Video sequences contain rich dynamic patterns, such as dynamic texture patterns that exhibit stationarity in the temporal domain, and action patterns that are non-stationary in either spatial or temporal domain. We show that a spatial-temporal generative ConvNet can be used to model and synthesize dynamic patterns. The…
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arXiv research
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.
169,291 papers · 148 categories
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48 results for “dynamical patterns”
Modeling and estimating dynamic graphs from binary pattern sequences.
problem Extracting dominant correlation structures from time-dependent binary patterns.
method State-space model of an Ising-type network composed of multiple undirected graphs, sequential Bayes algorithm.
result The method outperforms traditional methods in uncovering overlapping graphs and estimating dynamics of weights.
Study reveals patterns in daily bank transactions.
problem Understanding systemic risk in interbank networks.
method Analysis of daily interbank transactions and a simple model.
result Distinctive network-scale dynamics emerge from stable interaction patterns.
Learning Pattern-Specific Experts for Time Series Forecasting Under Patch-level Distribution Shiftcs.LG
TFPS improves time series forecasting by learning pattern-specific experts.
problem Challenges in forecasting time series data with varying patterns across segments.
method Dual-domain encoder, subspace clustering, pattern-specific experts.
result Significantly improved forecasting accuracy, especially in long-term forecasting.
Market Mill is a complex dependence pattern leading to nonlinear correlations and predictability in intraday dynamics of stock prices. The present paper puts together previous efforts to build a dynamical model reflecting the market mill asymmetries. We show that certain properties of the conditional dynamics at a sing…
Direct Mapping Hidden Excited State Interaction Patterns from ab initio Dynamics and Its Implications on Force Field Developmentphysics.chem-ph
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.
Generative memory model avoids vanishing gradients to robustly retrieve patterns.
problem Robust retrieval of stored patterns in the presence of interference and noise.
method Training a generative distributed memory without explicitly simulating attractor dynamics, using a likelihood-based Lyapunov function.
result The model converges to correct patterns upon iterative retrieval and achieves competitive performance as a memory model and a generative model.
This study explains RL training dynamics in LLMs, focusing on token-level optimization and reasoning pattern reshaping.
problem Understanding the training dynamics of RL in LLMs to improve their reasoning capabilities.
method Empirical analysis and theoretical modeling of RL training process, focusing on reasoning patterns and token optimization.
result RL primarily optimizes a sparse subset of critical tokens, reshaping reasoning pattern distributions and affecting model performance.
The majority of real-world networks are dynamic and extremely large (e.g., Internet Traffic, Twitter, Facebook, ...). To understand the structural behavior of nodes in these large dynamic networks, it may be necessary to model the dynamics of behavioral roles representing the main connectivity patterns over time. In th…
To understand the structural dynamics of a large-scale social, biological or technological network, it may be useful to discover behavioral roles representing the main connectivity patterns present over time. In this paper, we propose a scalable non-parametric approach to automatically learn the structural dynamics of …
Modeling dynamic user interests using neural matrix factorization.
problem Challenging extraction of valuable insights from unstructured, high-dimensional, and dynamic online content data.
method Combines matrix factorization with neural networks to model nonlinear user and content factors.
result Accurately identifies nuanced and coherent consumption patterns of Boston Globe readers over five years.
New Y-systems for Miquel dynamics are Möbius invariant.
problem Miquel dynamics circle centers are not Möbius invariant.
method Introduced new Y-systems involving only intersection points.
result New Y-systems are Möbius invariant and satisfy the transformation group principle.
TimeTrail detects financial fraud patterns through temporal correlation analysis.
problem Detecting and explaining complex financial fraud patterns.
method Temporal data enrichment, dynamic correlation analysis, interpretable pattern visualization.
result TimeTrail outperforms conventional methods in accuracy and interpretability.
dCMF models evolving patterns in multiway data with temporal dynamics.
problem Capturing evolving patterns in multiway datasets with temporal dependencies.
method Time-aware coupled factorization model constrained by LDS structure.
result dCMF outperforms alternatives in capturing complex dynamics.
DySAT learns dynamic graph node representations capturing structural and temporal patterns.
problem Learning latent representations of nodes in dynamic graphs.
method Dynamic Self-Attention Network (DySAT) that combines self-attention layers for structural and temporal dimensions.
result DySAT outperforms state-of-the-art baselines in link prediction on dynamic graphs.
Method analyzes large-scale network data to detect communication pattern shifts.
problem Analyzing large-scale time-series network data is challenging.
method Temporal encoder embedding method using ground-truth or estimated vertex labels.
result Detects communication pattern shifts across all levels of network structure.
