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On Elicitation Complexity and Conditional Elicitation  [PDF]
Rafael Frongillo,Ian A. Kash
Mathematics , 2015,
Abstract: Elicitation is the study of statistics or properties which are computable via empirical risk minimization. While several recent papers have approached the general question of which properties are elicitable, we suggest that this is the wrong question---all properties are elicitable by first eliciting the entire distribution or data set, and thus the important question is how elicitable. Specifically, what is the minimum number of regression parameters needed to compute the property? Building on previous work, we introduce a new notion of elicitation complexity and lay the foundations for a calculus of elicitation. We establish several general results and techniques for proving upper and lower bounds on elicitation complexity. These results provide tight bounds for eliciting the Bayes risk of any loss, a large class of properties which includes spectral risk measures and several new properties of interest. Finally, we extend our calculus to conditionally elicitable properties, which are elicitable conditioned on knowing the value of another property, giving a necessary condition for the elicitability of both properties together.
A simple test of muscle coactivation estimation using electromyography
Ervilha, U.F.;Graven-Nielsen, T.;Duarte, M.;
Brazilian Journal of Medical and Biological Research , 2012, DOI: 10.1590/S0100-879X2012007500092
Abstract: in numerous motor tasks, muscles around a joint act coactively to generate opposite torques. a variety of indexes based on electromyography signals have been presented in the literature to quantify muscle coactivation. however, it is not known how to estimate it reliably using such indexes. the goal of this study was to test the reliability of the estimation of muscle coactivation using electromyography. isometric coactivation was obtained at various muscle activation levels. for this task, any coactivation measurement/index should present the maximal score (100% of coactivation). two coactivation indexes were applied. in the first, the antagonistic muscle activity (the lower electromyographic signal between two muscles that generate opposite joint torques) is divided by the mean between the agonistic and antagonistic muscle activations. in the second, the ratio between antagonistic and agonistic muscle activation is calculated. moreover, we computed these indexes considering different electromyographic amplitude normalization procedures. it was found that the first algorithm, with all signals normalized by their respective maximal voluntary coactivation, generates the index closest to the true value (100%), reaching 92 ± 6%. in contrast, the coactivation index value was 82 ± 12% when the second algorithm was applied and the electromyographic signal was not normalized (p < 0.04). the new finding of the present study is that muscle coactivation is more reliably estimated if the emg signals are normalized by their respective maximal voluntary contraction obtained during maximal coactivation prior to dividing the antagonistic muscle activity by the mean between the agonistic and antagonistic muscle activations.
Cohesive Device Analysis in Humor  [cached]
Wei Liu
Journal of Language Teaching and Research , 2010, DOI: 10.4304/jltr.1.1.90-93
Abstract: Humor is popular among us. While we are familiar with humor in our daily conversation, this paper studies the humor in cohesive devices. Humor produced by speakers can arouse the audience to response with special effect. Therefore in humor a lot of cohesive devices are employed.
Probability Elicitation in Influence Diagram Modeling by Using Interval Probability  [PDF]
Xiaoxuan Hu, He Luo, Chao Fu
International Journal of Intelligence Science (IJIS) , 2012, DOI: 10.4236/ijis.2012.24012
Abstract: In decision modeling with influence diagrams, the most challenging task is probability elicitation from domain experts. It is usually very difficult for experts to directly assign precise probabilities to chance nodes. In this paper, we propose an approach to elicit probability effectively by using the concept of interval probability (IP). During the elicitation process, a group of experts assign intervals to probabilities instead of assigning exact values. Then the intervals are combined and converted into the point valued probabilities. The detailed steps of the elicitation process are given and illustrated by constructing the influence diagram for employee recruitment decision for a China’s IT Company. The proposed approach provides a convenient and comfortable way for experts to assess probabilities. It is useful in influence diagrams modeling as well as in other subjective probability elicitation situations.
We Are Humor Beings: Understanding and Predicting Visual Humor  [PDF]
Arjun Chandrasekaran,Ashwin K Vijayakumar,Stanislaw Antol,Mohit Bansal,Dhruv Batra,C. Lawrence Zitnick,Devi Parikh
Computer Science , 2015,
Abstract: Humor is an integral part of human lives. Despite being tremendously impactful, it is perhaps surprising that we do not yet have a detailed understanding of humor. In this work, we explore the novel problem of studying visual humor and designing computational models of humor using abstract scenes. We collect and make publicly available two datasets of abstract scenes: one that enables the study of humor at the scene-level and the other at the object-level. We study the funny scenes in our dataset and explore different types of humor depicted in these scenes. We model two tasks that we believe demonstrate an understanding of visual humor -- predicting the funniness of a scene and altering the funniness of a scene. We show that our models perform well using automatic evaluation as well as human studies.
Complexity of Terminating Preference Elicitation  [PDF]
Toby Walsh
Computer Science , 2009,
Abstract: Complexity theory is a useful tool to study computational issues surrounding the elicitation of preferences, as well as the strategic manipulation of elections aggregating together preferences of multiple agents. We study here the complexity of determining when we can terminate eliciting preferences, and prove that the complexity depends on the elicitation strategy. We show, for instance, that it may be better from a computational perspective to elicit all preferences from one agent at a time than to elicit individual preferences from multiple agents. We also study the connection between the strategic manipulation of an election and preference elicitation. We show that what we can manipulate affects the computational complexity of manipulation. In particular, we prove that there are voting rules which are easy to manipulate if we can change all of an agent's vote, but computationally intractable if we can change only some of their preferences. This suggests that, as with preference elicitation, a fine-grained view of manipulation may be informative. Finally, we study the connection between predicting the winner of an election and preference elicitation. Based on this connection, we identify a voting rule where it is computationally difficult to decide the probability of a candidate winning given a probability distribution over the votes.
Comment: Expert Elicitation for Reliable System Design  [PDF]
Norman Fenton,Martin Neil
Statistics , 2007, DOI: 10.1214/088342306000000529
Abstract: Comment: Expert Elicitation for Reliable System Design [arXiv:0708.0279]
Rejoinder: Expert Elicitation for Reliable System Design  [PDF]
Tim Bedford,John Quigley,Lesley Walls
Statistics , 2007, DOI: 10.1214/088342306000000556
Abstract: Rejoinder: Expert Elicitation for Reliable System Design [arXiv:0708.0279]
Comment: Expert Elicitation for Reliable System Design  [PDF]
Andrew Koehler
Statistics , 2007, DOI: 10.1214/088342306000000538
Abstract: Comment: Expert Elicitation for Reliable System Design [arXiv:0708.0279]
Comment: Expert Elicitation for Reliable System Design  [PDF]
Wenbin Wang
Statistics , 2007, DOI: 10.1214/088342306000000547
Abstract: Comment: Expert Elicitation for Reliable System Design [arXiv:0708.0279]
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