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    We use Pontryagin's minimum principle to optimize variational quantum algorithms. We show that for a fixed computation time, the optimal evolution has a bang-bang (square pulse) form, both for closed and open quantum systems with Markovian decoherence. Our findings support the choice of evolution ansatz in the recently proposed Quantum Approximate Optimization Algorithm. Focusing on the Sherrington-Kirkpatrick spin glass as an example, we find a system-size independent distribution of the duration of pulses, with characteristic time scale set by the inverse of the coupling constants in the Hamiltonian. We numerically demonstrate that our optimal nonadiabatic bang-bang protocols can outperform quantum annealing by orders of magnitude within the same running time. The optimality of the bang-bang protocols and the characteristic time scale of the pulses inform the search for effective hybrid (classical and quantum) machine learning schemes for tackling combinatorial optimization problems.
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    The surface code is a many-body quantum system, and simulating it in generic conditions is computationally hard. While the surface code is believed to have a high threshold, the numerical simulations used to establish this threshold are based on simplified noise models. We present a tensor-network algorithm for simulating error correction with the surface code under arbitrary local noise. Our simulation is exact within statistical fluctuations and we use it to study the threshold and the sub-threshold behaviour of the amplitude-damping and systematic rotation channels. We also compare these exact results to those obtained by making standard approximations to the noise models.
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    In this work we investigate the classical constraints imposed on the supergravity and super Yang-Mills backgrounds in the $\alpha'\to 0$ limit of the heterotic string using the pure spinor formalism. Guided by the recently observed sectorization of the model, we show that all the ten-dimensional constraints are elegantly obtained from the single condition of nilpotency of the BRST charge.
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    T CrB is a symbiotic recurrent nova known to exhibit active phases, characterised by apparent increases in the hot component temperature and the appearance of flickering, i.e. changes in the observed flux on the time-scale of minutes. Historical UV observations have ruled out orbital variability as an explanation for flickering and instead suggest flickering is caused by variable mass transfer. We have analysed optical and X-ray observations to investigate the nature of the flickering as well as the active phases in T CrB. The spectroscopic and photometric observations confirm that the active phases follow two periods of ~1000d and ~5000d. Flickering in the X-rays is detected and follows an amplitude-flux relationship similar to that observed in the optical. The flickering is most prominent at harder X-ray energies, suggesting that it originates in the boundary layer between the accretion disc and the white dwarf. The X-ray radiation from the boundary layer is then reprocessed by a thick accretion disc or a nebula into UV radiation. A more detailed understanding of flickering would benefit from long-term simultaneous X-ray and optical monitoring of the phenomena in symbiotic recurrent novae and related systems such as Z And type symbiotic stars.
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    Preserving details in restoring images highly corrupted by impulse noise remains a challenging problem. We proposed an algorithm based on radial basis functions (RBF) interpolation which estimates the intensities of corrupted pixels by their neighbors. In this algorithm, first intensity values of noisy pixels in the corrupted image are estimated using RBFs. Next, the image is smoothed. The proposed algorithm can effectively remove the highly dense impulse noise. Experimental results show the superiority of the proposed algorithm in comparison to the recent similar methods both in noise suppression and detail preservation. Extensive simulations show better results in measure of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), especially when the image is corrupted by very highly dense impulse noise.
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    Trimmed L-moments, were introduced by Elamir and Seheult(2003) to proposed a new estimation method for multi-parameter distributions when the mean doesn't exist or for heavy tailed distribution where the L-moments method which proposed by Hosking (1990) is not valid because the absence of theoretical L-moments. In this paper a new estimation method based on trimmed L-moments of multi-parameter copulas is proposed with a simulation study. The consistency and the asymptotic normality of the new estimator also established.
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    We study the problem of treatment effect estimation in randomized experiments with high-dimensional regression adjustments. We present an inferential approach that takes any risk-consistent regression adjustment and yields efficient estimates of the average treatment effect. This considerably extends the range of settings where high-dimensional regression adjustments are guaranteed to provide valid inference about the population average treatment effect. We also propose cross-estimation, a simple method for obtaining finite-sample-unbiased treatment effect estimates that leverages high-dimensional regression adjustments. Our method can be used when the regression model is estimated using the lasso, the elastic net, subset selection, or many other methods; we also extend our analysis to allow for adaptive specification search via cross-validation, and flexible non-parametric regression adjustments with, e.g., random forests or neural networks.
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    We extend the Rouse model of polymer dynamics to situations of non-stationary chain growth. For a dragged polymer chain of length $N(t) = t^\alpha$, we find two transitions in conformational dynamics. At $\alpha= 1/2$, the propagation of tension and the average shape of the chain change qualitatively, while at $\alpha = 1 $ the average center-of-mass motion stops. These transitions are due to a simple physical mechanism: a race duel between tension propagation and polymer growth. Therefore they should also appear for growing semi-flexible or stiff polymers. The generalized Rouse model inherits much of the versatility of the original Rouse model: it can be efficiently simulated and it is amenable to analytical treatment.
