Improved ROCKET algorithm for brain activity classification.
problem Classifying multivariate time series data from brain activity.
method Detach-Rocket Ensemble, leveraging pruning and ensemble methods.
result Competitive classification accuracy and interpretable channel relevance.
We study the Bondi-Sachs rockets with nonzero cosmological constant. We observe that the acceleration of the systems arises naturally in the asymptotic symmetries of (anti-) de Sitter spacetimes. Assuming the validity of the concepts of energy and mass previously introduced in asymptotically flat spacetimes, we find th…
Rocket algorithm classifies time-series data efficiently using random projections and natural sparsity.
problem Time-series classification challenges in diverse fields.
method Random convolutional kernels, non-linear transformation, compressed sensing framework.
result Rocket algorithm preserves discriminative patterns in time-series data and expresses inherent sparsity.
MINIROCKET speeds up time series classification by 75x.
problem High computational complexity in time series classification.
method Random convolutional kernels and linear classifier.
result Up to 75 times faster on larger datasets.
Paper uses deep reinforcement learning for better control of rocket engines during start-up phases.
problem Lack of optimal control during transient phases of liquid rocket engines.
method Deep reinforcement learning approach for optimal control of a gas-generator engine's continuous start-up phase.
result Deep reinforcement learning controller achieves highest performance and minimal computational effort.
Fusion framework improves time series classification across different datasets.
problem Kernel-based methods like Rocket perform inconsistently across datasets.
method Fusion-3 framework that adaptively fuses three representations (Rocket, SAX, SFA) based on dataset properties.
result Fusion-3 framework yields small but consistent average improvements over Rocket on 113 UCR datasets.
The paper proposes an ensemble of convolution-based methods for fault detection in gearboxes.
problem Fault detection in planetary gearboxes using vibration signals.
method Ensemble of three convolution kernel-based methods (ROCKET, 1D CNN with ResNet, FCN).
result Outperforms other approaches with over 98.8% accuracy.
We reinvestigate the "rockets and feathers" effect between retail gasoline and crude oil prices in a new framework of fractional integration, long-term memory and borderline (non-)stationarity. The most frequently used error-correction model is examined in detail and we find that the prices return to their equilibrium …
Models applied on real time response task, like click-through rate (CTR) prediction model, require high accuracy and rigorous response time. Therefore, top-performing deep models of high depth and complexity are not well suited for these applications with the limitations on the inference time. In order to further impro…
Paper studies Time Series Extrinsic Regression, improving on existing methods.
problem Learning relationship between time series and continuous scalar variables.
method Motivated and studied TSER, benchmarked TSC and ML algorithms.
result Rocket algorithm achieves highest overall accuracy in TSER.
Study on stability of network flow shrinkers with findings on instability of specific shapes.
problem Stability of regular shrinkers in network flow.
method Analysis of self-similarly shrinking solutions called regular shrinkers.
result All regular shrinkers with two or more enclosed regions can be perturbed away. Specific shapes like 4-ray star, 5-ray star, fish, and rocket are unstable among those with one enclosed region.
Most methods for time series classification that attain state-of-the-art accuracy have high computational complexity, requiring significant training time even for smaller datasets, and are intractable for larger datasets. Additionally, many existing methods focus on a single type of feature such as shape or frequency. …
We develop parallel predictive entropy search (PPES), a novel algorithm for Bayesian optimization of expensive black-box objective functions. At each iteration, PPES aims to select a batch of points which will maximize the information gain about the global maximizer of the objective. Well known strategies exist for sug…
A cased-based reasoning method predicts rare events on strategic sites using satellite imagery.
problem Manual prediction of rare events on strategic sites is impractical due to large datasets.
method Case-based reasoning approach incorporating expert knowledge for irregular time series and small datasets.
result The method significantly outperforms random selection on challenging applications.
Methane is considered being a good choice as a propellant for future reusable launch systems. However, the heat transfer prediction for supercritical methane flowing in cooling channels of a regeneratively cooled combustion chamber is challenging. Because accurate heat transfer predictions are essential to design relia…
iMOCA optimizes multiple objectives with continuous approximations for resource efficiency.
problem Optimizing multiple objectives with continuous function approximations that balance accuracy and evaluation cost.
method Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations (iMOCA) selects input and function approximations to maximize information gain per unit cost.
result iMOCA significantly improves over existing single-fidelity methods in approximating the optimal Pareto set.
Study benchmarks TSC algorithms in distinguishing diffusions using the likelihood ratio test.
problem Benchmarking optimality of TSC algorithms in distinguishing diffusion processes.
method Proposes to benchmark TSC algorithms using the likelihood ratio test (LRT).
result LRT benchmarks are computationally efficient and can be applied to various time series types.
To coordinate actions with an interaction partner requires a constant exchange of sensorimotor signals. Humans acquire these skills in infancy and early childhood mostly by imitation learning and active engagement with a skilled partner. They require the ability to predict and adapt to one's partner during an interacti…
Generative Bayesian Computation improves surrogates for expensive simulations.
problem Limitations of Gaussian process surrogates in handling complex, non-stationary data.
method Generative Bayesian Computation via Implicit Quantile Networks (IQNs).
result Generative Bayesian Computation outperforms traditional Gaussian process methods across various benchmarks.
The present study provides a comparative assessment of non-invasive sensors as means of estimating the microbial contamination and time-on-shelf (i.e. storage time) of leafy green vegetables, using a novel unified spectra analysis workflow. Two fresh ready-to-eat green salads were used in the context of this study for …
Bayesian deep learning predicts satellite collisions.
problem Space debris poses planetary risk.
method Bayesian deep learning with LSTM networks.
result Predicts conjunction event evolution with uncertainties.
Generative model uses random convolutional features to create financial time series.
problem Generating realistic financial time series with limited data and avoiding overfitting.
method Train generators by matching random convolutional features of real and generated time series, using SOCK (SOft Competing Kernels) feature map.
result Generators trained with random SOCK features outperform baselines across various financial datasets.
New method constructs geometric flat outputs for robotic systems using symmetry.
problem Finding flat outputs for arbitrary robotic systems remains an open question.
method Employing symmetry directly to construct a flat output.
result Demonstrated geometric flat outputs for various robotic systems.
Study assesses data-driven and physics-based SGS models for transcritical combustion.
problem Challenges in simulating high-pressure combustion systems due to complex fluid behaviors.
method Comparison of physics-based and random forest machine learning models in turbulent transcritical non-premixed flames.
result Random forest models can effectively model subgrid stresses, providing insight into their formation.