arXiv subjects

Rahul Sharma

Publications and source records attributed to Rahul Sharma.

At least 109 records · Page 6Linked to original sources

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↗

arXiv2020

On Scaling Data-Driven Loop Invariant Inference

Automated synthesis of inductive invariants is an important problem in software verification. Once all the invariants have been specified, software verification reduces to checking of verification conditions. Although static analyses to infer invariants have been studied for over forty years, recent years have seen a flurry of data-driven invariant inference techniques which guess invariants from examples instead of analyzing program text. However, these techniques have been demonstrated to scale only to programs with a small number of variables. In this paper, we study these scalability issues and address them in our tool oasis that improves the scale of data-driven invariant inference and outperforms state-of-the-art systems on benchmarks from the invariant inference track of the Syntax Guided Synthesis competition.

cs.LG↗

arXiv2020

Broad-band spectral analysis of LMXB XTE J1710-281 with Suzaku

This work presents the broad-band time-averaged spectral analysis of neutron star low-mass X-ray binary, XTE J1710-281 by using the Suzaku archival data. The source was in a hard or an intermediate spectral state during this observation. This is the first time that a detailed spectral analysis of the persistent emission spectra of XTE J1710-281 has been done up to 30 keV with improved constraints on its spectral parameters. By simultaneously fitting the XIS (0.6-9.0 keV) and the HXD-PIN (15.0-30.0 keV) data, we have modelled the persistent spectrum of the source with models comprising a soft component from accretion disc and/or neutron star surface/boundary layer and a hard Comptonizing component. The 0.6-30 keV continuum with neutral absorber can be described by a multi-colour disc blackbody with an inner disc temperature of $kT_{\rm disc} = 0.28$ keV, which is significantly Comptonized by the hot electron cloud with electron temperature of $kT_{\rm e} \approx 5$ keV and described by photon index $Γ= 1.86$. A more complex three-component model comprising a multi-colour disc blackbody $\approx 0.30$ keV, single temperature blackbody $\approx 0.65$ keV and Comptonization from the disc, partially absorbed (about 38 per cent) by an ionized absorber (log($ξ$) $\approx$ 4) describes the broad-band spectrum equally well.

astro-ph.HE↗

arXiv2020

CrypTFlow: Secure TensorFlow Inference

We present CrypTFlow, a first of its kind system that converts TensorFlow inference code into Secure Multi-party Computation (MPC) protocols at the push of a button. To do this, we build three components. Our first component, Athos, is an end-to-end compiler from TensorFlow to a variety of semi-honest MPC protocols. The second component, Porthos, is an improved semi-honest 3-party protocol that provides significant speedups for TensorFlow like applications. Finally, to provide malicious secure MPC protocols, our third component, Aramis, is a novel technique that uses hardware with integrity guarantees to convert any semi-honest MPC protocol into an MPC protocol that provides malicious security. The malicious security of the protocols output by Aramis relies on integrity of the hardware and semi-honest security of MPC. Moreover, our system matches the inference accuracy of plaintext TensorFlow. We experimentally demonstrate the power of our system by showing the secure inference of real-world neural networks such as ResNet50 and DenseNet121 over the ImageNet dataset with running times of about 30 seconds for semi-honest security and under two minutes for malicious security. Prior work in the area of secure inference has been limited to semi-honest security of small networks over tiny datasets such as MNIST or CIFAR. Even on MNIST/CIFAR, CrypTFlow outperforms prior work.

cs.CR↗

arXiv2020

Momentum Resolved Superconducting Energy Gaps of Sr$_2$RuO$_4$ from Quasiparticle Interference Imaging

