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 couldn’t locate an available parking lot. Not because there isn\u27t available parking but because they don’t 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 Tennessee’s 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 schools’ 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