LSTM neural networks improve fiber nonlinearities in coherent systems.
problem Compensating fiber nonlinearities in digital coherent systems.
method Utilization of Long short-term memory (LSTM) neural networks.
result LSTM neural networks provide superior performance compared to digital back propagation, especially in multi-channel scenarios.
Improved sample-efficient learning for non-coherent digital jamming.
problem Learning optimal jamming strategies in non-coherent digital modulation schemes without prior knowledge.
method Introduced a linear bandit algorithm that accounts for action similarities and integrates context features.
result Significantly improved convergence behavior compared to prior art.
The paper addresses speckle noise in coherent imaging systems.
problem Speckle noise degrades image quality in coherent imaging systems.
method Theoretical and algorithmic analysis of likelihood-based approaches for multilook coherent imaging.
result Established the first theoretical upper bound on MSE of the maximum likelihood estimator.
A method for fast, accurate cross-temporal forecasts using machine learning.
problem Inconsistent forecasts across different levels of platform data.
method Non-linear hierarchical forecast reconciliation using machine learning.
result Automated direct production of reconciled forecasts for high-frequency decision making.
GenInSAR uses CNNs to filter InSAR phase and estimate coherence without supervision.
problem Improving accuracy in InSAR phase filtering and coherence estimation.
method Unsupervised CNN-based generative model for joint phase filtering and coherence estimation.
result GenInSAR outperforms five related methods in residue reduction and coherence estimation.
Optimizes biomanufacturing processes with a new digital twin calibration method.
problem Lack of interpretability and sample efficiency in traditional DoE methods.
method Developed a computational approach to calibrate Bio-SoS digital twin model.
result Guides sample-efficient and interpretable DoEs by quantifying sub-model parameter estimation errors.
Solves division problem for L. Hörmander's systems.
problem Division problem for L. Hörmander's overdetermined systems.
method Formulates and proves divisibility criterion, coherence theorem.
result Establishes effective divisibility criterion and extends coherence theorem.
Proposes a probabilistic digital twin for dynamical systems using sparse Bayesian learning.
problem Creating and updating accurate digital twins for complex dynamical systems.
method Sparse Bayesian machine learning, two approaches: input-output and output-only.
result Identifies correct perturbation terms and associated parameters in dynamical systems.
Coherent Multiplex analyzes real-time wavelet coherence among multiple signals.
problem Identifying and visualizing coherence among multiple time series.
method Fast spectral similarity based on cosine similarity metrics of Fourier-transformed signals and sparse time-frequency wavelet coherence.
result Scalable real-time system for low-latency inference and monitoring of inter-signal relationships.
Let C be an algebraic curve of genus g. A coherent system on C consists of a pair (E,V), where E is an algebraic vector bundle over C of rank n and degree d and V is a subspace of dimension k of the space of sections of E. The stability of the coherent system depends on a parameter α. We study t…
Surrogate models enhance digital twin technology for dynamic systems.
problem Lack of clarity and effective methods for digital twin applications.
method Exploration of Gaussian process (GP) emulators within digital twin framework.
result Surrogate models like GP emulators are effective for digital twin development.
A digital twin for multi-scale systems uses physics-based and machine learning models.
problem Lack of application-specific details in digital twin technology.
method Strategically separates into physics-based and data-driven models; uses mixture of experts with Gaussian Process.
result Robust and accurate predictions at future time-steps for multi-scale systems.
Develops a machine learning model to predict ALS progression and assistive device use.
problem Challenges in predicting clinically meaningful milestones in ALS.
method Integrates longitudinal ALSFRS-R trajectories with survival modeling.
result Generates individualized survival curves and predicts wheelchair-free survival.
Novel digital twin for complex systems improves performance.
problem Lack of practical implementation details for stochastic nonlinear MDOF systems.
method Decouples time-scales, uses physics-based model, Bayesian filtering, and machine learning.
result Excellent performance of proposed digital twin framework validated by examples.
Enhanced multi-fidelity models improve digital twin accuracy and uncertainty quantification.
problem Lack of detailed application-specific data and inaccurate sensor data hinder surrogate model learning for digital twins.
method Proposes a multi-fidelity surrogate model framework integrating PCFE and GP, and deep-HPCFE with auto-regression schemes.
result Demonstrates improved accuracy and uncertainty quantification in digital twin systems.
Automates translating natural language to Verilog for digital design.
problem Manual translation of natural language specifications to Verilog is time-consuming and error-prone.
method Fine-tuned GPT-2 to derive Verilog from English, using a dataset of design tasks.
result GPT-2 achieved 94.8% correct translation across simple and abstract design tasks.
