ShantanuT01/DACTYL-Pretraining huggingface dataset:
We propose a topological plasmonic crystal structure composed of an array of parallel nanowires with unequal spacing. In the paraxial approximation, the Helmholtz equation that describes the propagation of light along the nanowires maps onto the Schr\"{o}dinger equation of the Su-Schrieffer-Heeger (SSH) model. Using full three-dimensional finite difference time domain solution of the Maxwell equations we demonstrate the existence of topological defect modes, with sub-wavelength localization, bound to kinks of the plasmonic crystal. Furthermore, we show that by manipulating kinks we can construct spatial mode filters, that couple bulk modes to topological defect modes, and topological beam-splitters that couple two topological defect modes. Finally, we show that the structures are robust to fabrication errors with inverse length-scale smaller than the topological band gap.
Strangeness flavor has turned out to be a very effective diagnostic tool of relativistic heavy ion physics. The absolute yield provides information about conditions arising in first instants of the interaction. Strange hadrons are abundant allowing a precise study of the chemical freeze-out conditions in the dense matter fireball. The thermal and chemical freeze-out universality seen in strange hadrons confirms sudden hadronization as the breakup mechanism. A plausible cause of sudden fireball breakup is the mechanical instability arising when a quark-gluon plasma phase supercools deeply in its adiabatic expansion. Applying these ideas, we interpret CERN-SPS and the first results on hadronic particle ratios obtained at RHI
Superconductivity in the iron pnictides emerges from metallic parent compounds exhibiting intertwined stripe-type magnetic order and nematic order, with itinerant electrons suggested to be essential for both. Here we use X-ray and neutron scattering to show that a similar intertwined state is realized in semiconducting KFe$_{0.8}$Ag$_{1.2}$Te$_2$ (K$_5$Fe$_4$Ag$_6$Te$_{10}$) without itinerant electrons. We find Fe atoms in KFe$_{0.8}$Ag$_{1.2}$Te$_2$ form isolated $2\times2$ blocks, separated by nonmagnetic Ag atoms. Long-range magnetic order sets in below $T_{\rm N}\approx35$ K, with magnetic moments within the $2\times2$ Fe blocks ordering into the stripe-type configuration. A nematic order accompanies the magnetic transition, manifest as a structural distortion that breaks the fourfold rotational symmetry of the lattice. The nematic orders in KFe$_{0.8}$Ag$_{1.2}$Te$_2$ and iron pnictide parent compounds are similar in magnitude and how they relate to the magnetic order, indicating a common origin. Since KFe$_{0.8}$Ag$_{1.2}$Te$_2$ is a semiconductor without itinerant electrons, this indicates that local-moment magnetic interactions are integral to its magnetic and nematic orders, and such interactions may play a key role in iron-based superconductivity.
The goal of this paper is to study the family of snake polyominoes. More precisely, we focus our attention on the class of partially directed snakes. We establish functional equations and length generating functions of two dimensional, three dimensional and then $N$ dimensional partially directed snake polyominoes. We then turn our attention to partially directed snakes inscribed in a $b\times k$ rectangle and we establish two-variable generating functions, with respect to height $k$ and length $n$ of the snakes. We include observations on the relationship between snake polyominoes and self-avoiding walks. We conclude with a discussion on inscribed snakes polyominoes of maximal length which lead us to the formulation of a conjecture encountered in the course of our investigations.
Allcock-Carlson-Toledo defined a period map for cubic threefolds which takes values in a ball quotient of dimension 10. A theorem of Voisin implies that this is an open embedding. We determine its image and show that on the algebraic level this amounts to an identification of the algebra of SL(5,C)-invariant polynomials on the third symmetric power of the dual of the tautological representation of SL(5,C) with an explicitly described algebra of meromorphic automorphic forms on the complex 10-ball.
