ShantanuT01/DACTYL-Pretraining huggingface dataset:
Terahertz (THz) radiation has uses from security to medicine; however, sensitive room-temperature detection of THz is notoriously difficult. The hot-electron photothermoelectric effect in graphene is a promising detection mechanism: photoexcited carriers rapidly thermalize due to strong electron-electron interactions, but lose energy to the lattice more slowly. The electron temperature gradient drives electron diffusion, and asymmetry due to local gating or dissimilar contact metals produces a net current via the thermoelectric effect. Here we demonstrate a graphene thermoelectric THz photodetector with sensitivity exceeding 10 V/W (700 V/W) at room temperature and noise equivalent power less than 1100 pW/Hz^1/2 (20 pW/Hz^1/2), referenced to the incident (absorbed) power. This implies a performance which is competitive with the best room-temperature THz detectors for an optimally coupled device, while time-resolved measurements indicate that our graphene detector is eight to nine orders of magnitude faster than those. A simple model of the response, including contact asymmetries (resistance, work function and Fermi-energy pinning) reproduces the qualitative features of the data, and indicates that orders-of-magnitude sensitivity improvements are possible.
In this paper we generalize the results of Sharma, Awasthi and Gupta (see \cite{SAG}). We work over a field of any characteristic with $q = p^k$ elements and we give a sufficient condition for the existence of a primitive element $\alpha \in \mathbb{F}_{p^k}$ such that $f(\alpha)$ is also primitive in $\mathbb{F}_{p^k}$, where $f(x) \in \mathbb{F}_{p^k}(x)$ is a quotient of polynomials with some restrictions. We explicitly determine the values of $k$ for which such a pair exists for $p=2,3,5$ and $7$.
The classifications approaches for the crystallographic symmetries of patterns that are more or less periodic in two dimensions are critically reviewed and their relative performance qualitatively evaluated. The information theory based approach of the author utilizes digital images and turns out to be the only one that allows for fully objective classifications of the crystallographic symmetries, i.e. Bravais lattice type, Laue class, and plane symmetry group, of noisy real-world images. His information theory based crystallographic symmetry classifications utilize geometric bias-corrected sums of squared residuals, i.e. pertinent first order information, and enable the most meaningful crystallographic averaging in the spatial frequency domain, which suppresses generalized noise much more effectively than traditional Fourier filtering. Taking account of the fact that it is fundamentally unsound to assign an abstract mathematical concept such as a single symmetry type, class, or group with 100 % certainty to a more or less 2D periodic record of a noisy real-world imaging experiment that involved a real-world sample, the information theory based approach to crystallographic symmetry classifications delivers probabilistic classifications. Recent applications of deep convolutional neural networks to classifications of crystallographic translation symmetries in 2D and crystals in three dimensions are discussed as these machines deliver probabilistic classifications by non-analytical means.
We show that the functions g and gs introduced by Guo Wuwen in [4] are continuous and semialgebraic. We use this fact to prove that the set Nn of ordered n-tuples of real numbers, realizable by nonnegative matrices, is a closed set.
We derive the one-loop perturbative formula of the redshift-space matter power spectrum after density field reconstruction in the Zeldovich approximation. We find that the reconstruction reduces the amplitudes of nonlinear one-loop perturbative terms significantly by partially erasing the nonlinear mode-coupling between density and velocity fields. In comparison with N-body simulations, we find that both the monopole and quadrupole spectra of reconstructed matter density fields agree with the one-loop perturbation theory up to higher wavenumber than those before reconstruction. We also evaluate the impact on cosmic growth rate assuming the survey volume and the number density like the Baryon Oscillation Spectroscopic Survey and find that the total error, including statistical and systematic ones due to one-loop approximation, decreases by half.
We consider a general method for the approximation of the distribution of a process conditioned to not hit a given set. Existing methods are based on particle system that are failable, in the sense that, in many situations , they are not well defined after a given random time. We present a method based on a new particle system which is always well define. Moreover , we provide sufficient conditions ensuring that the particle method converges uniformly in time. We also show that this method provides an approximation method for the quasi-stationary distribution of Markov processes. Our results are illustrated by their application to a neutron transport model.
