sub-string âiPodâ in their titles; we will call these iPod- related records from now on. The algorithms were implemented in Java, and our ex- periments were run ...
Text S1 - Additional Information. Sidhartha Goyala, Jie Yuanb, Thomas Chenc, Josh D. Rabinowitzb, and Ned S. Wingreend. aDepartment of Physics ...
Experiments on synthetic data. We begin by illustrating the ability of our method to extract âtrueâ mutation signatures on simulated data. In these simulations a ...
Supporting text S2: Examples of simulation experiments: Simulating the. Repressilator. The following examples are MIASE compliant descriptions of three ...
S =(0,1) and the integrals were summed for cases where r, p1 or p2 is either 0 or 1. ..... The following table lists the cases and corresponding integral forms.
S3 Text: Three additional test cases. Yannick G. Spill and Michael Nilges. Here, we report three additional test cases extracted from a recent SAXS bench- mark1.
Feb 8, 2008 - the most remarkable result to date is the fact that no known physical con-. Preprint submitted .... Values for Rc obtained with expressions (4) and (6) are compared in. Table 1. .... can in principle produce scattering of higher l-waves
1. S1 Appendix. Additional methodological information. This appendix provides additional information on the methodology used to carry out the analyses on the.
Oct 8, 2013 - Obesity (Silver Spring). 14 Suppl 5: 242Sâ249S. 3. ... Pitt B, Byington RP, Furberg CD, Hunninghake DB, Mancini GB, et al. (2000). Effect of ...
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As reasoned in Ref [35] (for the S-mobility FOI with denominator Ni, and in the absence of. 6 age-mixing), if there exists a solution z such that za,i = xa, i.e. final ...
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have instructed the judges to use several confluent criteria that we list here in no ... Acc: accusing somebody in the talk of POV, not reading comments, not ...
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Table S2. Indirect reductive amination experiments. Entry. Aditivea. (solvent). Temp. b. Time. (days). [H]. Time. HPLC peaks area (%)c. 2a. 2a. 3a. 3b. 3c Interm.
Apr 30, 2014 - suboscines are mainly monogamous with long-term social bonds, our results are consistent with the view that sexual selection promotes song ...
the P/R break-even point which improves F-score. AUC is in general less sensitive to slight changes in the location of the separating surface, and, consequently, ...
Additional experiments Cross-learning without AIMed We observed that the models trained on AIMed are the worst when applied to other corpora. Therefore, we investigated how CL performance (Table 3) of the best performing kernels changes when AIMed is discarded. The results in Table S7 show that AUC values change insignificantly, but F-scores increase by 3–6%. This experiment again proves that the difference between AIMed and the other 4 corpora is the largest, and that characteristics of a particular learning corpus have a considerable impact on performance. Transductive SVM Erkan et al applied the SVM with transductive learning strategy, which improved the F-score due to its higher recall especially for edit kernel [22]. Transductive learning also takes into account test examples when building a model. First, labels are assigned to test examples using the standard SVM. Then, the transductive SVM (TSVM) optimizes the class separating surface by switching the labels of two selected test data so that the overall objective function of the learner improves. TSVM was found to be superior to SVM for text classification [74], and transductive version is a built-in feature in SVM-Light. We investigated the effect of using the TSVM with three selected kernels: kBSPS, cosine, and edit (application to AGP would be more complicated since its implementation is not based on SVM). The results in Table S8 show that in some particular cases, the F-score gain is remarkable. This happens when the difference between the original recall and precision values is large, and the F-score is dominated by the lower value. TSVM in those cases moves the separating surface towards the P/R break-even point which improves F-score. AUC is in general less sensitive to slight changes in the location of the separating surface, and, consequently, our observed AUC hardly changes (and if yes, then mostly negative). Overall, our results give no clear argument for the advantage of transductive learning, especially when an application needs either higher recall or higher precision. Note that TVSM drastically increases training time.
