Units in the VO Version REC-1.0
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HARD DRIVES How Is Data Read and Stored on a Hard Drive?
HARD DRIVES Alternatively referred to as a hard disk drive and abbreviated as HD or HDD, thehard drive is the computer's main storage media device that permanently stores all data on the computer. The hard drive was first introduced on September 13, 1956 and consists of one or more hard drive platters inside of air sealed casing. Most computer hard drives are in an internal drive bay at the front of the computer and connect to themotherboard using either ATA, SCSI, or a SATA cable and power cable. Below, is a picture of what the inside of a hard drive looks like for a desktop and laptop hard drive. As can be seen in the above picture, the desktop hard drive has six components: the head actuator, read/write actuator arm, read/write head, spindle, and platter. On the back of a hard drive is a circuit board called the disk controller. Tip: New users often confuse memory (RAM) with disk drive space. See our memory definition for a comparison between memory and storage. How is data read and stored on a hard drive? Data sent to and from the hard drive is interpreted by the disk controller, which tells the hard drive what to do and how to move the components within the drive. When the operating system needs to read or write information, it examines the hard drives File Allocation Table (FAT) to determine file location and available areas. Once this has been determined, the disk controller instructs the actuator to move the read/write arm and align the read/write head. -
Efficient In-Memory Indexing with Generalized Prefix Trees
Efficient In-Memory Indexing with Generalized Prefix Trees Matthias Boehm, Benjamin Schlegel, Peter Benjamin Volk, Ulrike Fischer, Dirk Habich, Wolfgang Lehner TU Dresden; Database Technology Group; Dresden, Germany Abstract: Efficient data structures for in-memory indexing gain in importance due to (1) the exponentially increasing amount of data, (2) the growing main-memory capac- ity, and (3) the gap between main-memory and CPU speed. In consequence, there are high performance demands for in-memory data structures. Such index structures are used—with minor changes—as primary or secondary indices in almost every DBMS. Typically, tree-based or hash-based structures are used, while structures based on prefix-trees (tries) are neglected in this context. For tree-based and hash-based struc- tures, the major disadvantages are inherently caused by the need for reorganization and key comparisons. In contrast, the major disadvantage of trie-based structures in terms of high memory consumption (created and accessed nodes) could be improved. In this paper, we argue for reconsidering prefix trees as in-memory index structures and we present the generalized trie, which is a prefix tree with variable prefix length for indexing arbitrary data types of fixed or variable length. The variable prefix length enables the adjustment of the trie height and its memory consumption. Further, we introduce concepts for reducing the number of created and accessed trie levels. This trie is order-preserving and has deterministic trie paths for keys, and hence, it does not require any dynamic reorganization or key comparisons. Finally, the generalized trie yields improvements compared to existing in-memory index structures, especially for skewed data. -
Armenian Alphabet Confusing Letters
Armenian Alphabet: Help to Distinguish the “Look-Alike” (confusing) 12 of the 38 Letters of the Armenian Alphabet 6. B b z zebra 27. A a j jet (ch) church 17. ^ 6 dz (adze) (ts) cats 25. C c ch church 19. ch unaspirated ch/j (j) jet Q q • 14. ts unaspirated ts/dz (dz) adze ) 0 • 21. & 7 h hot initial y Maya medial y final in monosyllable except verbs silent final elsewhere 35. W w p piano 36. @ 2 k kid 34. U u v violin 31. t step unaspirated t/d (d) dance K k • 38. ~ ` f fantastic ARMENIAN ALPHABET First created in the 5th c. by Mesrop Mashtots. Eastern Armenian Dialect Transliteration. (Western Dialect, when different, follows in parentheses). 1. a father 14. ts unaspirated ts/dz (dz) adze G g ) 0 • 2. E e b boy (p) piano 15. k skip unaspirated k/g (g) go I i • 3. D d g go (k) kid 16. L l h hot 4. X x d dance (t) ten 17. ^ 6 dz (adze) (ts) cats 5. F f ye yet initial e pen medial 18. * 8 gh Parisian French Paris e pen initial ft, fh, fh2, fr you are 19. ch unaspirated ch/j (j) jet Q q • y yawn before vowel 20. T t m meet 6. B b z zebra 21. h hot initial 7. e pen & 7 + = y Maya medial 8. O o u focus y final in monosyllable except verbs silent final elsewhere 9. P p t ten 22. H h n nut 10. -
