text
stringlengths 11
9.77k
| label
stringlengths 2
104
|
|---|---|
We study the effect of the second virial coefficient on the adiabatic lapse rate of a dry atmosphere. To this end, we compute the corresponding adiabatic curves, the internal energy, and the heat capacity, among other thermodynamic parameters. We apply these results to Earth, Mars, Venus, Titan, and the exoplanet G1 851d, considering three physically relevant virial coefficients in each case: the hard-sphere, van der Waals, and the square-well potential. These examples illustrate under which atmospheric conditions the effect of the second virial coefficient is relevant. Taking the latter into account yields corrections towards the experimental values of the lapse rates of Venus and Titan in some instances. | physics |
We present results from an asymptotic magnetohydrodynamic model that is suited for studying the rapidly rotating, low viscosity regime typical of the electrically conducting fluid interiors of planets and stars. We show that the presence of sufficiently strong magnetic fields prevents the formation of large-scale vortices and saturates the inverse cascade at a finite length-scale. This saturation corresponds to an equilibrated state in which the energetics of the depth-averaged flows are characterized by a balance of convective power input and ohmic dissipation. A quantitative criteria delineating the transition between finite-size flows and domain-filling (large-scale) vortices in electrically conducting fluids is found. By making use of the inferred and observed properties of planetary interiors, our results suggest that convection-driven large-scale vortices do not form in the electrically conducting regions of many bodies. | physics |
We investigate testing of the hypothesis of independence between a covariate and the marks in a marked point process. It would be rather straightforward if the (unmarked) point process were independent of the covariate and the marks. In practice, however, such an assumption is questionable, and possible dependence between the point process and the covariate or the marks may lead to incorrect conclusions. Hence we propose to investigate the complete dependence structure in the triangle points-marks-covariates together. We take advantage of the recent development of the nonparametric random shift methods, namely the new variance correction approach, and propose tests of the null hypothesis of independence between the marks and the covariate and between the points and the covariate. We present a detailed simulation study showing the performance of the methods, and provide two theorems establishing the appropriate form of the correction factors for the variance correction. Finally, we illustrate the use of the proposed methods in two real applications. | statistics |
This paper shows that a relation can be found between the voltage at the terminals of an inverter-interfaced Renewable Energy Source RES and its optimal reactive power support. This relationship, known as Volt-Var Curve VVC, enables the decentral operation of RES for Active Voltage Management (AVM). In this paper, the decentralized AVM technique is modified to consider the effects of the realistic operational constraints of RES. The AVM technique capitalizes on the reactive power support capabilities of inverters to achieve the desired objective in unbalanced active Low-Voltage Distribution Systems LVDSs. However, as the results show, this AVM technique fails to satisfy the operator objective when the network structure dynamically changes. By updating the VVCs according to the system configuration and components availability, the objective functions will be significantly improved, and the AVM method remains resilient against the network changes. To keep the decentralized structure, the impedance identification capability of inverters is used to find the system configuration locally. Adaptive VVCs enable the decentralized control of inverters in an online setting. A real-life suburban residential LV-DS in Dublin, Ireland is used to showcasing the proposed method, and the effectiveness of proposed resilient active voltage management technique is demonstrated. | electrical engineering and systems science |
In this paper, we show that Quantum Mechanics does not admit ontological models, in the sense that the quantum state of a system cannot correspond to a set of physical states representing the independent reality of the system. We show, via two thought experiments based on the Wigner's friend scenario, that if the ontic state of physical systems in the lab is the same for Wigner and for his friend, one of the following will be violated: PBR theorem, Quantum-theoretic predictions, Causality and the "No-superdeterminism" assumption. | quantum physics |
The AdS/CFT understanding of CFT entanglement is based on HRT surfaces in the dual bulk spacetime. While such surfaces need not exist in sufficiently general spacetimes, the maximin construction demonstrates that they can be found in any smooth asymptotically locally AdS spacetime without horizons or with only Kasner-like singularities. In this work, we introduce restricted maximin surfaces anchored to a particular boundary Cauchy slice $C_\partial$. We show that the result agrees with the original unrestricted maximin prescription when the restricted maximin surface lies in a smooth region of spacetime. We then use this construction to extend the existence theorem for HRT surfaces to generic charged or spinning AdS black holes whose mass-inflation singularities are not Kasner-like. We also discuss related issues in time-independent charged wormholes. | high energy physics theory |
We present 3D general relativistic magnetohydrodynamic (GRMHD) simulations of the accretion flow surrounding Sagittarius A* that are initialized using larger-scale MHD simulations of the $\sim$ 30 Wolf--Rayet (WR) stellar winds in the Galactic center. The properties of the resulting accretion flow on horizon scales are set not by ad hoc initial conditions but by the observationally constrained properties of the WR winds with limited free parameters. For this initial study we assume a non-spinning black hole. Our simulations naturally produce a $\sim 10^{-8} M_\odot$ yr$^{-1}$ accretion rate, consistent with previous phenomenological estimates. We find that a magnetically arrested flow is formed by the continuous accretion of coherent magnetic field being fed from large radii. Near the event horizon, the magnetic field is so strong that it tilts the gas with respect to the initial angular momentum and concentrates the originally quasi-spherical flow to a narrow disk-like structure. We also present 230 GHz images calculated from our simulations where the inclination angle and physical accretion rate are not free parameters but are determined by the properties of the WR stellar winds. The image morphology is highly time variable. Linear polarization on horizon scales is coherent with weak internal Faraday rotation. | astrophysics |
The production of hidden-bottom pentaquark $P_{b}$ states via $\gamma p$ and $\pi ^{-}p$ scatterings is studied within an effective Lagrangian approach and the vector-meson-dominance mechnism. For the $P_{b}$ production in the process $\gamma p\rightarrow \Upsilon p$, the dipole Pomeron model is employed to calculate the background contribution, and the experimental data can be well described. For the process $\pi ^{-}p\rightarrow \Upsilon n$, the Reggeized $t$-channel with $\pi $ exchange is considered as the main background for the $P_{b}$ production. Near the threshold, two-peak structure from the states $P_{b (11080)$ and $P_{b}(11125)$ can be observed if energy bin width is chosen as 0.01 GeV, and the same result is obtained in the $\pi ^{-}p$ scattering. Moreover, by taking the branching ratio of Br$[{P_{b}\rightarrow \pi N}]\simeq 0.05\%$, the numerical result shows that the average value of cross section from the $P_{b}(11080)$ state produced in the $\gamma p$ or $\pi ^{-}p$ scattering reaches at least 0.1 nb with a bin of 0.1 GeV. Even if we reduce the branching ratio of the $P_{b}$ state into $\pi N$ channel by one order, the theoretical average of the cross section from $P_{b}(11080)$ production in $\pi ^{-}p$ scattering can reach the order of 0.01 nb with a bin of 0.1 GeV, which means that the signal can still be clearly distinguished from the background. The experimental measurements and studies on the hidden-bottom pentaquark $P_{b}$ state production in the $\gamma p $ or $\pi ^{-}p$ scattering near-threshold energy region around $W\simeq 11$ GeV are strongly suggested, which are accessible at COMPASS and JPARC. Particularly, the result of the photoproduction suggests that it is very promising to observe the hidden-bottom pentaquark at proposed EicC facility in China. | high energy physics phenomenology |
Context: Stellar clusters are benchmarks for theories of star formation and evolution. The high precision parallax data of the Gaia mission allows significant improvements in the distance determination to stellar clusters and its stars. In order to have accurate and precise distance determinations, systematics like the parallax spatial correlations need to be accounted for, especially for stars in small sky regions. Aims: Provide the astrophysical community with a free and open code designed to simultaneously infer cluster parameters (i.e. distance and size) and the distances to its stars using Gaia parallax measurements. It includes cluster oriented prior families and is specifically designed to deal with the Gaia parallax spatial correlations. Methods: A Bayesian hierarchical model is created to allow the inference of both the cluster parameters and distances to its stars. Results: Using synthetic data that mimics Gaia parallax uncertainties and spatial correlations, we observe that our cluster oriented prior families result in distance estimates with smaller errors than those obtained with an exponentially decreasing space density prior. In addition, the treatment of the parallax spatial correlations minimizes errors in the estimated cluster size and stellar distances and avoids the underestimation of uncertainties. Although neglecting the parallax spatial correlations has no impact on the accuracy of cluster distance determinations, it underestimates the uncertainties and may result in measurements that are incompatible with the true value. Conclusions: The combination of prior knowledge with the treatment of Gaia parallax spatial correlations produces accurate (error <10%) and trustworthy estimates (i.e. true values contained within the 2$\sigma$ uncertainties) of clusters distances for clusters up to ~5 kpc, and cluster sizes for clusters up to ~1 kpc. | astrophysics |
Density Functional Tight Binding (DFTB) is an attractive method for accelerated quantum simulations of condensed matter due to its enhanced computational efficiency over standard Density Functional Theory approaches. However, DFTB models can be challenging to determine for individual systems of interest, especially for metallic and interfacial systems where different bonding arrangements can lead to significant changes in electronic states. In this regard, we have created a rapid-screening approach for determining systematically improvable DFTB interaction potentials that can yield transferable models for a variety of conditions. Our method leverages a recent reactive molecular dynamics force field where many-body interactions are represented by linear combinations of Chebyshev polynomials. This allows for the efficient creation of multi-center representations with relative ease, requiring only a small investment in initial DFT calculations. We have focused our workflow on TiH$_2$ as a model system and show that a relatively small training set based on unit-cell sized calculations yields a model accurate for both bulk and surface properties. Our approach is easy to implement and can yield accurate DFTB models over a broad range of thermodynamic conditions, where physical and chemical properties can be difficult to interrogate directly and there is historically a significant reliance on theoretical approaches for interpretation and validation of experimental results. | condensed matter |
We present a generalization of the so-called Maxwellian extended Bargmann algebra by considering a non-relativistic limit to a generalized Maxwell algebra defined in three spacetime dimensions. The non-relativistic Chern-Simons gravity theory based on this new algebra is also constructed and discussed. We point out that the extended Bargmann and its Maxwellian generalization are particular sub-cases of the generalized Maxwellian extended Bargmann gravity introduced here. The extension of our results using the semigroup expansion method is also discussed. | high energy physics theory |
We provide an algorithm for preparing the thermofield double (TFD) state of the Sachdev-Ye-Kitaev model without the need for an auxiliary bath. Following previous work, the TFD can be cast as the approximate ground state of a Hamiltonian, $H_{\text{TFD}}$. Using variational quantum circuits, we propose and implement a gradient-based algorithm for learning parameters that find this ground state, an application of the variational quantum eigensolver. Concretely, we find quantum circuits that prepare the ground state of $H_{\text{TFD}}$ for the $q=4$ SYK model up to $N=12$. | high energy physics theory |
The measurement based, or one-way, model of quantum computation for continuous variables uses a highly entangled state called a cluster state to accomplish the task of computing. Cluster states that are universal for computation are a subset of a class of states called graph states. These states are Gaussian states and therefore require that the homodyne detection (Gaussian measurement) scheme is supplemented with a non-Gaussian measurement for universal computation, a significant experimental challenge. Here we define a new non-Gaussian class of states based on hypergraphs which satisfy the requirements of the Lloyd-Braunstein criteria while restricted to a Gaussian measurement strategy. Our main result is to show that, taking advantage of the intrinsic multimode nonlinearity, a hypergraph consisting of 3-edges can be used to apply a three-mode operation to an input three-mode state. As a special case, this technique can be used to apply the cubic phase gate to a single mode. | quantum physics |
