ShantanuT01/DACTYL-Pretraining huggingface dataset:
We measure and study the evolution of the UV galaxy Luminosity Function (LF) at z=3-5 from the largest high-redshift survey to date, the Deep part of the CFHT Legacy Survey. We also give accurate estimates of the SFR density at these redshifts. We consider ~100,000 Lyman-break galaxies at z~3.1, 3.8 & 4.8 selected from very deep ugriz images of this data set and estimate their rest-frame 1600A luminosity function. Due to the large survey volume, cosmic variance plays a negligible role. Furthermore, we measure the bright end of the LF with unprecedented statistical accuracy. Contamination fractions from stars and low-z galaxy interlopers are estimated from simulations. To correct for incompleteness, we study the detection rate of simulated galaxies injected to the images as a function of magnitude and redshift. We estimate the contribution of several systematic effects in the analysis to test the robustness of our results. We find the bright end of the LF of our u-dropout sample to deviate significantly from a Schechter function. If we modify the function by a recently proposed magnification model, the fit improves. For the first time in an LBG sample, we can measure down to the density regime where magnification affects the shape of the observed LF because of the very bright and rare galaxies we are able to probe with this data set. We find an increase in the normalisation, $\phi^{*}$, of the LF by a factor of 2.5 between z~5 and z~3. The faint-end slope of the LF does not evolve significantly between z~5 and z~3. We do not find a significant evolution of the characteristic magnitude in the studied redshift interval. The SFR density is found to increase by a factor of ~2 from z~5 to z~4. The evolution from z~4 to z~3 is less eminent.
We demonstrate theoretically that a strong high-frequency circularly polarized electromagnetic field can turn a two-dimensional periodic array of interconnected quantum rings into a topological insulator. The elaborated approach is applicable to calculate and analyze the electron energy spectrum of the array, the energy spectrum of the edge states and the corresponding electronic densities. As a result, the present theory paves the way to optical control of the topological phases in ring-based mesoscopic structures.
Detection and classification of ships based on their silhouette profiles in natural imagery is an important undertaking in computer science. This problem can be viewed from a variety of perspectives, including security, traffic control, and even militarism. Therefore, in each of the aforementioned applications, specific processing is required. In this paper, by applying the "bag of words" (BoW), a new method is presented that its words are the features that are obtained using pre-trained models of deep convolutional networks. , Three VGG models are utilized which provide superior accuracy in identifying objects. The regions of the image that are selected as the initial proposals are derived from a greedy algorithm on the key points generated by the Scale Invariant Feature Transform (SIFT) method. Using the deep features in the BOW method provides a good improvement in the recognition and classification of ships. Eventually, we obtained an accuracy of 91.8% in the classification of the ships which shows the improvement of about 5% compared to previous methods.
Graviational radiation is described by canonical Yang-Mills wave equations on the curved space-time manifold, together with evolution equations for the metric in the tangent bundle. The initial data problem is described in Yang-Mills scalar and vector potentials, resulting in Lie-constraints in addition to the familiar Gauss-Codacci relations.
Three-nucleon forces, which compose an up-to-date subject in few-nucleon systems, provide a good account of the triton binding energy and the cross section minimum in proton-deuteron elastic scattering, while do not succeed in explaining spin observables such as the nucleon and deuteron analyzing powers, suggesting serious defects in their spin dependence. We study the spin structure of nucleon-deuteron elastic amplitudes by decomposing them into spin-space tensors and examine effects of three-nucleon forces to each component of the amplitudes obtained by solving the Faddeev equation. Assuming that the spin-scalar amplitudes dominate the others, we derive simple expressions for spin observables in the nucleon-deuteron elastic scattering. The expressions suggest that a particular combination of spin observables in the scattering provides direct information of scalar, vector, or tensor component of the three-nucleon forces. These effects are numerically investigated by the Faddeev calculation.
