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CSC 599: Computational Scientific Discovery

Lecture 3: Data, Data Structures, and Reasoning in Science. CSC 599: Computational Scientific Discovery. Outline. Computers Helping Scientists Data in Science Data Catalogs Data structures for scientific knowledge Equations Symbolic knowledge Simulations and scientific reasoning

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CSC 599: Computational Scientific Discovery

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  1. Lecture 3: Data, Data Structures, and Reasoning in Science CSC 599: Computational Scientific Discovery

  2. Outline Computers Helping Scientists Data in Science • Data • Catalogs Data structures for scientific knowledge • Equations • Symbolic knowledge Simulations and scientific reasoning • Do simulations • Architectures for sci. reasoning • QSIM Next time: • Probabilistic reason and graphical models

  3. Computers cooperating with Scientists Play to the strengths of computers • Fast • Accurate (we hope!)‏ • Don't get bored • Good for simulations of complex systems! Avoiding their weaknesses • No “common sense” • Garbage In, Garbage out • Calculation bugs • Time to develop/maintain code • Use someone else's program, if exists

  4. Don't Just Code-It-Up! Do you really need a computer? • Analytical solutions are cooler • Integrate! (If you can)‏ • Ordinary differential equation • System of eqns & derivatives w.r.t. one independ var • Eg. time • Laplace and z-transformations • Perturbation expansions and discrete time eqns • Partial differential equations • Another variable can change • For fluids: pressure, temperature, etc. • Separation of variables • See Gershenfeld “The Nature of Mathematical Modeling”

  5. Data in Science You make a measurement, should record? • Value (numeric or conceptual)‏ • Precision • For measured numbers: Mean and std deviation • For concepts: set identity “car” vs. “Toyota” vs. “Prius” • Domain • System constraints • Range definition limits • System extent limits • Saturation limits • Instruments constraints • Range definition limits • Detection limits • Reliable limits • Data constraints • Observed min/max w/in system extent and detect limits

  6. Data in Science (2)‏ You make a measurement, should record? • Units and Dimensions • When and Where • Speed of light expected to be observer-independent • Count of robins? First bloom of dandelions? • With what equipment • Eg. camera model and film type • By whom? • Implicit paradigm • Yesterday's Chinese “guest stars” are today's supernovas • Why? • What are you trying to look for? • Might hint at what your data do not show

  7. Laboratories: Where the Data Comes From Places where observations are made • Astronomical Observatories • Particle Physics Accelerators • CERN • FermiLab • Biology Labs • Human Genome Project Issues • Distributed observations • Human Genome Project • Collaborative observations

  8. Catalogs What is (and is not) Recorded Distributed Nature of Catalogs Synonyms in Catalogs Missing and Error Values in Catalogs Permanence of Catalogs

  9. What is (and is not) recorded Not record “outside of bounds” • Space • Outside of area of interest • Time (maybe Money!)‏ • Budget ran out, no more money for sensing • Magnitude (strength)‏ “Outside of bounds” may be a “soft” concept • USGS records earthquake • All Mw > 7 • Most Mw > 6 • Has Mw≈ 5 and below close to seismometers Q: Is it true that the only small earthquakes are close to seismometers?

  10. Distributed Nature of Catalogs Distributed databases • Hopefully designed properly • Redundancy • Reliably online up-time Central repository • Centralizes data available from other dbs • Example: some biochemical dbs • May use • inconsistent terms, • different missing value conventions, etc • May be out-of-date relative to source dbs • Format may be “least common denominator” of more specialized formats

  11. Synonyms and Different Meanings for a Given Word Concepts • ASCII or UNICODE string Consider the term “robin” • In N. America: • “American robin” • “Turdus migratorius • In Europe • “European robin” • “Erithacus rubecula” • What about these terms? • “thrush family bird” • “songbird” • “bird” Ontologies can help (if used)‏

  12. Missing and Errors Values Missing values often coded as sentinel values • There might be more than one! • Esp. for “required” fields • Clerks were required to get customer telephone # • Most common tele number? (111) 111-1111 Errors values • Probably a bigger problem before computers • Computers can solve some problems: • Sanity rules • Parity, checksum (corruption), nonce (security)‏ • Computers can add problems too • NASA launched satellite to look at atmospheric ozone • Satellite reported very low O3 levels by south pole • Data was not believed, BUT WAS TRUE (Ozone hole)‏

