Ppt on machine translation wikipedia

Unit One Basic Concepts. Learning Objectives In this unit, basic concepts and ideas about language, communication and translation methodology are required.

and says its meaning aloud in another language. Sight translation is made when written translation is not needed or there is no time to do it. Machine translation (MT) or Automatic translation Machine translation is done by a computer instead of a human being. An automated translation will not be as accurate as a translation conducted by a minded being. Machines lack the thinking capacity endowed to man by God/


Computational terminography: extracting knowledge from texts Špela Vintar Dept. of Translation Studies University of Ljubljana Terminology Symposium Zadar,

techniques such as term & definition extraction, aka Text Mining & Knowledge Extraction. ❖ Knowledge structures can be used in: ❖ (Machine) Translation ❖ Question Answering ❖ Automatic Document Indexing & Classification ❖ Building user- and context-aware applications ❖ … Source: Ackoff, R/ collectively called speleothems are formed by deposition of calcium carbonate and other dissolved minerals. Source: Wikipedia, “Karst”, August 2014. TS Terminology Symposium Zadar, 22-23 August 2014 Termine: 34 terms/


© Jude Shavlik 2006 David Page 2007 CS 760 – Machine Learning (UW-Madison)Lecture #7, Slide 1 Learning from Examples: Standard Methodology for Evaluation.

HYPO DISTRIBUTION © Jude Shavlik 2006 David Page 2007 CS 760 – Machine Learning (UW-Madison)Lecture #7, Slide 35 From Wikipedia (http://en.wikipedia.org/wiki/P-value) http://en.wikipedia.org/wiki/P-value The p-value of an observed value X / © Jude Shavlik 2006 David Page 2007 CS 760 – Machine Learning (UW-Madison)Lecture #7, Slide 80 Constructing the Achievable Curve Given: Set of PR points, fixed number positive and negative examples Translate PR points to ROC pointsTranslate PR points to ROC points/


1 Gholamreza Haffari Simon Fraser University MT Summit, August 2009 Machine Learning approaches for dealing with Limited Bilingual Data in SMT.

Some slides are adapted or used from  Chris Callison Burch  Trevor Cohn  Dragos Stefan Munteanu 3 Statistical Machine TranslationTranslate from a source language to a target language by computer using a statistical model  M F  E / not parallel in the strict sense but convey overlapping information  Wikipedia pages  New agencies: BBC, CNN  From comparable corpora, we can extract sentence pairs which are (approximately) translation of each other 15 Extracting Parallel Sentences (Munteanu & Marcu, /


Introduction to Statistical Machine Translation Philipp Koehn Kevin Knight USC/Information Sciences Institute USC/Computer Science Department CSAIL Massachusetts.

–Other applications •Information extraction •Information retrieval •Question answering/summarization Language translation Yo quiero Taco Bell MT Systems Where have you seen machine translation systems? Machine Translation 美国关岛国际机场及其办公室均接获一 名自称沙地阿拉伯富商拉登等发出的电 子邮件,威胁将会向机场等公众地方发 动生化袭击後,关岛经保持高度戒备。 The U.S. island of Guam is maintaining/you don’t get the characters right… ISO-8859-2 (Latin2) ISO-8859-6 (Arabic) http://en.wikipedia.org/wiki/ISO/IEC_8859 Chinese? •GB Code •GBK Code •Big 5 Code •CNS-11643-1992 •… /


Soul of a New Machine Jeff Chase Duke University.

then the core raises a CPU exception (a fault). x86 control registers The details aren’t important. See [en.wikipedia.org/wiki/Control_register] Entering the kernel Suppose a CPU core is running user code in user mode: The user program /using load/store instructions Demand-paged virtual memory Illusion of near-infinite memory, backed by disk or memory on other machines Address Translation (even more) Checkpoint/restart Transparently save a copy of a process, without stopping the program while the save /


