ShantanuT01/DACTYL-Pretraining huggingface dataset:
This paper proposes a fusion-based gender recognition method which uses facial images as input. Firstly, this paper utilizes pre-processing and a landmark detection method in order to find the important landmarks of faces. Thereafter, four different frameworks are proposed which are inspired by state-of-the-art gender recognition systems. The first framework extracts features using Local Binary Pattern (LBP) and Principal Component Analysis (PCA) and uses back propagation neural network. The second framework uses Gabor filters, PCA, and kernel Support Vector Machine (SVM). The third framework uses lower part of faces as input and classifies them using kernel SVM. The fourth framework uses Linear Discriminant Analysis (LDA) in order to classify the side outline landmarks of faces. Finally, the four decisions of frameworks are fused using weighted voting. This paper takes advantage of both texture and geometrical information, the two dominant types of information in facial gender recognition. Experimental results show the power and effectiveness of the proposed method. This method obtains recognition rate of 94% for neutral faces of FEI face dataset, which is equal to state-of-the-art rate for this dataset.
Classical and quantum measurement theories are usually held to be different because the algebra of classical measurements is commutative, however the Poisson bracket allows noncommutativity to be added naturally. After we introduce noncommutativity into classical measurement theory, we can also add quantum noise, differentiated from thermal noise by Poincar\'e invariance. With these two changes, the extended classical and quantum measurement theories are equally capable, so we may speak of a single "measurement theory". The reconciliation of general relativity and quantum theory has been long delayed because classical and quantum systems have been thought to be very different, however this unification allows us to discuss a unified measurement theory for geometry in physics.
This thesis explore different approaches using Convolutional and Recurrent Neural Networks to classify and temporally localize activities on videos, furthermore an implementation to achieve it has been proposed. As the first step, features have been extracted from video frames using an state of the art 3D Convolutional Neural Network. This features are fed in a recurrent neural network that solves the activity classification and temporally location tasks in a simple and flexible way. Different architectures and configurations have been tested in order to achieve the best performance and learning of the video dataset provided. In addition it has been studied different kind of post processing over the trained network's output to achieve a better results on the temporally localization of activities on the videos. The results provided by the neural network developed in this thesis have been submitted to the ActivityNet Challenge 2016 of the CVPR, achieving competitive results using a simple and flexible architecture.
We show that, contrary to common belief, the propagation of a spin-2 field in an electromagnetic background is {\em causal}. The proof will be given in the Fierz formalism which, as we shall see, is free of the ambiguity present in the more usual Einstein representation. We shall also review the proof in this latter representation.
Current stellar model predictions of adiabatic oscillation frequencies differ significantly from the corresponding observed frequencies due to the non-adiabatic and poorly understood near-surface layers of stars. However, certain combinations of frequencies -- known as frequency ratios -- are largely unaffected by the uncertain physical processes as they are mostly sensitive to the stellar core. Furthermore, the seismic signature of helium ionization provides envelope properties while being almost independent of the outermost layers. We have developed an advanced stellar modelling approach in which we complement frequency ratios with parameters of the helium ionization zone while taking into account all possible correlations to put the most stringent constraints on the stellar internal structure. We have tested the method using the Kepler benchmark star 16 Cyg A and have investigated the potential of the helium glitch parameters to constrain the basic stellar properties in detail. It has been explicitly shown that the initial helium abundance and mixing-length parameters are well constrained within our framework, reducing systematic uncertainties on stellar mass and age arising for instance from the well-known anti-correlation between the mass and initial helium abundance. The modelling of six additional Kepler stars including 16 Cyg B reinforces the above findings and also confirms that our approach is mostly independent from model uncertainties associated with the near-surface layers. Our method is relatively computationally expensive, however, it provides stellar masses, radii and ages precisely in an automated manner, paving the way for analysing numerous stars observed in the future during the ESA PLATO mission.
We give a necessary and sufficient condition on a sequence of functions on a set $\Omega$ under which there is a measure on $\Omega$ which renders the given sequence of functions a martingale. Further such a measure is unique if we impose a natural maximum entropy condition on the conditional probabilities.
