arXiv subjects

Gaurav Kumar

Publications and source records attributed to Gaurav Kumar.

At least 55 records · Page 3Linked to original sources

arXiv2022

Hybrid 2D-QD MoS2 PbSe Quantum Dot Broadband Photodetectors with High-Sensitivity and Room-Temperature Operation at 2.5 μm

Broadband infrared photodetectors have profound importance in diverse applications including security, gas sensing, bioimaging, spectroscopy for food quality, and recycling, just to name a few. Yet, these applications can currently be served by expensive epitaxially grown photodetectors, limiting their market potential and social impact. The use of colloidal quantum dots (CQDs) and 2D-materials in a hybrid layout is an attractive alternative to design low-cost CMOS-compatible infrared photodetectors. However, the spectral sensitivity of these conventional hybrid detectors has been restricted to 2.1 um. Herein, we present a hybrid structure comprising MoS2 with PbSe CQDs to extend their sensitivity further towards the mid-wave infrared, up to 3 um. We achieve room temperature responsivity of 137.6 A/W and a detectivity of 7.7 10^10 Jones at 2.55 um owing to highly efficient photoexcited carrier separation at the interface of MoS2 and PbSe in combination with an oxide-coating to reduce dark current; the highest value yet for a PbSe based hybrid device. These findings strongly support the successful fabrication of hybrid devices which may pave the pathway for cost-effective, high performance, next-generation, novel photodetectors.

physics.app-ph↗

arXiv2022

Computation offloading to hardware accelerators in Intel SGX and Gramine Library OS

The Intel Software Guard Extensions (SGX) technology enables applications to run in an isolated SGX enclave environment, with elevated confidentiality and integrity guarantees. Gramine Library OS facilitates execution of existing unmodified applications in SGX enclaves, requiring only an accompanying manifest file that describes the application's security posture and configuration. However, Intel SGX is a CPU-only technology, thus Gramine currently supports CPU-only workloads. To enable a broader class of applications that offload computations to hardware accelerators - GPU offload, NIC offload, FPGA offload, TPM communications - Gramine must be augmented with device-backed mmap support and generic ioctl support. In this paper, we describe the design and implementation of this newly added support, the corresponding changes to the manifest-file syntax and the requisite deep copy algorithm. We evaluate our implementation on Intel Media SDK workloads and discuss the encountered caveats and limitations. Finally, we outline a use case for the presented mmap/ioctl support beyond mere device communication, namely the mechanism to slice the application into the trusted enclave part (where the core application executes) and the untrusted shared-memory part (where insecure shared libraries execute).

cs.CR↗

arXiv2021

Membrane budding driven by intra-cellular ESCRT-III filaments

Exocytosis is a common transport mechanism via which cells transport out non-essential macro-molecules (cargo) into the extra cellular space. ESCRT-III proteins are known to help in this. They polymerize into a conical spring like structure and help deform the cell membrane locally into a bud which wrapps the outgoing cargo. we model this process using a continuum energy functional. It consists of elastic energies of the membrane and the semi-rigid ESCRT-III filament, favorable adhesion energy between the cargo and the membrane, and affinity among the ESCRT-III filaments. We take the free energy minimization route to identify the sequence of composite structures which form during the process. We show that membrane adhesion of the cargo is the driving force for this budding process and not the buckling of ESCRT-III filaments from flat spiral to conical spring shape. However ESCRT-III stabilizes the bud once it forms. Further we conclude that a non-equilibrium process is needed to pinch off/separate the stable bud (containing the cargo) from the cell body.

cond-mat.soft↗

arXiv2021

3-phonon scattering pathways for vibrational energy transfer in crystalline RDX

A long-held belief is that shock energy induces initiation of an energetic material through an indirect energy up-pumping mechanism involving phonon scattering through doorway modes. In this paper, a 3-phonon theoretical analysis of energy up-pumping in RDX is presented that involves both direct and indirect pathways where the direct energy transfer dominates. The calculation considers individual phonon modes which are then analyzed in bands. Scattering is handled up to the third order term in the Hamiltonian based on Fermi's Golden Rule. On average, modes with frequencies up to 90 cm-1 scatter quickly and redistribute the energy to all the modes. This direct stimulation occurs rapidly, within 0.16 ps, and involves distortions to NN bonds. Modes from 90 to 1839 cm-1 further up-pump the energy to NN bond distortion modes through an indirect route within 5.6 ps. The highest frequency modes have the lowest contribution to energy transfer due to their lower participation in phonon-phonon scattering. The modes stimulated directly by the shock with frequencies up to 90 cm-1 are estimated to account for 52 to 89\% of the total energy transfer to various NN bond distorting modes.

