Generalized Backward Induction: Justification for a Folk Algorithm

I introduce axiomatically infinite sequential games that extend Kuhn’s classical framework. Infinite games allow for (a) imperfect information, (b) an infinite horizon, and (c) infinite action sets. A generalized backward induction (GBI) procedure is defined for all such games over the roots of subgames. A strategy profile that survives backward pruning is called a backward induction solution (BIS). The main result of this paper finds that, similar to finite games of perfect information, the sets of BIS and subgame perfect equilibria (SPE) coincide for both pure strategies and for behavioral strategies that satisfy the conditions of finite support and finite crossing. Additionally, I discuss five examples of well-known games and political economy models that can be solved with GBI but not classic backward induction (BI). The contributions of this paper include (a) the axiomatization of a class of infinite games, (b) the extension of backward induction to infinite games, and (c) the proof that BIS and SPEs are identical for infinite games

Deep ensemble model for segmenting microscopy images in the presence of limited labeled data

Obtaining large amounts of high quality labeled microscopy data is expensive and time-consuming. To overcome this issue, we propose a deep ensemble model which aims to utilise limited labeled training data. We train multiple identical Convolutional Neural Network (CNN) segmentation models on training data that is partitioned into folds in two steps. First, the data is split based on sample diversity or expert knowledge reflecting the possible {\em modes} of the underlying data distribution. In the second step, these partitions are split into random folds like in a cross-validation setting. Segmentation models based on the U-net architecture are trained on each of these resulting folds yielding the candidate models for our deep ensemble model which are aggregated to obtain the final prediction. The proposed deep ensemble model is compared to relevant baselines, in their ability to segment interneurons in microscopic images of mice spinal cord, showing improved performance on an independent test set

Deep ensemble model for segmenting microscopy images in the presence of limited labeled data

Obtaining large amounts of high quality labeled microscopy data is expensive and time-consuming. To overcome this issue, we propose a deep ensemble model which aims to utilise limited labeled training data. We train multiple identical Convolutional Neural Network (CNN) segmentation models on training data that is partitioned into folds in two steps. First, the data is split based on sample diversity or expert knowledge reflecting the possible {\em modes} of the underlying data distribution. In the second step, these partitions are split into random folds like in a cross-validation setting. Segmentation models based on the U-net architecture are trained on each of these resulting folds yielding the candidate models for our deep ensemble model which are aggregated to obtain the final prediction. The proposed deep ensemble model is compared to relevant baselines, in their ability to segment interneurons in microscopic images of mice spinal cord, showing improved performance on an independent test set

Arterial stiffness and the non-dipping pattern in type 1 diabetes males with and without erectile dysfunction

Abstract Arterial stiffness (AS) and non-dipping pattern are early predictors of cardiovascular diseases but are not used in clinical practice. We aimed to assess if AS and the non-dipping pattern are more prevalent in the erectile dysfunction (ED) group than in the non-ED group among subjects with type 1 diabetes (T1DM). The study group consisted of adults with T1DM. Aortic pulse wave velocity (PWV Ao)—a marker of increased AS, central systolic blood pressure, and heart rate (HR) were measured with a brachial oscillometric device (Arteriograph 24). Erectile dysfunction (ED) was assessed by the International Index of Erectile Function-5. A comparison between the groups with and without ED was performed. Of 34 investigated men with T1DM, 12 (35.3%) suffered from ED. The group with ED had higher mean 24 h HR (77.7 [73.7–86.5] vs 69.9 [64.0–76.8]/min; p = 0.04, nighttime PWV Ao (8.1 [6.8–8.5] vs 6.8 [6.1–7.5] m/s; p = 0.015) and prevalence of non-dipping SBP Ao pattern (11 [91.7] vs 12 [54.5]%; p = 0.027) than individuals without ED. The presence of ED detected a central non-dipping pattern with a sensitivity of 47.8% and a specificity of 90.9%. The central non-dipping pattern was more prevalent and the nighttime PWV was higher in T1DM subjects with ED than in those without ED