arXiv subjects

Ashish Kumar

Publications and source records attributed to Ashish Kumar.

At least 73 records · Page 4Linked to original sources

arXiv2020

Accurate effective potential for density amplitude and the corresponding Kohn-Sham exchange-correlation potential calculated from approximate wavefunctions

Over the past few years it has been pointed out that direct inversion of accurate but approximate ground state densities leads to Kohn-Sham exchange-correlation (xc) potentials that can differ significantly from the exact xc potential of a given system. On the other hand, the corresponding wavefunction based construction of exchange-correlation potential as done by Baerends et al. and Staroverov et al. obviates such problems and leads to potentials that are very close to the true xc potential. In this paper, we provide an understanding of why the wavefunction based approach gives the exchange-correlation potential accurately. Our understanding is based on the work of Levy, Perdew and Sahni (LPS) who gave an equation for the square root of density (density amplitude) and the expression for the associated effective potential in the terms of the corresponding wavefunction. We show that even with the use of approximate wavefunctions the LPS expression gives accurate effective and exchange-correlation potentials. Based on this we also identify the source of difference between the potentials obtained from a wavefunction and those given by the inversion of the associated density. Finally, we suggest exploring the possibility of obtaining accurate ground-state density from an approximate wavefunction for a system by making use of the LPS effective potential.

physics.atom-ph↗

arXiv2020

Distress propagation on production networks: Coarse-graining and modularity of linkages

Distress propagation occurs in connected networks, its rate and extent being dependent on network topology. To study this, we choose economic production networks as a paradigm. An economic network can be examined at many levels: linkages among individual agents (microscopic), among firms/sectors (mesoscopic) or among countries (macroscopic). New emergent dynamical properties appear at every level, so the granularity matters. For viral epidemics, even an individual node may act as an epicenter of distress and potentially affect the entire network. Economic networks, however, are known to be immune at the micro-levels and more prone to failure in the meso/macro-levels. We propose a dynamical interaction model to characterize the mechanism of distress propagation, across different modules of a network, initiated at different epicenters. Vulnerable modules often lead to large degrees of destabilization. We demonstrate our methodology using a unique empirical data-set of input-output linkages across 0.14 million firms in one administrative state of India, a developing economy. The network has multiple hub-and-spoke structures that exhibits moderate disassortativity, which varies with the level of coarse-graining. The novelty lies in characterizing the production network at different levels of granularity or modularity, and finding `too-big-to-fail' modules supersede `too-central-to-fail' modules in distress propagation.

physics.soc-ph↗

arXiv2020

Domain Independent Unsupervised Learning to grasp the Novel Objects

One of the main challenges in the vision-based grasping is the selection of feasible grasp regions while interacting with novel objects. Recent approaches exploit the power of the convolutional neural network (CNN) to achieve accurate grasping at the cost of high computational power and time. In this paper, we present a novel unsupervised learning based algorithm for the selection of feasible grasp regions. Unsupervised learning infers the pattern in data-set without any external labels. We apply k-means clustering on the image plane to identify the grasp regions, followed by an axis assignment method. We define a novel concept of Grasp Decide Index (GDI) to select the best grasp pose in image plane. We have conducted several experiments in clutter or isolated environment on standard objects of Amazon Robotics Challenge 2017 and Amazon Picking Challenge 2016. We compare the results with prior learning based approaches to validate the robustness and adaptive nature of our algorithm for a variety of novel objects in different domains.

cs.CV↗

arXiv2019

Investigation of Thermoelectric properties of Magnetic Insulator FeRuTiSi Using First Principle Calculation

In this work, we have investigated the electronic structure and thermoelectric properties of quaternary heusler alloy, FeRuTiSi, using first principle DFT tools implemented in WIEN2k and BoltzTraP code. Electronic structure calculations using TB-mBJ potential shows appearance of flat band at the conduction band edge, thus electron in conduction band have the large effective mass (me*), and therefore mainly contribute for negatively large value of Seebeck coefficient (S). This alloy has indirect band gap of 0.59 eV, and shows the n-type transport behavior. Under the constant relaxation time approximation (tau = 10 -14 s), temperature dependent Seebeck coefficient, electrical conductivity (sigma), and electronic thermal conductivity (ke) were also estimated. The maximum figure-of-merit (ZT), for the FeRuTiSi compound is found to be ~0.86 at 840 K, with n-type doping, which suggests that this quaternary alloy can be a good candidate among the n-type material for thermoelectric applications in high-temperature reg

cond-mat.str-el↗

arXiv2019

Thermoelectric properties of Half-Metallic FeMnScGa Using First Principle Calculation

