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satisfied: team success! • Preconditions of all executed actions met • No missed deadlines Concurrent Plan Recognition & Execution for Human-Robot Teams | Steven J. Levine Slide 40 Concurrent Plan Recognition & Execution for Human-Robot Teams | Steven J. Levine Slide 41 Compile constraints with ATMS • Compile const...
https://ocw.mit.edu/courses/16-412j-cognitive-robotics-spring-2016/497c66f8f0334208d69fc5a29ff2a956_MIT16_412JS16_L13.pdf
)(cid:3)(cid:3) (cid:14)(cid:15)(cid:16)(cid:3)(cid:5)(cid:17)(cid:8)(cid:2)(cid:3)(cid:3) (cid:18)(cid:8)(cid:9)(cid:7)(cid:5)(cid:17)(cid:8)(cid:2)(cid:3)(cid:3)(cid:5)(cid:13)(cid:9)(cid:6) (cid:18)(cid:8)(cid:9)(cid:7)(cid:5)(cid:17)(cid:8)(cid:2)(cid:3)(cid:3)(cid:5)(cid:13)(cid:9)(cid:6) Concurrent Plan Recogniti...
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−3 100 Offline Compilation Time 101 102 Worst Online Commit Latency 103 • Randomly-generated TPNU’s with randomly-generated causal link structure (probably harder) • Compilation time roughly 104 proportional to candidate subplans, (large variance) • Reactive online performance 101 103 102 Number of Candidate Subplans 10...
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Labeled all-pairs shortest path (APSP) (cid:6) [6, 11] [2, 11] if x = 1 if x = 2 • Causal link extraction: provides ordering • In this case, producer guaranteed to precede consumer • Labeled causal link extracted Concurrent Plan Recognition & Execution for Human-Robot Teams | Steven J. Levine Slide 55 Labeled Value ...
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each pair of graph, representing shortest distance as function of environment • Generalization of Floyd Warshall algorithm that uses LVS operations instead of standard + and < • See (Conrad 2010) for details Concurrent Plan Recognition & Execution for Human-Robot Teams | Steven J. Levine Slide 58 Optimization: Causal ...
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A3 = bagel} • Concurrent Plan Recognition & Execution for Human-Robot Teams | Steven J. Levine Slide 66 Environments and subsumption • Environment subsumes iff contains all e2 e2 e1 assignments in e1 • ex., subsumes xR1 = j } {xR1 = juice { uice, xA3 = bagel} • Intuitively, all subplans represented by also e2 represe...
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pi ⇒ (cid:3) (cid:2) (cid:4) ∧ φec from epi to ec end foreach epi ∈ P do foreach eti ∈ T do ∧ φec then ∧ φeti (cid:5) (cid:5) (cid:4) φC φC ← {aec,p = epi } ∧ φepi (cid:5) (cid:5) if epi ≺ eti φC AddConstraint and eti ≺ ec (cid:2) ¬φC and eti (cid:9) ec φC Add [(cid:4), ∞] : φC from ec to eti (cid:5) (cid:5) and eti ≺ ...
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8)(cid:11)(cid:9) Concurrent Plan Recognition & Execution for Human-Robot Teams | Steven J. Levine Slide 73 Labeled causal link extraction • Uses an LVS, except: • Values are TPNU events, rather than numbers • Relation R is not < but rather succession (via labeled APSP): (cid:6) e R e = a b if Qdea e (φa φb) ∪ True → ...
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2 ∧ z = 1) At least 1 causal link holds Threat resolved Concurrent Plan Recognition & Execution for Human-Robot Teams | Steven J. Levine Slide 79 Causal link extraction in a nutshell* • For each precondition of each consumer event: • Find all producer provably before or during consumer Concurrent Plan Recognition & Ex...
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Chapter 4 and 5 Wear Mechanisms 1 Delamination Wear Mechanisms • Four mechanisms of delamination wear 1. Plastic deformation of the surface 2. Crack nucleation at the sub-surface due to plastic deformation 3. Crack propagation from these nucleated cracks due to plastic deformation 4. Creation of loose wear s...
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∂(σ ) xz ∂x r + r + ∂ ( σ xz ) r = 0 ∂z ∂(σ zz ) r = 0 ∂z f 3 = c 1 f 2 = c 2 (σ ) = f ( z ) r xx r (σ yy ) = νf ( z ) (σ ) = 0 r (σ ) = 0 r xz zz 10 Plastic Deformation of a Semi-Infinite Elastoplastic Solid Residual Stress Calculation -- Equilibrium Equations (σzz )' r ( ) = − ε zz r ( γxz r ) = − 1− 2...
