ShantanuT01/DACTYL-Pretraining huggingface dataset:
X-rays offer a reliable method to identify Active Galactic Nuclei (AGN). However, in the high-redshift Universe X-ray AGN are poorly sampled due to their relatively low space density and the small areas covered by X-ray surveys. In addition to wide-area X-ray surveys, it is important to have deep optical data in order to locate the optical counterparts and determine their redshifts. In this work, we build a high-redshift ($z\geqslant3.5$) X-ray selected AGN sample in the XMM-XXL northern field using the most updated [0.5-2 keV] catalogue along with a plethora of new spectroscopic and multi-wavelength catalogues, including the deep optical Subaru Hyper Suprime-Cam (HSC) data reaching magnitude limits i$\sim$26 mag. We select all the spectroscopically confirmed AGN and complement this sample with high-redshift candidates that are HSC g- and r-band dropouts. To confirm the dropouts, we derive their photometric redshifts using spectral energy distribution techniques. We end up with a sample of 54 high-z sources (28 with spec-z), the largest in this field so far (almost three times larger than in previous studies), and we estimate the possible contamination and completeness. We calculate the number counts (logN-logS) in different redshift bins and compare our results with previous studies and models. We provide the strongest high-redshift AGN constraints yet at bright fluxes ($\rm f_{0.5-2~keV} > 10^{-15} erg~s^{-1}~cm^{-2}$). The samples of $z\geqslant3.5$, $z\geqslant4$ and $z\geqslant5$ are in agreement with an exponential decline model similar to that witnessed at optical wavelengths. Our work emphasizes the importance of using wide area X-ray surveys with deep optical data to uncover high redshift AGN.
The contributions of Lucio Russo to the mathematics of percolation and disordered systems are outlined. The context of his work is explained, and its ongoing impact on current work is described and amplified.
Dynamic skeletal data, represented as the 2D/3D coordinates of human joints, has been widely studied for human action recognition due to its high-level semantic information and environmental robustness. However, previous methods heavily rely on designing hand-crafted traversal rules or graph topologies to draw dependencies between the joints, which are limited in performance and generalizability. In this work, we present a novel decoupled spatial-temporal attention network(DSTA-Net) for skeleton-based action recognition. It involves solely the attention blocks, allowing for modeling spatial-temporal dependencies between joints without the requirement of knowing their positions or mutual connections. Specifically, to meet the specific requirements of the skeletal data, three techniques are proposed for building attention blocks, namely, spatial-temporal attention decoupling, decoupled position encoding and spatial global regularization. Besides, from the data aspect, we introduce a skeletal data decoupling technique to emphasize the specific characteristics of space/time and different motion scales, resulting in a more comprehensive understanding of the human actions.To test the effectiveness of the proposed method, extensive experiments are conducted on four challenging datasets for skeleton-based gesture and action recognition, namely, SHREC, DHG, NTU-60 and NTU-120, where DSTA-Net achieves state-of-the-art performance on all of them.
The purpose of this article is to study very weak solutions of elliptic equation $$ -\Delta u+g(u)=2k\frac{\partial \delta_0}{\partial x_N }+j\delta_0\quad {\rm in}\quad\ \ B_1(0),\qquad u=0\quad {\rm on}\quad\ \ \partial B_1(0), $$ where $k>0$, $j\ge0$, $B_1(0)$ denotes the unit ball centered at the origin in $\mathbb{R}^N$ with $N\geq2$, $g:\mathbb{R}\to\mathbb{R}$ is an odd, nondecreasing and $C^1$ function, $\delta_0$ is the Dirac mass concentrated at the origin and $\frac{\partial\delta_0}{\partial x_N}$ is defined in the distribution sense that $$ \langle\frac{\partial \delta_0}{\partial x_N},\zeta\rangle=\frac{\partial\zeta(0)}{\partial x_N} , \qquad \forall \zeta\in C^1_0(B_1(0)). $$ We obtain that the above problem admits a unique very weak solution $u_{k,j}$ under the integral subcritical assumption $$\int_1^{\infty}g(s)s^{-1-\frac{N+1}{N-1}}ds<+\infty.$$ Furthermore, we prove that $u_{k,j}$ has anisotropic singularity at the origin and we consider the odd property $u_{k,0}$ and limit of $\{u_{k,0}\}_k$ as $k\to\infty$.   We pose the constraint on nonlienarity $g(u)$ that we only require integrability in the principle value sense, due to the singularities only at the origin. This makes us able to search the very weak solutions in a larger scope of the nonlinearity.
