arXiv subjects

Rahul Sharma

Publications and source records attributed to Rahul Sharma.

At least 91 records · Page 5Linked to original sources

arXiv2021

Timing and Spectral Analysis of HMXB OAO 1657-415 with NuSTAR

This work presents a comprehensive timing and spectral analysis of high-mass X-ray binary pulsar, OAO 1657-415 by using the observation made with Nuclear Spectroscopic Telescope Array (NuSTAR) on June 2019. During this observation, OAO 1657-415 exhibited X-ray variability by a factor of about 3. X-ray pulsations at 37.03322(14) s were observed up to 70 keV. OAO 1657-415 was undergoing a spin-down phase with $\dot{P} = 9(1) \times 10^{-8}$ s s$^{-1}$. This is an order of about 100 larger than the long-term spin-up rate. The pulse profile evolved marginally during the observation. We have discussed the long-term pulse period history of the source spanning a time-base of 43 years, including the latest Fermi/GBM data. The 3-70 keV source spectrum is described by a partially covered cutoff power-law, an Fe K$_α$ line at 6.4 keV and a Compton shoulder at 6.3 keV. We report the presence of a cyclotron absorption feature around 40 keV, which is indicative of a surface magnetic field strength of $3.59 \pm 0.06 \ (1 + z)^{-1} \times 10^{12}$ and $3.29_{-0.22}^{+0.23} \ (1 + z)^{-1} \times 10^{12}$ G. This work shows the first robust presence of cyclotron absorption feature in the source.

astro-ph.HE↗

arXiv2021

Cross modal video representations for weakly supervised active speaker localization

An objective understanding of media depictions, such as inclusive portrayals of how much someone is heard and seen on screen such as in film and television, requires the machines to discern automatically who, when, how, and where someone is talking, and not. Speaker activity can be automatically discerned from the rich multimodal information present in the media content. This is however a challenging problem due to the vast variety and contextual variability in the media content, and the lack of labeled data. In this work, we present a cross-modal neural network for learning visual representations, which have implicit information pertaining to the spatial location of a speaker in the visual frames. Avoiding the need for manual annotations for active speakers in visual frames, acquiring of which is very expensive, we present a weakly supervised system for the task of localizing active speakers in movie content. We use the learned cross-modal visual representations, and provide weak supervision from movie subtitles acting as a proxy for voice activity, thus requiring no manual annotations. We evaluate the performance of the proposed system on the AVA active speaker dataset and demonstrate the effectiveness of the cross-modal embeddings for localizing active speakers in comparison to fully supervised systems. We also demonstrate state-of-the-art performance for the task of voice activity detection in an audio-visual framework, especially when speech is accompanied by noise and music.

cs.CV↗

arXiv2021

Phonetic Word Embeddings

This work presents a novel methodology for calculating the phonetic similarity between words taking motivation from the human perception of sounds. This metric is employed to learn a continuous vector embedding space that groups similar sounding words together and can be used for various downstream computational phonology tasks. The efficacy of the method is presented for two different languages (English, Hindi) and performance gains over previous reported works are discussed on established tests for predicting phonetic similarity. To address limited benchmarking mechanisms in this field, we also introduce a heterographic pun dataset based evaluation methodology to compare the effectiveness of acoustic similarity algorithms. Further, a visualization of the embedding space is presented with a discussion on the various possible use-cases of this novel algorithm. An open-source implementation is also shared to aid reproducibility and enable adoption in related tasks.