LIGO and Virgo have reported the detection of GW190521, from the merger of a binary black hole (BBH) with a total mass around $150$ M$_\odot$. While current stellar models limit the mass of any black hole (BH) remnant to about $40 - 50$ M$_\odot$, more massive BHs can be produced dynamically through repeated mergers in the core of a dense star cluster. The process is limited by the recoil kick (due to anisotropic emission of gravitational radiation) imparted to merger remnants, which can escape the parent cluster, thereby terminating growth. We study the role of the host cluster metallicity and escape speed in the buildup of massive BHs through repeated mergers. Almost independent of host metallicity, we find that a BBH of about $150$ M$_\odot$ could be formed dynamically in any star cluster with escape speed $\gtrsim 200$ km s$^{-1}$, as found in galactic nuclear star clusters as well as the most massive globular clusters and super star clusters. Using an inspiral-only waveform, we compute the detection probability for different primary masses ($\ge 60$ M$_\odot$) as a function of secondary mass and find that the detection probability increases with secondary mass and decreases for larger primary mass and redshift. Future additional detections of massive BBH mergers will be of fundamental importance for understanding the growth of massive BHs through dynamics and the formation of intermediate-mass BHs. | astrophysics |
Pickling behaviour of the oxide layer on hot-rolled 2205 duplex stainless steel (DSS) was studied in H2SO4 solutions with electrolytic workstation. | physics |
Recent work has demonstrated that deep neural networks are vulnerable to adversarial examples---inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network. In fact, some of the latest findings suggest that the existence of adversarial attacks may be an inherent weakness of deep learning models. To address this problem, we study the adversarial robustness of neural networks through the lens of robust optimization. This approach provides us with a broad and unifying view on much of the prior work on this topic. Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal. In particular, they specify a concrete security guarantee that would protect against any adversary. These methods let us train networks with significantly improved resistance to a wide range of adversarial attacks. They also suggest the notion of security against a first-order adversary as a natural and broad security guarantee. We believe that robustness against such well-defined classes of adversaries is an important stepping stone towards fully resistant deep learning models. Code and pre-trained models are available at https://github.com/MadryLab/mnist_challenge and https://github.com/MadryLab/cifar10_challenge. | statistics |
We consider the problem of finding a marked vertex in a graph from an arbitrary starting distribution, using a quantum walk based algorithm. We work in the framework introduced by Belovs which showed how to detect the existence of a marked vertex in $O(\sqrt{RW})$ quantum walk steps, where $R$ is the effective resistance and $W$ is the total weight of the graph. Our algorithm outputs a marked vertex in the same runtime up to a logarithmic factor in the number of marked vertices. When starting in the stationary distribution, this recovers the recent results of Ambainis et al. We also describe a new algorithm to estimate the effective resistance $R$. | quantum physics |
Silicon photonics lacks a second-order nonlinear optical response in general because the typical constituent materials are centro-symmetric and lack inversion symmetry, which prohibits second-order nonlinear processes such as second harmonic generation (SHG). Here, for the first time, we realize efficient SHG in a silicon-based optical microresonator by combining a strong photo-induced effective second-order nonlinearity with resonant enhancement and perfect-phase matching. We show a record-high conversion efficiency of 2,500 %/W, which is 2 to 4 orders of magnitude larger than previous works. In particular, our devices realize mW-level SHG output powers with > 20 % power conversion efficiency. This demonstration is a major breakthrough in realizing efficient second-order nonlinear processes in silicon photonics, and paves the way for integrated self-referencing of Kerr frequency combs for compact optical frequency synthesis and optical clock technologies. | physics |
Modelling the extremal dependence structure of spatial data is considerably easier if that structure is stationary. However, for data observed over large or complicated domains, non-stationarity will often prevail. Current methods for modelling non-stationarity in extremal dependence rely on models that are either computationally difficult to fit or require prior knowledge of covariates. Sampson and Guttorp (1992) proposed a simple technique for handling non-stationarity in spatial dependence by smoothly mapping the sampling locations of the process from the original geographical space to a latent space where stationarity can be reasonably assumed. We present an extension of this method to a spatial extremes framework by considering least squares minimisation of pairwise theoretical and empirical extremal dependence measures. Along with some practical advice on applying these deformations, we provide a detailed simulation study in which we propose three spatial processes with varying degrees of non-stationarity in their extremal and central dependence structures. The methodology is applied to Australian summer temperature extremes and UK precipitation to illustrate its efficacy compared to a naive modelling approach. | statistics |
Multi-user multi-keyword ranked search scheme in arbitrary language is a novel multi-keyword rank searchable encryption (MRSE) framework based on Paillier Cryptosystem with Threshold Decryption (PCTD). Compared to previous MRSE schemes constructed based on the k-nearest neighbor searcha-ble encryption (KNN-SE) algorithm, it can mitigate some draw-backs and achieve better performance in terms of functionality and efficiency. Additionally, it does not require a predefined keyword set and support keywords in arbitrary languages. However, due to the pattern of exact matching of keywords in the new MRSE scheme, multilingual search is limited to each language and cannot be searched across languages. In this pa-per, we propose a cross-lingual multi-keyword rank search (CLRSE) scheme which eliminates the barrier of languages and achieves semantic extension with using the Open Multilingual Wordnet. Our CLRSE scheme also realizes intelligent and per-sonalized search through flexible keyword and language prefer-ence settings. We evaluate the performance of our scheme in terms of security, functionality, precision and efficiency, via extensive experiments. | computer science |
We present analyses of Spitzer observations of 29P/Schwassmann-Wachmann 1 using 16 $\mu$m IRS "blue" peak-up (PU) and 24 $\mu$m and 70 $\mu$m MIPS images obtained on UT 2003 November 23 and 24 that characterize the Centaur's large-grain (10-100 $\mu$m) dust coma during a time of non-outbursting "quiescent" activity. Estimates of $\epsilon f \rho$ for each band (16 $\mu$m (2600 $\pm$ 43 cm), 24 $\mu$m (5800 $\pm$ 63 cm), and 70 $\mu$m (1800 $\pm$ 900 cm)) follow the trend between nucleus size vs. $\epsilon f \rho$ that was observed for the WISE/NEOWISE comet ensemble. A coma model was used to derive a dust production rate in the range of 50-100 kg/s. For the first time, a color temperature map of SW1's coma was constructed using the 16 $\mu$m and 24 $\mu$m imaging data. With peaks at $\sim$ 140K, this map implies that coma water ice grains should be slowly sublimating and producing water gas in the coma. We analyzed the persistent 24 $\mu$m "wing" (a curved southwestern coma) feature at 352,000 km (90$''$) from the nucleus attributed by Stansberry et al. (2004) to nucleus rotation and instead propose that it is largely created by solar radiation pressure and gravity acting on micron sized grains. We performed coma removal to the 16 $\mu$m PU image in order to refine the nucleus' emitted thermal flux. A new application of the Near Earth Asteroid Thermal Model (NEATM; Harris 1998) at five wavelengths (5.730 $\mu$m, 7.873 $\mu$m, 15.80 $\mu$m, 23.68 $\mu$m, and 71.42 $\mu$m) was then used to refine SW1's effective radius measurement to $R = 32.3 \pm 3.1$ km and infrared beaming parameter to $\eta = 1.1 \pm 0.2$, respectively. | astrophysics |
We present first predictions of the cross sections and differential distributions for the exclusive reaction $pp \to pp K^{*0} \bar{K}^{*0}$ contributing to the $K^{+} K^{-} \pi^{+} \pi^{-}$ channel. The amplitudes for the reaction are formulated within the nonperturbative tensor-pomeron approach. We consider separately the $f_{2}(1950)$ $s$-channel exchange mechanism and the $K^{*0}$ $t/u$-channel exchange mechanism, focusing on their specificities. First mechanism is a candidate for the central diffractive production of tensor glueball and the second one is an irreducible continuum. We adjust parameters of our model, assuming the dominance of pomeron-pomeron fusion, to the WA102 experimental data. We find that including the continuum contribution alone one can describe the WA102 data reasonably well. We present predictions for the reaction $pp \to pp (K^{*0} \bar{K}^{*0} \to K^{+} K^{-} \pi^{+} \pi^{-})$ for the ALICE, ATLAS, CMS and LHCb experiments including typical kinematical cuts. We find from our model a cross sections of $\sigma \cong 17-250$ nb for the LHC experiments, depending on the assumed cuts. Absorption effects are included in our analysis. | high energy physics phenomenology |
As it has been demonstrated that trapped ion systems have unmatched long-lived quantum-bit (qubit) coherence and can support high-fidelity quantum manipulations, how to scale up the system size becomes an inevitable task for practical purposes. In this work, we theoretically analyse the physical limitation of scalability with a trapped ion array, and propose a feasible scheme of architecture that in principle allows an arbitrary number of ion qubits, for which the overhead only scales linearly with the system size. This scheme relies on the combined ideas of a trap architecture of tunable size, stabilisation of an ion crystal by optical tweezers, and continuous sympathetic cooling without touching the stored information. We demonstrate that illumination of optical tweezers modifies the motional spectrum by effectively pinning the ions, lifting the frequencies of the motional ground modes. By doing so, we make the structure of the array less vulnerable from thermal excitations, and suppress the the position fluctuations to insure faithful gate operations. Finally, we also explore the local behaviour of cooling when a sub-array is isolated by optical tweezers from other parts of the crystal. | quantum physics |
Prophet inequalities compare the expected performance of an online algorithm for a stochastic optimization problem to the expected optimal solution in hindsight. They are a major alternative to classic worst-case competitive analysis, of particular importance in the design and analysis of simple (posted-price) incentive compatible mechanisms with provable approximation guarantees. A central open problem in this area concerns subadditive combinatorial auctions. Here $n$ agents with subadditive valuation functions compete for the assignment of $m$ items. The goal is to find an allocation of the items that maximizes the total value of the assignment. The question is whether there exists a prophet inequality for this problem that significantly beats the best known approximation factor of $O(\log m)$. We make major progress on this question by providing an $O(\log \log m)$ prophet inequality. Our proof goes through a novel primal-dual approach. It is also constructive, resulting in an online policy that takes the form of static and anonymous item prices that can be computed in polynomial time given appropriate query access to the valuations. As an application of our approach, we construct a simple and incentive compatible mechanism based on posted prices that achieves an $O(\log \log m)$ approximation to the optimal revenue for subadditive valuations under an item-independence assumption. | computer science |
Within a 400 pc sphere around the Sun, we search for Praesepe's tidal tails in the Gaia DR2 dataset. We used a modified convergent-point method to search for stars with space velocities close to the space velocity of the Praesepe cluster. We find a clear indication for the existence of Praesepe's tidal tails, both extending up to 165~pc from the centre of the cluster. A total of 1393 stars populate the cluster and its tails, giving a total mass of 794 M_Sun. We determined a tidal radius of 10.77 pc for the cluster and a tidal mass of 483 M_Sun. The corresponding half-mass radius is 4.8 pc. We also found clear indication for mass segregation in the cluster. The tidal tails outside 2 tidal radii are populated by 389 stars. The total contamination of our sample by field stars lies between 50 to 100 stars or 3.6 to 7.2 per cent. We used an astrometrically and photometrically clean sub-sample of Gaia DR2 which makes our Praesepe sample incomplete beyond M_G ~ 12.0 mag, which corresponds to about 0.25 M_Sun. A comparison with an N-body model of the cluster and its tails shows remarkably good coincidence. Three new white dwarfs are found in the tails. | astrophysics |
