Computational Astrobiology Summer School Introduction

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Computational Astrobiology Summer School
Introduction

• Kim Binsted
• NASA Astrobiology Institute
• and
• Information Computer Sciences Dept
• University of Hawaii
• binsted_at_hawaii.edu

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Introductions
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Admin
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Today

• Overview of CASS
• Campus orientation and poster posting
• Lunch with NAI postdocs

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Travel expenses

• Before you go, give me
• WH-1 form, signed
• Non-employee reimbursement form
• Receipts
• Boarding passes (if you can print them
  otherwise, send as soon as you can)
• Youll receive a fixed amount (500), less taxes
  (if you dont give me the boarding passes).

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Schedule

• See
• http//www2.hawaii.edu/binsted/CASS/schedule.html
  
• There are still some readings etc. to be
  uploaded, so please check every day or so.
• Note the two locations POST 126 (here) and the
  Waterhole (will show later)

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Daily routine

• 9am lecture starts
• 1015am coffee break
• 1030-12 Discussion
• 12 session ends
• The discussion is not for just asking questions
  about the lecture it is for brainstorming ideas
  for projects. Please be prepared to talk.

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Afternoons

• Free time, but please use productively
• Doing readings
• Library
• Drafting project ideas
• Surfing

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Public Lecture

• Searching for Life in the Universe
• Seth Shotak and Chris McKay
• Friday July 28, 730pm-9pm
• Arts 132 (see campus map http//www.hawaii.edu/ca
  mpusmap/)
• Reception at 630pm in the Arts building please
  attend!

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Ocean Field Trip

• Details to follow, but roughly
• Carpool to Kaneohe, to arrive at noon
• Morning Tour of Coconut Island
• Afternoon Kayaking on Kaneohe Bay
• Carpool back to Honolulu, returning about 7pm.

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Big Island Field Trip

• Sat Aug 5, 710am HNL Hilo. Be at the airport
  by 610am.
• Drop bags at hotel, go to Mauna Kea for summit
  tour and stargazing at vistors center.
• Sun Aug 6 Volcanoes National Park.
• 843pm Hilo HNL.
• Anyone NOT going, let me know now!!

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Project Presentations

• On the last day, please be ready to give a short
  (15min) presentation on what you plan to do for
  your project.
• Give as much detail as possible scope,
  interface, languages, expected completion date
  etc. Dont worry, you can change your mind on
  these things!
• Email your presentation (pref. PowerPoint) to me
  before you go.

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On your return

• Meet with your mentor, and discuss your project
  plan. Modify as necessary.
• Send me a monthly progress report (casual email
  OK).
• Youre not finished until I have a
  well-documented version of your code!
• Aim to have something to present/demonstrate at
  Bioastronomy 2007 (July 16-20, Arecibo). Travel
  funding will (hopefully) be available.

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Overview
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What is Astrobiology?

• Scientific research into the origin,
  distribution, and destiny of life in the
  universe.
• Practically speaking astronomers, biologists,
  chemists, geologists, computer scientists, etc.
  doing their research in a larger context Earth
  as one (very rich!) data point, rather than the
  whole story.
• Inherently interdisciplinary.

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Drake equation

• NRfp ne fl fifcL , where
• N number of civilizations in our galaxy whose
  electromagnetic emissions are detectable.
• R rate of formation of stars.
• fp fraction of those stars with planetary
  systems.
• ne number of planets, per solar system, with an
  environment suitable for life.

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Drake equation (2)

• fl fraction of suitable planets on which life
  actually appears.
• fi fraction of life bearing planets on which
  intelligent life emerges.
• fc fraction of civilizations that develop a
  technology that releases detectable signs of
  their existence into space.
• L The length of time such civilizations release
  detectable signals into space.

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Astrobiology and SETI

• Either sharply distinguished or muddled together,
  depending on funding atmosphere (currently one
  happy family)
• Roughly, astrobiology works on the first four
  factors of the Drake equation, and SETI works on
  the last three (as well as attempts at direct
  detection)
• The SETI Institute is a team member in the
  distributed NASA Astrobiology Institute.

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Astrobiology at UH

• Part of (and funded by) the NAI
• 18 co-Is, from astronomy, chemistry, planetary
  geology, biology, oceanography, computer science
• 11 postdocs
• Many grad students
• Theme The origin, history, distribution and role
  of water as it relates to life in the universe.

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How does CS fit in?

• Astrobiology research can benefit from using
  up-to-date computational techniques and resources
• Astrobiologists may or may not be aware of
  developments in computer science/engineering
• Computer scientists may or may not be aware of
  what astrobiologists need
• We need to bring them together!

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Our approach

• Encourage computer science students to tackle
  problems in astrobiology, via
• NASA Space Grants for undergraduate research
• ICS 691 Graduate seminar/project course,
  Computational Astrobiology
• CASS Computational Astrobiology Summer School
  2006
• Encourage astrobiologists to talk to ICS
  students, especially those that need to do
  capstone projects.

