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30 Pages

### probcfg

Course: CMPT 413, Fall 2009
School: Sveriges...
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Word Count: 1755

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Computational CMPT-413 Linguistics Anoop Sarkar http://www.cs.sfu.ca/anoop March 28, 2007 1 / 30 Probabilistic CFG (PCFG) S VP VP PP NP NP NP NP V P NP VP 1 V NP 0.9 VP PP 0.1 P NP 1 NP PP 0.25 Calvin 0.25 monsters 0.25 school 0.25 imagined 1 in 1 tree P (input) = P (tree | input) P (Calvin imagined monsters in school) =? Notice that P (VP V NP ) + P (VP VP PP ) = 1.0 2 / 30 Probabilistic CFG...

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Computational CMPT-413 Linguistics Anoop Sarkar http://www.cs.sfu.ca/anoop March 28, 2007 1 / 30 Probabilistic CFG (PCFG) S VP VP PP NP NP NP NP V P NP VP 1 V NP 0.9 VP PP 0.1 P NP 1 NP PP 0.25 Calvin 0.25 monsters 0.25 school 0.25 imagined 1 in 1 tree P (input) = P (tree | input) P (Calvin imagined monsters in school) =? Notice that P (VP V NP ) + P (VP VP PP ) = 1.0 2 / 30 Probabilistic CFG (PCFG) P (Calvin imagined monsters in school) =? (S (NP Calvin) (VP (V imagined) (NP (NP monsters) (PP (P in) (NP school))))) (S (NP Calvin) (VP (VP (V imagined) (NP monsters)) (PP (P in) (NP school)))) 3 / 30 Probabilistic CFG (PCFG) (S (NP Calvin) (VP (V imagined) (NP (NP monsters) (PP (P in) (NP school))))) P (tree1 ) = P (S NP VP ) P (NP Calvin) P (VP V NP ) P (V imagined ) P (NP NP PP ) P (NP monsters ) P (PP P NP ) P (P in) P (NP school ) = 1 0.25 0.9 1 0.25 0.25 1 1 0.25 = .003515625 4 / 30 Probabilistic CFG (PCFG) (S (NP Calvin) (VP (VP (V imagined) (NP monsters)) (PP (P in) (NP school)))) P (tree2 ) = P (S NP VP ) P (NP Calvin) P (VP VP PP ) P (VP V NP ) P (V imagined ) P (NP monsters ) P (PP P NP ) P (P in) P (NP school ) = 1 0.25 0.1 0.9 1 0.25 1 1 0.25 = .00140625 5 / 30 Probabilistic CFG (PCFG) P (Calvin imagined monsters in school) = P (tree1 ) + P (tree2 ) = .003515625 + .00140625 Most likely tree is tree1 (S (NP Calvin) (VP (V imagined) (NP (NP monsters) (PP (P in) (NP school))))) (S (NP Calvin) (VP (VP (V imagined) (NP monsters)) (PP (P in) (NP school)))) 6 / 30 = .004921875 arg max = P (tree | input) tree PCFG Central condition: P (A ) = 1 f (A ,) f (A ) Called a proper PCFG if this condition holds Note that this means P (A ) = P ( | A ) = P (T | S ) = P (T ,S ) P (S ) = P (T , S ) = i P (RHSi | LHSi ) 7 / 30 PCFG What is the PCFG that can be extracted from this single tree: (S (NP (Det the) (NP man)) (VP (VP (V played) (NP (Det a) (NP game))) (PP (P with) (NP (Det the) (NP dog))))) How many different rhs exist for A where A can be S, NP, VP, PP, Det, N, V, P 8 / 30 PCFG S NP NP NP NP VP VP PP Det Det V P NP VP Det NP man game dog VP PP V NP P NP the a played with c c c c c c c c c c c c =1 =3 =1 =1 =1 =1 =1 =1 =2 =1 =1 =1 p p p p p p p p p p p p = 1/1 = 3/6 = 1/6 = 1/6 = 1/6 = 1/2 = 1/2 = 1/1 = 2/3 = 1/3 = 1/1 = 1/1 = 1.0 = 0.5 = 0.1667 = 0.1667 = 0.1667 = 0.5 = 0.5 = 1.0 = 0.67 = 0.33 = 1.0 = 1.0 We can do this with multiple trees. Simply count occurrences of CFG rules over all the trees. A repository of such trees labelled by a human is called a TreeBank. 