Reverse Pre-Empting the Federal Arbitration Act: Alleviating the Arbitration Crisis in Nursing Homes

In Casarotto, the Supreme Court enunciated that Montana\u27s notice requirement conflicted with the goals and policies of the FAA. The inequities associated with the process of pre-dispute arbitration agreements in nursing homes, however, confirm that the FAA\u27s goals and policies \u27 conflict with accepted principles of contract law \u27 in this context. Long standing principles of contract law that predate the FAA, as well as basic human morality, should supersede the interests of efficiency and convenience purportedly served by the general enforceability of the statute. State case law as well as attempted state legislation already evince an underlying public policy to protect nursing home residents from the harsh effects of unconscionable arbitration agreements. Despite court decisions declaring certain practices unconscionable, however, nursing homes continue to employ these procedures. It is therefore Congress\u27 obligation to recognize this impropriety and grant relief possibly in the form of legislation similar to the McCarran Act that would essentially consent to state regulation of nursing home admission agreements. Moreover, legislative relief would serve policy interests by providing an efficient, inexpensive, and fair forum for dispute resolution; pursue the goals of states by protecting residents; and open the door of redress that had previously been closed or impossible to reach for many nursing home residents. This legislation is vital to nursing home residents because whatever Congress meant when it sought to make arbitration agreements as enforceable as other contracts; and whatever the Supreme Court meant when it interpreted the FAA to apply to consumer disputes in federal and state court, surely it did not mean this

Discovering and Understanding High Performance Materials using Density Functional Theory: Quantum Mechanical Simulations and the Consequences of Symmetry

There are two primary ways that atomic level modeling data is used: materials prediction and understanding materials properties. This dissertation work encom- passes two studies, each of which explore one application. Both studies rely on the highly successful density functional theory (DFT) formalism but differ in that two different implementations of DFT are used on two different high performance materials. The first study on bulk magnesium (Mg) metal alloys explores materials prediction and relies on VASP, a commercially maintained plane-wave DFT code which has been used extensively to successfully study a wide range of materials. [1] The approach used in this first study is to ‘experiment’ within computational quantum mechanical simulations to improve the elastic properties of bulk Mg by altering its HCP lattice structure. We systematically study the influence of adding lithium (Li) as an alloy for two reasons: to maintain the lightweight benefits of Mg, and Li naturally occurs in a body centered cubic (BCC) crystal structure. The hypothesis is that an alloy with a more symmetric crystal structure will show im- proved properties, however we do not place any symmetry restrictions on the results of the structure search. We find that the addition of Li to Mg does improve the elastic properties of the resulting alloys; however it does not necessarily increase the symmetry. Five structures are found which belong to the convex hull, three of which are previously unreported. The second DFT study seeks to understand the electronic environment within lead sulfur (PbS) semiconductor nano-structures and utilizes the open-source Octopus code, designed for electron-ion dynamics in finite systems using time-dependent DFT in real time and real space and which has also been bench-marked extensively [2]. The aim of the second study is to understand at the most fundamental levels the impact reduced symmetry has on the electronic states and transitions at the level of the individual IR-light-absorbing quantum dot. We employ three toy models to isolate the impacts of reduced coordination, Pb-rich structures, and Peierls distortions. An in-depth analysis of the bonding through the charge density and electron localization function shows that the metavalent bonding observed in bulk PbS persists in the nanoscale regime. Changing the stoichiometry too far away from Pb:S = 1:1 results in the loss of semiconducting character and an overall metallic character prevails. When we place particular attention on the effects of atomic coordination, we observe enhanced electron localization clustered around the lowest coordinated atoms. Peierls distortions intensify the clustering behavior which lowers the energy of the occupied electronic states and increases the energy of the unoccupied states as deduced from density of states plots. The change in the electron localization is substantial only for a significant amount of low-coordinated atoms. A conclusion is made with an outlook to future work

A GIS-based approach for mapping hazardous areas due to geological slope processes: case study for Jesenice municipality in Slovenia

Hazard

Randomly Weighted Neuromodulation in Neural Networks Facilitates Learning of Manifolds Common Across Tasks

Geometric Sensitive Hashing functions, a family of Local Sensitive Hashing functions, are neural network models that learn class-specific manifold geometry in supervised learning. However, given a set of supervised learning tasks, understanding the manifold geometries that can represent each task and the kinds of relationships between the tasks based on them has received little attention. We explore a formalization of this question by considering a generative process where each task is associated with a high-dimensional manifold, which can be done in brain-like models with neuromodulatory systems. Following this formulation, we define \emph{Task-specific Geometric Sensitive Hashing~(T-GSH)} and show that a randomly weighted neural network with a neuromodulation system can realize this function.Comment: 10 pages, 7 figures, 1 table, Appear in NeurIPS 202