arXiv subjects

Rahul Sharma

Publications and source records attributed to Rahul Sharma.

At least 91 records · Page 5Linked to original sources

arXiv2022

Small-scale solar jet formation and their associated waves and instabilities

Studies on small-scale jets' formation, propagation, evolution, and role, such as type I and II spicules, mottles, and fibrils in the lower solar atmosphere's energetic balance, have progressed tremendously thanks to the combination of detailed observations and sophisticated mathematical modelling. This review provides a survey of the current understanding of jets, their formation in the solar lower atmosphere, and their evolution from observational, numerical, and theoretical perspectives. First, we review some results to describe the jet properties, acquired numerically, analytically and through high-spatial and temporal resolution observations. Further on, we discuss the role of hydrodynamic and magnetohydrodynamic instabilities, namely Rayleigh-Taylor and Kelvin-Helmholtz instabilities, in jet evolution and their role in the energy transport through the solar atmosphere in fully and partially ionised plasmas. Finally, we discuss several mechanisms of magnetohydrodynamic wave generation, propagation, and energy transport in the context of small-scale solar jets in detail. This review identifies several gaps in the understanding of small-scale solar jets and some misalignments between the observational studies and knowledge acquired through theoretical studies and numerical modelling. It is to be expected that these gaps will be closed with the advent of high-resolution observational instruments, such as Daniel K. Inouye Solar Telescope, Solar Orbiter, Parker Solar Probe, and Solar CubeSats for Linked Imaging Spectropolarimetry, combined with further theoretical and computational developments.

astro-ph.SR↗

arXiv2022

Federated Learning with Noisy User Feedback

Machine Learning (ML) systems are getting increasingly popular, and drive more and more applications and services in our daily life. This has led to growing concerns over user privacy, since human interaction data typically needs to be transmitted to the cloud in order to train and improve such systems. Federated learning (FL) has recently emerged as a method for training ML models on edge devices using sensitive user data and is seen as a way to mitigate concerns over data privacy. However, since ML models are most commonly trained with label supervision, we need a way to extract labels on edge to make FL viable. In this work, we propose a strategy for training FL models using positive and negative user feedback. We also design a novel framework to study different noise patterns in user feedback, and explore how well standard noise-robust objectives can help mitigate this noise when training models in a federated setting. We evaluate our proposed training setup through detailed experiments on two text classification datasets and analyze the effects of varying levels of user reliability and feedback noise on model performance. We show that our method improves substantially over a self-training baseline, achieving performance closer to models trained with full supervision.

cs.LG↗

arXiv2022

On the Existence of Photoluminescence and Room-Temperature Spin Polarization in Ambipolar V doped MoS$_2$ Monolayers

Opto-spintronics is an emerging field where ultra-thin magnetic-semiconductors having high spin-valley coupling play an important role. Here, we demonstrate substitutional vanadium (V) doping in MoS$_2$ lattice in different extent, leading to the coexistence of photoluminescence (PL), valleypolarization (~32%), and valley splitting (~28 meV shift in PL with helicity $σ^+$ and $σ^-$ of light excitation). A large V doping causes semiconductor to metal transition in MoS$_2$ but with medium level causing the existence of photoluminescence with high spin polarization. The ambipolar nature of medium level V doped MoS$_2$ is shown here indicating its potential as an opto-electronic material. The presence of V-dopants and their different level of content are proven by both spectroscopic and microscopic methods.A detailed temperature and power dependent photoluminescence studies along with density functional theory-based calculations in support unravels the emergence of the co-existence of spin-valley coupling and photoluminescence. This study shows the potential of doping MoS$_2$ for deriving new materials for next generation room temperature opto-spintronics.

physics.app-ph↗

arXiv2022

Using Active Speaker Faces for Diarization in TV shows

Speaker diarization is one of the critical components of computational media intelligence as it enables a character-level analysis of story portrayals and media content understanding. Automated audio-based speaker diarization of entertainment media poses challenges due to the diverse acoustic conditions present in media content, be it background music, overlapping speakers, or sound effects. At the same time, speaking faces in the visual modality provide complementary information and not prone to the errors seen in the audio modality. In this paper, we address the problem of speaker diarization in TV shows using the active speaker faces. We perform face clustering on the active speaker faces and show superior speaker diarization performance compared to the state-of-the-art audio-based diarization methods. We additionally report a systematic analysis of the impact of active speaker face detection quality on the diarization performance. We also observe that a moderately well-performing active speaker system could outperform the audio-based diarization systems.

cs.MM↗

arXiv2022

Audio visual character profiles for detecting background characters in entertainment media

An essential goal of computational media intelligence is to support understanding how media stories -- be it news, commercial or entertainment media -- represent and reflect society and these portrayals are perceived. People are a central element of media stories. This paper focuses on understanding the representation and depiction of background characters in media depictions, primarily movies and TV shows. We define the background characters as those who do not participate vocally in any scene throughout the movie and address the problem of localizing background characters in videos. We use an active speaker localization system to extract high-confidence face-speech associations and generate audio-visual profiles for talking characters in a movie by automatically clustering them. Using a face verification system, we then prune all the face-tracks which match any of the generated character profiles and obtain the background character face-tracks. We curate a background character dataset which provides annotations for background character for a set of TV shows, and use it to evaluate the performance of the background character detection framework.

cs.CV↗

arXiv2022

Neutral atom scattering based mapping of atomically thin layers

Imaging surfaces using low energy neutral atom scattering is a relatively recent development in the field of microscopy. In this work we demonstrate that this technique is sensitive enough to distinguish films as thin as a single monolayer from the underlying substrate. Using collimated beams of He and Kr atoms as an incident probe on MoS$_2$ films grown on SiO$_2$/Si substrate, we observe systematic changes in the scattered atom flux which allows us to map the thin MoS$_2$ films. Measurements carried out by varying incidence energy using both He and Kr provides insights into the details of atom-surface collision dynamics and its role in contrast generation.

physics.chem-ph↗

arXiv2021

Jigsaw: Large Language Models meet Program Synthesis

Large pre-trained language models such as GPT-3, Codex, and Google's language model are now capable of generating code from natural language specifications of programmer intent. We view these developments with a mixture of optimism and caution. On the optimistic side, such large language models have the potential to improve productivity by providing an automated AI pair programmer for every programmer in the world. On the cautionary side, since these large language models do not understand program semantics, they offer no guarantees about quality of the suggested code. In this paper, we present an approach to augment these large language models with post-processing steps based on program analysis and synthesis techniques, that understand the syntax and semantics of programs. Further, we show that such techniques can make use of user feedback and improve with usage. We present our experiences from building and evaluating such a tool jigsaw, targeted at synthesizing code for using Python Pandas API using multi-modal inputs. Our experience suggests that as these large language models evolve for synthesizing code from intent, jigsaw has an important role to play in improving the accuracy of the systems.

cs.SE↗

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↗