cs.CL↗

arXiv2021

Application and Benchmark of SPH for Modeling the Impact in Thermal Spraying

The properties of a thermally sprayed coating, such as its durability or thermal conductivity depend on its microstructure, which is in turn directly related to the particle impact process. To simulate this process we present a 3D Smoothed Particle Hydrodynamics (SPH) model, which represents the molten droplet as an incompressible fluid, while a semi-implicit Enthalpy-Porosity method is applied for the mushy zone during solidification. In addition, we present an implicit correction for SPH simulations, based on well known approaches, from which we can observe improved performance and simulation stability. We apply our SPH method to the impact and solidification of Al$_2$O$_3$ droplets onto a free slip substrate and perform a rigorous quantitative comparison of our method with the commercial software Ansys Fluent using the Volume of Fluid (VOF) approach, while taking identical physical effects into consideration. The results are evaluated in depth and we discuss the applicability of either method for the simulation of thermal spray deposition. We show that SPH is an excellent method for solving this free surface problem accurately and efficiently.

physics.flu-dyn↗

arXiv2021

Quantitative Evaluation of SPH in TIG Spot Welding

While the application of the Smoothed Particle Hydrodynamics (SPH) method for the modeling of welding processes has become increasingly popular in recent years, little is yet known about the quantitative predictive capability of this method. We propose a novel SPH model for the simulation of the tungsten inert gas (TIG) spot welding process and conduct a thorough comparison between our SPH implementation and two Finite Element Method (FEM) based models. In order to be able to quantitatively compare the results of our SPH simulation method with grid based methods we additionally propose an improved particle to grid interpolation method based on linear least-squares with an optional hole-filling pass which accounts for missing particles. We show that SPH is able to yield excellent results, especially given the observed deviations between the investigated FEM methods and as such, we validate the accuracy of the method for an industrially relevant engineering application.

physics.flu-dyn↗

arXiv2021

Multi-institution encrypted medical imaging AI validation without data sharing

Adoption of artificial intelligence medical imaging applications is often impeded by barriers between healthcare systems and algorithm developers given that access to both private patient data and commercial model IP is important to perform pre-deployment evaluation. This work investigates a framework for secure, privacy-preserving and AI-enabled medical imaging inference using CrypTFlow2, a state-of-the-art end-to-end compiler allowing cryptographically secure 2-party Computation (2PC) protocols between the machine learning model vendor and target patient data owner. A common DenseNet-121 chest x-ray diagnosis model was evaluated on multi-institutional chest radiographic imaging datasets both with and without CrypTFlow2 on two test sets spanning seven sites across the US and India, and comprising 1,149 chest x-ray images. We measure comparative AUROC performance between secure and insecure inference in multiple pathology classification tasks, and explore model output distributional shifts and resource constraints introduced by secure model inference. Secure inference with CrypTFlow2 demonstrated no significant difference in AUROC for all diagnoses, and model outputs from secure and insecure inference methods were distributionally equivalent. The use of CrypTFlow2 may allow off-the-shelf secure 2PC between healthcare systems and AI model vendors for medical imaging, without changes in performance, and can facilitate scalable pre-deployment infrastructure for real-world secure model evaluation without exposure to patient data or model IP.

cs.CR↗

arXiv2021

MAFIA: Machine Learning Acceleration on FPGAs for IoT Applications

Recent breakthroughs in ML have produced new classes of models that allow ML inference to run directly on milliwatt-powered IoT devices. On one hand, existing ML-to-FPGA compilers are designed for deep neural-networks on large FPGAs. On the other hand, general-purpose HLS tools fail to exploit properties specific to ML inference, thereby resulting in suboptimal performance. We propose MAFIA, a tool to compile ML inference on small form-factor FPGAs for IoT applications. MAFIA provides native support for linear algebra operations and can express a variety of ML algorithms, including state-of-the-art models. We show that MAFIA-generated programs outperform best-performing variant of a commercial HLS compiler by 2.5x on average.

