ShantanuT01/DACTYL-Pretraining huggingface dataset:
We use the star formation history map of the Large Magellanic Cloud to study the sites of the eight smallest (and presumably youngest) supernova remnants in the Cloud: SN 1987A, N158A, N49, and N63A (core collapse remnants), 0509-67.5, 0519-69.0, N103B, and DEM L71 (Type Ia remnants). The local star formation histories provide unique insights into the nature of the supernova progenitors, which we compare with the properties of the supernova explosions derived from the remnants themselves and from supernova light echoes. We find that all the core collapse supernovae that we have studied are associated with vigorous star formation in the recent past. Stars more massive than 21.5Msun are very scarce around SNR N49, implying that the magnetar SGR 0526-66 in this SNR was either formed elsewhere or came from a progenitor with a mass well below the 30Msun threshold suggested in the literature. Three of our four Ia SNRs are associated with old, metal poor stellar populations. This includes SNR 0509-67.5, which is known to have been originated by an extremely bright Type Ia event, and yet is located very far away from any sites of recent star formation, in a population with a mean age of 7.9 Gyr. The Type Ia SNR N103B, on the other hand, is associated with recent star formation, and might have had a relatively younger and more massive progenitor with substantial mass loss before the explosion. We discuss these results in the context of our present understanding of core collapse and Type Ia supernova progenitors.
We investigate $2d$ sigma-models with a $2+N$ dimensional Minkowski signature target space metric and Killing symmetry, specifically supersymmetrized, and see under which conditions they might lead to corresponding exact string vacua. It appears that the issue relies heavily on the properties of the vector $M_{\mu}$, a reparametrization term, which needs to possess a definite form for the Weyl invariance to be satisfied. We give, in the $n = 1$ supersymmetric case, two non-renormalization theorems from which we can relate the $u$ component of $M_{\mu}$ to the $\beta^G_{uu}$ function. We work out this $(u,u)$ component of the $\beta^G$ function and find a non-vanishing contribution at four loops. Therefore, it turns out that at order $\alpha^{\prime 4}$, there are in general non-vanishing contributions to $M_u$ that prevent us from deducing superstring vacua in closed form.
Fracture in aluminum alloys with precipitates involves at least two mechanisms, namely, ductile fracture of the aluminum-rich matrix and brittle fracture of the precipitates. In this work, a coupled crystal plasticity-phase field model for mixed ductile-brittle failure modes is formulated and used to investigate the effect of precipitate morphology and size distribution on damage evolution in aluminum alloys. A thermodynamically consistent framework for elastic and plastic work dissipation in the fracture process zone is used to formulate the coupled constitutive behavior for brittle and ductile damage, respectively. Representative Volume Elements (RVE) with varying particle morphology and orientation were generated and their uni-axial loading was simulated to assess the damage resistance of the different model microstructures. For critical energy release rate $G_c$ values of the aluminum matrix ranging from 4 to 8~Jm$^{-2}$ for single crystals and from 10 to 16~Jm$^{-2}$ for polycrystals, the model predicts a change in failure modes from particle debonding to cracking followed by ductile matrix failure. The change of particle failure mechanism as a result of increased $G_c$ is observed for both, single and polycrystalline model microstructures. For the case of particle debonding, microstructures with circular or ellipsoidal particles (with the major axis perpendicular to the loading direction) show a higher ductility and fracture work compared to the other studied cases. In the case of particle cracking, microstructures with ellipsoidal particles aligned parallel to the loading axis show a higher ductility and fracture work among the investigated cases. The simulations qualitatively reproduce the experimentally observed particle failure mechanisms (cracking and debonding) for two different matrix alloy classes (commercially pure and 2xxx alloys) reinforced with ceramic particles.
A model is suggested for the bursting x-ray pulsar, GRO J1744-28, where the compact object of the pulsar is proposed to be a strange matter star with crusts. It is suggested that two envelope crusts shield the two polar caps of an accreting strange stars owing to strong polar-cap magnetic fields. GRO J1744-28 might not have a unified crust, but two polar crusts. Bursts are the result of phase transition of polar crusts if one accreting crust is heavier than that the Coulomb force can support. It is shown that there should be phase-lags when bursting crust is expanded about one hundred meters. Many calculated properties of this model, such as the phase-lags, the bursting luminosity and the characteristic photon energy, are in agreement with observations.
