c # – & # 39; Answer & # 39; does not contain a definition to & # 39; Redirect & # 39;

I'm trying to use Response.Redirect (url) in a .NET project, but I'm having some problems.

I am also using the AspNetSaml nuget package.

This is the error:

Error CS0117 & # 39; Answer & # 39; does not contain a definition to & # 39; Redirect & # 39;

I am already using the System.Web namespace and also added System.Web.dll

Empty static redirection (Answer samlResponse)
{
// specify the SAML provider url here, also known as "endpoint"
var samlEndpoint = "https://saml.alight.com/idp/SSO.saml2";

Request AuthRequest = new AuthRequest (
"https://www.xxxx.com/", // put the "unique ID" of your application here
"https://www.xxxx.com/" // Consumer URL assertion: the redirect URL where the provider will send authenticated users
);

// Generate the provider URL
string url = request.GetRedirectUrl (samlEndpoint);

Response.Redirect (url);


}

calculation – Derive / find the value of an integral if an integral definition of pi is already given.

I am currently reading Tom Apostol's Vol.1 Calculus and somehow I am stuck in this problem: Having defined $ pi = 2 int _ {- 1} ^ {1} { sqrt {1-x ^ 2} dx} $ , use the properties of the integral to calculate the following in terms of pi: $$ int _ {- 3} ^ {3} { sqrt {9-x ^ 2} dx} $$
The properties that have been shown so far in this book are:
$$ int_a ^ b { sum_ {k = 1} ^ n {c_kf_k (x) dx} = sum_ {k = 1} ^ nc_k int_a ^ bf_k (x) dx} $$
$$ int_a ^ b {f (x) dx} + int_b ^ c {f (x) dx} = int_a ^ c {f (x) dx} $$
$$ int_a ^ b {f (x) dx} = int_ {a + c} ^ {b + c} {f (x-c) dx} $$
$$ int_a ^ b {g (x) dx} leq int_a ^ b f (x) dx ; text {If} ; g (x) leq f (x) $$
Along with the proofs of these theorems. Now that the integrand here is represented by a semicircle of radius 3, it is quite obvious that the answer is $ frac {9 pi} {2} $ . However, it seems that I can not use the aforementioned properties to find it in the way the author wants me to do it. I assumed that I could somehow derive the following formula: $$ 9 int _ {- 1} ^ 1 sqrt {1-x ^ 2} dx = int _ {- 3} ^ {3} sqrt {9-x ^ 2} dx $$
And it would follow that the answer was $ frac {9 pi} {2} $ , but I'm still hitting dead ends. Could someone explain to me the method (which involves only the properties mentioned above) that should be used to solve this? I've been sitting on this for a while and I'm wondering if this is just a case of not being able to find the correct numbers or if I'm just not on the right track.

P.S .: This is my first post here / the first time I use Mathjax, so please excuse / point out any mistakes you may have made in the formulas.

operators – implicit definition of a coordinate in the differential equation

I would like to solve the following differential equation.

$ frac {d ^ 2 psi (r _ *)} {dr ^ 2_ *} + ( omega ^ 2 – V (r)) psi (r _ *) = 0 $

and the boundary conditions are $ psi ( inf) = psi (-inf) = 0 $. The problem is that the function V (r) is defined in $ r $do not $ r _ * $. However, I have the following relationship

$ r_ * = r + 2M ln (r-2M) $

which, clearly, can not be solved by $ r $.

My real problem is finding the proper values ​​for $ omega $, but any help to deal with this implicit definition of $ r $ you can help me

PS: I'm posting this here and not in the physics / math exchange because the function $ V (r) $ It's complicated and I need to solve it computationally. And no, I do not want to transform the whole equation to the $ r $ Coordinate and solve it there.

