pr s r t r pr s st
play

Pr srtr - PowerPoint PPT Presentation

Oct 31, 2023 •291 likes •1.01k views

Pr srtr Pr sst r rt

  1. Pr♦✈❛❜❧❡ ❈❤♦s❡♥✲❚❛r❣❡t✲❋♦r❝❡❞✲▼✐❞✜① Pr❡✐♠❛❣❡ ❘❡s✐st❛♥❝❡ ❊❧❡♥❛ ❆♥❞r❡❡✈❛ ❛♥❞ ❇❛rt ▼❡♥♥✐♥❦ ✭❑✳❯✳▲❡✉✈❡♥✮ ❙❡❧❡❝t❡❞ ❆r❡❛s ✐♥ ❈r②♣t♦❣r❛♣❤② ❚♦r♦♥t♦✱ ❈❛♥❛❞❛ ❆✉❣✉st ✶✶✱ ✷✵✶✶ ✶ ✴ ✶✺

  2. ▼ ✶ ♠♦❞ ♠ ▼ ✐♥❥❡❝t✐✈❡❧② ♣❛❞❞❡❞✿ ▼ ▼ ✶ ▼ ❦ ▼ ✶ ✵ ▼ ♠ ✵ ✶ ♥ ♠ ✵ ✶ ♥ ▼ ✐ ❝♦♠♣r❡ss❡❞ ✐t❡r❛t✐✈❡❧② ✉s✐♥❣ ❢ ▼❡r❦❧❡✲❉❛♠❣år❞ ❍❛s❤ ❋✉♥❝t✐♦♥ ❉❡s✐❣♥ ✭▼❉✮✿ ■♥tr♦❞✉❝t✐♦♥ ❍❛s❤ ❋✉♥❝t✐♦♥s ✷ ✴ ✶✺

  3. ▼ ✶ ♠♦❞ ♠ ▼ ✐♥❥❡❝t✐✈❡❧② ♣❛❞❞❡❞✿ ▼ ▼ ✶ ▼ ❦ ▼ ✶ ✵ ▼ ♠ ✵ ✶ ♥ ♠ ✵ ✶ ♥ ▼ ✐ ❝♦♠♣r❡ss❡❞ ✐t❡r❛t✐✈❡❧② ✉s✐♥❣ ❢ ■♥tr♦❞✉❝t✐♦♥ ❍❛s❤ ❋✉♥❝t✐♦♥s ▼❡r❦❧❡✲❉❛♠❣år❞ ❍❛s❤ ❋✉♥❝t✐♦♥ ❉❡s✐❣♥ ✭▼❉✮✿ ✷ ✴ ✶✺

  4. ✵ ✶ ♥ ♠ ✵ ✶ ♥ ▼ ✐ ❝♦♠♣r❡ss❡❞ ✐t❡r❛t✐✈❡❧② ✉s✐♥❣ ❢ ■♥tr♦❞✉❝t✐♦♥ ❍❛s❤ ❋✉♥❝t✐♦♥s ▼❡r❦❧❡✲❉❛♠❣år❞ ❍❛s❤ ❋✉♥❝t✐♦♥ ❉❡s✐❣♥ ✭▼❉✮✿ ▼ ✐♥❥❡❝t✐✈❡❧② ♣❛❞❞❡❞✿ ▼ �→ ▼ ✶ · · · ▼ ❦ = ▼ � ✶ � ✵ −| ▼ |− ✶ ♠♦❞ ♠ ��| ▼ |� ♠ ✷ ✴ ✶✺

  5. ■♥tr♦❞✉❝t✐♦♥ ❍❛s❤ ❋✉♥❝t✐♦♥s ▼❡r❦❧❡✲❉❛♠❣år❞ ❍❛s❤ ❋✉♥❝t✐♦♥ ❉❡s✐❣♥ ✭▼❉✮✿ ▼ ✐♥❥❡❝t✐✈❡❧② ♣❛❞❞❡❞✿ ▼ �→ ▼ ✶ · · · ▼ ❦ = ▼ � ✶ � ✵ −| ▼ |− ✶ ♠♦❞ ♠ ��| ▼ |� ♠ ▼ ✐ ❝♦♠♣r❡ss❡❞ ✐t❡r❛t✐✈❡❧② ✉s✐♥❣ ❢ : { ✵ , ✶ } ♥ + ♠ → { ✵ , ✶ } ♥ ✷ ✴ ✶✺

