Moving the Chains: Why the National NLRB\u27s Affirmation of the Decision in Northwestern v. Capa Is Necessary to Best Protect Collegiate Athletes

(Excerpt) This Note argues that the NLRB erred in their decision to decline to assert jurisdiction. Instead, it stresses that athletes should be allowed to unionize and collectively bargain, so they will finally receive the protections they need, both medical and financial. Without unionization, progress can be made, but a solution cannot be realized. This note analyzes the effect of Northwestern v. CAPA on the “student-athlete.” Part I of the Note discusses the relevant labor law. Part II examines the Northwestern case and the reasoning behind the decision of the NLRB Regional Director in Chicago. Part III discusses the advancements in player safety and compensation that have occurred since Northwestern and why those changes have not solved the problem. Part IV discusses counterarguments to athlete unionization and refutes those arguments. Finally, Part V proposes a scenario where the NCAA amicably negotiates an employment agreement with a college athlete player’s union and what items must be a part of that contract. By taking these measures, the NCAA can address the most immediate athlete needs, while preserving the appeal of amateurism

Nullifying the Inherent Bias of Non-invariant Exploratory Landscape Analysis Features

Exploratory landscape analysis (ELA) in single-objective black-box optimization relies on a comprehensive and large set of numerical features characterizing problem instances. Those foster problem understanding and serve as basis for constructing automated algorithm selection models choosing the best suited algorithm for a problem at hand based on the aforementioned features computed prior to optimization. This work specifically points to the sensitivity of a substantial proportion of these features to absolute objective values, i.e., we observe a lack of shift and scale invariance. We show that this unfortunately induces bias within automated algorithm selection models, an overfitting to specific benchmark problem sets used for training and thereby hinders generalization capabilities to unseen problems. We tackle these issues by presenting an appropriate objective normalization to be used prior to ELA feature computation and empirically illustrate the respective effectiveness focusing on the BBOB benchmark set.</p

Exploratory Landscape Analysis for Mixed-Variable Problems

Exploratory landscape analysis and fitness landscape analysis in general have been pivotal in facilitating problem understanding, algorithm design and endeavors such as automated algorithm selection and configuration. These techniques have largely been limited to search spaces of a single domain. In this work, we provide the means to compute exploratory landscape features for mixed-variable problems where the decision space is a mixture of continuous, binary, integer, and categorical variables. This is achieved by utilizing existing encoding techniques originating from machine learning. We provide a comprehensive juxtaposition of the results based on these different techniques. To further highlight their merit for practical applications, we design and conduct an automated algorithm selection study based on a hyperparameter optimization benchmark suite. We derive a meaningful compartmentalization of these benchmark problems by clustering based on the used landscape features. The identified clusters mimic the behavior the used algorithms exhibit. Meaning, the different clusters have different best performing algorithms. Finally, our trained algorithm selector is able to close the gap between the single best and the virtual best solver by 57.5% over all benchmark problems

Investigating the Viability of Existing Exploratory Landscape Analysis Features for Mixed-Integer Problems

Exploratory landscape analysis has been at the forefront of characterizing single-objective continuous optimization problems. Other variants, which can be summarized under the term landscape analysis, have been used in the domain of combinatorial problems. However, none to little has been done in this research area for mixed-integer problems. In this work, we evaluate the current state of existing exploratory landscape analysis features and their applicability on a subset of mixed-integer problems.</p

Flexible Transparent Barrier Applications of Oxide Thin Films Prepared by Photochemical Conversion at Low Temperature and Ambient Pressure

Photoconversion of metal-organic precursors to thin film metal oxides using ultraviolet (UV) radiation in oxidative atmosphere is an attractive technology because it can be applied at temperatures <80°C and at ambient pressure. Thus, it enables preparing this class of thin films in a cost-efficient manner on temperature sensitive substrates such as polymer films. In this article, various aspects of research and development in the field of photochemical thin-film fabrication, with particular focus to the application of the produced films as gas permeation barriers for the encapsulation of optoelectronic devices are reviewed. Thereby, it covers investigations on fundamental photochemically initiated reactions for precursor classes containing metal-oxygen and metal-nitrogen bonds, and emphazises the relevance of that understanding for applicative considerations like integration of the single-layer barrier films into relevant encapsulation films. Further perspectives are given concerning integration of additional functionalities like electrical conductivity to the flexible and transparent barrier films. © Copyright © 2020 With, Helmstedt and Prager

