ShantanuT01/DACTYL-Pretraining huggingface dataset:
The relaxation time limit of the one-point distribution of the spatially periodic totally asymmetric simple exclusion process is expected to be the universal one point distribution for the models in the KPZ universality class in a periodic domain. Unlike the infinite line case, the limiting one point distribution depends non-trivially on the scaled time parameter. We study several properties of this distribution for the case of the periodic step and flat initial conditions. We show that the distribution changes from a Tracy-Widom distribution in the small time limit to the Gaussian distribution in the large time limit, and also obtain right tail estimate for all time. Furthermore, we establish a connection to integrable differential equations such as the KP equation, coupled systems of mKdV and nonlinear heat equations, and the KdV equation.
X-ray powder diffraction and electrical resistivity measurements were performed on the tetragonal compound CeCoSi under pressure to elucidate the phase boundary of the pressure-induced structural transition and the change in the 4$f$ electronic state. The temperature-pressure phase diagram has been determined from the shift of the Bragg peaks and from the anomaly in the resistivity. The critical pressure, $P_{\rm s}$ $\sim$ 4.9 GPa at 300 K, decreases to $P_{\rm s}$ $\sim$ 3.6 GPa at 10 K. The decrease of $P_{\rm s}$ is due not only to the decrease in volume of the unit cell but also to an anisotropic shrinkage by cooling. When crossing the boundary to the high-pressure phase, the resistivity shows a significant drop to exhibit a metallic temperature dependence. The results of this study strongly suggest that the structural phase transition can be ascribed to valence instability of Ce-$4f$ electron.
The injection of secondary particles produced by Dark Matter (DM) annihilation at redshift 100<z<1000 affects the process of recombination, leaving an imprint on Cosmic Microwave Background (CMB) anisotropies. Here we provide a new assessment of the constraints set by CMB data on the mass and self-annihilation cross-section of DM particles. Our new analysis includes the most recent WMAP (7-year) and ACT data, as well as an improved treatment of the time-dependent coupling between the DM annihilation energy with the thermal gas. We show in particular that the improved measurement of the polarization signal places already stringent constraints on light DM particles, ruling out 'thermal' WIMPs with mass less then about 10 GeV.
Typically, the deployment of face recognition models in the wild needs to identify low-resolution faces with extremely low computational cost. To address this problem, a feasible solution is compressing a complex face model to achieve higher speed and lower memory at the cost of minimal performance drop. Inspired by that, this paper proposes a learning approach to recognize low-resolution faces via selective knowledge distillation. In this approach, a two-stream convolutional neural network (CNN) is first initialized to recognize high-resolution faces and resolution-degraded faces with a teacher stream and a student stream, respectively. The teacher stream is represented by a complex CNN for high-accuracy recognition, and the student stream is represented by a much simpler CNN for low-complexity recognition. To avoid significant performance drop at the student stream, we then selectively distil the most informative facial features from the teacher stream by solving a sparse graph optimization problem, which are then used to regularize the fine-tuning process of the student stream. In this way, the student stream is actually trained by simultaneously handling two tasks with limited computational resources: approximating the most informative facial cues via feature regression, and recovering the missing facial cues via low-resolution face classification. Experimental results show that the student stream performs impressively in recognizing low-resolution faces and costs only 0.15MB memory and runs at 418 faces per second on CPU and 9,433 faces per second on GPU.
We investigate the long time behavior of the survivors' area in the scaling state of two dimensional soap froth. We relate this problem to the recently studied temporal decay of the fraction of Potts spins that have never been flipped till time t. The results of our topological simulations are consistent with the value $\theta=1$ for the scaling exponent of the survivors' areas, in agreement with a recently obtained analytical result. We find, however, that the relaxation time needed to get into the scaling regime depends on the degree of randomness in the topological rearrangements and becomes very large in the deterministic limit.
