Hey, u have come so far across, you deserve an easy one now !!!
A professor thinks of 2 consecutive numbers between 1 & 10 (both inclusive). Student "A" knows one number & "B" knows another.
while they chat in lunchtime, i hear them
A says: I do not know your number
B says: Neither do i know your number
A says: Now i know your number
You have to find the possible (pair of numbers) (A,B), if none of them are lying
Hint: Suppose the answer is (2,1),(7,6),(4,5),(2,3) , You have to enter the next page's name as 21234576 (i.e. the pair sums in ascending order & pair order preserved)


class: center, middle, inverse, title-slide # Module 03 ## MPA 6010 ### Ani Ruhil ### (updated: 2018-11-10) ---  # Agenda 1. Sampling distributions 2. Interval estimation 3. Student's `t` distribution --- class: inverse, middle, center # Sampling --- `Sampling` is all about the science of drawing samples Many ways to do it but at its most basic, we could say it is about upholding two principles: (1) Each and every observation in the population has the same chance of being drawn into the sample (2) Each observation is drawn into the sample independently of all other observations Samples that meet criteria (1) and (2) are `simple random samples` Draw a distinction between `finite populations` (where we know the population size) and `infinite populations` (where we do not the population size) * Finite Population: Draw a sample of size `\(n\)` from the population such that each possible sample of size `\(n\)` has an identical probability of selection * We may sample with replacement (recommended), or * sample without replacement (the more common -- but not necessarily better -- approach) * Infinite Population: We sample such that * Each element is selected independently of all other elements * Each element has the same probability of being selected --- .pull-left[ A population of four numbers -- 2, 4, 6, and 8. All possible unique samples with `\(n = 2\)`? What sample means shows up most often? So the next time I draw a sample of `\(n = 2\)` from this population, what sample mean should I `expect` to see? Formally, the Sampling Distribution of `\(\bar{x}\)` is the probability distribution of all possible values of the sample mean `\(\bar{x}\)` `Expected Value` of `\(\bar{x}\)` is `\(E(\bar{x})=\mu\)` and Standard Deviation of `\(\bar{x}\)` is ... `Standard Error` of `\(\bar{x}\)`, calculated as `\(\sigma_{\bar{x}}=\dfrac{\sigma}{\sqrt{n}}\)` ] .pull-right[

An Example of Sampling Distributions

  Sample No. 	  x1 	  x2 	  Mean

  1 	  2 	  2 	  2

  2 	  2 	  4 	  3

  3 	  2 	  6 	  4

  4 	  2 	  8 	  5

  5 	  4 	  2 	  3

  6 	  4 	  4 	  4

  7 	  4 	  6 	  5

  8 	  4 	  8 	  6

  9 	  6 	  2 	  4

  10 	  6 	  4 	  5

  11 	  6 	  6 	  6

  12 	  6 	  8 	  7

  13 	  8 	  2 	  5

  14 	  8 	  4 	  6

  15 	  8 	  6 	  7

  16 	  8 	  8 	  8

 ] --- ## The Human Genome Let us see the length of the [human genome](http://phylo.bio.ku.edu/biostats/geneLenDemo.html)

Population Distribution of Human Gene Lengths

 --- ## `The Standard Error` A gauge of how much error should we expect to make because we are using a sample instead of population `$$\sigma_{\bar{x}} = \dfrac{\sigma}{\sqrt{n}}$$` Meaning? ... the average distance of a sample mean from the population mean for all samples of a given sample size drawn from a common population with standard deviation of `\(\sigma\)` Two things drive it ... (i) how much variability there is in the population, i.e., the population standard deviation `\((\sigma)\)`, and (ii) how large a sample we draw, i.e., `\((n)\)` Cannot influence `\(\sigma\)` but `\(n\)` is under our control. All else being equal, (i) the larger the `\(n\)` the smaller will `\(\sigma_{\bar{x}}\)` be, and (ii) the larger the `\(\sigma\)` the larger will `\(\sigma_{\bar{x}}\)` be --- ## `The Central Limit Theorem` > For any population with mean `\(\mu\)` and standard deviation `\(\sigma\)`, the distribution of sample means for samples of size `\(n\)` will approach the normal distribution with a mean of `\(\mu\)` and standard error of `\(\sigma_{\bar{x}}=\dfrac{\sigma}{\sqrt{n}}\)` as `\(n \rightarrow \infty\)` Since you never see the population, it turns out that if we have `\(n \geq 30\)` we can rely on this theorem to carry out some basic statistical tests with the `z-score` distribution --- Tuition costs at state universities is `\(\sim \mu=4260; \sigma=900\)`. Sample of `\(n=50\)` is drawn at random. (1) What is sampling distribution of mean tuition costs? `\(\mu = 4260; \sigma_{\bar{x}}=\dfrac{\sigma}{\sqrt{n}}=\dfrac{900}{\sqrt{50}}= 127.28\)` (2) What is `\(P(\bar{x})\)` within 250 of `\(\mu\)`? Sampling distribution is `\(\mu = 4260; \sigma_{\bar{x}} = 127.28\)` Within `\(250\)` implies `\(P(4010 \leq \mu \leq 4510)\)` `$$z_{4510}=\dfrac{4510-4260}{127.28}=1.96$$` `$$z_{4010} = \dfrac{4010-4260}{127.28} = - 1.96$$` `$$P(4010 \leq \mu \leq 4510) = 0.95$$` (3) What is `\(P(\bar{x})\)` within `\(100\)` of `\(\mu\)`? ... `\(P(4160 \leq \mu \leq 4360) = 0.5704\)` --- Average annual cost of automobile insurance is `\(\sim \mu=687; \sigma=230\)` and `\(n= 45\)` `\(\sigma_{\bar{x}}=\frac{\sigma}{\sqrt{n}}=\frac{230}{\sqrt{45}}= 34.29\)` Hence the sampling distribution is `\(\mu = 687; \sigma_{\bar{x}} = 34.29\)` What is `\(P(\bar{x})\)` within `\(100\)`? We need `\(P(587 \leq \mu \leq 787)\)` * `\(z_{787}=\frac{787-687}{34.29}=2.92\)` * `\(z_{587} = \frac{587-687}{34.29} = -2.92\)` `\(\therefore P(587 \leq \mu \leq 787) = 0.9964\)` What is `\(P(\bar{x})\)` within `\(25\)`? We need `\(P(662 \leq \mu \leq 712)\)` * `\(z_{712}=\frac{712-687}{34.29}=0.73\)` * `\(z_{662} = \frac{662-687}{34.29} = -0.73\)` `\(\therefore P(662 \leq \mu \leq 712) = 0.5346\)` If insurance agency wants to be within 25, would you recommend a larger sample? Yes, since `\(n = 45\)` is unlikely to yield the desired result --- ## `What about proportions?` The theory of sampling distributions applies to proportions as well Sample proportion is `\(\bar{p}=\dfrac{x}{n}\)` and on average `\(E(\bar{p}) = p\)`. The standard error (aka the standard deviation of `\(\bar{p}\)`) is `\(\sigma_{\bar{p}}={\sqrt{\dfrac{p(1-p)}{n}}}\)` We can assume sampling distribution of `\(\bar{p}\)` is approximately normally distributed if `\(np \geq 5\)` AND `\(n(1-p) \geq 5\)` (both conditions must be met)1 **Note:** When population proportion `\(p\)` is unknown, standard error is calculated as `\(s_{\bar{p}}={\sqrt{\dfrac{\bar{p}(1-\bar{p})}{n}}}\)` .footnote[[1] Technically, for a finite population, `\(\sigma_{\bar{p}}={\sqrt{\dfrac{N-n}{N-1}}}{\sqrt{\dfrac{p(1-p)}{n}}}\)` and for an infinite population `\(\sigma_{\bar{p}}={\sqrt{\dfrac{p(1-p)}{n}}}\)` ] --- The Governor's office reports 56% of US households have internet access. If `\(p=0.56\)` and `\(n=300\)`, what is: (1) The sampling distribution of `\(\bar{p}\)`? Given `\(n=300; p=0.56\)`, `\(\sigma_{\bar{p}}={\sqrt{\dfrac{0.56(1-0.56)}{300}}}=0.0287\)` (2) What is `\(Probability (\bar{p})\)` within `\(\pm 0.03\)`? We are looking for the interval `\((0.53,0.59)\)` and so we calculate: `$$z_{0.59}=\dfrac{0.59-0.56}{0.0287}=1.035$$` `$$z_{0.53}=\dfrac{0.53-0.56}{0.0287}=-1.035$$` and hence `\(P(0.53 \leq \bar{p} \leq 0.59)=0.7062\)` (3) Calculate (1) and (2) for `\(n=600\)` and `\(n=1000\)`, respectively --- class: inverse, middle, center # Point Estimates --- Statistics calculated for a population are `population parameters` (for e.g., `\(\mu\)`; `\(\sigma^{2}\)`; `\(\sigma\)`) Statistics calculated for a sample are `sample statistics` (for e.g., `\({\bar{x}}\)`; `\(s^{2}\)`; `\(s\)`) Desirable point estimators have the following properties: (1) The sampling distribution of the point estimator is `unbiased` -- it is centered around the population parameter (2) The point estimator is `efficient` -- it has the smallest possible variance (3) The point estimator is `consistent` -- it tends towards the population parameter as the sample size increases The easiest way to understand bias and efficiency is via the following graphic [authored by Sebastian Raschka](https://sebastianraschka.com):  --- ## Interval Estimates Although point estimates (for e.g., `\(\bar{x}\)`) tell us the expected value of a parameter based on a sample statistic, we know samples are not all the same even though they were drawn randomly, and have the same sample size. So we try to ask: How much confidence can we place in the point estimate? The `margin of error` helps us answer this question * Interval Estimate of `\(\mu = \bar{x} \pm \text{ Margin of error}\)` * Interval Estimate of `\(p = \bar{p} \pm \text{ Margin of error}\)` * Margin of Error `\(= \left( z \times \sigma \right)\)` In any normal distribution of sample means with mean `\(\mu\)` and standard deviation `\(\sigma\)`, the following statement is true: * Over all samples of size `\(n\)`, the probability is `\(0.95\)` for the event in question * That is, `\(-1.96\sigma \leq \bar{x}-\mu \leq +1.96\sigma\)` Rearranging the former identity yields: `\(\bar{x}-1.96\sigma \leq \mu \leq \bar{x} +1.96\sigma\)` This says that of all samples of identical size `\(n\)`, there is a 95% probability that `\(\mu\)` falls within `\(\bar{x} \pm 1.96\sigma\)` The range of values within `\(\bar{x} \pm 1.96\sigma\)` yield the 95% **confidence interval (CI)** of `\(\mu\)`, and the two boundaries are the 95% `confidence interval limits` --- Be careful! We aren't saying `\(\mu\)` falls within a known interval. Rather, that in repeated random sampling 95% of Confidence Intervals will include `\(\mu\)`  ---  --- ### Interval Estimate with Known `\(\sigma\)` `\(\bar{X} \pm z_{\alpha/2}\left(\dfrac{\sigma}{\sqrt{n}}\right)\)` where `\(z_{\frac{\alpha}{2}}\)` is the `\(z\)` value yielding an area of `\(\dfrac{\alpha}{2}\)` in the upper tail of the standard normal distribution | Confidence Level | `\(\alpha\)` | `\(\dfrac{\alpha}{2}\)` | `\(z_{\frac{\alpha}{2}}\)` | | :-- | :-- | :-- | :-- | | 90% | 0.10 | 0.050 | 1.645 | | 95% | 0.05 | 0.025 | 1.960 | | 99% | 0.01 | 0.005 | 2.576 | --- ## Confidence Intervals `$$\mu = \bar{x} \pm \text{ Margin of error}$$` When you calculate the interval estimate for a specific `\(z\)`, you get two values ... `$$\bar{x} - (z \times \sigma)$$` `$$\bar{x} + (z \times \sigma)$$` These are the lower and upper confidence interval limits, respectively If `\(z = 1.645\)` you get the 90% confidence intervals If `\(z = 1.960\)` you get the 95% confidence intervals If `\(z = 2.576\)` you get the 99% confidence intervals --- Let `\(\bar{x}=32; \sigma=6\)`. Let also `\(n=50\)`. (1) What is the standard error? `$$\sigma_{\bar{x}}=\dfrac{\sigma}{\sqrt{n}}=\dfrac{6}{\sqrt{50}}=0.8485$$` (2) Find `\(90\%\)`CI `$$= 32 \pm 1.645(0.8485)=32 \pm 1.3957=(30.6043; 33.3957)$$` (3) Find `\(95\%\)`CI `$$= 32 \pm 1.960(0.8485)=32 \pm 1.6630=(30.3370; 33.6630)$$` (4) Find `\(99\%\)`CI `$$= 32 \pm 2.576(0.8485)=32 \pm 2.1857=(29.8143; 34.1857)$$` The more confident you want to be, the wider the interval will be --- Let household mean television viewing time be `\(\sim \bar{x}=8.5; \sigma=3.5\)` and `\(n=300\)` Therefore `\(\sigma_{\bar{x}}=\dfrac{\sigma}{\sqrt{n}}=\dfrac{3.5}{\sqrt{300}}=0.2020\)` (1) Find the `\(95\)`% CI `$$= 8.5 \pm 1.960(0.2020)=8.5 \pm 0.3959 = (8.1041; 8.8959)$$` (2) What is the `\(99\)`% CI? `$$= 8.5 \pm 2.576(0.2020) = 8.5 \pm 0.520352 = (7.979648; 9.020352)$$` --- ## Confidence Intervals for Proportions Since we don't know the population proportion `\(p\)` we calculate the standard error `\(s_{\bar{p}}\)` as `$$s_{\bar{p}} = {\sqrt{\dfrac{\bar{p}(1-\bar{p})}{n}}}$$` Several formulas but most common are (1) Agresti-Coull confidence interval -- first calculate `\(p^{'} = \dfrac{x + 2}{n + 4}\)` and then calculate `$$p^{'} - z_{\alpha/2}\sqrt{ \dfrac{p^{'} \left(1-p^{'} \right) } {n+4} } \leq p \leq p^{'} + z _{\alpha/2}\sqrt{ \dfrac{p^{'} \left(1-p^{'} \right) } {n+4} }$$` (2) Wald confidence intervals are calculated with the usual standard error and as: `$$\bar{p} - z_{\alpha/2}\sqrt{ \dfrac{\bar{p} \left(1-\bar{p} \right) } {n} } \leq p \leq \bar{p} + z_{\alpha/2}\sqrt{ \dfrac{\bar{p} \left(1-\bar{p} \right) } {n} }$$` --- Agresti-Coull and Wald too conservative (i.e., too wide) when (i) `\(n\)` is small or (ii) `\(p\)` is close to `\(0\)` or `\(1\)` In these situations Wilson's Interval is preferred `$$\dfrac{n}{n + z^2_{\alpha/2}}\left[ \left(\bar{p} + \dfrac{z^2_{\alpha/2}}{2n} \right) \pm z_{\alpha/2} \sqrt{\dfrac{\bar{p}(1-\bar{p})}{n} + \dfrac{z^2_{\alpha/2}}{4n^2}} \right ]$$` which converts to `$$\dfrac{n}{n + z^2_{\alpha/2}}\left[ \left(\bar{p} + \dfrac{z^2_{\alpha/2}}{2n} \right) - z_{\alpha/2} \sqrt{\dfrac{\bar{p}(1-\bar{p})}{n} + \dfrac{z^2_{\alpha/2}}{4n^2}} \right ] \leq p \leq \dfrac{n}{n + z^2_{\alpha/2}}\left[ \left(\bar{p} + \dfrac{z^2_{\alpha/2}}{2n} \right) + z_{\alpha/2} \sqrt{\dfrac{\bar{p}(1-\bar{p})}{n} + \dfrac{z^2_{\alpha/2}}{4n^2}} \right ]$$` --- #### `An interesting issue` .pull-left[

