Domains – Can you use a subdomain that points to an IP for SSH?

I have a website and would like to use it for SSH purposes. I hate having to remember the IP address of my computer, so I hoped to do something like:

ssh uname@me.example.com -p 1234

or

ssh uname@eexample.com/me -p 1234

or anything, where I do not have to remember my IP, I just need the address. I really do not know much about networking, but I think so example.com/me This would not work because it is done by the web server (no matter which GitHub page is used) and therefore can not be redirected to a reasonable IP address. But I suppose I could do that with a subdomain me.example.comand that would be great.

So, can you use a subdomain that points to an IP for SSH?

For your information, I currently have my domain on Google Domains.

Graphics – Create a nine-region cluster diagram with tooltip to display labels for 2D points

I'm trying to create a cluster graph with nine regions using the tooltip to show labels associated with 2D points that are also displayed on them. I DO NOT want to use "FindClusters". I want to design my own cluster diagram, as explained below. The following dummy data consists of 2-D points, each point having an identifier named "NAICS" (NAICS stands for "North American Industry")
Classification system ").

mockdataWithNAICS = {{"29-1141", 186, 112}, {"41-2031", 123, 92}, {"41-1011", 65, 404}, {"43-4051", 108, 646}, {"31-1014", 643, 246},
        {"49-9071", 356, 363}, {"43-1011", 543, 381}, {"43-5081", 268, 674}, {"53-3032", 416, 653}, {"37-3011", 514, 428}, {"37-2012", 501, 58},
        {"33-9032", 441, 598}, {"35-2014", 633, 138}, {"29-2061", 414, 590}, {"53-3033", 98, 155}, {"35-3031", 179, 431}, {"49-3023", 93, 623},
        {"35-3021", 37, 578}, {"41-2011", 256, 237}, {"37-2011", 302, 50}, {"47-1011", 518, 2}, {"11-9111", 313, 294}, {"31-9092", 698, 136},
        {"43-3031", 608, 610}, {"43-6013", 562, 515}, {"13-2011", 415, 327}, {"21-1093", 191, 72}, {"41-4012", 212, 92}, {"41-3031", 546, 418},
        {"31-1011", 591, 49}, {"47-2031", 405, 526}, {"41-3021", 191, 297}, {"15-1151", 442, 445}, {"43-6011", 118, 185}, {"49-1011", 472, 402},
        {"53-7062", 348, 552}, {"43-4171", 409, 396}, {"43-6014", 348, 247}, {"53-1031", 629, 59}, {"47-2061", 589, 534}, {"27-1026", 22, 377},
        {"29-1069", 445, 74}, {"49-9021", 647, 539}, {"43-9061", 25, 543}, {"11-1021", 19, 165}, {"41-1012", 482, 199}, {"29-1062", 1, 68},
        {"43-4081", 217, 112}, {"41-3099", 663, 66}, {"11-3031", 329, 392}, {"53-7061", 357, 515}, {"35-2021", 488, 245}, {"31-9091", 318, 679},
        {"51-1011", 650, 349}, {"11-9051", 38, 145}, {"53-3031", 166, 691}, {"39-9021", 561, 127}, {"39-5012", 665, 362}, {"47-2111", 397, 532},
        {"43-3071", 326, 271}, {"29-2034", 361, 450}, {"35-9011", 12, 366}, {"29-1123", 16, 211}, {"15-1142",162, 662}, {"11-2021", 520, 164},
        {"29-2031", 339, 619}, {"25-2011", 263, 564}, {"41-4011", 551, 301}, {"29-2055", 76, 549}, {"29-2052", 285, 640}, {"13-2072", 370, 542},
        {"35-2012", 512, 547}, {"11-2022", 130, 154}, {"15-1132", 188, 274}, {"13-2052", 338, 587}, {"15-1199", 455, 5}, {"35-9031", 595, 472},
        {"17-2051", 648, 481}, {"11-9199", 430, 189}, {"39-3091", 29, 396}, {"47-2152", 412, 342}, {"29-1122", 582, 20}, {"11-9141", 276, 4},
        {"25-2021", 666, 617}, {"15-1134", 236, 334}, {"37-1011", 407, 664}, {"29-1063", 260, 278}, {"49-3031", 161, 354}, {"41-9022", 185, 144},
        {"41-9041", 551, 628}, {"25-2031", 529, 505}, {"29-2071", 548, 296}, {"29-1127", 373, 124}, {"21-1023", 473, 71}, {"29-1067", 489, 569},
        {"29-1071", 539, 277}, {"11-3121", 390, 348}, {"11-9021", 634, 20}, {"53-3041", 589, 258}, {"49-3021", 47, 206}};

L = Length(mockdataWithNAICS);
counter = Range(L);
counter = Range(L);
mock2Dvalues = {mockdataWithNAICS((#))((2)), mockdataWithNAICS((#))((3))} & /@ counter

