### Table 1: Examples of time series clinical studies Reference Treatment /

"... In PAGE 1: ... For example, the Inflammation and the Host Response to Injury research program [3], a consortium of several leading research hospitals, stud- ies the response of over a hundred trauma and burn patients using time series expression data. Table1 lists a number of other examples of such studies. Time series expression experiments present a number of computational prob- lems [4].... In PAGE 2: ... Table1 . Examples of a number of time series clinical studies.... In PAGE 3: ... Most previous papers describing such data have relied on simple techniques such as averaging. See Table1 for some examples. As mentioned above, there have been a number of methods suggested for aligning two time series expression datasets.... In PAGE 11: ... These results are in good agreement with the (anecdotal) observations in the original paper mentioned above. As mentioned before ( Table1 ), previous attempts to combine patient expres- sion data used the average expression value for each time point. To compare our results with these methods we used k-means to cluster the average and median expression values from all six patients.... ..."

### Table 1: The results of parameter estimation for GFP-GR using an experimental time series obtained by the Fluorescence Recovery after Photobleaching (FRAP) technique.

2007

"... In PAGE 6: ... Model calibration The developed methodology was then used to estimate diffusion coefficient and binding rate parameters of GFP- tagged glucocorticoid receptor (GFP-GR). The results are given in Table1 and Figure 2 (the experimental FRAP time series data are from McNally, personal communication). The Root Mean Squared Error (RMSE) and Coefficient of Determination (R2) were calculated, using equations (14) and (15), for every set of optimized parameters and pre- sented in the last two columns of Table 1.... In PAGE 6: ... The results are given in Table 1 and Figure 2 (the experimental FRAP time series data are from McNally, personal communication). The Root Mean Squared Error (RMSE) and Coefficient of Determination (R2) were calculated, using equations (14) and (15), for every set of optimized parameters and pre- sented in the last two columns of Table1 . The values for diffusion coefficient, binding rate parameters, and corre- sponding indices estimated by [25] are given as the first run in Table 1 and Figure 2 for sake of comparison.... In PAGE 6: ... The Root Mean Squared Error (RMSE) and Coefficient of Determination (R2) were calculated, using equations (14) and (15), for every set of optimized parameters and pre- sented in the last two columns of Table 1. The values for diffusion coefficient, binding rate parameters, and corre- sponding indices estimated by [25] are given as the first run in Table1 and Figure 2 for sake of comparison. Table 1 and Figure 2 indicate that many combinations of the three parameters can essentially produce the same error level (RMSE) and yields equally excellent fits.... In PAGE 7: ... Our analysis, however, suggests that using FRAP, one cannot conclude how much of the bio-macromolecule under study is free and how much is bound. As Table1 shows, the concentra- tion of free GFP-GR ranges from zero to 100 per cent. The same is true for the concentration of the bound complex.... In PAGE 7: ... Note that both parameter sets produce excellent fits with the same RMSE and coefficient of determination (see Figure 2). Analysis of Table1 indicates that the average binding time per vacant site, calculated by tb = 1/Kd [25], ranges between 0.3 and 500 mili-seconds.... In PAGE 9: ... The following null and alternative hypotheses were formu- lated: To verify if the residuals have constant variance they were divided into different sections. One of the possible solu- tions in Table1 was chosen and the residual plot versus laser beam recovery (Fig. 3) was divided into three regions.... ..."

### (Table 5Erreur ! Source du renvoi introuvable.). The distributions of the PROSPECT+SAIL input variables, and the corresponding simulated fAPAR, fCover are presented in Figure 2 and Figure 3. Despite the emphasis put on low LAI values, fAPAR and fCover distributions have more frequent higher values (Figure 3 a and b). Note that, in this study, fAPAR is defined as that observed at 10:00 local solar time when the canopy is only illuminated by the sun (black sky). It is thus rather close to the black sky fAPAR values at the time of satellite overpass used by other products (MODIS, MGVI). It corresponds also to the value the closest to the daily integrated black sky value (results not shown here) required by the majority of users. A 4% Gaussian noise with no bias was added to the reflectances. It corresponds roughly to MERIS performances as evaluated over vicarious calibration exercises (Zurita-Milla et al., 2006). The distribution of reflectances in the 11 MERIS bands show the contrast between the visible (lower values) and near infrared bands (higher values) with the red-edge bands being intermediate (Figure 3 c).