The Binomial Proportion Revisited

  Proportion 	  1 - Proportion 	  (Proportion) * (1 - Proportion)

  0.00 	  1.00 	  0.0000

  0.05 	  0.95 	  0.0475

  0.10 	  0.90 	  0.0900

  0.15 	  0.85 	  0.1275

  0.20 	  0.80 	  0.1600

  0.25 	  0.75 	  0.1875

  0.30 	  0.70 	  0.2100

  0.35 	  0.65 	  0.2275

  0.40 	  0.60 	  0.2400

  0.45 	  0.55 	  0.2475

  0.50 	  0.50 	  0.2500

  0.55 	  0.45 	  0.2475

  0.60 	  0.40 	  0.2400

  0.65 	  0.35 	  0.2275

  0.70 	  0.30 	  0.2100

  0.75 	  0.25 	  0.1875

  0.80 	  0.20 	  0.1600

  0.85 	  0.15 	  0.1275

  0.90 	  0.10 	  0.0900

  0.95 	  0.05 	  0.0475

  1.00 	  0.00 	  0.0000

 ] .pull-right[ Note what happens when `\(\bar{p}=0\)` and `\(\bar{p}=1\)`: Your numerator in the formula `\(s_{\bar{p}} = \sqrt{\dfrac{\bar{p}(1-\bar{p})}{n}}\)` is driven to `\(0\)` and hence so is the standard error Where the numerator `\(\bar{p}(1 - \bar{p})\)` is the widest is when `\(\bar{p}=0.50\)`; as `\(\bar{p}\)` moves away towards `\(0\)` or `\(1\)`, `\(\bar{p}(1 - \bar{p})\)` steadily decreases At the extremes, the Wilson will do well but not so the Wald or Agresti-Coull Use Wilson when `\(p\)` is close to `\(0\)` or `\(1\)` or if the sample size is `small` and the Agresti-Coull method when the sample size is `large` and `\(p\)` is not close to `\(0\)` or `\(1\)` ] --- In a survey of a small town in Vermont, voters were asked if they would support closing the local schools and sending the local kids to schools in the neighboring town to more efficiently utilize local tax dollars. A random sample of 153 voters yields 43.1% favoring school closures. What is the 95% confidence interval for the population proportion? Use the Wald approach. We have `\(\bar{p}=0.431\)` and `\(n = 153\)`, yielding a standard error of `$$s_{\bar{p}} = \sqrt{\dfrac{0.431(1-0.431)}{153}} = 0.04003585$$` Given `\(z = \pm 1.96\)` the confidence interval is: `$$0.431 \pm 1.96(0.04003585) = 0.3525297 \text{ and } 0.5094703$$` Loosely interpreted as: We are about 95% confident that the population proportion of the town's voters that support school closures lies in the interval given by 0.3525 and 0.5094 If you need to, you can [use this online calculator for exact confidence intervals](http://statpages.info/confint.html) --- class: inverse, middle, center # Student's `t-distribution`  --- Extension of the large sample theory of sampling distributions to small samples Assume `\(X \sim N(13,2)\)`. If `\(x = 12, n=16\)`, what is `\(z_{x=12}\)`? It turns out that `\(z_{x=12}=2\)`. Now, if we don't know `\(\mu\)` and `\(\sigma\)` and draw three samples of `\(n=16\)` each but `\(s\)` differs in each sample? What is `\(z\)` in each?

The t distribution: An Example

  Sample No. 	  Std. Dev. 	  Std. Error 	  z-score

  1 	  4 	  1.00 	  -1

  2 	  2 	  0.50 	  -2

  3 	  1 	  0.25 	  -4

 Notice what happens here: Each sample yields a different value of `\(z\)` for the same `\(x=12\)` simply because each sample is generating differing `\(\bar{x}\)` and `\(s\)`. If you use `\(t\)` instead of `\(z\)` you gain some precision `$$t = \dfrac{x - \bar{x}}{s_{\bar{x}}}$$` where `\(s_{\bar{x}} = \dfrac{s}{\sqrt{n}}\)` and `\(t\)` is distributed with `\(n-1\)` degrees of freedom --- ### Various `t` distributions

