16 research outputs found
An intelligent modular real-time vision-based system for environment perception
A significant portion of driving hazards is caused by human error and
disregard for local driving regulations; Consequently, an intelligent
assistance system can be beneficial. This paper proposes a novel vision-based
modular package to ensure drivers' safety by perceiving the environment. Each
module is designed based on accuracy and inference time to deliver real-time
performance. As a result, the proposed system can be implemented on a wide
range of vehicles with minimum hardware requirements. Our modular package
comprises four main sections: lane detection, object detection, segmentation,
and monocular depth estimation. Each section is accompanied by novel techniques
to improve the accuracy of others along with the entire system. Furthermore, a
GUI is developed to display perceived information to the driver. In addition to
using public datasets, like BDD100K, we have also collected and annotated a
local dataset that we utilize to fine-tune and evaluate our system. We show
that the accuracy of our system is above 80% in all the sections. Our code and
data are available at
https://github.com/Pandas-Team/Autonomous-Vehicle-Environment-PerceptionComment: Accepted in NeurIPS 2022 Workshop on Machine Learning for Autonomous
Drivin
Practical Policy Optimization with Personalized Experimentation
Many organizations measure treatment effects via an experimentation platform
to evaluate the casual effect of product variations prior to full-scale
deployment. However, standard experimentation platforms do not perform
optimally for end user populations that exhibit heterogeneous treatment effects
(HTEs). Here we present a personalized experimentation framework, Personalized
Experiments (PEX), which optimizes treatment group assignment at the user level
via HTE modeling and sequential decision policy optimization to optimize
multiple short-term and long-term outcomes simultaneously. We describe an
end-to-end workflow that has proven to be successful in practice and can be
readily implemented using open-source software.Comment: 5 pages, 2 figure
Exploring the Efficacy of Pooled Stools in Fecal Microbiota Transplantation for Microbiota-Associated Chronic Diseases.
Fecal microbiota transplantation is being assessed as a treatment for chronic microbiota-related diseases such as ulcerative colitis. Results from an initial randomized trial suggest that remission rates depend on unobservable features of the fecal donors and observable features of the patients. We use mathematical modeling to assess the efficacy of pooling stools from different donors during multiple rounds of treatment. In the model, there are two types of patients and two types of donors, where the patient type is observable and the donor type (effective or not effective) is not observable. In the model, clinical outcomes from earlier rounds of treatment are used to estimate the current likelihood that each donor is effective, and then each patient in each round is treated by a pool of donors that are currently deemed to be the most effective. Relative to the no-pooling case, pools of size two or three significantly increase the proportion of patients in remission during the first several rounds of treatment. Although based on data from a single randomized trial, our modeling suggests that pooling of stools - via daily cycling of encapsulated stool from several different donors - may be beneficial in fecal microbiota transplantation for chronic microbiota-related diseases
A graphical depiction of Eq (12).
<p>A graphical depiction of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163956#pone.0163956.e034" target="_blank">Eq (12)</a>.</p
Results for the independence version of the problem with <i>D</i><sub>1</sub> = 5 initial donors.
<p>Results for the independence version of the problem with <i>D</i><sub>1</sub> = 5 initial donors.</p
Bayesian Denoising: From MAP to MMSE Using Consistent Cycle Spinning
We introduce a new approach for the implementation of minimum mean-square error (MMSE) denoising for signals with decoupled derivatives. Our method casts the problem as a penalized least-squares regression in the redundant wavelet domain. It exploits the link between the discrete gradient and Haar-wavelet shrinkage with cycle spinning. The redundancy of the representation implies that some wavelet-domain estimates are inconsistent with the underlying signal model. However, by imposing additional constraints, our method finds wavelet-domain solutions that are mutually consistent. We confirm the MMSE performance of our method through statistical estimation of Levy processes that have sparse derivatives
A tutorial on Thompson sampling
The objective of this tutorial is to explain when, why, and how to apply Thompson sampling