ShantanuT01/DACTYL-Pretraining huggingface dataset:
\emph{Bidirected graphs} (a sort of nonstandard graphs introduced by Edmonds and Johnson) provide a natural generalization to the notions of directed and undirected graphs. By a \emph{weakly (node- or edge-) acyclic} bidirected graph we mean such graph having no (node- or edge-) simple cycles. We call a bidirected graph \emph{strongly acyclic} if it has no cycles (even non-simple). Unlike the case of standard graphs, a bidirected graph may be weakly acyclic but still have non-simple cycles.   Testing a given bidirected graph for weak acyclicity is a challenging combinatorial problem, which also has a number of applications (e.g. checking a perfect matching in a general graph for uniqueness). We present (generalizing results of Gabow, Kaplan, and Tarjan) a modification of the depth-first search algorithm that checks (in linear time) if a given bidirected graph is weakly acyclic (in case of negative answer a simple cycle is constructed).   Our results are best described in terms of \emph{skew-symmetric graphs} (the latter give another, somewhat more convenient graph language which is essentially equivalent to the language of bidirected graphs).   We also give structural results for the class of weakly acyclic bidirected and skew-symmetric graphs explaining how one can construct any such graph starting from strongly acyclic instances and, vice versa, how one can decompose a weakly acyclic graph into strongly acyclic ``parts''. Finally, we extend acyclicity test to build (in linear time) such a decomposition.
We study critical GI/G/1 queues under finite second moment assumptions. We show that the busy period distribution is regularly varying with index half. We also review previously known M/G/1/ and M/M/1 derivations, yielding exact asymptotics as well as a similar derivation for GI/M/1. The busy period asymptotics determine the growth rate of moments of the renewal process counting busy cycles. We further use this to demonstrate a BRAVO phenomenon (Balancing Reduces Asymptotic Variance of Outputs) for the work-output process (namely the busy-time). This yields new insight on the BRAVO effect.   A second contribution of the paper is in settling previous conjectured results about GI/G/1 and GI/G/s BRAVO. Previously, infinite buffer BRAVO was generally only settled under fourth-moment assumptions together with an assumption about the tail of the busy-period. In the current paper we strengthen the previous results by reducing to assumptions to existence of $2+\epsilon$ moments.
We prove several results on the number of rational points on open subsets of Kummer varieties of arbitrary dimension. Some of our results are unconditional, and others depend on the Parity Conjecture (a corollary of the Conjecture of Birch and Swinnerton-Dyer). As examples, we show that (conditional on the Parity Conjecture) all odd-dimension Kummer varieties over Q which are quotients of absolutely simple abelian varieties have dense rational points, and we construct an infinite family of K3 surfaces with dense rational points.
We investigate the probability that a random odd composite number passes a random Fermat primality test, improving on earlier estimates in moderate ranges. For example, with random numbers to $2^{200}$, our results improve on prior estimates by close to 3 orders of magnitude.
It has been reported that some X-ray spectra of gamma-ray burst (GRB) afterglows cannot be fitted by a simple power law. A blackbody component is added to precisely fit the thermal feature in these spectra. Alternatively, we propose that bremsstrahlung radiation can also be one possible mechanism to explain the thermal component of the GRB X-ray afterglow. In particular, we examine the X-ray afterglow of the ultra-long GRB 130925A in this paper. By our calculation, we find that the X-ray thermal component observed by both Swift-XRT and NuSTAR can be well explained by the bremsstrahlung radiation. Our results indicate that the GRBs with the bremsstrahlung emission in the X-ray afterglow could be born in a metal-rich and dusty environment.
The investigation of the orientation of galaxies is a standard test concerning to scenarios of galaxy formation, because different theories of galaxy formation make various predictions regarding to the angular momentum of galaxies. The new method of analysis of the alignment of galaxies in clusters was proposed in the paper Godlowski (2012) and now is improved. We analyzed the distribution of the position angles of the galaxy major axes, as well as the distribution of two angles describing the spatial orientation of galaxy plane, which gives the information about galaxy angular momenta. We discuss the orientation of galaxies in groups and clusters of galaxies. The results show the dependence of the alignment with respect to clusters richness. The implications of the results for theories of galaxy formation are discussed as well.