CS61C L13 CALL I (1) Chae, Summer 2008 © UCB Albert Chae, Instructor inst.eecs.berkeley.edu/~cs61c CS61C : Machine Structures Lecture #13 – Compiling,

so with minimal disruption to programmer, and especially the user -Rosetta allows old PowerPC programs to run on the new x86 systems by runtime translation -Universal Binaries contain the machine code for both platforms, so both systems can run at native speeds Did a similar thing 13 years ago when they switched from Motorola 680x0/ adds quite a bit of complexity to the compiler, linker, and operating system. However, it provides many benefits that often outweigh these. en.wikipedia.org/wiki/Dynamic_linking


Language Translators By: Henry Zaremba. Origins of Translator Technology ▫1954- IBM gives a demo of a translation program called the “Georgetown-IBM experiment”

specific nouns Available Machine Translators Free online- ▫Google translate, Anuusaraka Commercially available Software ▫Babylon ($116.40), IdiomaX ($149.95) Works Cited Google Translate. N.p., n.d. Web. 19 Apr. 2011. O’Grady, William, et al. Contemporary Linguistics. N.p.: n.p., n.d. Print. Rev. of Babylon Language Translation Software. Top Ten Reviews.com. N.p., n.d. Web. 19 Apr. 2011. “Translation.” Wikipedia. N.p/


Robert Crawford, MBA West Middle School.  Explain how the binary system is used by computers.  Describe how software is written and translated  Summarize.

machine code [or executable] might then be stored for execution at a later time. ◦ [most software is distributed as an executable]  Alternatively, an interpreter can be used to analyze and perform the outcomes of the source code program directly on the fly. http://en.wikipedia/and Linux.  Explain how the binary system is used by computers.  Describe how software is written and translated ◦ Programmer, Source Code, compiled, Interpreted  Summarize the tasks of the operating system  Identify two leading /


 Google started to suggest a number of offensive synonyms for the word “gay.” According to All Out organisation: “500 million people use Google Translate.

Wikipedia is translated into 287 languages. The first commercial website is Apple’s, which is translated into some 130 languages. [Source: www.pangeanic.com] 5  IBM launched model customization for the IBM Watson Translation Service, further enhancing the company’s flagship machine translation/ 20.  BBC News Labs creates a new “virtual voiceover” tool. The new tool uses Google Translate to machine translate the scripts of English-language video and convert it to Japanese for BBC Japan. Source: Slator.com /


Vamshi Ambati | Stephan Vogel | Jaime Carbonell Language Technologies Institute Carnegie Mellon University A ctive Learning and C rowd-Sourcing for Machine.

a.k.a. loss function  Sampling Strategy: Crowd Sourcing Review  Definition  Broadcasting tasks to a broad audience  Voluntary (Wikipedia), for fun (ESP) or pay (Mechanical Turk)  In Natural Language Processing  Information Extraction (Snow et al 2008)  /Model-driven and Decoding-based Active Learning strategies for sentence selection  Explore crowd-landscape on Mechanical Turk for Machine Translation (Ambati and Vogel, Mturk Workshop at NAACL 2010)  Cost and Quality trade-off working with multiple/


Machine Translation A Presentation by: Julie Conlonova, Rob Chase, and Eric Pomerleau.

and precision Decoding Language Alignment Goal: Produce a word-aligned set from a sentence-aligned dataset First step on the road toward Statistical Machine Translation Example Problem:  The motion to adjourn the House is now deemed to have been adopted.  La motion portant que la /1.Decoding the meaning of the source text, and 2.Re-encoding this meaning in the target language.” - “Translation Process”, Wikipedia, May 2006 Decoding How to go from the T-matrix and A-matrix to a word alignment? There are /


RANLP tutorial, September 2013, Hissar, Bulgaria Violeta Seretan Department of Translation Technology Faculty of Translation and Interpreting University.

