Flux compactification on smooth, compact three-dimensional toric varieties

Three-dimensional smooth, compact toric varieties (SCTV), when viewed as real six-dimensional manifolds, can admit G-structures rendering them suitable for internal manifolds in supersymmetric flux compactifications. We develop techniques which allow us to systematically construct G-structures on SCTV and read off their torsion classes. We illustrate our methods with explicit examples, one of which consists of an infinite class of toric CP^1 bundles. We give a self-contained review of the relevant concepts from toric geometry, in particular the subject of the classification of SCTV in dimensions less or equal to 3. Our results open up the possibility for a systematic construction and study of supersymmetric flux vacua based on SCTV.Comment: 27 pages, 10 figures; v2: references, minor typos & improvement

Effect of Curcumma, Zn-Proteinate, and Cu-Proteinate Supplements on Milk Production of Subclinical Mastitis Fries Holland Cows

The objective of the research was to find out the effect of Curcumma, Zn-proteinate, and Cu-proteinate supplementation on subclinical mastitis status in term of 4% FCM milk production. The research was conducted using 24 heads of lactating dairy cows in Randomized Block Design with six treatments, and four groups of milk production as replication i.e. I= >14 kg/day ; II= 12-13.99 kg/day ; III= 10-11.99 kg/day; IV= <10 kg/day. Treatments were: R1 (Control); R2 (R1 + 2% Zn proteinate); R3(R1 + 2% Cu-proteinate); R4(R1 + 2% Curcumma); R5(R1 + 2% Zn-proteinate + 2% Cu-proteinate); R6 (R1 + 2% Zn-proteinate + 2% Cu-proteinate + 2% Curcumma). Parameters observed were 4%FCM milk production and subclinis status. The results showed that ration supplemented with Curcumma, Zn-proteinate, and Cu-proteinate decreased mastitis subclinic status and 4% FCM milk production increased significantly. Supplementation of Curcumma, Zn-proteinate, and Cu-proteinate resulted is the best for decrease in subclinical mastitis indicator and increase 4% FCM milk production. (Animal Production 12(1): 16-20 (2010

Discussion on the paper ‘Statistical contributions to bioinformatics: Design, modelling, structure learning and integration’ by Jeffrey S. Morris and Veerabhadran Baladandayuthapani

Bioinformatics is an important research area for statisticians. This discussion provides some additional topics to the paper, namely on statistical contributions to detect differential expressed genes, for protein structure prediction, and for the analysis of highly correlated features in Glycomics datasets

Thermalization from gauge/gravity duality: Evolution of singularities in unequal time correlators

We consider a gauge/gravity dual model of thermalization which consists of a collapsing thin matter shell in asymptotically Anti-de Sitter space. A central aspect of our model is to consider a shell moving at finite velocity as determined by its equation of motion, rather than a quasi-static approximation as considered previously in the literature. By applying a divergence matching method, we obtain the evolution of singularities in the retarded unequal time correlator $G^R(t,t')$, which probes different stages of the thermalization. We find that the number of singularities decreases from a finite number to zero as the gauge theory thermalizes. This may be interpreted as a sign of decoherence. Moreover, in a second part of the paper, we show explicitly that the thermal correlator is characterized by the existence of singularities in the complex time plane. By studying a quasi-static state, we show the singularities at real times originate from contributions of normal modes. We also investigate the possibility of obtaining complex singularities from contributions of quasi-normal modes.Comment: 35 pages, 4 figure

Holographic dilepton production in a thermalizing plasma

We determine the out-of-equilibrium production rate of dileptons at rest in strongly coupled N=4 Super Yang-Mills plasma using the AdS/CFT correspondence. Thermalization is achieved via the gravitational collapse of a thin shell of matter in AdS_5 space and the subsequent formation of a black hole, which we describe in a quasistatic approximation. Prior to thermalization, the dilepton spectral function is observed to oscillate as a function of frequency, but the amplitude of the oscillations decreases when thermal equilibrium is approached. At the same time, we follow the flow of the quasinormal spectrum of the corresponding U(1) vector field towards its equilibrium limit.Comment: 21 pages, 7 figures. v2: Version accepted for publication in JHEP; minor modifications, added reference

