The $P$-$V$ phase transition and critical behavior in the extended phase space of asymptotic Anti-de Sitter (AdS) black holes have been widely investigated, in which four critical exponents around critical point are found to be consistent with values
in mean field theory. Recently, another critical exponent $ u$ related to divergent correlation length at critical point has been investigated by using thermodynamic curvature scalar $R_N$ at critical point in charged AdS black hole. Moreover, one finds that the divergence behavior of $R_N$ at critical point indicates a universal property, i.e. characterized by a dimensionless constant that is identical to that for a van der Waals fluid. In this paper, we further check out this universal property through investigating thermodynamic curvature scalar in de Rham-Gabadadze-Tolley (dRGT) massive gravity, and find that this dimensionless constant is also indeed independent of horizon topology, massive graviton and dimension of spacetime. Furthermore, we investigate divergence behavior of thermodynamic curvature scalar at critical point in generic asymptotic Anti-de Sitter (AdS) black holes, and demonstrate the universality in this generic case. Those results give new insights into the microstructure of black holes.
Prompted by the open questions in Gibilisco [Int. J. Software Informatics, 8(3-4): 265, 2014], in which he introduced a family of measurement-induced quantum uncertainty measures via metric adjusted skew informations, we investigate these measures fu
ndamental properties (including basis independence and spectral representation), and illustrate their applications to detect quantum nonlocality and entanglement.
Developing an accurate and reliable injury predictor is central to the biomechanical studies of traumatic brain injury. State-of-the-art efforts continue to rely on empirical, scalar metrics based on kinematics or model-estimated tissue responses exp
licitly pre-defined in a specific brain region of interest. They could suffer from loss of information. A single training dataset has also been used to evaluate performance but without cross-validation. In this study, we developed a deep learning approach for concussion classification using implicit features of the entire voxel-wise white matter fiber strains. Using reconstructed American National Football League (NFL) injury cases, leave-one-out cross-validation was employed to objectively compare injury prediction performances against two baseline machine learning classifiers (support vector machine (SVM) and random forest (RF)) and four scalar metrics via univariate logistic regression (Brain Injury Criterion (BrIC), cumulative strain damage measure of the whole brain (CSDM-WB) and the corpus callosum (CSDM-CC), and peak fiber strain in the CC). Feature-based deep learning and machine learning classifiers consistently outperformed all scalar injury metrics across all performance categories in cross-validation (e.g., average accuracy of 0.844 vs. 0.746, and average area under the receiver operating curve (AUC) of 0.873 vs. 0.769, respectively, based on the testing dataset). Nevertheless, deep learning achieved the best cross-validation accuracy, sensitivity, and AUC (e.g., accuracy of 0.862 vs. 0.828 and 0.842 for SVM and RF, respectively). These findings demonstrate the superior performances of deep learning in concussion prediction, and suggest its promise for future applications in biomechanical investigations of traumatic brain injury.
We examine the photonic spin Hall effect (SHE) in a graphene-substrate system with the presence of external magnetic field. In the quantum Hall regime, we demonstrate that the in-plane and transverse spin-dependent splittings in photonic SHE exhibit
different quantized behaviors. The quantized SHE can be described as a consequence of a quantized geometric phase (Berry phase), which corresponds to the quantized spin-orbit interaction. Furthermore, an experimental scheme based on quantum weak value amplification is proposed to detect the quantized SHE in terahertz frequency regime. By incorporating the quantum weak measurement techniques, the quantized photonic SHE holds great promise for detecting quantized Hall conductivity and Berry phase. These results may bridge the gap between the electronic SHE and photonic SHE in graphene.
The photonic spin Hall effect (SHE) can be regarded as a direct optical analogy of the SHE in electronic systems where a refractive index gradient plays the role of electric potential. However, it has been demonstrated that the effective refractive i
ndex fails to adequately explain the lightmatter interaction in atomically thin crystals. In this paper, we examine the spin-orbit interaction on the surface of the freestanding atomically thin crystals. We find that it is not necessary to involve the effective refractive index to describe the spin-orbit interaction and the photonic SHE in the atomically thin crystals. The strong spin-orbit interaction and giant photonic SHE have been predicted, which can be explained as the large polarization rotation of plane-wave components in order to satisfy the transversality of photon.
We report the observation of the Goos-Hanchen effect in graphene via a weak value amplification scheme. We demonstrate that the amplified Goos-Hanchen shift in weak measurements is sensitive to the variation of graphene layers. Combining the Goos-Han
chen effect with weak measurements may provide important applications in characterizing the parameters of graphene.
