Bespoke: A Block-Level Neural Network Optimization Framework for Low-Cost Deployment

As deep learning models become popular, there is a lot of need for deploying them to diverse device environments. Because it is costly to develop and optimize a neural network for every single environment, there is a line of research to search neural networks for multiple target environments efficiently. However, existing works for such a situation still suffer from requiring many GPUs and expensive costs. Motivated by this, we propose a novel neural network optimization framework named Bespoke for low-cost deployment. Our framework searches for a lightweight model by replacing parts of an original model with randomly selected alternatives, each of which comes from a pretrained neural network or the original model. In the practical sense, Bespoke has two significant merits. One is that it requires near zero cost for designing the search space of neural networks. The other merit is that it exploits the sub-networks of public pretrained neural networks, so the total cost is minimal compared to the existing works. We conduct experiments exploring Bespoke's the merits, and the results show that it finds efficient models for multiple targets with meager cost.Comment: This is the extended version of our AAAI-2023 paper (https://ojs.aaai.org/index.php/AAAI/article/view/26020

Cell and gene therapy regulatory, pricing, and reimbursement framework: With a focus on South Korea and the EU

Ever since relevant bioengineering technologies have sufficiently matured to the platformizable commercialization stage, a slew of money has flocked to the cell and gene therapy market over the last few years, resulting in an abundance of clinical studies in the field. Newer modalities have brought up a string of regulatory and legislative tasks, such as developing guidelines and legislative rules to systematically regulate newer pharmaceutical products. Accordingly, another layer of legislation and guidelines tailored for cell and gene therapies has been introduced and is expected to evolve on par with technological progress. Furthermore, authorities have shifted to pricing and reimbursement policies that can share risks for cost and outcome among stakeholders altogether, such as developers and the government, while expanding the accessibility of patients to innovative cell and gene therapies. This review attempts to capture the salient regulatory features of the cell and gene therapy market in the context of South Korea and the European Union and points out where two sovereign entities currently stand on each policy element and how each tackles regulatory challenges. We can observe the converging trend where regulatory, pricing and reimbursement rules of adjoining countries in the supranational union or member countries of a consortium are getting more aligned. Evidently, concerted efforts to share regulatory science knowledge and embrace reference pricing have played their parts. The authors argue that policy priorities should be placed on initiatives to harmonize with other medical authorities to better the rights of patients and clear out the uncertainties of developers, ultimately to share and advance regulatory science and layout forward-looking policies at opportune times

SRZoo: An integrated repository for super-resolution using deep learning

Deep learning-based image processing algorithms, including image super-resolution methods, have been proposed with significant improvement in performance in recent years. However, their implementations and evaluations are dispersed in terms of various deep learning frameworks and various evaluation criteria. In this paper, we propose an integrated repository for the super-resolution tasks, named SRZoo, to provide state-of-the-art super-resolution models in a single place. Our repository offers not only converted versions of existing pre-trained models, but also documentation and toolkits for converting other models. In addition, SRZoo provides platform-agnostic image reconstruction tools to obtain super-resolved images and evaluate the performance in place. It also brings the opportunity of extension to advanced image-based researches and other image processing models. The software, documentation, and pre-trained models are publicly available on GitHub.Comment: Accepted in ICASSP 2020, code available at https://github.com/idearibosome/srzo

Price increase negotiations to address drug shortages in South Korea’s national health insurance

South Korea has adopted a unique approach to address drug shortages by increasing reimbursement prices within its National Health Insurance Service. This study aims to analyze the characteristics, increase rates, affecting factors, and budget impacts of products that have increased price through the negotiation system. Between 2007 and 2022, there were price increase negotiations over 244 items. Of these, price increase negotiations were successful for 217 items, resulting in an agreement rate of 89%. The average rate of price increase for the agreed-upon products was 37.8%, and the overall budget increase for drugs with price increases (n = 217) was approximately 24.5%. Budget impact of each variable of the negotiated agreements showed that the number of negotiated agreement items was smaller after 2015 than before 2015, but each total budget impact (initial budget, increased budget, and final budget) and the average budget impact were higher. Although domestic companies accounted for a larger overall budget, the average budget per item was larger for multinational companies. The correlation analysis of the ratio of price increase and budget impact variables showed that the ratio of price increase was positively and significantly correlated with the increased budget, while it was negatively but not significantly correlated with the initial and final budgets. The South Korean model of increasing reimbursement prices in public insurance for drugs at risk of shortages serves as an exemplary case for not only securing patient access but also considering budget management