arXiv subjects

Sandeep Kumar

Publications and source records attributed to Sandeep Kumar.

At least 127 records · Page 7Linked to original sources

arXiv2021

Large Magnetic Moment in Flexoelectronic Silicon at Room Temperature

Time-dependent rotational electric polarizations have been proposed to generate temporally varying magnetic moments, for example, through a combination of ferroelectric polarization and optical phonons. This phenomenon has been called dynamical multiferroicity, but explicit experimental demonstrations have been elusive to date. Here, we report the detection of a temporal magnetic moment as high as 1.2 mu_B/atom in charge-doped thin film of silicon under flexural strain. We demonstrate that the magnetic moment is generated by a combination of electric polarization arising from a flexoelectronic charge separation along the strain gradient and the deformation potential of phonons. The effect can be controlled by adjusting the external strain gradient, doping concentration and dopant, and can be regarded as a dynamical multiferroic effect involving flexoelectronics polarization instead of ferroelectricity. The discovery of a large magnetic moment in silicon may enable the use of non-magnetic and non-ferroelectric semiconductors in various multiferroic and spintronic applications.

cond-mat.mtrl-sci↗

arXiv2021

Sub-bandgap activated charges transfer in a graphene-MoS2-graphene heterostructure

Monolayers of transition metal dichalcogenides are semiconducting materials which offer many prospects in optoelectronics. A monolayer of molybdenum disulfide (MoS2) has a direct bandgap of 1.88 eV. Hence, when excited with optical photon energies below its bandgap, no photocarriers are generated and a monolayer of MoS2 is not of much use in either photovoltaics or photodetection. Here, we demonstrate that large size MoS2 monolayer sandwiched between two graphene layers makes this heterostructure optically active well below the band gap of MoS2. An ultrafast optical pump-THz probe experiment reveals in real-time, transfer of carriers between graphene and MoS2 monolayer upon photoexcitation with photon energies down to 0.5 eV. It also helps to unravel an unprecedented enhancement in the broadband transient THz response of this tri-layer material system. We propose possible mechanism which can account for this phenomenon. Such specially designed heterostructures, which can be easily built around different transition metal dichalcogenide monolayers, will considerably broaden the scope for modern optoelectronic applications at THz bandwidth.

cond-mat.mes-hall↗

arXiv2021

Page Table Management for Heterogeneous Memory Systems

Modern enterprise servers are increasingly embracing tiered memory systems with a combination of low latency DRAMs and large capacity but high latency non-volatile main memories (NVMMs) such as Intel's Optane DC PMM. Prior works have focused on efficient placement and migration of data on a tiered memory system, but have not studied the optimal placement of page tables. Explicit and efficient placement of page tables is crucial for large memory footprint applications with high TLB miss rates because they incur dramatically higher page walk latency when page table pages are placed in NVMM. We show that (i) page table pages can end up on NVMM even when enough DRAM memory is available and (ii) page table pages that spill over to NVMM due to DRAM memory pressure are not migrated back later when memory is available in DRAM. We study the performance impact of page table placement in a tiered memory system and propose an efficient and transparent page table management technique that (i) applies different placement policies for data and page table pages, (ii) introduces a differentiating policy for page table pages by placing a small but critical part of the page table in DRAM, and (iii) dynamically and judiciously manages the rest of the page table by transparently migrating the page table pages between DRAM and NVMM. Our implementation on a real system equipped with Intel's Optane NVMM running Linux reduces the page table walk cycles by 12% and total cycles by 20% on an average. This improves the runtime by 20% on an average for a set of synthetic and real-world large memory footprint applications when compared with various default Linux kernel techniques.

cs.DC↗

arXiv2021

THz pulses from optically excited Fe, Pt and Ta based Spintronic Heterostructures

Spintronic heterostructures are considered to be the new generation THz sources for their capability in producing high power and broadband THz radiation. Here, we provide a brief review on the state-of-the-art in this field. The optically excited bi- and tri-layer combinations of ferromagnetic and nonmagnetic thin films have become increasingly popular. Towards optimizing the THz conversion efficiency and broadband gapless spectrum from these THz emitters, various control parameters need to be taken into consideration. The inverse spin Hall effect in the heavy metal layer of the heterostructure is primarily responsible for the generation of THz pulses. A few new results on iron, platinum and tantalum based heterostructures have also been reported here. It is observed that the Ta(2nm)/Fe(2nm)/Pt(2nm) tri-layer heterostructure generates ~40(250)% stronger THz signal as compared to the counterpart Fe(2nm)/Pt(2nm) (Fe(3nm)/Ta(2nm)) bi-layer heterostructure.

