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Comprehensive muon spin rotation/relaxation (muSR) and neutron powder diffraction (NPD) studies supported via bulk measurements have been performed on the ordered double perovskite Sr2YbRuO6 to investigate the nature of the magnetic ground state. Two sharp transitions at TN1 ~ 42 K and TN2 ~ 36 K have been observed in the static and dynamic magnetization measurements, coinciding with the heat capacity data. In order to confirm the origin of the observed phase transitions and the magnetic ground state, microscopic evidences are presented here. An initial indication of long-range magnetic ordering comes from a sharp drop in the muon initial asymmetry and a peak in the relaxation rate near TN1. NPD confirms that the magnetic ground state of Sr2YbRuO6 consists of an antiferromagnetic (AFM) structure with interpenetrating lattices of parallel Yb3+ and Ru5+ moments lying in the ab-plane and adopting a A-type AFM structure. Intriguingly, a small but remarkable change is observed in the long-range ordering parameters at TN2 confirming the presence of a weak spin reorientation (i.e. change in spin configuration) transition of Ru and Yb moments, as well as a change in the magnetic moment evolution of the Yb3+ spins at TN2. The temperature dependent behaviour of the Yb3+ and Ru5+ moments suggests that the 4d-electrons of Ru5+ play a dominating role in stabilizing the long range ordered magnetic ground state in the double perovskite Sr2YbRuO6 whereas only the Yb3+ moments show an arrest at TN2. The observed magnetic structure and the presence of a ferromagnetic interaction between Ru- and Yb- ions are explained with use of the Goodenough-Kanamori-Anderson (GKA) rules. Possible reasons for the presence of the second magnetic phase transition and of a compensation point in the magnetization data are linked to competing mechanisms of magnetic anisotropy., Comment: 28 pages, 12 figures

In this paper, we study a new discrete tree and the resulting branching process, which we call the \textbf{E}rlang \textbf{W}eighted \textbf{T}ree(\textbf{EWT}). The EWT appears as the local weak limit of a random graph model proposed in~\cite{La2015}. In contrast to the local weak limit of well-known random graph models, the EWT has an interdependent structure. In particular, its vertices encode a multi-type branching process with uncountably many types. We derive the main properties of the EWT, such as the probability of extinction, growth rate, etc. We show that the probability of extinction is the smallest fixed point of an operator. We then take a point process perspective and analyze the growth rate operator. We derive the Krein--Rutman eigenvalue $\beta_0$ and the corresponding eigenfunctions of the growth operator, and show that the probability of extinction equals one if and only if $\beta_0 \leq 1$.

In this paper, we propose a novel end-to-end approach for AI-assisted code completion called Pythia. It generates ranked lists of method and API recommendations which can be used by software developers at edit time. The system is currently deployed as part of Intellicode extension in Visual Studio Code IDE. Pythia exploits state-of-the-art large-scale deep learning models trained on code contexts extracted from abstract syntax trees. It is designed to work at a high throughput predicting the best matching code completions on the order of 100 $ms$. We describe the architecture of the system, perform comparisons to frequency-based approach and invocation-based Markov Chain language model, and discuss challenges serving Pythia models on lightweight client devices. The offline evaluation results obtained on 2700 Python open source software GitHub repositories show a top-5 accuracy of 92\%, surpassing the baseline models by 20\% averaged over classes, for both intra and cross-project settings., Comment: Published in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '19)

This article examines large time behaviour of finite state mean-field interacting particle systems. Our first main result is a sharp estimate (in the exponential scale) on the time required for convergence of the empirical measure process of the $N$-particle system to its invariant measure; we show that when time is of the order of $\exp\{N\Lambda\}$ for a suitable constant $\Lambda \geq 0$, the process has mixed well and it is close to its invariant measure. We then obtain large-$N$ asymptotics of the second largest eigenvalue of the generator associated with the empirical measure process when it is reversible with respect to its invariant measure. We show that its absolute value scales as $\exp\{-N\Lambda\}$. The main tools used in establishing our results are the large deviation properties of the empirical measure process from its large-$N$ limit. As an application of the study of large time behaviour, we also show convergence of the empirical measure of the system of particles to a global minimum of a certain `entropy' function when particles are added over time in a controlled fashion. The controlled addition of particles is analogous to the cooling schedule associated with the search for a global minimum of a function using the simulated annealing algorithm., Comment: 33 pages. Added references and re-organised the introduction

