This paper compares supervised learning methods for identifying phytoplankton species.
problem Identifying phytoplankton species due to their variability and ambiguity.
method Proposed a new features type from raw signals and compared various classifiers.
result Random Forest using the proposed features gave the best classification results with 98.24% accuracy.
This review assesses statistical and machine learning methods for coral bleaching.
problem Coral bleaching due to rising sea temperatures and environmental factors.
method Statistical and machine learning models for predicting and analyzing coral bleaching.
result Statistical and machine learning methods are crucial for effective reef management.
Deep learning detects schools of herring from echograms.
problem Manual interpretation of echograms is time-consuming and inconsistent.
method Deep learning framework for automatic detection.
result Deep learning outperforms traditional machine learning.
The paper improves marine buoy placement to detect ships robustly against disruptions.
problem Detecting fishing vessels in the presence of natural and man-made disruptions.
method Formulated as a clustering problem, used dropout k-means and k-median to improve buoy placement robustness.
result Improved ship detection probability with dropout k-means compared to classic methods.
Deep learning model detects and classifies marine microfossils.
problem Manual identification of microfossils is time-consuming and error-prone.
method Transfer learning from ImageNet dataset to classify foraminifera.
result Proposed model achieves high accuracy on foraminifera classification.
A new method for marine robot navigation using sparse Gaussian Process TD learning.
problem Data efficiency and online prediction challenges in marine robot navigation.
method Sparse Gaussian Process Temporal Difference (SPGP-TD) learning with a sparse approximation.
result SPGP-SARSA outperforms state-of-the-art sparse methods and replicates exact prediction quality.
ConvNet classifies whale vocalizations and ambient noise in acoustic recordings.
problem Automated detection and classification of marine mammal vocalizations in acoustic recordings.
method Convolutional Neural Network with a novel acoustic representation.
result Classifier accurately detects and classifies whale vocalizations and ambient noise.
Framework improves marine mammal monitoring in noisy underwater environments.
problem Underwater bioacoustic monitoring challenges due to overlapping calls and variable noise.
method Multi-step attention-guided framework with segmentation and mid-level fusion.
result Improved signal discrimination, reduced false positives, reliable representations.
A Deep Zero-Inflated Model for Detecting North Atlantic Right Whale Presence
problem Balancing marine conservation and blue economy management
method Deep Zero-Inflated Bernoulli model
result Improved model adequacy and predictive performance
New method predicts VIV in 3D currents for marine risers.
problem Uncertainty in predicting VIV due to 3D current effects.
method Data-driven modeling using random forest regression.
result Data-driven method outperforms traditional models in 3D current conditions.
Machine learning models predict bluebottles' presence on beaches, addressing class imbalance and unreliable absence data.
problem Predicting bluebottles' presence on beaches with machine learning, tackling class imbalance and unreliable absence data.
method Used Multilayer Perceptron, Random Forest, and XGBoost models; employed data augmentation techniques like SMOTE, Random Undersampling, and Synthetic Negative Approach.
result Random Forests combined with Synthetic Negative Approach provided the best predictive model, identifying wind direction as a key factor.
The paper analyzes sectoral diversity in startup ecosystems in Europe and the USA.
problem Investigating sectoral diversity in startup ecosystems.
method Analysis of 20+ startup ecosystems in Europe and the USA using a new visualization tool and numerical simulations.
result Emerging diversity of startup ecosystems can be explained by a preferential attachment model based on sectoral funding.
Hybrid AI methods optimize renewable energy and facilities management.
problem Optimizing renewable energy resources and facilities management in oceanic engineering and airports.
method Hybrid neuro-evolutionary algorithms and machine learning.
result Proposed methodologies for predicting key parameters in renewable energy and facilities management.
Completes results on complex braid group parabolic subgroups.
problem Proves properties of complex braid group parabolic subgroups.
method Uses Garside groupoid structure of B(G31) to extend results.
result Proves main theorems for B(G31) parabolic subgroups.
LightAutoML automates ML for a large financial services company.
problem Building high-quality ML models for a complex financial ecosystem.
method Developed an AutoML system tailored to a large European financial services company's unique requirements.
result LightAutoML outperformed experienced data scientists and other open-source solutions.
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.
