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The superconducting coplanar waveguide (SCPW) cavity plays an essential role in various areas like superconducting qubits, parametric amplifiers, radiation detectors, and studying magnon-photon and photon-phonon coupling. Despite its wide-ranging applications, the use of SCPW cavities to study various van der Waals 2D materials is relatively unexplored. The resonant modes of the SCPW cavity exquisitely sense the dielectric environment. In this work, we measure the charge compressibility of bilayer graphene coupled to a half-wavelength SCPW cavity. Our approach provides a means to detect subtle changes in the capacitance of the bilayer graphene heterostructure, which depends on the compressibility of bilayer graphene, manifesting as shifts in the resonant frequency of the cavity. This method holds promise for exploring a wide class of van der Waals 2D materials, including transition metal dichalcogenides (TMDs) and their moir\'e where DC transport measurement is challenging.

We consider the best arm identification problem in the stochastic multi-armed bandit framework where each arm has a tiny probability of realizing large rewards while with overwhelming probability the reward is zero. A key application of this framework is in online advertising where click rates of advertisements could be a fraction of a single percent and final conversion to sales, while highly profitable, may again be a small fraction of the click rates. Lately, algorithms for BAI problems have been developed that minimise sample complexity while providing statistical guarantees on the correct arm selection. As we observe, these algorithms can be computationally prohibitive. We exploit the fact that the reward process for each arm is well approximated by a Compound Poisson process to arrive at algorithms that are faster, with a small increase in sample complexity. We analyze the problem in an asymptotic regime as rarity of reward occurrence reduces to zero, and reward amounts increase to infinity. This helps illustrate the benefits of the proposed algorithm. It also sheds light on the underlying structure of the optimal BAI algorithms in the rare event setting., Comment: 32 pages

We perform an analysis of the properties of radio-loud (RL) and radio-quiet (RQ) quasars with MgII broad emission line (i-band magnitude $\leq 19.1$ and z $\leq 1.9$), selected from the parent sample of SDSS DR7 catalogue. For sources with full-width half maxima (FWHM) greater than \mbox{15,000 km s$^{-1}$} (very broad line sample; VBL) we find the radio loud fraction (RLF) to be about 40\%. To further investigate this result we compare the bolometric luminosity, optical continuum luminosity, black hole (BH) mass and Eddington ratios of our VBL sample of RL and RQ quasars. Our analysis shows that in our VBL sample space, RL quasars have higher luminosities and BH mass than RQ quasars. The similarity in the distribution of their covering fraction (CF) shows that there is no difference in dust distribution between VBL RL and RQ quasars and hence dust is not affecting our results. We also find that there is no correlation of RL quasar properties with optical continuum luminosity and BH mass., Comment: 6 pages, 4 figures, Accepted to the Special Issue "A New Window on the Radio Emission from Galaxies, Galaxy Clusters and Cosmic Web: Current Status and Perspectives" of MDPI Galaxies

Qubit coherence and gate fidelity are typically considered the two most important metrics for characterizing a quantum processor. An equally important metric is inter-qubit connectivity as it minimizes gate count and allows implementing algorithms efficiently with reduced error. However, inter-qubit connectivity in superconducting processors tends to be limited to nearest neighbour due to practical constraints in the physical realization. Here, we introduce a novel superconducting architecture that uses a ring resonator as a multi-path coupling element with the qubits uniformly distributed throughout its circumference. Our planar design provides significant enhancement in connectivity over state of the art superconducting processors without any additional fabrication complexity. We theoretically analyse the qubit connectivity and experimentally verify it in a device capable of supporting up to twelve qubits where each qubit can be connected to nine other qubits. Our concept is scalable, adaptable to other platforms and has the potential to significantly accelerate progress in quantum computing, annealing, simulations and error correction., Comment: Main manuscript and supplementary contain 16 pages, 10 figures, 3 tables