We propose a new spatio-temporal attention based mechanism for human action recognition able to automatically attend to the hands most involved into the studied action and detect the most discriminative moments in an action. Attention is handled in a recurrent manner employing Recurrent Neural Network (RNN) and is fully-differentiable. In contrast to standard soft-attention based mechanisms, our approach does not use the hidden RNN state as input to the attention model. Instead, attention distributions are extracted using external information: human articulated pose. We performed an extensive ablation study to show the strengths of this approach and we particularly studied the conditioning aspect of the attention mechanism. We evaluate the method on the largest currently available human action recognition dataset, NTU-RGB+D, and report state-of-the-art results. Other advantages of our model are certain aspects of explanability, as the spatial and temporal attention distributions at test time allow to study and verify on which parts of the input data the method focuses.
High resolution x-ray powder diffraction measurements on poled PbZr(1-x)TixO3 (PZT) ceramic samples close to the rhombohedral-tetragonal phase boundary (the so-called morphotropic phase boundary, MPB) have shown that for both rhombohedral and tetragonal compositions, the piezoelectric elongation of the unit cell does not occur along the polar directions but along those directions associated with the monoclinic distortion. This work provides the first direct evidence for the origin of the very high piezoelectricity in PZT.
The set of Bell-diagonal states for two qubits can be depicted as a tetrahedron in three dimensions. We consider the level surfaces of entanglement and quantum discord for Bell-diagonal states. This provides a complete picture of the structure of entanglement and discord for this simple case and, in particular, of their nonanalytic behavior under decoherence. The pictorial approach also indicates how to show that discord is neither convex nor concave.
This paper concerns the diffusion-homogenization of transport equations when both the adimensionalized scale of the heterogeneities $\alpha$ and the adimensionalized mean-free path $\eps$ converge to 0. When $\alpha=\eps$, it is well known that the heterogeneous transport solution converges to a homogenized diffusion solution. We are interested here in the situation where $0<\eps\ll\alpha\ll1$ and in the respective rates of convergences to the homogenized limit and to the diffusive limit. Our main result is an approximation to the transport solution with an error term that is negligible compared to the maximum of $\alpha$ and $\frac\eps\alpha$. After establishing the diffusion-homogenization limit to the transport solution, we show that the corrector is dominated by an error to homogenization when $\alpha^2\ll\eps$ and by an an error to diffusion when $\eps\ll\alpha^2$.   Our regime of interest involves singular perturbations in the small parameter $\eta=\frac\eps\alpha$. Disconnected local equilibria at $\eta=0$ need to be reconnected to provide a global equilibrium on the cell of periodicity when $\eta>0$. This reconnection between local and global equilibria is shown to hold when sufficient {\em no-drift} conditions are satisfied. The Hilbert expansion methodology followed in this paper builds on corrector theories for the result developed in \cite{NBAPuVo}.
We obtain lower bounds on the rank of the kappa ring of the Delign-Mumford compactification of the moduli space of curves in different degrees. For this purpose, we introduce a quotient of the kappa ring, the combinatorial kappa ring, and show that the rank of this latter ring in degree $d$ is bounded below by $|P(d,3g-2+n-d)|$ where $P(d,r)$ denotes the set of partitions of the positive integer $d$ into at most $r$ parts. In codimension 1 (i.e. $d=3g-4+n$) we show that the rank of the kappa ring is equal to $n-1$ for $g=1$, and is equal to $\lceil \frac{(n+1)(g+1)}{2}\rceil-1$ for $g>1$. Furthermore, in codimension $e=3g-3+n-d$, the rank of the kappa ring (as $g$ and $e$ remain fixed and $n$ grows large) is asymptotic to $\frac{{n+e\choose e}{g+e\choose e}}{(e+1)!}$.