The following measurements are required to create a raster:

minx = Round(Min(Table({mock2Dvalues((i))((1))}, {i, 1, L})));
maxx = Round(Max(Table({mock2Dvalues((i))((1))}, {i, 1, L})));

dx = Round(Subdivide(minx, maxx, 3));

miny = Round(Min(Table({mock2Dvalues((i))((2))}, {i, 1, L})));

maxy = Round(Max(Table({mock2Dvalues((i))((2))}, {i, 1, L})));

dy = Round(Subdivide(miny, maxy, 3));

gridpts = Tuples({dx, dy});

Use the following diagram to visualize the grid and all data points:

plt1 = ListPlot(gridpts, Frame -> True, PlotStyle -> Black, 
  GridLines -> {dx, dy}, Epilog :> {Blue, Point(mock2Dvalues)})

The following diagram shows the grid
Points using the tooltip to define the following nine regions:

plt2 = ListPlot(Tooltip(gridpts), Frame -> True, PlotStyle -> Black, 
  GridLines -> {dx, dy})

Here we define the nine cluster regions. We assign colors to the region name to map the 2D points to the regions to which they belong, depending on the color. The colors are arbitrary:

    magentaregion = Rectangle(gridpts((1)), gridpts((6)))

    orangeregion = Rectangle(gridpts((5)), gridpts((10)))

    cyanregion = Rectangle(gridpts((9)), gridpts((14)))

    redregion = Rectangle(gridpts((2)), gridpts((7)))

    blueregion = Rectangle(gridpts((6)), gridpts((11)))

    greenregion = Rectangle(gridpts((10)), gridpts((15)))

    lightgrayregion = Rectangle(gridpts((3)), gridpts((8)))

    yellowregion = Rectangle(gridpts((7)), gridpts((12)))

    blackregion = Rectangle(gridpts((11)), gridpts((16)))

Here the points are paid out by region, to which they belong:

remagentapts = Select(mock2Dvalues, # (Element) magentaregion &)

orangepts = Select(mock2Dvalues, # (Element) orangeregion &)

cyanpts = Select(mock2Dvalues, # (Element) cyanregion &)

redpts = Select(mock2Dvalues, # (Element) redregion &)

bluepts = Select(mock2Dvalues, # (Element) blueregion &)

greenpts = Select(mock2Dvalues, # (Element) greenregion &)

lightgraypts = Select(mock2Dvalues, # (Element) lightgrayregion &)

yellowpts = Select(mock2Dvalues, # (Element) yellowregion &)

blackpts = Select(mock2Dvalues, # (Element) blackregion &) 

The following diagrams are required to create the following final diagram:

plt3 = ListPlot(magentapts, Frame -> True, PlotStyle -> Magenta, 
   GridLines -> {dx, dy}, PlotRange -> {{minx, maxx}, {miny, maxy}}, 
   AspectRatio -> 1);

plt4 = ListPlot(orangepts, Frame -> True, PlotStyle -> Orange, 
   GridLines -> {dx, dy}, PlotRange -> {{minx, maxx}, {miny, maxy}}, 
   AspectRatio -> 1);

plt5 = ListPlot(cyanpts, Frame -> True, PlotStyle -> Cyan, 
   GridLines -> {dx, dy}, PlotRange -> {{minx, maxx}, {miny, maxy}}, 
   AspectRatio -> 1);

plt6 = ListPlot(redpts, Frame -> True, PlotStyle -> Red, 
   GridLines -> {dx, dy}, PlotRange -> {{minx, maxx}, {miny, maxy}}, 
   AspectRatio -> 1);

plt7 = ListPlot(bluepts, Frame -> True, PlotStyle -> Blue, 
   GridLines -> {dx, dy}, PlotRange -> {{minx, maxx}, {miny, maxy}}, 
   AspectRatio -> 1);

plt8 = ListPlot(greenpts, Frame -> True, PlotStyle -> Green, 
   GridLines -> {dx, dy}, PlotRange -> {{minx, maxx}, {miny, maxy}}, 
   AspectRatio -> 1);

plt9 = ListPlot(lightgraypts, Frame -> True, PlotStyle -> LightGray, 
   GridLines -> {dx, dy}, PlotRange -> {{minx, maxx}, {miny, maxy}}, 
   AspectRatio -> 1);

plt10 = ListPlot(yellowpts, Frame -> True, PlotStyle -> Yellow, 
   GridLines -> {dx, dy}, PlotRange -> {{minx, maxx}, {miny, maxy}}, 
   AspectRatio -> 1);

plt11 = ListPlot(blackpts, Frame -> True, PlotStyle -> Black, 
   GridLines -> {dx, dy}, PlotRange -> {{minx, maxx}, {miny, maxy}}, 
   AspectRatio -> 1);

This diagram shows the cluster diagram with nine regions with the points colored by the region to which they belong:

finalchart = Show({plt3, plt4, plt5, plt6, plt7, plt8, plt9, plt10, plt11})

At last; We make an association between the NAICS codes and their 2D points as follows:

mockdataWithNAICSlabels = 
  Association(#((1)) -> #((2 ;; 3)) & /@ mockdataWithNAICS);

Dataset(mockdataWithNAICSlabels)

My question is: how can I map the NAICS codes to their respective points BY COLOR REGION so that when I use tooltips; The NAICS codes are displayed for each item in the final chart above. Thanks!