2005

"... In PAGE 17: ....2.2. Simulations The input variables of the PROSPECT+SAIL model are set to follow Gaussian distributions within their respective definition interval (Table5 ). The distributions derive from empirical knowledge and are assumed independent.... ..."

### Table 1: Implementation results for chaotic time-series prediction

1998

"... In PAGE 4: ...Matlab neural network toolbox and trained using conventional backpropagation algorithms. A summary of the implementation results obtained are presented in Table1 . Two different simulation approaches were used for the chaotic time series prediction problem.... In PAGE 4: ...ifference between the predicted and actual results, in terms of the prediction error is illustrated in Fig. 5. This compares favourably with a conventional fuzzy approach which employed an even finer-grained partitioning strategy ranging from 15 to 29 fuzzy sets to achieve a similar accuracy [Wang92]. For further comparison, the results using a conventional neural network approach which contains 40 nodes in the hidden layer are also included in Table1 . Previous work demonstrated that this size of network resulted in a similar degree of accuracy as a conventional fuzzy reasoning approach employing seven fuzzy sets on each input domain [Wang92].... In PAGE 4: ... Previous work demonstrated that this size of network resulted in a similar degree of accuracy as a conventional fuzzy reasoning approach employing seven fuzzy sets on each input domain [Wang92]. Table1 illustrates that the FNN approach provides a more accurate prediction of the time-series as compared to the conventional neural network approach. However, these results do not highlight that the training time of the conventional neural network was more than a factor of two slower than the largest FNN employed.... ..."

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### Table 1: Time Series data

"... In PAGE 2: ...pproaches (e.g., least squares methods). To the experiments carried out in this work, a set of ten series was selected ( Table1 ), ranging from financial mar- kets to natural processes [3][14][10] (Figure 4). The last two series were artificially created, using the chaotic formulas: DCD8 BP CPDCD8A0BDB4BD A0 DCD8A0BDB5BN DCBC BP BCBMBEBN CP BP BG for the quadratic series [15]; and DCD8 BP BD A0 CPDCBE D8A0BD B7 CQDCD8A0BE, CP BP BDBMBG, CQ BP BCBMBF, DCBC BP BCBMBDBD for the henon one [2].... In PAGE 3: ... - the use of decomposable information; i.e., AF CBCCCF BPBO BDBN C3BN C3 B7BD BQ if the series is seasonal (period C3) and trended; AF CBCCCF BPBO BDBN C3 BQ if the series is seasonal ; and AF CBCCCF BPBO BD BQ and CBCCCF BPBO BDBN BE BQ if the series trended. Several FNNs, with a number of hidden nodes (D2CW) rang- ing from 0 to 13, were used to explore all sliding windows for each TS of Table1 . Each model was trained with 90% of the series elements, being the rest 10% used for the forecasts.... In PAGE 5: ... 1 2 13 12 11 8 3 Figure 3: The best model for the sunspots series. Table 5 shows the best models achieved by the GEA, for all series of Table1 . As an example, Figure 3 plots the best ANN topology for the sunspots series.... ..."

