arXiv research

This paper improves OMP-based sparse subspace clustering with data-adaptive capability.

Study introduces a probabilistic framework for air-sea fluxes using neural networks.

Generative AI predicts Arctic sea ice dynamics over decades.

Neural-network emulators predict sea-level changes due to Antarctic ice melt.

New method corrects seasonal Arctic sea ice predictions with probabilistic models.

New model uses heteroscedastic Gaussian process for alkenone SST proxy.

Develops probabilistic forecasting for Sea Level Anomalies using Conformal Prediction on functional time series.

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

Combines ocean surface and interior data to study ocean dynamics.

Study optimizes climate adaptation strategies for NYC.

Forecast dam inflow using sea surface feature weights.

Study compares machine learning algorithms for predicting SST in the Great Barrier Reef.

Heat demand prediction is a prominent research topic in the area of intelligent energy networks. It has been well recognized that periodicity is one of the important characteristics of heat demand. Seasonal-trend decomposition based on LOESS (STL) algorithm can analyze the periodicity of a heat demand series, and decom…

Improved regret bounds for online convex optimization under stochastic and adversarial settings.

New model combines physics and machine learning for ocean dynamics.

BALLAST optimizes Lagrangian observer placement for ocean vector fields.

Super-resolution is a classical problem in image processing, with numerous applications to remote sensing image enhancement. Here, we address the super-resolution of irregularly-sampled remote sensing images. Using an optimal interpolation as the low-resolution reconstruction, we explore locally-adapted multimodal conv…

We investigate three-dimensional surfaces where the normal vector forms a constant angle with the radius vector. These surfaces naturally extend equiangular (logarithmic) spirals in the plane.

Study examines grain futures connectedness during Russia-Ukraine conflict.

The forecasting and reconstruction of ocean and atmosphere dynamics from satellite observation time series are key challenges. While model-driven representations remain the classic approaches, data-driven representations become more and more appealing to benefit from available large-scale observation and simulation dat…

New neural network enforces mass conservation for better ice flow predictions.

Pattern ensembling fills in missing or inaccurate trajectory data.

Study uses ANFIS to assess wind power under climate change.

Deep CNN models improve spatio-temporal forecasting efficiency.

New algorithms compute Koopman operators on RKHSs efficiently and accurately.

Neural point estimators improve parameter estimation from replicated data.

New method for PKM inverse dynamics second derivatives efficiently.

In this study, the wind data series from five locations in Aegean Sea islands, the most active `hotspots' in terms of refugee influx during the Oct/2015 - Jan/2016 period, are investigated. The analysis of the three-per-site data series includes standard statistical analysis and parametric distributions, auto-correlati…

DYffusion improves diffusion models for spatiotemporal forecasting.

Paper introduces MTCM to measure multivariate tail dependence.

There are several ways of a construction of a boundary of a symmetric space using pencils of geodesics: the Karpelevich boundary, the visibility boundary, the associahedral boundary, and the sea urchin. We give explicit descriptions of these boundaries. We obtain some moduli space like polyhedra as sections of these co…

Flexible XVAE model for efficient spatial extremes simulation.

This review assesses statistical and machine learning methods for coral bleaching.

CVAE detects weak complex signals in maritime radar, improving detection over classical methods.

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

We propose a formulation of a Lorentzian quantum geometry based on the framework of causal fermion systems. After giving the general definition of causal fermion systems, we deduce space-time as a topological space with an underlying causal structure. Restricting attention to systems of spin dimension two, we derive th…

Space partitions of $\mathbb{R}^d$ underlie a vast and important class of fast nearest neighbor search (NNS) algorithms. Inspired by recent theoretical work on NNS for general metric spaces [Andoni, Naor, Nikolov, Razenshteyn, Waingarten STOC 2018, FOCS 2018], we develop a new framework for building space partitions re…

Information-theoretic quantities, such as entropy, are used to quantify the amount of information a given variable provides. Entropies can be used together to compute the mutual information, which quantifies the amount of information two variables share. However, accurately estimating these quantities from data is extr…

Omega ratio is shown to be equivalent to Sharpe ratio under certain distributional assumptions.

Study examines spillovers between BRICS and U.S. staple grain futures markets.

We discuss - in what is intended to be a pedagogical fashion - generalized "mean-to-risk" ratios for portfolio optimization. The Sharpe ratio is only one example of such generalized "mean-to-risk" ratios. Another example is what we term the Fano ratio (which, unlike the Sharpe ratio, is independent of the time horizon)…

This paper introduces a new scalable multi-objective deep reinforcement learning (MODRL) framework based on deep Q-networks. We develop a high-performance MODRL framework that supports both single-policy and multi-policy strategies, as well as both linear and non-linear approaches to action selection. The experimental …

New algorithms reduce online learning regret by tracking gradient variation.

FORE evaluates occupancy ratios without requiring Bellman completeness.

Optimal option portfolios under Sharpe Ratio maximization with skew-elliptical t-distributed returns

We present a new methodology of computing incremental contribution for performance ratios for portfolio like Sharpe, Treynor, Calmar or Sterling ratios. Using Euler's homogeneous function theorem, we are able to decompose these performance ratios as a linear combination of individual modified performance ratios. This a…

New method forecasts multilinear data using tensor autoregression.

The paper proposes an asset allocation strategy using the Sortino ratio for better performance.