One-shot face recognition measures the ability to identify persons with only seeing them at one glance, and is a hallmark of human visual intelligence. It is challenging for conventional machine learning approaches to mimic this way, since limited data are hard to effectively represent the data variance. The goal of one-shot face recognition is to learn a large-scale face recognizer, which is capable to fight off the data imbalance challenge. In this paper, we propose a novel generative adversarial one-shot face recognizer, attempting to synthesize meaningful data for one-shot classes by adapting the data variances from other normal classes. Specifically, we target at building a more effective general face classifier for both normal persons and one-shot persons. Technically, we design a new loss function by formulating knowledge transfer generator and a general classifier into a unified framework. Such a two-player minimax optimization can guide the generation of more effective data, which effectively promote the underrepresented classes in the learned model and lead to a remarkable improvement in face recognition performance. We evaluate our proposed model on the MS-Celeb-1M one-shot learning benchmark task, where we could recognize 94.98% of the test images at the precision of 99% for the one-shot classes, keeping an overall Top1 accuracy at $99.80\%$ for the normal classes. To the best of our knowledge, this is the best performance among all the published methods using this benchmark task with the same setup, including all the participants in the recent MS-Celeb-1M challenge at ICCV 2017\footnote{http://www.msceleb.org/challenge2/2017}.
The notion of information scrambling is tied to the long-time behaviour of a system and therefore is related to its infra-red dynamics. In fast scramblers, information spreads as a logarithmic function of the number of degrees of freedom. Ordinarily, under an RG-flow, scrambling will become faster in the IR since many degrees of freedom are integrated out, as a consequence of the c-theorem. In this article, we consider a class of Holographic quantum field theories (QFT), which are strongly coupled large $N$ gauge theories with large number of adjoint and fundamental matter, in which scrambling slows down in the IR. This happens since more degrees of freedom are inserted in the IR, compared to the UV, in a precise sense. For generic large $N$ gauge theories, we also explore general, perturbative flow features of the corresponding Lyapunov exponent, based on the Callan-Symanzik equation.
In this note we show that the degree of the interpolation polynomial for equidistant base points is characterized by the regularity of matrices of combinatorical type.
The magnification effect due to gravitational lensing enhances the chances of detecting moderate-redshift ($z \sim 1$) sources in very-high-energy (VHE; $E > 100$ GeV) $\gamma$-rays by ground-based Atmospheric Cherenkov Telescope facilities. It has been shown in previous work that this prospect is not hampered by potential $\gamma-\gamma$ absorption effects by the intervening (lensing) galaxy, nor by any individual star within the intervening galaxy. In this paper, we expand this study to simulate the light-bending effect of a realistic ensemble of stars. We first demonstrate that, for realistic parameters of the galaxy's star field, it is extremely unlikely (probability $\lesssim 10^{-6}$) that the direct line of sight between the $\gamma$-ray source and the observer passes by any star in the field close enough to be subject to significant $\gamma\gamma$ absorption. Our simulations then focus on the rare cases where $\gamma\gamma$ absorption by (at least) one individual star might be non-negligible. We show that gravitational light bending will have the effect of avoiding the $\gamma-\gamma$ absorption spheres around massive stars in the intervening galaxy. This confirms previous results by Barnacka et al. and re-inforces arguments in favour of VHE $\gamma$-ray observations of lensed moderate-redshift blazars to extend the redshift range of objects detected in VHE $\gamma$-rays, and to probe the location of the $\gamma$-ray emission region in those blazars.
