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.
Model explains asset price fluctuations using interacting neurons.
problem Understanding volatility and price dynamics in financial markets.
method Structural model based on interacting neurons, generating functional analysis, simulations.
result Model predicts fat-tailed and broader distributions of asset returns.
We study warped compactifications of string/M theory with the help of effective potentials, continuing previous work of the last two authors and Michael R. Douglas presented in arXiv:1206.1885. The dynamics of the conformal factor of the internal metric, which is responsible for instabilities in these constructions, is…
Study confirms complex crypto market dynamics via non-linear potentials.
problem Linear models fail to capture complex financial market dynamics.
method Analyzed high-frequency crypto currency data to confirm non-linear drift and potential functions.
result Markets exhibit either single-well or double-well potentials, indicating varying levels of uncertainty or stress.
FlowLLM uses LLMs and flow matching to efficiently generate novel materials.
problem Challenging material discovery due to vast chemical space.
method Combines LLMs and Riemannian flow matching to design novel crystalline materials.
result Significantly increases generation rate of stable materials and unique crystals.
We find numerical and empirical evidence for dynamical, structural and topological phase transitions on the (German) Frankfurt Stock Exchange (FSE) in the temporal vicinity of the worldwide financial crash. Using the Minimal Spanning Tree (MST) technique, a particularly useful canonical tool of the graph theory, two tr…
Python tool detects economic crises from S&P500 correlation data.
problem Identifying economic crises from market correlation data.
method Bayesian multi-trend change point analysis.
result Mean market correlation correlates with major economic events.
The paper extends algebraic expression for subjective spatial patterns to include temporal patterns.
problem Studying subjective spatial and temporal patterns in machine learning.
method Develops X-form for algebraic expression of subjective spatial patterns and extends it to temporal patterns.
result Established algebraic expressions for both spatial and temporal patterns.
Safe Pattern Pruning reduces pattern explosion in predictive pattern mining.
problem Exponential growth of patterns in structured data.
method Safe Pattern Pruning (SPP) method.
result Effective model building in practical data analysis.
BN^2MF identifies unknown exposure patterns in environmental mixtures.
problem Identifying unknown exposure patterns in environmental mixtures.
method Bayesian non-parametric non-negative matrix factorization (BN^2MF) with non-negative continuous priors and a non-parametric sparse prior.
result Estimates patterns of chemical exposures without specifying the number of patterns.
RestoreAI predicts landmine risk from patterns, improving clearance efficiency.
problem Predicting landmine risk from spatial patterns to enhance clearance efficiency.
method RestoreAI uses landmine patterns for risk prediction, implementing three deminers: linear, curved, and Bayesian.
result RestoreAI significantly boosts clearance efficiency, achieving a 14.37 percentage point increase in cleared landmines per timestep.
Study of knots with generalized Mazur patterns and their invariants.
problem Understanding the invariants and properties of knots with generalized Mazur patterns.
method Computational analysis of τ and ε invariants for n-twisted satellites. result None of the n-twisted patterns from the family act surjectively on the smooth or rational concordance group. Paper proves rigidity of spherical ring patterns on surfaces.
problem Proving rigidity of spherical orthogonal ring patterns on closed surfaces.
method Modification of combinatorial total geodesic curvature and variational principles.
result Rigidity of spherical orthogonal ring patterns on closed surfaces proved.
Two approaches detect EV charging patterns at stations.
problem Identify charging patterns at electric vehicle charging stations.
method Two approaches: rule-based and hierarchical clustering.
result Hierarchical clustering revealed unexpected charging patterns.
Proves existence of circle patterns on surfaces with cusps.
problem Existence of circle patterns with prescribed angles on surfaces with cusps.
method Introduced combinatorial Ricci and Calabi flows to prove longtime existence and convergence.
result Existence of generalized circle patterns with prescribed angles on surfaces with cusps.
Study of combinatorial Calabi flow on ideal circle patterns.
problem Finding ideal circle patterns with prescribed curvatures.
method Combinatorial Calabi flow in hyperbolic and Euclidean geometry.
result Flow converges exponentially to ideal circle patterns.