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primary wear-rate controlling mechanism. 14 Crack Nucleation Imaginary sphere A B (a) (b) Figure 4.36 Deformed sphere of the Imaginary sphere 15 Criteria for Crack Nucleation Energy Criterion and Strength Criterion Energy criterion e , n i a r t S Strength criterion d* Particle size, d Figure 4.37...
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=6k Graph removed for copyright reasons. See Figure 4.44 in [Suh 1986]. 23 Crack Propagation • In fracture mechanics, crack propagation is classified in terms of three modes. 1. The load is applied perpendicular to the crack. 2. The load is applied parallel to the crack direction. 3. The load is applied tran...
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, H-C. “Surface Fraction and Crack Propagation in Delamination Wear.” Ph.D. Thesis, MIT, 1981. 31 Shear strain as a function of distance from the left tip for different depths of crack location Source: Sin, H-C. “Surface Fraction and Crack Propagation in Delamination Wear.” Ph.D. Thesis, MIT, 1981. 32 Void ...
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39 Fretting Wear Graph removed for copyright reasons. See Figure 5.10 in [Suh 1986]. 40 Source: Sin, H-C. “Surface Fraction and Crack Propagation in Delamination Wear.” Ph.D. Thesis, MIT, 1981. 41 Sequence of graphs and photos removed for copyright reasons. See Figures 5.12-5.23 in [Suh 1986]. 42
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Fast Fourier Transform: Theory and Algorithms Lecture 8 Vladimir Stojanović 6.973 Communication System Design – Spring 2006 Massachusetts Institute of Technology Cite as: Vladimir Stojanovic, course materials for 6.973 Communication System Design, Spring 2006. MIT OpenCourseWare (http://ocw.mit.edu/), Massachusetts I...
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for 6.973 Communication System Design, Spring 2006. MIT OpenCourseWare (http://ocw.mit.edu/), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY]. Divide and conquer ‰ Divide and conquer always has less computations Suppose all Il sets have same number of elements N1 so, N=N1*N2, r=N2 Each inner-m...
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and conquer ‰ Different types balance mapping with subproblem cost ‰ E.g. in radix-2 „ subproblems are trivial (only sum and differences) „ Mapping requires twiddle factors (large number of multiplies) ‰ E.g. in prime-factor algorithm „ Subproblems are DFTs with coprime lengths (costly) „ Mapping trivial (no ar...
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: N multiplications with twiddle factors ‰ Step 3: Evaluate N2 DFTs of length N1 ‰ Vector xi mapped to matrix xn1,n2 (N1xN2) ‰ Compute N1 DFTs of length N2 on each row ‰ Point-to-point multiply with twiddle factors ‰ Compute N2 DFTs of length N1 on the columns 6.973 Communication System Design 9 Cite as: Vladim...
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1 x2 x3 x4 x5 x10 x6 x11 x7 x12 x8 x13 x x 14 9 „ Not just transpose Figure by MIT OpenCourseWare. 6.973 Communication System Design 10 Cite as: Vladimir Stojanovic, course materials for 6.973 Communication System Design, Spring 2006. MIT OpenCourseWare (http://ocw.mit.edu/), Massachusetts Institute of Technology. ...
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X7 DFT N = 4 { }x2i DFT N = 4 { } x2i+1 w1 8 w2 8 w3 8 Division into even and odd numbered sequences DFT of N / 2 Multiplication by twiddle factors DFT N = 2 DFT N = 2 DFT N = 2 DFT N = 2 DFT of 2 X0 X4 X1 X5 X2 X6 X3 X7 Which type is this implementation? Figure by MIT OpenCourseWare. 6.973 Communication System Desig...
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}x2k+1 X0 X2 X4 X6 X1 X3 X5 X7 DFT of 2 Multiplication by twiddle factors DFT of N / 2 6.973 Communication System Design 13 Figure by MIT OpenCourseWare. Cite as: Vladimir Stojanovic, course materials for 6.973 Communication System Design, Spring 2006. MIT OpenCourseWare (http://ocw.mit.edu/), Massachusetts Institute...
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by MIT OpenCourseW. are. ‰ Which one is DIT (DIF)? ‰ How can we get one from another? 6.973 Communication System Design 14 Cite as: Vladimir Stojanovic, course materials for 6.973 Communication System Design, Spring 2006. MIT OpenCourseWare (http://ocw.mit.edu/), Massachusetts Institute of Technology. Downloaded o...