Financial markets have been extensively studied as highly complex evolving systems. In this paper, we quantify financial price fluctuations through a coupled dynamical system composed of phase oscillators. We find a Financial Coherence and Incoherence (FCI) coexistence collective behavior emerges as the system evolves …
Optical Coherence Tomography allows ophthalmologist to obtain cross-section imaging of eye retina. Assisted with digital image analysis methods, effective disease detection could be performed. Various methods exist to extract feature from OCT images. The proposed study aims to compare the effectiveness of handcrafted a…
Predicts coherence from quantum heat engine noise using machine learning.
problem Predicting coherence in quantum heat engines from nonequilibrium fluctuations.
method Developed a machine learning protocol using K-Nearest Neighbor (KNN) model.
result Machine learning successfully predicts coherence from quantum heat engine noise.
Framework predicts responses in misspecified systems using GPLFM and BNNs.
problem Predicting responses in dynamical systems with model misspecification.
method Integrates GPLFM and BNNs for uncertainty-aware inference and prediction.
result Systematic propagation of uncertainty from diagnosis to prediction.
Quantum reservoir computing needs coherence influx for effective information processing.
problem Understanding and optimizing quantum reservoir computing.
method Theoretical and numerical analysis of quantum systems, focusing on coherence influx and spectral radius of Pauli transfer matrix.
result Coherence influx is essential for realizing nonstationary echo state property in quantum reservoir computing.
This study identifies financial risk paths in digital-transformed enterprises.
problem Identifying financial risks in digital-transformed enterprises.
method DEMATEL-ISM-MICMAC method.
result Political and economic environment affects enterprise's financial structure.
Deep generative model for healthcare data identifies coherent substructures and mutational clusters.
problem Analytical challenges in healthcare data, including sparsity, missingness, and small sample sizes.
method Proposes a deep generative Bayesian model with collapsed Gibbs sampling for multinomial count data.
result Identifies coherent substructures and biologically meaningful mutational clusters in cancer data.
Machine learning identifies types of alterations in historical manuscripts.
problem Understanding and categorizing alterations in historical manuscripts.
method Alteration Latent Dirichlet Allocation (alterLDA) model.
result High performance in recognizing alterations on labelled data, and interesting insights on unlabelled data.
CMC method detects causal relationships in time series data.
problem Understanding causal relationships in nonlinear systems.
method Cross-Mapping Coherence method, based on nonlinear state-space reconstruction and coherence metrics.
result CMC accurately identifies causal connections in various systems.
Paper reviews neuromorphic engineering features and compares analog vs digital systems.
problem Lack of consensus and unclear features in neuromorphic engineering.
method Review of recent work, comparison of machine learning accelerator chips.
result Analog processing and reduced bit precision architectures offer best efficiencies.
Develops a framework to assess systemic risk in the economy using bank-firm network data.
problem Measuring systemic risk in the economy using multilayer network data.
method Unified framework combining techniques to reconstruct multilayer economy structure from bank and firm balance sheets, and dynamics of shock propagation.
result Identifies systemically important firms and banks, and assesses systemic risk determinants.
Paper introduces probabilistic digital twins for optimal decision making under uncertainty.
problem Optimal sequential decision making under incomplete and uncertain information.
method Formal definition of epistemic uncertainty using measure theory, and solution via deep reinforcement learning.
result Proposes a generic approximate solution for optimal sequential decision making.
DeepONets improve surrogate modeling for engineering systems.
problem Accurately modeling complex PDEs for engineering systems.
method DeepONets specialize in approximating mathematical operators for PDEs.
result DeepONets achieve high prediction accuracy and zero-shot capability.
The Clock Drawing Test (CDT) is a rapid, inexpensive, and popular neuropsychological screening tool for cognitive conditions. The Digital Clock Drawing Test (dCDT) uses novel software to analyze data from a digitizing ballpoint pen that reports its position with considerable spatial and temporal precision, making possi…
Adler had shown in 1979 that the Toda system can be given a coad- joint orbit description. We quantize the Toda system by viewing it as a single orbit of a multiplicative group of lower triangular matrices of determinant one with pos- itive diagonal entries. We get a unitary representation of the group with square inte…
A new method streamlines digital payment programming using smart contracts.
problem High costs and security challenges in programming smart contracts for digital payments.
method Transforming digital currencies into token streams and using configurable templates to generate specialized smart contracts.
result Reduces payment programming costs and enhances security, self-enforcement, adaptability, and controllability.
