Model estimation of cerebral hemodynamics between blood flow and volume changes: a data-based modeling approach

It is well known that there is a dynamic relationship between cerebral blood flow (CBF) and cerebral blood volume (CBV). With increasing applications of functional MRI, where the blood oxygen-level-dependent signals are recorded, the understanding and accurate modeling of the hemodynamic relationship between CBF and CBV becomes increasingly important. This study presents an empirical and data-based modeling framework for model identification from CBF and CBV experimental data. It is shown that the relationship between the changes in CBF and CBV can be described using a parsimonious autoregressive with exogenous input model structure. It is observed that neither the ordinary least-squares (LS) method nor the classical total least-squares (TLS) method can produce accurate estimates from the original noisy CBF and CBV data. A regularized total least-squares (RTLS) method is thus introduced and extended to solve such an error-in-the-variables problem. Quantitative results show that the RTLS method works very well on the noisy CBF and CBV data. Finally, a combination of RTLS with a filtering method can lead to a parsimonious but very effective model that can characterize the relationship between the changes in CBF and CBV

Photoemission Spectroscopy of Magnetic and Non-magnetic Impurities on the Surface of the Bi2_2Se3_3 Topological Insulator

Dirac-like surface states on surfaces of topological insulators have a chiral spin structure that suppresses back-scattering and protects the coherence of these states in the presence of non-magnetic scatterers. In contrast, magnetic scatterers should open the back- scattering channel via the spin-flip processes and degrade the state's coherence. We present angle-resolved photoemission spectroscopy studies of the electronic structure and the scattering rates upon adsorption of various magnetic and non-magnetic impurities on the surface of Bi$_2$Se$_3$, a model topological insulator. We reveal a remarkable insensitivity of the topological surface state to both non-magnetic and magnetic impurities in the low impurity concentration regime. Scattering channels open up with the emergence of hexagonal warping in the high-doping regime, irrespective of the impurity's magnetic moment.Comment: 5 pages, 4 figure

Class reconstruction driven adversarial domain adaptation for hyperspectral image classification

We address the problem of cross-domain classification of hyperspectral image (HSI) pairs under the notion of unsupervised domain adaptation (UDA). The UDA problem aims at classifying the test samples of a target domain by exploiting the labeled training samples from a related but different source domain. In this respect, the use of adversarial training driven domain classifiers is popular which seeks to learn a shared feature space for both the domains. However, such a formalism apparently fails to ensure the (i) discriminativeness, and (ii) non-redundancy of the learned space. In general, the feature space learned by domain classifier does not convey any meaningful insight regarding the data. On the other hand, we are interested in constraining the space which is deemed to be simultaneously discriminative and reconstructive at the class-scale. In particular, the reconstructive constraint enables the learning of category-specific meaningful feature abstractions and UDA in such a latent space is expected to better associate the domains. On the other hand, we consider an orthogonality constraint to ensure non-redundancy of the learned space. Experimental results obtained on benchmark HSI datasets (Botswana and Pavia) confirm the efficacy of the proposal approach