Fast Binary and Multiway Prefix Searches for Packet Forwarding
Computer Networks 51 (2007) 588–605 www.elsevier.com/locate/comnet Fast binary and multiway prefix searches for packet forwarding Yeim-Kuan Chang * Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan, ROC Received 24 June 2005; received in revised form 24 January 2006; accepted 12 May 2006 Available online 21 June 2006 Responsible Editor: J.C. de Oliveira Abstract Backbone routers with tens-of-gigabits-per-second links are indispensable communication devices to deploy on the Inter- net. The IP lookup operation is the most critical task that must be improved in routers. In this paper, we first present a systematic method to compare prefixes of different lengths. The list of prefixes can then be sorted and stored in a sequential array, which is contrary to the linked lists used in most of trie-based structures. Next, fast binary and multiway prefix searches assisted by auxiliary prefixes are proposed. We also developed a 32-bit representation to encode the prefixes of different lengths. For the large routing tables currently available on the Internet, the proposed multiway prefix search can achieve the worst-case number of memory accesses of three and four if the sizes of the CPU cache lines are 64 bytes and 32 bytes, respectively. The IPv4 simulation results show that the proposed prefix searches outperform the existing IP lookup schemes in terms of lookup times and memory consumption. The simulations using IPv6 routing tables also show the performance advantages of the proposed binary prefix searches. We also analyze the performance of the existing lookup schemes by con- currently considering the lookup speed, the update speed, and the memory consumption. -
Fonts for Latin Paleography
FONTS FOR LATIN PALEOGRAPHY Capitalis elegans, capitalis rustica, uncialis, semiuncialis, antiqua cursiva romana, merovingia, insularis majuscula, insularis minuscula, visigothica, beneventana, carolina minuscula, gothica rotunda, gothica textura prescissa, gothica textura quadrata, gothica cursiva, gothica bastarda, humanistica. User's manual 5th edition 2 January 2017 Juan-José Marcos [email protected] Professor of Classics. Plasencia. (Cáceres). Spain. Designer of fonts for ancient scripts and linguistics ALPHABETUM Unicode font http://guindo.pntic.mec.es/jmag0042/alphabet.html PALEOGRAPHIC fonts http://guindo.pntic.mec.es/jmag0042/palefont.html TABLE OF CONTENTS CHAPTER Page Table of contents 2 Introduction 3 Epigraphy and Paleography 3 The Roman majuscule book-hand 4 Square Capitals ( capitalis elegans ) 5 Rustic Capitals ( capitalis rustica ) 8 Uncial script ( uncialis ) 10 Old Roman cursive ( antiqua cursiva romana ) 13 New Roman cursive ( nova cursiva romana ) 16 Half-uncial or Semi-uncial (semiuncialis ) 19 Post-Roman scripts or national hands 22 Germanic script ( scriptura germanica ) 23 Merovingian minuscule ( merovingia , luxoviensis minuscula ) 24 Visigothic minuscule ( visigothica ) 27 Lombardic and Beneventan scripts ( beneventana ) 30 Insular scripts 33 Insular Half-uncial or Insular majuscule ( insularis majuscula ) 33 Insular minuscule or pointed hand ( insularis minuscula ) 38 Caroline minuscule ( carolingia minuscula ) 45 Gothic script ( gothica prescissa , quadrata , rotunda , cursiva , bastarda ) 51 Humanist writing ( humanistica antiqua ) 77 Epilogue 80 Bibliography and resources in the internet 81 Price of the paleographic set of fonts 82 Paleographic fonts for Latin script 2 Juan-José Marcos: [email protected] INTRODUCTION The following pages will give you short descriptions and visual examples of Latin lettering which can be imitated through my package of "Paleographic fonts", closely based on historical models, and specifically designed to reproduce digitally the main Latin handwritings used from the 3 rd to the 15 th century. -
