9,629 research outputs found
Learning user-specific latent influence and susceptibility from information cascades
Predicting cascade dynamics has important implications for understanding
information propagation and launching viral marketing. Previous works mainly
adopt a pair-wise manner, modeling the propagation probability between pairs of
users using n^2 independent parameters for n users. Consequently, these models
suffer from severe overfitting problem, specially for pairs of users without
direct interactions, limiting their prediction accuracy. Here we propose to
model the cascade dynamics by learning two low-dimensional user-specific
vectors from observed cascades, capturing their influence and susceptibility
respectively. This model requires much less parameters and thus could combat
overfitting problem. Moreover, this model could naturally model
context-dependent factors like cumulative effect in information propagation.
Extensive experiments on synthetic dataset and a large-scale microblogging
dataset demonstrate that this model outperforms the existing pair-wise models
at predicting cascade dynamics, cascade size, and "who will be retweeted".Comment: from The 29th AAAI Conference on Artificial Intelligence (AAAI-2015
High-responsivity vertical-illumination Si/Ge uni-traveling-carrier photodiodes based on silicon-on-insulator substrate
Si/Ge uni-traveling carrier photodiodes exhibit higher output current when
space-charge effects are overcome and thermal effects are suppressed, which is
highly beneficial for increasing the dynamic range of various microwave
photonic systems and simplifying high-bit-rate digital receivers in different
applications. From the point of view of packaging, detectors with
vertical-illumination configuration can be easily handled by pick-and-place
tools and are a popular choice for making photo-receiver modules. However,
vertical-illumination Si/Ge uni-traveling carrier (UTC) devices suffer from
inter-constraint between high speed and high responsivity. Here, we report a
high responsivity vertical-illumination Si/Ge UTC photodiode based on a
silicon-on-insulator substrate. The maximum absorption efficiency of the
devices was 2.4 times greater than the silicon substrate owing to constructive
interference. The Si/Ge UTC photodiode was successfully fabricated and had a
dominant responsivity at 1550 nm of 0.18 A/W, a 50% improvement even with a 25%
thinner Ge absorption layer.Comment: 5pages,2figure
A Novel Method of the Generalized Interval-Valued Fuzzy Rough Approximation Operators
Rough set theory is a suitable tool for dealing with the imprecision, uncertainty, incompleteness, and vagueness of knowledge. In this paper, new lower and upper approximation operators for generalized fuzzy rough sets are constructed, and their definitions are expanded to the interval-valued environment. Furthermore, the properties of this type of rough sets are analyzed. These operators are shown to be equivalent to the generalized interval fuzzy rough approximation operators introduced by Dubois, which are determined by any interval-valued fuzzy binary relation expressed in a generalized approximation space. Main properties of these operators are discussed under different interval-valued fuzzy binary relations, and the illustrative examples are given to demonstrate the main features of the proposed operators
PottsMGNet: A Mathematical Explanation of Encoder-Decoder Based Neural Networks
For problems in image processing and many other fields, a large class of
effective neural networks has encoder-decoder-based architectures. Although
these networks have made impressive performances, mathematical explanations of
their architectures are still underdeveloped. In this paper, we study the
encoder-decoder-based network architecture from the algorithmic perspective and
provide a mathematical explanation. We use the two-phase Potts model for image
segmentation as an example for our explanations. We associate the segmentation
problem with a control problem in the continuous setting. Then, multigrid
method and operator splitting scheme, the PottsMGNet, are used to discretize
the continuous control model. We show that the resulting discrete PottsMGNet is
equivalent to an encoder-decoder-based network. With minor modifications, it is
shown that a number of the popular encoder-decoder-based neural networks are
just instances of the proposed PottsMGNet. By incorporating the
Soft-Threshold-Dynamics into the PottsMGNet as a regularizer, the PottsMGNet
has shown to be robust with the network parameters such as network width and
depth and achieved remarkable performance on datasets with very large noise. In
nearly all our experiments, the new network always performs better or as good
on accuracy and dice score than existing networks for image segmentation
Connections between Operator-splitting Methods and Deep Neural Networks with Applications in Image Segmentation
Deep neural network is a powerful tool for many tasks. Understanding why it
is so successful and providing a mathematical explanation is an important
problem and has been one popular research direction in past years. In the
literature of mathematical analysis of deep deep neural networks, a lot of
works are dedicated to establishing representation theories. How to make
connections between deep neural networks and mathematical algorithms is still
under development. In this paper, we give an algorithmic explanation for deep
neural networks, especially in their connection with operator splitting and
multigrid methods. We show that with certain splitting strategies,
operator-splitting methods have the same structure as networks. Utilizing this
connection and the Potts model for image segmentation, two networks inspired by
operator-splitting methods are proposed. The two networks are essentially two
operator-splitting algorithms solving the Potts model. Numerical experiments
are presented to demonstrate the effectiveness of the proposed networks
Dual Skipping Networks
Inspired by the recent neuroscience studies on the left-right asymmetry of
the human brain in processing low and high spatial frequency information, this
paper introduces a dual skipping network which carries out coarse-to-fine
object categorization. Such a network has two branches to simultaneously deal
with both coarse and fine-grained classification tasks. Specifically, we
propose a layer-skipping mechanism that learns a gating network to predict
which layers to skip in the testing stage. This layer-skipping mechanism endows
the network with good flexibility and capability in practice. Evaluations are
conducted on several widely used coarse-to-fine object categorization
benchmarks, and promising results are achieved by our proposed network model.Comment: CVPR 2018 (poster); fix typ
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