Human motion retargeting aims to transfer the motion of one person in a "driving" video or set of images to another person. Existing efforts leverage a long training video from each target person to train a subject-specific motion transfer model. However, the scalability of such methods is limited, as each model can only generate videos for the given target subject, and such training videos are labor-intensive to acquire and process. Few-shot motion transfer techniques, which only require one or a few images from a target, have recently drawn considerable attention. Methods addressing this task generally use either 2D or explicit 3D representations to transfer motion, and in doing so, sacrifice either accurate geometric modeling or the flexibility of an end-to-end learned representation. Inspired by the Transformable Bottleneck Network, which renders novel views and manipulations of rigid objects, we propose an approach based on an implicit volumetric representation of the image content, which can then be spatially manipulated using volumetric flow fields. We address the challenging question of how to aggregate information across different body poses, learning flow fields that allow for combining content from the appropriate regions of input images of highly non-rigid human subjects performing complex motions into a single implicit volumetric representation. This allows us to learn our 3D representation solely from videos of moving people. Armed with both 3D object understanding and end-to-end learned rendering, this categorically novel representation delivers state-of-the-art image generation quality, as shown by our quantitative and qualitative evaluations.
The goal of this paper is to perform 3D object detection in the context of autonomous driving. Our method first aims at generating a set of high-quality 3D object proposals by exploiting stereo imagery. We formulate the problem as minimizing an energy function that encodes object size priors, placement of objects on the ground plane as well as several depth informed features that reason about free space, point cloud densities and distance to the ground. We then exploit a CNN on top of these proposals to perform object detection. In particular, we employ a convolutional neural net (CNN) that exploits context and depth information to jointly regress to 3D bounding box coordinates and object pose. Our experiments show significant performance gains over existing RGB and RGB-D object proposal methods on the challenging KITTI benchmark. When combined with the CNN, our approach outperforms all existing results in object detection and orientation estimation tasks for all three KITTI object classes. Furthermore, we experiment also with the setting where LIDAR information is available, and show that using both LIDAR and stereo leads to the best result.
We define zeta functions for the adjoint action of GL(n) on its Lie algebra and study their analytic properties. For n<4 we are able to fully analyse these functions, and recover the Shintani zeta function for the prehomogeneous vector space of binary quadratic forms for n=2. Our construction naturally yields a regularisation, which is necessary for the improvement of the properties of these zeta function, in particular for the analytic continuation if n>2.   We further obtain upper and lower bounds on the mean value X^{-5/2}\sum_{E} \res_{s=1}\zeta_E(s) as X->\infty, where E runs over totally real cubic number fields whose second successive minimum of the trace form on its ring of integers is bounded by X. To prove the upper bound we use our new zeta function for GL(3). These asymptotic bounds are a first step towards a generalisation of density results obtained by Datskovsky in case of quadratic field extensions.
We model the broad-band spectral energy distribution of the innermost "core-jet" region of the redshift z=1.187 quasar PKS 1127-145. We propose a scenario where the high energy photons are produced via the Compton scattering of thermal IR radiation by the relativistic particles in a parsec-scale jet. The high energy spectrum, together with the observed radio variability and superluminal expansion, suggest that PKS 1127-145 may be a blazar, despite the fact that its optical/UV component is likely dominated by thermal radiation from an accretion disk. The relation of PKS 1127-145 to MeV - blazars is discussed.
The structure of graphs with a 2-vertex-cut that are critical with respect to the Euler genus is studied. A general theorem describing the building blocks is presented. These constituents, called hoppers and cascades, are classified for the case when Euler genus is small. As a consequence, the complete list of obstructions of connectivity 2 for embedding graphs into the Klein bottle is obtained.