We present a simple version of hadron-quark hybrid (HQH) model in the $\mu_B$--$T$ plain, where $T$ is temperature and $\mu_{B}$ is the baryon-number chemical potential. The model is composed of the independent-quark model for quark-gluon states and an improved version of excluded-volume hadron resonance gas (EV-HRG) model for hadronic states. In the improved version of EV-HRG, the pressure has charge conjugation and is obtained by a simple analytic form. The switching function from hadron states to quark-gluon states in the present model has no chemical potential dependence. The simple HQH model is successful in reproducing LQCD results on the transition region of chiral crossover and the EoS in $\mu_{B} \leq 400$ MeV. We then predict the chiral-crossover region in $400 \leq \mu_{B} \leq 800$ MeV. We also predict a transition line derived from isentropic trajectories in $0 \leq \mu_{B} \leq 800$ MeV and find that the effect of strangeness neutrality is small there. | high energy physics phenomenology |
A common way to manipulate a quantum system, for example spins or artificial atoms, is to use properly tailored control pulses. In order to accomplish quantum information tasks before coherence is lost, it is crucial to implement the control in the shortest possible time. Here we report the near time-optimal preparation of a Bell state with fidelity higher than $99\%$ in an NMR experiment, which is feasible by combining the synergistic capabilities of modelling and experiments operating in tandem. The pulses preparing the Bell state are found by experiments that are recursively assisted with a gradient-based optimization algorithm working with a model. Thus, we explore the interplay between model-based numerical optimal design and experimental-based learning control. Utilizing the balanced synergism between the dual approaches should have broad applications for accelerating the search for optimal quantum controls. | quantum physics |
Two models are first presented, of one-dimensional discrete-time quantum walk (DTQW) with temporal noise on the internal degree of freedom (i.e., the coin): (i) a model with both a coin-flip and a phase-flip channel, and (ii) a model with random coin unitaries. It is then shown that both these models admit a common limit in the spacetime continuum, namely, a Lindblad equation with Dirac-fermion Hamiltonian part and, as Lindblad jumps, a chirality flip and a chirality-dependent phase flip, which are two of the three standard error channels for a two-level quantum system. This, as one may call it, Dirac Lindblad equation, provides a model of quantum relativistic spatial diffusion, which is evidenced both analytically and numerically. This model of spatial diffusion has the intriguing specificity of making sense only with original unitary models which are relativistic in the sense that they have chirality, on which the noise is introduced: The diffusion arises via the by-construction (quantum) coupling of chirality to the position. For a particle with vanishing mass, the model of quantum relativistic diffusion introduced in the present work, reduces to the well-known telegraph equation, which yields propagation at short times, diffusion at long times, and exhibits no quantumness. Finally, the results are extended to temporal noises which depend smoothly on position. | quantum physics |
Accuracy of crop price forecasting techniques is important because it enables the supply chain planners and government bodies to take appropriate actions by estimating market factors such as demand and supply. In emerging economies such as India, the crop prices at marketplaces are manually entered every day, which can be prone to human-induced errors like the entry of incorrect data or entry of no data for many days. In addition to such human prone errors, the fluctuations in the prices itself make the creation of stable and robust forecasting solution a challenging task. Considering such complexities in crop price forecasting, in this paper, we present techniques to build robust crop price prediction models considering various features such as (i) historical price and market arrival quantity of crops, (ii) historical weather data that influence crop production and transportation, (iii) data quality-related features obtained by performing statistical analysis. We additionally propose a framework for context-based model selection and retraining considering factors such as model stability, data quality metrics, and trend analysis of crop prices. To show the efficacy of the proposed approach, we show experimental results on two crops - Tomato and Maize for 14 marketplaces in India and demonstrate that the proposed approach not only improves accuracy metrics significantly when compared against the standard forecasting techniques but also provides robust models. | statistics |
We consider a security setting in which the Cyber-Physical System (CPS) is composed of subnetworks where each subnetwork is under ownership of one defender. Such CPS can be represented by an attack graph where the defenders are required to invest (subject to a budget constraint) on the graph's edges in order to protect their critical assets (where each defender's critical asset has a certain value to the defender if compromised). We model such CPS using Hybrid Input-Output Automaton (HIOA) where each subnetwork is represented by a HIOA module. We first establish the building blocks needed in our setting. We then present our model that characterizes the continuous time evolution of the investments and discrete transitions between different states (where each state represents different condition and/or perturbation) within the system. Finally, we provide a real-world CPS example to validate our modeling. | electrical engineering and systems science |
UAVs have been widely used in visual inspections of buildings, bridges and other structures. In either outdoor autonomous or semi-autonomous flights missions strong GPS signal is vital for UAV to locate its own positions. However, strong GPS signal is not always available, and it can degrade or fully loss underneath large structures or close to power lines, which can cause serious control issues or even UAV crashes. Such limitations highly restricted the applications of UAV as a routine inspection tool in various domains. In this paper a vision-model-based real-time self-positioning method is proposed to support autonomous aerial inspection without the need of GPS support. Compared to other localization methods that requires additional onboard sensors, the proposed method uses a single camera to continuously estimate the inflight poses of UAV. Each step of the proposed method is discussed in detail, and its performance is tested through an indoor test case. | computer science |
In this paper, we first propose a novel maneuvering technique compatible with displacement-consensus-based formation controllers. We show that the formation can be translated with an arbitrary velocity by modifying the weights in the consensus Laplacian matrix. In fact, we demonstrate that the displacement-consensus-based formation control is a particular case of our more general method. We then uncover robustness issues with undesired steady-state motions and resultant distorted shapes in undirected displacement-consensus-based formation control. In particular, these issues are triggered when neighboring agents mismeasure their relative positions, e.g., their onboard sensors are misaligned and have different scale factors. We will show that if all the sensing is close to perfect but different among the agents, then the stability of the system is compromised. Explicit expressions for the eventual non-desired velocity and shape's distortion are given as functions of the scale factors and misalignments for formations based on tree graphs. | electrical engineering and systems science |
In a recent work the Green's functions of the $\mathcal{PT}$-symmetric scalar theory $g \phi^{2}(i\phi)^\epsilon$ were calculated at the first order of the logarithmic expansion, i.e. at first order in $\epsilon$, and it was proposed to use this expansion in powers of $\epsilon$ to implement a systematic renormalization of the theory. Using techniques that we recently developed for the analysis of an ordinary (hermitian) scalar theory, in the present work we calculate the Green's functions at $O(\epsilon^2)$, pushing also the analysis to higher orders. We find that, at each finite order in $\epsilon$, the theory is non-interacting for any dimension $d \geq 2$. We then conclude that by no means this expansion can be used for a systematic renormalization of the theory. We are then lead to consider resummations, and we start with the leading contributions. Unfortunately, the results are quite poor. Specifying to the physically relevant $i g \phi^3$ model, we show that this resummation simply gives the trivial lowest order results of the weak-coupling expansion. We successively resum subleading diagrams, but again the results are rather poor. All this casts serious doubts on the possibility of studying the theory $g \phi^{2}(i\phi)^\epsilon$ with the help of such an expansion. We finally add that the findings presented in this work were obtained by us some time ago (December 2019), and we are delighted to see that these results, that we communicated to C.M. Bender in December 2019, are confirmed in a recent preprint (e-Print:2103.07577) of C.M. Bender and collaborators. | high energy physics theory |
The design of sampling set (DoS) for bandlimited graph signals (GS) has been extensively studied in recent years, but few of them exploit the benefits of the stochastic prior of GS. In this work, we introduce the optimization framework for Bayesian DoS of bandlimited GS. We also illustrate how the choice of different sampling sets affects the estimation error and how the prior knowledge influences the result of DoS compared with the non-Bayesian DoS by the aid of analyzing Gershgorin discs of error metric matrix. Finally, based on our analysis, we propose a heuristic algorithm for DoS to avoid solving the optimization problem directly. | electrical engineering and systems science |
Nearly one third of all nitrogen oxides are removed from the atmosphere through the reactive uptake of N$_2$O$_5$ into aqueous aerosol. The primary step in reactive uptake is the rapid hydrolysis of N$_2$O$_5$, yet despite significant study, the mechanism and rate of this process are unknown. Here we use machine learning-based reactive many body potentials and methods of importance sampling molecular dynamics simulations to study the solvation and subsequent hydrolysis of N$_2$O$_5$. We find that hydrolysis to nitric acid proceeds through the coordinated fluctuation of intramolecular charge separation and solvation, and its characteristic rate is 4.1 ns$^{-1}$, orders of magnitude faster than traditionally assumed. This large rate calls into question standard models of reactive uptake that envision local equilibration between the gas and the bulk solution. We propose an alternative model based on interfacial reactivity that can explain existing experimental observations and is corroborated by explicit simulations. | physics |
Recently proposed L2-norm linear discriminant analysis criterion via the Bhattacharyya error bound estimation (L2BLDA) is an effective improvement of linear discriminant analysis (LDA) for feature extraction. However, L2BLDA is only proposed to cope with vector input samples. When facing with two-dimensional (2D) inputs, such as images, it will lose some useful information, since it does not consider intrinsic structure of images. In this paper, we extend L2BLDA to a two-dimensional Bhattacharyya bound linear discriminant analysis (2DBLDA). 2DBLDA maximizes the matrix-based between-class distance which is measured by the weighted pairwise distances of class means and meanwhile minimizes the matrix-based within-class distance. The weighting constant between the between-class and within-class terms is determined by the involved data that makes the proposed 2DBLDA adaptive. In addition, the criterion of 2DBLDA is equivalent to optimizing an upper bound of the Bhattacharyya error. The construction of 2DBLDA makes it avoid the small sample size problem while also possess robustness, and can be solved through a simple standard eigenvalue decomposition problem. The experimental results on image recognition and face image reconstruction demonstrate the effectiveness of the proposed methods. | computer science |
Deconvolution is the most commonly used image processing method to remove the blur caused by the point-spread-function (PSF) in optical imaging systems. While this method has been successful in deblurring, it suffers from several disadvantages including being slow, since it takes many iterations, suboptimal, in cases where experimental operator chosen to represent PSF is not optimal. In this paper, we are proposing a deep-learning-based deblurring method applicable to optical microscopic imaging systems. We tested the proposed method in database data, simulated data, and experimental data (include 2D optical microscopic data and 3D photoacoustic microscopic data), all of which showed much improved deblurred results compared to deconvolution. To quantify the improved performance, we compared our results against several deconvolution methods. Our results are better than conventional techniques and do not require multiple iterations or pre-determined experimental operator. Our method has the advantages of simple operation, short time to compute, good deblur results and wide application in all types of optical microscopic imaging systems. The deep learning approach opens up a new path for deblurring and can be applied in various biomedical imaging fields. | electrical engineering and systems science |
