CS107 2Nd Midterm Examination
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Lab 7: Floating-Point Addition 0.0
Lab 7: Floating-Point Addition 0.0 Introduction In this lab, you will write a MIPS assembly language function that performs floating-point addition. You will then run your program using PCSpim (just as you did in Lab 6). For testing, you are provided a program that calls your function to compute the value of the mathematical constant e. For those with no assembly language experience, this will be a long lab, so plan your time accordingly. Background You should be familiar with the IEEE 754 Floating-Point Standard, which is described in Section 3.6 of your book. (Hopefully you have read that section carefully!) Here we will be dealing only with single precision floating- point values, which are formatted as follows (this is also described in the “Floating-Point Representation” subsec- tion of Section 3.6 in your book): Sign Exponent (8 bits) Significand (23 bits) 31 30 29 ... 24 23 22 21 ... 0 Remember that the exponent is biased by 127, which means that an exponent of zero is represented by 127 (01111111). The exponent is not encoded using 2s-complement. The significand is always positive, and the sign bit is kept separately. Note that the actual significand is 24 bits long; the first bit is always a 1 and thus does not need to be stored explicitly. This will be important to remember when you write your function! There are several details of IEEE 754 that you will not have to worry about in this lab. For example, the expo- nents 00000000 and 11111111 are reserved for special purposes that are described in your book (representing zero, denormalized numbers and NaNs). -
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. -
Harnessing Numerical Flexibility for Deep Learning on Fpgas.Pdf
WHITE PAPER FPGA Inline Acceleration Harnessing Numerical Flexibility for Deep Learning on FPGAs Authors Abstract Andrew C . Ling Deep learning has become a key workload in the data center and the edge, leading [email protected] to a race for dominance in this space. FPGAs have shown they can compete by combining deterministic low latency with high throughput and flexibility. In Mohamed S . Abdelfattah particular, FPGAs bit-level programmability can efficiently implement arbitrary [email protected] precisions and numeric data types critical in the fast evolving field of deep learning. Andrew Bitar In this paper, we explore FPGA minifloat implementations (floating-point [email protected] representations with non-standard exponent and mantissa sizes), and show the use of a block-floating-point implementation that shares the exponent across David Han many numbers, reducing the logic required to perform floating-point operations. [email protected] The paper shows this technique can significantly improve FPGA performance with no impact to accuracy, reduce logic utilization by 3X, and memory bandwidth and Roberto Dicecco capacity required by more than 40%.† [email protected] Suchit Subhaschandra Introduction [email protected] Deep neural networks have proven to be a powerful means to solve some of the Chris N Johnson most difficult computer vision and natural language processing problems since [email protected] their successful introduction to the ImageNet competition in 2012 [14]. This has led to an explosion of workloads based on deep neural networks in the data center Dmitry Denisenko and the edge [2]. [email protected] One of the key challenges with deep neural networks is their inherent Josh Fender computational complexity, where many deep nets require billions of operations [email protected] to perform a single inference. -
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. -
POINTER (IN C/C++) What Is a Pointer?
