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.
PPES selects batches of points for parallel optimization of expensive functions.
problem Optimizing expensive, black-box functions efficiently.
method Parallel Predictive Entropy Search (PPES) for batch Bayesian optimization.
result PPES outperforms greedy batch selection in optimization performance.
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.
ANN predicts methane heat transfer in rocket engines efficiently.
problem Accurate heat transfer prediction for supercritical methane in rocket engine cooling channels.
method Artificial neural networks trained on CFD simulation data.
result ANN model predicts maximum wall temperature with convincing precision.
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.
ROCKET speeds up time series classification without sacrificing accuracy.
problem High computational complexity and intractability of existing time series classification methods.
method Simple linear classifiers using random convolutional kernels.
result Achieves state-of-the-art accuracy with significantly reduced computational expense.
A new method trains lightweight neural networks using a more complex 'booster' network.
problem Training high-performing neural networks for real-time tasks like CTR prediction.
method Rocket launching framework: a cumbersome net guides the training of a lightweight net.
result Lightweight models achieve performance previously only possible with complex models.
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 …
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.
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.
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.
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.
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.
This paper reflects on teaching Data Science to diverse students.
problem Adapting learning practices to diverse backgrounds in Data Science.
method Summarizes experiences from teaching a postgraduate Data Science module.
result Draws lessons relevant to teaching Data Science.
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.
Economics debated as a natural science.
problem Whether Economics should be considered a natural science.
method Discussion of the debate in a special issue of Euro. Phys. J. Special Topic.
result Economics remains a social science, not a natural science.
Data science teams collaborate extensively, using various tools and stakeholders.
problem Lack of understanding in how data science workers collaborate in practice.
method Conducted an online survey with 183 data science workers.
result Data science teams are highly collaborative and use multiple tools and stakeholders.
Data science redefines causal inference from observational data, classifying tasks into description, prediction, and counterfactual prediction.
problem Widespread misunderstandings about data science's role in causal inference from observational data.
method Organizing data science tasks into three classes: Description, prediction, and counterfactual prediction (including causal inference).
result The necessity of subject-matter expert knowledge for causal analyses in data science.
Machine learning improves wildfire science and management, but requires expert knowledge.
problem Improving wildfire science and management through AI.
method Review of popular ML approaches and their application in six wildfire science domains.
result Opportunities exist for applying more advanced ML methods in wildfire science.
ML methods improve planetary science data analysis.
problem Insufficient use of ML in planetary science.
method Ten recommendations for integrating ML in planetary science.
result Expanding planetary science insights from large datasets.
TGDS integrates scientific theory into data science for better model interpretation and discovery.
problem Limited applicability of data science models in scientific problems involving complex phenomena.
method Integrating scientific theory into data science models to improve model effectiveness and interpretability.
result TGDS aims to advance scientific understanding by discovering novel insights.
Baccalaureate institutions seek to integrate statistics into data science.
problem Graduate statisticians losing ground in data science.
method Reviewing historical contributions of statisticians and calling for integration.
result Baccalaureate institutions need to integrate statistics into data science education.
Foundation models alter medical data science workflow, challenging veridical data science principles.
problem Foundation models disrupt traditional data science practices in medicine.
method Critically examined the medical foundation model lifecycle and its deviation from veridical data science principles.
result Foundation models challenge veridical data science principles of predictability, computability, and stability.
Defines data science as a natural ecosystem with challenges and missions.
problem Challenges and missions in data science due to 5D complexities and data life cycle phases.
method Systemic and data-centric view of data science as a fusion of data universe and its challenges, formalizing a general-purpose architecture.
result Essential data science as a natural ecosystem integrating specific disciplines and high-impact applications.
Robot learns to imitate human interactions through deep learning.
problem Teaching robots to coordinate actions with human partners.
method Deep learning framework for motion embedding, prediction, and trajectory generation.
result Importance of predictive and adaptive components for successful imitation.
Data science enhances knot theory by analyzing invariant relations.
problem Understanding the complex relations between knot invariants.
method Topological data analysis applied to knot theory.
result New insights into long-standing conjectures about knots.
