Balanced excitation and inhibition enhance neuronal selectivity and robustness.
problem Ensuring robust neuronal responses in noisy environments.
method Investigated the conditions for balanced excitation and inhibition to enhance robustness of single neurons and network attractor states.
result Balanced excitation and inhibition are crucial for high-capacity, noise-resistant neuronal selectivity.
Study constructs balanced datasets for seismic failure prediction.
problem Imbalanced datasets limit machine learning performance in seismic failure prediction.
method Framework with three steps: GMF identification, probability density estimation, and sample transformation.
result Framework improves machine learning performance in seismic failure mode prediction.
Machine learning speeds up quantum chemical calculations of excited states.
problem Accurate quantum chemical calculations of excited states are computationally expensive.
method Employing machine learning to speed up and advance excited-state simulations in various fields.
result Machine learning techniques can significantly reduce computational time for excited-state simulations.
New self-exciting random evolutions (SEREs) for modeling traffic and transport processes.
problem Modeling self-exciting and clustering effects in traffic and transport processes.
method Introducing a new process based on a superposition of a Markov chain and a Hawkes process, and constructing self-exciting random evolutions (SEREs).
result Developed new models and limit theorems for SEREs, including averaging and diffusion approximation.
New method clusters ab initio dynamics to predict excited state properties.
problem Complex excited state dynamics in polyatomic systems.
method Time series guided clustering algorithm to generate meta-stable patterns.
result Accurate prediction of ground and excited state properties.
Machine learning aids excited-state molecular dynamics studies.
problem Challenges in studying electronically excited states of molecules.
method Employing machine learning techniques for excited-state molecular dynamics.
result Highlight successes and challenges in machine learning for excited-state processes.
Optimal reinsurance strategy analyzed for dynamic risk model with self- and externally-excited jumps.
problem Optimal reinsurance in a dynamic contagion model with self-exciting and externally-exciting risks.
method Two methodologies: classical HJB approach and BSDE approach, focusing on Markovian setting.
result Comparison of self-exciting and externally-exciting risks highlights heightened risk from self-exciting component.
We study a linear price impact model including other liquidity takers, whose flow of orders either follows a Poisson or a Hawkes process. The optimal execution problem is solved explicitly in this context, and the closed-formula optimal strategy describes in particular how one should react to the orders of other trader…
In Levin-Wen (LW) models, a wide class of exactly solvable discrete models, for two dimensional topological phases, it is relatively easy to describe only single fluxon excitations, but not the charge and dyonic as well as many-fluxon excitations. To incorporate charged and dyonic excitations in (doubled) topological p…
Paper presents a method for estimating Hawkes process parameters.
problem Estimating parameters of Hawkes processes with self-excitation or inhibition.
method Maximum likelihood estimation for Hawkes processes with self-excitation or inhibition.
result The proposed estimator provides more accurate estimations in the inhibition context.
3D ConvNets improved with Project & Excite for medical imaging segmentation.
problem Improving segmentation performance in 3D medical imaging.
method Proposed Project & Excite (PE) modules for 3D F-CNNs, extending 2D recalibration methods.
result Project & Excite modules boost segmentation performance up to 0.3 in Dice Score.
Paper explores ML for UV spectra, showing transferability in chemical space.
problem Modeling excited states and predicting properties of unseen molecules.
method Adapting charge model for excited states, using SchNarc approach.
result ML models can predict properties of unseen molecules and different excited states.
Deep QMC method accurately computes electronic excited states.
problem Accurate calculation of electronic excited states in large systems.
method Extends variational QMC with deep neural networks for excited states.
result Consistently achieves high accuracy for low-lying excited states.
Paper tackles robust control policy learning for uncertain systems.
problem Learning control policies for an unknown linear dynamical system with quadratic cost.
method Convex optimization method balancing exploitation and exploration.
result Minimizes worst-case cost by reducing uncertainty in model parameters.
Given a graph embedded in an orientable surface, a process consisting of random excitations and random node and face balancing is constructed and analyzed. It is shown that given a priori bounds g' on the genus and n' on the number of nodes, one can determine the genus of the surface from local observations of the proc…
Deep RL estimates muscle excitations in biomechanical simulations.
problem Estimating muscle excitations from biomechanical systems.
method NAF reinforcement learning with custom reward function, episode-based hard update, and dual buffer experience replay.
result Models learned muscle excitations for given motions after 100,000 steps with <1% error.
Study shows self-exciting shocks increase systemic risk in interbank networks.
problem Systemic risk in interbank lending networks with self-exciting shocks.
method Mean-field model, weak convergence analysis, measure-valued process, law of large numbers.
result Self-exciting shocks increase systemic risk in interbank networks.