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    We present numerical tests and predictions of the KrkNLO method for matching of NLO QCD corrections to hard processes with LO parton shower Monte Carlo generators. This method was described in detail in our previous publications, where its advantages over other approaches, such as MCatNLO and POWHEG, were pointed out. Here we concentrate on presenting some numerical results (cross sections and distributions) for $Z/\gamma^*$ (Drell-Yan) and Higgs-boson production processes at the LHC. The Drell--Yan process is used mainly to validate the KrkNLO implementation in the Herwig 7 program with respect to the previous implementation in Sherpa. We also show predictions for this process with the new, complete, MC-scheme parton distribution functions and compare them with our previously published results. Then, we present the first results of the KrkNLO method for the Higgs production in gluon--gluon fusion at the LHC and compare them with the predictions of other programs, such as MCFM, MCatNLO, POWHEG and HNNLO, as well as with experimental data from the ATLAS collaboration.
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    Debris disks often take the form of eccentric rings with azimuthal asymmetries in surface brightness. Such disks are often described as showing "pericenter glow", an enhancement of the disk brightness in regions nearest the central star. At long wavelengths, however, the disk apocenters should appear brighter than their pericenters: in the long wavelength limit, we find the apocenter/pericenter flux ratio scales as 1+e for disk eccentricity e. We produce new models of this "apocenter glow" to explore its causes and wavelength dependence and study its potential as a probe of dust grain properties. Based on our models, we argue that several far-infrared and (sub)millimeter images of the Fomalhaut and epsilon Eridani debris rings obtained with Herschel, JCMT, SHARC II, ALMA, and ACTA should be reinterpreted as suggestions or examples of apocenter glow. This reinterpretation yields new constraints on the disks' dust grain properties and size distributions.
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    In this paper we proposed an ordered patch based method using Conditional Random Field (CRF) in order to encode local properties and their spatial relationship in images to address texture classification, face recognition, and scene classification problems. Typical image classification approaches work without considering spatial causality among distinctive properties of an image for image representation in feature space. In this method first, each image is encoded as a sequence of ordered patches, including local properties. Second, the sequence of these ordered patches is modeled as a probabilistic feature vector by CRF to model spatial relationship of these local properties. And finally, image classification is performed on such probabilistic image representation. Experimental results on several standard image datasets indicate that proposed method outperforms some of existing image classification methods.
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    Metastable dynamics of a hyperbolic variation of the Allen-Cahn equation with homogeneous Neumann boundary conditions are considered. Using the "dynamical approach" proposed by Carr-Pego [10] and Fusco-Hale [19] to study slow-evolution of solutions in the classic parabolic case, we prove existence and persistence of metastable patterns for an exponentially long time. In particular, we show the existence of an "approximately invariant" $N$-dimensional manifold $\mathcal{M}_0$ for the hyperbolic Allen-Cahn equation: if the initial datum is in a tubular neighborhood of $\mathcal{M}_0$, the solution remains in such neighborhood for an exponentially long time. Moreover, the solution has $N$ transition layers and the transition points move with exponentially small velocity. In addition, we determine the explicit form of a system of ordinary differential equations describing the motion of the transition layers and we analyze the differences with the corresponding motion valid for the parabolic case.
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    Social media have great potential to support diverse information sharing, but there is widespread concern that platforms like Twitter do not result in communication between those who hold contradictory viewpoints. Because users can choose whom to follow, prior research suggests that social media users exist in 'echo chambers' or become polarized. We seek evidence of this in a complete cross section of hyperlinks posted on Twitter, using previously validated measures of the political slant of news sources to study information diversity. Contrary to prediction, we find that the average account posts links to more politically moderate news sources than the ones they receive in their own feed. However, members of a tiny network core do exhibit cross-sectional evidence of polarization and are responsible for the majority of tweets received overall due to their popularity and activity, which could explain the widespread perception of polarization on social media.
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    Low level images representation in feature space performs poorly for classification with high accuracy since this level of representation is not able to project images into the discriminative feature space. In this work, we propose an efficient image representation model for classification. First we apply Hidden Markov Model (HMM) on ordered grids represented by different type of image descriptors in order to include causality of local properties existing in image for feature extraction and then we train up a separate classifier for each of these features sets. Finally we ensemble these classifiers efficiently in a way that they can cancel out each other errors for obtaining higher accuracy. This method is evaluated on 15 natural scene dataset. Experimental results show the superiority of the proposed method in comparison to some current existing methods
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    In this paper, we study the effect of a single link on the capacity of a network of error-free bit pipes. More precisely, we study the change in network capacity that results when we remove a single link of capacity $\delta$. In a recent result, we proved that if all the sources are directly available to a single super-source node, then removing a link of capacity $\delta$ cannot change the capacity region of the network by more than $\delta$ in each dimension. In this paper, we extend this result to the case of multi-source, multi-sink networks for some special network topologies.
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    The rate-distortion dimension (RDD) of an analog stationary process is studied as a measure of complexity that captures the amount of information contained in the process. It is shown that the RDD of a process, defined as two times the asymptotic ratio of its rate-distortion function $R(D)$ to $\log {1\over D}$ as the distortion $D$ approaches zero, is equal to its information dimension (ID). This generalizes an earlier result by Kawabata and Dembo and provides an operational approach to evaluate the ID of a process, which previously was shown to be closely related to the effective dimension of the underlying process and also to the fundamental limits of compressed sensing. The relation between RDD and ID is illustrated for a piecewise constant process.
