264 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
Attention Optimization for Abstractive Document Summarization
Attention plays a key role in the improvement of sequence-to-sequence-based
document summarization models. To obtain a powerful attention helping with
reproducing the most salient information and avoiding repetitions, we augment
the vanilla attention model from both local and global aspects. We propose an
attention refinement unit paired with local variance loss to impose supervision
on the attention model at each decoding step, and a global variance loss to
optimize the attention distributions of all decoding steps from the global
perspective. The performances on the CNN/Daily Mail dataset verify the
effectiveness of our methods
Image Feature Information Extraction for Interest Point Detection: A Comprehensive Review
Interest point detection is one of the most fundamental and critical problems
in computer vision and image processing. In this paper, we carry out a
comprehensive review on image feature information (IFI) extraction techniques
for interest point detection. To systematically introduce how the existing
interest point detection methods extract IFI from an input image, we propose a
taxonomy of the IFI extraction techniques for interest point detection.
According to this taxonomy, we discuss different types of IFI extraction
techniques for interest point detection. Furthermore, we identify the main
unresolved issues related to the existing IFI extraction techniques for
interest point detection and any interest point detection methods that have not
been discussed before. The existing popular datasets and evaluation standards
are provided and the performances for eighteen state-of-the-art approaches are
evaluated and discussed. Moreover, future research directions on IFI extraction
techniques for interest point detection are elaborated
Dynamic response analysis of rutting resistance performance of high modulus asphalt concrete pavement
In order to systematically study the rutting resistance performance of High-Modulus Asphalt Concrete (HMAC) pavements, a finite element method model of HMAC pavement was established using ABAQUS software. Based on the viscoelasticity theory of asphalt, the stress and deformation distribution characteristics of HMAC pavement were studied and compared to conventional asphalt pavement under moving loads. Then, the pavement temperature field model was established to study the temperature variation and the thermal stress in HMAC pavement. Finally, under the condition of continuous temperature variation, the creep behavior and permanent deformation of HMAC pavement were investigated. The results showed that under the action of moving loads, the strain and displacement generated in HMAC pavement were lower than those in conventional asphalt pavement. The upper surface layer was most obviously affected by outside air temperature, resulting in maximum thermal stress. Lastly, under the condition of continuous temperature change, HMAC pavement could greatly reduce the deformation of asphalt material in each surface layer compared to conventional asphalt pavement
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Characterization of aquifer heterogeneity using transient hydraulic tomography
Hydraulic tomography is a cost -effective technique for characterizing the heterogeneity of hydraulic parameters in the subsurface. During hydraulic tomography surveys, a large number of hydraulic heads (i.e., aquifer responses) are collected from a series of pumping or injection tests in an aquifer. These responses are then used to interpret the spatial distribution of hydraulic parameters of the aquifer using inverse modeling. In this study, we developed an efficient sequential successive linear estimator (SSLE) for interpreting data from transient hydraulic tomography to estimate three-dimensional hydraulic conductivity and specific storage fields of aquifers. We first explored this estimator for transient hydraulic tomography in a hypothetical one-dimensional aquifer. Results show that during a pumping test, transient heads are highly correlated with specific storage at early time but with hydraulic conductivity at late time. Therefore, reliable estimates of both hydraulic conductivity and specific storage must exploit the head data at both early and late times. Our study also shows that the transient heads are highly correlated over time, implying only infrequent head measurements are needed during the estimation. Applying this sampling strategy to a well -posed problem, we show that our SSLE can produce accurate estimates of both hydraulic conductivity and specific storage fields. The benefit of hydraulic tomography for ill -posed problems is then demonstrated. Finally, to affirm the robustness of our SSLE approach, we apply the SSLE approach to transient hydraulic tomography in a hypothetical two- dimensional aquifer with nonstationary hydraulic properties, as well as a hypothetical three-dimensional heterogeneous aquifer.This title from the Hydrology & Water Resources Technical Reports collection is made available by the Department of Hydrology & Atmospheric Sciences and the University Libraries, University of Arizona. If you have questions about titles in this collection, please contact [email protected]
An improved positioning algorithm in a long-range asymmetric perimeter security system
In this paper, an improved positioning algorithm is proposed for a long-range asymmetric perimeter security system. This algorithm employs zero-crossing rate to detect the disturbance starting point, and then utilizes an improved empirical mode decomposition to obtain the effective time-frequency distribution of the extracted signal. In the end, a cross-correlation is used to estimate the time delay of the effective extracted signal. The scheme is also verified and analyzed experimentally. The field test results demonstrate that the proposed scheme can achieve a detection of 96.60% of positioning errors distributed within the range of 0-±20 m at the sensing length of 75 km, which significantly improves the positioning accuracy for the long-range asymmetric fence perimeter application
Interface induced Zeeman-protected superconductivity in ultrathin crystalline lead films
Two dimensional (2D) superconducting systems are of great importance to
exploring exotic quantum physics. Recent development of fabrication techniques
stimulates the studies of high quality single crystalline 2D superconductors,
where intrinsic properties give rise to unprecedented physical phenomena. Here
we report the observation of Zeeman-type spin-orbit interaction protected
superconductivity (Zeeman-protected superconductivity) in 4 monolayer (ML) to 6
ML crystalline Pb films grown on striped incommensurate (SIC) Pb layers on
Si(111) substrates by molecular beam epitaxy (MBE). Anomalous large in-plane
critical field far beyond the Pauli limit is detected, which can be attributed
to the Zeeman-protected superconductivity due to the in-plane inversion
symmetry breaking at the interface. Our work demonstrates that in
superconducting heterostructures the interface can induce Zeeman-type
spin-orbit interaction (SOI) and modulate the superconductivity
Silicon Layer Intercalation of Centimeter-Scale, Epitaxially-Grown Monolayer Graphene on Ru(0001)
We develop a strategy for graphene growth on Ru(0001) followed by
silicon-layer intercalation that not only weakens the interaction of graphene
with the metal substrate but also retains its superlative properties. This
G/Si/Ru architecture, produced by silicon-layer intercalation approach (SIA),
was characterized by scanning tunneling microscopy/spectroscopy and angle
resolved electron photoemission spectroscopy. These experiments show high
structural and electronic qualities of this new composite. The SIA allows for
an atomic control of the distance between the graphene and the metal substrate
that can be used as a top gate. Our results show potential for the next
generation of graphene-based materials with tailored properties.Comment: 13 pages, 4 figures, to be published in Appl. Phys. Let
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