Here, we have investigated the thermoelectric properties of FeMnScGa alloy by combined use of full potential linearized augmented plane-wave (FP-LAPW) method and Boltzmann transport theory implemented in Wien2K and BoltzTraP code, respectively. Using the TB-mBJ potential, half-metallic nature is clearly observed with energy band gap of ~0.41 eV for down spin channel. Transport coefficients in 200-1000 K temperature range are investigated under the constant relaxation time approximations (tau=10-14 s). Total contributions to the Seebeck coefficients from up and dn-spin channels are estimated by using two-current model. The calculated values of Seebeck coefficients are found to be negative in the entire temperature region under present study, which suggests the n-type characteristic of this alloy. The magnitude of Seebeck coefficient is found to be increasing with temperature and exhibit the large value ~ -87 micro-V/K in 700-1000 K temperature range. The maximum in magnitude of total electrical conductivity value is found to be ~2.0 x 105 ohm-1m-1 at 1000 K. The present study shows, with half-metallic electronic structure, FeMnScGa have very good thermoelectric behavior, and can be suitable for thermoelectric application in high temperature region.

cond-mat.str-el↗

arXiv2019

Learning Navigation Subroutines from Egocentric Videos

Planning at a higher level of abstraction instead of low level torques improves the sample efficiency in reinforcement learning, and computational efficiency in classical planning. We propose a method to learn such hierarchical abstractions, or subroutines from egocentric video data of experts performing tasks. We learn a self-supervised inverse model on small amounts of random interaction data to pseudo-label the expert egocentric videos with agent actions. Visuomotor subroutines are acquired from these pseudo-labeled videos by learning a latent intent-conditioned policy that predicts the inferred pseudo-actions from the corresponding image observations. We demonstrate our proposed approach in context of navigation, and show that we can successfully learn consistent and diverse visuomotor subroutines from passive egocentric videos. We demonstrate the utility of our acquired visuomotor subroutines by using them as is for exploration, and as sub-policies in a hierarchical RL framework for reaching point goals and semantic goals. We also demonstrate behavior of our subroutines in the real world, by deploying them on a real robotic platform. Project website: https://ashishkumar1993.github.io/subroutines/.

cs.RO↗

arXiv2019

Apparatus for Seebeck coefficient measurement of wire, thin film and bulk materials in the wide temperature range (80-650K)

A Seebeck coefficient measurement apparatus has been designed and developed, which is very effective for accurate characterization of different type of samples in a wide temperature range (80 - 650K) simultaneously covering low as well as the high-temperature regime. Reducing the complexity of the technical design of sample holder and data collections has always been challenging to implement in a single instrument when samples are in different geometrical shape and electronic structure. Our unique design of sample holder with pressure probes covers measurements of different samples shapes (wires, thin films and pellets) as well as different resistivity ranges (metals, semiconductors and insulators). It is suitable for characterization of different samples sizes (3-12 mm). A double heater configuration powered by a dual channel source meter is employed for maintaining a desired constant temperature difference across the sample for the whole temperature range. Two K-type thermocouples are used for simultaneously reading of temperatures and Seebeck voltages by utilizing different channels of a multichannel digital multimeter. Calibration of the system has been carried out using constantan, chromel and alumel materials and recorded data is found to be very accurate and consistent with earlier reports. The Seebeck coefficients of standard samples of constantan (wire) and GaN (thin film) have been reported, which shows the measurement capability of designed setup with versatile samples.

physics.app-ph↗

arXiv2019

Unified Graph based Multi-Cue Feature Fusion for Robust Visual Tracking

Visual Tracking is a complex problem due to unconstrained appearance variations and dynamic environment. Extraction of complementary information from the object environment via multiple features and adaption to the target's appearance variations are the key problems of this work. To this end, we propose a robust object tracking framework based on Unified Graph Fusion (UGF) of multi-cue to adapt to the object's appearance. The proposed cross-diffusion of sparse and dense features not only suppresses the individual feature deficiencies but also extracts the complementary information from multi-cue. This iterative process builds robust unified features which are invariant to object deformations, fast motion, and occlusion. Robustness of the unified feature also enables the random forest classifier to precisely distinguish the foreground from the background, adding resilience to background clutter. In addition, we present a novel kernel-based adaptation strategy using outlier detection and a transductive reliability metric.