We classify nonconstant entire local minimizers of the standard Ginzburg-Landau functional for maps in $H^{1}_{loc}(R^3;R^3)$ satisfying a natural energy bound. Up to translations and rotations, such solutions of the Ginzburg-Landau system are given by an explicit solution equivariant under the action of the orthogonal group.
This paper introduces EXMOVES, learned exemplar-based features for efficient recognition of actions in videos. The entries in our descriptor are produced by evaluating a set of movement classifiers over spatial-temporal volumes of the input sequence. Each movement classifier is a simple exemplar-SVM trained on low-level features, i.e., an SVM learned using a single annotated positive space-time volume and a large number of unannotated videos.   Our representation offers two main advantages. First, since our mid-level features are learned from individual video exemplars, they require minimal amount of supervision. Second, we show that simple linear classification models trained on our global video descriptor yield action recognition accuracy approaching the state-of-the-art but at orders of magnitude lower cost, since at test-time no sliding window is necessary and linear models are efficient to train and test. This enables scalable action recognition, i.e., efficient classification of a large number of different actions even in large video databases. We show the generality of our approach by building our mid-level descriptors from two different low-level feature representations. The accuracy and efficiency of the approach are demonstrated on several large-scale action recognition benchmarks.
Monte Carlo simulation using a combination of Wang Landau (WL) and Transition Matrix (TM) Monte Carlo algorithms to simulate two lattice spin models with continuous energy is described. One of the models, the one dimensional Lebwohl-Lasher model has an exact solution and we have used this to test the performance of the mixed algorithm (WLTM). The other system we have worked on is the two dimensional XY-model. The purpose of the present work is to test the performance of the WLTM algorithm in continuous models and to suggest methods for obtaining best results in such systems using this algorithm.
We have performed large-scale shell-model calculations of the half-lives and neutron-branching probabilities of the r-process waiting point nuclei at the magic neutron numbers N=50, 82, and 126. The calculations include contributions from allowed Gamow-Teller and first-forbidden transitions. We find good agreement with the measured half-lives for the N=50 nuclei with charge numbers Z=28-32 and for the N=82 nuclei 129Ag and 130Cd. The contribution of forbidden transitions reduce the half-lives of the N=126 waiting point nuclei significantly, while they have only a small effect on the half-lives of the N=50 and 82 r-process nuclei.
We introduce degenerate Hermite polynomials as a degenerate version of the ordinary Hermite polynomials. Then, among other things, by using the formula about representing one lambda-Sheffer polynomial in terms of other lambda-Sheffer polynomials we represent the degenerate Hermite polynomials in terms of the higher-order degenerate Bernoulli, Euler, and Frobenius-Euler polynomials and vice versa.
We carried out a principal component analysis (PCA) of the fluxes of five polycyclic aromatic hydrocarbon (PAH) bands at 6.2, 7.7, 8.6, 11.0, and 11.2 $\mu$m in the reflection nebula NGC 7023 comprising of the photodissociation region (PDR) and a cavity. We find that only two principal components (PCs) are required to explain the majority of the observed variance in PAH fluxes (98 %). The first PC ($PC_{1}$), which is the primary driver of the variance, represents the total PAH emission. The second PC ($PC_{2}$) is related to the ionization state of PAHs across the nebula. This is consistent with the results of a similar analysis of the PAH emission in NGC 2023. The biplots and the correlations of PCs with the various PAH ratios show that there are two subsets of ionic bands with the 6.2 and 7.7 $\mu$m bands forming one subset and the 8.6 and 11.0 $\mu$m bands the other. However, the distinction between these subsets is only present in the PDR. We have also carried out a separate PCA analysis of the PAH fluxes, this time only considering variations in the cavity. This shows that in the cavity, $PC_{2}$ is not related to the charge state of PAHs, but possibly to structural molecular changes.
We describe some studies related to the frequency of prime values of integer polynomials.
