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.
Discovering statistically significant patterns from databases is an important challenging problem. The main obstacle of this problem is in the difficulty of taking into account the selection bias, i.e., the bias arising from the fact that patterns are selected from extremely large number of candidates in databases. In …
A cornerstone of human statistical learning is the ability to extract temporal regularities / patterns from random sequences. Here we present a method of computing pattern time statistics with generating functions for first-order Markov trials and independent Bernoulli trials. We show that the pattern time statistics c…
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.
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.
Graph Neural Networks improve financial time series forecasting accuracy.
problem Forecasting univariate financial time series with statistical significance.
method Introducing the Time-Geometric model combining geometric and temporal patterns.
result Statistically significant improvements in forecasting accuracy through geometric patterns.
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.
Research optimizes C++ patterns for HFT, reducing latency and improving profitability.
problem Optimizing latency-critical code for high-frequency trading systems.
method Creation of a Low-Latency Programming Repository, optimisation of trading strategy, implementation of Disruptor pattern.
result Significant performance improvements in speed and profitability.
Significant pattern mining, the problem of finding itemsets that are significantly enriched in one class of objects, is statistically challenging, as the large space of candidate patterns leads to an enormous multiple testing problem. Recently, the concept of testability was proposed as one approach to correct for mult…
MCRapper efficiently computes patterns in data using Monte-Carlo Rademacher Averages.
problem Finding statistically significant patterns in data with limited samples.
method Monte-Carlo Empirical Rademacher Averages (MCERA) for poset families.
result MCRapper provides upper bounds to the discrepancy of functions, enabling efficient pattern mining.
R-GPM enables efficient graph pattern mining through user-defined relations.
problem Efficient graph pattern mining through user-defined relations.
method Parallel computing framework with MCMC sampling algorithm and optimizations.
result Efficient estimators for graph pattern statistics with up to 3-orders-of-magnitude computational cost reduction.
HYPA-DBGNN detects anomalous sequential patterns in temporal graphs.
problem Modeling temporal patterns in dynamic graphs, especially considering deviations from random shuffling.
method Two-step approach combining null model inference and neural message passing.
result HYPA-DBGNN outperforms baseline methods in static node classification tasks.
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.
While Multiple Instance (MI) data are point patterns -- sets or multi-sets of unordered points -- appropriate statistical point pattern models have not been used in MI learning. This article proposes a framework for model-based MI learning using point process theory. Likelihood functions for point pattern data derived …
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.
We derive generalized estimators for a number of spatial statistics that have been used in the analysis of spatially resolved omics data, such as Ripley's K, H and L functions, clustering index, and degree of clustering, which allow these statistics to be calculated on data modelled by arbitrary random measures (RMs). …
New statistical measures assess group separability in low-dimensional geometrical spaces.
problem Lack of statistical measures to evaluate group separability in low-dimensional geometrical spaces.
method Proposed three statistical measures (PSI-ROC, PSI-PR, PSI-P) based on Projection Separability rationale.
result Statistical-based measures outperform traditional cluster validity indices in evaluating group separability.
New method learns domain-invariant local feature patterns for unsupervised domain adaptation.
problem Performance degradation due to domain-shift in unsupervised domain adaptation.
method Jointly learns domain-invariant local feature patterns and holistic feature distributions.
result Superior performance on benchmark datasets compared to state-of-the-art methods.
New neural network can learn multiple patterns at once.
problem Learning multiple patterns simultaneously in neural networks.
method Multitasking Hebbian Network (MHN) for parallel learning of sparse data-sets.
result MHN can handle multiple patterns hierarchically or in parallel, depending on dilution.
Study high-frequency trading patterns in cryptocurrencies.
problem Understanding automated trading algorithms in cryptocurrency markets.
method Analyzes intraday trading data of cryptocurrencies, focusing on returns, volumes, and volatility.
result Provides insights into predictability of economic value in cryptocurrency markets.
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.
Bayesian method finds patterns of mutual independence in data.
problem Investigating mutual independence in statistics.
method Bayesian model comparison using Markov chain Monte Carlo (MCMC).
result Automated search for patterns of mutual independence.
Driving styles have a great influence on vehicle fuel economy, active safety, and drivability. To recognize driving styles of path-tracking behaviors for different divers, a statistical pattern-recognition method is developed to deal with the uncertainty of driving styles or characteristics based on probability density…
Point patterns are sets or multi-sets of unordered elements that can be found in numerous data sources. However, in data analysis tasks such as classification and novelty detection, appropriate statistical models for point pattern data have not received much attention. This paper proposes the modelling of point pattern…
Predict cell loads in cellular networks using statistical learning of geometric marks.
problem Predicting cell loads in cellular networks using geometric marks.
method Statistical regression model and scattering moments of random measures.
result Scattering moments can capture similar geometry information as baseline approach and improve performance.
This paper formalizes a latent variable inference problem we call {\em supervised pattern discovery}, the goal of which is to find sets of observations that belong to a single ``pattern.'' We discuss two versions of the problem and prove uniform risk bounds for both. In the first version, collections of patterns can be…
We present a novel algorithm, Westfall-Young light, for detecting patterns, such as itemsets and subgraphs, which are statistically significantly enriched in one of two classes. Our method corrects rigorously for multiple hypothesis testing and correlations between patterns through the Westfall-Young permutation proced…
New test assesses shared brain activity across different cognitive modalities.
problem Determining if different cognitive modalities use overlapping neural representations.
method Formulated a statistical hypothesis testing approach using permutation testing.
result New test (CMPT) has greater statistical power than cross-modal decoding while maintaining low Type I errors.
