restart; with(Statistics):
X := RandomVariable(Normal(0, 1)): Y := RandomVariable(Uniform(-2, 2)):
Probability(X*Y < 0);

crashes my comp in approximately 600 s. Mma produces 1/2 on my comp in 0.078125 s.

Let us consider

with(Statistics):
X1 := RandomVariable(Normal(0, 1)):
X2 := RandomVariable(Normal(0, 1)):
X3 := RandomVariable(Uniform(0, 1)): 
X4 := RandomVariable(Uniform(0, 1)):
Z := max(X1, X2, X3, X4); CDF(Z, t);

int((1/2)*(_t0*Heaviside(_t0-1)-_t0*Heaviside(_t0)-Heaviside(1-_t0)*Heaviside(-_t0)+Heaviside(-_t0)+Heaviside(1-_t0)-1)*(1+erf((1/2)*_t0*2^(1/2)))*(2^(1/2)*Heaviside(_t0-1)*exp(-(1/2)*_t0^2)*_t0-2^(1/2)*Heaviside(_t0)*exp(-(1/2)*_t0^2)*_t0-2^(1/2)*Heaviside(-_t0)*Heaviside(1-_t0)*exp(-(1/2)*_t0^2)-Pi^(1/2)*undefined*erf((1/2)*_t0*2^(1/2))*Dirac(_t0)-Pi^(1/2)*undefined*erf((1/2)*_t0*2^(1/2))*Dirac(_t0-1)+2^(1/2)*Heaviside(-_t0)*exp(-(1/2)*_t0^2)+2^(1/2)*Heaviside(1-_t0)*exp(-(1/2)*_t0^2)-Pi^(1/2)*undefined*Dirac(_t0)-Pi^(1/2)*undefined*Dirac(_t0-1)+Pi^(1/2)*Heaviside(_t0-1)*erf((1/2)*_t0*2^(1/2))-Pi^(1/2)*Heaviside(_t0)*erf((1/2)*_t0*2^(1/2))-exp(-(1/2)*_t0^2)*2^(1/2)+Pi^(1/2)*Heaviside(_t0-1)-Pi^(1/2)*Heaviside(_t0))/Pi^(1/2), _t0 = -infinity .. t)

whereas Mma 11 produces the correct piecewise expression (see that here screen15.11.16.docx).

Edit. Mma output.

Let us consider 

J := int(x^n/sqrt(1+x^n), x = 0 .. 1) assuming n > 0;

2*(2^(1/2)-hypergeom([1/2, 1/n], [(n+1)/n], -1))/(2+n)

limit(J,n=infinity);
FAIL
MultiSeries:-limit(J,n=infinity);
FAIL

Mma 11 finds the limit is zero. Hope one feels the difference.

limit((x^2-1)*sin(1/(x-1)), x = infinity, complex);
infinity-infinity*I
MultiSeries:-limit((x^2-1)*sin(1/(x-1)), x = infinity, complex);
infinity

whereas the same outputs are expected. The help http://www.maplesoft.com/support/help/Maple/view.aspx?path=infinity&term=infinity does not shed light on the problem. Here are few pearls:

• infinity is used to denote a mathematical infinity, and hence it is usually used as a symbol by itself or as -infinity.
• The quantities infinity, -infinity, infinity*I, -infinity*I, infinity + y*I, -infinity + y*I, x + infinity*I and x - infinity*I, where x and y are finite, are all considered to be distinct in Maple. However, all 2-component complex numerics in which both components are infinity are considered to be the same (representing the single point at the "north pole" of the Riemann sphere).
• The type cx_infinity can be used to recognize this "north pole" infinity.

This MaplePrimes guest blog post is from Dr. James Smith, an Assistant Lecturer in the Electrical Engineering and Computer Science Department of York University’s Lassonde School of Engineering. His team has been working with Maplesim to improve the design of assistive devices.

As we go through our everyday lives, we rarely give much thought to the complex motions and movements our bodies go through on a regular basis. Motions and movements that seem so simple on the surface require more strength and coordination to execute than we realize. And these are made far more difficult as we age or when our health is in decline. So what can be done to assist us with these functions?

