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Found 16 papers, 6 papers with code
CGNSDE: Conditional Gaussian Neural Stochastic Differential Equation for Modeling Complex Systems and Data Assimilation
Then, neural networks are supplemented to the knowledge-based model in a specific way, which not only characterizes the remaining features that are challenging to model with simple forms but also advances the use of analytic formulae to efficiently compute the nonlinear DA solution.
Benchmarking Data Science Agents
In this paper, we introduce DSEval -- a novel evaluation paradigm, as well as a series of innovative benchmarks tailored for assessing the performance of these agents throughout the entire data science lifecycle.
Integrating Active Learning in Causal Inference with Interference: A Novel Approach in Online Experiments
In the domain of causal inference research, the prevalent potential outcomes framework, notably the Rubin Causal Model (RCM), often overlooks individual interference and assumes independent treatment effects.
FlowHON: Representing Flow Fields Using Higher-Order Networks
FlowHON captures the inherent higher-order dependencies in flow fields as nodes and estimates the transitions among them as edges.
A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions
However, these methods often suffer from data imbalance, a common issue in graph data where certain segments possess abundant data while others are scarce, thereby leading to biased learning outcomes.
CEBoosting: Online Sparse Identification of Dynamical Systems with Regime Switching by Causation Entropy Boosting
In addition, the CEBoosting method is applied to a nonlinear paradigm model for topographic mean flow interaction, demonstrating the online detection of regime switching in the presence of strong intermittency and extreme events.
A Causality-Based Learning Approach for Discovering the Underlying Dynamics of Complex Systems from Partial Observations with Stochastic Parameterization
In this paper, a new iterative learning algorithm for complex turbulent systems with partial observations is developed that alternates between identifying model structures, recovering unobserved variables, and estimating parameters.
LASSO-Based Multiple-Line Outage Identification In Partially Observable Power Systems
Tested on IEEE 39-bus system with 25% and 50% PMU coverage, the proposed identification method is 93% and 80% accurate for single- and double-line outages.
MC-UNet Multi-module Concatenation based on U-shape Network for Retinal Blood Vessels Segmentation
The proposed network structure retains three layers the essential structure of U-Net, in which the atrous convolution combining the multi-kernel pooling blocks are designed to obtain more contextual information.
Dynamic Power Systems Line Outage Detection Using Particle Filter and Partially Observed States
Real-time transmission line outage detection is difficult because of partial phasor measurement unit (PMU) deployment and varying outage signal strength.
Dynamic RAN Slicing for Service-Oriented Vehicular Networks via Constrained Learning
To obtain an optimal RAN slicing policy for accommodating the spatial-temporal dynamics of vehicle traffic density, we first formulate a constrained RAN slicing problem with the objective to minimize long-term system cost.
A New Multiple Source Domain Adaptation Fault Diagnosis Method between Different Rotating Machines
To solve this issue, transfer learning is proposed by leveraging knowl- edge learned from source domain to target domain.
Visual Causality Analysis of Event Sequence Data
Unfortunately, recovering causalities from observational event sequences is challenging, as the heterogeneous and high-dimensional event variables are often connected to rather complex underlying event excitation mechanisms that are hard to infer from limited observations.
Energy and Information Management of Electric Vehicular Network: A Survey
In this paper, we provide a comprehensive survey on the deployment and management of EVN considering all three aspects of energy flow, data communication, and computation.
Task-Oriented Optimal Sequencing of Visualization Charts
A chart sequence is used to describe a series of visualization charts generated in the exploratory analysis by data analysts.
Assessing four Neural Networks on Handwritten Digit Recognition Dataset (MNIST)
Another inspiring result is that CapsNet only needs a small amount of data to get excellent performance.