Graph learning captures financial dynamics over time.
problem Understanding the evolving patterns in financial interactions.
method Graph Representation Learning applied to a dynamic financial graph.
result Captured latent trajectories reveal insights into economic events.
LLMs detect market patterns through causal reasoning, not just temporal association.
problem Detecting structural market patterns in financial data.
method Obfuscation testing using the WHO-WHOM-WHAT framework.
result LLMs achieve 71.5% detection rate of market patterns without temporal context.
Transformers learn to integrate information from past positions incrementally, specializing heads in distinct patterns.
problem How transformers learn to integrate information from multiple past positions with varying statistical significance.
method High-order Markov chain task, incremental learning, sparse attention patterns, simplified differential equations, stage-wise convergence, early stopping as regularizer.
result Transformers learn to specialize heads in distinct patterns, shifting from competitive to cooperative learning dynamics.
ASRNN adapts scales dynamically for better sequence modeling.
problem Fixed scales in multiscale RNNs don't match temporal patterns.
method Adaptively learns and adjusts scales based on temporal contexts.
result ASRNNs yield better performances with dynamical scaling.
Dynamic model clusters interactions over time, improving prediction.
problem Sparse, evolving interaction graphs with temporal dynamics.
method Structured, nonparametric edge-exchangeable model for dynamic clustering.
result Improved predictive performance compared to static models.
Dynamic time warping applied to temporal graphs for pattern recognition.
problem Comparing temporal graphs using a proximity measure.
method Dynamic Time Warping (DTGW) on temporal graphs.
result DTGW is a flexible measure for temporal graph comparison, NP-hard in general but with polynomial-time solvable special cases.
Enhances load forecasting for multiple entities with dynamic similarities.
problem Inaccurate probabilistic load predictions due to uncertainties and dynamic changes.
method Online multi-task learning for probabilistic load forecasting.
result Significantly enhances load forecasting accuracy across various scenarios.
DDP models dynamic comorbidity networks from event data.
problem Understanding complex temporal patterns of co-occurring diseases.
method Developed deep diffusion processes (DDP) to model dynamic comorbidity networks.
result DDP enables accurate risk prediction and interpretable disease trajectories.
Enhanced deep learning model forecasts household leverage series accurately.
problem Forecasting household leverage series due to complex temporal-spatial dynamics.
method TSEN model with multiple RNN-based layers and an attention layer.
result Captures temporal-spatial dynamics and provides more accurate predictions.
Flexible log file parsing using HMM adapts to evolving content.
problem Dynamic log file processing with evolving content.
method Modeling frequent patterns into HMM for flexible log file parsing.
result High accuracy (over 99%) in parsing different system log files.
New model clusters online learning activity patterns.
problem Understanding and clustering online learning activity patterns.
method Hierarchical Dirichlet Hawkes process (HDHP) for continuous-time grouped streaming data.
result HDHP can recover meaningful learning patterns and track user interests over time.
AdaKoop efficiently models nonlinear dynamics from nonstationary data streams.
problem Capturing nonlinear dynamics in nonstationary data streams with computational efficiency.
method Koopman operator theory and probabilistic framework for streaming data.
result AdaKoop outperforms state-of-the-art methods in real-time forecasting accuracy and efficiency.
Proposes a new stock prediction method that accounts for market dynamics.
problem The dynamic nature of the stock market invalidates traditional machine learning assumptions.
method Develops a second-order learning paradigm with multi-scale patterns.
result Demonstrates effectiveness in stock prediction on real-world data.
Two-layer CNN learns key patterns in specific data distributions.
problem Understanding why CNNs work well in computer vision.
method Investigated learning dynamics of a two-layer nonlinear CNN over specific data distributions.
result Convolutional filters learn key patterns in data and dominate during training.
A new associative memory uses Sinkhorn divergence for efficient pattern retrieval.
problem Efficiently retrieving patterns from large datasets of weighted point clouds.
method Derived retrieval dynamics as a SHK gradient flow, discretized for a deterministic algorithm.
result Proved basin invariance, geometric convergence, and robust recovery from perturbations.
Chaos in cerebellar cells enhances complexity of neural patterns.
problem Understanding how cerebellar granular layer represents complex information.
method Constructed a model of cerebellar granular layer with gap junctions, evaluated using reservoir computing.
result Chaotic dynamics in the cerebellar granular layer produce complex and diverse output patterns.
FLUID-LLM: Learning Computational Fluid Dynamics with Spatiotemporal-aware Large Language Modelscs.LG
FLUID-LLM uses LLMs to predict fluid dynamics with improved accuracy.
problem Leveraging LLMs for CFD due to their pattern recognition abilities but struggles with fluid dynamics complexities.
method Combines pre-trained LLMs with spatiotemporal-aware encoding to predict unsteady fluid dynamics.
result Significant performance improvements in CFD predictions across various datasets.