cond-mat.mes-hall↗

arXiv2021

TabPert: An Effective Platform for Tabular Perturbation

To truly grasp reasoning ability, a Natural Language Inference model should be evaluated on counterfactual data. TabPert facilitates this by assisting in the generation of such counterfactual data for assessing model tabular reasoning issues. TabPert allows a user to update a table, change its associated hypotheses, change their labels, and highlight rows that are important for hypothesis classification. TabPert also captures information about the techniques used to automatically produce the table, as well as the strategies employed to generate the challenging hypotheses. These counterfactual tables and hypotheses, as well as the metadata, can then be used to explore an existing model's shortcomings methodically and quantitatively.

cs.CL↗

arXiv2021

Learning Policies for Multilingual Training of Neural Machine Translation Systems

Low-resource Multilingual Neural Machine Translation (MNMT) is typically tasked with improving the translation performance on one or more language pairs with the aid of high-resource language pairs. In this paper, we propose two simple search based curricula -- orderings of the multilingual training data -- which help improve translation performance in conjunction with existing techniques such as fine-tuning. Additionally, we attempt to learn a curriculum for MNMT from scratch jointly with the training of the translation system with the aid of contextual multi-arm bandits. We show on the FLORES low-resource translation dataset that these learned curricula can provide better starting points for fine tuning and improve overall performance of the translation system.

cs.CL↗

arXiv2021

Learning Feature Weights using Reward Modeling for Denoising Parallel Corpora

Large web-crawled corpora represent an excellent resource for improving the performance of Neural Machine Translation (NMT) systems across several language pairs. However, since these corpora are typically extremely noisy, their use is fairly limited. Current approaches to dealing with this problem mainly focus on filtering using heuristics or single features such as language model scores or bi-lingual similarity. This work presents an alternative approach which learns weights for multiple sentence-level features. These feature weights which are optimized directly for the task of improving translation performance, are used to score and filter sentences in the noisy corpora more effectively. We provide results of applying this technique to building NMT systems using the Paracrawl corpus for Estonian-English and show that it beats strong single feature baselines and hand designed combinations. Additionally, we analyze the sensitivity of this method to different types of noise and explore if the learned weights generalize to other language pairs using the Maltese-English Paracrawl corpus.

cs.CL↗

arXiv2021

Role of corner flow separation in unsteady dynamics of hypersonic flow over a double wedge geometry

This study investigates the origin and sustenance of self induced oscillations of shock structures in a hypersonic flow over a double wedge configuration. Previously, various researchers have considered the double wedge flow configuration for inviscid flow with variations of different inflows as well as geometric parameters such as inflow Mach number , wedge angles, and wedge lengths. Few recently published articles reveal an unsteady flow physics involved with the hypersonic viscous flow for double wedge configuration with large second wedge angles. However, the reason for such self sustained flow oscillations is not completely clear. The present work seeks out to investigate the origin of such oscillations in a low enthalpy hypersonic flow with different aft wedge angles and wedge length ratios.In the current study, viscous flow over a double wedge at Mach 7 and fore wedge angle of 30° is considered. An improved version of rhoCentralFoam solver in OpenFOAM is used to investigate the double wedge flow over different grid resolutions in the separation region and shock shock interaction region. This study corroborates the observation from the previous literature with an improvement in the range of parameters which results in a self sustained periodic oscillation. The present study also suggests that the unsteadiness becomes possible when the incidence shock is in the proximity of the aft wedge expansion corner as a consequence of different wedge length ratios(L1/L2) or aft wedge angles (θ2). Flow can still be steady at a large aft wedge angle if the incidence shock is far ahead of the aft wedge expansion corner.

physics.flu-dyn↗

arXiv2021

Prediction of Separation Induced Transition on Thick Airfoil Using Nonlinear URANS Based Turbulence Model