Surveys have shown radio-loud (RL) quasars constitute $10 \%$-$15 \%$ of the total quasar population. However, it is unknown if the radio-loud fraction (RL quasars/Total quasars) remains consistent among different parameter spaces. This study shows that radio-loud fraction increases for increasing full width half maximum (FWHM) velocity of the H$\beta$ and MgII broad emission line. Our data has been obtained from Shen et al. (2011) catalogue. To investigate the reason, in this preliminary study we analyse various properties like bolometric luminosity, optical continuum luminosity, black hole (BH) mass and accretion rate of RL quasars and RQ quasars sample which have FWHM greater than 15000km/s (High broad line). From the distributions we can conclude for all the properties in high broad line, RL quasars are having higher values than RQ quasars. We have predicted RL quasars are intrinsically brighter than RQ quasars and also predicted BH mass-jet connection and accretion disk-jet connection from our results but to conclude anything more analysis is needed., Comment: Accepted in Astronomische Nachrichten (AN) Journal. IWARA 2020 conference proceeding

Simulation of unsteady creeping flows in complex geometries has traditionally required the use of a time-stepping procedure, which is typically costly and unscalable. To reduce the cost and allow for computations at much larger scales, we propose an alternative approach that is formulated based on the unsteady Stokes equation expressed in the time-spectral domain. This transformation results in a boundary value problem with an imaginary source term proportional to the computed mode that is discretized and solved in a complex-valued finite element solver using Bubnov-Galerkin formulation. This transformed spatio-spectral formulation presents several advantages over the traditional spatio-temporal techniques. Firstly, for cases with boundary conditions varying smoothly in time, it provides a significant saving in computational cost as it can resolve time-variation of the solution using a few modes rather than thousands of time steps. Secondly, in contrast to the traditional time integration scheme with a finite order of accuracy, this method exhibits a super convergence behavior versus the number of computed modes. Thirdly, in contrast to the stabilized finite element methods for fluid, no stabilization term is employed in our formulation, producing a solution that is consistent and more accurate. Fourthly, the proposed approach is embarrassingly parallelizable owing to the independence of the solution modes, thus enabling scalable calculations at a much larger number of processors. The comparison of the proposed technique against a standard stabilized finite element solver is performed using two- and three-dimensional canonical and complex geometries. The results show that the proposed method can produce more accurate results at 1% to 11% of the cost of the standard technique for the studied cases.

We highlight the usefulness of city-scale agent-based simulators in studying various non-pharmaceutical interventions to manage an evolving pandemic. We ground our studies in the context of the COVID-19 pandemic and demonstrate the power of the simulator via several exploratory case studies in two metropolises, Bengaluru and Mumbai. Such tools become common-place in any city administration's tool kit in our march towards digital health., Comment: 56 pages

Deep Learning (DL) model-based AI services are increasingly offered in a variety of predictive analytics services such as computer vision, natural language processing, speech recognition. However, the quality of the DL models can degrade over time due to changes in the input data distribution, thereby requiring periodic model updates. Although cloud data-centers can meet the computational requirements of the resource-intensive and time-consuming model update task, transferring data from the edge devices to the cloud incurs a significant cost in terms of network bandwidth and are prone to data privacy issues. With the advent of GPU-enabled edge devices, the DL model update can be performed at the edge in a distributed manner using multiple connected edge devices. However, efficiently utilizing the edge resources for the model update is a hard problem due to the heterogeneity among the edge devices and the resource interference caused by the co-location of the DL model update task with latency-critical tasks running in the background. To overcome these challenges, we present Deep-Edge, a load- and interference-aware, fault-tolerant resource management framework for performing model update at the edge that uses distributed training. This paper makes the following contributions. First, it provides a unified framework for monitoring, profiling, and deploying the DL model update tasks on heterogeneous edge devices. Second, it presents a scheduler that reduces the total re-training time by appropriately selecting the edge devices and distributing data among them such that no latency-critical applications experience deadline violations. Finally, we present empirical results to validate the efficacy of the framework using a real-world DL model update case-study based on the Caltech dataset and an edge AI cluster testbed.

