arXiv subjects

Rahul Sharma

Publications and source records attributed to Rahul Sharma.

At least 109 records · Page 6Linked to original sources

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↗

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↗