48,935 research outputs found
Asymptotically Safe Dark Matter
We introduce a new paradigm for dark matter (DM) interactions in which the
interaction strength is asymptotically safe. In models of this type, the
coupling strength is small at low energies but increases at higher energies,
and asymptotically approaches a finite constant value. The resulting
phenomenology of this "asymptotically safe DM" is quite distinct. One
interesting effect of this is to partially offset the low-energy constraints
from direct detection experiments without affecting thermal freeze-out
processes which occur at higher energies. High-energy collider and indirect
annihilation searches are the primary ways to constrain or discover
asymptotically safe dark matter.Comment: 5 pages, 2 multi-panel figures, PRD versio
On the Performance Limits of Pilot-Based Estimation of Bandlimited Frequency-Selective Communication Channels
In this paper the problem of assessing bounds on the accuracy of pilot-based
estimation of a bandlimited frequency selective communication channel is
tackled. Mean square error is taken as a figure of merit in channel estimation
and a tapped-delay line model is adopted to represent a continuous time channel
via a finite number of unknown parameters. This allows to derive some
properties of optimal waveforms for channel sounding and closed form Cramer-Rao
bounds
Multigranular scale speech recognition: tehnological and cognitive view
We propose a Multigranular Automatic Speech Recognizer. The hypothesis is that
speech signal contains information distributed on more different time scales.
Many works from various scientific fields ranging from neurobiology to speech
technologies, seem to concord on this assumption. In a broad sense, it seems
that speech recognition in human is optimal because of a partial
parallelization process according to which the left-to-right stream of
speech is captured in a multilevel grid in which several linguistic analyses take
place contemporarily. Our investigation aims, in this view, to apply these new
ideas to the project of more robust and efficient recognizers
A multi-class approach for ranking graph nodes: models and experiments with incomplete data
After the phenomenal success of the PageRank algorithm, many researchers have
extended the PageRank approach to ranking graphs with richer structures beside
the simple linkage structure. In some scenarios we have to deal with
multi-parameters data where each node has additional features and there are
relationships between such features.
This paper stems from the need of a systematic approach when dealing with
multi-parameter data. We propose models and ranking algorithms which can be
used with little adjustments for a large variety of networks (bibliographic
data, patent data, twitter and social data, healthcare data). In this paper we
focus on several aspects which have not been addressed in the literature: (1)
we propose different models for ranking multi-parameters data and a class of
numerical algorithms for efficiently computing the ranking score of such
models, (2) by analyzing the stability and convergence properties of the
numerical schemes we tune a fast and stable technique for the ranking problem,
(3) we consider the issue of the robustness of our models when data are
incomplete. The comparison of the rank on the incomplete data with the rank on
the full structure shows that our models compute consistent rankings whose
correlation is up to 60% when just 10% of the links of the attributes are
maintained suggesting the suitability of our model also when the data are
incomplete
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