1,566 research outputs found
Phase space localization for anti-de Sitter quantum mechanics and its zero curvature limit
Using techniques of geometric quantization and SO(sub 0)(3,2)-coherent states, a notion of optimal localization on phase space is defined for the quantum theory of a massive and spinning particle in anti-de Sitter space time. It is shown that this notion disappears in the zero curvature limit, providing one with a concrete example of the regularizing character of the constant (nonzero) curvature of the anti-de Sitter space time. As a byproduct a geometric characterization of masslessness is obtained
On the Supersymplectic Homogeneous Superspace Underlying the OSp(1/2) Coherent States
In this work we extend Onofri and Perelomov's coherent states methods to the
recently introduced coherent states. These latter are shown to be
parametrized by points of a supersymplectic supermanifold, namely the
homogeneous superspace , which is clearly identified with a
supercoadjoint orbit of by exhibiting the corresponding equivariant
supermoment map. Moreover, this supermanifold is shown to be a nontrivial
example of Rothstein's supersymplectic supermanifolds. More precisely, we show
that its supersymplectic structure is completely determined in terms of
-invariant (but unrelated) K\"ahler -form and K\"ahler metric on
the unit disc. This result allows us to define the notions of a superK\"ahler
supermanifold and a superK\"ahler superpotential, the geometric structure of
the former being encoded into the latter.Comment: 19 pgs, PlainTeX, Preprint CRM-185
The Ubiquity of Large Graphs and Surprising Challenges of Graph Processing: Extended Survey
Graph processing is becoming increasingly prevalent across many application
domains. In spite of this prevalence, there is little research about how graphs
are actually used in practice. We performed an extensive study that consisted
of an online survey of 89 users, a review of the mailing lists, source
repositories, and whitepapers of a large suite of graph software products, and
in-person interviews with 6 users and 2 developers of these products. Our
online survey aimed at understanding: (i) the types of graphs users have; (ii)
the graph computations users run; (iii) the types of graph software users use;
and (iv) the major challenges users face when processing their graphs. We
describe the participants' responses to our questions highlighting common
patterns and challenges. Based on our interviews and survey of the rest of our
sources, we were able to answer some new questions that were raised by
participants' responses to our online survey and understand the specific
applications that use graph data and software. Our study revealed surprising
facts about graph processing in practice. In particular, real-world graphs
represent a very diverse range of entities and are often very large,
scalability and visualization are undeniably the most pressing challenges faced
by participants, and data integration, recommendations, and fraud detection are
very popular applications supported by existing graph software. We hope these
findings can guide future research
Electroweak Phase Transition in the U(1)' MSSM
In this work, we have investigated the nature of the electroweak phase
transition in the U(1) extended minimal supersymmetric standard model without
introducing any exotic fields. The effective potential has been estimated
exactly at finite temperature taking into account the whole particle spectrum.
For reasonable values of the lightest Higgs and neutralino, we found that the
electroweak phase transition could be strongly first order due to: (1) the
interactions of the singlet with the doublets in the effective potential, and
(2) the evolution of the wrong vacuum that delays the transition.Comment: substantial changes, references added, 18 pages, 4 figure
Unknown Health States Recognition With Collective Decision Based Deep Learning Networks In Predictive Maintenance Applications
At present, decision making solutions developed based on deep learning (DL)
models have received extensive attention in predictive maintenance (PM)
applications along with the rapid improvement of computing power. Relying on
the superior properties of shared weights and spatial pooling, Convolutional
Neural Network (CNN) can learn effective representations of health states from
industrial data. Many developed CNN-based schemes, such as advanced CNNs that
introduce residual learning and multi-scale learning, have shown good
performance in health state recognition tasks under the assumption that all the
classes are known. However, these schemes have no ability to deal with new
abnormal samples that belong to state classes not part of the training set. In
this paper, a collective decision framework for different CNNs is proposed. It
is based on a One-vs-Rest network (OVRN) to simultaneously achieve
classification of known and unknown health states. OVRN learn state-specific
discriminative features and enhance the ability to reject new abnormal samples
incorporated to different CNNs. According to the validation results on the
public dataset of Tennessee Eastman Process (TEP), the proposed CNN-based
decision schemes incorporating OVRN have outstanding recognition ability for
samples of unknown heath states, while maintaining satisfactory accuracy on
known states. The results show that the new DL framework outperforms
conventional CNNs, and the one based on residual and multi-scale learning has
the best overall performance
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