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Open Access Issue
Power of Second Opportunity: Dynamic Pricing with Second Chance
Tsinghua Science and Technology 2025, 30(2): 543-560
Published: 09 December 2024
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In this paper, we consider the following dynamic pricing problem. Suppose the market price vt of an item arriving at time t is determined by vt=θTxt, where xt is the feature vector of that item and θ is an unknown vector parameter. The seller has to post prices without knowing θ such that the total regret in time span T is minimized. Considering real-world scenarios in which people may negotiate prices, we propose a model called Second Chance Pricing, in which a seller has a second opportunity to post a price after the first offer is declined. Theoretical analysis shows that a second chance of pricing results in a total regret between O(lnTnlnn+1n) and O(n2lnT), where n is the dimension of the feature space. Experiments on both synthetic data and real data demonstrate significant benefits brought about by the second chance where the regret is only 13% of that of one chance.

Regular Paper Issue
DG-CNN: Introducing Margin Information into Convolutional Neural Networks for Breast Cancer Diagnosis in Ultrasound Images
Journal of Computer Science and Technology 2022, 37(2): 277-294
Published: 31 March 2022
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Although using convolutional neural networks (CNNs) for computer-aided diagnosis (CAD) has made tremendous progress in the last few years, the small medical datasets remain to be the major bottleneck in this area. To address this problem, researchers start looking for information out of the medical datasets. Previous efforts mainly leverage information from natural images via transfer learning. More recent research work focuses on integrating knowledge from medical practitioners, either letting networks resemble how practitioners are trained, how they view images, or using extra annotations. In this paper, we propose a scheme named Domain Guided-CNN (DG-CNN) to incorporate the margin information, a feature described in the consensus for radiologists to diagnose cancer in breast ultrasound (BUS) images. In DG-CNN, attention maps that highlight margin areas of tumors are first generated, and then incorporated via different approaches into the networks. We have tested the performance of DG-CNN on our own dataset (including 1485 ultrasound images) and on a public dataset. The results show that DG-CNN can be applied to different network structures like VGG and ResNet to improve their performance. For example, experimental results on our dataset show that with a certain integrating mode, the improvement of using DG-CNN over a baseline network structure ResNet18 is 2.17% in accuracy, 1.69% in sensitivity, 2.64% in specificity and 2.57% in AUC (Area Under Curve). To the best of our knowledge, this is the first time that the margin information is utilized to improve the performance of deep neural networks in diagnosing breast cancer in BUS images.

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