In recent years, my research team and I have been working on developing more practical and streamlined devices to assist humans with everyday movements, such as standing and sitting. Our objective was to determine if energy could be regenerated in prosthetic devices during these movements, similar to the way in which hybrid electric vehicles recover waste heat from braking and convert it into useable energy.

People use – and potentially generate – more energy than they realize in carrying out common, everyday movements. Our research for this project focused on the leg joints, and investigated which of the three joints (ankle, knee or hip) was able to regenerate the most energy throughout a sitting or standing motion. We were confident that determining this would lead to the development of more efficient locomotive devices for people suffering from diseases or disabilities affecting the muscles around these joints.

In order to identify the point at which regenerative power is at its peak, we determined that MapleSim was the best tool to help us gather the desired data. We took biomechanical data from actual human trials and applied them to a robotic model that mimics human movements when transitioning between sitting and standing positions. We created models to measure unique movements and energy consumption at each joint throughout the identified movements to determine where the greatest regeneration occurred.

To successfully carry out our research, it was essential that we were able to model the complex chemical reactions that occur within the battery needed to power the assistive device. It is a challenge finding this feature in many engineering software programs and MapleSim’s battery modeling library saved our team a great deal of time and effort during the process, as we were able to use an existing MapleSim model and simply make adjustments to fit our project.

Using MapleSim, we developed a simplified model of the human leg with a foot firmly planted on the ground, followed by a more complex model with a realistic human foot that could be raised off the ground. The first model was used to create a simplified model-based motion controller that was then applied to the second model. The human trials we conducted produced the necessary data for input into a multi-domain MapleSim model that was used to accurately simulate the necessary motions to properly analyze battery autonomy.

The findings that resulted from our research have useful and substantial applications for prostheses and orthoses designs. If one is able to determine the most efficient battery autonomy, operation of these assistive devices can be prolonged, and smaller, lighter batteries can be used to power them. Ultimately, our simulations and the resulting data create the possibility of more efficient devices that can reduce joint loads during standing to sitting processes, and vice versa.

Let us consider 

MmaTranslator:-FromMma(" Table[0,{n=10},{m=2}]");

[seq([seq(0,i=1..(m := 2))],j=1..(n := 10))]

The above result is syntactically incorrect in Maple language. The translation should be 

Matrix(10,2)
#or
[seq([seq(0,i=1.. 2)],j=1..10)]

up to the Mmma's result

Table[0, {n = 10}, {m = 2}]

{{0, 0}, {0, 0}, {0, 0}, {0, 0}, {0, 0}, {0, 0}, {0, 0}, {0, 0}, {0,0}, {0, 0}}

Another bug is as follows.

MmaTranslator:-FromMma("Sinc[x]");

Sinc(x)

whereas the expected result is piecewise(x=0,1,sin(x)/x) up to http://reference.wolfram.com/language/ref/Sinc.html

In general, the MmaTranslator package is outdated. It often returns working Mma's commands as incorrect (Concrete examples are long and need a context. These may be exposed on demand.). The question arises about the quality of other Maple translations. 

The command

plots:-implicitplot(evalc(argument((1+x+I*y)/(1-x-I*y))) <= (1/4)*Pi, x = -5 .. 5, y = -5 .. 5, crossingrefine = 1, gridrefine = 2, rational = true, filled, signchange = true, resolution = 1000);

produces an incorrect result

in view of

evalf(argument((1-4+4*I)/(1+4-4*I)));
                          2.889038378

There is a workaround 

plots:-inequal(evalc(argument((1+x+I*y)/(1-x-I*y))) <= (1/4)*Pi, x = -5 .. 5, y = -5 .. 5);

The command 

restart; st := time(): FunctionAdvisor(EllipticE); time()-st;

produces the result on my comp in 805.484 s. Too much time.

