Nonperturbative signatures in pair production for general elliptic polarization fields

The momentum signatures in nonperturbative multiphoton pair production for general elliptic polarization electric fields are investigated by employing the real-time Dirac-Heisenberg-Wigner formalism. For a linearly polarized electric field we find that the positions of the nodes in momenta spectra of created pairs depend only on the electric field frequency. The polarization of external fields could not only change the node structures or even make the nodes disappear but also change the thresholds of pair production. The momentum signatures associated to the node positions in which the even-number-photon pair creation process is forbid could be used to distinguish the orbital angular momentum of created pairs on the momenta spectra. These distinguishable momentum signatures could be relevant for providing the output information of created particles and also the input information of ultrashort laser pulses.Comment: 8 pages, 4 figures, submitted to Europhysics Letter

Tunneling into Multiwalled Carbon Nanotubes: Coulomb Blockade and Fano Resonance

Tunneling spectroscopy measurements of single tunnel junctions formed between multiwalled carbon nanotubes (MWNTs) and a normal metal are reported. Intrinsic Coulomb interactions in the MWNTs give rise to a strong zero-bias suppression of a tunneling density of states (TDOS) that can be fitted numerically to the environmental quantum-fluctuation (EQF) theory. An asymmetric conductance anomaly near zero bias is found at low temperatures and interpreted as Fano resonance in the strong tunneling regime.Comment: 4 pages, 4 figure

Co-training an improved recurrent neural network with probability statistic models for named entity recognition

© Springer International Publishing AG 2017. Named Entity Recognition (NER) is a subtask of information extraction in Natural Language Processing (NLP) field and thus being wildly studied. Currently Recurrent Neural Network (RNN) has become a popular way to do NER task, but it needs a lot of train data. The lack of labeled train data is one of the hard problems and traditional co-training strategy is a way to alleviate it. In this paper, we consider this situation and focus on doing NER with co-training using RNN and two probability statistic models i.e. Hidden Markov Model (HMM) and Conditional Random Field (CRF). We proposed a modified RNN model by redefining its activation function. Compared to traditional sigmoid function, our new function avoids saturation to some degree and makes its output scope very close to [0, 1], thus improving recognition accuracy. Our experiments are conducted ATIS benchmark. First, supervised learning using those models are compared when using different train data size. The experimental results show that it is not necessary to use whole data, even small part of train data can also get good performance. Then, we compare the results of our modified RNN with original RNN. 0.5% improvement is obtained. Last, we compare the co-training results. HMM and CRF get higher improvement than RNN after co-training. Moreover, using our modified RNN in co-training, their performances are improved further