We report the discovery of linear magnetoelectric effect in the well-known green phase compound, Sm2BaCuO5, which crystallizes in the centrosymmetric orthorhombic (Pnma) structure. Magnetization and specific heat measurements reveal the long-range antiferromagnetic ordering of Cu2+ and Sm3+-ions moments at TN1 = 23 K and TN2 = 5 K, respectively. Applied magnetic field induces dielectric anomaly at TN1 whose magnitude increases with field, which results in significant (1.7%) magnetocapacitance effect. On the other hand, the dielectric anomaly observed in zero-applied magnetic field at TN2 shows a small (0.4%) magnetocapacitance effect. Interestingly, applied magnetic field induces an electric polarization below TN1 and the polarization varies linearly up to the maximum applied field of 9 T with the magnetoelectric coefficient {\alpha} ~ 4.4 ps/m, demonstrating high magnetoelectric coupling. Below TN2, the electric polarization decreases from 35 to 29 {\mu}C/m2 at 2 K and 9 T due to ordering of Sm-sublattice. The observed linear magnetoelectricity in Sm2BaCuO5 is explained using symmetry analysis., Comment: 22 pages, 5 figures (to be published in Phys. Rev. B)

The magnetic ground state of double perovskite Sr2DyRuO6 has been investigated using muon spin rotation and relaxation (muSR), neutron powder diffraction (NPD) and inelastic neutron scattering (INS), in addition to heat capacity and magnetic susceptibility (ac and dc) measurements. A clear signature of a long-range ordered magnetic ground state has been observed in the heat capacity data, which exhibit two sharp anomalies at 39.5 and 36 K found as well in the magnetic data. Further confirmation of long-range magnetic ordering comes from a sharp drop in the muon initial asymmetry and a peak in the relaxation rate at 40 K, along with a weak anomaly near 36 K. Based on temperature dependent NPD, the low temperature magnetic structure contains two interpenetrating lattices of Dy and Ru5, forming an antiferromagnetic ground state below 39.5 K with magnetic propagation vector k = (0,0,0). The magnetic moments of Dy and Ru at 3.5 K are pointing along the crystallographic b-axis with values of muDy = 4.92(10) muB and muRu = 1.94(7) muB, respectively. The temperature dependence of the Ru moments follows a mean field type behaviour, while that of the Dy moments exhibits a deviation indicating that the primary magnetic ordering is induced by the order of the 4d electrons of Ru rather than that of its proper 4f Dy electrons. The origin of the second anomaly observed in the heat capacity data at 36.5 K must be connected to a very small spin reorientation as the NPD studies do not reveal any clear change in the observed magnetic Bragg peaks positions or intensities between these two transitions. INS measurements reveal the presence of crystal field excitations (CEF) in the paramagnetic state with overall CEF splitting of 73.8 meV, in agreement with the point change model calculations., Comment: 25 pages, 14 figures

The ability to generate and verify multipartite entanglement is an important benchmark for near-term quantum devices devices. We develop a scalable entanglement metric based on multiple quantum coherences, and demonstrate experimentally on a 20-qubit superconducting device - the IBM Q System One. We report a state fidelity of 0.5165$\pm$0.0036 for an 18-qubit GHZ state, indicating multipartite entanglement across all 18 qubits. Our entanglement metric is robust to noise and only requires measuring the population in the ground state; it can be readily applied to other quantum devices to verify multipartite entanglement., Comment: 7+4 pages, comments welcome

We study the idea of information design for inducing prosocial behavior in the context of electricity consumption. We consider a continuum of agents. Each agent has a different intrinsic motivation to reduce her power consumption. Each agent models the power consumption of the others via a distribution. Using this distribution, agents will anticipate their reputational benefit and choose a power consumption by trading off their own intrinsic motivation to do a prosocial action, the cost of this prosocial action and their reputation. Initially, the service provider can provide two types of quantized feedbacks of the power consumption. We study their advantages and disadvantages. For each feedback, we characterize the corresponding mean field equilibrium, using a fixed point equation. Besides computing the mean field equilibrium, we highlight the need for a systematic study of information design, by showing that revealing less information to the society can lead to more prosociality. In the last part of the paper, we introduce the notion of privacy and provide a new quantized feedback, more flexible than the previous ones, that respects agents' privacy concern but at the same time improves prosociality. The results of this study are also applicable to generic resource sharing problems., Comment: 24 pages, 10 figures

A multi-armed bandit with finitely many arms is studied when each arm is a homogeneous Markov process on an underlying finite state space. The transition law of one of the arms, referred to as the odd arm, is different from the common transition law of all other arms. A learner, who has no knowledge of the above transition laws, has to devise a sequential test to identify the index of the odd arm as quickly as possible, subject to an upper bound on the probability of error. For this problem, we derive an asymptotic lower bound on the expected stopping time of any sequential test of the learner, where the asymptotics is as the probability of error vanishes. Furthermore, we propose a sequential test, and show that the asymptotic behaviour of its expected stopping time comes arbitrarily close to that of the lower bound. Prior works deal with independent and identically distributed arms, whereas our work deals with Markov arms. Our analysis of the rested Markov setting is a key first step in understanding the difficult case of restless Markov setting, which is still open.

