Moore's law might continue for a while more.
Here is an announcement which just appeared on the day of lecture: bilayer graphene for
transistors.
space discrete continuous
time -----------------------------
discrete life Henon map
continuous atom Kepler
discrete space and continuous time can make sense
in the quantum world, where a system jumps from
one energy level to an other.
T=Compile[{c},({c,#} &) /@ Union[Drop[NestList[c*#(1-#) &,0.3,500],400]]];
d=0.001;ListPlot[Flatten[Table[T[c],{c,2,4,d}],1],PlotStyle->PointSize->d]
M=Compile[{x,y},Block[{z=x+I y,k=0},While[Abs[z]<2&&k<50,z=z^2+x+I y;++k];k]];
DensityPlot[50-M[x,y],{x,-2,1},{y,-1.5,1.5},PlotPoints->200]
M=100; V=CellularAutomaton[18,Table[Random[Integer,1],{k,M}],M];
ListDensityPlot[Reverse[V],Mesh->False,Axes->False,Frame->False]
X=x[t];Y=y[t];Z=z[t];L=NDSolve[{x'[t]==10(Y-X),y'[t]==-X*Z+28X-Y,z'[t]==X*Y-8Z/3,
x[0]==z[0]==0,y[0]==1},{x,y,z},{t,0,40}];ParametricPlot3D[{X,Y,Z}/.L,{t,0,40}]
Here is a 140 character
version. Needed to change 10 to 9 and leave out some semicolons to make it work.
S=NDSolve[{x'[t]==y[t],y'[t]==-x[t]-(x[t]^2-1)y[t],x[0]==0,y[0]==.2},{x,y},{t,0,90}];
ParametricPlot[Evaluate[{x[t],y[t]}] /. S[[1]],{t,0,90}]
T[{x_,y_}] :={1-1.4*x^2+y,.3*x};c=Last[NestList[T,{0,0},1000]];
Graphics[{PointSize[0.001],Map[Point,NestList[T,c,10000]]},AspectRatio->1]
t = "We the People of the United States"; text = ToLowerCase[t];
a ={"a","b","c","d","e","f","g","h","i","j","k","l","m","n","o","p","q","r","s","t","u","v","w","x","y","z"};
Caesar[s_,n_]:=StringReplace[s,Prepend[Table[a[[i]]->a[[Mod[i+n-1,26]+1]],{i,26}]," "->" "]];
T = Characters[text]; t1=StringJoin[Table[Caesar[T[[k]],3],{k,Length[T]}]]
Reverse[Sort[Table[{StringCount[t1,a[[i]]],a[[i]]},{i,26}]]]
We can do this also with longer texts. Lets take the novel "Jane Eyre" from Charlotte Bronte,
and make a frequency analysis:
t = Import["http://www.gutenberg.org/cache/epub/1260/pg1260.txt"]; text=ToLowerCase[t];
a ={"a","b","c","d","e","f","g","h","i","j","k","l","m","n","o","p","q","r","s","t","u","v","w","x","y","z"};
u=Reverse[Sort[Table[{StringCount[t,a[[i]]],a[[i]]},{i,26}]]]
The output is
u={{101955, "e"}, {67232, "t"}, {62718, "a"}, {61555, "o"}, {54830,
"n"}, {50048, "s"}, {48994, "i"}, {47775, "r"}, {45340,
"h"}, {37580, "d"}, {32581, "l"}, {23943, "u"}, {20882,
"m"}, {19024, "c"}, {18169, "w"}, {17025, "y"}, {16859,
"f"}, {15008, "g"}, {12268, "p"}, {10767, "b"}, {7651, "v"}, {6145,
"k"}, {1244, "x"}, {943, "q"}, {671, "j"}, {327, "z"}}
BarChart[Table[u[[k, 1]], {k, Length[u]}]]
M=9; L=100; K=1.4; KK=3*K^2;
T[{x_, y_, z_}] := If[x^2+y^2+z^2==0.0, 0.0,
Module[{phi, theta, rho}, rho = N[Sqrt[x^2+y^2+z^2]];
phi=M*ArcCos[z/rho];theta=M*Arg[x+I y];
N[rho^M {Cos[theta] Sin[phi],Sin[theta] Sin[phi],Cos[phi]}]]];
Mandelbulb[c_] := Module[{x=0,y=0,z=0,i=0},
While[iKK,{z,-K,K}, {y,-K,K},{x,-K,K},
ColorFunction -> Function[{x,y,z},Hue[(x^2+y^2+z^2)/KK]],
PlotStyle -> {Specularity[White, 20]},
Mesh->False,PlotPoints ->40];
Here is the result with PlotPoints->400 (you have to wait a while to get it appear,
RegionPlot3D is very slow).