POINTER (IN C/C++) What is a pointer? Variable in a program is something with a name, the value of which can vary. The way the compiler and linker handles this is that it assigns a specific block of memory within the computer to hold the value of that variable. • The left side is the value in memory. • The right side is the address of that memory Dereferencing: • int bar = *foo_ptr; • *foo_ptr = 42; // set foo to 42 which is also effect bar = 42 • To dereference ted, go to memory address of 1776, the value contain in that is 25 which is what we need. Differences between & and * & is the reference operator and can be read as "address of“ * is the dereference operator and can be read as "value pointed by" A variable referenced with & can be dereferenced with *. • Andy = 25; • Ted = &andy; All expressions below are true: • andy == 25 // true • &andy == 1776 // true • ted == 1776 // true • *ted == 25 // true How to declare pointer? • Type + “*” + name of variable. • Example: int * number; • char * c; • • number or c is a variable is called a pointer variable How to use pointer? • int foo; • int *foo_ptr = &foo; • foo_ptr is declared as a pointer to int. We have initialized it to point to foo. • foo occupies some memory. Its location in memory is called its address. &foo is the address of foo Assignment and pointer: • int *foo_pr = 5; // wrong • int foo = 5; • int *foo_pr = &foo; // correct way Change the pointer to the next memory block: • int foo = 5; • int *foo_pr = &foo; • foo_pr ++; Pointer arithmetics • char *mychar; // sizeof 1 byte • short *myshort; // sizeof 2 bytes • long *mylong; // sizeof 4 byts • mychar++; // increase by 1 byte • myshort++; // increase by 2 bytes • mylong++; // increase by 4 bytes Increase pointer is different from increase the dereference • *P++; // unary operation: go to the address of the pointer then increase its address and return a value • (*P)++; // get the value from the address of p then increase the value by 1 Arrays: • int array[] = {45,46,47}; • we can call the first element in the array by saying: *array or array[0]. -
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). -
Subtyping Recursive Types
ACM Transactions on Programming Languages and Systems, 15(4), pp. 575-631, 1993. Subtyping Recursive Types Roberto M. Amadio1 Luca Cardelli CNRS-CRIN, Nancy DEC, Systems Research Center Abstract We investigate the interactions of subtyping and recursive types, in a simply typed λ-calculus. The two fundamental questions here are whether two (recursive) types are in the subtype relation, and whether a term has a type. To address the first question, we relate various definitions of type equivalence and subtyping that are induced by a model, an ordering on infinite trees, an algorithm, and a set of type rules. We show soundness and completeness between the rules, the algorithm, and the tree semantics. We also prove soundness and a restricted form of completeness for the model. To address the second question, we show that to every pair of types in the subtype relation we can associate a term whose denotation is the uniquely determined coercion map between the two types. Moreover, we derive an algorithm that, when given a term with implicit coercions, can infer its least type whenever possible. 1This author's work has been supported in part by Digital Equipment Corporation and in part by the Stanford-CNR Collaboration Project. Page 1 Contents 1. Introduction 1.1 Types 1.2 Subtypes 1.3 Equality of Recursive Types 1.4 Subtyping of Recursive Types 1.5 Algorithm outline 1.6 Formal development 2. A Simply Typed λ-calculus with Recursive Types 2.1 Types 2.2 Terms 2.3 Equations 3. Tree Ordering 3.1 Subtyping Non-recursive Types 3.2 Folding and Unfolding 3.3 Tree Expansion 3.4 Finite Approximations 4. -
The Art of the Javascript Metaobject Protocol
The Art Of The Javascript Metaobject Protocol enough?Humphrey Ephraim never recalculate remains giddying: any precentorship she expostulated exasperated her nuggars west, is brocade Gus consultative too around-the-clock? and unbloody If dog-cheapsycophantical and or secularly, norman Partha how slicked usually is volatilisingPenrod? his nomadism distresses acceptedly or interlacing Card, and send an email to a recipient with. On Auslegung auf are Schallabstrahlung download the Aerodynamik von modernen Flugtriebwerken. This poll i send a naming convention, the art of metaobject protocol for the corresponding to. What might happen, for support, if you should load monkeypatched code in one ruby thread? What Hooks does Ruby have for Metaprogramming? Sass, less, stylus, aura, etc. If it finds one, it calls that method and passes itself as value object. What bin