ShantanuT01/DACTYL-Pretraining huggingface dataset:
M stars are ideal targets to search for Earth-like planets. However, they usually have high levels of magnetic activity, which could affect their habitability and make difficult the detection of exoplanets orbiting around them. Unfortunately, long-term variability of dM stars has not been extensively studied, due to their low intrinsic brightness. For this reason, in 1999 we started the HKalpha project, which systematically observes the spectra of a large number of stars, in particular dM stars, at the Complejo Astronomico El Leoncito (CASLEO). In this work, we study the long-term activity of the young active dM1 star AU Microscopii. We analyze the Mount Wilson index S derived from CASLEO spectra obtained between 2004 and 2016, which we complement with the S-index derived from HARPS, FEROS and UVES public spectra. We also analyze the simultaneous photometric counterpart provided by the ASAS public database for this star between 2000 and 2009, and our own photometry. In both totally independent time series, we detect a possible activity cycle of period around 5 years. We also derived a precise rotation period for this star P rot = 4.85 days, consistent with the literature. This activity cycle reflects that an alphaOmega dynamo could be operating in this star.
Finding the electromagnetic (EM) counterpart of binary compact star merger, especially the binary neutron star (BNS) merger, is critically important for gravitational wave (GW) astronomy, cosmology and fundamental physics. On Aug. 17, 2017, Advanced LIGO and \textit{Fermi}/GBM independently triggered the first BNS merger, GW170817, and its high energy EM counterpart, GRB 170817A, respectively, resulting in a global observation campaign covering gamma-ray, X-ray, UV, optical, IR, radio as well as neutrinos. The High Energy X-ray telescope (HE) onboard \textit{Insight}-HXMT (Hard X-ray Modulation Telescope) is the unique high-energy gamma-ray telescope that monitored the entire GW localization area and especially the optical counterpart (SSS17a/AT2017gfo) with very large collection area ($\sim$1000 cm$^2$) and microsecond time resolution in 0.2-5 MeV. In addition, \textit{Insight}-HXMT quickly implemented a Target of Opportunity (ToO) observation to scan the GW localization area for potential X-ray emission from the GW source. Although it did not detect any significant high energy (0.2-5 MeV) radiation from GW170817, its observation helped to confirm the unexpected weak and soft nature of GRB 170817A. Meanwhile, \textit{Insight}-HXMT/HE provides one of the most stringent constraints (~10$^{-7}$ to 10$^{-6}$ erg/cm$^2$/s) for both GRB170817A and any other possible precursor or extended emissions in 0.2-5 MeV, which help us to better understand the properties of EM radiation from this BNS merger. Therefore the observation of \textit{Insight}-HXMT constitutes an important chapter in the full context of multi-wavelength and multi-messenger observation of this historical GW event.
We report on some properties of the $\xi(s)$ function and its value on the critical line, $\Xi(t)=\xi\left(\tfrac{1}{2}+it\right)$. First, we present some identities that hold for the log derivatives of a holomorphic function. We then re-examine Hadamard's product-form representation of the $\xi(s)$ function, and present a simple proof of the horizontal monotonicity of the modulus of $\xi(s)$. We then show that the $\Xi(t)$ function can be interpreted as the autocorrelation function of a weakly stationary random process, whose power spectral function $S(\omega)$ and $\Xi(t)$ form a Fourier transform pair. We then show that $\xi(s)$ can be formally written as the Fourier transform of $S(\omega)$ into the complex domain $\tau=t-i\lambda$, where $s=\sigma+it=\tfrac{1}{2}+\lambda+it$. We then show that the function $S_1(\omega)$ studied by P\'{o}lya has $g(s)$ as its Fourier transform, where $\xi(s)=g(s)\zeta(s)$. Finally we discuss the properties of the function $g(s)$, including its relationships to Riemann-Siegel's $\vartheta(t)$ function, Hardy's Z-function, Gram's law and the Riemann-Siegel asymptotic formula.
We study a 2-spin quantum Turing architecture, in which discrete local rotations \alpha_m of the Turing head spin alternate with quantum controlled NOT-operations. We show that a single chaotic parameter input \alpha_m leads to a chaotic dynamics in the entire Hilbert space. The instability of periodic orbits on the Turing head and `chaos swapping' onto the Turing tape are demonstrated explicitly as well as exponential parameter sensitivity of the Bures metric.
