Can an invisible creature with Mind Blank be seen by a creature with True Seeing?

True Seeing is a Divination spell that states

For the Duration, the creature has Truesight

While Mind Blank states

one willing creature you touch is immune to […] Divination Spells

Let’s say that the Rogue was cast Invisibility and Mind Blank, and the Wizard cast True Seeing on himself. The discussion at my table hinges on the fact that True Seeing doesn’t target or affect the Mind Blanked rogue, so his immunity is not relevant. The Wizard would simply gain Truesight and would spot him.

How do these spells interact?

SPFX rest call response seen in network tab but I cant read it on console

I got an issue I’m doing a rest call to get columns of a list, the call success because I can see the response in the network tab of the developer tools, here is a picture: enter image description here for some reason when I try to console it it looks like that with and without calling the .json() method : enter image description here here is my method:

   export const getListFieldsByGuid = (guid: string, context: WebPartContext)   => {   return context.spHttpClient     .get(   context.pageContext.web.absoluteUrl +     `/_api/web/lists(guid'$  {guid}')/Fields?$  filter=Hidden eq false and    ReadOnlyField eq false`,   SPHttpClient.configurations.v1 ) .then(response => {   console.log("fields without .json():", response);   return response.json(); }); }; 

what am I doing wrong? Moreover, If anyone has a better way to get columns of out specific view of a list I would thank a lot!

Where have you seen an application of “arrangement spaces”?

I am compiling a paper in which I advertise (and use) the following notion of arrangement spaces (I made up the name, as I found no standard name in the literature).

Let $ v_i\in\Bbb R^d,i\in N:=\{1,…,n\}$ be a finite family of points. Let’s call this an arrangement and write $ v$ when referring to all the points at once.

Definition. The arrangement space $ U\subseteq\Bbb R^n$ of $ v$ is the column span of the matrix $ M$ in which the $ v_i$ ‘s are the rows.

The definition is motivated by a recurring idea in geometry, and despite its simplicity has some interesting and non-trivial applications. While I have seen numerous computations that could have been formulated with this concept, I have never seen anyone giving a name to this specific idea.

So I wonder where some of you came across this idea, how you have used it, and where you have already seen it under a different name. I will start to name a few:

  • The arrangement space determines the arrangement up to invertible linear transformations. It is therefore of interest wherever people study linear, affine and convex dependencies between points (point configurations, oriented matroids, …)
  • The arrangement space determines all properties that are determined up to invertible linear transformations. E.g. the dimension of the span of the points, or whether the points are a linear transformation of a rational arrangement or a 01-arrangement.
  • The space of possible $ d$ -dimensional arrangement spaces in $ \Bbb R^n$ is the Grassmannian $ \mathrm{Gr}(n,d)$ . We therefore can parametrize arrangments via the Grassmannian, where distinct points in $ \mathrm{Gr}(n,d)$ describe arrangements that are not related by linear transformations.
  • Arrangement spaces give a natural definition of a form of Gale duality, which I have only seen defined in artificial and technical ways. The idea is as follows: $ $ v\;\;\mapsto \;\;U\;\;\mapsto\;\; U^\bot\;\;\mapsto\;\;\bar v.$ $ Two arrangements are Gale duals of each other, if and only if their arrangement spaces are orthogonal complements of each other.
  • The linear matroid on $ v$ can equivalently be defined by just knowing its arrangement space $ U$ : the independent sets are $ I\subseteq N$ for which $ U^\bot$ has trivial intersection with $ \mathrm{span}\{e_i\mid i\in I\}$ . In this form it is especially easy to prove that the dual matroid is again a linear matroid, namely, it is the linear matroid of the Gale dual of $ v$ (as defined above).
  • Let $ \Gamma\subseteq\mathrm{Sym}(N)$ be a permutation group that acts on $ \Bbb R^n$ via permutation matrices. If the arrangement space of $ v$ is $ \Gamma$ -invariant, then the arrangement expresses certain symmetries prescribed by $ \Gamma$ . The arrangement space can hence be used in the study of symmetric arrangements.
  • If $ G=(N,E)$ is a graph on $ N$ , and the arrangement space of $ v$ is an eigenspace of $ G$ , then the points in $ v$ are in a certain balanced configuration w.r.t. the edges of $ G$ . This has then be applied in graph drawings, stable arrangements from strongly regular graphs, eigenpolytopes, etc.

Note how all these are connected via their common use of the arrangement space. Hence, if you provide an answer, please explain how the arrangment space can be used to simplify what you are doing.

Can the effective topos be seen as symmetric monoidal?

In

Example(s) of monoidal symmetric closed category with NNO without infinite coproducts?

user Zhen Lin states the effective topos is locally cartesian closed. On nLab we have that locally cartesian closed with terminal object implies cartesian closed, and Hyland states (in his original paper on the effective topos) that there is such a terminal object and he calls it $ 1$ as usual. So cartesian closed implies cartesian monoidal implies symmetric monoidal. Is this line of reasoning alright? Am I missing something?

Also, since we can represent morphisms in a symmetric monoidal category as string diagrams (from Joyal and Street) does this mean we can do this for the effective topos? I would like to draw these!

If so, could someone help me there? My knowledge on all this is quite narrow and I only made this connection successfully.

Matrix Inversion in the complexity class $P$ seen as a decision problem

If the set $ P$ is defined as the set of decision problems that can be solved by a deterministic Turing Machine in polynomial time, and matrix inversion using Gaussian elimination is $ O (n^3)$ , then how can I relate these two concepts to conclude that inverting a matrix is in $ P$ ?

I suppose I need a way of converting a description of Gaussian elimination into a decision problem? Or maybe I am confused about the fundamentals