Ninidze, Mariam " a New Point of Vew on the Origin of the Georgian Alphabet"
Maia Ninidze A New Point of View on the Origin of the Georgian Alphabet The history of the development of different scripts shows that writing may assume the organized systemic appearance that Georgian asomtavruli (capital alphabet) had in the 5th century only as a result of reform. No graphic system has received such refined appearance through its independent evolution. Take, for example, the development of the Greek alphabet down to 403 B.C. In reconstructing the original script of the Georgian alphabet I was guided by the point of view that "the first features and the later developed or introduced foreign elements should be demarked and in this way comparative chronology be defined" (10, p. 50) and that "knowledge of the historical development of the outline of letters will enable us to form an idea of how the change of the outline of letters occurred and ... perceive the type of the preceding change, no imprints on monuments of which have come down to us" (12. p. 202). When one alphabet evinces similarity with another in the outline of corresponding graphemes of similar sounds, this suggests their common provenance or one's derivation from the other. But closeness existing at the level of general systemic peculiarities is a sign of later, artificial assimilation. The systemic similarity of Georgian asomtavruli with Greek would seem to suggest that it was reformed on the basis of the Greek counterpart. The earliest Georgian inscriptions in the asomtavruli are characterized by similarity to Greek in: 1) equal height of letters, 2) geometric roundness of graphemes and linearity-angularity, 3) the direction of writing from left to right, 4) the order of the alphabet and the numerical value of graphemes, but not similarity in the form of individual graphemes. -
Dictionary Budget: 11
USOO7609179B2 (12) United States Patent (10) Patent No.: US 7,609,179 B2 Diaz-Gutierrez et al. (45) Date of Patent: Oct. 27, 2009 (54) METHOD FOR COMPRESSED DATAWITH (56) References Cited REDUCED DICTIONARY SIZES BY CODING U.S. PATENT DOCUMENTS VALUE PREFIXES 5,363,098 A * 1 1/1994 Antoshenkov ............... 341.95 6,518,895 B1 2/2003 Weiss et al. (75) Inventors: Pablo Diaz-Gutierrez, Granada (ES); 6,529,912 B2 * 3/2003 Satoh et al. ................. 707/101 Vijayshankar Raman, Sunnyvale, CA 7,088,271 B2 * 8/2006 Marpe et al. ................ 341,107 (US); Garret Swart, Palo Alto, CA (US) * cited by examiner Assignee: International Business Machines Primary Examiner Peguy Jean Pierre (73) (74) Attorney, Agent, or Firm—IP Authority, LLC; Ramraj Corporation, Armonk, NY (US) Soundararajan (*) Notice: Subject to any disclaimer, the term of this patent is extended or adjusted under 35 (57) ABSTRACT U.S.C. 154(b) by 0 days. The speed of dictionary based decompression is limited by the cost of accessing random values in the dictionary. If the (21) Appl. No.: 11/970,844 size of the dictionary can be limited so it fits into cache, decompression is made to be CPU bound rather than memory bound. To achieve this, a value prefix coding scheme is pre (22) Filed: Jan. 8, 2008 sented, wherein value prefixes are stored in the dictionary to get good compression from Small dictionaries. Also pre sented is an algorithm that determines the optimal entries for (65) Prior Publication Data a value prefix dictionary. Once the dictionary fits in cache, decompression speed is often limited by the cost of mispre US 2009/O174583 A1 Jul. -
Parallel-Prefix Structures for Binary and Modulo
