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Supreme Court of the State of New York Appellate Division: Second Judicial Department
Supreme Court of the State of New York Appellate Division: Second Judicial Department A GLOSSARY OF TERMS FOR FORMATTING COMPUTER-GENERATED BRIEFS, WITH EXAMPLES The rules concerning the formatting of briefs are contained in CPLR 5529 and in § 1250.8 of the Practice Rules of the Appellate Division. Those rules cover technical matters and therefore use certain technical terms which may be unfamiliar to attorneys and litigants. The following glossary is offered as an aid to the understanding of the rules. Typeface: A typeface is a complete set of characters of a particular and consistent design for the composition of text, and is also called a font. Typefaces often come in sets which usually include a bold and an italic version in addition to the basic design. Proportionally Spaced Typeface: Proportionally spaced type is designed so that the amount of horizontal space each letter occupies on a line of text is proportional to the design of each letter, the letter i, for example, being narrower than the letter w. More text of the same type size fits on a horizontal line of proportionally spaced type than a horizontal line of the same length of monospaced type. This sentence is set in Times New Roman, which is a proportionally spaced typeface. Monospaced Typeface: In a monospaced typeface, each letter occupies the same amount of space on a horizontal line of text. This sentence is set in Courier, which is a monospaced typeface. Point Size: A point is a unit of measurement used by printers equal to approximately 1/72 of an inch. -
Unicode Nearly Plain-Text Encoding of Mathematics Murray Sargent III Office Authoring Services, Microsoft Corporation 4-Apr-06
Unicode Nearly Plain Text Encoding of Mathematics Unicode Nearly Plain-Text Encoding of Mathematics Murray Sargent III Office Authoring Services, Microsoft Corporation 4-Apr-06 1. Introduction ............................................................................................................ 2 2. Encoding Simple Math Expressions ...................................................................... 3 2.1 Fractions .......................................................................................................... 4 2.2 Subscripts and Superscripts........................................................................... 6 2.3 Use of the Blank (Space) Character ............................................................... 7 3. Encoding Other Math Expressions ........................................................................ 8 3.1 Delimiters ........................................................................................................ 8 3.2 Literal Operators ........................................................................................... 10 3.3 Prescripts and Above/Below Scripts........................................................... 11 3.4 n-ary Operators ............................................................................................. 11 3.5 Mathematical Functions ............................................................................... 12 3.6 Square Roots and Radicals ........................................................................... 13 3.7 Enclosures..................................................................................................... -
Proquest Dissertations
Automated learning of protein involvement in pathogenesis using integrated queries Eithon Cadag A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Washington 2009 Program Authorized to Offer Degree: Department of Medical Education and Biomedical Informatics UMI Number: 3394276 All rights reserved INFORMATION TO ALL USERS The quality of this reproduction is dependent upon the quality of the copy submitted. In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if material had to be removed, a note will indicate the deletion. UMI Dissertation Publishing UMI 3394276 Copyright 2010 by ProQuest LLC. All rights reserved. This edition of the work is protected against unauthorized copying under Title 17, United States Code. uest ProQuest LLC 789 East Eisenhower Parkway P.O. Box 1346 Ann Arbor, Ml 48106-1346 University of Washington Graduate School This is to certify that I have examined this copy of a doctoral dissertation by Eithon Cadag and have found that it is complete and satisfactory in all respects, and that any and all revisions required by the final examining committee have been made. Chair of the Supervisory Committee: Reading Committee: (SjLt KJ. £U*t~ Peter Tgffczy-Hornoch In presenting this dissertation in partial fulfillment of the requirements for the doctoral degree at the University of Washington, I agree that the Library shall make its copies freely available for inspection. I further agree that extensive copying of this dissertation is allowable only for scholarly purposes, consistent with "fair use" as prescribed in the U.S. -
5 the Rules of Typography
