arXiv subjects

Himanshu Pant

Publications and source records attributed to Himanshu Pant.

6 recordsLinked to original sources

arXiv2024

Coupling of magnetism and transport properties to the lattice degrees of freedom in NdBaCo$_2$O$_{5+δ}$ ($δ\sim 0.65$)

We have studied the origin of zero volume expansion below the Curie temperature (Tc), variable range hopping (VRH) behaviour using structural, magnetic, transport and thermal studies on the oxygen deficient double perovskite NdBaCo$_2$O$_{5+δ}$ ($δ\sim 0.65$). The valence state of Co ions and the possible properties exhibited by such compound were studied using electronic structure calculations for $δ= 0.75$. Careful investigation of structure shows that the compound stabilizes in tetragonal structure (P4/mmm) having $2a_p \times 2a_p \times 2a_p$ (222) superstructure, where $a_p$ is the cubic perovskite lattice parameter. The compound exhibits a minimum in resistivity, ferromagnetic and ferrimagnetic transitions around 375 K, 120 K ($T_c$) and 60 K, respectively with signature of Griffiths phase above $T_c$. Our detailed structural analysis suggests signature of the onset of the above magnetic transitions at temperatures well above its stabilisation at long range level thereby leading to VRH behaviour. The observed zero thermal expansion in volume below Tc appears to be due to competing magnetic interactions within and between the magnetic sublattices. Our electronic structure calculations show (a) the importance of electron-electron correlation in Nd $4f$ and Co $3d$ states (b) Co ions stabilize in intermediate spin (IS) state, having oxidation state less than +3 (c) half metallicity. Our results show the possibility of coupling between magnetism and ferroelectricity. We believe that our results especially on the valence state of the Co ion, zero thermal expansion in volume, short range magnetic orderings and the connection between different degrees of freedom will be helpful in clearing the ambiguities existing in literature on the nature of magnetism and thereby aiding in designing new functionalities.

cond-mat.str-el↗

arXiv2022

Evidence of Lattice Strain as a Precursor to Superconductivity in BaPb$_{0.75}$Bi$_{0.25}$O$_3$

In this work, we have investigated the precursor effects to superconductivity in BaPb$_{0.75}$Bi$_{0.25}$O$_3$ using temperature dependent resistivity, x-ray diffraction technique and photoemission spectroscopy. The present compound exhibits superconductivity around 11 K ($T_C$). The synthesis procedure adopted is much simpler as compared to the procedure available in the literature. In the temperature range (10 K-25 K) i.e. above $T_C$, our results show an increase in both the orthorhombic and tetragonal strain. The well screened features observed in Bi and Pb 4$f_{7/2}$ core levels are indicative of the metallic nature of the sample. The compound exhibits finite intensity at the Fermi level at 300 K and this intensity decreases with decrease in temperature and develops into a pseudogap; the energy dependence of the spectral density of states suggests disordered metallic state. Furthermore, our band structure calculations reveal that the structural transition upon Pb doping results in the closing of the band gap at the Fermi level.

cond-mat.supr-con↗

arXiv2021

Learning Neural Network Classifiers with Low Model Complexity

Modern neural network architectures for large-scale learning tasks have substantially higher model complexities, which makes understanding, visualizing and training these architectures difficult. Recent contributions to deep learning techniques have focused on architectural modifications to improve parameter efficiency and performance. In this paper, we derive a continuous and differentiable error functional for a neural network that minimizes its empirical error as well as a measure of the model complexity. The latter measure is obtained by deriving a differentiable upper bound on the Vapnik-Chervonenkis (VC) dimension of the classifier layer of a class of deep networks. Using standard backpropagation, we realize a training rule that tries to minimize the error on training samples, while improving generalization by keeping the model complexity low. We demonstrate the effectiveness of our formulation (the Low Complexity Neural Network - LCNN) across several deep learning algorithms, and a variety of large benchmark datasets. We show that hidden layer neurons in the resultant networks learn features that are crisp, and in the case of image datasets, quantitatively sharper. Our proposed approach yields benefits across a wide range of architectures, in comparison to and in conjunction with methods such as Dropout and Batch Normalization, and our results strongly suggest that deep learning techniques can benefit from model complexity control methods such as the LCNN learning rule.

cs.LG↗

arXiv2021

Twin Augmented Architectures for Robust Classification of COVID-19 Chest X-Ray Images

The gold standard for COVID-19 is RT-PCR, testing facilities for which are limited and not always optimally distributed. Test results are delayed, which impacts treatment. Expert radiologists, one of whom is a co-author, are able to diagnose COVID-19 positivity from Chest X-Rays (CXR) and CT scans, that can facilitate timely treatment. Such diagnosis is particularly valuable in locations lacking radiologists with sufficient expertise and familiarity with COVID-19 patients. This paper has two contributions. One, we analyse literature on CXR based COVID-19 diagnosis. We show that popular choices of dataset selection suffer from data homogeneity, leading to misleading results. We compile and analyse a viable benchmark dataset from multiple existing heterogeneous sources. Such a benchmark is important for realistically testing models. Our second contribution relates to learning from imbalanced data. Datasets for COVID X-Ray classification face severe class imbalance, since most subjects are COVID -ve. Twin Support Vector Machines (Twin SVM) and Twin Neural Networks (Twin NN) have, in recent years, emerged as effective ways of handling skewed data. We introduce a state-of-the-art technique, termed as Twin Augmentation, for modifying popular pre-trained deep learning models. Twin Augmentation boosts the performance of a pre-trained deep neural network without requiring re-training. Experiments show, that across a multitude of classifiers, Twin Augmentation is very effective in boosting the performance of given pre-trained model for classification in imbalanced settings.

cs.CV↗

arXiv2020

Complexity Controlled Generative Adversarial Networks

One of the issues faced in training Generative Adversarial Nets (GANs) and their variants is the problem of mode collapse, wherein the training stability in terms of the generative loss increases as more training data is used. In this paper, we propose an alternative architecture via the Low-Complexity Neural Network (LCNN), which attempts to learn models with low complexity. The motivation is that controlling model complexity leads to models that do not overfit the training data. We incorporate the LCNN loss function for GANs, Deep Convolutional GANs (DCGANs) and Spectral Normalized GANs (SNGANs), in order to develop hybrid architectures called the LCNN-GAN, LCNN-DCGAN and LCNN-SNGAN respectively. On various large benchmark image datasets, we show that the use of our proposed models results in stable training while avoiding the problem of mode collapse, resulting in better training stability. We also show how the learning behavior can be controlled by a hyperparameter in the LCNN functional, which also provides an improved inception score.

cs.LG↗

arXiv2018

Scalable Twin Neural Networks for Classification of Unbalanced Data

Twin Support Vector Machines (TWSVMs) have emerged an efficient alternative to Support Vector Machines (SVM) for learning from imbalanced datasets. The TWSVM learns two non-parallel classifying hyperplanes by solving a couple of smaller sized problems. However, it is unsuitable for large datasets, as it involves matrix operations. In this paper, we discuss a Twin Neural Network (Twin NN) architecture for learning from large unbalanced datasets. The Twin NN also learns an optimal feature map, allowing for better discrimination between classes. We also present an extension of this network architecture for multiclass datasets. Results presented in the paper demonstrate that the Twin NN generalizes well and scales well on large unbalanced datasets.

cs.LG↗