ShantanuT01/DACTYL-Pretraining huggingface dataset:
We study the photocenter position variability due to variations in the quasar inner structure. We consider variability in the accretion disk emissivity and torus structure variability due to different illumination by the central source. We discuss possible detection of these effects by Gaia. Observations of the photocenter variability in two AGNs, SDSS J121855+020002 and SDSS J162011+1724327 have been reported and discussed. With investigation of the variations in the quasar inner structure we explore how much this effect can affect the position determination and whether it can be (or not) detected with Gaia mission. We used (a) a model of a relativistic disk, including the perturbation that can increase brightness of a part of the disk, and consequently offset the photocenter position, and (b) a model of a dusty torus which absorbs and re-emits the incoming radiation from accretion disk. We estimated the value of the photocenter offset due to these two effects. We found that perturbations in the inner structure can significantly offset the photocenter. It depends on the characteristics of perturbation and accretion disk and structure of the torus. In the case of two considered QSOs the observed photocenter offsets cannot be explained by variations in the accretion disk and other effects should be considered. We discussed possibility of exploding stars very close to the AGN source, and also possibility that there are two variable sources in the center of these two AGNs that may indicate a binary super-massive black hole system on a kpc (pc) scale. The Gaia mission seems to be very perspective, not only for astrometry, but also for exploring the inner structure of AGNs. We conclude that variations in the quasar inner structure can affect the observed photocenter (up to several mas). There is a chance to observe such effect in the case of bright and low-redshifted QSOs.
Let $X \subset \mathbb P^3$ be a smooth hypersurface of degree $6$ over complex numbers. In this paper, we give a characterization of initialized and ACM bundles of rank $1$ on $X$ with respect to the line bundle given by a smooth hyperplane section of $X$.
In spin crossover materials, an abrupt phase transition between a low spin state and a high spin state can be driven by temperature, pressure or by light irradiation. Of a special relevance are Fe(II) based coordination polymers where, in contrast to molecular systems, the phase transition between a spin S=0 and a spin S=2 state shows a pronounced hysteresis which is desirable for technical applications. A satisfactory microscopic explanation of this large cooperative phenomenon has been sought for a long time. The lack of X-ray data has been one of the reasons for the absence of microscopic studies. In this work, we present an efficient route to prepare reliable model structures and within an ab initio density functional theory analysis and effective model considerations we show that in polymeric spin crossover compounds magnetic exchange between high spin Fe(II) centers is as important as elastic couplings for understanding the phase transition. We discuss the relevance of these interactions for the cooperative behavior in these materials.
We examined the sky confusion noise in 40 sky regions by analysing 175 far-infrared (90--200 $\mu$m) maps obtained with ISOPHOT, the photometer on-board the Infrared Space Observatory. For cirrus fields with $\rm < \rm B > > 5$ MJysr$^{-1}$ the formula based on IRAS data (Helou & Beichman, \cite{Helou+Beichman_90}) predicts confusion noise values within a factor of 2 to our measurements. The dependence of the sky confusion noise on the surface brightness was determined for the wavelength range 90 $\le \lambda \le$ 200 $\mu$m. We verified that the confusion noise scales as N ~ <B>^{1.5}, independent of the wavelength and confirmed N ~ lambda^{2.5}$ for lambda >= 100$um. The scaling of the noise value at different separations between target and reference positions was investigated for the first time, providing a practical formula. Since our results confirm the applicability of the Helou & Beichman (1990) formula, the cirrus confusion noise predictions made for future space missions with telescopes of a similar size can be trusted. At 90 and 170um a noise term with a Poissonian spatial distribution was detected in the faintest fields (<B> <= 3-5 MJysr^{-1}), which we interpret as fluctuations in the Cosmic Far-Infrared Background (CFIRB). Applying ratios of the fluctuation amplitude to the absolute level of 10% and 7% at 90 and 170um, respectively, as supported by model calculations, we achieved a new simultaneous determination of the fluctuation amplitudes and the surface brightness of the CFIRB. The fluctuation amplitudes are 7(+/-)2 mJy and 15(+/-)4 mJy at 90 and 170um, respectively. We obtained a CFIRB surface brightness of B(0)=0.8(+/-)0.2 MJysr^{-1} (nuI_nu=14(+/-)3nWm^-2sr^-1) at 170um and an upper limit of 1.1 MJysr^{-1} (nuI_nu=37 nWm^-2sr^-1) at 90um.
