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Table 1. Expression profiles of adult human tissues

in
by S. Bortoluzzi, C. Romualdi, A. Bisognin, G. A. Danieli, P. Wong, A. Fritz, D. Frishman, S. Bortoluzzi, C. Romualdi, A. Bisognin, G. A. Danieli 2003
"... In PAGE 4: ... In every considered tissue, a relatively small number of highly expressed genes appeared to account for a very large fraction of the total transcriptional activity, as shown in Fig. 1 and reported in Table1 (columns 5 and 6). In general, slightly more than 10% of the total number of expressed genes accounts for one-half of the total transcriptional activity of any given tissue.... ..."

Table 1 Illustrative models of numerical cognition

in A Neural Model of How the Brain Represents and Compares Multi-Digit Numbers: Spatial and Categorical Processes
by Stephen Grossberg, Dmitry V. Repin
"... In PAGE 5: ...ohen, 1994, 1997; Dehaene et al., 1999). Together with the psychophysical data from human infants and adults, and various animal species, they have provided explanatory targets for models of human and animal numerical abilities. Table1 summarizes some illustrative and widely cited models. The classification is based on the domain of application (function, mechanism and representation addressed) and the experimental data explained.... ..."

Table 1: A comparison of select proxemic factors that differ between adult humans and QRIO (equipped with standard optics).

in Behavioral overlays for non-verbal communication expression on a humanoid robot, Auton
by Andrew G. Brooks 2006
"... In PAGE 4: ... In addition, the size of the robot (which is not limited to the range fixed by human biology) may have to be taken into account when considering the proxemics that a human might be likely to find nat- ural or comfortable. See Table1 for a comparison of several proxemic factors in the case of adult humans and QRIO, and Figure 3 for the general proxemic zones that were selected for QRIO. There has been little exploration of the use of prox- emics in human-robot interaction to date.... ..."
Cited by 2

Table 1: A comparison of select proxemic factors that differ between adult humans and QRIO (equipped with standard optics).

in Behavioral overlays for non-verbal communication expression on a humanoid robot, Auton
by Andrew G. Brooks 2006
"... In PAGE 4: ... In addition, the size of the robot (which is not limited to the range fixed by human biology) may have to be taken into account when considering the proxemics that a human might be likely to find nat- ural or comfortable. See Table1 for a comparison of several proxemic factors in the case of adult humans and QRIO, and Figure 3 for the general proxemic zones that were selected for QRIO. There has been little exploration of the use of prox- emics in human-robot interaction to date.... ..."
Cited by 2

Table X. Methods and relevant parametric variables in studies of numerical abilities in human infants/children and nonhuman animals

in Evolutionary and developmental foundations of human knowledge: a case study of mathematics
by Marc D. Hauser, Elizabeth Spelke 2004
Cited by 7

Table 2 Ability of Different Human PEX Genes to Restore Peroxisomal Protein Import in Patient PBD222 Cells

in unknown title
by unknown authors 1998

Table 1. Performance changes for sensing abilities

in Sponsored by The Boeing Company
by Mike Barley, Fabio Massacci, Haralambos Mouratidis 2005
"... In PAGE 54: ... It would be useful to understand which of our key assumptions in the model has led to such a mismatch in prediction. Table1 . Total amount of allocations given.... In PAGE 56: ... One reduction in EQH could be because subjects simply did not send as many allocations totally over the course of the experiments. This, however is not the case as can be seen in Table1 where for 6 agents, the total amount of allocations given is com- parable to that of 4 agents. To investigate further, we checked if the quality of human allocation had degraded.... In PAGE 63: ... In the second phase, roles of the Buddy and the PA are reversed. Table1 documents the agent roles undertaken by the community agents in the 7-14 model. In the table PA denotes Protected Agents and B denotes Buddy Agents.... In PAGE 98: ... Thus, BRLP provides a very favorable trade off of run-time to entropy, and we use this method in the multiagent case. Table1 shows the runtime results and entropy (in parenthesis) for d varying from 1 to 0.125 versus two values of percentage threshold reward (10% and 50%) with T set to 2.... In PAGE 99: ...Table1 . Average run time(sec)/Entropy for RDR, T = 2 Reward Loss 1 .... In PAGE 103: ... Vertical scales on the gure are V s that are normalize with the high score. Table1 shows scores of three teams at the same map with varying sensing conditions. Simulation results at column r1 are the results of simulations that the seeing ability of agents are set the half of normal sensing ability s.... In PAGE 112: ... CASPER uses its model to continuously generate and repair schedules, tracking the current spacecraft state and resources, the expected evolution of state and resources, and the effects on planned activities. Table1 : Sample safety analysis for two risks Instruments overheat from being left on too long Instruments exposed to sun Operations For each turn on command, look for the following turn off command. Verify that they are within the maximum separation.... ..."

Table 3 FDp% for atenolol, ganciclovir, and naproxen in humans and dogs Humans Dogs

in
by D. A. Norris, G. D. Leesman, P. J. Sinko, G. M. Grass 1999
"... In PAGE 7: ... model. Table3 , which shows the extent of absorp- In summary, a physiologically based computer tion for three representative compounds, reinforces model to simulate oral absorption in mammals was the ability of the model to predict FDp% for both developed and tested. The model was found to well and poorly absorbed compounds.... ..."

Table 6.12: Direct comparison to human abilities. Mean and standard deviation of the recognition rates of classiflers using GMM and HMM.

in unknown title
by unknown authors 2003

Table 3. Noise elimination results with various subset numbers (Adult dataset)

in Eliminating class noise in large datasets
by Xingquan Zhu, Xindong Wu, Qijun Chen 2003
"... In PAGE 6: ... The results of noise elimination and classification accuracy improvements are given in Table 3. Table3 demonstrates that when using more subsets, the non-objection scheme prevents more noise from being eliminated. One of the worst consequences of keeping much noise is that the classifier learned for the dataset would have very limited improvement (sometimes no improvement).... In PAGE 6: ... Actually, there is a contradiction between the number of subsets and the system performance: the more the subsets, the larger is the number of decision committee members (because we take each subset as a decision maker to identify noise), and meanwhile, the weaker is the ability of each committee member (because with the increase of the subset number, less information is contained in each subset). From Table3 , one can intuitively conclude that the larger the number of subsets, the worse is the system performance. Actually, at the beginning, there is a trend that the accuracy increases with the increase of the subset number, but after a certain number it begins to drop.... In PAGE 6: ...g., 127 subsets, Table3 indicates that the majority scheme can still attain a good performance. In most of our experiments, we use 5 subsets.... ..."
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