Tort Law - Reckless Misconduct in Sports

The United States Court of Appeals for the Tenth Circuit has allowed a professional athlete to recover for an injury caused during a game by the reckless misconduct of an opponent in violation of game rules and customs. Hackbart v. Cincinnati Bengals, Inc., 601 F.2d 516 (10th Cir.), cert. denied, 444 U.S. 931 (1979)

Substantial Justification Further Defined by Phillips

The Tax Court in Phillips (referred to as Phillips II) has set helpful guidelines for making a determination of when the IRS has taken a position which is not substantially justified. This is one of the tests that must be satisfied before a court of proper jurisdiction can grant a motion for litigation costs pursuant to Section 7430. Phillips I also addresses the issue of settlement attempts, and whether they are needed to exhaust administrative remedies, which is a further requirement for Section 7430 treatment. The following discussion describes the legislative purpose of Section 7430 and its effectiveness in promoting fee-shifting. Attention will be focused upon Phillips and other cases in analyzing the tests of Section 7430

Observation of large-scale multi-agent based simulations

The computational cost of large-scale multi-agent based simulations (MABS) can be extremely important, especially if simulations have to be monitored for validation purposes. In this paper, two methods, based on self-observation and statistical survey theory, are introduced in order to optimize the computation of observations in MABS. An empirical comparison of the computational cost of these methods is performed on a toy problem

Optical implementation of systolic array processing

Algorithms for matrix vector multiplication are implemented using acousto-optic cells for multiplication and input data transfer and using charge coupled devices detector arrays for accumulation and output of the results. No two dimensional matrix mask is required; matrix changes are implemented electronically. A system for multiplying a 50 component nonnegative real vector by a 50 by 50 nonnegative real matrix is described. Modifications for bipolar real and complex valued processing are possible, as are extensions to matrix-matrix multiplication and multiplication of a vector by multiple matrices

In vivo effects on intron retention and exon skipping by the U2AF large subunit and SF1/BBP in the nematode Caenorhabditis elegans

The in vivo analysis of the roles of splicing factors in regulating alternative splicing in animals remains a challenge. Using a microarray-based screen, we identified a Caenorhabditis elegans gene, tos-1, that exhibited three of the four major types of alternative splicing: intron retention, exon skipping, and, in the presence of U2AF large subunit mutations, the use of alternative 3' splice sites. Mutations in the splicing factors U2AF large subunit and SF1/BBP altered the splicing of tos-1. 3' splice sites of the retained intron or before the skipped exon regulate the splicing pattern of tos-1. Our study provides in vivo evidence that intron retention and exon skipping can be regulated largely by the identities of 3' splice sites

Is the Most Accurate AI the Best Teammate? Optimizing AI for Teamwork

AI practitioners typically strive to develop the most accurate systems, making an implicit assumption that the AI system will function autonomously. However, in practice, AI systems often are used to provide advice to people in domains ranging from criminal justice and finance to healthcare. In such AI-advised decision making, humans and machines form a team, where the human is responsible for making final decisions. But is the most accurate AI the best teammate? We argue "No" -- predictable performance may be worth a slight sacrifice in AI accuracy. Instead, we argue that AI systems should be trained in a human-centered manner, directly optimized for team performance. We study this proposal for a specific type of human-AI teaming, where the human overseer chooses to either accept the AI recommendation or solve the task themselves. To optimize the team performance for this setting we maximize the team's expected utility, expressed in terms of the quality of the final decision, cost of verifying, and individual accuracies of people and machines. Our experiments with linear and non-linear models on real-world, high-stakes datasets show that the most accuracy AI may not lead to highest team performance and show the benefit of modeling teamwork during training through improvements in expected team utility across datasets, considering parameters such as human skill and the cost of mistakes. We discuss the shortcoming of current optimization approaches beyond well-studied loss functions such as log-loss, and encourage future work on AI optimization problems motivated by human-AI collaboration.Comment: v

Invasion speeds for structured populations in fluctuating environments

We live in a time where climate models predict future increases in environmental variability and biological invasions are becoming increasingly frequent. A key to developing effective responses to biological invasions in increasingly variable environments will be estimates of their rates of spatial spread and the associated uncertainty of these estimates. Using stochastic, stage-structured, integro-difference equation models, we show analytically that invasion speeds are asymptotically normally distributed with a variance that decreases in time. We apply our methods to a simple juvenile-adult model with stochastic variation in reproduction and an illustrative example with published data for the perennial herb, \emph{Calathea ovandensis}. These examples buttressed by additional analysis reveal that increased variability in vital rates simultaneously slow down invasions yet generate greater uncertainty about rates of spatial spread. Moreover, while temporal autocorrelations in vital rates inflate variability in invasion speeds, the effect of these autocorrelations on the average invasion speed can be positive or negative depending on life history traits and how well vital rates ``remember'' the past

On Counting Triangles through Edge Sampling in Large Dynamic Graphs

Traditional frameworks for dynamic graphs have relied on processing only the stream of edges added into or deleted from an evolving graph, but not any additional related information such as the degrees or neighbor lists of nodes incident to the edges. In this paper, we propose a new edge sampling framework for big-graph analytics in dynamic graphs which enhances the traditional model by enabling the use of additional related information. To demonstrate the advantages of this framework, we present a new sampling algorithm, called Edge Sample and Discard (ESD). It generates an unbiased estimate of the total number of triangles, which can be continuously updated in response to both edge additions and deletions. We provide a comparative analysis of the performance of ESD against two current state-of-the-art algorithms in terms of accuracy and complexity. The results of the experiments performed on real graphs show that, with the help of the neighborhood information of the sampled edges, the accuracy achieved by our algorithm is substantially better. We also characterize the impact of properties of the graph on the performance of our algorithm by testing on several Barabasi-Albert graphs.Comment: A short version of this article appeared in Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2017