Potential precision of a direct measurement of the Higgs boson total width at a muon colliderr

In the light of the discovery of a 126 GeV Standard-Model-like Higgs boson at the LHC, we evaluate the achievable accuracies for direct measurements of the width, mass, and the s-channel resonant production cross section of the Higgs boson at a proposed muon collider. We find that with a beam energy resolution of R=0.01% (0.003%) and integrated luminosity of 0.5 fb^{-1} (1 fb^{-1}), a muon collider would enable us to determine the Standard-Model-like Higgs width to +/- 0.35 MeV (+/- 0.15 MeV) by combining two complementary channels of the WW^* and b\bar b final states. A non-Standard-Model Higgs with a broader width is also studied. The unparalleled accuracy potentially attainable at a muon collider would test the Higgs interactions to a high precision.Comment: 7 pages, 5 figures. Version appeared on Physical Review

Exploiting Tradeoff Between Transmission Diversity and Content Diversity in Multi-Cell Edge Caching

Caching in multi-cell networks faces a well-known dilemma, i.e., to cache same contents among multiple edge nodes (ENs) to enable transmission cooperation/diversity for higher transmission efficiency, or to cache different contents to enable content diversity for higher cache hit rate. In this work, we introduce a partition-based caching to exploit the tradeoff between transmission diversity and content diversity in a multi-cell edge caching networks with single user only. The performance is characterized by the system average outage probability, which can be viewed as the sum of the cache hit outage probability and cache miss probability. We show that (i) In the low signal-to-noise ratio(SNR) region, the ENs are encouraged to cache more fractions of the most popular files so as to better exploit the transmission diversity for the most popular content; (ii) In the high SNR region, the ENs are encouraged to cache more files with less fractions of each so as to better exploit the content diversity.Comment: Accepted by IEEE International Conference on Communications (ICC), Kansas City, MO, USA, May 201

Advanced LSTM: A Study about Better Time Dependency Modeling in Emotion Recognition

Long short-term memory (LSTM) is normally used in recurrent neural network (RNN) as basic recurrent unit. However,conventional LSTM assumes that the state at current time step depends on previous time step. This assumption constraints the time dependency modeling capability. In this study, we propose a new variation of LSTM, advanced LSTM (A-LSTM), for better temporal context modeling. We employ A-LSTM in weighted pooling RNN for emotion recognition. The A-LSTM outperforms the conventional LSTM by 5.5% relatively. The A-LSTM based weighted pooling RNN can also complement the state-of-the-art emotion classification framework. This shows the advantage of A-LSTM