Multistage Robust Mixed-Integer Optimization with Adaptive Partitions

We present a new partition-and-bound method for multistage adaptive mixed-integer optimization (AMIO) problems that extends previous work on finite adaptability. The approach analyzes the optimal solution to a static (nonadaptive) version of an AMIO problem to gain insight into which regions of the uncertainty set are restricting the objective function value. We use this information to construct partitions in the uncertainty set, leading to a finitely adaptable formulation of the problem. We use the same information to determine a lower bound on the fully adaptive solution. The method repeats this process iteratively to further improve the objective until a desired gap is reached. We provide theoretical motivation for this method, and characterize its convergence properties and the growth in the number of partitions. Using these insights, we propose and evaluate enhancements to the method such as warm starts and smarter partition creation. We describe in detail how to apply finite adaptability to multistage AMIO problems to appropriately address nonanticipativity restrictions. Finally, we demonstrate in computational experiments that the method can provide substantial improvements over a nonadaptive solution and existing methods for problems described in the literature. In particular, we find that our method produces high-quality solutions versus the amount of computational effort, even as the problem scales in the number of time stages and the number of decision variables

Reusing routinely collected clinical data for medical device surveillance

Background Following the public healthcare scandal surrounding Poly Implant Prothèse breast implants, there is increased focus on the surveillance of medical devices. A number of clinical specialties in the UK prospectively collect clinical data on procedures performed. We explore a surveillance programme in the case study of prosthetic aortic valve heart implants, reusing routinely collected data. Methods Demographic, comorbidity and operative pseudonymised data from the UK National Adult Cardiac Surgery Audit registry were extracted for all patients undergoing an aortic valve replacement (AVR) operation from 1998-onwards. Rules were developed to classify implants, recorded as free-text, by manufacturer, series, model and prosthesis type, and cleaning algorithms applied to the dataset. Patient outcomes are assessed across implants. Long-term mortality follow-up was tracked by record linkage to the Office for National Statistics death register, and surgical re-intervention tracked by reoccurrence in the registry. Results Data on 95,000 AVR operations were extracted. Prosthetic implants were classified into 97 models from ten manufacturers. There were substantial differences in implant volumes by manufacturers, deconstructed into temporal trends, prosthesis type and models, and healthcare providers. Significant differences were observed in outcomes between models. These differences are influenced by case-mix selection bias. Conclusion Reuse of routinely collected clinical data for medical device surveillance is viable and economically effective. Data collected, when properly analysed, can potentially be used to detect inferior devices, inform manufacturers and clinicians of device quality, supplement research, facilitate development of (inter-) national clinical guidelines for implant choice and inform businesses and healthcare procurement officers about market access. Linkage to other routinely collected data, including Hospital Episode Statistics, product data and other audits, offer richer surveillance capabilities

The Hanabi Challenge: A New Frontier for AI Research

From the early days of computing, games have been important testbeds for studying how well machines can do sophisticated decision making. In recent years, machine learning has made dramatic advances with artificial agents reaching superhuman performance in challenge domains like Go, Atari, and some variants of poker. As with their predecessors of chess, checkers, and backgammon, these game domains have driven research by providing sophisticated yet well-defined challenges for artificial intelligence practitioners. We continue this tradition by proposing the game of Hanabi as a new challenge domain with novel problems that arise from its combination of purely cooperative gameplay with two to five players and imperfect information. In particular, we argue that Hanabi elevates reasoning about the beliefs and intentions of other agents to the foreground. We believe developing novel techniques for such theory of mind reasoning will not only be crucial for success in Hanabi, but also in broader collaborative efforts, especially those with human partners. To facilitate future research, we introduce the open-source Hanabi Learning Environment, propose an experimental framework for the research community to evaluate algorithmic advances, and assess the performance of current state-of-the-art techniques.Comment: 32 pages, 5 figures, In Press (Artificial Intelligence

Proteinase-activated receptor 2 modulates OA-related pain, cartilage and bone pathology

Objective Proteinase-activated receptor 2 (PAR2) deficiency protects against cartilage degradation in experimental osteoarthritis (OA). The wider impact of this pathway upon OA-associated pathologies such as osteophyte formation and pain is unknown. Herein, we investigated early temporal bone and cartilage changes in experimental OA in order to further elucidate the role of PAR2 in OA pathogenesis. Methods OA was induced in wild-type (WT) and PAR2-deficient (PAR2−/−) mice by destabilisation of the medial meniscus (DMM). Inflammation, cartilage degradation and bone changes were monitored using histology and microCT. In gene rescue experiments, PAR2−/− mice were intra-articularly injected with human PAR2 (hPAR2)-expressing adenovirus. Dynamic weight bearing was used as a surrogate of OA-related pain. Results Osteophytes formed within 7 days post-DMM in WT mice but osteosclerosis was only evident from 14 days post induction. Importantly, PAR2 was expressed in the proliferative/hypertrophic chondrocytes present within osteophytes. In PAR2−/− mice, osteophytes developed significantly less frequently but, when present, were smaller and of greater density; no osteosclerosis was observed in these mice up to day 28. The pattern of weight bearing was altered in PAR2−/− mice, suggesting reduced pain perception. The expression of hPAR2 in PAR2−/− mice recapitulated osteophyte formation and cartilage damage similar to that observed in WT mice. However, osteosclerosis was absent, consistent with lack of hPAR2 expression in subchondral bone. Conclusions This study clearly demonstrates PAR2 plays a critical role, via chondrocytes, in osteophyte development and subchondral bone changes, which occur prior to PAR2-mediated cartilage damage. The latter likely occurs independently of OA-related bone changes