Category Theory – Definition of $[mathscr A^{text{op}}, textbf{Set}](H_A, X) a [mathscr A^{text{op}}, textbf{Set}](H_B, X) $ in the Yoneda slogan test

With respect to the test of Yoneda's motto on p.98:

How do you get that map $$[mathscr A^{text{op}}, textbf{Set}](H_A, X) a [mathscr A^{text{op}}, textbf{Set}](H_B, X) $$ is defined by $ – circ H_f $
? (The definition of $ H_f $ it is given on p.90.)

And how do you get that map? $$[mathscr A^{text{op}}, textbf{Set}](H_A, X) a [mathscr A^{text{op}}, textbf{Set}](H_A, X & # 39;) $$ is defined by $ theta circ – $ ?

Geometry of ag.algebraic: definition of retraction of Chow groups under a special type of morphism

Leave $ X $ be a normal complex projective variety (not necessarily smooth), and leave $ Y $ Being a complex projective variety without problems. Leave $ Z subset X $ Be a soft closed sub-variety.

Leave $ pi: Y rightarrow X $ Be a map with the property that. $ pi $ it's an isomorphism about $ X setminus Z $, plus $ pi $ is a $ mathbb {P} ^ n $– package about $ Z $ for some $ n $.

(Note that $ pi $ It can not be an explosion if codim$ Z neq n + 1 $, for example.)

My question is: Can we define? pull back Map $ pi ^ *: CH_k (X) rightarrow CH_k (Y) $ for chow groups? In general, pull-backs can be defined when $ pi $ is flat or a local full intersection The map, but in my case, clearly. $ pi $ It's not flat, and I'm not sure if $ pi $ It's actually a local full intersection.

Any help or reference would be welcome.

python: the definition of steps_per_epoch considerably increases training time

This is my training function:

model.fit (train_states, train_plays, epochs = numEpochs,
validation_data = (test_states, test_plays),
shuffle = True) 

When I do not define steps by time, I get this:

Train in 78800 samples, validate in 33780 samples.
Period 1/100

32/78800 [..............................] - ETA: 6:37 - loss: 4.8805 - acc: 0.0000e + 00
640/78800 [..............................] - ETA: 26s - loss: 4.1140 - acc: 0.0844
1280/78800 [..............................] - ETA: 16s - loss: 3.7132 - acc: 0.1172
1920/78800 [..............................] - ETA: 12s - loss: 3.5422 - acc: 0.1354
2560/78800 [..............................] - ETA: 11s - loss: 3.4102 - acc: 0.1582
3200/78800 [>.............................] - ETA: 10s - loss: 3.3105 - acc: 0.1681
3840/78800 [>.............................] - ETA: 9s - loss: 3.2102 - acc: 0.1867
...

But when I need to define:

model.fit (train_states, train_plays, epochs = numEpochs,
validation_data = (test_states, test_plays),
steps_per_epoch = 78800,
validation_steps = 33780,
shuffle = True) 

The training time for each season increases absurdly, although it is 78800:

Period 1/100

1/78800 [..............................] - ETA: 35:39:24 - loss: 4.8044 - acc: 0.0172
2/78800 [..............................] - ETA: 34:48:03 - loss: 4.7417 - acc: 0.0114
3/78800 [..............................] - ETA: 34:04:17 - loss: 4,6801 - acc: 0.0369
4/78800 [..............................] - ETA: 33:59:25 - loss: 4.6148 - acc: 0.0528
5/78800 [..............................] - ETA: 33:47:50 - loss: 4.5438 - acc: 0.0622

and even if I set batch_size, it goes one by one

So I need help to understand what is happening and what the solution would be

I'm using keras

This is the template, if necessary:

model = keras.Sequential ([
        keras.layers.Flatten(input_shape=(8, 4)),
        keras.layers.Dense(300, activation=tf.nn.relu),
        keras.layers.Dense(300, activation=tf.nn.relu),
        keras.layers.Dense(300, activation=tf.nn.relu),
        keras.layers.Dense(128, activation=tf.nn.softmax)
    ])

model.compile (optimizer = & # 39; adam & # 39 ;,
loss = & # 39; categorical_crossentropy & # 39 ;,
metrics =['accuracy'])

usa – On the definition of "Country of residence" on the US customs form. UU

When it arrives in the USA UU., You are given a customs form (CBP Traveler Registration Form) to complete it. One of the questions is "Country of residence". If one lives temporarily in the US UU (For example, with an F1 visa), what should they respond? Is the United States your country of residence, or is the country of residence defined as the country of your permanent residence (which in most cases is the country of your citizenship)?