Hybrid quantum/classical variational algorithms can be implemented on noisy intermediate-scale quantum computers and can be used to find solutions for combinatorial optimization problems. Approaches discussed in the literature minimize the expectation of the problem Hamiltonian for a parameterized trial quantum state. The expectation is estimated as the sample mean of a set of measurement outcomes, while the parameters of the trial state are optimized classically. This procedure is fully justified for quantum mechanical observables such as molecular energies. In the case of classical optimization problems, which yield diagonal Hamiltonians, we argue that aggregating the samples in a different way than the expected value is more natural. In this paper we propose the Conditional Value-at-Risk as an aggregation function. We empirically show -- using classical simulation as well as quantum hardware -- that this leads to faster convergence to better solutions for all combinatorial optimization problems tested in our study. We also provide analytical results to explain the observed difference in performance between different variational algorithms.
The most common search channel for heavy neutral Higgses in models with an extension of the Standard Model Higgs sector is $A/H^0\rightarrow \tau\tau$, which becomes ineffective when new decay modes of $A/H^0$ open. In this paper, we analyzed two such channels involving charged Higgses in the final states: $A/H^0 \rightarrow W^\pm H^\mp$ and $H^0 \rightarrow H^+H^-$. With the consequent decay of $H^\pm\rightarrow \tau\nu$, we found that the limits for $\sigma\times{BR}(gg \rightarrow A/H^0 \rightarrow W^\pm H^\mp)\times {BR}(H^\pm \rightarrow \tau \nu)$ vary from 30 to 10 fb for $m_{A/H^0}$ between 300 and 1000 GeV for 95% C.L. exclusion, and about 80 to 30 fb for 5$\sigma$ discovery. For $H^+H^-$ mode, 95% C.L. limits on $\sigma\times {BR}(gg\to H^0\to H^+ H^-)\times {BR}^2(H^\pm\to \tau\nu)$ vary from 9 to 4 fb for $m_{H^0}$ between 400 and 1000 GeV, while the 5$\sigma$ reach is about 20 to 10 fb. We further interpret the cross section limits in the Type II 2HDM parameter space. While $A\rightarrow W^\pm H^\mp$ offers great sensitivity in both $\sin(\beta-\alpha)$ versus $\tan\beta$ and $m_A$ versus $\tan\beta$ parameter space, $H^0\rightarrow H^+ H^-$ can cover most of the parameter space for $H^0$. Reach in $H^0\rightarrow W^\pm H^\mp$ is more limited, especially for $m_{H^0}>2 m_{H^\pm}$. It is, however, complementary to $H^0\rightarrow H^+ H^-$ when ${BR}(H^0\rightarrow H^+ H^-)$ is accidentally suppressed.
Stability conditions are a mathematical way to understand $\Pi$-stability for D-branes in string theory. Spaces of stability conditions seem to be related to moduli spaces of conformal field theories. This is a survey article describing what is currently known about spaces of stability conditions, and giving some pointers for future research.
Existing domain adaptation methods on visual sentiment classification typically are investigated under the single-source scenario, where the knowledge learned from a source domain of sufficient labeled data is transferred to the target domain of loosely labeled or unlabeled data. However, in practice, data from a single source domain usually have a limited volume and can hardly cover the characteristics of the target domain. In this paper, we propose a novel multi-source domain adaptation (MDA) method, termed Multi-source Sentiment Generative Adversarial Network (MSGAN), for visual sentiment classification. To handle data from multiple source domains, it learns to find a unified sentiment latent space where data from both the source and target domains share a similar distribution. This is achieved via cycle consistent adversarial learning in an end-to-end manner. Extensive experiments conducted on four benchmark datasets demonstrate that MSGAN significantly outperforms the state-of-the-art MDA approaches for visual sentiment classification.
We use the Landweber method for numerical simulations for the multiwave tomography problem with a reflecting boundary and compare it with the averaged time reversal method. We also analyze the rate of convergence and the dependence on the step size for the Landweber iterations on a Hilbert space.