Production of strange hadrons in elementary and heavy-ion reactions is studied with the hadronic transport approach SMASH (Simulating Many Accelerated Strongly-interacting Hadrons). The poorly known branching ratios of the relevant hadronic resonances are constrained from the known elementary hadronic cross sections and from invariant mass spectra of dileptons. The constrained model is employed as a baseline to compare to heavy-ion-collision experiments at low energies ($E_\text{kin} = 1-2A\,\text{GeV}$) and to predict some of the upcoming pion-beam results by HADES, which are expected to be sensitive to the resonance properties. The employed vacuum-resonance approach proves to be viable for small systems at these energies, but for large systems additional in-medium effects might be required.
As is well-known, the requirement that gamma ray bursts (GRB's) be optically thin to high energy photons yields a lower limit on the Lorentz factor (\gamma) of the expansion. In this paper, we provide a simple derivation of the lower limit on \gamma due to the annihilation of photon pairs, and correct the errors in some of the previous calculations of this limit. We also derive a second limit on \gamma due to scattering of photons by pair-created electrons and positrons. For some bursts, this limit is the more stringent. In addition, we show that a third limit on \gamma, which is obtained by considering the scattering of photons by electrons which accompany baryons, is nearly always less important than the second limit. Finally, we evaluate these limits for a number of bursts.
We present the first results of a near-infrared (0.96-2.31 micron) spectroscopic survey of M, L, and T dwarfs obtained with NIRSPEC on the Keck II telescope. Our new survey has a resolving power of R = 2000 and is comprised of two major data sets: 53 J-band (1.14-1.36 micron) spectra covering all spectral types from M6 to T8 with at least two members in each spectral subclass (wherever possible), and 25 flux-calibrated spectra from 1.14 to 2.31 microns for most spectral classes between M6 and T8. Sixteen of these 25 objects have additional spectral coverage from 0.96-1.14 microns to provide overlap with optical spectra. Spectral flux ratio indexes for prominent molecular bands are derived and equivalent widths (EWs) for several atomic lines are measured. We find that a combination of four H2O and two CH4 band strengths can be used for spectral classification. Weak (EW~1-2 angstrom) atomic lines of Al I and Ca I disappear at the boundary between M and L types.
Neural painting refers to the procedure of producing a series of strokes for a given image and non-photo-realistically recreating it using neural networks. While reinforcement learning (RL) based agents can generate a stroke sequence step by step for this task, it is not easy to train a stable RL agent. On the other hand, stroke optimization methods search for a set of stroke parameters iteratively in a large search space; such low efficiency significantly limits their prevalence and practicality. Different from previous methods, in this paper, we formulate the task as a set prediction problem and propose a novel Transformer-based framework, dubbed Paint Transformer, to predict the parameters of a stroke set with a feed forward network. This way, our model can generate a set of strokes in parallel and obtain the final painting of size 512 * 512 in near real time. More importantly, since there is no dataset available for training the Paint Transformer, we devise a self-training pipeline such that it can be trained without any off-the-shelf dataset while still achieving excellent generalization capability. Experiments demonstrate that our method achieves better painting performance than previous ones with cheaper training and inference costs. Codes and models are available.
We calculate the thermomechanical properties of $\alpha$-iron, and in particular its isothermal and adiabatic elastic constants, using first-principles total-energy and lattice-dynamics calculations, minimizing the quasi-harmonic vibrational free energy under finite strain deformations. Particular care is made in the fitting procedure for the static and temperature-dependent contributions to the free energy, in discussing error propagation for the two contributions separately, and in the verification and validation of pseudopotential and all-electron calculations. We find that the zero-temperature mechanical properties are sensitive to the details of the calculation strategy employed, and common semi-local exchange-correlation functionals provide only fair to good agreement with experimental elastic constants, while their temperature dependence is in excellent agreement with experiments in a wide range of temperature almost up to the Curie transition.
We establish a large deviation principle for the largest eigenvalue of a rank one deformation of a matrix from the GUE or GOE. As a corollary, we get another proof of the phenomenon, well-known in learning theory and finance, that the largest eigenvalue separates from the bulk if the perturbation is large enough. A large part of the paper is devoted to an auxiliary result on the continuity of spherical integrals, in the case when one of the matrix is of rank one, as studied in a previous work.
