Holographic quantum singularity

In this study, we addressed the influence of quantum singularity on the topological state. The quantum singularity creates the defect in the momentum space ubiquitously and leads to the phase transition for the topological material. The kinetic equation reveals that the defect generates an anomaly without the characteristic energy scale. In the holographic model, the three-dimensional dislocations map into the gravitational bulk as domain walls extending along the AdS radial direction from the boundary. The creation/annihilation of the domain wall causes the quantum phase transition by ’t Hooft anomaly generation and is controlled by the gauge field. In other words, the phase transition is realized by the anomaly inflow. This ’t Hooft anomaly is caused by a phase ambiguity of the ground state resulting from the singularity in parameter space. This singularity gives the basis for the boundary’s topological state with the Berry connection. ’t Hooft anomaly’s renormalization group invariance shows that the total Berry flux is conserved in the UV layer to the IR layer. Phase transition entails domain wall constitution, which generates the entropy from the non-universal form or quantum entropy correction.

Logarithmic corrections to black hole entropy in matter coupled $$ \mathcal{N} $$ ≥ 1 Einstein-Maxwell supergravity

We calculate the first three Seeley-DeWitt coefficients for fluctuation of the massless fields of a $$ \mathcal{N} $$ N = 2 Einstein-Maxwell supergravity theory (EMSGT) distributed into different multiplets in d = 4 space-time dimensions. By utilizing the Seeley-DeWitt data in the quantum entropy function formalism, we then obtain the logarithmic correction contribution of individual multiplets to the entropy of extremal Kerr-Newman family of black holes. Our results allow us to find the logarithmic entropy corrections for the extremal black holes in a fully matter coupled $$ \mathcal{N} $$ N = 2, d = 4 EMSGT, in a particular class of $$ \mathcal{N} $$ N = 1, d = 4 EMSGT as consistent decomposition of $$ \mathcal{N} $$ N = 2 multiplets ($$ \mathcal{N} $$ N = 2 → $$ \mathcal{N} $$ N = 1) and in $$ \mathcal{N} $$ N ≥ 3, d = 4 EMSGTs by decomposing them into $$ \mathcal{N} $$ N = 2 multiplets ($$ \mathcal{N} $$ N ≥ 3 → $$ \mathcal{N} $$ N = 2). For completeness, we also obtain logarithmic entropy correction results for the non-extremal Kerr-Newman black holes in the matter coupled $$ \mathcal{N} $$ N ≥ 1, d = 4 EMSGTs by employing the same Seeley-DeWitt data into a different Euclidean gravity approach developed in [17].

Synthetic protein alignments by CCMgen quantify noise in residue-residue contact prediction

Compensatory mutations between protein residues that are in physical contact with each other can manifest themselves as statistical couplings between the corresponding columns in a multiple sequence alignment (MSA) of the protein family. Conversely, high coupling coefficients predict residues contacts. Methods for de-novo protein structure prediction based on this approach are becoming increasingly reliable. Their main limitation is the strong systematic and statistical noise in the estimation of coupling coefficients, which has so far limited their application to very large protein families. While most research has focused on boosting contact prediction quality by adding external information, little progress has been made to improve the statistical procedure at the core. In that regard, our lack of understanding of the sources of noise poses a major obstacle. We have developed CCMgen, the first method for simulating protein evolution by providing full control over the generation of realistic synthetic MSAs with pairwise statistical couplings between residue positions. This procedure requires an exact statistical model that reliably reproduces observed alignment statistics. With CCMpredPy we also provide an implementation of persistent contrastive divergence (PCD), a precise inference technique that enables us to learn the required high-quality statistical models. We demonstrate how CCMgen can facilitate the development and testing of contact prediction methods by analysing the systematic noise contributions from phylogeny and entropy. For that purpose we propose a simple entropy correction (EC) strategy which disentangles the correction for both sources of noise. We find that entropy contributes typically roughly twice as much noise as phylogeny.