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    Coulomb breakup is used to infer radiative-capture cross sections at astrophysical energies. We test theoretically the accuracy of this indirect technique in the particular case of 15C, for which both the Coulomb breakup to ^14C+n and the radiative capture 14C(n,\gamma)15C have been measured. We analyse the dependance of Coulomb-breakup calculations on the projectile description in both its initial bound state and its continuum. Our calculations depend not only on the Asymptotic Normalisation Coefficient (ANC) of the 15C ground state, but also on the 14C-n continuum. This questions the method proposed by Summers and Nunes [Phys. Rev. C 78, 011601 (2008), ibid. 78, 069908 (2008)], which assumes that an ANC can be directly extracted from the comparison of calculations to breakup data. Fortunately, the sensitivity to the continuum description can be absorbed in a normalisation constant obtained by a simple \chi2 fit of our calculations to the measurements. By restricting this fit to low 14C-n energy in the continuum, we can achieve a better agreement between the radiative-capture cross sections inferred from the Coulomb-breakup method and the exact ones. This result revives the Coulomb-breakup technique to infer neutron radiative-capture capture to loosely-bound states, which would be very useful for r- and s-process modelling in explosive stellar environments.
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    We consider the coupling of the magnons in both quantum ferromagnets and antiferromagnets to the longitudinal order-parameter fluctuations, and the resulting nonanalytic behavior of the longitudinal susceptibility. In classical magnets it is well known that long-range correlations induced by the magnons lead to a singular wave-number dependence of the form $1/k^{4-d}$ in all dimensions 2<d<4, for both ferromagnets and antiferromagnets. At zero temperature we find a profound difference between the two cases. Consistent with naive power counting, the longitudinal susceptibility in a quantum antiferromagnet scales as $k^{d-3}$ for 1<d<3, whereas in a quantum ferromagnet the analogous result, $k^{d-2}$, is absent due to a zero scaling function. This absence of a nonanalyticity in the longitudinal susceptibility is due to the lack of magnon number fluctuations in the ground state of a quantum ferromagnet; correlation functions that are sensitive to other fluctuations do exhibit the behavior predicted by simple power counting. Also of interest is the dynamical behavior as expressed in the longitudinal part of the dynamical structure factor, which is directly measurable via neutron scattering. For both ferromagnets and antiferromagnets there is a logarithmic singularity at the magnon frequency with a prefactor that vanishes as $T\to 0$. In the antiferromagnetic case there also is a nonzero contribution at T=0 that is missing for ferromagnets. Magnon damping due to quenched disorder restores the expected scaling behavior of the longitudinal susceptibility in the ferromagnetic case; it scales as $k^{d-2}$ if the order parameter is not conserved, or as $k^d$ if it is. Detailed predictions are made for both two- and three-dimensional systems at both T=0 and in the limit of low temperatures, and the physics behind the various nonanalytic behaviors is discussed.
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    Structure-property relationships is a key materials science concept that enables the design of new materials. In the case of materials for application in radiation environments, correlating radiation tolerance with fundamental structural features of a material enables materials discovery. Here, we use a machine learning model to examine the factors that govern amorphization resistance in the complex oxide pyrochlore ($A_2B_2$O$_7$). We examine the fidelity of predictions based on cation radii and electronegativities, the oxygen positional parameter, and the energetics of disordering and amorphizing the material. No one factor alone adequately predicts amorphization resistance. We find that, when multiple families of pyrochlores (with different B cations) are considered, radii and electronegativities provide the best prediction but when the machine learning model is restricted to only the $B$=Ti pyrochlores, the energetics of disordering and amorphization are optimal. This work provides new insight into the factors that govern the amorphization susceptibility and highlights the ability of machine learning approaches to generate that insight.
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    Equilibrium is a central concept of statistical mechanics. In previous work we introduced the notions of a Boltzmannian alpha-epsilon-equilibrium and a Boltzmannian gamma-varepsilon-equilibrium (Werndl and Frigg 2015a, 2015b). This was done in a deterministic context. We now consider systems with a stochastic micro-dynamics and transfer these notions from the deterministic to the stochastic context. We then prove stochastic equivalents of the Dominance Theorem and the Prevalence Theorem. This establishes that also in stochastic systems equilibrium macro-regions are large in requisite sense.
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    We propose a modular and scalable framework for dense coregistration and cosegmentation with two key characteristics: first, we substitute ground truth data with the semantic map output of a classifier; second, we combine this output with population deformable registration to improve both alignment and segmentation. Our approach deforms all volumes towards consensus, taking into account image similarities and label consistency. Our pipeline can incorporate any classifier and similarity metric. Results on two datasets, containing annotations of challenging brain structures, demonstrate the potential of our method.
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    We present how to further the search for Lorentz and CPT violation in the top-quark sector after the first measurement in this sector by D0. We compute the Lorentz-violating matrix element for top pair production via gluon fusion, which allows a similar but more precise measurement at the LHC. For CPT violation, we analyze single-top production, derive the modified matrix elements, and predict possible signals.
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    A residual design ${\cal{D}}_B$ with respect to a block $B$ of a given design $\cal{D}$ is defined to be linearly embeddable over $GF(p)$ if the $p$-ranks of the incidence matrices of ${\cal{D}}_B$ and $\cal{D}$ differ by one. A sufficient condition for a residual design to be linearly embeddable is proved in terms of the minimum distance of the linear code spanned by the incidence matrix, and this condition is used to show that the residual designs of several known infinite classes of designs are linearly embeddable. A necessary condition for linear embeddability is proved for affine resolvable designs and their residual designs. As an application, it is shown that a residual design of the classical affine design of the planes in $AG(3,2^2)$ admits two nonisomorphic embeddings over $GF(2)$ that give rise to the only known counter-examples of Hamada's conjecture over a field of non-prime order.
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    We prove that, for every integer $n \ge 2$, a finite or infinite countable group $G$ can be embedded into a 2-generated group $H$ in such a way that the solvability of quadratic equations of length at most $n$ is preserved, i.e., every quadratic equation over $G$ of length at most $n$ has a solution in $G$ if and only if this equation, considered as an equation over $H$, has a solution in $H$.
