I've spent all my morning trying to get rid of all fireflies in the final render, but the result still shows too much noise. How can I fix it (without changing materials)?  Thank you.
I know that this question is a bit old or maybe overdone, but I'm running the latest edition of Blender and when I use the Cycles engine I get really fuzzy results and I can't find a simple, non-compromising solution to the quality.  I don't know exactly what I'm doing wrong here, but I'm sure others share my frustration.  Leaving everything in default settings, I started a new project and switched to the Cycles render.  I duplicated the cube and set colors for two Diffuse BSDF shaders, then went ahead and made walls and a floor all sharing the same diffuse shader.  I made two windows to emit blue light.  The screenshot has gaps between the walls because of the setup:  I'm aware that it's considered very incomplete and many have come to terms with some amount of noise, but is this normal?  Furthermore, with default settings or not, I can't change the amount of noise produced.  Supposedly, using "progressively refine" in the rendering tab should make it refine itself indefinitely, however Cycles only counts to 10 refines and then stops, creating a render which is a pixel-perfect match of the ordinary rendering method when it's completed.  I understand that there are tricks that can be used to reduce noise, but at the cost of extra processing time and usually small quality conflicts, like blurring.  My question essentially boils down to is this Cycles' current limit?
This question was , but I think that the only answer didn't quite understand the question so I thought I'd try again. I'm writing a paper in which I need to have references show up in two places:  a special list of &quot;key references&quot; in its own section, and a bibliography containing the rest of the references that were cited in the text. I thought that bibentry would do it, so the document looks something like: \documentclass[11pt]{article} \usepackage{natbib} \usepackage{bibentry} \nobibliography*  \begin{document}  \section{Amazing idea}  Chicken chicken chicken \citep{chicken:2010fj}.  Chicken chicken, chicken \citep{turkey:2010jk}.  \section{Key References}  \begin{enumerate} \item \bibentry{lemur:2009ii} \item \bibentry{gibbon:2011jk} \end{enumerate}  \bibliographystyle{apalike} \bibliography{amazing.bib} \end{document}  Unfortunately, this doesn't quite do it, as the resulting document presents the lemur and gibbon bibentry references correctly in the &quot;Key References&quot; section, but also prints them in the bibliography at the end of the document.  Ideally, the only entries in the bibliography would be the chicken and turkey \citep references.  I'd like it to look like:  Key references  Lemur, L. (2009). Lemur, 12(4):1-7. Gibbon, G. (2011). Gibbon, 135:1002-1009.  References Chicken, C. (2010). Chicken. Chicken Press, Coop. Turkey, T. (2010). Turkey, 7(4):E231.  What it actually looks like:  Key references  Lemur, L. (2009). Lemur, 12(4):1-7. Gibbon, G. (2011). Gibbon, 135:1002-1009.  References Chicken, C. (2010). Chicken. Chicken Press, Coop. Gibbon, G. (2011). Gibbon, 135:1002-1009. Lemur, L. (2009). Lemur, 12(4):1-7. Turkey, T. (2010). Turkey, 7(4):E231.  Is there any way to achieve this with the current method, or is there some other (possibly easier!?) way to do this? How will multiple GPUs improve Cycles' rendering performance? I have an NVIDIA Titan GPU and I'm able to add another card, but I wonder whether  Blender will benefit from more than one GPUs?  Should I do it as SLI or should I just do it as an added card? I just need some help in understanding how Blender will use either SLI or a second GPU.  Note that I only use Cycles. How to calculate polygon areas and perimeters using QGIS? Using QGIS, if I load a polygon layer and make a new column, how do I fill that column with the:   area of each polygon and another column with length of the perimeter of each polygon? Birthday Problem applied to collisions I'm trying to extend the birthday problem to detect collision probability in a hashing scheme. Here is my problem. I use the letters and numbers [A-Z][a-z][0-9] to make a set of keys by randomly choosing from that set 10 times and concatenating them all together. So A24cv30kf2 could be one key for example. This implies that the base set has $62^{10}$ possible keys. Next, I want to compute the probability that I would see a collision if I picked a key from that set and issued it to $n$ users. If $M$ represents the size of the set (= $62^{10}$) and $P(M,n)$ the probability then  $$P(M,n) = \frac{M!}{M^{n}(M-n)!