ShantanuT01/DACTYL-Pretraining huggingface dataset:
Coronal mass ejections (CMEs) and corotating interaction regions (CIRs) as well as their source regions are important because of their space weather consequences. The current understanding of CMEs primarily comes from the Solar and Heliospheric Observatory (SOHO) and the Solar Terrestrial Relations Observatory (STEREO) missions, but these missions lacked some key measurements: STEREO did not have a magnetograph; SOHO did not have in-situ magnetometer. SOHO and other imagers such as the Solar Mass Ejection Imager (SMEI) located on the Sun-Earth line are also not well-suited to measure Earth-directed CMEs. The Earth-Affecting Solar Causes Observatory (EASCO) is a proposed mission to be located at the Sun-Earth L5 that overcomes these deficiencies. The mission concept was recently studied at the Mission Design Laboratory (MDL), NASA Goddard Space Flight Center, to see how the mission can be implemented. The study found that the scientific payload (seven remote-sensing and three in-situ instruments) can be readily accommodated and can be launched using an intermediate size vehicle; a hybrid propulsion system consisting of a Xenon ion thruster and hydrazine has been found to be adequate to place the payload at L5. Following a 2-year transfer time, a 4-year operation is considered around the next solar maximum in 2025.
We propose a twistor--like formulation of N=1, D=3,4,6 and 10 null superstrings. The model possesses N=1 target space supersymmetry and n=D-2 local worldsheet supersymmetry, the latter replaces the kappa-symmetry of the conventional approach to the strings. Adding a Wess--Zumino term to a null superstring action we observe a string tension generation mechanism: the induced worldsheet metric becomes non-degenerate and the resulting model turns out to be classically equivalent to the heterotic string.
Single-particle properties at superdeformation are investigated within the Cranked Relativistic Mean Field (CRMF) theory on the example of superdeformed rotational bands observed in the A~140-150 mass region. Applying the effective alignment approach it is shown that CRMF theory provides a reasonable description of the alignment properties of the single-particle orbitals. The agreement with experiment is good in most of the cases. This suggests that many features of the observed superdeformed bands can be well understood in terms of an almost undisturbed single-particle motion. The stability of the results with respect to the parameterizations used in Relativistic Mean Field (RMF) theory is also investigated, employing some frequently used non-linear effective forces. It turns out, with the exception of the single-particle ordering in superdeformed minimum, that the dependence of the calculated observables on the parameterization is rather small.
[Shortened abstract:] In this thesis we investigate a solution to the `problem of time' in canonical quantum gravity by splitting spacetime into surfaces of constant mean curvature parameterised by York time. We argue that there are reasons to consider York time a viable candidate for a physically meaningful notion of time. We investigate a number York-time Hamiltonian-reduced cosmological models and explore some technical aspects, such as the non-canonical Poisson structure. We develop York-time Hamiltonian-reduced cosmological perturbation theory by solving the Hamiltonian constraint perturbatively around a homogeneous background for the physical (non-vanishing) Hamiltonian that is the momentum conjugate to the York time parameter. We proceed to canonically quantise the cosmological models and the perturbation theory and discuss a number of conceptual and technical points, such as volume eigenfunctions and the absence of a momentum representation due to the non-standard commutator structure. We propose an alternative, wavefunction-free method of quantisation based on an ensemble of trajectories and a dynamical configuration-space geometry and discuss its application to gravity. We conclude by placing the York-time theories explored in this thesis in the wider context of a search for a satisfactory theory of quantum gravity.
Anisotropic spherically symmetric systems are studied in the connection and densitized triad variables used in loop quantum gravity. The material source is an anisotropic fluid, which is arguably the most commonly used source term in anisotropic studies within general relativity. The gravitational+anisotropic fluid constraints are derived and analyzed and then quantum gravity inspired holonomy replacements are performed. The quantum properties of the fluid are dictated by the modified constraint equations. Particular attention is paid to wormhole throats, as they provide a simplistic model of the structures thought to be ubiquitous in the quantum gravity space-time foam at high energy scales. In comparison to the purely classical theory, the quantum corrections act to increase the energy density of the fluid, which indicates that they may lessen the energy condition violation present in the classical theory. Related to this, in principle it would be possible to have scenarios where the classical solution yields everywhere negative (with a zero at the throat) fluid energy density but the corresponding quantum corrected theory possesses only small regions of negative energy density or even everywhere non-negative energy density.
