clinicaltrials.gov Identifier: NCT00091637.

ABSTRACT

Diabetic macular edema (DME), being a frequent manifestation of DR, disrupts the retinal symmetry. This event is particularly triggered by vascular endothelial growth factors (VEGF). Intravitreal injections of anti-VEGFs have been the most practiced treatment but an expensive option. A major challenge associated with this treatment is determining an optimal treatment regimen and differentiating patients who do not respond to anti-VEGF. As it has a significant burden for both the patient and the health care providers if the patient is not responding, any clinically acceptable method to predict the treatment outcomes holds huge value in the efficient management of DME. In such situations, artificial intelligence (AI) or machine learning (ML)-based algorithms come useful as they can analyze past clinical details of the patients and help clinicians to predict the patient's response to an anti-VEGF agent. The work presented here attempts to review the literature that is available from the peer research community to discuss solutions provided by AI/ML methodologies to tackle challenges in DME management. Lastly, a possibility for using two different types of data has been proposed, which is believed to be the key differentiators as compared to the similar and recent contributions from the peer research community.

The problem of creating efficient mappings of dataflow graphs onto specific architectures (i.e., solving the place and route problem) is incredibly challenging. The difficulty is especially acute in the area of Coarse-Grained Reconfigurable Architectures (CGRAs) to the extent that solving the mapping problem may remove a significant bottleneck to adoption. We believe that the next generation of mapping algorithms will exhibit pattern recognition, the ability to learn from experience, and identification of creative solutions, all of which are human characteristics. This manuscript describes our game UNTANGLED, developed and fine-tuned over the course of a year to allow us to capture and analyze human mapping strategies. It also describes our results to date. We find that the mapping problem can be crowdsourced very effectively, that players can outperform existing algorithms, and that successful player strategies share many elements in common. Based on our observations and analysis, we make concrete recommendations for future research directions for mapping onto CGRAs.

One of the grand challenges in the design of portable/wearable electronics is to achieve optimal efficiency and flexibility in a tiny low power package. Coarse grained reconfigurable architectures (CGRAs) hold great promise for low power, high performance, and flexible designs for a domain of applications. CGRAs are very promising due to the ability to highly customize such architectures to an application domain. However, greater customization makes the mapping of applications onto these architectures very challenging. Good tools and fast, effective mapping algorithms are needed to support design space exploration for CGRAs. In particular, the mapping problem has been difficult to solve in a satisfying and general way. In this paper, we present an architectural design flow using crowdsourcing to provide mappings of benchmarks onto new architectures. We show that the crowd can provide high quality, reliable mappings, significantly outperforming our custom Simulated Annealing algorithm in almost all cases. We further show that the crowd can provide other types of feedback that are difficult to obtain from an automatic mapping algorithm. Our proof of concept cross-architectural study supports an 8Way or 4Way1Hop architecture as a top choice, concludes that a custom modification that constrains inputs and outputs consumes less energy but requires more area than its less constrained counterpart, and suggests that Stripe architectures are interesting to consider because they perform nearly as well as our mesh variants and may present a more straightforward mapping problem for the crowd or an automatic mapping algorithm.