In this work, we study the problem of training deep networks for semantic image segmentation using only a fraction of annotated images, which may significantly reduce human annotation efforts. Particularly, we propose a strategy that exploits the unpaired image style transfer capabilities of CycleGAN in semi-supervised segmentation. Unlike recent works using adversarial learning for semi-supervised segmentation, we enforce cycle consistency to learn a bidirectional mapping between unpaired images and segmentation masks. This adds an unsupervised regularization effect that boosts the segmentation performance when annotated data is limited. Experiments on three different public segmentation benchmarks (PASCAL VOC 2012, Cityscapes and ACDC) demonstrate the effectiveness of the proposed method. The proposed model achieves 2-4% of improvement with respect to the baseline and outperforms recent approaches for this task, particularly in low labeled data regime.
Metal production in galaxies traces star formation, and is highly concentrated toward the centers of galactic discs. This suggests that galaxies should have inhomogeneous metal distributions with strong radial gradients, but observations of present-day galaxies show only shallow gradients with little azimuthal variation, implying the existence of a redistribution mechanism. We study the role of gravitational instability-driven turbulence as a mixing mechanism by simulating an isolated galactic disc at high resolution, including metal fields treated as passive scalars. Since any cylindrical field can be decomposed into a sum of Fourier-Bessel basis functions, we set up initial metal fields characterized by these functions and study how different modes mix. We find both shear and turbulence contribute to mixing, but the mixing strongly depends on the symmetries of the mode. Non-axisymmetric modes have decay times smaller than the galactic orbital period because shear winds them up to small spatial scales, where they are erased by turbulence. The decay timescales for axisymmetric modes are much greater, though for all but the largest-scale inhomogeneities the mixing timescale is still short enough to erase chemical inhomogeneities over cosmological times. These different timescales provide an explanation for why galaxies retain metallicity gradients while there is almost no variation at a fixed radius. Moreover, the comparatively long timescales required for mixing axisymmetric modes may explain the greater diversity of metallicity gradients observed in high redshift galaxies as compared to local ones: these systems have not yet reached equilibrium between metal production and diffusion.
A two-layer system coupled via tunneling and with different carrier masses in each layer is investigated in the integer quantum Hall regime. Striking deviations of the one-layer Hall conductivity from the usual quantization are found, if resonance between Landau levels of different layers is achieved. The appearance of negative jumps in the Hall conductivity is also predicted under suitable conditions. The results obtained are robust against disorder in the system.
Density fluctuations produced by supersonic turbulence are of great importance to astrophysical chemical models. A property of these density fluctuations is that the two point correlation function decreases with increasing scale separation. The relation between the density decorrelation length scale ($L_{\rm dec}$) and the turbulence driving scale ($L_{\rm drive}$) determines how turbulence affects the density and chemical structures in the interstellar medium (ISM), and is a key component for using observations of atomic and molecular tracers to constrain turbulence properties. We run a set of numerical simulations of supersonic magnetohydrodynamic turbulence, with different sonic Mach numbers ($\mathcal{M}_s=4.5, 7$) , and driven on varying scales (1/2.5, 1/5, 1/7) the box length. We derive the $L_{\rm dec}-L_{\rm drive}$ relation as a function of Mach number, driving scale, and the orientation of the line-of-sight (LOS) in respect to the magnetic-field. We find that the mean ratio $L_{\rm dec}/L_{\rm drive} = 0.19 \pm 0.10$, when averaged over snapshots, Mach numbers, driving lengths, and the three LOSs. For LOS parallel to the magnetic field the density structures are statistically smaller and the $L_{\rm dec}-L_{\rm drive}$ relation is tighter, with $L_{\rm dec}/L_{\rm drive} = 0.112 \pm 0.024$. We discuss our results in the context of using observations of chemical tracers to constrain the dominant turbulence driving scale in the ISM.