$Context$. The mean pressure profile of the galaxy cluster population plays an essential role in cosmological analyses. An accurate characterization of the shape, intrinsic scatter, and redshift evolution of this profile is necessary to estimate some of the biases and systematic effects that currently prevent cosmological analyses based on thermal Sunyaev-Zel'dovich (tSZ) surveys from obtaining precise and unbiased cosmological constraints. This is one of the main goals of the ongoing NIKA2 SZ large program, which aims at mapping the tSZ signal of a representative cluster sample selected from the $Planck$ and ACT catalogs and spans a redshift range $0.5 < z < 0.9$. $Aims$. To estimate the impact of intracluster medium (ICM) disturbances that can be detected by NIKA2 on the mean pressure profile of galaxy clusters, we realized a study based on a synthetic cluster sample that is similar to that of the NIKA2 SZ large program. $Methods$. To reach this goal we employed the hydrodynamical simulation $Marenostrum~MUltidark~SImulations~of~galaxy~Clusters$ (MUSIC). We simulated realistic NIKA2 and $Planck$ tSZ observations, which were jointly analyzed to estimate the ICM pressure profile of each cluster. A comparison of the deprojected profiles with the true radial profiles directly extracted from the MUSIC simulation allowed us to validate the NIKA2 tSZ pipeline and to study the impact of ICM disturbances on the characterization of the ICM pressure distribution even at high redshift. After normalizing each profile by the integrated quantities estimated under the hydrostatic equilibrium hypothesis, we evaluated the mean pressure profile of the twin sample and show that it is compatible with that extracted directly from the MUSIC simulation in the scale range that can be recovered by NIKA2. We studied the impact of cluster dynamical state on both its shape and associated scatter. (abridged)
We show that an electrically soft ferroelectric host can be used to tune the photoluminescence (PL) response of rare-earth emitter ions by external electric field. The proof of this concept is demonstrated by changing the PL response of Eu+3 ion by electric field on a model system Eu-doped 0.94Na1/2Bi1/2TiO3-0.06BaTiO3. We also show that new channels of radiative transitions, forbidden otherwise, open up due to positional disorder in the system, which can as well be tuned by electric field.
In this paper, we present a large scale unlabeled person re-identification (Re-ID) dataset "LUPerson" and make the first attempt of performing unsupervised pre-training for improving the generalization ability of the learned person Re-ID feature representation. This is to address the problem that all existing person Re-ID datasets are all of limited scale due to the costly effort required for data annotation. Previous research tries to leverage models pre-trained on ImageNet to mitigate the shortage of person Re-ID data but suffers from the large domain gap between ImageNet and person Re-ID data. LUPerson is an unlabeled dataset of 4M images of over 200K identities, which is 30X larger than the largest existing Re-ID dataset. It also covers a much diverse range of capturing environments (eg, camera settings, scenes, etc.). Based on this dataset, we systematically study the key factors for learning Re-ID features from two perspectives: data augmentation and contrastive loss. Unsupervised pre-training performed on this large-scale dataset effectively leads to a generic Re-ID feature that can benefit all existing person Re-ID methods. Using our pre-trained model in some basic frameworks, our methods achieve state-of-the-art results without bells and whistles on four widely used Re-ID datasets: CUHK03, Market1501, DukeMTMC, and MSMT17. Our results also show that the performance improvement is more significant on small-scale target datasets or under few-shot setting.
We provide an atomistic tight-binding description of a few carriers confined in ambipolar (n-p) double quantum dots defined in a semiconducting carbon nanotube. We focus our attention on the charge state of the system in which Pauli blockade of the current flow is observed [F. Pei et al., Nat. Nanotechnol. 7, 630 (2012); E. A. Laird et al., ibid 8, 565 (2013)] with a single excess electron in the n-dot and a single hole in the p-dot. We use the configuration interaction approach to determine the spin-valley structure of the states near the neutrality point and discuss its consequences for the interdot exchange interaction, the degeneracy of the energy spectrum and the symmetry of the confined states. We calculate the transition energies lifting the Pauli blockade and analyze their dependence on the magnetic field vector. Furthermore, we introduce bending of the nanotube and demonstrate its influence on the transition energy spectra. The best qualitative agreement with the experimental data is observed for nanotubes deflected in the gated areas in which the carrier confinement is induced.