cs.CV↗

arXiv2019

Semiconductor to metal transition for Ni doped CdO thin films

In the present investigation resistivity Vs temperature measurements have been performed for solgel prepared pure and Ni doped CdO (NDO) thin films. The semiconducting behavior i.e. negative temperature coefficient of resistivity (TCR) of pure CdO thin film through-out the whole temperature range can be attributed mainly due to concentration dependent thermally activation mechanism of the carriers. The semiconductor to metal transition (SMT) for NDO thin films has been elucidated via combined effect of involved grain boundaries, ionic impurities, phonon scattering and carrier activation.

cond-mat.mtrl-sci↗

arXiv2019

FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network

This paper develops the FastRNN and FastGRNN algorithms to address the twin RNN limitations of inaccurate training and inefficient prediction. Previous approaches have improved accuracy at the expense of prediction costs making them infeasible for resource-constrained and real-time applications. Unitary RNNs have increased accuracy somewhat by restricting the range of the state transition matrix's singular values but have also increased the model size as they require a larger number of hidden units to make up for the loss in expressive power. Gated RNNs have obtained state-of-the-art accuracies by adding extra parameters thereby resulting in even larger models. FastRNN addresses these limitations by adding a residual connection that does not constrain the range of the singular values explicitly and has only two extra scalar parameters. FastGRNN then extends the residual connection to a gate by reusing the RNN matrices to match state-of-the-art gated RNN accuracies but with a 2-4x smaller model. Enforcing FastGRNN's matrices to be low-rank, sparse and quantized resulted in accurate models that could be up to 35x smaller than leading gated and unitary RNNs. This allowed FastGRNN to accurately recognize the "Hey Cortana" wakeword with a 1 KB model and to be deployed on severely resource-constrained IoT microcontrollers too tiny to store other RNN models. FastGRNN's code is available at https://github.com/Microsoft/EdgeML/.

cs.LG↗

arXiv2018

Visual Memory for Robust Path Following

Humans routinely retrace paths in a novel environment both forwards and backwards despite uncertainty in their motion. This paper presents an approach for doing so. Given a demonstration of a path, a first network generates a path abstraction. Equipped with this abstraction, a second network observes the world and decides how to act to retrace the path under noisy actuation and a changing environment. The two networks are optimized end-to-end at training time. We evaluate the method in two realistic simulators, performing path following and homing under actuation noise and environmental changes. Our experiments show that our approach outperforms classical approaches and other learning based baselines.

cs.CV↗

arXiv2018

Robust Object Tracking with Crow Search Optimized Multi-cue Particle Filter

Particle Filter(PF) is used extensively for estimation of target Non-linear and Non-gaussian state. However, its performance suffers due to inherent problem of sample degeneracy and impoverishment. In order to address this, we propose a novel resampling method based upon Crow Search Optimization to overcome low performing particles detected as outlier. Proposed outlier detection mechanism with transductive reliability achieve faster convergence of proposed PF tracking framework. In addition, we present an adaptive fuzzy fusion model to integrate multi-cue extracted for each evaluated particle. Automatic boosting and suppression of particles using proposed fusion model not only enhances performance of resampling method but also achieve optimal state estimation. Performance of the proposed tracker is evaluated over 12 benchmark video sequences and compared with state-of-the-art solutions. Qualitative and quantitative results reveals that the proposed tracker not only outperforms existing solutions but also efficiently handle various tracking challenges. On average of outcome, we achieve CLE of 7.98 and F-measure of 0.734.

cs.CV↗

arXiv2018

Universal nature of different methods of obtaining the exact Kohn-Sham exchange-correlation potential for a given density

An interesting fundamental problem in density-functional theory of electronic structure of matter is to construct the exact Kohn-Sham (KS) potential for a given density. The exact potential can then be used to assess the accuracy of approximate functionals and the corresponding potentials. Besides its practical usefulness, such a construction by itself is a challenging inverse problem. Over the past three decades, many seemingly disjoint methods have been proposed to solve this problem. We show that these emanate from a single algorithm based on the Euler equation for the density. This provides a mathematical foundation for all different density-based methods that are used to construct the KS system from a given density and reveals their universal character.

physics.atm-clus↗

arXiv2017

Exact solution of long-range electron transfer through conjugated molecular bridge