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    A background model describes a scene without any foreground objects and has a number of applications, ranging from video surveillance to computational photography. Recent studies have introduced the method of Dynamic Mode Decomposition (DMD) for robustly separating video frames into a background model and foreground components. While the method introduced operates by converting color images to grayscale, we in this study propose a technique to obtain the background model in the color domain. The effectiveness of our technique is demonstrated using a publicly available Scene Background Initialisation (SBI) dataset. Our results both qualitatively and quantitatively show that DMD can successfully obtain a colored background model.
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    Most studies of disease spreading consider the underlying social network as obtained without the contagion, though epidemic influences peoples willingness to contact others: A friendly contact may be turned to unfriendly to avoid infection. We study the susceptible-infected (SI) disease spreading model on signed networks, in which each edge is associated with a positive or negative sign representing the friendly or unfriendly relation between its end nodes. In a signed network, according to Heiders theory, edge signs evolve such that finally a state of structural balance is achieved, corresponding to no frustration in physics terms. However, the danger of infection affects the evolution of its edge signs. To describe the coupled problem of the sign evolution and disease spreading, we generalize the notion of structural balance by taking into account the state of the nodes. We introduce an energy function and carry out Monte-Carlo simulations on complete networks to test the energy landscape, where we find local minima corresponding to the so called jammed states. In the stationary state, the network is split into balanced clusters of purely susceptible or infected nodes with only friendly edges within the cluster, while edges between clusters are unfriendly and prevent the disease to spread. In this case, a coexistence occurs between susceptible and infected nodes in the system even for large values of infection rates.
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    A major challenge in machine learning is covariate shift, i.e., the problem of training data and test data coming from different distributions. This paper studies the feasibility of tackling this problem by means of sparse filtering. We show that the sparse filtering algorithm intrinsically addresses this problem, but it has limited capacity for covariate shift adaptation. To overcome this limit, we propose a novel semi-supervised sparse filtering algorithm, named periodic sparse filtering. Our proposed algorithm is formally analyzed and empirically evaluated with an elaborated synthetic data set and real speech emotion data sets. As a result, we argue that, as an alternative methodology, feature distribution learning has enormous potential in carrying out covariate shift adaptation.
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    We apply a recently developed theory for metastability in open quantum systems to a one-dimensional dissipative quantum Ising model. Earlier results suggest this model features either a non-equilibrium phase transition or a smooth but sharp crossover, where the stationary state changes from paramagnetic to ferromagnetic, accompanied by strongly intermittent emission dynamics characteristic of first-order coexistence between dynamical phases. We show that for a range of parameters close to this transition/crossover point the dynamics of the finite system displays pronounced metastability, i.e., the system relaxes first to long-lived metastable states, before eventual relaxation to the true stationary state. From the spectral properties of the quantum master operator we characterise the low-dimensional manifold of metastable states, which are shown to be probability mixtures of two, paramagnetic and ferromagnetic, metastable phases. We also show that for long times the dynamics can be approximated by a classical stochastic dynamics between the metastable phases that is directly related to the intermittent dynamics observed in quantum trajectories and thus the dynamical phases.
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    Analysing multiple evidence sources is often feasible only via a modular approach, with separate submodels specified for smaller components of the available evidence. Here we introduce a generic framework that enables fully Bayesian analysis in this setting. This requires joining the submodels together into a full, joint model, so that all uncertainty can be fully propagated between all components. We propose a generic method for forming a suitable joint model from submodels, and a convenient computational algorithm for fitting this joint model in stages, rather than as a single monolithic model. The approach also enables splitting of large joint models into smaller submodels, allowing inference for the original joint model to be conducted via our multi-stage algorithm. We motivate and demonstrate our approach through two examples: joining components of an evidence synthesis of A/H1N1 influenza, and splitting a large ecology model.
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    Here, we study the ultimately bounded stability of network of mismatched systems using Lyapunov direct method. We derive an upper bound on the norm of the error of network states from its average states, which it achieves in finite time. Then, we devise a decentralized compensator to asymptotically pin the network of mismatched systems to a desired trajectory. Next, we design distributed estimators to compensate for the mismatched parameters performances of adaptive decentralized and distributed compensations are analyzed. Our analytical results are verified by several simulations in a network of globally connected Lorenz oscillators.
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    We have used the SmallGroups library of groups, together with the computer algebra systems GAP and Mathematica, to search for groups with a three-dimensional irreducible representation in which one of the group generators has a twice-degenerate eigenvalue while another generator has non-degenerate eigenvalues. By assuming one of these group generators to commute with the charged-lepton mass matrix and the other one to commute with the neutrino (Dirac) mass matrix, one derives group-theoretical predictions for the moduli of the matrix elements of either a row or a column of the lepton mixing matrix. Our search has produced several realistic predictions for either the second row, or the third row, or for any of the columns of that matrix.