Recently, Vision-Language Pre-training (VLP) techniques have greatly benefited various vision-language tasks by jointly learning visual and textual representations, which intuitively helps in Optical Character Recognition (OCR) tasks due to the rich visual and textual information in scene text images. However, these methods cannot well cope with OCR tasks because of the difficulty in both instance-level text encoding and image-text pair acquisition (i.e. images and captured texts in them). This paper presents a weakly supervised pre-training method, oCLIP, which can acquire effective scene text representations by jointly learning and aligning visual and textual information. Our network consists of an image encoder and a character-aware text encoder that extract visual and textual features, respectively, as well as a visual-textual decoder that models the interaction among textual and visual features for learning effective scene text representations. With the learning of textual features, the pre-trained model can attend texts in images well with character awareness. Besides, these designs enable the learning from weakly annotated texts (i.e. partial texts in images without text bounding boxes) which mitigates the data annotation constraint greatly. Experiments over the weakly annotated images in ICDAR2019-LSVT show that our pre-trained model improves F-score by +2.5\% and +4.8\% while transferring its weights to other text detection and spotting networks, respectively. In addition, the proposed method outperforms existing pre-training techniques consistently across multiple public datasets (e.g., +3.2\% and +1.3\% for Total-Text and CTW1500).
Accurate parallaxes from Gaia DR1 (TGAS) are combined with GALEX visual Nuv magnitudes to produce absolute Mnuv magnitudes and an ultraviolet HR diagram for a large sample of astrometric stars. A functional fit is derived of the lower envelope main sequence of the nearest 1403 stars (distance < 40 pc), which should be reddening-free. Using this empirical fit, 50 nearby stars are selected with significant Nuv excess. These are predominantly late K and early M dwarfs, often associated with X-ray sources, and showing other manifestations of magnetic activity. The sample may include systems with hidden white dwarfs, stars younger than the Pleiades, or, most likely, tight interacting binaries of the BY Dra-type. A separate collection of 40 stars with precise trigonometric parallaxes and Nuv - G colors bluer than 2 mag is presented. It includes several known novae, white dwarfs, and binaries with hot subdwarf (sdOB) components, but most remain unexplored.
Since z~1, the stellar mass density locked in low mass groups and clusters has grown by a factor of ~8. Here we make the first statistical measurements of the stellar mass content of low mass X-ray groups at 0.5<z<1, enabling the calibration of stellar-to-halo mass scales for wide-field optical and infrared surveys. Groups are selected from combined Chandra and XMM-Newton X-ray observations in the Chandra Deep Field South (CDFS). These ultra-deep observations allow us to identify bona fide low mass groups at high redshift and enable measurements of their total halo masses. We compute aggregate stellar masses for these halos using galaxies from the Carnegie-Spitzer-IMACS (CSI) spectroscopic redshift survey. Stars comprise ~3-4% of the total mass of group halos with masses 10^{12.8}<M200/Msun<10^{13.5} (about the mass of Fornax and 1/50th the mass of Virgo). Complementing our sample with higher mass halos at these redshifts, we find that the stellar-to-halo mass ratio decreases toward higher halo masses, consistent with other work in the local and high redshift universe. The observed scatter about the stellar-halo mass relation is ~0.25 dex, which is relatively small and suggests that total group stellar mass can serve as a rough proxy for halo mass. We find no evidence for any significant evolution in the stellar-halo mass relation since z<1. Quantifying the stellar content in groups since this epoch is critical given that hierarchical assembly leads to such halos growing in number density and hosting increasing shares of quiescent galaxies.
Nowadays, distributed smart cameras are deployed for a wide set of tasks in several application scenarios, ranging from object recognition, image retrieval, and forensic applications. Due to limited bandwidth in distributed systems, efficient coding of local visual features has in fact been an active topic of research. In this paper, we propose a novel approach to obtain a compact representation of high-dimensional visual data using sensor fusion techniques. We convert the problem of visual analysis in resource-limited scenarios to a multi-view representation learning, and we show that the key to finding properly compressed representation is to exploit the position of cameras with respect to each other as a norm-based regularization in the particular signal representation of sparse coding. Learning the representation of each camera is viewed as an individual task and a multi-task learning with joint sparsity for all nodes is employed. The proposed representation learning scheme is referred to as the multi-view task-driven learning for visual sensor network (MT-VSN). We demonstrate that MT-VSN outperforms state-of-the-art in various surveillance recognition tasks.
