Sentinel analyzes Twitter streams in real-time with high accuracy.
problem Real-time analytics on fast data streams with minimal delay and high accuracy.
method Distributed system Sentinel using Apache Storm and SpaceSaving for summary storage.
result Sentinel achieves high analytical accuracy on Twitter streams.
Framework uses DL and STORM priors for FBU cardiac MRI reconstruction.
problem Reconstructing FBU cardiac MRI from undersampled data.
method Model-based reconstruction with DL and STORM priors.
result Demonstrates potential for accelerating FBU cardiac MRI.
Spark implementation for distributed function minimization.
problem Optimization of functions in distributed computing environments.
method Gradient and quasi-Newton methods on Apache Spark.
result Scalable solution for classification and regression problems.
STORM-PG uses momentum for faster policy gradient updates.
problem Improving policy gradient methods for reinforcement learning.
method Introduces STORM-PG, a SARAH-based algorithm with exponential moving average.
result Achieves O(1/ε3) sample complexity, matching best-known rate. Improved neural network predicts tropical storm trajectories and Bayesian intervals.
problem Accurately predicting the trajectories of tropical storms to prevent damage.
method Developed an improved RNN model with dropout to predict Bayesian intervals.
result Neural network dropout values significantly affect prediction accuracy and intervals.
STORM enables edge computing for empirical risk minimization.
problem Training models on edge devices for streaming data.
method Online sketching for empirical risk minimization.
result STORM can estimate least-squares objective accurately.
Study examines stock price reactions to Texas winter storm power outages.
problem Impact of natural disasters on stock market values.
method Used four benchmark models to measure abnormal returns.
result Firms experienced significant stock price drops after the Texas winter storm.
New method clusters hydrological and sediment data for storm event analysis.
problem Analyzing storm events for water quality constituents like turbidity.
method Multivariate time series clustering of river discharge and sediment data.
result Clusters differ from 2-D hysteresis loop classifications.
Unified machine learning framework for deep learning and web services.
problem Deep learning and web service challenges.
method MMLSpark expands Spark to deep learning, micro-service orchestration, etc.
result Unified API for deep learning and web services.
A new framework combines Spark and deep learning for big data analysis.
problem Efficient big data analysis for AI problems.
method Combines Apache Spark's distributed computing with deep learning's MLP architecture.
result Empirical analysis shows the new framework outperforms traditional methods.
GluonCV and GluonNLP simplify deep learning for CV and NLP.
problem Lack of easy-to-use deep learning tools for CV and NLP.
method Developed modular APIs and pre-trained models for rapid prototyping.
result Facilitates rapid prototyping and reproducible research in CV and NLP.
New algorithm STORM reduces variance in non-convex optimization without large batches.
problem Improving convergence in non-convex optimization problems.
method Adaptive learning rates and momentum-based variance reduction.
result Achieves optimal convergence rate without batch sizes or knowledge of variance.
ST-STORM separates semantic and appearance features for robust representation learning.
problem Traditional SSL methods fail to capture appearance cues in critical applications.
method Hybrid SSL framework with two latent streams, Content and Style, disentangled through gating mechanisms.
result The Style branch effectively isolates complex appearance phenomena without degrading semantic performance.
A framework for training deep networks in Apache Spark.
problem Expensive and time-consuming training of deep networks with large data and model parameters.
method Data and model-parallel, distributed training over Apache Spark clusters.
result Significant speedup and scalability for deep network training.
DPASF stream preprocesses Big Data streams efficiently.
problem Efficient preprocessing of streaming Big Data.
method Implemented six preprocessing algorithms in Apache Flink.
result Preprocessing improves data accuracy in streaming Big Data.
Apache Spark is a popular open-source platform for large-scale data processing that is well-suited for iterative machine learning tasks. In this paper we present MLlib, Spark's open-source distributed machine learning library. MLlib provides efficient functionality for a wide range of learning settings and includes sev…
Deep learning predicts tropical cyclone tracks efficiently.
problem Forecasting tropical cyclone trajectories with high precision and speed.
method Fused neural network model using past trajectory data and reanalysis atmospheric images.
result Deep learning can provide valuable and complementary predictions for tropical cyclone tracks.