We consider the transport of rigid objects with internal structure in a flashing ratchet potential by investigating the overdamped behavior of a rod-like chain of evenly spaced point particles. In 1D, analytical arguments show that the velocity can reverse direction multiple times in response to changing the size of the chain or the temperature of the heat bath. The physical reason is that the effective potential experienced by the mechanically coupled objects can have a different symmetry than that of individual objects. All analytical predictions are confirmed by Brownian dynamics simulations. These results may provide a route to simple, coarse-grained models of molecular motor transport that incorporate an object's size and rotational degrees of freedom into the mechanism of transport.
To address the problem of long-tail distribution for the large vocabulary object detection task, existing methods usually divide the whole categories into several groups and treat each group with different strategies. These methods bring the following two problems. One is the training inconsistency between adjacent categories of similar sizes, and the other is that the learned model is lack of discrimination for tail categories which are semantically similar to some of the head categories. In this paper, we devise a novel Adaptive Class Suppression Loss (ACSL) to effectively tackle the above problems and improve the detection performance of tail categories. Specifically, we introduce a statistic-free perspective to analyze the long-tail distribution, breaking the limitation of manual grouping. According to this perspective, our ACSL adjusts the suppression gradients for each sample of each class adaptively, ensuring the training consistency and boosting the discrimination for rare categories. Extensive experiments on long-tail datasets LVIS and Open Images show that the our ACSL achieves 5.18% and 5.2% improvements with ResNet50-FPN, and sets a new state of the art. Code and models are available at https://github.com/CASIA-IVA-Lab/ACSL.
The TeV blazar Markarian 421 underwent multi-TeV flaring during April 2004 and simultaneously observed in x-ray and TeV energies. It was observed that the TeV outbursts had no counterparts in the lower energies, which implies that this might be an orphan flare. In the context of hadronic model, we have shown that this multi-TeV flaring can be produced due to the interaction of Fermi-accelerated protons of energy $\lesssim 168$ TeV with the background photons in the low energy tail of the synchrotron self-Compton spectrum of the blazar jet. We fit very well the flaring spectrum with this model. Based on this study, we speculate that Mrk 501 and PG 1553+113 are possible candidates for orphan flaring in the future.
An elliptic curve $E$ over $\mathbb{Q}$ is said to be good if $N_{E}^{6}<\max\!\left\{ \left\vert c_{4}^{3}\right\vert ,c_{6}^{2}\right\} $ where $N_{E}$ is the conductor of $E$ and $c_{4}$ and $c_{6}$ are the invariants associated to a global minimal model of $E$. In this article, we generalize Masser's Theorem on the existence of infinitely many good elliptic curves with full $2$-torsion. Specifically, we prove via constructive methods that for each of the fifteen torsion subgroups $T$ allowed by Mazur's Torsion Theorem, there are infinitely many good elliptic curves $E$ with $E\!\left(\mathbb{Q}\right) _{\text{tors}}\cong T$.
The recent observation of high-lying Rydberg states of excitons in semiconductors with relatively high binding energy motivates exploring their applications in quantum nonlinear optics and quantum information processing. Here, we study Rydberg excitation dynamics of a mesoscopic array of excitons to demonstrate its application in simulation of quantum many-body dynamics. We show that the $\mathbb{Z}_2$-ordered phase can be reached using physical parameters available for cuprous oxide (Cu$_2$O) by optimizing driving laser parameters such as duration, intensity, and frequency. In an example, we study the application of our proposed system to solving the Maximum Independent Set (MIS) problem based on the Rydberg blockade effect.
The nonelliptic $\mathsf{A_2}$-webs with $k$ "$+$"s on the top boundary and $3n-2k$ "$-$"s on the bottom boundary combinatorially model the space $\mathsf{Hom}_{\mathfrak{sl}_3}(\mathsf{V}^{\otimes (3n-2k)}, \mathsf{V}^{\otimes k})$ of $\mathfrak{sl}_3$-module maps on tensor powers of the natural $3$-dimensional $\mathfrak{sl}_3$-module $\mathsf{V}$, and they have connections with the combinatorics of Springer varieties. Petersen, Pylyavskyy, and Rhodes showed that the set of such $\mathsf{A_2}$-webs and the set of semistandard tableaux of shape $(3^n)$ and type $\{1^2,\dots,k^2,k+1,\dots, 3n-k\}$ have the same cardinalities. In this work, we use the $\sf{m}$-diagrams introduced by Tymoczko and the Robinson-Schensted correspondence to construct an explicit bijection, different from the one given by Russell, between these two sets. In establishing our result, we show that the pair of standard tableaux constructed using the notion of path depth is the same as the pair constructed from applying the Robinson-Schensted correspondence to a $3\,2\,1$-avoiding permutation. We also obtain a bijection between such pairs of standard tableaux and Westbury's $\mathsf{A_2}$ flow diagrams.
