ShantanuT01/DACTYL-Pretraining huggingface dataset:
We study a two-dimensional spin model obtained by "Higgsing" the rank-2 U(1) lattice gauge theory (LGT) with scalar or vector charges on the L_x * L_y square lattice under the periodic boundary condition (PBC). There are p degrees of freedom per orbital and three orbitals per unit cell in the spin model. The resulting spin model is a stabilizer code consisting of three mutually commuting projectors that are, in turn, obtained by Higgsing the mutually commuting Gauss's law operators and the magnetic field operators in the underlying LGT. The spin model thus obtained is exactly solvable, with the ground state degeneracy (GSD) D given by log_p D=2+(1+delta_{L_x mod p,0})(1+delta_{L_y mod p,0}) when p is a prime number. Two types of dipole excitations, pristine and emergent, are identified. Both the monopoles and the dipoles are free to move, with restrictions on monopoles to hop only by p lattice spacing along with certain directions. The monopole-monopole braiding phase depends on the separation of the x or y coordinates of the initial monopole positions, making it distinct from the ordinary anyon braiding statistics. The monopole-dipole braiding obeys the usual anyonic statistics. Despite the oddity, the monopole-monopole braiding phase can be understood as the Aharonov-Bohm phase of some emergent vector potentials.
State-of-the-art deep learning methods for image processing are evolving into increasingly complex meta-architectures with a growing number of modules. Among them, region-based fully convolutional networks (R-FCN) and deformable convolutional nets (DCN) can improve CAD for mammography: R-FCN optimizes for speed and low consumption of memory, which is crucial for processing the high resolutions of to 50 micrometers used by radiologists. Deformable convolution and pooling can model a wide range of mammographic findings of different morphology and scales, thanks to their versatility. In this study, we present a neural net architecture based on R-FCN / DCN, that we have adapted from the natural image domain to suit mammograms -- particularly their larger image size -- without compromising resolution. We trained the network on a large, recently released dataset (Optimam) including 6,500 cancerous mammograms. By combining our modern architecture with such a rich dataset, we achieved an area under the ROC curve of 0.879 for breast-wise detection in the DREAMS challenge (130,000 withheld images), which surpassed all other submissions in the competitive phase.
We present extended simulations of the distribution of galaxy clusters in different dark matter models, using an optimized version of the Zel'dovich approximation. Six different dark matter models are studied: Standard CDM, Open CDM, Tilted CDM, Cold + Hot DM, low-density CDM with cosmological constant, and low Hubble constant ($h=0.3$) CDM. We compare simulations with an Abell/ACO redshift sample. We find that the models that best reproduce the clustering of the real data sets are the Cold + Hot DM model and the CDM model with a cosmological constant. The probability density function of all models and data is always well approximated by a lognormal distribution. We also find that the linear biasing parameter for the simulated clusters is nearly constant over a large range of scales, but its value depends on the model. We also note that the abundances of clusters predicted by the Press-Schechter theory provide strong constraints on these models: only the Cold + Hot DM model and the low-H CDM model appear to produce the correct number-density of clusters. Taking this constraint together with the cluster clustering statistics leads one to conclude that the best of our models is the Cold + Hot DM scenario.
Under certain plausible assumptions, M. Rubinstein and P. Sarnak solved the Shanks--R\'enyi race problem, by showing that the set of real numbers $x\geq 2$ such that $\pi(x;q,a_1)>\pi(x;q,a_2)>...>\pi(x;q,a_r)$ has a positive logarithmic density $\delta_{q;a_1,...,a_r}$. Furthermore, they established that if $r$ is fixed, $\delta_{q;a_1,...,a_r}\to 1/r!$ as $q\to \infty$. In this paper, we investigate the size of these densities when the number of contestants $r$ tends to infinity with $q$. In particular, we deduce a strong form of a recent conjecture of A. Feuerverger and G. Martin which states that $\delta_{q;a_1,...,a_r}=o(1)$ in this case. Among our results, we prove that $\delta_{q;a_1,...,a_r}\sim 1/r!$ in the region $r=o(\sqrt{\log q})$ as $q\to\infty$. We also bound the order of magnitude of these densities beyond this range of $r$. For example, we show that when $\log q\leq r\leq \phi(q)$, $\delta_{q;a_1,...,a_r}\ll_{\epsilon} q^{-1+\epsilon}$.
