Computer Basics, Representation of Characters in Computers
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Package 'Pinsplus'
Package ‘PINSPlus’ August 6, 2020 Encoding UTF-8 Type Package Title Clustering Algorithm for Data Integration and Disease Subtyping Version 2.0.5 Date 2020-08-06 Author Hung Nguyen, Bang Tran, Duc Tran and Tin Nguyen Maintainer Hung Nguyen <[email protected]> Description Provides a robust approach for omics data integration and disease subtyping. PIN- SPlus is fast and supports the analysis of large datasets with hundreds of thousands of sam- ples and features. The software automatically determines the optimal number of clus- ters and then partitions the samples in a way such that the results are ro- bust against noise and data perturbation (Nguyen et.al. (2019) <DOI: 10.1093/bioinformat- ics/bty1049>, Nguyen et.al. (2017)<DOI: 10.1101/gr.215129.116>). License LGPL Depends R (>= 2.10) Imports foreach, entropy , doParallel, matrixStats, Rcpp, RcppParallel, FNN, cluster, irlba, mclust RoxygenNote 7.1.0 Suggests knitr, rmarkdown, survival, markdown LinkingTo Rcpp, RcppArmadillo, RcppParallel VignetteBuilder knitr NeedsCompilation yes Repository CRAN Date/Publication 2020-08-06 21:20:02 UTC R topics documented: PINSPlus-package . .2 AML2004 . .2 KIRC ............................................3 PerturbationClustering . .4 SubtypingOmicsData . .9 1 2 AML2004 Index 13 PINSPlus-package Perturbation Clustering for data INtegration and disease Subtyping Description This package implements clustering algorithms proposed by Nguyen et al. (2017, 2019). Pertur- bation Clustering for data INtegration and disease Subtyping (PINS) is an approach for integraton of data and classification of diseases into various subtypes. PINS+ provides algorithms support- ing both single data type clustering and multi-omics data type. PINSPlus is an improved version of PINS by allowing users to customize the based clustering algorithm and perturbation methods. -
Generalized Linear Models (Glms)
San Jos´eState University Math 261A: Regression Theory & Methods Generalized Linear Models (GLMs) Dr. Guangliang Chen This lecture is based on the following textbook sections: • Chapter 13: 13.1 – 13.3 Outline of this presentation: • What is a GLM? • Logistic regression • Poisson regression Generalized Linear Models (GLMs) What is a GLM? In ordinary linear regression, we assume that the response is a linear function of the regressors plus Gaussian noise: 0 2 y = β0 + β1x1 + ··· + βkxk + ∼ N(x β, σ ) | {z } |{z} linear form x0β N(0,σ2) noise The model can be reformulate in terms of • distribution of the response: y | x ∼ N(µ, σ2), and • dependence of the mean on the predictors: µ = E(y | x) = x0β Dr. Guangliang Chen | Mathematics & Statistics, San Jos´e State University3/24 Generalized Linear Models (GLMs) beta=(1,2) 5 4 3 β0 + β1x b y 2 y 1 0 −1 0.0 0.2 0.4 0.6 0.8 1.0 x x Dr. Guangliang Chen | Mathematics & Statistics, San Jos´e State University4/24 Generalized Linear Models (GLMs) Generalized linear models (GLM) extend linear regression by allowing the response variable to have • a general distribution (with mean µ = E(y | x)) and • a mean that depends on the predictors through a link function g: That is, g(µ) = β0x or equivalently, µ = g−1(β0x) Dr. Guangliang Chen | Mathematics & Statistics, San Jos´e State University5/24 Generalized Linear Models (GLMs) In GLM, the response is typically assumed to have a distribution in the exponential family, which is a large class of probability distributions that have pdfs of the form f(x | θ) = a(x)b(θ) exp(c(θ) · T (x)), including • Normal - ordinary linear regression • Bernoulli - Logistic regression, modeling binary data • Binomial - Multinomial logistic regression, modeling general cate- gorical data • Poisson - Poisson regression, modeling count data • Exponential, Gamma - survival analysis Dr. -
"Computers" Abacus—The First Calculator