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    Four rubidium fountains at the U.S. Naval Observatory (USNO) have been in operation for 4.5 years. Each fountain has demonstrated periods of stability marked by Total or Theo deviation below 10^-16. Occasional frequency changes, on order of 1.5 times per year per fountain, introduce deviations from white-frequency noise behavior. Averaged together, the four fountains form an ensemble with a white-frequency noise level of 10^-13 and excellent long-term stability as compared to the primary frequency standards contributing to TAI. Progress on using the clocks at USNO for improving limits on coupling of fundamental constants to gravity by measuring the universality of the gravitational redshift for different types of clocks is discussed.
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    This paper is devoted to a systematic study and classification of invariant affine or metric connections on certain classes of naturally reductive spaces. For any non-symmetric, effective, strongly isotropy irreducible homogeneous Riemannian manifold $(M=G/K, g)$, we compute the dimensions of the spaces of $G$-invariant affine and metric connections. For such manifolds we also describe the space of invariant metric connections with skew-torsion. For the compact Lie group ${\rm U}_{n}$ we classify all bi-invariant metric connections, by introducing a new family of bi-invariant connections whose torsion is of vectorial type. Next we present applications related with the notion of $\nabla$-Einstein manifolds with skew-torsion. In particular, we classify all such invariant structures on any non-symmetric strongly isotropy irreducible homogeneous space.
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    We present direct estimates of the mean sky brightness temperature in observing bands around 99GHz and 242GHz due to line emission from distant galaxies. These values are calculated from the summed line emission observed in a blind, deep survey for specrtal line emission from high redshift galaxies using ALMA (the 'ASPECS' survey). In the 99 GHz band, the mean brightness will be dominated by rotational transitions of CO from intermediate and high redshift galaxies. In the 242GHz band, the emission could be a combination of higher order CO lines, and possibly [CII] 158$\mu$m line emission from very high redshift galaxies ($z \sim 6$ to 7). The mean line surface brightness is a quantity that is relevant to measurements of spectral distortions of the cosmic microwave background, and as a potential tool for studying large-scale structures in the early Universe using intensity mapping. While the cosmic volume and the number of detections are admittedly small, this pilot survey provides a direct measure of the mean line surface brightness, independent of conversion factors, excitation, or other galaxy formation model assumptions. The mean surface brightness in the 99GHZ band is: $T_B = 0.94\pm 0.09$ $\mu$K. In the 242GHz band, the mean brightness is: $T_B = 0.55\pm 0.033$ $\mu$K. These should be interpreted as lower limits on the average sky signal, since we only include lines detected individually in the blind survey, while in a low resolution intensity mapping experiment, there will also be the summed contribution from lower luminosity galaxies that cannot be detected individually in the current blind survey.
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    We present a search for [CII] line and dust continuum emission from optical dropout galaxies at $z>6$ using ASPECS, our ALMA Spectroscopic Survey in the Hubble Ultra-Deep Field (UDF). Our observations, which cover the frequency range $212-272$ GHz, encompass approximately the range $6<z<8$ for [CII] line emission and reach a limiting luminosity of L$_{\rm [CII]}\sim$(1.6-2.5)$\times$10$^{8}$ L$_{\odot}$. We identify fourteen [CII] line emitting candidates in this redshift range with significances $>$4.5 $\sigma$, two of which correspond to blind detections with no optical counterparts. At this significance level, our statistical analysis shows that about 60\% of our candidates are expected to be spurious. For one of our blindly selected [CII] line candidates, we tentatively detect the CO(6-5) line in our parallel 3-mm line scan. None of the line candidates are individually detected in the 1.2 mm continuum. A stack of all [CII] candidates results in a tentative detection with $S_{1.2mm}=14\pm5\mu$Jy. This implies a dust-obscured star formation rate (SFR) of $(3\pm1)$ M$_\odot$ yr$^{-1}$. We find that the two highest--SFR objects have candidate [CII] lines with luminosities that are consistent with the low-redshift $L_{\rm [CII]}$ vs. SFR relation. The other candidates have significantly higher [CII] luminosities than expected from their UV--based SFR. At the current sensitivity it is unclear whether the majority of these sources are intrinsically bright [CII] emitters, or spurious sources. If only one of our line candidates was real (a scenario greatly favored by our statistical analysis), we find a source density for [CII] emitters at $6<z<8$ that is significantly higher than predicted by current models and some extrapolations from galaxies in the local universe.
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    We study the molecular gas properties of high-$z$ galaxies observed in the ALMA Spectroscopic Survey (ASPECS) that targets a $\sim1$ arcmin$^2$ region in the Hubble Ultra Deep Field (UDF), a blind survey of CO emission (tracing molecular gas) in the 3mm and 1mm bands. Of a total of 1302 galaxies in the field, 56 have spectroscopic redshifts and correspondingly well-defined physical properties. Among these, 11 have infrared luminosities $L_{\rm{}IR}>10^{11}$ L$_\odot$, i.e. a detection in CO emission was expected. Out these, 7 are detected at various significance in CO, and 4 are undetected in CO emission. In the CO-detected sources, we find CO excitation conditions that are lower than typically found in starburst/SMG/QSO environments. We use the CO luminosities (including limits for non-detections) to derive molecular gas masses. We discuss our findings in context of previous molecular gas observations at high redshift (star-formation law, gas depletion times, gas fractions): The CO-detected galaxies in the UDF tend to reside on the low-$L_{\rm{}IR}$ envelope of the scatter in the $L_{\rm{}IR}-L'_{\rm{}CO}$ relation, but exceptions exist. For the CO-detected sources, we find an average depletion time of $\sim$ 1 Gyr, with significant scatter. The average molecular-to-stellar mass ratio ($M_{\rm{}H2}$/$M_*$) is consistent with earlier measurements of main sequence galaxies at these redshifts, and again shows large variations among sources. In some cases, we also measure dust continuum emission. On average, the dust-based estimates of the molecular gas are a factor $\sim$2-5$\times$ smaller than those based on CO. Accounting for detections as well as non-detections, we find large diversity in the molecular gas properties of the high-redshift galaxies covered by ASPECS.