cs.AR↗

arXiv2021

Atomic-scale Visualization of Electronic Fluid Flow

The most essential characteristic of any fluid is the velocity field v(r) and this is particularly true for macroscopic quantum fluids. Although rapid advances have occurred in quantum fluid v(r) imaging, the velocity field of a charged superfluid - a superconductor - has never been visualized. Here we use superconductive-tip scanning tunneling microscopy to image the electron-pair density \r{ho}_S(r) and velocity v_S(r) fields of the flowing electron-pair fluid in superconducting NbSe2. Imaging v_S(r) surrounding a quantized vortex finds speeds reaching 10,000 km/hr. Together with independent imaging of \r{ho}_S(r) via Josephson tunneling, we visualize the supercurrent density j_S(r)=\r{ho}_S(r)v_S(r), which peaks above 3 x 10^7 A/cm^2. The spatial patterns in electronic fluid flow and magneto-hydrodynamics reveal hexagonal structures co-aligned to the crystal lattice and quasiparticle bound states, as long anticipated. These novel techniques pave the way for electronic fluid flow visualization in many other quantum fluids.

cond-mat.supr-con↗

arXiv2021

Discovery of a Cooper-Pair Density Wave State in a Transition-Metal Dichalcogenide

Pair density wave (PDW) states are defined by a spatially modulating superconductive order-parameter. To search for such states in transition metal dichalcogenides (TMD) we use high-speed atomic-resolution scanned Josephson-tunneling microscopy (SJTM). We detect a PDW state whose electron-pair density and energy-gap modulate spatially at the wavevectors of the preexisting charge density wave (CDW) state. The PDW couples linearly to both the s-wave superconductor and to the CDW, and exhibits commensurate domains with discommensuration phase-slips at the boundaries, conforming to those of the lattice-locked commensurate CDW. Nevertheless, we find a global $δΦ\sim \pm2π/3$ phase difference between the PDW and CDW states, possibly owing to the Cooper-pair wavefunction orbital content. Our findings presage pervasive PDW physics in the many other TMDs that sustain both CDW and superconducting states.

cond-mat.supr-con↗

arXiv2021

SIRNN: A Math Library for Secure RNN Inference

Complex machine learning (ML) inference algorithms like recurrent neural networks (RNNs) use standard functions from math libraries like exponentiation, sigmoid, tanh, and reciprocal of square root. Although prior work on secure 2-party inference provides specialized protocols for convolutional neural networks (CNNs), existing secure implementations of these math operators rely on generic 2-party computation (2PC) protocols that suffer from high communication. We provide new specialized 2PC protocols for math functions that crucially rely on lookup-tables and mixed-bitwidths to address this performance overhead; our protocols for math functions communicate up to 423x less data than prior work. Some of the mixed bitwidth operations used by our math implementations are (zero and signed) extensions, different forms of truncations, multiplication of operands of mixed-bitwidths, and digit decomposition (a generalization of bit decomposition to larger digits). For each of these primitive operations, we construct specialized 2PC protocols that are more communication efficient than generic 2PC, and can be of independent interest. Furthermore, our math implementations are numerically precise, which ensures that the secure implementations preserve model accuracy of cleartext. We build on top of our novel protocols to build SIRNN, a library for end-to-end secure 2-party DNN inference, that provides the first secure implementations of an RNN operating on time series sensor data, an RNN operating on speech data, and a state-of-the-art ML architecture that combines CNNs and RNNs for identifying all heads present in images. Our evaluation shows that SIRNN achieves up to three orders of magnitude of performance improvement when compared to inference of these models using an existing state-of-the-art 2PC framework.

cs.CR↗

arXiv2021

Broad-band spectral study of LMXB black hole candidate 4U 1957+11 with NuSTAR

We present here the results of broadband spectral analysis of low-mass X-ray binary and a black hole candidate 4U 1957+11. The source was observed nine times with the Nuclear Spectroscopic Telescope Array (NuSTAR) between 2018 September and 2019 November. During these observations, the spectral state of 4U 1957+11 evolved marginally. The disc dominant spectra are well described with a hot, multicolour disc blackbody with disc temperature varying in the range $kT_{\rm in} \sim$ 1.35-1.86 keV and a non-thermal component having a steep slope ($Γ=$ 2-3). A broad Fe emission line feature (5-8 keV) was observed in the spectra of all the observations. The relativistic disc model was used to study the effect of distance, inclination, and the black hole mass on its spin. Simulations indicate a higher spin for smaller distances and lower black hole mass. At smaller distances and higher mass, spin is maximum and almost independent of the distance. An inverse correlation exists between the spin and the spectral hardening factor for all the cases. The system prefers a moderate spin of about 0.85 for black hole masses between 4-6 M_sun for a 7 kpc distance.