Algorithms – Create a priority search tree to determine the number of points in the range [-inf, qx] X [qy, qy’] from a series of points sorted by y-coordinates

A priority search tree can be created for a set of points P in time O (n log (n)). However, if the points are sorted by the y-coordinates, it takes O (n). I find algorithms for constructing the tree if the points are not sorted.

I found a way to do this as follows:

  1. Construct a BST on the points.
    Since the points are sorted, it will take O (n) time

  2. Min-heap the BST on the x-coordinates
    This will take theta (n) time

The total time complexity will be O (n).

Is this a valid approach to creating a priority search tree in O (n) time?

Color – Transform 2D points into a regular grid or grid

I've generated a series of points in 2D by a DimensionReduce on a list of colors:

colors = RandomColor(100);
coords = DimensionReduce(colors, 2, Method -> "TSNE");
ListPlot(Thread(Style(coords, colors, PointSize -> .05)))

Enter image description here

However, I would like to arrange these points in a regular 2D grid (or other grid) while preserving the neighborhoods as much as possible to achieve the following:

Enter image description here

(This is just a model)

Is there a function to provide this transformation? Or do I rather write something to mix the colors in a grid to minimize the distances between the neighbors? Here is my rough attempt of such a process:

dm = DistanceMatrix(colors, DistanceFunction -> ColorDistance);
g = System`GridGraph({10, 10}, VertexSize -> .8);
adj = AdjacencyMatrix@g;
vneighbors = 
  GatherBy(Position(adj // Normal, 1), First)((;; , ;; , 2));
vlabels = Range(100);
Fold(SetProperty({#1, #2}, 
   VertexStyle -> colors((vlabels((#2))))) &, g, Range(100))

Enter image description here

Do(
  While({swapi, swapj} = RandomInteger({1, 100}, 2); swapi == swapj);
  cost = Total@
     dm((vlabels((swapi)), vlabels((vneighbors((swapi)))))) + 
    Total@dm((vlabels((swapj)), vlabels((vneighbors((swapj))))));
  swapcost = 
   Total@dm((vlabels((swapj)), vlabels((vneighbors((swapi)))))) + 
    Total@dm((vlabels((swapi)), vlabels((vneighbors((swapj))))));
  If(cost > swapcost, temp = vlabels((swapi)); 
   vlabels((swapi)) = vlabels((swapj)); vlabels((swapj)) = temp), 
  10000);
Fold(SetProperty({#1, #2}, 
   VertexStyle -> colors((vlabels((#2))))) &, g, Range(100))

Enter image description here

Is this the best solution to the problem? There has to be something cleverer that Mathematica can give me.

Adjustment – better for points with exaggerated deviations

Does the function have to go through all the points? If not, how about customizing instead of interpolating?

dados16 = {{10, 0.37}, {15, 0.47}, {20, 0.54}, {25, 0.61}, {30, 0.70}, {40, 0.80},{50, 0.90}, {60, 1.01}, {70, 1.10}, {80, 1.20}, {90, 1.31}, {100, 1.42}, {110, 1.53}};

fit = Fit(dados16, {1, x, x^(1/2)}, x)

0.104065 + 0.0701194 sqrt (x) + 0.00609645 x

Plot(fit, {x, 0, 110}, 
 Epilog -> {{Red, PointSize(.02), Point(dados16)}})

Enter image description here

Abstract Algebra – Why should I care if $ V $ is an open subset of $ R ^ n $ with a volume of $> 1 $, then there are 2 rational points $ p, q in V $ s.t. $ p-q in Z ^ n $?

Lemma. If $ V $ is a limited open subset of $ R ^ n $ with volume $> 1 $there are 2 rational points $ p, q in V, p neq q $ s.t. $ p-q in Z ^ n $?

$ textbf {Q:} $ Why should I be interested in rational points? $ p, q $ Here? Most of the time I've seen evidence of using the Minkowski theorem $ p, q in V $ without knowing $ p, q $ rational. This is a well-established version of the statement used to prove Minkowski's theorem, but it seems very plausible if you do not wiggle rationally $ p, q $ to get a bit rational $ p, q $, What are possible application of the above-mentioned lemma?

Ref. Siegel, Lectures on the Geometry of Numbers, Lec II, para. 2.

Live View – Does a manufacturer's EVF camera (or alternative firmware) implement multicolored / incorrect focus points and / or histogram positions?

It would be logical and helpful to use wrong colors (possibly superimposed on a b / w EVF display) to indicate how strong a peak contrast is. Alternatively, this could be a more precise way to For example, to display zone system zones at selected exposure (not just zone 9/10, as the usual "zebra" feature does).

Has a manufacturer already implemented such a function? Could it be realistically implemented in eg Magic Lantern?

Get 100 BMF points – Easy task

Get 100 BMF points when you join a telegram group and do 1-2 simple social media tasks (Twitter) through my Reference link !!

STEPS –

1) Go to this link and start the telegram bot

2) After launch, the bot requests a simple confirmation captcha and completes it to prove that you are human.

3) Complete all tasks defined by the bot (joining the telegram group, channel, main channel, connecting to Twitter …

Get 100 BMF points – Easy task