We establish formulae for the derivative of the Poincar\'e constant of Gibbs measures on both compact domains and all of $\R^d$. As an application, we show that if the (not necessarily convex) Hamiltonian is an increasing function, then the Poincar\'e constant is strictly decreasing in the inverse temperature, and vice versa. Applying this result to the $O(2)$ model allows us to give a sharpened upper bound on its Poincar\'e constant. We further show that this model exhibits a qualitatively different zero-temperature behavior of the Poincar\'e and Log-Sobolev constants.
We investigate a zero-range process where the underlying one-particle stationary distribution has multifractality. The multiparticle stationary probability measure can be written in a factorized form. If the number of the particles is sufficiently large, a great part of the particles condense at the site with the highest measure of the one-particle problem. The number of the particles out of the condensate scales algebraically with the system size and the exponent depends on the strength of the disorder. These results can be well reproduced by a branching process, with similar multifractal property.
A new two-loop radiative Majorana neutrino mass model is constructed from the gauge-invariant effective operator $L^i L^j Q^k d^c Q^l d^c \epsilon_{ik} \epsilon_{jl}$ that violates lepton number conservation by two units. The ultraviolet completion features two scalar leptoquark flavors and a color-octet Majorana fermion. We show that there exists a region of parameter space where the neutrino oscillation data can be fitted while simultaneously meeting flavor-violation and collider bounds. The model is testable through lepton flavor-violating processes such as ${\mu} \to e{\gamma}$, $\mu \to eee$, and $\mu N \to eN$ conversion, as well as collider searches for the scalar leptoquarks and color-octet fermion. We computed and compiled a list of necessary Passarino-Veltman integrals up to boxes in the approximation of vanishing external momenta and made them available as a Mathematica package, denoted as ANT.
Every graph $\Gamma$ can be embedded in the plane with a minimal number of edge intersections, called its classical crossing number $\text{cross}\left(\Gamma\right)$. In this paper, we prove that if $\Gamma$ is a metric graph it can be realized as a tropical curve in the plane with exactly $\text{cross}\left(\Gamma\right)$ crossings, where the tropical curve is equipped with the lattice length metric. Our result has an application in algebraic geometry, as it enables us to construct a rational map of non-Archimedean curves into the projective plane, whose tropicalization is almost faithful when restricted to their skeleton.
Let $\{X, X_n, n\geq 1\}$ be a sequence of independent identically distributed non-degenerate random variables. Put $S_0=0, S_n = \sum^n_{i=1} X_i$ and $V_n^2=\sum^n_{i=1} X_i^2, n\ge 1.$ A weak convergence theorem is established for the self-normalized partial sums processes $\{S_{[nt]}/V_n, 0\le t\le 1\}$ when $X$ belongs to the domain of attraction of a stable law with index $\alpha \in (0,2]$. The respective limiting distributions of the random variables ${\max_{1\le i\le n}|X_i|}/{S_n}$ and ${\max_{1\le i\le n}|X_i|}/{V_n}$ are also obtained under the same condition.
We provide a uniform solution to 4d N=2 gauge theory with a single gauge group G=A,D,E when the one-loop contribution to the beta function from any irreducible component R of the hypermultiplets is less than or equal to half of that of the adjoint representation. The solution is given by a non-compact Calabi-Yau geometry, whose defining equation is built from explicitly known polynomials W_G and X_R, associated respectively to the gauge group G and each irreducible component R. We provide many pieces of supporting evidence, for example by analyzing the system from the point of view of the 6d N=(2,0) theory compactified on a sphere.