For a non-orientable closed surface standardly embedded in the 4-sphere, a diffeomorphism over this surface is extendable if and only if this diffeomorphism preserves the Guillou-Marin quadratic form of this embedded surface.
YOLOv3 detects ships in real-time with high accuracy.
problem Real-time target detection in maritime scenarios.
method YOLOv3 model trained on a large dataset of marine vessels.
result Average Precision up to 96% for IoU of 0.5.
Novel hybrid model combines image feature discovery and topic modeling for marine robot mission data.
problem Insufficient analysis of robot mission data due to data complexity and lack of suitable models.
method Convolutional autoencoders for feature discovery in images and Bayesian nonparametric topic models for thematic structure.
result Hybrid model outperforms state-of-the-art approaches in unsupervised seafloor terrain characterization.
This study uses RNNs to diagnose faults in underwater thrusters.
problem Fault detection and diagnosis of underwater thrusters in harsh marine environments.
method Data-driven fault detection using Recurrent Neural Networks (RNNs) with empirical data.
result RNNs outperform residual-based feature extraction for fault classification.
FLUXtrapolation benchmarks machine learning for extrapolating ecosystem fluxes under distribution shifts.
problem Machine learning challenges in extrapolating ecosystem fluxes under distribution shifts.
method Defined temporal, spatial, and temperature-based extrapolation scenarios; evaluated performance across domains, temporal aggregations, and tail errors.
result Baselines perform similarly under median hourly RMSE but differ under tail-focused and multi-scale evaluations.
Proposes PFM to analyze journalist behaviors in online ecosystems.
problem Understanding the inner workings of journalist interactions and feedback loops.
method Bayesian Poisson Factorization Machine (PFM) with tensor-like structure, batch and stochastic variational inference.
result Deep architecture of PFM layers can be used for data exploration of latent behaviors.
Debt-financed collateral in DeFi increases stability risks.
problem Financial stability risks in DeFi ecosystems due to debt-financed collateral.
method Categorization and classification algorithm to measure debt-financed collateral.
result Wide-spread use of stablecoins as debt-financed collateral increases financial stability risks.
Measures regulatory activity and complexity over 23 years.
problem Lack of large-scale, longitudinal data on regulatory activity and complexity.
method Analyzed over 4.5 million references to U.S. Federal Acts and Agencies in Form 10-Ks.
result Regulatory energy per filing (temperature) and diversity have increased over 23 years.
Extends Shifts dataset for MS lesion segmentation and marine vessel power estimation.
problem Distributional shift in training and deployment data for ML models.
method Develops new datasets for high-risk industrial applications.
result Demonstrates robustness and uncertainty estimation in new industrial tasks.
Symmetry reduction simplifies nonlinear trajectory planning for ships.
problem Nonlinear trajectory planning for under-actuated ships.
method Symmetry reduction and reconstruction in contact geometry.
result Explicit, exact solutions to trajectory planning problems.
P. Arnoux and A. Marin showed that any triangulation of RPn contains more than 2(n+1)(n+2) vertices if n≥3. We construct some natural triangulation of RPn with 2n(n+5)−1 vertices for all n≥3. Previously, it was known that RPn has Z2n-e…
Stablecoins are reshaping global monetary systems, offering hybrid structures with public and private monies.
problem The evolution of stablecoins from crypto innovation to a global monetary component.
method Econometric analysis and hybrid system design modeling.
result Stablecoins maintain strong peg stability, and a hybrid system design ensures financial resilience.
The paper develops sampling methods for ocean phenomena based on temperature and salinity measurements.
problem Improving oceanographic sampling with limited resources.
method Design criterion based on uncertainty in excursions of vector-valued Gaussian random fields.
result Demonstrates effective exploration of ambiguous regions for data-driven sampling.
SFC aims to protect the Amazon with a digital currency and smart contracts.
problem Protecting the Amazon's ecosystem and ensuring resource credibility.
method Blockchain, digital contracts, smart contracts with oracles.
result Ensures credibility and security for financial resources invested in Amazon projects.
Unified framework connects physical laws and machine learning.
problem Combining physical laws and machine learning for scientific applications.
method Universal Differential Equations (UDEs) as a unifying framework.
result Wide variety of applications can be efficiently handled through UDE formalism.