  6. ▼✉❧t✐❝♦❧❧✐s✐♦♥ r❡s✐st❛♥❝❡ ❙❡❝✉r✐t② ❛❣❛✐♥st ❧❡♥❣t❤ ❡①t❡♥s✐♦♥ ❛tt❛❝❦ ❈❤♦s❡♥✲t❛r❣❡t✲❢♦r❝❡❞✲♣r❡✜① ♣r❡✐♠❛❣❡ r❡s✐st❛♥❝❡ ✳✳✳✳✳✳✳ ❈❤♦s❡♥✲t❛r❣❡t✲❢♦r❝❡❞✲♣r❡✜① ✭❈❚❋P✮ ♣r❡✐♠❛❣❡ r❡s✐st❛♥❝❡ ✭s❡❝✉r✐t② ❛❣❛✐♥st ❤❡r❞✐♥❣ ❛tt❛❝❦✮ ❈❤♦♦s❡ ② ✱ ❣✐✈❡♥ P ✱ ✜♥❞ ❘ s✉❝❤ t❤❛t P ❘ ② ❆♣♣❧✐❝❛t✐♦♥s✿ ♣r❡❞✐❝t✐♥❣ ❡❧❡❝t✐♦♥s✱ s♣♦rts ❣❛♠❡s✱ ❡t❝✳ ■❞❡❛❧❧②✱ ❈❚❋P ❛tt❛❝❦ r❡q✉✐r❡s ✷ ♥ ✇♦r❦ ■♥tr♦❞✉❝t✐♦♥ ❍❛s❤ ❋✉♥❝t✐♦♥ ❙❡❝✉r✐t② ❘❡q✉✐r❡♠❡♥ts Pr❡✐♠❛❣❡ r❡s✐st❛♥❝❡ ❙❡❝♦♥❞ ♣r❡✐♠❛❣❡ r❡s✐st❛♥❝❡ ❈♦❧❧✐s✐♦♥ r❡s✐st❛♥❝❡ ✸ ✴ ✶✺

  7. ❈❤♦s❡♥✲t❛r❣❡t✲❢♦r❝❡❞✲♣r❡✜① ✭❈❚❋P✮ ♣r❡✐♠❛❣❡ r❡s✐st❛♥❝❡ ✭s❡❝✉r✐t② ❛❣❛✐♥st ❤❡r❞✐♥❣ ❛tt❛❝❦✮ ❈❤♦♦s❡ ② ✱ ❣✐✈❡♥ P ✱ ✜♥❞ ❘ s✉❝❤ t❤❛t P ❘ ② ❆♣♣❧✐❝❛t✐♦♥s✿ ♣r❡❞✐❝t✐♥❣ ❡❧❡❝t✐♦♥s✱ s♣♦rts ❣❛♠❡s✱ ❡t❝✳ ■❞❡❛❧❧②✱ ❈❚❋P ❛tt❛❝❦ r❡q✉✐r❡s ✷ ♥ ✇♦r❦ ■♥tr♦❞✉❝t✐♦♥ ❍❛s❤ ❋✉♥❝t✐♦♥ ❙❡❝✉r✐t② ❘❡q✉✐r❡♠❡♥ts Pr❡✐♠❛❣❡ r❡s✐st❛♥❝❡ ❙❡❝♦♥❞ ♣r❡✐♠❛❣❡ r❡s✐st❛♥❝❡ ❈♦❧❧✐s✐♦♥ r❡s✐st❛♥❝❡ ▼✉❧t✐❝♦❧❧✐s✐♦♥ r❡s✐st❛♥❝❡ ❙❡❝✉r✐t② ❛❣❛✐♥st ❧❡♥❣t❤ ❡①t❡♥s✐♦♥ ❛tt❛❝❦ ❈❤♦s❡♥✲t❛r❣❡t✲❢♦r❝❡❞✲♣r❡✜① ♣r❡✐♠❛❣❡ r❡s✐st❛♥❝❡ ✳✳✳✳✳✳✳ ✸ ✴ ✶✺

