Investor emotions predict earnings announcements, but excitement lowers returns.
problem The impact of investor emotions on earnings announcements and their returns.
method Social media data analysis over a decade to test the relationship between investor emotions and earnings announcements.
result Excitement about earnings announcements is associated with lower announcement returns.
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.
This chapter teaches how to model social media events using Hawkes processes.
problem Modeling discrete, inter-dependent events over continuous time in social media.
method Introduction to point processes, Hawkes process, event intensity function, event simulation, parameter estimation.
result Demonstrates modeling retweet cascades using a Hawkes self-exciting process.
New model for clustering dependent community Hawkes processes in temporal networks.
problem Modeling strong dependence and community structure in temporal networks.
method Dependent Community Hawkes (DCH) models combining stochastic block models and Hawkes processes.
result Spectral clustering error bound derived for DCH models.
Novel framework for spatio-temporal event analysis using Hawkes processes.
problem Inference of dynamics in spatio-temporal event sequences.
method Randomized Fourier feature-based transformations and gradient descent.
result Improved fitting capability in synthetic and real datasets.
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.
New equations for Cosserat media motions derived from bundle automorphisms.
problem Modeling deformations in Cosserat media.
method Euler-type equations derived from SO(3)-bundle automorphisms.
result Presented new equations for Cosserat media motions.
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.
Unified deep network learns shared representation and cross-media similarity metric for multimedia data.
problem Improving cross-media retrieval by capturing complex correlations among multiple media types.
method Unified Network for Cross-media Similarity Metric (UNCSM) that combines shared representation learning and distance metric calculation.
result UNCSM outperforms state-of-the-art methods on 4 cross-media datasets.
This article asks how planning scholarship may effectively gain impact in planning practice through media exposure. In liberal democracies the public sphere is dominated by mass media. Therefore, working with such media is a prerequisite for effective public impact of planning research. Using the example of megaproject…
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.
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.
Study compares sentiment spillover networks from news and social media in tech companies.
problem Understanding how sentiment information flows between companies through news and social media.
method Network-based transfer entropy method to measure and compare sentiment spillover.
result News shows stronger information flow among tech companies after COVID-19.
The paper introduces new uniformity and homogeneity concepts for Cosserat media.
problem Characterizing uniformity and homogeneity in Cosserat media.
method Using groupoids and smooth distributions, the authors derive three canonical equations to characterize uniformity and homogeneity.
result The paper provides a unique and maximal division of Cosserat media into uniform and second-grade parts.
Social media enhances or diminishes scientific status, depending on usage.
problem Impact of social media on scientific stratification and mobility.
method Logistic Attribution Analysis combining statistical and machine learning methods.
result Social media promotes stratification and mobility, but beyond a threshold, it negatively impacts status.
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.
Social media has nonlinear causal effects on stock market prices, according to new research.
problem Understanding the nonlinear relationship between social media and stock market dynamics.
method Analyzed extensive social media data related to DJIA index components using information-theoretic measures.
result Social media has significant nonlinear causality on stocks' returns, with social media dominating the directional coupling.
Media tone around earnings announcements predicts stock returns.
problem Determining if media tone around earnings announcements provides useful information for stock prices.
method Conducted an event study on media tone around earnings announcements for nonfinancial S&P 500 firms.
result Media tone around earnings announcements predicts abnormal stock returns.
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.
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.
Bayesian method predicts social unrest from social media data.
problem Uncertainty in predicting events from diverse, unstructured social media data.
method Machine learning for classification and empirical Bayesian approach for event probability calculation.
result Predicted social unrest events in Australian cities with high accuracy.
EmTract extracts emotions from financial social media text.
problem Understanding investor emotions in financial markets.
method Annotated data, DistilBERT model, embedding space augmentation.
result EmTract outperforms existing emotion classifiers.
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.
This paper studies how social media posts, especially by executives, affect stock prices.
problem Predicting stock market movements using social media data.
method Integrated sentiment analysis of Twitter and Reddit posts with historical stock data using time series models and deep learning.
result Improvements in stock price prediction when social media data, especially executive posts, are included.
Paper proposes using word embeddings to detect trolls in social media debates.
problem Preventing online harassment through rapid detection of offensive posts.
method Word embedding models for identifying fast-changing topics and negative content.
result GloVe model helps in discovering new keywords for trolling detection.
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.
Social media reduces individual investors' disposition effect through negative information.
problem The disposition effect in individual investors selling profitable assets too early and holding onto losing assets for too long.
method Analysis of post data and trading data from Xueqiu.com.
result Social media information significantly reduces the disposition effect.
Analyze food popularity trends using social media data.
problem Identify trends and popularity of cuisine types across space and time.
method Use off-the-shelf machine learning techniques, Kernel Density Estimation, and Bayesian Networks.
result Model dependencies among food cuisines popularity.
Deep learning ensemble detects social media rumors faster.
problem Timely detection of rumors on social media platforms.
method Ensemble model using deep neural networks and time-series tweet data.
result Improved rumor detection performance by 7.9%.
New framework uses social media images to estimate wildlife populations.
problem Lack of basic data for wildlife species due to inadequate traditional methods.
method Developed a new computer vision tool to account for social media bias.
result Showed that wildlife population size estimates are learnable from social media.
Study models fractures in porous media using geometric analysis.
problem Analyzing fluid flow in fractures with complex geometries.
method Developed a geometric model using Riemannian manifold and Laplace Beltrami operators.
result Reduced model accurately approximates flow in complex fractures.
Paper uses GAN to predict opioid relapse from social media data.
problem Accurate relapse prediction for opioid addiction.
method Generative Adversarial Networks (GAN) model trained on sentiment images and social influences.
result GAN model predicts relapse better than alternatives, showing relapse is linked to joy and negative emotions.
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.
The paper proposes a method to learn user representations invariant to social media behavior changes.
problem Difficulty in comparing users over time due to evolving behavior.
method Learning a mapping from user activity to a vector space capturing invariant features.
result The learned mapping enables efficient comparisons of users not seen at training time.
Deep learning predicts stock movements using social media data.
problem Predicting stock movements with traditional methods is challenging.
method Graph neural network combining financial data and social media.
result Improvement of 28% in cumulative returns.
The tools of presymplectic geometry are used to study light rays trajectories in anisotropic media.
Study travel time tomography for transversely isotropic media using modified pseudodifferential calculus.
problem Travel time tomography problem for transversely isotropic media.
method Modified scattering pseudodifferential calculus to solve the tomography problem.
result Construction and use of modified pseudodifferential calculus to solve the tomography problem.
Paper models user behavior in online social media using HMMs.
problem Understanding user behavior in online social media platforms.
method Leveraging Hidden Markov Models (HMMs) to represent user behavior, deriving a model-based distance, and using spectral clustering.
result Clusters of users with similar behavioral trajectories identified.