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8 X0 X14 X1 X15 DFT 4 DFT 4 DFT 4 DFT 4 X0 X12 X1 X13 X2 X14 X3 X15 ‰ Reduces the number of stages to log4N Figure by MIT OpenCourseWare. „ Radix-8 can reduce number of operations even more 6.973 Communication System Design 1 6 Cite as: Vladimir Stojanovic, course materials for 6.973 Communication System Design, Sp...
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4 X1 X13 X3 X15 R2 R4 S-R Figure by MIT OpenCourseWare. 6.973 Communication System Design Cite as: Vladimir Stojanovic, course materials for 6.973 Communication System Design, Spring 2006. 17 MIT OpenCourseWare (http://ocw.mit.edu/), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY]. Split-radix ...
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N / 4)], ‰ Even samples X2k in DIF should be computed separately from other samples „ With same algorithm (recursively) as the original sequence ‰ No general rule for odd samples „ Radix-4 is more efficient than radix-2 „ Higher radices are inefficient DFT of 2 Multiplication by twiddle factors DFT of N / 2 X4k...
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conquer requirements „ N-long DFT computed from DFTs with lengths that are factors of N (allows the inner sum to be a DFT) „ Provided that subsets Il guarantee periodic xi „ When N factors into co-prime factors N=N1*N2 „ Starting from any xi form subset with compatible periodicity (the periodicity of the subset d...
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N1 and N2 are coprime ‰ All congruences modulo N1 obtained „ For a given congruence modulo N2 and vice versa Figure by MIT OpenCourseW. are. 6.973 Communication System Design 21 Cite as: Vladimir Stojanovic, course materials for 6.973 Communication System Design, Spring 2006. MIT OpenCourseWare (http://ocw.mit.edu...
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3 8 4 14 „ Reversing N1 and N2 „ Results in transposed mapping 6.973 Figure by MIT OpenCourseW. are. 22 Cite as: Vladimir Stojanovic, course materials for 6.973 Communication System Design, Spring 2006. MIT OpenCourseWare (http://ocw.mit.edu/), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY]. ...
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0 n1k1 n2k2 Figure by MIT OCW. x12 x9 x6 DFT 3 x3 x0 x5 x10 DFT 5 X9 X4 X14 X8 X2 X11 X5 Figure by MIT OpenCourseWare. True bidimensional transform! (no extra twiddle factors) 6.973 Communication System Design 23 Cite as: Vladimir Stojanovic, course materials for 6.973 Communication System Design, Spring 2006. MIT Op...
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4 X8 X2 X11 X5 ‰ Efficient small DFTs are a key to the feasibility of this algorithm Figure by MIT OpenCourseWare. 6.973 Communication System Design 26 Cite as: Vladimir Stojanovic, course materials for 6.973 Communication System Design, Spring 2006. MIT OpenCourseWare (http://ocw.mit.edu/), Massachusetts Institute...
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Fast Fourier Transform: Practical aspects and Basic Architectures Lecture 9 Vladimir Stojanović 6.973 Communication System Design – Spring 2006 Massachusetts Institute of Technology Cite as: Vladimir Stojanovic, course materials for 6.973 Communication System Design, Spring 2006. MIT OpenCourseWare (http://ocw.mit.edu/...
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1100 68 136 276 632 2524 1572 5804 3548 17660 9492 N Radix 2 Radix 4 SRFFT PFA Winograd 152 408 1032 2504 5896 13566 30728 68616 148 976 5488 28336 148 388 964 2308 5380 12292 27652 61444 30 60 120 240 504 1008 2520 384 888 2076 4812 384 888 2076 5016 13388 14540 29548 34668 84076 99628 16 32 64 128 256 512 1024 2048 1...
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(cid:137) In-place computation (cid:137) Regularity (cid:132) Computation (cid:132) Interconnect (cid:137) Parallelism and pipelining (cid:137) Quantization noise Cite as: Vladimir Stojanovic, course materials for 6.973 Communication System Design, Spring 2006. MIT OpenCourseWare (http://ocw.mit.edu/), Massachusetts In...
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(cid:137) Cooley-Tukey and SRFFT are most compatible with longer size FFTs Cite as: Vladimir Stojanovic, course materials for 6.973 Communication System Design, Spring 2006. MIT OpenCourseWare (http://ocw.mit.edu/), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY]. 6.973 Communication System Desig...
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) Four error sources per butterfly (variance 2-2B/12) (cid:132) Total variance per butterfly 2-2B/3 (cid:132) Each output node receives signals from a total of N-1 butterflies in the flow graph (N/2 from the first stage, N/4 in the second, …) (cid:132) Total variance for each output ~ N/3*2-2B (cid:132) Assuming input...
