Light rings and shadows of rotating black holes in the semiclassical gravity with trace anomaly

In a recent work by Fernandes [arXiv:2305.10382], an exact stationary and axisymmetric solution was discovered in semiclassical gravity with type-A trace anomaly, identified as a quantum-corrected version of the Kerr black hole. This discovery presents exciting research opportunities for observing non-circular spacetimes. In this study, we explore the light rings and shadow of this black hole solution. Our investigation reveals that there exist prograde and retrograde normal light rings, whose radii increase monotonically with the coupling parameter $\alpha$. We also observe that when $\alpha$ is negative, the shadow area for the quantum-corrected black hole is smaller than that of the Kerr black hole, whereas when $\alpha$ is positive, the area is larger. Furthermore, the NHEKline for nearly extreme black hole disappears when $\alpha$ is greater than zero, while it appears for negative $\alpha$, even if the spin is not too high. Such line sinks in the middle part when $|\alpha|$ is relatively large if $\alpha$ is less than zero.Comment: 14 pages, 7 figure

A new analytical model of magnetofluids surrounding rotating black holes

In this study, we develop a simplified magnetofluid model in the framework of GRMHD. We consider an ideal, adiabatic fluid composed of two components, ions and electrons, having a constant ratio between their temperatures. The flows are assumed to be governed by gravity, enabling us to employ the ballistic approximation, treating the streamlines as timelike geodesics. We show that the model is analytically soluble around a rotating black hole if the angular velocity of the geodesic $u^\theta$ is vanishing. In the corresponding solution, which is named the conical solution, we derive a comprehensive set of explicit expressions for the thermodynamics and the associated magnetic field. Furthermore, we explore the potential applications of our model to describe the thick disks and the jets at the horizon scale. Our model provides a direct pathway for the study of black hole imaging.Comment: 27 pages, 4 figure

Electromagnetic effects on charged particles in NHEK

We investigate the motions of charged particles in the near horizon region of an extreme Kerr black hole with weak electromagnetic fields. There is an enhanced symmetry in the NHEK geometry. We find that when the electromagnetic field respects this enhanced symmetry, which we refer to as the maximally symmetric electromagnetic (MSEM) field, the equations of motion of charged particles get simplified into a set of decoupled first-order differential equations. We discuss the motions of charged particles in two MSEM fields, one being the force-free field and the other being the vacuum fields. Even though the radial motions are similar to the geodesics in NHEK geometry, the angular motions could be affected by the electromagnetic field significantly. In particular, for the vacuum solution which is produced by a weakly charged black hole, there exist stable vortical motions if the electromagnetic parameter is above the critical value \mB_c = \sqrt{3}. These vortical motions do not cross the equatorial planes, and the charged particles in them radiate non-thermally. We discuss the corresponding astrophysical implications.Comment: 26 pages, 4 figure

Polarized images of charged particles in vortical motions around a magnetized Kerr black hole

In this work, we study the images of a Kerr black hole (BH) immersed in uniform magnetic fields, illuminated by the synchrotron radiation of charged particles in the jet. We particularly focus on the spontaneously vortical motions (SVMs) of charged particles in the jet region and investigate the polarized images of electromagnetic radiations from the trajectories along SVMs. We notice that there is a critical value $\omega_c$ for charged particle released at a given initial position and subjected an outward force, and once $|qB_0/m|=|\omega_B|>|\omega_c|$ charged particles can move along SVMs in the jet region. We obtain the polarized images of the electromagnetic radiations from the trajectories along SVMs. Our simplified model suggests that the SVM radiations can act as the light source to illuminate the BH and form a photon ring structure.Comment: 24 pages, 8 figure