Mergers between a spiral and an elliptical (S+E mergers) are poorly studied so far despite the importance for galaxy evolution. NGC4441 is a nearby candidate for an advanced remnant of such a merger, showing typical tidal structures like an optical tail and two shells as well as two HI tails. The study of the molecular gas content gives clues on the impact of the recent merger event on the star formation. Simulations of S+E mergers predict contradictory scenarios concerning the strength and the extent of an induced starburst. Thus, observations of the amount and the distribution of the molecular gas, the raw material of star formation, are needed to understand the influence of the merger on the star formation history. 12CO and 13CO (1-0) and (2-1) observations were obtained using the Onsala Space Observatory 20m and IRAM 30m telescope as well as the Plateau de Bure interferometer. These data allow us to carry out a basic analysis of the molecular gas properties such as estimates of the molecular gas mass, its temperature and density and the star formation efficiency. The CO observations reveal an extended molecular gas reservoir out to ~4kpc, with a total molecular gas mass of ~5x10^8 M_sun. Furthermore, high resolution imaging shows a central molecular gas feature, most likely a rotating disc hosting most of the molecular gas ~4x10^8 M_sun. This nuclear disc shows a different sense of rotation than the large-scale HI structure, indicating a kinematically decoupled core. (abbreviated)
A common ploy to reduce the switching current and energy dissipation in spin-transfer-torque driven magnetization switching of shape-anisotropic single-domain nanomagnets is to employ magnets with low saturation magnetization $M_s$ and high shape-anisotropy. The high shape-anisotropy compensates for low $M_s$ to keep the static switching error rate constant. However, this ploy increases the switching delay, its variance in the presence of thermal noise, and the dynamic switching error rate. Using the stochastic Landau-Lifshitz-Gilbert equation with a random torque emulating thermal noise, we show that pumping some excess spin-polarized current into the nanomagnet during switching will keep the mean switching delay and its variance constant as we reduce $M_s$, while still reducing the energy dissipation significantly.
Injury analysis may be one of the most beneficial applications of deep learning based human pose estimation. To facilitate further research on this topic, we provide an injury specific 2D dataset for alpine skiing, covering in total 533 images. We further propose a post processing routine, that combines rotational information with a simple kinematic model. We could improve detection results in fall situations by up to 21% regarding the PCK@0.2 metric.
We have theoretically investigated electromechanical properties of freely suspended carbon nanotubes when a current is injected into the tubes using a scanning tunneling microscope. We show that a shuttle-like electromechanical instability can occur if the bias voltage exceeds a dissipation-dependent threshold value. An instability results in large amplitude vibrations of the carbon nanotube bending mode, which modify the current-voltage characteristics of the system.
We present the Spitzer Space Telescope Infrared Array Camera (IRAC) observations for a sample of local elliptical galaxies to study later stages of AGN activities. A sample of 36 elliptical galaxies is selected from the Palomar spectroscopic survey. We detect nuclear non-stellar infrared emission in 9 of them. There is unambiguous evidence of circumnuclear dust in these 9 galaxies in their optical images. We also find a remarkable correlation between the infrared excess emission and the nuclear radio/X-ray emission, suggesting that infrared excess emission is tightly related with nuclear activity. Possible origin of infrared excess emission from hot dust heated by the central AGN is supported by spectral indices of IR excess emission.
We summarize recent developments in the study of the origin of halo spin profiles and preliminary implications on disk formation. The specific angular-momentum distributions within halos in N-body simulations match a universal shape, M(<j) \propto j/(j_0+j). It is characterized by a power law over most of the mass, and one shape parameter in addition to the spin parameter \lambda. The angular momentum tends to be aligned throughout the halo and of cylindrical symmetry. Even if angular momentum is conserved during baryonic infall, the resultant disk density profile is predicted to deviate from exponential, with a denser core and an extended tail. A slightly corrected version of the scaling relation due to linear tidal-torque theory is used to explain the origin of a typical power-law profile in shells, j(M) \propto M^s with s \gsim 1. While linear theory crudely predicts the amplitudes of halo spins, it is not a good predictor of their directions. Independently, mergers of halos are found to produce a similar profile due to j transfer from the orbit to the product halo via dynamical friction and tidal stripping. The halo spin is correlated with having a recent major merger, though this correlation is weakened by mass loss. These two effects are due to a correlation between the spins of neighboring halos and their orbit, leading to prograde mergers.