Sr$_2$RuO$_4$ has long been the focus of intense research interest because of conjectures that it is a correlated topological superconductor. It is the momentum space (k-space) structure of the superconducting energy gap $Δ_i(\mathbf{k})$ on each band $i$ that encodes its unknown superconducting order-parameter. But, because the energy scales are so low, it has never been possible to directly measure the $Δ_i(\mathbf{k})$ of Sr$_2$RuO$_4$. Here we implement Bogoliubov quasiparticle interference (BQPI) imaging, a technique capable of high-precision measurement of multiband $Δ_i(\mathbf{k})$. At T=90 mK we visualize a set of Bogoliubov scattering interference wavevectors $q_j:j=1-5$ consistent with eight gap nodes/minima, that are all closely aligned to the $(\pm1,\pm1)$ crystal-lattice directions on both the $α$-and $β$-bands. Taking these observations in combination with other very recent advances in directional thermal conductivity (E. Hassinger et al. Phys. Rev. X 7, 011032 (2017)), temperature dependent Knight shift (A. Pustogow et al. Nature 574, 72 (2019)), time-reversal symmetry conservation (S. Kashiwaya et al. arXiv:1907.030939) and theory (A.T. Romer et al. Phys. Rev. Lett. 123, 247001 (2019); H. S. Roising et al. Phys. Rev. Research 1, 033108 (2019),O. Gingras et al. Phys. Rev. Lett. 123, 217005 (2019)), the BQPI signature of Sr$_2$RuO$_4$ appears most consistent with $Δ_i(\mathbf{k})$ having $d_{x^2-y^2}$ $(B_{1g})$ symmetry.

cond-mat.supr-con↗

arXiv2020

A broadband look of the Accreting Millisecond X-ray Pulsar SAX J1748.9-2021 using AstroSat and XMM-Newton

SAX J1748.9-2021 is a transient accretion powered millisecond X-ray pulsar located in the Globular cluster NGC 6440. We report on the spectral and timing analysis of SAX J1748.9-2021 performed on AstroSat data taken during its faint and short outburst of 2017. We derived the best-fitting orbital solution for the 2017 outburst and obtained an average local spin frequency of 442.361098(3) Hz. The pulse profile obtained from 3-7 keV and 7-20 keV energy bands suggest constant fractional amplitude ~0.5% for fundamental component, contrary to previously observed energy pulse profile dependence. Our AstroSat observations revealed the source to be in a hard spectral state. The 1-50 keV spectrum from SXT and LAXPC on-board AstroSat can be well described with a single temperature blackbody and thermal Comptonization. Moreover, we found that the combined spectra from XMM-Newton (EPIC-PN) and AstroSat (SXT+LAXPC) indicated the presence of reflection features in the form of iron (Fe K$α$) line that we modeled with the reflection model xillvercp. One of the two X-ray burst observed during the AstroSat/LAXPC observation showed hard X-ray emission (>30 keV) due to Compton up-scattering of thermal photons by the hot corona. Time resolved analysis performed on the bursts revealed complex evolution in emission radius of blackbody for second burst suggestive of mild photospheric radius expansion.

astro-ph.HE↗

arXiv2019

LoopInvGen: A Loop Invariant Generator based on Precondition Inference

We describe the LoopInvGen tool for generating loop invariants that can provably guarantee correctness of a program with respect to a given specification. LoopInvGen is an efficient implementation of the inference technique originally proposed in our earlier work on PIE (https://doi.org/10.1145/2908080.2908099). In contrast to existing techniques, LoopInvGen is not restricted to a fixed set of features -- atomic predicates that are composed together to build complex loop invariants. Instead, we start with no initial features, and use program synthesis techniques to grow the set on demand. This not only enables a less onerous and more expressive approach, but also appears to be significantly faster than the existing tools over the SyGuS-COMP 2018 benchmarks from the INV track.