Measuring + Analyzing + Interpreting interactions and associations between people, topics and ideas.” (http://en.wikipedia.org/wiki/Social_analytics)http://en.wikipedia.org/wiki/Social_analytics 6 http://www.submitedge.com http://irevolution.net You shall know someone … / of co-occurrences/collocations/cum-corpus 15 Beauchesne, 2001 Charest et al., 2012 Relevance for Natural Language Processing Machine translation EN ask a question – FR poser `put’ une question – ES hacer `make’ una pregunta “collocations/


MonoTrans2: A New Human Computation System to Support Monolingual Translation Chang Hu, Benjamin B. Bederson, Philip Resnik and Yakov Kronrod Translating.

delivery Delmas 31 Munro, Robert. 2010. Crowdsourced translation for emergency response and beyond. NSF Workshop on crowdsourcing and translation, University of Maryland. Uncommon Languages Bilingual Translators are Hard to Find Machine Translation? Large volume, cheap, fast Unreliable quality Translation with bilingual translators vs. 1,200,000 contributors Wikipedia: 900 translators Translate with the Monolingual Crowd Chang Hu. Collaborative Translation by Monolingual Users, CHI 09 Chang Hu, Benjamin/


Anusaaraka: An Approach to Machine Translation Akshar Bharati, Vineet Chaitanya 1, Amba kulkarni 2 1 Chinmaya International Foundation stationed at Rashtriya.

to the destination, but the journey is not comfortable.) Anusaaraka is -  Completely Transparent The whole process of Machine Translation is transparent even to a layman Human Understandable Outputs For example Chunking: Color Scheme Parsed output: Modifier-Modified Tree /play a crucial Role. Examples of What people in general can do when provided with proper environment/tools --- Wikipedia, ConceptNet 03/01/0775 Anusaaraka provides the Right kind of Environment for the people to Contribute at their level/


Machine Learning Crash Course Computer Vision James Hays Slides: Isabelle Guyon, Erik Sudderth, Mark Johnson, Derek Hoiem Photo: CMU Machine Learning Department.

Derek Hoiem K-means algorithm Illustration: http://en.wikipedia.org/wiki/K-means_clusteringhttp://en.wikipedia.org/wiki/K-means_clustering 1. Randomly select K centers/B. Sarel Mean shift Simple Mean Shift procedure: Compute mean shift vector Translate the Kernel window by m(x) Computing the Mean Shift Slide by Y/L1 distance, χ 2 distance, quadratic distance, histogram intersection Support vector machines Linear classifiers Margin maximization The kernel trick Kernel functions: histogram intersection, /


Semantics in Statistical Machine Translation Jan Odijk MA-Rotation Lecture Utrecht March10, 2011 1.

of MT technology for all EU language pairs Efficient inclusion of linguistic knowledge into statistical machine translation The development and testing of hybrid architectures for the integration of rule-based and statistical / EuroWordNet, BalkaNet, WordNets for several languagesWordNetEuroWordNetBalkaNetWordNets for several languages –Knowledge Repositories: OpenCyc, Wikipedia, DBpediaOpenCycWikipediaDBpedia MWE Lexica: SAID, DUELMESAIDDUELME 33 Semantics Resources CoNLL 2009 Shared Task on syntactic/


Using Encyclopedic Knowledge for Named Entity Disambiguation Razvan Bunescu Machine Learning Group Department of Computer Sciences University of Texas.

Using Encyclopedic Knowledge for Named Entity Disambiguation Razvan Bunescu Machine Learning Group Department of Computer Sciences University of Texas at Austin razvan@cs.utexas.edu Marius Pasca/out. Use a linear ranking function: One feature for the context-article similarity: Each word-category pair  w,c   V  C is translated into a feature: One special feature for out-of-Wikipedia entities:  [  cos |  w,c |  out ] Ranking Formulation: Example “… this past weekend. John Williams and the Boston Pops/


ELEC6200, Fall 07, Oct 29 Westrom: Virtual Machines 1 Kenneth Westrom ELEC-6620.

