Scheme for suppressing atom expansion induced contrast loss in atom interferometers

The loss of contrast due to atom expansion induced non-perfect Raman pulse area in atom interferometers is investigated systematically. Based on the theoretical simulation, we find that the expansion of the atomic cloud results in a decrease of the {\pi} pulse fidelity and a change of the {\pi} pulse duration, which lead to a significant reduction in fringe contrast. We propose a mitigation strategy of increasing the intensities of the second and third Raman pulses. Simulation results show that the fringe contrast can be improved by 13.6% in a typical atom interferometer gravimeter using this intensity compensation strategy. We also evaluate the effects of this mitigation strategy in the case of a lower atomic cloud temperature and a larger Raman beam size under different Raman pulse time interval conditions. This mitigation strategy has potential applications in increasing the sensitivity of atom interferometer-based precision measuring, including precision measuring of the gravity, gravity gradient, rotation, and magnetic field gradient, as well as testing of the Einstein equivalence principle.Comment: 14 pages, 8 figure

Half-titanocene 5-t-butyl-2-(1-(arylimino)methyl)quinolin-8-olate chlorides: Synthesis, characterization and ethylene (co-) polymerization behavior

A series of half-titanocene chloride complexes bearing 5-t-butyl-2-(1-(arylimino)methyl)quinolin-8-olate ligands (L), CpTiLCl₂, has been synthesized in acceptable yields by the stoichiometric reaction of CpTiCl₃ with the respective potassium 5-t-butyl-2-(1-(arylimino)methyl)quinolin-8-olate. All half-titanocene complexes were fully characterized by elemental analysis and NMR spectroscopy, and the molecular structures of the representative complexes C1 and C2 were confirmed as pseudo octahedral at titanium by single-crystal X-ray diffraction. When activated with methylaluminoxane (MAO) or modified methylaluminoxane (MMAO), all titanium complexes exhibited good activities (up to 4.8 × 10⁵ g mol⁻¹(Ti) h⁻¹) towards ethylene polymerization. The obtained polyethylene exhibited ultra-high molecular weight (up to 11.82 × 10⁵ g mol⁻¹) with narrow polydispersity. Furthermore, effective co-polymerization of ethylene with 1-hexene or 1-octene was achieved with several percentages of co-monomer incorporation in the resultant polyethylenes

Dual Long Short-Term Memory Networks for Sub-Character Representation Learning

Characters have commonly been regarded as the minimal processing unit in Natural Language Processing (NLP). But many non-latin languages have hieroglyphic writing systems, involving a big alphabet with thousands or millions of characters. Each character is composed of even smaller parts, which are often ignored by the previous work. In this paper, we propose a novel architecture employing two stacked Long Short-Term Memory Networks (LSTMs) to learn sub-character level representation and capture deeper level of semantic meanings. To build a concrete study and substantiate the efficiency of our neural architecture, we take Chinese Word Segmentation as a research case example. Among those languages, Chinese is a typical case, for which every character contains several components called radicals. Our networks employ a shared radical level embedding to solve both Simplified and Traditional Chinese Word Segmentation, without extra Traditional to Simplified Chinese conversion, in such a highly end-to-end way the word segmentation can be significantly simplified compared to the previous work. Radical level embeddings can also capture deeper semantic meaning below character level and improve the system performance of learning. By tying radical and character embeddings together, the parameter count is reduced whereas semantic knowledge is shared and transferred between two levels, boosting the performance largely. On 3 out of 4 Bakeoff 2005 datasets, our method surpassed state-of-the-art results by up to 0.4%. Our results are reproducible, source codes and corpora are available on GitHub.Comment: Accepted & forthcoming at ITNG-201