Miniature lenses with tunable focus are essential components for many modern applications involving compact optical systems. While several tunable lenses have been reported with various tuning mechanisms, they often still face challenges in power consumption, tuning speed, fabrication cost, or production scalability. In this work, we have adapted the mechanism of an Alvarez lens - a varifocal composite lens in which lateral shifts of two optical elements with cubic phase surfaces give rise to a change in optical power - to construct a miniature, MEMS-actuated metasurface Alvarez lens. The implementation based on electrostatic microelectromechanical systems (MEMS) generates fast and controllable actuation with low power consumption. The utilization of metasurfaces, ultrathin and subwavelength-patterned diffractive optics, as the optical elements greatly reduces the device volume compared to systems using conventional freeform lenses. The entire MEMS Alvarez metalens is fully compatible with modern semiconductor fabrication technologies, granting it the potential to be mass-produced at a low unit cost. In the reported prototype to operate at 1550 nm wavelength, a total uniaxial displacement of 6.3 um is achieved in the Alvarez metalens with direct-current (DC) voltage application up to 20 V, modulating the focal position within a total tuning range of 68 um, producing more than an order of magnitude change in focal length and 1460 diopters change in optical power. The MEMS Alvarez metalens has a robust design that can potentially generate a much larger tuning range without substantially increasing device volume or energy consumption, making it desirable for a wide range of imaging and display applications. | physics |
We advance a holographic construction for the entanglement negativity of bipartite mixed state configurations of two disjoint intervals in $(1+1)$ dimensional conformal field theories ($CFT_{1+1}$) through the $AdS_3/CFT_2$ correspondence. Our construction constitutes the large central charge analysis of the entanglement negativity for mixed states under consideration and involves a specific algebraic sum of bulk space like geodesics anchored on appropriate intervals in the dual $CFT_{1+1}$. The construction is utilized to compute the holographic entanglement negativity for such mixed states in $CFT_{1+1}$s dual to bulk pure $AdS_3$ geometries and BTZ black holes respectively. Our analysis exactly reproduces the universal features of corresponding replica technique results in the large central charge limit which serves as a consistency check. | high energy physics theory |
The apparent detection of an exoplanet orbiting Fomalhaut was announced in 2008. However, subsequent observations of Fomalhaut b raised questions about its status: Unlike other exoplanets, it is bright in the optical and nondetected in the infrared, and its orbit appears to cross the debris ring around the star without the expected gravitational perturbations. We revisit previously published data and analyze additional Hubble Space Telescope (HST) data, finding that the source is likely on a radial trajectory and has faded and become extended. Dynamical and collisional modeling of a recently produced dust cloud yields results consistent with the observations. Fomalhaut b appears to be a directly imaged catastrophic collision between two large planetesimals in an extrasolar planetary system. Similar events should be very rare in quiescent planetary systems of the age of Fomalhaut, suggesting that we are possibly witnessing the effects of gravitational stirring due to the orbital evolution of hypothetical planet(s) around the star. | astrophysics |
With ENZO simulations run on the J\"ulich supercomputers, we have investigated the evolution of magnetic fields in the largest cosmic structures (namely galaxy clusters and filaments connecting them) with unprecedented dynamical range. These simulations revealed the full development of the small-scale dynamo in Eulerian cosmological magneto-hydrodynamical simulations. The turbulent motions developed during the formation of clusters are energetic enough to foster the growth of magnetic fields by several orders of magnitude, starting from weak magnetic fields up strengths of $\sim \rm \mu G$ as observed. Furthermore, shock waves are launched during cluster formation and they are able to accelerate cosmic-ray electrons, that emit in the radio wavelengths. Radio observations of this emission provide information on the local magnetic field strength. We have incorporated, for the first time, the cooling of cosmic-ray electrons when modelling this emission. In this contribution, we present our advances in modelling these physical processes. Here, we mostly focus on the most interesting object in our sample of galaxy clusters, which shows the complexity of magnetic fields and the potential of existing and future multi-wavelengths observations in the study of the weakly collisional plasma on $\sim$ Megaparsecs scales. | astrophysics |
We consider a simple extension of the standard model, which could give a solution to its $CP$ issues through both the Peccei-Quinn mechanism and the Nelson-Barr mechanism. Its low energy effective model coincides with the scotogenic model in the leptonic sector. Although leptogenesis is known not to work well at lower reheating temperature than $10^9$ GeV in simple seesaw and scotogenic frameworks, such low reheating temperature could be consistent with both neutrino mass generation and thermal leptogenesis via newly introduced fields without referring to the resonance effect. An alternative dark matter candidate to axion is prepared as an indispensable ingredient of the model. | high energy physics phenomenology |
We study quantum degenerate Fermi gases of ${^6}$Li atoms at high densities ($10^{15}$ cm$^{-3}$) and observe elastic and inelastic $p$-wave collisions far away from any Feshbach resonance. $P$-wave evaporation reaches temperatures of $T/T_F=0.42$ partially limited by the slow transfer of energy from high to low velocities through $p$-wave collisions. Via cross-dimensional thermalization, the $p$-wave background scattering volume is determined to be $\lvert V_p \rvert =(39^{+1.3}_{-1.6}a_0)^3$. $P$-wave dipolar relaxation creates a metastable mixture of the lowest and highest hyperfine states. | condensed matter |
Online and in the real world, communities are bonded together by emotional consensus around core issues. Emotional responses to scientific findings often play a pivotal role in these core issues. When there is too much diversity of opinion on topics of science, emotions flare up and give rise to conflict. This conflict threatens positive outcomes for research. Emotions have the power to shape how people process new information. They can color the public's understanding of science, motivate policy positions, even change lives. And yet little work has been done to evaluate the public's emotional response to science using quantitative methods. In this paper, we use a dataset of responses to scholarly articles on Facebook to analyze the dynamics of emotional valence, intensity, and diversity. We present a novel way of weighting click-based reactions that increases their comprehensibility, and use these weighted reactions to develop new metrics of aggregate emotional responses. We use our metrics along with LDA topic models and statistical testing to investigate how users' emotional responses differ from one scientific topic to another. We find that research articles related to gender, genetics, or agricultural/environmental sciences elicit significantly different emotional responses from users than other research topics. We also find that there is generally a positive response to scientific research on Facebook, and that articles generating a positive emotional response are more likely to be widely shared---a conclusion that contradicts previous studies of other social media platforms. | computer science |
Oxide heterointerfaces constitute a rich platform for realizing novel functionalities in condensed matter. A key aspect is the strong link between structural and electronic properties, which can be modified by interfacing materials with distinct lattice symmetries. Here we determine the effect of the cubic-tetragonal distortion of $\text{SrTiO}_3$ on the electronic properties of thin films of $\text{SrIrO}_3$, a topological crystalline metal hosting a delicate interplay between spin-orbit coupling and electronic correlations. We demonstrate that below the transition temperature at 105 K, $\text{SrIrO}_3$ orthorhombic domains couple directly to tetragonal domains in $\text{SrTiO}_3$. This forces the in-phase rotational axis to lie in-plane and creates a binary domain structure in the $\text{SrIrO}_3$ film. The close proximity to the metal-insulator transition in ultrathin $\text{SrIrO}_3$ causes the individual domains to have strongly anisotropic transport properties, driven by a reduction of bandwidth along the in-phase axis. The strong structure-property relationships in perovskites make these compounds particularly suitable for static and dynamic coupling at interfaces, providing a promising route towards realizing novel functionalities in oxide heterostructures. | condensed matter |
We formulate a statistical model of two sequential measurements and prove a so-called J-equation that leads to various diversifications of the well-known Jarzynski equation including the Crooks dissipation theorem. Moreover, the J-equation entails formulations of the Second Law going back to Wolfgang Pauli. We illustrate this by an analytically solvable example of sequential discrete position-momentum measurements accompanied with the increase of Shannon entropy. The standard form of the J-equation extends the domain of applications of the quantum Jarzynski equation in two respects: It includes systems that are initially only in local equilibrium and it extends this equation to the cases where the local equilibrium is described by microcanononical, canonical or grand canonical ensembles. Moreover, the case of a periodically driven quantum system in thermal contact with a heat bath is shown to be covered by the theory presented here. Finally, we shortly consider the generalized Jarzynski equation in classical statistical mechanics. | quantum physics |
Absorption of fermionic dark matter leads to a range of distinct and novel signatures at dark matter direct detection and neutrino experiments. We study the possible signals from fermionic absorption by nuclear targets, which we divide into two classes of four Fermi operators: neutral and charged current. In the neutral current signal, dark matter is absorbed by a target nucleus and a neutrino is emitted. This results in a characteristically different nuclear recoil energy spectrum from that of elastic scattering. The charged current channel leads to induced $\beta$ decays in isotopes which are stable in vacuum as well as shifts of the kinematic endpoint of $ \beta$ spectra in unstable isotopes. To confirm the possibility of observing these signals in light of other constraints, we introduce UV completions of example higher dimensional operators that lead to fermionic absorption signals and study their phenomenology. Most prominently, dark matter which exhibits fermionic absorption signals is necessarily unstable leading to stringent bounds from indirect detection searches. Nevertheless, we find a large viable parameter space in which dark matter is sufficiently long lived and detectable in current and future experiments. | high energy physics phenomenology |
Low-energy transport in quantum Hall states is carried through edge modes, and is dictated by bulk topological invariants and possibly microscopic Boltzmann kinetics at the edge. Here we show how the presence or breaking of symmetries of the edge Hamiltonian underlie transport properties, specifically d.c. conductance and noise. We demonstrate this through the analysis of hole-conjugate states of the quantum Hall effect, specifically the $\nu=2/3$ case in a quantum point-contact (QPC) geometry. We identify two symmetries, a continuous $SU(3)$ and a discrete $Z_3$, whose presence or absence (different symmetry scenarios) dictate qualitatively different types of behavior of conductance and shot noise. While recent measurements are consistent with one of these symmetry scenarios, others can be realized in future experiments. | condensed matter |
A metric graph is a pair $(G,d)$, where $G$ is a graph and $d:E(G) \to\mathbb{R}_{\geq0}$ is a distance function. Let $p \in [1,\infty]$ be fixed. An isometric embedding of the metric graph $(G,d)$ in $\ell_p^k = (\mathbb{R}^k, d_p)$ is a map $\phi : V(G) \to \mathbb{R}^k$ such that $d_p(\phi(v), \phi(w)) = d(vw)$ for all edges $vw\in E(G)$. The $\ell_p$-dimension of $G$ is the least integer $k$ such that there exists an isometric embedding of $(G,d)$ in $\ell_p^k$ for all distance functions $d$ such that $(G,d)$ has an isometric embedding in $\ell_p^K$ for some $K$. It is easy to show that $\ell_p$-dimension is a minor-monotone property. In this paper, we characterize the minor-closed graph classes $\mathcal{C}$ with bounded $\ell_p$-dimension, for $p \in \{2,\infty\}$. For $p=2$, we give a simple proof that $\mathcal{C}$ has bounded $\ell_2$-dimension if and only if $\mathcal{C}$ has bounded treewidth. In this sense, the $\ell_2$-dimension of a graph is `tied' to its treewidth. For $p=\infty$, the situation is completely different. Our main result states that a minor-closed class $\mathcal{C}$ has bounded $\ell_\infty$-dimension if and only if $\mathcal{C}$ excludes a graph obtained by joining copies of $K_4$ using the $2$-sum operation, or excludes a M\"obius ladder with one `horizontal edge' removed. | mathematics |
