18 research outputs found
Kinematic space for conical defects
Kinematic space can be used as an intermediate step in the AdS/CFT dictionary
and lends itself naturally to the description of diffeomorphism invariant
quantities. From the bulk it has been defined as the space of boundary anchored
geodesics, and from the boundary as the space of pairs of CFT points. When the
bulk is not globally AdS the appearance of non-minimal geodesics leads to
ambiguities in these definitions. In this work conical defect spacetimes are
considered as an example where non-minimal geodesics are common. From the bulk
it is found that the conical defect kinematic space can be obtained from the
AdS kinematic space by the same quotient under which one obtains the defect
from AdS. The resulting kinematic space is one of many equivalent
fundamental regions. From the boundary the conical defect kinematic space can
be determined by breaking up OPE blocks into contributions from individual bulk
geodesics. A duality is established between partial OPE blocks and bulk fields
integrated over individual geodesics, minimal or non-minimal.Comment: 29 pages, 9 figures. As published in JHE
ProxyFL: Decentralized Federated Learning through Proxy Model Sharing
Institutions in highly regulated domains such as finance and healthcare often
have restrictive rules around data sharing. Federated learning is a distributed
learning framework that enables multi-institutional collaborations on
decentralized data with improved protection for each collaborator's data
privacy. In this paper, we propose a communication-efficient scheme for
decentralized federated learning called ProxyFL, or proxy-based federated
learning. Each participant in ProxyFL maintains two models, a private model,
and a publicly shared proxy model designed to protect the participant's
privacy. Proxy models allow efficient information exchange among participants
using the PushSum method without the need of a centralized server. The proposed
method eliminates a significant limitation of canonical federated learning by
allowing model heterogeneity; each participant can have a private model with
any architecture. Furthermore, our protocol for communication by proxy leads to
stronger privacy guarantees using differential privacy analysis. Experiments on
popular image datasets, and a pan-cancer diagnostic problem using over 30,000
high-quality gigapixel histology whole slide images, show that ProxyFL can
outperform existing alternatives with much less communication overhead and
stronger privacy
CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds
Precision measurements and new physics searches at the Large Hadron Collider
require efficient simulations of particle propagation and interactions within
the detectors. The most computationally expensive simulations involve
calorimeter showers. Advances in deep generative modelling - particularly in
the realm of high-dimensional data - have opened the possibility of generating
realistic calorimeter showers orders of magnitude more quickly than
physics-based simulation. However, the high-dimensional representation of
showers belies the relative simplicity and structure of the underlying physical
laws. This phenomenon is yet another example of the manifold hypothesis from
machine learning, which states that high-dimensional data is supported on
low-dimensional manifolds. We thus propose modelling calorimeter showers first
by learning their manifold structure, and then estimating the density of data
across this manifold. Learning manifold structure reduces the dimensionality of
the data, which enables fast training and generation when compared with
competing methods.Comment: Accepted to the Machine Learning and the Physical Sciences Workshop
at NeurIPS 202