arXiv subjects

Sandeep Kumar

Publications and source records attributed to Sandeep Kumar.

At least 91 records · Page 5Linked to original sources

arXiv2023

Schottky Barrier MOSFET Enabled Ultra-Low Power Real-Time Neuron for Neuromorphic Computing

Energy-efficient real-time synapses and neurons are essential to enable large-scale neuromorphic computing. In this paper, we propose and demonstrate the Schottky-Barrier MOSFET-based ultra-low power voltage-controlled current source to enable real-time neurons for neuromorphic computing. Schottky-Barrier MOSFET is fabricated on a Silicon-on-insulator platform with polycrystalline Silicon as the channel and Nickel/Platinum as the source/drain. The Poly-Si and Nickel make the back-to-back Schottky junction enabling ultra-low ON current required for energy-efficient neurons.

cs.ET↗

arXiv2023

Evidence of magnetoelectronic electromagnon mediated transport in flexoelectronic heterostructures

The superposition of atomic vibrations and flexoelectronic effect gives rise to a cross correlation between free charge carriers and temporal magnetic moment of phonons in conducting heterostructures under an applied strain gradient. The resulting dynamical coupling is expected to give rise to quasiparticle excitations called as magnetoelectronic electromagnon that carries electronic charge and temporal magnetic moment. Here, we report experimental evidence of magnetoelectronic electromagnon in the freestanding degenerately doped p-Si based heterostructure thin film samples. These quasiparticle excitations give rise to long-distance (>100um) spin transport; demonstrated using spatially modulated transverse magneto-thermoelectric and non-local resistance measurements. The magnetoelectronic electromagnons are non-reciprocal and give rise to large magnetochiral anisotropy (0.352 A-1T-1) that diminishes at lower temperatures. The superposition of non-reciprocal magnetoelectronic electromagnons gives rise to longitudinal and transverse modulations in charge carrier density, spin density and magnetic moment; demonstrated using the Hall effect and edge dependent magnetoresistance measurements, which can also be called as inhomogeneous magnetoelectronic multiferroic effect. These quasiparticle excitations are analogues to photons where time dependent polarization and temporal magnetic moment replaces electric and magnetic field, respectively and most likely topological because it manifests topological Nernst effect. Hence, the magnetoelectronic electromagnon can potentially give rise to quantum interference and entanglement effects in conducting solid state system at room temperature in addition to efficient spin transport.

cond-mat.mes-hall↗

arXiv2023

Bioconvection in a phototactic algae suspension with oblique irradiation and forward anisotropic scattering

In this study, we analyze the bioconvection in a suspension of phototactic algae that exhibits anisotropic scattering. The top layer of the suspension is illuminated by oblique collimated irradiation. During the study, the bottom boundary is considered as rigid whereas the top boundary is considered stress-free. In order to solve the eigenvalue problem, the Newton-Raphson-Kantorovich finite difference method of order four is used. Linear analysis of the basic state is performed using neutral curves. The results demonstrate a change in the most unstable mode from an overstable to a stationary state or vice versa for particular parameters in response to a variation in the incidence angle. The position of the maximum basic concentration shifts toward the top of the suspension as the incidence angle is increased. In most cases, the system becomes more unstable with an increment in the incidence angle.

math.DS↗

arXiv2022

On Lagrangian Coherent Structures in Laparoscopy

Laparoscopy is an electrosurgical medical operation often involving an application of high-frequency alternating current to remove undesired biological tissue from the insufflated abdomen accessible through inlet and outlets trocars. One of the main byproducts in this process are the gaseous particles, called surgical smoke, which is found hazardous for both the patient and the operating room staff. The elimination of this hazardous material is an area of active research in the medical community. Thus, understanding dynamics influenced by the underlying flow inside the abdomen is crucial. In this article, we propose a computational fluid dynamics model and analyse the velocity field in an insufflated abdomen shaped domain by identifying the Lagrangian Coherent Structures (LCS) that are responsible for the transportation, mixing and accumulation of the material particles in the flow. By calculating the mixing strength we show that the regions revealed by these material curves are dependent on the angle, positions and number of the outlets and inlets. Hence, a novel utility of LCS in medical surgery is presented that can detail the dynamics of surgical smoke informing better design of effective smoke removal technologies.

physics.flu-dyn↗

arXiv2022

Forecasting formation of a Tropical Cyclone Using Reanalysis Data

The tropical cyclone formation process is one of the most complex natural phenomena which is governed by various atmospheric, oceanographic, and geographic factors that varies with time and space. Despite several years of research, accurately predicting tropical cyclone formation remains a challenging task. While the existing numerical models have inherent limitations, the machine learning models fail to capture the spatial and temporal dimensions of the causal factors behind TC formation. In this study, a deep learning model has been proposed that can forecast the formation of a tropical cyclone with a lead time of up to 60 hours with high accuracy. The model uses the high-resolution reanalysis data ERA5 (ECMWF reanalysis 5th generation), and best track data IBTrACS (International Best Track Archive for Climate Stewardship) to forecast tropical cyclone formation in six ocean basins of the world. For 60 hours lead time the models achieve an accuracy in the range of 86.9% - 92.9% across the six ocean basins. The model takes about 5-15 minutes of training time depending on the ocean basin, and the amount of data used and can predict within seconds, thereby making it suitable for real-life usage.