Student's t versus the Standard Normal z Distribution

 --- [See this interactive applet](http://rpsychologist.com/d3/tdist/) * Use `\(t\)` distribution whenever `\(\sigma\)` is unknown and hence `\(s\)` must be used, `regardless of the sample size` * Use the `\(t\)` distribution whenever `\(n < 30\)` even if `\(\sigma\)` is known --- Find the following `\(t\)` score(s): (1) `\(t\)` leaves `\(0.025\)` in the Upper Tail with `\(df=12\)` Answer: `\(t=2.179\)` (2) `\(t\)` leaves `\(0.05\)` in the Lower Tail with `\(df=50\)` Answer: `\(t=1.676\)` (3) `\(t\)` leaves `\(0.01\)` in the Upper Tail with `\(df=30\)` Answer: `\(t=2.457\)` (4) `\(90\%\)` of the area falls between these `\(t\)` values with `\(df = 25\)` Answer: `\(t= \pm 1.708\)` (5) `\(95\%\)` of the area falls between these `\(t\)` values with `\(df = 45\)` Answer: `\(t= \pm 2.104\)` --- Simple random sample with `\(n=54\)` yielded `\(\bar{x} = 22.5\)` and `\(s=4.4\)`. (1) Calculate the standard error. `$$s_{\bar{x}}=\dfrac{s}{\sqrt{n}}=\dfrac{4.4}{\sqrt{54}}=\dfrac{4.4}{7.34}=0.59$$` (2) What is the `\(90\%\)` confidence interval? `$$\bar{x}\pm t(s_{\bar{x}})=22.5 \pm 1.674(0.59)=22.5 \pm 0.98=21.52; 23.48$$` (3) What is the `\(95\%\)` confidence interval? `$$\bar{x}\pm t(s_{\bar{x}})=22.5 \pm 2.006(0.59)=22.5 \pm 1.18=21.32; 23.68$$` (4) What is the `\(99\%\)` confidence interval? `$$\bar{x}\pm t(s_{\bar{x}})=22.5 \pm 2.672(0.59)=22.5 \pm 1.57=20.93; 24.07$$` (5) What happens to the margin of error and the width of the interval as we increase how "confident" we want to be? Answer: The margin of error increases and the confidence interval widens --- Continental Airlines' pilots fly on average 49 hours per month. This is based on `\(n=100\)` with `\(s=8.5\)` `\(\therefore s_{\bar{x}}=\dfrac{s}{\sqrt{n}}=\dfrac{8.5}{\sqrt{100}}=\dfrac{8.5}{10}=0.85\)` What is the margin of error at `\(95\%\)` CI? `\(t_{\alpha/2}(s_{\bar{x}})=1.984(0.85)=1.68\)` What is the `\(95\%\)`CI? `\(\bar{x}\pm t_{\alpha/2}(s_{\bar{x}})=49 \pm 1.984(0.85)=49 \pm 1.68=47.32; 50.58\)` --- ### Determining Needed Sample Size Margin of Error `\(=z_{\alpha/2}\left(\dfrac{\sigma}{\sqrt{n}}\right)\)` (1) Select the desired confidence level `\((1-\alpha)\)` (2) Select our best guess of `\(\sigma\)`; If you don't know `\(\sigma\)`, use `\(\sigma \approx \frac{Range}{4}\)` (3) Solve for `\(n\)` in the formula for the Margin of Error `\begin{eqnarray*} E=z_{\alpha/2}\left(\dfrac{\sigma}{\sqrt{n}}\right) \\ \therefore \sqrt{n}(E)= (z_{\alpha/2})(\sigma) \\ \therefore \sqrt{n}=\dfrac{(z_{\alpha/2})(\sigma)}{E} \\ \therefore n = \dfrac{(z_{\alpha/2})^{2}(\sigma)^{2}}{E^{2}} \end{eqnarray*}` --- Range for a sample is `\(36\)` We don't have `\(\sigma\)` so we calculate `\(\sigma \approx \dfrac{Range}{4}=\dfrac{36}{4}=9\)` At `\(95\%\)`CI, how large an `\(n\)` would give give margin of error of 3? Given `\((1-\alpha)=0.95; \sigma^{2}=81; E=3; \therefore z=1.96\)` `\(\therefore n=\dfrac{(z_{\alpha/2})^{2}(\sigma)^{2}}{E^{2}}\)` `\(=\dfrac{(1.96)^{2}(81)}{9}\)` `\(=(1.96)^{2}(9)=34.5744 \approx 35\)` ... `(Note: we are rounding up)` At `\(95\%\)`CI, how large an `\(n\)` would give give margin of error of 2? Given `\((1-\alpha)=0.95; \sigma^{2}=81; E=2; \therefore z=1.96\)` `\(\therefore n=\dfrac{(z_{\alpha/2})^{2}(\sigma)^{2}}{E^{2}}\)` `\(=\dfrac{(1.96)^{2}(81)}{4}\)` `\(=\dfrac{311.1696}{4} = 77.7924 \approx 78\)` ... `(Note: we are rounding up)` --- The New York Times reports mean Bar Mitzvah costs in New York City to be `\(19,000\)` dollars. Let `\(\sigma = 9400\)`. They also want you to use a `\(95\%\)` CI. (1) Recommend `\(n\)` if desired margin of error is `\(1000\)` `\(n=\dfrac{(z_{\alpha/2})^{2}(\sigma)^{2}}{E^{2}}\)` `\(=\dfrac{(1.96)^{2}(9400)^{2}}{(1000)^{2}} =(1.96)^{2}(88.36)= 339.4437 \approx 340\)` (2) What if the margin of error desired is `\(500\)`? `\(n=\dfrac{(z_{\alpha/2})^{2}(\sigma)^{2}}{E^{2}}\)` `\(=\dfrac{(1.96)^{2}(9400)^{2}}{(500)^{2}} = (1.96)^{2}(353.44)= 1357.7751 \approx 1358\)` (3) What if it is `\(200\)`? `\(n=\dfrac{(z_{\alpha/2})^{2}(\sigma)^{2}}{E^{2}}\)` `\(=\dfrac{(1.96)^{2}(9400)^{2}}{(200)^{2}} = (1.96)^{2}(2209)= 8486.0944 \approx 8487\)` ... `The closer we want to be to the true value, the larger the sample size needed` --- ### Population Proportion & Sample Size Determination Interval estimate of population proportion is `\(\bar{p}=\pm\)` Margin of Error If `\(np \geq 5\)` and `\(n(1-p) \geq 5\)`, the sampling distribution of `\(\bar{p}\)` is approximately Normal `\(\sigma_{\bar{p}}=\sqrt{\dfrac{{\bar{p}}(1-{\bar{p}})}{n}}\)` Margin of error is `\(E = t_{\alpha/2}\sqrt{\dfrac{\bar{p}(1-\bar{p})}{n}}\)` So the interval estimate for `\(p\)` `\(=\bar{p} \pm t_{\alpha/2}\sqrt{\dfrac{\bar{p}(1-\bar{p})}{n}}\)` `\(n=\dfrac{(z_{\alpha/2})^{2}(p^{*})(1-p^{*})}{E^{2}}; p^{*}=\)` best guess or `\(0.50\)` --- ### An Example A simple random sample of `\(n=800\)` generates `\(\bar{p} = 0.70\)` Given `\(n=800; \bar{p}=0.70\)`, `\(\sigma_{\bar{p}}=\sqrt{\dfrac{p(1-p)}{n}} =\sqrt{\dfrac{0.7(0.3)}{800}}=0.0162\)` What is `\(90\%\)`CI for `\(p\)`? `\(t_{\alpha/2}=1.645\)`, therefore 90%CI `\(=0.7 \pm 1.645(0.0162) = 0.7 \pm 0.0266 = 0.6734; 0.7266\)` What is `\(95\%\)`CI for `\(p\)`? `\(t_{\alpha/2}=1.96\)`, therefore 95%CI `\(=0.7 \pm 1.96(0.0162) = 0.7 \pm 0.0317 = 0.6683; 0.7317\)` --- ### Another Example Audience profile data for the ESPN SportsZone website show `\(26\%\)` of users to be women in a sample of `\(n=400\)` users. What is the margin of error at `\(95\%\)` CI? `\(E = t_{\alpha/2}\sqrt{\dfrac{\bar{p}(1-\bar{p})}{n}} = 1.96\sqrt{\dfrac{0.26(74)}{400}} = 0.0429\)` What is the `\(95\%\)` CI? 95%CI `\(=0.26 \pm 0.0429 = 0.2171; 0.3029\)` How large a sample do we need if desired margin of error is `\(0.03\)`? Given `\(E=0.03\)`, `\(n=\dfrac{(1.96)^{2}(0.26)(0.74)}{0.0009} = 821.2487 \approx 822\)`


class Builder : ObjectBuilder<Stripe3ds2ToolbarCustomization>
<init> |
Builder() |
build |
Build the toolbar customization fun build(): Stripe3ds2ToolbarCustomization |
setBackgroundColor |
Set the toolbar's background color fun setBackgroundColor(hexColor: String): Builder |
setButtonText |
Set the toolbar's cancel button text fun setButtonText(buttonText: String): Builder |
setHeaderText |
Set the toolbar's title fun setHeaderText(headerText: String): Builder |
setStatusBarColor |
Set the status bar color, if not provided a darkened version of the background color will be used. fun setStatusBarColor(hexColor: String): Builder |
setTextColor |
Set the color of the title text fun setTextColor(hexColor: String): Builder |
setTextFontName |
Set the font for the title text fun setTextFontName(fontName: String): Builder |
setTextFontSize |
Set the title text's font size fun setTextFontSize(fontSize: Int): Builder |


