Holographic QCD with Topologically Charged Domain-Wall/Membranes

We study the thermodynamical phase structures of holographic QCD with nontrivial topologically charged domain-wall/membranes which are originally related to the multiple $\theta$-vacua in the large $N_c$ limit. We realize the topologically charged membranes as the holographic D6-brane fluxes in the Sakai-Sugimoto model. The D6-brane fluxes couple to the probe D8-anti-D8 via Chern-Simon term, and act as the source for the baryonic current density of QCD. We find rich phase structures of the dual meson system by varying asymptotic separation of D8 and anti-D8. Especially, there can be a thermodynamically favored and stable phase of finite baryonic current density. This provides the supporting evidence for the discovery of the topologically charged membranes found in the lattice QCD calculations. We also find a crossover phase with the limiting baryonic current density and temperature which suggest a Hagedorn-like phase transition of meson dissociation.Comment: 23 pages, 19 figures;v2 typos corrected;v3 text improve

Phenotype-based and Self-learning Inter-individual Sleep Apnea Screening with a Level IV Monitoring System

Purpose: We propose a phenotype-based artificial intelligence system that can self-learn and is accurate for screening purposes, and test it on a Level IV monitoring system. Methods: Based on the physiological knowledge, we hypothesize that the phenotype information will allow us to find subjects from a well-annotated database that share similar sleep apnea patterns. Therefore, for a new-arriving subject, we can establish a prediction model from the existing database that is adaptive to the subject. We test the proposed algorithm on a database consisting of 62 subjects with the signals recorded from a Level IV wearable device measuring the thoracic and abdominal movements and the SpO2. Results: With the leave-one cross validation, the accuracy of the proposed algorithm to screen subjects with an apnea-hypopnea index greater or equal to 15 is 93.6%, the positive likelihood ratio is 6.8, and the negative likelihood ratio is 0.03. Conclusion: The results confirm the hypothesis and show that the proposed algorithm has great potential to screen patients with SAS