9 / 30 Ambiguity Part of Speech ambiguity saw noun saw verb Structural ambiguity: Prepositional Phrases I saw (the man) with the telescope I saw (the man with the telescope) Structural ambiguity: Coordination a program to promote safety in ((trucks) and (minivans)) a program to promote ((safety in trucks) and (minivans)) ((a program to promote safety in trucks) and (minivans)) 10 / 30 Ambiguity attachment choice in alternative parses NP NP a program to promote NP safety in VP VP NP PP NP trucks and minivans NP a program NP VP to promote NP safety in PP trucks VP NP and NP minivans 11 / 30 Parsing as a machine learning problem S = a sentence T = a parse tree A statistical parsing model defines P (T | S ) Find best parse: P (T | S ) = Best parse: P (T ,S ) P (S ) arg max T P (T | S ) = P (T , S ) P (T , S ) i =1...n arg max T e.g. for PCFGs: P (T , S ) = P (RHSi | LHSi ) 12 / 30 Prepositional Phrases noun attach: I bought the shirt with pockets verb attach: I washed the shirt with soap As in the case of other attachment decisions in parsing: it depends on the meaning of the entire sentence needs world knowledge, etc. Maybe there is a simpler solution: we can attempt to solve it using heuristics or associations between words 13 / 30 Structure Based Ambiguity Resolution Right association: a constituent (NP or PP) tends to attach to another constituent immediately to its right (Kimball 1973) Minimal attachment: a constituent tends to attach to an existing non-terminal using the fewest additional syntactic nodes (Frazier 1978) These two principles make opposite predictions for prepositional phrase attachment Consider the grammar: VP NP V NP PP NP PP (1) (2) for input: I [VP saw [NP the man . . . [PP with the telescope ], RA predicts that the PP attaches to the NP, i.e. use rule (2), and MA predicts V attachment, i.e. use rule (1) 14 / 30 Structure Based Ambiguity Resolution Garden-paths look structural: The emergency crews hate most is domestic violence Neither MA or RA account for more than 55% of the cases in real text Psycholinguistic experiments using eyetracking show that humans resolve ambiguities as soon as possible in the left to right sequence using the words to disambiguate Garden-paths are caused by a combination of lexical and structural effects: The flowers delivered for the patient arrived 15 / 30 Ambiguity Resolution: Prepositional Phrases in English Learning Prepositional Phrase Attachment: Annotated Data v n1 p n2 Attachment join board as director V is chairman of N.V. N using crocidolite in filters V bring attention to problem V is asbestos in products N making paper for filters N including three with cancer N . . . . . . . . . . . . . . . 16 / 30 Prepositional Phrase Attachment Method Always noun attachment Most likely for each preposition Average Human (4 head words only) Average Human (whole sentence) Accuracy 59.0 72.2 88.2 93.2 17 / 30 Back-off Smoothing Let 1 represent noun attachment. We want to compute probability noun of attachment: p (1 | v , n1, p , n2). Probability of verb attachment is 1 - p (1 | v , n1, p , n2). 