Most of the turbulence models in practice are based on the assumption of a linear relation between Reynolds stresses and mean flow strain rates which generally provides a good approximation in case of attached and fully turbulent flows. A two dimensional numerical study has been carried out over NACA 0021 with k-ωSST model with non-linear correction at Re = 120,000 for various angles of attack which experiences the formation of a laminar separation bubble (LSB). A correct prediction of LSB requires an accurate resolution of anisotropy in Reynolds stresses. For comparison with other linear models, the simulations are also performed with k-kl-ω, k-ωSST and Spalart Allmaras. The performance of these models is assessed through aerodynamic lift, drag, pressure and friction coefficients. It is found that the non-linear k-ωSST and k-kl-ωtransition model provide comparable quality of prediction in lift and drag coefficients (in spite of the fact that non-linear k-ωSST involves solving less number of transport equation than the transition model) as observed in the experiments whereas k-ωSST and SA models under predict the drag coefficient value at low angle of attack due to inability to capture the separation induced transition. It is also observed that the location of laminar separation bubble is captured accurately when nonlinear or transition model is used as opposed to the SA or linear SST models, which lack in the ability to predict the same.

physics.flu-dyn↗

arXiv2021

Investigation of flow structures in a turbulent separating flow using hybrid RANS-LES model

Hybrid Reynolds-averaged Navier Stokes large eddy simulation (RANS LES) methods have become popular for simulation of massively separated flows at high Reynolds numbers due to their reduced computational cost and good accuracy. The current study aims to examine the performance of LES and hybrid RANS-LES model for a given grid resolution. For better assessment and contrast of model performance, both mean and instantaneous flow fields have been investigated. For studying instantaneous flow, proper orthogonal decomposition has been used. Current analysis shows that hybrid RANS-LES is capable of achieving similar accuracy in prediction of both mean and instantaneous flow fields at a very coarse grid as compared to LES. Focusing mostly on the practical applications of computation, most of the attention has been given to the prediction of one-point flow statistics and little consideration has been put to two-point statistics. Here, two-point statistics has been considered using POD to investigate unsteady turbulent flow.

physics.flu-dyn↗

arXiv2021

Investigation of asymmetrically pitching airfoil at high reduced frequency

The expanding application in Micro-Air Vehicles has encouraged many researchers to understand the unsteady flow around a flapping foil at a low Reynolds number. We numerically investigate an incompressible unsteady flow around a two-dimensional pitching airfoil (SD7003) at high reduced frequency in the laminar regime. This study interrogates the effect of different unsteady parameters, namely amplitude (A), reduced frequency (k), Reynolds number (Re), and asymmetry parameter (S) for pitching motion on the force coefficients. The inviscid theoretical model is utilized to calculate the lift coefficient for sinusoidal motion in the viscous regime, and a comparison is made with the numerical results. The theoretical analysis identifies the influence of the non-circulatory lift over circulatory lift at a high reduced frequency. Further, the results indicate that the reduced frequency (k) and asymmetry parameter (S) have a significant impact on the instantaneous and time-averaged force coefficients as well as on the vortex structure in the wake. Finally, the Fast Fourier Transformation (FFT) analysis is carried out over a simulated case with fixed amplitude and Reynolds number for distinct k and S values. The findings confirm that the dominant frequency in the flow (k*) has a direct correlation to the airfoil pitching frequency (k).

physics.flu-dyn↗

arXiv2020

Solving Physics Puzzles by Reasoning about Paths

We propose a new deep learning model for goal-driven tasks that require intuitive physical reasoning and intervention in the scene to achieve a desired end goal. Its modular structure is motivated by hypothesizing a sequence of intuitive steps that humans apply when trying to solve such a task. The model first predicts the path the target object would follow without intervention and the path the target object should follow in order to solve the task. Next, it predicts the desired path of the action object and generates the placement of the action object. All components of the model are trained jointly in a supervised way; each component receives its own learning signal but learning signals are also backpropagated through the entire architecture. To evaluate the model we use PHYRE - a benchmark test for goal-driven physical reasoning in 2D mechanics puzzles.

cs.AI↗

arXiv2020

EinsteinPy: A Community Python Package for General Relativity

This paper presents EinsteinPy (version 0.3), a community-developed Python package for gravitational and relativistic astrophysics. Python is a free, easy to use a high-level programming language which has seen a huge expansion in the number of its users and developers in recent years. Specifically, a lot of recent studies show that the use of Python in Astrophysics and general physics has increased exponentially. We aim to provide a very high level of abstraction, an easy to use interface and pleasing user experience. EinsteinPy is developed keeping in mind the state of a theoretical gravitational physicist with little or no background in computer programming and trying to work in the field of numerical relativity or trying to use simulations in their research. Currently, EinsteinPy supports simulation of time-like and null geodesics and calculates trajectories in different background geometries some of which are Schwarzschild, Kerr, and KerrNewmann along with coordinate inter-conversion pipeline. It has a partially developed pipeline for plotting and visualization with dependencies on libraries like Plotly, matplotlib, etc. One of the unique features of EinsteinPy is a sufficiently developed symbolic tensor manipulation utilities which are a great tool in itself for teaching yourself tensor algebra which for many beginner students can be overwhelmingly tricky. EinsteinPy also provides few utility functions for hypersurface embedding of Schwarzschild spacetime which further will be extended to model gravitational lensing simulation.