Meyniel's conjecture is one of the deepest open problems on the cop number of a graph. It states that for a connected graph $G$ of order $n,$ $c(G) = O(\sqrt{n}).$ While largely ignored for over 20 years, the conjecture is receiving increasing attention. We survey the origins of and recent developments towards the solution of the conjecture. We present some new results on Meyniel extremal families containing graphs of order $n$ satisfying $c(G) \ge d\sqrt{n},$ where $d$ is a constant.
To evaluate the impact of stellar feedback, it is critical to estimate the mass outflow rates of galaxies. Past estimates have been plagued by uncertain assumptions about the outflow geometry, metallicity, and ionization fraction. Here we use Hubble Space Telescope ultraviolet spectroscopic observations of the nearby starburst NGC 6090 to demonstrate that many of these quantities can be constrained by the data. We use the Si~{\sc IV} absorption lines to calculate the scaling of velocity (v), covering fraction (C$_f$), and density with distance from the starburst (r), assuming the Sobolev optical depth and a velocity law of the form: v$~\propto(1 -\mathrm{R}_\mathrm{i}/\mathrm{r} )^\beta$ (where R$_\mathrm{i}$ is the inner outflow radius). We find that the velocity ($\beta$=0.43) is consistent with an outflow driven by an r$^{-2}$ force with the outflow radially accelerated, while the scaling of the covering fraction ($C_f \propto \mathrm{r}^{-0.82}$) suggests that cool clouds in the outflow are in pressure equilibrium with an adiabatically expanding medium. We use the column densities of four weak metal lines and CLOUDY photoionization models to determine the outflow metallicity, the ionization correction, and the initial density of the outflow. Combining these values with the profile fitting, we find R$_\mathrm{i}$ = 63 pc, with most of the mass within 300~pc of the starburst. Finally, we find that the maximum mass outflow rate is 2.3~M$_\odot$ yr$^{-1}$ and the mass loading factor (outflow divided by the star formation rate) is 0.09, a factor of 10 lower than the value calculated using common assumptions for the geometry, metallicity and ionization structure of the outflow.
We expose a double-copy structure in the scattering amplitudes of the generic Jordan family of N=2 Maxwell-Einstein and Yang-Mills/Einstein supergravity theories in four and five dimensions. The Maxwell-Einstein supergravity amplitudes are obtained through the color/kinematics duality as a product of two gauge-theory factors; one originating from pure N=2 super-Yang-Mills theory and the other from the dimensional reduction of a bosonic higher-dimensional pure Yang-Mills theory. We identify a specific symplectic frame in four dimensions for which the on-shell fields and amplitudes from the double-copy construction can be identified with the ones obtained from the supergravity Lagrangian and Feynman-rule computations. The Yang-Mills/Einstein supergravity theories are obtained by gauging a compact subgroup of the isometry group of their Maxwell-Einstein counterparts. For the generic Jordan family this process is identified with the introduction of cubic scalar couplings on the bosonic gauge-theory side, which through the double copy are responsible for the non-abelian vector interactions in the supergravity theory. As a demonstration of the power of this structure, we present explicit computations at tree-level and one loop. The double-copy construction allows us to obtain compact expressions for the supergravity superamplitudes which are naturally organized as polynomials in the gauge coupling constant.
