1,811 research outputs found
Symmetry-Enriched Quantum Spin Liquids in
We use the intrinsic one-form and two-form global symmetries of (3+1)
bosonic field theories to classify quantum phases enriched by ordinary
(-form) global symmetry. Different symmetry-enriched phases correspond to
different ways of coupling the theory to the background gauge field of the
ordinary symmetry. The input of the classification is the higher-form
symmetries and a permutation action of the -form symmetry on the lines and
surfaces of the theory. From these data we classify the couplings to the
background gauge field by the 0-form symmetry defects constructed from the
higher-form symmetry defects. For trivial two-form symmetry the classification
coincides with the classification for symmetry fractionalizations in .
We also provide a systematic method to obtain the symmetry protected
topological phases that can be absorbed by the coupling, and we give the
relative 't Hooft anomaly for different couplings. We discuss several examples
including the gapless pure gauge theory and the gapped Abelian finite
group gauge theory. As an application, we discover a tension with a conjectured
duality in for gauge theory with two adjoint Weyl fermions
An Interpretable Deep Hierarchical Semantic Convolutional Neural Network for Lung Nodule Malignancy Classification
While deep learning methods are increasingly being applied to tasks such as
computer-aided diagnosis, these models are difficult to interpret, do not
incorporate prior domain knowledge, and are often considered as a "black-box."
The lack of model interpretability hinders them from being fully understood by
target users such as radiologists. In this paper, we present a novel
interpretable deep hierarchical semantic convolutional neural network (HSCNN)
to predict whether a given pulmonary nodule observed on a computed tomography
(CT) scan is malignant. Our network provides two levels of output: 1) low-level
radiologist semantic features, and 2) a high-level malignancy prediction score.
The low-level semantic outputs quantify the diagnostic features used by
radiologists and serve to explain how the model interprets the images in an
expert-driven manner. The information from these low-level tasks, along with
the representations learned by the convolutional layers, are then combined and
used to infer the high-level task of predicting nodule malignancy. This unified
architecture is trained by optimizing a global loss function including both
low- and high-level tasks, thereby learning all the parameters within a joint
framework. Our experimental results using the Lung Image Database Consortium
(LIDC) show that the proposed method not only produces interpretable lung
cancer predictions but also achieves significantly better results compared to
common 3D CNN approaches
Agile Corpus Annotation in Practice: An Overview of Manual and Automatic Annotation of CVs
This paper describes work testing agile data annotation by moving away from the traditional, linear phases of corpus creation towards iterative ones and by recognizing the potential for sources of error occurring throughout the annotation process.JRC.DG.G.2-Global security and crisis managemen
- …