In this paper we present a comprehensive survey of 41 X-ray binaries (10 HMXBs and 31 LMXBs) with Chandra, with specific emphasis on the Fe K region and the narrow Fe Kalpha line, at the highest resolution possible. We find that: a) The Fe Kalpha line is always centered at 1.9387 +- 0.0016 Angstroms, compatible with Fe I up to Fe X; we detect no shifts to higher ionization states nor any difference between HMXBs and LMXBs. b) The line is very narrow, with FWHM < 5 mAngstroms, which means that the reprocessing material is not rotating at high speeds. c) Fe Kalpha fluorescence is present in all the HMXB in the survey while such emissions are very rare (~ 10% ) among LMXBs. d) The lack of Fe line emission is always accompanied by the lack of any detectable K edge. e) We obtain the empirical curve of growth of the equivalent width of the Fe Kalpha line versus the density column of the reprocessing material, i.e. EW_{Kalpha} vs N_{H}, and show that it is consistent with a reprocessing region spherically distributed around the compact object. f) We show that fluorescence in X-ray binaries follows the X-ray Baldwin effect. We interpret this finding as evidence of decreasing neutral Fe abundance with increasing X-ray illumination and use it to explain some spectral states of Cyg X-1 and as a possible cause of the lack of narrow Fe line emission in LMXBs. g) Finally, we study anomalous morphologies. We present the first evidence of a Compton shoulder in the HMXB X1908+075. Also the Fe Kalpha lines of 4U1700-37 and LMC X-4 present asymmetric wings suggesting the presence of highly structured stellar winds in these systems.
A new procedure aiming at disentangling the instrumental profile broadening and the relevant X-ray powder diffraction (XRPD) profile shape is presented. The technique consists of three steps: denoising by means of wavelet transforms, background suppression by morphological functions and deblurring by a Lucy--Richardson damped deconvolution algorithm. Real XRPD intensity profiles of ceria samples are used to test the performances. Results show the robustness of the method and its capability of efficiently disentangling the instrumental broadening affecting the measurement of the intrinsic physical line profile. These features make the whole procedure an interesting and user-friendly tool for the pre-processing of XRPD data.
Galactic diffuse TeV gamma-ray emission is produced by the interaction of high-energy cosmic-ray particles with matter and radiation in our Galaxy. The measurement of Galactic diffuse TeV gamma-ray emission would provide strong constraints on the acceleration and propagation of Galactic cosmic rays. The High Altitude Water Cherenkov (HAWC) Observatory, located in central Mexico at 4100 m above sea level, is sensitive to gamma rays between a few hundreds GeV and about 100 TeV. Thanks to its large field of view of 2 steradians and excellent background estimation, HAWC has a unique capability to observe large extended sources such as Galactic diffuse emission. We present the analysis strategy to detect the Galactic diffuse emission with HAWC, including techniques to remove contaminations from localized sources.
In this paper, a novel video classification method is presented that aims to recognize different categories of third-person videos efficiently. Our motivation is to achieve a light model that could be trained with insufficient training data. With this intuition, the processing of the 3-dimensional video input is broken to 1D in temporal dimension on top of the 2D in spatial. The processes related to 2D spatial frames are being done by utilizing pre-trained networks with no training phase. The only step which involves training is to classify the 1D time series resulted from the description of the 2D signals. As a matter of fact, optical flow images are first calculated from consecutive frames and described by pre-trained CNN networks. Their dimension is then reduced using PCA. By stacking the description vectors beside each other, a multi-channel time series is created for each video. Each channel of the time series represents a specific feature and follows it over time. The main focus of the proposed method is to classify the obtained time series effectively. Towards this, the idea is to let the machine learn temporal features. This is done by training a multi-channel one dimensional Convolutional Neural Network (1D-CNN). The 1D-CNN learns the features along the only temporal dimension. Hence, the number of training parameters decreases significantly which would result in the trainability of the method on even smaller datasets. It is illustrated that the proposed method could reach the state-of-the-art results on two public datasets UCF11, jHMDB and competitive results on HMDB51.