Long-term object detection requires the integration of frame-based results over several seconds. For non-deformable objects, long-term detection is often addressed using object detection followed by video tracking. Unfortunately, tracking is inapplicable to objects that undergo dramatic changes in appearance from frame to frame. As a related example, we study hand detection over long video recordings in collaborative learning environments. More specifically, we develop long-term hand detection methods that can deal with partial occlusions and dramatic changes in appearance.   Our approach integrates object-detection, followed by time projections, clustering, and small region removal to provide effective hand detection over long videos. The hand detector achieved average precision (AP) of 72% at 0.5 intersection over union (IoU). The detection results were improved to 81% by using our optimized approach for data augmentation. The method runs at 4.7x the real-time with AP of 81% at 0.5 intersection over the union. Our method reduced the number of false-positive hand detections by 80% by improving IoU ratios from 0.2 to 0.5. The overall hand detection system runs at 4x real-time.
We study analytically the tracer particle mobility in single-file systems with distributed friction constants. Our system serves as a prototype for non-equilibrium, heterogeneous, strongly interacting Brownian systems. The long time dynamics for such a single-file setup belongs to the same universality class as the Rouse model with dissimilar beads. The friction constants are drawn from a density $\varrho(\xi)$ and we derive an asymptotically exact solution for the mobility distribution $P[\mu_0(s)]$, where $\mu_0(s)$ is the Laplace-space mobility. If $\varrho$ is light-tailed (first moment exists) we find a self-averaging behaviour: $P[\mu_0(s)]=\delta[\mu_0(s)-\mu(s)]$ with $\mu(s)\propto s^{1/2}$. When $\varrho(\xi)$ is heavy-tailed, $\varrho(\xi)\simeq \xi^{-1-\alpha} \ (0<\alpha<1)$ for large $\xi$ we obtain moments $\langle [\mu_s(0)]^n\rangle \propto s^{\beta n}$ where $\beta=1/(1+\alpha)$ and no self-averaging. The results are corroborated by simulations.
Hyperspectral image (HSI) classification is widely used for the analysis of remotely sensed images. Hyperspectral imagery includes varying bands of images. Convolutional Neural Network (CNN) is one of the most frequently used deep learning based methods for visual data processing. The use of CNN for HSI classification is also visible in recent works. These approaches are mostly based on 2D CNN. Whereas, the HSI classification performance is highly dependent on both spatial and spectral information. Very few methods have utilized the 3D CNN because of increased computational complexity. This letter proposes a Hybrid Spectral Convolutional Neural Network (HybridSN) for HSI classification. Basically, the HybridSN is a spectral-spatial 3D-CNN followed by spatial 2D-CNN. The 3D-CNN facilitates the joint spatial-spectral feature representation from a stack of spectral bands. The 2D-CNN on top of the 3D-CNN further learns more abstract level spatial representation. Moreover, the use of hybrid CNNs reduces the complexity of the model compared to 3D-CNN alone. To test the performance of this hybrid approach, very rigorous HSI classification experiments are performed over Indian Pines, Pavia University and Salinas Scene remote sensing datasets. The results are compared with the state-of-the-art hand-crafted as well as end-to-end deep learning based methods. A very satisfactory performance is obtained using the proposed HybridSN for HSI classification. The source code can be found at \url{https://github.com/gokriznastic/HybridSN}.
It is shown that the set of decimal palindromes is an additive basis for the natural numbers. Specifically, we prove that every natural number can be expressed as the sum of forty-nine (possibly zero) decimal palindromes.
This is a detailed study of the infinitesimal variation of the variety of lines through a point of a low degree hypersurface in pro jective space. The motion is governed by a system of partial differential equations which we describe explicitly.