astro-ph.HE↗

arXiv2021

Variational Rejection Particle Filtering

We present a variational inference (VI) framework that unifies and leverages sequential Monte-Carlo (particle filtering) with \emph{approximate} rejection sampling to construct a flexible family of variational distributions. Furthermore, we augment this approach with a resampling step via Bernoulli race, a generalization of a Bernoulli factory, to obtain a low-variance estimator of the marginal likelihood. Our framework, Variational Rejection Particle Filtering (VRPF), leads to novel variational bounds on the marginal likelihood, which can be optimized efficiently with respect to the variational parameters and generalizes several existing approaches in the VI literature. We also present theoretical properties of the variational bound and demonstrate experiments on various models of sequential data, such as the Gaussian state-space model and variational recurrent neural net (VRNN), on which VRPF outperforms various existing state-of-the-art VI methods.

cs.LG↗

arXiv2021

Multi-Atom Quasiparticle Scattering Interference for Superconductor Energy-Gap Symmetry Determination

Complete theoretical understanding of the most complex superconductors requires a detailed knowledge of the symmetry of the superconducting energy-gap $Δ_\mathbf{k}^α$, for all momenta $\mathbf{k}$ on the Fermi surface of every band $α$. While there are a variety of techniques for determining $|Δ_\mathbf{k}^α|$, no general method existed to measure the signed values of $Δ_\mathbf{k}^α$. Recently, however, a new technique based on phase-resolved visualization of superconducting quasiparticle interference (QPI) patterns centered on a single non-magnetic impurity atom, was introduced. In principle, energy-resolved and phase-resolved Fourier analysis of these images identifies wavevectors connecting all k-space regions where $Δ_\mathbf{k}^α$ has the same or opposite sign. But use of a single isolated impurity atom, from whose precise location the spatial phase of the scattering interference pattern must be measured is technically difficult. Here we introduce a generalization of this approach for use with multiple impurity atoms, and demonstrate its validity by comparing the $Δ_\mathbf{k}^α$ it generates to the $Δ_\mathbf{k}^α$ determined from single-atom scattering in FeSe where $s_{\pm}$ energy-gap symmetry is established. Finally, to exemplify utility, we use the multi-atom technique on LiFeAs and find scattering interference between the hole-like and electron-like pockets as predicted for $Δ_\mathbf{k}^α$ of opposite sign.

cond-mat.supr-con↗

arXiv2020

Secure Medical Image Analysis with CrypTFlow

We present CRYPTFLOW, a system that converts TensorFlow inference code into Secure Multi-party Computation (MPC) protocols at the push of a button. To do this, we build two components. Our first component is an end-to-end compiler from TensorFlow to a variety of MPC protocols. The second component is an improved semi-honest 3-party protocol that provides significant speedups for inference. We empirically demonstrate the power of our system by showing the secure inference of real-world neural networks such as DENSENET121 for detection of lung diseases from chest X-ray images and 3D-UNet for segmentation in radiotherapy planning using CT images. In particular, this paper provides the first evaluation of secure segmentation of 3D images, a task that requires much more powerful models than classification and is the largest secure inference task run till date.

cs.CR↗

arXiv2020

Severe Dirac Mass Gap Suppression in Sb_2Te_3-based Quantum Anomalous Hall Materials