Component 4: Introduction to Information and Computer Science Unit 1: Basic Computing Concepts, Including History Lecture 4 BMI540/640 Week 1 This material was developed by Oregon Health & Science University, funded by the Department of Health and Human Services, Office of the National Coordinator for Health Information Technology under Award Number IU24OC000015. The First "Computers" • The word "computer" was first recorded in 1613 • Referred to a person who performed calculations • Evidence of counting is traced to at least 35,000 BC Ishango Bone Tally Stick: Science Museum of Brussels Component 4/Unit 1-4 Health IT Workforce Curriculum 2 Version 2.0/Spring 2011 Abacus—The First Calculator • Invented by Babylonians in 2400 BC — many subsequent versions • Used for counting before there were written numbers • Still used today The Chinese Lee Abacus http://www.ee.ryerson.ca/~elf/abacus/ Component 4/Unit 1-4 Health IT Workforce Curriculum 3 Version 2.0/Spring 2011 1 Slide Rules John Napier William Oughtred • By the Middle Ages, number systems were developed • John Napier discovered/developed logarithms at the turn of the 17 th century • William Oughtred used logarithms to invent the slide rude in 1621 in England • Used for multiplication, division, logarithms, roots, trigonometric functions • Used until early 70s when electronic calculators became available Component 4/Unit 1-4 Health IT Workforce Curriculum 4 Version 2.0/Spring 2011 Mechanical Computers • Use mechanical parts to automate calculations • Limited operations • First one was the ancient Antikythera computer from 150 BC Used gears to calculate position of sun and moon Fragment of Antikythera mechanism Component 4/Unit 1-4 Health IT Workforce Curriculum 5 Version 2.0/Spring 2011 Leonardo da Vinci 1452-1519, Italy Leonardo da Vinci • Two notebooks discovered in 1967 showed drawings for a mechanical calculator • A replica was built soon after Leonardo da Vinci's notes and the replica The Controversial Replica of Leonardo da Vinci's Adding Machine . -
UNIVAC I Computer System
Saved from the Internet on 3/9/2010 Created by Allan Reiter UNIVAC I Computer System After you look at this yellow page go to the blue page to find out how UNIVAC I really worked. My name is Allan Reiter and in 1954 began my career with a company in St Paul, Minnesota called Engineering Research Associates (ERA) that was part of the Remington Rand Corporation. I was hired with 3 friends, Paul S. Lawson, Vernon Sandoz, and Robert Kress. We were buddies who met in the USAF where we were trained and worked on airborne radar on B-50 airplanes. In a way this was the start of our computer career because the radar was controlled by an analog computer known as the Q- 24. After discharge from the USAF Paul from Indiana and Vernon from Texas drove up to Minnesota to visit me. They said they were looking for jobs. We picked up a newspaper and noticed an ad that sounded interesting and decided to check it out. The ad said they wanted people with military experience in electronics. All three of us were hired at 1902 West Minnehaha in St. Paul. We then looked up Robert Kress (from Iowa) and he was hired a few days later. The four of us then left for Philadelphia with three cars and one wife. Robert took his wife Brenda along. This was where the UNIVAC I was being built. Parts of the production facilities were on an upper floor of a Pep Boys building. Another building on Allegheny Avenue had a hydraulic elevator operated with water pressure. -
Bit Nove Signal Interface Processor
www.audison.eu bit Nove Signal Interface Processor POWER SUPPLY CONNECTION Voltage 10.8 ÷ 15 VDC From / To Personal Computer 1 x Micro USB Operating power supply voltage 7.5 ÷ 14.4 VDC To Audison DRC AB / DRC MP 1 x AC Link Idling current 0.53 A Optical 2 sel Optical In 2 wire control +12 V enable Switched off without DRC 1 mA Mem D sel Memory D wire control GND enable Switched off with DRC 4.5 mA CROSSOVER Remote IN voltage 4 ÷ 15 VDC (1mA) Filter type Full / Hi pass / Low Pass / Band Pass Remote OUT voltage 10 ÷ 15 VDC (130 mA) Linkwitz @ 12/24 dB - Butterworth @ Filter mode and slope ART (Automatic Remote Turn ON) 2 ÷ 7 VDC 6/12/18/24 dB Crossover Frequency 68 steps @ 20 ÷ 20k Hz SIGNAL STAGE Phase control 0° / 180° Distortion - THD @ 1 kHz, 1 VRMS Output 0.005% EQUALIZER (20 ÷ 20K Hz) Bandwidth @ -3 dB 10 Hz ÷ 22 kHz S/N ratio @ A weighted Analog Input Equalizer Automatic De-Equalization N.9 Parametrics Equalizers: ±12 dB;10 pole; Master Input 102 dBA Output