We theoretically analyze the problem of testing for $p$-hacking based on distributions of $p$-values across multiple studies. We provide general results for when such distributions have testable restrictions (are non-increasing) under the null of no $p$-hacking. We find novel additional testable restrictions for $p$-values based on $t$-tests. Speci...
A general framework for the description of classic wave propagation is introduced. This relies on a cone structure $C$ determined by an intrinsic space $\Sigma$ of velocities of propagation (point, direction and time-dependent) and an observers' vector field $\partial_t$ whose integral curves provide both a Zermelo problem for the wave and an auxil...
We present nearly self-consistent stellar-halo models of the Fornax dwarf spheroidal galaxy associated with the Milky Way galaxy. Such galaxies are dominated by dark matter and have almost no gas in the system. Therefore, they are excellent objects for N-body modeling that takes into account visible and dark matter halo components. In order to mode...
The development of low-threshold detectors for neutrinos and dark matter necessitates low-energy calibration. We suggest this can be provided by the nuclear recoils resulting from the $\gamma$ emission following neutron capture. There is often a well defined MeV-scale $\gamma$ associated to a nuclear recoil in the 100 eV range. Using the FIFRELIN c...
We propose a simple model of spacetime vacuum fluctuations motivated by AdS/CFT, where the vacuum is described by a thermal density matrix, $\rho = \frac{e^{-K}}{\mbox{Tr}(e^{-K})}$ with $K$ the modular Hamiltonian. In AdS/CFT, both the expectation value of $K$ and its fluctuations $\langle \Delta K^2 \rangle$ have been calculated; both obey an are...
While deep reinforcement learning excels at solving tasks where large amounts of data can be collected through virtually unlimited interaction with the environment, learning from limited interaction remains a key challenge. We posit that an agent can learn more efficiently if we augment reward maximization with self-supervised objectives based on s...
In the framework of the relativistic mean field model with Thomas-Fermi approximation, we study the structures of low density nuclear matter in a three-dimensional geometry with reflection symmetry. The numerical accuracy and efficiency are improved by expanding the mean fields according to fast cosine transformation and considering only one octant...
Today many mobile users in various zones are invited to sense and send back real-time useful information (e.g., traffic observation and sensor data) to keep the freshness of the content updates in such zones. However, due to the sampling cost in sensing and transmission, a user may not have the incentive to contribute the real-time information to h...
In neural network-based monaural speech separation techniques, it has been recently common to evaluate the loss using the permutation invariant training (PIT) loss. However, the ordinary PIT requires to try all $N!$ permutations between $N$ ground truths and $N$ estimates. Since the factorial complexity explodes very rapidly as $N$ increases, a PIT...
Published in Journal of High Energy Physics
The large proper-time behaviour of expanding boost-invariant fluids has provided many crucial insights into quark-gluon plasma dynamics. Here we formulate and explore the late-time behaviour of nonequilibrium dynamics at the level of linearized perturbations of equilibrium, but without any special symmetry assumptions. We introduce a useful quantit...