Recently, segmentation-based methods are quite popular in scene text detection, as the segmentation results can more accurately describe scene text of various shapes such as curve text. However, the post-processing of binarization is essential for segmentation-based detection, which converts probability maps produced by a segmentation method into bounding boxes/regions of text. In this paper, we propose a module named Differentiable Binarization (DB), which can perform the binarization process in a segmentation network. Optimized along with a DB module, a segmentation network can adaptively set the thresholds for binarization, which not only simplifies the post-processing but also enhances the performance of text detection. Based on a simple segmentation network, we validate the performance improvements of DB on five benchmark datasets, which consistently achieves state-of-the-art results, in terms of both detection accuracy and speed. In particular, with a light-weight backbone, the performance improvements by DB are significant so that we can look for an ideal tradeoff between detection accuracy and efficiency. Specifically, with a backbone of ResNet-18, our detector achieves an F-measure of 82.8, running at 62 FPS, on the MSRA-TD500 dataset. Code is available at: https://github.com/MhLiao/DB
Using a new method to estimate total galaxy mass (M$_{\rm T}$) and two samples of low luminosity galaxies containing measurements of the number of globular clusters (GCs) per galaxy (N$_{\rm GC}$), we revisit the N$_{\rm GC}-$M$_{\rm T}$ relation using a total of 203 galaxies, 157 of which have M$_{\rm T}$ $\ \le 10^{10}$ M$_\odot$. We find that the relation is nearly linear, N$_{\rm GC} \propto$ M$_{\rm T}^{0.92\pm0.08}$ down to at least M$_{\rm T} \sim 10^{8.75}$ M$_\odot$. Because the relationship extends to galaxies that average less than one GC per galaxy and to a mass range in which mergers are relatively rare, the relationship cannot be solely an emergent property of hierarchical galaxy formation. The character of the radial GC distribution in low mass galaxies, and the lack of mergers at these galaxy masses, also appears to challenge models in which the GCs form in central, dissipatively concentrated high-density, high-pressure regions and are then scattered to large radius. The slight difference between the fitted power-law exponent and a value of one, leaves room for a shallow M$_{\rm T}$-dependent variation in the mean mass per GC that would allow the relation between total mass in GCs and M$_{\rm T}$ to be linear.
The recognition of color texture under varying lighting conditions is still an open issue. Several features have been proposed for this purpose, ranging from traditional statistical descriptors to features extracted with neural networks. Still, it is not completely clear under what circumstances a feature performs better than the others. In this paper we report an extensive comparison of old and new texture features, with and without a color normalization step, with a particular focus on how they are affected by small and large variation in the lighting conditions. The evaluation is performed on a new texture database including 68 samples of raw food acquired under 46 conditions that present single and combined variations of light color, direction and intensity. The database allows to systematically investigate the robustness of texture descriptors across a large range of variations of imaging conditions.
It is shown that the interacting Pais-Uhlenbeck oscillator necessarily leads to a description with a Hamiltonian that contains positive and negative energies associated with two oscillators. Descriptions with a positive definite Hamiltonians, considered by some authors, can hold only for a free Pais-Uhlenbeck oscillator. We demonstrate that the solutions of a self-interacting Pais-Uhlenbeck oscillator are stable on islands in the parameter space, as already observed in the literature. If we slightly modify the system, by considering a sine interaction term, and/or by taking unequal masses of the two oscillators, then the system is stable on the continents that extend from zero to infinity in the parameter space. Therefore, the Pais-Uhlenbeck oscillator is quite acceptable physical system.
We study here some consequences of the nonlinearities of the electromagnetic field acting as a source of Einstein's equations on the propagation of photons. We restrict to the particular case of a ``regular black hole'', and show that there exist singularities in the effective geometry. These singularities may be hidden behind a horizon or naked, according to the value of a parameter. Some unusual properties of this solution are also analyzed.