Paper proposes efficient algorithm for recovering sparsity pattern from deterministic missing data.
problem Recovering sparsity pattern from datasets with deterministic missing structure.
method Proposes an efficient algorithm for missing value imputation using topological property of censorship filter.
result Consistently recovers the sparsity pattern with high probability in polynomial time and logarithmic sample complexity.
The study improves the perceptron's storage capacity by optimizing variable selection.
problem Distinguishing genuine structure from random correlations in high-dimensional data.
method Replica method from statistical mechanics for optimal variable selection.
result Optimal variable selection can surpass the Cover--Gardner bound for pattern classification.
In this paper, we consider the block-sparse signals recovery problem in the context of multiple measurement vectors (MMV) with common row sparsity patterns. We develop a new method for recovery of common row sparsity MMV signals, where a pattern-coupled hierarchical Gaussian prior model is introduced to characterize bo…
Information mapping is a popular application of Multivoxel Pattern Analysis (MVPA) to fMRI. Information maps are constructed using the so called searchlight method, where the spherical multivoxel neighborhood of every voxel (i.e., a searchlight) in the brain is evaluated for the presence of task-relevant response patte…
Bayesian method clusters, discovers patterns, detects anomalies in diverse time series.
problem Analyzing and describing heterogeneous time series data.
method Interpretable kernel embedding for heterogeneous time series, leveraging intermediate results.
result Practical utility in clustering, pattern discovery, and anomaly detection.
Probabilistic graphical models (PGMs) have become a popular tool for computational analysis of biological data in a variety of domains. But, what exactly are they and how do they work? How can we use PGMs to discover patterns that are biologically relevant? And to what extent can PGMs help us formulate new hypotheses t…
Study measures irreversibility in crypto trends using Kullback-Leibler divergence.
problem Assessing irreversibility in cryptocurrency trends.
method Defined irreversibility index using Kullback-Leibler divergence between uptrend and downtrend distributions.
result Strong irreversibility in all analyzed cryptocurrencies, with trends evolving over time.
Discovering statistical structure from links is a fundamental problem in the analysis of social networks. Choosing a misspecified model, or equivalently, an incorrect inference algorithm will result in an invalid analysis or even falsely uncover patterns that are in fact artifacts of the model. This work focuses on uni…
Detects backdoors in trained models without poisoned training data.
problem Detecting backdoors in DNNs trained without access to the poisoned training set.
method Proposes a novel detector using the maximum achievable misclassification fraction (MAMF) statistic.
result Detects backdoors and infers source and target classes.
Cryptocurrencies show varying levels of efficiency over time, forming clusters with younger ones mimicking older ones.
problem Determining the efficiency of cryptocurrencies over time.
method Permutation entropy and statistical complexity over sliding time-windows of price log returns.
result 37% of cryptocurrencies are efficient over 80% of the time, while 20% are efficient in less than 20% of the time.
This study reveals statistical patterns in ERC20 token transactions on Ethereum blockchain.
problem Understanding transactional dynamics in decentralized systems.
method Examined over 44 million ERC20 token transfers, categorized by address type (EOA or SC), and analyzed using scaling laws.
result EOA-driven transactions exhibit consistent statistical behavior, while SC-driven activity displays sublinear scaling and bursty activity.
Deep learning uncovers patterns between knot types.
problem Discovering connections between combinatorial and hyperbolic knot invariants.
method Statistical approach using linear regression and deep learning.
result Revealed empirical connections between knot types.
New method for MTL with varying sparsity patterns across tasks.
problem Jointly training multiple linear models with differing sparsity patterns.
method Mixed-integer programming formulation and scalable algorithms.
result Our methods leverage shared support information to improve variable selection.
Quantitative CT predicts ILD patterns and prognosis.
problem Diagnosing and predicting prognosis of fibrosing ILD patterns.
method High-resolution CT texture features, TM model for classification and survival analysis.
result TM model outperforms histogram-based model in distinguishing UIP from non-UIP patterns and allows for survival group partitioning.
Minimal DAMs can recognize patterns in high noise, even with minimal data.
problem Pattern recognition in high noise conditions with limited data.
method Interpolating between DAMs and spin glasses, using minimal dense associative networks and extremizing quenched free-energy.
result Minimal DAMs can correctly recognize patterns even when the signal is very weak and noise is high.
EMODM detects abnormal patterns in complex systems.
problem Detecting abnormal patterns in complex systems.
method Probabilistic models and statistical algorithms.
result EMODM detects abnormal patterns in real-time raw data.
Identifying anomalous patterns in real-world data is essential for understanding where, when, and how systems deviate from their expected dynamics. Yet methods that separately consider the anomalousness of each individual data point have low detection power for subtle, emerging irregularities. Additionally, recent dete…
Audio fingerprinting, also named as audio hashing, has been well-known as a powerful technique to perform audio identification and synchronization. It basically involves two major steps: fingerprint (voice pattern) design and matching search. While the first step concerns the derivation of a robust and compact audio si…
A new distributed algorithm for fitting sparse additive models with feature division and decorrelation.
problem Fitting high-dimensional sparse additive models efficiently and accurately.
method Divide, decorrelate, and conquer approach.
result Effective and efficient recovery of sparsity patterns and statistical inference for each component.
Randomization helps verify if data mining results are due to inherent patterns.
problem Verify if data mining results are due to inherent patterns or coincidental findings.
method Metropolis sampling based on local swaps to randomize data while preserving discovered patterns.
result Randomized data often reveals that clustering results imply frequent pattern discovery.