A collection of deep learning-based feature-free approaches for characterizing single-objective continuous fitness landscapes

Exploratory Landscape Analysis is a powerful technique for numerically characterizing landscapes of single-objective continuous optimization problems. Landscape insights are crucial both for problem understanding as well as for assessing benchmark set diversity and composition. Despite the irrefutable usefulness of these features, they suffer from their own ailments and downsides. Hence, in this work we provide a collection of different approaches to characterize optimization landscapes. Similar to conventional landscape features, we require a small initial sample. However, instead of computing features based on that sample, we develop alternative representations of the original sample. These range from point clouds to 2D images and, therefore, are entirely feature-free. We demonstrate and validate our devised methods on the BBOB testbed and predict, with the help of Deep Learning, the high-level, expert-based landscape properties such as the degree of multimodality and the existence of funnel structures. The quality of our approaches is on par with methods relying on the traditional landscape features. Thereby, we provide an exciting new perspective on every research area which utilizes problem information such as problem understanding and algorithm design as well as automated algorithm configuration and selection.</p

A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes

Exploratory Landscape Analysis is a powerful technique for numerically characterizing landscapes of single-objective continuous optimization problems. Landscape insights are crucial both for problem understanding as well as for assessing benchmark set diversity and composition. Despite the irrefutable usefulness of these features, they suffer from their own ailments and downsides. Hence, in this work we provide a collection of different approaches to characterize optimization landscapes. Similar to conventional landscape features, we require a small initial sample. However, instead of computing features based on that sample, we develop alternative representations of the original sample. These range from point clouds to 2D images and, therefore, are entirely feature-free. We demonstrate and validate our devised methods on the BBOB testbed and predict, with the help of Deep Learning, the high-level, expert-based landscape properties such as the degree of multimodality and the existence of funnel structures. The quality of our approaches is on par with methods relying on the traditional landscape features. Thereby, we provide an exciting new perspective on every research area which utilizes problem information such as problem understanding and algorithm design as well as automated algorithm configuration and selection

Automated Algorithm Selection in Single-Objective Continuous Optimization:A Comparative Study of Deep Learning and Landscape Analysis Methods

In recent years, feature-based automated algorithm selection using exploratory landscape analysis has demonstrated its great potential in single-objective continuous black-box optimization. However, feature computation is problem-specific and can be costly in terms of computational resources. This paper investigates feature-free approaches that rely on state-of-the-art deep learning techniques operating on either images or point clouds. We show that point-cloud-based strategies, in particular, are highly competitive and also substantially reduce the size of the required solver portfolio. Moreover, we highlight the effect and importance of cost-sensitive learning in automated algorithm selection models.</p

Helium transmission rate as a rapid and reliable method for assessing the water vapour transmission rate of transparent PET-SiOx barrier foils

A single quadrupole mass spectrometer coupled measuring setup was developed for the investigation of the helium transmission rate (HeTR) of SiOx-coated polyethylene terephthalate (PET) barrier films. The setup allows the pressure-less and time-resolved measurements of the helium permeation at transient and steady-state conditions. Whereas standard water vapour transmission rate (WVTR) experiments took extended test times (in the range of several days), HeTR measurements were finished after 1 h. For the material system investigated here, an excellent linear correlation of WVTR and HeTR was proven over two orders of magnitude (regarding WVTR). Experiments with application of different strain loads on the coated films revealed a significant increase of both, HeTR and WVTR. Scanning electron microscope (SEM) measurements evidenced multiple ruptures of the SiOx coating depending on the applied strain and initial thickness of the SiOx layer. Considering virgin barrier films and strain-ruptured barrier films, a good correlation of WVTR and HeTR was shown

Fecundity of Blue Crab, Callinectes Sapidus, in Chesapeake Bay: Biological, Statistical and Management Considerations

Ovigerous blue crabs were collected from the mouth of Chesapeake Bay during the 1986 and 1987 spawning seasons. Mean carapace width was 14.7 cm; mean fecundity was 3.2×106 eggs. Fecundity was significantly related to carapace width, and did not vary significantly with developmental stage of the eggs. Mean fecundities were 2.6×106 eggs in 1986, and 4.0×106 eggs in 1987. An additive model with year and size effects described the observed fecundities reasonably well, was compact, and was easier to interpret than a multiplicative model. To fit a more general model without year effects, the authors took the mean of 1986 and 1987 results, and modeled fecundity as E=-2.25+0.38W, where E is predicted fecundity (106 eggs), and W is carapace width (cm)