The differential acceleration between a rotating mechanical gyroscope and a non-rotating one is directly measured by using a double free-fall interferometer, and no apparent differential acceleration has been observed at the relative level of 2x10{-6}. It means that the equivalence principle is still valid for rotating extended bodies, i.e., the spin-gravity interaction between the extended bodies has not been observed at this level. Also, to the limit of our experimental sensitivity, there is no observed asymmetrical effect or anti-gravity of the rotating gyroscopes as reported by hayasaka et al.
The $J_1-J_3$ Heisenberg spin models with nearest-neighbor ($J_1$) and additional isotropic three-site ($J_3$) spin interactions remain relatively less explored, although such types of competing exchange terms can naturally emerge from different sources, including the strong-coupling expansion of the multiorbital Hubbard model. Below we present a short survey of the recently published research in this field, the emphasis being on the characteristics of the variety of quantum phases supported by a few generic uniform- and alternating-spin $J_1-J_3$ Heisenberg chains. For the reason that the positive ($J_3>0$) three-site couplings tend towards the formation of local quantum dimers, the $J_1-J_3$ spin models typically experience some spontaneous dimerization upon increasing $J_3$. Actually, it occurred that the established dimer phases in spin-$S$ $J_1-J_3$ Heisenberg chains ($S>{1}/{2}$) serve as complete analogues of the famous gapped Majumdar-Ghosh dimer phase in the spin-${1}/{2}$ Heisenberg chain with next-nearest-neighbor couplings. The same dimerizations have been observed in the alternating-spin ($S,\sigma$) $J_1-J_3$ chains ($S>\sigma$), provided that the cell spin $S+\sigma=\rm{integer}$, whereas for half-integer cell spin, the local dimer formation produces gapless spin-liquid ground states. The alternating-spin $J_1-J_3$ chains also provide some typical examples of spin models supporting the so-called non-Lieb-Mattis magnetic phases.
We introduce multigrid Predictive Filter Flow (mgPFF), a framework for unsupervised learning on videos. The mgPFF takes as input a pair of frames and outputs per-pixel filters to warp one frame to the other. Compared to optical flow used for warping frames, mgPFF is more powerful in modeling sub-pixel movement and dealing with corruption (e.g., motion blur). We develop a multigrid coarse-to-fine modeling strategy that avoids the requirement of learning large filters to capture large displacement. This allows us to train an extremely compact model (4.6MB) which operates in a progressive way over multiple resolutions with shared weights. We train mgPFF on unsupervised, free-form videos and show that mgPFF is able to not only estimate long-range flow for frame reconstruction and detect video shot transitions, but also readily amendable for video object segmentation and pose tracking, where it substantially outperforms the published state-of-the-art without bells and whistles. Moreover, owing to mgPFF's nature of per-pixel filter prediction, we have the unique opportunity to visualize how each pixel is evolving during solving these tasks, thus gaining better interpretability.
Let $Q$ be an affine quartic which does not intersect transversely with the line at infinity $L_{\infty}$. In this paper, we show the existence of a $(2,3)$ torus decomposition of the defining polynomial of $Q$ and its uniqueness except for one class.
We present a subtle idea to economically improve message-unilaterally-transmitted quantum secure direct communication (QSDC) protocols to realize two-way secure direct communication.
Although it is widely accepted that photospheric motions provide the energy source and that the magnetic field must play a key role in the process, the detailed mechanisms responsible for heating the Sun's corona and accelerating the solar wind are still not fully understood. Cranmer et al. (2007) developed a sophisticated, 1D, time-steady model of the solar wind with turbulence dissipation. By varying the coronal magnetic field, they obtain, for a single choice of wave properties, a realistic range of slow and fast wind conditions with a sharp latitudinal transition between the two streams. Using a 1D, time-dependent model of the solar wind of Lionello et al. (2014), which incorporates turbulent dissipation of Alfv\'en waves to provide heating and acceleration of the plasma, we have explored a similar configuration, obtaining qualitatively equivalent results. However, our calculations suggest that the rapid transition between slow and fast wind suggested by this 1D model may be disrupted in multidimensional MHD simulations by the requirement of transverse force balance.
We apply systematic methods previously used by Mihalkovic et al. to predict the structure of the `basic' Co-rich modification of the decagonal Al70 Co20 Ni10 layered quasicrystal, based on known lattice constants and previously calculated pair potentials. The modelling is based on Penrose tile decoration and uses Monte Carlo annealing to discover the dominant motifs, which are converted into rules for another level of description. The result is a network of edge-sharing large decagons on a binary tiling of edge 10.5 A. A detailed analysis is given of the instability of a four-layer structure towards $c$-doubling and puckering of the atoms out of the layers, which is applied to explain the (pentagonal) orientational order.