At very large densities and/or temperatures a quark-hadron phase transition is expected to take place. Simulations of QCD on lattice at zero baryon density indicate that the transition occurs at $T_c \sim 150-170$ MeV. The calculations indicate that transition is likely to be second order or a cross over phenomenon. Although the lattice simulations have not given any indication on when the transition occurs at nonzero baryon density, the transition is expected to occur around the densities of few times nuclear matter density. Also, there is a strong reason to believe that the quark matter formed after the phase transition is in colour superconducting phase. The matter densities in the interior of neutron stars are expected to be several times the nuclear matter density and therefore the neutron star cores may possibly consist of quark matter. One then expects that this quark matter is formed during the collapse of supernova. Starting with the assumption that the quark matter, when formed consists of predominantly u and d quarks, we consider the evolution of strange quarks by weak interactions in the present work. The reaction rates and time required to reach the chemical equilibrium are computed here. Our calculations show that the chemical equilibrium is reached in about $10^{-7}$ seconds. Further more during and immediately after the equilibration process enormous amount of energy is released and copious numbers of neutrinos are produced. We show that for reasonable models of nuclear equations of state the amount of energy released could be as high as $10^{53}$ ergs and as many as $10^{58}$ neutrinos may be emitted during the quark matter formation.
We study the long-time behavior of solutions of the one-phase Stefan problem in inhomogeneous media in dimensions $n \geq 2$. Using the technique of rescaling which is consistent with the evolution of the free boundary, we are able to show the homogenization of the free boundary velocity as well as the locally uniform convergence of the rescaled solution to a self-similar solution of the homogeneous Hele-Shaw problem with a point source. Moreover, by viscosity solution methods, we also deduce that the rescaled free boundary uniformly approaches a sphere with respect to Hausdorff distance.
The problem of a particle confined in a box with moving walls is studied, focusing on the case of small perturbations which do not alter the shape of the boundary (\lq pantography\rq). The presence of resonant transitions involving the natural transition frequencies of the system and the Fourier transform of the velocity of the walls of the box is brought to the light. The special case of a pantographic change of a circular box is analyzed in dept, also bringing to light the fact that the movement of the boundary cannot affect the angular momentum of the particle.
The quantum Hall effect in ultra-high mobility GaAs/AlGaAs has been measured and plateaus are found at many different fractions. The resistivity is quantized as \rho =h/ie^2 where i exhibits many different values. The fractions 5/3, 8/5, 11/7, 14/9, 17/11 fit the formula, i=3p\pm 2/(2p \pm 1) and it is claimed that 2p flux quanta are attached to the electron. The fractions 4/11, 7/11, 12/7, 13/8 and 15/11 do not fit the expression for i, even then the authors insist that flux quanta are attached to the electron and hence composite fermions (CF) are formed. We report that the interpretation of the experimental data in terms of CF is incorrect.
The emergence of ferromagnetism in two-dimensional van der Waals materials has aroused broad interest. However, the ferromagnetic instability has been a problem remained. In this work, by using the first-principles calculations, we identified the critical ranges of strain and doping for the bilayer Cr2Ge2Te6 within which the ferromagnetic stability can be enhanced. Beyond the critical range, the tensile strain can induce the phase transition from the ferromagnetic to the antiferromagnetic, and the direction of magnetic easy axis can be converted from out-of-plane to in-plane due to the increase of compressive strain, or electrostatic doping. We also predicted an electron doping range, within which the ferromagnetism can be enhanced, while the ferromagnetic stability was maintained. Moreover, we found that the compressive strain can reverse the spin polarization of electrons at the conduction band minimum, so that two categories of half-metal can be induced by controlling electrostatic doping in the bilayer Cr2Ge2Te6. These results should shed a light on achieving ferromagnetic stability for low-dimensional materials.
Auto-Monitoring and Short-Messaging-Service System is a real-time monitoring system for any critical operational environments. It detects an undesired event occurring in the environment, generates an alert with detailed message and sends it to the user to prevent hazards. This system employs a Friendly ARM as main controller while, sensors and terminals to interact with the real world. A GSM network is utilized to bridge the communication between monitoring system and user. This paper presents details of prototyping the system.
We propose Guided Zoom, an approach that utilizes spatial grounding of a model's decision to make more informed predictions. It does so by making sure the model has "the right reasons" for a prediction, defined as reasons that are coherent with those used to make similar correct decisions at training time. The reason/evidence upon which a deep convolutional neural network makes a prediction is defined to be the spatial grounding, in the pixel space, for a specific class conditional probability in the model output. Guided Zoom examines how reasonable such evidence is for each of the top-k predicted classes, rather than solely trusting the top-1 prediction. We show that Guided Zoom improves the classification accuracy of a deep convolutional neural network model and obtains state-of-the-art results on three fine-grained classification benchmark datasets.