We compute the masses of the pseudoscalar mesons $\pi^+$ , $K^0$ and $D^+$ at finite temperature and baryon chemical potential. The computations are based on a symmetry- preserving Dyson-Schwinger equation treatment of a vector-vector four quark contact interaction. The results found for the temperature dependence of the meson masses are in qualitative agreement with lattice QCD data and QCD sum rules calculations. The chemical potential dependence of the masses provide a novel prediction of the present computation.
A beautiful conjecture of Erd\H{o}s-Simonovits and Sidorenko states that if H is a bipartite graph, then the random graph with edge density p has in expectation asymptotically the minimum number of copies of H over all graphs of the same order and edge density. This conjecture also has an equivalent analytic form and has connections to a broad range of topics, such as matrix theory, Markov chains, graph limits, and quasirandomness. Here we prove the conjecture if H has a vertex complete to the other part, and deduce an approximate version of the conjecture for all H. Furthermore, for a large class of bipartite graphs, we prove a stronger stability result which answers a question of Chung, Graham, and Wilson on quasirandomness for these graphs.
Successful visual recognition networks benefit from aggregating information spanning from a wide range of scales. Previous research has investigated information fusion of connected layers or multiple branches in a block, seeking to strengthen the power of multi-scale representations. Despite their great successes, existing practices often allocate the neurons for each scale manually, and keep the same ratio in all aggregation blocks of an entire network, rendering suboptimal performance. In this paper, we propose to learn the neuron allocation for aggregating multi-scale information in different building blocks of a deep network. The most informative output neurons in each block are preserved while others are discarded, and thus neurons for multiple scales are competitively and adaptively allocated. Our scale aggregation network (ScaleNet) is constructed by repeating a scale aggregation (SA) block that concatenates feature maps at a wide range of scales. Feature maps for each scale are generated by a stack of downsampling, convolution and upsampling operations. The data-driven neuron allocation and SA block achieve strong representational power at the cost of considerably low computational complexity. The proposed ScaleNet, by replacing all 3x3 convolutions in ResNet with our SA blocks, achieves better performance than ResNet and its outstanding variants like ResNeXt and SE-ResNet, in the same computational complexity. On ImageNet classification, ScaleNets absolutely reduce the top-1 error rate of ResNets by 1.12 (101 layers) and 1.82 (50 layers). On COCO object detection, ScaleNets absolutely improve the mmAP with backbone of ResNets by 3.6 (101 layers) and 4.6 (50 layers) on Faster RCNN, respectively. Code and models are released at https://github.com/Eli-YiLi/ScaleNet.
The distributions of solar flare energies and waiting times have not been described simultaneously by a single physical model, yet. In this research, we investigate whether recent avalanche models can describe the distributions for both the released energies and waiting times of flares in an active region. Flaring events are simulated using the modified Lu and Hamilton model (Lu and Hamilton (1991), ApJ, 380, 89) and also the optimized model (Farhang et al. (2018), ApJ, 859, 41). Applying a probability balance equation approach, we study the statistics of the simulated flaring events and investigate the origin of the observed power law in the flare frequency-size distribution. The results indicate that the power law originates in the distribution of transition rates (the distribution of the probabilities of transitions between different energies) rather than the distribution of the energy of the active region. It is also observed that the waiting-time distribution of simulated flaring events follows a q-exponential function which approximates a simple Poisson distribution.
We recall a formulation of super-membrane theory in terms of certain matrix models. These models are known to have a mass spectrum given by the positive half-axis. We show that, for the simplest such matrix model, a normalizable zero-mass ground state does not exist.