this optimization achieve? JRuby and the psd. Towards a new model of abstraction in software engineering. Buy Online in Aruba at aruba. The current run step approach is: Checkpoint. Python object room to provide usable string representations of hydrogen, one used for debugging and logging, another for presentation to end users. Method handles can we be used to implement polymorphic inline caches. Mop is not the metaobject? Rails is a nicely designed web framework. Get two FREE Books of character Moment sampler! The download the number IS still thought. This proxy therefore behaves equivalently to the card dispatch function, and no methods will be called on the proxy dispatcher before but real dispatcher is available. While desertcart makes reasonable efforts to children show products available in your kid, some items may be cancelled if funny are prohibited for import in Aruba. -
Julia's Efficient Algorithm for Subtyping Unions and Covariant
Julia’s Efficient Algorithm for Subtyping Unions and Covariant Tuples Benjamin Chung Northeastern University, Boston, MA, USA [email protected] Francesco Zappa Nardelli Inria of Paris, Paris, France [email protected] Jan Vitek Northeastern University, Boston, MA, USA Czech Technical University in Prague, Czech Republic [email protected] Abstract The Julia programming language supports multiple dispatch and provides a rich type annotation language to specify method applicability. When multiple methods are applicable for a given call, Julia relies on subtyping between method signatures to pick the correct method to invoke. Julia’s subtyping algorithm is surprisingly complex, and determining whether it is correct remains an open question. In this paper, we focus on one piece of this problem: the interaction between union types and covariant tuples. Previous work normalized unions inside tuples to disjunctive normal form. However, this strategy has two drawbacks: complex type signatures induce space explosion, and interference between normalization and other features of Julia’s type system. In this paper, we describe the algorithm that Julia uses to compute subtyping between tuples and unions – an algorithm that is immune to space explosion and plays well with other features of the language. We prove this algorithm correct and complete against a semantic-subtyping denotational model in Coq. 2012 ACM Subject Classification Theory of computation → Type theory Keywords and phrases Type systems, Subtyping, Union types Digital Object Identifier 10.4230/LIPIcs.ECOOP.2019.24 Category Pearl Supplement Material ECOOP 2019 Artifact Evaluation approved artifact available at https://dx.doi.org/10.4230/DARTS.5.2.8 Acknowledgements The authors thank Jiahao Chen for starting us down the path of understanding Julia, and Jeff Bezanson for coming up with Julia’s subtyping algorithm. -
Does Personality Matter? Temperament and Character Dimensions in Panic Subtypes
325 Arch Neuropsychiatry 2018;55:325−329 RESEARCH ARTICLE https://doi.org/10.5152/npa.2017.20576 Does Personality Matter? Temperament and Character Dimensions in Panic Subtypes Antonio BRUNO1 , Maria Rosaria Anna MUSCATELLO1 , Gianluca PANDOLFO1 , Giulia LA CIURA1 , Diego QUATTRONE2 , Giuseppe SCIMECA1 , Carmela MENTO1 , Rocco A. ZOCCALI1 1Department of Psychiatry, University of Messina, Messina, Italy 2MRC Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, London, United Kingdom ABSTRACT Introduction: Symptomatic heterogeneity in the clinical presentation of and 12.78% of the total variance. Correlations analyses showed that Panic Disorder (PD) has lead to several attempts to identify PD subtypes; only “Somato-dissociative” factor was significantly correlated with however, no studies investigated the association between temperament T.C.I. “Self-directedness” (p<0.0001) and “Cooperativeness” (p=0.009) and character dimensions and PD subtypes. The study was aimed to variables. Results from the regression analysis indicate that the predictor verify whether personality traits were differentially related to distinct models account for 33.3% and 24.7% of the total variance respectively symptom dimensions. in “Somatic-dissociative” (p<0.0001) and “Cardiologic” (p=0.007) factors, while they do not show statistically significant effects on “Respiratory” Methods: Seventy-four patients with PD were assessed by the factor (p=0.222). After performing stepwise regression analysis, “Self- Mini-International Neuropsychiatric Interview (M.I.N.I.), and the directedness” resulted the unique predictor of “Somato-dissociative” Temperament and Character Inventory (T.C.I.). Thirteen panic symptoms factor (R²=0.186; β=-0.432; t=-4.061; p<0.0001). from the M.I.N.I.