The paper studies circle patterns on surfaces with specific angles and curvature maps.
problem Investigating circle patterns with obtuse angles on surfaces of finite type.
method Characterizing curvature maps and establishing combinatorial Ricci flow conditions.
result Generalizations of circle pattern theorem and a computational method to find patterns.
Flexics samples patterns with guarantees, addressing flexibility and accuracy issues.
problem Pattern explosion and limited sampling accuracy with existing methods.
method Leverages SAT sampling and pattern mining algorithms to support flexible quality measures and constraints.
result Flexics provides strong guarantees on sampling accuracy while being flexible and efficient.
The paper extends circle pattern theory to include obtuse angles.
problem Existence and rigidity of circle patterns with non-obtuse exterior intersection angles.
method Topological degree theory, variational principle, Teichmüller theory, Sard's Theorem.
result The Circle Pattern Theorem is generalized to include obtuse angles.
CDPA identifies common and distinctive patterns in high-dimensional datasets.
problem Existing methods fail to capture the common pattern between coefficient matrices of shared latent factors.
method Proposes CDPA, an unsupervised learning method that incorporates both common and distinctive patterns of coefficient matrices.
result CDPA provides better characterization of common and distinctive patterns in high-dimensional datasets.
Study circle patterns on tori, linking symplectic forms and homeomorphisms.
problem Understanding circle patterns on tori and their symplectic properties.
method Investigates the space of circle patterns on closed tori with complex projective structures, embedding it into Teichmüller spaces and analyzing symplectic forms.
result Non-degeneracy of the pulled-back Weil-Petersson symplectic form and homeomorphism between circle patterns and Teichmüller spaces.
FSR efficiently discovers significant patterns with few resampled datasets.
problem Mining significant patterns in transactional data, especially subgroups.
method FSR uses resampling to bound the supremum deviation of quality statistics, providing rigorous guarantees on false discoveries.
result FSR effectively discovers significant subgroups with a small number of resampled datasets.
In this paper we study predictive pattern mining problems where the goal is to construct a predictive model based on a subset of predictive patterns in the database. Our main contribution is to introduce a novel method called safe pattern pruning (SPP) for a class of predictive pattern mining problems. The SPP method a…
Study uses CNN and LSTM to recognize stock chart patterns.
problem Recognizing stock chart patterns for trading.
method Used CNN and LSTM neural networks on historical stock data.
result Obtained accuracies for recognizing two common chart patterns.
Discovers discriminative patterns in two-class datasets.
problem Discovering patterns that occur more frequently in one class than the other.
method Proposes SSDPS algorithm with an original enumeration strategy exploiting anti-monotonicity.
result SSDPS outperforms other algorithms in terms of efficiency and pattern generation.
Method computes pattern time statistics from random sequences.
problem Extracting temporal regularities from random sequences.
method Using generating functions for Markov and Bernoulli trials.
result Pattern time statistics cover various measurements in learning.
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.
Paper discovers shifting patterns in sequence classification and proposes a method to improve performance.
problem Discriminative patterns in sequential data are time-dependent and degrade traditional classification methods.
method Proposes a novel sequence classification method using multi-instance learning and LSTM models to detect and model shifting patterns.
result Demonstrates superior sequence classification performance and detection of shifting patterns in cropland mapping and affective state recognition.
The paper integrates statistical significance and discriminative power in pattern discovery.
problem Discovering actionable patterns that meet rigorous statistical significance and discriminative power criteria.
method Integrates statistical significance and discriminative power criteria into state-of-the-art algorithms.
result Improves discriminative power and statistical significance of discovered patterns without quality deterioration.
Generative model attacks CNN on MNIST by subtly replacing input patterns.
problem Adversarial attacks on neural networks.
method Generative model that replaces input patterns with generated ones.
result Demonstrated effectiveness on MNIST dataset.
OCLEP+ detects anomalies and intrusions with short patterns.
problem Detecting anomalies and intrusions in minimal data.
method One-class Classification using Length statistics of Emerging Patterns Plus.
result Effective detection of anomalies and intrusions with minimal data.