### Table 1: Time Series data

2001

"... In PAGE 2: ...pproaches (e.g., least squares methods). To the experiments carried out in this work, a set of ten series was selected ( Table1 ), ranging from financial mar- kets to natural processes [3][14][10] (Figure 4). The last two series were artificially created, using the chaotic formulas: a0 a1 a7 a18a17a33a0 a1 a49 a22 a5a20a19 a10 a0 a1 a49 a22 a7a9a21 a0 a23a22 a7 a18a24a23a25 a26a27a21a28a17 a7 a18a29 for the quadratic series [15]; and a0 a1 a7 a30a19 a10 a31a17a33a0a2a25 a1 a49 a22 a33a32a35a34 a0 a1 a49 a25 , a17 a7 a36a19a37a25 a29 , a34 a7 a38a24a27a25 a39 , a0 a23a22 a7 a40a24a23a25a41a19a37a19 for the henon one [2].... In PAGE 3: ... - the use of decomposable information; i.e., a41 a11 a1a0a3a2 a7 a5a4a4a19 a21 a25a42 a21 a43a42 a32 a19 a8 if the series is seasonal (period a42 ) and trended; a41 a11 a1a0a3a2 a7 a5a4a4a19 a21 a25a42 a8 if the series is seasonal ; and a41 a11 a1a0a3a2 a7 a5a4 a19 a8 and a11 a1a0a3a2 a7 a5a4 a19 a21 a26 a8 if the series trended. Several FNNs, with a number of hidden nodes (a10a44a38 ) rang- ing from 0 to 13, were used to explore all sliding windows for each TS of Table1 . Each model was trained with 90% of the series elements, being the rest 10% used for the forecasts.... In PAGE 5: ... 1 2 13 12 11 8 3 Figure 3: The best model for the sunspots series. Table 5 shows the best models achieved by the GEA, for all series of Table1 . As an example, Figure 3 plots the best ANN topology for the sunspots series.... ..."

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### Table 1. Fits for Each Cluster or Group for the Near{Infrared FP

"... In PAGE 6: ...or group, are listed in Table1 . Since the rms is quoted in units of log re , the uncertainty on each intercept is therefore rms=pN ? 1.... In PAGE 6: ... This is possible because virtually every study of the FP (optical and near{ infrared) obtains a similar value for this parameter, hence it should be possible to constrain its value a priori. These ts show signi cantly smaller uncertainty in their determination of a than the unconstrained ts, and are listed in Table1 . In this case, seven of the 11 clusters have a slope a within one standard deviation of the value from the simultaneous t.... ..."

### Table 1. Comparison of the Slope of the FP in the Optical and Near{Infrared

"... In PAGE 7: ... For this reason, separate comparisons are made for each literature source for those galaxies in common, but keeping the data in the original bandpass. All of these ts are listed in Table1 . Note that the catalogs prepared for the purpose of this comparison have been put onto a common extinction scale as described by Pahre (1998b).... In PAGE 7: ... The statistical signi cance of each regression for the comparison of log re ? 0:32h ie is at the 2{6 con dence level (CL), while the signi cance for the log DK ? log Dopt comparison is at the 3{10 CL. As a demonstration of how the method adopted here is superior to the alternate method of tting the optical and near{infrared FP relations independently and then comparing their slopes, notice that the 0:06 dex uncertainty in Table1 for the J rgensen et al. (1996) r{band subsample is nearly a factor of two smaller than the 0:11 dex uncertainty derived when the independently tted slopes were compared in x4.... In PAGE 8: ... The present paper includes larger samples of galaxies both in Coma and Hydra{Centaurus, as well as other rich clusters and low density environments, hence this e ect can be re{analyzed. The data were broken down into six regions of the sky or similar density environments, compared to the overall solution (as listed in Table1 ), and are displayed in Figure 4. Guzm an (1995) found that log DK ? log DV at a given log 0 was 0:05 dex larger in Hydra{Centaurus region than in Coma, but panel (d) of Figure 4 shows that it is lt; 0:03 dex smaller.... In PAGE 32: ... The positive value of the linear regression in each case signi es a steepening of the FP as the wavelength moves from the optical to the near{infrared. The slopes and intercepts of these comparisons are listed in Table1 . The literature comparisons are: Faber et al.... ..."

### TABLE 8 NEAR-INFRARED SPECTRAL PROPERTIES OF TDWARF SUBTYPES

### Table 6. Comparison of Model and CMDL CH4 Concentration Time Series Site Latitude

"... In PAGE 16: ... Sites affected by frequent pollution episodes contributed by local emissions, such as Tae-ahn Peninsula, Mace Head, and Barrow, are more difficult to simulate with the coarse-resolution model. Values of r2 for model-observation correlations and the average model bias are included in Table6 .... In PAGE 16: ...harmonic and quadratic fit) are removed from the time series, r2 values drop to 0.15-0.50 for many sites. Correlations are still significant at a 99% confidence level for many of the sites, as indicated in Table6 . The average model biases are generally close to zero, but higher in NH mid- and high-latitude source regions and lower in the SH for the High OH simulation than for Low OH .... ..."

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