The process of sunspot formation is considered with the account of heat effects. According to the Le Chatelier principle, a local overheating must precede to the cooling of solar surface in the places of sunspot formation. The sunspot dynamics is a process close to the surface nucleate-free boiling in a thin layer with formation of bubbles (or craters), so we focus on the analogy between these two processes. Solar spots and surface nucleate-free boiling in a thin layer have similarities in formation conditions, results of impact on the surface were they have been formed, periodicity, and their place in the hierarchy of self-organization in complex systems. The difference is in the working medium and method of channelling of extra energy from the overheated surface -for boiling process, the energy is forwarded to generation of vapor, and in sunspots the solar energy is consumed to formation of a strong magnetic field. This analogy explains the problem of a steady brightness (temperature) of a spot that is independent of the spot size and other characteristics.
Spin precession was nonthermally induced by an ultrashort laser pulse in orthoferrite DyFeO3 with a pump-probe technique. Both circularly and linearly polarized pulses led to spin precessions; these phenomena are interpreted as the inverse Faraday effect and the inverse Cotton-Mouton effect, respectively. For both cases, the same mode of spin precession was excited; the precession frequencies and polarization were the same, but the phases of oscillations were different. We have shown theoretically and experimentally that the analysis of phases can distinguish between these two mechanisms. We have demonstrated experimentally that in the visible region, the inverse Faraday effect was dominant, whereas the inverse Cotton-Mouton effect became relatively prominent in the near-infrared region.
Charge-neutral 180$^\circ$ domain walls that separate domains of antiparallel polarization directions are common structural topological defects in ferroelectrics. In normal ferroelectrics, charged 180$^\circ$ domain walls running perpendicular to the polarization directions are highly energetically unfavorable because of the depolarization field and are difficult to stabilize. We explore both neutral and charged 180$^\circ$ domain walls in hyperferroelectrics, a class of proper ferroelectrics with persistent polarization in the presence of a depolarization field, using density functional theory. We obtain zero temperature equilibrium structures of head-to-head and tail-to-tail walls in recently discovered $ABC$-type hexagonal hyperferroelectrics. Charged domain walls can also be stabilized in canonical ferroelectrics represented by LiNbO$_3$ without any dopants, defects or mechanical clamping. First-principles electronic structure calculations show that charged domain walls can reduce and even close the band gap of host materials and support quasi-two-dimensional electron(hole) gas with enhanced electrical conductivity.
We present a new procedure to measure the large-scale carbon monoxide (CO) emissions across cosmic history. As a tracer of large-scale structure (LSS), the CO gas content as a function of redshift can be quantified by its three-dimensional fluctuation power spectra. Furthermore, cross-correlating CO emission with other LSS tracers offers a way to measure the emission as a function of scale and redshift. Here we introduce the model relevant for such a cross-correlation measurement between CO and other LSS tracers, and between different CO rotational lines. We propose a novel use of Cosmic Microwave Background (CMB) data and attempt to extract redshifted CO emissions embedded in the Wilkinson Microwave Anisotropy Probe (WMAP) dataset. We cross-correlate the all-sky WMAP7 data with LSS data sets, namely, the photometric quasar sample and the luminous red galaxy sample from the Sloan Digital Sky Survey Data Release 6 and 7 respectively. We are unable to detect a cross-correlation signal with either CO(1-0) nor CO(2-1) lines, mainly due to the instrumental noise in the WMAP data. However, we are able to rule out models more than three times greater than our more optimistic model. We discuss the cross-correlation signal from the thermal Sunyaev-Zeldovich effect and dust as potential contaminants, and quantify their impact for our CO measurements. We discuss forecasts for current CMB experiments and a hypothetical future CO focused experiment, and propose to cross-correlate CO temperature data with the Hobby-Eberly Telescope Dark Energy Experiment Ly-alpha-emitter sample, for which a signal-to-noise ratio of 58 is possible.
