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 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.
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
Research into automated systems for detecting and classifying marine mammals in acoustic recordings is expanding internationally due to the necessity to analyze large collections of data for conservation purposes. In this work, we present a Convolutional Neural Network that is capable of classifying the vocalizations o…
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.
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.
We present a method for Temporal Difference (TD) learning that addresses several challenges faced by robots learning to navigate in a marine environment. For improved data efficiency, our method reduces TD updates to Gaussian Process regression. To make predictions amenable to online settings, we introduce a sparse app…
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.
Phytoplankton plays an important role in marine ecosystem. It is defined as a biological factor to assess marine quality. The identification of phytoplankton species has a high potential for monitoring environmental, climate changes and for evaluating water quality. However, phytoplankton species identification is not …
This Ph.D. thesis deals with the optimization of several renewable energy resources development as well as the improvement of facilities management in oceanic engineering and airports, using computational hybrid methods belonging to AI to this end. Energy is essential to our society in order to ensure a good quality of…
Slender marine structures such as deep-water marine risers are subjected to currents and will normally experience Vortex Induced Vibrations (VIV), which can cause fast accumulation of fatigue damage. The ocean current is often three-dimensional (3D), i.e., the direction and magnitude of the current vary throughout the …
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.
We show that up to automorphisms of PC2 there are 5 homogeneous convex foliations of degree four on PC2. Using this result, we give a partial answer to a question posed in 2013 by D. {Marín} and J. {Pereira} about the classification of reduced convex foliations on~$\mat…
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…
Let n≥3. In this paper, we study the problem of whether a given finite group G embeds in a quotient of the form Bn/Γk(Pn), where Bn is the n-string Artin braid group, k∈{2,3}, and {Γl(Pn)}l∈N is the lower central series of the n-string pure braid group Pn. Previous …
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.
The gap between our ability to collect interesting data and our ability to analyze these data is growing at an unprecedented rate. Recent algorithmic attempts to fill this gap have employed unsupervised tools to discover structure in data. Some of the most successful approaches have used probabilistic models to uncover…
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.
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.
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.
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.
Tracking the abundance of underwater species is crucial for understanding the effects of climate change on marine ecosystems. Biologists typically monitor underwater sites with echosounders and visualize data as 2D images (echograms); they interpret these data manually or semi-automatically, which is time-consuming and…
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…
In this paper we study the tensor powers of the standard representation of the quantum super-algebra Uq(sl(2∣1), focusing on the rings of its algebra endomorphisms, called centraliser algebras and denoted by LGn. Their dimensions were conjectured by I. Marin and E. Wagner \cite{MW}. We prove this conjecture, desc…
We first introduce a novel profile-based alignment algorithm, the multiple continuous Signal Alignment algorithm with Gaussian Process Regression profiles (SA-GPR). SA-GPR addresses the limitations of currently available signal alignment methods by adopting a hybrid of the particle smoothing and Markov-chain Monte Carl…
Noncritical soft-faults and model deviations are a challenge for Fault Detection and Diagnosis (FDD) of resident Autonomous Underwater Vehicles (AUVs). Such systems may have a faster performance degradation due to the permanent exposure to the marine environment, and constant monitoring of component conditions is requi…
A big challenge in environmental monitoring is the spatiotemporal variation of the phenomena to be observed. To enable persistent sensing and estimation in such a setting, it is beneficial to have a time-varying underlying environmental model. Here we present a planning and learning method that enables an autonomous ma…
New skein theory for Links-Gould polynomial simplifies link evaluations.
problem Computing Links-Gould polynomial for oriented links.
method Developed a cubic braid-type skein theory.
result Skein theory can evaluate any oriented link.
We show that up to automorphisms of PC2 there are 14 homogeneous convex foliations of degree 5 on PC2. We establish some properties of the Fermat foliation F0d of degree d≥2 and of the Hilbert modular foliation FH5 of degree 5. As a…
Alternative approach to regularize time-dependent singular Lagrangian systems.
problem Regularizing time-dependent singular Lagrangian systems.
method Employing the coisotropic embedding theorem and the Tulczyjew isomorphism.
result Uniqueness of the Lagrangian regularization to first order.
Novel defects in hyperbolic sheets explain complex wrinkling patterns in nature.
problem Understanding complex wrinkling patterns in thin elastic hyperbolic surfaces.
method Non-Euclidean plate theory and investigation of branch points.
result Branch points are natural defects in hyperbolic sheets, influencing their morphology robustly.