physics.ao-ph↗

arXiv2022

Magnetic induction processes in Hot Jupiters, application to KELT-9b

The small semi-major axes of Hot Jupiters lead to high atmospheric temperatures of up to several thousand Kelvin. Under these conditions, thermally ionised metals provide a rich source of charged particles and thus build up a sizeable electrical conductivity. Subsequent electromagnetic effects, such as the induction of electric currents, Ohmic heating, magnetic drag, or the weakening of zonal winds have thus far been considered mainly in the framework of a linear, steady-state model of induction. For Hot Jupiters with an equilibrium temperature $T_{eq} > 1500$ K, the induction of atmospheric magnetic fields is a runaway process that can only be stopped by non-linear feedback. For example, the back-reaction of the magnetic field onto the flow via the Lorentz force or the occurrence of magnetic instabilities. Moreover, we discuss the possibility of self-excited atmospheric dynamos. Our results suggest that the induced atmospheric magnetic fields and electric currents become independent of the electrical conductivity and the internal field, but instead are limited by the planetary rotation rate and wind speed. As an explicit example, we characterise the induction process for the hottest exoplanet, KELT-9b by calculating the electrical conductivity along atmospheric $P-T$-profiles for the day- and nightside. Despite the temperature varying between 3000 K and 4500 K, the resulting electrical conductivity attains an elevated value of roughly 1 S/m throughout the atmosphere. The induced magnetic fields are predominately horizontal and might reach up to a saturation field strength of 400 mT, exceeding the internal field by two orders of magnitude.

astro-ph.EP↗

arXiv2022

A Unified Framework for Optimization-Based Graph Coarsening

Graph coarsening is a widely used dimensionality reduction technique for approaching large-scale graph machine learning problems. Given a large graph, graph coarsening aims to learn a smaller-tractable graph while preserving the properties of the originally given graph. Graph data consist of node features and graph matrix (e.g., adjacency and Laplacian). The existing graph coarsening methods ignore the node features and rely solely on a graph matrix to simplify graphs. In this paper, we introduce a novel optimization-based framework for graph dimensionality reduction. The proposed framework lies in the unification of graph learning and dimensionality reduction. It takes both the graph matrix and the node features as the input and learns the coarsen graph matrix and the coarsen feature matrix jointly while ensuring desired properties. The proposed optimization formulation is a multi-block non-convex optimization problem, which is solved efficiently by leveraging block majorization-minimization, $\log$ determinant, Dirichlet energy, and regularization frameworks. The proposed algorithms are provably convergent and practically amenable to numerous tasks. It is also established that the learned coarsened graph is $ε\in(0,1)$ similar to the original graph. Extensive experiments elucidate the efficacy of the proposed framework for real-world applications.

stat.ML↗

arXiv2022

Decentralized and stable matching in Peer-to-Peer energy trading

In peer-to-peer (P2P) energy trading, a secured infrastructure is required to manage trade and record monetary transactions. A central server/authority can be used for this. But there is a risk of central authority influencing the energy price. So blockchain technology is being preferred as a secured infrastructure in P2P trading. Blockchain provides a distributed repository along with smart contracts for trade management. This reduces the influence of central authority in trading. However, these blockchain-based systems still rely on a central authority to pair/match sellers with consumers for trading energy. The central authority can interfere with the matching process to profit a selected set of users. Further, a centralized authority also charges for its services, thereby increasing the cost of energy. We propose two distributed mechanisms to match sellers with consumers. The first mechanism doesn't allow for price negotiations between sellers and consumers, whereas the second does. We also calculate the time complexity and the stability of the matching process for both mechanisms. Using simulation, we compare the influence of centralized control and energy prices between the proposed and the existing mechanisms. The overall work strives to promote the free market and reduce energy prices.

cs.GT↗

arXiv2022

On the Integral and Derivative Identities of Bivariate Fox H-Function: Application in Wireless System Performance Analysis

The present work proposes analytical solutions for the integral of bivariate Fox H-function in combination with algebraic, exponential, and complementary error functions. In addition, the work also presents the derivative identities with respect to function arguments. Further, the suitability of the proposed mathematical solutions is verified with reference to wireless communication environment, where a fading behaviour of the channel acquired the bivariate Fox H-function structure. Further more, asymptotic results for the outage probability and average symbol error probability are presented utilizing the origin probability density function based approach. The obtained results are free from complex analytical functions. At last, the analytical findings of the paper are compared with the numerical results and also with the Monte-Carlo simulation results to confirm their accuracy.