Structured representations such as scene graphs serve as an efficient and compact representation that can be used for downstream rendering or retrieval tasks. However, existing efforts to generate realistic images from scene graphs perform poorly on scene composition for cluttered or complex scenes. We propose two contributions to improve the scene composition. First, we enhance the scene graph representation with heuristic-based relations, which add minimal storage overhead. Second, we use extreme points representation to supervise the learning of the scene composition network. These methods achieve significantly higher performance over existing work (69.0% vs 51.2% in relation score metric). We additionally demonstrate how scene graphs can be used to retrieve pose-constrained image patches that are semantically similar to the source query. Improving structured scene graph representations for rendering or retrieval is an important step towards realistic image generation., Comment: To appear in CVPRW 2019 (CEFRL)

Recent progress in the field of multiferroics led to the discovery of many new materials in which ferroelectricity is induced by cycloidal spiral orders. The direction of the electric polarization is typically constrained by spin anisotropies and magnetic field. Here, we report that the mixed rare-earth manganite, Gd$_{0.5}$Dy$_{0.5}$MnO$_3$, exhibits a spontaneous electric polarization along a general direction in the crystallographic ac-plane, which is suppressed below 10 K but re-emerges in an applied magnetic field. Neutron diffraction measurements show that the polarization direction results from a large tilt of the spiral plane with respect to the crystallographic axes and that the suppression of ferroelectricity is caused by the transformation of a cycloidal spiral into a helical one, a unique property of this rare-earth manganite. The freedom in the orientation of the spiral plane allows for a fine magnetic control of ferroelectricity, i.e. a rotation as well as a strong enhancement of the polarization depending on the magnetic field direction. We show that this unusual behavior originates from the coupling between the transition metal and rare-earth magnetic subsystems., Comment: 20 pages, 8 figures (to be published in Physical Review Materials)

SrTi0.5Mn0.5O3 (STMO) is a chemically disordered perovskite having random distribution of Ti and Mn over 1b site. Striking discrepancies about the structural and magnetic properties of STMO demands detailed analysis which is addressed. To explore the magnetic ground state of STMO, static and dynamic magnetic properties were studied over a broad temperature range (2-300 K). The dc, ac magnetization show a cusp like peak at Tf ~ 14 K, which exhibits field and frequency dependence. The thermoremanent magnetization is characterized by using stretched exponential function and characteristic time suggests the existence of spin clusters. Also the other features observed in magnetic memory effect, muon spin resonance/rotation and neutron powder diffraction confirm the existence of cluster spin glass state in STMO, rather than the long range ordered ground state. Intriguingly, the observed spin relaxation can be attributed to the dilute magnetism due to non-magnetic doping at Mn-site and competing antiferromagnetic and ferromagnetic interactions resulting from the site disorder., Comment: 17 pages, 7 figures

We consider a multi-population epidemic model with one or more (almost) isolated communities and one mobile community. Each of the isolated communities has contact within itself and, in addition, contact with the outside world but only through the mobile community. The contact rate between the mobile community and the other communities is assumed to be controlled. We first derive a multidimensional ordinary differential equation (ODE) as a mean-field fluid approximation to the process of the number of infected nodes, after appropriate scaling. We show that the approximation becomes tight as the sizes of the communities grow. We then use a singular perturbation approach to reduce the dimension of the ODE and identify an optimal control policy on this system over a fixed time horizon via Pontryagin's minimum principle. We then show that this policy is close to optimal, within a certain class, on the original problem for large enough communities. From a phenomenological perspective, we show that the epidemic may sustain in time in all communities (and thus the system has a nontrivial metastable regime) even though in the absence of the mobile nodes the epidemic would die out quickly within each of the isolated communities., Comment: 16 pages (two column format), 4 figures

We consider a double-auction mechanism, which was recently proposed in the context of rate allocation in mobile data-offloading markets. Network operators (users) derive benefit from offloading their traffic to third party WiFi or femtocell networks (link-suppliers). Link-suppliers experience costs for the additional capacity that they provide. Users and link-suppliers (collectively referred to as agents) have their pay-offs and cost functions as private knowledge. A network-manager decomposes the problem into a network problem and agent problems. The surrogate pay-offs and cost functions are modulated by the agents' bids. Agents' payoffs and costs are then determined by the allocations and prices set by the network-manager. Under this design, so long as the agents do not anticipate the effect of their actions on the prices set by the network-manager (i.e., price-taking agents), a competitive equilibrium exists as a solution to the network and agent problems, and this equilibrium optimizes the sum utility of all agents. However, this design fails when the agents are all strategic (price-anticipating). Specifically, the presence of a strategic link-supplier drives the system to an undesirable equilibrium with zero participation resulting in an efficiency loss of 100%. This is in stark contrast to an earlier setting where the users alone are strategic but the link-supplier is not - the efficiency loss is known to be at most 34%. The paper then proposes a Stackelberg game modification where the efficiency loss can be characterized in terms of the link-supplier's cost function when the users' pay-off functions are linear. Specifically, when the link-supplier's cost function is quadratic, the worst case efficiency loss is 25%. Further, the loss in efficiency improves for polynomial cost functions of higher degree.

