## What is the point of hiding a webpage until load?

I have noticed that whenever I go to namecheap.com, the page loads abnormally. Instead of the gradual loading like most websites, namecheap stays completely blank for a few seconds, and then suddenly everything appears. I got curious, and I looked at the page source and found this script:

(function(a,s,y,n,c,h,i,d,e){s.className+=' '+y;h.start=1*new Date;                                 h.end=i=function(){s.className=s.className.replace(RegExp(' ?'+y),'')};                                 (a[n]=a[n]||[]).hide=h;setTimeout(function(){i();h.end=null},c);h.timeout=c;                                 })(window,document.documentElement,'async-hide','dataLayer',4000,                                 {'GTM-544JFM':true}); 

Which goes through all the elements in the page, and removes the async-hide CSS class from them. There is also a CSS rule: .async-hide { opacity: 0 !important} that hides elements with this class.

To me, this seems very bad from a UX stance, but I am wondering if there is any good reason to do this?

## How to point abc.xxxexpress.com on another server to mydomain.com on my server?

The domain name of my business partner in China is xxxexpress.com. This domain is for their server in China.

## bare hand integration of euler class of blow up of a complex surface at a point

let X be a product of two Riemann surface of genus $$\geq1$$ and p be a point of X. If X’ is the blow up of X at p. Let E denote the exceptional divisor I am aware that the the second Chern number of X’ since it’s the euler characteristic. I would to verify this by integration of the second chern class. Up to now I try as follows:

Let W be a tubular neighborhood of the exceptional divisor E. Let N be the normal bundle of E $$c_2(X’) = p^*(c_2(X))+c_2(TN)=p^*(c_2(X))+ c_1(TE)c_1(N)$$ Now I am not sure how to proceed. $$\int_{X’}p^*(c_2(X))+ c_1(TE)c_1(N)$$ This reduces to $$\int_{X’}c_1(TE)c_1(N)$$ which I tend to understand as the intersection number. But usually one performs this computation in $$\mathbb{C}P^n$$, which is not the case here. Could anybody give me some help? Thanks.

## Determining Sample Spaces for Sylvester’s Four Point Problem

I don’t have any background in geometric probability, so may I ask for forgiveness if the following is wrong or doesn’t make sense:

Assumptions:

• no three sampled points are collinear
• the (naive) geometric probability in euclidean space is equal to the ratio of lengths or areas
• the probabilites do not change under isometric transformations and/or uniform scaling
• the probability of a set $$P4:=\lbrace p_1,\ p_2,\ p_3,\ p_4\rbrace$$ of four points being in convex configuration in the euclidean plane is independent of the order in which the elements are drawn by the sampling process; that in turn means that we assume that
• $$\lbrace p_1,\ p_2,\ p_3\rbrace$$ resembles the corners of $$T_{max}$$, the triangle of largest area
• $$\lbrace p_1,\ p_2,\ p_3\rbrace$$ have been drawn uniformly from the boundary of their circumcircle, which has implications on the probability of encountering $$T_{max}$$ with specific values for the pair of smallest central angles.
• the smallest circle enclosing $$P4$$ is the unit circle centered at the origin
• the longest side of $$T_{max}$$ is bisected by the non-negative part of the x-axis

Under the above assumptions the probability of encountering a convex quadrilateral equals the blue area divided by the blue plus red area in the images below:

the sampling area in case of acute $$T_{max}$$ equals the entire unit disk

the sampling area in case of acute $$T_{max}$$ equals the unit disk with a notch

The notch in the case of obtuse $$T_{max}$$ is owed to the assumption that the first three points resemble $$T_{max}$$ which implies that points outside $$T_{max}$$ that generate a deltoid configuration would contradict the maximality of the area of $$T_{max}$$

If the above makes sense, then the probability of four points being in convex configuration could be calculated by integrating over the ratios of the blue areas over the entire sampling area as defined by the $$T_{max}$$ multiplied with the probability of $$T_{max}$$ as a consequence of uniform sampling on the boundary of the unit circle.