The command

J := int(sin(x)/(x*(1-2*a*cos(x)+a^2)), x = 0 .. infinity)assuming a::real,a^2 <>0;

outputs 

(infinity*I)*signum(a^3*(Sum(a^_k1, _k1 = 0 .. infinity))-a^2*(Sum(a^_k1, _k1 = 0 .. infinity))-a*(Sum(a^(-_k1), _k1 = 0 .. infinity))+a^2+Sum(a^(-_k1), _k1 = 0 .. infinity)+a)

which is wrong in view of 

evalf(eval(J, a = 1/2));
                       Float(undefined) I

The correct answer is Pi/(4*a)*(abs((1+a)/(1-a))-1) according to G&R 3.792.6. Numeric calculations confirm it.

Both the commands 

maximize(x*sin(t)+y*sin(2*t), t = 0 .. 2*Pi)assuming x>=0,y>=0;
minimize(x*sin(t)+y*sin(2*t), t = 0 .. 2*Pi)assuming x>=0,y>=0;

output 0. Simply no words.

The following three commands 

plots:-implicitplot(3*cos(x) = tan(y)^3, x = -Pi .. Pi, y = -(1/2)*Pi-1 .. (1/2)*Pi+1, thickness = 3, crossingrefine = 1, rational = true, signchange = true, resolution = 1000, gridrefine = 2);
plots:-implicitplot(3*cos(x) = tan(y)^3, x = -Pi .. Pi, y = -(1/2)*Pi-1 .. (1/2)*Pi+1, thickness = 3, crossingrefine = 1, rational = true, signchange = false, resolution = 1000, gridrefine = 2);
plots:-implicitplot(3*cos(x) = tan(y)^3, x = -Pi .. Pi, y = -(1/2)*Pi-1 .. (1/2)*Pi+1, thickness = 3, crossingrefine = 1, rational = true, resolution = 1000, gridrefine = 2);

produce the same incorrect plot 

It is clear the sraight lines given by y=Pi/2 and y=-Pi/2 are superfluous. It should be noticed that the Mmma's ContourPlot command without any options produces a correct plot.

Up to http://www.maplesoft.com/support/help/Maple/view.aspx?path=solve&term=solve

 Let us consider 

solve({x > -Pi, (tan(x)-tan(x)^2)^2-cos(x+4*tan(x)) = -1, x < Pi}, [x]);
                               []

We see the command omits the solution x=0 without any warning. It should be noticed that Mathematica solves it, outputting

{{x -> 0}, {x -> 0}}

and the warning

Solve::incs: Warning: Solve was unable to prove that the solution set found is complete.

One may draw a conclusion on her/his own.

Just a simple graphical view of Maple releases over the years.

with(plots):
MapleVersions := [[1, 1982], [1.1, 1982.05], [2, 1982.33], [2.1, 1982.42], [2.15, 1982.58], [2.2, 1982.92], [3, 1983.17], [3.1, 1983.75], [3.2, 1984.25], [3.3, 1985.17], [4, 1986.25], [4.1, 1987.33], [4.2, 1987.92], [4.3, 1989.17], [5.1, 1990.58], [5.2, 1992.83], [5.3, 1994.17], [5.4, 1996], [5.5, 1997.83], [6, 1999.92], [7, 2001.5], [8, 2002.25], [9, 2003.42], [9.5, 2004.25], [10, 2005.33], [10.01, 2005.58], [10.02, 2005.83], [10.03, 2006.17], [10.04, 2006.42], [10.05, 2006.5], [10.06, 2006.75], [11, 2007.08], [11.01, 2007.5], [11.02, 2007.83], [12, 2008.33], [12.01, 2008.75], [12.02, 2008.92], [13, 2009.25], [13.01, 2009.5], [13.02, 2009.75], [14, 2010.25], [14.01, 2010.75], [15, 2011.25], [15.01, 2011.42], [16, 2012.17], [16.01, 2012.33], [17, 2013.17], [17.01, 2013.5], [18, 2014.17], [18.01, 2014.33], [18.015, 2014.5], [18.02, 2014.83], [19, 2015.17], [19.1, 2015.33], [20, 2016.17], [20.1, 2016.25], [20.15, 2016.30]]
a:=map(ListTools:-Reverse,MapleVersions): #swap x-y axis
plot(a, style = point, symbol = point)

Hi Mapleprimes,

I have made this little procedure with Maple. 

check_g_conjecture_10.pdf

similar to this next one check_g_conjecture_10.mw

This may be worth a look.