The Rules of Typography Typographic Terminology THE RULES OF TYPOGRAPHY Typographic Terminology TYPEFACE VS. FONT: Two Definitions Typeface Font THE FULL FAMILY vs. ONE WEIGHT A full family of fonts A member of a typeface family example: Helvetica Neue example: Helvetica Neue Bold THE DESIGN vs. THE DIGITAL FILE The intellectual property created A digital file of a typeface by a type designer THE RULES OF TYPOGRAPHY Typographic Terminology LEADING 16/20 16/29 “In a badly designed book, the letters “In a badly designed book, the letters Leading refers to the amount of space mill and stand like starving horses in a mill and stand like starving horses in a between lines of type using points as field. In a book designed by rote, they the measurement. The name was sit like stale bread and mutton on field. In a book designed by rote, they derived from the strips of lead that the page. In a well-made book, where sit like stale bread and mutton on were used during the typesetting designer, compositor and printer have process to create the space. Now we all done their jobs, no matter how the page. In a well-made book, where perform this digitally—also with many thousands of lines and pages, the designer, compositor and printer have consideration to the optimal setting letters are alive. They dance in their for any particular typeface. seats. Sometimes they rise and dance all done their jobs, no matter how When speaking about leading we in the margins and aisles.” many thousands of lines and pages, the first say the type size “on” the leading. -
Book Typography 101 at the End of This Session, Participants Should Be Able To: 1
3/21/2016 Objectives Book Typography 101 At the end of this session, participants should be able to: 1. Evaluate typeset pages for adherence Dick Margulis to traditional standards of good composition 2. Make sensible design recommendations to clients based on readability of text and clarity of communication © 2013–2016 Dick Margulis Creative Services © 2013–2016 Dick Margulis Creative Services What is typography? Typography encompasses • The design and layout of the printed or virtual page • The selection of fonts • The specification of typesetting variables • The actual composition of text © 2013–2016 Dick Margulis Creative Services © 2013–2016 Dick Margulis Creative Services What is typography? What is typography? The goal of good typography is to allow Typography that intrudes its own cleverness the unencumbered communication and interferes with the dialogue of the author’s meaning to the reader. between author and reader is almost always inappropriate. Assigned reading: “The Crystal Goblet,” by Beatrice Ward http://www.arts.ucsb.edu/faculty/reese/classes/artistsbooks/Beatrice%20Warde,%20The%20Crystal%20Goblet.pdf (or just google it) © 2013–2016 Dick Margulis Creative Services © 2013–2016 Dick Margulis Creative Services 1 3/21/2016 How we read The basics • Saccades • Page size and margins The quick brown fox jumps over the lazy dog. Mary had a little lamb, a little bread, a little jam. • Line length and leading • Boules • Justification My very educated mother just served us nine. • Typeface My very educated mother just served us nine. -
Generating Postscript Names for Fonts Using Opentype Font Variations
bc Adobe Enterprise & Developer Support Adobe Technical Note #5902 Generating PostScript Names for Fonts Using OpenType Font Variations Version 1.0, September 14 2016 1 Introduction This document specifies how to generate PostScript names for OpenType variable font instances. Please see the OpenType specification chapter, OpenType Font Variations Overview for an overview of variable fonts and related terms, and the chapter fvar - Font Variations Table for the terms specific to this discussion. The ‘fvar’ table optionally allows for PostScript names for named instances of an OpenType variations fonts to be specified. However, the PostScript names for named instances may be omitted, and there is no mechanism to provide a PostScript name for instances at arbitrary points in the variable font design space. This document describes how to generate PostScript names for such cases. This is useful in several workflows. For the foreseeable future, arbitrary instances of a variable font must be exported as a non- variable font for legacy applications and for printing: a PostScript name is required for this. The primary goal of the document is to provide a standard method for providing human readable PostScript names, using the instance font design space coordinates and axis tags. A secondary goal is to allow the PostScript name for a variable font instance to be used as a font reference, such that the design space coordinates of the instance can be recovered from the PostScript name. However, a descriptive PostScript name is possible only for a limited number of design axes, and some fonts may exceed this. For such fonts, a last resort method is described which serves the purpose of generating a PostScript name, but without semantic content. -
5Lesson 5: Web Page Layout and Elements