physics.app-ph↗

arXiv2020

Enhancement in optically induced ultrafast THz response of MoSe2MoS2 heterobilayer

THz conductivity of large area MoS2 and MoSe2 monolayers as well as their vertical heterostructure, MoSe2MoS2 is measured in the 0.3-5 THz frequency range. Compared to the monolayers, the ultrafast THz reflectivity of the MoSe2MoS2 heterobilayer is enhanced many folds when optically excited above the direct band gap energies of the constituting monolayers. The free carriers generated in the heterobilayer evolve with the characteristic times found in each of the two monolayers. Surprisingly, the same enhancement is recorded in the ultrafst THz reflectivity of the heterobilayer when excited below the MoS2 bandgap energy. A mechanism accounting for these observations is proposed.

physics.app-ph↗

arXiv2020

A comparison study of extrapolation models and empirical relations in forecasting solar wind

Coronal mass ejections (CMEs) and high speed solar streams serve as perturbations to the background solar wind that have major implications in space weather dynamics. Therefore, a robust framework for accurate predictions of the background wind properties is a fundamental step towards the development of any space weather prediction toolbox. In this pilot study, we focus on the implementation and comparison of various models that are critical for a steady state, solar wind forecasting framework. Specifically, we perform case studies on Carrington rotations 2053, 2082 and 2104, and compare the performance of magnetic field extrapolation models in conjunction with velocity empirical formulations to predict solar wind properties at Lagrangian point L1. Two different models to extrapolate the solar wind from the coronal domain to the inner-heliospheric domain are presented, namely, (a) Kinematics based (Heliospheric Upwind eXtrapolation [HUX]) model and (b) Physics based model. The physics based model solves a set of conservative equations of hydrodynamics using the PLUTO code and can additionally predict the thermal properties of solar wind. The assessment in predicting solar wind parameters of the different models is quantified through statistical measures. We further extend this developed framework to also assess the polarity of inter-planetary magnetic field at L1. Our best models for the case of CR2053 gives a very high correlation coefficient ($\sim$ 0.73-0.81) and has an root mean square error of ($\sim$ 75-90 kms$^{-1}$). Additionally, the physics based model has a standard deviation comparable with that obtained from the hourly OMNI solar wind data and also produces a considerable match with observed solar wind proton temperatures measured at L1 from the same database.

astro-ph.SR↗

arXiv2020

Safe trajectory of a piece moved by a robot

In this work, we propose a mathematical model for a physical problem based on the movement of a metal piece held by a robot. Using the principles of Kirchoff plate theory, a set of equations determining stresses and deformations caused during the motion, have been provided. We also discuss possible numerical treatment of these equations and finally, a solution to the one-dimensional analog of the problem has been presented.

cs.RO↗

arXiv2020

EIS -- a family of activation functions combining Exponential, ISRU, and Softplus

Activation functions play a pivotal role in the function learning using neural networks. The non-linearity in the learned function is achieved by repeated use of the activation function. Over the years, numerous activation functions have been proposed to improve accuracy in several tasks. Basic functions like ReLU, Sigmoid, Tanh, or Softplus have been favorite among the deep learning community because of their simplicity. In recent years, several novel activation functions arising from these basic functions have been proposed, which have improved accuracy in some challenging datasets. We propose a five hyper-parameters family of activation functions, namely EIS, defined as, \[ \frac{x(\ln(1+e^x))^α}{\sqrt{β+γx^2}+δe^{-θx}}. \] We show examples of activation functions from the EIS family which outperform widely used activation functions on some well known datasets and models. For example, $\frac{x\ln(1+e^x)}{x+1.16e^{-x}}$ beats ReLU by 0.89\% in DenseNet-169, 0.24\% in Inception V3 in CIFAR100 dataset while 1.13\% in Inception V3, 0.13\% in DenseNet-169, 0.94\% in SimpleNet model in CIFAR10 dataset. Also, $\frac{x\ln(1+e^x)}{\sqrt{1+x^2}}$ beats ReLU by 1.68\% in DenseNet-169, 0.30\% in Inception V3 in CIFAR100 dataset while 1.0\% in Inception V3, 0.15\% in DenseNet-169, 1.13\% in SimpleNet model in CIFAR10 dataset.

