What Can We Say about Information? Agreeing a Narrative

The nature of information remains contested. This paper proposes a set of principles for a narrative of information, and explores the consequences of taking these principles as normative in the present rhetoric of the information society

Introduction: The difference that makes a difference

This article introduces TripleC’s Special Issue on The Difference That Makes a Difference, containing papers arising from a workshop of the same name that ran in Milton Keynes in September 2011. The background to the workshop is explained, workshop sessions are summarised, and the content of the papers introduced. Finally, some provisional outcomes from the workshop and the Special Issue are described

Do the design concepts used for the space flight hardware directly affect cell structure and/or cell function ground based simulations

The use of clinostats and centrifuges to explore the hypogravity range between zero and 1 g is described. Different types of clinostat configurations and clinostat-centrifuge combinations are compared. Some examples selected from the literature and current research in gravitational physiology are presented to show plant responses in the simulated hypogravity region of the g-parameter (0 is greater than g is greater than 1). The validation of clinostat simulation is discussed. Examples in which flight data can be compared to clinostat data are presented. The data from 3 different laboratories using 3 different plant species indicate that clinostat simulation in some cases were qualitatively similar to flight data, but that in all cases were quantitatively different. The need to conduct additional tests in weightlessness is emphasized

Analgesic Effects of Fatty Acid Amide Hydrolase Inhibition in a Rat Model of Neuropathic Pain

Cannabinoid-based medicines have therapeutic potential for the treatment of pain. Augmentation of levels of endocannabinoids with inhibitors of fatty acid amide hydrolase (FAAH) is analgesic in models of acute and inflammatory pain states. The aim of this study was to determine whether local inhibition of FAAH alters nociceptive responses of spinal neurons in the spinal nerve ligation model of neuropathic pain. Electrophysiological studies were performed 14-18 days after spinal nerve ligation or sham surgery, and the effects of the FAAHinhibitor cyclohexylcarbamic acid 3-carbamoyl biphenyl-3-yl ester (URB597) on mechanically evoked responses of spinal neurons and levels of endocannabinoids were determined. Intraplantar URB597 (25 _g in 50 _l) significantly ( p _ 0.01) attenuated mechanically evoked responses of spinal neurons in sham-operated rats. Effects of URB597 were blocked by the cannabinoid 1 receptor (CB1 ) antagonist AM251 [N-1-(2,4-dichlorophenyl)-5-(4-iodophenyl)-4-methyl-N-1-piperidinyl-1H-pyrazole-3-carboxamide] (30_g in50_l) and the opioid receptor antagonist naloxone. URB597 treatment increased levels of anandamide, 2-arachidonyl glycerol, and oleoyl ethanolamide in the ipsilateral hindpaw of shamoperated rats. Intraplantar URB597 (25 _g in 50 _l) did not, however, alter mechanically evoked responses of spinal neurons in spinal nerve ligated (SNL) rats or hindpaw levels of endocannabinoids. Intraplantar injection of a higher dose of URB597 (100 _g in 50 _l) significantly ( p_0.05) attenuated evoked responses of spinal neurons in SNL rats but did not alter hindpaw levels of endocannabinoids. Spinal administration of URB597 attenuated evoked responses of spinal neurons and elevated levels of endocannabinoids in shamoperated and SNL rats. These data suggest that peripheral FAAH activity may be altered or that alternative pathways of metabolism have greater importance in SNL rats

Linear Approximations and Tests of Conditional Pricing Models

We construct a simple reduced-form example of a conditional pricing model with modest intrinsic nonlinearity. The theoretical magnitude of the pricing errors (alphas) induced by the application of standard linear conditioning are derived as a direct consequence of an omitted variables bias. When the model is calibrated to either characteristics sorted or industry portfolios, we find that the alphas generated by approximation-induced specification error are economically large. A Monte Carlo analysis shows that finite-sample alphas are even larger. It also shows that the power to detect omitted nonlinear factors through tests based on estimated risk premiums can sometimes be quite low, even when the effect of misspecification on alphas is large.

Cognitive Cliches

This paper is an exploration of a wide class of mental structures called cognitive cliches that support intermediate methods that are moderately general purpose, in that a few of them will probably be applicable to any given task; efficient; but not individually particularly powerful. These structures are useful in representation, learning, and reasoning of various sorts. Together they form a general theory of special cases. A cognitive cliche is a pattern that is commonly found in representations and, when recognized, can be exploited by applying the intermediate methods attached to it. The flavor of the idea is perhaps best conveyed by some examples: TRANSITIVITY, CROSS PRODUCTS, SUCCESSIVE APPROXIMATION, CONTAINMENT, ENABLEMENT, PATHS, RESOURCES, and PROPAGATION are all cognitive cliches.MIT Artificial Intelligence Laborator

Naive Problem Solving and Naive Mathematics

AI problem solvers have almost always been given a complete and correct axiomatization of their problem domain and of the operators available to change it. Here I discuss a paradigm for problem solving in which the problem solver initially is given only a list of available operators, with no indication as to the structure of the world or the behavior of the operators. Thus, to begin it is "blind" and can only stagger about in the world tripping over things until it begins to understand what is going on. Eventually it will learn enough to solve problems in the world as well as if it the world had been explained to it initially. I call this paradigm naive problem solving. The difficulty of adequately formalizing all but the most constrained domains makes naive problem solving desirable. I have implemented a naive problem solver that learns to stack blocks and to use an elevator. It learns by finding instances of "naive mathematical cliches" which are common mental models that are likely to be useful in any domain.MIT Artificial Intelligence Laborator