Contributing¶
Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given.
Types of Contributions¶
You can contribute in many ways:
Create Cookiecutter Templates¶
Some other Cookiecutter templates to list in the README would be great.
If you create a Cookiecutter template, submit a pull request adding it to README.md.
Report Bugs¶
Report bugs at https://github.com/cookiecutter/cookiecutter/issues.
If you are reporting a bug, please include:
Your operating system name and version.
Any details about your local setup that might be helpful in troubleshooting.
If you can, provide detailed steps to reproduce the bug.
If you don’t have steps to reproduce the bug, just note your observations in as much detail as you can. Questions to start a discussion about the issue are welcome.
Fix Bugs¶
Look through the GitHub issues for bugs. Anything tagged with “bug” is open to whoever wants to implement it.
Implement Features¶
Look through the GitHub issues for features. Anything tagged with “enhancement” and “please-help” is open to whoever wants to implement it.
Please do not combine multiple feature enhancements into a single pull request.
Note: this project is very conservative, so new features that aren’t tagged with “please-help” might not get into core. We’re trying to keep the code base small, extensible, and streamlined. Whenever possible, it’s best to try and implement feature ideas as separate projects outside of the core codebase.
Write Documentation¶
Cookiecutter could always use more documentation, whether as part of the official Cookiecutter docs, in docstrings, or even on the web in blog posts, articles, and such.
If you want to review your changes on the documentation locally, you can do:
pip install -r docs/requirements.txt
make servedocs
This will compile the documentation, open it in your browser and start watching the files for changes, recompiling as you save.
Submit Feedback¶
The best way to send feedback is to file an issue at https://github.com/cookiecutter/cookiecutter/issues.
If you are proposing a feature:
Explain in detail how it would work.
Keep the scope as narrow as possible, to make it easier to implement.
Remember that this is a volunteer-driven project, and that contributions are welcome :)
Setting Up the Code for Local Development¶
Here’s how to set up cookiecutter
for local development.
Fork the
cookiecutter
repo on GitHub.Clone your fork locally:
git clone git@github.com:your_name_here/cookiecutter.git
Install your local copy into a virtualenv. Assuming you have virtualenvwrapper installed, this is how you set up your fork for local development:
mkvirtualenv cookiecutter
cd cookiecutter/
python setup.py develop
Create a branch for local development:
git checkout -b name-of-your-bugfix-or-feature
Now you can make your changes locally.
When you’re done making changes, check that your changes pass the tests and lint check:
pip install tox
tox
Please note that tox runs lint check automatically, since we have a test environment for it.
If you feel like running only the lint environment, please use the following command:
tox -e lint
Ensure that your feature or commit is fully covered by tests. Check report after regular tox run. You can also run coverage only report and get html report with statement by statement highlighting:
tox -e cov-report
You report will be placed to htmlcov
directory. Please do not include this directory to your commits. By default this directory in our .gitignore
file.
Commit your changes and push your branch to GitHub:
git add .
git commit -m "Your detailed description of your changes."
git push origin name-of-your-bugfix-or-feature
Submit a pull request through the GitHub website.
Contributor Guidelines¶
Pull Request Guidelines¶
Before you submit a pull request, check that it meets these guidelines:
The pull request should include tests.
If the pull request adds functionality, the docs should be updated. Put your new functionality into a function with a docstring, and add the feature to the list in README.md.
The pull request must pass all CI/CD jobs before being ready for review.
If one CI/CD job is failing for unrelated reasons you may want to create another PR to fix that first.
Coding Standards¶
PEP8
Functions over classes except in tests
Quotes via http://stackoverflow.com/a/56190/5549
Use double quotes around strings that are used for interpolation or that are natural language messages
Use single quotes for small symbol-like strings (but break the rules if the strings contain quotes)
Use triple double quotes for docstrings and raw string literals for regular expressions even if they aren’t needed.
Example:
LIGHT_MESSAGES = {
'English': "There are %(number_of_lights)s lights.",
'Pirate': "Arr! Thar be %(number_of_lights)s lights."
}
def lights_message(language, number_of_lights):
"""Return a language-appropriate string reporting the light count."""
return LIGHT_MESSAGES[language] % locals()
def is_pirate(message):
"""Return True if the given message sounds piratical."""
return re.search(r"(?i)(arr|avast|yohoho)!", message) is not None
Write new code in Python 3.
Testing with tox¶
Tox uses py.test under the hood, hence it supports the same syntax for selecting tests.
For further information please consult the pytest usage docs
.
To run a particular test class with tox:
tox -e py '-k TestFindHooks'
To run some tests with names matching a string expression:
tox -e py '-k generate'
Will run all tests matching “generate”, test_generate_files for example.
To run just one method:
tox -e py '-k "TestFindHooks and test_find_hook"'
To run all tests using various versions of python in virtualenvs defined in tox.ini, just run tox:
tox
This configuration file setup the pytest-cov plugin and it is an additional dependency. It generate a coverage report after the tests.
It is possible to tests with some versions of python, to do this the command is:
tox -e py36,pypy3
Will run py.test with the python3.6 and pypy3 interpreters, for example.
Core Committer Guide¶
Vision and Scope¶
Core committers, use this section to:
Guide your instinct and decisions as a core committer
Limit the codebase from growing infinitely
Command-Line Accessible¶
Provides a command-line utility that creates projects from cookiecutters
Extremely easy to use without having to think too hard
Flexible for more complex use via optional arguments
API Accessible¶
Entirely function-based and stateless (Class-free by intentional design)
Usable in pieces for developers of template generation tools
Being Jinja2-specific¶
Sets a standard baseline for project template creators, facilitating reuse
Minimizes the learning curve for those who already use Flask or Django
Minimizes scope of Cookiecutter codebase
Extensible¶
Being extendable by people with different ideas for Jinja2-based project template tools.
Entirely function-based
Aim for statelessness
Lets anyone write more opinionated tools
Freedom for Cookiecutter users to build and extend.
No officially-maintained cookiecutter templates, only ones by individuals
Commercial project-friendly licensing, allowing for private cookiecutters and private Cookiecutter-based tools
Fast and Focused¶
Cookiecutter is designed to do one thing, and do that one thing very well.
Cover the use cases that the core committers need, and as little as possible beyond that :)
Generates project templates from the command-line or API, nothing more
Minimize internal line of code (LOC) count
Ultra-fast project generation for high performance downstream tools
Inclusive¶
Cross-platform and cross-version support are more important than features/functionality
Fixing Windows bugs even if it’s a pain, to allow for use by more beginner coders
Stable¶
Aim for 100% test coverage and covering corner cases
No pull requests will be accepted that drop test coverage on any platform, including Windows
Conservative decisions patterned after CPython’s conservative decisions with stability in mind
Stable APIs that tool builders can rely on
New features require a +1 from 3 core committers
VCS-Hosted Templates¶
Cookiecutter project templates are intentionally hosted VCS repos as-is.
They are easily forkable
It’s easy for users to browse forks and files
They are searchable via standard Github/Bitbucket/other search interface
Minimizes the need for packaging-related cruft files
Easy to create a public project template and host it for free
Easy to collaborate
Process: Pull Requests¶
If a pull request is untriaged:
Look at the roadmap
Set it for the milestone where it makes the most sense
Add it to the roadmap
How to prioritize pull requests, from most to least important:
Fixes for broken tests. Broken means broken on any supported platform or Python version.
Extra tests to cover corner cases.
Minor edits to docs.
Bug fixes.
Major edits to docs.
Features.
Ensure that each pull request meets all requirements in checklist.
Process: Issues¶
If an issue is a bug that needs an urgent fix, mark it for the next patch release.
Then either fix it or mark as please-help.
For other issues: encourage friendly discussion, moderate debate, offer your thoughts.
New features require a +1 from 2 other core committers (besides yourself).
Process: Roadmap¶
The roadmap located here
Due dates are flexible. Core committers can change them as needed. Note that GitHub sort on them is buggy.
How to number milestones:
Follow semantic versioning. Look at: http://semver.org
Milestone size:
If a milestone contains too much, move some to the next milestone.
Err on the side of more frequent patch releases.
Process: Pull Request merging and HISTORY.md maintenance¶
If you merge a pull request, you’re responsible for updating AUTHORS.md
and HISTORY.md
When you’re processing the first change after a release, create boilerplate following the existing pattern:
## x.y.z (Development)
The goals of this release are TODO: release summary of features
Features:
* Feature description, thanks to [@contributor](https://github.com/contributor) (#PR).
Bug Fixes:
* Bug fix description, thanks to [@contributor](https://github.com/contributor) (#PR).
Other changes:
* Description of the change, thanks to [@contributor](https://github.com/contributor) (#PR).
Process: Accepting Template Pull Requests¶
Run the template to generate the project.
Attempt to start/use the rendered project.
Merge the template in.
Update the history file.
note: Adding a template doesn’t give authors credit.
Process: Your own code changes¶
All code changes, regardless of who does them, need to be reviewed and merged by someone else.
This rule applies to all the core committers.
Exceptions:
Minor corrections and fixes to pull requests submitted by others.
While making a formal release, the release manager can make necessary, appropriate changes.
Small documentation changes that reinforce existing subject matter. Most commonly being, but not limited to spelling and grammar corrections.
Responsibilities¶
Ensure cross-platform compatibility for every change that’s accepted. Windows, Mac, Debian & Ubuntu Linux.
Ensure that code that goes into core meets all requirements in this checklist: https://gist.github.com/audreyr/4feef90445b9680475f2
Create issues for any major changes and enhancements that you wish to make. Discuss things transparently and get community feedback.
Don’t add any classes to the codebase unless absolutely needed. Err on the side of using functions.
Keep feature versions as small as possible, preferably one new feature per version.
Be welcoming to newcomers and encourage diverse new contributors from all backgrounds. Look at Code of Conduct.
Becoming a Core Committer¶
Contributors may be given core commit privileges. Preference will be given to those with:
Past contributions to Cookiecutter and other open-source projects. Contributions to Cookiecutter include both code (both accepted and pending) and friendly participation in the issue tracker. Quantity and quality are considered.
A coding style that the other core committers find simple, minimal, and clean.
Access to resources for cross-platform development and testing.
Time to devote to the project regularly.


Research
Dr. Mao’s research interests mainly focus on the fields of advanced computing systems, such as cloud computing, data-intensive platforms, quantum computing and quantum-based applications.
We develop algorithms to improve the performance of existing systems and propose novel system architectures to address practical issues in industry. Specifically, Dr. Mao’s investigate the following research problems.
-
Quantum systems and applications: we develop algorithms based on the utilize quantum bits (qubits) to improve the classic applications, such as deep neural networks (Tensorflow Quantum and Qiskit).
-
Cloud systems and applications: we build efficient cluster management algorithms for virtualized computing platforms, such as Docker and Kubernetes, to efficiently schedule the resources and improve the performance.
Quantum systems and applications
- We developed a Quantum GAN (QuGAN), a Generative Adversarial Network (GAN) through quantum states, to improve the important and successful deep learning applications (e.g. Selfies to Emojis, 3D Object Generation, and Face Aging). With the potential quantum speedup, we designed and implemented QuGAN architecture that provides a stable convergence of the model trained and tested on the real datasets. Compared to classical GANs and other quantum-based in the literature, QuGAN achieved significantly better results in terms of training efficiency, up to a 98% parameter count reduction, as well as the measured similarity between the generated distribution and original distribution, with up to a 125% improvement.
- From a practical system point of view, we proposed QuClassi, a hybrid quantum-classical deep neural network architecture for classification problems (e.g. face recognition). With a limited number of qubits, our novel system is able to reduce 95% of the size of the classic learning models. To the best of our knowledge, QuClassi is the first practical solution that tackles image classification problems in the quantum setting. Additionally, comparing QuClassi to the other similarly parameterized classical neural networks, QuClassi outperformed them by learning significantly faster (up to 53%) and achieved up to 215% higher accuracy in our experiments. Besides experiments on local simulators, we conducted our experiments on real quantum computers, IBM-Q Experience, to evaluate the proposed system.


