2,063 research outputs found
Mirror effect induced by the dilaton field on the Hawking radiation
We discuss the string creation in the near-extremal NS1 black string
solution. The string creation is described by an effective field equation
derived from a fundamental string action coupled to the dilaton field in a
conformally invariant manner. In the non-critical string model the dilaton
field causes a timelike mirror surface outside the horizon when the size of the
black string is comparable to the Planck scale. Since the fundamental strings
are reflected by the mirror surface, the negative energy flux does not
propagate across the surface. This means that the evaporation stops just before
the naked singularity of the extremal black string appears even though the
surface gravity is non-zero in the extremal limit.Comment: 15 page
Critical fluctuations in cortical models near instability
Australian Research Council, the National Health and Medical Research Council, the Brain Network Recovery Group Grant JSMF22002082, and Netherlands Organization for Scientific Research (NWO #451–10-030
Improving novelty detection using the reconstructions of nearest neighbours
We show that using nearest neighbours in the latent space of autoencoders
(AE) significantly improves performance of semi-supervised novelty detection in
both single and multi-class contexts. Autoencoding methods detect novelty by
learning to differentiate between the non-novel training class(es) and all
other unseen classes. Our method harnesses a combination of the reconstructions
of the nearest neighbours and the latent-neighbour distances of a given input's
latent representation. We demonstrate that our nearest-latent-neighbours (NLN)
algorithm is memory and time efficient, does not require significant data
augmentation, nor is reliant on pre-trained networks. Furthermore, we show that
the NLN-algorithm is easily applicable to multiple datasets without
modification. Additionally, the proposed algorithm is agnostic to autoencoder
architecture and reconstruction error method. We validate our method across
several standard datasets for a variety of different autoencoding architectures
such as vanilla, adversarial and variational autoencoders using either
reconstruction, residual or feature consistent losses. The results show that
the NLN algorithm grants up to a 17% increase in Area Under the Receiver
Operating Characteristics (AUROC) curve performance for the multi-class case
and 8% for single-class novelty detection
Learning to detect radio frequency interference in radio astronomy without seeing it
Radio Frequency Interference (RFI) corrupts astronomical measurements, thus
affecting the performance of radio telescopes. To address this problem,
supervised segmentation models have been proposed as candidate solutions to RFI
detection. However, the unavailability of large labelled datasets, due to the
prohibitive cost of annotating, makes these solutions unusable. To solve these
shortcomings, we focus on the inverse problem; training models on only
uncontaminated emissions thereby learning to discriminate RFI from all known
astronomical signals and system noise. We use Nearest-Latent-Neighbours (NLN) -
an algorithm that utilises both the reconstructions and latent distances to the
nearest-neighbours in the latent space of generative autoencoding models for
novelty detection. The uncontaminated regions are selected using weak-labels in
the form of RFI flags (generated by classical RFI flagging methods) available
from most radio astronomical data archives at no additional cost. We evaluate
performance on two independent datasets, one simulated from the HERA telescope
and another consisting of real observations from LOFAR telescope. Additionally,
we provide a small expert-labelled LOFAR dataset (i.e., strong labels) for
evaluation of our and other methods. Performance is measured using AUROC, AUPRC
and the maximum F1-score for a fixed threshold. For the simulated data we
outperform the current state-of-the-art by approximately 1% in AUROC and 3% in
AUPRC for the HERA dataset. Furthermore, our algorithm offers both a 4%
increase in AUROC and AUPRC at a cost of a degradation in F1-score performance
for the LOFAR dataset, without any manual labelling
Holographic Kondo Model in Various Dimensions
We study the addition of localised impurities to U(N) Supersymmetric
Yang-Mills theories in (p+1)-dimensions by using the gauge/gravity
correspondence. From the gravity side, the impurities are introduced by
considering probe D(8-p)-branes extendingalong the time and radial directions
and wrapping an (7-p)-dimensional submanifold of the internal (8-p)-sphere, so
that the degrees of freedom are point-like from the gauge theory perspective.
We analyse both the configuration in which the branes generate straight flux
tubes -corresponding to actual single impurities - and the one in which
connected flux tubes are created- corresponding to dimers. We discuss the
thermodynamics of both the configurations and the related phase transition. In
particular, the specific heat of the straight flux-tube configuration is
negative for p<3, while it is never the case for the connected one. We study
the stability of the system by looking at the impurity fluctuations. Finally,
we characterise the theory by computing one- and two-point correlators of the
gauge theory operators dual to the impurity fluctuations. Because of the
underlying generalised conformal structure, such correlators can be expressed
in terms of an effective coupling constant (which runs because of its
dimensionality) and a generalised conformal dimension.Comment: 56 pages, 3 figures; v2: typos correcte
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