1,557 research outputs found
Reflected BSDE driven by a marked point process with a convex/concave generator
In this paper, a class of reflected backward stochastic differential
equations (RBSDE) driven by a marked point process (MPP) with a convex/concave
generator is studied. Based on fixed point argument, -method and
truncation technique, the well-posedness of this kind of RBSDE with unbounded
terminal condition and obstacle is investigated. Besides, we present an
application on the pricing of American options via utility maximization, which
is solved by constructing an RBSDE with a convex generator.Comment: arXiv admin note: substantial text overlap with arXiv:2310.1472
Mean reflected BSDE driven by a marked point process and application in insurance risk management
This paper aims to solve a super-hedging problem along with insurance
re-payment under running risk management constraints. The initial endowment for
the super-heding problem is characterized by a class of mean reflected backward
stochastic differential equation driven by a marked point process (MPP) and a
Brownian motion. By Lipschitz assumptions on the generators and proper
integrability on the terminal value, we give the well-posedness of this kind of
BSDEs by combining a representation theorem with the fixed point argument.Comment: arXiv admin note: text overlap with arXiv:2310.1472
Quadratic exponential BSDEs driven by a marked point process
In this paper, the well-posedness of quadratic exponential backward
stochastic differential equations driven by marked point process (MPP) under
unbounded terminal condition is studied based on a fixed point argument,
-method and an approximation procedure. We also prove the solvability
of the mean reflected quadratic exponential backward stochastic differential
equations driven by marked point process via -method
Towards Grouping in Large Scenes with Occlusion-aware Spatio-temporal Transformers
Group detection, especially for large-scale scenes, has many potential
applications for public safety and smart cities. Existing methods fail to cope
with frequent occlusions in large-scale scenes with multiple people, and are
difficult to effectively utilize spatio-temporal information. In this paper, we
propose an end-to-end framework,GroupTransformer, for group detection in
large-scale scenes. To deal with the frequent occlusions caused by multiple
people, we design an occlusion encoder to detect and suppress severely occluded
person crops. To explore the potential spatio-temporal relationship, we propose
spatio-temporal transformers to simultaneously extract trajectory information
and fuse inter-person features in a hierarchical manner. Experimental results
on both large-scale and small-scale scenes demonstrate that our method achieves
better performance compared with state-of-the-art methods. On large-scale
scenes, our method significantly boosts the performance in terms of precision
and F1 score by more than 10%. On small-scale scenes, our method still improves
the performance of F1 score by more than 5%. The project page with code can be
found at http://cic.tju.edu.cn/faculty/likun/projects/GroupTrans.Comment: 11 pages, 5 figure
Investor Attention and Crowdfunding Performance
Today's digital era facilitates the rise of crowdfunding markets by allowing entrepreneurs to seek funding directly from crowds. Crowdfunding, as IT-enabled disintermediation, lowers entry barriers for crowds to invest in business projects and entrepreneurs to obtain funding, yet may exacerbate information asymmetry and absorb investor attention to process information about the potential projects. We develop a model wherein investors with limited attention aggregate personalized information about (reward-based) crowdfunding projects and conduct comparative analyses on how rises in investors’ unit attention cost (associated with greater distractions) affect investor attention, investment decisions, and crowdfunding performance. We then exploit a novel measure of distraction---news pressure---to test the effects of distraction on investor engagement and crowdfunding performance empirically, and the results support our model predictions
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