Cosmic strings are predicted to form kinks and cusps that travel along the string at close to the speed of light. These disturbances are radiated away as highly beamed gravitational waves that produce a burst like pulse as the cone of emission sweeps past an observer. Gravitational wave detectors such as the Laser Interferometer Space Antenna (LISA) and the Laser Interferometer Gravitational wave Observatory (LIGO) will be capable of detecting these bursts for a wide class of string models. Such a detection would illuminate the fields of string theory, cosmology, and relativity. Here we develop template based Markov Chain Monte Carlo (MCMC) techniques that can efficiently detect and characterize the signals from cosmic string cusps. We estimate how well the signal parameters can be recovered by the advanced LIGO-Virgo network and the LISA detector using a combination of MCMC and Fisher matrix techniques. We also consider joint detections by the ground and space based instruments. We show that a parallel tempered MCMC approach can detect and characterize the signals from cosmic string cusps, and we demonstrate the utility of this approach on simulated data from the third round of Mock LISA Data Challenges (MLDCs).
Recent observations by the Fermi satellite suggest that a photosphere emission component is contributing to the observed spectrum of many GRBs. One important question is whether the photosphere component can interpret the typical ``Band'' function of GRBs with a typical low energy photon spectral index $\alpha \sim -1$. We perform a detailed study of the photosphere emission spectrum by progressively introducing several physical ingredients previously not fully incorporated, including the probability distribution of the location of a dynamically evolving photosphere, superposition of emission from an equal-arrival-time ``volume'' in a continuous wind, the evolution of optical depth of a wind with finite but evolving outer boundary, as well as the effect of different top-hat wind luminosity ($L_w$) profiles. By assuming a co-moving blackbody spectrum emerging from the photosphere, we find that for an outflow with a constant or increasing $L_w$, the low-energy spectrum below the peak energy ($E_{p}$), can be modified to $F_\nu \sim \nu^{1.5}$ ($\alpha \sim +0.5$). A softer ($-1<\alpha<+0.5$) or flat ($\alpha=-1$) spectrum can be obtained during the $L_w$ decreasing phase or high-latitude-emission-dominated phase. We also study the evolution of $E_{p}$ as a function of wind and photosphere luminosity in this photosphere model. An $E_p-L$ tracking pattern can be reproduced if a certain positive dependence between the dimensionless entropy $\eta$ and $L_w$ is introduced. However, the hard-to-soft evolution pattern cannot be reproduced unless a contrived condition is invoked. In order to interpret the Band spectrum, a more complicated photosphere model or a different energy dissipation and radiation mechanism are needed.
In this work, we present galaxy stellar and baryonic (stars plus cold gas) mass functions (SMF and BMF) and their halo mass dependence for two volume-limited data sets. The first, RESOLVE-B, coincides with the Stripe 82 footprint and is extremely complete down to baryonic mass Mbary ~ 10^9.1 Msun, probing the gas-rich dwarf regime below Mbary ~ 10^10 Msun. The second, ECO, covers a ~40 times larger volume (containing RESOLVE-A) and is complete to Mbary ~ 10^9.4 Msun. To construct the SMF and BMF we implement a new "cross-bin sampling" technique with Monte Carlo sampling from the full likelihood distributions of stellar or baryonic mass. Our SMFs exhibit the "plateau" feature starting below Mstar ~ 10^10 Msun that has been described in prior work. However, the BMF fills in this feature and rises as a straight power law below ~10^10 Msun, as gas-dominated galaxies become the majority of the population. Nonetheless, the low-mass slope of the BMF is not as steep as that of the theoretical dark matter halo MF. Moreover, we assign group halo masses by abundance matching, finding that the SMF and BMF separated into four physically motivated halo mass regimes reveal complex structure underlying the simple shape of the overall MFs. In particular, the satellite MFs are depressed below the central galaxy MF "humps" in groups with mass <10^13.5 Msun yet rise steeply in clusters. Our results suggest that satellite destruction and/or stripping are active from the point of nascent group formation. We show that the key role of groups in shaping MFs enables reconstruction of a given survey's SMF or BMF based on its group halo mass distribution.
In this article the interaction of branes at angles with respect to each other with non-zero internal gauge fields are calculated by construction of the boundary states in spacetime in which some of its directions are compact on tori. The interaction depends on both angle and fields.