Research in texture recognition often concentrates on the problem of material recognition in uncluttered conditions, an assumption rarely met by applications. In this work we conduct a first study of material and describable texture at- tributes recognition in clutter, using a new dataset derived from the OpenSurface texture repository. Motivated by the challenge posed by this problem, we propose a new texture descriptor, D-CNN, obtained by Fisher Vector pooling of a Convolutional Neural Network (CNN) filter bank. D-CNN substantially improves the state-of-the-art in texture, mate- rial and scene recognition. Our approach achieves 82.3% accuracy on Flickr material dataset and 81.1% accuracy on MIT indoor scenes, providing absolute gains of more than 10% over existing approaches. D-CNN easily trans- fers across domains without requiring feature adaptation as for methods that build on the fully-connected layers of CNNs. Furthermore, D-CNN can seamlessly incorporate multi-scale information and describe regions of arbitrary shapes and sizes. Our approach is particularly suited at lo- calizing stuff categories and obtains state-of-the-art re- sults on MSRC segmentation dataset, as well as promising results on recognizing materials and surface attributes in clutter on the OpenSurfaces dataset.
As part of the 100IAS survey, a program aimed to obtain nebular-phase spectra for a volume-limited and homogeneous sample of Type Ia supernovae (SNe Ia), we observed ASASSN-18tb (SN 2018fhw) at 139 days past maximum light. ASASSN-18tb was a fast-declining, sub-luminous event that fits well within the observed photometric and spectroscopic distributions of the SN Ia population. We detect a prominent H$\alpha$ emission line of $L_{{\rm H}\alpha}=2.2\pm0.2\times10^{38}$ ergs s$^{-1}$ with FWHM $\approx1100$ km s$^{-1}$ in the nebular-phase spectrum of this SN Ia. High luminosity H$\alpha$ emission ($L_{{\rm H}\alpha}\gtrsim 10^{40}$ ergs~s$^{-1}$) has previously been discovered in a rare class of SNe Ia-like objects showing CSM interactions (SNe Ia-CSM). They predominantly belong to over-luminous ($M_{\rm max}<-19$ mag in optical) 1991T-like SNe Ia and are exclusively found in star-forming galaxies. By contrast, ASASSN-18tb is a sub-luminous SN Ia ($M_{B, {\rm max}}\sim -17.7$ mag) found in an early-type galaxy dominated by old stellar populations. We discuss possible origins for the observed hydrogen. Out of 75 SNe Ia for which we have so far obtained nebular spectra in 100IAS, no other SN shows a $\sim 1000 \,{\rm km s^{-1}}$ H$\alpha$ emission line with comparable line luminosity as ASASSN-18tb, emphasizing the rarity of such emission in the nebular phase. Based on preliminary results from our survey, the rate for ASASSN-18tb-like nebular H$\alpha$ emission could be as high as $\sim 10\%$ level among sub-luminous SNe Ia.
We present MMT/Megacam imaging of the Leo~IV dwarf galaxy in order to investigate its structure and star formation history, and to search for signs of association with the recently discovered Leo~V satellite. Based on parameterized fits, we find that Leo~IV is round, with $\epsilon < 0.23$ (at the 68\% confidence limit) and a half-light radius of $r_{h} \simeq 130$ pc. Additionally, we perform a thorough search for extended structures in the plane of the sky and along the line of sight. We derive our surface brightness detection limit by implanting fake structures into our catalog with stellar populations identical to that of Leo~IV. We show that we are sensitive to stream-like structures with surface brightness $\mu_{r}\lesssim29.6$ mag arcsec$^{-2}$, and at this limit, we find no stellar bridge between Leo IV (out to a radius of $\sim$0.5 kpc) and the recently discovered, nearby satellite Leo V. Using the color magnitude fitting package StarFISH, we determine that Leo~IV is consistent with a single age ($\sim$14 Gyr), single metallicity ($[Fe/H]\sim-2.3$) stellar population, although we can not rule out a significant spread in these value. We derive a luminosity of $M_{V}=-5.5\pm0.3$. Studying both the spatial distribution and frequency of Leo~IV's 'blue plume' stars reveals evidence for a young ($\sim$2 Gyr) stellar population which makes up $\sim$2\% of its stellar mass. This sprinkling of star formation, only detectable in this deep study, highlights the need for further imaging of the new Milky Way satellites along with theoretical work on the expected, detailed properties of these possible 'reionization fossils'.
