Supplementary Table 4 - Table S4: Results of Mann-Whitney U test (mean rank and sum of ranks) for comparison of bacterial density (cells/mL X 108) in the ...
We perform 10 Ã (number of participants) Ã (number of events) swaps. .... rand. 10-3. 10-2. 10-1. 100. 100. 101. 102. 103. LVHK x data rand. Figure E: The ...
disease categories using Comparative Toxicogenomics Database's (CTD) MEDIC disease vocabulary [2]. We then used Fisher's exact test comparing the ...
Alexander J. Stewart, Andrew Pomiankowski, Robert M. Seymour and Max Reuter. Negatively autoregulating genes in yeast, Drosophila and humans. Below we ...
A literal listener R0 would interpret âred hatâ as referring equally likely to the green monster with the red hat and the robot with the red hat. The Gricean speaker ...
Jul 1, 2017 - Richard Sandstrom, Bradley Bernstein, M. A. Bender, Mark Groudine, Andreas Gnirke, John. Stamatoyannopoulos, Leonid A. Mirny, Eric S.
Mayer DC, Kaneko O, Hudson-Taylor DE, Reid ME, Miller LH (2001). Characterization of a Plasmodium falciparum erythrocyte-binding protein paralogous to ...
Bernoulli trial at that sample location (as in the above Measurement model section). Next, the discrete dynamics of p(li(t) = 1) were 'transferred' to the continuous.
Davis IW, Baker D (2009) RosettaLigand docking with full ligand and ... Meiler J, Baker D (2006) ROSETTALIGAND: protein-small molecule docking with full side ...
Modelling influence and opinion evolution in online collective behaviour. Corentin ..... Journal of research in personality. 2010 ... (A) Gauging - RMSE, detailed error bars. Individual past information. Null. Cons. Unp. 1. 5. 10. 15. RMSE. 5.35. 8.
mutations as a function of the probability of emergence of a strain with ... birth-death process, since the infected host can either give rise to a new strain individual, a new mutant individual, or die, and these three events happen at three .... ex
The Oncomine database [6] was used to monitor gene expression of genes ..... identifies chromosomal loci with altered gene expression in cervical cancer.
modifications in the regulation of the CLDN4 promoter in ovarian cancer cells. J Biol Chem. 2006;281:21433-44. 18. Landers KA, Samaratunga H, Teng L, et al.
For model inversion, parameters were modelled as log-normal distributions. .... during binocular rivalry or continuous structure-from-motion, which are different to ...
When b â â, the product between the burst size and the critical failure probability simplifies to bηc = 1 â κ(1 â α) f1. + O(bâ1) . (12). This means that, apart from ...
even if a tunable free parameter included in our the measure), a single ... greedy merging is therefore essential for reliable reconstruction of the network. .... The DASH database, downloaded in June 2010 contained N0 = 918 journals .... [2] Wu F, H
Our algorithm assumes that the nucleus is a rounded surface, whose area is around ... where Ii is the intensity of the pixel i, A is the area of the circle, A0 is a ...
C. Determination of kinetic rates from FRAP experiments. 6. D. Time-resolved hypocotyl growth. 7 ... An experimental fluence rate response curve always exhibits the same ... However, for intermediate light intensities, the hypocotyl growth shows a ..
practice, a large number of steady-state gene expression measurement data (e.g. ..... as Fas-associated death domain protein (FADD) on the cytoplasmic side of ...
Frank and Goodman, 2012): Imagine someone is talking to you in an alien language and uses a word you don't know to refer to one of these creatures.
Supplementary Information 4 Reasoning in Reference Games: Individual- vs. Population-Level Probabilistic Modeling
Experiment 3: prior elicitation In order to evaluate higher-level listener behavior, it was necessary to obtain estimates of the prior salience of each of the three creatures in the display for each display used in Experiment 1. Experiment 3 was conducted to collect these estimates. Participants. 129 participants were recruited via Amazon’s Web Service Mechanical Turk. Participants’ IP address was limited to US addresses only. Only participants with a past work approval rate of at least 95% were accepted. Ethics statement. This study was conducted with the approval of the Stanford University research subjects review board. All participants gave written consent and received $0.30 for their participation (hourly rate of $10.00) according to the policies set forth by the Stanford University research subjects review board. Procedure and materials. Trials were similar to comprehension trials in Experiment 1. On each trial, participants chose one object from a triple of objects. But instead of receiving a message, participants were instead told (following Frank and Goodman, 2012): Imagine someone is talking to you in an alien language and uses a word you don’t know to refer to one of these creatures. Which creature do you think the person is talking about? Click on that creature. Triples and their spatial arrangement were randomly sampled from the 157 different visual displays (grids) of referents that occurred in Experiment 1. Each participant completed 26 experimental trials. Thus, each unique grid was was seen between participants on average 21 times. Each grid was seen at least 16 times and no grid was seen more than 31 times. Results. From the obtained choice distributions over objects in each grid, salience priors were computed for each of the four reference game types that we considered: simple, complex, unambiguous and ambiguous. Since the same grid appeared in different conditions in Experiment 1, we determined for each condition the set of grids that occurred with that condition in Experiment 1, together with its occurrence frequency for that condition. The salience priors for each condition are then the average of the choices from Experiment 3 weighted by the frequency of occurring with the relevant condition in Experiment 1. The resulting salience priors are given in Table 4. Table 4: Results from the salience prior elicitation in Experiment 3.
simple complex unambiguous ambiguous
target
competitor
distractor
.340 .221 .280 .119
.253 .380 .203 .121
.407 .399 .518 .760
1
References Frank, Michael C. and Noah D. Goodman (2012). “Predicting Pragmatic Reasoning in Language Games”. In: Science 336.6084, p. 998.