  13. Permanence of Catalog How long does the media last? • Is the data viewed as temporary or worth archiving? • Is more data generated than can be stored? • Proprietary? • Subject to privacy issues? • Backup policy? • Organization that holds the data • Tech savvy? • Have money? • Well-organized in general? • Redundant sites? • Banks do this, but they have $ and are protecting $

  14. The Importance of Prediction Scientists do a lot of things: • Prediction underlies much Scientific assertions • Numbers: Equations • Concepts: Rules, decision trees, production sys • Probabilities: Graphical models • Next time!

  15. Equations “Strongest” form of knowledge Defines relationships between quantities F = ma • Force and acceleration acting in same direction • Mass is scalar For N variables: N-1 knowns -> 1 unknown Scalar or Vector F = ma

  16. Equations, cont'd Are they always generalizations? The Drake Equation: N = R* * fp * ne * fl * fi * fc * L Where: N = Number of civilizations emitting radio transmissions R* = rate of Sun-like star formation fp = fraction of stars with planets ne = average number of inhabitable planets fl,fi,fc = probability of life, intelligence and civilization L = average duration of a civilization So far N = 0, is this equation useful? Do we know any of these terms well? What if N was non-zero, would the eqn be useful?

  17. Computing with symbols Rules • If dog(X) and wet(X) then smelly(X)‏ Decision trees Production systems • Like rules, but special memory to hold what is true

  18. Simulations Dimensions • Deterministic or stochastic? • Discrete or continuous? • Steady-state or time-varying? • Linear or non-linear? • Batch or real-time? Phases of development • Real system to logical abstraction • Appropriate datastructs/algorithms/math technique • Implement algorithm as program • Validate implementation

  19. Real System to Logical Abstraction • Define the problem • What are the objectives? • What question(s) would you like to answer? • What do you anticipate answering in the future? • When in doubt, leave detail out • You may not need it • “A theory should be as simple as it needs to be, and no simpler” A. Einstein • You can always add it in future • Spend the time turning vague statement of goals into better defined one(s)‏ • Revisit as necessary • (That's just good software engineering!)‏

  20. Appropriate Datastructure/Algorithm/Math Technique Setting up the model • Start with what's known • Go simple -> complex • Iterate • Develop large models modularly • Model only what is necessary • Make (and state) assumptions and hypotheses • State constraints • Alternate between top-down & bottom up Best data structure/algorithm? • Data Structures and algorithms class Best math technique? • Numerical methods class

  21. Validation Define some “test cases” before hand • Define (input,output) pairings • Should range in complexity • Should be checked: • By scientists, or • By well-known (e.g. “textbook”) cases, or • By hand (and then double-checked by someone else)‏ Just good software engineering!

  22. Implementation Procedural languages Object oriented languages Artificial Intelligence Languages Production systems Simulation languages

  23. Procedural Languages Examples • Fortran, C Fast • Compilers map language to machine code easily • Decades worth of optimizing compilers Large body of libraries for scientific • Among first programming languages • Decades worth of optimizing libraries Have to think in machine terms, not domain terms

  24. Object Oriented Languages Examples • C++, Java, C#, (Eiffel, Smalltalk)‏ Object Oriented Nature • Classes can represent scientific objects • Methods can represent scientific transitions • C++ can link with C libraries Still may be a hassle to program objects

  25. Artificial Intelligence Languages Examples • Prolog, Lisp, Scheme Prolog: Predicate Logic (Horn Clause) based Facts: sentiveNose(sniffer). dog(fido). wet(fido). Rules: smelly(X) :- dog(X), wet(X). offendedBy(Y,X) :- smelly(X), sensitiveNose(Y). Possible queries: Is sniffer offended by fido? offendedBy(sniffer,fido). Does fido offend anyone? offendedBy(A,fido). Is sniffer offended by anyone? offendedBy(sniffer,B). Is anyone offended by anyone else? offendedBy(A,B).