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.973 Communication System Design 8 Particular cases (cid:137) DFT algorithms for real data sequence xk (cid:132) Xk has Hermitian symmetry (XN-k=Xk *) (cid:132) X0 is real, and when N even, XN/2 real as well (cid:132) N input values map to (cid:132) 2 real and N/2-1 complex conjugate values when N even (cid:132) 1 re...
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) Can be obtained by simply pruning the FFT flow graph (cid:132) Alternately, looks just like a recursive 1-tap filter for each tone x(n) X(k) z-1 -k WN Cite as: Vladimir Stojanovic, course materials for 6.973 Communication System Design, Spring 2006. MIT OpenCourseWare (http://ocw.mit.edu/), Massachusetts Institute o...
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973 Communication System Design 12 Related transforms: DCT (cid:137) Lots of applications in image and video processing (cid:137) Scale factor of 1/sqrt(2) for X0 left out (cid:132) Formula above appears as a sub-problem in length-4N real DFT (cid:132) Multiplicative complexity can be related to real DFT (cid:132) Pr...
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-2 + (3.2n-2 - 4) multiplications + (2n +3.2n-2 -n) additions 1 real DFT 2n-1 + 2 DCTs 2n-2 + 3.2n-1 -2 additions 1 real DFT 2n + (3.2n-1 -2) multiplications + (3.2n-1 -3) additions 1 odd DFT 2n-1+ 1 complex DFT 2n-1 +2n+1 additions 2 complex DFT's 2n-2 + 2(3.2n-2-4) multiplications + (2n+3.2n-1 -8) additions 1 DHT 2n ...
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s Cite as: Vladimir Stojanovic, course materials for 6.973 Communication System Design, Spring 2006. MIT OpenCourseWare (http://ocw.mit.edu/), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY]. 6.973 Communication System Design 15 Implementation on general purpose computers (cid:137) FFT algorithms...
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Design, Spring 2006. MIT OpenCourseWare (http://ocw.mit.edu/), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY]. 6.973 Communication System Design 17 Vector and multi-processors (cid:137) Must deal with two interconnected problems (cid:132) The vector size of the data that can be processed at the ...
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2) Communication cost very hard to estimate (cid:137) Dedicated arithmetic units (cid:132) Butterfly unit (cid:132) CORDIC unit (cid:137) Still, many heuristics and local tricks to reduce complexity and improve communication Cite as: Vladimir Stojanovic, course materials for 6.973 Communication System Design, Spring 2...
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UTTERFLY DATA OUT FFT RAM Figure by MIT OpenCourseWare. N CONTROL (cid:137) For large FFTs storage of intermediate results is a problem (cid:132) N-long FFT requires (cid:132) N/r*logrN, radix-r butterfly operations (cid:132) 2N*logrN read or write RAM accesses (cid:132) E.g. to do the 8K FFT in 1ms, need to access int...
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of 2 Multiplication by twiddle factors DFT of N / 2 Figure by MIT OpenCourseWare. (cid:132) Produces the output values in bit-reversed order Cite as: Vladimir Stojanovic, course materials for 6.973 Communication System Design, Spring 2006. MIT OpenCourseWare (http://ocw.mit.edu/), Massachusetts Institute of Technology....
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73 Communication System Design 24 FFT network (cid:137) Inputs are in “bit-shuffled” order (decimated) (cid:137) Outputs are in “bit-reversed” order (cid:132) Minimizes the amount of interconnects X0X4 X1X5 X2X6 X3X7 (cid:137) General scheme for interconnections (cid:132) Number the cells naturally (cid:132) 0 to N/2-...
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X2,X3 X4,X5 X6,X7 Figure by MIT OpenCourseWare. (cid:137) Each multiply-add cell associated with xk and xk+1 (k- even number between 0 and N-1) (cid:137) A connection from cell with xk to cell with xj when j=2k mod N-1 (this mapping is one-to-one) (cid:132) Represents “circular left shift” of the logN-bit binary repr...
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137) View as time-multiplexed version of the FFT network (cid:132) In each step, N/2 nodes take the role of N/2 cells Figure by MIT OpenCourseWare. in FFT network (cid:132) Other half routes the data other nodes Cite as: Vladimir Stojanovic, course materials for 6.973 Communication System Design, Spring 2006. MIT Ope...
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, course materials for 6.973 Communication System Design, Spring 2006. MIT OpenCourseWare (http://ocw.mit.edu/), Massachusetts Institute of Technology. Downloaded on [DD Month YYYY]. 6.973 Communication System Design 29 Readings (cid:137) [1] C.D. Thompson "Fourier Transforms in VLSI," no. UCB/CSD-82-105, 1982. (cid...
