A mathematical framework for operational fine tunings

In the framework of ontological models, the features of quantum mechanics that emerge as inherently nonclassical always involve properties that are fine tuned, i.e. properties that hold at the operational level but break at the ontological level (they only hold for fine tuned values of the ontic parameters). Famous examples of such features are contextuality and nonlocality. We here develop a precise theory-independent mathematical framework for characterizing operational fine tunings. These are distinct from causal fine tunings - already introduced by Wood and Spekkens in [NJP,17 033002(2015)] - as they do not involve any assumption on the underlying causal structure. We show how all the already known examples of operational fine tunings fit into our framework, we discuss possibly new fine tunings and we use the framework to shed new light on the relation between nonlocality and generalized contextuality, where the former can involve a causal fine tuning too, unlike the latter. The framework is also formulated in the language of category theory and functors.Comment: 26 pages, 6 figure

Spike voltage topography in temporal lobe epilepsy

We investigated the voltage topography of interictal spikes in patients with temporal lobe epilepsy (TLE) to see whether topography was related to etiology for TLE. Adults with TLE, who had epilepsy surgery for drug-resistant seizures from 2011 until 2014 at Jefferson Comprehensive Epilepsy Center were selected. Two groups of patients were studied: patients with mesial temporal sclerosis (MTS) on MRI and those with other MRI findings. The voltage topography maps of the interictal spikes at the peak were created using BESA software. We classified the interictal spikes as polar, basal, lateral, or others. Thirty-four patients were studied, from which the characteristics of 340 spikes were investigated. The most common type of spike orientation was others (186 spikes; 54.7%), followed by lateral (146; 42.9%), polar (5; 1.5%), and basal (3; 0.9%). Characteristics of the voltage topography maps of the spikes between the two groups of patients were somewhat different. Five spikes in patients with MTS had polar orientation, but none of the spikes in patients with other MRI findings had polar orientation (odds ratio = 6.98, 95% confidence interval = 0.38 to 127.38; p = 0.07). Scalp topographic mapping of interictal spikes has the potential to offer different information than visual inspection alone. The present results do not allow an immediate clinical application of our findings; however, detecting a polar spike in a patient with TLE may increase the possibility of mesial temporal sclerosis as the underlying etiology

Session D, 2017 Third Place: The Effects of Sunscreen on Photosynthetic Filamentous Algae

Algae are photosynthetic, often single celled aquatic organisms which serve as one of the most basic prey organisms in a given ecosystem. They are susceptible to changes in the aquatic ecosystem, and when chemicals found in sunscreen are introduced, they may be affected. Based upon a previous study we hypothesize that filamentous algae will have a greater rate of photosynthesis in water where sunscreen is present as compared to water where sunscreen is not present due to the lack of UV light penetration. For this experiment, we collected algae from South Bay, and placed 4 mL samples into 118mL jars, with one control group and three experimental groups of varying sunscreen concentrate. The change in dissolved oxygen was recorded. We ran this test in outside conditions, and once under a UV and sun lamp. We then repeated the set of tests with less sunscreen to account for a potential error in light penetration, as well as a trial without UV to make sure it was in fact UV that impacted photosynthesis. Using ANOVA and Tukey statistical tests our results showed that our hypothesis was not supported, but there were other trends that were noticeable. This shows that sunscreen has a negative effect on algae photosynthesis

Control Force Compensation in Ground-Based Flight Simulators

This paper presents the results of a study that investigated if controller force compensations accounting for the inertial force and moment due to the aircraft motion during flight have a significant effect on pilot control behavior and performance. Seven rotorcraft pilots performed a side-step and precision hovering task in light turbulence in the Vertical Motion Simulator. The effects of force compensation were examined for two different simulated rotorcraft: linear and UH-60 dynamics with two different force gradient of the lateral stick control. Four motion configurations were used: large motion, hexapod motion, fixed-base motion, and fixed-base motion with compensation. Control-input variables and task performance such as the time to translate to the designated hover position, station-keeping position errors, and handling qualities ratings were used as measures. Control force compensation enabled pilot control behavior and performance more similar to that under high- or medium-fidelity motion to some extent only. Control force compensation did not improve overall task performance considering both rotorcraft models at the same time. The control force compensation had effects on the linear model with lighter force gradient, but only a minimal effect on pilots? control behavior and task performance for the UH-60 model, which had a higher force gradient. This suggests that the control force compensation has limited benefits for controllers that have higher stiffness

FAStEN: an efficient adaptive method for feature selection and estimation in high-dimensional functional regressions

Functional regression analysis is an established tool for many contemporary scientific applications. Regression problems involving large and complex data sets are ubiquitous, and feature selection is crucial for avoiding overfitting and achieving accurate predictions. We propose a new, flexible, and ultra-efficient approach to perform feature selection in a sparse high dimensional function-on-function regression problem, and we show how to extend it to the scalar-on-function framework. Our method combines functional data, optimization, and machine learning techniques to perform feature selection and parameter estimation simultaneously. We exploit the properties of Functional Principal Components, and the sparsity inherent to the Dual Augmented Lagrangian problem to significantly reduce computational cost, and we introduce an adaptive scheme to improve selection accuracy. Through an extensive simulation study, we benchmark our approach to the best existing competitors and demonstrate a massive gain in terms of CPU time and selection performance without sacrificing the quality of the coefficients' estimation. Finally, we present an application to brain fMRI data from the AOMIC PIOP1 study

Deep-Learning for Classification of Colorectal Polyps on Whole-Slide Images

Histopathological characterization of colorectal polyps is an important principle for determining the risk of colorectal cancer and future rates of surveillance for patients. This characterization is time-intensive, requires years of specialized training, and suffers from significant inter-observer and intra-observer variability. In this work, we built an automatic image-understanding method that can accurately classify different types of colorectal polyps in whole-slide histology images to help pathologists with histopathological characterization and diagnosis of colorectal polyps. The proposed image-understanding method is based on deep-learning techniques, which rely on numerous levels of abstraction for data representation and have shown state-of-the-art results for various image analysis tasks. Our image-understanding method covers all five polyp types (hyperplastic polyp, sessile serrated polyp, traditional serrated adenoma, tubular adenoma, and tubulovillous/villous adenoma) that are included in the US multi-society task force guidelines for colorectal cancer risk assessment and surveillance, and encompasses the most common occurrences of colorectal polyps. Our evaluation on 239 independent test samples shows our proposed method can identify the types of colorectal polyps in whole-slide images with a high efficacy (accuracy: 93.0%, precision: 89.7%, recall: 88.3%, F1 score: 88.8%). The presented method in this paper can reduce the cognitive burden on pathologists and improve their accuracy and efficiency in histopathological characterization of colorectal polyps, and in subsequent risk assessment and follow-up recommendations