Modeling aggressive market order arrivals using Hawkes factor models.
problem Aggressive market order placements and their impact on stock prices.
method Bivariate marked Hawkes process with self-excitation and cross-excitation components.
result The Hawkes model with an exponential kernel produces better calibration than a monotonous exponential kernel.
Develops machine learning models for excited states of CH2NH2+.
problem Accurately predicting excited-state properties and couplings for CH2NH2+.
method Combines neural networks and kernel ridge regression, encoding electronic states in inputs.
result Improved accuracy in predicting excited-state properties and couplings.
A new model predicts discrete events with flexible, nonparametric baseline and excitation.
problem Limited flexibility in discrete Hawkes models for event prediction.
method Gaussian Process Discrete Hawkes Process (GP-DHP) with collapsed latent representation.
result Improves predictive log-likelihood for diverse event patterns.
We extend Hawkes processes by treating self-excitation levels as stochastic processes.
problem Approximating events and intensities that accelerate each other in correlated contagion.
method Proposed an extension to Hawkes processes with stochastic excitation levels, generalized algorithm for simulation, and hybrid MCMC approach for model fitting.
result Our approach allows better approximation in domains where events and intensities accelerate each other.
Persistency of excitation ensures correct parameter estimation in neural networks.
problem Ensuring correct parameter estimation in neural networks during training.
method Analyzed gradient descent dynamics in a two-layer neural network and proposed a new algorithm.
result Conditions for persistent excitation of network weights are difficult to satisfy in multi-layer networks.
Lower bounds and upper bounds on sample complexity for identifying linear dynamical systems.
problem Identifying an unknown linear dynamical system with limited data.
method Sample complexity lower and upper bounds, persistent excitation condition, active learning algorithm.
result Lower and upper bounds share the same dependency on key problem parameters.
Paper analyzes coexisting hidden and self-excited attractors in an economic system.
problem Existence of coexisting hidden and self-excited attractors in economic systems.
method Integer and fractional order analysis of an economic system.
result Integer-order system exhibits multiple combinations of coexisting hidden and self-excited attractors.
Researchers use Hawkes processes to analyze credit trades, revealing self-excitement and volume impacts.
problem Understanding the dynamics of credit market trades and their interactions.
method Simple method for fitting multidimensional Hawkes processes with exponential kernels using maximum likelihood non-convex optimization.
result Quantification of self-excitement and volume impacts in credit trades.
Optimal noise excitation for linear system identification reduces sample complexity.
problem Efficiently identifying linear systems with minimal data.
method Active learning algorithm using ordinary least squares and semidefinite programming.
result The proposed algorithm matches lower bounds on sample complexity for any active learning method.
New algorithm learns LQR with O ( T ) O(\sqrt{T}) O ( T ) regret using Langevin dynamics and excitation.
problem Learning LQR with a O ( T ) O(\sqrt{T}) O ( T ) regret bound. method Thompson sampling with Langevin dynamics and excitation mechanism.
result Achieved O ( T ) O(\sqrt{T}) O ( T ) regret bound for LQR learning. Paper forecasts financial trading durations using a new point process model.
problem Forecasting limit order book durations in high-frequency financial data.
method Self-exciting flexible residual point process incorporating empirical distributional features.
result The model achieves strong predictive performance compared to alternative approaches.
The Hawkes process is a simple point process, whose intensity function depends on the entire past history and is self-exciting and has the clustering property. The Hawkes process is in general non-Markovian. The linear Hawkes process has immigration-birth representation. Based on that, Fierro et al. recently introduced…
Study on stochastic volatility models with external shocks triggering jump cascades.
problem Analyzing the impact of external shocks on jump dynamics in stochastic volatility models.
method Establishing scaling limits for a class of stochastic volatility models with self-exciting jump dynamics.
result External shocks can trigger endogenous jump cascades in asset returns and volatility.
Study of knots in excitable media shows stable minimal lengths.
problem Understanding the dynamics and minimal lengths of knots in excitable media.
method Numerical simulations of the FitzHugh-Nagumo equation for various torus knots.
result FitzHugh-Nagumo evolution preserves knot topology and yields stable minimal lengths.
Develops a goodness-of-fit test for self-exciting processes.
problem Quantifying how well generative models capture self-exciting point processes.
method Connects to Quasi-maximum-likelihood estimator (QMLE) theory and develops a non-parametric self-normalizing statistic, the Generalized Score (GS) statistics.
result Validates the proposed GS test's good performance through numerical simulation and real-data experiments.