cs.PL↗

arXiv2019

Refined $α$-Divergence Variational Inference via Rejection Sampling

We present an approximate inference method, based on a synergistic combination of Rényi $α$-divergence variational inference (RDVI) and rejection sampling (RS). RDVI is based on minimization of Rényi $α$-divergence $D_α(p||q)$ between the true distribution $p(x)$ and a variational approximation $q(x)$; RS draws samples from a distribution $p(x) = \tilde{p}(x)/Z_{p}$ using a proposal $q(x)$, s.t. $Mq(x) \geq \tilde{p}(x), \forall x$. Our inference method is based on a crucial observation that $D_\infty(p||q)$ equals $\log M(θ)$ where $M(θ)$ is the optimal value of the RS constant for a given proposal $q_θ(x)$. This enables us to develop a \emph{two-stage} hybrid inference algorithm. Stage-1 performs RDVI to learn $q_θ$ by minimizing an estimator of $D_α(p||q)$, and uses the learned $q_θ$ to find an (approximately) optimal $\tilde{M}(θ)$. Stage-2 performs RS using the constant $\tilde{M}(θ)$ to improve the approximate distribution $q_θ$ and obtain a sample-based approximation. We prove that this two-stage method allows us to learn considerably more accurate approximations of the target distribution as compared to RDVI. We demonstrate our method's efficacy via several experiments on synthetic and real datasets.

cs.LG↗

arXiv2019

Deep-Learning continuous gravitational waves

We present a first proof-of-principle study for using deep neural networks (DNNs) as a novel search method for continuous gravitational waves (CWs) from unknown spinning neutron stars. The sensitivity of current wide-parameter-space CW searches is limited by the available computing power, which makes neural networks an interesting alternative to investigate, as they are extremely fast once trained and have recently been shown to rival the sensitivity of matched filtering for black-hole merger signals. We train a convolutional neural network with residual (short-cut) connections and compare its detection power to that of a fully-coherent matched-filtering search using the WEAVE pipeline. As test benchmarks we consider two types of all-sky searches over the frequency range from $20\,\mathrm{Hz}$ to $1000\,\mathrm{Hz}$: an `easy' search using $T=10^5\,\mathrm{s}$ of data, and a `harder' search using $T=10^6\,\mathrm{s}$. Detection probability $p_\mathrm{det}$ is measured on a signal population for which matched filtering achieves $p_\mathrm{det}=90\%$ in Gaussian noise. In the easiest test case ($T=10^5\,\mathrm{s}$ at $20\,\mathrm{Hz}$) the DNN achieves $p_\mathrm{det}\sim88\%$, corresponding to a loss in sensitivity depth of $\sim5\%$ versus coherent matched filtering. However, at higher-frequencies and longer observation time the DNN detection power decreases, until $p_\mathrm{det}\sim13\%$ and a loss of $\sim 66\%$ in sensitivity depth in the hardest case ($T=10^6\,\mathrm{s}$ at $1000\,\mathrm{Hz}$). We study the DNN generalization ability by testing on signals of different frequencies, spindowns and signal strengths than they were trained on. We observe excellent generalization: only five networks, each trained at a different frequency, would be able to cover the whole frequency range of the search.

gr-qc↗

arXiv2019

Overfitting in Synthesis: Theory and Practice (Extended Version)

In syntax-guided synthesis (SyGuS), a synthesizer's goal is to automatically generate a program belonging to a grammar of possible implementations that meets a logical specification. We investigate a common limitation across state-of-the-art SyGuS tools that perform counterexample-guided inductive synthesis (CEGIS). We empirically observe that as the expressiveness of the provided grammar increases, the performance of these tools degrades significantly. We claim that this degradation is not only due to a larger search space, but also due to overfitting. We formally define this phenomenon and prove no-free-lunch theorems for SyGuS, which reveal a fundamental tradeoff between synthesizer performance and grammar expressiveness. A standard approach to mitigate overfitting in machine learning is to run multiple learners with varying expressiveness in parallel. We demonstrate that this insight can immediately benefit existing SyGuS tools. We also propose a novel single-threaded technique called hybrid enumeration that interleaves different grammars and outperforms the winner of the 2018 SyGuS competition (Inv track), solving more problems and achieving a $5\times$ mean speedup.