Deep learning is usually described as an experiment-driven field under continuous criticizes of lacking theoretical foundations. This problem has been partially fixed by a large volume of literature which has so far not been well organized. This paper reviews and organizes the recent advances in deep learning theory. The literature is categorized in six groups: (1) complexity and capacity-based approaches for analyzing the generalizability of deep learning; (2) stochastic differential equations and their dynamic systems for modelling stochastic gradient descent and its variants, which characterize the optimization and generalization of deep learning, partially inspired by Bayesian inference; (3) the geometrical structures of the loss landscape that drives the trajectories of the dynamic systems; (4) the roles of over-parameterization of deep neural networks from both positive and negative perspectives; (5) theoretical foundations of several special structures in network architectures; and (6) the increasingly intensive concerns in ethics and security and their relationships with generalizability. | computer science |
The spin Seebeck effect (SSE) has generated interest in the thermoelectric and magnetic communities for potential high efficiency energy harvesting applications, and spintronic communities as a source of pure spin current. To understand the underlying mechanisms requires characterisation of potential materials across a range of temperatures, however, for thin films the default measurement of an applied temperature gradient (across the sample) has been shown to be compromised by the presence of thermal resistances. Here, we demonstrate a method to perform low temperature SSE measurements where instead of monitoring the temperature gradient, the heat flux passing through the sample is measured using two calibrated heat flux sensors. This has the advantage of measuring the heat loss through the sample as well as providing a reliable method to normalise the SSE response of thin film samples. We demonstrate this method with an $\text{SiO}_{2}/\text{Fe}_{3}O_{4}/\text{Pt}$ sample, where a semiconducting-insulating transition occurs at the Verwey transition, $T_{\text{V}}$, of $\text{Fe}_{3}\text{O}_{4}$ and quantify the thermomagnetic response above and below $T_{\text{V}}$. | condensed matter |
The performance of high-order gas-kinetic scheme (HGKS) has been investigated for the direct numerical simulation (DNS) of isotropic compressible turbulence up to the supersonic regime. Due to the multi-scale nature and coupled temporal-spatial evolution process, HGKS provides a valid tool for the numerical simulation of compressible turbulent flow. Based on the domain decomposition and message passing interface (MPI), a parallel HGKS code is developed for large-scale computation in this paper. The standard tests from the nearly incompressible flow to the supersonic one, including Taylor-Green vortex problem, turbulent channel flow and isotropic compressible turbulence, are presented to validate the parallel scalability, efficiency, accuracy and robustness of parallel implementation. The performance of HGKS for the nearly incompressible turbulence is comparable with the high-order finite difference scheme, including the resolution of flow structure and efficiency of computation. Based on the accuracy of the numerical solution, the numerical dissipation of the scheme in the turbulence simulation is quantitatively evaluated. As a mesoscopic method, HGKS performs better than both lattice Boltzmann method (LBM) and discrete unified gas-kinetic scheme (DUGKS), due to its high-order accuracy. Meanwhile, based on the kinetic formulation HGKS shows advantage for supersonic turbulent flow simulation with its accuracy and robustness. The current work demonstrates the capability of HGKS as a powerful DNS tool from the low speed to supersonic turbulence study, which is less reported under the framework of finite volume scheme. | physics |
In a remarkable recent work [arXiv : 1711.09102] by Arkani-Hamed et al, the amplituhedron program was extended to the realm of non-supersymmetric scattering amplitudes. In particular it was shown that for tree-level planar diagrams in massless $\phi^{3}$ theory (and its close cousin, bi-adjoint $\phi^{3}$ theory) a polytope known as the associahedron sits inside the kinematic space and is the amplituhedron for the theory. Precisely as in the case of amplituhedron, it was shown that scattering amplitude is nothing but residue of the canonical form associated to the associahedron. Combinatorial and geometric properties of associahedron naturally encode properties like locality and unitarity of (tree level) scattering amplitudes. In this paper we attempt to extend this program to planar amplitudes in massless $\phi^{4}$ theory. We show that tree-level planar amplitudes in this theory can be obtained from geometry of objects known as the Stokes polytope which sits naturally inside the kinematic space. As in the case of associahedron we show that residues of the canonical form on these Stokes polytopes can be used to compute scattering amplitudes for quartic interactions. However unlike associahedron, Stokes polytope of a given dimension is not unique and as we show, one must sum over all of them to obtain the complete scattering amplitude. Not all Stokes polytopes contribute equally and we argue that the corresponding weights depend on purely combinatorial properties of the Stokes polytopes. As in the case of $\phi^{3}$ theory, we show how factorization of Stokes polytope implies unitarity and locality of the amplitudes. | high energy physics theory |
The reduced inertia levels in low-carbon power grids necessitate explicit constraints to limit frequency's nadir and rate of change during scheduling. This can result in significant curtailment of renewable energy due to the minimum generation of thermal plants that are needed to provide frequency response (FR) and inertia. Additional consideration of fast FR, a dynamically reduced largest loss and under frequency load shedding (UFLS) allows frequency security to be achieved more cost effectively. This paper derives a novel nadir constraint from the swing equation that, for the first time, provides a framework for the optimal comparison of all these services. We demonstrate that this constraint can be accurately and conservatively approximated for moderate UFLS levels with a second order cone, resulting in highly tractable convex problems. Case studies performed on a Great Britain 2030 system demonstrate that UFLS as an option to contain single plant outages can reduce annual operational costs by up to {\pounds}559m, 52% of frequency security costs. The sensitivity of this value to wind penetration, abundance of alternative frequency services, UFLS amount and cost is explored. | electrical engineering and systems science |
The observation of fluid-like behavior in nucleus-nucleus, proton-nucleus and high-multiplicity proton-proton collisions motivates systematic studies of how different measurements approach their fluid-dynamic limit. We have developed numerical methods to solve the ultra-relativistic Boltzmann equation for systems of arbitrary size and transverse geometry. Here, we apply these techniques for the first time to the study of azimuthal flow coefficients $v_n$ including non-linear mode-mode coupling and to an initial condition with realistic event-by-event fluctuations. We show how both linear and non-linear response coefficients extracted from $v_n$ develop as a function of opacity from free streaming to perfect fluidity. We note in particular that away from the fluid-dynamic limit, the signal strength of linear and non-linear response coefficients does not reduce uniformly, but that their hierarchy and relative size shows characteristic differences. | high energy physics phenomenology |
We consider reusing established non-probabilistic output analyses (either forward or backwards) that yield over-approximations of a program's pre-image or image relation, e.g., interval analyses. We assume a probability measure over the program input and present two techniques (one for forward and one for backward analyses) that both derive upper and lower probability bounds for the output events. We demonstrate the most involved technique, namely the forward technique, for two examples and compare their results to a cutting-edge probabilistic output analysis. | computer science |
Identified-hadron (PID) spectra from 2.76 TeV Pb-Pb and $p$-$p$ collisions are analyzed via a two-component (soft + hard) model (TCM) of hadron production in high-energy nuclear collisions. The PID TCM is adapted with minor changes from a recent analysis of PID hadron spectra from 5 TeV $p$-Pb collisions. Results from LHC data are compared with a PID TCM for 200 GeV Au-Au pion and proton spectra. 2.76 TeV proton spectra exhibit strong inefficiencies above 1 GeV/c estimated by comparing the $p$-$p$ spectrum with the corresponding TCM. After inefficiency correction Pb-Pb proton spectra are very similar to Au-Au proton spectra. PID A-A spectra are generally inconsistent with radial flow. Jet-related Pb-Pb and Au-Au spectrum hard components exhibit strong suppression at higher $p_t$ in more-central collisions corresponding to results from spectrum ratio $R_{AA}$ but also, for pions and kaons, exhibit dramatic enhancements below $p_t = 1$ GeV/c that are concealed by $R_{AA}$. In contrast, enhancements of proton hard components appear only above 1 GeV/c suggesting that the baryon/meson "puzzle" is a jet phenomenon. Modification of spectrum hard components in more-central A-A collisions is consistent with increased gluon splitting during jet formation but with approximate conservation of leading-parton energy within a jet via the lower-$p_t$ enhancements. | high energy physics phenomenology |
We give a classification of fully supersymmetric chiral ${\cal N}=(8,0)$ AdS$_3$ vacua in general three-dimensional half-maximal gauged supergravities coupled to matter. These theories exhibit a wealth of supersymmetric vacua with background isometries given by the supergroups OSp$(8|2,\mathbb{R})$, F(4), SU$(4|1,1)$, and OSp$(4^*|4)$, respectively. We identify the associated embedding tensors and the structure of the associated gauge groups. We furthermore compute the mass spectra around these vacua. As an off-spin we include results for a number of ${\cal N}=(7,0)$ vacua with supergroups OSp$(7|2,\mathbb{R})$ and G$(3)$, respectively. We also comment on their possible higher-dimensional uplifts. | high energy physics theory |
We study the behaviour of the Negativity of Wigner Function (NWF) as a measure of entanglement in non-Gaussian states under quantum polarisation converter devices. We analyze comparatively this quantity with other measures of entanglement in a system prepared in a superposition of two-mode coherent states. We show that the (WF) can be identified as a quantifier of non-Gaussian entanglement. | quantum physics |
The main focus of this work is a novel framework for the joint reconstruction and segmentation of parallel MRI (PMRI) brain data. We introduce an image domain deep network for calibrationless recovery of undersampled PMRI data. The proposed approach is the deep-learning (DL) based generalization of local low-rank based approaches for uncalibrated PMRI recovery including CLEAR [6]. Since the image domain approach exploits additional annihilation relations compared to k-space based approaches, we expect it to offer improved performance. To minimize segmentation errors resulting from undersampling artifacts, we combined the proposed scheme with a segmentation network and trained it in an end-to-end fashion. In addition to reducing segmentation errors, this approach also offers improved reconstruction performance by reducing overfitting; the reconstructed images exhibit reduced blurring and sharper edges than independently trained reconstruction network. | electrical engineering and systems science |
We revisit the evolution of the scale factor in a flat FRW spacetime with a new generalized decay rule for the dynamic $\Lambda$-term under modified theories of gravity. It analyses certain cosmological parameters and examines their behaviours in this generalized setting which includes several decay laws in the literature. We have also obtained observational constraints on various model parameters and estimated the present values of cosmological parameters $\{\Omega_{m_0}$, $\Omega_{\Lambda_0}$, $q_{0}, t_{0}$, $\omega_{0}\}$ and have discussed with various observational results. Finite time past and future singularities in this model are also discussed. \end{abstract} | physics |
We study the displacements between the centres of galaxies and their supermassive black holes (BHs) in the cosmological hydrodynamical simulation Horizon-AGN, and in a variety of observations from the literature. The BHs in Horizon-AGN feel a sub-grid dynamical friction force, sourced by the surrounding gas, which prevents recoiling BHs being ejected from the galaxy. We find that i) the fraction of spatially offset BHs increases with cosmic time, ii) BHs live on prograde orbits in the plane of the galaxy with an orbital radius that decays with time but stalls near $z=0$, and iii) the magnitudes of offsets from the galaxy centres are substantially larger in the simulation than in observations. We attribute the stalling of the infall and excessive offset magnitudes to the fact that dynamical friction from stars and dark matter is not modelled in the simulation, and hence provide a way to improve the black hole dynamics of future simulations. | astrophysics |
Near lightcone correlators are dominated by operators with the lowest twist. We consider the contributions of such leading lowest twist multi-stress tensor operators to a heavy-heavy-light-light correlator in a CFT of any even dimensionality with a large central charge. An infinite number of such operators contribute, but their sum is described by a simple ansatz. We show that the coefficients in this ansatz can be determined recursively, thereby providing an operational procedure to compute them. This is achieved by bootstrapping the corresponding near lightcone correlator: conformal data for any minimal-twist determines that for the higher minimal-twist and so on. To illustrate this procedure in four spacetime dimensions we determine the contributions of double- and triple-stress tensors. We compute the OPE coefficients; whenever results are available in the literature, we observe complete agreement. We also compute the contributions of double-stress tensors in six spacetime dimensions and determine the corresponding OPE coefficients. In all cases the results are consistent with the exponentiation of the near lightcone correlator. This is similar to the situation in two spacetime dimensions for the Virasoro vacuum block. | high energy physics theory |