We discuss the possibility of implementing axiomatic nonextensive statistics, where it is conjectured that the phase-space volume determines the (non)extensive entropy, on the particle production at NICA energies. Both Boltzmann-Gibbs and Tsallis statistics are very special cases of this generic (non)extensivity. We conclude that the lattice thermodynamics is {\it ab initio} extensive and additive and thus the nonextensive approaches including Tsallis statistics categorically are not matching with them, while the particle production, for instance the particle ratios at various center-of-mass energies, is likely a nonextensive process but certainly not of Tsallis type. The resulting freezeout parameters, the temperature and the chemical potentials, are approximately compatible with the ones deduced from Boltzmann-Gibbs statistics.
The surface critical behavior of bcc binary alloys undergoing a continuous B2-A2 order-disorder transition is investigated in the mean-field (MF) approximation. Our main aim is to provide clear evidence for the fact that surfaces which break the two-sublattice symmetry generically display the critical behavior of the NORMAL transition, whereas symmetry-preserving surfaces exhibit ORDINARY surface critical behavior. To this end we analyze the lattice MF equations for both types of surfaces in terms of nonlinear symplectic maps and derive a Ginzburg-Landau model for the symmetry-breaking (100) surface. The crucial feature of the continuum model is the emergence of an EFFECTIVE ORDERING (``staggered'') SURFACE FIELD, which depends on temperature and the other lattice model parameters, and which explains the appearance of NORMAL critical behavior for symmetry-breaking surfaces.
The convergence problem for scattering states is studied in detail within the framework of the Algebraic Model, a representation of the Schrodinger equation in an L^2 basis. The dynamical equations of this model are reformulated featuring new "Dynamical Coefficients", which explicitly reveal the potential effects. A general analysis of the Dynamical Coefficients leads to an optimal basis yielding well converging, precise and stable results. A set of strategies for solving the equations for non-optimal bases is formulated based on the asymptotic behaviour of the Dynamical Coefficients. These strategies are shown to provide a dramatically improved convergence of the solutions.
$\Kbar N$ interactions are investigated {\it via} an effective non-linear chiral meson-baryon Lagrangian. The adjustable parameters are determined by a fitting procedure on the $K^-p$ threshold branching ratios and total cross-section data for $p^{lab}_K\le$ 250 MeV/c. We produce predictions for the $\Sigma \pi$ mass spectrum, and scattering lenghts $a_{K^-p}$, $a_n(K^-n \to K^-n)$, $a_n0(\Kbar0 n \to \Kbar0 n)$, and $a_{ex}(K^-p \to \Kbar0 n)$. The $\Kbar N$ amplitudes thus obtained, as well as those for other two-body channels ($\pi N$, $NN$, and $YN$) are used as input to predict the scattering length $A_{K^-d}$, for which we have devised a relativistic version of the three-body Faddeev equations. Results for all two- and three-body coupled channels are reported both in isospin and particle bases. All available $\Kbar N$ data are well reproduced and our best results for the $K^-p$ and $K^-d$ scattering lenghts are $a_{K^-p} = (-0.90 + i 0.87) fm$ and $A_{K^-d} = (-1.80 + i 1.55) fm$.
This paper describes an efficient approach for human face recognition based on blood perfusion data from infra-red face images. Blood perfusion data are characterized by the regional blood flow in human tissue and therefore do not depend entirely on surrounding temperature. These data bear a great potential for deriving discriminating facial thermogram for better classification and recognition of face images in comparison to optical image data. Blood perfusion data are related to distribution of blood vessels under the face skin. A distribution of blood vessels are unique for each person and as a set of extracted minutiae points from a blood perfusion data of a human face should be unique for that face. There may be several such minutiae point sets for a single face but all of these correspond to that particular face only. Entire face image is partitioned into equal blocks and the total number of minutiae points from each block is computed to construct final vector. Therefore, the size of the feature vectors is found to be same as total number of blocks considered. For classification, a five layer feed-forward backpropagation neural network has been used. A number of experiments were conducted to evaluate the performance of the proposed face recognition system with varying block sizes. Experiments have been performed on the database created at our own laboratory. The maximum success of 91.47% recognition has been achieved with block size 8X8.
We propose a generalization of classical statistical mechanics which describes the behavior of dissipative systems placed in contact with a heat bath. In contrast to conventional statistical mechanics, which assigns probabilities to the states of the system, the generalized theory assigns probabilities to the trajectories of the system. The conditional probability of pairs of states at two different times is given by a path integral. We present two simple analytically-tractable examples which illustrate the predicted effect of temperature on the mean trajectories, hysteresis and drift of the system.