Contact charging between insulators is one of the most basic, yet least well understood, of physical processes. For example we have no clear theory for how insulators recruit enough charge carriers to deposit charge but not enough to discharge. In this letter we note that charging and discharging kinetics may be distinct, and from this observation we develop a mathematical model. The model surprisingly predicts that charging can decrease as contact frequency increases: we confirm this prediction experimentally and propose future steps.

We consider a simultaneous small noise limit for a singularly perturbed coupled diffusion described by \begin{eqnarray*} dX^{\varepsilon}_t &=& b(X^{\varepsilon}_t, Y^{\varepsilon}_t)dt + \varepsilon^{\alpha}dB_t, dY^{\varepsilon}_t &=& - \frac{1}{\varepsilon} \nabla_yU(X^{\varepsilon}_t, Y^{\varepsilon}_t)dt + \frac{s(\varepsilon)}{\sqrt{\varepsilon}} dW_t, \end{eqnarray*} where $B_t, W_t$ are independent Brownian motions on ${\mathbb R}^d$ and ${\mathbb R}^m$ respectively, $b : \mathbb{R}^d \times \mathbb{R}^m \rightarrow \mathbb{R}^d$, $U : \mathbb{R}^d \times \mathbb{R}^m \rightarrow \mathbb{R}$ and $s :(0,\infty) \rightarrow (0,\infty)$. We impose regularity assumptions on $b$, $U$ and let $0 < \alpha < 1.$ When $s(\varepsilon)$ goes to zero slower than a prescribed rate as $\varepsilon \rightarrow 0$, we characterize all weak limit points of $X^{\varepsilon}$, as $\varepsilon \rightarrow 0$, as solutions to a differential equation driven by a measurable vector field. Under an additional assumption on the behaviour of $U(x, \cdot)$ at its global minima we characterize all limit points as Filippov solutions to the differential equation., Comment: Added References and Corrected Typos

In small-cell wireless networks where users are connected to multiple base stations (BSs), it is often advantageous to switch off dynamically a subset of BSs to minimize energy costs. We consider two types of energy cost: (i) the cost of maintaining a BS in the active state, and (ii) the cost of switching a BS from the active state to inactive state. The problem is to operate the network at the lowest possible energy cost (sum of activation and switching costs) subject to queue stability. In this setting, the traditional approach -- a Max-Weight algorithm along with a Lyapunov-based stability argument -- does not suffice to show queue stability, essentially due to the temporal co-evolution between channel scheduling and the BS activation decisions induced by the switching cost. Instead, we develop a learning and BS activation algorithm with slow temporal dynamics, and a Max-Weight based channel scheduler that has fast temporal dynamics. We show using convergence of time-inhomogeneous Markov chains, that the co-evolving dynamics of learning, BS activation and queue lengths lead to near optimal average energy costs along with queue stability., Comment: A shorter version of this paper appeared in the Proceedings of IEEE Conference on Computer Communications (IEEE Infocom 2017)

Open-access domestic broadband connection sharing constitutes a voluntary practice that is associated with societal, economic and public-safety benefits. Despite this fact, broadband subscribers are usually hesitant to freely share their broadband connection with guests for a multitude of reasons; one of them being sharing their network might hinder their own broadband quality of experience. In this paper, we investigate experimentally the impact of uplink guest traffic on the sharer's broadband quality of experience under both generic and broadband-sharing-specific packet scheduling policies. Both guest-user traffic and access point profiles employed in our study are developed by analyzing real-world traffic traces and measurements, captured from actual broadband sharing networking environments. Our results validate the suitability of hybrid packet scheduling policies for broadband sharing schemes and show that only a few dozen kilobytes per second of uplink guest traffic can be tolerated by sharers without hampering their broadband quality of experience. In this context, we show that the selection of the most appropriate packet scheduling policy for broadband sharing, as well as its respective configuration, depend largely on the capacity of the broadband connection and the policy's packet-dropping behavior on guest traffic.

The group affect or emotion in an image of people can be inferred by extracting features about both the people in the picture and the overall makeup of the scene. The state-of-the-art on this problem investigates a combination of facial features, scene extraction and even audio tonality. This paper combines three additional modalities, namely, human pose, text-based tagging and CNN extracted features / predictions. To the best of our knowledge, this is the first time all of the modalities were extracted using deep neural networks. We evaluate the performance of our approach against baselines and identify insights throughout this paper.