The transport properties on the two-dimensional surface of coupled multilayer heterostructures are studied in the integer quantum Hall states. We emphasize the criticality of the surface state and the phase coherent transport properties in the thermodynamic limit. A new, stable numerical algorithm for large scale conductance calculations in the transfer matrix approach is discussed in detail. It is then applied to a directed network model describing the quantum mechanical tunneling and impurity scattering of the multilayer edge states. We calculate the two-probe conductance in the direction parallel to the external magnetic field, its fluctuations and statistical distributions as a function of the interlayer tunneling strength. Using finite size scaling, the asymptotic scaling functions of the ensemble averaged conductance and the conductance fluctuations are calculated for a fixed aspect ratio and found to be in remarkable agreement with the analytical results obtained using the supersymmetric nonlinear $\sigma$-model approach. The conductance distribution is determined in the quasi-one-dimensional metallic, insulating, as well as the crossover regime where comparisons are made to that at the single-layer quantum Hall transition. We present, for the first time, a detailed study of the level statistics in the eigenvalue spectrum of the transfer matrix. Coexistence of metallic and insulating statistics is observed in the crossover regime, which is attributed to the emergence of a finite range level repulsion in the crossover regime, separating the metallic (Wigner-surmise) behavior at small level spacings from the insulating (uncorrelated or Poisson) behavior at large level spacings.
A relatively recent study by Mars and Senovilla provided us with a uniqueness result for the exterior vacuum gravitational field of global models describing finite isolated rotating bodies in equilibrium in General Relativity (GR). The generalisation to exterior electrovacuum gravitational fields, to include charged rotating objects, is presented here.
We study the impact of maximal zeros of the Dirac mass matrix on neutrino phenomenology and dark matter within the framework of an inverse seesaw ISS $(2,3)$. ISS $(2,3)$ is obtained by adding two right handed neutrinos and three gauge singlets sterile fermions to the standard model leading to an extra sterile state in the keV range. The model is more predictive because of the presence of fewer numbers of right handed neutrinos than the conventional inverse seesaw. Moreover, texture zeros in the structures of the mass matrices involved in the model can reduce the free parameters. We extensively study the effect of different textures of Dirac mass matrix on sterile neutrino dark matter phenomenology. Our study also includes the implications of these texture zero mass matrices on low energy phenomena like neutrinoless double beta decay (0$\nu\beta\beta$). The zero textures highly constrain the parameter space of the model. Based on the allowed cosmological ranges of the relic abundance, decay rates, and dark matter mixing with the active neutrinos, we study the viability of the different textures within the framework.
We consider the problem of determining the Galaxy's gravitational potential from a star catalogue. We show that orbit-based approaches to this problem suffer from unacceptable numerical noise deriving from the use of only a finite number of orbits. An alternative approach, which requires an ability to determine the model's phase-space density at predetermined positions and velocities, has a level of numerical noise that lies well below the intrinsic uncertainty associated with the finite size of the catalogue analysed. A catalogue of 10000 stars brighter than V=17 and distributed over the sky at b>30 degrees enables us to determine the scaleheight of the disc that contributes to the potential with an uncertainty below 20pc if the catalogue gives proper motions, line-of-sight velocities and parallaxes with errors typical of the Gaia Catalogue, rising to 36pc if only proper motions are available. The uncertainty in the disc's scalelength is significantly smaller than 0.25kpc.
The metric for plane gravitational waves is quantized using the Ashtekar field variables. The z axis (direction of travel of the waves) is taken to be the entire real line. Solutions to the constraints are proposed; they involve open-ended flux lines running along the entire z axis. These solutions are annihilated by the constraints except at the two boundary points, where the Gauss constraint does not annihilate the solutions. This result is in sharp contrast to the situation in the general, 3+1 dimensional case without planar symmetry, where the Gauss constraints do not contribute at boundaries because the Lagrange multipliers for the Gauss constraints vanish there. The constraints annihilate the solutions if classical matter is included (so that flux lines are terminated on the matter). The SU(2) holonomy matrices used in the solutions are (2j+1) dimensional, where j may be any spin, not necessarily j = 1/2. In this respect the solutions resemble the spin network states recently constructed by Rovelli and Smolin in loop space.
