"... In PAGE 5: ... We recognize in this picture sev- eral well-known features mentioned in previous studies, as well as new ones. The two main differences between previously reported nucleotide features and the features highlighted in Table3 and Figure 2 are that (i) the features obtained by the LASSO result from a global analysis of the complete dataset, as opposed to statistical analysis of sub- Huesken et al. [33]), and (ii) we provide a precise quanti- tative assessment of the importance of each feature, the weight of a feature being its contribution in the final pre- dicted silencing efficacy.... In PAGE 7: ... The comparison of the two models learned to each other highlights the conservation of most motifs discussed above, suggesting that they are not just an artifact of the training set but might be related to some biological function. In fact, as observed in Table3 , very few positions seem to be without influence on the efficacy of the siRNA. A ques- tion worth investigating is whether all the features appear- ing in Table 3 and Figure 2 really help predict efficacy, or whether some of them may be discarded.... In PAGE 7: ... In fact, as observed in Table 3, very few positions seem to be without influence on the efficacy of the siRNA. A ques- tion worth investigating is whether all the features appear- ing in Table3 and Figure 2 really help predict efficacy, or whether some of them may be discarded. The fact that the LASSO model tries to find parsimonious models based on as few features as possible to predict accuracy suggests that all detected motifs indeed play a role.... ..."

"... In PAGE 7: ... To quanti- tatively evaluate the relevance of the feature vectors-based clustering of the database, we computed the mean and stan- dard deviation of the vertical coordinate of the images in each cluster. Table1 presents the values we obtained. The standard deviations are all inferior to 0.... ..."

"... In PAGE 4: ... The sources vary as to the extent of formalization. The set of usability-related activities proposed in the HCI field are detailed in Table1 , where sources follow an order of increasing formalization from left to right. We have analyzed the activities proposed by the different authors in order to extract the common ones or, at least, the activities that are at the same abstraction level and are common to several sources.... In PAGE 5: ...The resulting usability activities (the left column in Table1 ) are represented in Fig. 1, grouped according to the generic kind of activity to which they belong: Analy- sis, design or evaluation.... ..."

"... In PAGE 7: ... NMA performed comparably to AMA for small Nr, but performed worse than both GTT and AMA for large Nr. Later ( Table5 ) we present quanti- tative comparisons of all four techniques on real-world data. The top-left quadrant of Table 3 shows the results of varying the number of nodes N, while holding Nr = 100, k = 2, and with K - [1; : : : ; N].... ..."

"... In PAGE 7: ...ates of the center pole of cluster 1 are (0.27, 0.25). Concisely, as suggested by the method of the multidi- mensional reduction of [17], we can rely on the quanti- tative information obtained from the fuzzy c-mean [19] and the fuzzy classifier [21] computation to produce a sensitive representation of a data-space. Following that method, Table4 gives the x and y coordinates of the center pole of each cluster of the experiment related to Table 3. As we explained before, we can subsequently compute the distance of a document to the coordinates of the center pole of the mountain to which it belongs in the data-space.... ..."

"... In PAGE 7: ... The questions in the Table 2 represent the three factors of technology acceptance. Table3 indicates the Lambda-x estimates and standard errors as obtained for each of the observed variables from the confirmatory factor analysis, with their abbreviations, the names of the latent variables, response modes, and respective item means. Lambda-x values, which are the loadings of each observed variable on the respective latent variable, indicate reasonable sizes to support the idea of using these latent variables in the proposed path analytic model to explain significant factors in educational technology acceptance.... ..."

"... In PAGE 3: ... The subjects were volunteer students enrolled in a northeastern university in the United States, majoring in Information Systems amp; Technology, Information Studies, Telecommunication and Network Management, Linguistics, and Computer Engineering. Table3 shows the demographic data of the subjects participating in these studies. Among the 102 subjects, only two reported red and green color blindness.... ..."

"... In PAGE 4: ... Results A number of logistic regression models were fitted to a 50% random sub-sample of schools in Tanga Region. Table2 presents the final model results and shows that altitude has a negative effect on the probability of a school having prevalence gt; 50%, whereas minimum LST and mean NDVI both have a positive effect. The remaining 50% of schools in Tanga Region not selected to develop the model were used to assess the accuracy of the model.... ..."

"... In PAGE 3: ... Data for Factorial Analysis Levels Supplier Quality Levels Supplier On-time Delivery Levels Supplied Parts Current State Improved State 1 Improved State 2 Current State Improved State 1 Improved State 2 Frames 95% N/A 100% 78% 90% 100% Shafts 85% 95% 100% 55% 80% 100% Housings 60% 80% 100% 45% 80% 100% 3. Results Table3 summarizes the results for all levels of supplier quality while Table 4 summarizes the results for all levels of supplier on-time delivery. Table 5 summarizes the results from improving both supplier quality and supplier on- time delivery simultaneously.... ..."