cs.LG↗

arXiv2020

TanhSoft -- a family of activation functions combining Tanh and Softplus

Deep learning at its core, contains functions that are composition of a linear transformation with a non-linear function known as activation function. In past few years, there is an increasing interest in construction of novel activation functions resulting in better learning. In this work, we propose a family of novel activation functions, namely TanhSoft, with four undetermined hyper-parameters of the form tanh(αx+βe^{γx})ln(δ+e^x) and tune these hyper-parameters to obtain activation functions which are shown to outperform several well known activation functions. For instance, replacing ReLU with xtanh(0.6e^x)improves top-1 classification accuracy on CIFAR-10 by 0.46% for DenseNet-169 and 0.7% for Inception-v3 while with tanh(0.87x)ln(1 +e^x) top-1 classification accuracy on CIFAR-100 improves by 1.24% for DenseNet-169 and 2.57% for SimpleNet model.

cs.NE↗

arXiv2020

Interlacing properties of system-poles, system-zeros and spectral-zeros in MIMO systems

SISO passive systems with just one type of memory/storage element (either only inductive or only capacitative) are known to have real poles and zeros, and further, with the zeros interlacing poles (ZIP). Due to a variety of definitions of the notion of a system zero, and due to other reasons described in the paper, results involving ZIP have not been extended to MIMO systems. This paper formulates conditions under which MIMO systems too have interlaced poles and zeros. This paper next focusses on the notion of a `spectral zero' of a system, which has been well-studied in various contexts: for example, spectral factorization, optimal charging/discharging of a dissipative system, and even model order reduction. We formulate conditions under which the spectral zeros of a MIMO system are real, and further, conditions that guarantee that the system-zeros, spectral zeros, and the poles are all interlaced. The techniques used in the proofs involve new results in Algebraic Riccati equations (ARE) and Hamiltonian matrices, and these results help in formulating new notions of positive-real balancing, and inter-relations with the existing notion of positive-real balancing; we also relate the positive-real singular values with the eigenvalues of the extremal ARE solutions in the proposed `quasi-balanced' forms.

eess.SY↗

arXiv2020

Group ring based public key cryptosystems

In this paper, we propose two cryptosystems based on group rings and existing cryptosystem. First one is Elliptic ElGamal type group ring public key cryptosystem whose security is greater than security of cryptosystems based on elliptic curves discrete logarithmic problem (ECDLP). Second is ElGamal type group ring public key cryptosystem, which is analogous to ElGamal public key cryptosystem but has comparatively greater security. Examples are also given for both the proposed cryptosystems.

math.GR↗

arXiv2020

Magnetic field-dependent resistance crossover and logarithmic to non-saturating magnetoresistance in topological insulator Bi$_2$Te$_3$

We report a metal-insulator like transition in single crystalline 3D topological insulator Bi2Te3 at a temperature of 230K in presence of an external magnetic field applied normal to the surface. This transition becomes more prominent at larger magnetic field strength with the residual resistance value increasing linearly with the magnetic field. At low temperature, the magnetic field dependence of the magnetoresistance shows a transition from logarithmic to linear behavior and the onset magnetic field value for this transition decreases with increasing temperature. The logarithmic magnetoresistance indicates the weak anti-localization of the surface Dirac electrons while the high temperature behavior originates from the bulk carriers due to intrinsic impurities. At even higher temperatures beyond~230 K, a completely classical Lorentz model type quadratic behavior of the magnetoresistance is observed. We also show that the experimentally observed anomalies at ~230K in the magneto-transport properties do not originate from any stacking fault in Bi2Te3.

cond-mat.mes-hall↗

arXiv2020

Flexoelectric effect mediated spin-to-charge conversion at amorphous-Si thin film interfaces

Interfacial spin to charge conversion arises due to an electric potential perpendicular to the interface. The electric potential can be artificially induced, for example, using ferroelectric and piezoelectric thin films at the interface. An alternate way to induce the electric potential could be flexoelectric field. The flexoelectricity can be observed in all the material that either have or lack inversion symmetry, additionally no large gate bias is needed. In this experimental study, we report large spin to charge conversion (spin-Hall angle- 0.578) at Ni80Fe20/amorphous-Si interfaces attributed to flexoelectricity mediated Rashba spin-orbit coupling. The flexoelectricity at the interface also gave rise to interlayer spin-acoustic phonon or flexo-magnetoelastic coupling. In addition to spin-charge conversion, the strained interfaces also led to almost three-fold increase in anomalous Nernst effect. This strain engineering for spin dependent thermoelectric behavior at room temperature opens a new window to the realization of spintronics and spin-caloritronics devices.

cond-mat.mes-hall↗

arXiv2020

A Novel GDP Prediction Technique based on Transfer Learning using CO2 Emission Dataset

