1,621 research outputs found
Policy gradient learning methods for stochastic control with exit time and applications to share repurchase pricing
We develop policy gradients methods for stochastic control with exit time in
a model-free setting. We propose two types of algorithms for learning either
directly the optimal policy or by learning alternately the value function
(critic) and the optimal control (actor). The use of randomized policies is
crucial for overcoming notably the issue related to the exit time in the
gradient computation. We demonstrate the effectiveness of our approach by
implementing our numerical schemes in the application to the problem of share
repurchase pricing. Our results show that the proposed policy gradient methods
outperform PDE or other neural networks techniques in a model-based setting.
Furthermore, our algorithms are flexible enough to incorporate realistic market
conditions like e.g. price impact or transaction costs.Comment: 19 pages, 6 figure
Generative modeling for time series via Schr{\"o}dinger bridge
We propose a novel generative model for time series based on Schr{\"o}dinger
bridge (SB) approach. This consists in the entropic interpolation via optimal
transport between a reference probability measure on path space and a target
measure consistent with the joint data distribution of the time series. The
solution is characterized by a stochastic differential equation on finite
horizon with a path-dependent drift function, hence respecting the temporal
dynamics of the time series distribution. We can estimate the drift function
from data samples either by kernel regression methods or with LSTM neural
networks, and the simulation of the SB diffusion yields new synthetic data
samples of the time series. The performance of our generative model is
evaluated through a series of numerical experiments. First, we test with a toy
autoregressive model, a GARCH Model, and the example of fractional Brownian
motion, and measure the accuracy of our algorithm with marginal and temporal
dependencies metrics. Next, we use our SB generated synthetic samples for the
application to deep hedging on real-data sets. Finally, we illustrate the SB
approach for generating sequence of images
Chemical Synthesis of Astatine Labelled ASCT2 Inhibitor Analogue (HA01) as a Radiotherapeutic Agent for Theranostics
https://openworks.mdanderson.org/sumexp22/1049/thumbnail.jp
UC-492 LotSpotter
The parking issue has quietly become the cause of a lot of stress for travelers and other regular users. It\u27s nothing new that some people miss their flight and/or get late to other important meetings and appointments because they couldnât locate an available parking lot. Not because there isn\u27t available parking but because they donât know where it is! What if there was some way to solve that? Introducing LotSpotter! An application built to detect and navigate to vacant parking spaces across the United States. It will leverage various technologies, including image processing, sensors, AI and mobile app development, to achieve its goal with frameworks such as OpenCV and processes from Amazon Web Services such as DynamoDB. Additionally, it will all be run through RaspberryPi to take advantage of GPS, and camera functionality! Users will be able to create accounts, reserve spaces, and much more. The days of being restricted by the struggles of metropolis are no more! LotSpotter is here
Sustainability and Maturation of School Turnaround: A Multiyear Evaluation of Tennesseeâs Achievement School District and Local Innovation Zones
Recent evaluations of reforms to improve low-performing schools have almost exclusively focused on shorter term effects. In this study, we extend the literature by examining the sustainability and maturation of two turnaround models in Tennessee: the state-led Achievement School District (ASD) and district-led local Innovation Zones (iZones). Using difference-in-differences models, we find overall positive effects on student achievement in iZone schools and null effects in ASD schools. Additional findings suggest a linkage between staff turnover and the effectiveness of reforms. ASD schools experienced high staff turnover in every cohort, and iZone schools faced high turnover in its latest cohort, the only one with negative effects. We discuss how differences in the ASD and iZone interventions may help explain variation in the schoolsâ ability to recruit and retain effective teachers and principals
Combined Drug Efficacy of EGFR and ASCT2 Inhibitors in Preclinical Models of Colorectal Cancer
https://openworks.mdanderson.org/sumexp22/1082/thumbnail.jp
CHO stable pool fed-batch process development of SARS-CoV-2 spike protein production
Please click Additional Files below to see the full abstract
Data Assimilation using a GPU Accelerated Path Integral Monte Carlo Approach
The answers to data assimilation questions can be expressed as path integrals
over all possible state and parameter histories. We show how these path
integrals can be evaluated numerically using a Markov Chain Monte Carlo method
designed to run in parallel on a Graphics Processing Unit (GPU). We demonstrate
the application of the method to an example with a transmembrane voltage time
series of a simulated neuron as an input, and using a Hodgkin-Huxley neuron
model. By taking advantage of GPU computing, we gain a parallel speedup factor
of up to about 300, compared to an equivalent serial computation on a CPU, with
performance increasing as the length of the observation time used for data
assimilation increases.Comment: 5 figures, submitted to Journal of Computational Physic
Comparison of combination of dimensionality reduction and classification techniques for identifying tree species using integrated QuickBird imagery and Lidar data
Florbetapir PET-assessed demyelination is associated with faster tau accumulation in an APOE Δ4-dependent manner
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