arXiv research

Study reveals trade dynamics in dry bulk shipping networks, highlighting their randomness and periodic changes.

YOLOv3 detects ships in real-time with high accuracy.

Machine learning predicts ship performance changes over time.

Paper uses DBSCAN variation to detect ship anomalies.

Transformer learns shipping costs more accurately than traditional methods.

Adaptive rerouting reshapes impacts of maritime chokepoint disruptions

AI helps simplify complex ship finance processes.

Decision tool helps manage biofouling risks for ships in the Baltic Sea.

Marine buoys aid in the battle against Illegal, Unreported and Unregulated (IUU) fishing by detecting fishing vessels in their vicinity. Marine buoys, however, may be disrupted by natural causes and buoy vandalism. In this paper, we formulate marine buoy placement as a clustering problem, and propose dropout k-means an…

A hybrid ML method improves ship response predictions across different sea conditions.

Deep Learning is gaining traction with geophysics community to understand subsurface structures, such as fault detection or salt body in seismic data. This study describes using deep learning method for iceberg or ship recognition with synthetic aperture radar (SAR) data. Drifting icebergs pose a potential threat to ac…

Deep learning detects corrosion in nuclear fuel canisters.

This research proposes a method to hedge freight rate risk in shipping markets under model uncertainty.

This paper studies the market phenomenon of non-convergence between futures and spot prices in the grains market. We postulate that the positive basis observed at maturity stems from the futures holder's timing options to exercise the shipping certificate delivery item and subsequently liquidate the physical grain. In …

Image compression techniques reveal network structure for shipping box optimization.

Having the right assortment of shipping boxes in the fulfillment warehouse to pack and ship customer's online orders is an indispensable and integral part of nowadays eCommerce business, as it will not only help maintain a profitable business but also create great experiences for customers. However, it is an extremely …

Custom loss functions improve accuracy of wildfire rate of spread forecasts.

Botswana's agricultural credit scheme is unsustainable and needs reform.

New model identifies anticyclonic patterns causing drought and heat.

This study analyzes dynamic connectedness in global supply chain infrastructure portfolios, identifying key risk factors and extreme events.

Machine learning predicts liquid water properties from cluster data.

Recently we introduced T-duality in the study of topological insulators. In this paper, we study the bulk-boundary correspondence for three phenomena in condensed matter physics, namely, the quantum Hall effect, the Chern insulator, and time reversal invariant topological insulators. In all of these cases, we show that…

We develop a systematic method for renormalizing the AdS/CFT prescription for computing correlation functions. This involves regularizing the bulk on-shell supergravity action in a covariant way, computing all divergences, adding counterterms to cancel them and then removing the regulator. We explicitly work out the ca…

In this work we use a proven model to study a dynamic duopolistic competition between an old and a new technology which, through improved technical performance - e.g. data transmission capacity - fight in order to conquer market share. The process whereby an old technology fights a new one off through own improvements …

New geometric approach realizes 5D bulk theories with 4D edge modes.

Using holographic renormalization coupled with the Caffarelli/Silvestre\cite{caffarelli} extension theorem, we calculate the precise form of the boundary operator dual to a bulk scalar field rather than just its average value. We show that even in the presence of interactions in the bulk, the boundary operator dual to …

Recently we introduced T-duality in the study of topological insulators, and used it to show that T-duality trivialises the bulk-boundary correspondence in 2 dimensions. In this paper, we partially generalise these results to higher dimensions and briefly discuss the 4D quantum Hall effect.

Deep learning automates biofouling detection in ship hull images.

New method calibrates ambiguity sets for robust decision-making under contamination.

Modeling vessel speed to balance efficiency and environmental risks in Arctic shipping.

New method accelerates neural network training by focusing on flat directions.

Crypto markets show negative spillovers between chains, not positive co-movements.

The study shows how geometric Weyl bulk-density exponent rigidifies spectral encodings in O-regularly varying classes.

3D dual field theories for Virasoro minimal models constructed using Seifert fiber spaces.

We generalize the Zermelo navigation problem and its solution on Riemannian manifolds $(M, h)$ admitting a space dependence of a ship's own speed $|u(x)|_h\leq1$ in the presence of a perturbation $W$ determined by a mild velocity vector field $|W(x)|_h<|u(x)|_h$, with application of Finsler metric of Randers type.

We propose a family of differential models for B-type open-closed topological Landau-Ginzburg theories defined by a pair $(X,W)$, where $X$ is any non-compact Calabi-Yau manifold and $W$ is any holomorphic complex-valued function defined on $X$ whose critical set is compact. The models are constructed at cochain level …

A 'holographic formula' expressing the functional determinant of the scattering operator in an asymptotically locally anti-de Sitter(ALAdS) space has been proposed in terms of a relative functional determinant of the scalar Laplacian in the bulk. It stems from considerations in AdS/CFT correspondence of a quantum corre…

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…

Extends results of math-ph/0407067

New methods improve Archimax copula inference and sampling.

We generalize the Zermelo navigation problem and its solution on Riemannian manifolds $(M, h)$ admitting a space dependence of a ship's speed $0<|u(x)|_h\leq1$ in the presence of a perturbation $\tilde{W}$ determined by a strong velocity vector field satisfying $|\tilde{W}(x)|_h=|u(x)|_h$, with application of Finsler m…

Extends results of math-ph/0407067

We consider IIA compactifications on solvmanifolds with O6/D6 branes and study the conditions for obtaining de Sitter vacua in ten dimensions. While this is a popular set-up for searching de Sitter vacua, we propose a new method to include supersymmetry breaking sources. For space-time filling branes preserving bulk su…

The study examines spectral dynamics in deep neural networks, predicting how outliers evolve during training.

We study the effect of a relevant double-trace deformation on the partition function (and conformal anomaly) of a CFT at large N and its dual picture in AdS. Three complementary previous results are brought into full agreement with each other: bulk and boundary computations, as well as their formal identity. We show th…

We consider the Zermelo navigation problem on the ellipsoid of revolution (spheroid) in the presence of a perturbation $W$ determined by a mild velocity vector field, $|W|<1$, with application of Finsler metric of Randers type in the context of the corresponding optimal control represented by a time-efficient ship's he…

On conformally compact manifolds of arbitrary signature, we use conformal geometry to identify a natural (and very general) class of canonical boundary problems. It turns out that these encompass and extend aspects of already known holographic bulk-boundary problems, the conformal scattering description of boundary con…

Optimizes package types for e-commerce to reduce damage and costs.