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 $0.2 ^\circ$C in city-wide temperature sensing task and $10$ 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