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Lecture 4 van Emde Boas 6.046J Spring 2015 Lecture 4: Divide and Conquer: van Emde Boas Trees • Series of Improved Data Structures • Insert, Successor • Delete • Space This lecture is based on personal communication with Michael Bender, 2001. Goal We want to maintain n elements in the range {0, 1, 2, . ....
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3 0 4 0 5 0 6 0 7 0 8 0 9 1 10 1 11 0 12 0 13 0 14 0 15 1 Figure 1: Bit vector for u = 16. THe current set is {1, 9, 10, 15}. Split Universe into Clusters √ We can improve performance by splitting up the range {0, 1, 2, . . . , u − 1} into u clusters of size u. If x = i u + j, then V[x] = V.Clust...
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) √ O( u) √ O( u) √ Total = O( u) The three operations in Successor are also Successor calls to vectors of size u. We can use recursion to speed things up. • V.cluster[i] is a size- u van Emde Boas structure (∀ 0 ≤ i < u) √ √ √ • V.summary is a size- u van Emde Boas structure √ • V.summary[i] indicates...
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each structure. This gives an O(1) time overhead for each Insert operation. SUCCESSOR(V, x) i = high(x) 1 if low(x) < V.cluster[i].max 2 j = Successor(V.cluster[i], low(x)) 3 4 else i = Successor(V.summary, high(x)) 5 6 return index(i, j) j = V.cluster[i].min √ T(u) = T( u) + O(1) =⇒ T(u) = O(log ...
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I Now we update V.max 11 12 13 else 14 15 if x == V.max if V.summary.max = None i = V.summary.max V.max = index(i, V.cluster[i].max) I O(1) time I Unstore new min I First Call I Second Call If the second call is executed, the first call only takes O(1) time. So √ T(u) = T( u) + O(1) =⇒ T(u) = O(log log ...
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Details: Split/Merge small structures · log log u) = O(n) space for large 6 MIT OpenCourseWare http://ocw.mit.edu 6.046J / 18.410J Design and Analysis of Algorithms Spring 2015 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.
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6.897: Selected Topics in Cryptography Lectures 7 and 8 Lecturer: Ran Canetti Highlights of past lectures • Presented a basic framework for analyzing the security of protocols for multi-party function evaluation. • Presented the notion of modular composition. • Stated and proved the non-concurrent. composition theo...
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parties. • Only function evaluation. • Environment interacts with the computation only at the beginning and the end. • Only non-concurrent composition. Wish-list for a more general framework • Deal with more “real-life” settings such as: – Asynchronous communication – Unreliable and unauthenticated communication ...
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value for the security parameter. In each activation an ITM may request to write to at most one tape of another ITM. A request includes: • – Identity of the requesting ITM – Identity of the target ITM and tape, code for the target ITM. – Contents If the control function C allows the tuple (source id, target id,...
https://ocw.mit.edu/courses/6-897-selected-topics-in-cryptography-spring-2004/4a4bd69a1e3c2fe6208174e2d38a9146_lecture7_8.pdf
output tape of Z. – All other ITMs can write to the incoming comm. tape of A, can invoke new ITMs, and can write to the subroutine output tape of their invoker and the input tapes of their subroutines. – Modeling corruptions: A can write a “corrupt” message on incoming comm. Tape of ITM M. Then: • M writes “Corru...
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• Copy all outputs from F to the subroutine output tape of its invoker. The ideal process The ideal process for evaluating functionality F with environment Z and adversary S is the following system of interacting ITMs: Initial ITM: – Environment Z (the initial ITM, with fixed ID) • • Control function: – Z can ...
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The ensemble {IDEALf S,Z (k,z)} (k in N, z in {0,1}*) Definition of security: Protocol P emulates the ideal process for F if for any adversary A there exists an adversary S such that for all Z we have: IDEALF S,Z ~ EXECP,A,Z . In this case we say that protocol P securely realizes F. Note: There is no parameterizati...
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that for all Z we have IDEALF S,Z ~ EXECP,Ad,Z . • Claim: P realizes F w.r.t. the dummy adversary iff it realizes F. Proof: • • Assume P realizes F then w.r.t. dummy adversaries. That is, If P realizes F then it also realizes F w.r.t the dummy adversary. Sd,Z ~ EXECP,Ad,Z . Now, let A be an arbitrary adversary. t...