Surveys have shown radio-loud (RL) quasars constitute 10%-15% of the total quasar population and rest are radio-quiet (RQ). However, it is unknown if this radio-loud fraction (RLF) remains consistent among different parameter spaces. This study shows that RLF increases for increasing full width half maximum (FWHM) velocity of the H$\beta$ broad emission line (z < 0.75). To analyse the reason, we compared bolometric luminosity of RL and RQ quasars sample which have FWHM of H$\beta$ broad emission line greater than 15000km/s (High Broad Line or HBL) with which have FWHM of H$\beta$ emission line less than 2500km/s (Low Broad Line or LBL). From the distributions we can conclude for the HBL, RQ and RL quasars are peaking separately and RL quasars are having higher values whereas for the LBL the peaks are almost indistinguishable. We predicted selection effects could be the possible reason but to conclude anything more analysis is needed. Then we compared our result with Wills & Browne (1986) and have shown that some objects from our sample do not follow the pattern of the logR vs FWHM plot where R is the ratio of 5 GHz radio core flux density with the extended radio lobe flux density., Comment: Proceedings paper of the IAU symposium "Nuclear Activity in Galaxies Across Cosmic Time" (Ethiopia) accepted to be published under the Cambridge University Press, eds. M. Povic, P. Marziani, J. Masegosa, H. Netzer, S. H. Negu, and S. B. Tessema

Existing scalable superconducting quantum processors have only nearest-neighbor coupling. This leads to reduced circuit depth, requiring large series of gates to perform an arbitrary unitary operation in such systems. Recently, multi-modal devices have been demonstrated as a promising candidate for small quantum processor units. Always on longitudinal coupling in such circuits leads to implementation of native high fidelity multi-qubit gates. We propose an architecture using such devices as building blocks for a highly connected larger quantum circuit. To demonstrate a quantum operation between such blocks, a standard transmon is coupled to the multi-modal circuit using a 3D bus cavity giving rise to small exchange interaction between the transmon and one of the modes. We study the cross resonance interaction in such systems and characterize the entangling operation as well as the unitary imperfections and cross-talk as a function of device parameters. Finally, we tune up the cross resonance drive to implement multi-qubit gates in this architecture., Comment: 10 pages, 7 figures, main text and supplementary information

Services hosted in multi-tenant cloud platforms often encounter performance interference due to contention for non-partitionable resources, which in turn causes unpredictable behavior and degradation in application performance. To grapple with these problems and to define effective resource management solutions for their services, providers often must expend significant efforts and incur prohibitive costs in developing performance models of their services under a variety of interference scenarios on different hardware. This is a hard problem due to the wide range of possible co-located services and their workloads, and the growing heterogeneity in the runtime platforms including the use of fog and edge-based resources, not to mention the accidental complexity in performing application profiling under a variety of scenarios. To address these challenges, we present FECBench, a framework to guide providers in building performance interference prediction models for their services without incurring undue costs and efforts. The contributions of the paper are as follows. First, we developed a technique to build resource stressors that can stress multiple system resources all at once in a controlled manner to gain insights about the interference on an application's performance. Second, to overcome the need for exhaustive application profiling, FECBench intelligently uses the design of experiments (DoE) approach to enable users to build surrogate performance models of their services. Third, FECBench maintains an extensible knowledge base of application combinations that create resource stresses across the multi-dimensional resource design space. Empirical results using real-world scenarios to validate the efficacy of FECBench show that the predicted application performance has a median error of only 7.6% across all test cases, with 5.4% in the best case and 13.5% in the worst case.

