The Archimedean trap: Why traditional reinforcement learning will probably not yield AGI

After generalizing the Archimedean property of real numbers in such a way as to make it adaptable to non-numeric structures, we demonstrate that the real numbers cannot be used to accurately measure non-Archimedean structures. We argue that, since an agent with Artificial General Intelligence (AGI) should have no problem engaging in tasks that inherently involve non-Archimedean rewards, and since traditional reinforcement learning rewards are real numbers, therefore traditional reinforcement learning probably will not lead to AGI. We indicate two possible ways traditional reinforcement learning could be altered to remove this roadblock

Widely Received: Payoffs to Player Attributes in the NFL

We examine wide receivers drafted into the NFL to assess what attributes explain draft rank and correspond to high salaries and performance in their first year in the league. We find that tangible measures of player quality are valuable signals. Consistent with expectations, faster and more accomplished college receivers are drafted earlier and earn more. However, we find no significant relationship between 40-yard dash times and first year performance for wide receivers. In addition, media exposure received by players prior to the draft is positively related to draft placement and higher earnings even after controlling for measured physical attributes.Sports Economics, NFL, Draft, Media

A Framework for Image Segmentation Using Shape Models and Kernel Space Shape Priors

©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/TPAMI.2007.70774Segmentation involves separating an object from the background in a given image. The use of image information alone often leads to poor segmentation results due to the presence of noise, clutter or occlusion. The introduction of shape priors in the geometric active contour (GAC) framework has proved to be an effective way to ameliorate some of these problems. In this work, we propose a novel segmentation method combining image information with prior shape knowledge, using level-sets. Following the work of Leventon et al., we propose to revisit the use of PCA to introduce prior knowledge about shapes in a more robust manner. We utilize kernel PCA (KPCA) and show that this method outperforms linear PCA by allowing only those shapes that are close enough to the training data. In our segmentation framework, shape knowledge and image information are encoded into two energy functionals entirely described in terms of shapes. This consistent description permits to fully take advantage of the Kernel PCA methodology and leads to promising segmentation results. In particular, our shape-driven segmentation technique allows for the simultaneous encoding of multiple types of shapes, and offers a convincing level of robustness with respect to noise, occlusions, or smearing

Statistical Shape Analysis using Kernel PCA

©2006 SPIE--The International Society for Optical Engineering. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. The electronic version of this article is the complete one and can be found online at: http://dx.doi.org/10.1117/12.641417DOI:10.1117/12.641417Presented at Image Processing Algorithms and Systems, Neural Networks, and Machine Learning, 16-18 January 2006, San Jose, California, USA.Mercer kernels are used for a wide range of image and signal processing tasks like de-noising, clustering, discriminant analysis etc. These algorithms construct their solutions in terms of the expansions in a high-dimensional feature space F. However, many applications like kernel PCA (principal component analysis) can be used more effectively if a pre-image of the projection in the feature space is available. In this paper, we propose a novel method to reconstruct a unique approximate pre-image of a feature vector and apply it for statistical shape analysis. We provide some experimental results to demonstrate the advantages of kernel PCA over linear PCA for shape learning, which include, but are not limited to, ability to learn and distinguish multiple geometries of shapes and robustness to occlusions

Molecular simulations of entangled defect structures around nanoparticles in nematic liquid crystals

We investigate the defect structures forming around two nanoparticles in a Gay-Berne nematic liquid crystal using molecular simulations. For small separations, disclinations entangle both particles forming the figure of eight, the figure of omega and the figure of theta. These defect structures are similar in shape and occur with a comparable frequency to micron-sized particles studied in experiments. The simulations reveal fast transitions from one defect structure to another suggesting that particles of nanometre size cannot be bound together effectively. We identify the 'three-ring' structure observed in previous molecular simulations as a superposition of the different entangled and non-entangled states over time and conclude that it is not itself a stable defect structure.Comment: keywords: molecular-simulation, defects, nematic, disclination, algorithmic classification ; 8 pages, 7 figures, 1 tabl

The Fluoride Recharging Capability of an Orthodontic Primer: an in vitro study

Objective: The purpose of this study was to determine the fluoride recharging capability of Opal Seal, a fluoride releasing orthodontic primer, as compared to Transbond XT, the control. Material and Methods: 1mm x 5mm disks of Opal Seal and Transbond were prepared according to the respective manufacturer’s instructions. Initially, the samples were stored in deionized water (DI) for 8 weeks. The samples were then randomly divided into one of two groups: Over-the-counter (OTC) fluoride mouthwash and prescription strength (PS) fluoride mouthwash. The OTC group samples were immersed in 5mL of 0.0219% sodium fluoride containing mouthwash for one minute every day for seven days. The PS group samples were immersed in 5mL of 0.2% sodium fluoride containing mouthwash for one minute. All of the samples were suspended in 5mL fresh DI water and fluoride release measurements were taken at baseline (the end of initial 8 weeks of storage), 24 hours, 3 days, 5 days, 7 days, and 14 days. Results: Opal Seal samples treated with the OTC fluoride mouthwash exhibited significant fluctuation in fluoride ion release across time (p=0.0058). However, there were no statistically significant differences in fluoride ion release between the individual timepoints and baseline. Similarly, Opal Seal samples treated with the PS fluoride mouthwash exhibited significant variation in the fluoride ion concentration across time (p\u3c 0.001), and a statistically significant increase over baseline was seen at 24 hours only (p= 0.0006). The control group samples treated either with the OTC or PS mouthwash did not exhibit any significant difference in fluoride ion release between any individual timepoint and baseline. Conclusion: For Opal Seal and Transbond XT, there were no statistically significant differences of fluoride concentration at any timepoint compared to baseline measurements when using OTC mouthwash. When using PS mouthwash, there was a small, statistically significant increase of fluoride concentration of the Opal Seal samples after 24 hours but no differences were seen at any other timepoints. Opal Seal did not demonstrate a substantial amount of fluoride recharge when fluoride mouthwash is used as a fluoride delivery vehicle. Future well-designed randomized controlled trials are needed to evaluate the efficacy of Opal Seal primer when coupled with the use of fluoride mouthwashes

Intimate interfaces in action: assessing the usability and subtlety of emg-based motionless gestures

Mobile communication devices, such as mobile phones and networked personal digital assistants (PDAs), allow users to be constantly connected and communicate anywhere and at any time, often resulting in personal and private communication taking place in public spaces. This private -- public contrast can be problematic. As a remedy, we promote intimate interfaces: interfaces that allow subtle and minimal mobile interaction, without disruption of the surrounding environment. In particular, motionless gestures sensed through the electromyographic (EMG) signal have been proposed as a solution to allow subtle input in a mobile context. In this paper we present an expansion of the work on EMG-based motionless gestures including (1) a novel study of their usability in a mobile context for controlling a realistic, multimodal interface and (2) a formal assessment of how noticeable they are to informed observers. Experimental results confirm that subtle gestures can be profitably used within a multimodal interface and that it is difficult for observers to guess when someone is performing a gesture, confirming the hypothesis of subtlety