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• State the UC theorem • Discuss some implications • Prove the theorem Modular composition: The basic idea for a single copy of f Q Q Q P Q P Q Q Î Q P Q P F The basic idea for multiple calls to F: Q Q Q Q Î Q P P PP PPP P Q P P F F F Q P P P P P P Q P P P PPP PPP The “hybrid model” fo...
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• M writes “Corrupted” on subr. output tape of Z • From now on, in each activation M sends its entire state to Z • A assumes all write privileges of M. • Corruption messages to copies of F are treated as in the ideal process (I.e., up to the discretion of F), with the exception that the “corrupted” outputs are wri...
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ALF Q,H,Z ~ EXECQp,A,Z . S,Z ~ EXECQ,H,Z . The corollary follows. Implications of the UC theorem 1. Can design and analyze protocols in a modular way: – Partition a given task T to simpler sub-tasks T1…Tk – Construct protocols for realizing T1…Tk. – Construct a protocol for T assuming ideal access to T1…Tk. – U...
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exactly like Ad.) – Messages sent to and from each instance of P are treated as follows: • • For each instance of P, H keeps a simulated copy of S. All messages from Z to parties of P are forwarded to the corresponding instance of S. • Messages generated by each instance of S are forwarded to Z. • Messages fro...
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notifies S that it received input from (id). When receiving “ok” from S, the shell forwards the input to the main program. – Whenever the main program wishes to write an output to party (id), the shell tells S that it wants to give output to (id). When receiving “ok” from S, the shell forwards the output to (id). ...
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Send (sid,pida,pidb) to S • • Halt. Example: The key-exchange functionality FKE (II) Wait to receive: (sid,pida,“exchange”,pidb) from party (sid,pida) (sid,pidb,“exchange”,pida) from party (sid,pidb) • • Then: • If one of the parties is corrupted then obtain a value a from S. Else, choose a Å{0,1}k • Outpu...
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,pid) where pid is in T, do: – Output r to the party, plus all the messages addressed to it that were not yet delivered. – Obtain from the party a list of messages to be delivered in the next round. Send this list to S. 3. Once all the parties in T have sent their messages for this round, increment r Å r+1 and r...
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= T η i η t cos θ t cos θ i L10-3 SOLVE TE BOUNDARY EQUATIONS We found: 1+ Γ = T and Solving yields: 1− Γ = T ηi cos θt ηt cos θi ) = Γ θ( i t η cos θ − η cos θ t = η cos θ + η cos θ t i t i i i η −' 1 n ' η + 1 n where η ' (cid:22) n t η cos θ i η cos θ t i Normal incidence: θ = 0, ...
https://ocw.mit.edu/courses/6-013-electromagnetics-and-applications-spring-2009/4a6f6970935940d8e2b391f9dbadf598_MIT6_013S09_lec10.pdf
TE solution: E → H E H → − ε ↔ μ Our original equations : Become these: E ∇ × = −μ H ∂ t ∂ E ∂ t ∂ E 0 H ∇ × = ε ∇ ε = i H E ∂ → ∇ × = ε t ∂ H ∂ → − ∇ × = μ t ∂ E → ∇ μ = H 0 i ∇ μ = H 0 i → ∇ ε = E 0 i Are they valid? Under what circumstances does the solution to the resulti...
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L10-7 TE AND TM WAVE REFLECTIONS TE TM Γ -1 0 1 |Γ|2 1 TE |Γ|2 1 TE Critical angle θ 90o 0 TM 90o θ 0 TM θ 90o Brewster’s angle θB Brewster’s angle θB High power laser beam Brewster’s angle laser window Horizontally polarized glasses cut glare Ocean wave No reflection at θB L10-8 M...
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Massachusetts Institute of Technology 6.270 Autonomous LEGO Robot Competition IAP 2005: Attack of the Drones Workshop 1 — Basic LEGO Structure and Bracing Monday, January 3, and Tuesday, January 4, 2005 1 Items to Bring • All of your LEGOs 2 Reading • Section 6.3 of course notes 3 LEGO Measurements • A Funda...
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work if the number of nubs (FLUs) in the beam conform to Pythagoras’ theorem. Using Pythagoras’ theorem not only leads to stronger bracing, but also saves beams. For the purposes of LEGO, this means that the only useful combinations are 3-4-5, 5-12-13, and 6-8-10 (Figure 4). • Other Bracing. Try experimenting with u...
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a cube at least 10 FLUs on a side that can survive a 6 droptest. ′ For more practice, try constructing the motor jig as shown in Section 4.4. You will also see some creative uses of bracing when you complete Assignment 1. 6 A few things for the future • Drop Test Blues Doing the drop test can really scary when it i...