5Lesson 5: Web Page Layout and Elements Objectives By the end of this lesson, you will be able to: 1.1.14: Apply branding to a Web site. 2.1.1: Define and use common Web page design and layout elements (e.g., color, space, font size and style, lines, logos, symbols, pictograms, images, stationary features). 2.1.2: Determine ways that design helps and hinders audience participation (includes target audience, stakeholder expectations, cultural issues). 2.1.3: Manipulate space and content to create a visually balanced page/site that presents a coherent, unified message (includes symmetry, asymmetry, radial balance). 2.1.4: Use color and contrast to introduce variety, stimulate users and emphasize messages. 2.1.5: Use design strategies to control a user's focus on a page. 2.1.6: Apply strategies and tools for visual consistency to Web pages and site (e.g., style guides, page templates, image placement, navigation aids). 2.1.7: Convey a site's message, culture and tone (professional, casual, formal, informal) using images, colors, fonts, content style. 2.1.8: Eliminate unnecessary elements that distract from a page's message. 2.1.9: Design for typographical issues in printable content. 2.1.10: Design for screen resolution issues in online content. 2.2.1: Identify Web site characteristics and strategies to enable them, including interactivity, navigation, database integration. 2.2.9: Identify audience and end-user capabilities (e.g., lowest common denominator in usability). 3.1.3: Use hexadecimal values to specify colors in X/HTML. 3.3.7: Evaluate image colors to determine effectiveness in various cultures. -
Application Note C/C++ Coding Standard
Application Note C/C++ Coding Standard Document Revision J April 2013 Copyright © Quantum Leaps, LLC www.quantum-leaps.com www.state-machine.com Table of Contents 1 Goals..................................................................................................................................................................... 1 2 General Rules....................................................................................................................................................... 1 3 C/C++ Layout........................................................................................................................................................ 2 3.1 Expressions................................................................................................................................................... 2 3.2 Indentation..................................................................................................................................................... 3 3.2.1 The if Statement.................................................................................................................................. 4 3.2.2 The for Statement............................................................................................................................... 4 3.2.3 The while Statement........................................................................................................................... 4 3.2.4 The do..while Statement.....................................................................................................................5 -
Ryan Tibshirani
Ryan Tibshirani Carnegie Mellon University http://www.stat.cmu.edu/∼ryantibs/ Depts. of Statistics and Machine Learning [email protected] 229B Baker Hall 412.268.1884 Pittsburgh, PA 15213 Academic Positions Associate Professor (Tenured), Depts. of Statistics and Machine Learning, July 2016 { present Carnegie Mellon University Joint Appointment, Dept. of Machine Learning, Carnegie Mellon University Oct 2013 { present Assistant Professor, Dept. of Statistics, Carnegie Mellon University Aug 2011 { June 2016 Education Ph.D. in Statistics, Stanford University. Sept 2007 { Aug 2011 Thesis: \The Solution Path of the Generalized Lasso". Advisor: Jonathan Taylor. B.S. in Mathematics, Stanford University. Sept 2003 { June 2007 Minor in Computer Science. Grants, Awards, Honors \Theoretical Foundations of Deep Learning", Department of Defense (DoD) May 2020 { Apr 2025 Multidisciplinary University Research Initiative (MURI) grant. (co-PI, Rich Baraniuk is PI) \Delphi Influenza Forecasting Center of Excellence", Centers for Disease Sept 2019 | Aug 2024 Control and Prevention (CDC) grant no. U01IP001121, total award amount $3,000,000 (co-PI, Roni Rosenfeld is PI) \Improved Nowcasting via Adaptive Boosting of Highly Variable Biosurveil- Nov 2017 { May 2020 lance Data Sources", Defense Threat Reduction Agency (DTRA) grant no. HDTRA1-18-C-0008, total award amount $1,016,057 (co-PI, Roni Rosenfeld is PI) Teaching Innovation Award from Carnegie Mellon University Apr 2017 \Locally Adaptive Nonparametric Estimation for the Modern Age | New July 2016 { June 2021 Insights, Extensions, and Inference Tools", National Science Foundation (NSF) Division of Mathematical Sciences (DMS) CAREER grant no. 1554123, total award amount $400,000 (PI) \Graph Trend Filtering for Recommender Systems", Adobe Digital Marketing Sept 2014 { Sept 2015 Research Awards, total award amount $50,000, (co-PI, Alex Smola is PI) 1 \Advancing Theory and Computation in Statistical Learning Problems", July 2013 { June 2016 National Science Foundation (NSF) Division of Mathematical Sciences (DMS) grant no. -
High-Dimensional and Causal Inference by Simon James