cs.PL↗

arXiv2019

Evidence for a Vestigial Nematic State in the Cuprate Pseudogap Phase

The CuO$_2$ antiferromagnetic insulator is transformed by hole-doping into an exotic quantum fluid usually referred to as the pseudogap (PG) phase. Its defining characteristic is a strong suppression of the electronic density-of-states D(E) for energies |E|<$Δ^*$, where $Δ^*$ is the pseudogap energy. Unanticipated broken-symmetry phases have been detected by a wide variety of techniques in the PG regime, most significantly a finite Q density-wave (DW) state and a Q=0 nematic (NE) state. Sublattice-phase-resolved imaging of electronic structure allows the doping and energy dependence of these distinct broken symmetry states to be visualized simultaneously. Using this approach, we show that, even though their reported ordering temperatures T$_{DW}$ and T$_{NE}$ are unrelated to each other, both the DW and NE states always exhibit their maximum spectral intensity at the same energy, and using independent measurements that this is the pseudogap energy $Δ^*$. Moreover, no new energy-gap opening coincides with the appearance of the DW state (which should theoretically open an energy gap on the Fermi-surface), while the observed pseudogap opening coincides with the appearance of the NE state (which should theoretically be incapable of opening a Fermi-surface gap). We demonstrate how this perplexing phenomenology of thermal transitions and energy-gap opening at the breaking of two highly distinct symmetries can be understood as the natural consequence of a vestigial nematic state , within the pseudogap phase of Bi$_2$Sr$_2$CaCu$_2$O$_8$.

cond-mat.str-el↗

arXiv2018

Pitcher Plant Inspired Biomimetic Liquid Infused Slippery Surface Using Taro Leaf

Bio-inspired anti-wetting surfaces, such as lotus leaf or pitcher plant, have led to the development of stable liquid infused slippery surfaces for various scientific applications. The present work demonstrates the use of biomimetic superhydrophobic surface (inspired from taro leaf) for fabrication of a stable air-liquid film. The taro leaf replica is fabricated on PDMS using two step soft-molding technique, where microstructures of the replicated surface are used to retain silicone oil layer to form a stable slippery surface. The fabricated surface exhibits a low contact angle hysteresis (CAH) of 2°, with sliding angle (SA) of 1.1°, which further affirms the super-slippery nature of the surface. Furthermore, it also shows excellent self-repairing ability, thermal stability and long-term durability of the oil coating against high shear rates, high impact droplets etc. We thus envisage that the present method of fabrication of slippery surface is simple and economical, and thus useful for various applications such as water drag reduction, self-cleaning, anti-fogging, anti-fouling etc.

cond-mat.soft↗

arXiv2018

Study of the reflection spectra of SAX J1748.9-2021

We report the spectral analysis of accretion powered millisecond X-ray pulsar SAX J1748.9-2021 from the NuSTAR observation made during its 2015 outburst. The spectra displayed a broad emission line at $\sim 6.5$ keV with line width of $\sim 0.5$ keV and excess above $\sim 20$ keV due to Compton hump. The persistent emission of SAX J1748.9-2021 is described by a combination of soft thermal component with $kT =0.62^{+0.03}_{-0.05}$ keV and thermally Comptonized component with $kT_e=2.50^{+0.06}_{-0.03}$ keV reflected from the disc with reflection fraction of $0.30^{+0.08}_{-0.04}$. We have modeled the reflection spectrum with self-consistent model relxillCP and have found the inclination of the accretion disc to be $32.3^{^\circ +4.8}_{-4.7}$ and log $ξ$ is equal to $3.05^{+0.40}_{-0.34}$. We have obtained an upper limit of 57 km for inner disc radius; and the estimated magnetic field strength at the poles is less than $3.4 \times 10^9$ G. This kind of detailed investigation of reflection spectrum of SAX J1748.9-2021, especially above 10 keV, has been achieved only because of high statistics NuSTAR data of the source.