Synergy not loading at startup
-
ZoomAddict
Just updated to 10.5 and Synergy no longer loads at startup. This is a fresh install (not a migration) on a 2008 Mac Pro with a fresh install of Synergy 3.2.1. The "Load at Startup" box is checked in the Synergy prefs, but there isn't anything in the startup items (in the Log In prefs).
I've tried unchecking the "Load at Startup box", then hitting apply, then re-enabling the box and applying again. Also tried manually adding a couple of things Synergy related to the startup items but they all open Synergy prefs at Startup (I just want Synergy to startup in the background like it did before).
I've searched and cannot find reference to this bug. Is it a known issue & is there anything else I can try?
Thanks Wincent!
-
ZoomAddict
Didn't find the cause but poked around the website and found this workaround which works:
---------- "Launch Synergy at login" checkbox in the Preference Pane no longer works for Synergy Classic. The workaround is to navigate to ~/Library/PreferencePanes/Synergy.prefPane, ctrl+click, choose Show Package Contents from the contextual menu, navigate to Contents/Resources/Synergy and drag this icon to the Startup Items in the Accounts preference pane. ----------
This issue (failure of the "Launch at Startup" feature) in Synergy Classic was repeatable (same issue on both the Mac Pros tested here). If you are still doing fixes to the 3.2.x version this bug needs to be quashed. Thanks!
-
Greg Hurrell
The unchecking "Launch at login", "Apply", rechecking, "Apply" should have worked.
If you want to try adding it manually to the startup items the item you have to add is not
~/Library/PreferencePanes/Synergy.prefPane
but~/Library/PreferencePanes/Synergy.prefPane/Contents/Resources/Synergy.app
.There might be something else going on here. Any error messages printed to the console?
-
Greg Hurrell
The "not launching at login" issue has come up a few times over the years (Synergy first came out in 2002, remember). Occasionally in an OS upgrade things would change in some subtle way and it would stop working. I don't think there were any "bugs" as such in the way Synergy added itself to the login items, but over the years I have had to implement a lot of workarounds to make sure that it got added when required.
It's actually been surprisingly complicated for what should be such a simple feature.
At the time of writing, I'm not aware of any real problems with 3.2.1 and the latest version of Mac OS X, and your report is an isolated one. If it were a general issue I would expect literally dozens of reports from different people. But still, it's something to keep an eye on to see if there's been yet another change in the way login items works which is causing problems.
-
anonymous
MacBook Pro (Unibody) 10.5.6
Synergy does not autostart for me. And has not, over multiple machines (I have a PPC desktop, and another standard MacBook Pro) all on 10.5.6
Works fine once I click start in the prefPane.
-
Greg Hurrell
Most likely stale entries in your login items. Please see my previous comments above for how to force those settings to be refreshed (basically boils down to 'The unchecking "Launch at login", "Apply", rechecking, "Apply"').
-
anonymous
Same thing here, with a new install of 10.5 on a MacBookPro unibody. I tried the unchecking/checking "Launch at Startup" thing to no avail. I've now added Synergy.app to the login items as described above, and will see what happens at the next startup.
-
Greg Hurrell
I think the important thing to note here is that it's not enough to just uncheck then recheck; you actually have to click "Apply" after each.
-
Fuga
OS X 10.5.7 Synergy 3.4
No Apply button option. it's there but not clickable/active. Not on any of the tabs.
-
Greg Hurrell
It should become active as soon as you actually make a change to one of the preferences. If you change nothing then there is nothing to apply, so the button remains inactive.
-
Fuga
Oh, I just ticked the Start button. Nevertheless I just did the add to login items thing. Thanks again for Synergy. I keep trying them all but keep with Synergy.
Reply
This topic is now closed.


{% extends 'base.html' %} {% block content %} Welcome to Crypto News Up To The Minute Crypto Currency and Blockchain News {% include 'partials/_price_table.html' %} {% include 'partials/_news.html' %} {% endblock %}
Up To The Minute Crypto Currency and Blockchain News


Techequilla
Achievements
Announcements
Teams
Contact Us
Projects
Our Team
Smaranjit Ghose
Anush Bhatia
Suhrid Datta
Sukkrit Sharma
swat1998


The Conversation Inspector for IBM Watson is a tool for developers to build conversational user experiences with
Watson Conversation.
The purpose of the tool is to display JSON data that is sent between applications and the Watson Conversation
service. This is useful when, for example, you use context variables extensively to manage state information.
Functionality:
Displays JSON data of incoming messages (sample) and provides filters, for example only context variables (sample)


This is a static dictionary entry from /data/dictionary: {{ 'styleguide-sample' | translate }}
/data/dictionary
Show in English Show in Danish
The current language is: {{ translate.currentLang }}


Options
Option
Value
Description
Static color
Color to use if adaptive mode is disabled, or unsupported by your OS.
Enable adaptive
Use the operating system's accent colors (on Windows only, in Personalization Settings).
Unfocused theme
Light toolbar
Light titlebar
Light toolbar and titlebar
Faded toolbar
Default reset
Dimmed text
Color scheme of unfocused windows, when you're clicked away from Firefox.


{% extends "home/base.html" %} {% load static %} {% block addon_css %} {% endblock %} {% block content %}
Smiles:
{{answer.smiles}}
Netcharge:
{{answer.netcharge}}
InChIKey:
{{answer.inchikey}}
InChI:
{{answer.inchi}}
InChIcol:
{{answer.inchicol}}
More information (from its Standard Form):
Complex structures in which this molecule appears :
References in which this molecule is mentioned:
{{reference.authors}}. {{reference.pub_year}}. {{reference.title}}. {{reference.journal}} {{reference.volume}} ({{reference.issue}}). doi: {{reference.doi}}. Available in: Pubmed Link
{{reference.authors}}. {{reference.pub_year}}. {{reference.title}}. {{reference.journal}} {{reference.volume}} ({{reference.issue}}). doi: {{reference.doi}}.


John Siracusa has, in his laser-focused, analytical style, perfectly captured something about technology that I always knew, but never knew I knew. His latest post, Technological Conservatism is a must read:
Beneath what seems like a reasonable feature request lurks the heart of technological conservatism: what was and is always shall be.
(emphasis his).
I love everything about this, and it’s a helluva lot better than [Steve Yegge’s stinker][yegge].
Siracusa’s article inspires me in two ways:
- Stop holding “what I know” so dearly. “Lead or bleed”, as Chad Fowler says
- Stop accepting unjustified idioms & conventions as being “more correct”. “That’s just the way it’s done” is often the best explanation for doing things a particular way, but it’s not actually a legitimate reason for anything.


Apache Hadoop
The Apache™ Hadoop® project develops open-source software for reliable, scalable, distributed computing.
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.
This is a release of Apache Hadoop 3.3 line.
It contains 117 bug fixes, improvements and enhancements since 3.3.5. Users of Apache Hadoop 3.3.5 and earlier should upgrade to this release.
Feature highlights:
Starting from this release, Hadoop publishes Software Bill of Materials (SBOM) using CycloneDX Maven plugin. For more information on SBOM, please go to SBOM.
HDFS Router-Router Based Federation now supports storing delegation tokens on MySQL, HADOOP-18535 which improves token operation through over the original Zookeeper-based implementation.
HADOOP-18671 moved a number of HDFS-specific APIs to Hadoop Common to make it possible for certain applications that depend on HDFS semantics to run on other Hadoop compatible file systems.
In particular, recoverLease() and isFileClosed() are exposed through LeaseRecoverable interface, while setSafeMode() is exposed through SafeMode interface.
Users are encouraged to read the overview of major changes since release 3.3.5. For details of 117 bug fixes, improvements, and other enhancements since the previous 3.3.5 release, please check release notes and changelog.
This is a release of Apache Hadoop 3.3 line.
Key changes include
Users of Apache Hadoop 3.3.4 and earlier should upgrade to this release.
All users are encouraged to read the overview of major changes since release 3.3.4.
For details of bug fixes, improvements, and other enhancements since the previous 3.3.4 release, please check release notes and changelog.
The ABFS connector has a critical bug fix https://issues.apache.org/jira/browse/HADOOP-18546: ABFS. Disable purging list of in-progress reads in abfs stream close().
All users of the abfs connector in hadoop releases 3.3.2+ MUST either upgrade
to this release or disable prefetching by setting
fs.azure.readaheadqueue.depth
to 0
.
This is a release of Apache Hadoop 3.3 line.
It contains a small number security and critical integration fixes since 3.3.3.
Users of Apache Hadoop 3.3.3 should upgrade to this release.
Users of hadoop 2.x and hadoop 3.2 should also upgrade to the 3.3.x line. As well as feature enhancements, this is the sole branch currently receiving fixes for anything other than critical security/data integrity issues.
Users are encouraged to read the overview of major changes since release 3.3.3. For details of bug fixes, improvements, and other enhancements since the previous 3.3.3 release, please check release notes and changelog.
This is the third stable release of Apache Hadoop 3.2 line.
It contains 153 bug fixes, improvements and enhancements since 3.2.3.
Users are encouraged to read the overview of major changes since 3.2.3. For details of 153 bug fixes, improvements, and other enhancements since the previous 3.2.3 release, please check release notes and changelog.
This is the second stable release of Apache Hadoop 2.10 line.
It contains 211 bug fixes, improvements and enhancements since 2.10.1.
Users are encouraged to read the overview of major changes since 2.10.1. For details of 211 bug fixes, improvements, and other enhancements since the previous 2.10.1 release, please check release notes and changelog.
The project includes these modules:
A wide variety of companies and organizations use Hadoop for both research and production. Users are encouraged to add themselves to the Hadoop PoweredBy wiki page.
Other Hadoop-related projects at Apache include:


Compiling ‘pg’ on MS Windows
In order to build this extension on MS Windows you will need a couple things.
First, a compiler. For the one click installer this means you should either use VC++ 6.0 or the compiler that comes with cygwin or mingw if you’re building on that platform.
If you’ve built Ruby yourself, you should use the same compiler to build this library that you used to build Ruby.
Second, PostgreSQL. Be sure you installed it with the development header files if you installed it using the standard PostgreSQL installer for Windows. If you didn’t, you can run the installer again, select “modify”, and then select the ‘development headers’ option to install them.
I recommend making sure that ‘pg_config.exe’ is in your PATH. The PostgreSQL installer for Windows does not necessarily update your PATH when it installs itself, so you may need to do this manually. This isn’t strictly necessary, however.
In order to build ruby-pg, just run ‘rake’. If the pg_config.exe executable is not in your PATH, you’ll need to explicitly point ruby-pg to where your PostgreSQL headers and libraries are with something like this:
rake --with-pg-dir=c:/progra~1/postgr~1/8.3
Adjust your path accordingly. BE SURE TO USE THE SHORT PATH NAMES! If you try to use a path with spaces in it, the nmake.exe program will choke.
Cross compiling for mswin32
Using rake-compiler a cross compiled pg gem can be build on a Linux or MacOS X host for the win32 platform. The generated gem is statically linked against libpq and libssl. OpenSSL and PostgreSQL are downloaded and compiled from the sources. There are no runtime dependencies to any but the standard Windows DLLs.
Install mingw32 using the instructions in rake-compiler’s README. For Debian/Ubuntu it is apt-get install gcc-mingw32 . Use ruby-1.8.7 for the following commands.
Download and cross compile ruby 1.8 and 1.9 for win32 with:
rake-compiler cross-ruby VERSION=1.8.7-p352 rake-compiler cross-ruby VERSION=1.9.2-p290
Download and cross compile pg for win32:
rake cross native gem
or with custom versions:
rake cross native gem RUBY_CC_VERSION=1.8.7:1.9.2 \ OPENSSL_VERSION=1.0.0e POSTGRESQL_VERSION=9.1.1
If everything works, there should be pg-VERSION-x86-mingw32.gem in the pkg directory.
Reporting Problems
If you have any problems you can submit them via [the project’s issue-tracker][bitbucket]. And submit questions, problems, or solutions, so that it can be improved.