  8. ■♥tr♦❞✉❝t✐♦♥ ❍❛s❤ ❋✉♥❝t✐♦♥ ❙❡❝✉r✐t② ❘❡q✉✐r❡♠❡♥ts Pr❡✐♠❛❣❡ r❡s✐st❛♥❝❡ ❙❡❝♦♥❞ ♣r❡✐♠❛❣❡ r❡s✐st❛♥❝❡ ❈♦❧❧✐s✐♦♥ r❡s✐st❛♥❝❡ ▼✉❧t✐❝♦❧❧✐s✐♦♥ r❡s✐st❛♥❝❡ ❙❡❝✉r✐t② ❛❣❛✐♥st ❧❡♥❣t❤ ❡①t❡♥s✐♦♥ ❛tt❛❝❦ ❈❤♦s❡♥✲t❛r❣❡t✲❢♦r❝❡❞✲♣r❡✜① ♣r❡✐♠❛❣❡ r❡s✐st❛♥❝❡ ✳✳✳✳✳✳✳ ❈❤♦s❡♥✲t❛r❣❡t✲❢♦r❝❡❞✲♣r❡✜① ✭❈❚❋P✮ ♣r❡✐♠❛❣❡ r❡s✐st❛♥❝❡ ✭s❡❝✉r✐t② ❛❣❛✐♥st ❤❡r❞✐♥❣ ❛tt❛❝❦✮ ❈❤♦♦s❡ ② ✱ ❣✐✈❡♥ P ✱ ✜♥❞ ❘ s✉❝❤ t❤❛t H ( P � ❘ ) = ② ❆♣♣❧✐❝❛t✐♦♥s✿ ♣r❡❞✐❝t✐♥❣ ❡❧❡❝t✐♦♥s✱ s♣♦rts ❣❛♠❡s✱ ❡t❝✳ ■❞❡❛❧❧②✱ ❈❚❋P ❛tt❛❝❦ r❡q✉✐r❡s ✷ ♥ ✇♦r❦ ✸ ✴ ✶✺

  9. ❡❧♦♥❣❛t❡❞ ❤❡r❞✐♥❣ ♥ ✷ ✷ ♥ ✸ ✷ r ✸ ✷ r ❖ ♥ ❜❧♦❝❦s ✭✵ r ♥ ✷✮ ❛tt❛❝❦ ▲ ▼ ❝♦♠♣❧❡①✐t② ✭ ❢ ✲❝❛❧❧s✮ ♥ ✷ ✷ ♥ ✸ ❤❡r❞✐♥❣ ❖ ♥ ❜❧♦❝❦s ❤ ✶ ❤ ✷ ❤ ✸ P ❤ ✹ ✐✈ ❤ ② ❤ ✺ ❤ ✻ ❤ ✼ ❤ ✽ ■♥tr♦❞✉❝t✐♦♥ ❍❡r❞✐♥❣ ❆tt❛❝❦ ❢♦r ▼❉ ❬❑❡❧s❡② ✫ ❑♦❤♥♦✱ ✵✻❪ P❤❛s❡ ✷ P❤❛s❡ ✶ ✹ ✴ ✶✺

  10. ❡❧♦♥❣❛t❡❞ ❤❡r❞✐♥❣ ♥ ✷ ✷ ♥ ✸ ✷ r ✸ ✷ r ❖ ♥ ❜❧♦❝❦s ✭✵ r ♥ ✷✮ ❛tt❛❝❦ ▲ ▼ ❝♦♠♣❧❡①✐t② ✭ ❢ ✲❝❛❧❧s✮ ♥ ✷ ✷ ♥ ✸ ❤❡r❞✐♥❣ ❖ ♥ ❜❧♦❝❦s P ✐✈ ❤ ② ■♥tr♦❞✉❝t✐♦♥ ❍❡r❞✐♥❣ ❆tt❛❝❦ ❢♦r ▼❉ ❬❑❡❧s❡② ✫ ❑♦❤♥♦✱ ✵✻❪ P❤❛s❡ ✷ P❤❛s❡ ✶ ❤ ✶ ❤ ✷ ❤ ✸ ❤ ✹ ❤ ✺ ❤ ✻ ❤ ✼ ❤ ✽ ✹ ✴ ✶✺