We compute presentations for a family of semigroup algebras related to the problem of decomposing $sl_3(\mathbb{C})$ tensor products. Along the way we find new toric degenerations of the Grassmannian variety $Gr_3(\mathbb{C}^n)$ which $T-$invariant for $T \subset GL_n(\mathbb{C})$ the diagonal torus.
While the observed nearly scale-invariant initial power spectrum is regarded as one of the favorable evidence of the standard inflationary cosmology, precision observations of the Cosmic Microwave Background (CMB) anisotropies also suggest possible existence of nontrivial features such as those observed around multipoles $\ell\sim120$ by WMAP. Here, we examine the Planck data and investigate the effects of these features on the cosmological parameter estimation performing the Markov-Chain Monte-Carlo (MCMC) analysis. We find that the features exist in the Planck data at the same position as the case of the WMAP data but they do not affect the cosmological parameter estimation significantly.
We use Herschel-PACS far-infrared data, combined with previous multi-band information and mid-IR spectra, to properly account for the presence of an active nucleus and constrain its energetic contribution in luminous infrared (IR) sources at z\sim2. The sample is composed of 24 sources in the GOODS-South field, with typical IR luminosity of 10^{12} Lo. Data from the 4 Ms Chandra X-ray imaging in this field are also used to identify and characterize AGN emission. We reproduce the observed spectral energy distribution (SED), decomposed into a host-galaxy and an AGN component. A smooth-torus model for circum-nuclear dust is used to account for the direct and re-processed contribution from the AGN. We confirm that galaxies with typical L_{8-1000um}\sim10^{12}Lo at z\sim2 are powered predominantly by star-formation. An AGN component is present in nine objects (\sim35% of the sample) at the 3sigma confidence level, but its contribution to the 8-1000 um emission accounts for only \sim5% of the energy budget. The AGN contribution rises to \sim23% over the 5-30 um range (in agreement with Spitzer IRS results) and to \sim60% over the narrow 2-6 um range. The presence of an AGN is confirmed by X-ray data for 3 (out of nine) sources, with X-ray spectral analysis indicating the presence of significant absorption, i.e. NH\sim10^{23}-10^{24} cm^{-2}. An additional source shows indications of obscured AGN emission from X-ray data. The comparison between the mid-IR--derived X-ray luminosities and those obtained from X-ray data suggests that obscuration is likely present also in the remaining six sources that harbour an AGN according to the SED-fitting analysis.
Inverse imaging problems are inherently under-determined, and hence it is important to employ appropriate image priors for regularization. One recent popular prior---the graph Laplacian regularizer---assumes that the target pixel patch is smooth with respect to an appropriately chosen graph. However, the mechanisms and implications of imposing the graph Laplacian regularizer on the original inverse problem are not well understood. To address this problem, in this paper we interpret neighborhood graphs of pixel patches as discrete counterparts of Riemannian manifolds and perform analysis in the continuous domain, providing insights into several fundamental aspects of graph Laplacian regularization for image denoising. Specifically, we first show the convergence of the graph Laplacian regularizer to a continuous-domain functional, integrating a norm measured in a locally adaptive metric space. Focusing on image denoising, we derive an optimal metric space assuming non-local self-similarity of pixel patches, leading to an optimal graph Laplacian regularizer for denoising in the discrete domain. We then interpret graph Laplacian regularization as an anisotropic diffusion scheme to explain its behavior during iterations, e.g., its tendency to promote piecewise smooth signals under certain settings. To verify our analysis, an iterative image denoising algorithm is developed. Experimental results show that our algorithm performs competitively with state-of-the-art denoising methods such as BM3D for natural images, and outperforms them significantly for piecewise smooth images.