Users of cloud platforms often must expend significant manual efforts in the deployment and orchestration of their services on cloud platforms due primarily to having to deal with the high variabilities in the configuration options for virtualized environment setup and meeting the software dependencies for each service. Despite the emergence of many DevOps cloud automation and orchestration tools, users must still rely on specifying low-level scripting details for service deployment and management using Infrastructure-as-Code (IAC). Using these tools required domain expertise along with a steep learning curve. To address these challenges in a tool-and-technology agnostic manner, which helps promote interoperability and portability of services hosted across cloud platforms, we present initial ideas on a GUI based cloud automation and orchestration framework called CloudCAMP. It incorporates domain-specific modeling so that the specifications and dependencies imposed by the cloud platform and application architecture can be specified at an intuitive, higher level of abstraction without the need for domain expertise using Model-Driven Engineering(MDE) paradigm. CloudCAMP transforms the partial specifications into deployable Infrastructure-as-Code (IAC) using the Transformational-Generative paradigm and by leveraging an extensible and reusable knowledge base. The auto-generated IAC can be handled by existing tools to provision the services components automatically. We validate our approach quantitatively by showing a comparative study of savings in manual and scripting efforts versus using CloudCAMP.

With the proliferation of machine learning (ML) libraries and frameworks, and the programming languages that they use, along with operations of data loading, transformation, preparation and mining, ML model development is becoming a daunting task. Furthermore, with a plethora of cloud-based ML model development platforms, heterogeneity in hardware, increased focus on exploiting edge computing resources for low-latency prediction serving and often a lack of a complete understanding of resources required to execute ML workflows efficiently, ML model deployment demands expertise for managing the lifecycle of ML workflows efficiently and with minimal cost. To address these challenges, we propose an end-to-end data analytics, a serverless platform called Stratum. Stratum can deploy, schedule and dynamically manage data ingestion tools, live streaming apps, batch analytics tools, ML-as-a-service (for inference jobs), and visualization tools across the cloud-fog-edge spectrum. This paper describes the Stratum architecture highlighting the problems it resolves.

Pre-trained deep learning models are increasingly being used to offer a variety of compute-intensive predictive analytics services such as fitness tracking, speech and image recognition. The stateless and highly parallelizable nature of deep learning models makes them well-suited for serverless computing paradigm. However, making effective resource management decisions for these services is a hard problem due to the dynamic workloads and diverse set of available resource configurations that have their deployment and management costs. To address these challenges, we present a distributed and scalable deep-learning prediction serving system called Barista and make the following contributions. First, we present a fast and effective methodology for forecasting workloads by identifying various trends. Second, we formulate an optimization problem to minimize the total cost incurred while ensuring bounded prediction latency with reasonable accuracy. Third, we propose an efficient heuristic to identify suitable compute resource configurations. Fourth, we propose an intelligent agent to allocate and manage the compute resources by horizontal and vertical scaling to maintain the required prediction latency. Finally, using representative real-world workloads for urban transportation service, we demonstrate and validate the capabilities of Barista.

We use data from the All Wavelength Extended Groth Strip International Survey to construct stacked X-ray maps of optically bright active galaxies (AGN) and an associated control sample of galaxies at high redshift (z less than 0.6). From our analysis of the surface brightness profiles obtained from these X-ray maps, we find evidence of feedback from the active nuclei. We find that excluding galaxies and AGN, residing in group environments, from our samples enhances the significance of our detection. Our results support the tentative findings of Chatterjee et al. who use X-ray selected AGN for their analysis. We discuss the implications of these results in the context of quantifying AGN feedback and show that the current method can be used to extract X-ray source population in high redshift galaxies., Comment: The Astrophysical Journal, Volume 872, Number 1, Page- 35

