Apparent horizon and gravitational thermodynamics of Universe in the Eddington-Born-Infeld theory

The thermodynamics of Universe in the Eddington-Born-Infeld (EBI) theory was restudied by utilizing the holographic-style gravitational equations that dominate the dynamics of the cosmical apparent horizon $\Upsilon_{A}$ and the evolution of Universe. We started in rewriting the EBI action of the Palatini approach into the Bigravity-type action with an extra metric $q_{\mu\nu}$. With the help of the holographic-style dynamical equations, we discussed the property of the cosmical apparent horizon $\Upsilon_{A}$ including timelike, spacelike and null characters, which depends on the value of the parameter of state $w_{m}$ in EBI Universe. The unified first law for the gravitational thermodynamics and the total energy differential for the open system enveloped by $\Upsilon_{A}$ in EBI Universe were obtained. Finally, applying the positive-heat-out sign convention, we derived the generalized second law of gravitational thermodynamics in EBI universe.Comment: 23 pages, 0 figure

Sigma-1 Receptor-Modulated Neuroinflammation in Neurological Diseases

A large body of evidence indicates that sigma-1 receptors (Sig-1R) are important drug targets for a number of neuropsychiatric disorders. Sig-1Rs are enriched in central nervous system (CNS). In addition to neurons, both cerebral microglia and astrocytes express Sig-1Rs. Activation of Sig-1Rs is known to elicit potent neuroprotective effects and promote neuronal survival via multiple mechanisms, including promoting mitochondrial functions, decreasing oxidative stress and regulating neuroimmnological functions. In this review article, we focus on the emerging role of Sig-1Rs in regulating neuroinflammation and discuss the recent advances on the Sig-1R-modulating neuroinflammation in the pathophysiology and therapy of neurodegenerative disorders

Learning Disentangled Representation Implicitly via Transformer for Occluded Person Re-Identification

Person re-identification (re-ID) under various occlusions has been a long-standing challenge as person images with different types of occlusions often suffer from misalignment in image matching and ranking. Most existing methods tackle this challenge by aligning spatial features of body parts according to external semantic cues or feature similarities but this alignment approach is complicated and sensitive to noises. We design DRL-Net, a disentangled representation learning network that handles occluded re-ID without requiring strict person image alignment or any additional supervision. Leveraging transformer architectures, DRL-Net achieves alignment-free re-ID via global reasoning of local features of occluded person images. It measures image similarity by automatically disentangling the representation of undefined semantic components, e.g., human body parts or obstacles, under the guidance of semantic preference object queries in the transformer. In addition, we design a decorrelation constraint in the transformer decoder and impose it over object queries for better focus on different semantic components. To better eliminate interference from occlusions, we design a contrast feature learning technique (CFL) for better separation of occlusion features and discriminative ID features. Extensive experiments over occluded and holistic re-ID benchmarks (Occluded-DukeMTMC, Market1501 and DukeMTMC) show that the DRL-Net achieves superior re-ID performance consistently and outperforms the state-of-the-art by large margins for Occluded-DukeMTMC

A γ\gamma-ray Quasi-Periodic modulation in the Blazar PKS 0301−-243?

We report a nominally high-confidence $\gamma$-ray quasi-periodic modulation in the blazar PKS 0301$-$243. For this target, we analyze its \emph{Fermi}-LAT Pass 8 data covering from 2008 August to 2017 May. Two techniques, i.e., the maximum likelihood optimization and the exposure-weighted aperture photometry, are used to build the $\gamma$-ray light curves. Then both the Lomb-Scargle Periodogram and the Weighted Wavelet Z-transform are applied to the light curves to search for period signals. A quasi-periodicity with a period of $2.1\pm0.3$ yr appears at the significance level of $\sim5\sigma$, although it should be noted that this putative quasi-period variability is seen in a data set barely four times longer. We speculate that this $\gamma$-ray quasi-periodic modulation might be evidence of a binary supermassive black hole.Comment: 9 pages, 8 figures; Accepted for publication in Ap