562 research outputs found
Earnings Manipulation and Risky Investment
This paper develops a formal model to study earnings manipulation. It analyzes the effects of real earnings auditor quality and at-risk incentive on management's earnings manipulation decision. It shows that the management has the incentive to smooth corporate earnings even when the employment contract is linear. It also demonstrates that adding the ability to manipulate earnings to the principal-agent model drastically changes the management's attitude towards risk. The management will become risk seeking in the company's earnings when cumulative earnings management in previous periods is high even if the management has a risk-averse utility function
SpreadDetect: Detection of spreading change in a network over time
Change-point analysis has been successfully applied to the detect changes in
multivariate data streams over time. In many applications, when data are
observed over a graph/network, change does not occur simultaneously but instead
spread from an initial source coordinate to the neighbouring coordinates over
time. We propose a new method, SpreadDetect, that estimates both the source
coordinate and the initial timepoint of change in such a setting. We prove that
under appropriate conditions, the SpreadDetect algorithm consistently estimates
both the source coordinate and the timepoint of change and that the minimal
signal size detectable by the algorithm is minimax optimal. The practical
utility of the algorithm is demonstrated through numerical experiments and a
COVID-19 real dataset.Comment: 26 pages,3 figures, 2 table
CoupleNet: Coupling Global Structure with Local Parts for Object Detection
The region-based Convolutional Neural Network (CNN) detectors such as Faster
R-CNN or R-FCN have already shown promising results for object detection by
combining the region proposal subnetwork and the classification subnetwork
together. Although R-FCN has achieved higher detection speed while keeping the
detection performance, the global structure information is ignored by the
position-sensitive score maps. To fully explore the local and global
properties, in this paper, we propose a novel fully convolutional network,
named as CoupleNet, to couple the global structure with local parts for object
detection. Specifically, the object proposals obtained by the Region Proposal
Network (RPN) are fed into the the coupling module which consists of two
branches. One branch adopts the position-sensitive RoI (PSRoI) pooling to
capture the local part information of the object, while the other employs the
RoI pooling to encode the global and context information. Next, we design
different coupling strategies and normalization ways to make full use of the
complementary advantages between the global and local branches. Extensive
experiments demonstrate the effectiveness of our approach. We achieve
state-of-the-art results on all three challenging datasets, i.e. a mAP of 82.7%
on VOC07, 80.4% on VOC12, and 34.4% on COCO. Codes will be made publicly
available.Comment: Accepted by ICCV 201
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