635 research outputs found
Quantum resource studied from the perspective of quantum state superposition
Quantum resources,such as discord and entanglement, are crucial in quantum
information processing. In this paper, quantum resources are studied from the
aspect of quantum state superposition. We define the local superposition (LS)
as the superposition between basis of single part, and nonlocal superposition
(NLS) as the superposition between product basis of multiple parts. For quantum
resource with nonzero LS, quantum operation must be introduced to prepare it,
and for quantum resource with nonzero NLS, nonlocal quantum operation must be
introduced to prepare it. We prove that LS vanishes if and only if the state is
classical and NLS vanishes if and only if the state is separable. From this
superposition aspect, quantum resources are categorized as superpositions
existing in different parts. These results are helpful to study quantum
resources from a unified frame.Comment: 9 pages, 4 figure
No-Service Rail Surface Defect Segmentation via Normalized Attention and Dual-scale Interaction
No-service rail surface defect (NRSD) segmentation is an essential way for
perceiving the quality of no-service rails. However, due to the complex and
diverse outlines and low-contrast textures of no-service rails, existing
natural image segmentation methods cannot achieve promising performance in NRSD
images, especially in some unique and challenging NRSD scenes. To this end, in
this paper, we propose a novel segmentation network for NRSDs based on
Normalized Attention and Dual-scale Interaction, named NaDiNet. Specifically,
NaDiNet follows the enhancement-interaction paradigm. The Normalized
Channel-wise Self-Attention Module (NAM) and the Dual-scale Interaction Block
(DIB) are two key components of NaDiNet. NAM is a specific extension of the
channel-wise self-attention mechanism (CAM) to enhance features extracted from
low-contrast NRSD images. The softmax layer in CAM will produce very small
correlation coefficients which are not conducive to low-contrast feature
enhancement. Instead, in NAM, we directly calculate the normalized correlation
coefficient between channels to enlarge the feature differentiation. DIB is
specifically designed for the feature interaction of the enhanced features. It
has two interaction branches with dual scales, one for fine-grained clues and
the other for coarse-grained clues. With both branches working together, DIB
can perceive defect regions of different granularities. With these modules
working together, our NaDiNet can generate accurate segmentation map. Extensive
experiments on the public NRSD-MN dataset with man-made and natural NRSDs
demonstrate that our proposed NaDiNet with various backbones (i.e., VGG,
ResNet, and DenseNet) consistently outperforms 10 state-of-the-art methods. The
code and results of our method are available at
https://github.com/monxxcn/NaDiNet.Comment: 10 pages, 6 figures, Accepted by IEEE Transactions on Instrumentation
and Measurement 202
Positive solutions of boundary value problem for singular positone and semi-positone third-order difference equations
Applications of Schauder’s Fixed Point Theorem to Semipositone Singular Differential Equations
We study the existence of positive periodic solutions of second-order singular differential equations. The proof relies on Schauder’s fixed point theorem. Our results generalized and extended those results contained in the studies by Chu and Torres (2007) and Torres (2007)
. In some suitable weak singularities, the existence of periodic solutions may help
FAN: Fatigue-Aware Network for Click-Through Rate Prediction in E-commerce Recommendation
Since clicks usually contain heavy noise, increasing research efforts have
been devoted to modeling implicit negative user behaviors (i.e., non-clicks).
However, they either rely on explicit negative user behaviors (e.g., dislikes)
or simply treat non-clicks as negative feedback, failing to learn negative user
interests comprehensively. In such situations, users may experience fatigue
because of seeing too many similar recommendations. In this paper, we propose
Fatigue-Aware Network (FAN), a novel CTR model that directly perceives user
fatigue from non-clicks. Specifically, we first apply Fourier Transformation to
the time series generated from non-clicks, obtaining its frequency spectrum
which contains comprehensive information about user fatigue. Then the frequency
spectrum is modulated by category information of the target item to model the
bias that both the upper bound of fatigue and users' patience is different for
different categories. Moreover, a gating network is adopted to model the
confidence of user fatigue and an auxiliary task is designed to guide the
learning of user fatigue, so we can obtain a well-learned fatigue
representation and combine it with user interests for the final CTR prediction.
Experimental results on real-world datasets validate the superiority of FAN and
online A/B tests also show FAN outperforms representative CTR models
significantly
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