18 / 30 Back-off Smoothing ^ 1. If f (v , n1, p , n2) > 0 and p 0.5 f (1, v , n1, p , n2) f (v , n1, p , n2) ^ p (1 | v , n1, p , n2) = 2. Else if f (v , n1, p ) + f (v , p , n2) + f (n1, p , n2) > 0 ^ and p 0.5 ^ p (1 | v , n1, p , n2) = f (1, v , n1, p ) + f (1, v , p , n2) + f (1, n1, p , n2) f (v , n1, p ) + f (v , p , n2) + f (n1, p , n2) f (1, v , p ) + f (1, n1, p ) + f (1, p , n2) f (v , p ) + f (n1, p ) + f (p , n2) f (1, p ) f (p ) 19 / 30 3. Else if f (v , p ) + f (n1, p ) + f (p , n2) > 0 ^ p (1 | v , n1, p , n2) = 4. Else if f (p ) > 0 ^ p (1 | v , n1, p , n2) = ^ 5. Else p (1 | v , n1, p , n2) = 1.0 Prepositional Phrase Attachment: (Collins and Brooks 1995) Results: 84.5% accuracy with the use of some limited word classes for dates, numbers, etc. Using complex word classes taken from WordNet (which we shall be looking at later in this course) increases accuracy to 88% (Stetina and Nagao 1998) We can improve on parsing performance with Probabilistic CFGs by using the insights taken from PP attachment. Modify the PCFG model to be sensitive to words and other context-sensitive features of the input. And generalizing to other kinds of attachment problems, like coordination or deciding which constituent is an argument of a verb. 20 / 30 Some other studies Toutanova, Manning, and Ng, 2004: use sophisticated smoothing model for PP attachment 86.18% with words & stems; with word classes: 87.54% Merlo, Crocker and Berthouzoz, 1997: test on multiple PPs, generalize disambiguation of 1 PP to 2-3 PPs 14 structures possible for 3PPs assuming a single verb: all 14 are attested in the Treebank same model as CB95; but generalized to dealing with upto 3PPs 1PP: 84.3% 2PP: 69.6% 3PP: 43.6% Note that this is still not the real problem faced in parsing natural language 21 / 30 Adding Lexical Information to PCFG S .. VB{indicated} indicated VP{indicated} NP{difference} difference PP{in} P in NP .. 22 / 30 Adding Lexical Information to PCFG (Collins 99, Charniak 00) VP{indicated} VB{+H:indicated} VP{indicated} STOP .. VB{+H:indicated} VP{indicated} VB{+H:indicated} NP{difference} VP{indicated} VB{+H:indicated} .. PP{in} VP{indicated} VB{+H:indicated} .. STOP Ph (VB | VP, indicated) Pl (STOP | VP, VB, indicated) Pr (NP(difference) | VP, VB, indicated) Pr (PP(in) | VP, VB, indicated) Pr (STOP | VP, VB, indicated) 23 / 30 Evaluation of Parsing Consider a candidate parse to be evaluated against the truth (or gold-standard parse): candidate: (S (A (P this) (Q is)) (A (R a) (T test))) gold: (S (A (P this)) (B (Q is) (A (R a) (T test)))) In order to evaluate this, we list all the constituents Candidate (0,4,S) (0,2,A) (2,4,A) Gold (0,4,S) (0,1,A) (1,4,B) (2,4,A) Skip spans of length 1 which would be equivalent to part of speech tagging accuracy. Precision is defined as #correct #proposed = 2 3 and recall as #correct #in gold = 2. 4 Another measure: crossing brackets, candidate: [ an [incredibly expensive] coat ] (1 CB) gold: [ an [incredibly [expensive coat]] 24 / 30 Evaluation of Parsing Bracketing recall R Bracketing precision P Complete match Average crossing No crossing 2 or less crossing = = = = = = num of correct constituents num of constituents in the goldfile num of correct c...