astro-ph.HE↗

arXiv2018

Spectral properties of MXB 1658-298 in the low/hard and high/soft state

We report results from a broadband spectral analysis of the low mass X-ray binary MXB 1658-298 with the Swift/XRT and NuSTAR observations made during its 2015-2017 outburst. The source showed different spectral states and accretion rates during this outburst. The source was in low/hard state during 2015; and it was in high/soft state during the 2016 NuSTAR observations. This is the first time that a comparison of the soft and hard spectral states during an outburst is being reported in MXB 1658-298. Compared with the observation of 2015, the X-ray luminosity was about four times higher during 2016. The hard state spectrum can be well described with models consisting of a single-temperature blackbody component along with Comptonized disc emission, or three component model comprising of multi-colour disc, single-temperature blackbody and thermal Comptonization components. The soft state spectrum can be described with blackbody or disc blackbody and Comptonization component, where the neutron star surface (or boundary layer) is the dominant source for the Comptonization seed photons. We have also found a link between the spectral state of the source and the Fe K absorber. The absorption features due to highly ionized Fe were observed only in the soft state. This suggests a probable connection between accretion disc wind (or atmosphere) and spectral state (or accretion state) of MXB 1658-298.

astro-ph.HE↗

arXiv2018

Active Learning of Points-To Specifications

When analyzing programs, large libraries pose significant challenges to static points-to analysis. A popular solution is to have a human analyst provide points-to specifications that summarize relevant behaviors of library code, which can substantially improve precision and handle missing code such as native code. We propose ATLAS, a tool that automatically infers points-to specifications. ATLAS synthesizes unit tests that exercise the library code, and then infers points-to specifications based on observations from these executions. ATLAS automatically infers specifications for the Java standard library, and produces better results for a client static information flow analysis on a benchmark of 46 Android apps compared to using existing handwritten specifications.

cs.PL↗

arXiv2018

Regularization and Variable Selection with Copula Prior

In this work, we show that under specific choices of the copula, the lasso, elastic net, and $g$-prior are particular cases of `copula prior,' for regularization and variable selection method. We present `lasso with Gauss copula prior' and `lasso with t-copula prior.' The simulation study and real-world data for regression, classification, and large time-series data show that the `copula prior' often outperforms the lasso and elastic net while having a comparable sparsity of representation. Also, the copula prior encourages a grouping effect. The strongly correlated predictors tend to be in or out of the model collectively under the copula prior. The `copula prior' is a generic method, which can be used to define the new prior distribution. The application of copulas in modeling prior distribution for Bayesian methodology has not been explored much. We present the resampling-based optimization procedure to handle big data with copula prior.

stat.ME↗

arXiv2017

Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time

User experience in modern content discovery applications critically depends on high-quality personalized recommendations. However, building systems that provide such recommendations presents a major challenge due to a massive pool of items, a large number of users, and requirements for recommendations to be responsive to user actions and generated on demand in real-time. Here we present Pixie, a scalable graph-based real-time recommender system that we developed and deployed at Pinterest. Given a set of user-specific pins as a query, Pixie selects in real-time from billions of possible pins those that are most related to the query. To generate recommendations, we develop Pixie Random Walk algorithm that utilizes the Pinterest object graph of 3 billion nodes and 17 billion edges. Experiments show that recommendations provided by Pixie lead up to 50% higher user engagement when compared to the previous Hadoop-based production system. Furthermore, we develop a graph pruning strategy at that leads to an additional 58% improvement in recommendations. Last, we discuss system aspects of Pixie, where a single server executes 1,200 recommendation requests per second with 60 millisecond latency. Today, systems backed by Pixie contribute to more than 80% of all user engagement on Pinterest.

cs.IR↗

arXiv2017

Eventually Sound Points-To Analysis with Missing Code

Static analyses make the increasingly tenuous assumption that all source code is available for analysis; for example, large libraries often call into native code that cannot be analyzed. We propose a points-to analysis that initially makes optimistic assumptions about missing code, and then inserts runtime checks that report counterexamples to these assumptions that occur during execution. Our approach guarantees eventual soundness, i.e., the static analysis is sound for the available code after some finite number of counterexamples. We implement Optix, an eventually sound points-to analysis for Android apps, where the Android framework is missing. We show that the runtime checks added by Optix incur low overhead on real programs, and demonstrate how Optix improves a client information flow analysis for detecting Android malware.

cs.PL↗