Related papers: Constraints on fNL from Wilkinson Microwave Anisot…
This paper studies the use of Metropolis-Hastings sampling for training Spiking Neural Network (SNN) hardware subject to strong unknown non-idealities, and compares the proposed approach to the common use of the backpropagation of error…
Naive Bayes Nearest Neighbour (NBNN) is a simple and effective framework which addresses many of the pitfalls of K-Nearest Neighbour (KNN) classification. It has yielded competitive results on several computer vision benchmarks. Its central…
A very important property of a statistical distribution is to know whether it obeys Gaussian statistics or not. On the one hand, it is of paramount importance in the context of CMB anisotropy studies, since deviations from a Gaussian…
We describe and discuss the application of Gumbel statistics, which model extreme events, to WMAP 5-year measurements of the cosmic microwave background. We find that temperature extrema of the CMB are well modelled by the Gumbel formalism…
We present an analysis of the constraints on the amplitude of primordial non-Gaussianity of local type described by the dimensionless parameter $f_{\rm NL}$. These constraints are set by the auto-correlation functions (ACFs) of two large…
In this paper we propose a new method for training neural networks (NNs) for frequency modulated continuous wave (FMCW) radar mutual interference mitigation. Instead of training NNs to regress from interfered to clean radar signals as in…
We study the statistical properties of the 1st year WMAP data on different scales using the spherical mexican hat wavelet transform. Consistent with the results of Vielva et al. (2003) we find a deviation from Gaussianity in the form of…
We present a new approach for statistical inference on noise properties of CMB anisotropy data. We consider a Maximum Likelihood parametric estimator to recover the full dependence structure of the noise process. We also consider a…
We measure the 2-1 cumulant correlator power spectrum $C^{21}_l$, a degenerate bispectrum, from the second data release of the Wilkinson Microwave Anisotropy Probe (WMAP). Our high resolution measurements with SpICE span a large…
The Planck CMB experiment has delivered the best constraints so far on primordial non-Gaussianity, ruling out early-Universe models of inflation that generate large non-Gaussianity. Although small improvements in the CMB constraints are…
We study observational constraints on the non-metricity $f(Q)$-gravity which reproduces an exact $\Lambda$CDM background expansion history while modifying the evolution of linear perturbations. To this purpose we use Cosmic Microwave…
Neural networks in general, from MLPs and CNNs to attention-based Transformers, are constructed from layers of linear combinations followed by nonlinear operations such as ReLU, Sigmoid, or Softmax. Despite their strength, these…
In this paper we present forecasts of the contamination on different shapes of the primordial non-Gaussianity fnl parameter -- detectable on future Cosmic Microwave Background (CMB) high--resolution anisotropy maps -- produced by unresolved…
We search the high-latitude portion of the COBE Differential Microwave Radiometers (DMR) 4-year sky maps for evidence of a non-Gaussian temperature distribution in the cosmic microwave background. The genus, 3-point correlation function,…
In our previous work the nonlinearity parameter f_NL, which characterizes nongaussianity in the cosmic microwave background, was estimated for a class of inflationary models based on nonlocal field theory. These models include p-adic…
Most parameter constraints obtained from cosmic microwave background (CMB) anisotropy data are based on power estimates and rely on approximate likelihood functions; computational difficulties generally preclude an exact analysis based on…
We present measurements of the clustering of hot and cold patches in the microwave background sky as measured from the Wilkinson Microwave Anisotropy Probe (WMAP) five-year data. These measurements are compared with theoretical predictions…
Multitude of deep learning models have been proposed for node classification in graphs. However, they tend to perform poorly under labeled-data scarcity. Although Few-shot learning for graphs has been introduced to overcome this problem,…
We propose a set of statistics $S_q$ for detecting non-gaussianity in CMBR anisotropy data sets. These statistics are both simple and, according to calculations over a space of linear combinations of three-point functions, nearly optimal at…
The peculiar motion of an observer with respect to the CMB rest frame induces a deflection in the arrival direction of the observed photons (also known as CMB aberration) and a Doppler shift in the measured photon frequencies. As a…