If the ultrahigh-energy (UHE) neutrino fluxes produced from a distant astrophysical source can be measured at a km^3-size neutrino telescope, they will provide a promising way to help determine the flavor mixing pattern of three active neutrinos. Considering the conventional UHE neutrino source with the flavor ratio \phi_e : \phi_\mu : \phi_\tau = 1 : 2 : 0, I show that \phi^D_e : \phi^D_\mu : \phi^D_\tau = (1 -2 \Delta) : (1 +\Delta) : (1 +\Delta) holds at the detector of a neutrino telescope, where \Delta characterizes the effect of \mu-\tau symmetry breaking (i.e., \theta_13 \neq 0 and \theta_23 \neq \pi/4). Current experimental data yield -0.1 \leq \Delta \leq +0.1. It is also possible to probe \Delta by detecting the \bar{\nu}_e flux of E_\bar{\nu}_e \approx 6.3 PeV via the Glashow resonance channel \bar{\nu}_e e \to W^- \to anything. Finally, I give some brief comments on the possibility to constrain the mixing between active and sterile neutrinos by using the UHE neutrino telescopes.
Phenomenological equations describing the Seebeck, Hall, Nernst, Peltier, Ettingshausen, and Righi-Leduc effects are numerically solved for the temperature, electric current, and electrochemical potential distributions of semiconductors under magnetic field. The results are compared to experiments.
We consider an evolution equation inspired by a model in peridynamics, with a singular pairwise interaction force term, and we give global in time existence, uniqueness and stability results for the Cauchy problem.
The variability of the high-energy gamma ray sources in the Third EGRET catalog is analyzed by a new method. We re-analyze the EGRET data to calculate a likelihood function for the flux of each source in each observation, both for detections and upper limits. These functions can be combined in a uniform manner with a simple model of the flux distribution to characterize the flux variation by a confidence interval for the relative standard deviation of the flux. The main result is a table of these values for almost all the cataloged sources. As expected, the identified pulsars are steady emitters and the blazars are mostly highly variable. The unidentified sources are heterogeneous, with greater variation at higher Galactic latitude. There is an indication that pulsar wind nebulae are associated with variable sources. There is a population of variable sources along the Galactic plane, concentrated in the inner spiral arms.
In the low redshift Universe (z<0.3), our view of galaxy evolution is primarily based on fibre optic spectroscopy surveys. Elaborate methods have been developed to address aperture effects when fixed aperture sizes only probe the inner regions for galaxies of ever decreasing redshift or increasing physical size. These aperture corrections rely on assumptions about the physical properties of galaxies. The adequacy of these aperture corrections can be tested with integral-field spectroscopic data. We use integral-field spectra drawn from 1212 galaxies observed as part of the SAMI Galaxy Survey to investigate the validity of two aperture correction methods that attempt to estimate a galaxy's total instantaneous star formation rate. We show that biases arise when assuming that instantaneous star formation is traced by broadband imaging, and when the aperture correction is built only from spectra of the nuclear region of galaxies. These biases may be significant depending on the selection criteria of a survey sample. Understanding the sensitivities of these aperture corrections is essential for correct handling of systematic errors in galaxy evolution studies.
We report on the pressure effects on the orbital polaron lattice in the lightly doped manganites $\mathrm{La_{1-x}Sr_xMnO_{3}}$, with $x\sim 1/8$. The dependence of the orbital polaron lattice on $negative$ chemical pressure is studied by substituting Pr for La in $\mathrm{(La_{1-y}Pr_y)_{7/8}Sr_{1/8}MnO_{3}}$. In addition, we have studied its hydrostatic pressure dependence in $\mathrm{(La_{0.9}Pr_{0.1})_{7/8}Sr_{1/8}MnO_{3}}$. Our results strongly indicate that the hopping $t$ significantly contributes to the stabilization of the orbital polaron lattice and that the orbital polarons are ferromagnetic objects which get stabilized by local double exchange processes. The analysis of short range orbital correlations and the verification of the Grueneisen scaling by hard x-ray, specific heat and thermal expansion data reinforces our conclusions.
