Search results for "Gaussia"

Quantum fluctuations and correlations in equilibrium and nonequilibrium thermodynamics

2014

CLUSTERING INCOMPLETE SPECTRAL DATA WITH ROBUST METHODS

2018

Abstract. Missing value imputation is a common approach for preprocessing incomplete data sets. In case of data clustering, imputation methods may cause unexpected bias because they may change the underlying structure of the data. In order to avoid prior imputation of missing values the computational operations must be projected on the available data values. In this paper, we apply a robust nan-K-spatmed algorithm to the clustering problem on hyperspectral image data. Robust statistics, such as multivariate medians, are more insensitive to outliers than classical statistics relying on the Gaussian assumptions. They are, however, computationally more intractable due to the lack of closed-for…

Crop Nitrogen Retrieval Methods for Simulated Sentinel-2 Data Using In-Field Spectrometer Data.

2021

Nitrogen (N) is one of the key nutrients supplied in agricultural production worldwide. Over-fertilization can have negative influences on the field and the regional level (e.g., agro-ecosystems). Remote sensing of the plant N of field crops presents a valuable tool for the monitoring of N flows in agro-ecosystems. Available data for validation of satellite-based remote sensing of N is scarce. Therefore, in this study, field spectrometer measurements were used to simulate data of the Sentinel-2 (S2) satellites developed for vegetation monitoring by the ESA. The prediction performance of normalized ratio indices (NRIs), random forest regression (RFR) and Gaussian processes regression (GPR) f…

Bayesian semiparametric long memory models for discretized event data

2020

We introduce a new class of semiparametric latent variable models for long memory discretized event data. The proposed methodology is motivated by a study of bird vocalizations in the Amazon rain forest; the timings of vocalizations exhibit self-similarity and long range dependence. This rules out Poisson process based models where the rate function itself is not long range dependent. The proposed class of FRActional Probit (FRAP) models is based on thresholding, a latent process. This latent process is modeled by a smooth Gaussian process and a fractional Brownian motion by assuming an additive structure. We develop a Bayesian approach to inference using Markov chain Monte Carlo and show g…

Noise phenomena and soliton dynamics in long Josephson junctions

2013

In this work we computationally explore the transient dynamics of a noisy Josephson junction (JJ). Principal purpose is to investigate the behavior of the lifetime of the superconductive state as a function of the system and noise source parameters. The relations between the emerging phenomena and the evolution of the JJ order parameter φ, that is the phase difference between the macroscopic wave functions describing the superconducting condensate in the two electrodes, is deeply investigated. We focus our interest on the switching events from the superconducting metastable state, and in particular on the mean escape time (MET). In the used model, a long JJ can be represented by a string co…

A novel heuristic algorithm for the modeling and risk assessment of the COVID-19 pandemic phenomenon

2020

This article belongs to the special issue: Soft computing techniques in materials science and engineering Summarization: The modeling and risk assessment of a pandemic phenomenon such as COVID-19 is an important and complicated issue in epidemiology, and such an attempt is of great interest for public health decision-making. To this end, in the present study, based on a recent heuristic algorithm proposed by the authors, the time evolution of COVID-19 is investigated for six different countries/states, namely New York, California, USA, Iran, Sweden and UK. The number of COVID-19-related deaths is used to develop the proposed heuristic model as it is believed that the predicted number of dai…

Do the Mega and Titan Tests Yield Accurate Results? An Investigation into Two Experimental Intelligence Tests

2020

The Mega and Titan Tests were designed by Ronald K. Hoeflin to make fine distinctions in the intellectual stratosphere. The Mega Test purported to measure above-average adult IQ up to and including scores with a rarity of one in a million of the general population. The Titan Test was billed as being even more difficult than the Mega Test. In this article, these claims are subjected to scrutiny. Both tests are renormed using the normal curve of distribution. It is found that the Mega Test has a higher ceiling and a lower floor than the Titan Test. While the Mega Test may thus seem preferable as a psychometric instrument, it is somewhat marred by a number of easy items in its verbal section. …

Correspondence between generalized binomial field states and coherent atomic states

2008

We show that the N-photon generalized binomial states of electromagnetic field may be put in a bijective mapping with the coherent atomic states of N two-level atoms. We exploit this correspondence to simply obtain both known and new properties of the N-photon generalized binomial states. In particular, an over-complete basis of these binomial states and an orthonormal basis are obtained. Finally, the squeezing properties of generalized binomial state are analyzed.

Jet fragmentation transverse momentum distributions in pp and p-Pb collisions at √s, √sNN = 5.02 TeV

2021

Jet fragmentation transverse momentum (jT) distributions are measured in proton-proton (pp) and proton-lead (p-Pb) collisions at √sNN = 5.02 TeV with the ALICE experiment at the LHC. Jets are reconstructed with the ALICE tracking detectors and electromagnetic calorimeter using the anti-kT algorithm with resolution parameter R = 0.4 in the pseudorapidity range |η| < 0.25. The jT values are calculated for charged particles inside a fixed cone with a radius R = 0.4 around the reconstructed jet axis. The measured jT distributions are compared with a variety of parton-shower models. Herwig and Pythia 8 based models describe the data well for the higher jT region, while they underestimate the low…

Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes

2018

In this work we evaluate multi-output (MO) Gaussian Process (GP) models based on the linear model of coregionalization (LMC) for estimation of biophysical parameter variables under a gap filling setup. In particular, we focus on LAI and fAPAR over rice areas. We show how this problem cannot be solved with standard single-output (SO) GP models, and how the proposed MO-GP models are able to successfully predict these variables even in high missing data regimes, by implicitly performing an across-domain information transfer. CICYT TIN2015-64210-R In this work we evaluate multi-output (MO) Gaussian Process (GP) models based on the linear model of coregionalization (LMC) for estimation of biophy…