We present results from multi-epoch neutral hydrogen (HI) absorption observations of six bright pulsars with the Arecibo telescope. Moving through the interstellar medium (ISM) with transverse velocities of 10--150 AU/yr, these pulsars have swept across 1--200 AU over the course of our experiment, allowing us to probe the existence and properties of the tiny scale atomic structure (TSAS) in the cold neutral medium (CNM). While most of the observed pulsars show no significant change in their HI absorption spectra, we have identified at least two clear TSAS-induced opacity variations in the direction of B1929+10. These observations require strong spatial inhomogeneities in either the TSAS clouds' physical properties themselves or else in the clouds' galactic distribution. While TSAS is occasionally detected on spatial scales down to 10 AU, it is too rare to be characterized by a spectrum of turbulent CNM fluctuations on scales of 10-1000 AU, as previously suggested by some work. In the direction of B1929+10, an apparent correlation between TSAS and interstellar clouds inside the warm Local Bubble (LB) indicates that TSAS may be tracing the fragmentation of the LB wall via hydrodynamic instabilities. While similar fragmentation events occur frequently throughout the ISM, the warm medium surrounding these cold cloudlets induces a natural selection effect wherein small TSAS clouds evaporate quickly and are rare, while large clouds survive longer and become a general property of the ISM.
For reaction-diffusion processes without exclusion, in which the particles can exist in the same site of a one-dimensional lattice, we study all the integrable models which can be obtained by imposing a boundary condition on the master equation of the asymmetric diffusion process. The annihilation process is also added. The Bethe ansatz solution and the exact N-particle conditional probabilities are obtained.
Spin-resolved band structures of L-gap surface states on Ag(111) and Cu(111) are investigated by spin- and angle-resolved photoelectron spectroscopy (SARPES) with a vacuum-ultra-violet laser. The observed spin textures of the Ag(111) and Cu(111) surface states agree with that expected by the conventional Rashba effect. The Rashba parameter of the Ag(111) surface state is estimated quantitatively and is 80% of that of Cu(111). The surface-state wave function is found to be predominantly of even mirror-symmetry with negligible odd contribution by SARPES using a linearly polarized light. The results are consistent with our theoretical calculations for the orbital-resolved surface state.
Differentiable real function reproducing primes up to a given number and having a differentiable inverse function is constructed. This inverse function is compared with the Riemann-Von Mangoldt exact expression for the number of primes not exceeding a given value. Software for computation of the direct and inverse functions and their derivatives is developed. Examples of approximate solution of Diophantine equations on the primes are given.
We study the production of a W boson in association with n hard QCD jets (for n>=2), with a particular emphasis on results relevant for the Large Hadron Collider (7 TeV and 8 TeV). We present predictions for this process from High Energy Jets, a framework for all-order resummation of the dominant contributions from wide-angle QCD emissions. We first compare predictions against recent ATLAS data and then shift focus to observables and regions of phase space where effects beyond NLO are expected to be large.
The low temperature properties of glass are distinct from those of crystals due to the presence of poorly understood low-energy excitations. The tunneling model proposes that these are atoms tunneling between nearby equilibria, forming tunneling two level systems (TLSs). This model is rather successful, but it does not explain the remarkably universal value of the mechanical dissipation $Q^{-1}$ near 1 kelvin. The only known exceptions to this universality are the $Q^{-1}$ of certain thin films of amorphous silicon, carbon and germanium. Recently, it was found that $Q^{-1}$ of amorphous silicon (a-Si) films can be reduced by two orders of magnitude by increasing the temperature of the substrate during deposition. According to the tunneling model, the reduction in $Q^{-1}$ at 1 kelvin implies a reduction in $P_{0}\gamma ^{2}$, where $P_{0}$ is the density of TLSs and $\gamma $ is their coupling to phonons. In this preliminary report, we demonstrate elastic measurements of a-Si films down to 20 mK. This will allow us, in future work, to determine whether $P_{0}$ or $\gamma $ is responsible for the reduction in $Q^{-1}$ with deposition temperature.