Example 1
By the selector
A
B
C
A
A
B
A
B
C
A
Example 2
By array of the elements
A
B
C
A
A
B
A
B
C
A
Example 3
In greater parent
A
B
C
Example 4
In smaller parent
A
B
C
A
B


You head to the armory in search for arrows but you don't know where the arrows are kept Do you Ask the salesman or Look around the shop


0..31
and long shift operations outside of the
range 0..63
are reported. Shifting by negative or overly large values is almost certainly
a coding error.
Example:
int shiftSize = 32; // Warning: shift by 32 bits is equivalent to shift by 0 bits, so there's no shift at all. int mask = (1 << shiftSize) - 1;


{% include "includes/metas.html" %} {% include "includes/jsons.html" %} {% include "includes/icons.html" %}


Host Process Environment Information:
BenchmarkDotNet.Core=v<IP_ADDRESS>
OS=Microsoft Windows NT 6.2.9200.0
Processor=Intel(R) Core(TM) i5-4210M CPU 2.60GHz, ProcessorCount=4
Frequency=2533211 ticks, Resolution=394.7559 ns, Timer=TSC
CLR=MS.NET 4.0.30319.42000, Arch=64-bit RELEASE [RyuJIT]
GC=Concurrent Workstation
JitModules=clrjit-v4.6.1586.0
Type=batchConfigurationDictionaryBenchmark Mode=Throughput
| Method | Median | StdDev | Scaled | Scaled-SD | Gen 0 | Gen 1 | Gen 2 | Bytes Allocated/Op |
|---|
| batchConfigurationDictionary | 1.3216 us | 0.1016 us | 1.00 | 0.00 | 3,417.00 | - | - | 280.81 |
| batchConfigurationDictionaryRefactoring | 1.2220 us | 0.0964 us | 0.95 | 0.10 | 3,218.12 | - | - | 264.44 |


Genesis 43:6
And Israel said, Wherefore dealt ye so ill with me, as to tell the man whether ye had yet a brother?


amazon-chime-sdk / com.amazonaws.services.chime.sdk.meetings.audiovideo.video / VideoPauseState / PausedByUserRequest
PausedByUserRequest
PausedByUserRequest
The video tile has been paused by the user, and will only be unpaused if the user requests it to resume.


Product Items Selection
Select Product Item
Select Product Item
Laptop
Mobile
HeadPhone
AirPod
PlayStation
Author:
Mahesh Muttinti


Recommended download
git clone -b release https://gitlab.com/petsc/petsc.git petsc
- Use
git pull
anytime to obtain new patches that have been added since yourgit clone
or lastgit pull
. - Use
git checkout v3.14.4
to checkout a particular version.
Please join the petsc-announce and petsc-users mailing lists.
Documentation of changes/patches
Downloads if you cannot use git
- petsc-lite-3.14.4.tar.gz (use with online documentation)
- petsc-3.14.4.tar.gz (includes documentation, recommended for off-line use)
Use mirror below if GitLab and our primary download server are unavailable
- https://ftp.mcs.anl.gov/pub/petsc/release-snapshots/ (primary server)
- https://www.mcs.anl.gov/petsc/mirror/release-snapshots/ (mirror)


Hi! I'm Simon, a young, passionate and (hopefully) talented software developer. I do mostly full-stack web development but I'm pretty much interested about everything related to computer science including but not limited to these very cool things: WebGL, DevOps, Immutable Servers, Deep Learning and Natural Language Processing.
If I have aroused your interest and you want to learn more about me, you can either keep scrolling this page or take a look at the project section.


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| lennongrad.com | v1.13 (Rehaul Update) | Icons by Lorc, Delapouite, and Felbrigg. Retrieved from http://game-icons.net. |


Front End Developer
Jani Momolu Anderson
About Me
Contact Me
Résumé
Creative
Code
UX Diary
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Your scores indicate that you are probably a nice person but not a fit for us.
Excellent! Your mindset works for us!
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By filling out this form, you're indicating your desire to submit your name
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candidate for South Africa's 2019 elections.
We will do
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registration details with the Independent Electoral Commission.
Other than for such purposes of checking your eligibility, your data will not be shared with anyone.


| Application Path | Rails Version | Brakeman Version | Report Time | Checks Performed |
|---|---|---|---|---|
| /src | <IP_ADDRESS> | 3.5.0 |
2018-06-13 10:17:16 +0000 1.0245963 seconds |
BasicAuth, BasicAuthTimingAttack, ContentTag, CreateWith, CrossSiteScripting, DefaultRoutes, Deserialize, DetailedExceptions, DigestDoS, DynamicFinders, EscapeFunction, Evaluation, Execute, FileAccess, FileDisclosure, FilterSkipping, ForgerySetting, HeaderDoS, I18nXSS, JRubyXML, JSONEncoding, JSONParsing, LinkTo, LinkToHref, MailTo, MassAssignment, MimeTypeDoS, ModelAttrAccessible, ModelAttributes, ModelSerialize, NestedAttributes, NestedAttributesBypass, NumberToCurrency, QuoteTableName, Redirect, RegexDoS, Render, RenderDoS, RenderInline, ResponseSplitting, RouteDoS, SQL, SQLCVEs, SSLVerify, SafeBufferManipulation, SanitizeMethods, SelectTag, SelectVulnerability, Send, SendFile, SessionManipulation, SessionSettings, SimpleFormat, SingleQuotes, SkipBeforeFilter, StripTags, SymbolDoSCVE, TranslateBug, UnsafeReflection, ValidationRegex, WithoutProtection, XMLDoS, YAMLParsing |
| Scanned/Reported | Total |
|---|---|
| Controllers | 17 |
| Models | 11 |
| Templates | 24 |
| Errors | 3 |
| Security Warnings | 16 (12) |
| Ignored Warnings | 0 |
| Warning Type | Total |
|---|---|
| Command Injection | 1 |
| Cross Site Scripting | 1 |
| Cross-Site Request Forgery | 1 |
| Dangerous Send | 1 |
| File Access | 1 |
| Format Validation | 1 |
| Mass Assignment | 1 |
| Remote Code Execution | 4 |
| SQL Injection | 2 |
| Session Setting | 3 |
| Confidence | Class | Method | Warning Type | Message |
|---|---|---|---|---|
| High | DashboardController | change_graph | Dangerous Send | |
| High | BenefitFormsController | download | File Access | |
| High | Api::V1::MobileController | show | Remote Code Execution | |
| High | Api::V1::MobileController | index | Remote Code Execution | |
| High | BenefitFormsController | download | Remote Code Execution | |
| High | Session Setting | |||
| High | Session Setting | |||
| High | Session Setting | |||
| High | UsersController | update | SQL Injection | |
| Medium | Benefits | Benefits.make_backup | Command Injection | |
| Medium | UsersController | user_params | Mass Assignment | |
| Medium | Analytics | hits_by_ip | SQL Injection | |
| Medium | PasswordResetsController | reset_password | Remote Code Execution |
Controller Warnings
| Confidence | Controller | Warning Type | Message |
|---|---|---|---|
| High | ApplicationController | Cross-Site Request Forgery |
Model Warnings
| Confidence | Model | Warning Type | Message |
|---|---|---|---|
| High | User | Format Validation |
View Warnings
| Confidence | Template | Warning Type | Message |
|---|---|---|---|
| High | layouts/application (AdminController#dashboard) | Cross Site Scripting |