Machine learning monitors detect motor overheating, adapting to concept drift.
problem Early detection of motor overheating in ships' propulsion systems.
method Machine learning and statistical methods using historical data to adapt to concept drift.
result The proposed monitors provide early detection of overheating during and after concept drifts.
5 homogeneous convex foliations of degree 4 found on complex projective plane.
problem Classifying reduced convex foliations on complex projective plane.
method Analyzing homogeneous foliations of degree 4.
result 5 homogeneous convex foliations of degree four identified.
The paper studies embedding finite groups in specific quotient spaces.
problem Embedding finite groups in quotient spaces of Artin and pure braid groups.
method Analyzes conditions for embedding finite groups in Bn/Γk(Pn), proving necessary conditions and constructing explicit embeddings. result Finite groups of odd order embed in Bn/Γ2(Pn) and Bn/Γ3(Pn), extending previous results. The paper analyzes security issues in blockchain ecosystems with multiple SSPs and proposes two models for better stake management.
problem Security issues in blockchain ecosystems with multiple SSPs and stake fragmentation.
method Formalized the Multiple SSP Problem and analyzed two architectures: Model M and Model S through convex optimization and game-theoretic lens. result Model S achieves tighter security guarantees through single validator sets and aggregated slashing logic. Flexible XVAE model for efficient spatial extremes simulation.
problem Complex tail dependence structures in spatial extremes processes.
method Variational autoencoder (XVAE) for modeling flexible and non-stationary dependence.
result XVAE provides fast inference and outperforms traditional models in high dimensions.
The green area of economy is the key of healthy living. It is necessary to convene economic and ecologic framework to establish a market attentive to drastic reduction of emissions damaging our climate and landscapes in rural areas, to the protection of biological diversity of the planet, to stop producing nuclear wast…
Study quantifies systemic risk in DeFi using network analysis.
problem Systemic risk in decentralized finance (DeFi) ecosystem.
method Network-based fragility analysis of TVL dynamics.
result Developed CFI and RCS to quantify structural fragility and risk contribution.
FST.ai 2.0 improves Taekwondo decision-making with AI, reducing review time and increasing trust.
problem Fair, transparent, and explainable decision-making in Taekwondo.
method Pose-based action recognition, epistemic uncertainty modeling, interactive dashboards.
result 85% reduction in decision review time, 93% referee trust in AI-assisted decisions.
Paper develops a neural-fuzzy controller for GPS-intelligent buoys.
problem Optimally track dynamically positioned marine buoys with unknown parameters.
method Dynamic system modeling using neural-fuzzy networks with backstepping technique.
result The controller minimizes position errors and adjusts buoy positions accurately.
Study examines European banks' digital transformation strategies.
problem Lack of a common framework for open banking innovation in banking sector.
method Qualitative analysis of partnerships and API development.
result European banks are diversifying and boosting customer relationship management.
Study examines machine learning competitions' impact on AI development.
problem Fostering innovation and skill development in AI.
method Analysis of major competition platforms, workflows, and participant demographics.
result MLCs promote collaboration, reproducibility, and continuous innovation in AI.
Enhances VC startup success predictions using graph augmented time series models.
problem Challenges in predicting startup success due to limited financial data and subjective forecasts.
method Integrates inter-company relationships into time series analysis using GraphRAG.
result Significantly outperforms previous models in startup success predictions.
AIBC uses AI and blockchain for efficient resource sharing.
problem Efficient resource sharing in large-scale decentralized systems.
method Two-consensus scheme with DABFT and DPoEV.
result Predictive deep learning for optimal BFT algorithm selection.
We develop obstructions to a knot K in the 3-sphere bounding a smooth punctured Klein bottle in the 4-ball. The simplest of these is based on the linking form of the 2-fold branched cover of the 3-sphere branched over K. Stronger obstructions are based on the Ozsvath-Szabo correction term in Heegaard-Floer homology, al…
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.
Study maps research streams in biodiversity finance, identifies key areas.
problem Biodiversity loss and need for finance to reverse trends.
method Quantitative bibliometric analysis of 189,456 references.
result Identifies eight primary research streams in biodiversity finance.
Advances in Python and data standards boost reproducibility in neuroimaging.
problem Lack of reproducibility in scientific research.
method Open data sharing resources, data standards, open-source Python, and software engineering advances.
result Reproducibility in neuroimaging has improved with new tools and practices.