This paper proposes a new approach for Cross Modal Matching, i.e. the matching of patterns represented in different modalities, when pairs of same/different data are available for training (e.g. faces of same/different persons). In this situation, standard approaches such as Partial Least Squares (PLS) or Canonical Correlation Analysis (CCA), map the data into a common latent space that maximiz...

Abstract We present an extended validation of semi-analytical, semi-empirical covariance matrices for the two-point correlation function (2PCF) on simulated catalogs representative Luminous Red Galaxies (LRG) data collected during initial two months operations Stage-IV ground-based Dark Energy Spectroscopic Instrument (DESI). run pipeline multiple Zel’dovich (EZ) mock galaxy with corresponding ...

We specify a class of Gaussian rank-r latent models for cross-covariance. We show by construction that any variance-covariance matrix for the observed variables induced by rank-r reduced-rank regression can be induced by a rank-r latent model. 1 Model specification Basic terms are introduced which will be used to state the result. 1.1 Rank-r constraint models Let p be the number of X-variables ...

In-motion alignment of Strapdown Inertial Navigation Systems (SINS) without any geodetic-frame observations is one of the toughest challenges for Autonomous Underwater Vehicles (AUV). This paper presents a novel scheme for Doppler Velocity Log (DVL) aided SINS alignment using Unscented Kalman Filter (UKF) which allows large initial misalignments. With the proposed mechanism, a nonlinear SINS er...

The article describes the R package constrainedKriging, a tool for spatial prediction problems that involve change of support. The package provides software for spatial interpolation by constrained (CK), covariance-matching constrained (CMCK) and customary universal (UK) kriging. CK and CMCK yield approximately unbiased predictions of nonlinear functionals of target quantities under change of s...

To make sense of the world our brains must analyze high-dimensional datasets streamed by our sensory organs. Because such analysis begins with dimensionality reduction, modelling early sensory processing requires biologically plausible online dimensionality reduction algorithms. Recently, we derived such an algorithm, termed similarity matching, from a Multidimensional Scaling (MDS) objective f...

Tang, Gnecco, and Geller (1989, Biometrika 76, 577-583) proposed an approximate likelihood ratio (ALR) test of the null hypothesis that a normal mean vector equals a null vector against the alternative that all of its components are nonnegative with at least one strictly positive. This test is useful for comparing a treatment group with a control group on multiple endpoints, and the data from t...

The accurate estimation of measurements covariance is a fundamental problem in sensors fusion algorithms and is crucial for the proper operation of filtering algorithms. This paper provides an innovative solution for this problem and realizes the proposed solution on a 2D indoor navigation system for unmanned ground vehicles (UGVs) that fuses measurements from a MEMS-grade gyroscope, speed meas...

the most important factor in donor-recipient matching for hematopoietic stem cell transplantation is their hla typing. for many patients, we can not find a full-matched donor. in high risk patients, one locus  mismatched related donors are suitable alternatives. (1, 2) since the matching of patients and their siblings usually is based on low resolution hla typing, the matching of one locus mism...