ShantanuT01/DACTYL-Pretraining huggingface dataset:
ABRIDGED-This paper presents the first direct estimate of the 3D clustering properties of far-infrared sources up to z~3. This has been possible thanks to the Pacs Evolutionary Probe (PEP) survey of the GOODS South field performed with the PACS instrument onboard the Herschel Satellite. An analysis of the two-point correlation function over the whole redshift range spanned by the data reports for the correlation length, r_0~6.3 Mpc and r_0~6.7 Mpc, respectively at 100um and 160um, corresponding to dark matter halo masses M>~10^{12.4} M_sun. Objects at z~2 instead seem to be more strongly clustered, with r_0~19 Mpc and r_0~17 Mpc in the two considered PACS channels. This dramatic increase of the correlation length between z~1 and z~2 is connected with the presence of a wide, M>~10^{14} M_sun, filamentary structure which includes more than 50% of the sources detected at z~2. An investigation of the properties of such sources indicates the possibility for boosted star-forming activity in those which reside within the overdense environment with respect of more isolated galaxies found in the same redshift range. Lastly, we also present our results on the evolution of the relationship between luminous and dark matter in star-forming galaxies between z~1 and z~2. We find that the increase of (average) stellar mass in galaxies <M*> between z~1 and z~2 is about a factor 10 lower than that of the dark matter haloes hosting such objects (<M*>[z~1]/<M*>[z~2] ~ 0.4 vs M_{halo}[z~1]/M_{halo}[z~2] ~ 0.04). Our findings agree with the evolutionary picture of downsizing whereby massive galaxies at z~2 were more actively forming stars than their z~1 counterparts, while at the same time contained a lower fraction of their mass in the form of luminous matter.
We study, numerically and analytically, the stability of synchronization for an ensemble of coupled phase oscillators with attractive and repulsive interactions, as a function of the number of repulsive couplings and their intensity. Scaling properties of the desynchronization transition are disclosed, and it is shown that unsynchronized configurations have different symmetries depending on the intensity of the repulsive interaction. The concept of frustration minimization helps understanding the main features observed in the collective dynamics of the oscillator network.
Traditional anomaly detection methods focus on detecting inter-class variations while medical image novelty identification is inherently an intra-class detection problem. For example, a machine learning model trained with normal chest X-ray and common lung abnormalities, is expected to discover and flag idiopathic pulmonary fibrosis which a rare lung disease and unseen by the model during training. The nuances from intra-class variations and lack of relevant training data in medical image analysis pose great challenges for existing anomaly detection methods. To tackle the challenges, we propose a hybrid model - Transformation-based Embedding learning for Novelty Detection (TEND) which without any out-of-distribution training data, performs novelty identification by combining both autoencoder-based and classifier-based method. With a pre-trained autoencoder as image feature extractor, TEND learns to discriminate the feature embeddings of in-distribution data from the transformed counterparts as fake out-of-distribution inputs. To enhance the separation, a distance objective is optimized to enforce a margin between the two classes. Extensive experimental results on both natural image datasets and medical image datasets are presented and our method out-performs state-of-the-art approaches.
We review basic examples of classical string solutions in AdS5xS5. We concentrate on simplest rigid closed string solutions of circular or folded type described by integrable 1-d Neumann system but mention also various generalizations and related open-string solutions.
A ``friends-of-friends'' percolation algorithm has been used to extract a catalogue of dn/n = 80 density enhancements (groups) from the six slices of the Las Campanas Redshift Survey (LCRS). The full catalogue contains 1495 groups and includes 35% of the LCRS galaxy sample. A clean sample of 394 groups has been derived by culling groups from the full sample which either are too close to a slice edge, have a crossing time greater than a Hubble time, have a corrected velocity dispersion of zero, or contain a 55-arcsec ``orphan'' (a galaxy with a mock redshift which was excluded from the original LCRS redshift catalogue due to its proximity to another galaxy -- i.e., within 55 arcsec). Median properties derived from the clean sample include: line-of-sight velocity dispersion sigma_los = 164km/s, crossing time t_cr = 0.10/H_0, harmonic radius R_h = 0.58/h Mpc, pairwise separation R_p = 0.64/h Mpc, virial mass M_vir = (1.90x10^13)/h M_sun, total group R-band luminosity L_tot = (1.30x10^11)/h^2 L_sun, and R-band mass-to-light ratio M/L = 171h M_sun/L_sun; the median number of observed members in a group is 3.
We consider properties of the box polynomials, a one variable polynomial defined over all integer partitions $\lambda$ whose Young diagrams fit in an $m$ by $n$ box. We show that these polynomials can be expressed by the finite difference operator applied to the power $x^{m+n}$. Evaluating box polynomials yields a variety of identities involving set partition enumeration. We extend the latter identities using restricted growth words and a new operator called the fast Fourier operator, and consider connections between set partition enumeration and the chromatic polynomial on graphs. We also give connections between the box polynomials and the excedance matrix, which encodes combinatorial data from a noncommutative quotient algebra motivated by the recurrence for the excedance set statistic on permutations.
