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We study the iron-pnictide compound BaFe$_2$(As$_{1-x}$P$_x$)$_2$ for x $\sim$0.23, with a doping concentration near quantum criticality and enhanced nematic fluctuating state in the doping-temperature phase diagram. Transport measurements confirm the presence of a magneto-structural transition at 60K from the tetragonal to the orthorhombic phase, followed by a superconducting transition below 16K. The temperature and polarisation dependent Raman spectra reveal that there is a phonon mode at 211 cm$^{-1}$, followed by two broad modes (BM) between 400 and 700 cm$^{-1}$, having an energy difference of 15 meV, in the temperature range between 300K and 80K. In the non-superconducting state, the phonon mode exhibits expected polarization dependence as well as temperature evolution due to anharmonicity, strong anisotropic and thermally inert behaviour are observed for the BM. Electronic structure calculations for doped and undoped BaFe$_2$As$_2$ show that while Fe $d_{xz}$ and $d_{yz}$ orbitals do not split in the tetragonal phase, the splitting energy is 13.5 meV in the orthorhombic phase of the doped system, which is reasonably close to the experimentally observed value of the energy separation of the BM. We believe that reported BM possibly are the signature of electronic Raman scattering involving the crystal field levels of $d$-orbitals of Fe$^{2+}$ due to local breaking of the C$_4$ symmetry of the parent compound in the doped system.

The spin-orbit coupling (SOC) is a key to understand the magnetically driven superconductivity in iron-based superconductors, where both local and itinerant electrons are present and the orbital angular momentum is not completely quenched. Here, we report a neutron scattering study on the bilayer compound CaK(Fe$_{0.96}$Ni$_{0.04}$)$_4$As$_4$ with superconductivity coexisting with a non-collinear spin-vortex crystal magnetic order that preserves the tetragonal symmetry of Fe-Fe plane. In the superconducting state, two spin resonance modes with odd and even $L$ symmetries due to the bilayer coupling are found similar to the undoped compound CaKFe$_4$As$_4$ but at lower energies. Polarization analysis reveals that the odd mode is $c-$axis polarized, and the low-energy spin anisotropy can persist to the paramagnetic phase at high temperature, which closely resembles other systems with in-plane collinear and $c-$axis biaxial magnetic orders. These results provide the missing piece of the puzzle on the SOC effect in iron-pnictide superconductors, and also establish a common picture of $c-$axis preferred magnetic excitations below $T_c$ regardless of the details of magnetic pattern or lattice symmetry., Comment: 14 pages, 11 figures, including Supplementary Materials. Accepted for publication on Physical Review Letters

The effect of hydrostatic pressure on the electronic structures of iron pnictide superconducting compounds LiFeAs and NaFeAs through density functional theory based first principles studies are presented, using the predicted crystal structures at very high pressure by Zhang et al. [1]. The orbital selective pressure induced modifications in the partial density of states and electronic band structures reveal mixed multi-band multi-orbital nature of these compounds, with energetically degenerate dxz/dyz orbitals at ambient pressure. Due to larger hydrostatic pressures some of the electron/hole bands crosses the Fermi level, leading to significant topological modifications in Fermi surfaces known as Lifshitz transition. Interrelation between Lifshitz transition and superconductivity-an important subject matter of current research are described. Based on such interconnection the current study predicts the superconducting-Tc in 111 compounds can not be raised at such high pressures. Wannier functions based electronic structure investigation reveal a relatively larger hybridization between the Fe-3d and As-4p orbitals at higher pressures, as an effect of which the orbital degeneracy of dxz and dyz orbitals are lifted. Different hybridization contributions in the crystal-field splitting are separated using the Wannier functions based formalism, by incorporating different bands with a particular orbital character in the Wannier function construction. Pressure dependence of the intra/inter orbital hopping amplitudes between Fe-d orbitals has been discussed using low energy tight binding model.

In order to train robust deep learning models, large amounts of labelled data is required. However, in the absence of such large repositories of labelled data, unlabeled data can be exploited for the same. Semi-Supervised learning aims to utilize such unlabeled data for training classification models. Recent progress of self-training based approaches have shown promise in this area, which leads to this study where we utilize an ensemble approach for the same. A by-product of any semi-supervised approach may be loss of calibration of the trained model especially in scenarios where unlabeled data may contain out-of-distribution samples, which leads to this investigation on how to adapt to such effects. Our proposed algorithm carefully avoids common pitfalls in utilizing unlabeled data and leads to a more accurate and calibrated supervised model compared to vanilla self-training based student-teacher algorithms. We perform several experiments on the popular STL-10 database followed by an extensive analysis of our approach and study its effects on model accuracy and calibration.

The growth and success of deep learning approaches can be attributed to two major factors: availability of hardware resources and availability of large number of training samples. For problems with large training databases, deep learning models have achieved superlative performances. However, there are a lot of \textit{small sample size or $S^3$} problems for which it is not feasible to collect large training databases. It has been observed that deep learning models do not generalize well on $S^3$ problems and specialized solutions are required. In this paper, we first present a review of deep learning algorithms for small sample size problems in which the algorithms are segregated according to the space in which they operate, i.e. input space, model space, and feature space. Secondly, we present Dynamic Attention Pooling approach which focuses on extracting global information from the most discriminative sub-part of the feature map. The performance of the proposed dynamic attention pooling is analyzed with state-of-the-art ResNet model on relatively small publicly available datasets such as SVHN, C10, C100, and TinyImageNet., Comment: 3 figures, 2 tables, accepted in BigMM 2020

Electron energy loss near edge spectra (ELNES) has been calculated for two different Fe based superconducting material FeSe and FeTe using density functional theory (DFT). Fe K-edge absorption spectra consist of several features, origins of which are thoroughly described in light of partial density of states of constituent atoms. Here we have included "core-hole effect" and found drastic change in absorption spectra. Our results including core hole effect matches very well with experimental X-ray Absorption Near Edge Structure (XANES) results available in literature., Comment: conference paper

Deep metric learning algorithms have been utilized to learn discriminative and generalizable models which are effective for classifying unseen classes. In this paper, a novel noise tolerant deep metric learning algorithm is proposed. The proposed method, termed as Density Aware Metric Learning, enforces the model to learn embeddings that are pulled towards the most dense region of the clusters for each class. It is achieved by iteratively shifting the estimate of the center towards the dense region of the cluster thereby leading to faster convergence and higher generalizability. In addition to this, the approach is robust to noisy samples in the training data, often present as outliers. Detailed experiments and analysis on two challenging cross-modal face recognition databases and two popular object recognition databases exhibit the efficacy of the proposed approach. It has superior convergence, requires lesser training time, and yields better accuracies than several popular deep metric learning methods., Comment: Accepted in IEEE CVPR 2019

Mobile biometric approaches provide the convenience of secure authentication with an omnipresent technology. However, this brings an additional challenge of recognizing biometric patterns in unconstrained environment including variations in mobile camera sensors, illumination conditions, and capture distance. To address the heterogeneous challenge, this research presents a novel heterogeneity aware loss function within a deep learning framework. The effectiveness of the proposed loss function is evaluated for periocular biometrics using the CSIP, IMP and VISOB mobile periocular databases. The results show that the proposed algorithm yields state-of-the-art results in a heterogeneous environment and improves generalizability for cross-database experiments.