  11. ❡❧♦♥❣❛t❡❞ ❤❡r❞✐♥❣ ♥ ✷ ✷ ♥ ✸ ✷ r ✸ ✷ r ❖ ♥ ❜❧♦❝❦s ✭✵ r ♥ ✷✮ ❛tt❛❝❦ ▲ ▼ ❝♦♠♣❧❡①✐t② ✭ ❢ ✲❝❛❧❧s✮ ♥ ✷ ✷ ♥ ✸ ❤❡r❞✐♥❣ ❖ ♥ ❜❧♦❝❦s P ✐✈ ❤ ② ■♥tr♦❞✉❝t✐♦♥ ❍❡r❞✐♥❣ ❆tt❛❝❦ ❢♦r ▼❉ ❬❑❡❧s❡② ✫ ❑♦❤♥♦✱ ✵✻❪ P❤❛s❡ ✷ PP P❤❛s❡ ✶ ❤ ✶ q P ✶ ✏ ✏✏ ❤ ✷ PP ❤ ✸ P q ✏ ✶ ✏✏ ❤ ✹ PP ❤ ✺ q P ✶ ✏ ✏✏ ❤ ✻ PP ❤ ✼ P q ✏ ✶ ✏✏ ❤ ✽ ✹ ✴ ✶✺

  12. ❡❧♦♥❣❛t❡❞ ❤❡r❞✐♥❣ ♥ ✷ ✷ ♥ ✸ ✷ r ✸ ✷ r ❖ ♥ ❜❧♦❝❦s ✭✵ r ♥ ✷✮ ❛tt❛❝❦ ▲ ▼ ❝♦♠♣❧❡①✐t② ✭ ❢ ✲❝❛❧❧s✮ ♥ ✷ ✷ ♥ ✸ ❤❡r❞✐♥❣ ❖ ♥ ❜❧♦❝❦s P ✐✈ ❤ ② ■♥tr♦❞✉❝t✐♦♥ ❍❡r❞✐♥❣ ❆tt❛❝❦ ❢♦r ▼❉ ❬❑❡❧s❡② ✫ ❑♦❤♥♦✱ ✵✻❪ P❤❛s❡ ✷ PP P❤❛s❡ ✶ ❤ ✶ q P ❍❍ ✶ ✏ ✏✏ ❍ ❥ ❤ ✷ PP ✯ ✟ ❤ ✸ ✟✟ q P ✏ ✶ ✏✏ ❤ ✹ PP ❤ ✺ q P ❍❍ ✏ ✶ ✏✏ ❥ ❍ ❤ ✻ PP ✯ ✟ ❤ ✼ ✟✟ q P ✶ ✏ ✏✏ ❤ ✽ ✹ ✴ ✶✺

  13. ❡❧♦♥❣❛t❡❞ ❤❡r❞✐♥❣ ♥ ✷ ✷ ♥ ✸ ✷ r ✸ ✷ r ❖ ♥ ❜❧♦❝❦s ✭✵ r ♥ ✷✮ ❛tt❛❝❦ ▲ ▼ ❝♦♠♣❧❡①✐t② ✭ ❢ ✲❝❛❧❧s✮ ♥ ✷ ✷ ♥ ✸ ❤❡r❞✐♥❣ ❖ ♥ ❜❧♦❝❦s P ✐✈ ❤ ■♥tr♦❞✉❝t✐♦♥ ❍❡r❞✐♥❣ ❆tt❛❝❦ ❢♦r ▼❉ ❬❑❡❧s❡② ✫ ❑♦❤♥♦✱ ✵✻❪ P❤❛s❡ ✷ PP P❤❛s❡ ✶ ❤ ✶ P q ❍❍ ✏ ✶ ✏✏ ❥ ❍ ❤ ✷ PP ✯ ✟ ❤ ✸ ❅ ✟✟ q P ❅ ✶ ✏ ✏✏ ❅ ❘ ❤ ✹ ② PP ❤ ✺ q P � ✒ ❍❍ ✶ ✏ � ✏✏ ❍ ❥ ❤ ✻ � PP ✯ ✟ ❤ ✼ ✟✟ P q ✶ ✏ ✏✏ ❤ ✽ ✹ ✴ ✶✺