Questions:

• have similar ways of defining the sample spaces for Sylvester’s Four Point Problem already been described or investigated?
• what are objections against the proposed against the proposed definition of the available sample space on basis of $$T_{max}$$?

Remark:

in case of obtuse $$T_{max}$$ the area of the sample space can be calculated on basis of angles $$\alpha$$ and $$\beta$$ that are adjacent to the longest side of $$T_{max}$$ as follows, keeping in mind that that longest edge is the diameter of the unit circle:

• the area of the lower blue half-disk equals $$\ \frac{\pi}{2}\$$
• the area $$\ A_{\alpha}\$$ of the union of $$\ T_{max}\$$ with the blue region opposite to angle $$\ \alpha$$ equals $$\ \alpha+\sin(\alpha)cos(\alpha)\$$ and analogous $$\ A_{\beta}\$$for angle $$\ \beta\$$
• the area of $$\ A_{T_{max}}\$$ of $$\ T_{max}\$$ equals $$\ \frac{1}{\cot(\alpha)+\cot(\beta)}\$$

The area of the sampling area for obtuse $$\ T_{max}\$$ then equals $$\ \frac{\pi}{2}+A_{\alpha}+A_{\beta}-A_{T_{max}}$$

## At what point is brevity no longer a virtue?

A recent bug fix required me to go over code written by other team members, where I found this (it’s C#):

return (decimal)CostIn > 0 && CostOut > 0 ? (((decimal)CostOut - (decimal)CostIn) / (decimal)CostOut) * 100 : 0; 

Now, allowing there’s a good reason for all those casts, this still seems very difficult to follow. There was a minor bug in the calculation and I had to untangle it to fix the issue.

I know this person’s coding style from code review, and his approach is that shorter is almost always better. And of course there’s value there: we’ve all seen unnecessarily complex chains of conditional logic that could be tidied with a few well-placed operators. But he’s clearly more adept than me at following chains of operators crammed into a single statement.

This is, of course, ultimately a matter of style. But has anything been written or researched on recognizing the point where striving for code brevity stops being useful and becomes a barrier to comprehension?

The reason for the casts is Entity Framework. The db needs to store these as nullable types. Decimal? is not equivalent to Decimal in C# and needs to be cast.

## Can wireshark can capture the exact payload and end point of API used by my Mobile APP?

I was just wondering if someone having a total control over his/her network, is running my mobile app. Also wireshark is capturing every requests made using the network. My app is calling API Endpoint like http://bob.com/alise/param1/param2 and also passing the HTTP parameters.

Can wireshark is capable to capture the network requests like this, Is url is visible?

Also is it possible for someone to track the HTTP parameters I am passing.

Is it plain text if I am not using HTTPS? what is its just HTTP call?

if not using wireshark,is there any other way to capture network calls made by app (android/iOS/PhoneGap/Ionic)?

## Price point for this product?

I was thinking of launching a server line that would be a budget service.

This line of servers would have little restrictions compared to o… | Read the rest of http://www.webhostingtalk.com/showthread.php?t=1768593&goto=newpost

## Converting between CSS pixel, iOS point, and Android dp

I’m developing a web application that will be viewed on mobile devices and I need to determine how large in CSS pixels to make the touch targets.

The Web Content Accessibility Guidelines (WCAG) 2.1 success criterion 2.5.5 requires touch targets of at least 44 x 44 CSS pixels. Apple’s Human Interface Guidelines (HIG) for iOS recommends 44 x 44 points and Google’s Material Design guidelines recommends 48 x 48 dp.

I’d like to compare the HIG and Material Design guidelines to WCAG. How can I compare CSS pixels, iOS points, and Android dp? Preferably, I’d like to learn how to convert iOS points and Android dp to CSS pixels.