Probabilistic modeling of temporal phenomena is of central importance in a variety of fields ranging from neuroscience to economics to speech recognition. While the task has received extensive attention in recent decades, learning temporal models for multivariate real-valued data that is non-Gaussian is still a formidable challenge. Recently, the power of copulas, a framework for representing c...

The article studies multi-modal (in particular temporal, tense, logics) possessing universal formulas. We prove (Theorems 11 and 12) that the admissibility problem for inference rules (inference rules with parameters) is decidable for all decidable multi-modal (in particular, temporal) logics possessing an universal formula and decidable w.r.t. unification (unification with parameters). These t...

This paper introduces a perturbative inversion algorithm for determining sea floor acoustic properties, which uses modal amplitudes as input data. Perturbative inverse methods have been used in the past to estimate bottom acoustic properties in sediments, but up to this point these methods have used only the modal eigenvalues as input data. As with previous perturbative inversion methods, the o...

Modal analysis is extensively used to study dynamic behavior of continuous and lumped parameter linear systems. More specifically, modal analysis can be used for the analysis and controller design of dynamic systems. In both cases, reduction of model size without degrading the accuracy is often required for the efficient use of the model. The reduction of modal models has been extensively studi...

In this research, the modal parameters of a beam in free-free condition are extracted by performing different experiments in laboratory. For this purpose, two different techniques are employed. The first methodology is considered as a time domain method in Operational Modal Analysis. While the other one is frequency domain impact hammer test which is categorized as an Experimental Modal Analysi...

The selection of a good model for forecasting a time series is a task that involves experience and knowledge. Employing machine learning algorithms is a promising approach to acquiring knowledge in regards to this task. A supervised classification method originating from the symbolic data analysis field is proposed for the model selection problem. This method was applied in the task of selectin...

Operational Modal Analysis (OMA) is a technique of extracting modal parameters of a system from output responses only. It is an emerging field in structural dynamics and has been applied to complex structures that are often difficult to analyze using traditional Experimental Modal Analysis (EMA) techniques. Since the input information is unavailable in OMA, the technique makes use of certain as...

In this paper an analytic approach is devised to represent, and study the behavior of, nonlinear dynamic chaotic Genesio system using general nonlinear modal representation. In this approach, the original nonlinear ordinary differential equations (ODEs) of model transforms to a sequence of linear timeinvariant ODEs. By solving the proposed linear ODEs sequence, the exact solution of the origina...

• PASTIS-R first open-access multimodal dataset with panoptic annotations. Thorough evaluation of different fusion schemes on three crop mapping tasks. Multimodality brings consistent improvement in performance and cloud robustness. Optical radar satellite time series are synergetic: optical images contain rich spectral information, while C-band captures useful geometrical information is immune...