In this paper, we first establish Hopf's lemmas for parabolic fractional equations and parabolic fractional $p$-equations. Then we derive an asymptotic Hopf's lemma for antisymmetric solutions to parabolic fractional equations. We believe that these Hopf's lemmas will become powerful tools in obtaining qualitative properties of solutions for nonlocal parabolic equations.
We provide a simple proof for the necessity of conditions for discriminating with minimum error between a known set of quantum states.
Information loss paradox is still a challenging problem in theoretical physics. In this essay, for statics BTZ black holes and Schwarzschild black holes, we propose a simple solution based on the similarity between black holes and topological insulators. That is, the Hawking radiation is pure due to the entanglement between the left-moving sector and right-moving sector of the Hawking radiation. And this entanglement may be detected in an analogue black hole in the near future.
Combinatorial Scoring games, with the property `extra pass moves for a player does no harm', are characterized. The characterization involves an order embedding of Conway's Normal-play games. Also, we give a theorem for comparing games with scores (numbers) which extends Ettinger's work on dicot Scoring games.
Radiative feedback can influence subsequent star formation. We quantify the heating from OB stars in the local star-forming regions in the JCMT Gould Belt survey. Dust temperatures are calculated from 450/850 micron flux ratios from SCUBA-2 observations at the JCMT assuming a fixed dust opacity spectral index $\beta=1.8$. Mean dust temperatures are calculated for each submillimetre clump along with projected distances from the main OB star in the region. Temperature vs. distance is fit with a simple model of dust heating by the OB star radiation plus the interstellar radiation field and dust cooling through optically thin radiation. Classifying the heating sources by spectral type, O-type stars produce the greatest clump average temperature rises and largest heating extent, with temperatures over 40 K and significant heating out to at least 2.4 pc. Early-type B stars (B4 and above) produce temperatures of over 20 K and significant heating over 0.4 pc. Late-type B stars show a marginal heating effect within 0.2 pc. For a given projected distance, there is a significant scatter in clump temperatures that is due to local heating by other luminous stars in the region, projection effects, or shadowing effects. Even in these local, `low-mass' star-forming regions, radiative feedback is having an effect on parsec scales, with 24% of the clumps heated to at least 3 K above the 15 K base temperature expected from heating by only the interstellar radiation field, and a mean dust temperature for heated clumps of 24 K.
Many power line companies are using UAVs to perform their inspection processes instead of putting their workers at risk by making them climb high voltage power line towers, for instance. A crucial task for the inspection is to detect and classify assets in the power transmission lines. However, public data related to power line assets are scarce, preventing a faster evolution of this area. This work proposes the Power Line Assets Dataset, containing high-resolution and real-world images of multiple high-voltage power line components. It has 2,409 annotated objects divided into five classes: transmission tower, insulator, spacer, tower plate, and Stockbridge damper, which vary in size (resolution), orientation, illumination, angulation, and background. This work also presents an evaluation with popular deep object detection methods, showing considerable room for improvement. The STN PLAD dataset is publicly available at https://github.com/andreluizbvs/PLAD.
A photonic crystal fiber based source of photon pairs in symmetric factorable state is experimentally demonstrated. The idler and signal photons, with almost the same bandwidth, are centering at 807.5 nm and 1364.1 nm, respectively. The property of the spectral de-correlation is confirmed by measuring the correlation dependence of photon pairs upon the spectra of pump, signal and idler photons.
