Bootstrapping Mixed Correlators in the Five Dimensional Critical O(N) Models

We use the conformal bootstrap approach to explore $5D$ CFTs with $O(N)$ global symmetry, which contain $N$ scalars $\phi_i$ transforming as $O(N)$ vector. Specifically, we study multiple four-point correlators of the leading $O(N)$ vector $\phi_i$ and the $O(N)$ singlet $\sigma$. The crossing symmetry of the four-point functions and the unitarity condition provide nontrivial constraints on the scaling dimensions ($\Delta_\phi$, $\Delta_\sigma$) of $\phi_i$ and $\sigma$. With reasonable assumptions on the gaps between scaling dimensions of $\phi_i$ ($\sigma$) and the next $O(N)$ vector (singlet) scalar, we are able to isolate the scaling dimensions $(\Delta_\phi$, $\Delta_\sigma)$ in small islands. In particular, for large $N=500$, the isolated region is highly consistent with the result obtained from large $N$ expansion. We also study the interacting $O(N)$ CFTs for $1\leqslant N\leqslant100$. Isolated regions on $(\Delta_\phi,\Delta_\sigma)$ plane are obtained using conformal bootstrap program with lower order of derivatives $\Lambda$; however, they disappear after increasing $\Lambda$. We think these islands are corresponding to interacting but nonunitary $O(N)$ CFTs. Our results provide a lower bound on the critical value $N_c>100$, below which the interacting $O(N)$ CFTs turn into nonunitary. The critical value is unexpectedly large comparing with previous estimations.Comment: 28 pages, 4 figure

Non-damping oscillations at flaring loops

Context. QPPs are usually detected as spatial displacements of coronal loops in imaging observations or as periodic shifts of line properties in spectroscopic observations. They are often applied for remote diagnostics of magnetic fields and plasma properties on the Sun. Aims. We combine imaging and spectroscopic measurements of available space missions, and investigate the properties of non-damping oscillations at flaring loops. Methods. We used the IRIS to measure the spectrum over a narrow slit. The double-component Gaussian fitting method was used to extract the line profile of Fe XXI 1354.08 A at "O I" window. The quasi-periodicity of loop oscillations were identified in the Fourier and wavelet spectra. Results. A periodicity at about 40 s is detected in the line properties of Fe XXI, HXR emissions in GOES 1-8 A derivative, and Fermi 26-50 keV. The Doppler velocity and line width oscillate in phase, while a phase shift of about Pi/2 is detected between the Doppler velocity and peak intensity. The amplitudes of Doppler velocity and line width oscillation are about 2.2 km/s and 1.9 km/s, respectively, while peak intensity oscillate with amplitude at about 3.6% of the background emission. Meanwhile, a quasi-period of about 155 s is identified in the Doppler velocity and peak intensity of Fe XXI, and AIA 131 A intensity. Conclusions. The oscillations at about 40 s are not damped significantly during the observation, it might be linked to the global kink modes of flaring loops. The periodicity at about 155 s is most likely a signature of recurring downflows after chromospheric evaporation along flaring loops. The magnetic field strengths of the flaring loops are estimated to be about 120-170 G using the MHD seismology diagnostics, which are consistent with the magnetic field modeling results using the flux rope insertion method.Comment: 9 pages, 9 figures, 1 table, accepted by A&

Microbial community pattern detection in human body habitats via ensemble clustering framework

The human habitat is a host where microbial species evolve, function, and continue to evolve. Elucidating how microbial communities respond to human habitats is a fundamental and critical task, as establishing baselines of human microbiome is essential in understanding its role in human disease and health. However, current studies usually overlook a complex and interconnected landscape of human microbiome and limit the ability in particular body habitats with learning models of specific criterion. Therefore, these methods could not capture the real-world underlying microbial patterns effectively. To obtain a comprehensive view, we propose a novel ensemble clustering framework to mine the structure of microbial community pattern on large-scale metagenomic data. Particularly, we first build a microbial similarity network via integrating 1920 metagenomic samples from three body habitats of healthy adults. Then a novel symmetric Nonnegative Matrix Factorization (NMF) based ensemble model is proposed and applied onto the network to detect clustering pattern. Extensive experiments are conducted to evaluate the effectiveness of our model on deriving microbial community with respect to body habitat and host gender. From clustering results, we observed that body habitat exhibits a strong bound but non-unique microbial structural patterns. Meanwhile, human microbiome reveals different degree of structural variations over body habitat and host gender. In summary, our ensemble clustering framework could efficiently explore integrated clustering results to accurately identify microbial communities, and provide a comprehensive view for a set of microbial communities. Such trends depict an integrated biography of microbial communities, which offer a new insight towards uncovering pathogenic model of human microbiome.Comment: BMC Systems Biology 201