Aims. We report results of an X-ray study of the supernova remnant (SNR) G344.7-0.1 and the point-like X-ray source located at the geometrical center of the SNR radio structure. Methods. The morphology and spectral properties of the remnant and the central X-ray point-like source were studied using data from the XMM-Newton and Chandra satellites. Archival radio data and infrared Spitzer observations at 8 and 24 $\mu$m were used to compare and study its multi-band properties at different wavelengths. Results. The XMM-Newton and Chandra observations reveal that the overall X-ray emission of G344.7-0.1 is extended and correlates very well with regions of bright radio and infrared emission. The X-ray spectrum is dominated by prominent atomic emission lines. These characteristics suggest that the X-ray emission originated in a thin thermal plasma, whose radiation is represented well by a plane-parallel shock plasma model (PSHOCK). Our study favors the scenario in which G344.7-0.1 is a 6 x 10^3 year old SNR expanding in a medium with a high density gradient and is most likely encountering a molecular cloud on the western side. In addition, we report the discovery of a soft point-like X-ray source located at the geometrical center of the radio SNR structure. The object presents some characteristics of the so-called compact central objects (CCO). However, its neutral hydrogen absorption column (N_{H}) is inconsistent with that of the SNR. Coincident with the position of the source, we found infrared and optical objects with typical early-K star characteristics. The X-ray source may be a foreground star or the CCO associated with the SNR. If this latter possibility were confirmed, the point-like source would be the farthest CCO detected so far and the eighth member of the new population of isolated and weakly magnetized neutron stars.
We study the dynamical effects of gravitational focusing by a binary companion on winds from late-type stars. In particular, we investigate the mass transfer and formation of accretion disks around the secondary in detached systems consisting of an AGB mass-losing star and an accreting companion. The presence of mass outflows is studied as a function of mass loss rate, wind temperature and binary orbital parameters. A 2-dimensional hydrodynamical model is used to study the stability of mass transfer in wind accreting symbiotic binary systems. In our simulations we use an adiabatic equation of state and a modified version of the isothermal approximation, where the temperature depends on the distance from the mass losing star and its companion. We explore the accretion flow between the components and formation of accretion disks for a range of orbital separations and wind parameters. Our results show the formation of stream flow between the stars and accretion disks of various sizes for certain orbital configurations. For a typical slow and massive wind from an AGB star the flow pattern is similar to a RLOF with accretion rates of 10% of the mass loss from the primary. Stable disks with exponentially decreasing density profiles and masses of the order $10^{-4}$ solar masses are formed when wind acceleration occurs at several stellar radii. The formation of tidal streams and accretion disks is found to be weakly dependent on the mass loss from the AGB star. Our simulations of gravitationally focused wind accretion in symbiotic binaries show the formation of stream flows and enhanced accretion rates onto the compact component.
Fix positive integers $k$ and $d$. We show that, as $n\to\infty$, any set system $\mathcal{A} \subset 2^{[n]}$ for which the VC dimension of $\{ \triangle_{i=1}^k S_i \mid S_i \in \mathcal{A}\}$ is at most $d$ has size at most $(2^{d\bmod{k}}+o(1))\binom{n}{\lfloor d/k\rfloor}$. Here $\triangle$ denotes the symmetric difference operator. This is a $k$-fold generalisation of a result of Dvir and Moran, and it settles one of their questions. A key insight is that, by a compression method, the problem is equivalent to an extremal set theoretic problem on $k$-wise intersection or union that was originally due to Erd\H{o}s and Frankl. We also give an example of a family $\mathcal{A} \subset 2^{[n]}$ such that the VC dimension of $\mathcal{A}\cap \mathcal{A}$ and of $\mathcal{A}\cup \mathcal{A}$ are both at most $d$, while $\lvert \mathcal{A} \rvert = \Omega(n^d)$. This provides a negative answer to another question of Dvir and Moran.
As an addendum to our previous evaluation of the weak-interaction corrections to hadronic top-quark pair production we determine the leading weak-interaction contributions due to the subprocesses $b {\bar b} \to t {\bar t}$ and $g q ({\bar q}) \to t {\bar t} q ({\bar q})$. For several distributions in $t {\bar t}$ production at the LHC we find that these contributions are non-negligible as compared to the weak corrections from the other partonic subprocesses.