning indicators approximate the square root of Fisher information.
Analyzes new economic paradigm for non-independent consumer choices.
problem Non-independent consumer choices due to firm supply and consumer information.
method Develops a new mathematical framework for economic systems.
result New paradigm for economic system description is necessary.
The paper sets sample complexity bounds for identifying LTI systems from a finite set.
problem Identifying an LTI system from a finite set of possible systems using trajectory data.
method Maximum likelihood estimator and information theory tools.
result Upper and lower bounds for sample complexity are derived, independent of stability assumption.
Paper improves speaker verification with federated learning and differential privacy.
problem Improving speaker verification accuracy using private data.
method Combining federated learning and differential privacy to train an auxiliary model that predicts vocal characteristics.
result 6% relative improvement in equal error rate over a baseline system.
Physics-informed neural networks improve model accuracy and efficiency.
problem Accurate dynamic models for technical systems are hard to achieve.
method Physics-informed neural ordinary differential equations (PINODE) integrating Lagrangian mechanics.
result Hybrid model combines physical insight and data approximation.
The paper explores how AI systems use information geometry to encode semantic structure.
problem How AI systems encode semantic structure into geometric representation spaces.
method Focuses on softmax distributions and develops dual steering method for robust concept manipulation.
result Dual steering optimally modifies target concepts while minimizing off-target changes.