Industrial smoke emissions present a serious threat to natural ecosystems and human health. Prior works have shown that using computer vision techniques to identify smoke is a low cost and convenient method. However, industrial smoke detection is a challenging task because industrial emission particles are often decay rapidly outside the stacks or facilities and steam is very similar to smoke. To overcome these problems, a novel Spatio-Temporal Cross Network (STCNet) is proposed to recognize industrial smoke emissions. The proposed STCNet involves a spatial pathway to extract texture features and a temporal pathway to capture smoke motion information. We assume that spatial and temporal pathway could guide each other. For example, the spatial path can easily recognize the obvious interference such as trees and buildings, and the temporal path can highlight the obscure traces of smoke movement. If the two pathways could guide each other, it will be helpful for the smoke detection performance. In addition, we design an efficient and concise spatio-temporal dual pyramid architecture to ensure better fusion of multi-scale spatiotemporal information. Finally, extensive experiments on public dataset show that our STCNet achieves clear improvements on the challenging RISE industrial smoke detection dataset against the best competitors by 6.2%. The code will be available at: https://github.com/Caoyichao/STCNet.
We study rare leptonic decays of vector $B$ mesons in the scalar leptoquark and family non-universal $Z^\prime$ models. We constrain these new couplings by using the measured branching fractions of $B_{s,d} \to \mu^+ \mu^-$ processes and the existing $B_{s,d}^0-\bar B_{s,d}^0$ mixing data. We estimate the branching fractions of $B_{s,d}^* \to \mu^+ \mu^-$ in both the models, which are found to be reasonably enhanced from their corresponding standard model values and within the reach of Run 2-3 of LHC.
We investigate D-branes on a noncompact singular Calabi-Yau manifold by using the boundary CFT description, and calculate the open string Witten indices between the boundary states. The B-type D-branes turn out to be characterized by the properties of a compact positively curved manifold. We give geometric interpretations to these boundary states in terms of coherent sheaves of the manifold.
I present an effective classical theory which describes the non-perturbative real-time dynamics in hot gauge theories and has the potential for numerical implementation.
The determination of the Hubble constant $H_0$ from the Cosmic Microwave Background by the Planck Collaboration [Aghanim et al. 2018] is in tension at $4.2\sigma$ with respect to the local determination of $H_0$ by the SH0ES collaboration [Reid et al. 2019]. Here, we improve upon the local determination, which fixes the deceleration parameter to the standard $\Lambda$CDM model value of $q_0=-0.55$, that is, uses information from observations beyond the local universe. First, we derive the effective calibration prior on the absolute magnitude $M_B$ of Supernovae Ia, which can be used in cosmological analyses in order to avoid the double counting of low-redshift supernovae. We find $M_B = -19.2334 \pm 0.0404$ mag. Then, we use the above $M_B$ prior in order to obtain a determination of the local $H_0$ which only uses local observations and only assumes the cosmological principle, that is, large-scale homogeneity and isotropy. This is achieved by adopting an uninformative flat prior for $q_0$ in the cosmographic expansion of the luminosity distance. We use the latest Pantheon sample and find $H_0= 75.35 \pm 1.68 \text{ km s}^{-1} {\rm Mpc}^{-1}$, which features a 2.2% uncertainty, close to the 1.9% error obtained by the SH0ES Collaboration. Our determination is at the higher tension of $4.5\sigma$ with the latest results from the Planck Collaboration that assume the $\Lambda$CDM model. Furthermore, we also constrain the deceleration parameter to $q_0= -1.08 \pm 0.29$, which disagrees with Planck at the $1.9\sigma$ level. These estimations only use supernovae in the redshift range $0.023\le z\le 0.15$.
We discuss the behavior of weakly bound bosonic dimers formed in a cold Fermi gas at a large positive scattering length $a$ for the interspecies interaction. We find the exact solution for the dimer-dimer elastic scattering and obtain a strong decrease of their collisional relaxation and decay with increasing $a$. The large ratio of the elastic to inelastic rate is promising for achieving Bose-Einstein condensation of the dimers and cooling the condensed gas to very low temperatures.