Equalizer 20 ÷ 20k Hz AUX Input 101.5 dBA OPTICAL IN1 / IN2 Inputs 110 dBA TIME ALIGNMENT Channel Separation @ 1 kHz 85 dBA Distance 0 ÷ 510 cm / 0 ÷ 200.8 inches Input sensitivity Pre Master 1.2 ÷ 8 VRMS Delay 0 ÷ 15 ms Input sensitivity Speaker Master 3 ÷ 20 VRMS Step 0,08 ms; 2,8 cm / 1.1 inch Input sensitivity AUX Master 0.3 ÷ 5 VRMS Fine SET 0,02 ms; 0,7 cm / 0.27 inch Input impedance Pre In / Speaker In / AUX 15 kΩ / 12 Ω / 15 kΩ GENERAL REQUIREMENTS Max Output Level (RMS) @ 0.1% THD 4 V PC connections USB 1.1 / 2.0 / 3.0 Compatible Microsoft Windows (32/64 bit): Vista, INPUT STAGE Software/PC requirements Windows 7, Windows 8, Windows 10 Low level (Pre) Ch1 ÷ Ch6; AUX L/R Video Resolution with screen resize min. -
Alternative Perspectives 3.1
Alternative Perspectives 3.1 Chapter 3: ALTERNATIVE PERSPECTIVES Extending Our Understanding of the Relationships Among Devices In the previous chapter, the grain at which we looked at input devices was fairly coarse, especially if our orientation is the user and usage, and not the technology. If we want to probe deeper, characterizing devices as "mice”, "tablets" or "joysticks" is not adequate. While useful, they are not detailed enough to provide us with the understanding that will enable us to make significant improvements in our interface designs. The design space of input devices is complex. In order to achieve a reasonable grasp of it, we have to refine the grain of our analysis to something far finer than has hitherto been the case. In the sections which follow, we explore some of the approaches to carving up this space in ways meaningful to the designer. If design is choice, then developing a more refined taxonomy will improve the range of choice. And, if the dimensions of the resultant taxonomy are appropriate, the model that emerges will afford better choices. As a start, let us take an example. It illustrates that - even at the top level - the dominant mouse, joystick, trackball ... categorization is not the only way to carve up the "pie." Figure 1 shows a caricature of four generic devices: a touch screen a light pen a touch tablet a tablet with a stylus. Haptic Input 14 September, 2009 Buxton Alternative Perspectives 3.2 (a) (b) (c) (d) Figure 1: Analogy and relationships among different devices The devices characterized in this figure possess some important properties that help us better understand input technologies in context. -
The Hexadecimal Number System and Memory Addressing
C5537_App C_1107_03/16/2005 APPENDIX C The Hexadecimal Number System and Memory Addressing nderstanding the number system and the coding system that computers use to U store data and communicate with each other is fundamental to understanding how computers work. Early attempts to invent an electronic computing device met with disappointing results as long as inventors tried to use the decimal number sys- tem, with the digits 0–9. Then John Atanasoff proposed using a coding system that expressed everything in terms of different sequences of only two numerals: one repre- sented by the presence of a charge and one represented by the absence of a charge. The numbering system that can be supported by the expression of only two numerals is called base 2, or binary; it was invented by Ada Lovelace many years before, using the numerals 0 and 1. Under Atanasoff’s design, all numbers and other characters would be converted to this binary number system, and all storage, comparisons, and arithmetic would be done using it. Even today, this is one of the basic principles of computers. Every character or number entered into a computer is first converted into a series of 0s and 1s. Many coding schemes and techniques have been invented to manipulate these 0s and 1s, called bits for binary digits. The most widespread binary coding scheme for microcomputers, which is recog- nized as the microcomputer standard, is called ASCII (American Standard Code for Information Interchange). (Appendix B lists the binary code for the basic 127- character set.) In ASCII, each character is assigned an 8-bit code called a byte. -
Midterm-2020-Solution.Pdf