In this paper, we propose a method for coarse camera pose computation which is robust to viewing conditions and does not require a detailed model of the scene. This method meets the growing need of easy deployment of robotics or augmented reality applications in any environments, especially those for which no accurate 3D model nor huge amount of ground truth data are available. It exploits the ability of deep learning techniques to reliably detect objects regardless of viewing conditions. Previous works have also shown that abstracting the geometry of a scene of objects by an ellipsoid cloud allows to compute the camera pose accurately enough for various application needs. Though promising, these approaches use the ellipses fitted to the detection bounding boxes as an approximation of the imaged objects. In this paper, we go one step further and propose a learning-based method which detects improved elliptic approximations of objects which are coherent with the 3D ellipsoid in terms of perspective projection. Experiments prove that the accuracy of the computed pose significantly increases thanks to our method and is more robust to the variability of the boundaries of the detection boxes. This is achieved with very little effort in terms of training data acquisition -- a few hundred calibrated images of which only three need manual object annotation. Code and models are released at https://github.com/zinsmatt/3D-Aware-Ellipses-for-Visual-Localization.
We present new measurements of the diffuse radio emission from the Coma cluster of galaxies at 1.4 GHz using the Effelsberg 100-m-telescope. The halo source Coma C has an extent down to noise of about 80 arcmin corresponding to 3 Mpc (H_0 = 50 km/s/Mpc) in Coma. The radio map reveals clear similarities with images of the extended X-ray halo of the Coma cluster. After subtracting the contributions from point sources we obtained an integrated diffuse flux density of S = 640 +- 35 mJy from Coma C. We derive relations between the various observationally determined spectral indices and the spectral index of the synchrotron emissivity, which allow one to achieve a rough estimate concerning the consistency of the presently available data at different frequencies and to place constraints on the emissivity index distribution. In the halo's core region, the inferred emissivity index between 0.3 GHz and 1.4 GHz appears to be in the range 0.4 - 0.75 implying that there must be some very effective mechanism for particle acceleration operating in the intracluster medium.
Recently, scene text detection has been a challenging task. Texts with arbitrary shape or large aspect ratio are usually hard to detect. Previous segmentation-based methods can describe curve text more accurately but suffer from over segmentation and text adhesion. In this paper, we propose attention-based feature decomposition-reconstruction network for scene text detection, which utilizes contextual information and low-level feature to enhance the performance of segmentation-based text detector. In the phase of feature fusion, we introduce cross level attention module to enrich contextual information of text by adding attention mechanism on fused multi-scaled feature. In the phase of probability map generation, a feature decomposition-reconstruction module is proposed to alleviate the over segmentation problem of large aspect ratio text, which decomposes text feature according to their frequency characteristic and then reconstructs it by adding low-level feature. Experiments have been conducted on two public benchmark datasets and results show that our proposed method achieves state-of-the-art performance.
We study an inhomogeneous critical Ising chain in a transverse field whose couplings decay exponentially from the center. In the strong inhomogeneity limit we apply Fisher's renormalization group to show that the ground state is formed by concentric singlets similar to those of the rainbow state of the XX model. In the weak inhomogeneity limit we map the model to a massless Majorana fermion living in a hyperbolic spacetime, where, using CFT techniques, we derive the entanglement entropy that is violated linearly. We also study an inhomogeneous non-critical Ising model that for weak inhomogeneity is mapped to a massive Majorana fermion, while for strong inhomogeneity regime it exhibits trivial and non-trivial topological phases and a separation between regions with high and low entanglement. We also present the entanglement Hamiltonian of the model.
We prove a Central Limit Theorem for the empirical measure in the one-dimensional Totally Asymmetric Zero-Range Process in the hyperbolic scaling $N$, starting from the equilibrium measure $\nu_{\rho}$. We also show that when taking the direction of the characteristics, the limit density fluctuation field does not evolve in time until $N^{4/3}$, which implies the current across the characteristics to vanish in this longer time scale.
We reexamine the functional renormalization-group theory of wetting transitions. As a starting point of the analysis we apply an exact equation describing renormalization group flow of the generating functional for irreducible vertex functions. We show how the standard nonlinear renormalization group theory of wetting transitions can be recovered by a very simple truncation of the exact flow equation. The derivation makes all the involved approximations transparent and demonstrates the applicability of the approach in any spatial dimension d\geq 2. Exploiting the non-uniqueness of the renormalization-group cutoff scheme, we find however, that the capillary parameter omega is a scheme-dependent quantity below d=3. For d=3 the parameter omega is perfectly robust against scheme variation.