Paper extends circle pattern theory to obtuse angles.
problem Circle patterns with obtuse angles not previously covered.
method Using topological degree theory, extends Koebe-Andreev-Thurston Theorem.
result Generalized Andreev's Theorem for obtuse dihedral angles.
Generative ConvNet models and synthesizes dynamic video patterns.
problem Modeling and synthesizing dynamic patterns in video sequences.
method A spatial-temporal generative ConvNet learns from training sequences through an iterative 'analysis by synthesis' algorithm.
result The model can synthesize realistic dynamic patterns.
Pattern sampling reduces time series classification complexity.
problem High computational complexity of exhaustive search for shapelets.
method Pattern sampling using a weighted trie to extract discriminative patterns.
result Significant reduction in computational and memory resources.
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 proves how to deform circle patterns with interstices.
problem Deforming circle patterns with interstices.
method Combining combinatorial type, exterior angles, and conformal structures.
result Complete description of circle patterns with interstices.
Symplectic forms match on circle pattern space.
problem Matching symplectic forms on circle pattern space.
method Pullback of symplectic forms to circle pattern space.
result Symplectic forms on circle pattern space coincide.
A new model for efficient sequential pattern mining without specific encoding schemes.
problem Mining relevant sequential patterns efficiently and effectively.
method Subsequence interleaving model based on probabilistic sequence database.
result Efficient inference through submodular optimization, resulting in low spuriousness and redundancy.
Study identifies clusters of EU countries with similar young mortality patterns.
problem Identify clusters of EU countries with similar mortality patterns in young population.
method Symbolic data analysis (SDA) with age, gender, and main causes of death dimensions.
result Identified clusters of EU countries with similar mortality patterns in young population.
This research improves LSTM for monthly electricity demand forecasting using pattern-based methods.
problem Forecasting mid-term monthly electricity demand with high accuracy.
method Developed a hybrid LSTM model using x-patterns and exponential smoothing.
result The hybrid model outperformed standard LSTM and classical models.
Proposes SCR-Apriori for efficient mining of SCR-patterns.
problem Mining high-quality `Set of Contrasting Rules'-pattern (SCR-pattern) efficiently.
method Integrates SCR-pattern structure into Apriori algorithm to prune search space.
result Significantly reduces computational cost compared to state-of-the-art.
The paper proposes a method to identify high-quality financial patterns using entropy.
problem Extracting reliable short-term patterns from noisy financial data.
method Entropy-assisted framework for clustering and pruning patterns.
result High-quality patterns with low local entropy and historical profitability.
We consider ``hyperideal'' circle patterns, i.e. patterns of disks appearing in the definition of the Delaunay decomposition associated to a set of disjoint disks, possibly with cone singularities at the center of those disks. Hyperideal circle patterns are associated to hyperideal hyperbolic polyhedra. We describe the…
LetSIP learns relevant patterns for user interests in data mining.
problem Redundancy in pattern mining makes it hard for analysts to identify relevant patterns.
method Combines pattern sampling with interactive data mining, using user feedback to learn sampling distribution.
result Favourable trade-offs in quality-diversity and exploitation-exploration compared to existing methods.
Model predicts Bitcoin's future movements using multimodal pattern matching.
problem Challenges in predicting Bitcoin's volatile future movements.
method Ranking similar past chart patterns given current chart information.
result Improves directional prediction of Bitcoin's future movements.
Method detects multi-timescale consumer spending patterns from receipts.
problem Understanding and managing consumer behavior in high-dimensional data.
method Non-negative tensor factorization (NTF) to extract multi-timescale expenditure patterns.
result Consumption patterns are characterized based on spending behavior over different timescales.
An empirical study of joint bivariate probability distribution of two consecutive price increments for a set of stocks at time scales ranging from one minute to thirty minutes reveals asymmetric structures with respect to the axes y=0, y=x, x=0 and y=-x. All four asymmetry patterns remarkably resemble a four-blade mill…
This paper presents a framework for exact discovery of the top-k sequential patterns under Leverage. It combines (1) a novel definition of the expected support for a sequential pattern - a concept on which most interestingness measures directly rely - with (2) SkOPUS: a new branch-and-bound algorithm for the exact disc…