Article calls for data scientists to join ethics debate.
problem Impact of data science on society and ethics.
method Systematic approach from CNIL, established ethical guidelines.
result Data science requires professional ethics to build trust.
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.
Complexity science integrates natural and social sciences to understand socio-economic dynamics.
problem Chronic poverty and lack of understanding of societal development.
method Use of tools from natural sciences to analyze socio-economic factors.
result Integration of natural and social sciences reveals synergistic mechanisms driving societal evolution.
This paper explores data science applications in economics using a taxonomy of models and hybrid models showing higher accuracy.
problem Investigating data science applications in economics.
method Systematic literature review using Prisma method.
result Hybrid models showed higher prediction accuracy than other algorithms.
Complexity science offers new insights into macroeconomics and finance.
problem Insufficient understanding of economic and financial phenomena.
method Adopting complexity science to better understand complex systems.
result Complex system characteristics can benefit financial analysts, regulators, and policymakers.
Simulation-based inference tackles complex inverse problems in science.
problem Challenging inverse problems in complex simulations.
method Review and identification of forces driving the field.
result Expanding the audience to appreciate the impact on science.
Proposes a methodology to improve data science ROI by addressing key business questions.
problem Companies often fail to maximize data science value, focusing on basic analysis.
method Categorizes and answers 'The Big Three' questions using data science methods.
result Shows how to apply the methodology to real business use cases.
Data science reveals unexpected tax manipulation patterns in Italian regions.
problem Detecting tax income manipulation in Italian municipalities.
method Adopting Benford's first digit law to analyze tax data.
result Marked disparities found in tax distributions across regions, contrary to expectations.
This study analyzes data science vocabulary changes over 13 years.
problem Understanding evolution of data science terms over time.
method Exploratory Data Analysis, Latent Semantic Analysis, Latent Dirichlet Analysis, N-grams Analysis.
result Identified new vocabulary and its incorporation into scientific literature.
Economies are complex man-made systems where organisms and markets interact according to motivations and principles not entirely understood yet. The increasing dissatisfaction with the postulates of traditional economics i.e. perfectly rational agents, interacting through efficient markets in the search of equilibrium,…
Kan extensions help in data science extrapolation and learning.
problem Generalizing functions over larger sets in data science.
method Kan extensions in category theory applied to data science problems.
result Kan extensions can be used to derive classification and clustering algorithms.
SCN learns and compensates for data bias in citizen science projects.
problem Data bias in citizen science datasets.
method End-to-end learning scheme that learns and compensates for data shift.
result SCN outperforms models that ignore data bias and handles massive high-dimensional data.
Crowdsourced science questions improve exam accuracy.
problem Generating high-quality, domain-specific multiple choice questions is challenging.
method Leveraging a corpus and existing questions, the method suggests document selection and answer distractors.
result Crowdsourced questions are indistinguishable from original questions and improve exam accuracy.
Paper tackles natural science exam questions, improving over previous systems.
problem Hard natural science exam questions requiring advanced logic reasoning.
method Constructs contextual knowledge graphs for questions and supporting sentences, learns to reason with neural embeddings.
result Model outperforms previous state-of-the-art QA systems on the ARC Challenge Set.
Machine learning's data-centric philosophy conflicts with natural sciences' standards.
problem Conflict between machine learning's ontology and epistemology and natural sciences' practices.
method Identifying and analyzing contexts where ML can be beneficial or harmful in natural sciences.
result ML can enhance trustworthiness in causal inference but introduces biases in emulation and labeling.
New geometric methods improve optimization and data science problems.
problem Improving optimization and data science problems.
method Geometric tools for high-dimensional optimization and statistical data science.
result New algorithms and statistical guarantees for optimization and data science.
Computer science scans LLMs to understand and manipulate their economic forecasts.
problem Understanding and controlling the reasoning of large language models in economics.
method Brain scanning techniques applied to LLMs to identify and manipulate underlying concepts.
result LLMs can be steered to generate forecasts with specific biases, allowing for correction or simulation.
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.