NOTICE: This software (or technical data) was produced for the U.S. Government under contract, and is subject to the Rights in Data-General Clause 52.227-14, Alt. IV (DEC 2007). Copyright 2023 The MITRE Corporation. All Rights Reserved.
Overview
OpenMPF provides a Markup component that can be used to draw bounding boxes and labels on images and videos. The
component provides one task called OCV GENERIC MARKUP TASK
that can be added to the end of any image and/or video
pipeline. By default, many other OpenMPF components provide * (WITH MARKUP)
pipelines that use this task. Note that
the Markup component will not appear in the list of components in the Component Registration web UI because it's a core
feature of OpenMPF.
Configuration
The following properties can be set as job properties or algorithm properties on the MARKUPCV
algorithm. Also, the
default values can be changed be setting the system property listed for each:
MARKUP_LABELS_ENABLED
- System property:
markup.labels.enabled
- Default value:
true
- If true, add a label to each detection box.
- System property:
MARKUP_LABELS_ALPHA
- System property:
markup.labels.alpha
- Default value:
0.5
- Value in range [0.0, 1.0] that specifies how transparent the labels and frame number overlay should be. 0.0 is invisible (not recommended) and 1.0 is fully opaque.
- System property:
MARKUP_LABELS_FROM_DETECTIONS
- System property:
markup.labels.from.detections
- Default value:
false
- If true, use detection-level details to populate the bounding box labels. Otherwise, use track-level details.
- System property:
MARKUP_LABELS_TRACK_INDEX_ENABLED
- System property:
markup.labels.track.index.enabled
- Default value:
true
- If true, add the track index to the start of every bounding box label.
- System property:
MARKUP_LABELS_TEXT_PROP_TO_SHOW
- System property:
markup.labels.text.prop.to.show
- Default value:
CLASSIFICATION
- Name of the text property to show in the label before the numeric property. If using track-level details, and this property is not present at the track level, then the detection property for the track's exemplar will be used. Leave empty to omit.
- System property:
MARKUP_TEXT_LABEL_MAX_LENGTH
- System property:
markup.labels.text.max.length
- Default value:
10
- The maximum length of the label selected by
MARKUP_LABELS_TEXT_PROP_TO_SHOW
. If the label is longer than the limit, characters after limit will not be displayed.
- System property:
MARKUP_LABELS_NUMERIC_PROP_TO_SHOW
- System property:
markup.labels.numeric.prop.to.show
- Default value:
CONFIDENCE
- Name of the numeric property to show in the label after the text property. If using track-level details, and this property is not present at the track level, then the detection property for the track's exemplar will be used. Leave empty to omit. Set to CONFIDENCE to use the confidence value.
- Numeric values are displayed with a precision of 3 decimal places in the label.
- System property:
MARKUP_LABELS_CHOOSE_SIDE_ENABLED
- System property:
markup.labels.choose.side.enabled
- Default value:
true
- Labels will always snap to the top-most corner of the box. If true, snap the label to the side of the corner that produces the least amount of overhang. If false, always show the label on the right side of the corner.
- System property:
MARKUP_BORDER_ENABLED
- System property:
markup.border.enabled
- Default value:
false
- If true, generate the marked-up frame with a black border. Can be useful if boxes or labels extend beyond frame boundaries.
- System property:
MARKUP_VIDEO_EXEMPLAR_ICONS_ENABLED
- System property:
markup.video.exemplar.icons.enabled
- Default value:
true
- If true, and labels are enabled, use an icon to indicate the exemplar detection for each track.
- The icons are only used in video markup. This is because every detection is an exemplar in image markup.
- System property:
MARKUP_VIDEO_BOX_SOURCE_ICONS_ENABLED
- System property:
markup.video.box.source.icons.enabled
- Default value:
false
- If true, and labels are enabled, use icons to indicate the source of each bounding box. For example, if the box is the result of an algorithm detection, tracking performing gap fill, or Workflow Manager animation.
- System property:
MARKUP_VIDEO_MOVING_OBJECT_ICONS_ENABLED
- System property:
markup.video.moving.object.icons.enabled
- Default value:
false
- If true, and labels are enabled, use icons to indicate if the object is considered moving or stationary. If using track-level details, and the
MOVING
property is not present at the track level, then the property for the track's exemplar will be used.
- System property:
MARKUP_VIDEO_FRAME_NUMBERS_ENABLED
- System property:
markup.video.frame.numbers.enabled
- Default value:
true
- If true, add the frame number to each marked-up frame. This setting is independent of
MARKUP_LABELS_ENABLED
.
- System property:
MARKUP_VIDEO_ENCODER
- System property:
markup.video.encoder
- Default value:
vp9
- Use
vp9
to generate VP9-encoded.webm
video files. Useh264
to generate H.264-encoded.mp4
files. Usemjpeg
to generate MJPEG-encoded.avi
files. The.webm
and.mp4
files can display in most browsers, and are higher quality, but take longer to generate. - Please review the Usage Royalties section of the License and Distribution page before using the H.264 encoder for commercial purposes.
- System property:
MARKUP_VIDEO_VP9_CRF
- System property:
markup.video.vp9.crf
- Default value:
31
- The CRF value can be from 0-63. Lower values mean better quality. Recommended values range from 15-35, with 31 being recommended for 1080p HD video. This property is only used if generating VP9-encoded
.webm
files
- System property:
MARKUP_ANIMATION_ENABLED
- System property:
markup.video.animation.enabled
- Default value:
true
- If true, draw bounding boxes to fill in the gaps between detections in each track. Interpolate size and position.
- System property:
Video Markup Icons
| Icon | Meaning | Setting |
|---|---|---|
| Track exemplar | MARKUP_VIDEO_EXEMPLAR_ICONS_ENABLED |
|
| Track or detection is moving | MARKUP_VIDEO_MOVING_OBJECT_ICONS_ENABLED |
|
| Track or detection is stationary | MARKUP_VIDEO_MOVING_OBJECT_ICONS_ENABLED |
|
| Detection is the direct result of a component detection algorithm | MARKUP_VIDEO_BOX_SOURCE_ICONS_ENABLED |
|
| Detection is the result of a component performing tracking in an attempt to fill in the gaps between algorithm detections | MARKUP_VIDEO_BOX_SOURCE_ICONS_ENABLED |
|
| Detection is the result of the Workflow Manager interpolating (animating) the size and position of the bounding box to fill gaps between detections in the track | MARKUP_VIDEO_BOX_SOURCE_ICONS_ENABLED |
Video Markup Examples
Above we show frame 94 of a marked up video. Frame numbers are enabled so the frame number is shown in the top-right corner. Exemplar icons are enabled, and since this detection is the track exemplar a star icon is shown in the label. Also, the label shows the track's CLASSIFICATION
property followed by the track confidence. All of the examples shown in this section will display track-level information because MARKUP_LABELS_FROM_DETECTIONS=false
. The circle represents the top-left corner of the detection. See this section of the C++ Batch Component API for more information on flip and rotation.
Above we show frame 25 of the marked up video. This time we configured markup to show a black border around the video frame. This is useful when the label extends beyond the edge of the original video frame, as shown here. Also, this time we configured markup to show icons indicating if the track is moving or stationary. The fast-forward icon at the start of the label indicates that this track is moving. Additionally, this time we configured markup to show icons indicating the bounding box source. The magnifying glass icon after the fast-forward icon indicates that this detection is a direct result of the component's detection algorithm. Note that the magnifying glass icon will be replaced with the star icon for exemplars.
The frame above shows a movie camera icon to indicate that the detection is the result of the Workflow Manager (WFM) interpolating (animating) the size and position of the bounding box to fill gaps between detections in the track. Considering how blurry the person appears in this frame, it's not surprising that the algorithm could not detect him. If you perform a job with FRAME_INTERVAL
greater than one, or otherwise perform frame skipping, then all bounding boxes in skipped frames will be the result of WFM animation. Note that the classification and confidence values are simply carried over from the last detection that was not the result of WFM animation.
The frame above shows a paper clip icon to indicate that the detection is the result of the component performing tracking in an attempt to fill in the gaps between algorithm detections. In general, these detections are more trustworthy than the ones resulting from WFM animation, but not as trustworthy as the ones directly resulting from the detection algorithm.
The frame above shows the person detection in addition to a new skis detection. The confidence for the latter is lower, which is good considering the algorithm misclassified the person's shadow as skis. The skis track is only a few frames long, so the WFM determined it was a non-moving (stationary) track. This is represented by the anchor icon at the start of the label. Also, notice that the labels are semi-transparent. This allows you to read labels and see frame content that would otherwise be hidden if the labels were completely opaque. Note that you may want to set MARKUP_LABELS_ALPHA
to 0.75
or greater when using the mjpeg
encoder.
Video Encoder Considerations
Performing markup on an image will always generate a .png
file. Performing markup on a video will generate a video file based on the value of MARKUP_VIDEO_ENCODER
. The vp9
, h264
, and mjpg
encoders are supported.
The vp9
and h264
encoders serve the same purpose in that both formats can be played in the WFM web UI in most web browsers, while the .avi
files resulting from the mjpeg
format must be downloaded and played using a separate program like VLC or mpv. In general, h264
encoding is much faster than vp9
encoding, so you may want to use it instead of vp9
. Please be aware that you may be required to pay Usage Royalties when using the h264
encoder for commercial purposes.
The mjpeg
encoder is faster than both the vp9
and h264
encoders, but produces lower quality video. Specifically, the label text is not as clear. You may want to use it when developing components or marking up large video files.
To give you a sense of performance, here are the results of a very limited batch of tests. Note that if you choose to use the vp9
encoder, you can increase the CRF value to reduce processing time at the cost of reduced video quality.
input media: 23 frames @ 3840x2160:
| Encoder | CRF | Time (secs) | Notes |
|---|---|---|---|
| mjpeg | 6.94 | ||
| h264 | 9.478 | ||
| vp9 | 60 | 13.194 | |
| vp9 | 31 | 21.431 |
input media: 509 frames @ 640x480:
| Encoder | CRF | Time (secs) | Notes |
|---|---|---|---|
| mjpeg | 6.927 | alpha 0.5 is hard to read when blended with dark background; 0.75 does better | |
| h264 | 11.259 | ||
| vp9 | 60 | 35.945 | text not acceptable due to low resolution |
| vp9 | 31 | 52.178 |


Open Pampas [au22], plain, in Mandoria, peasants: 188
Last updated Turn #148
Open Pampas [au22], plain, in Mandoria, peasants: 188
------------------------------------------------------------------------
30: The population grows by one peasant.
Routes leaving Open Pampas:
North, plain, to Open Pampas [at22], 7 days
Garrison [iy1u], garrison, uncontrolled, with six peasants
East, plain, to Open Pampas [au23], 7 days
South, plain, to Open Pampas [av22], 7 days
West, plain, to Open Pampas [au21], 7 days
Inner locations:
An Orc Stronghold [s68], orc-stronghold, defense 25, 2% damaged
Seen here:
Garrison [fc1j], garrison, uncontrolled, with six peasants


HyperText Markup Language (HTML) is a programmimg language that controls what you see on a web page,
the HTML allows you to properly fomat a web site so that you can display links to other pages and
correctly format any text and images that are required. It is the framework that creates web pages.
Designing a web page using boxes, this how a page is constructed. Everyhting on aweb page lies
inside a box, you can have multiple boxes, you can have boxes inside boxes.
The box model refers to the construction of all the boxes on a web page. Boxes are made up of four different
parts, the Content, Padding Border and Margin. The content has the html inside it, this include text and images,
the padding clears an area around the content, the border goes around the padding and the margin clears an area
outside the border. All these parts of the box allow developers to precisely create a web page for your viewing
pleasure.


--- title: "Ernesto Sirolli: Want to help someone?  layout: "post" permalink: "/2013/02/ernesto-sirolli-want-to-help-someone.html" uuid: "5379541478017895391" guid: "tag:blogger.com,1999:blog-4627720939429298175.post-5379541478017895391" date: "2013-02-28 03:03:00" updated: "2013-03-01 04:44:16" description: blogger: siteid: "4627720939429298175" postid: "5379541478017895391" comments: "0" categories: [tedtalks] author: name: "Gremsi" url: "http://www.blogger.com/profile/00614962079213173450?rel=author" image: "http://img2.blogblog.com/img/b16-rounded.gif" ---


This page can be used to push your firewall configuration out to several systems
simultaneously, so that you can manage a cluster of firewall systems from a single
interface. Before a server can be added to this page, it must have Webmin 1.170 or
above installed and be added in the Webmin Servers Index module. It must also have the
necessary ipi(6)tables programs installed and properly configured.


