Making and Breaking Impasses in International Regimes. The WTO, Seattle and Doha

WTO; international agreements; international trade; governance

Neurophysiological responses to stressful motion and anti-motion sickness drugs as mediated by the limbic system

Performance is characterized in terms of attention and memory, categorizing extrinsic mechanism mediated by ACTH, norepinephrine and dopamine, and intrinsic mechanisms as cholinergic. The cholinergic role in memory and performance was viewed from within the limbic system and related to volitional influences of frontal cortical afferents and behavioral responses of hypothalamic and reticular system efferents. The inhibitory influence of the hippocampus on the autonomic and hormonal responses mediated through the hypothalamus, pituitary, and brain stem are correlated with the actions of such anti-motion sickness drugs as scopolamine and amphetamine. These drugs appear to exert their effects on motion sickness symptomatology through diverse though synergistic neurochemical mechanisms involving the septohippocampal pathway and other limbic system structures. The particular impact of the limbic system on an animal's behavioral and hormonal responses to stress is influenced by ACTH, cortisol, scopolamine, and amphetamine

The tails in the Helix Nebula NGC 7293

We have examined a stream-source model for the production of the cometary tails observed in the Helix Nebula NGC 7293 in which a transonic or moderately supersonic stream of ionized gas overruns a source of ionized gas. Hydrodynamic calculations reveal velocity structures which are in good agreement with the observational data on tail velocities and are consistent with observations of the nebular structure. The results also are indicative of a stellar atmosphere origin for the cometary globules. Tail remnants persist for timescales long enough for their identification with faint striations visible in the nebula gas to be plausible.Comment: 7 pages, 6 figures, accepted for publication in A&

Fourth Conference on Artificial Intelligence for Space Applications

Proceedings of a conference held in Huntsville, Alabama, on November 15-16, 1988. The Fourth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: space applications of expert systems in fault diagnostics, in telemetry monitoring and data collection, in design and systems integration; and in planning and scheduling; knowledge representation, capture, verification, and management; robotics and vision; adaptive learning; and automatic programming

Renorming spaces with greedy bases

We study the problem of improving the greedy constant or the democracy constant of a basis of a Banach space by renorming. We prove that every Banach space with a greedy basis can be renormed, for a given \vare>0, so that the basis becomes (1+\vare)-democratic, and hence (2+\vare)-greedy, with respect to the new norm. If in addition the basis is bidemocratic, then there is a renorming so that in the new norm the basis is (1+\vare)-greedy. We also prove that in the latter result the additional assumption of the basis being bidemocratic can be removed for a large class of bases. Applications include the Haar systems in $L_p[0,1]$, $1<p<\infty$, and in dyadic Hardy space $H_1$, as well as the unit vector basis of Tsirelson space

Estimation of Proportions of Objects and Determination of Training Sample-Size in a Remote Sensing Application

One of the problems in remote sensing is estimating the expected proportions of certain categories of objects which cannot be observed directly or distinctly. For example, a multi-channel scanning device may fail to observe objects because of obstructions blocking the view, or different categories of objects may make up a resolution element giving rise to a single observation. This will require ground truth on any such categories of objects for estimating their expected proportions associated with various classes represented in the remote sensing data. Considering the classes to be distributed as multivariate normal with different mean vectors and common covariance, we give the maximum likelihood estimates for the expected proportions of objects associated with different classes, using the Bayes procedure for classification of individuals obtained from these classes. An approximate solution for simultaneous confidence intervals on these proportions is given, and thereby a sample-size needed to achieve a desired amount of accuracy for the estimates has been determined