Automated discovery of diverse self-organized patterns in complex systems.
problem Automated identification of interesting spatially localized patterns in self-organizing systems.
method Intrinsically motivated machine learning algorithms (POP-IMGEPs) combined with deep auto-encoders and CPPN primitives.
result Efficiency and effectiveness of the proposed method in discovering diverse patterns compared to baselines.
This paper explores non-periodic folding of Spidron units, revealing nonlinear dynamics.
problem Understanding the kinematics and nonlinear phenomena of Spidron units.
method Analysis of single unit cell kinematics and recursive construction of multiple cells.
result Non-periodic folding restricts isotropic folding as the number of unit cells increases.
GGP models multivariate time series with latent sub-sequences for diverse behaviors.
problem Modeling multivariate time series with diverse behaviors and patterns.
method Graph Gamma Process (GGP) linear dynamical systems with latent sub-sequences.
result GGP models exhibit good predictive performance and reveal interpretable latent patterns.
Paper introduces TSSDMN for modeling dynamic multilayer networks.
problem Capturing temporal and cross-layer dynamics in multilayer networks.
method Tensor State Space Model (TSSDMN) using symmetric Tucker decomposition.
result TSSDMN uniquely captures temporal dynamics within and across layers.
Dimers and Circle patternsmath-ph
The dimer model and circle patterns are linked via combinatorial and geometric transformations.
problem Understanding the dimer model and its geometric counterpart.
method Established a correspondence between dimer models and circle patterns, using combinatorial and geometric transformations.
result The Miquel dynamics on circle patterns is governed by the octahedron recurrence.
Paper proposes TC3 algorithm to predict crowdfunding funding progress.
problem Predicting the funding progress in crowdfunding dynamics.
method Trajectory-based Continuous Control for Crowdfunding (TC3) algorithm using actor-critic frameworks.
result TC3-Options model effectively predicts U-shaped funding patterns.
Meta-learning approach for fast and compressive energy-based memory models.
problem Learning associative memory with fast and compressive models for complex data.
method Meta-learning approach to energy-based memory models (EBMM) using arbitrary neural architectures.
result Demonstrated associative retrieval outperforming existing systems in reconstruction error and compression rate.
Taxonomy for ML in simulations, covering patterns and algorithms.
problem Enhancing simulations using machine learning.
method Presentation of eight patterns and three algorithmic areas.
result Catalog of activities and patterns for ML integration in simulations.
Active learning method for neural population dynamics using optogenetics.
problem Efficiently selecting neurons to stimulate for identifying neural population dynamics.
method Developed active learning procedure for low-rank regression to determine informative photostimulation patterns.
result Demonstrated a two-fold reduction in data required for predictive power using low-rank linear dynamical systems model.
Mechanisms of information communication and market price movements. The case of SP 500 marketq-fin.ST
The paper analyzes how market prices respond to information processing and non-linear dynamics.
problem Understanding how market prices change in response to information.
method Logistic Continuous Wavelet Transformation method applied to SP 500 market data.
result Identifies patterns in market dynamics and describes them using a new theory of reflexive communication.
New model captures sparse, evolving multigraph structures.
problem Understanding sparse, evolving multigraph structures in dynamic interaction data.
method Dynamic nonparametric Bayesian model combining sparsity and clustering.
result Improved held-out likelihood and predictive performance.
Improved dynamic classifier selection by refining regions of competence.
problem Limited performance of dynamic selection systems due to noisy regions.
method Integrates a filter and an adaptive distance to enhance regions of competence.
result Significant increase in recognition performance and decrease in computational cost.
LNUCB-TA improves MAB performance by dynamically adjusting exploration rates and recognizing spatiotemporal patterns.
problem Suboptimal performance in environments with rapidly changing reward structures and static exploration rates.
method Hybrid model combining linear and nonlinear estimation, with adaptive k-NN for temporal attention.
result Significantly outperforms state-of-the-art algorithms in cumulative and mean reward, convergence, and robustness.
Hierarchical Coupled Geometry Analysis for Neuronal Structure and Activity Pattern Discoveryq-bio.QM
In the wake of recent advances in experimental methods in neuroscience, the ability to record in-vivo neuronal activity from awake animals has become feasible. The availability of such rich and detailed physiological measurements calls for the development of advanced data analysis tools, as commonly used techniques do …
We demonstrate the application of an algorithmic trading strategy based upon the recently developed dynamic mode decomposition (DMD) on portfolios of financial data. The method is capable of characterizing complex dynamical systems, in this case financial market dynamics, in an equation-free manner by decomposing the s…