1,074 research outputs found
A Multi-task Learning Approach for Improving Product Title Compression with User Search Log Data
It is a challenging and practical research problem to obtain effective
compression of lengthy product titles for E-commerce. This is particularly
important as more and more users browse mobile E-commerce apps and more
merchants make the original product titles redundant and lengthy for Search
Engine Optimization. Traditional text summarization approaches often require a
large amount of preprocessing costs and do not capture the important issue of
conversion rate in E-commerce. This paper proposes a novel multi-task learning
approach for improving product title compression with user search log data. In
particular, a pointer network-based sequence-to-sequence approach is utilized
for title compression with an attentive mechanism as an extractive method and
an attentive encoder-decoder approach is utilized for generating user search
queries. The encoding parameters (i.e., semantic embedding of original titles)
are shared among the two tasks and the attention distributions are jointly
optimized. An extensive set of experiments with both human annotated data and
online deployment demonstrate the advantage of the proposed research for both
compression qualities and online business values.Comment: 8 Pages, accepted at AAAI 201
Research on Parametric Model for Surface Processing Prediction of Aero-Engine Blades
This paper presented a method for establishing a blade surface machining prediction model based on a parametric model. The abrasive grain state of the grinding tool was divided into initial wear stage, stable wear stage and sharp wear stage. Based on this, a parametric prediction model of engine blade surface material removal was established. In this paper, the simulation of blade surface machining was carried out. In this work, the blade was divided into several sections according to the direction from the blade root to the blade tip. A certain curve of the outer contour was fitted with a specific arc to reduce the calculation amount. Through a series of simulation calculations, the expressions of the above parametric prediction model were obtained, and several experiments were carried out to verify the feasibility of the prediction model, and the results were analyzed
Light-scattering study of the coil-to-globule transition of linear poly(N-isopropylacrylamide) ionomers in water
Sample Size Determination for Interval Estimation of the Prevalence of a Sensitive Attribute Under Randomized Response Models
© 2022 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.Studies with sensitive questions should include a sufficient number of respondents to adequately address the research interest. While studies with an inadequate number of respondents may not yield significant conclusions, studies with an excess of respondents become wasteful of investigators’ budget. Therefore, it is an important step in survey sampling to determine the required number of participants. In this article, we derive sample size formulas based on confidence interval estimation of prevalence for four randomized response models, namely, the Warner’s randomized response model, unrelated question model, item count technique model and cheater detection model. Specifically, our sample size formulas control, with a given assurance probability, the width of a confidence interval within the planned range. Simulation results demonstrate that all formulas are accurate in terms of empirical coverage probabilities and empirical assurance probabilities. All formulas are illustrated using a real-life application about the use of unethical tactics in negotiation.Peer reviewe
(E)-N′-[1-(4-Chlorophenyl)ethylidene]-2-hydroxybenzohydrazide
In the title compound, C15H13ClN2O2, the dihedral angle between the two benzene rings is 7.0 (1)°. An intramolecular N—H⋯O hydrogen bond is present and intermolecular O—H⋯O hydrogen bonds link the molecules into chains along [001]
Theoretical prediction of diffusive ionic current through nanopores under salt gradients
In charged nanopores, ionic diffusion current reflects the ionic selectivity
and ionic permeability of nanopores which determines the performance of osmotic
energy conversion, i.e. the output power and efficiency. Here, theoretical
predictions of the diffusive currents through cation-selective nanopores have
been developed based on the investigation of diffusive ionic transport under
salt gradients with simulations. The ionic diffusion current I satisfies a
reciprocal relationship with the pore length I correlates with a/L (a is a
constant) in long nanopores. a is determined by the cross-sectional areas of
diffusion paths for anions and cations inside nanopores which can be described
with a quadratic power of the diameter, and the superposition of a quadratic
power and a first power of the diameter, respectively. By using effective
concentration gradients instead of nominal ones, the deviation caused by the
concentration polarization can be effectively avoided in the prediction of
ionic diffusion current. With developed equations of effective concentration
difference and ionic diffusion current, the diffusion current across nanopores
can be well predicted in cases of nanopores longer than 100 nm and without
overlapping of electric double layers. Our results can provide a convenient way
for the quantitative prediction of ionic diffusion currents under salt
gradients
Curriculum development and students’ learning outcomes: a case study of Putonghua presentation skills
Asian Women's Perceived Impact of Leadership Characteristics and Skills on their UIUC Campus Life
Poster Presentation for EUI Spring 2017 Student Conference, concerning Asian women's perceived impact of leadership skills and characteristics on their UIUC campus life.Ope
5,6-Dimethyl-1,2,4-triazin-3-amine
In the crystal structure of the title compound, C5H8N4, adjacent molecules are connected through N—H⋯N hydrogen bonds, resulting in a zigzag chain along [100]. The amino groups and heterocyclic N atoms are involved in further N—H⋯N hydrogen bonds, forming R
2
2(8) motifs
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