Land cover classification of remote sensing images is a challenging task due to limited amounts of annotated data, highly imbalanced classes, frequent incorrect pixel-level annotations, and an inherent complexity in the semantic segmentation task. In this article, we propose a novel architecture called the dense dilated convolutions' merging network (DDCM-Net) to address this task. The proposed DDCM-Net consists of dense dilated image convolutions merged with varying dilation rates. This effectively utilizes rich combinations of dilated convolutions that enlarge the network's receptive fields with fewer parameters and features compared with the state-of-the-art approaches in the remote sensing domain. Importantly, DDCM-Net obtains fused local- and global-context information, in effect incorporating surrounding discriminative capability for multiscale and complex-shaped objects with similar color and textures in very high-resolution aerial imagery. We demonstrate the effectiveness, robustness, and flexibility of the proposed DDCM-Net on the publicly available ISPRS Potsdam and Vaihingen data sets, as well as the DeepGlobe land cover data set. Our single model, trained on three-band Potsdam and Vaihingen data sets, achieves better accuracy in terms of both mean intersection over union (mIoU) and F1-score compared with other published models trained with more than three-band data. We further validate our model on the DeepGlobe data set, achieving state-of-the-art result 56.2% mIoU with much fewer parameters and at a lower computational cost compared with related recent work. Code available at https://github.com/samleoqh/DDCM-Semantic-Segmentation-PyTorch
Let $G$ be a graph and $k$ a positive integer. A strong $k$-edge-coloring of $G$ is a mapping $\phi: E(G)\to \{1,2,\dots,k\}$ such that for any two edges $e$ and $e'$ that are either adjacent to each other or adjacent to a common edge, $\phi(e)\neq \phi(e')$. The strong chromatic index of $G$, denoted as $\chi'_{s}(G)$, is the minimum integer $k$ such that $G$ has a strong $k$-edge-coloring. Lv, Li and Zhang [Graphs and Combinatorics 38 (3) (2022) 63] proved that if $G$ is a claw-free subcubic graph other than the triangular prism then $\chi_s'(G)\le 8$. In addition, they asked if the upper bound $8$ can be improved to $7$. In this paper, we answer this question in the affirmative. Our proof implies a linear-time algorithm for finding strong $7$-edge-colorings of such graphs. We also construct infinitely many claw-free subcubic graphs with their strong chromatic indices attaining the bound $7$.
We show that the cone of primitive Heegner divisors is finitely generated for many orthogonal Shimura varieties, including the moduli space of polarized K3-surfaces. The proof relies on the growth of coefficients of modular forms.
Exploiting the valley degree of freedom to store and manipulate information provides a novel paradigm for future electronics. A monolayer transition metal dichalcogenide (TMDC) with broken inversion symmetry possesses two degenerate yet inequivalent valleys, offering unique opportunities for valley control through helicity of light. Lifting the valley degeneracy by Zeeman splitting has been demonstrated recently, which may enable valley control by a magnetic field. However, the realized valley splitting is modest, (~ 0.2 meV/T). Here we show greatly enhanced valley spitting in monolayer WSe2, utilizing the interfacial magnetic exchange field (MEF) from a ferromagnetic EuS substrate. A valley splitting of 2.5 meV is demonstrated at 1 T by magneto-reflectance measurements. Moreover, the splitting follows the magnetization of EuS, a hallmark of the MEF. Utilizing MEF of a magnetic insulator can induce magnetic order, and valley and spin polarization in TMDCs, which may enable valleytronic and quantum computing applications.
We characterize the observability property (and, by duality, the controllability and the stabilization) of the wave equation on a Riemannian manifold $\Omega,$ with or without boundary, where the observation (or control) domain is time-varying. We provide a condition ensuring observability, in terms of propagating bicharacteristics. This condition extends the well-known geometric control condition established for fixed observation domains. As one of the consequences, we prove that it is always possible to find a time-dependent observation domain of arbitrarily small measure for which the observability property holds. From a practical point of view, this means that it is possible to reconstruct the solutions of the wave equation with only few sensors (in the Lebesgue measure sense), at the price of moving the sensors in the domain in an adequate way.We provide several illustrating examples, in which the observationdomain is the rigid displacement in $\Omega$ of a fixed domain, withspeed $v,$ showing that the observability property depends both on $v$and on the wave speed. Despite the apparent simplicity of some of ourexamples, the observability property can depend on nontrivial arithmeticconsiderations.
Let $p$ be an odd prime number. Using modular arguments, we give an easy testable condition which allows often to prove Fermat's Last Theorem over the quadratic field ${\bf Q}(\sqrt{5})$ for the exponent $p$. It is related to the Wendt's resultant of the polynomials $X^n-1$ and $(X+1)^n-1$. We deduce Fermat's Last Theorem over this field in case one has $5\leq p<10^7$, and we obtain analogous results on Sophie Germain type criteria.