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Some current/past student projects

• From fairly basic web interfaces to ongoing
  research

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Web interface for Deep Impact ground based
observation database

• with Karen Meechstudent Johnny Saucedo

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Deep Impact
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Requirements

• Search for observations by institution,
  telescope, observatory, observer, operator,
  and/or instrument
• Edit magnitude, filter type, and error for any
  observation
• Help available via pop-up windows

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Status

• Completed
• Funded by UH-NASA Space Grant Fellowship for
  undergraduate research
• Won a UH undergraduate research prize

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Lowell Telescope Scheduler

• with Marc Buie (Lowell), Karen Meech, Mary
  Kadooka
• students Derek Shirae, Scott Ibara, Nick Bradley

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Motivation

• Many large and medium-size research telescopes
  are devoted to particular projects (e.g. Pluto
  observations), so sit unused when its target
  observations are not possible.
• Principal Investigators (PIs) would be happy to
  share with other observers, but are unwilling to
  spend time scheduling amateur observations every
  night.

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Telescope scheduler interface

• Needed
• A night-by-night observation scheduler for an
  automatic telescope
• A database of requested observations
• An interface that allows amateur astronomers to
  request a wide range of observations from the
  telescope, and retrieve the resulting data

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Scheduling Constraints

• The scheduler must
• Prioritize the PIs observations.
• Use observing time efficiently.
• Take advantage of unusual conditions.
• Complete observation sequences, even when
  disperse.
• Complete background tasks (e.g. calibration).
• Avoid damaging the telescope.
• Run quickly at the beginning of each observation
  period.

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Interface Requirements

• The interface must help students
• Decide what they want to observe.
• Specify observation constraints (preferably
  soft rather than hard)
• Make practical requests (i.e. likely to be
  granted)
• Use the telescopes capabilities (e.g. filters)
  effectively.

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Status

• Database Done.
• Interface Done, but should be improved to make
  easier for students to use.
• Scheduler Done, but PI needs improvements.
• Next steps
• Multi-night, dynamic scheduler
• Adaptive user interface
• Paper (co-authored by students) presented at
  IMSA05.

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Calibration/processing tool for in-situ
voltammeter

• with Brian Glazer, Jim Cowen
• students Kayo Fujiwara, Bryan Norman

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Requirements

• Consistently detect peaks and peak heights from
  voltammeter probe
• Determine calibration curve for each probe and
  element
• Efficiently process data, returning quantities of
  each element detected
• Interface to improve ease of use

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Status

• Peak detection algorithm done
• Calibration curve calculation done, needs
  improvement
• Interface done
• Future work In-situ data processing, allowing
  adaptive data gathering

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Simulation of adaptive intelligence

• with Sam Joseph
• student Cliff Tomosada

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Problem how adaptive is intelligence?

• To what range of conditions is intelligence
  adaptive?
• Is it just a peacocks tail?
• Is there a range of conditions under which we
  should expect intelligence to evolve?
• Hypothesis There are ideal frequency/severity
  profiles of environmental challenges that drives
  the evolution of intelligence.

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Our approach

• Define intelligence as
• adaptation on an individual time scale
• on-board simulation of environment
• ability to pass acquired information over
  generations
• Simulate species with varying intelligence,
  generation length, energistic requirements etc.
  under varying environmental regimes
• Compare simulation to known histories of species
  with varying brain/body ratios.

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Results (1)
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Results (2)
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Problems

• Sea lion EQ problematic due to difference in size
  between males and females (we used female size)
• Scaling factors on environmental disruptions
  principled, but need stronger justification.
• Simulation is crude, and arguably doesnt take
  into account differential effects of various
  kinds of disruptions.

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Status

• Beta version of simulation complete
• Seal / sea lion population comparisons not ideal
  looking for other data sources
• Next steps
• Refine simulation
• More species
• Make predictions re just right conditions for
  intelligence

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Wireless sensor networks for extreme environments

• with Edo Biagioni
• students Nitin Nagar, Faye Yuen, Mary Liang

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Problem

• Want to monitor conditions in extreme
  environments with minimal personal attention from
  humans
• Maximize data return by extending sensor life
• Prioritize any repair/maintenance attempts by
  sensors/robots/humans

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Status

• Adaptive protocol for sensor network developed,
  tested, deployed on small scale
• Basic hardware development in progress
• Hope to deploy in Atacama (Chris McKay),
  Kamchatka (Gilichinsky)
• Looking for funding

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Bioinformatics Tools

• with Andy Boal, Mark Brown
• students Derek Shirae, Charles Hall, Jimmy Saw

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Problem

• Andy Boals research (protein adaptation to
  extreme environments) requires efficient search
  and analysis tools.
• Also, can we generate/discover novel proteins
  that should be robust under extreme conditions?

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Status

• Basic bioinformatics tools (scripts) developed
  and used.
• More advanced version(s) with streamlined user
  interface developed by ICS 691 students.
• Three papers submitted, using analysis
• Genetic algorithm generates novel robust proteins
  with interesting features

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AFAR Adaptive Framework for Astrobiology Research

• with Rich Gazan
• student Marc LePape

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Problem

• Astrobiology is highly interdisciplinary no one
  person knows it all
• Identifying interesting unknowns (esp. for
  collaborative research) is difficult
• An in-depth overview would be useful for
  outreach/education efforts

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Our approach

• Develop adaptive, zoomable knowledge map of the
  state of astrobiology
• Make adding new knowledge automatic when
  possible, trivially easy when not
• Bootstrap with existing resources (e.g.
  astrobiology handbook, roadmap, etc.)

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Status

• Funding proposals submitted
• Hope to entice PhD student or CASS participant

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Questions?