arXiv subjects

Deepak Sharma

Publications and source records attributed to Deepak Sharma.

28 records · Page 2Linked to original sources

arXiv2020

High Field Magneto-Transport of Mixed Topological Insulators Bi2Se3-xTex (x = 0, 1, 2 & 3)

The article comprises structural, microstructural, and physical properties analysis of Bi2Se3-xTex (x= 0, 1, 2 and 3) mixed topological insulator (MTI) single crystals. All the crystals were grown through a well-optimized solid-state reaction route via the self-flux method. These MTI are well characterized through XRD (X-ray Diffraction), SEM (Scanning Electron Microscopy), EDAX (Energy Dispersive spectroscopy), and thereby, the physical properties are analyzed through the RT (Resistance vs temperature) down to 10K as well as the magneto-resistance (MR) measurements (at 5K) in a magnetic field of up to 10 Tesla. The MR drastically varies from x=0 to x=3 in MTI, from a huge 400 percent, it goes down to 20 percent and 5 percent and eventually back to 315 percent. This fascinated behaviour of MR is explained in this article through HLN (Hikami-Larkin-Nagaoka) equation and an additional term. This article not only proposed the mesmerizing behavior of MR in MTI but also explains the reason through competing WAL (Weak Anti-Localization) and WL (Weak Localization) conduction processes.

cond-mat.mtrl-sci↗

arXiv2020

Raman Spectroscopy of Bi2Se3-xTex (x= 0 to 3) Topological Insulator Crystals

We report crystal growth and Raman spectroscopy characterization of pure and mixed bulk topological insulators. The series comprises of both binary and ternary tetradymite topological insulators. We analyzed in detail the Raman peaks of vibrational modes as out of plane Ag, and in plane Eg for both binary and ternary tetradymite topological insulators. Both out of plane Ag exhibit obvious atomic size dependent peak shifts and the effect is much lesser for the former than the latter. The situation is rather interesting for in plane Eg, which not only shows the shift but rather a broader hump like structure. The de convolution of the same show two clear peaks, which are understood in terms of the presence of separate in plane BiSe and BiTe modes in mixed tetradymite topological insulators. Summarily, various Raman modes of well-characterized pure and mixed topological insulator single crystals are reported and discussed in this article.

cond-mat.mtrl-sci↗

arXiv2020

Broad Band Single Germanium Nanowire Photodetectors with Surface Oxide Controlled High Optical Gain

We have investigated photoconductive properties of single Germanium Nanowires(NWs)of diameter less than 100 nm in the spectral range of 300 to 1100 nm showing ultra large peak Responsivity in excess of 10^{7}AW^{-1}.The NWs were grown by Vapor Liquid Solid method using Au nanoparticle as catalyst. In this report we discuss the likely origin of the ultra large responsivity that may arise from a combination of various physical effects which are a): Ge and GeO_{x} interface states which act as scavengers of electrons from the photo-generated pairs,leaving the holes free to reach the electrodes,b) Schottky barrier at the metal and NW interface which gets lowered substantially due to carrier diffusion in contact region and (c) photodetector length being small (approximately few μm), negligible loss of photogenerated carriers due to recombination at defect sites. We have observed from power dependence of the optical gain that the gain is controlled by trap states. We find that the surface of the nanowire has presence of a thin layer of GeO_{x} (as evidenced from HRTEM study) which provide interface states. It is observed that these state play a crucial role to provide a radial field for separation of photogenerated electron and hole pair which in turn leads to very high effective photoconductive gain that reaches a very high at low illumination density.

physics.app-ph↗

arXiv2020

Deep interpretability for GWAS

Genome-Wide Association Studies are typically conducted using linear models to find genetic variants associated with common diseases. In these studies, association testing is done on a variant-by-variant basis, possibly missing out on non-linear interaction effects between variants. Deep networks can be used to model these interactions, but they are difficult to train and interpret on large genetic datasets. We propose a method that uses the gradient based deep interpretability technique named DeepLIFT to show that known diabetes genetic risk factors can be identified using deep models along with possibly novel associations.

cs.LG↗

arXiv2020

IterefinE: Iterative KG Refinement Embeddings using Symbolic Knowledge

Knowledge Graphs (KGs) extracted from text sources are often noisy and lead to poor performance in downstream application tasks such as KG-based question answering.While much of the recent activity is focused on addressing the sparsity of KGs by using embeddings for inferring new facts, the issue of cleaning up of noise in KGs through KG refinement task is not as actively studied. Most successful techniques for KG refinement make use of inference rules and reasoning over ontologies. Barring a few exceptions, embeddings do not make use of ontological information, and their performance in KG refinement task is not well understood. In this paper, we present a KG refinement framework called IterefinE which iteratively combines the two techniques - one which uses ontological information and inferences rules, PSL-KGI, and the KG embeddings such as ComplEx and ConvE which do not. As a result, IterefinE is able to exploit not only the ontological information to improve the quality of predictions, but also the power of KG embeddings which (implicitly) perform longer chains of reasoning. The IterefinE framework, operates in a co-training mode and results in explicit type-supervised embedding of the refined KG from PSL-KGI which we call as TypeE-X. Our experiments over a range of KG benchmarks show that the embeddings that we produce are able to reject noisy facts from KG and at the same time infer higher quality new facts resulting in up to 9% improvement of overall weighted F1 score

cs.AI↗

arXiv2019

Off-Policy Policy Gradient Algorithms by Constraining the State Distribution Shift