Vacuum Ambiguity in de Sitter Space at Strong Coupling

It is well known that in the weak coupling regime, quantum field theories in de Sitter space do not have a unique vacuum, but a class of vacua parametrized by a complex parameter $\alpha$, i.e., the so-called $\alpha$-vacua. In this article, using gauge/gravity duality, we calculate the symmetric two-point function of strongly coupled ${\cal N}=4$ supersymmetric Yang-Mills theory on $dS_3$. We find that there is a class of de Sitter invariant vacua, parametrized by a set of complex parameters $\{\alpha_{\nu}\}$.Comment: 17 pages in JHEP style, references adde

The problematic backreaction of SUSY-breaking branes

In this paper we investigate the localisation of SUSY-breaking branes which, in the smeared approximation, support specific non-BPS vacua. We show, for a wide class of boundary conditions, that there is no flux vacuum when the branes are described by a genuine delta-function. Even more, we find that the smeared solution is the unique solution with a regular brane profile. Our setup consists of a non-BPS AdS_7 solution in massive IIA supergravity with smeared anti-D6-branes and fluxes T-dual to ISD fluxes in IIB supergravity.Comment: 27 pages, Latex2e, 5 figure

Stability Constraints on Classical de Sitter Vacua

We present further no-go theorems for classical de Sitter vacua in Type II string theory, i.e., de Sitter constructions that do not invoke non-perturbative effects or explicit supersymmetry breaking localized sources. By analyzing the stability of the 4D potential arising from compactification on manfiolds with curvature, fluxes, and orientifold planes, we found that additional ingredients, beyond the minimal ones presented so far, are necessary to avoid the presence of unstable modes. We enumerate the minimal setups for (meta)stable de Sitter vacua to arise in this context.Comment: 18 pages; v2: argument improved, references adde

Prediction of vascular aging based on smartphone acquired PPG signals

Photoplethysmography (PPG) measured by smartphone has the potential for a large scale, non-invasive, and easy-to-use screening tool. Vascular aging is linked to increased arterial stiffness, which can be measured by PPG. We investigate the feasibility of using PPG to predict healthy vascular aging (HVA) based on two approaches: machine learning (ML) and deep learning (DL). We performed data preprocessing, including detrending, demodulating, and denoising on the raw PPG signals. For ML, ridge penalized regression has been applied to 38 features extracted from PPG, whereas for DL several convolutional neural networks (CNNs) have been applied to the whole PPG signals as input. The analysis has been conducted using the crowd-sourced Heart for Heart data. The prediction performance of ML using two features (AUC of 94.7%) \u2013 the a wave of the second derivative PPG and tpr, including four covariates, sex, height, weight, and smoking \u2013 was similar to that of the best performing CNN, 12-layer ResNet (AUC of 95.3%). Without having the heavy computational cost of DL, ML might be advantageous in finding potential biomarkers for HVA prediction. The whole workflow of the procedure is clearly described, and open software has been made available to facilitate replication of the results

Prediction of vascular aging based on smartphone acquired PPG signals

Photoplethysmography (PPG) measured by smartphone has the potential for a large scale, non-invasive, and easy-to-use screening tool. Vascular aging is linked to increased arterial stiffness, which can be measured by PPG. We investigate the feasibility of using PPG to predict healthy vascular aging (HVA) based on two approaches: machine learning (ML) and deep learning (DL). We performed data preprocessing, including detrending, demodulating, and denoising on the raw PPG signals. For ML, ridge penalized regression has been applied to 38 features extracted from PPG, whereas for DL several convolutional neural networks (CNNs) have been applied to the whole PPG signals as input. The analysis has been conducted using the crowd-sourced Heart for Heart data. The prediction performance of ML using two features (AUC of 94.7%) – the a wave of the second derivative PPG and tpr, including four covariates, sex, height, weight, and smoking – was similar to that of the best performing CNN, 12-layer ResNet (AUC of 95.3%). Without having the heavy computational cost of DL, ML might be advantageous in finding potential biomarkers for HVA prediction. The whole workflow of the procedure is clearly described, and open software has been made available to facilitate replication of the results