PPT-Nested Refinement Types

for Dynamic Languages Ravi Chugh 2 What are Dynamic Languages Untyped and characterized by common features Reflective typetests typeof x number Dictionarystyle objects . Maximize agreement with diffraction data. Minimize R-factor. Maximize ideality of stereochemistry. Minimize deviation from ideal bond lengths and angles. S. |F. obs. -F. calc. |. S. |F. obs. |. hkl. hkl. Swanand. Gore & Gerard . Kleywegt. May 6. th. 2010, 12-1 pm. Macromolecular Crystallography Course. Outline. Intuitive idea of resolution – why higher order diffraction is better.. Parameters, model, observations, refinement – more data is better.. Michael Butler. University of Southampton. users.ecs.soton.ac.uk/mjb. Motivation. In a refinement based approach it is beneficial to model systems . abstractly . with . little architectural structure . We cannot complete that refinement due to a little problem: in order to get the new values and , we need not only the values of and just produced by the recursive call, , which was not sav Maximize agreement with diffraction data. Minimize R-factor. Maximize ideality of stereochemistry. Minimize deviation from ideal bond lengths and angles. S. |F. obs. -F. calc. |. S. |F. obs. |. hkl. hkl. Niki. Vazou. 1. , Patrick M. Rondon. 2. , and . Ranjit. Jhala. 1. 1. UC San Diego . 2. Google . 1. Vanilla Types. 12 :: . Int. 2. Refinement Types. 12 :: . {. . v: . Int. . |. v > 10. Inference. Hiroshi Unno (University of Tsukuba). Joint work with: Naoki Kobayashi, . Tachio. Terauchi, . Ryosuke. Sato, Takuya . Kuwahara. , . Kodai. Hashimoto, . Sho. Torii. 2015/7/4. HOPA 2015. if. Lesson. CS1313 Spring 2017. 1. Nested. . if. . Lesson Outline. Nested. if. Lesson . Outline. A Complicated . if. Example #1. A Complicated . if. Example #2. A Complicated . if. Example #3. procedure for . native structure. phenix.refine. . yourcoords.pdb. . m230d_2017_scaled.mtz . refinement.input.xray_data.labels. =". FP_native-cris. . SIGFP_native-cris. “ . output.prefix. =. nativeround1. . Patrick M. . Rondon. , Ming Kawaguchi, . Ranjit. . Jhala. University of California, San Diego. Refinement Type Inference. (Invariant Discovery). Liquid Types. C Program Verification Vs. Liquid Types. Sarah E. Cusson. 1. , Marcel P. Georgin. 2. , Ethan T. Dale. 1. , . Vira. Dhaliwal. 1,. and Alec D. Gallimore. 1. 1. Department of Aerospace Engineering, University of Michigan; . 2. Applied Physics Program, University of Michigan . Marron out of town next week.. Please fill out Course Review. Image Object Representation. Major Approaches for Image Data Objects:. Landmark Representations. Boundary Representations. Medial Representations. Fig S2yLi EuA-DRietveld refinement of the SXRD patterns of Na099-xxAl1-xSi1xO4yLi 001EuNASO yLi Eu 0 y 015 samplesTable S1 Main parameters of processing and refinement of the NxASO 0 x 025 and NxASO J Gynecol Oncol Vol. 19, No. 4:251-255, 2008Hong-Bum Cho, et al.252Table 1. Sequences of consensus oligonucleotides used to amplifica-tion on and geneNameSequence (5' to 3')TargetReferencesGP5+GP6+G