HONOR CODE Questions Sheet. A Lets C. [6 Points] 1. What type of address (heap,stack,static,code) does each value evaluate to Book1, Book1->name, Book1->author, &Book2? [4] 2. Will all of the print statements execute as expected? If NO, write print statement which will not execute as expected?[2] B. Mystery [8 Points] 3. When the above code executes, which line is modified? How many times? [2] 4. What is the value of register a6 at the end ? [2] 5. What is the value of register a4 at the end ? [2] 6. In one sentence what is this program calculating ? [2] C. C-to-RISC V Tree Search; Fill in the blanks below [12 points] D. RISCV - The MOD operation [8 points] 19. The data segment starts at address 0x10000000. What are the memory locations modified by this program and what are their values ? E Floating Point [8 points.] 20. What is the smallest nonzero positive value that can be represented? Write your answer as a numerical expression in the answer packet? [2] 21. Consider some positive normalized floating point number where p is represented as: What is the distance (i.e. the difference) between p and the next-largest number after p that can be represented? [2] 22. Now instead let p be a positive denormalized number described asp = 2y x 0.significand. What is the distance between p and the next largest number after p that can be represented? [2] 23. Sort the following minifloat numbers. [2] F. Numbers. [5] 24. What is the smallest number that this system can represent 6 digits (assume unsigned) ? [1] 25. -
Lecture 2: Variables and Primitive Data Types
Lecture 2: Variables and Primitive Data Types MIT-AITI Kenya 2005 1 In this lecture, you will learn… • What a variable is – Types of variables – Naming of variables – Variable assignment • What a primitive data type is • Other data types (ex. String) MIT-Africa Internet Technology Initiative 2 ©2005 What is a Variable? • In basic algebra, variables are symbols that can represent values in formulas. • For example the variable x in the formula f(x)=x2+2 can represent any number value. • Similarly, variables in computer program are symbols for arbitrary data. MIT-Africa Internet Technology Initiative 3 ©2005 A Variable Analogy • Think of variables as an empty box that you can put values in. • We can label the box with a name like “Box X” and re-use it many times. • Can perform tasks on the box without caring about what’s inside: – “Move Box X to Shelf A” – “Put item Z in box” – “Open Box X” – “Remove contents from Box X” MIT-Africa Internet Technology Initiative 4 ©2005 Variables Types in Java • Variables in Java have a type. • The type defines what kinds of values a variable is allowed to store. • Think of a variable’s type as the size or shape of the empty box. • The variable x in f(x)=x2+2 is implicitly a number. • If x is a symbol representing the word “Fish”, the formula doesn’t make sense. MIT-Africa Internet Technology Initiative 5 ©2005 Java Types • Integer Types: – int: Most numbers you’ll deal with. – long: Big integers; science, finance, computing. – short: Small integers. -
Signedness-Agnostic Program Analysis: Precise Integer Bounds for Low-Level Code
Signedness-Agnostic Program Analysis: Precise Integer Bounds for Low-Level Code Jorge A. Navas, Peter Schachte, Harald Søndergaard, and Peter J. Stuckey Department of Computing and Information Systems, The University of Melbourne, Victoria 3010, Australia Abstract. Many compilers target common back-ends, thereby avoid- ing the need to implement the same analyses for many different source languages. This has led to interest in static analysis of LLVM code. In LLVM (and similar languages) most signedness information associated with variables has been compiled away. Current analyses of LLVM code tend to assume that either all values are signed or all are unsigned (except where the code specifies the signedness). We show how program analysis can simultaneously consider each bit-string to be both signed and un- signed, thus improving precision, and we implement the idea for the spe- cific case of integer bounds analysis. Experimental evaluation shows that this provides higher precision at little extra cost. Our approach turns out to be beneficial even when all signedness information is available, such as when analysing C or Java code. 1 Introduction The “Low Level Virtual Machine” LLVM is rapidly gaining popularity as a target for compilers for a range of programming languages. As a result, the literature on static analysis of LLVM code is growing (for example, see [2, 7, 9, 11, 12]). LLVM IR (Intermediate Representation) carefully specifies the bit- width of all integer values, but in most cases does not specify whether values are signed or unsigned. This is because, for most operations, two’s complement arithmetic (treating the inputs as signed numbers) produces the same bit-vectors as unsigned arithmetic. -