Visual perception of a person is easily influenced by many factors such as camera parameters, pose and viewpoint variations. These variations make person Re-Identification (ReID) a challenging problem. Nevertheless, human attributes usually stand as robust visual properties to such variations. In this paper, we propose a new method to leverage features from human attributes for person ReID. Our model uses a tensor to non-linearly fuse identity and attribute features, and then forces the parameters of the tensor in the loss function to generate discriminative fused features for ReID. Since tensor-based methods usually contain a large number of parameters, training all of these parameters becomes very slow, and the chance of overfitting increases as well. To address this issue, we propose two new techniques based on Structural Sparsity Learning (SSL) and Tensor Decomposition (TD) methods to create an accurate and stable learning problem. We conducted experiments on several standard pedestrian datasets, and experimental results indicate that our tensor-based approach significantly improves person ReID baselines and also outperforms state of the art methods.
We prove an extension to the classical continuity theorem in rough paths. We show that two $p$-rough paths are close in all levels of iterated integrals provided the first $\lfl p \rfl$ terms are close in a uniform sense. Applications include estimation of the difference of the signatures of two uniformly close paths and convergence rates of Gaussian rough paths.
Semi-analytical models of galaxy formation based on hierarchical clustering now make a wide range of predictions for observable properties of galaxies at low and high redshift. This article concentrates on 2 aspects: (1) Self-consistent modelling of dust absorption predicts a mean UV extinction A_{UV} ~ 1 mag, depending only weakly on redshift, and similar to observational estimates. (2) The models predict that the Lyman-break galaxies found at z ~ 3 should be strongly clustered with a comoving correlation length r_0 = 4-7 Mpc/h (depending on the cosmology), in good agreement with subsequent observational determinations.
Lieb-Robinson-type bounds are reported for a large class of classical Hamiltonian lattice models. By a suitable rescaling of energy or time, such bounds can be constructed for interactions of arbitrarily long range. The bound quantifies the dependence of the system's dynamics on a perturbation of the initial state. The effect of the perturbation is found to be effectively restricted to the interior of a causal region of logarithmic shape, with only small, algebraically decaying effects in the exterior. A refined bound, sharper than conventional Lieb-Robinson bounds, is required to correctly capture the shape of the causal region, as confirmed by numerical results for classical long-range $XY$ chains. We discuss the relevance of our findings for the relaxation to equilibrium of long-range interacting lattice models.
We study the rate of convergence of the Markov chain on $S_n$ which starts with a random $(n-k)$-cycle for a fixed $k \geq 1$, followed by random transpositions. The convergence to the stationary distribution turns out to be of order $n$. We show that after $cn + \frac{\ln k}{2}n$ steps for $c>0$, the law of the Markov chain is close to the uniform distribution. The character of the defining representation is used as test function to obtain a lower bound for the total variation distance. We identify the asymptotic distribution of the test function given the law of the Markov chain for the $(n-1)$-cycle case. The upper bound relies on estimates for the difference of normalized characters.
Multideterminant calculations have been performed on model systems to emphasize the role of many-body effects in the general description of charge quantization experiments. We show numerically and derive analytically that a closed-shell ansatz, the usual ingredient of mean-field methods, does not properly describe the step-like electron transfer characteristic in weakly coupled systems. With the multideterminant results as a benchmark, we have evaluated the performance of common ab initio mean field techniques, such as Hartree Fock (HF) and Density Functional Theory (DFT) with local and hybrid exchange correlation functionals, with a special focus on spin-polarization effects. For HF and hybrid DFT, a qualitatively correct open-shell solution with distinct steps in the electron transfer behaviour can be obtained with a spin-unrestricted (i.e., spin-polarized) ansatz though this solution differs quantitatively from the multideterminant reference. We also discuss the relationship between the electronic eigenvalue gap and the onset of charge transfer for both HF and DFT and relate our findings to recently proposed practical schemes for calculating the addition energies in the Coulomb blockade regime for single molecule junctions from closed-shell DFT within the local density approximation.
Comprehensive 3D scene understanding, both geometrically and semantically, is important for real-world applications such as robot perception. Most of the existing work has focused on developing data-driven discriminative models for scene understanding. This paper provides a new approach to scene understanding, from a synthesis model perspective, by leveraging the recent progress on implicit 3D representation and neural rendering. Building upon the great success of Neural Radiance Fields (NeRFs), we introduce Scene-Property Synthesis with NeRF (SS-NeRF) that is able to not only render photo-realistic RGB images from novel viewpoints, but also render various accurate scene properties (e.g., appearance, geometry, and semantics). By doing so, we facilitate addressing a variety of scene understanding tasks under a unified framework, including semantic segmentation, surface normal estimation, reshading, keypoint detection, and edge detection. Our SS-NeRF framework can be a powerful tool for bridging generative learning and discriminative learning, and thus be beneficial to the investigation of a wide range of interesting problems, such as studying task relationships within a synthesis paradigm, transferring knowledge to novel tasks, facilitating downstream discriminative tasks as ways of data augmentation, and serving as auto-labeller for data creation.