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    In this paper we use ASPECS, the ALMA Spectroscopic Survey in the \em Hubble Ultra Deep Field (UDF) in band 3 and band 6, to place blind constraints on the CO luminosity function and the evolution of the cosmic molecular gas density as a function of redshift up to $z\sim 4.5$. This study is based on galaxies that have been solely selected through their CO emission and not through any other property. In all of the redshift bins the ASPECS measurements reach the predicted `knee' of the CO luminosity function (around $5\times10^{9}$ K km/s pc$^2$). We find clear evidence of an evolution in the CO luminosity function with respect to $z\sim 0$, with more CO luminous galaxies present at $z\sim 2$. The observed galaxies at $z\sim 2$ also appear more gas-rich than predicted by recent semi-analytical models. The comoving cosmic molecular gas density within galaxies as a function of redshift shows a factor 3-10 drop from $z \sim 2$ to $z \sim 0$ (with significant error bars), and possibly a decline at $z>3$. This trend is similar to the observed evolution of the cosmic star formation rate density. The latter therefore appears to be at least partly driven by the increased availability of molecular gas reservoirs at the peak of cosmic star formation ($z\sim2$).
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    We present an analysis of a deep (1$\sigma$=13 $\mu$Jy) cosmological 1.2-mm continuum map based on ASPECS, the ALMA Spectroscopic Survey in the Hubble Ultra Deep Field. In the 1 arcmin$^2$ covered by ASPECS we detect nine sources at $>3.5\sigma$ significance at 1.2-mm. Our ALMA--selected sample has a median redshift of $z=1.6\pm0.4$, with only one galaxy detected at z$>$2 within the survey area. This value is significantly lower than that found in millimeter samples selected at a higher flux density cut-off and similar frequencies. Most galaxies have specific star formation rates similar to that of main sequence galaxies at the same epoch, and we find median values of stellar mass and star formation rates of $4.0\times10^{10}\ M_\odot$ and $\sim40~M_\odot$ yr$^{-1}$, respectively. Using the dust emission as a tracer for the ISM mass, we derive depletion times that are typically longer than 300 Myr, and we find molecular gas fractions ranging from $\sim$0.1 to 1.0. As noted by previous studies, these values are lower than using CO--based ISM estimates by a factor $\sim$2. The 1\u2009mm number counts (corrected for fidelity and completeness) are in agreement with previous studies that were typically restricted to brighter sources. With our individual detections only, we recover $55\pm4\%$ of the extragalactic background light (EBL) at 1.2 mm measured by the Planck satellite, and we recover $80\pm7\%$ of this EBL if we include the bright end of the number counts and additional detections from stacking. The stacked contribution is dominated by galaxies at $z\sim1-2$, with stellar masses of (1-3)$\times$10$^{10}$ M$_\odot$. For the first time, we are able to characterize the population of galaxies that dominate the EBL at 1.2 mm.
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    We present the rationale for and the observational description of ASPECS: The ALMA SPECtroscopic Survey in the Hubble Ultra-Deep Field (UDF), the cosmological deep field that has the deepest multi-wavelength data available. Our overarching goal is to obtain an unbiased census of molecular gas and dust continuum emission in high-redshift (z$>$0.5) galaxies. The $\sim$1$'$ region covered within the UDF was chosen to overlap with the deepest available imaging from HST. Our ALMA observations consist of full frequency scans in band 3 (84-115 GHz) and band 6 (212-272 GHz) at approximately uniform line sensitivity ($L'_{\rm CO}\sim$2$\times$10$^{9}$ K km/s pc$^2$), and continuum noise levels of 3.8 $\mu$Jy beam$^{-1}$ and 12.7 $\mu$Jy beam$^{-1}$, respectively. The molecular surveys cover the different rotational transitions of the CO molecule, leading to essentially full redshift coverage. The [CII] emission line is also covered at redshifts $6.0<z<8.0$. We present a customized algorithm to identify line candidates in the molecular line scans, and quantify our ability to recover artificial sources from our data. Based on whether multiple CO lines are detected, and whether optical spectroscopic redshifts as well as optical counterparts exist, we constrain the most likely line identification. We report 10 (11) CO line candidates in the 3mm (1mm) band, and our statistical analysis shows that $<$4 of these (in each band) are likely spurious. Less than 1/3 of the total CO flux in the low-J CO line candidates are from sources that are not associated with an optical/NIR counterpart. We also present continuum maps of both the band 3 and band 6 observations. The data presented here form the basis of a number of dedicated studies that are presented in subsequent papers.
  • PDF
    It is believed that most (perhaps all) gapped phases of matter can be described at long distances by Topological Quantum Field Theory (TQFT). On the other hand, it has been rigorously established that in 1+1d ground states of gapped Hamiltonians can be approximated by Matrix Product States (MPS). We show that the state-sum construction of 2d TQFT naturally leads to MPS in their standard form. In the case of systems with a global symmetry G, this leads to a classification of gapped phases in 1+1d in terms of Morita-equivalence classes of G-equivariant algebras. Non-uniqueness of the MPS representation is traced to the freedom of choosing an algebra in a particular Morita class. In the case of Short-Range Entangled phases, we recover the group cohomology classification of SPT phases.
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    Network creation games have been extensively studied, both from economists and computer scientists, due to their versatility in modeling individual-based community formation processes, which in turn are the theoretical counterpart of several economics, social, and computational applications on the Internet. In their several variants, these games model the tension of a player between her two antagonistic goals: to be as close as possible to the other players, and to activate a cheapest possible set of links. However, the generally adopted assumption is that players have a \emphcommon and complete information about the ongoing network, which is quite unrealistic in practice. In this paper, we consider a more compelling scenario in which players have only limited information about the network they are embedded in. More precisely, we explore the game-theoretic and computational implications of assuming that players have a complete knowledge of the network structure only up to a given radius $k$, which is one of the most qualified \emphlocal-knowledge models used in distributed computing. To this respect, we define a suitable equilibrium concept, and we provide a comprehensive set of upper and lower bounds to the price of anarchy for the entire range of values of $k$, and for the two classic variants of the game, namely those in which a player's cost --- besides the activation cost of the owned links --- depends on the maximum/sum of all the distances to the other nodes in the network, respectively. These bounds are finally assessed through an extensive set of experiments.
  • PDF
