Heterotic Action in SUGRA-SYM Background

We consider the generalization of the heterotic action considered by Cherkis and Schwarz where the chiral bosons are introduced in a manifestly covariant way using an auxiliary field. In particular, we construct the kappa-symmetric heterotic action in ten-dimensional supergravity background coupled to super Yang-Mills theory and prove its kappa-symmetry. The usual Bianchi identity of Type I supergravity with super Yang-Mills dH_3= -\tr F\wedge F is crucially used. For technical reason, the Yang-Mills field is restricted to be abelian.Comment: 12 pages, no figures, added comments in the acknowledgmen

Defining the optimal technique for endoscopic ultrasound shear wave elastography: a combined benchtop and animal model study with comparison to transabdominal shear wave elastography

Background/Aims Shear wave elastography (SWE) is used for liver fibrosis staging based on stiffness measurements. It can be performed using endoscopic ultrasound (EUS) or a transabdominal approach. Transabdominal accuracy can be limited in patients with obesity because of the thick abdomen. Theoretically, EUS-SWE overcomes this limitation by internally assessing the liver. We aimed to define the optimal technique for EUS-SWE for future research and clinical use and compare its accuracy with that of transabdominal SWE. Methods Benchtop study: A standardized phantom model was used. The compared variables included the region of interest (ROI) size, depth, and orientation and transducer pressure. Porcine study: Phantom models with varying stiffness values were surgically implanted between the hepatic lobes. Results For EUS-SWE, a larger ROI size of 1.5 cm and a smaller ROI depth of 1 cm demonstrated a significantly higher accuracy. For transabdominal SWE, the ROI size was nonadjustable, and the optimal ROI depth ranged from 2 to 4 cm. The transducer pressure and ROI orientation did not significantly affect the accuracy. There were no significant differences in the accuracy between transabdominal SWE and EUS-SWE in the animal model. The variability among the operators was more pronounced for the higher stiffness values. Small lesion measurements were accurate only when the ROI was entirely situated within the lesion. Conclusions We defined the optimal viewing windows for EUS-SWE and transabdominal SWE. The accuracy was comparable in the non-obese porcine model. EUS-SWE may have a higher utility for evaluating small lesions than transabdominal SWE

Multilabel Classification Models for the Prediction of Cross-Coupling Reaction Conditions

Machine-learned ranking models have been developed for the prediction of substrate-specific cross-coupling reaction conditions. Data sets of published reactions were curated for Suzuki, Negishi, and C–N couplings, as well as Pauson–Khand reactions. String, descriptor, and graph encodings were tested as input representations, and models were trained to predict the set of conditions used in a reaction as a binary vector. Unique reagent dictionaries categorized by expert-crafted reaction roles were constructed for each data set, leading to context-aware predictions. We find that relational graph convolutional networks and gradient-boosting machines are very effective for this learning task, and we disclose a novel reaction-level graph attention operation in the top-performing model

Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions

We present a systematic investigation using graph neural networks (GNNs) to model organic chemical reactions. To do so, we prepared a dataset collection of four ubiquitous reactions from the organic chemistry literature. We evaluate seven different GNN architectures for classification tasks pertaining to the identification of experimental reagents and conditions. We find that models are able to identify specific graph features that affect reaction conditions and lead to accurate predictions. The results herein show great promise in advancing molecular machine learning

Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions

We present a systematic investigation using graph neural networks (GNNs) to model organic chemical reactions. To do so, we prepared a dataset collection of four ubiquitous reactions from the organic chemistry literature. We evaluate seven different GNN architectures for classification tasks pertaining to the identification of experimental reagents and conditions. We find that models are able to identify specific graph features that affect reaction conditions and lead to accurate predictions. The results herein show great promise in advancing molecular machine learning.Comment: 23 pages, 10 tables, 13 figures, to appear in the ICML 2020 Workshop on Graph Representation Learning and Beyond (GRLB

Multilabel Classification Models for the Prediction of Cross-Coupling Reaction Conditions

Machine-learned ranking models have been developed for the prediction of substrate-specific cross-coupling reaction conditions. Data sets of published reactions were curated for Suzuki, Negishi, and C–N couplings, as well as Pauson–Khand reactions. String, descriptor, and graph encodings were tested as input representations, and models were trained to predict the set of conditions used in a reaction as a binary vector. Unique reagent dictionaries categorized by expert-crafted reaction roles were constructed for each data set, leading to context-aware predictions. We find that relational graph convolutional networks and gradient-boosting machines are very effective for this learning task, and we disclose a novel reaction-level graph attention operation in the top-performing model

Dispersive coupling between MoSe2 and an integrated zero-dimensional nanocavity

Establishing a coherent interaction between a material resonance and an optical cavity is a necessary first step to study semiconductor quantum optics. Here we report on the signature of a coherent interaction between a two-dimensional excitonic transition in monolayer MoSe2 and a zero-dimensional, ultra-low mode volume, V∼ 2(λ/n)^3, on-chip photonic crystal nanocavity. This coherent interaction manifests as a dispersive shift of the cavity transmission spectrum, when the exciton-cavity detuning is decreased via temperature tuning. The exciton-cavity coupling is estimated to be about 6.5 meV, with a cooperativity of about 4.0 at 80 K, showing our material system is on the verge of strong coupling. The small mode-volume of the resonator is instrumental in reaching the strongly nonlinear regime, while on-chip cavities will help create a scalable quantum photonic platform

Chiral primary cubic interactions from pp-wave supergravity

We explicitly construct cubic interaction light-cone Hamiltonian for the chiral primary system involving the metric fields and the self-dual four-form fields in the IIB pp-wave supergravity. The background fields representing pp-waves exhibit SO(4)*SO(4)*Z_2 invariance. It turns out that the interaction Hamiltonian is precisely the same as that for the dilaton-axion system, except for the fact that the chiral primary system fields have the opposite parity to that of the dilaton-axion fields under the Z_2 transformation that exchanges two SO(4)'s.Comment: 14 pages, A few comments are adde

Gauge Symmetry Enhancement and Radiatively Induced Mass in the Large N Nonlinear Sigma Model

We consider a hybrid of nonlinear sigma models in which two complex projective spaces are coupled with each other under a duality. We study the large N effective action in 1+1 dimensions. We find that some of the dynamically generated gauge bosons acquire radiatively induced masses which, however, vanish along the self-dual points where the two couplings characterizing each complex projective space coincide. These points correspond to the target space of the Grassmann manifold along which the gauge symmetry is enhanced, and the theory favors the non-Abelian ultraviolet fixed point.Comment: 11 pages, REVTEX, typos are corrected, version to appear in Phys. Rev.