Pecha Kucha - Kelsey Patterson
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This document provides a 3-sentence summary of how to use Voyant Tools to analyze Mary Shelley's novel "Frankenstein". It introduces Voyant Tools as a free web-based text analysis tool that can be used to analyze texts. It then demonstrates how Voyant Tools can be used to create a stop list, graph word frequencies, analyze word lemmatization, and favorite, organize and share analyzed texts. The document serves as a brief tutorial for using Voyant Tools to dissect elements of "Frankenstein".
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This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Taking AI to the Next Level in Manufacturing.pdf
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
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Pecha Kucha - Kelsey Patterson
- 1. W I T H A L I T T L E B I T O F H E L P F R O M M A R Y S H E L L E Y ’ S “ F R A N K E N S T E I N ” A Dissection of Voyant Tools N O T F R A N K E N S T E I N - > < - F R A N K E N S T E I N By Kelsey Patterson1
- 3. But What’s “Voyeur”? Accommodating Easy to use Analyze texts Beta Stage
- 4. Beta? 1 2 3 * I understand the pronunciation of betta fish
- 7. STOP LIST 1
- 8. STOP LIST 1
- 9. 1 2 3 4 5
- 10. Time
- 11. Little Bit of Extra Context
- 15. 1. Select like words 2. Graph! But What About Lemmas?
- 16. Where’s Clerval? Introduction to Henry Clerval and his background relationship w/ Victor Clerval’s nurses an ill Frankenstein and joins him on a journey Clerval’s untimely end
- 17. Where’s Clerval? Introduction to Henry Clerval and his background relationship w/ Victor Clerval’s nurses an ill Frankenstein and joins him on a journey Clerval’s untimely end
- 18. Playing Favorites Have something you want to remember? Just click on that heart with plus! The heart to the left will take you back to your list of favorites!
- 20. Don’t Forget to Share!
Editor's Notes
- Hi, my name’s Kelsey and I’m here to take you on a tour of the text analysis website “Voyant Tools”. You’ll be able to see how this resource can be used to discover symbols, plot points, and more all in one place. Along the way, we’ll get a little help from Mary Shelley’s “Frankenstein”.
- Born in 1797. Hung in the literary circle of Romantic greats such as Lord Byron and her husband Percy Bysshe Shelley. Tells the story of Victor Frankenstein’s horrific experiment of building a monster from cadaver parts. The plot follows the path of the creature’s destruction as well as Victor’s inability to own up to his mistake and take responsibility.
- Accomodating:use texts in a variety of formats including plain text, HTML, XML, PDF, RTF and MS Worduse texts from different locations, including URLs and uploaded files
- lack of more advanced linguistic processing (lemmatization, parts of speech, semantic awareness)lack of XML-aware analytic features (though XML is a valid input format)the current default skin (configuration of tools) is not well-suited to reading textssome of the user documentation is a bit bareother funcitonality:proximity searching of termsmulti-word (n-gram) views (though you can search for specific phrases)
- The start screen. When all components are open, the screen consists of three columns with the text set in the middle. Both side columns have three windows which all utilize different tools to analyze the one text. It’s a lot to handle at once, so why don’t we magnify just one section.
- Got rid of common english words as well as said and shall
- Got rid of common english words as well as said and shall
- Key word in context
- Raw frequency
- Raw frequency
- Relative frequencies = how often the word occurs in proportion to the rest of the words in that section