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Effective Innovation Don Clausing ESD 33, MIT July 2004 Three types of innovations • Launch • Growth • Library July 2004 MIT 2 Enterprise Processes INTEGRATION & DIRECTION July 2004 MIT 3 Product acquisition process GLOBAL ECONOMY I S E G O L O N H C E T E L B A L I A V A L L A PRODUCT PORTFOLIO ARCHITECTURE BUSIN...
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t - t i f e n e B γ β n ptimizatio O “Renaissance” Decline Saturation Present system Next-generation system Leapfrog α Infancy Rapid growth Maturity Typical steps of evolution of technological systems can be illustrated by an S-shaped curve that reflects changes of the system's benefit-to cost ratio with time since the...
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is relatively fixed, then the performance alone can be used. If the performance is relatively fixed, then the reciprocal of cost can be used alone. July 2004 MIT 8 Evolution of inventive activity S CURVE NUMBER OF INVENTIONS LEVEL OF INVENTIONS PROFITABILITY OF INVENTIONS July 2004 MIT 9 TIME Levels of invention • 1...
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Law of Increasing Degree of Ideality. Degree of Ideality is defined as a ratio of Index of Functionality to Index of Cost, where cost can be expressed in dollars, or units of size or weight, etc. It is, essentially, the Benefit-to-Cost ratio. A truly ideal system in most cases is a virtual reality, it exists only in...
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approach REQUIRED TRADE-OFF Compromise doesn’t satisfy either requirement. July 2004 MIT 16 Separation of physical conflicts • Separation of opposite properties in time • Separation of opposite properties in space • Separation of opposite properties between the whole and its parts These simple ideas lead to many inve...
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• Apply the first three (above) for a specific purpose; e.g., mitigate a harmful effect July 2004 MIT 25 Structural change in Sufield CONTACT FORCE PAPER ROLL SHAFT FIELD July 2004 MIT 26 Changes to fields • Change from one type of field to another • Intensify • Concentrate in a smaller region • Vary strength of fie...
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250 MM/SEC VELOCITY 300 MM/SEC PAPER STACK STACK FORCE 0.7 LB GUIDE: ANGLE 45 MOUTH OPENING 7 MM FRICTION 1.0 RETARD: RADIUS 25 MM FRICTION 1.5 Critical parameters guide the detailed design: assure robustness Fig. 5.18 July 2004 MIT 32 Technology readiness SELECTION PRODUCT N PRODUCT DEVELOPMENT AND COMMERCIALIZATI...
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of xerographic copiers July 2004 MIT 38 5 problems of EI management • Innovation done ineffectively; EI process not followed • EI not well integrated into PA • EI not well integrated with other enterprise processes • Spending on EI is at wrong level • EI had wrong people July 2004 MIT 39 Management for success • R...
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Statistics as a Catalyst p1.pdf Box_Statistics as a Catalyst p2.pdf Frey_One Factor at a Time.pdf DeWeck_Isoperforamnce.pdf Senin_Wallace_Distributed Modeling.pdf Hazelrigg_Role and Use of Models.pdf Taguchi_Clausing_Robust Quality.pdf Ulrich_Eppinger_Product Design and Dev ch13.pdf Beck_Extreme Programming.pdf...
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MIT #1 #2 #3 #4 #5 #1 #2 #3 #4 #6 #5 #6 #7 #8 #7 #8 42 Next Steps • Due date changed for HW #4 – Due 8:30AM Thurs 8 July • Reading assignment – Crevelling_Critical Paramter Management.pdf – Frey_Error Budgeting.pdf • See you at the ne...
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18.417 Introduction to Computational Molecular Biology Lecture 5: September 23, 2004 Lecturer: Ross Lippert Scribe: Tony Scelfo Editor: Athicha Muthitacharoen Local/Multi Alignments Introduction Last Time • Global Alignment This Time • Local Alignment Method of aligning two sequences that share a highly commo...
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(ascii) • asn - another format that isn’t as popular Homeobox Genes Homeobox Genes are good to study when looking at local alignments. The reason for this is that the region that codes for the Homeobox Gene has been identified in many species and biologists can look for the Homeobox Gene in new species or test the ...
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�, j�) in the edit graph. Figure 5.1: Figure showing Local Alignment region Problem Statement Input: S1, S2, � Output: maxi� �i,j��j GlobalScore(S1[i� · · · i], S2[j� · · · j]) Time: O(n4 · n2) = O(n6) cell(i,j) = GlobalAlignmentScore(S1 [1 · · · i], S2[1 · · · j]) • reduces local alignment to O(n4). By using the ...