  26. Artificial Intelligence Langs (2)‏ Lisp • It's all functions operating on lists: (defun computeProperties (object newObj)‏ (if (and (getProperties object dog)‏ (getProperties object wet)‏ )‏ (addProperty object smelly newObj)‏ )‏ )‏

  27. Artificial Intelligence Langs (3)‏ Advantages • Encourage thinking in domain space • Prolog: modular rules • Lisp: functions operating on lists good for symbolic processing Disadvantages: • Prolog: not good for floating pt numbers • Exact matching not compatible with float pt rounding • Lisp: • Handling loops and variables somewhat contrived

  28. Production Systems Examples • SOAR, OPS5, Mycin? Models human thought Production sys = Rules + Working Memory • Rules = if-then statements if dog(X) and wet(X) then smelly(X)‏ if smelly(X) and sensitiveNose(Y) then offendedBy(Y,X)‏ • Working memory = models human memory dog(fido), wet(fido), sensitiveNose(sniffer)‏ • Computation 1st round: compute smelly(fido)‏ 2nd round: compute offendedBy(sniffer,fido)‏

  29. Production Systems, cont'd Advantages • Productions are inherently modular • Can have constraints on working memory elements (“wme's”) • Better models human cognitive limitations • RETE algorithm matches wmes and rules efficiently • Rules = laws, WMEs = state When rules conflict, what happens? • Architecture has to do something • OPS5: More specific rule wins • SOAR: Create problem space to resolve issue • MYCIN: Rules w/certainty factors (weighted vote)‏ • Is what the architecture does what you want?

  30. Simulations Languages Examples • QSIM, SPICE (analog circuits), Scilab Qualitative simulations • Very precise, not very general: d2x/dt2(t) = -9.8 m/sec2, x(t0) = 2m, v(t0) = 0 m/sec • Less precise, more general d2x/dt2(t) = -g, x(t0) = x0, v(t0) = v0 • Even less precise, even more general dx/dt = M- (monotonically dec. fnc), x(t0) = 0 .. x0 .. infinity

  31. QSIM (1)‏ Values Either landmark values 0, MAX_CAPACITY, t0, inf Or ranges between landmark values Not full but not empty: (0,MAX_CAPACITY), Time before t1 but after t0: (t0,t1)‏ Variables: Have both a value and derivative <0,inc> <(0,MAX_CAPACITY),inc> <MAX_CAPACITY,std> Functions: Either monotonically increasing or decreasing M+, M- Derivatives: either increasing, decreasing or steady: inc, dec, std.

  32. QSIM (2): Classic Example U-Tube: Two tubes connected by pipe • Both have • Some initial fluid level amtA: 0..AMAX..inf amtB: 0..BMAX..inf • Pressure dependent upon level pressureA = M+(amtA) pressureB = M+(amtB)‏

  33. QSIM (3)‏ Other constraints: • Total fluid only in A & B, and is constant: amtA + amtB = total constant(total)‏ • Flow depends on pressure difference: pAB = pressureA – pressureB flowAB = M+(pAB)‏ d(amtB)/dt = flowAB d(amtA)/dt = -flowAB • Knowledge of quantities: total: 0..inf amtA: 0..AMAX..inf amtB: 0..BMAX..inf pressureA: 0..inf pressureB: 0..inf pAB: -inf..0..inf flowAB: -inf..0..inf • Correspondence between values pressureA & amtA: (0,0), (inf,inf)‏ pressureB & amtB: (0,0), (inf,inf)‏ flowAB & pAB: (-inf, -inf), (0,0), (inf,inf)‏

  34. QSIM (4)‏ Note correspondence w/ordinary diffy eqn d(amtB)/dt = f(g(amtA) – h(amtB))‏ f,g,h  M+ Qualitative state is dynamic Imagine filling tank A (ignore tank B)‏ Need to represent amtA and its derivative: amtA(t): t0: <0,inc> (t0,t1): <(0,AMAX),inc> t1: <AMAX,std>

  35. QSIM (5): Predicting behavior 1. Give initial (at least some) conditions: “Tank A full” t = t0: amtA = <AMAX,?> “Tank B empty” t = t0: amtB = <0,?>

  36. QSIM (6): 2. Correspondence propagation on init state amtB = 0 therefore pressureB = 0 amtA = AMAX therefore pressureA = (0,inf)‏ amtA=AMAX && amtB = 0 therefore total = (0,inf)‏ pressureA = (0,inf) and pressureB = 0 therefore pAB = (0,inf)‏ constant(total) therefore d(total)/dt = std pAB = (0,inf) therefore flowAB = (0,inf)‏ flowAB = (0,inf) therefore d(amtA)/dt = dec d(amtB)/dt = inc d(amtA)/dt = dec therefore d(pressureA)/dt = dec. d(amtB)/dt = dec therefore d(pressureB)/dt = dec.