Graph embedding is a powerful method to represent graph neurological data (e.g., brain connectomes) in a low dimensional space for brain connectivity mapping, prediction and classification. However, existing embedding algorithms have two major limitations. First, they primarily focus on preserving one-to-one topological relationships between nodes (i.e., regions of interest (ROIs) in a connectome), but they have mostly ignored many-to-many relationships (i.e., set to set), which can be captured using a hyperconnectome structure. Second, existing graph embedding techniques cannot be easily adapted to multi-view graph data with heterogeneous distributions. In this paper, while cross-pollinating adversarial deep learning with hypergraph theory, we aim to jointly learn deep latent embeddings of subject0specific multi-view brain graphs to eventually disentangle different brain states. First, we propose a new simple strategy to build a hyperconnectome for each brain view based on nearest neighbour algorithm to preserve the connectivities across pairs of ROIs. Second, we design a hyperconnectome autoencoder (HCAE) framework which operates directly on the multi-view hyperconnectomes based on hypergraph convolutional layers to better capture the many-to-many relationships between brain regions (i.e., nodes). For each subject, we further regularize the hypergraph autoencoding by adversarial regularization to align the distribution of the learned hyperconnectome embeddings with that of the input hyperconnectomes. We formalize our hyperconnectome embedding within a geometric deep learning framework to optimize for a given subject, thereby designing an individual-based learning framework. Our experiments showed that the learned embeddings by HCAE yield to better results for brain state classification compared with other deep graph embedding methods methods.
We consider functions $f:B\to\Rset$ that obey tropical analogs of classical Pl\"ucker relations on minors of a matrix. The most general set $B$ that we deal with in this paper is of the form $\{x\in \Zset^n\colon 0\le x\le a, m\le x_1+...+x_n\le m'\}$ (a rectangular integer box ``truncated from below and above''). We construct a basis for the set $\Tscr$ of tropical Pl\"ucker functions on $B$, a subset $\Bscr\subseteq B$ such that the restriction map $\Tscr\to\Rset^\Bscr$ is bijective. Also we characterize, in terms of the restriction to the basis, the classes of submodular, so-called skew-submodular, and discrete concave functions in $\Tscr$, discuss a tropical analogue of the Laurentness property, and present other results.
Recently there has been considerable interest in the Fluctuation Theorem (FT). The FT shows how time reversible microscopic dynamics leads to irreversible macroscopic behavior as the system size or observation time increases. We show that the argument of the Evans-Searles FT, the dissipation function, plays a central role in nonlinear response theory and derive the Dissipation Theorem, giving exact relations for nonlinear response of classical N-body systems. These expressions should be verifiable experimentally. When linearized they reduce to the Green-Kubo expressions for linear response.
Traditional 3D convolutions are computationally expensive, memory intensive, and due to large number of parameters, they often tend to overfit. On the other hand, 2D CNNs are less computationally expensive and less memory intensive than 3D CNNs and have shown remarkable results in applications like image classification and object recognition. However, in previous works, it has been observed that they are inferior to 3D CNNs when applied on a spatio-temporal input. In this work, we propose a convolutional block which extracts the spatial information by performing a 2D convolution and extracts the temporal information by exploiting temporal differences, i.e., the change in the spatial information at different time instances, using simple operations of shift, subtract and add without utilizing any trainable parameters. The proposed convolutional block has same number of parameters as of a 2D convolution kernel of size nxn, i.e. n^2, and has n times lesser parameters than an nxnxn 3D convolution kernel. We show that the 3D CNNs perform better when the 3D convolution kernels are replaced by the proposed convolutional blocks. We evaluate the proposed convolutional block on UCF101 and ModelNet datasets.