  14. ❡❧♦♥❣❛t❡❞ ❤❡r❞✐♥❣ ♥ ✷ ✷ ♥ ✸ ✷ r ✸ ✷ r ❖ ♥ ❜❧♦❝❦s ✭✵ r ♥ ✷✮ ❛tt❛❝❦ ▲ ▼ ❝♦♠♣❧❡①✐t② ✭ ❢ ✲❝❛❧❧s✮ ♥ ✷ ✷ ♥ ✸ ❤❡r❞✐♥❣ ❖ ♥ ❜❧♦❝❦s ■♥tr♦❞✉❝t✐♦♥ ❍❡r❞✐♥❣ ❆tt❛❝❦ ❢♦r ▼❉ ❬❑❡❧s❡② ✫ ❑♦❤♥♦✱ ✵✻❪ P❤❛s❡ ✷ PP P❤❛s❡ ✶ ❤ ✶ q P ❍❍ ✶ ✏ ✏✏ ❍ ❥ ❤ ✷ PP ✯ ✟ ❤ ✸ ❅ ✟✟ P q ❅ ✏ ✶ ✏✏ ❅ ❘ P ✲ ❤ ✹ ✐✈ ❤ ② PP ❤ ✺ q P � ✒ ❍❍ ✏ ✶ � ✏✏ ❥ ❍ ❤ ✻ � PP ✟ ✯ ❤ ✼ ✟✟ P q ✏ ✶ ✏✏ ❤ ✽ ✹ ✴ ✶✺

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

Gaussian Cheap Talk Game with Quadratic Cost Functions: When Herding between Strategic Senders Is

Gaussian Cheap Talk Game with Quadratic Cost Functions: When Herding between Strategic Senders Is

Gaussian Cheap Talk Game with Quadratic Cost Functions: When Herding between Strategic Senders Is a Virtue Farhad Farokhi , Andr e Teixeira , and C edric Langbort KTH Royal Institute of Technology, Sweden University of

275 views • 25 slides
Information Cascades - Also following the crowd or herding - Example: Choosing a

Information Cascades - Also following the crowd or herding - Example: Choosing a

Information Cascades - Also following the crowd or herding - Example: Choosing a restaurant in an unfamiliar town you do your research, and plan to go to restaurant A When you arrive, A is empty and B next door is full if you believe

587 views • 3 slides
Herding networking cats: Integrating Linux routing with FusionCLI Stephen Hemminger

Herding networking cats: Integrating Linux routing with FusionCLI Stephen Hemminger

Herding networking cats: Integrating Linux routing with FusionCLI Stephen Hemminger shemminger@vyatta.com Vyatta versions http://vyatta.org http://vyatta.com Free download Subscription livecd Update 2x year Update 4x

645 views • 19 slides
Zalandos Open Source Infrastructure on AWS with Docker bernd.herding@zalando.de GOTO Con

Zalandos Open Source Infrastructure on AWS with Docker bernd.herding@zalando.de GOTO Con

Zalandos Open Source Infrastructure on AWS with Docker bernd.herding@zalando.de GOTO Con Berlin 2015, 2015-12-04 @01k One of Europes largest online Fashion Retailers 15 countries 3 fulfillment centers 17+ million active customers 2.2+

486 views • 35 slides
Projection based transfer learning Christian Poelitz Dortmund Technical University Christian

Projection based transfer learning Christian Poelitz Dortmund Technical University Christian

Projection based transfer learning Christian Poelitz Dortmund Technical University Christian Poelitz Dortmund Technical University Projection based transfer learning Transfer Learning We want to reuse a trained model or information from

158 views • 15 slides
Behavioural models Cognitive biases Marcus Bendtsen Department of Computer and Information

Behavioural models Cognitive biases Marcus Bendtsen Department of Computer and Information

Behavioural models Cognitive biases Marcus Bendtsen Department of Computer and Information Science (IDA) Division for Database and Information Techniques (ADIT) Judgement under uncertainty Humans are not necessarily very good at estimating

