1,095 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
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 investigatorsâ 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 Warnerâs 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)-NâČ-[1-(4-ChloroÂphenÂyl)ethylÂidene]-2-hydroxyÂbenzohydrazide
In the title compound, C15H13ClN2O2, the dihedral angle between the two benzene rings is 7.0â
(1)°. An intraÂmolecular NâHâŻO hydrogen bond is present and interÂmolecular OâHâŻO hydrogen bonds link the molÂecules 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
Multi-device wind turbine power generation forecasting based on hidden feature embedding
In recent years, the global installed capacity of wind power has grown rapidly. Wind power forecasting, as a key technology in wind turbine systems, has received widespread attention and extensive research. However, existing studies typically focus on the power prediction of individual devices. In the context of multi-turbine scenarios, employing individual models for each device may introduce challenges, encompassing data dilution and a substantial number of model parameters in power generation forecasting tasks. In this paper, a single-model method suitable for multi-device wind power forecasting is proposed. Firstly, this method allocates multi-dimensional random vectors to each device. Then, it utilizes space embedding techniques to iteratively evolve the random vectors into representative vectors corresponding to each device. Finally, the temporal features are concatenated with the corresponding representative vectors and inputted into the model, enabling the single model to accomplish multi-device wind power forecasting task based on device discrimination. Experimental results demonstrate that our method not only solves the data dilution issue and significantly reduces the number of model parameters but also maintains better predictive performance. Future research could focus on using more interpretable space embedding techniques to observe representation vectors of wind turbine equipment and further explore their semantic features
Modulation Mechanism of Ionic Transport through Short Nanopores by Charged Exterior Surfaces
Short nanopores have various applications in biosensing, desalination, and
energy conversion. Here, the modulation of charged exterior surfaces on ionic
transport is investigated through simulations with sub-200 nm long nanopores
under applied voltages. Detailed analysis of ionic current, electric field
strength, and fluid flow inside and outside nanopores reveals that charged
exterior surfaces can increase ionic conductance by increasing both the
concentration and migration speed of charge carriers. The electric double
layers near charged exterior surfaces provide an ion pool and an additional
passageway for counterions, which lead to enhanced exterior surface conductance
and ionic concentrations at pore entrances and inside the nanopore. We also
report that charges on the membrane surfaces increase electric field strengths
inside nanopores. The effective width of a ring with surface charges placed at
pore entrances (Lcs) is considered as well by studying the dependence of the
current on Lcs. We find a linear relationship between the effective Lcs and the
surface charge density and voltage, and an inverse relationship between the
geometrical pore length and salt concentration. Our results elucidate the
modulation mechanism of charged exterior surfaces on ionic transport through
short nanopores, which is important for the design and fabrication of porous
membranes.Comment: 27 pages, 6 figure
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