The present study deals with the effect of interfacial slip on the deformation and emulsion rheology of a dilute suspension of droplets in a linear flow. The droplets are laden with surfactants that are bulk-insoluble and get transported only along the interface. An asymptotic approach is adopted for the present analysis in order to tackle the nonlinearity present due to deformation of droplets. The analysis is carried out under two different limiting scenarios namely: surface diffusion-dominated-surfactant transport and surface convection-dominated surfactant transport. For either of the limiting cases we look into the droplet dynamics for two commonly encountered bulk flows - uniaxial extensional and simple shear flow. Under the assumption of negligible fluid inertia in either phase, it is shown that slip at the droplet interface significantly affects the surfactant-induced Marangoni stress and hence droplet deformation and emulsion rheology. Presence of interfacial slip not only brings about a decrease in the droplet deformation but also reduces the effective viscosity of the emulsion. The fall in both droplet deformation and effective viscosity is found to be more severe for the limiting case of surface convection-dominated surfactant transport. For the case of an imposed simple shear flow, the normal stress differences generated due to droplet deformation are affected as well due to the presence of interfacial slip.

Inter-qubit coupling and qubit connectivity in a processor are crucial for achieving high fidelity multi-qubit gates and efficient implementation of quantum algorithms. Typical superconducting processors employ relatively weak transverse inter-qubit coupling which are activated via frequency tuning or microwave drives. Here, we propose a class of multi-mode superconducting circuits which realize multiple transmon qubits with all-to-all longitudinal coupling. These "artificial molecules" directly implement a multi-dimensional Hilbert space that can be easily manipulated due to the always-on longitudinal coupling. We describe the basic technique to analyze such circuits, compute the relevant properties and discuss how to optimize them to create efficient small-scale quantum processors with universal programmability., Comment: 16 pages, 12 figures; Revised version with three new sections and updated references

We analyze orbital solutions for 48 massive multiple-star systems in the Cygnus OB2 Association, 23 of which are newly presented here, to find that the observed distribution of orbital periods is approximately uniform in log P for P<45 d, but it is not scale-free. Inflections in the cumulative distribution near 6 d, 14, d, and 45 d, suggest key physical scales of ~0.2, ~0.4, and ~1 A.U. where yet-to-be-identified phenomena create distinct features. No single power law provides a statistically compelling prescription, but if features are ignored, a power law with exponent beta = -0.22 provides a crude approximation over P=1.4 -- 2000 d, as does a piece-wise linear function with a break near 45 d. The cumulative period distribution flattens at P > 45 d, even after correction for completeness, indicating either a lower binary fraction or a shift toward low-mass companions. A high degree of similarity (91% likelihood) between the Cyg OB2 period distribution and that of other surveys suggests that the binary properties at P<25 d are determined by local physics of disk/clump fragmentation and are relatively insensitive to environmental and evolutionary factors. Fully 30% of the unbiased parent sample is a binary with period P < 45 d. Completeness corrections imply a binary fraction near 55% for P < 5000 d. The observed distribution of mass ratios 0.2

Mid-infrared images from the Spitzer Space Telescope Galactic Legacy Infrared MidPlane Survey Extraordinaire program reveal that the infrared source IRAS 03063+5735 is a bowshock nebula produced by an early B star, 2MASS 03101044+5747035. We present new optical spectra of this star, classify it as a B1.5 V, and determine a probable association with a molecular cloud complex at V_LSR=-38 -- -42 km/s in the outer Galaxy near l=140.59 degr, b=-0.250 degr. On the basis of spectroscopic parallax, we estimate a distance of 4.0 +/-1 kpc to both the bowshock nebula and the molecular complex. One plausible scenario is that this a high-velocity runaway star impinging upon a molecular cloud. We identify the HII region and stellar cluster associated with IRAS 03064+5638 at a projected distance of 64 pc as one plausible birth site. The spectrophotometric distance and linkage to a molecular feature provides another piece of data helping to secure the ill-determined rotation curve in the outer Galaxy. As a by-product of spectral typing this star, we present empirical spectral diagnostic diagrams suitable for approximate spectral classification of O and B stars using He lines in the little-used yellow-red portion of the optical spectrum., Comment: Accepted for publication in the Astronomical Journal