Multi-person tracking plays a critical role in the analysis of surveillance video. However, most existing work focus on shorter-term (e.g. minute-long or hour-long) video sequences. Therefore, we propose a multi-person tracking algorithm for very long-term (e.g. month-long) multi-camera surveillance scenarios. Long-term tracking is challenging because 1) the apparel/appearance of the same person will vary greatly over multiple days and 2) a person will leave and re-enter the scene numerous times. To tackle these challenges, we leverage face recognition information, which is robust to apparel change, to automatically reinitialize our tracker over multiple days of recordings. Unfortunately, recognized faces are unavailable oftentimes. Therefore, our tracker propagates identity information to frames without recognized faces by uncovering the appearance and spatial manifold formed by person detections. We tested our algorithm on a 23-day 15-camera data set (4,935 hours total), and we were able to localize a person 53.2% of the time with 69.8% precision. We further performed video summarization experiments based on our tracking output. Results on 116.25 hours of video showed that we were able to generate a reasonable visual diary (i.e. a summary of what a person did) for different people, thus potentially opening the door to automatic summarization of the vast amount of surveillance video generated every day.
We report on NMR and torque measurements on the frustrated quasi-two-dimensional spin-dimer system SrCu2(BO3)2 in magnetic fields up to 34 T that reveal a sequence of magnetization plateaus at 1/8, 2/15, 1/6, and 1/4 of the saturation and two incommensurate phases below and above the 1/6 plateau. The magnetic structures determined by NMR involve a stripe order of triplets in all plateaus, suggesting that the incommensurate phases originate from proliferation of domain walls. We propose that the magnetization process of SrCu2(BO3)2 is best described as an incomplete devil's staircase.
Whenever a compound exhibits a spontaneous muSR oscillation, long-range magnetic ordering is usually inferred. Here we show that some caution is required. The coherence length needs not to be large for a spontaneous muon spin precession to be observed. Establishing the incommensurate nature of a magnetic structure, solely based on muSR measurements, may not be reliable. The absence of a spontaneous muon precession at low temperature does not mean that the system under investigation does not display long-range magnetic ordering.   The relaxation measured in zero and longitudinal field in the quasi-static limit is usually analyzed in the framework of the strong-collision model, the static polarization function being taken to be the famous Kubo-Toyabe function. This might not be satisfactory if short-range correlation effects are strong. Here we propose a method based on the maximum entropy concept and reverse Monte Carlo technique which gives results consistent with those obtained in 2013 by analytical means for the considered example.
Some analytical spherical-symmetric equilibrium solutions are cosidered in the Newtonian theory of gravitation with deviations from 1/r law. Stability of star against a first-order phase-transition is shown to be particularly influenced by the deviations of degree in the power-law potential.
Convolutional neural networks (CNNs) have shown dramatic improvements in single image super-resolution (SISR) by using large-scale external samples. Despite their remarkable performance based on the external dataset, they cannot exploit internal information within a specific image. Another problem is that they are applicable only to the specific condition of data that they are supervised. For instance, the low-resolution (LR) image should be a "bicubic" downsampled noise-free image from a high-resolution (HR) one. To address both issues, zero-shot super-resolution (ZSSR) has been proposed for flexible internal learning. However, they require thousands of gradient updates, i.e., long inference time. In this paper, we present Meta-Transfer Learning for Zero-Shot Super-Resolution (MZSR), which leverages ZSSR. Precisely, it is based on finding a generic initial parameter that is suitable for internal learning. Thus, we can exploit both external and internal information, where one single gradient update can yield quite considerable results. (See Figure 1). With our method, the network can quickly adapt to a given image condition. In this respect, our method can be applied to a large spectrum of image conditions within a fast adaptation process.