Score matching method improves density estimation for truncated data on manifolds.
problem Density estimation for truncated data on manifolds with intractable normalising constant.
method Truncated score matching extended to Riemannian manifolds with boundary.
result Score matching estimator approximates true parameter values with low error.
Single-pass algorithm for low-rank approximation of matrix products.
problem Efficiently computing low-rank approximations of matrix products.
method A single pass algorithm that retains additional summary information about matrices A and B.
result Comparable spectral norm guarantees to existing two-pass methods with improved performance.
Kerckhoff and Storm conjectured that compact hyperbolic n-orbifolds with totally geodesic boundary are infinitesimally rigid when n>3. This paper verifies this conjecture for a specific example based on the 4-dimensional hyperbolic 120-cell.
FCNv2 robustness tested under noise and random initial conditions.
problem Assessing AI weather forecasting model robustness to input noise.
method Two experiments with varying noise levels and random initial conditions.
result FCNv2 preserves hurricane features under low to moderate noise, but underestimates intensity and persistence.
Improved sample complexity for actor-critic algorithms in MDPs.
problem Achieving optimal policies with limited data in reinforcement learning.
method Single-timescale actor-critic with STORM (STOchastic Recursive Momentum) and a sample buffer.
result Optimal sample complexity of O(ε−2) for ε-optimal policies. PyODDS automates outlier detection for new data sources.
problem Manual outlier detection is inefficient and domain-specific.
method Automated end-to-end system with Apache Spark and database support.
result PyODDS optimizes outlier detection pipelines automatically.
A new algorithm predicts periodic time series data efficiently in cloud environments.
problem Efficiently identifying and predicting periodic patterns in large-scale time-series data.
method Proposes a Periodicity-based Parallel Time Series Prediction (PPTSP) algorithm using TSDCA, MTSPPR, and PTSP methods.
result Significant improvements in prediction accuracy and performance compared to existing algorithms.
This paper is a follow-up to our joint paper with I. Agol, P. Storm and K. Whyte "Finiteness of arithmetic hyperbolic reflection groups". The main purpose is to investigate the effective side of the method developed there and its possible application to the problem of classification of arithmetic hyperbolic reflection …
Neural network predicts stock prices using technical indicators.
problem Predicting stock prices for optimal trading.
method Converted financial data into buy-sell-hold signals, trained MLP ANN model with Apache Spark.
result Neural network model performs comparably to Buy and Hold strategy.
Study examines equity in post-Snow Uri recovery, finds disparities.
problem Disproportionate impacts on vulnerable populations during recovery.
method County and census tract level data analysis, satellite imagery, statistical procedures.
result Negative associations between non-Hispanic whites and outages, positive associations with certain demographic variables.
Study character varieties of a Coxeter group in hyperbolic and Anti-de Sitter spaces.
problem Characterize the geometric transitions of a Coxeter group's holonomy representations.
method Analysis of rigidity properties and character varieties in hyperbolic and Anti-de Sitter spaces.
result Description of singularity at the collapse of a right-angled cuboctahedron.
BreachRadar detects points-of-compromise in bank transactions to prevent fraud.
problem Detecting and preventing bank transaction fraud caused by data breaches.
method A distributed alternating algorithm that assigns probabilities to different locations being compromised.
result BreachRadar achieves over 90% precision and recall in detecting compromised cards.
DiCFS improves CFS for big data, handling large datasets efficiently.
problem Efficient feature selection for large datasets in big data.
method Distributed CFS (DiCFS) using Apache Spark for scalability and parallel processing.
result DiCFS outperforms WEKA in terms of time-efficiency and scalability.
Study optimizes climate adaptation strategies for NYC.
problem Catastrophic damages from extreme weather in NYC.
method Real options analysis and extreme value theory.
result Optimal adaptation pathways identified for NYC.
Built the smallest non-commensurable hyperbolic 4-manifold.
problem Finding the smallest non-commensurable hyperbolic 4-manifold.
method Gluing copies of a polytope to build the manifold.
result The constructed manifold has twice the minimal volume.
