A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Paper explores physics-informed deep learning for system reliability assessment.
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.
Paper proposes a method to verify PINN fidelity using Fisher information from dynamical systems.
Controller seeks informative system observations to predict nonlinear dynamics.
Greedy policy maximizes information in unknown linear systems.
In modern recommender systems, both users and items are associated with rich side information, which can help understand users and items. Such information is typically heterogeneous and can be roughly categorized into flat and hierarchical side information. While side information has been proved to be valuable, the maj…
Proposes a new method to enhance neural learning by maximizing information gain.
New method estimates spin system mutual information using neural networks.
Complete criterion for VoI in multi-decision influence diagrams established.
Complex dynamical systems driven by the unravelling of information can be modelled effectively by treating the underlying flow of information as the model input. Complicated dynamical behaviour of the system is then derived as an output. Such an information-based approach is in sharp contrast to the conventional mathem…
We propose a physics-informed Echo State Network (ESN) to predict the evolution of chaotic systems. Compared to conventional ESNs, the physics-informed ESNs are trained to solve supervised learning tasks while ensuring that their predictions do not violate physical laws. This is achieved by introducing an additional lo…
A dynamical system can be regarded as an information processing apparatus that encodes input streams from the external environment to its state and processes them through state transitions. The information processing capacity (IPC) is an excellent tool that comprehensively evaluates these processed inputs, providing de…
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.
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 …
Paper formulates mutual information optimal control for discrete-time systems.
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.
The article reviews how gradient flow systems on hypergraphs connect to information geometry and nonequilibrium physics.
Researchers infer firm-level supply chain networks from sector-level data to assess systemic risk.
Calculates local Granger causality for Gaussian and nonlinear systems.
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 () in the brain is related to the level of consciousness. IIT proposes that to quantify information i…
Interpretable machine learning has gained much attention recently. Briefness and comprehensiveness are necessary in order to provide a large amount of information concisely when explaining a black-box decision system. However, existing interpretable machine learning methods fail to consider briefness and comprehensiven…
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.
Physics-informed model reduces RBC simulation costs.
Paper proposes a method for weather-informed probabilistic forecasting and scenario generation in power systems.
Meta-active learning optimizes control of safety-critical systems by efficiently learning dynamics and configurations.
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…
This paper uses information theory to improve risk modeling in big data.
EIDGM model estimates DE parameters from RCS data.
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 financial market entropy is modeled using open quantum systems.
New bandit algorithm maximizes information gain.
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…
Growing number of network devices and services have led to increasing demand for protective measures as hackers launch attacks to paralyze or steal information from victim systems. Intrusion Detection System (IDS) is one of the essential elements of network perimeter security which detects the attacks by inspecting net…
Proposes PEID for analyzing synergistic causation in complex systems.
A new method extracts features and reconstructs moments in dynamical systems using information geometry.
Machine learning approximates phase transitions using Fisher information.
Analyzes new economic paradigm for non-independent consumer choices.
Standard accuracy metrics indicate that modern reading comprehension systems have achieved strong performance in many question answering datasets. However, the extent these systems truly understand language remains unknown, and existing systems are not good at distinguishing distractor sentences, which look related but…
The paper sets sample complexity bounds for identifying LTI systems from a finite set.
Paper improves speaker verification with federated learning and differential privacy.
Physics-informed neural networks improve model accuracy and efficiency.
The paper explores how AI systems use information geometry to encode semantic structure.
Visual exploration of high-dimensional real-valued datasets is a fundamental task in exploratory data analysis (EDA). Existing methods use predefined criteria to choose the representation of data. There is a lack of methods that (i) elicit from the user what she has learned from the data and (ii) show patterns that she…
Accurately determining dependency structure is critical to discovering a system's causal organization. We recently showed that the transfer entropy fails in a key aspect of this---measuring information flow---due to its conflation of dyadic and polyadic relationships. We extend this observation to demonstrate that this…