Intermolecular electron transfer reaction often occurs over long range distances (i.e. up to several tens of angstroms) and plays a key role in various physical, chemical and biological processes. In these reactions the rate constant of long range electron transfer depends upon electronic coupling between the donor and acceptor. The coupling between donor and acceptor may increases by the atoms located between them which form a kind of bridge for electron tunneling. By using exact analytical method we calculated the value of electronic coupling for the above said processes in which the interaction of an electron with the donor, acceptor are represented as Dirac delta functions and conjugated bridge is represented by finite square well.

physics.chem-ph↗

arXiv2017

Decentralized demand response for temperature-constrained appliances

The evolution of the power grid towards the so-called Smart Grid, where information technologies help improve the efficiency of electricity production, distribution and consumption, allows to use the fine-grained control brought by the Internet of Things capabilities to perform distributed demand response when requested by the grid. In this paper, we study the demand-response potential of coordinated large numbers of appliances which have to maintain some temperature within a fixed range through the ON/OFF functioning of a temperature modifier. We introduce a mathematical model and methods to coordinate appliances with given requirements, in order to offer a global energy demand reduction for a desirable duration while still satisfying the temperature constraints, and with limited communication overhead. We quantify the maximum power reduction that can be attained, as a function of the reduction duration asked by the grid.

math.OC↗

arXiv2017

Design and Development of an automated Robotic Pick & Stow System for an e-Commerce Warehouse

In this paper, we provide details of a robotic system that can automate the task of picking and stowing objects from and to a rack in an e-commerce fulfillment warehouse. The system primarily comprises of four main modules: (1) Perception module responsible for recognizing query objects and localizing them in the 3-dimensional robot workspace; (2) Planning module generates necessary paths that the robot end- effector has to take for reaching the objects in the rack or in the tote; (3) Calibration module that defines the physical workspace for the robot visible through the on-board vision system; and (4) Gripping and suction system for picking and stowing different kinds of objects. The perception module uses a faster region-based Convolutional Neural Network (R-CNN) to recognize objects. We designed a novel two finger gripper that incorporates pneumatic valve based suction effect to enhance its ability to pick different kinds of objects. The system was developed by IITK-TCS team for participation in the Amazon Picking Challenge 2016 event. The team secured a fifth place in the stowing task in the event. The purpose of this article is to share our experiences with students and practicing engineers and enable them to build similar systems. The overall efficacy of the system is demonstrated through several simulation as well as real-world experiments with actual robots.

cs.RO↗

arXiv2015

Multi-orbital quantum antiferromagnetism in iron pnictides --- effective spin couplings and quantum corrections to sublattice magnetization

Towards understanding the multi-orbital quantum antiferromagnetism in iron pnictides, effective spin couplings and spin fluctuation induced quantum corrections to sublattice magnetization are obtained in the $(π,0)$ AF state of a realistic three band interacting electron model involving $xz$, $yz$, and $xy$ Fe 3d orbitals. The $xy$ orbital is found to be mainly responsible for the generation of strong ferromagnetic spin coupling in the $b$ direction, which is critically important to fully account for the spin wave dispersion as measured in inelastic neutron scattering experiments. The ferromagnetic spin coupling is strongly suppressed as the $xy$ band approaches half filling, and is ascribed to particle-hole exchange in the partially filled $xy$ band. The strongest AF spin coupling in the $a$ direction is found to be in the orbital off diagonal sector involving the $xz$ and $xy$ orbitals. First order quantum corrections to sublattice magnetization are evaluated for the three orbitals, and yield a significant $37\%$ average reduction from the Hartree-Fock value.

cond-mat.str-el↗

arXiv2015

The effect of premature wall yield on creep testing of strongly-flocculated suspensions

Measuring yielding in cohesive suspensions is often hampered by slip at measurement surfaces. This paper presents creep data for strongly-flocculated suspensions obtained using vane-in-cup tools with differing cup-to-vane diameter ratios. The three suspensions were titania and alumina aggregated at their isoelectric points and polymer-flocculated alumina. The aim was to find the diameter ratio where slip or premature yielding at the cup wall had no effect on the transient behaviour. The large diameter ratio results showed readily understandable material behaviour comprising linear viscoelasticity at low stresses, strain-softening close to yielding, time-dependent yield across a range of stresses and then viscous flow. Tests in small ratio geometries however showed more complex responses. Effects attributed to the cup wall included delayed softening, slip, multiple yielding and stick-slip events, and unsteady flow. The conclusion was that cups have to be relatively large to eliminate wall artefacts. A diameter ratio of three was sufficient in practice, although the minimum ratio must be material dependent.

cond-mat.soft↗