Off-policy deep reinforcement learning (RL) algorithms are incapable of learning solely from batch offline data without online interactions with the environment, due to the phenomenon known as \textit{extrapolation error}. This is often due to past data available in the replay buffer that may be quite different from the data distribution under the current policy. We argue that most off-policy learning methods fundamentally suffer from a \textit{state distribution shift} due to the mismatch between the state visitation distribution of the data collected by the behavior and target policies. This data distribution shift between current and past samples can significantly impact the performance of most modern off-policy based policy optimization algorithms. In this work, we first do a systematic analysis of state distribution mismatch in off-policy learning, and then develop a novel off-policy policy optimization method to constraint the state distribution shift. To do this, we first estimate the state distribution based on features of the state, using a density estimator and then develop a novel constrained off-policy gradient objective that minimizes the state distribution shift. Our experimental results on continuous control tasks show that minimizing this distribution mismatch can significantly improve performance in most popular practical off-policy policy gradient algorithms.

cs.LG↗

arXiv2019

Hikami-Larkin-Nagaoka (HLN) Fitting of Magneto Transport of Bi2Se3 Single Crystal in Different Magnetic Field Ranges

We report the detailed study of structural micro-structuraland high magnetic field magneto transport propertiesof Bi2Se3single crystal. Bi2Se3 single crystal is grown through conventional solid-state reaction route via the self-flux method. Rietveld analysis on Powder X-ray Diffraction showed that the studied Bi2Se3 crystal is crystallized in single-phase without any impurity. The surface morphology analyzed through Scanning Electron Microscopy study which shows that as-grown single crystal exhibit layered type structure and the quantitative weight of the atomic constituents (Bi and Se) are found to be closeto the stoichiometric amount in energy-dispersive X-ray spectroscopy analysis. Low temperature (2.5K) magneto-resistance (MR) exhibited a v-type cusp around origin at lower magnetic field, which is the sign of weak anti-localization effect. Further, Bi2Se3 single crystal magneto conductivity data is fitted by well-known HLN equation in different magnetic field range of 2Tesla, 4Tesla and 6Tesla and the resultant found that the conduction mechanism of Bi2Se3 is dominated by WAL state.

cond-mat.mtrl-sci↗

arXiv2018

Matching Disparate Image Pairs Using Shape-Aware ConvNets

An end-to-end trainable ConvNet architecture, that learns to harness the power of shape representation for matching disparate image pairs, is proposed. Disparate image pairs are deemed those that exhibit strong affine variations in scale, viewpoint and projection parameters accompanied by the presence of partial or complete occlusion of objects and extreme variations in ambient illumination. Under these challenging conditions, neither local nor global feature-based image matching methods, when used in isolation, have been observed to be effective. The proposed correspondence determination scheme for matching disparate images exploits high-level shape cues that are derived from low-level local feature descriptors, thus combining the best of both worlds. A graph-based representation for the disparate image pair is generated by constructing an affinity matrix that embeds the distances between feature points in two images, thus modeling the correspondence determination problem as one of graph matching. The eigenspectrum of the affinity matrix, i.e., the learned global shape representation, is then used to further regress the transformation or homography that defines the correspondence between the source image and target image. The proposed scheme is shown to yield state-of-the-art results for both, coarse-level shape matching as well as fine point-wise correspondence determination.

cs.CV↗

arXiv2015

Evolution of structural (α) relaxation-time anomalies in GexSe1-x Chalcogenide glasses

We examine enthalpy relaxation across the chalcogenide glass series GexSe1-x, prepared over close-by compositions using conventional melt-quenching technique. We estimate the timescale τ(Tg) characterizing enthalpic relaxation near the kinetic glass transition temperature, using the non-reversing heat-flow data obtained from MDSC (modulated differential scanning calorimetry) measurements over a wide range of compositions (2.1 {\lessthanequalto} r {\lessthanequalto} 2.8, r = 2x+2). Anomalies in the enthalpy-relaxation characteristic-times τg(r) are identified as marking rigidity-transitions encountered in successive Ge-doping of polymeric selenium chains.

cond-mat.dis-nn↗

arXiv2012

A Neuro-Fuzzy Technique for Implementing the Half-Adder Circuit Using the CANFIS Model

A Neural Network, in general, is not considered to be a good solver of mathematical and binary arithmetic problems. However, networks have been developed for such problems as the XOR circuit. This paper presents a technique for the implementation of the Half-adder circuit using the CoActive Neuro-Fuzzy Inference System (CANFIS) Model and attempts to solve the problem using the NeuroSolutions 5 Simulator. The paper gives the experimental results along with the interpretations and possible applications of the technique.

cs.NE↗