We study the fluctuation of the number of particles in ideal Bose-Einstein condensates, both within the canonical and the microcanonical ensemble. Employing the Mellin-Barnes transformation, we derive simple expressions that link the canonical number of condensate particles, its fluctuation, and the difference between canonical and microcanonical fluctuations to the poles of a Zeta function that is determined by the excited single-particle levels of the trapping potential. For the particular examples of one- and three-dimensional harmonic traps we explore the microcanonical statistics in detail, with the help of the saddle-point method. Emphasizing the close connection between the partition theory of integer numbers and the statistical mechanics of ideal Bosons in one-dimensional harmonic traps, and utilizing thermodynamical arguments, we also derive an accurate formula for the fluctuation of the number of summands that occur when a large integer is partitioned.
Salient object detection has been long studied to identify the most visually attractive objects in images/videos. Recently, a growing amount of approaches have been proposed all of which rely on the contour/edge information to improve detection performance. The edge labels are either put into the loss directly or used as extra supervision. The edge and body can also be learned separately and then fused afterward. Both methods either lead to high prediction errors near the edge or cannot be trained in an end-to-end manner. Another problem is that existing methods may fail to detect objects of various sizes due to the lack of efficient and effective feature fusion mechanisms. In this work, we propose to decompose the saliency detection task into two cascaded sub-tasks, \emph{i.e.}, detail modeling and body filling. Specifically, the detail modeling focuses on capturing the object edges by supervision of explicitly decomposed detail label that consists of the pixels that are nested on the edge and near the edge. Then the body filling learns the body part which will be filled into the detail map to generate more accurate saliency map. To effectively fuse the features and handle objects at different scales, we have also proposed two novel multi-scale detail attention and body attention blocks for precise detail and body modeling. Experimental results show that our method achieves state-of-the-art performances on six public datasets.
Consider a scenario where we are supplied with a number of ready-to-use models trained on a certain source domain and hope to directly apply the most appropriate ones to different target domains based on the models' relative performance. Ideally we should annotate a validation set for model performance assessment on each new target environment, but such annotations are often very expensive. Under this circumstance, we introduce the problem of ranking models in unlabeled new environments. For this problem, we propose to adopt a proxy dataset that 1) is fully labeled and 2) well reflects the true model rankings in a given target environment, and use the performance rankings on the proxy sets as surrogates. We first select labeled datasets as the proxy. Specifically, datasets that are more similar to the unlabeled target domain are found to better preserve the relative performance rankings. Motivated by this, we further propose to search the proxy set by sampling images from various datasets that have similar distributions as the target. We analyze the problem and its solutions on the person re-identification (re-ID) task, for which sufficient datasets are publicly available, and show that a carefully constructed proxy set effectively captures relative performance ranking in new environments. Code is available at \url{https://github.com/sxzrt/Proxy-Set}.
Normalization methods improve both optimization and generalization of ConvNets. To further boost performance, the recently-proposed switchable normalization (SN) provides a new perspective for deep learning: it learns to select different normalizers for different convolution layers of a ConvNet. However, SN uses softmax function to learn importance ratios to combine normalizers, leading to redundant computations compared to a single normalizer.   This work addresses this issue by presenting Sparse Switchable Normalization (SSN) where the importance ratios are constrained to be sparse. Unlike $\ell_1$ and $\ell_0$ constraints that impose difficulties in optimization, we turn this constrained optimization problem into feed-forward computation by proposing SparsestMax, which is a sparse version of softmax. SSN has several appealing properties. (1) It inherits all benefits from SN such as applicability in various tasks and robustness to a wide range of batch sizes. (2) It is guaranteed to select only one normalizer for each normalization layer, avoiding redundant computations. (3) SSN can be transferred to various tasks in an end-to-end manner. Extensive experiments show that SSN outperforms its counterparts on various challenging benchmarks such as ImageNet, Cityscapes, ADE20K, and Kinetics.