of converted code for repeated execution ELEC6200, Fall 07, Oct 29 Westrom: Virtual Machines 8 Process VM, continued Optimizers, Same ISA Perform code optimization during translation and execution High-level-language VM Cross-platform compatibiltiy Programs written for an abstract machine, which is mapped to real hardware through a virtual machine Sun Microsystems Java VM Microsoft Common Language Infrastructure,.NET framework ELEC6200, Fall 07/


Operating System Support for Virtual Machines Samuel T. King, George W. Dunlap,Peter M.Chen Presented By, Rajesh 1 References [1] Virtual Machines: Supporting.

different ISA same ISA Classic OS VMs Dynamic Binary Optimizers Dynamic Translators Hosted VMs Operating System Support for Virtual Machine Introduction Types of VMM UMLinux UMLinux Performance Issues Proposed Solution Evaluation of Proposed Solution Conclusion 13 Introduction Virtual Machine (VM) ◦ A software implementation of a machine that executes programs like a physical machine Virtual Machine Monitor (VMM) ◦ A layer of s/w that emulates the h/


Simple Machines Simple Machines Making Work Easier….YEAH!!!

machines Six simple machines: Lever, Inclined Plane, Wheel and Axle, Screw, Wedge, Pulley NSF North Mississippi GK8 Mechanical Advantage We know that a machine/machine multiplies that force is the mechanical advantage of the machine Abbreviated MA We know that a machine/machine/to translate /to translate force/simple machines Much/ machines NSF/ machines / Simple machines change/machine/ work the machine produces versus /.org. 2006. Simple Machines. Accessed 3 February 2006/Machines. Accessed 2 February 2006. http/


CS12420 Sequence Diagrams – a UML notation for modelling bahaviour Lynda Thomas Images from Wikipedia unless credited or mine.

things happen or how to write the methods Sequence diagrams do that Let’s use google http://en.wikipedia.org/wiki/Sequence_diagrams http://www.ibm.com/developerworks/rational/libr ary/3101.htmlhttp://www.ibm.com/developerworks/rational//at checkout() … Decide to use setCheckedout(aTrans) in DocInfo … here is pseudocode translator=aTrans translator.doEmer() Now need to elaborate doEmer() in Translator Here is a vending machine State diagrams look at the internals of an object – more next year Flow charts /


Simple Machines Simple Machines Matt Aufman and Steve Case University of Mississippi NSF NMGK-8 February 2006 Matt Aufman and Steve Case University of.

Machines Have few or no moving parts Make work easier Can be combined to create complex machines Six simple machines/machines Six simple machines/ machine multiplies/machine multiplies that force is the mechanical advantage of the machine Abbreviated MA We know that a machine/machine/translate/translate / machines /machines NSF North Mississippi GK8 The trick is WORK Simple machines/ machines /machine/work the machine produces /org. 2006. Simple Machines. Accessed 3 February/Machines. Accessed 2 February 2006. http/


Simple Machines Simple Machines Matt Aufman and Steve Case University of Mississippi NSF NMGK-8 February 2006 Matt Aufman and Steve Case University of.

Machines Have few or no moving parts Make work easier Can be combined to create complex machines Six simple machines/machines Six simple machines/a machine multiplies /machine multiplies that force is the mechanical advantage of the machine Abbreviated MA We know that a machine/machine/ to translate force / translate force/simple machines / machines / machines /Simple machines change/machine/ work the machine produces versus/org. 2006. Simple Machines. Accessed 3 February 2006/Machines. Accessed 2 February 2006. http/


Jeff Howbert Introduction to Machine Learning Winter 2014 1 Machine Learning Natural Language Processing.

