No abstract

No abstract

Halide perovskite‐based photovoltaic (PV) devices have recently emerged for low energy consumption electronic devices such as Internet of Things (IoT). In this work, an effective strategy to form a hole‐selective layer using phenethylammonium iodide (PEAI) salt is presented that demonstrates unprecedently high open‐circuit voltage of 0.9 V with 18 µW cm−2 under 200 lux (cool white light‐emitting diodes). An appropriate post‐deposited amount of PEAI (2 mg) strongly interacts with the perovskite surface forming a conformal coating of PEAI on the perovskite film surface, which improves the crystallinity and absorption of the film. Here, Kelvin probe force microscopy results indicate the diminished potential difference across the grain boundaries and grain interiors after the PEAI deposition, constructing an electrically and chemically homogeneous surface. Also, the surface becomes more p‐type with a downshift of a valence band maximum, confirmed by ultraviolet photoelectron spectroscopy measurement, facilitating the transport of holes to the hole transport layer (HTL). The hole‐selective layer‐deposited devices exhibit reduced hysteresis in light current density–voltage curves and maintain steadily high fill factor across the different light intensities (200–1000 lux). This work highlights the importance of the HTL/perovskite interface that prepares the indoor halide perovskite PV devices for powering IoT device.

This paper presents the techniques and results of landing-site topographic mapping and rover localization using orbital, descent and rover images in the Chang'e-4 mission. High-resolution maps of the landing site are generated from orbital and descent images. Local digital elevation models and digital orthophoto maps with 0.02 m resolution are generated at each waypoint. The location of the lander is determined as (177.588 • E, 45.457 • S) using festure-matching techniques. The cross-site visual localization method is routinely used to localize the rover at each waypoint to reduce error accumulation from wheel slippage and IMU drift in dead reckoning. After the first five lunar days, the rover travels 186.66 m from the lander, according to the cross-site visual localization. The developed methods and results have been directly utilized to support the mission's operations. The maps and localization information are also valuable for supporting multiple scientific explorations of the landing site.

No abstract

This paper describes our system (HIT-SCIR) for the CoNLL 2017 shared task: Multilingual Parsing from Raw Text to Universal Dependencies. Our system includes three pipelined components: tokenization, Part-of-Speech (POS) tagging and dependency parsing. We use character-based bidirectional long shortterm memory (LSTM) networks for both tokenization and POS tagging. Afterwards, we employ a list-based transitionbased algorithm for general non-projective parsing and present an improved Stack-LSTM-based architecture for representing each transition state and making predictions.Furthermore, to parse low/zero-resource languages and cross-domain data, we use a model transfer approach to make effective use of existing resources. We demonstrate substantial gains against the UDPipe baseline, with an average improvement of 3.76% in LAS of all languages. And finally, we rank the 4th place on the official test sets.

No abstract

In this paper, we propose a new rich resource enhanced AMR aligner which produces multiple alignments and a new transition system for AMR parsing along with its oracle parser. Our aligner is further tuned by our oracle parser via picking the alignment that leads to the highestscored achievable AMR graph. Experimental results show that our aligner outperforms the rule-based aligner in previous work by achieving higher alignment F1 score and consistently improving two open-sourced AMR parsers. Based on our aligner and transition system, we develop a transition-based AMR parser that parses a sentence into its AMR graph directly. An ensemble of our parsers with only words and POS tags as input leads to 68.4 Smatch F1 score, which outperforms the parser of Wang and Xue (2017).