PARALLEL-PREFIX STRUCTURES FOR BINARY AND MODULO f2n − 1; 2n; 2n + 1g ADDERS By JUN CHEN Bachelor of Science in Information and Control Engineering Shanghai Jiao Tong University Shanghai, China 2000 Master of Science in Electrical Engineering Illinois Institute of Technology Chicago, IL, U.S.A. 2004 Submitted to the Faculty of the Graduate College of Oklahoma State University in partial fulfillment of the requirements for the Degree of DOCTOR OF PHILOSOPHY December, 2008 PARALLEL-PREFIX STRUCTURES FOR BINARY AND MODULO f2n − 1; 2n; 2n + 1g ADDERS Dissertation Approved: Dr. James E. Stine Dissertation Advisor Dr. Louis G. Johnson Dr. Sohum Sohoni Dr. Hermann G. W. Burchard Dr. A. Gordon Emslie Dean of the Graduate College ii ACKNOWLEDGMENTS First, I would like to thank my advisor, Dr. James E. Stine, for his continuous efforts on helping me with the work through a Ph.D. degree. His enthusiasm in teaching, positive thinking, along with his strong professional strengths, guided me towards the completion of this degree. My family's support is also greatly appreciated. This work cannot be done without the support from my parents Guangzhao Chen and Shengyuan Chen and sister Yu Chen. I am very proud of being able to work with Ivan, my fellow at VLSI Computer Archi- tecture Group (VCAG). Thanks also go to Johannes Grad, an alumni of Illinois Institute of Technology, who helped me much on EDA tools. Finally, I would like to thank all the committee members, Dr. Johnson, Dr. Sohoni, Dr. Burchard and Dr. Stine. Thank you all for everything you have done to help through. -
Vocabularies in the VO Version 2.0
International Virtual Observatory Alliance Vocabularies in the VO Version 2.0 IVOA Working Draft 2020-03-26 Working group Semantics This version http://www.ivoa.net/documents/Vocabularies/20200326 Latest version http://www.ivoa.net/documents/Vocabularies Previous versions WD-20190905 Author(s) Markus Demleitner Editor(s) Markus Demleitner Abstract In this document, we discuss practices related to the use of RDF-based vocabularies in the VO. This primarily concerns the creation, publication, and maintenance of vocabularies agreed upon within the IVOA. To cover the wide range of use cases envisoned, we define three flavours of such vo- cabularies: SKOS for informal knowledge organisation on the one hand, and strict hierarchies of classes and properties on the other. While the framework rests on the solid foundations of W3C RDF, provisions are made to facili- tate using IVOA vocabularies without specific RDF tooling. Non-normative appendices detail the current vocabulary-related tooling. Status of this document This is an IVOA Working Draft for review by IVOA members and other interested parties. It is a draft document and may be updated, replaced, or obsoleted by other documents at any time. It is inappropriate to use IVOA Working Drafts as reference materials or to cite them as other than “work in progress”. A list of current IVOA Recommendations and other technical documents can be found at http://www.ivoa.net/documents/. Contents 1 Introduction4 1.1 Role within the VO Architecture................5 1.2 Relationship to Vocabularies in the VO Version 1.......6 1.3 Reading Guide..........................7 1.4 Terminology, Conventions, Typography.............7 2 Derivation of Requirements (Non-Normative)8 2.1 Use Cases.............................8 2.1.1 Controlled Vocabulary in VOResource.........8 2.1.2 Controlled Vocabularies in VOTable..........8 2.1.3 Datalink Link Selection.................8 2.1.4 VOEvent Filtering, Query Expansion..........9 2.1.5 Vocabulary Updates in VOResource..........9 2.1.6 Discovering Meanings................. -
Megabyte from Wikipedia, the Free Encyclopedia
Megabyte From Wikipedia, the free encyclopedia The megabyte is a multiple of the unit byte for digital information storage or transmission with two different values depending on context: 1 048 576 bytes (220) generally for computer memory;[1][2] and one million bytes (106, see prefix mega-) generally for computer storage.[1][3] The IEEE Standards Board has decided that "Mega will mean 1 000 000", with exceptions allowed for the base-two meaning.[3] In rare cases, it is used to mean 1000×1024 (1 024 000) bytes.[3] It is commonly abbreviated as Mbyte or MB (compare Mb, for the megabit). Multiples of bytes Contents SI decimal prefixes Binary IEC binary prefixes Name Value usage Name Value 1 Definition (Symbol) (Symbol) 2 Examples of use 3 10 10 3 See also kilobyte (kB) 10 2 kibibyte (KiB) 2 4 References megabyte (MB) 106 220 mebibyte (MiB) 220 5 External links gigabyte (GB) 109 230 gibibyte (GiB) 230 terabyte (TB) 1012 240 tebibyte (TiB) 240 Definition petabyte (PB) 1015 250 pebibyte (PiB) 250 exabyte (EB) 1018 260 exbibyte (EiB) 260 The term "megabyte" is commonly used to mean either zettabyte (ZB) 1021 270 zebibyte (ZiB) 270 10002 bytes or 10242 bytes. This originated as yottabyte (YB) 1024 280 yobibyte (YiB) 280 compromise technical jargon for the byte multiples that needed to be expressed by the powers of 2 but lacked See also: Multiples of bits · Orders of magnitude of data a convenient name. As 1024 (210) approximates 1000 (103), roughly corresponding SI multiples began to be used for binary multiples. -