We describe a family of rational affine surfaces S with huge groups of automorphisms in the following sense: the normal subgroup of Aut(S) generated by all its algebraic subgroups is not generated by any countable family of such subgroups, and the quotient of Aut(S) by this subgroup contains a free group over an uncountable set of generators.
Contactless characterization of mechanical resonances using Fabry-Perot interferometry is a powerful tool to study the mechanical and dynamical properties of atomically thin membranes. However, amplitude calibration is often not performed, or only possible by making assumptions on the device parameters such as its mass or the temperature. In this work, we demonstrate a calibration technique that directly measures the oscillation amplitude by detecting higher harmonics that arise from nonlinearities in the optical transduction. Employing this technique, we calibrate the resonance amplitude of two-dimensional nanomechanical resonators, without requiring knowledge of their mechanical properties, actuation force, geometric distances or the laser intensity.
At low values of external doping graphene displays a wealth of unconventional transport properties. Perhaps most strikingly, it supports a robust 'metallic' regime, with universal conductance of the order of the conductance quantum. We here apply a combination of mean field and bosonization methods to explore the large scale transport properties of the system. We find that, irrespective of the doping level, disordered graphene is subject to common mechanisms of Anderson localization. However, at low doping a number of renormalization mechanisms conspire to protect the conductivity of the system, to an extend that strong localization may not be seen even at temperatures much smaller than those underlying present experimental work.
We present here experimental results obtained on Bi2FeCrO6 (BFCO) epitaxial films deposited by laser ablation directly on SrTiO3 substrates. It has been theoretically predicted, by Baettig and Spaldin, using first-principles density functional theory that BFCO is ferrimagnetic (with a magnetic moment of 2 Bohr magneton per formula unit) and ferroelectric (with a polarization of ~80 microC/cm2 at 0K). The crystal structure has been investigated using X-ray diffraction which shows that the films are epitaxial with a high crystallinity and have a degree of orientation depending of the deposition conditions and that is determined by the substrate crystal structure. Chemical analysis carried out by X-ray Microanalysis and X-ray Photoelectron Spectroscopy (XPS) indicates the correct cationic stoichiometry in the BFCO layer, namely (Bi:Fe:Cr = 2:1:1). XPS depth profiling revealed that the oxidation state of Fe and Cr ions in the film remains 3+ throughout the film thickness and that both Fe and Cr ions are homogeneously distributed throughout the depth. Cross-section high-resolution transmission electron microscopy images together with selected area electron diffraction confirm the crystalline quality of the epitaxial BFCO films with no identifiable foreign phase or inclusion. The multiferroic character of BFCO is proven by ferroelectric and magnetic measurements showing that the films exhibit ferroelectric and magnetic hysteresis at room temperature. In addition, local piezoelectric measurements carried out using piezoresponse force microscopy (PFM) show the presence of ferroelectric domains and their switching at the sub-micron scale.
Feature pyramid network (FPN) is a critical component in modern object detection frameworks. The performance gain in most of the existing FPN variants is mainly attributed to the increase of computational burden. An attempt to enhance the FPN is enriching the spatial information by expanding the receptive fields, which is promising to largely improve the detection accuracy. In this paper, we first investigate how expanding the receptive fields affect the accuracy and computational costs of FPN. We explore a baseline model called inception FPN in which each lateral connection contains convolution filters with different kernel sizes. Moreover, we point out that not all objects need such a complicated calculation and propose a new dynamic FPN (DyFPN). The output features of DyFPN will be calculated by using the adaptively selected branch according to a dynamic gating operation. Therefore, the proposed method can provide a more efficient dynamic inference for achieving a better trade-off between accuracy and computational cost. Extensive experiments conducted on MS-COCO benchmark demonstrate that the proposed DyFPN significantly improves performance with the optimal allocation of computation resources. For instance, replacing inception FPN with DyFPN reduces about 40% of its FLOPs while maintaining similar high performance.
