1,935 research outputs found
Behavior Based Manipulation
If investors are not fully rational, what can smart money do? This paper provides an example in which smart money can strategically take advantage of investors’ behavioral biases and manipulate the price process to make profit. The paper considers three types of traders, behavior-driven investors who have two behavioral biases (momentum trading and dispositional effect), arbitrageurs, and a manipulator who can influence asset prices. We show that, due to the investors’ behavioral biases and the limit of arbitrage, the manipulator can profit from a "pump and dump" trading strategy by accumulating the speculative asset while pushing the asset price up, and then selling the asset at high prices. Since nobody has private information, manipulation investigated here is completely trade-based. The paper also endogenously derives several asset pricing anomalies, including the high volatility of asset prices, momentum and reversal
Ray-tracing-based reconstruction algorithms for digital breast tomosynthesis
As a breast-imaging technique, digital breast tomosynthesis has great potential to improve the diagnosis of early breast cancer over mammography. Ray-tracing-based reconstruction algorithms, such as ray-tracing back projection, maximum-likelihood expectation maximization (MLEM), ordered-subset MLEM (OS-MLEM), and simultaneous algebraic reconstruction technique (SART), have been developed as reconstruction methods for different breast tomosynthesis systems. This paper provides a comparative study to investigate these algorithms by computer simulation and phantom study. Experimental results suggested that, among the four investigated reconstruction algorithms, OS-MLEM and SART performed better in interplane artifact removal with a fast speed convergence
Quantized passive filtering for switched delayed neural networks
The issue of quantized passive filtering for switched delayed neural networks with noise interference is studied in this paper. Both arbitrary and semi-Markov switching rules are taken into account. By choosing Lyapunov functionals and applying several inequality techniques, sufficient conditions are proposed to ensure the filter error system to be not only exponentially stable, but also exponentially passive from the noise interference to the output error. The gain matrix for the proposed quantized passive filter is able to be determined through the feasible solution of linear matrix inequalities, which are computationally tractable with the help of some popular convex optimization tools. Finally, two numerical examples are given to illustrate the usefulness of the quantized passive filter design methods
Acupoint Herbal Patching with or without Conventional Treatment for Stable Chronic Obstructive Pulmonary Disease: a Systematic Review of RCT
Life-Cycle Monitoring and Safety Evaluation of Critical Energy Infrastructure Using Full-Scale Distributed Optical Fiber Sensors
Improving the Generalizability of Trajectory Prediction Models with Frenet-Based Domain Normalization
Predicting the future trajectories of nearby objects plays a pivotal role in
Robotics and Automation such as autonomous driving. While learning-based
trajectory prediction methods have achieved remarkable performance on public
benchmarks, the generalization ability of these approaches remains
questionable. The poor generalizability on unseen domains, a well-recognized
defect of data-driven approaches, can potentially harm the real-world
performance of trajectory prediction models. We are thus motivated to improve
generalization ability of models instead of merely pursuing high accuracy on
average. Due to the lack of benchmarks for quantifying the generalization
ability of trajectory predictors, we first construct a new benchmark called
argoverse-shift, where the data distributions of domains are significantly
different. Using this benchmark for evaluation, we identify that the domain
shift problem seriously hinders the generalization of trajectory predictors
since state-of-the-art approaches suffer from severe performance degradation
when facing those out-of-distribution scenes. To enhance the robustness of
models against domain shift problem, we propose a plug-and-play strategy for
domain normalization in trajectory prediction. Our strategy utilizes the Frenet
coordinate frame for modeling and can effectively narrow the domain gap of
different scenes caused by the variety of road geometry and topology.
Experiments show that our strategy noticeably boosts the prediction performance
of the state-of-the-art in domains that were previously unseen to the models,
thereby improving the generalization ability of data-driven trajectory
prediction methods.Comment: This paper was accepted by 2023 IEEE International Conference on
Robotics and Automation (ICRA
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