Bits, Bytes and Digital Information
Today’s lecture Understand the difference between analogue Bits, bytes and and digital information Convert between decimal numbers and binary digital numbers information Lecture 2 – COMPSCI111/111G SS 2018 Analogue vs digital information Encoding information Information in the real world is continuous Real world information is stored by a computer Continuous signal using numbers Weight Visual information shown 11111111111111111111111 01111111111111111111111 00001111111111111111111 Real Weight 00000011111111111111111 00000000011111111111111 44444000001111111111111 75444000000011111111111 Information stored by a computer is digital 55554401000000111111111 33367544000000011111111 Represented by discrete numbers 22283554444000000111111 99928357544000000011111 99999233657504000001111 99999983666554400000011 Weight 99999928338674400000001 shown Image Pixels 1. Give each pixel colour a number. 2. Let the computer draw the numbers as Real Weight coloured pixels (eg. black = 0). Encoding information Numbers and Computing Sound information Numbers are used to represent all information manipulated by a computer. Sound Computers use the binary number system: ― Binary values are either 0 or 1. Waveform Samples 1. Give each sample a number (height of We use the decimal number system: green box). ― 0 to 9 are decimal values. 2. Let the computer move the loudspeaker membrane according to the samples. Number Systems Positional Notation Base: Any number can be expressed as: ― Specifies the number of digits used by the system. ― Binary is base 2. ― Decimal is base 10. ∗ ∗ ⋯ ∗ Positional notation: where is the digit at position , and is the ― Describes how numbers are written. base. ⋯ Most significant digit Least significant digit Decimal Examples Storing Decimal Numbers in a Computer 657 Series of dials: 6∗10 5∗10 7∗10 ― Each dial goes from 0 to 9. Information is stored digitally: 600 50 7 657 ― Finite number of states – 10 per dial. -
2020 Global Payments Guide
FS TREASURY SERVICES 2020 Global Payments Guide Your Guide To Making Cross-Currency Payments in over 160 Countries with Ease. 2020 Global Payments Guide Last Updated: November 13, 2020 For the most up-to-date version, please visit jpmorgan.com/visit/guide | 2 The J.P. Morgan Global Payments Guide is your desktop resource to help you make timely and accurate payments to beneficiaries around the world. Work with J.P. Morgan to get the global payment support that your business Setting up your payment Cross-Border Payment Requirement demands It is best practice to include the below standard information in Intermediary banks are often used when a payment is made in a With employees, suppliers and operations located around the payment instructions to avoid potential delays or returns: currency that is different from the local currency. When making a globe, ensuring prompt payments in multiple currencies is a payment through an intermediary bank, their SWIFT BIC must be Ordering Customer challenge. Your business requires a partner who takes the time to – included. understand your needs and helps ensure your payments are Account number processed smoothly. – Full name (no initials) Key Terms – Full address International Bank Account Number (IBANN As one of the top-ranked cash management and payments – Street address (avoid P.O. Box numbers) The International Bank Account Number, IBAN, is an internationally processors in the world, J.P. Morgan is able to offer the tools that – City agreed standard to identify an individual’s account at a financial help you manage your day-to-day global operations, along with – State Code institution.