Regards,

Matthew

P.S.   see  https://en.wikipedia.org/wiki/Goldbach%27s_conjecture

In My Humble Opinion, Wikipedia is a good crowd sourced resource.

This post - this is a generalization of the question from  here .
Suppose we have  m  divisible objects that need to be divided equally between n persons, and so that the total number of parts (called  N  in the text of the procedure) after cutting should be a minimum. Cutting procedure exactly solves this problem. It can be proved that the estimate holds  n<=N<=n+m-1, and  N<n+m-1 if and only if there are several objects (< m), whose measures sum to be a multiple of the share (Obj in the text of the procedure).

In the attached file you can find also the text of the second procedure Cutting1, which is approximately solves the problem. The procedure Cutting1 is much faster than Cutting. But the results of their work are usually the same or Cutting procedure gives a slightly better result than Cutting1.

Required parameters of the procedure: L is the list of the measures of the objects to be cutted, n is the number of persons. The optional parameter  Name is a name or the list of names of the objects of L (if the latter then should be nops(L)=nops(Name) ).

Cutting:=proc(L::list(numeric), n::posint, Name::{name,list(name)}:=Object)

local m, n1, L1, L11, mes, Obj, It, M, N;

uses combinat, ListTools;

m:=nops(L); L1:=sort([seq([`if`(Name::name,Name||i,Name[i]),L[i]], i=1..m)], (a,b)->a[2]<=b[2]);

mes:=table(map(t->t[1]=t[2],L1));

Obj:=`+`(L[])/n;

It:=proc(L1, n)

local i, M, m1, S, n0, a, L2;

if nops(L1)=1 then return [[[L1[1,1],Obj]] $ n] fi;

if n=1 then return [L1] fi;

for i from 1 while `+`(seq(L1[k,2],k=1..i))<=Obj do

od;

M:=[seq(choose(L1,k)[], k=1..ceil(nops(L1)/2))];

S:=[];

for m1 in M while nops(S)=0 do n0:=`+`(seq(m1[k,2],k=1..nops(m1)))/Obj;

if type(n0,integer) then S:=m1 fi;

od;

if nops(S)=0 then

a:=Obj-`+`(seq(L1[k,2],k=1..i-1));

L2:=[[L1[i,1],L1[i,2]-a],seq(L1[k],k=i+1..nops(L1))];

 [[seq(L1[k], k=1..i-1),`if`(a=0,NULL,[L1[i,1],a])],It(L2,n-1)[]] else L2:=sort(convert(convert(L1,set) minus convert(S,set), list),(a,b)->a[2]<=b[2]);

[It(S,n0)[], It(L2,n-n0)[]] fi;

end proc;

M:=It(L1,n);

N:=add(nops(M[i]), i=1 ..nops(M));

Flatten(M, 1);

[Categorize((a,b)->a[1]=b[1],%)];

print(``);

print(cat(`Cutting scheme (total  `, N, `  parts):`) );

print(map(t->[seq(t[k,2]/`+`(seq(t[k,2],k=1..nops(t)))*t[1,1],k=1..nops(t))], %)[]);

print(``);

print(`Scheme of sharing out:`);

seq([Person||k,`+`(seq(M[k,i,2]/mes[M[k,i,1]]*M[k,i,1], i=1..nops(M[k])))],k=1..n);

end proc:

Examples of use.

First example from the link above:

Cutting([225,400,625], 4, Cake);  # 3 cakes must be equally divided by 4 persons

eval(%,[Cake1,Cake2,Cake3]=~[225,400,625]);  # Check

Second example (the same for 10 persons):

Cutting([225,400,625], 10, Cake);

Third example (7 identical apples should be divided between 12 persons):

Cutting([1 $ 7], 12, apple); 

Cutting.mw

 Edited:

1. Fixed a bug in the procedure Cutting  (I forgot sort the list  L2  in sub-procedure  It  if  nops(S)<>0 ).

2. Changes made to the sub-procedure  It  for the case if there are several objects (>1  and  < m), whose measures                     sum to be a multiple of the share  Obj .