Competitions devoted to the specific task Major tasks in NLP (Wikipedia) http://en.wikipedia.org/wiki/Natural_language_processing Jeff Howbert Introduction to Machine Learning Winter 2014 14 l Automatically translate text from one human language to another. –This is / (grammar, semantics, facts about the real world, etc.) in order to solve properly. Machine translation Jeff Howbert Introduction to Machine Learning Winter 2014 15 l The grammar for natural languages is ambiguous and typical sentences have /


Simple Machines. Work or Not? What is work and what is not? a teacher lecturing to her class a mouse pushing a piece of cheese with its nose across the.

distance than the axle A larger circular wheel affixed to a smaller rigid rod at its center Used to translate force across horizontal distances (like a car) Trade off: the wheel must be rotated through a greater /August 2004. Simple Machines: inclined planes. Accessed 2 February 2006. http://www.professorbeaker.com/planefact.html http://www.professorbeaker.com/planefact.html Wikepedia. Accessed 3 February 2006. http://en.wikipedia.org/wiki/Mechanicaladvantage http://en.wikipedia.org/wiki/Mechanicaladvantage /


#APMP2016. Submitting proposals in more than one language: a survival guide Considering language and translation as a key component of your value proposition.

units (headings, titles or elements in a list) that have previously been translated, in order to aid human translators. The translation memory stores the source text and its corresponding translation in language pairs called “translation units”. Individual words are handled by terminology bases and are not within the domain of TM. Machine translation (MT) From Wikipedia: a a sub-field of computational linguistics that investigates the use of/


Promoting Science and Technology Exchange using Machine Translation Toshiaki Nakazawa Japan Science and Technology Agency Oct. 30, PSLT2015.

Extremely poor at foreign languages – Made a Nobel Lecture in Japanese – Poorly written English papers 7 Photo from Wikipedia “English is just one of the tools” Juichi Yamagiwa ( 山極寿一 ) World-renowned expert in the study of gorillas/using the existing resources – 3.6M entries (about 90% accuracy) 16 Large-scale Dictionary Construction via Pivot-based Statistical Machine Translation with Significance Pruning and Neural Network Features Raj Dabre 1, Chenhui Chu 2, Fabien Cromieres 2, Toshiaki Nakazawa 2,/


Virtual Machine Overview

or application runtime. Example: Hardware virtual machine: VMWare, Xen, VirtualBOX .. Application virtual machine: Java Virtual Machine, .NET Framework (From Wikipedia) 系統virtual machine=硬體virtual machine Process virtual machine=application machine把硬體和作業系統從軟體抽離出來 How can virtualization help us ? 05/host machine itself; This approach can be useful to set up an isolated virtual network. In this configuration, the virtual machine cannot connect to the Internet. Network Address Translation The virtual machine /


New Paradigms for MT and IR Carnegie Mellon University & Meaningful Machines Jaime Carbonell Machine Translation 1. Context-Based Machine Translation 2.

“…right language” “…right level of detail” IR (search engines) Routing, personalization Anticipatory analysis Info extraction, speech Machine translation Summarization, expansion 3 Exploiting Context for LT’s Machine Translation –Why context is so necessary –Context-Based MT (new paradigm) –Finding near-synonym phrases (via context) Information/ & SECONDARY WORKS: Mark Twain: life and works Wikipedia: “Tom Sawyer” Literature chat room: Tom Sawyer On merchandising Huck Finn and Tom Sawyer RELATED INFORMATION:


Machine Programming - Introduction CENG331: Introduction to Computer Systems 5 th Lecture Instructor: Erol Sahin Acknowledgement: Most of the slides are.

time ArchitecturesProcessors MMX SSE SSE2 SSE3 SSE4 Intel x86 Processors, contd. Machine Evolution  48619891.9M  Pentium19933.1M  Pentium/MMX19974.5M /conditional operations Linux/GCC Evolution  Very limited More Information Intel processors (Wikipedia)Wikipedia Intel microarchitecturesmicroarchitectures New Species: ia64, then IPF, then Itanium,… NameDateTransistors / 0x03 0x45 0x08 0x89 0xec 0x5d 0xc3 Object Code Assembler  Translates.s into.o  Binary encoding of each instruction  Nearly/


Overview of technologies for translators and language service providers Belinda Maia University of Porto.

