233 research outputs found
Pricing Ad Slots with Consecutive Multi-unit Demand
We consider the optimal pricing problem for a model of the rich media
advertisement market, as well as other related applications. In this market,
there are multiple buyers (advertisers), and items (slots) that are arranged in
a line such as a banner on a website. Each buyer desires a particular number of
{\em consecutive} slots and has a per-unit-quality value (dependent on
the ad only) while each slot has a quality (dependent on the position
only such as click-through rate in position auctions). Hence, the valuation of
the buyer for item is . We want to decide the allocations and
the prices in order to maximize the total revenue of the market maker.
A key difference from the traditional position auction is the advertiser's
requirement of a fixed number of consecutive slots. Consecutive slots may be
needed for a large size rich media ad. We study three major pricing mechanisms,
the Bayesian pricing model, the maximum revenue market equilibrium model and an
envy-free solution model. Under the Bayesian model, we design a polynomial time
computable truthful mechanism which is optimum in revenue. For the market
equilibrium paradigm, we find a polynomial time algorithm to obtain the maximum
revenue market equilibrium solution. In envy-free settings, an optimal solution
is presented when the buyers have the same demand for the number of consecutive
slots. We conduct a simulation that compares the revenues from the above
schemes and gives convincing results.Comment: 27page
Robust Outlier Detection Method Based on Local Entropy and Global Density
By now, most outlier-detection algorithms struggle to accurately detect both
point anomalies and cluster anomalies simultaneously. Furthermore, a few
K-nearest-neighbor-based anomaly-detection methods exhibit excellent
performance on many datasets, but their sensitivity to the value of K is a
critical issue that needs to be addressed. To address these challenges, we
propose a novel robust anomaly detection method, called Entropy Density Ratio
Outlier Detection (EDROD). This method incorporates the probability density of
each sample as the global feature, and the local entropy around each sample as
the local feature, to obtain a comprehensive indicator of abnormality for each
sample, which is called Entropy Density Ratio (EDR) for short in this paper. By
comparing several competing anomaly detection methods on both synthetic and
real-world datasets, it is found that the EDROD method can detect both point
anomalies and cluster anomalies simultaneously with accurate performance. In
addition, it is also found that the EDROD method exhibits strong robustness to
the number of selected neighboring samples, the dimension of samples in the
dataset, and the size of the dataset. Therefore, the proposed EDROD method can
be applied to a variety of real-world datasets to detect anomalies with
accurate and robust performances
Learning Discriminative Shrinkage Deep Networks for Image Deconvolution
Most existing methods usually formulate the non-blind deconvolution problem
into a maximum-a-posteriori framework and address it by manually designing
kinds of regularization terms and data terms of the latent clear images.
However, explicitly designing these two terms is quite challenging and usually
leads to complex optimization problems which are difficult to solve. In this
paper, we propose an effective non-blind deconvolution approach by learning
discriminative shrinkage functions to implicitly model these terms. In contrast
to most existing methods that use deep convolutional neural networks (CNNs) or
radial basis functions to simply learn the regularization term, we formulate
both the data term and regularization term and split the deconvolution model
into data-related and regularization-related sub-problems according to the
alternating direction method of multipliers. We explore the properties of the
Maxout function and develop a deep CNN model with a Maxout layer to learn
discriminative shrinkage functions to directly approximate the solutions of
these two sub-problems. Moreover, given the fast-Fourier-transform-based image
restoration usually leads to ringing artifacts while conjugate-gradient-based
approach is time-consuming, we develop the Conjugate Gradient Network to
restore the latent clear images effectively and efficiently. Experimental
results show that the proposed method performs favorably against the
state-of-the-art ones in terms of efficiency and accuracy
- …