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3, 2004 5-5 Hi,j = max ⎨ Di,j = max ⎨ Vi,j = max ⎨ Hi,j−1 − λ D i,j−1 − � − � Vi,j , Hi,j Di−1,j−1 + �(S1(i), S2(j)) Vi−1,j − � D i−1,j − � − � Multiple Alignment S1, S2, S3, · · · , Sd are the sequences to be aligned. �(S1, S2, · · ·) d-way score measures the distance for all possible pairwise alignments a...
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ai, aj ) ⎩ 5-6 Lecture 5: September 23, 2004 When aligning k sequences, the running time is O(2knd) Progressive alignment • e.g. clustal (based on aligning to an alignment) • does not use ( � 2 ) pairwise alignments, instead aligns first pair and then aligns next sequence to the ...
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Computational Ocean Acoustics • Ray Tracing • Wavenumber Integration • Normal Modes • Parabolic Equation 13.853 COMPUTATIONAL OCEAN ACOUSTICS Lecture 19 Parabolic Equation • Mathematical Derivation (6.2) – Phase Errors and Angular Limitations (6.2.4) • Starting Fields (6.4) – Modal starter – PE Self Starter – Analyti...
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ICS Lecture 19 [See Fig 6.8, 6.10-6.12 in Jensen, Kuperman, Porter and Schmidt. Computational Ocean Acoustics. New York: Springer-Verlag, 2000.] Upslope: Energy loss Downslope: Energy Gain Accounts for density variation Energy Conserving 13.853 COMPUTATIONAL OCEAN ACOUSTICS Lecture 19 Student Demos Wavenumber Integ...
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18.405J/6.841J: Advanced Complexity Theory Spring 2016 Prof. Dana Moshkovitz Lecture 5: Toda's Theorem Scribe: Ilya Razenshteyn Scribe Date: Fall 2012 1 Overview In the last lecture we covered Valiant–Vazirani reduction and investigated the complexity of ap- proximate counting. In this lecture we prove Toda’s Theorem:...
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quantified Boolean formula ϕ(x) with n variables (x stands for the set of free variables), and a parameter m and outputs a formula (cid:76) y ψ(x, y) such that for every x Pr[ϕ(x) = (cid:77) y ψ(x, y)] ≥ 1 − 2−m. The running time of the reduction is (nm)Oc(1). 1 To prove this theorem we first need to establish some prop...
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)] ≥ 1 − 2−(m+n+10). (cid:76) y τ (x1, x, y) such that for every x1, x y Now by union bound we have that with probability at least 1 − 2−(m+10) we have ψ(x1, x) for every x, x1. remove the outer quantifier. For this we use Valiant–Vazirani somewhat “abstract” setting. In this case we have ϕ(x) = ∃x1 y τ (x1, x, y) = y τ...
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we see that with probability at least (1 − 1/8n)N (1) is equal to ϕ(x). So, if we choose N = O(nm) we can make this probability at least 1 − 2−(m+10). Thus, the total probability of failure is at most 2−(m+9) (cid:28) 2−m. It is left to observe that we can (and should!) transform (1) to a ⊕SAT instance using tricks fro...
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of possible values of this formula are disjoint. So, we can compute this sum using an oracle from #P (since T and f are polynomial-time deterministic reductions), and, thus, decide ϕ. So, it is left to prove the Lemma. For l = 1 there is nothing to prove: we can take the reduction to be the identity. By the inductive h...
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Lecture Notes on Wave Optics (04/07/14) 2.71/2.710 Introduction to Optics –Nick Fang Outline: A. Imaging with coherent light B. Optical Spatial Filtering C. The significance of PSF and ATF, and effect of coherence D. Phase Contrast Imaging: Zernike and Schlieren methods A. Imaging with Coherent Light Recap: a c...
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0 Introduction to Optics –Nick Fang By cascading two lenses together, we can reveal Abbe’s theory of imaging process: Ideally, applying two forward Fourier transforms recovers the original function of the object field, with a reversal in the coordinates: 𝐸𝑖𝑚𝑎𝑔𝑒(𝑥", 𝑦") ≈ ∬ 𝐸(𝑥′, 𝑦′)exp { } 𝑑𝑥′𝑑𝑦′ ⁡ ...
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aveilluminationobject:decomposed intoHuygens waveletsimageplaneplanewaveilluminationobject:decomposed intospatial frequenciesimageplaneFourier (pupil)planediffraction ordercomes to focus Lecture Notes on Wave Optics (04/07/14) 2.71/2.710 Introduction to Optics –Nick Fang 𝐸𝑖𝑚𝑎𝑔...
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