cs.IT↗

arXiv2022

Enhancement to Training of Bidirectional GAN : An Approach to Demystify Tax Fraud

Outlier detection is a challenging activity. Several machine learning techniques are proposed in the literature for outlier detection. In this article, we propose a new training approach for bidirectional GAN (BiGAN) to detect outliers. To validate the proposed approach, we train a BiGAN with the proposed training approach to detect taxpayers, who are manipulating their tax returns. For each taxpayer, we derive six correlation parameters and three ratio parameters from tax returns submitted by him/her. We train a BiGAN with the proposed training approach on this nine-dimensional derived ground-truth data set. Next, we generate the latent representation of this data set using the $encoder$ (encode this data set using the $encoder$) and regenerate this data set using the $generator$ (decode back using the $generator$) by giving this latent representation as the input. For each taxpayer, compute the cosine similarity between his/her ground-truth data and regenerated data. Taxpayers with lower cosine similarity measures are potential return manipulators. We applied our method to analyze the iron and steel taxpayers data set provided by the Commercial Taxes Department, Government of Telangana, India.

cs.LG↗

arXiv2022

Robust Graph Neural Networks using Weighted Graph Laplacian

Graph neural network (GNN) is achieving remarkable performances in a variety of application domains. However, GNN is vulnerable to noise and adversarial attacks in input data. Making GNN robust against noises and adversarial attacks is an important problem. The existing defense methods for GNNs are computationally demanding and are not scalable. In this paper, we propose a generic framework for robustifying GNN known as Weighted Laplacian GNN (RWL-GNN). The method combines Weighted Graph Laplacian learning with the GNN implementation. The proposed method benefits from the positive semi-definiteness property of Laplacian matrix, feature smoothness, and latent features via formulating a unified optimization framework, which ensures the adversarial/noisy edges are discarded and connections in the graph are appropriately weighted. For demonstration, the experiments are conducted with Graph convolutional neural network(GCNN) architecture, however, the proposed framework is easily amenable to any existing GNN architecture. The simulation results with benchmark dataset establish the efficacy of the proposed method, both in accuracy and computational efficiency. Code can be accessed at https://github.com/Bharat-Runwal/RWL-GNN.

cs.LG↗

arXiv2022

Probing of large interfacial contribution to spin orbit coupling in CoFeB/Ta heterostructure by ultrafast THz emission spectroscopy

Ultrafast THz radiation generation from ferromagnetic/nonmagnetic bilayer heterostructure-based spintronic emitters generally exploits the conversion from spin- to charge-current within the nonmagnetic layer and its interface with the ferromagnetic layer. Various possible sub-contributions to the underlying mechanism of inverse spin Hall effect for the THz emission from such structures, need to be exploited for not only investigating the intricacies at the fundamental level in the material properties themselves but also for improving their performance for broadband and high-power THz emission. Here, we report ultrafast THz emission from CoFeB/Ta bilayer at varying sample temperatures in a large range to unravel the role of intrinsic and extrinsic spin to charge conversion processes. In addition to an enhancement in the THz emission, its temperature dependence shows a THz signal polarity reversal if the CoFeB/Ta sample is annealed at an elevated temperature. We extract the behaviour of the spin Hall resistivity, determine the intrinsic spin Hall conductivity contribution in it and compare those with the standard Fe/Pt system. Our results clearly demonstrate a giant interfacial contribution to the overall spin Hall angle arising from the modified interface in the annealed CoFeB/Ta, where a sign reversal in the corresponding spin Hall angle is manifested from the THz amplitude variation with the temperature.

cond-mat.mes-hall↗

arXiv2022

A New Monte-Carlo Radiative Transfer Simulation of Cyclotron Resonant Scattering Features

We present a new Monte-Carlo radiative transfer code, which we have used to model the cyclotron line features in the environment of a variable magnetic field and plasma density. The code accepts an input continuum and performs only the line transfer by including the three cyclotron resonant processes (cyclotron absorption, cyclotron emission, cyclotron scattering). Subsequently, the effects of gravitational red-shift and light bending on the emergent spectra are computed. We have applied our code to predict the observable spectra from three different emission geometries; 1) an optically thin slab near the stellar surface, 2) an accretion mound formed by the accumulation of the accreted matter, 3) an accretion column representing the zone of a settling flow onto the star. Our results show that the locally emergent spectra from the emission volume are significantly anisotropic. However, in the presence of strong light bending the anisotropy reduces considerably. This averaging also drastically reduces the strength of harmonics higher than second in the observable cyclotron spectra. We find that uniform field slabs produce line features that are too narrow, and mounds with large magnetic distortions produce features that are too wide compared to the average widths of the spectral features observed from various sources. The column with a gently varying (dipole) field produces widths in the intermediate range, similar to those observed.

