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Register a new userWhat can we learn from higher multipole power spectra of galaxy distribution in redshift space?
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We investigate a potential of the higher multipole power spectra of the galaxy distribution in redshift space as a cosmological probe on halo scales. Based on the fact that a halo model explains well the multipole power spectra of the luminous red galaxy (LRG) sample in the Sloan Digital Sky Survey (SDSS), we focus our investigation on the random motions of the satellite LRGs that determine the higher multipole spectra at large wavenumbers. We show that our theoretical model fits the higher multipole spectra at large wave numbers from N-body numerical simulations and we apply these results for testing the gravity theory and the velocity structure of galaxies on the halo scales. In this analysis, we use the multipole spectra P_4(k) and P_6(k) on the small scales of the range of wavenumber 0.3<k/[h{Mpc}^{-1}]<0.6, which is in contrast to the usual method of testing gravity by targeting the linear growth rate on very large scales. We demonstrate that our method could be useful for testing gravity on the halo scales.
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We investigate the effects of multi-task learning using the recently introduced task of semantic tagging. We employ semantic tagging as an auxiliary task for three different NLP tasks: part-of-speech tagging, Universal Dependency parsing, and Natural Language Inference. We compare full neural network sharing, partial neural network sharing, and what we term the learning what to share setting where negative transfer between tasks is less likely. Our findings show considerable improvements for all tasks, particularly in the learning what to share setting, which shows consistent gains across all tasks.
Clustering of the large scale structure provides complementary information to the measurements of the cosmic microwave background anisotropies through power spectrum and bispectrum of density perturbations. Extracting the bispectrum information, however, is more challenging than it is from the power spectrum due to the complex models and the computational cost to measure the signal and its covariance. To overcome these problems, we adopt a proxy statistic, skew spectrum which is a cross-spectrum of the density field and its quadratic field. By applying a large smoothing filter to the density field, we show the theory fits the simulations very well. With the spectra and their full covariance estimated from $N$-body simulations as our mock Universe, we perform a global fits for the cosmological parameters. The results show that adding skew spectrum to power spectrum the $1sigma$ marginalized errors for parameters $ b_1^2A_s, n_s$ and $f_{rm NL}^{rm loc}$ are reduced by $31%, 22%, 44%$, respectively. This is the answer to the question posed in the title and indicates that the skew spectrum will be a fast and effective method to access complementary information to that enclosed in the power spectrum measurements, especially for the forthcoming generation of wide-field galaxy surveys.
Learning problems form an important category of computational tasks that generalizes many of the computations researchers apply to large real-life data sets. We ask: what concept classes can be learned privately, namely, by an algorithm whose output does not depend too heavily on any one input or specific training example? More precisely, we investigate learning algorithms that satisfy differential privacy, a notion that provides strong confidentiality guarantees in contexts where aggregate information is released about a database containing sensitive information about individuals. We demonstrate that, ignoring computational constraints, it is possible to privately agnostically learn any concept class using a sample size approximately logarithmic in the cardinality of the concept class. Therefore, almost anything learnable is learnable privately: specifically, if a concept class is learnable by a (non-private) algorithm with polynomial sample complexity and output size, then it can be learned privately using a polynomial number of samples. We also present a computationally efficient private PAC learner for the class of parity functions. Local (or randomized response) algorithms are a practical class of private algorithms that have received extensive investigation. We provide a precise characterization of local private learning algorithms. We show that a concept class is learnable by a local algorithm if and only if it is learnable in the statistical query (SQ) model. Finally, we present a separation between the power of interactive and noninteractive local learning algorithms.
We discuss the features of instabilities in binary systems, in particular, for asymmetric nuclear matter. We show its relevance for the interpretation of results obtained in experiments and in ab initio simulations of the reaction between $^{124}Sn+^{124}Sn$ at 50AMeV.}
Different from other multiple top-quark productions, triple top-quark production requires the presence of both flavor violating neutral interaction and flavor conserving neutral interaction. We describe the interaction of triple top-quarks and up-quark in terms of two dimension-6 operators; one can be induced by a new heavy vector resonance, the other by a scalar resonance. Combining same-sign top-quark pair production and four top-quark production, we explore the potential of the 13 TeV LHC on searching for the triple top-quark production.
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