    The optimized gradient method (OGM) was recently developed by optimizing the step coefficients of first-order methods with respect to the function value. This OGM is as efficient as Nesterov's fast gradient method (FGM) and satisfies a worst-case convergence bound of the function value that is twice smaller than that of FGM. Moreover, OGM was shown recently to achieve the optimal cost function complexity bound of first-order methods for smooth convex minimization. Considering that OGM is superior to the widely used FGM for smooth convex minimization, it is desirable to further understand the formulation and convergence analysis of OGM. Therefore, this paper studies a generalized formulation of OGM and its convergence analysis in terms of both the function value and the gradient. We then optimize the step coefficients of first-order methods with respect to the gradient norm, leading to a new algorithm called OGM-OG. We show that OGM-OG achieves a worst-case convergence bound for the gradient norm that is smaller than the best known analytical bound for first-order methods.
  • PDF
    When it comes to datasets with a tremendous amount of predictors, variable reduction techniques such as PCA or FA are often used. In this paper, the elastic net, which lies in between the LASSO method and ridge regression, is used as a variable reduction technique followed by further analysis with multivariate regression. Specifically, a messy only dataset is used to show how it can be 'tidied' up and broken down into sensible subsets using the aforementioned method.
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    We define a relationally exchangeable structure as a random combinatorial structure whose law is invariant with respect to relabeling its relations, instead of its elements. Examples of relationally exchangeable structures include edge exchangeable random graphs and hypergraphs and also exchangeable random set partitions (when viewed from a certain perspective). Relationally exchangeable models arise most naturally in certain network science applications, particularly network datasets generated processes of interactions. We prove a representation theorem for the class of relationally exchangeable structures and discuss some consequences and open problems. The representation refines Kingman's paintbox correspondence for exchangeable random partitions.
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    We study a six matrix model with global $SO(3)\times SO(3)$ symmetry containing at most quartic powers of the matrices. This theory exhibits a phase transition from a geometrical phase at low temperature to a Yang-Mills matrix phase with no background geometrical structure at high temperature. This is an exotic phase transition in the same universality class as the three matrix model but with important differences. The geometrical phase is determined dynamically, as the system cools, and is given by a fuzzy four-sphere background ${\bf S}^2_N\times{\bf S}^2_N$, with an Abelian gauge field which is very weakly coupled to two normal scalar fields playing the role of dark energy.
  • PDF
    We have studied how the energy spectrum of muons at production affects some of the most common measurements related to muons in extensive air shower studies, namely, the number of muons at the ground, the slope of the lateral distribution of muons, the apparent muon production depth, and the arrival time delay of muons at ground. We found that by changing the energy spectrum by an amount consistent with the difference between current models (namely EPOS-LHC and QGSjet-II.04), the muon surface density at ground increases $5\%$ at $20^\circ$ zenith angle and $17\%$ at $60^\circ$ zenith angle. This effect introduces a zenith angle dependence on the reconstructed number of muons which might be experimentally observed. The maximum of the muon production depth distribution at $40^\circ$ increases $\sim10\text{ g/cm}^2$ and $\sim0\text{ g/cm}^2$ at $60^\circ$, which, from pure geometrical considerations, increases the arrival time delay of muons. There is an extra contribution to the delay due to the subluminal velocities of muons of the order of $\sim3$ ns at all zenith angles. Finally, changes introduced in the logarithmic slope of the lateral density function are less than 2%.
  • PDF
    The Defense Advanced Research Projects Agency (DARPA) Real-time Adversarial Intelligence and Decision-making (RAID) program is investigating the feasibility of "reading the mind of the enemy" - to estimate and anticipate, in real-time, the enemy's likely goals, deceptions, actions, movements and positions. This program focuses specifically on urban battles at echelons of battalion and below. The RAID program leverages approximate game-theoretic and deception-sensitive algorithms to provide real-time enemy estimates to a tactical commander. A key hypothesis of the program is that these predictions and recommendations will make the commander more effective, i.e. he should be able to achieve his operational goals safer, faster, and more efficiently. Realistic experimentation and evaluation drive the development process using human-in-the-loop wargames to compare humans and the RAID system. Two experiments were conducted in 2005 as part of Phase I to determine if the RAID software could make predictions and recommendations as effectively and accurately as a 4-person experienced staff. This report discusses the intriguing and encouraging results of these first two experiments conducted by the RAID program. It also provides details about the experiment environment and methodology that were used to demonstrate and prove the research goals.
  • PDF
    The metal-insulator transitions and the intriguing physical properties of rare-earth perovskite nickelates have attracted considerable attention in recent years. Nonetheless, a complete understanding of these materials remains elusive. Here, taking a NdNiO3 thin film as a representative example, we utilize a combination of x-ray absorption and resonant inelastic x-ray scattering (RIXS) spectroscopies to resolve important aspects of the complex electronic structure of the rare-earth nickelates. The unusual coexistence of bound and continuum excitations observed in the RIXS spectra provides strong evidence for the abundance of oxygen 2p holes in the ground state of these materials. Using cluster calculations and Anderson impurity model interpretation, we show that these distinct spectral signatures arise from a Ni 3d8 configuration along with holes in the oxygen 2p valence band, confirming suggestions that these materials do not obey a conventional positive charge-transfer picture, but instead exhibit a negative charge-transfer energy, in line with recent models interpreting the metal to insulator transition in terms of bond disproportionation.
  • PDF
    The Colouring problem is that of deciding whether a given graph $G$ admits a (proper) $k$-colouring for a given integer $k$. A graph is $(H_1,H_2)$-free for a pair of graphs $H_1,H_2$ if it contains no induced subgraph isomorphic to $H_1$ or $H_2$. We continue a study into the complexity of Colouring for $(H_1,H_2)$-free graphs. The complement $\bar{H}$ of a graph $H$ has the same vertices as $H$ and an edge between two distinct vertices if and only if these vertices are not adjacent in $H$. By combining new and known results we classify the computational complexity of Colouring for $(H,\bar{H})$-free graphs except when $H=sP_1+P_3$ or $H=sP_1+P_4$ for $s\geq 2$. We also show that these are the only open cases when considering all bigenic graph classes closed under complementation.
  • PDF
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Recent comments