astro-ph.HE↗

arXiv2022

Parallel Multiphysics Simulation for the Stabilized Optimal Transportation Meshfree (OTM)

This paper presents a parallel \PG{implementation} for the Optimal Transportation Meshfree (OTM) method on large CPU clusters. Communications are handled with the Message Passing Interface (MPI). The Recursive Coordinate Bisection (RCB) algorithm is utilized for domain decomposition and for implementing dynamic load-balancing strategy. This work involves three new concepts to reduce the computational efforts: Dynamic halo regions, Efficient data management strategies for ease of addition and deletion of nodes and material points using advanced STL container, and nearest neighborhood communication for detection of neighbors and communication. Also, Linked Cell approach has been implemented to further reduce the computational efforts. Parallel performance analysis is investigated for challenging multiphysics applications like Taylor rod impact and serrated chip formation process. Adequate scalability of parallel implementation for these applications is reported.

cs.CE↗

arXiv2022

A Comprehensive Benchmark Suite for Intel SGX

Trusted execution environments (TEEs) such as \intelsgx facilitate the secure execution of an application on untrusted machines. Sadly, such environments suffer from serious limitations and performance overheads in terms of writing back data to the main memory, their interaction with the OS, and the ability to issue I/O instructions. There is thus a plethora of work that focuses on improving the performance of such environments -- this necessitates the need for a standard, widely accepted benchmark suite (something similar to SPEC and PARSEC). To the best of our knowledge, such a suite does not exist. Our suite, SGXGauge, contains a diverse set of workloads such as blockchain codes, secure machine learning algorithms, lightweight web servers, secure key-value stores, etc. We thoroughly characterizes the behavior of the benchmark suite on a native platform and on a platform that uses a library OS-based shimming layer (GrapheneSGX). We observe that the most important metrics of interest are performance counters related to paging, memory, and TLB accesses. There is an abrupt change in performance when the memory footprint starts to exceed the size of the EPC size in Intel SGX, and the library OS does not add a significant overhead (~ +- 10%).

cs.CR↗

arXiv2022

SMU: smooth activation function for deep networks using smoothing maximum technique

Deep learning researchers have a keen interest in proposing two new novel activation functions which can boost network performance. A good choice of activation function can have significant consequences in improving network performance. A handcrafted activation is the most common choice in neural network models. ReLU is the most common choice in the deep learning community due to its simplicity though ReLU has some serious drawbacks. In this paper, we have proposed a new novel activation function based on approximation of known activation functions like Leaky ReLU, and we call this function Smooth Maximum Unit (SMU). Replacing ReLU by SMU, we have got 6.22% improvement in the CIFAR100 dataset with the ShuffleNet V2 model.

cs.LG↗

arXiv2022

ErfAct and Pserf: Non-monotonic Smooth Trainable Activation Functions

An activation function is a crucial component of a neural network that introduces non-linearity in the network. The state-of-the-art performance of a neural network depends also on the perfect choice of an activation function. We propose two novel non-monotonic smooth trainable activation functions, called ErfAct and Pserf. Experiments suggest that the proposed functions improve the network performance significantly compared to the widely used activations like ReLU, Swish, and Mish. Replacing ReLU by ErfAct and Pserf, we have 5.68% and 5.42% improvement for top-1 accuracy on Shufflenet V2 (2.0x) network in CIFAR100 dataset, 2.11% and 1.96% improvement for top-1 accuracy on Shufflenet V2 (2.0x) network in CIFAR10 dataset, 1.0%, and 1.0% improvement on mean average precision (mAP) on SSD300 model in Pascal VOC dataset.

cs.NE↗

arXiv2022

Flexoelectronic doping of the degenerate silicon and the correlated electron behavior

In metal/degenerately doped silicon bilayer structure, the interfacial flexoelectric effect due to strain gradient leads to charge carrier transfer from metal layer to the silicon layer. This excess charge carrier concentration is called flexoelectronic doping or flexoelectronic charge transfer, which gives rise to an electronically polarized (order of magnitude larger than ferroelectric materials) silicon layer. In the transport measurements, the charge carrier concentration in silicon is found to increase by two orders of magnitude due to flexoelectronic doping, which changes the Fermi level and the Hall response. The flexoelectronic charge accumulation modifies the electron-electron and the electron phonon coupling, which gives rise to Mott metal-insulator transition and magnetism of phonons, respectively. The coexistence of flexoelectronic polarization and magnetism gives rise to a new class of materials called electronic multiferroics. By controlling the flexoelectronic doping, material behavior can potentially be engineered for quantum, spintronics and electronics applications in semiconductor materials.

cond-mat.mtrl-sci↗