Massive stars play an important role in both cluster and galactic evolution and the rate at which they lose mass is a key driver of both their own evolution and their interaction with the environment up to and including their SNe explosions. Young massive clusters provide an ideal opportunity to study a co-eval population of massive stars. We performed 3mm continuum observations with the Atacama Large Millimetre/submillimetre Array of the Galactic cluster Westerlund 1, to study the constituent massive stars and determine mass-loss rates for the diverse post-main sequence population.   We detected emission from 50 stars in Westerlund 1, comprising all 21 Wolf-Rayets within the field of view, eight cool and 21 OB super-/hypergiants. Emission nebulae were associated with a number of the cool hypergiants while, unexpectedly, a number of hot stars also appear spatially resolved. We measured the mass-loss rates for a unique population of massive post-main sequence stars at every stage of evolution, confirming a significant increase as stars transition from OB supergiant to WR states. The range of spectral types exhibited provides a critical test of radiatively driven wind theory and the reality of the bi-stability jump. The extreme mass-loss rate inferred for the interacting binary Wd1-9 in comparison to other cluster members confirmed the key role binarity plays in massive stellar evolution. The presence of compact nebulae around a number of OB and WR stars is unexpected; by analogy to the cool super-/hypergiants we attribute this to confinement and sculpting of the stellar wind via interaction with the intra-cluster medium/wind. Given the morphology of core collapse SNe depend on the nature of the pre-explosion circumstellar environment, if this hypothesis is correct then the properties of the explosion depend not just on the progenitor, but also the environment in which it is located.
Dark matter halos are built from accretion and merging. During merging some of the dark matter particles may be ejected with velocities higher than the escape velocity. We use both N-body simulations and single-particle smooth-field simulations to demonstrate that rapid changes to the mean field potential are responsible for such ejection, and in particular that dynamical friction plays no significant role in it. Studying a range of minor mergers, we find that typically between 5-15% of the particles from the smaller of the two merging structures are ejected. We also find that the ejected particles originate essentially from the small halo, and more specifically are particles in the small halo which pass later through the region in which the merging occurs.
We study zero-temperature XX chains and transverse Ising chains and join an initially separate finite piece on one or on both sides to an infinite remainder. In both critical and non-critical systems we find a typical increase of the entanglement entropy after the quench, followed by a slow decay towards the value of the homogeneous chain. In the critical case, the predictions of conformal field theory are verified for the first phase of the evolution, while at late times a step structure can be observed.
We show that the field $\mathbb{Q}(x,y)$, generated by two singular moduli~$x$ and~$y$, is generated by their sum ${x+y}$, unless~$x$ and~$y$ are conjugate over~$\mathbb{Q}$, in which case ${x+y}$ generates a subfield of degree at most~$2$. We obtain a similar result for the product of two singular moduli.
The solution of physical problems discretized using the finite element methods using quantum computers remains relatively unexplored. Here, we present a unified formulation (FEqa) to solve such problems using quantum annealers. FEqa is a hybrid technique in which the finite element problem is formulated on a classical computer, and the residual is minimized using a quantum annealer. The advantages of FEqa include utilizing a single qubit per degree of freedom, enforcing Dirichlet boundary conditions a priori, reaching arbitrary solution precision, and eliminating the possibility of the annealer generating invalid results. FEqa is scalable on the classical portion of the algorithm due to its Single Program Multiple Data (SPMD) nature and does not rely on ground state solutions from the annealer. The exponentially large number of collocation points used in quantum annealing are investigated for their cosine measures, and new iterative techniques are developed to exploit their properties. The quantum annealer has clear advantages in computational time over simulated annealing, for the example problems presented in this paper solved on the D-Wave machine. The presented work provides a pathway to solving physical problems using quantum annealers.
To evaluate ESR spectrum at finite temperatures for specified spatial configurations of spins is very important issue to study quantum spin systems. Although a direct numerical estimation of the Kubo formula provides exact data, the application is limited to small size of the system because of the restriction of the computer capacity. The method of the Fourier transform of the autocorrelation function improved the restriction. As an extension of the method, we propose a new method for numerical calculation of the ESR spectrum from the time evolution of the magnetization by making use of the Wiener-Khinchin theorem.
Given an endomorphism f of projective space, we exhibit explicit bounds on the difference between the naive height of a divisor and its canonical height relative to f.