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Sveriges lantbruksuniversitet - CMPT - 413
CMPT-413 Computational LinguisticsAnoop Sarkar http:/www.cs.sfu.ca/anoopApril 17, 20071 / 34Writing a grammar for natural language: Grammar DevelopmentGrammar development is the process of writing a grammar for a particular language This can
Sveriges lantbruksuniversitet - CMPT - 413
Sveriges lantbruksuniversitet - CMPT - 413
CMPT-413 Computational LinguisticsAnoop Sarkar http:/www.cs.sfu.ca/anoopMarch 28, 20071 / 19Lexical SemanticsSo far, we have listed words in our lexicon or vocabulary assuming a single meaning per word: Consider n-grams P (wi | wi -2 , wi -1
Sveriges lantbruksuniversitet - CMPT - 413
CMPT-413 Computational LinguisticsAnoop Sarkar http:/www.cs.sfu.ca/anoopApril 4, 20071 / 28Discourse ProcessingMultiple sentences, dialogs Human-human (Switchboard corpus) and human-computer interaction (ATIS corpus) New phenomena at the dis
Berkeley - IEOR - 263
Fall 08, IEOR 263A Homework 10 Solutions 1. We have that the transition probability matrix of {Xnd } is P d and by definition of d, gcd{n 1 : (nd) pii &gt; 0} = 1, then {Xnd } is aperiodic. Consider the matrix P = 0 1 1 . 0In this case d = 2, {Xn } i
Iowa State - ECON - 101
Economics &quot;The study of choice under conditions of scarcity&quot; Scarcity Individual consumers: Spending power (or income) &amp; time Society: Factors of Production: (Land, Labor, Capital, Human Capital) Land: All gifts of nature (land, natural resources, et
Wisconsin - BOTANY - 422
PERSPECTIVES2006. The number of grains will be limited (~100 interstellar and ~1000 cometary grains), but but determination of cosmic ray exposure ages of interstellar dust, cometary GEMS/IDPs, and crystalline silicates will be very revealing. Techn
Iowa State - CPRE - 185
Prelab for Lab 2For CprE 185 labs January 22 and 23You should have a basic understanding of variables and how to declare them in C before you come to lab this week. This prelab is meant to help you learn these concepts.References The following re
Iowa State - CPRE - 185
Problems Boolean logic program to review lab 9 o Write a program that will use logical operations to model the following truth table. A B C O 0 0 0 0 1 1 1 1 0 0 1 1 0 0 1 1 0 1 0 1 0 1 0 1 1 1 0 0 1 1 0 0Play with structures: o Create a structure
Berkeley - ASTRO - 00177408
-55.955000 -42.915000 148.22206 47.985281 -42.915000 -34.765000 203.50748 60.794655 -34.765000 -20.095000 138.50015 45.039814 -20.095000 -7.0550000 159.26452
Berkeley - ASTRO - 00177408
-55.955000 -42.915000 148.22206 47.985281 -42.915000 -34.765000 203.50748 60.794655 -34.765000 -20.095000 138.50015 45.039814 -20.095000 -7.0550000 159.26452
Berkeley - ASTRO - 00177408
# Time [days] Mag Magerr Band Uplim Ref 0.00120 19.5 -0.2 V yes GCN4516 0.00138 18 -0.2 V yes GCN4509 0.00377 20.4 -0.2 B yes GCN4516 0.0
Berkeley - ASTRO - 00177408
360.288 -0.2 0 no GCN4510 1139.62 19.1 0.5 Rc no 1735.78 18.1 0.2 none yes 2039.9 19.2 0.4 Rc no 2879.71 19.1 0
Berkeley - ASTRO - 00177408
360.288 5.139853e+03 0.000000e+00 no GCN4510 1139.62 9.793788e-05 4.510206e-05 Rc no 1735.78 2.460088e-04 4.531650e-05 none yes 2039.9 8.932041e-05 3.290686e-05
Berkeley - ASTRO - 00177408
1735.78 2.460088e-04 4.531650e-05 none yes 2879.71 9.793788e-05 1.804082e-05 Rc yes 42120 3.555910e-04 6.550228e-05 R yes 48960.3 1.073868e-05 1.978137e-06
