Observations of Orbiting Hot Spots around Naked Singularities

Recently, it has been reported that photons can traverse naked singularities in the Janis-Newman-Winicour and Born-Infeld spacetimes when these singularities are appropriately regularized. In this paper, we investigate observational signatures of hot spots orbiting these naked singularities, with a focus on discerning them from black holes. In contrast to Schwarzschild black holes, we unveil the presence of multiple additional image tracks within critical curves in time integrated images capturing a complete orbit of hot spots. Moreover, these new images manifest as a more pronounced second-highest peak in temporal magnitudes when observed at low inclinations.Comment: 24 pages, 6 figures, and 3 animated images of intensity for ancillary. arXiv admin note: text overlap with arXiv:2305.1741

Observations of Orbiting Hot Spots around Scalarized Reissner-Nordstr\"om Black Holes

This paper investigates the observational signatures of hot spots orbiting scalarized Reissner-Nordstr\"om black holes, which have been reported to possess multiple photon spheres. In contrast to the single-photon sphere case, hot spots orbiting black holes with two photon spheres produce additional image tracks in time integrated images capturing a complete orbit of hot spots. Notably, these newly observed patterns manifest as a distinct second-highest peak in temporal magnitudes when observed at low inclination angles. These findings offer promising observational probes for distinguishing black holes with multiple photon spheres from their single-photon sphere counterparts.Comment: 22 pages, 5 figure

Mapping EEG Signals to Visual Stimuli: A Deep Learning Approach to Match vs. Mismatch Classification

Existing approaches to modeling associations between visual stimuli and brain responses are facing difficulties in handling between-subject variance and model generalization. Inspired by the recent progress in modeling speech-brain response, we propose in this work a ``match-vs-mismatch'' deep learning model to classify whether a video clip induces excitatory responses in recorded EEG signals and learn associations between the visual content and corresponding neural recordings. Using an exclusive experimental dataset, we demonstrate that the proposed model is able to achieve the highest accuracy on unseen subjects as compared to other baseline models. Furthermore, we analyze the inter-subject noise using a subject-level silhouette score in the embedding space and show that the developed model is able to mitigate inter-subject noise and significantly reduce the silhouette score. Moreover, we examine the Grad-CAM activation score and show that the brain regions associated with language processing contribute most to the model predictions, followed by regions associated with visual processing. These results have the potential to facilitate the development of neural recording-based video reconstruction and its related applications

Coarse-to-Fine: Facial Structure Editing of Portrait Images via Latent Space Classifications

Facial structure editing of portrait images is challenging given the facial variety, the lack of ground-truth, the necessity of jointly adjusting color and shape, and the requirement of no visual artifacts. In this paper, we investigate how to perform chin editing as a case study of editing facial structures. We present a novel method that can automatically remove the double chin effect in portrait images. Our core idea is to train a fine classification boundary in the latent space of the portrait images. This can be used to edit the chin appearance by manipulating the latent code of the input portrait image while preserving the original portrait features. To achieve such a fine separation boundary, we employ a carefully designed training stage based on latent codes of paired synthetic images with and without a double chin. In the testing stage, our method can automatically handle portrait images with only a refinement to subtle misalignment before and after double chin editing. Our model enables alteration to the neck region of the input portrait image while keeping other regions unchanged, and guarantees the rationality of neck structure and the consistency of facial characteristics. To the best of our knowledge, this presents the first effort towards an effective application for editing double chins. We validate the efficacy and efficiency of our approach through extensive experiments and user studies

Gravitational Lensing by Born-Infeld Naked Singularities

We examine the gravitational lensing phenomenon caused by photon spheres in the Born-Infeld naked singularity spacetime, where gravity is coupled with Born-Infeld electrodynamics. Specifically, our focus lies on relativistic images originating from a point-like light source generated by strong gravitational lensing near photon spheres, as well as images of a luminous celestial sphere. It shows that Born-Infeld naked singularities consistently exhibit one or two photon spheres, which project onto one or two critical curves on the image plane. Interestingly, we discover that the nonlinearity nature of the Born-Infeld electrodynamics enables photons to traverse the singularity, leading to the emergence of new relativistic images within the innermost critical curve. Furthermore, the presence of two photon spheres doubles the number of relativistic images compared to the scenario with only a single photon sphere. Additionally, the transparency inherent to Born-Infeld naked singularities results in the absence of a central shadow in the images of celestial spheres.Comment: 7 figures, 2 table

Coarse-to-Fine: Facial Structure Editing of Portrait Images via Latent Space Classifications

Facial structure editing of portrait images is challenging given the facial variety, the lack of ground-truth, the necessity of jointly adjusting color and shape, and the requirement of no visual artifacts. In this paper, we investigate how to perform chin editing as a case study of editing facial structures. We present a novel method that can automatically remove the double chin effect in portrait images. Our core idea is to train a fine classification boundary in the latent space of the portrait images. This can be used to edit the chin appearance by manipulating the latent code of the input portrait image while preserving the original portrait features. To achieve such a fine separation boundary, we employ a carefully designed training stage based on latent codes of paired synthetic images with and without a double chin. In the testing stage, our method can automatically handle portrait images with only a refinement to subtle misalignment before and after double chin editing. Our model enables alteration to the neck region of the input portrait image while keeping other regions unchanged, and guarantees the rationality of neck structure and the consistency of facial characteristics. To the best of our knowledge, this presents the first effort towards an effective application for editing double chins. We validate the efficacy and efficiency of our approach through extensive experiments and user studies