Neural encoder-decoder models have shown great success in many sequence generation tasks. However, previous work has not investigated situations in which we would like to control the length of encoder-decoder outputs. This capability is crucial for applications such as text summarization, in which we have to generate concise summaries with a desired length. In this paper, we propose methods for controlling the output sequence length for neural encoder-decoder models: two decoding-based methods and two learning-based methods.1 Results show that our learning-based methods have the capability to control length without degrading summary quality in a summarization task.

Comprehension of spoken natural language is an essential skill for robots to communicate with humans effectively. However, handling unconstrained spoken instructions is challenging due to (1) complex structures and the wide variety of expressions used in spoken language, and (2) inherent ambiguity of human instructions. In this paper, we propose the first comprehensive system for controlling robots with unconstrained spoken language, which is able to effectively resolve ambiguity in spoken instructions. Specifically, we integrate deep learning-based object detection together with natural language processing technologies to handle unconstrained spoken instructions, and propose a method for robots to resolve instruction ambiguity through dialogue. Through our experiments on both a simulated environment as well as a physical industrial robot arm, we demonstrate the ability of our system to understand natural instructions from human operators effectively, and show how higher success rates of the object picking task can be achieved through an interactive clarification process. 1

SummaryArbuscular mycorrhizal fungi translocate polyphosphate through hyphae over a long distance to deliver to the host. More than three decades ago, suppression of host transpiration was found to decelerate phosphate delivery of the fungal symbiont, leading us to hypothesize that transpiration provides a primary driving force for polyphosphate translocation, probably via creating hyphal water flow in which fungal aquaporin(s) may be involved.The impact of transpiration suppression on polyphosphate translocation through hyphae of Rhizophagus clarus was evaluated. An aquaporin gene expressed in intraradical mycelia was characterized and knocked down by virus-induced gene silencing to investigate the involvement of the gene in polyphosphate translocation.Rhizophagus clarus aquaporin 3 (RcAQP3) that was most highly expressed in intraradical mycelia encodes an aquaglyceroporin responsible for water transport across the plasma membrane. Knockdown of RcAQP3 as well as the suppression of host transpiration decelerated polyphosphate translocation in proportion to the levels of knockdown and suppression, respectively.These results provide the first insight into the mechanism underlying long-distance polyphosphate translocation in mycorrhizal associations at the molecular level, in which host transpiration and the fungal aquaporin play key roles. A hypothetical model of the translocation is proposed for further elucidation of the mechanism.

We present high-resolution two-color photoassociation spectroscopy of Bose-Einstein condensates of ytterbium atoms. The use of narrow Raman resonances and careful examination of systematic shifts enabled us to measure 13 bound state energies for three isotopologues of the ground state ytterbium molecule with standard uncertainties on the order of 500 Hz. The atomic interactions are modeled using an ab initio based mass scaled Born-Oppenheimer potential whose long range van der Waals parameters and total WKB phase are fitted to experimental data. We find that the quality of the fit of this model, of about 112.9 kHz (RMS) can be significantly improved by adding the recently calculated beyond-Born-Oppenheimer (BBO) adiabatic corrections [

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