Differential geometry. Problem with the definition of the cross product.

Throughout the question, keep in mind that I know very little about differential geometry. That is, only the intrinsic definitions of differentiable / Riemannian manifolds and the metric tensor, etc. I'm trying to understand the definition of the cross product given by Wikipedia here:

https://en.wikipedia.org/wiki/Cross_product#Index_notation_for_tensors

The article says that we can define the cross product. $ c $ of two vectors $ u $,$ v $ given an adequate "knit product" $ eta ^ {mi} $ as follows

$ c ^ m: = sum_ {i = 1} ^ 3 sum_ {j = 1} ^ 3 sum_ {k = 1} ^ 3 eta ^ {mi} epsilon_ {ijk} u ^ jv ^ k $

To demonstrate my current understanding of this definition, I will introduce some notation and terminology. Then I will show where my confusion arises with an example. I apologize in advance for the length of this post.

Leave $ M $ be a mild Riemannian variety in $ mathbb {R} ^ 3 $ with the metric tensor $ g $. Choose a coordinate table $ (U, phi) $ with $ phi $ A diffeomorphism. We define a collection. $ beta = {b_i: U to TM | i in {1,2,3 } } $ of vector fields, called coordinate vectors, as follows

$ b_i (x): = Big (x, big ( delta_x circ frac { partial { phi ^ {- 1}}} { partial {q_i}} circ phi big) (x ) Big) $

where $ delta_x: mathbb {R} ^ 3 to T_xM $ Denotes the canonical bijection. The coordinate vectors induce a natural base. $ gamma_x $ at each point $ x in U $ through the tangent space $ T_xM $. Leave $[g_x]_S $ denotes the matrix representation of the metric tensor at the point $ x $ in the standard basis for $ T_xM $ and let $[g_x]_ { gamma_x} $ denote the matrix representation in the base $ gamma_x $.

My understanding of the previous definition of the cross product now follows. Leave $ u, v in T_xM $ be tangent vectors and let

$[u]_ { gamma_x} = begin {bmatrix}
u_1 \
u_2 \
u_3
end {bmatrix} $
$ space space space space space space [v]_ { gamma_x} = begin {bmatrix}
v_1 \
v_2 \
v_3
end {bmatrix} $

denotes the coordinates of $ u, v $ at the base $ gamma_x $. Then we define the $ m $The coordinate of the cross product. $ u times v in T_xM $ at the base $ gamma_x $ as

$ big ([u times v]_ { gamma_x} big) _m: = sum_ {i = 1} ^ 3 sum_ {j = 1} ^ 3 sum_ {k = 1} ^ 3 big ([g_x]_ { gamma_x} big) _ {mi} epsilon_ {ijk} u_jv_k $

Now I will demonstrate my apparent misunderstanding with an example. Leave the multiple $ M $ be the usual Riemannian collector in $ mathbb {R} ^ 3 $ and let $ phi $ be given by

$ phi (x_1, x_2, x_3) = (x_1, x_2, x_3-x_1 ^ 2-x_2 ^ 2) $

$ phi ^ {- 1} (q_1, q_2, q_3) = (q_1, q_2, q_3 + q_1 ^ 2 + q_2 ^ 2) $

The Jacobian matrix $ J $ of $ phi ^ {- 1} $ is

$ J = begin {bmatrix}
1 & 0 & 0 \
0 & 1 & 0 \
2q_1 and 2q_2 and 1
end {bmatrix} $
$ space space space space space space J ^ {{1} = begin {bmatrix}
1 & 0 & 0 \
0 & 1 & 0 \
-2q_1 & -2q_2 & 1
end {bmatrix} $