Berkeley - ASTRO - 00177408
11.410000 34.230003 4.8801518 0.15476824 25.162871 34.230003 55.420002 -7.1583672 3.4018788 327.59974 55.420002 84.760002 15.829904 -2.3030588 299.73961
Berkeley - ASTRO - 00177408
11.4100 34.2300 -0.697075 0.799869 34.2300 55.4200 6.68627 1.05591 55.4200 84.7600 -3.93089 0.648640 84.7600 141.810 9.20444 1.45795 141.810 146.700 27.497
Berkeley - ASTRO - 00177408
chi^2/nu= 277.78127 / 193The fit is rejectable at 99.993871 % Confidence -42.9150 -34.7650 201.17938 -34.7650 -20.0950 219.12440 -20.0950 -7.05500 233.67896 -7.05500 -3.7950
Berkeley - ASTRO - 00177408
&lt;html&gt;&lt;head&gt;&lt;title&gt;Your NED Search Results&lt;/title&gt;&lt;/head&gt;&lt;body background=&quot;/pics/NEDbgHelp.gif&quot; bgcolor=&quot;#FFFFFF&quot;&gt;&lt;center&gt;&lt;font size=6 color=&quot;#CC3333&quot;&gt;&lt;b&gt;N&lt;/b&gt;&lt;/font&gt;&lt;font size=4 color=&quot;#000000&quot;&gt;&lt;b&gt;ASA/IPAC&lt;/b&gt;&lt;/font&gt;&amp;nbsp;&lt;font size=6 color=&quot;#CC
Berkeley - ASTRO - 00177408
120.556 121.049 45.8159 10.81121.049 121.336 39.0268 12.7458121.336 121.994 48.5438 9.27936121.994 122.412 49.6199 11.6955122.412 122.716 73.6888 17.0173122.716 123.857 46.1726 6.85523123.857 123.997 80.005 26.129123.997 124.293 42.361 13.6598
Berkeley - ASTRO - 00177408
Source Contamination: 5.26E-08 +/- 1.5E-08 cts/s
Berkeley - ASTRO - 00177408
#ra dec hmag dhmag053.962761 17.109814 13.829 0.025053.944143 17.116085 16.804 0.137053.989731 17.119774 16.009 0.077053.955925 17.112076 13.050 0.024054.056998 17.110207 12.890 0.025054.025218 17.114105 15.171 0.044054.046284 17.102865
Berkeley - ASTRO - 00177408
;instrument XRT;exposure 64937.415;xunit kev;bintype counts 0.0000000 0.0049999999 13.416170 1.00000 0.0049999999 0.0099999998 13.464069 1.00000 0.0099999998 0.015000000 13.511968 1.0
Berkeley - ASTRO - 00177408
;instrument XRT;exposure 269.05439;xunit kev;bintype counts 0.0000000 0.0049999999 14.516683 1.00000 0.0049999999 0.0099999998 14.568504 1.00000 0.0099999998 0.015000000 14.620325 1.0
Berkeley - ASTRO - 00177408
#ra dec rmag drmag54.22221817.13006516.2050.00553.98003217.11030819.2820.07054.22023517.10499716.2420.00554.26303917.10549519.6410.09854.28926317.10327016.6720.00754.28071717.10326216.8100.00853.83690717.09809516.8320.008
Berkeley - ASTRO - 00177408
chi^2/nu= 90.229770 / 328.000The fit is rejectable at 2.1718713e-40 % Confidence#index t1 t2 fade_index delta_mag_pk hindex dhindex rate1 drate1 rate2 drate2 logr dlogr 0 0.1206 0.2380 -3.01 0.0 0.17 0.08 1.15E+0
Berkeley - ASTRO - 00177408
# t1 t2 hardness error 0.12055600 0.12133600 -0.14044738 0.19090244 0.12133600 0.12199400 0.082234260 0.19072434 0.12199400 0.12271600 0.26708790 0.15921771 0.12271600 0.12385700
Berkeley - ASTRO - 00177408
output00177408000_999/sw00177408000xpcw2po_cl.evtoutput00177408001_999/sw00177408001xpcw2po_cl.evtoutput00177408002_999/sw00177408002xpcw2po_cl.evtoutput00177408003_999/sw00177408003xpcw2po_cl.evtoutput00177408004_999/sw00177408004xpcw2po_cl.evt
Berkeley - ASTRO - 00177408
# t1 t2 dt rad_min rad_max cts err scl bg bg_rat wt 0.120556 0.120813 0.000257 0. 16. 10.60 3.52 0.867841 5.000000 0.279570 1 0.120813 0.121049 0.000237 0. 16. 9.00
Berkeley - ASTRO - 00177408
tmin 2.0695142e-05tmin 0.00011504599
Berkeley - ASTRO - 00177408