In this brief note I draw attention to the structure of the stationary Halpha line as a function of orbital phase, using a data set compiled over some years. These data are compared with a set of nightly observations over some 2 orbits. The structures are very similar and this is highly relevant to the question of persistence and stability of spectral features which have been attributed to a circumbinary disk.
For a semistable family of varieties over a curve in characteristic $p$, we prove the existence of a "Clemens-Schmid type" long exact sequence for the $p$-adic cohomology. The cohomology groups appearing in such a long exact sequence are defined locally
We discuss strangeness production in heavy-ion collisions in the broad energy range --- from SIS energies through AGS-SPS-RHIC and upto LHC energies. On several examples we demonstrate how the strange particle production can reveal information about the collision dynamics and about possible modifications of particle properties in medium. In particular the production of hadrons containing two and more strange quarks, like $\Xi$ and $\Omega$ baryons, or a $\phi$ meson is of interest. We conduct our discussion in the framework of the minimal statistical model, in which the total strangeness yield is fixed by the $K^+$ multiplicity. It is emphasized that in collisions with a small number of produced strange particles, the exact strangeness conservation in each collision event must be explicitly preserved.
We study the overall spin precession frequency of a test gyroscope around a neutron star. The precession of the test gyroscope gives the signatures of the general relativistic effects that are present in the region of strong gravity of an NS. Using a numerical code, we find the precession of the test gyroscope for a rotating and a strongly magnetized neutron star. The magnetic field distribution inside the neutron star is assumed either to be poloidal or toroidal. The overall spin precession rate is obtained by setting the orbital frequency of the gyroscope to a non-zero value but restricted to a time-like observer. The gyro frequency differs depending on the central object being a black hole or a neutron star. For neutron star, the gyro precession can even be calculated inside the star. We find that the gyroscope precession frequency depends on the stars mass, rotation rate, and magnetic field configuration.
This work presents a new method to compute time and wavelength dependent center-to-limb brightness distributions for Classical Cepheids. Our model atmospheres are based on second-order accurate 1-D hydrodynamic calculations, performed in spherical geometry. The brightness intensity distributions, and the resulting limb darkening, are computed through the dynamic atmospheres by using a full set of atomic and molecular opacities. Our results confirm important differences with respect to equivalent hydrostatic models. The amount of limb darkening, and the shape of the limb profiles, show a strong dependence on the pulsational phase of the Cepheid, which cannot be reproduced by static models. Non-linear effects in our hydrodynamic equations add a new level of complexity in the wavelength dependence of our limb profiles, which are affected by the presence of shock-waves traveling through the atmosphere. These effects, already detectable by present-day interferometers, should be taken into consideration when deriving limb darkened diameters for nearby Cepheids with the accuracy required to measure their radial pulsations.
In this note, we discuss the low and high temperature contribution of Thermodynamic Bethe Ansatz (TBA) dressed excitations in the thermodynamics and energy-magnetization relaxation within the Generalized Hydrodynamics approach in the linear response regime. In particular, we show how the temperature dependent dispersions of the excitations reproduce well known behavior of the specific heat, magnetic susceptibility, spin and energy Drude weights. In this context, we derive a further formulation of the Drude weights from the finite wavevector relaxation. Furthermore, we contrast the TBA description of thermodynamics and dynamics in terms of a multitude of string excitations to that in terms of a single quasi-particle in low energy effective theories.
We study sandpile models with stochastic toppling rules and having sticky grains so that with a non-zero probability no toppling occurs, even if the local height of pile exceeds the threshold value. Dissipation is introduced by adding a small probability of particle loss at each toppling. Generically, for models with a preferred direction, the avalanche exponents are those of critical directed percolation clusters. For undirected models, avalanche exponents are those of directed percolation clusters in one higher dimension.
In this work first we consider a physiologically structured population model with a distributed recruitment process. That is, our model allows newly recruited individuals to enter the population at all possible individual states, in principle. The model can be naturally formulated as a first order partial integro-differential equation, and it has been studied extensively. In particular, it is well-posed on the biologically relevant state space of Lebesgue integrable functions. We also formulate a delayed integral equation (renewal equation) for the distributed birth rate of the population. We aim to illustrate the connection between the partial integro-differential and the delayed integral equation formulation of the model utilising a recent spectral theoretic result. In particular, we consider the equivalence of the steady state problems in the two different formulations, which then leads us to characterise irreducibility of the semigroup governing the linear partial integro-differential equation. Furthermore, using the method of characteristics, we investigate the connection between the time dependent problems. In particular, we prove that any (non-negative) solution of the delayed integral equation determines a (non-negative) solution of the partial differential equation and vice versa. The results obtained for the particular distributed states at birth model then lead us to present some very general results, which establish the equivalence between a general class of partial differential and delay equation, modelling physiologically structured populations.