And the matrix representation of the metric tensor in the base. $ gamma_x $ is

$[g_x]_ { gamma_x} = J ^ T[g_x]_SJ = begin {bmatrix}
1 + 4q_1 ^ 2 and 4q_1q_2 and 2q_1 \
4q_1q_2 & 1 + 4q_2 ^ 2 & 2q_2 \
2q_1 and 2q_2 and 1
end {bmatrix} $

Now choose $ x = (1,1, -1) $. The coordinates of $ x $ they are obviously $ phi (x) = (1,1,1) $ and the three above matrices are converted

$ J = begin {bmatrix}
1 & 0 & 0 \
0 & 1 & 0 \
2 and 2 and 1
end {bmatrix} $
$ space space space space space space J ^ {{1} = begin {bmatrix}
1 & 0 & 0 \
0 & 1 & 0 \
-2 & -2 & 1
end {bmatrix} $
$ space space space space space space [g_x]_ { gamma_x} = begin {bmatrix}
5 & ​​4 & 2 \
4 & 5 & 2 \
2 and 2 and 1
end {bmatrix} $

Now we calculate the cross product in the base. $ gamma_x $. Using my understanding of the definition as described above, I get

$[u times v]_ { gamma_x} = begin {bmatrix}
36 \
35 \
sixteen
end {bmatrix} $

If, on the other hand, we calculate the cross product on the standard basis, then, using my understanding of the definition, I get

$[u times v]_S = begin {bmatrix}
0 \
-one \
two
end {bmatrix} $

Naturally, these results should coincide if we make a base change in $[u times v]_ { gamma_x} $. Doing just that, I get

$[u times v]_S = J[u times v]_ { gamma_x} = begin {bmatrix}
1 & 0 & 0 \
0 & 1 & 0 \
2 and 2 and 1
end {bmatrix} begin {bmatrix}
36 \
35 \
sixteen
end {bmatrix} = begin {bmatrix}
36 \
35 \
158
end {bmatrix} $

Clearly, these do not agree. I can think of several reasons for this. Perhaps the definition given in Wikipedia is erroneous or only works for orthogonal coordinates. Maybe I am misinterpreting the definition given in Wikipedia. Or maybe I made a mistake somewhere in my calculation. My question then is as follows. How should I interpret the definition given in Wikipedia, and how should that definition be expressed using the notation provided here?

ct.category theory – Definition of infinite regular category

In

Makkai, A theorem on exact categories, with an infinite generalization.

an infinite regular category definition is given: a complete regular category $ C $ with the additional requirement

(DC) for each diagram $ F: alpha ^ { text {op}} a C $, $ alpha $ an ordinal, such that each $ F ( beta + 1) a F ( beta) $ it's regular epi and $ F ( lambda) = lim _ { beta < lambda} F ( beta) $ for each ordinal limit $ lambda $, then each projection $ lim F a F ( beta) $ it must be regular epi.

In

Carboni-vitale, Regular and exact terminations.

another definition is given (called "completely regular"): a complete regular category such that the products of regular episodes are regular epi.

The first definition implies the second. Are the two definitions equivalent?

Performance: What is the exact definition of what is included in the Google Search Console: "time dedicated to download a page" metrics?

I have searched high and low for an official definition of what is included in that graph in the search console. It seems that I can not find any authoritative resource for this among the Google documentation. I have read in articles and comments that it is the "Google robots http request time", but what does that really mean?

  • Is it strictly just the page itself, or would it include a redirect?
  • Is DNS search included?
  • I suppose it includes the round trip of the network, and so the question arises: from where in the world does Google crawl my website hosted in Germany? (eg, from the USA, would it mean a little static overload?)
  • Is it only the 200 status code that is included or 304 requests are included in the statistics as well? And what about 400 and 500 answers?
  • I understand that statistics are DO NOT It's first byte time, but what are they then? Time for the last byte?

Waiting for some clarity from a credible source