A new approach to compute Feynman Integrals is presented. It relies on an integral representation of a given Feynman Integral in terms of simpler ones. Using this approach, we present, for the first time, results for a certain family of non-planar five-point two-loop Master Integrals with one external off-shell particle, relevant for instance for $H+2$ jets production at the LHC, in both Euclidean and physical kinematical regions. | high energy physics phenomenology |
A large number of applications in classical and quantum photonics require the capability of implementing arbitrary linear unitary transformations on a set of optical modes. In a seminal work by Reck et al. it was shown how to build such multiport universal interferometers with a mesh of beam splitters and phase shifters, and this design became the basis for most experimental implementations in the last decades. However, the design of Reck et al. is difficult to scale up to a large number of modes, which would be required for many applications. Here we present a constructive proof that it is possible to realize a multiport universal interferometer on N modes with a succession of 6N Fourier transforms and 6N+1 phase masks, for any even integer N. Furthermore, we provide an algorithm to find the correct succesion of Fourier transforms and phase masks to realize a given arbitrary unitary transformation. Since Fourier transforms and phase masks are routinely implemented in several optical setups and they do not suffer from the scalability issues associated with building extensive meshes of beam splitters, we believe that our design can be useful for many applications in photonics. | quantum physics |
Regression models, in which the observed features $X \in \R^p$ and the response $Y \in \R$ depend, jointly, on a lower dimensional, unobserved, latent vector $Z \in \R^K$, with $K< p$, are popular in a large array of applications, and mainly used for predicting a response from correlated features. In contrast, methodology and theory for inference on the regression coefficient $\beta$ relating $Y$ to $Z$ are scarce, since typically the un-observable factor $Z$ is hard to interpret. Furthermore, the determination of the asymptotic variance of an estimator of $\beta$ is a long-standing problem, with solutions known only in a few particular cases. To address some of these outstanding questions, we develop inferential tools for $\beta$ in a class of factor regression models in which the observed features are signed mixtures of the latent factors. The model specifications are practically desirable, in a large array of applications, render interpretability to the components of $Z$, and are sufficient for parameter identifiability. Without assuming that the number of latent factors $K$ or the structure of the mixture is known in advance, we construct computationally efficient estimators of $\beta$, along with estimators of other important model parameters. We benchmark the rate of convergence of $\beta$ by first establishing its $\ell_2$-norm minimax lower bound, and show that our proposed estimator is minimax-rate adaptive. Our main contribution is the provision of a unified analysis of the component-wise Gaussian asymptotic distribution of $\wh \beta$ and, especially, the derivation of a closed form expression of its asymptotic variance, together with consistent variance estimators. The resulting inferential tools can be used when both $K$ and $p$ are independent of the sample size $n$, and when both, or either, $p$ and $K$ vary with $n$, while allowing for $p > n$. | statistics |
The large scale features of the solar wind are examined in order to predict small scale features of turbulence in unexplored regions of the heliosphere. The strategy is to examine how system size, or effective Reynolds number, varies, and then how this quantity influences observable statistical properties, including intermittency properties of solar wind turbulence. The expectation based on similar hydrodynamics scalings, is that the kurtosis, of the small scale magnetic field increments, will increase with increasing Reynolds number. Simple theoretical arguments as well as Voyager observations indicate that effective interplanetary turbulence Reynolds number decreases with increasing heliocentric distance. The decrease of scale-dependent magnetic increment kurtosis with increasing heliocentric distance, is verified using a newly refined Voyager magnetic field dataset. We argue that these scalings continue to much smaller heliocentric distances approaching the Alfven critical region, motivating a prediction that the Parker Solar Probe spacecraft will observe increased magnetic field intermittency, stronger current sheets, and more localized dissipation, as its perihelion approaches the critical regions. Similar arguments should be applicable to turbulence in other expanding astrophysical plasmas. | physics |
The easily tunable emission of halide perovskite nanocrystals throughout the visible spectrum makes them an extremely promising material for light-emitting applications. Whereas high quantum yields and long-term colloidal stability have already been achieved for nanocrystals emitting in the red and green spectral range, the blue region currently lags behind, with low quantum yields, broad emission profiles and insufficient colloidal stability. In this work, we present a facile synthetic approach for obtaining two-dimensional CsPbBr3 nanoplatelets with monolayer-precise control over their thickness, resulting in sharp photoluminescence and electroluminescence peaks with a tunable emission wavelength between 432 and 497 nm due to quantum confinement. Subsequent addition of a PbBr2-ligand solution repairs surface defects likely stemming from bromide and lead vacancies in a sub-ensemble of weakly emissive nanoplatelets. The overall photoluminescence quantum yield of the blue-emissive colloidal dispersions is consequently enhanced up to a value of 73+-2 %. Transient optical spectroscopy measurements focusing on the excitonic resonances further confirm the proposed repair process. Additionally, the high stability of these nanoplatelets in films and to prolonged UV light exposure is shown. | condensed matter |
Trajectory optimization and model predictive control are essential techniques underpinning advanced robotic applications, ranging from autonomous driving to full-body humanoid control. State-of-the-art algorithms have focused on data-driven approaches that infer the system dynamics online and incorporate posterior uncertainty during planning and control. Despite their success, such approaches are still susceptible to catastrophic errors that may arise due to statistical learning biases, unmodeled disturbances or even directed adversarial attacks. In this paper, we tackle the problem of dynamics mismatch and propose a distributionally robust optimal control formulation that alternates between two relative-entropy trust region optimization problems. Our method finds the worst-case maximum-entropy Gaussian posterior over the dynamics parameters and the corresponding robust optimal policy. We show that our approach admits a closed-form backward-pass for a certain class of systems and demonstrate the resulting robustness on linear and nonlinear numerical examples. | electrical engineering and systems science |
In this paper, we examine the viability of Bianchi type V universe in $f(R,T)$ theory of gravitation. To solve the field equations, we have considered the power law for scale factor and constructed a singular Lagrangian model which is based on the coupling between Ricci scalar R and trace of energy-momentum tensor T. We find the constraints on Hubble constant $H_{0}$ and free parameter $n$ with 46 observational Hubble dataset and obtain pretty satisfactory results. The physical features of the model and transitional behavior of equation of state (EOS) parameter are analyzed. We examine the nature of physical parameters and validity of energy conditions as well as stability condition. We also present the Om(z) and statefinder diagnostic analysis for the derived model. | physics |
In recent years, significant attention has been devoted towards integrating deep learning technologies in the healthcare domain. However, to safely and practically deploy deep learning models for home health monitoring, two significant challenges must be addressed: the models should be (1) robust against noise; and (2) compact and energy-efficient. We propose REST, a new method that simultaneously tackles both issues via 1) adversarial training and controlling the Lipschitz constant of the neural network through spectral regularization while 2) enabling neural network compression through sparsity regularization. We demonstrate that REST produces highly-robust and efficient models that substantially outperform the original full-sized models in the presence of noise. For the sleep staging task over single-channel electroencephalogram (EEG), the REST model achieves a macro-F1 score of 0.67 vs. 0.39 achieved by a state-of-the-art model in the presence of Gaussian noise while obtaining 19x parameter reduction and 15x MFLOPS reduction on two large, real-world EEG datasets. By deploying these models to an Android application on a smartphone, we quantitatively observe that REST allows models to achieve up to 17x energy reduction and 9x faster inference. We open-source the code repository with this paper: https://github.com/duggalrahul/REST. | electrical engineering and systems science |
We give an example of a finite-dimensional algebra with a 2-cluster tilting module and a simple module which has infinite complexity. This answers a question of Erdmann and Holm. | mathematics |
We exhibit the first examples of compact orientable hyperbolic manifolds that do not have any spin structure. We show that such manifolds exist in all dimensions $n \geq 4$. The core of the argument is the construction of a compact orientable hyperbolic $4$-manifold $M$ that contains a surface $S$ of genus $3$ with self intersection $1$. The $4$-manifold $M$ has an odd intersection form and is hence not spin. It is built by carefully assembling some right angled $120$-cells along a pattern inspired by the minimum trisection of $\mathbb{C}\mathbb{P}^2$. The manifold $M$ is also the first example of a compact orientable hyperbolic $4$-manifold satisfying any of these conditions: 1) $H_2(M,\mathbb{Z})$ is not generated by geodesically immersed surfaces. 2) There is a covering $\tilde{M}$ that is a non-trivial bundle over a compact surface. | mathematics |
Electromagnetically neutral dark sector particles may directly couple to the photon through higher dimensional effective operators. Considering electric and magnetic dipole moment, anapole moment, and charge radius interactions, we derive constraints from stellar energy loss in the Sun, horizontal branch and red giant stars, as well as from cooling of the proto-neutron star of SN1987A. We provide the exact formula for in-medium photon-mediated pair production to leading order in the dark coupling, and compute the energy loss rates explicitly for the most important processes, including a careful discussion on resonances and potential double counting between the processes. Stringent limits for dark states with masses below $3\,$keV ($40\,$MeV) arise from red giant stars (SN1987A), implying an effective lower mass-scale of approximately $10^9\,$GeV ($10^7\,$GeV) for mass-dimension five, and $100\,$GeV ($2.5\,$TeV) for mass-dimension six operators as long as dark states stream freely; for the proto-neutron star, the trapping of dark states is also evaluated. Together with direct limits previously derived by us in Chu et al. (2018), this provides the first comprehensive overview of the viability of effective electromagnetic dark-state interactions below the GeV mass-scale. | high energy physics phenomenology |
Clustering is a fundamental task in machine learning. One of the most successful and broadly used algorithms is DBSCAN, a density-based clustering algorithm. DBSCAN requires $\epsilon$-nearest neighbor graphs of the input dataset, which are computed with range-search algorithms and spatial data structures like KD-trees. Despite many efforts to design scalable implementations for DBSCAN, existing work is limited to low-dimensional datasets, as constructing $\epsilon$-nearest neighbor graphs is expensive in high-dimensions. In this paper, we modify DBSCAN to enable use of $\kappa$-nearest neighbor graphs of the input dataset. The $\kappa$-nearest neighbor graphs are constructed using approximate algorithms based on randomized projections. Although these algorithms can become inaccurate or expensive in high-dimensions, they possess a much lower memory overhead than constructing $\epsilon$-nearest neighbor graphs ($\mathcal{O}(nk)$ vs. $\mathcal{O}(n^2)$). We delineate the conditions under which $k$NN-DBSCAN produces the same clustering as DBSCAN. We also present an efficient parallel implementation of the overall algorithm using OpenMP for shared memory and MPI for distributed memory parallelism. We present results on up to 16 billion points in 20 dimensions, and perform weak and strong scaling studies using synthetic data. Our code is efficient in both low and high dimensions. We can cluster one billion points in 3D in less than one second on 28K cores on the Frontera system at the Texas Advanced Computing Center (TACC). In our largest run, we cluster 65 billion points in 20 dimensions in less than 40 seconds using 114,688 x86 cores on TACC's Frontera system. Also, we compare with a state of the art parallel DBSCAN code; on 20d/4M point dataset, our code is up to 37$\times$ faster. | computer science |