MultiChoose[n_, k_] := Module[{a=Range[n],s={}},
Do[u=RandomChoice[a];
a=Delete[a,Position[a,u][[1]]];
s=Append[s,u],{k}];s];
MultiChoose[49, 6]
|
The probability of answering this question correctly by guessing at random is a) 25 percent b) 50 percent c) 60 percent d) 25 percent |
Solve[ 2 x^4 + x^3 + x + 1 == 0, x]And here is an attempt to render this in MathML also directly exported from Mathematica:
Export["out",MathMLForm[Solve[2x^4 + x^3 + x + 1==0,x]]]
| 0 1 0 |
| 1 0 0 |
| 0 0 1 |
which has determinant -1. The parity is -1.
Theorem: A cat has nine tails.
Proof: No cat has eight tails. A cat has one tail more than no cat. Therefore, a cat has nine tails.
|
Here is again the Fasskreis theorem. I think there had been a sign wrong during
the lecture. If you find a reference, let me know. I myself learned it in high school.
Given a circle of radius 1 and a point P inside the circle. The theorem tells
1-|PO|2 = |PA| |PB|The proof is done by brute force using Descartes idea of using coordinates. P=(a,b) A=(a, (1-a2)1/2) B=(a,-(1-a2)1/2) |PA| |PB| = ((1-a2)1/2-b) ((1-a2)1/2+b) = -b2+1-a2 = 1-|PO|2Questions:
|
Decimal Sexagesimal
--------------------------
1 = 1
60 = 1 0
87 = 1 27
123 = 2 3
600 = 10 0
3600 = 1 0 0
You see from the last two examples that it can be tricky to separate 10 0
and 1 0 0. Thats why it is not a very convenient
system, unless we would have 60 symbols
For hexadecimal, we have that
10=A, 11=B,12=C, 13=D, 14=E, 15=F | I was not completely sure on the idea of which numbers are irrational. I followed the proof idea you gave and said that the numbers were irrational. But then you said that we all believe it is true but hasn't been proven. |
There were two open questions I have mentioned, one is whether pie is
irrational or not and the other was about the structure of the digits appearing
in sqrt(2). The following Mathematica code makes a histogram of the frequencies
of the first 1000 digits of sqrt(2): F[n_]:=Histogram[IntegerDigits[Floor[(10^n) N[Sqrt[2],n]],10]]; F[1000]Nobody knows whether the statistics will eventually flatten out. Everybody believes, it will. |
![]() A page sent from a student |
|
During the break of the first lecture, we discussed a bit about cryptology. Actually, the mystery structure we have
looked at in the Worksheet is also important in
cryptology. We will look at that in the number theory lecture. It turns out that the quadratic
function f(x)=x2 holds the holy grail for integer factorization. If n=p q is the
product of two large primes like 200 digit primes then finding p and q is impossible with current
computers and mathematics. If we can find x for which x2 mod n is a small y2,
then x2 - y2 = (x-y) (x+y) is a multiple of n. This means that the greatest common
divisor of (x-y) and n gives a factor of n.
Here are some slides
about the networks. And
Here is the project page. One question was about the terminology used for the networks studied in the last part of the lecture. The jargon can vary a bit. I did not get too much into the graph theory jargon but the more colloquial network theory language: In network theory one talks about "nodes" while in graph theory, one has "vertices". In network theory one talks about "links" or "connections" while in graph theory, one has "edges". One nice thing about networks is that they are very intuitive. We are familiar with networks when looking at subway maps, street maps, organisatorial structures, processes, mindmaps, etc. It is a natural object which is known to us since we were kids. |