Quark-antiquark pair (or dijet) production at the electron-ion collider (EIC) has been argued to be one of most important processes that allowing to access the Weizs\"acker-Williams (WW) gluon distributions at small $x$ limit. Within the framework of Color Glass Condensate (CGC) effective field theory (EFT), we calculated the dijet cross sections and the azimuthal correlations by including the Sudakov resummations, numerical results shown that the back-to-back correlations are significantly suppressed when the Sudakov resummations are taken into account. In addition, by using the solutions of running-coupling Balitsky-Kovchegov (rcBK) equation, the unpolarized and linearly polarized WW gluon distributions both in coordinate and momentum space are presented.
At first we introduce the Neutrino in the standard Model, then the Dirac and Majorana Masses. After introducing the See-Saw Mechanism, we discuss the neutrino oscillations and the neutrino in astrophysics and cosmology. We finish this paper with a brief summary of the neutrino experiments.
We address the task of 3D semantic scene completion, i.e. , given a single depth image, we predict the semantic labels and occupancy of voxels in a 3D grid representing the scene. In light of the recently introduced generative adversarial networks (GAN), our goal is to explore the potential of this model and the efficiency of various important design choices. Our results show that using conditional GANs outperforms the vanilla GAN setup. We evaluate these architecture designs on several datasets. Based on our experiments, we demonstrate that GANs are able to outperform the performance of a baseline 3D CNN in case of clean annotations, but they suffer from poorly aligned annotations.
We briefly review the experiments of KEK and FINUDA, that claim evidence for deeply bound kaon states, from the perspective of recent theoretical papers and experiments that provide an alternative explanation of the peaks seen. At the same time we show that recent criticisms raised by Akaishi and Yamazaki, and exposed by Akaishi in this Conference, have no base.
The transversal number $\tau(H)$ of a hypergraph $H$ is the minimum number of vertices that intersect every edge of $H$. A linear hypergraph is one in which every two distinct edges intersect in at most one vertex. A $k$-uniform hypergraph has all edges of size $k$. It is known that $\tau(H) \le (n + m)/(k+1)$ holds for all $k$-uniform, linear hypergraphs $H$ when $k \in \{2,3\}$ or when $k \ge 4$ and the maximum degree of $H$ is at most two. It has been conjectured that $\tau(H) \le (n+m)/(k+1)$ holds for all $k$-uniform, linear hypergraphs $H$. We disprove the conjecture for large $k$, and show that the best possible constant $c_k$ in the bound $\tau(H) \le c_k (n+m)$ has order $\ln(k)/k$ for both linear (which we show in this paper) and non-linear hypergraphs. We show that for those $k$ where the conjecture holds, it is tight for a large number of densities if there exists an affine plane $AG(2,k)$ of order $k \ge 2$. We raise the problem to find the smallest value, $k_{\min}$, of $k$ for which the conjecture fails. We prove a general result, which when applied to a projective plane of order $331$ shows that $k_{\min} \le 166$. Even though the conjecture fails for large $k$, our main result is that it still holds for $k=4$, implying that $k_{\min} \ge 5$. The case $k=4$ is much more difficult than the cases $k \in \{2,3\}$, as the conjecture does not hold for general (non-linear) hypergraphs when $k=4$. Key to our proof is the completely new technique of the deficiency of a hypergraph introduced in this paper.
The Ly$\alpha$ escape fraction is a key measure to constrain the neutral state of the intergalactic medium and then to understand how the universe was fully reionized. We combine deep narrowband imaging data from the custom-made filter NB393 and the $H_{2}S$1 filter centered at 2.14 $\mu$m to examine the Ly$\alpha$ emitters and H$\alpha$ emitters at the same redshift $z=2.24$. The combination of these two populations allows us to determine the Ly$\alpha$ escape fraction at $z=2.24$. Over an area of 383 arcmin$^{2}$ in the Extended Chandra Deep Field South (ECDFS), 124 Ly$\alpha$ emitters are detected down to NB393 = 26.4 mag at the 5$\sigma$ level, and 56 H$\alpha$ emitters come from An14. Of these, four have both Ly$\alpha$ and H$\alpha$ emissions (LAHAEs). We measure the individual/volumetric Ly$\alpha$ escape fraction by comparing the observed Ly$\alpha$ luminosity/luminosity density to the extinction-corrected H$\alpha$ luminosity/luminosity density. We revisit the extinction correction for H$\alpha$ emitters using the Galactic extinction law with the color excess for nebular emission. We also adopt the Calzetti extinction law together with an identical color excess for stellar and nebular regions to explore how the uncertainties in extinction correction affect our results. In both cases, an anti-correlation between the Ly$\alpha$ escape fraction and dust attenuation is found among the LAHAEs, suggesting that dust absorption is responsible for the suppression of the escaping Ly$\alpha$ photons. However, the estimated Ly$\alpha$ escape fraction of individual LAHAEs varies up to ~3 percentage points between the two methods of extinction correction. We find the global Ly$\alpha$ escape fraction at $z=2.24$ to be ($3.7\pm1.4$)% in the ECDFS. The variation in the color excess of the extinction causes a discrepancy of ~1 percentage point in the global Ly$\alpha$ escape fraction.