In the last 150 years, CO2 concentration in the atmosphere has increased from 280 parts per million to 400 parts per million. This has caused an increase in the average global temperatures by nearly 0.7 degree centigrade due to the greenhouse effect. However, the most prosperous states are the highest emitters of greenhouse gases (specially, CO2). This indicates a strong relationship between gaseous emissions and the gross domestic product (GDP) of the states. Such a relationship is highly volatile and nonlinear due to its dependence on the technological advancements and constantly changing domestic and international regulatory policies and relations. To analyse such vastly nonlinear relationships, soft computing techniques has been quite effective as they can predict a compact solution for multi-variable parameters without any explicit insight into the internal system functionalities. This paper reports a novel transfer learning based approach for GDP prediction, which we have termed as Domain Adapted Transfer Learning for GDP Prediction. In the proposed approach per capita GDP of different nations is predicted using their CO2 emissions via a model trained on the data of any developed or developing economy. Results are comparatively presented considering three well-known regression methods such as Generalized Regression Neural Network, Extreme Learning Machine and Support Vector Regression. Then the proposed approach is used to reliably estimate the missing per capita GDP of some of the war-torn and isolated countries.

eess.SP↗

arXiv2020

Photo-Seebeck effect in single-crystalline bismuth telluride topological insulator

Bismuth telluride is a low energy bulk band-gap topological system with conducting surface states. Besides its very good thermoelectric properties, it also makes a very good candidate for broadband photodetectors. Here, we report temperature-dependent photo-Seebeck effect in a bulk single crystalline bismuth telluride. On light illumination, an electrically biased sample shows distinguishable contributions in the measured current due to both the Seebeck effect and the normal photo-generated carriers within a narrow layer of the sample. Detailed experiments are performed to elucidate the distinction between the Seebeck contribution and the photogenerated current. The temperature-dependence of the photocurrent without Seebeck contribution shows a sign reversal from negative to positive at a specific temperature depending on the wavelength of photoexcitation light.

cond-mat.mes-hall↗

arXiv2020

Large spin-Hall effect in Si at room temperature

Silicon's weak intrinsic spin-orbit coupling and centrosymmetric crystal structure are a critical bottleneck to the development of Si spintronics, because they lead to an insignificant spin-Hall effect (spin current generation) and inverse spin-Hall effect (spin current detection). Here, we undertake current, magnetic field, crystallography dependent magnetoresistance and magneto thermal transport measurements to study the spin transport behavior in freestanding Si thin films. We observe a large spin-Hall magnetoresistance in both p-Si and n-Si at room temperature and it is an order of magnitude larger than that of Pt. One explanation of the unexpectedly large and efficient spin-Hall effect is spin-phonon coupling instead of spin-orbit coupling. The macroscopic origin of the spin-phonon coupling can be large strain gradients that can exist in the freestanding Si films. This discovery in a light, earth abundant and centrosymmetric material opens a new path of strain engineering to achieve spin dependent properties in technologically highly-developed materials.

cond-mat.mes-hall↗

arXiv2020

Deep Bayesian Network for Visual Question Generation

Generating natural questions from an image is a semantic task that requires using vision and language modalities to learn multimodal representations. Images can have multiple visual and language cues such as places, captions, and tags. In this paper, we propose a principled deep Bayesian learning framework that combines these cues to produce natural questions. We observe that with the addition of more cues and by minimizing uncertainty in the among cues, the Bayesian network becomes more confident. We propose a Minimizing Uncertainty of Mixture of Cues (MUMC), that minimizes uncertainty present in a mixture of cues experts for generating probabilistic questions. This is a Bayesian framework and the results show a remarkable similarity to natural questions as validated by a human study. We observe that with the addition of more cues and by minimizing uncertainty among the cues, the Bayesian framework becomes more confident. Ablation studies of our model indicate that a subset of cues is inferior at this task and hence the principled fusion of cues is preferred. Further, we observe that the proposed approach substantially improves over state-of-the-art benchmarks on the quantitative metrics (BLEU-n, METEOR, ROUGE, and CIDEr). Here we provide project link for Deep Bayesian VQG \url{https://delta-lab-iitk.github.io/BVQG/}

cs.CV↗

arXiv2019

Multimodal Differential Network for Visual Question Generation

Generating natural questions from an image is a semantic task that requires using visual and language modality to learn multimodal representations. Images can have multiple visual and language contexts that are relevant for generating questions namely places, captions, and tags. In this paper, we propose the use of exemplars for obtaining the relevant context. We obtain this by using a Multimodal Differential Network to produce natural and engaging questions. The generated questions show a remarkable similarity to the natural questions as validated by a human study. Further, we observe that the proposed approach substantially improves over state-of-the-art benchmarks on the quantitative metrics (BLEU, METEOR, ROUGE, and CIDEr).

cs.CL↗