This paper is devoted to find out cylindrically symmetric kinematic self-similar perfect fluid and dust solutions. We study the cylindrically symmetric solutions which admit kinematic self-similar vectors of second, zeroth and infinite kinds, not only for the tilted fluid case but also for the parallel and orthogonal cases. It is found that the parallel case gives contradiction both in perfect fluid and dust cases. The orthogonal perfect fluid case yields a vacuum solution while the orthogonal dust case gives contradiction. It is worth mentioning that the tilted case provides solution both for the perfect as well as dust cases.
In this paper, we consider the lifespan of solution to the MHD boundary layer system as an analytic perturbation of general shear flow. By using the cancellation mechanism in the system observed in \cite{LXY1}, the lifespan of solution is shown to have a lower bound in the order of $\varepsilon^{-2+}$ if the strength of the perturbation is of the order of $\varepsilon$. Since there is no restriction on the strength of the shear flow and the lifespan estimate is larger than the one obtained for the classical Prandtl system in this setting, it reveals the stabilizing effect of the magnetic field on the electrically conducting fluid near the boundary.
The Chan-Vese (CV) model is a classic region-based method in image segmentation. However, its piecewise constant assumption does not always hold for practical applications. Many improvements have been proposed but the issue is still far from well solved. In this work, we propose an unsupervised image segmentation approach that integrates the CV model with deep neural networks, which significantly improves the original CV model's segmentation accuracy. Our basic idea is to apply a deep neural network that maps the image into a latent space to alleviate the violation of the piecewise constant assumption in image space. We formulate this idea under the classic Bayesian framework by approximating the likelihood with an evidence lower bound (ELBO) term while keeping the prior term in the CV model. Thus, our model only needs the input image itself and does not require pre-training from external datasets. Moreover, we extend the idea to multi-phase case and dataset based unsupervised image segmentation. Extensive experiments validate the effectiveness of our model and show that the proposed method is noticeably better than other unsupervised segmentation approaches.
The bilateral series corresponding to many of the third-, fifth-, sixth- and eighth order mock theta functions may be derived as special cases of $_2\psi_2$ series \[ \sum_{n=-\infty}^{\infty}\frac{(a,c;q)_n}{(b,d;q)_n}z^n. \] Three transformation formulae for this series due to Bailey are used to derive various transformation and summation formulae for both these mock theta functions and the corresponding bilateral series. \\ New and existing summation formulae for these bilateral series are also used to make explicit in a number of cases the fact that for a mock theta function, say $\chi(q)$, and a root of unity in a certain class, say $\zeta$, that there is a theta function $\theta_{\chi}(q)$ such that \[ \lim_{q \to \zeta}(\chi(q) - \theta_{\chi}(q)) \] exists, as $q \to \zeta$ from within the unit circle.
We present a numerical algorithm for predicting the optimal conditions for the effective alignment of magnetic particles in dense powders during the compactization process using an externally applied field. This task is especially important for the permanent magnets development due to the fact that alignment of anisotropy axes of nanocomposite grains increases both remanence and coercivity of magnetic materials. In contrast to previously known methods where magnetic moment of each particle was assumed to be 'fixed' with respect to the particle itself, our approach takes into account the (field-dependent) deviation of this moment from the particle anisotropy axis that occurs even for magnetically 'hard' particles possessing a strong mechanical contact. We show, that this deviation leads to the existence of the optimal value of the applied field for which the particle orientation (or alignment) time is minimal. The influence of the external pressure and internal mechanical friction on the details of the compactization/orientation process is also studied.
We classify elliptic curves over the rationals whose N\'eron model over the integers is semi-abelian, with good reduction at p=2, and whose Mordell--Weil group contains an element of order two that stays non-trivial at p=2. Furthermore, we describe those curves where the element of order two is narrow, or where another element of order two exists, and also express our findings in terms of Deligne--Mumford stacks of pointed curves of genus one.
The interband and intraband conductivities of doped graphene were theoretically investigated beyond the linear response. The new dependences of induced currents on frequency and amplitude of external electric field, the graphene temperature and chemical potential were determined for sufficiently strong electric fields in the resonant approximation. Particularly, the saturation of induced currents and the non-linear increase of optical transparency with wave intensity growth were obtained for arbitrary temperatures and doped situation. As contrasted to increase for the interband transitions at fixed intensity, the transmission coefficient of intraband mechanism decreases with rise of the chemical potential and temperature.