}$$  This follows by replacing 365 in the well known birthday problem shown   What I want to understand is the following: I want to check and see if my choice of concatenating 10 is right, when I know that my $n$ is of the order of a few millions and my $1 - P(M,n)$ needs to be less than 0.0001 or some small number. The large values of M &amp; n make the above intractable to compute. Is there an easier way to compute this? What is actually the characteristic length in fluid dynamics description? In many fluid-dynamics models and quantities (, ... generally various  descriptions) there is a parameter (usually $L$ or $\ell$) descripted as characteristic linear dimension or characteristic length. What is that in intuitive and physical meaning? I understand the typical examples ( etc.) but I can't see so far any systematic generalisation.  What is the definition? (Or, maybe: What is its role during the derivation?) How do I set read/write permissions my hard drives? Find the smallest disk in complex plane containing the eigenvalues of a matrix (NBHM 2012) Consider the given matrix  $$\left[\begin{matrix}1&amp; -2&amp; 3&amp; -2 \\1&amp; 1&amp; 0&amp; 3\\-1&amp; 1&amp; 1&amp; -1\\0&amp; -3&amp; 1&amp; 1&amp;\end{matrix}\right]$$  Find out the smallest disk like ($|z-1| &lt; r$ ) in the complex plane containing the eigenvalues of the given matrix.  One easiest method is to find out the eigenvalues from the characteristic equation of the matrix. I can do it so no problem with it.   I am looking for a method to solve the problem without the above method that can be evaluated in very short time. This type of method is possible, I believe.  Thank you for your help $$\sum_{n = 1}^\infty \frac{(-1)^n}{p_n}$$ where $p_n$ is the $n$th prime.  I have computed this to 10000 rather than infinity. My results suggest that convergence does happen but it's very slow. But I can't even be sure about the first few digits: $-0.26959$?  I have looked at the "questions that may already have your answer" and some of the "similar questions," but they involve somewhat different formulas. How can an encryption scheme be indistinguishable with multiple messages but vulnerable to an adaptive chosen-plaintext attack? In order to be indistinguishable with respect to multiple messages, wouldn't the scheme need to be nondeterministic? How would this then possibly be vulnerable to an adaptive CPA? Prove that   $\dfrac {\left( 5^{125}-1\right)}{\left( 5^{25}-1\right)}$ is composite number using number theory. Do not use calculator or Wolfram alpha or anything like that. Elastic net penalty I have a question about the elastic net penalty as implemented in glmnet in R compared to the original paper by Zou and Hastie (2005). In glmnet the penalty is listed as   $$ (1-\alpha)/2||\beta||_2^2+α||\beta||_1. $$  but in the paper it is  $$(1-\alpha)||\beta||_1 + \alpha||\beta||_2^2.  $$  Does anyone know where the factor $\frac12$ some from? (Never mind the fact that the $\alpha$'s were swapped between the two parameterisations.) In both cases the penalties are multiplied by $\lambda$, but what are the mathematical/technical arguments for not using a simple convex combination of the lasso and ridge penalties? Neutrino Oscillations and Conservation of Momentum I would like to better understand how neutrino oscillations are consistent with conservation of momentum because I'm encountering some conceptual difficulties when thinking about it. I do have a background in standard QM but only rudimentary knowledge of particle physics.   If the velocity expectation value of a neutrino in transit is constant, then it would appear to me that conservation of momentum could be violated when the flavor eigenstate at the location of the neutrino source is different from that at the location of the interaction, since they are associated with different masses.  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When I use logistic regression, the prediction is always all '1' (which means good loan).  I have never seen this before, and do not know where to start in terms of trying to sort out the issue.  There are 22 columns with 600K rows.  When I decrease the # of columns I get the same result with logistic regression.   Why could the logistic regression be so wrong?  **Actual from the data**  0 :   41932  1 :   573426  **Logistic regression output**   prediction for 1 when actually 0: 41932 prediction for 1 when actually 1:573426  A**s you can see, it always predicts a 1**   **Random forests does better:**  actual 0, pred 0 : 38800  actual 1, pred 0 : 27  actual 0, pred 1 : 3132 actual 1, pred 1 : 573399 Show that $a^{q-1} \equiv 1 \pmod{pq}$ Assume that $p$ and $q$ are distinct add primes such that $p-1\mid q-1$. 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