The paper reveals the hidden costs of digitizing commodity money and proposes a new stable-coin system.
problem Depreciation of banknotes due to high logistics costs after digitization.
method Analyzing the functions of money from a logistics perspective and comparing commodity money to digital currency.
result There is no honest money that is both a store of value and has negligible logistics costs.
On certain manifolds, the phase which appears in the scalar product of two coherent state vectors is twice the symplectic area of the geodesic triangle determined by the corresponding points on the manifold and the origin of the system of coordinates. This result is proved for compact Hermitian symmetric spaces using t…
We present a method for identifying the coherent structures associated with individual Lagrangian flow trajectories even where only sparse particle trajectory data is available. The method, based on techniques in spectral graph theory, uses the Coherent Structure Coloring vector and associated eigenvectors to analyze t…
Digital twin reduces costs in various fields.
problem High costs in decision-making processes.
method Use of digital twin model for cost reduction.
result Digital twin acts as a cost reduction method.
Anosov flow found in specific partially hyperbolic systems.
problem Characterizing partially hyperbolic diffeomorphisms with center foliation.
method Analyzing transitive dynamically coherent systems with one-dimensional center foliation.
result Discretized Anosov flow found in systems satisfying f(W)=W for center leaves. New method produces coherent forecasts for long-range data.
problem Inaccurate and non-coherent forecasts on long-horizon data.
method Probabilistic forecasting with KL-divergence for coherent aggregates.
result Improves forecast performance across base levels and aggregates.
The abstract explores a new wave equation linking quantum mechanics and complex adaptive systems.
problem Understanding the underlying mechanism of distribution formation in complex quantum entanglement.
method Exploring the logical relationship between Schrödinger's wave equation and Shi's trading volume-price wave equation in finance.
result A non-localized wave equation in quantum mechanics reveals the invariance of interaction as a universal law.
Viewing a data set such as the clouds of Jupiter, coherence is readily apparent to human observers, especially the Great Red Spot, but also other great storms and persistent structures. There are now many different definitions and perspectives mathematically describing coherent structures, but we will take an image pro…
Study examines Trump's crypto influence on markets, revealing conflicts and vulnerabilities.
problem Presidential power and cryptocurrency markets during Trump's second term.
method Mixed-methods approach combining quantitative and qualitative data.
result Political-linked digital assets became a distinct class with systemic vulnerabilities.
Mathematical advances needed for Digital Twins, differing from traditional models.
problem Foundational mathematical advances required for Digital Twins.
method Multi-scale, multi-physics modeling and coupling, different reliability criteria and uncertainty assessments.
result AI/ML methods can perform well in biomedical problems but fail in simple engineering systems.
CrowdLLM uses LLMs and generative models to create diverse digital populations.
problem Lack of diversity and accuracy in digital populations created by LLMs.
method Integrates pretrained LLMs and generative models to enhance diversity and fidelity.
result CrowdLLM achieves promising performance in accuracy and distributional fidelity.
Introduces MLM dataset for multitask learning across multiple languages and modalities.
problem Training and evaluating multitask systems on diverse data.
method Develops a new benchmark dataset with semantic data and modalities.
result Demonstrates challenges in generalizing on diverse data.
Improved aircraft structure prediction using derivative-enhanced sparse Cholesky GP method.
problem Accurate real-time prediction of aircraft structure performance.
method Combining derivative data with a modified dynamic sparse Cholesky linear system solver.
result Improved prediction accuracy of aircraft structure performance.
The paper quantifies and attributes uncertainty in complex system simulations.
problem Uncertainty in complex system simulations due to unknown or approximated subprocesses.
method Developed a framework for quantifying and attributing submodel uncertainty using bootstrapping, Bayesian model averaging, and tree-based methods.
result Individual submodels contribute to overall uncertainty, and their importance can be quantified.
Deep RL optimizes sensor placement in digital twins for dynamic data acquisition.
problem Limited applicability of traditional sensor placement techniques for online applications.
method Formulates sensor placement as a Markov decision process and uses deep reinforcement learning.
result Improves predictive accuracy and reliability of digital twins through adaptive sensor repositioning.
DIGIT is a low-cost tactile sensor for in-hand manipulation.
problem Difficulty in sensing contact forces limits robotic manipulation.
method DIGIT miniaturizes and improves a vision-based tactile sensor.
result DIGIT enables better control of interactions with the environment.