Valentin Zauner-Stauber Jul 18 2016 09:54 UTC

Conjugate Gradient IS a Krylov-space method...

Renato Renner Jul 09 2016 06:29 UTC

I am afraid that you may have misunderstood my previous answer. I did not at all mean to claim that we *cannot* apply QM to brains. Rather, my point was that F1, after she prepared the electron, *doesn't need to* include her own brain in her analysis (especially because she will no longer interact w

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Tony Sudbery Jul 08 2016 19:25 UTC

Where is it written that quantum mechanics cannot be applied to brains? And if it is so written, how is it possible to have measurements like those that you assign to Wigner and his assistant? Indeed, we don't (yet) apply QM to our brains, because we don't have sufficient knowledge or computing powe

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Renato Renner Jul 08 2016 06:07 UTC

I completely agree with your analysis, which describes the gedankenexperiment from a global (“outside”) perspective, according to the laws of Bohmian Mechanics (BM). And, indeed, it shows that the "memory" of a measurement outcome cannot assumed to be permanent, i.e., it may change (according to BM)

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Tony Sudbery Jul 02 2016 19:09 UTC

Roger Colbeck drew our attention to this paper in the York QFIT group, and we met to discuss it last week. I would like to comment on the relation of Bohmian quantum mechanics to the extended Wigner's friend experiment. As generalised by John Bell, Bohmian qm can be applied to this experiment to yie

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Lidia del Rio Jun 26 2016 17:51 UTC

Video of a *journal club* session about this paper, with a 20min explanation of the paradox and a long audience discussion about implications and interpretations:

http://pirsa.org/16060101/

Notation: "for simplicity" some names have been revealed (like the friends'), some have been changed

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Mario Jun 08 2016 06:58 UTC

Too bad, the paper has been withdrawn due to a mistake :-/

Renato Renner Jun 07 2016 16:09 UTC

Maybe. But if your solution was not only self-consistent but also "single-world" then it would certainly contradict our theorem.

@Jonathan: I am unsure whether the concern you had is related to ($\star$), too, but I would anyway be interested to know whether it is this type of statements that you

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André Xuereb Jun 06 2016 08:07 UTC

If I had a self-consistent answer to that I'd be able to solve the measurement problem, no?

Renato Renner Jun 06 2016 07:47 UTC

OK, then we have identified the point where we disagree. Our assumption (QT) indeed corresponds to ($\star$), together with the understanding that the state is physically relevant.

Having said this, I am still curious to know how, in your opinion, experimenter F1 should derive her prediction ab

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