High-dimensional and causal inference by Simon James Sweeney Walter A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Statistics in the Graduate Division of the University of California, Berkeley Committee in charge: Professor Bin Yu, Co-chair Professor Jasjeet Sekhon, Co-chair Professor Peter Bickel Assistant Professor Avi Feller Fall 2019 1 Abstract High-dimensional and causal inference by Simon James Sweeney Walter Doctor of Philosophy in Statistics University of California, Berkeley Professor Bin Yu, Co-chair Professor Jasjeet Sekhon, Co-chair High-dimensional and causal inference are topics at the forefront of statistical re- search. This thesis is a unified treatment of three contributions to these literatures. The first two contributions are to the theoretical statistical literature; the third puts the techniques of causal inference into practice in policy evaluation. In Chapter 2, we suggest a broadly applicable remedy for the failure of Efron’s bootstrap in high dimensions is to modify the bootstrap so that data vectors are broken into blocks and the blocks are resampled independently of one another. Cross- validation can be used effectively to choose the optimal block length. We show both theoretically and in numerical studies that this method restores consistency and has superior predictive performance when used in combination with Breiman’s bagging procedure. This chapter is joint work with Peter Hall and Hugh Miller. In Chapter 3, we investigate regression adjustment for the modified outcome (RAMO). An equivalent procedure is given in Rubin and van der Laan [2007] and then in Luedtke and van der Laan [2016]; philosophically similar ideas appear to originate in Miller [1976]. -
Pcredux Package - an Overview Stefan Rödiger, Michał Burdukiewicz, Andrej-Nikolai Spiess 2017-11-20
PCRedux Package - An Overview Stefan Rödiger, Michał Burdukiewicz, Andrej-Nikolai Spiess 2017-11-20 Contents 1 Introduction to the Analysis of Sigmoid Shaped Curves – or the Analysis of Amplification Curve Data from Quantitative real-time PCR Experiments 2 2 Aims of the PCRedux Package 3 3 Functions of the PCRedux Package 4 3.1 autocorrelation_test() - A Function to Detect Positive Amplification Curves . .4 3.2 decision_modus() - A Function to Get a Decision (Modus) from a Vector of Classes . .5 3.3 earlyreg() - A Function to Calculate the Slope and Intercept of Amplification Curve Data from a qPCR Experiment . 10 3.4 head2tailratio() - A Function to Calculate the Ratio of the Head and the Tail of a Quanti- tative PCR Amplification Curve . 14 3.5 hookreg() and hookregNL() - Functions to Detect Hook Effekt-like Curvatures . 16 3.6 mblrr() - A Function Perform the Quantile-filter Based Local Robust Regression . 18 3.7 pcrfit_single() - A Function to Extract Features from an Amplification Curve . 23 3.8 performeR() - Performance Analysis for Binary Classification . 29 3.9 qPCR2fdata() - A Helper Function to Convert Amplification Curve Data to the fdata Format 29 3.10 visdat_pcrfit() - A Function to Visualize the Content of Data From an Analysis with the pcrfit_single() Function . 34 4 Data Sets 36 4.1 Classified Data Sets . 36 4.2 Amplification Curve Data in the RDML Format . 37 5 Summary and Conclusions 37 References 37 PCRedux 1 C127EGHP data set htPCR data set A B 8 1.5 6 1.0 RFU RFU 4 0.5 2 0 0 10 20 30 40 0 5 10 15 20 25 30 35 Cycle Cycle Figure 1: Sample data from A) the C127EGHP data set with 64 amplification curves (chipPCR package, (Rödiger, Burdukiewicz, and Schierack 2015)) and B) from the htPCR data set with 8858 amplification curves (qpcR package, (Ritz and Spiess 2008)). -
Mathematics People
NEWS Mathematics People or up to ten years post-PhD, are eligible. Awardees receive Braverman Receives US$1 million distributed over five years. NSF Waterman Award —From an NSF announcement Mark Braverman of Princeton University has been selected as a Prizes of the Association cowinner of the 2019 Alan T. Wa- terman Award of the National Sci- for Women in Mathematics ence Foundation (NSF) for his work in complexity theory, algorithms, The Association for Women in Mathematics (AWM) has and the limits of what is possible awarded a number of prizes in 2019. computationally. According to the Catherine Sulem of the Univer- prize citation, his work “focuses on sity of Toronto has been named the Mark Braverman complexity, including looking at Sonia Kovalevsky Lecturer for 2019 by algorithms for optimization, which, the Association for Women in Math- when applied, might mean planning a route—how to get ematics (AWM) and the Society for from point A to point B in the most efficient way possible. Industrial and Applied Mathematics “Algorithms are everywhere. Most people know that (SIAM). The citation states: “Sulem every time someone uses a computer, algorithms are at is a prominent applied mathemati- work. But they also occur in nature. Braverman examines cian working in the area of nonlin- randomness in the motion of objects, down to the erratic Catherine Sulem ear analysis and partial differential movement of particles in a fluid. equations. She has specialized on “His work is also tied to algorithms required for learning, the topic of singularity development in solutions of the which serve as building blocks to artificial intelligence, and nonlinear Schrödinger equation (NLS), on the problem of has even had implications for the foundations of quantum free surface water waves, and on Hamiltonian partial differ- computing.