Contender

A crossover between Archivist 12 (Arc) of the Minaverse, and the Seventh Doctor and Ace of Doctor WhoOriginally published at http://dwfiction.livejournal.com/272681.htm

The comic contender

spring 2015"Mizzou alumnus, All-American wrestler and comedian Greg Warren returns to Déjà Vu."Story by Kelsey Allen ; Photos by Rob Hill, courtesy Greg Warre

The Killing Game: Reputation and Knowledge in Politics of Succession

The winner of a battle for a throne can either execute or spare the loser; if the loser is spared, he contends the throne in the next period. Executing the losing contender gives the winner an additional quiet period, but then his life is at risk if he loses to some future contender. The trade-off is analyzed within an infinite-time complete information game. Our theory predicts that we would witness more killings along the succession lines in countries where a ‘circle of potential contenders’ is limited, and that executions of the predecessor are autocorrelated. In particular, with a dynastic rule in place, incentives, to kill the predecessor are much higher than in a non- hereditary dictatorships, e.g. in 19th century Latin America. Our analysis of historical material demonstrates that long succession lines indeed exhibit patterns predicted by our model.succession, dictatorship,

Low temperature specific heat of the heavy fermion superconductor PrOs4_4Sb12_{12}

We report the magnetic field dependence of the low temperature specific heat of single crystals of the first Pr-based heavy fermion superconductor PrOs$_4$Sb$_{12}$. The low temperature specific heat and the magnetic phase diagram inferred from specific heat, resistivity and magnetisation provide compelling evidence of a doublet ground state and hence superconductivity mediated by quadrupolar fluctuations. This establishes PrOs$_4$Sb$_{12}$ as a very strong contender of superconductive pairing that is neither electron-phonon nor magnetically mediated.Comment: 4 pages, 4 figure

Toward Contention Analysis for Parallel Executing Real-Time Tasks

In measurement-based probabilistic timing analysis, the execution conditions imposed to tasks as measurement scenarios, have a strong impact to the worst-case execution time estimates. The scenarios and their effects on the task execution behavior have to be deeply investigated. The aim has to be to identify and to guarantee the scenarios that lead to the maximum measurements, i.e. the worst-case scenarios, and use them to assure the worst-case execution time estimates. We propose a contention analysis in order to identify the worst contentions that a task can suffer from concurrent executions. The work focuses on the interferences on shared resources (cache memories and memory buses) from parallel executions in multi-core real-time systems. Our approach consists of searching for possible task contenders for parallel executions, modeling their contentiousness, and classifying the measurement scenarios accordingly. We identify the most contentious ones and their worst-case effects on task execution times. The measurement-based probabilistic timing analysis is then used to verify the analysis proposed, qualify the scenarios with contentiousness, and compare them. A parallel execution simulator for multi-core real-time system is developed and used for validating our framework. The framework applies heuristics and assumptions that simplify the system behavior. It represents a first step for developing a complete approach which would be able to guarantee the worst-case behavior

Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction

This paper reports the results of the NN3 competition, which is a replication of the M3 competition with an extension of the competition towards neural network (NN) and computational intelligence (CI) methods, in order to assess what progress has been made in the 10 years since the M3 competition. Two masked subsets of the M3 monthly industry data, containing 111 and 11 empirical time series respectively, were chosen, controlling for multiple data conditions of time series length (short/long), data patterns (seasonal/non-seasonal) and forecasting horizons (short/medium/long). The relative forecasting accuracy was assessed using the metrics from the M3, together with later extensions of scaled measures, and non-parametric statistical tests. The NN3 competition attracted 59 submissions from NN, CI and statistics, making it the largest CI competition on time series data. Its main findings include: (a) only one NN outperformed the damped trend using the sMAPE, but more contenders outperformed the AutomatANN of the M3; (b) ensembles of CI approaches performed very well, better than combinations of statistical methods; (c) a novel, complex statistical method outperformed all statistical and Cl benchmarks; and (d) for the most difficult subset of short and seasonal series, a methodology employing echo state neural networks outperformed all others. The NN3 results highlight the ability of NN to handle complex data, including short and seasonal time series, beyond prior expectations, and thus identify multiple avenues for future research. (C) 2011 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved

Portrait of a Contender: Corey Morris

“Language at Work – Bridging Theory and Practice” caught up with busy Corey Morris on a cold and rainy November day in Denmark. We had two main reasons for looking up Corey at his work. First, we knew that his work takes him to the four corners of the world of branding on a daily basis. Second, we were well aware that Corey would be the right person to ask questions as to where branding is going these days

"i have a feeling trump will win..................": Forecasting Winners and Losers from User Predictions on Twitter

Social media users often make explicit predictions about upcoming events. Such statements vary in the degree of certainty the author expresses toward the outcome:"Leonardo DiCaprio will win Best Actor" vs. "Leonardo DiCaprio may win" or "No way Leonardo wins!". Can popular beliefs on social media predict who will win? To answer this question, we build a corpus of tweets annotated for veridicality on which we train a log-linear classifier that detects positive veridicality with high precision. We then forecast uncertain outcomes using the wisdom of crowds, by aggregating users' explicit predictions. Our method for forecasting winners is fully automated, relying only on a set of contenders as input. It requires no training data of past outcomes and outperforms sentiment and tweet volume baselines on a broad range of contest prediction tasks. We further demonstrate how our approach can be used to measure the reliability of individual accounts' predictions and retrospectively identify surprise outcomes.Comment: Accepted at EMNLP 2017 (long paper