14,596 research outputs found
The Iwasawa decomposition and the Bruhat decomposition of the automorphism group on certain exceptional Jordan algebra
Let be the real form of a complex simple Jordan algebra such
that the automorphism group is . By using some orbit types
of on , for ,
explicitly, we give the Iwasawa decomposition, the Oshima--Sekiguchi's
Iwasawa decomposition, the Matsuki decomposition, and the Bruhat
and Gauss decompositions.Comment: v3, 30 pages, 1 figure, major changes from v2 by separating section
9--14, reformatted, and the title changed to appear in Tsukuba J. of Mat
Orbit decomposition of Jordan matrix algebras of order three under the automorphism groups
The orbit decomposition is given under the automorphism group on the real
split Jordan algebra of all hermitian matrices of order three corresponding to
any real split composition algebra, or the automorphism group on the
complexification, explicitly, in terms of the cross product of H. Freudenthal
and the characteristic polynomial.Comment: v2, 32 pages, presentation improved, minor errors corrected, and the
title changed as appeared in J. Math. Sci. Univ. Toky
Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
We envision a mobile edge computing (MEC) framework for machine learning (ML)
technologies, which leverages distributed client data and computation resources
for training high-performance ML models while preserving client privacy. Toward
this future goal, this work aims to extend Federated Learning (FL), a
decentralized learning framework that enables privacy-preserving training of
models, to work with heterogeneous clients in a practical cellular network. The
FL protocol iteratively asks random clients to download a trainable model from
a server, update it with own data, and upload the updated model to the server,
while asking the server to aggregate multiple client updates to further improve
the model. While clients in this protocol are free from disclosing own private
data, the overall training process can become inefficient when some clients are
with limited computational resources (i.e. requiring longer update time) or
under poor wireless channel conditions (longer upload time). Our new FL
protocol, which we refer to as FedCS, mitigates this problem and performs FL
efficiently while actively managing clients based on their resource conditions.
Specifically, FedCS solves a client selection problem with resource
constraints, which allows the server to aggregate as many client updates as
possible and to accelerate performance improvement in ML models. We conducted
an experimental evaluation using publicly-available large-scale image datasets
to train deep neural networks on MEC environment simulations. The experimental
results show that FedCS is able to complete its training process in a
significantly shorter time compared to the original FL protocol
Stabilization of Stochastic Quantum Dynamics via Open and Closed Loop Control
In this paper we investigate parametrization-free solutions of the problem of
quantum pure state preparation and subspace stabilization by means of
Hamiltonian control, continuous measurement and quantum feedback, in the
presence of a Markovian environment. In particular, we show that whenever
suitable dissipative effects are induced either by the unmonitored environment
or by non Hermitian measurements, there is no need for feedback control to
accomplish the task. Constructive necessary and sufficient conditions on the
form of the open-loop controller can be provided in this case. When open-loop
control is not sufficient, filtering-based feedback control laws steering the
evolution towards a target pure state are provided, which generalize those
available in the literature
- …