Quantum anomalous Hall (QAH) effect appears in ferromagnetic topological insulators (FMTI) when a Dirac mass gap opens in the spectrum of the topological surface states (SS). Unaccountably, although the mean mass gap can exceed 28 meV (or ~320 K), the QAH effect is frequently only detectable at temperatures below 1 K. Using atomic-resolution Landau level spectroscopic imaging, we compare the electronic structure of the archetypal FMTI Cr_0.08(Bi_0.1Sb_0.9)_1.92Te_3 to that of its non-magnetic parent (Bi_0.1Sb_0.9)_2Te_3, to explore the cause. In (Bi_0.1Sb_0.9)_2Te_3, we find spatially random variations of the Dirac energy. Statistically equivalent Dirac energy variations are detected in Cr_0.08(Bi_0.1Sb_0.9)_1.92Te_3 with concurrent but uncorrelated Dirac mass gap disorder. These two classes of SS electronic disorder conspire to drastically suppress the minimum mass gap to below 100 μeV for nanoscale regions separated by <1 μm. This fundamentally limits the fully quantized anomalous Hall effect in Sb_2Te_3-based FMTI materials to very low temperatures.

cond-mat.mes-hall↗

arXiv2020

CrypTFlow2: Practical 2-Party Secure Inference

We present CrypTFlow2, a cryptographic framework for secure inference over realistic Deep Neural Networks (DNNs) using secure 2-party computation. CrypTFlow2 protocols are both correct -- i.e., their outputs are bitwise equivalent to the cleartext execution -- and efficient -- they outperform the state-of-the-art protocols in both latency and scale. At the core of CrypTFlow2, we have new 2PC protocols for secure comparison and division, designed carefully to balance round and communication complexity for secure inference tasks. Using CrypTFlow2, we present the first secure inference over ImageNet-scale DNNs like ResNet50 and DenseNet121. These DNNs are at least an order of magnitude larger than those considered in the prior work of 2-party DNN inference. Even on the benchmarks considered by prior work, CrypTFlow2 requires an order of magnitude less communication and 20x-30x less time than the state-of-the-art.

cs.CR↗

arXiv2020

On the partial eruption of a bifurcated solar filament structure

The partial eruption of a filament channel with bifurcated substructures is investigated using datasets obtained from both ground-based and space-borne facilities. Small-scale flux reconnection/cancellation events in the region triggered the pile-up of ambient magnetic field, observed as bright EUV loops in close proximity of the filament channel. This led to the formation of a V-shaped cusp structure at the site of interaction between the coalesced EUV loops and the filament channel, with the presence of distinct plasmoid structures and associated bidirectional flows. Analysis of imaging data from SDO/AIA further suggests the vertical split of the filament structure into two substructures. The perturbed upper branch of the filament structure rose up and erupted with the onset of an energetic GOES M1.4 flare at 04:30 UT on January 28, 2015. The estimated twist number and squashing factor obtained from nonlinear force free-field extrapolation of the magnetic field data support the vertical split in filament structure with high twist in upper substructure. The loss in equilibrium of the upper branch due to torus instability, implying this as a potential triggering mechanism of the observed partial eruption.

astro-ph.SR↗

arXiv2020

Critical Science Plan for the Daniel K. Inouye Solar Telescope (DKIST)

The Daniel K. Inouye Solar Telescope (DKIST) will revolutionize our ability to measure, understand and model the basic physical processes that control the structure and dynamics of the Sun and its atmosphere. The first-light DKIST images, released publicly on 29 January 2020, only hint at the extraordinary capabilities which will accompany full commissioning of the five facility instruments. With this Critical Science Plan (CSP) we attempt to anticipate some of what those capabilities will enable, providing a snapshot of some of the scientific pursuits that the Daniel K. Inouye Solar Telescope hopes to engage as start-of-operations nears. The work builds on the combined contributions of the DKIST Science Working Group (SWG) and CSP Community members, who generously shared their experiences, plans, knowledge and dreams. Discussion is primarily focused on those issues to which DKIST will uniquely contribute.

astro-ph.SR↗