A graph is called a chain graph if it is bipartite and the neighborhoods of the vertices in each color class form a chain with respect to inclusion. A threshold graph can be obtained from a chain graph by making adjacent all pairs of vertices in one color class. Given a graph $G$, let $\lambda$ be an eigenvalue (of the adjacency matrix) of $G$ with multiplicity $k \geq 1$. A vertex $v$ of $G$ is a downer, or neutral, or Parter depending whether the multiplicity of $\lambda$ in $G-v$ is $k-1$, or $k$, or $k+1$, respectively. We consider vertex types in the above sense in threshold and chain graphs. In particular, we show that chain graphs can have neutral vertices, disproving a conjecture by Alazemi {\em et al.}
The phase oscillator model with global coupling is extended to the case of finite-range nonlocal coupling. Under suitable conditions, peculiar patterns emerge in which a quasi-continuous array of identical oscillators separates sharply into two domains, one composed of mutually synchronized oscillators with unique frequency and the other composed of desynchronized oscillators with distributed frequencies. We apply a theory similar to the one which successfully explained the onset of collective synchronization in globally coupled phase oscillators with frequency distribution. A space-dependent order parameter is thus introduced, and an exact functional self-consistency equation is derived for this quantity. Its numerical solution is confirmed to reproduce the simulation results accurately.
We classify some special classes of non-rational Fano threefolds with terminal singularities. In particular, all such hyperelliptic and trigonal varieties are found.
The general idea of determining cosmological parameters with gravitational lensing statistics is outlined, and then recent work---with an emphasis on applicability to all cosmological models, observational bias, better statistics and robustness testing through numerical simulations---is discussed with relevance to a scheme originally introduced by Kochanek to avoid some of the uncertainties which plague other methods. The main result is that the observations at present---and probably in the future as well---are more indicative of intrinsic scatter than a hint of the correct cosmological model.
We propose and discuss two postulates on the nature of errors in highly correlated noisy physical stochastic systems. The first postulate asserts that errors for a pair of substantially correlated elements are themselves substantially correlated. The second postulate asserts that in a noisy system with many highly correlated elements there will be a strong effect of error synchronization. These postulates appear to be damaging for quantum computers.
We predict a new type of the negative-$U$ Anderson impurity formed in a triple quantum dot. The two dots of the system behave as a negative-$U$ impurity preferring zero or double electron occupancy rather than single occupancy, and the third dot stabilizes the attractive interaction of $U < 0$ via Coulomb repulsion. Using a bosonization method, we find that the system has the two different phases of massive or vanishing charge fluctuations between the two occupancies at low temperature, which are equivalent with the antiferromagnetic and ferromagnetic phases of the anisotropic Kondo model, respectively. The phase transition is experimentally accessible and identifiable by electron conductance, offering the possibility of experimentally exploring the anisotropic Kondo model.
Cosmic shear tomography has emerged as one of the most promising tools to both investigate the nature of dark energy and discriminate between General Relativity and modified gravity theories. In order to successfully achieve these goals, systematics in shear measurements have to be taken into account; their impact on the weak lensing power spectrum has to be carefully investigated in order to estimate the bias induced on the inferred cosmological parameters. To this end, we develop here an efficient tool to compute the power spectrum of systematics by propagating, in a realistic way, shear measurement, source properties and survey setup uncertainties. Starting from analytical results for unweighted moments and general assumptions on the relation between measured and actual shear, we derive analytical expressions for the multiplicative and additive bias, showing how these terms depend not only on the shape measurement errors, but also on the properties of the source galaxies (namely, size, magnitude and spectral energy distribution). We are then able to compute the amplitude of the systematics power spectrum and its scaling with redshift, while we propose a multigaussian expansion to model in a non-parametric way its angular scale dependence. Our method allows to self-consistently propagate the systematics uncertainties to the finally observed shear power spectrum, thus allowing us to quantify the departures from the actual spectrum. We show that even a modest level of systematics can induce non-negligible deviations, thus leading to a significant bias on the recovered cosmological parameters.