524 research outputs found
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 CāN couplings, as well as PausonāKhand 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 CāN couplings, as well as PausonāKhand 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, Vā¼ 2(Ī»/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.
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