Impulse control problem on finite horizon with execution delay

We consider impulse control problems in finite horizon for diffusions with decision lag and execution delay. The new feature is that our general framework deals with the important case when several consecutive orders may be decided before the effective execution of the first one. This is motivated by financial applications in the trading of illiquid assets such as hedge funds. We show that the value functions for such control problems satisfy a suitable version of dynamic programming principle in finite dimension, which takes into account the past dependence of state process through the pending orders. The corresponding Bellman partial differential equations (PDE) system is derived, and exhibit some peculiarities on the coupled equations, domains and boundary conditions. We prove a unique characterization of the value functions to this nonstandard PDE system by means of viscosity solutions. We then provide an algorithm to find the value functions and the optimal control. This easily implementable algorithm involves backward and forward iterations on the domains and the value functions, which appear in turn as original arguments in the proofs for the boundary conditions and uniqueness results

Hybrid Rocket Engine Ignition and Control

Control of a hybrid rocket engine is dependent upon a robust system capable of executing commands at precise times. In order to accomplish this, hardware systems must be in place to control the flow of a pressurized gas and provide feedback to launch site personnel. Through the use of solenoid valves and wireless transceivers, control over the thrust of a rocket can be accomplished. In order to understand this information and provide a user-friendly interface to complete this, a launch control module is used. Through the combined capabilities of the two system it becomes possible to test and launch a hybrid engine rocket in a safe and efficient manner

Study of meta-analysis strategies for network inference using information-theoretic approaches

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Reverse engineering of gene regulatory networks (GRNs) from gene expression data is a classical challenge in systems biology. Thanks to high-throughput technologies, a massive amount of gene-expression data has been accumulated in the public repositories. Modelling GRNs from multiple experiments (also called integrative analysis) has; therefore, naturally become a standard procedure in modern computational biology. Indeed, such analysis is usually more robust than the traditional approaches focused on individual datasets, which typically suffer from some experimental bias and a small number of samples. To date, there are mainly two strategies for the problem of interest: the first one (”data merging”) merges all datasets together and then infers a GRN whereas the other (”networks ensemble”) infers GRNs from every dataset separately and then aggregates them using some ensemble rules (such as ranksum or weightsum). Unfortunately, a thorough comparison of these two approaches is lacking. In this paper, we evaluate the performances of various metaanalysis approaches mentioned above with a systematic set of experiments based on in silico benchmarks. Furthermore, we present a new meta-analysis approach for inferring GRNs from multiple studies. Our proposed approach, adapted to methods based on pairwise measures such as correlation or mutual information, consists of two steps: aggregating matrices of the pairwise measures from every dataset followed by extracting the network from the meta-matrix.Peer ReviewedPostprint (author's final draft

Distributionally Robust Classification on a Data Budget

Real world uses of deep learning require predictable model behavior under distribution shifts. Models such as CLIP show emergent natural distributional robustness comparable to humans, but may require hundreds of millions of training samples. Can we train robust learners in a domain where data is limited? To rigorously address this question, we introduce JANuS (Joint Annotations and Names Set), a collection of four new training datasets with images, labels, and corresponding captions, and perform a series of carefully controlled investigations of factors contributing to robustness in image classification, then compare those results to findings derived from a large-scale meta-analysis. Using this approach, we show that standard ResNet-50 trained with the cross-entropy loss on 2.4 million image samples can attain comparable robustness to a CLIP ResNet-50 trained on 400 million samples. To our knowledge, this is the first result showing (near) state-of-the-art distributional robustness on limited data budgets. Our dataset is available at \url{https://huggingface.co/datasets/penfever/JANuS_dataset}, and the code used to reproduce our experiments can be found at \url{https://github.com/penfever/vlhub/}.Comment: TMLR 2023; openreview link: https://openreview.net/forum?id=D5Z2E8CNs

Team One Carbon Catcher Design Report

Overview The burning of fossil fuels largely contributes to the increase of CO2 in the atmosphere. The US Department of Transportation alone contributed almost 6 million metric tons of carbon dioxide emissions in 2018 (EIA). Due to this, this report proposes recycling captured CO2 into a base for cleaner burning fuel in order to reduce emissions from the transportation industry and many others, which has the potential to impact many areas. Extraction of atmospheric CO2 is possible through a membrane filtration system based on traditional nitrogen generation. The passive filtration system autonomously separates the CO2 from other air components, thereby reducing energy consumption. The system's working sensors and actuators utilize similar energy saving strategies, such as distributing cloud-computing services over multiple servers and mainframes to reduce computing power. The movement of air is directed by a scalable fan device, which is presented as a modular design to allow customization of fan parts to specific size and installation requirements. As an integrated device, Team 1’s Carbon Catcher operates with a high efficiency in order to maximize the commercial opportunity of converting captured CO2 into cleaner fuel while also reducing CO2 emissions and the greenhouse effect. Goal The goal of Team 1’s Carbon Catcher project proposal is to design a cost-effective, scalable, and modular atmospheric carbon dioxide removal system that is capable of being utilized in a range of urban environments and may fit a variety of different customer requirements or requests