tmin 32.099833tmin 181.93968 364.16423 2064.0582 0.20484306 0.033045668 5 2064.0582 46869.069 0.12453600 0.012811805 9tmax 46869.069
Berkeley - ASTRO - 00177408
# t1 t2 dt rad_min rad_max cts err scl bg bg_rat wt 0.120556 0.120813 0.000257 0. 16. 10.60 3.52 0.867841 5.000000 0.279570 1 0.120813 0.121049 0.000237 0. 16. 9.00
Berkeley - ASTRO - 00177408
# tmin tmax 0.254029 468.41556 [ksec];instrument XRT;exposure 60143.753;xunit kev;bintype counts0.000000 0.010000 0.000000 0.0000000.010000 0.020000 0.000000 0.0000000.020000 0.030000 0.000000 0.0000000.030000 0.040000 0.00000
Berkeley - ASTRO - 00177408
# tmin tmax 0.254029 468.41556 [ksec];instrument XRT;exposure 60143.753;xunit kev;bintype counts0.000000 0.010000 0.000000 0.0000000.010000 0.020000 0.000000 0.0000000.020000 0.030000 0.000000 0.0000000.030000 0.040000 0.00000
Berkeley - ASTRO - 00177408
# tmin tmax 0.12055600 5.38475 [ksec];instrument XRT;exposure 266.08283;xunit kev;bintype counts0.000000 0.010000 0.000000 0.0000000.010000 0.020000 0.000000 0.0000000.020000 0.030000 0.000000 0.0000000.030000 0.040000 0.00000
Berkeley - ASTRO - 00177408
# tmin tmax 0.12055600 5.38475 [ksec];instrument XRT;exposure 266.08283;xunit kev;bintype counts0.000000 0.010000 0.000000 0.0000000.010000 0.020000 0.000000 0.0000000.020000 0.030000 0.000000 0.0000000.030000 0.040000 0.00000
Berkeley - ASTRO - 00177408
Wavdetect Sources with S/N&gt;3: # ra dec err [&quot;] signif counts steady? -log10(Prob_steady) 054.03492117.3458570.16673.8725.0 0-323.0 154.10150117.3244240.51014.141.8 1-0.7 254.01671417.2622120.65812.338.3 1-0.1 354.155
Berkeley - ASTRO - 00177408
output00177408000_999/sw00177408000xwtw2po_cl.evtoutput00177408001_999/sw00177408001xwtw2po_cl.evtoutput00177408002_999/sw00177408002xwtw2po_cl.evtoutput00177408003_999/sw00177408003xwtw2po_cl.evtoutput00177408004_999/sw00177408004xwtw2po_cl.evt
Berkeley - ASTRO - 00177408
SIMPLE = T / file does conform to FITS standardBITPIX = 8 / number of bits per data pixelNAXIS = 0 / number of data axesEXTEND = T / FITS dataset may contain extensio
Berkeley - ASTRO - 00177408
# Ep dEp lprob lEiso dlEiso67.040 0.054 3.49e-05 121.421 0.07467.097 0.061 3.07e-04 121.521 0.08467.163 0.070 5.88e-04 121.521 0.08467.238 0.081 8.49e-04 121.521 0.08467.325 0.092 1.10e-03 121.521 0.08467.423 0.106 1.29e-03 121.521 0.08467.537
Berkeley - ASTRO - 00177408
# Ep lEiso37.400 121.94840.123 121.88245.004 121.87446.499 121.58146.920 121.42847.877 121.33148.473 121.58348.868 121.63749.323 121.34449.341 121.34849.662 121.44650.219 121.40150.425 121.54350.808 121.47751.066 121.54551.173 121.583
Berkeley - ASTRO - 00177408
# Ep dEp lprob lNiso dlNiso67.040 0.054 3.49e-05 137.769 0.38167.097 0.061 3.06e-04 137.769 0.38067.163 0.070 5.87e-04 137.769 0.38067.238 0.081 8.65e-04 137.769 0.38067.325 0.092 1.10e-03 137.769 0.38067.423 0.106 1.29e-03 137.769 0.38067.537
Berkeley - ASTRO - 00177408
# Ep lNiso37.362 137.60340.096 137.25245.016 138.22146.520 138.10846.941 137.70147.899 137.88448.495 138.38548.881 138.62249.337 137.33249.355 137.35049.676 137.78350.234 137.58650.440 138.20350.817 137.91451.076 137.64251.182 137.219
Berkeley - ASTRO - 00177408
# x=Log_e(Beaming Fraction) y=Log_e(Egam/10^52 erg) z=Log_e(Tjet/days)# mean(x)= -4.6913 xdown= -5.0763 xup= -4.3622# mean(y)= -2.8349 ydown= -3.4214 yup= -2.2964# mean(z)= 1.8541 zdown= 1.3195 zup= 2.2750-5.9761 -3.7625 0.1152-5.8878 -3.7402
Berkeley - ASTRO - 00177408