We give a backward jeu de taquin slide analogue on semistandard reverse composition tableaux. These tableaux were first studied by Haglund, Luoto, Mason and van Willigenburg when defining quasisymmetric Schur functions. Our algorithm for performing backward jeu de taquin slides on semistandard reverse composition tableaux results in a natural operator on compositions that we call the jdt operator. This operator in turn gives rise to a new poset structure on compositions whose maximal chains we enumerate. As an application, we also give a noncommutative Pieri rule for noncommutative Schur functions that uses the jdt operators.
It has been suggested by Weinberg et al. (2013) that an instability due to the nonlinear coupling of a neutron star's tide to its $p$- and $g$-modes could affect the gravitational-wave phase evolution of a neutron-star binary. Weinberg (2016) suggests that this instability can turn on as the gravitational-waves pass through the sensitive band of ground-based detectors, although the size of the effect is not known. The discovery of the binary neutron star merger GW170817 provides an opportunity to look for evidence of nonlinear tides from $p$-$g$ mode coupling. We compute Bayesian evidences that compare waveform models that include the $p$-$g$ mode coupling to models that do not. Using the waveform model and priors of Essick et al. (2016) we find that the observation of GW170817 is consistent with $p$-$g$ mode coupling, in agreement with Abbott, B. P., et al. (2019a). We investigate the properties of the model and find that this consistency is due to degeneracy in a large region of the parameter space between the the model that includes nonlinear tides from $p$-$g$ mode coupling and the standard post-Newtonian model that does not. We investigate the consistency of the GW170817 signal with regions of the parameter space where the effect of nonlinear tides is not degenerate with the standard model. Regions of the nonlinear tide parameter space that have a fitting factor of less than 99$\%$ (98.5$\%$) are disfavored by a Bayes factor of 15 (25). We conclude that the consistency of the GW170817 signal with the model of Essick et al. (2016) is due to parameter degeneracy and that regions where nonlinear tides produce a measurable effect are strongly disfavored.
We present 8-13 micron spectra of eight red giants in the globular cluster 47 Tucanae (NGC 104), obtained at the European Southern Observatory 3.6m telescope. These are the first mid-infrared spectra of metal-poor, low-mass stars. The spectrum of at least one of these, namely the extremely red, large-amplitude variable V1, shows direct evidence of circumstellar grains made of amorphous silicate.
Cellular Automata are discrete--time dynamical systems on a spatially extended discrete space which provide paradigmatic examples of nonlinear phenomena. Their stochastic generalizations, i.e., Probabilistic Cellular Automata, are discrete time Markov chains on lattice with finite single--cell states whose distinguishing feature is the \textit{parallel} character of the updating rule. We review some of the results obtained about the metastable behavior of Probabilistic Cellular Automata and we try to point out difficulties and peculiarities with respect to standard Statistical Mechanics Lattice models.
Let p be a prime number, F a totally real field such that [F(mu_p): F]=2 and [F:Q] is odd. For delta \in F^times, let [delta] denote its class in F^times/F^{times p}. In this paper, we show   Main Theorem. There are infinitely many classes [delta]\in F^times/F^{times p} such that the twisted affine Fermat curves W_delta: X^p+Y^p=delta have no F-rational points.
We consider one dimensional diffusive search strategies subjected to external potentials. The location of a single target is drawn from a given probability density function (PDF) $f_G(x)$ and is fixed for each stochastic realization of the process. We optimize the quality of the search strategy as measured by the mean first passage time (MFPT) to the position of the target. For a symmetric but otherwise arbitrary distribution $f_G(x)$ we find the optimal potential that minimizes the MFPT. The minimal MFPT is given by a nonstandard measure of the dispersion, which can be related to the cumulative R\'enyi entropy. We compare optimal times in this model with optimal times obtained for the model of diffusion with stochastic resetting, in which the diffusive motion is interrupted by intermittent jumps (resets) to the initial position. Additionally, we discuss an analogy between our results and a so-called square-root principle.