Quantization of a 2D Minisuperspace Model in Dilaton-Einstein Gravity

We investigate a minisuperspace model of Einstein gravity plus dilaton that describes a static spherically symmetric configuration or a Kantowski - Sachs like universe. We develop the canonical formalism and identify canonical quantities that generate rigid symmetries of the Hamiltonian. Quantization is performed by the Dirac and the reduced methods. Both approaches lead to the same positive definite Hilbert space.Comment: 10 pages, plain tex, no figure

On a Quantum Universe Filled with Yang - Mills Radiation

We investigate the properties of a quantum Robertson - Walker universe described by the Wheeler -- DeWitt equation. The universe is filled with a quantum Yang -- Mills uniform field. This is then a quantum mini copy of the standard model of our universe. We discuss the interpretation of the Wheeler -- DeWitt wave function using the correspondence principle to connect $\vert\psi\vert^2$ for large quantum numbers to the classical probability for a radiation dominated universe. This can be done in any temporal gauge. The correspondence principle determines the Schr\"odinger representation of the momentum associated to the gravitational degree of freedom. We also discuss the measure in the mini--superspace needed to ensure invariance of the quantum description under change of the temporal gauge. Finally, we examine the behaviour of $\vert\psi\vert^2$ in inflationary conditions.Comment: 12 pages, PLAIN-TEX, Report: DFTT 23/9

Quantization of the String Inspired Dilaton Gravity and the Birkhoff Theorem

We develop a simple scheme of quantization for the dilaton CGHS model without scalar fields, that uses the Gupta-Bleuler approach for the string fields. This is possible because the constraints can be linearized classically, due to positivity conditions that are present in the model (and not in the general string case). There is no ambiguity nor anomalies in the quantization. The expectation values of the metric and dilaton fields obey the classical requirements, thus exhibiting at the quantum level the Birkhoff theorem.Comment: 15 pages, Plain TeX, a shortened version will appear in Physics Letters

Anisotropic Wormhole: Tunneling in Time and Space

We discuss the structure of a gravitational Euclidean instanton obtained through coupling of gravity to electromagnetism. This Euclidean solution can be interpreted as a tunneling to a hyperbolic space (baby universe) or alternatively as a static wormhole that joins two asymptotically flat spaces of a Reissner-Nordström type solution

An Anisotropic Wormhole:TUNNELLING in Time and Space

We discuss the structure of a gravitational euclidean instanton obtained through coupling of gravity to electromagnetism. Its topology at fixed $t$ is $S^1\times S^2$. This euclidean solution can be interpreted as a tunnelling to a hyperbolic space (baby universe) at $t=0$ or alternatively as a static wormhole that joins the two asymptotically flat spaces of a Reissner--Nordstr\"om type solution with $M=0$.Comment: PLAIN-TEX, 16 pages (4 figures not included), Report DFTT 2/9

Exploring Subgroup Performance In End-to-End Speech Models

End-to-End Spoken Language Understanding models are generally evaluated according to their overall accuracy, or separately on (a priori defined) data subgroups of interest. We propose a technique for analyzing model performance at the subgroup level, which considers all subgroups that can be defined via a given set of metadata and are above a specified minimum size. The metadata can represent user characteristics, recording conditions, and speech targets. Our technique is based on advances in model bias analysis, enabling efficient exploration of resulting subgroups. A fine-grained analysis reveals how model performance varies across subgroups, identifying modeling issues or bias towards specific subgroups. We compare the subgroup-level performance of models based on wav2vec 2.0 and HuBERT on the Fluent Speech Commands dataset. The experimental results illustrate how subgroup-level analysis reveals a finer and more complete picture of performance changes when models are replaced, automatically identifying the subgroups that most benefit or fail to benefit from the chang