92,661 research outputs found
Minimal Composite Higgs Models at the LHC
We consider composite Higgs models where the Higgs is a pseudo-Nambu
Goldstone boson arising from the spontaneous breaking of an approximate global
symmetry by some underlying strong dynamics. We focus on the SO(5) -> SO(4)
symmetry breaking pattern, assuming the partial compositeness paradigm. We
study the consequences on Higgs physics of the fermionic representations
produced by the strong dynamics, that mix with the Standard Model (SM) degrees
of freedom. We consider models based on the lowest-dimensional representations
of SO(5) that allow for the custodial protection of the Z -> b b coupling, i.e.
the 5, 10 and 14. We find a generic suppression of the gluon fusion process,
while the Higgs branching fractions can be enhanced or suppressed compared to
the SM. Interestingly, a precise measurement of the Higgs boson couplings can
distinguish between different realizations in the fermionic sector, thus
providing crucial information about the nature of the UV dynamics.Comment: 55 pages, 18 figures, References adde
An Overview of Multi-Processor Approximate Message Passing
Approximate message passing (AMP) is an algorithmic framework for solving
linear inverse problems from noisy measurements, with exciting applications
such as reconstructing images, audio, hyper spectral images, and various other
signals, including those acquired in compressive signal acquisiton systems. The
growing prevalence of big data systems has increased interest in large-scale
problems, which may involve huge measurement matrices that are unsuitable for
conventional computing systems. To address the challenge of large-scale
processing, multiprocessor (MP) versions of AMP have been developed. We provide
an overview of two such MP-AMP variants. In row-MP-AMP, each computing node
stores a subset of the rows of the matrix and processes corresponding
measurements. In column- MP-AMP, each node stores a subset of columns, and is
solely responsible for reconstructing a portion of the signal. We will discuss
pros and cons of both approaches, summarize recent research results for each,
and explain when each one may be a viable approach. Aspects that are
highlighted include some recent results on state evolution for both MP-AMP
algorithms, and the use of data compression to reduce communication in the MP
network
Closed-loop Bayesian Semantic Data Fusion for Collaborative Human-Autonomy Target Search
In search applications, autonomous unmanned vehicles must be able to
efficiently reacquire and localize mobile targets that can remain out of view
for long periods of time in large spaces. As such, all available information
sources must be actively leveraged -- including imprecise but readily available
semantic observations provided by humans. To achieve this, this work develops
and validates a novel collaborative human-machine sensing solution for dynamic
target search. Our approach uses continuous partially observable Markov
decision process (CPOMDP) planning to generate vehicle trajectories that
optimally exploit imperfect detection data from onboard sensors, as well as
semantic natural language observations that can be specifically requested from
human sensors. The key innovation is a scalable hierarchical Gaussian mixture
model formulation for efficiently solving CPOMDPs with semantic observations in
continuous dynamic state spaces. The approach is demonstrated and validated
with a real human-robot team engaged in dynamic indoor target search and
capture scenarios on a custom testbed.Comment: Final version accepted and submitted to 2018 FUSION Conference
(Cambridge, UK, July 2018
Extremal optimization for sensor report pre-processing
We describe the recently introduced extremal optimization algorithm and apply
it to target detection and association problems arising in pre-processing for
multi-target tracking.
Here we consider the problem of pre-processing for multiple target tracking
when the number of sensor reports received is very large and arrives in large
bursts. In this case, it is sometimes necessary to pre-process reports before
sending them to tracking modules in the fusion system. The pre-processing step
associates reports to known tracks (or initializes new tracks for reports on
objects that have not been seen before). It could also be used as a pre-process
step before clustering, e.g., in order to test how many clusters to use.
The pre-processing is done by solving an approximate version of the original
problem. In this approximation, not all pair-wise conflicts are calculated. The
approximation relies on knowing how many such pair-wise conflicts that are
necessary to compute. To determine this, results on phase-transitions occurring
when coloring (or clustering) large random instances of a particular graph
ensemble are used.Comment: 10 page
Contributions of plasma physics to chaos and nonlinear dynamics
This topical review focusses on the contributions of plasma physics to chaos
and nonlinear dynamics bringing new methods which are or can be used in other
scientific domains. It starts with the development of the theory of Hamiltonian
chaos, and then deals with order or quasi order, for instance adiabatic and
soliton theories. It ends with a shorter account of dissipative and high
dimensional Hamiltonian dynamics, and of quantum chaos. Most of these
contributions are a spin-off of the research on thermonuclear fusion by
magnetic confinement, which started in the fifties. Their presentation is both
exhaustive and compact. [15 April 2016
Structure Learning in Coupled Dynamical Systems and Dynamic Causal Modelling
Identifying a coupled dynamical system out of many plausible candidates, each
of which could serve as the underlying generator of some observed measurements,
is a profoundly ill posed problem that commonly arises when modelling real
world phenomena. In this review, we detail a set of statistical procedures for
inferring the structure of nonlinear coupled dynamical systems (structure
learning), which has proved useful in neuroscience research. A key focus here
is the comparison of competing models of (ie, hypotheses about) network
architectures and implicit coupling functions in terms of their Bayesian model
evidence. These methods are collectively referred to as dynamical casual
modelling (DCM). We focus on a relatively new approach that is proving
remarkably useful; namely, Bayesian model reduction (BMR), which enables rapid
evaluation and comparison of models that differ in their network architecture.
We illustrate the usefulness of these techniques through modelling
neurovascular coupling (cellular pathways linking neuronal and vascular
systems), whose function is an active focus of research in neurobiology and the
imaging of coupled neuronal systems
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