Density Deconvolution with Normalizing Flows

Density deconvolution is the task of estimating a probability density function given only noise-corrupted samples. We can fit a Gaussian mixture model to the underlying density by maximum likelihood if the noise is normally distributed, but would like to exploit the superior density estimation performance of normalizing flows and allow for arbitrary noise distributions. Since both adjustments lead to an intractable likelihood, we resort to amortized variational inference. We demonstrate some problems involved in this approach, however, experiments on real data demonstrate that flows can already out-perform Gaussian mixtures for density deconvolution.Comment: Appearing at the second workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models (ICML 2020), Virtual Conference. 8 pages, 6 figures, 5 table

Game State and Action Abstracting Monte Carlo Tree Search for General Strategy Game-Playing

When implementing intelligent agents for strategy games, we observe that search-based methods struggle with the complexity of such games. To tackle this problem, we propose a new variant of Monte Carlo Tree Search which can incorporate action and game state abstractions. Focusing on the latter, we developed a game state encoding for turn-based strategy games that allows for a flexible abstraction. Using an optimization procedure, we optimize the agent's action and game state abstraction to maximize its performance against a rule-based agent. Furthermore, we compare different combinations of abstractions and their impact on the agent's performance based on the Kill the King game of the Stratega framework. Our results show that action abstractions have improved the performance of our agent considerably. Contrary, game state abstractions have not shown much impact. While these results may be limited to the tested game, they are in line with previous research on abstractions of simple Markov Decision Processes. The higher complexity of strategy games may require more intricate methods, such as hierarchical or time-based abstractions, to further improve the agent's performance

Generating Diverse and Competitive Play-Styles for Strategy Games

Designing agents that are able to achieve different play-styles while maintaining a competitive level of play is a difficult task, especially for games for which the research community has not found super-human performance yet, like strategy games. These require the AI to deal with large action spaces, long-term planning and partial observability, among other well-known factors that make decision-making a hard problem. On top of this, achieving distinct play-styles using a general algorithm without reducing playing strength is not trivial. In this paper, we propose Portfolio Monte Carlo Tree Search with Progressive Unpruning for playing a turn-based strategy game (Tribes) and show how it can be parameterized so a quality-diversity algorithm (MAP-Elites) is used to achieve different play-styles while keeping a competitive level of play. Our results show that this algorithm is capable of achieving these goals even for an extensive collection of game levels beyond those used for training

Prioritizing persons deprived of liberty in global guidelines for tuberculosis preventive treatment

In this Policy Forum piece, Aditya Narayan and colleagues discuss the challenges and opportunities for tuberculosis preventive treatment in carceral settings

Dedifferentiation of Human Primary Thyrocytes into Multilineage Progenitor Cells without Gene Introduction

While identification and isolation of adult stem cells have potentially important implications, recent reports regarding dedifferentiation/reprogramming from differentiated cells have provided another clue to gain insight into source of tissue stem/progenitor cells. In this study, we developed a novel culture system to obtain dedifferentiated progenitor cells from normal human thyroid tissues. After enzymatic digestion, primary thyrocytes, expressing thyroglobulin, vimentin and cytokeratin-18, were cultured in a serum-free medium called SAGM. Although the vast majority of cells died, a small proportion (∼0.5%) survived and proliferated. During initial cell expansion, thyroglobulin/cytokeratin-18 expression was gradually declined in the proliferating cells. Moreover, sorted cells expressing thyroid peroxidase gave rise to proliferating clones in SAGM. These data suggest that those cells are derived from thyroid follicular cells or at least thyroid-committed cells. The SAGM-grown cells did not express any thyroid-specific genes. However, after four-week incubation with FBS and TSH, cytokeratin-18, thyroglobulin, TSH receptor, PAX8 and TTF1 expressions re-emerged. Moreover, surprisingly, the cells were capable of differentiating into neuronal or adipogenic lineage depending on differentiating conditions. In summary, we have developed a novel system to generate multilineage progenitor cells from normal human thyroid tissues. This seems to be achieved by dedifferentiation of thyroid follicular cells. The presently described culture system may be useful for regenerative medicine, but the primary importance will be as a tool to elucidate the mechanisms of thyroid diseases

Multi-center real-world comparison of the fully automated Idylla (TM) microsatellite instability assay with routine molecular methods and immunohistochemistry on formalin-fixed paraffin-embedded tissue of colorectal cancer

Microsatellite instability (MSI) is present in 15-20% of primary colorectal cancers. MSI status is assessed to detect Lynch syndrome, guide adjuvant chemotherapy, determine prognosis, and use as a companion test for checkpoint blockade inhibitors. Traditionally, MSI status is determined by immunohistochemistry or molecular methods. The Idylla (TM) MSI Assay is a fully automated molecular method (including automated result interpretation), using seven novel MSI biomarkers (ACVR2A, BTBD7, DIDO1, MRE11, RYR3, SEC31A, SULF2) and not requiring matched normal tissue. In this real-world global study, 44 clinical centers performed Idylla (TM) testing on a total of 1301 archived colorectal cancer formalin-fixed, paraffin-embedded (FFPE) tissue sections and compared Idylla (TM) results against available results from routine diagnostic testing in those sites. MSI mutations detected with the Idylla (TM) MSI Assay were equally distributed over the seven biomarkers, and 84.48% of the MSI-high samples had >= 5 mutated biomarkers, while 98.25% of the microsatellite-stable samples had zero mutated biomarkers. The concordance level between the Idylla (TM) MSI Assay and immunohistochemistry was 96.39% (988/1025); 17/37 discordant samples were found to be concordant when a third method was used. Compared with routine molecular methods, the concordance level was 98.01% (789/805); third-method analysis found concordance for 8/16 discordant samples. The failure rate of the Idylla (TM) MSI Assay (0.23%; 3/1301) was lower than that of referenced immunohistochemistry (4.37%; 47/1075) or molecular assays (0.86%; 7/812). In conclusion, lower failure rates and high concordance levels were found between the Idylla (TM) MSI Assay and routine tests.Peer reviewe