265 research outputs found
Draw-down Parisian ruin for spectrally negative L\'{e}vy process
In this paper we study the draw-down related Parisian ruin problem for
spectrally negative L\'{e}vy risk processes. We introduce the draw-down
Parisian ruin time and solve the corresponding two-sided exit time via
excursion theory. We also obtain an expression of the potential measure for the
process killed at the draw-down Parisian time. As applications, new results are
obtained for spectrally negative L\'{e}vy risk process with dividend barrier
and Parisian ruin
Network On Network for Tabular Data Classification in Real-world Applications
Tabular data is the most common data format adopted by our customers ranging
from retail, finance to E-commerce, and tabular data classification plays an
essential role to their businesses. In this paper, we present Network On
Network (NON), a practical tabular data classification model based on deep
neural network to provide accurate predictions. Various deep methods have been
proposed and promising progress has been made. However, most of them use
operations like neural network and factorization machines to fuse the
embeddings of different features directly, and linearly combine the outputs of
those operations to get the final prediction. As a result, the intra-field
information and the non-linear interactions between those operations (e.g.
neural network and factorization machines) are ignored. Intra-field information
is the information that features inside each field belong to the same field.
NON is proposed to take full advantage of intra-field information and
non-linear interactions. It consists of three components: field-wise network at
the bottom to capture the intra-field information, across field network in the
middle to choose suitable operations data-drivenly, and operation fusion
network on the top to fuse outputs of the chosen operations deeply. Extensive
experiments on six real-world datasets demonstrate NON can outperform the
state-of-the-art models significantly. Furthermore, both qualitative and
quantitative study of the features in the embedding space show NON can capture
intra-field information effectively
Herding Effect based Attention for Personalized Time-Sync Video Recommendation
Time-sync comment (TSC) is a new form of user-interaction review associated
with real-time video contents, which contains a user's preferences for videos
and therefore well suited as the data source for video recommendations.
However, existing review-based recommendation methods ignore the
context-dependent (generated by user-interaction), real-time, and
time-sensitive properties of TSC data. To bridge the above gaps, in this paper,
we use video images and users' TSCs to design an Image-Text Fusion model with a
novel Herding Effect Attention mechanism (called ITF-HEA), which can predict
users' favorite videos with model-based collaborative filtering. Specifically,
in the HEA mechanism, we weight the context information based on the semantic
similarities and time intervals between each TSC and its context, thereby
considering influences of the herding effect in the model. Experiments show
that ITF-HEA is on average 3.78\% higher than the state-of-the-art method upon
F1-score in baselines.Comment: ACCEPTED for ORAL presentation at IEEE ICME 201
Quality Assessment of Stereoscopic 360-degree Images from Multi-viewports
Objective quality assessment of stereoscopic panoramic images becomes a
challenging problem owing to the rapid growth of 360-degree contents. Different
from traditional 2D image quality assessment (IQA), more complex aspects are
involved in 3D omnidirectional IQA, especially unlimited field of view (FoV)
and extra depth perception, which brings difficulty to evaluate the quality of
experience (QoE) of 3D omnidirectional images. In this paper, we propose a
multi-viewport based fullreference stereo 360 IQA model. Due to the freely
changeable viewports when browsing in the head-mounted display (HMD), our
proposed approach processes the image inside FoV rather than the projected one
such as equirectangular projection (ERP). In addition, since overall QoE
depends on both image quality and depth perception, we utilize the features
estimated by the difference map between left and right views which can reflect
disparity. The depth perception features along with binocular image qualities
are employed to further predict the overall QoE of 3D 360 images. The
experimental results on our public Stereoscopic OmnidirectionaL Image quality
assessment Database (SOLID) show that the proposed method achieves a
significant improvement over some well-known IQA metrics and can accurately
reflect the overall QoE of perceived images
ChartDETR: A Multi-shape Detection Network for Visual Chart Recognition
Visual chart recognition systems are gaining increasing attention due to the
growing demand for automatically identifying table headers and values from
chart images. Current methods rely on keypoint detection to estimate data
element shapes in charts but suffer from grouping errors in post-processing. To
address this issue, we propose ChartDETR, a transformer-based multi-shape
detector that localizes keypoints at the corners of regular shapes to
reconstruct multiple data elements in a single chart image. Our method predicts
all data element shapes at once by introducing query groups in set prediction,
eliminating the need for further postprocessing. This property allows ChartDETR
to serve as a unified framework capable of representing various chart types
without altering the network architecture, effectively detecting data elements
of diverse shapes. We evaluated ChartDETR on three datasets, achieving
competitive results across all chart types without any additional enhancements.
For example, ChartDETR achieved an F1 score of 0.98 on Adobe Synthetic,
significantly outperforming the previous best model with a 0.71 F1 score.
Additionally, we obtained a new state-of-the-art result of 0.97 on
ExcelChart400k. The code will be made publicly available
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