## Prevent Visio 2016 from creating curve connector bend points?

I have a state machine diagram that consists of a series of shapes, and many curve connectors that connect from the center of one shape to somewhere on the radius of another shape. There are many cases where for two shapes A and B, there are multiple connectors that connect the center of A to the radius of B. I have two problems:

1. When I create a new connector, visio always uses a straight-downward initial angle for the connector, even if the destination shape is above the source connection point.

2. Visio always creates excessive amounts of bend points in my curves, especially when it has to bend over backwards to accommodate an initial downward angle, and each of these bend points must tediously be deleted so that changing the initial angle doesn’t make the curve look insane.

3. When attempting to move the shapes themselves, visio replaces the bend points that I deleted.

The deal breaker is really point 3 above, since the only reason I’m even putting myself through using Visio is to allow for changes to the layout, but when it completely changes the structure of the connectors like this the value is lost.

Is there a way to prevent visio from adding bend points to my curves? I really only want the angular controls at each of the connection points, with no bend points unless absolutely necessary. At present I don’t know that that necessity exists.

## How would a pH curve look like for titration of diluted weak base compared to concentrated one?

Let’s say I take \$pu{80 g}\$ of a weak base, dilute it with \$pu{50 ml}\$ of water and titrate it with a strong acid. I get a titration curve. Now I take again \$pu{80 g}\$ of the same weak base but this time I dilute it with \$pu{200 ml}\$ of water and titrate it with the strong acid. What will be the new titration curve compared to the first one?

And here is what confuses me: On one hand, the diluted base should have lower \$mathrm{pH}\$. But on the other hand, at half equivalence point, \$K_mathrm{b}=mathrm{pOH}\$, and since it’s the same base, \$K_mathrm{b}\$ is the same, and thus \$mathrm{pOH}\$ and \$mathrm{pH}\$ at half equivalence point should be the same. I am also not sure about the end point.

## Curve drawing in TIKZ – evenly spaced points

I have a certain number of points, using which I want to draw a spline/bezier curve “through” the points. When the curve is drawn, I want to automagically put along the curve p equally spaced points. (So: If my original number of points defining the curve is n, there is no relation between n and p (n can be larger than, equal to, or less than p)

## Reconstructing a curve in \$S^2\$ from intersections with great circles

Take $$S^2$$ with its standard metric. The space of great circles in $$S^2$$ can be identified with the real projective plane $$mathbb{R}P^2$$. Let $$X$$ be an embedded circle in $$S^2$$; associate to it a function $$f_X:mathbb{R}P^2rightarrow mathbb{Z}cup{infty}$$ which counts the number of intersection points (with multiplicity) of $$X$$ with given great circle. Can we reconstruct $$X$$ from $$f_X$$?

Remark: I think from some version of Crofton formula, it should be possible to determine the length of $$X$$ from $$f_X$$.

## Stratified log-rank test for Kaplan-Meier curve after propensity score matching

I am studying the effect of amputation vs limb-sparing surgery for tumor patients. I have created two equal cohorts (amputation vs limb-sparing surgery) through propensity score matching, matched on all other relevant parameters (demographics, socioeconomic status, tumor grade/stage, etc). I am now trying to conduct a Kaplan-Meier survival analysis on survival after amputation vs limb-sparing surgery using the survival package in R. I know that the normal log-rank test is inadequate for propensity score matched cohorts, so I’m trying to conduct a stratified log rank test. However, I am confused as to what I should stratify with.

My R code for a normal log rank test is this:
survfit(Surv(Survival_time, DIED) ~ Amp_or_not, data = my_data)

Would the stratified log rank test look like this?
survdiff(Surv(Survival_time, DIED) ~ strata(Amp_or_not), data=my_data)

However, this raises an error “No groups to test”. But given that the only groups I want to test are amputation and limb-sparing surgery, how do I conduct the stratified log rank test?

## Implied term structure from risky discount curve: does it make sense?

We know that, taken every discount curve, it’s possible to calculate its forward rates according to our tenor preferences.

We know also that it’s actually possible to extract an implied term structure from every discount curve simply using the appropriate forward rates.

This is indeed true for risk free discount curves, because the arbitrage-free assumption which returns the forward rates is fair.

Now let a risky discount curve, that is, every discount curve obtained from liquid issues’ yields possibly interpolated and bootstrapped… and so on.

Questions:

1. does it make sense to extract an implied term structure from it like we’re used to do with a risk free zero rate curve?
2. Is the implied term structure obtained in such a way an arbitrage-free forecast of the future yield curve shape?
3. If not so, is the assumption that allows to calculate the forward rates valid for risk-free curves only?
4. Does it make sense to obtain an implied term structure from, say, a CDS spread curve? Why?
5. If the answer to 5. was affirmative, would that implied term structure be an arbitrage-free forecast of the issuer’s default risk?

## Deep Learning ROC and Average Precision Curve Results

I used Vgg16 to create a deep learning model and the dataset is imbalanced so, I used class_weight argument in fit_generator method.

The model result as the following:
accuracy= 98.9% and loss= 0.1731

And I got the following ROC and Average Precision curves:

Why I got these results?
Is there any problem in ROC and average precision?

## Problem understanding a solenoidal vector field that is not a curve.

Problem

In Apostal’s calculus volume 2 , there is an example which shows that a solenoidal vector field that is not a curl. Example states that proof is difficult at this stage . Can anyone please me some understanding why this can happen. That is on what kind of open sets a solenoidal vector field is always a curl of some other vector field in that set?

NB-Currently a sophomore .

## “plane Cut” on a curve

I have a cosplay prop that I am trying to cut up into pieces so I can print them out in different color filament eliminating the need for any painting (it’s the Handle in https://www.thingiverse.com/thing:2416804). I was going along great using Meshmixer creating face groups, then generating complex, then separating complex, until a certain part where I tried to separate a part that has a hole going through the middle and smaller holes for screws. (using this tutorial https://www.youtube.com/watch?v=xw_ClxnJ1_U). I think Meshmixer is getting confused though since the topology of the part technically meets up to itself and has what could be considered multiple joining edges.

I was able to fire up Netfabb and get something close by doing a Polygon cut, but it’s not accurate enough. It left a bunch of tiny pieces all over the place where I didn’t quite get right at the vertices.

Ideally I would like to have a cutting tool exactly like the polygon cut in Netfabb but that allows you to choose actual Mesh triangle edges as your cutting edge to project through your mesh. Anyone know if there is a piece of software that can do something like that? I also considered attempting to “fill in the hole”, separate my complexes, then Cut the hole back out, but I don’t think that would work very well.

For reference here is what the part looks like. I’m trying to separate the green piece from the rest of it.