435 views • 28 slides
Kernel Recursive ABC: Point Estimation with Intractable Likelihood Motonobu Kanagawa EURECOM,

Kernel Recursive ABC: Point Estimation with Intractable Likelihood Motonobu Kanagawa EURECOM,

Kernel Recursive ABC: Point Estimation with Intractable Likelihood Motonobu Kanagawa EURECOM, Sophia Antipolis, France (Previously U. Tbingen) ISM-UUlm Workshop, October 2019 1 / 44 Contents of This Talk 1. Kernel Recursive ABC: Point

1.47k views • 131 slides
CAPS Community Accreditation for Produce Safety If you buy from CAPS buys you LOCAL FARMS

CAPS Community Accreditation for Produce Safety If you buy from CAPS buys you LOCAL FARMS

CAPS Community Accreditation for Produce Safety If you buy from CAPS buys you LOCAL FARMS ASSURANCE And shows you CARE about the Freshness, Quality and Safety of your Fruits and Contact Hans Estrin, hestrin@uvm.edu, 802 380 2109

388 views • 5 slides
Inference in ecology and evolution beyond generalised linear mixed models Reinder Radersma

Inference in ecology and evolution beyond generalised linear mixed models Reinder Radersma

Inference in ecology and evolution beyond generalised linear mixed models Reinder Radersma Dept. of Biology Lund University reinder.radersma@biol.lu.se Structure of GLMMs random effects fixed effects z 1 x 1 z 2 y x 2

330 views • 14 slides
Parallel Programming and Heterogeneous Computing Shared-Nothing Parallelism Models Max Plauth,

Parallel Programming and Heterogeneous Computing Shared-Nothing Parallelism Models Max Plauth,

Parallel Programming and Heterogeneous Computing Shared-Nothing Parallelism Models Max Plauth, Sven Khler, Felix Eberhardt, Lukas Wenzel and Andreas Polze Operating Systems and Middleware Group Theoretical Models for Parallel Computers

754 views • 20 slides
A DOMA MAIN S SPECIF ECIFIC A IC APPR PROACH CH TO H HETER EROGENEO ENEOUS US PARALLE

A DOMA MAIN S SPECIF ECIFIC A IC APPR PROACH CH TO H HETER EROGENEO ENEOUS US PARALLE

A DOMA MAIN S SPECIF ECIFIC A IC APPR PROACH CH TO H HETER EROGENEO ENEOUS US PARALLE LLELI LISM Hassan Chafi , Arvind Sujeeth, Kevin Brown, HyoukJoong Lee, Anand Atreya, Kunle Olukotun Stanford University Pervasive Parallelism

772 views • 40 slides
Upwind Summation By Parts Methods for Large Scale Elastic Wave Equation ICERM, Brown University

Upwind Summation By Parts Methods for Large Scale Elastic Wave Equation ICERM, Brown University

Upwind Summation By Parts Methods for Large Scale Elastic Wave Equation ICERM, Brown University October 27, 2020 Kenneth Duru Kenneth Duru: Upwind Summation By Parts Methods for Large Scale Elastic Wave Equation ICERM, Brown University 1 /

770 views • 47 slides
Parallel Programming and Heterogeneous Computing A4 Workloads & Fosters Methodology

Parallel Programming and Heterogeneous Computing A4 Workloads & Fosters Methodology

Parallel Programming and Heterogeneous Computing A4 Workloads & Fosters Methodology Max Plauth, Sven Khler, Felix Eberhardt, Lukas Wenzel, and Andreas Polze Operating Systems and Middleware Group Example: Can You Easily Parallelize

669 views • 28 slides
Integrating Maude into Hets Mihai Codescu, 1 Till Mossakowski, 1 Adri an Riesco 2 and Christian

Integrating Maude into Hets Mihai Codescu, 1 Till Mossakowski, 1 Adri an Riesco 2 and Christian

Integrating Maude into Hets Mihai Codescu, 1 Till Mossakowski, 1 Adri an Riesco 2 and Christian Maeder 1 1 DFKI GmbH Bremen and University of Bremen, Germany 2 Facultad de Inform atica, Universidad Complutense de Madrid, Spain June 24, 2010,

439 views • 30 slides
Improving NAMD Performance and Scaling on Heterogeneous Architectures David J. Hardy and Julio D.