Ep=55.60 Chi/nu= 51.27/57 (0.899)
Berkeley - ASTRO - 00177408
#file=swb15-350lc.txt dt=1.0 tstart=-10.095 tstop=113.025#t90 dt90 t50 dt50 rt90 drt90 rt50 drt50 rt45 drt45 tav dtav tmax dtmax trise dtrise tfall dtfall cts cts_err pk_rate dpk_rate band 109.000 1.932 86.000 2.765 58.000
Berkeley - ASTRO - 00177408
# S/N T1 T2 T90 T50# Estimated T100 Interval: -13.065 150.555 T90= 136.350 30.5 80.085 107.625 23.220 8.910 17.0 -7.395 19.335 23.490 9.180 5.5 124.095 150.555
Berkeley - ASTRO - 00177408
;instrument XRT;exposure 61149.182;xunit kev;bintype counts 0.0000000 0.0049999999 13.666843 1.00000 0.0049999999 0.0099999998 13.715614 1.00000 0.0099999998 0.015000000 13.764385 1.0
Berkeley - ASTRO - 00177408
# tmin tmax 10.0000 468.416 [ksec];instrument XRT;exposure 56383.773;xunit kev;bintype counts0.000000 0.010000 0.000000 0.0000000.010000 0.020000 0.000000 0.0000000.020000 0.030000 0.000000 0.0000000.030000 0.040000 0.000000 0
Berkeley - ASTRO - 00177408
# tmin tmax 10.0000 468.416 [ksec];instrument XRT;exposure 56383.773;xunit kev;bintype counts0.000000 0.010000 0.000000 0.0000000.010000 0.020000 0.000000 0.0000000.020000 0.030000 0.000000 0.0000000.030000 0.040000 0.000000 0
Berkeley - TMP - 00177408
Power-Law Model FitNorm@15keV 1.1934e-02 (1.0364e-02 1.3625e-02)alpha -1.7375 (-1.8620 -1.6154)Energy Fluence (15-350 keV) 2.5948e-06 (2.4156e-06 2.7852e-06) erg cm^-2Eiso (1-10^4 keV, host-frame) 1.6697e+53 (1.5405e+53 1.8461e+53) ergChi/nu=
Iowa State - DATA - 1336
Palmer Constitution 2008PreambleWe, the members of Palmer House do establish and adopt this constitution in order to form an efficient house, ensure equal representation of each resident, and secure an environment that stimulates intellectual, soci
Drexel - MK - 489
11 July 2007By: Lucian Dorneanu, Science EditorNanomachines Powered by BacteriaMicroscopic organisms can move tiny structures when stimulated with UV lightA new discovery in the field of nanotechnology could produce the smallest &quot;engines&quot; in th
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FORMULA SHEET For 1-D heat transfer problems, the general weak form is dT dT Ak dx = dx dx T AgdxFor a 2-node conduction element, when the area, conductivity, density, and internal heat generation are constant we have K= kA Le 1 -1 -1 1 2 1 1 2 1 1
Maryland - ENEE - 646
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Maryland - ENEE - 646
ENEE 646: Digital Computer DesignFall 2004 Handout #1Course Information and PolicyRoom:CHE 2108 TTh 2:00p.m. - 3:15p.m. http:/www.ece.umd.edu/class/enee646 Donald Yeung 1327 A. V. Williams (301) 405-3649 yeung@eng.umd.edu http:/www.ece.umd.edu
Maryland - ENEE - 646
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Maryland - ENEE - 646
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Maryland - ENEE - 646
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Maryland - ENEE - 646
ENEE 646: Digital Computer DesignFall 2004 Handout #11Problem Set # 2Due: October 12Pipelining and ILPProblem 1Consider the following 6-stage pipline:F D X1 X2 M WInstruction fetch Instruction decode, register read First cycle of ALU, b
Maryland - ENEE - 646
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