gr-qc↗

arXiv2019

AMUSED: A Multi-Stream Vector Representation Method for Use in Natural Dialogue

The problem of building a coherent and non-monotonous conversational agent with proper discourse and coverage is still an area of open research. Current architectures only take care of semantic and contextual information for a given query and fail to completely account for syntactic and external knowledge which are crucial for generating responses in a chit-chat system. To overcome this problem, we propose an end to end multi-stream deep learning architecture which learns unified embeddings for query-response pairs by leveraging contextual information from memory networks and syntactic information by incorporating Graph Convolution Networks (GCN) over their dependency parse. A stream of this network also utilizes transfer learning by pre-training a bidirectional transformer to extract semantic representation for each input sentence and incorporates external knowledge through the the neighborhood of the entities from a Knowledge Base (KB). We benchmark these embeddings on next sentence prediction task and significantly improve upon the existing techniques. Furthermore, we use AMUSED to represent query and responses along with its context to develop a retrieval based conversational agent which has been validated by expert linguists to have comprehensive engagement with humans.

cs.CL↗

arXiv2019

FastEstimator: A Deep Learning Library for Fast Prototyping and Productization

As the complexity of state-of-the-art deep learning models increases by the month, implementation, interpretation, and traceability become ever-more-burdensome challenges for AI practitioners around the world. Several AI frameworks have risen in an effort to stem this tide, but the steady advance of the field has begun to test the bounds of their flexibility, expressiveness, and ease of use. To address these concerns, we introduce a radically flexible high-level open source deep learning framework for both research and industry. We introduce FastEstimator.

cs.CV↗

arXiv2019

Curriculum Learning for Domain Adaptation in Neural Machine Translation

We introduce a curriculum learning approach to adapt generic neural machine translation models to a specific domain. Samples are grouped by their similarities to the domain of interest and each group is fed to the training algorithm with a particular schedule. This approach is simple to implement on top of any neural framework or architecture, and consistently outperforms both unadapted and adapted baselines in experiments with two distinct domains and two language pairs.

cs.CL↗

arXiv2019

Phonon Lifetimes and Thermal Conductivity of the Molecular Crystal $α$-RDX

The heat transfer properties of the organic molecular crystal $α$-RDX were studied using three phonon-based thermal conductivity models. It was found that the widely used Peierls-Boltzmann model for thermal transport in crystalline materials breaks down for $α$-RDX. We show this breakdown is due to a large degree of anharmonicity that leads to a dominance of diffusive-like carriers. Despite being developed for disordered systems, the Allen-Feldman theory for thermal conductivity actually gives the best description of thermal transport. This is likely because diffusive carriers contribute to over 95% of the thermal conductivity in $α$-RDX. The dominance of diffusive carriers is larger than previously observed in other fully ordered crystalline systems. These results indicate than van-der Waals bonded organic crystalline solids conduct heat in a manner more akin to amorphous materials than simple atomic crystals.

cond-mat.mtrl-sci↗

arXiv2019

Complexity-Weighted Loss and Diverse Reranking for Sentence Simplification

Sentence simplification is the task of rewriting texts so they are easier to understand. Recent research has applied sequence-to-sequence (Seq2Seq) models to this task, focusing largely on training-time improvements via reinforcement learning and memory augmentation. One of the main problems with applying generic Seq2Seq models for simplification is that these models tend to copy directly from the original sentence, resulting in outputs that are relatively long and complex. We aim to alleviate this issue through the use of two main techniques. First, we incorporate content word complexities, as predicted with a leveled word complexity model, into our loss function during training. Second, we generate a large set of diverse candidate simplifications at test time, and rerank these to promote fluency, adequacy, and simplicity. Here, we measure simplicity through a novel sentence complexity model. These extensions allow our models to perform competitively with state-of-the-art systems while generating simpler sentences. We report standard automatic and human evaluation metrics.

cs.CL↗