Improving NAMD Performance and Scaling on Heterogeneous Architectures David J. Hardy and Julio D.

Improving NAMD Performance and Scaling on Heterogeneous Architectures David J. Hardy and Julio D. C. Maia NIH Center for Macromolecular Modeling and Bioinformatics Theoretical and Computational Biophysics Group Beckman Institute for Advanced

655 views • 23 slides
Similarity Measures There are an enormous number of ways in which we can measure similarity

Similarity Measures There are an enormous number of ways in which we can measure similarity

Similarity Measures There are an enormous number of ways in which we can measure similarity They vary depending on whether the items we are interested in analysing come from one sample or two; are qualitative or quantitative; binary,

355 views • 7 slides
OpenMP and GPU Programming GPU Intro Emanuele Ruffaldi

OpenMP and GPU Programming GPU Intro Emanuele Ruffaldi

OpenMP and GPU Programming GPU Intro Emanuele Ruffaldi https://github.com/eruffaldi/course_openmpgpu PERCeptual RObotics Laboratory, TeCIP Scuola Superiore SantAnna Pisa,Italy e.ruffaldi@sssup.it April 12,2016 GPU Graphic Processing

652 views • 39 slides
NEAR DATA PROCESSING Mahdi Nazm Bojnordi Assistant Professor School of Computing University of

NEAR DATA PROCESSING Mahdi Nazm Bojnordi Assistant Professor School of Computing University of

NEAR DATA PROCESSING Mahdi Nazm Bojnordi Assistant Professor School of Computing University of Utah CS/ECE 7810: Advanced Computer Architecture Requirement for Your Presentations Prepare for exactly 20m talk followed by 5m Q&A

929 views • 28 slides
HPC for Computational Astrophysics: Looking Forward Ann Almgren Center for Computational

HPC for Computational Astrophysics: Looking Forward Ann Almgren Center for Computational

HPC for Computational Astrophysics: Looking Forward Ann Almgren Center for Computational Sciences and Engineering Lawrence Berkeley National Laboratory asalmgren@lbl.gov July 26, 2011 lab-logo Almgren, LBNL HPC for Astrophysics

633 views • 9 slides
Inference and Learning for Probabilistic Logic Programming Fabrizio Riguzzi Dipartimento di

Inference and Learning for Probabilistic Logic Programming Fabrizio Riguzzi Dipartimento di

Inference and Learning for Probabilistic Logic Programming Fabrizio Riguzzi Dipartimento di Matematica e Informatica Universit di Ferrara F. Riguzzi (DMI) Inference and learning for PLP 1 / 25 Outline Probabilistic Logic Languages 1

405 views • 25 slides
Programming for Hybrid Architectures John E. Stone Theoretical and Computational Biophysics Group

Programming for Hybrid Architectures John E. Stone Theoretical and Computational Biophysics Group

Programming for Hybrid Architectures John E. Stone Theoretical and Computational Biophysics Group Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign http://www.ks.uiuc.edu/Research/gpu/ GPGPU 2015:

607 views • 25 slides
Lattice QCD, Programming Models and Porting LQCD codes to Exascale Blint Jo - Jefferson Lab

Lattice QCD, Programming Models and Porting LQCD codes to Exascale Blint Jo - Jefferson Lab

Lattice QCD, Programming Models and Porting LQCD codes to Exascale Blint Jo - Jefferson Lab Feb 19, 2020 HPC Roundtable Thomas Jefferson National Accelerator Facility LQCD as an application Replace Spacetime with a 4-Dimentional

753 views • 34 slides
Optimization and Parallelization of the Boundary Element Method for the Wave Equation in Time

Optimization and Parallelization of the Boundary Element Method for the Wave Equation in Time

Optimization and Parallelization of the Boundary Element Method for the Wave Equation in Time Domain B erenger Bramas Inria, Bordeaux - Sud-Ouest PhD defense - Feb. 15th 2016 Advisor: Oliver Coulaud (Inria) Industrial co-advisor: Guillaume

1.61k views • 144 slides
t rts r

t rts r

t rts r rrt str rs t ttr s t t

479 views • 5 slides

More recommend