The results will be in the future But the future is coming! Technology FOR Translators Machine translation (MT) Machine assisted translation (MAT) Internet for information retrieval Corpora use Terminology Management Multimedia tools Summarisation and / Internet information Eurodicautom, online terminology, glossaries, dictionaries On-line encyclopedias – e.g. Wikipedia Translators’ pages Translators’ forums and mailing lists Systematic finding, analysing and storage of relevant information / knowledge/


July 24, 2007GALE Update: Alon Lavie1 Statistical Transfer and MEMT Activities Multi-Engine Machine Translation –MEMT service within the cross-GALE IOD.

July 24, 2007GALE Update: Alon Lavie1 Statistical Transfer and MEMT Activities Multi-Engine Machine Translation –MEMT service within the cross-GALE IOD –MEMT system combination for GNG evaluation within Rosetta consortium –/ lexical entries –ADSO bilingual lexicon 104513 lexical entries –LDC word bilingual glossary 78173 lexical entries –Wikipedia extracted bilingual lexicon 51009 lexical entries –Phrases from Parallel Corpus 44968 lexical entries –Manual bilingual lexicon (high freq) 1149 lexical entries


Monotrans: Human-Computer Collaborative Translation Chang Hu, Ben Bederson, Philip Resnik Human-Computer Interaction Lab Computational Linguistics and.

(MT) Large volume, cheap, fastUnreliable quality ( = restaurant, dining hall) Professional Translators High quality, but slow and expensive (even for common language pairs) Translation with the Crowd Bottle neck: bilingual people Translation with the Crowd vs. 75,000 contributors Wikipedia: 800 translators Translation with the Monolingual Crowd Quality Affordability Machine Translation Machine Translation Professional Bilingual Human Participation Amateur Bilingual Human Participation Monolingual Human/


Introduction Client Motivations  Tasks Categories Crowd Motivation Pros & Cons Quality Management Scale up with Machine Learning Workflows for Complex.

bad increased 6. More good workers leave the market… death spiral http://en.wikipedia.org/wiki/The_Market_for_Lemons Reputation systems Great number of reputation mechanisms Challenges in the Design of/ workers Workers have constrained capacity (cannot do more than xxhours per day)  Machine Learning Techniques No “price premium” for high ‐ quality workers It is the / A. Hosaka, MSNBC. "Facebook asks users to translate for free“,2008."Facebook asks users to translate for free“ Daren C. Brabham. "Moving the /


Translation by Collaboration among Monolingual Users Benjamin B. Bederson Computer Science Department Human-Computer.

for common language pairs) High quality, but slow and expensive (even for common language pairs) Amateur Translators Online Labor Markets The key idea Translation with the Crowd vs. 1,200,000 contributors Wikipedia: 900 translators Translate with the Monolingual Crowd Quality Speed / Affordability Machine Translation Machine Translation Professional Bilingual Human Participation Amateur Bilingual Human Participation Monolingual Human Participation Monolingual Human Participation Monolingual collaboration/


Rapid Prototyping of a Transfer-based Hebrew-to-English Machine Translation System Alon Lavie Language Technologies Institute Carnegie Mellon University.

statistical Language Model for the target language June 20, 2007ISCOL/BISFAI-20074 Current State-of-the-art in Machine Translation Phrase-based MT State-of-the-art: –Requires minimally several million words of parallel text for adequate/closed-class entries (pronouns, prepositions, etc.) Had to deal with spelling conventions Recently augmented with ~50K translation pairs extracted from Wikipedia (mostly proper names and named entities) June 20, 2007ISCOL/BISFAI-200719 Manual Transfer Grammar (human-developed/