Evaluating the degree of partisan districting (Gerrymandering) in a statistical framework typically requires an ensemble of districting plans which are drawn from a prescribed probability distribution that adheres to a realistic and non-partisan criteria. In this article we introduce novel non-reversible Markov chain Monte-Carlo (MCMC) methods for the sampling of such districting plans which have improved mixing properties in comparison to previously used (reversible) MCMC algorithms. In doing so we extend the current framework for construction of non-reversible Markov chains on discrete sampling spaces by considering a generalization of skew detailed balance. We provide a detailed description of the proposed algorithms and evaluate their performance in numerical experiments. | statistics |
Mixed outcome endpoints that combine multiple continuous and discrete components to form co-primary, multiple primary or composite endpoints are often employed as primary outcome measures in clinical trials. There are many advantages to joint modelling the individual outcomes using a latent variable framework, however in order to make use of the model in practice we require techniques for sample size estimation. In this paper we show how the latent variable model can be applied to the three types of joint endpoints and propose appropriate hypotheses, power and sample size estimation methods for each. We illustrate the techniques using a numerical example based on the four dimensional endpoint in the MUSE trial and find that the sample size required for the co-primary endpoint is larger than that required for the individual endpoint with the smallest effect size. Conversely, the sample size required for the multiple primary endpoint is reduced from that required for the individual outcome with the largest effect size. We show that the analytical technique agrees with the empirical power from simulation studies. We further illustrate the reduction in required sample size that may be achieved in trials of mixed outcome composite endpoints through a simulation study and find that the sample size primarily depends on the components driving response and the correlation structure and much less so on the treatment effect structure in the individual endpoints. | statistics |
Dynamical decoupling is a technique that protects qubits against noise. The ability to preserve quantum coherence in the presence of noise is essential for the development of quantum devices. Here the Rigetti quantum computing platform was used to test different dynamical decoupling sequences. The performance of the sequences was characterized by Quantum Process Tomography and analyzed using the quantum channels formalism. It is shown that dynamical decoupling can reduce qubit dephasing but cannot protect against spontaneous emission. Furthermore, from process tomography results, it was also possible to conclude that the action of dynamical decoupling cannot be understood as a simple modification of the qubit coherence time. It is also shown here that the performance of dynamical decoupling on the Rigetti's qubits is limited by pulse imperfections. However, the performance can be improved using robust dynamical decoupling, i.e. sequences that are robust against experimental imperfections. The sequences tested here outperformed previous dynamical decoupling sequences tested in the same platform. | quantum physics |
Probabilistically creating n perfect clones from m copies for one of N priori known quantum states with minimum failure probability is a long-standing problem. We provide a rigorous proof for the geometric approach to this probabilistic quantum cloning problem when N = 2. Then, we give the general geometric form of the sufficient and necessary condition of probabilistic cloning for N known quantum states. By this general geometric approach, we realize the optimal probabilistic quantum cloning of N known quantum states with priori probabilities. The results are also applicable to the identification of those N quantum states. | quantum physics |
Hadronic matrix elements of local four-quark operators play a central role in non-leptonic kaon decays, while vacuum matrix elements involving the same kind of operators appear in inclusive dispersion relations, such as those relevant in $\tau$-decay analyses. Using an $SU(3)_L\otimes SU(3)_R$ decomposition of the operators, we derive generic relations between these matrix elements, extending well-known results that link observables in the two different sectors. Two relevant phenomenological applications are presented. First, we determine the electroweak-penguin contribution to the kaon CP-violating ratio $\varepsilon'/\varepsilon$, using the measured hadronic spectral functions in $\tau$ decay. Second, we fit our $SU(3)$ dynamical parameters to the most recent lattice data on $K\to\pi\pi$ matrix elements. The comparison of this numerical fit with results from previous analytical approaches provides an interesting anatomy of the $\Delta I = \frac{1}{2}$ enhancement, confirming old suggestions about its underlying dynamical origin. | high energy physics phenomenology |
Identifying the angular degrees $l$ of oscillation modes is essential for asteroseismology and depends on visual tagging before fitting power spectra in a so-called peakbagging analysis. In oscillating subgiants, radial ($l$= 0) mode frequencies distributed linearly in frequency, while non-radial ($l$ >= 1) modes are p-g mixed modes that having a complex distribution in frequency, which increased the difficulty of identifying $l$. In this study, we trained a 1D convolutional neural network to perform this task using smoothed oscillation spectra. By training simulation data and fine-tuning the pre-trained network, we achieved a 95 per cent accuracy on Kepler data. | astrophysics |
B-mode ultrasound imaging is a popular medical imaging technique. Like other image processing tasks, deep learning has been used for analysis of B-mode ultrasound images in the last few years. However, training deep learning models requires large labeled datasets, which is often unavailable for ultrasound images. The lack of large labeled data is a bottleneck for the use of deep learning in ultrasound image analysis. To overcome this challenge, in this work we exploit Auxiliary Classifier Generative Adversarial Network (ACGAN) that combines the benefits of data augmentation and transfer learning in the same framework. We conduct experiment on a dataset of breast ultrasound images that shows the effectiveness of the proposed approach. | electrical engineering and systems science |
We propose a theory of chiral fermion dark matter (DM) with an isospin-3/2 fermion of a dark sector $SU(2)_D$ gauge symmetry, which is arguably the simplest chiral theory. An isospin-3 scalar breaks $SU(2)_D$ down to a discrete non-Abelian group $T'$ and generates the DM mass. The $SU(2)_D$ gauge symmetry protects the DM mass and guarantees its stability. We derive consistency conditions for the theory and study its DM phenomenology. In some regions of parameters of the theory a two-component DM scenario is realized, consisting of a fermion and a boson, with the boson being the lightest $T'$ nonsinglet field. In the case of single component fermionic DM, we find that internal consistency of the theory, perturbativity arguments, and the observed relic abundance limit the DM mass to be less than $280$ GeV, except when $s$-channel resonance regions are open for annihilation. For a significant part of the parameter space, the theory can be tested in DM direct detection signals at the LZ and XENONnT experiments. | high energy physics phenomenology |
Bluetooth is an omnipresent technology, available on billions of devices today. While it has been traditionally limited to peer-to-peer communication and star networks, the recent Bluetooth Mesh standard extends it to multi-hop networking. In addition, the Bluetooth 5 standard introduces new modes to allow for increased reliability. In this paper, we evaluate the feasibility of concurrent transmissions (CT) in Bluetooth via modeling and controlled experiments and then devise an efficient network-wide data dissemination protocol, BlueFlood, based on CT for multi-hop Bluetooth networks. First, we model and analyze how CT distorts the received waveform and characterize the Bit Error Rate of a Frequency-Shift Keying receiver to show that CT is feasible over Bluetooth. Second, we verify our analytic results with a controlled experimental study of CT over Bluetooth PHY. Third, we present BlueFlood, a fast and efficient network-wide data dissemination in multi-hop Bluetooth networks. In our experimental evaluation, in two testbeds deployed in university buildings, we show that BlueFlood achieves 99.9% end-to-end delivery ratio with a duty-cycle of 0.4% for periodic dissemination of advertising packets of 38 bytes with 200 milliseconds intervals at 2 Mbps. Moreover, we show that BlueFlood can be received by off-the-shelf devices such as smartphones, paving a seamless integration with existing technologies. | computer science |
Two approaches to spectral theory of order unit spaces are compared: the spectral duality of Alfsen and Shultz and the spectral compression bases due to Foulis. While the former approach uses the geometric properties of an order unit space in duality with a base norm space, the latter notion is purely algebraic. It is shown that the Foulis approach is strictly more general and contains the Alfsen-Shultz approach as a special case. This is demonstrated on two types of examples: the JB-algebras which are Foulis spectral if and only if they are Rickart, and the centrally symmetric state spaces, which may be Foulis spectral while not necessarily Alfsen-Shultz spectral. | quantum physics |
Atmospheric turbulence significantly affects imaging systems which use light that has propagated through long atmospheric paths. Images captured under such condition suffer from a combination of geometric deformation and space varying blur. We present a deep learning-based solution to the problem of restoring a turbulence-degraded face image where prior information regarding the amount of geometric distortion and blur at each location of the face image is first estimated using two separate networks. The estimated prior information is then used by a network called, Turbulence Distortion Removal Network (TDRN), to correct geometric distortion and reduce blur in the face image. Furthermore, a novel loss is proposed to train TDRN where first and second order image gradients are computed along with their confidence maps to mitigate the effect of turbulence degradation. Comprehensive experiments on synthetic and real face images show that this framework is capable of alleviating blur and geometric distortion caused by atmospheric turbulence, and significantly improves the visual quality. In addition, an ablation study is performed to demonstrate the improvements obtained by different modules in the proposed method. | computer science |
With the birth of the next-generation GPS III constellation and the upcoming launch of the Navigation Technology Satellite-3 (NTS-3) testing platform to explore future technologies for GPS, we are indeed entering a new era of satellite navigation. Correspondingly, it is time to revisit the design methods of the GPS spreading code families. In this work, we develop a natural evolution strategy (NES) machine learning algorithm with a Gaussian proposal distribution which constructs high-quality families of spreading code sequences. We demonstrate the ability of our algorithm to achieve better mean-squared auto- and cross-correlation than well-chosen families of equal-length Gold codes and Weil codes, for sequences of up to length-1023 and length-1031 bits and family sizes of up to 31 codes. Furthermore, we compare our algorithm with an analogous genetic algorithm implementation assigned the same code evaluation metric. To the best of the authors' knowledge, this is the first work to explore using a machine learning approach for designing navigation spreading code sequences. | electrical engineering and systems science |
Efficiently processing basic linear algebra subroutines is of great importance for a wide range of computational problems. In this paper, we consider techniques to implement matrix functions on a quantum computer, which are composed of basic matrix operations on a set of matrices. These matrix operations include addition, multiplication, Kronecker sum, tensor product, Hadamard product, and single-matrix functions. We discuss the composed matrix functions in terms of the estimation of scalar quantities such as inner products, trace, determinant and Schatten p-norms. We thus provide a framework for compiling instructions for linear algebraic computations into gate sequences on actual quantum computers. | quantum physics |
PointGoal navigation has seen significant recent interest and progress, spurred on by the Habitat platform and associated challenge. In this paper, we study PointGoal navigation under both a sample budget (75 million frames) and a compute budget (1 GPU for 1 day). We conduct an extensive set of experiments, cumulatively totaling over 50,000 GPU-hours, that let us identify and discuss a number of ostensibly minor but significant design choices -- the advantage estimation procedure (a key component in training), visual encoder architecture, and a seemingly minor hyper-parameter change. Overall, these design choices to lead considerable and consistent improvements over the baselines present in Savva et al. Under a sample budget, performance for RGB-D agents improves 8 SPL on Gibson (14% relative improvement) and 20 SPL on Matterport3D (38% relative improvement). Under a compute budget, performance for RGB-D agents improves by 19 SPL on Gibson (32% relative improvement) and 35 SPL on Matterport3D (220% relative improvement). We hope our findings and recommendations will make serve to make the community's experiments more efficient. | computer science |