Image of Kerr-Melvin black hole with thin accretion disk

In this present work, we study the observational appearance of Kerr-Melvin black hole (KMBH) illuminated by an accretion disk. The accretion disk is assumed to be located on the equatorial plane and be thin both geometrically and optically. Considering the fact that outside the innermost stable circular orbit (ISCO) the accretion flow moves in prograde or retrograde circular orbit and falls towards the horizon along plunging orbit inside the ISCO, we develop the numerical backward ray-tracing method and obtain the images of KMBH accompanying with the accretion disk for various black hole spins, strengths of magnetic fields and inclination angles of observers. We present the intensity distribution horizontally and longitudinally and show the profiles of the red-shift for the direct and lensed images. Our study suggests that the inner shadow and critical curves can be used to estimate the magnetic field around a black hole without degeneration.Comment: 24 pages, 10 figure

An Unified Search and Recommendation Foundation Model for Cold-Start Scenario

In modern commercial search engines and recommendation systems, data from multiple domains is available to jointly train the multi-domain model. Traditional methods train multi-domain models in the multi-task setting, with shared parameters to learn the similarity of multiple tasks, and task-specific parameters to learn the divergence of features, labels, and sample distributions of individual tasks. With the development of large language models, LLM can extract global domain-invariant text features that serve both search and recommendation tasks. We propose a novel framework called S\&R Multi-Domain Foundation, which uses LLM to extract domain invariant features, and Aspect Gating Fusion to merge the ID feature, domain invariant text features and task-specific heterogeneous sparse features to obtain the representations of query and item. Additionally, samples from multiple search and recommendation scenarios are trained jointly with Domain Adaptive Multi-Task module to obtain the multi-domain foundation model. We apply the S\&R Multi-Domain foundation model to cold start scenarios in the pretrain-finetune manner, which achieves better performance than other SOTA transfer learning methods. The S\&R Multi-Domain Foundation model has been successfully deployed in Alipay Mobile Application's online services, such as content query recommendation and service card recommendation, etc.Comment: CIKM 2023,6 page

Efficient Vision Transformers via Fine-Grained Manifold Distillation

This paper studies the model compression problem of vision transformers. Benefit from the self-attention module, transformer architectures have shown extraordinary performance on many computer vision tasks. Although the network performance is boosted, transformers are often required more computational resources including memory usage and the inference complexity. Compared with the existing knowledge distillation approaches, we propose to excavate useful information from the teacher transformer through the relationship between images and the divided patches. We then explore an efficient fine-grained manifold distillation approach that simultaneously calculates cross-images, cross-patch, and random-selected manifolds in teacher and student models. Experimental results conducted on several benchmarks demonstrate the superiority of the proposed algorithm for distilling portable transformer models with higher performance. For example, our approach achieves 75.06% Top-1 accuracy on the ImageNet-1k dataset for training a DeiT-Tiny model, which outperforms other ViT distillation methods

TinySAM: Pushing the Envelope for Efficient Segment Anything Model

Recently segment anything model (SAM) has shown powerful segmentation capability and has drawn great attention in computer vision fields. Massive following works have developed various applications based on the pretrained SAM and achieved impressive performance on downstream vision tasks. However, SAM consists of heavy architectures and requires massive computational capacity, which hinders the further application of SAM on computation constrained edge devices. To this end, in this paper we propose a framework to obtain a tiny segment anything model (TinySAM) while maintaining the strong zero-shot performance. We first propose a full-stage knowledge distillation method with hard prompt sampling and hard mask weighting strategy to distill a lightweight student model. We also adapt the post-training quantization to the promptable segmentation task and further reduce the computational cost. Moreover, a hierarchical segmenting everything strategy is proposed to accelerate the everything inference by $2\times$ with almost no performance degradation. With all these proposed methods, our TinySAM leads to orders of magnitude computational reduction and pushes the envelope for efficient segment anything task. Extensive experiments on various zero-shot transfer tasks demonstrate the significantly advantageous performance of our TinySAM against counterpart methods. Pre-trained models and codes are available at https://github.com/xinghaochen/TinySAM and https://gitee.com/mindspore/models/tree/master/research/cv/TinySAM