We aim to detect and identify multiple objects using multiple cameras and computer vision for disaster response drones. The major challenges are taming detection errors, resolving ID switching and fragmentation, adapting to multi-scale features and multiple views with global camera motion. Two simple approaches are proposed to solve these issues. One is a fast multi-camera system that added a tracklet association, and the other is incorporating a high-performance detector and tracker to resolve restrictions. (...) The accuracy of our first approach (85.71%) is slightly improved compared to our baseline, FairMOT (85.44%) in the validation dataset. In the final results calculated based on L2-norm error, the baseline was 48.1, while the proposed model combination was 34.9, which is a great reduction of error by a margin of 27.4%. In the second approach, although DeepSORT only processes a quarter of all frames due to hardware and time limitations, our model with DeepSORT (42.9%) outperforms FairMOT (71.4%) in terms of recall. Both of our models ranked second and third place in the `AI Grand Challenge' organized by the Korean Ministry of Science and ICT in 2020 and 2021, respectively. The source codes are publicly available at these URLs (github.com/mlvlab/drone_ai_challenge, github.com/mlvlab/Drone_Task1, github.com/mlvlab/Rony2_task3, github.com/mlvlab/Drone_task4).
A stable-like process is a Feller process $(X_t)_{t\geq 0}$ taking values in $\mathbb{R}^d$ and whose generator behaves, locally, like an $\alpha$-stable L\'evy process, but the index $\alpha$ and all other characteristics may depend on the state space. More precisely, the jump measure need not to be symmetric and it strongly depends on the current state of the process; moreover, we do not require the gradient term to be dominated by the pure jump part. Our approach is to understand the above phenomena as suitable microstructural perturbations.   We show that the corresponding martingale problem is well-posed, and its solution is a strong Feller process which admits a transition density. For the transition density we obtain a representation as a sum of an explicitly given principal term -- this is essentially the density of an $\alpha$-stable random variable whose parameters depend on the current state $x$ -- and a residual term; the $L^\infty\otimes L^1$-norm of the residual term is negligible and so is, under an additional structural assumption, the $L^\infty\otimes L^\infty$-norm. Concrete examples illustrate the relation between the assumptions and possible transition density estimates.
Current research in Computer Vision has shown that Convolutional Neural Networks (CNN) give state-of-the-art performance in many classification tasks and Computer Vision problems. The embedding of CNN, which is the internal representation produced by the last layer, can indirectly learn topological and relational properties. Moreover, by using a suitable loss function, CNN models can learn invariance to a wide range of non-linear distortions such as rotation, viewpoint angle or lighting condition. In this work, new insights are discovered about CNN embeddings and a new loss function is proposed, derived from the contrastive loss, that creates models with more predicable mappings and also quantifies distortions. In typical distortion-dependent methods, there is no simple relation between the features corresponding to one image and the features of this image distorted. Therefore, these methods require to feed-forward inputs under every distortions in order to find the corresponding features representations. Our contribution makes a step towards embeddings where features of distorted inputs are related and can be derived from each others by the intensity of the distortion.
The increase in luminosity as a star evolves on the red-giant branch is interrupted briefly when the hydrogen-burning shell reaches the vicinity of the composition discontinuity left behind from the first convective dredge-up. The non-monotonic variation of luminosity causes an accumulation of stars, known as the `bump', in the distribution of stars in the colour-magnitude diagrams of stellar clusters, which has substantial diagnostic potential. Here I present numerical results on this behaviour and discuss the physical reason for the luminosity variation, with the goal of strengthening the understanding of origin of the phenomenon and hence of its diagnostic potential.