Standing slow mode waves in hot flaring loops are exclusively observed in spectrometers and are used to diagnose the magnetic field strength and temperature of the loop structure. Due to the lack of spatial information, the longitudinal mode cannot be effectively identified. In this study, we simulate standing slow mode waves in flaring loops and compare the synthesized line emission properties with SUMER spectrographic and SDO/AIA imaging observations. We find that the emission intensity and line width oscillations are a quarter period out of phase with Doppler shift velocity both in time and spatial domain, which can be used to identify a standing slow mode wave from spectroscopic observations. However, the longitudinal overtones could be only measured with the assistance of imagers. We find emission intensity asymmetry in the positive and negative modulations, this is because the contribution function pertaining to the atomic emission process responds differently to positive and negative temperature variations. One may detect \textbf{half} periodicity close to the loop apex, where emission intensity modulation is relatively small. The line-of-sight projection affects the observation of Doppler shift significantly. A more accurate estimate of the amplitude of velocity perturbation is obtained by de-projecting the Doppler shift by a factor of $1-2\theta/\pi$ rather than the traditionally used $\cos\theta$. \textbf{If a loop is heated to the hotter wing, the intensity modulation could be overwhelmed by background emission, while the Doppler shift velocity could still be detected to a certain extent.
We present an approach to generalised fermionic statistics which relates the existence of a generalised statistical behaviour to non-invariant ground states. Considering the thermal average of an operatorial generalization of the Heisenberg algebra, we get an occupation number which depends on the degree of mixing between symmetric and antisymmetric sectors of the ground state. A natural prescription is given for the construction of a supersymmetric statistics. We also show that the structure of the vacuum, and therefore the statistical behaviour of the system, can be accounted for in terms of a second order phase transition.
We describe high order accurate and stable finite difference schemes for the initial-boundary value problem associated with the magnetic induction equations. These equations model the evolution of a magnetic field due to a given velocity field. The finite difference schemes are based on Summation by Parts (SBP) operators for spatial derivatives and a Simultaneous Approximation Term (SAT) technique for imposing boundary conditions. We present various numerical experiments that demonstrate both the stability as well as high order of accuracy of the schemes.
Knowledge transfer impacts the performance of deep learning -- the state of the art for image classification tasks, including automated melanoma screening. Deep learning's greed for large amounts of training data poses a challenge for medical tasks, which we can alleviate by recycling knowledge from models trained on different tasks, in a scheme called transfer learning. Although much of the best art on automated melanoma screening employs some form of transfer learning, a systematic evaluation was missing. Here we investigate the presence of transfer, from which task the transfer is sourced, and the application of fine tuning (i.e., retraining of the deep learning model after transfer). We also test the impact of picking deeper (and more expensive) models. Our results favor deeper models, pre-trained over ImageNet, with fine-tuning, reaching an AUC of 80.7% and 84.5% for the two skin-lesion datasets evaluated.
Theoretically motivated smallness of the penguin amplitude in $B \to \pi \pi$ decays allowes to calculate the value of the unitarity triangle angle $\alpha (\phi_2)$ with good accuracy. The relatively large branching ratio of the decay into $\pi^0 \pi^0$ is explained by the large value of FSI phase difference between $\Delta I = 1/2$ and $\Delta I = 3/2$ tree amplitudes.
The structure of amorphous materials has been debated since the 1930's as a binary question: amorphous materials are either Zachariasen continuous random networks (Z-CRNs) or Z-CRNs containing crystallites. It was recently demonstrated, however, that amorphous diamond can be synthesized in either form. Here we address the question of the structure of single-atom-thick amorphous monolayers. We reanalyze the results of prior simulations for amorphous graphene and report kinetic Monte Carlo simulations based on alternative algorithms. We find that crystallite-containing Z-CRN is the favored structure of elemental amorphous graphene, as recently fabricated, whereas the most likely structure of binary monolayer amorphous BN is altogether different than either of the two long-debated options: it is a compositionally disordered "pseudo-CRN" comprising a mix of B-N and noncanonical B-B and N-N bonds and containing "pseudocrystallites", namely honeycomb regions made of noncanonical hexagons. Implications for other non-elemental 2D and bulk amorphous materials are discussed.