  37. QSIM (7)‏ (Init state correspondence propagate, cont'd)‏ Additional constraint d(pressureA)/dt = dec && d(pressureB)/dt = inc therefore d(pAB)/dt = dec Last propagation d(pAB)/dt = dec therefore d(flowAB)/dt = dec So we have at t = t0: amtA = <AMAX,dec> pressureA = <(0,inf),dec> amtB = <0,inc> pressureB = <0,inc> pAB = <(0,inf), dec> flowAB = <(0,inf), dec> Total = <(0,inf), std>

  38. QSIM (8)‏ 3. Predicting the next state: What events can make the next state? • A variable can reach a limit or landmark • A variable may move off a landmark • A variable may start or stop moving 6 variables moving to limits/landmarks (theoretically): 46 - 1 = 4095 possibilities (actual): constraints & correspondences reduce choices t = (t0,t1): amtA = <(0,AMAX),dec> pressureA = <(0,inf),dec> amtB = <(0,BMAX),inc> pressureB = <(0,inf),inc> pAB = <(0,inf), dec> flowAB = <(0,inf), dec> Total = <(0,inf), std>

  39. QSIM (9)‏ What happens after? t = (t0,t1): amtA = <(0,AMAX),dec> pressureA = <(0,inf),dec> amtB = <(0,BMAX),inc> pressureB = <(0,inf),inc> pAB = <(0,inf), dec> flowAB = <(0,inf), dec> total = <(0,inf), std> Well: amtA is decreasing toward 0 (won't get there)‏ amtB is increasing toward BMAX. flowAB is decreasing toward 0. So either: • flowAB gets to 0 AND amtB gets to BMAX, or • flowAB gets to 0 BEFORE amtB gets to BMAX, or • amtB gets to BMAX BEFORE flowAB gets to 0 (Tank B overflows)‏

  40. QSIM (10)‏ • flowAB gets to 0 AND amtB gets to BMAX: t = t1(a): amtA = <(0,AMAX),std> pressureA = <(0,inf),std> amtB = <BMAX,std> pressureB = <(0,inf),std> pAB = <0,std> flowAB = <0,std> Total = <(0,inf), std> 3. amtB gets to BMAX BEFORE flowAB gets to 0 (Tank B overflows)‏ t = t1(c): amtA = <(0,AMAX),dec> pressureA = <(0,inf),dec> amtB = <BMAX,inc> pressureB = <(0,inf),inc> pAB = <(0,inf),dec> flowAB = <(0,inf),dec> Total = <(0,inf),std>

  41. QSIM (11)‏ 2. flowAB gets to 0 BEFORE amtB gets to BMAX t = t1(b): amtA = <(0,AMAX),std> pressureA = <(0,inf),std> amtB = <(0,BMAX),std> pressureB = <(0,inf),std> pAB = <0,std> flowAB = <0,std> Total = <(0,inf), std> amtB's level becomes a new landmark so: amtA: 0..a0..AMAX..inf pressureA: 0..p0..inf amtB: 0..a1..BMAX..inf pressureB: 0..p1..inf pAB: -inf..0..inf flowAB: -inf..0..inf total: 0..to0..inf

  42. QSIM (12)‏ Output: graph of state transitions:

  43. QSIM (13)‏ QSIM algorithm: • Init queue w/QState(t0), complete its description • If queue empty or exceed resource limit then stop, otherwise pop state S from queue • For each var vi in S get successors from table • Determine all successor states consistent w/restrictions • If S is time-interval, delete copies of itself • For each successor Si, assert: successor(S,Si) predecessor(Si,S)‏ • Apply global filters on successors • Add successors to queue Don't add inconsistent, quiescent, cycles, transition or t = inf states • Go to 2

  44. Next Time Probabilistic reasoning • Graphical Models Machine Learning and CSD

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