We present an analytical investigation of the exciton optical absorption in a narrow armchair graphene nanoribbon (AGNR) in the presence of a longitudinal external electric field directed parallel to the ribbon axis. The two-body 2D Dirac equation for the massless electron and hole subject to the ribbon confinement, Coulomb interaction and electric field is employed. The ribbon confinement is assumed to be much stronger than the internal exciton electric field which in turn considerably exceeds the external electric field. This justifies an adiabatic approximation implying a slow longitudinal and fast transverse electron-hole relative motion governed by the exciton attraction and accompanied by the external electric field and ribbon confinement effects, respectively. In the single subband approximation of the isolated size-quantized subbands induced by the ribbon confinement the exciton electroabsorption coefficient is determined in an explicit form. The pronounced dependencies of the exciton peak positions, widths and intensities on the ribbon width and electric field strength are traced. The electron-hole exciton attraction modifies remarkably the Franz-Keldysh electroabsorption in the frequency region both below and above the edges determined by the size-quantized energy levels. In the double-subband approximation of the interacting ground and first excited subbands the combined effect of the exciton electro- and autoionization caused by the electric field and intersubband coupling, respectively, are explored. Estimates of the expected experimental values for the typically employed AGNR show that for a weak electric field the exciton quasi-discrete states remain sufficiently stable to be observed in optical experiments, while relatively strong fields free the captured carriers to further restore their contribution to the transport.
Materials exhibiting controllable magnetic phase transitions are currently in demand for many spintronics applications. Here we investigate from first principles the electronic structure and intrinsic anomalous Hall, spin Hall and anomalous Nernst response properties of the FeRh metallic alloy which undergoes a thermally driven antiferromagnetic-to-ferromagnetic phase transition. We show that the energy band structures and underlying Berry curvatures have important signatures in the various Hall effects. Specifically, the suppression of the anomalous Hall and Nernst effects in the AFM state and a sign change in the spin Hall conductivity across the transition are found. It is suggested that the FeRh can be used a spin current detector capable of differentiating the spin Hall effect from other anomalous transverse effects. The implications of this material and its thermally driven phases as a spin current detection scheme are also discussed.
A number of geometric properties of $\Omega$-groups from a given variety of $\Omega$-groups can be characterized using the notions of domain and equational domain. An $\Omega$-group $H$ of a variety $\Theta$ is an equational domain in $\Theta$ if the union of algebraic varieties over $H$ is an algebraic variety. We give necessary and sufficient conditions for an $\Omega$-group $H$ in $\Theta$ to be an equational domain in this variety.
We have extracted PSF-fitted stellar photometry from near-ultraviolet, optical and near-infrared images, obtained with the Hubble Space Telescope, of the nearby (D ~ 5.5 Mpc) SBm galaxy NGC 1311. The ultraviolet and optical data reveal a population of hot main sequence stars with ages of 2-10 Myr. We also find populations of blue supergiants with ages between 10 and 40 Myr and red supergiants with ages between 10 and 100 Myr. Our near-infrared data shows evidence of star formation going back ~1 Gyr, in agreement with previous work. Fits to isochrones indicate a metallicity of Z ~ 0.004. The ratio of blue to red supergiants is consistent with this metallicity. This indicates that NGC 1311 follows the well-known luminosity-metallicity relation for late-type dwarf galaxies. About half of the hot main sequence stars and blue supergiants are found in two regions in the inner part of NGC 1311. These two regions are each about 200 pc across, and thus have crossing times roughly equal to the 10 Myr age we find for the dominant young population. The Luminosity Functions of the supergiants indicate a slowly rising star formation rate (of 0.001 Solar masses per year) from ~100 Myr ago until ~15 Myr ago, followed by a strong enhancement (to 0.01 Solar Masses per year) at ~10 Myr ago. We see no compelling evidence for gaps in the star-forming history of NGC 1311 over the last 100 Myr, and, with lower significance, none over the last Gyr. This argues against a bursting mode, and in favor of a gasping or breathing mode for the recent star-formation history.
Primary features of a new cosmological model, which is based on conjectures about an existence of the graviton background and superstrong gravitational quantum interaction, are considered. An expansion of the universe is impossible in such the model because of deceleration of massive objects by the graviton background, which is similar to the one for the NASA deep space probes Pioneer 10, 11. Redshifts of remote objects are caused in the model by interaction of photons with the graviton background, and the Hubble constant depends on an intensity of interaction and an equivalent temperature of the graviton background. Virtual massive gravitons would be dark matter particles. They transfer energy, lost by luminous matter radiation, which in a final stage may be collected with black holes and other massive objects.