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