We present surface photometry and stellar kinematics of NGC 4264, a lenticular galaxy in the region of the Virgo Cluster undergoing a tidal interaction with its neighbour, NGC 4261. We measured the bar radius and strength from SDSS imaging and the bar pattern speed from MUSE integral-field spectroscopy. We find that NGC 4264 hosts a strong and large bar, which is rotating fast. The accurate measurement of the bar rotation rate allows us to exclude that the formation of the bar was triggered by the ongoing interaction.
The gauge model of nonrelativistic particles on a line interacting with nonstandard gravitational fields [5] is supplemented by the addition of a (non)-Abelian gauge interaction. Solving for the gauge fields we obtain equations, in closed form, for a classical two particle system. The corresponding Schr\"{o}dinger equation, obtained by the Moyal quantization procedure, is solved analytically. Its solutions exhibit two different confinement mechanisms - dependent on the sign of the coupling $\lambda$ to the nonstandard gravitational fields. For $\lambda>0$ confinement is due to a rising potential whereas for $\lambda<0$ it is due to to the dynamical (geometric) bag formation. Numerical results for the corresponding energy spectra are given. For a particular relation between two coupling constants the model fits into the scheme of supersymmetrical quantum mechanics.
We report on a dispersion relation for the \gamma\gamma\to (\pi\pi)_I S-wave in isospin I emphasizing the low energy region. The f_0(980) signal that emerges in \gamma\gamma\to \pi\pi is also discussed. Our results could be used to distinguish between different \pi\pi isoscalar S-wave parameterizations. We also calculate the width of the \sigma resonance to \gamma\gamma and obtain the value \Gamma(\sigma\to\gamma\gamma)=(1.68\pm 0.15) KeV. Finally, we elaborate on the size of the f_0(980) coupling to \pi\pi and show that its smallness compared to the K\bar{K} one is not related to the OZI rule.
The diquark model is used to calculate the electromagnetic polarizabilities and charge radii of the nucleons for three different potentials. Making the scalar diquark lower in mass introduces a mixing angle $\theta $ between the $\left| 56\right\rangle $ and $\left| 70\right\rangle $ states ,which allows an improvement in value of all 6 properties. Generalizing the Gamov-Teller matrix and the magnetic moment operator to the diquark model gives constraints on this mixing. We obtain for the Richardson potential $\theta =23.2^{\circ },$ $\overline{\alpha }_p=7.9_{-0.9}^{+1.0}\times 10^{-4}fm^3,$ $\overline{\alpha }_n=7.7_{-0.6}^{+0.3}\times 10^{-4}fm^3,$ $\overline{\beta }_p=5.4_{-0.4}^{+1.6}\times 10^{-4}fm^3,$ $\overline{\beta }% _n=6.7_{-0.7}^{+1.3}\times 10^{-4}fm^3,$ $\left\langle r^2\right\rangle _p=0.37_{-0.03}^{+0.02}fm^2,$ $\left\langle r^2\right\rangle _n=-0.07_{-0.02}^{+0.03}fm^2.$ Additional pion cloud contributions could improve on all six results.
In a robotised warehouse, as in any place where robots move autonomously, a major issue is the localization or detection of human operators during their intervention in the work area of the robots. This paper introduces a wearable human localization system for large warehouses, which utilize preinstalled infrastructure used for localization of automated guided vehicles (AGVs). A monocular down-looking camera is detecting ground nodes, identifying them and computing the absolute position of the human to allow safe cooperation and coexistence of humans and AGVs in the same workspace. A virtual safety area around the human operator is set up and any AGV in this area is immediately stopped. In order to avoid triggering an emergency stop because of the short distance between robots and human operators, the trajectories of the robots have to be modified so that they do not interfere with the human. The purpose of this paper is to demonstrate an absolute visual localization method working in the challenging environment of an automated warehouse with low intensity of light, massively changing environment and using solely monocular camera placed on the human body.