1 Machine Learning and Open Courseware Colin de la Higuera

18 Cdlh 2014 Experience from LaVie Were used for recommendation Transcriptions Graphs of authors Related pdfs Other ontologies (Wikipedia, DBLP and Google) 19 Cdlh 2014 Topic and user modeling 7 features: L c = current lecture / approach 1. Automatic Speech Recognition (ASR) and Machine Translation (MT) Adaptation: Taking advantage of the characteristics of video lecture repositories High-quality automatic transcriptions and translations 2. Interactive postediting: intelligent interaction for reduced effort/


Translation Tutorial Lauri Carlson MOLTO kickoff meeting Barcelona March 2010.

tutorial2003.pdf Koehn Edinburgh 2008 http://www.inf.ed.ac.uk/teaching/courses/emnlp/ Open problem FAHQMT fully automatic human quality machine translation Not in sight, and everybody knows why: not the right knowledge sources not enough data not enough computing power ● /into i18n Newer document production models DITA Web distribution platforms MLWCM (compare Aarnes idea of Wikipedia editor) So Maybe look at new content production workflows instead of messing with legacy CAT Start from the hi tech /


GEMET human and machine readable interfaces WIKTIONARY Stefan Jensen, EEA, Copenhagen.

human and machine readable interfaces WIKTIONARY/ -> agricultural production -> agriculture 5. fetch cluster - will return all the information for the requested concept (definition, translations, themes, scope note, groups) and all the information for its broader, narrower, related concepts 6. fetch siblings/integration project Integrate GEMET into Wiktionary to allow maintenance by a wider community Linking GEMET Wikipedia and web service Thesaurus Y Thesaurus X Glossary X Glossary Y GEMET web service Feeds/


INF 212 ANALYSIS OF PROG. LANGS Virtual Machines Instructors: Crista Lopes Copyright © Instructors.

two previous approaches  Interpretation: Translated from a high-level language to machine code continuously during every execution  Static (ahead-of-time) compilation: Translated into machine code before execution, and only requires this translation once. Just-In-Time /watch?v=Ls0tM-c4Vfo http://www.youtube.com/watch?v=Ls0tM-c4Vfo Comparison of Various VMs http://en.wikipedia.org/wiki/Comparison_of_application_virtual_machines Smalltalk  Brief History  Developed by Alan Kay et al. in Xerox PARC /


Tools Textbook –Machining Fundamentals, John R. Walker, Goodheart-Willcox Company, Tinley Park IL (2004). $60 new, $35 used from Amazon. pp. 55-182. (Remainder.

as a threaded fastener used to hold objects together, and as a simple machine used to translate torque into linear force. Wikipedia (The Wikipedia entry for “screw” is excellent!helicalthreadfastener simple machinetorque –Machine screws –Wood screws –Sheet metal screws; self-tapping screws Head types /–Plug and bottoming taps Dies –Used on rod to “make your own screw” More on Taps from Wikipedia During operation, it is necessary with a hand tap to periodically reverse rotation to break the chip formed during/


Computers: How They Work 1. What is a Computer 2. Components of Computer 3. World’s First Computers and CPUs 4. Mother Board 5. Machine Code and the processor’s.

integrated into a single chip (i.e. constructed as an integrated circuit on a single piece of Silicon) http://en.wikipedia.org/wiki/Central_processing_unit Components of a Computer ► Processor ► Memory ► Input/Output Processor Input (Keyboard, Mouse USB Drive, DSL/ add eax,1 004015EB mov dword ptr [ebp-14h],eax 19: } C++ high level codes gets compiled/translated into low level machine codes Fetch-Decode-Execute Cycle ► Fetch an Instruction:  Fetch instruction at address stored in address register  /


Ohad Hageby IDC 2008 1 Support Vector Machines & Kernel Machines IP Seminar 2008 IDC Herzliya.

margin classifiers. supervised learninglinear classifierssupervised learninglinear classifiers (from Wikipedia) (from Wikipedia) Ohad Hageby IDC 2008 3 Introduction Continued Often we are interested in classifying data as a part of a machine-learning process. Each data point will be represented/ in the higher Dimension The Lagrangian will be: At the optimal point – “saddle point equations”: Which translate to: Ohad Hageby IDC 2008 35 And the optimization problem Ohad Hageby IDC 2008 36 The Decision Function /