A methodology for extracting neutron direct capture rates from Coulomb dissociation data is developed and applied to the Coulomb dissociation of 15C on 208Pb at 68 MeV/nucleon. Full Continuum Discretized Coupled Channel calculations are performed and an asymptotic normalization coefficient is determined from a fit to the breakup data. Direct neutron capture calculations using the extracted asymptotic normalization coefficient provide $(n,\gamma)$ cross sections consistent with direct measurements. Our results show that the Coulomb Dissociation data can be reliably used for extracting the cross section for 14C(n,g)15C if the appropriate reaction theory is used. The resulting error bars are of comparable magnitude to those from the direct measurement. This procedure can be used more generally to extract capture cross sections from breakup reactions whenever the desired capture process is fully peripheral.
The consequences of Gauge-Yukawa Unification (GYU) in supersymmetric unified models on low energy physics are analyzed. We find that the observed top-bottom mass hierarchy can be explained by supersymmetric GYU and different models can be experimentally distinguished if the top quark mass does not exceed the value $\sim 185$ GeV.
The critical point in particle physics at high temperature is studied through the ideal gas of scalars, the dilatons, in the model that implies the spontaneous breaking of an approximate scale symmetry. We consider the dynamical system of identical particles weakly interacting to each other. The critical point with the temperature as a function of a dilaton mass is established. The fluctuation of particle density grows up very sharply at critical point. Our results also suggest that the critical point may be identified through the fluctuation in yield of primary photons induced by conformal anomaly of strong and electromagnetic sectors of matter.
We suggest to test the premise of ``macroscopic local realism'' which is sufficient to derive Bell inequalities when measurements of photon number are only accurate to an uncertainty of order $n$ photons, where $n$ is macroscopic. Macroscopic local realism is only sufficient to imply, in the context of the original Einstein-Podolsky-Rosen argument, fuzzy ``elements of reality'' which have a macroscopic indeterminacy. We show therefore how the violation of local realism in the presence of macroscopic uncertainties implies the failure of ``macroscopic local realism''. Quantum states violating this macroscopic local realism are presented.
One of the most influential fundamental tools in harmonic analysis is Riesz transform. It maps $L^p$ functions to $L^p$ functions for any $p\in (1,\infty)$ which plays an important role in singular operators. As an application in fluid dynamics, the norm equivalence between $\|\nabla u\|_{L^p}$ and $\|\mbox{div} u\|_{L^p}+\|\mbox{curl} u\|_{L^p}$ is well established for $p\in (1,\infty)$. However, since Riesz operators sent bounded functions only to BMO functions, there is no hope to bound $\|\nabla u\|_{L^\infty}$ in terms of $\|\mbox{div} u\|_{L^\infty}+\|\mbox{curl} u\|_{L^\infty}$. As pointed out by Hoff[{\it SIAM J. Math. Anal.} {\bf 37}(2006), No. 6, 1742-1760], this is the main obstacle to obtain uniqueness of weak solutions for isentropic compressible flows.   Fortunately, based on new observations, see Lemma \ref{Riesz}, we derive an exact estimate for $\|\nabla u\|_{L^\infty}\le (2+1/N)\|\mbox{div} u\|_{L^\infty}$ for any N-dimensional radially symmetric vector functions $u$. As a direct application, we give an affirmative answer to the open problem of uniqueness of some weak solutions to the compressible spherically symmetric flows in a bounded ball.
We study the boundary value problem with Radon measures for nonnegative solutions of $L_Vu:=-\Delta u+Vu=0$ in a bounded smooth domain $\Gw$, when $V$ is a locally bounded nonnegative function. Introducing some specific capacity, we give sufficient conditions on a Radon measure $\gm$ on $\prt\Gw$ so that the problem can be solved. We study the reduced measure associated to this equation as well as the boundary trace of positive solutions. In the appendix A. Ancona solves a question raised by M. Marcus and L. V\'eron concerning the vanishing set of the Poisson kernel of $L_V$ for an important class of potentials $V$.