Image feature matching is a fundamental part of many geometric computer vision applications, and using multiple images can improve performance. In this work, we formulate multi-image matching as a graph embedding problem then use a Graph Convolutional Network to learn an appropriate embedding function for aligning image features. We use cycle consistency to train our network in an unsupervised fashion, since ground truth correspondence is difficult or expensive to aquire. In addition, geometric consistency losses can be added at training time, even if the information is not available in the test set, unlike previous approaches that optimize cycle consistency directly. To the best of our knowledge, no other works have used learning for multi-image feature matching. Our experiments show that our method is competitive with other optimization based approaches. | computer science |
This paper proposes a discrete knowledge graph (KG) embedding (DKGE) method, which projects KG entities and relations into the Hamming space based on a computationally tractable discrete optimization algorithm, to solve the formidable storage and computation cost challenges in traditional continuous graph embedding methods. The convergence of DKGE can be guaranteed theoretically. Extensive experiments demonstrate that DKGE achieves superior accuracy than classical hashing functions that map the effective continuous embeddings into discrete codes. Besides, DKGE reaches comparable accuracy with much lower computational complexity and storage compared to many continuous graph embedding methods. | computer science |
Asteroseismology of white dwarf (WD) stars is a powerful tool that allows to reveal the hidden chemical structure of WD and infer details about their evolution by comparing the observed periods with those obtained from stellar models. A recent asteroseismological study has reproduced the period spectrum of the helium rich pulsating WD KIC 08626021 with an unprecedented precision. The chemical structure derived from that analysis is notably different from that expected for a WD according to currently accepted formation channels, thus posing a challenge to the theory of stellar evolution. We explore the relevant micro- and macro-physics processes acting during the formation and evolution of KIC 08626021 that could lead to a chemical structure similar to that found through asteroseismology. We quantify to which extent is necessary to modify the physical processes that shapes the chemical structure, in order to reproduce the most important features of the asteroseismic model. We model the previous evolution of KIC 08626021 by exploring specific changes in the 12C+alpha reaction rate, screening processes, microscopic diffusion, as well as convective boundary mixing during core-He burning. We find that, in order to reproduce the core chemical profile derived for KIC 0862602, the 12C+alpha nuclear reaction rate has to be increased by a factor of $\sim$ 10 during the helium-core burning, and reduced by a factor of $\sim$ 1000 during the following helium-shell burning, as compared with the standard predictions for this rate. In addition, the main chemical structures derived for KIC 0862602 cannot be reconciled with our present knowledge of white dwarf formation. We find that within our current understanding of white dwarf formation and evolution, it is difficult to reproduce the most important asteroseismologically-derived features of the chemical structure of KIC 08626021. | astrophysics |
The nearby system 4C12.50, also known as IRAS 13451+1217 and PKS 1345+12, is a merger of gas-rich galaxies with infrared and radio activity. It has a perturbed interstellar medium (ISM) and a dense configuration of gas and dust around the nucleus. The radio emission at small ($\sim$100 pc) and large ($\sim$100 kpc) scales, as well as the large X-ray cavity in which the system is embedded, are indicative of a jet that could have affected the ISM. We carried out observations of the CO(1-0), (3-2), and (4-3) lines with the Atacama Large Millimeter Array (ALMA) to determine basic properties (i.e., extent, mass, and excitation) of the cold molecular gas in this system, including its already-known wind. The CO emission reveals the presence of gaseous streams related to the merger, which result in a small ($\sim$4kpc-wide) disk around the western nucleus. The disk reaches a rotational velocity of 200 $kms^{-1}$ , and has a mass of 3.8($\pm$0.4)$\times$10${^9}M_{\odot}$. It is truncated at a gaseous ridge north of the nucleus that is bright in [O III]. Regions with high-velocity CO emission are seen at signal-to-noise ratios of between 3 and 5 along filaments that radially extend from the nucleus to the ridge and that are bright in [O III] and stellar emission. A tentative wind detection is also reported in the nucleus and in the disk. The molecular gas speed could be as high as 2200 $kms^{-1}$ and the total wind mass could be as high as 1.5($\pm$0.1)$\times$10$^9M_{\odot}$. Energetically, it is possible that the jet, assisted by the radiation pressure of the active nucleus or the stars, accelerated clouds inside an expanding bubble. | astrophysics |
The dynamical behavior of social systems can be described by agent-based models. Although single agents follow easily explainable rules, complex time-evolving patterns emerge due to their interaction. The simulation and analysis of such agent-based models, however, is often prohibitively time-consuming if the number of agents is large. In this paper, we show how Koopman operator theory can be used to derive reduced models of agent-based systems using only simulation data. Our goal is to learn coarse-grained models and to represent the reduced dynamics by ordinary or stochastic differential equations. The new variables are, for instance, aggregated state variables of the agent-based model, modeling the collective behavior of larger groups or the entire population. Using benchmark problems with known coarse-grained models, we demonstrate that the obtained reduced systems are in good agreement with the analytical results, provided that the numbers of agents is sufficiently large. | mathematics |
In statistical process control, procedures are applied that require relatively strict conditions for their use. If such assumptions are violated, these methods become inefficient, leading to increased incidence of false signals. Therefore, a robust version of control charts is sought to be less sensitive with respect to a breach of normality and independence in measurements. Robust control charts, however, usually increase the delay in the detection of assignable causes. This negative effect can, to some extent, be removed with the aid of an adaptive approach. | statistics |
We introduce BayesSim, a framework for robotics simulations allowing a full Bayesian treatment for the parameters of the simulator. As simulators become more sophisticated and able to represent the dynamics more accurately, fundamental problems in robotics such as motion planning and perception can be solved in simulation and solutions transferred to the physical robot. However, even the most complex simulator might still not be able to represent reality in all its details either due to inaccurate parametrization or simplistic assumptions in the dynamic models. BayesSim provides a principled framework to reason about the uncertainty of simulation parameters. Given a black box simulator (or generative model) that outputs trajectories of state and action pairs from unknown simulation parameters, followed by trajectories obtained with a physical robot, we develop a likelihood-free inference method that computes the posterior distribution of simulation parameters. This posterior can then be used in problems where Sim2Real is critical, for example in policy search. We compare the performance of BayesSim in obtaining accurate posteriors in a number of classical control and robotics problems. Results show that the posterior computed from BayesSim can be used for domain randomization outperforming alternative methods that randomize based on uniform priors. | computer science |
We describe a functional framework suitable to the analysis of the Cahn-Hilliard equation on an evolving surface whose evolution is assumed to be given \textit{a priori}. The model is derived from balance laws for an order parameter with an associated Cahn-Hilliard energy functional and we establish well-posedness for general regular potentials, satisfying some prescribed growth conditions, and for two singular nonlinearities -- the thermodynamically relevant logarithmic potential and a double obstacle potential. We identify, for the singular potentials, necessary conditions on the initial data and the evolution of the surfaces for global-in-time existence of solutions, which arise from the fact that integrals of solutions are preserved over time, and prove well-posedness for initial data on a suitable set of admissible initial conditions. We then briefly describe an alternative derivation leading to a model that instead preserves a weighted integral of the solution, and explain how our arguments can be adapted in order to obtain global-in-time existence without restrictions on the initial conditions. Some illustrative examples and further research directions are given in the final sections. | mathematics |
We suggest that narrow, long radio filaments near the Galactic Center arise as kinetic jets - streams of high energy particles escaping from ram-pressure confined pulsar wind nebulae (PWNe). The reconnection between the PWN and interstellar magnetic field allows pulsar wind particles to escape, creating long narrow features. They are the low frequency analogs of kinetic jets seen around some fast-moving pulsars, such as The Guitar and The Lighthouse PWNe. The radio filaments trace a population of pulsars also responsible for the Fermi GeV excess produced by the Inverse Compton scattering by the pulsar wind particles. The magnetic flux tubes are stretched radially by the large scale Galactic winds. In addition to PWNe accelerated particles can be injected at supernovae remnants. The model predicts variations of the structure of the largest filaments on scales of $\sim$ dozens of years - smaller variations can occur on shorter time scales. We also encourage targeted observations of the brightest sections of the filaments and of the related unresolved point sources in search of the powering PWNe and pulsars. | astrophysics |
This paper evaluates the dynamic response of economic activity to shocks in uncertainty as percieved by agents.The study focuses on the comparison between the perception of economic uncertainty by manufacturers and consumers.Since uncertainty is not directly observable, we approximate it using the geometric discrepancy indicator of Claveria et al.(2019).This approach allows us quantifying the proportion of disagreement in business and consumer expectations of eleven European countries and the Euro Area.First, we compute three independent indices of discrepancy corresponding to three dimensions of uncertainty (economic, inflation and employment) and we average them to obtain aggregate disagreement measures for businesses and for consumers.Next, we use a bivariate Bayesian vector autoregressive framework to estimate the impulse response functions to innovations in disagreement in every country.We find that the effect on economic activity of shocks to the perception of uncertainty differ markedly between manufacturers and consumers.On the one hand, shocks to consumer discrepancy tend to be of greater magnitude and duration than those to manufacturer discrepancy.On the other hand, innovations in disagreement between the two collectives have an opposite effect on economic activity:shocks to manufacturer discrepancy lead to a decrease in economic activity, as opposed to shocks to consumer discrepancy.This finding is of particular relevance to researchers when using cross-sectional dispersion of survey-based expectations, since the effect on economic growth of shocks to disagreement depend on the type of agent. | statistics |
The recently claimed discovery of a massive ($M_\mathrm{BH}=68^{+11}_{-13}\,M_\odot$) black hole in the Galactic solar neighborhood has led to controversial discussions because it severely challenges our current view of stellar evolution. A crucial aspect for the determination of the mass of the unseen black hole is the precise nature of its visible companion, the B-type star LS V+22 25. Because stars of different mass can exhibit B-type spectra during the course of their evolution, it is essential to obtain a comprehensive picture of the star to unravel its nature and, thus, its mass. To this end, we study the spectral energy distribution of LS V+22 25 and perform a quantitative spectroscopic analysis that includes the determination of chemical abundances for He, C, N, O, Ne, Mg, Al, Si, S, Ar, and Fe. Our analysis clearly shows that LS V+22 25 is not an ordinary main sequence B-type star. The derived abundance pattern exhibits heavy imprints of the CNO bi-cycle of hydrogen burning, that is, He and N are strongly enriched at the expense of C and O. Moreover, the elements Mg, Al, Si, S, Ar, and Fe are systematically underabundant when compared to normal main-sequence B-type stars. We suggest that LS V+22 25 is a stripped helium star and discuss two possible formation scenarios. Combining our photometric and spectroscopic results with the Gaia parallax, we infer a stellar mass of $1.1\pm0.5\,M_\odot$. Based on the binary system's mass function, this yields a minimum mass of $2-3\,M_\odot$ for the compact companion, which implies that it may not necessarily be a black hole but a massive neutron- or main sequence star. The star LS V+22 25 has become famous for possibly having a very massive black hole companion. However, a closer look reveals that the star itself is a very intriguing object. Further investigations are necessary for complete characterization of this object. | astrophysics |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.