Translating for the European Commission Francisco De Vicente, Director Directorate for Translation Strategy and Multilingualism, DGT.

with experts in the Commission, the other institutions or the Member States Libraries (Brussels and Luxembourg) Reference works Electronic translation tools Terminology IATE (public) EUR-Lex (public) Quest Metasearch DGT Vista Electronic dictionaries, glossaries, Google, Wikipedia, etc. Translation tools Translation memories, Euramis Speech recognition Machine translation EUR-Lex: free public access to EU law eur-lex.europa.eu IATE: public multilingual term base iate.europa.eu/


March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics Translational eScience Ida Sim, MD, PhD March 16, 2010 Division of General Internal.

March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics These Problems.......will increasingly limit the clinical and translational research we want and need to do –“The ‘clinical research grid’ is failing” (Crowley, et al, JAMA/ Crowds Tapping into distributed intelligence of people –wikipedia (as accurate as Encyclopedia Britannica) –www.intrade.com: “stock market” for health care reform passage –e.g., Google Flu Use distributed machine and people resources –parallel computing for cheap/


Machine Safety Training for Beginners

field, the measured voltage is insignificant The hall voltage is directly proportional to the strength of the magnetic field Image source = Wikipedia Hall Voltage 1 – Electrons 2 – Conductor (Hall sensor) 3 – Magnet 4 – Magnetic field 5 – Power supply Miniature/ risk of electrical origin, related to the machine or not Trigger action & non trigger action What is a « Trigger action  » product When the operator is pushing the mushroom head (actuator), the head is translating to a position where, even if the/


The Linguistic-Core Approach to Structured Translation and Analysis of Low-Resource Languages November 2014 Project Review at ARL Jaime Carbonell (CMU)

monolingual text (need to decipher [Dou & Knight 13]) Decipherment helps Word Alignment Decipherment helps Machine Translation joint Bleu ISI jointly with CMU/Texas/MIT Graph Formalisms for Language Understanding and Generation String /Gazetteers (Pat, Chris) Brown Clusters (Kartik) Tajik Corpus from Leipzig Archive Supervision (Azim) Supervision (David) Tajik and Persian Wikipedias Tajik Reference Grammar (Perry, 2005) PerLex Persian Lexicon (Sagot and Walther, 2010) IPA Converter (Kartik, Pat, Chris) Named/


Machine Learning Computer Vision James Hays, Brown Slides: Isabelle Guyon, Erik Sudderth, Mark Johnson, Derek Hoiem Photo: CMU Machine Learning Department.

Sudderth, Mark Johnson, Derek Hoiem Photo: CMU Machine Learning Department protests G20 Clustering: group together similar/Cluster center Data Slide: Derek Hoiem K-means algorithm Illustration: http://en.wikipedia.org/wiki/K-means_clusteringhttp://en.wikipedia.org/wiki/K-means_clustering 1. Randomly select K centers 2. Assign each /by Y. Ukrainitz & B. Sarel Mean shift Simple Mean Shift procedure: Compute mean shift vector Translate the Kernel window by m(x) Computing the Mean Shift Slide by Y. Ukrainitz & B/


attributed copies permitted 1 The Brain is a Pattern matching Machine Intelligence and Patterns.

a Dictionary of Shape-Components in Visual Cortex: Comparison with Neurons, Humans and Machines, Ph.D. dissertation, Department of Brain and Cognitive Sciences, Massachusetts Institute of/copies permitted 18 Cortical minicolumn Retrieved from "http://en.wikipedia.org/wiki/Cortical_minicolumn"http://en.wikipedia.org/wiki/Cortical_minicolumn A cortical minicolumn is a vertical /known as synapses. In a column with 10,000 neurons, this translates into trillions of possible connections. The Blue Gene is used in /


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