Chapter 4 Variables and Data Types
PROG0101 Fundamentals of Programming PROG0101 FUNDAMENTALS OF PROGRAMMING Chapter 4 Variables and Data Types 1 PROG0101 Fundamentals of Programming Variables and Data Types Topics • Variables • Constants • Data types • Declaration 2 PROG0101 Fundamentals of Programming Variables and Data Types Variables • A symbol or name that stands for a value. • A variable is a value that can change. • Variables provide temporary storage for information that will be needed during the lifespan of the computer program (or application). 3 PROG0101 Fundamentals of Programming Variables and Data Types Variables Example: z = x + y • This is an example of programming expression. • x, y and z are variables. • Variables can represent numeric values, characters, character strings, or memory addresses. 4 PROG0101 Fundamentals of Programming Variables and Data Types Variables • Variables store everything in your program. • The purpose of any useful program is to modify variables. • In a program every, variable has: – Name (Identifier) – Data Type – Size – Value 5 PROG0101 Fundamentals of Programming Variables and Data Types Types of Variable • There are two types of variables: – Local variable – Global variable 6 PROG0101 Fundamentals of Programming Variables and Data Types Types of Variable • Local variables are those that are in scope within a specific part of the program (function, procedure, method, or subroutine, depending on the programming language employed). • Global variables are those that are in scope for the duration of the programs execution. They can be accessed by any part of the program, and are read- write for all statements that access them. 7 PROG0101 Fundamentals of Programming Variables and Data Types Types of Variable MAIN PROGRAM Subroutine Global Variables Local Variable 8 PROG0101 Fundamentals of Programming Variables and Data Types Rules in Naming a Variable • There a certain rules in naming variables (identifier). -
Class-4 Computer L-2 Input and Output Devices
CLASS-4 COMPUTER L-2 INPUT AND OUTPUT DEVICES BOOK EXERCISE A. Tick () the correct options. 1. Which of the following is NOT an input device? a. touchpad ( ) b. projector () c. MICR ( ) 2. What does OCR stands for? a. Optical Character Recognition () b. Oriented Character Recognition ( ) c. Optical Copy Recognition ( ) 3. A plotter prints on paper by using . a. A stylus ( ) b. pencils ( ) c. pens () 4. Which of the following is an output device? a. projector ( ) b. laser printer ( ) c. both a and b () B. Fill in the blanks. Picture barcode biometric projection MICR typeface 1. A barcode is a pattern of parallel lines of varying width printed on different products. 2. OCR does not treat the text as picture. 3. A projector projects an image (or moving images) onto a large surface known as projection screen. 4. The MICR technology recognizes the data printed bin the MICR typeface. 5. A biometric device uses fingerprint, facial scans or voice recognition to identify users. CLASS-4 COMPUTER L-2 INPUT AND OUTPUT DEVICES C. Identify each of the following as input or output devices. Projector, Light pen, Touchpad, Touchscreen, web-cam, Monitor, Printer, Plotter, Keyboard, Mouse, MICR, Speakers, Scanner, OCR, Microphone. Ans: Input Devices Output Devices MICR Projector Touchpad Monitor Scanner Printer Touchscreen Speakers Keyboard Plotter OCR Web Cam Mouse Microphone D. Answer in one word- 1. A latest input device enables you to choose options on the computer screen by simply touching with a finger. (Touchscreen) 2. A device that projects an image onto a large surface. (Projector) 3. A device that draws on paper with one or more automated pens.