Novel approach uses ENN for UQ in gust predictions, reducing RMSE and improving confidence.
problem Reducing bias and uncertainty in wind gust predictions.
method Evidential Neural Network (ENN) with Explainable AI.
result 47% reduction in RMSE, 95% coverage of observed gusts at 179 out of 266 stations.
Counterexamples found for volume entropy conjecture in hyperbolic 3-manifolds.
problem Volume entropy conjecture in hyperbolic 3-manifolds.
method Construction of metrics with specific curvature properties.
result Found counterexamples to the volume entropy conjecture.
JAMPI improves matrix multiplication in Spark, boosting performance by up to 24%.
problem Efficiently performing matrix multiplication in Spark.
method Combining asynchronous network IO, auto-vectorization, and barrier execution mode.
result Up to 24% performance increase in distributed matrix multiplication.
Study of four-dimensional hyperbolic Dehn filling.
problem Understanding four-dimensional analogues of Thurston's hyperbolic Dehn filling.
method Construction of an analytic path of complete, finite-volume cone four-manifolds interpolating between two hyperbolic four-manifolds.
result Construction of a path of complete, finite-volume cone four-manifolds that interpolates between two hyperbolic four-manifolds.
GraSPy simplifies graph analysis in Python.
problem Analyzing and understanding graphs.
method Scikit-learn compliant API for statistical inference and machine learning.
result Flexible algorithms for graph statistics.
DADApy analyzes high-dimensional data manifolds in Python.
problem Analyzing complex, high-dimensional data.
method Estimating intrinsic dimension, density, clustering, comparing distance metrics.
result Effective analysis of data manifolds in Python.
We prove that for any closed surface of genus at least four, and any punctured surface of genus at least two, the space of ending laminations is connected. A theorem of E. Klarreich implies that this space is homeomorphic to the Gromov boundary of the complex of curves. It follows that the boundary of the complex of cu…
Bitcoin treasury companies leverage stock to grow, using advanced statistical methods.
problem Leverage in Bitcoin treasury companies.
method Extended Kelly criterion to incorporate uncertainty.
result Advanced statistical methods can better model leverage in Bitcoin treasury companies.
The Hessian of the renormalized volume of geometrically finite hyperbolic 3-manifolds without rank-1 cusps, computed at the hyperbolic metric g with totally geodesic boundary of the convex core, is shown to be a strictly positive bilinear form on the tangent space to Teichmüller space. The metric g is known fro…
TailedTS dataset benchmarks heavy-tailed time series forecasting and periodicity quantification.
problem Benchmarking robustness of time series models under heavy-tailed distributions.
method Derived from Wikipedia page views, introduces periodicity quantification and robust loss functions.
result Standard Gaussian models degrade on high-volume page categories, while robust alternatives perform consistently.
Following the previous work of Nikulin and Agol, Belolipetsky, Storm, and Whyte it is known that there exist only finitely many (totally real) number fields that can serve as fields of definition of arithmetic hyperbolic reflection groups. We prove a new bound on the degree nk of these fields in dimension 3: nk d…
Geometrically transitions hyperbolic to anti-de Sitter structures in 4D.
problem Creating geometric transitions between hyperbolic and anti-de Sitter structures in 4D.
method Deformation of hyperbolic 4-polytopes and joining with anti-de Sitter polytopes.
result Existence of geometric transition examples in 4D.
We survey recent work on the dynamics of the outer automorphism group of a word hyperbolic group on spaces of (conjugacy classes of) representations ofthe group into a semi-simple Lie group G. All these results are motivated by the fact that the mapping class group of a closed surface acts properly discontinuously on t…
torchsom simplifies SOMs in PyTorch with GPU acceleration and scikit-learn API.
problem Efficient implementation and usability of SOMs in PyTorch.
method PyTorch backend, GPU acceleration, scikit-learn API, 90% test coverage.
result Ease of use and scalability for SOMs in PyTorch.
2L-FUSE enhances feature sparsity through kernel learning.
problem Sparsity and feature selection in regression tasks.
method 2-Layered kernel machines for learning a shape matrix and feature direction identification.
result Minimal yet informative feature sets are identified without losing predictive performance.
Study uses deep neural networks for flood forecasting.
problem Accurate flood predictions everywhere.
method Artificial deep neural networks for time-series forecasting.
result Neural networks improve flood predictions.