Artificial neural networks reduce computational costs for predicting excitation energy transfer properties in light-harvesting systems.
problem Computational limitations in predicting excitation energy transfer properties in light-harvesting systems.
method Use of artificial neural networks to bypass the computational limitations of established techniques.
result Artificial neural networks predict transfer times and transfer efficiencies with similar or higher accuracy than frequently used approximate methods.
This study improves text-to-speech synthesis using GANs for glottal excitation.
problem Slow inference and computational cost of WaveNet and difficulty in parallel training of GANs.
method Adopted GANs for parallel waveform generation in speech signal and glottal excitation.
result GAN-based glottal excitation model achieves quality and voice similarity on par with WaveNet.
Study state-dependent Hawkes processes for limit order book modeling.
problem Modeling feedback loop between order flow and limit order book shape.
method Existence and uniqueness of state-dependent Hawkes processes, simulation, maximum likelihood estimation.
result Excitation effects in order flow are strongly state-dependent.
Multiplex Network Hawkes model for systemic risk measurement
problem Investigate how contagion in financial networks is affected by different transmission channels
method Multiplex Network Hawkes model
result Sparse contagion pathways, with systemic-risk transmission concentrated in outward flows from a small number of influential institutions
Safe control of systems with unknown dynamics using persistent excitation.
problem Tension between safety and exploration in data-driven control.
method System identification through persistent excitation, robust constraint satisfaction, and synthesis of feedback controllers.
result Non-asymptotic guarantees on estimation and controller performance.
Study optimal dividend and capital injection in insurance portfolios with self-exciting claim arrivals.
problem Optimal dividend and capital injection in insurance portfolios with Hawkes process claim arrivals.
method Analytical properties, explicit threshold, HJB variational inequality, finite-difference scheme, policy-gradient, actor-critic methods.
result Learned strategies closely match the PDE benchmark and remain stable across initial conditions.
Paper introduces MSPD for multivariate risk processes with dependencies.
problem Computing risk valuations with dynamic dependencies between frequency and severity.
method Combines Poisson imbedding, pseudo-chaotic expansion, and Malliavin calculus.
result Explicit general correlation formula for MSPDs.
Paper detects communities from graph signals using low-rank excitation modeling.
problem Detect communities in graphs from noisy signals.
method Model signals as graph filter outputs, apply spectral method to covariance matrix.
result Community structure can be retrieved directly from graph signals.
Estimates network structure from spike train data using point process models.
problem Estimating network structure from self-exciting point process data.
method Incorporates saturation, low-dimensional structural assumptions, and long-range memory effects.
result Provides theoretical guarantees for high-dimensional self-exciting point processes.
This study defines a multivariate Self--Exciting Threshold Autoregressive with eXogenous input (MSETARX) models and present an estimation procedure for the parameters. The conditions for stationarity of the nonlinear MSETARX models is provided. In particular, the efficiency of an adaptive parameter estimation algorithm…
Proposes a speaker-independent GlotNet vocoder using WaveNet for speech generation.
problem Lack of efficient multi-speaker WaveNet models with limited resources.
method Uses source-filter model of speech production to train a WaveNet for glottal excitation.
result Proposed GlotNet vocoder performs favorably to direct WaveNet vocoder in speech quality.
GAttNHP predicts future events in temporal knowledge graphs by encoding long-range dependencies and handling mutual excitation.
problem Forecasting future events in temporal knowledge graphs due to long-range dependencies, mutual excitation, and heavy-tailed inter-arrival times.
method GAttNHP uses a self-attention encoder, semantic soft-grouping, and NCQ regression to address these issues.
result GAttNHP improves entity and time prediction on six benchmark TKG datasets compared to state-of-the-art baselines.
Comment on entropy learning for dynamic treatment regimes.
problem Evaluating dynamic treatment regimes using entropy loss.
method Optimization-based alternative to IPW estimate.
result Suggests optimization-based approach for evaluation.
Tiled Squeeze-and-Excite improves channel attention with local spatial context.
problem Improving channel attention mechanisms in neural networks.
method Proposes tiled squeeze-and-excite (TSE) framework for channel attention.
result Local context of 7 rows or columns is sufficient for matching global context performance.
New method interprets recurrent neural networks via excitable network attractors.
problem Understanding the inner workings of recurrent neural networks.
method Excitable network attractors for mechanistic interpretation.
result Recurrent neural networks' behavior can be interpreted using excitable network attractors.
MEG models for dynamic networks estimate dependencies and shared latent space relationships.
problem Modeling dynamic networks with shared latent space relationships and dependencies.
method MEG combines mutually exciting point processes and latent space models to estimate node-specific parameters and unobserved edges.
result MEG models can estimate intensities for unobserved edges, useful for anomaly detection in real-world applications.