The small-scale environment characterized by the local density is known to play a crucial role in deciding the galaxy properties but the role of large-scale environment on galaxy formation and evolution still remain a less clear issue. We propose an information theoretic framework to investigate the influence of large-scale environment on galaxy properties and apply it to the data from the Galaxy Zoo project which provides the visual morphological classifications of $\sim 1$ million galaxies from the Sloan Digital Sky Survey. We find a non-zero mutual information between morphology and environment which decreases with increasing length scales but persists throughout the entire length scales probed. We estimate the conditional mutual information and the interaction information between morphology and environment by conditioning the environment on different length scales and find a synergic interaction between them which operates upto at least a length scales of $ \sim 30 \, h^{-1}\, {\rm Mpc}$. Our analysis indicates that these interactions largely arise due to the mutual information shared between the environments on different length scales.
We present and show the features of the FRUITY database, an interactive web-based interface devoted to the nucleosynthesis in AGB stars. We describe the current available set of AGB models (largely expanded with respect to the original one) with masses in the range 1.3<=M/M_SUN<=3.0 and metallicities -2.15<=[Fe/H]<=+0.15. We illustrate the details of our s-process surface distributions and we compare our results to observations. Moreover, we introduce a new set of models where the effects of rotation are taken into account. Finally, we shortly describe next planned upgrades.
Human visual attention is subjective and biased according to the personal preference of the viewer, however, current works of saliency detection are general and objective, without counting the factor of the observer. This will make the attention prediction for a particular person not accurate enough. In this work, we present the novel idea of personalized attention prediction and develop Personalized Attention Network (PANet), a convolutional network that predicts saliency in images with personal preference. The model consists of two streams which share common feature extraction layers, and one stream is responsible for saliency prediction, while the other is adapted from the detection model and used to fit user preference. We automatically collect user preference from their albums and leaves them freedom to define what and how many categories their preferences are divided into. To train PANet, we dynamically generate ground truth saliency maps upon existing detection labels and saliency labels, and the generation parameters are based upon our collected datasets consists of 1k images. We evaluate the model with saliency prediction metrics and test the trained model on different preference vectors. The results have shown that our system is much better than general models in personalized saliency prediction and is efficient to use for different preferences.
We report the detection of a circular polarization signature in the Stokes $V$ profile of a 25 GHz Class I CH$_3$OH maser toward the high mass star forming region OMC-1. Such a feature usually constitutes a detection of the Zeeman effect. If due to a magnetic field in OMC-1, this would represent the first detection and discovery of the Zeeman effect in the 25 GHz Class I CH$_3$OH maser. The feature in Stokes $V$ is detected in two observations with different angular resolutions taken eight years apart with the Very Large Array (VLA); for our 2009 D-configuration observations, the fitted value for $z B_\text{los}$ is $152 \pm 12$ Hz, where $z$ is the Zeeman splitting factor and $B_\text{los}$ is the line-of-sight magnetic field. For our 2017 C-configuration observations, the fitted value for $zB_\text{los} = 149 \pm 19~$Hz, likely for the same maser spot. These correspond to $B_\text{los}$ in the range 171-214 mG, depending on which hyperfine transition is responsible for the maser line. While these $B_\text{los}$ values are high, they are not implausible. If the magnetic field increases in proportion to the molecular hydrogen density in shocked regions, then our detected fields predict values for the pre-shock magnetic field that are in agreement with observations. With $B_\text{los}$ =171-214 mG, the magnetic energy in the post-shocked regions where these 25 GHz Class I CH$_3$OH masers occur would dominate over the kinetic energy density and be at least of the order of the pressure in the shock, implying that the magnetic field would exert significant influence over the dynamics of these regions.
The main paradigm of smoothed analysis on graphs suggests that for any large graph $G$ in a certain class of graphs, perturbing slightly the edges of $G$ at random (usually adding few random edges to $G$) typically results in a graph having much "nicer" properties. In this work we study smoothed analysis on trees or, equivalently, on connected graphs. Given an $n$-vertex connected graph $G$, form a random supergraph $G^*$ of $G$ by turning every pair of vertices of $G$ into an edge with probability $\frac{\epsilon}{n}$, where $\epsilon$ is a small positive constant. This perturbation model has been studied previously in several contexts, including smoothed analysis, small world networks, and combinatorics.   Connected graphs can be bad expanders, can have very large diameter, and possibly contain no long paths. In contrast, we show that if $G$ is an $n$-vertex connected graph then typically $G^*$ has edge expansion $\Omega(\frac{1}{\log n})$, diameter $O(\log n)$, vertex expansion $\Omega(\frac{1}{\log n})$, and contains a path of length $\Omega(n)$, where for the last two properties we additionally assume that $G$ has bounded maximum degree. Moreover, we show that if $G$ has bounded degeneracy, then typically the mixing time of the lazy random walk on $G^*$ is $O(\log^2 n)$. All these results are asymptotically tight.