In many real-world networks, nodes have class labels, attributes, or variables that affect the network's topology. If the topology of the network is known but the labels of the nodes are hidden, we would like to select a small subset of nodes such that, if we knew their labels, we could accurately predict the labels of…
This study introduces a framework for the forecasting, reconstruction and feature engineering of multivariate processes along with its renewable energy applications. We integrate derivative-free optimization with an ensemble of sequence-to-sequence networks and design a new resampling technique called additive resampli…
We study control systems invariant under a Lie group with application to the problem of nonlinear trajectory planning. A theory of symmetry reduction of exterior differential systems is employed to demonstrate how symmetry reduction and reconstruction is effective in the explicit, exact construction of planned system t…
Develops discrete geometry for non-constant curvature surfaces.
problem Modeling surfaces of non-constant curvature, especially with non-constant negative curvature.
method Derived and numerically integrated Lelieuvre formulas for C1,1 hyperbolic surfaces. Proposed iterative and fast marching methods for solving implicit equations and computing geodesic distances. result Explicit construction of immersions is not provided, but equations are described implicitly.
This paper discusses challenges and opportunities in vessel behavior detection using machine and deep learning.
problem Real-time analysis of vessel behaviors is crucial for maritime safety and protection.
method Comparison of classical machine learning and deep learning approaches for vessel event and anomaly detection.
result Novel methods and tools are needed to address challenges in vessel behavior detection.
BALLAST optimizes Lagrangian observer placement for ocean vector fields.
problem Optimizing Lagrangian observer placement for time-dependent ocean vector fields.
method Bayesian active learning with look-ahead amendment for sea-drifter trajectories using a physics-informed spatio-temporal Gaussian process surrogate model.
result Noticeable benefits of BALLAST-aided observer placement strategies on synthetic and high-fidelity ocean models.
Hybrid model outperforms benchmarks in financial forecasting.
problem Robust asset price forecasting in finance.
method Combining LSTM with Neural Levy Processes using Grey Wolf Optimizer and ANN calibration.
result Hybrid model outperforms base LSTM and other models.
Machine learning predicts ship performance changes over time.
problem Estimating ship hydrodynamic performance over time.
method Machine learning methods (NL-PCR, NL-PLSR, probabilistic ANN) calibrated with in-service data.
result Probabilistic ANN model performs best in predicting ship performance changes.
Study uses AI and ML to predict and optimize corrosion resistance of aluminum alloys.
problem Corrosion resistance of aluminum alloys in marine environments.
method Investigated two ML approaches: direct and inverse, using Random Forest, neural network, and Gaussian Process Regression.
result Gaussian Process Regression with hybrid kernel functions provided superior predictive performance.
We consider a one-parameter family of Grushin-type singularities on surfaces, and discuss the possible diffusions that extend Brownian motion to the singularity. This gives a quick proof and clear intuition for the fact that heat can only cross the singularity for an intermediate range of the parameter. When crossing i…
Paper estimates spectral risk measures for insurance data with truncated and censored data.
problem Estimating spectral risk measures for insurance data with left truncation and right censoring.
method Proposes a non-parametric estimator using product limit estimator and establishes asymptotic normality.
result Proposed estimator outperforms existing methods for small k and small sample sizes.
Deep learning automates biofouling detection in ship hull images.
problem Automating biofouling assessment from ship hull images.
method Deep learning model trained on expert-annotated images.
result Deep learning model agrees with expert annotations.
The study establishes a criterion for the holomorphy of curvature in smooth webs and applies it to dual webs of homogeneous foliations.
problem Establishing conditions for the holomorphy of curvature in smooth webs and their duals.
method Developed an effective criterion for the holomorphy of curvature in smooth d-webs and applied it to dual webs of homogeneous foliations. result Characterized the holomorphy of the curvature of dual webs of homogeneous foliations on PC2. Holomorphic curvature of planar webs along invariant curves
problem Holomorphic curvature of planar webs
method Show that curvature is holomorphic along invariant curves
result Curvature is holomorphic along invariant curves if and only if curvature of subwebs is holomorphic
Nodes in real world networks often have class labels, or underlying attributes, that are related to the way in which they connect to other nodes. Sometimes this relationship is simple, for instance nodes of the same class are may be more likely to be connected. In other cases, however, this is not true, and the way tha…