For nuclei with N = Z, the isospin degree of freedom is important and, for deformed systems, rotational bands of different isospin may be expected at low excitation energies. We have investigated, in a simple model space, the influence of the isospin-breaking Coulomb interaction on the degree of chaoticity of these rotational bands. The statistical measures used rely on an analysis of level-spacing distributions, which are extremely difficult to measure experimentally. We show, however, that the overlap intergrals between states of similar frequency reflect well the degree of chaoticity. This quantity is closely related to the experimentally more accessible gamma-decay ``spreading width''.
We introduce a generic visual descriptor, termed as distribution aware retinal transform (DART), that encodes the structural context using log-polar grids for event cameras. The DART descriptor is applied to four different problems, namely object classification, tracking, detection and feature matching: (1) The DART features are directly employed as local descriptors in a bag-of-features classification framework and testing is carried out on four standard event-based object datasets (N-MNIST, MNIST-DVS, CIFAR10-DVS, NCaltech-101). (2) Extending the classification system, tracking is demonstrated using two key novelties: (i) For overcoming the low-sample problem for the one-shot learning of a binary classifier, statistical bootstrapping is leveraged with online learning; (ii) To achieve tracker robustness, the scale and rotation equivariance property of the DART descriptors is exploited for the one-shot learning. (3) To solve the long-term object tracking problem, an object detector is designed using the principle of cluster majority voting. The detection scheme is then combined with the tracker to result in a high intersection-over-union score with augmented ground truth annotations on the publicly available event camera dataset. (4) Finally, the event context encoded by DART greatly simplifies the feature correspondence problem, especially for spatio-temporal slices far apart in time, which has not been explicitly tackled in the event-based vision domain.
Fusion data for $^{16}$O+$^{16}$O are analyzed by coupled-channels calculations. It is shown that the calculated cross sections are sensitive to the couplings to the $2^+$ and $3^-$ excitation channels even at low energies, where these channels are closed. The sensitivity to the ion-ion potential is investigated by applying a conventional Woods-Saxon potential and the M3Y+repulsion potential, consisting of the M3Y double-folding potential and a repulsive term that simulates the effect of the nuclear incompressibility. The best overall fit to the data is obtained with a M3Y+repulsion potential which produces a shallow potential in the entrance channel. The stepwise increase in measured fusion cross sections at high energies is also consistent with such a shallow potential. The steps are correlated with overcoming the barriers for the angular momenta $L$ = 12, 14, 16, and 18. To improve the fit to the low-energy data requires a shallower potential and this causes a even stronger hindrance of fusion at low energies. It is therefore difficult, based on the existing fusion data, to make an accurate extrapolation to energies that are of interest to astrophysics.
Scene text image super-resolution (STISR) has been regarded as an important pre-processing task for text recognition from low-resolution scene text images. Most recent approaches use the recognizer's feedback as clues to guide super-resolution. However, directly using recognition clue has two problems: 1) Compatibility. It is in the form of probability distribution, has an obvious modal gap with STISR - a pixel-level task; 2) Inaccuracy. it usually contains wrong information, thus will mislead the main task and degrade super-resolution performance. In this paper, we present a novel method C3-STISR that jointly exploits the recognizer's feedback, visual and linguistical information as clues to guide super-resolution. Here, visual clue is from the images of texts predicted by the recognizer, which is informative and more compatible with the STISR task; while linguistical clue is generated by a pre-trained character-level language model, which is able to correct the predicted texts. We design effective extraction and fusion mechanisms for the triple cross-modal clues to generate a comprehensive and unified guidance for super-resolution. Extensive experiments on TextZoom show that C3-STISR outperforms the SOTA methods in fidelity and recognition performance. Code is available in https://github.com/zhaominyiz/C3-STISR.
We overview the potential of airborne X-ray and gamma-ray telescopes to probe the axion-like particles parameter space.
Anticipating future events is an essential feature for intelligent systems and embodied AI. However, compared to the traditional recognition task, the uncertainty of future and reasoning ability requirement make the anticipation task very challenging and far beyond solved. In this filed, previous methods usually care more about the model architecture design or but few attention has been put on how to train an anticipation model with a proper learning policy. To this end, in this work, we propose a novel training scheme called Dynamic Context Removal (DCR), which dynamically schedules the visibility of observed future in the learning procedure. It follows the human-like curriculum learning process, i.e., gradually removing the event context to increase the anticipation difficulty till satisfying the final anticipation target. Our learning scheme is plug-and-play and easy to integrate any reasoning model including transformer and LSTM, with advantages in both effectiveness and efficiency. In extensive experiments, the proposed method achieves state-of-the-art on four widely-used benchmarks. Our code and models are publicly released at https://github.com/AllenXuuu/DCR.