9,289 research outputs found
A new insight into the phase transition in the early Universe with two Higgs doublets
We study the electroweak phase transition in the alignment limit of the
CP-conserving two-Higgs-doublet model (2HDM) of Type I and Type II. The
effective potential is evaluated at one-loop, where the thermal potential
includes Daisy corrections and is reliably approximated by means of a sum of
Bessel functions. Both 1-stage and 2-stage electroweak phase transitions are
shown to be possible, depending on the pattern of the vacuum development as the
Universe cools down. For the 1-stage case focused on in this paper, we analyze
the properties of phase transition and discover that the field value of the
electroweak symmetry breaking vacuum at the critical temperature at which the
first order phase transition occurs is largely correlated with the vacuum depth
of the 1-loop potential at zero temperature.
We demonstrate that a strong first order electroweak phase transition
(SFOEWPT) in the 2HDM is achievable and establish benchmark scenarios leading
to different testable signatures at colliders. In addition, we verify that an
enhanced triple Higgs coupling (including loop corrections) is a typical
feature of the SFOPT driven by the additional doublet. As a result, SFOEWPT
might be able to be probed at the LHC and future lepton colliders through Higgs
pair production.Comment: 43 pages, 18 figures, minor revision and match to the published
versio
Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction
Most existing event extraction (EE) methods merely extract event arguments
within the sentence scope. However, such sentence-level EE methods struggle to
handle soaring amounts of documents from emerging applications, such as
finance, legislation, health, etc., where event arguments always scatter across
different sentences, and even multiple such event mentions frequently co-exist
in the same document. To address these challenges, we propose a novel
end-to-end model, Doc2EDAG, which can generate an entity-based directed acyclic
graph to fulfill the document-level EE (DEE) effectively. Moreover, we
reformalize a DEE task with the no-trigger-words design to ease the
document-level event labeling. To demonstrate the effectiveness of Doc2EDAG, we
build a large-scale real-world dataset consisting of Chinese financial
announcements with the challenges mentioned above. Extensive experiments with
comprehensive analyses illustrate the superiority of Doc2EDAG over
state-of-the-art methods. Data and codes can be found at
https://github.com/dolphin-zs/Doc2EDAG.Comment: Accepted by EMNLP 201
CSWA: Aggregation-Free Spatial-Temporal Community Sensing
In this paper, we present a novel community sensing paradigm -- {C}ommunity
{S}ensing {W}ithout {A}ggregation}. CSWA is designed to obtain the environment
information (e.g., air pollution or temperature) in each subarea of the target
area, without aggregating sensor and location data collected by community
members. CSWA operates on top of a secured peer-to-peer network over the
community members and proposes a novel \emph{Decentralized Spatial-Temporal
Compressive Sensing} framework based on \emph{Parallelized Stochastic Gradient
Descent}. Through learning the \emph{low-rank structure} via distributed
optimization, CSWA approximates the value of the sensor data in each subarea
(both covered and uncovered) for each sensing cycle using the sensor data
locally stored in each member's mobile device. Simulation experiments based on
real-world datasets demonstrate that CSWA exhibits low approximation error
(i.e., less than C in city-wide temperature sensing task and
units of PM2.5 index in urban air pollution sensing) and performs comparably to
(sometimes better than) state-of-the-art algorithms based on the data
aggregation and centralized computation.Comment: This paper has been accepted by AAAI 2018. First two authors are
equally contribute
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