Guru-Shishya
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This website contains complementary data to the paper.
Mosquito-borne diseases like Zika have negatively affect the citizens of economically emerging countries. Nevertheless, the current public healthcare solutions are still insufficient to support either disease surveillance or control. The engagement of citizens in reporting mosquito breeding sites remains difficult, especially when constant reports are fundamental to guide health agents in preventing disease outbreaks. This paper introduces VazaZika: a software platform aimed to support the surveillance and control of mosquito-borne diseases. This platform is built upon a legacy platform called VazaDengue, introducing the idea of gamification, That is, through game elements and rules, VazaZika aims to make enjoyable and challenging the constant report of mosquito breeding sites. Citizens are continuously rewarded with points and badges as they perform tasks performed in the systems. They progress in levels that enable new tasks and jump in rankings according to the citizens' location. Citizens can also join teams to engage with challenges, which helps developing a sense of belonging and connection against the prevalence of mosquito breeding sites. This paper also reports the process of gamifying VazaDengue, as well as the VazaZika platform user interface and its conceptual model for supporting their reuse by other software engineers.
| # | Artifact | Description |
|---|
Any questions and/or suggestions should be sent to the paper authors as follows.
| # | Name | |
|---|---|---|
| 1 | Eduardo Fernandes |<EMAIL_ADDRESS>|
| 2 | Anderson Uchôa |<EMAIL_ADDRESS>|
| 3 | Leonardo Sousa |<EMAIL_ADDRESS>|
| 4 | Anderson Oliveira |<EMAIL_ADDRESS>|
| 5 | Rafael de Mello |<EMAIL_ADDRESS>|
| 6 | Luiz Paulo Barroca |<EMAIL_ADDRESS>|
| 7 | Diogo Carvalho |<EMAIL_ADDRESS>|
| 8 | Alessandro Garcia |<EMAIL_ADDRESS>|
| 9 | Baldoino Fonseca |<EMAIL_ADDRESS>|
| 10 | Leopoldo Teixeira |<EMAIL_ADDRESS>|


home
tags
Liang Siyv's Blog
论文笔记：WHAT IS THE STATE OF NEURAL NETWORK PRUNING?
WHAT IS THE STATE OF NEURAL NETWORK PRUNING?
Hello Jekyll
Transform your plain text into static websites and blogs.


This section describes the two main aspects of user interaction, displaying messages and querying the user. We will deal with these two issues in turn.


In this p5 program, we have disabled the draw() loop. So how is the animation being updated? A custom dataChanged() function is run every time that the data changes, which updates the canvas.
This is possible thanks to Vue's ability to 'watch' data. Data watchers are useful if you want to perform a computationally expensive operation in response to changing data. The alternative approach would be to check for a change in data on each frame of the draw() loop, which can be less efficient.


Go Pro Demo (ionic)Go Pro Demo (ionic)
{{ bluetoothIsScanning?"Scaning":"Scan for bluetooth devices" }}
0">
{{scanResult.device?.name ??


EILPoint Class Reference
| Inherits from | NSObject |
|---|---|
| Conforms to | NSCoding NSCopying |
| Declared in | EILPoint.h EILPoint.m |
Properties
x
X coordinate of the point.
@property (nonatomic, assign, readonly) double x
Declared In
EILPoint.h
y
Y coordinate of the point.
@property (nonatomic, assign, readonly) double y
Declared In
EILPoint.h
Creating and Initializing Points
Operations on point
– distanceToPoint:
Computes distance between given point and this point.
- (double)distanceToPoint:(EILPoint *)point
Parameters
point |
Given point to which distance will be computed. |
|---|
Return Value
Distance between points.
Declared In
EILPoint.h
Serialization
– toDictionary
Serializes the point to NSDictionar
y.
- (NSDictionary *)toDictionary
Return Value
A point representation in NSDictionary.
Declared In
EILPoint.h
Identifying and Comparing Objects
– isEqual:
Returns a boolean value that indicates whether given point is equal to this one.
- (BOOL)isEqual:(id)other
Parameters
other |
The object to be compared to this point. May be nil. |
|---|
Return Value
YES if points are equal, otherwise NO.
Declared In
EILPoint.h
– hash
Returns an integer that can be used as a table address in a hash table structure.
- (NSUInteger)hash
Declared In
EILPoint.h
Describing Objects
Encoding and Decoding
– initWithCoder:
Returns a point initialized from data in a given unarchiver.
- (nullable instancetype)initWithCoder:(NSCoder *)decoder
Parameters
decoder |
An unarchiver object. |
|---|
Return Value
A new point initialized using the data in decoder.
Declared In
EILPoint.h
– encodeWithCoder:
Encodes the point using a given archiver.
- (void)encodeWithCoder:(NSCoder *)coder
Parameters
An |
archiver object. |
|---|
Declared In
EILPoint.h


US elections 2020: When are the debates, primaries, conventions?
A guide to all the important dates for the 2020 US presidential election.
Quick guide
- Nov 20, 2019 - Next Democratic presidential debate
- Feb 3, 2020 - Iowa caucuses
- Mar 3, 2020 - Super Tuesday
- July 13-16 2020 - Democratic National Convention
- Aug 24-27 2020 - Republican National Convention
- Sept 29, 2020 - First presidential debate
- Nov 3, 2020 - Election day
The United States is less than three months out from the Iowa caucus, the kick-off event for the 2020 presidential primary and caucus voting season.
After Iowa, a series of primaries and caucuses will be held, leading up to the Democratic and Republican conventions in which the parties will formally choose their candidates.
More:
-
Who are the 2020 US Democratic presidential candidates?
-
US Democratic Representative Tim Ryan ends presidential bid
-
'I am back,' Bernie Sanders tells supporters at NYC rally
Until the conventions, at least eight more Democratic debates will be held. After the conventions, at least three presidential debates and one vice presidential debate will take place.
As the election season heats up, here's a guide to the key dates for the 2020 presidential election:
Key caucuses and primaries*
February 2020
February 3 - Iowa caucuses
February 11 - New Hampshire primaries
February 22 - Nevada caucuses
February 29 - South Carolina primaries
March 2020
March 3 - "Super Tuesday" - Alabama, American Samoa, Arkansas, California, Colorado, Maine, Massachusetts, Minnesota, North Carolina, Oklahoma, Tennessee, Texas, Utah, Vermont, Virginia and Democrats abroad primaries and caucuses
March 8 - Puerto Rico Republican primary
March 10 - Hawaii Republican, Idaho, Michigan, Mississippi, Missouri, North Dakota Democratic and Washington primaries and caucuses
March 12 - Virgin Island Republican caucus
US President Donald Trump holds a campaign rally in Minneapolis, Minnesota [File: Leah Millis/Reuters]
March 14 - Northern Marianas Democratic convention, Guam Republican convention, Wyoming Republican convention, District of Columbia Republican convention
March 17 - Arizona Democratic, Florida, Illinois and Ohio primaries, as well as the Northern Marianas Republican convention
March 21 - Kentucky Republican caucuses
April 2020
April 3 - North Dakota Republican state convention
April 4 - Alaska Democratic and Hawaii Democratic primaries, Louisiana primary and Wyoming Democratic caucuses
April 7 - Wisconsin primaries
April 28 - Connecticut, Delaware, Maryland, Pennsylvania, Rhode Island and New York primaries
May 2020
May 2 - Kansas and Guam Democratic primaries and caucuses
May 5 - Indiana primaries
May 12 - Nebraska and West Virginia primaries
May 19 - Kentucky Democratic and Oregon primaries
June 2020
June 2 - Montana, New Jersey, New Mexico and South Dakota primaries, District of Columbia Democratic primary
June 6 - Virgin Islands Democratic caucuses
*Dates are subject to change; Republican primaries and caucuses have been cancelled in some states.
National Conventions
July 13-16, 2020 - Democratic National Convention
August 24-27, 2020 - Republican National Convention
Debates
Democratic presidential debates
June 26-27, 2019 - First Democratic debate. Read what the candidates said here and here.
July 30-31, 2019 - Second Democratic debate. Read the key takeaways here and here.
September 12, 2019 - Third Democratic debate. Read what happened here.
Democratic presidential candidates before a Democratic presidential primary debate hosted by ABC at Texas Southern University in Houston [File: David J Phillip/AP Photo]
October 15, 2019 - Fourth Democratic debate. Read what the candidates said here.
November 20, 2019 - Fifth Democratic debate.
December 19, 2019 - Sixth Democratic debate
January - April, 2020 - Six more Democratic presidential debates will be held.
Presidential debates
September 29, 2020 - First presidential debate
October 7, 2020 - First vice presidential debate
October 15, 2020 - Second presidential debate
October 22 - Third presidential debate
Election day
November 3, 2020


Royal Oak |
Royal Oak |
Royal Oak |
Royal Oak |
Royal Oak |
Royal Oak |
Royal Oak |
Royal Oak |
Royal Oak |
Royal Oak |
Royal Oak |
Royal Oak |
Royal Oak |
Royal Oak |
Royal Oak |
Royal Oak |
Royal Oak |
Royal Oak |
La Mare De Effendi migrated from France to Indonesia in 1992 with his wife and his newborn wife as of request of his good friend Warkof to oversee his tea factory in Jakarta. On 1995, When Mare contracts are finished, he was offered another contract by Warkof but Mare declined politely as he was eager to seek a better life for his family.
Mare saw an opportunity to sell imported tea to the foreigners and expatriates since tea of other nationality was not fervently imported into Indonesia. Me&Tea, a tea shop of various imported teas was established in Jakarta, Indonesia by La Mare de Effendi to cater for the needs of the expats and foreigners living in late 1995. Maison used his expertise gain from his time working in the tea factory and his previous tea study in France to quickly gain foothold in Jakarta. He imported various high end teas from all over the world to Jakarta and in two years his tea shop was becoming very lucrative due to the amounts of regular customers.
During the 1998 riot in Jakarta, even though his shop was burnt and destroyed to the ground, Mare saw an opportunity to expand quickly as land prices are plummeting everywhere. Six months after the crisis was over, he bought three more property around Jakarta and established more tea shop with Me&Tea brand. With the increase in tea shops, Mare start to stock and serve not only imported teas but also local teas and various local dessert as well as international dessert.
Now in 2016, 24 years after Me&Tea was established, it has 10 branches all over Indonesia and one mega factory and are enjoyed by locals and foreigners. Mare is retiring and Me&Tea will be managed by his only son, Marquis De Effendi.
Tab #3 content goes here!
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--- layout: post title: 'PICNIC07: The Art of Paul Pope' date: 2007-09-27 00:00:00 category: play ---
PICNIC07: The Art of Paul Pope
Works exclusively in analogue. Most interested in comics and picture-making. Brought in influences from rock art albums as much as comics he grew up with, and sees a relationship between music iconography and comics. Loved silent film.
Lots of Japanese manga artists worked with avant-garde theatre in the 60s and 70s. Shows a piece of work he did, borderline-plagiarised: you can do cover versions of songs, why not other art?
Currently working on a line of clothers with DKNY, and a store installation for Diesel in Hollywood.
Batman: there have been many batmen, and everyone has a version of it in their head. How can you make it new and your own? (Aside from PP: is Popeye the first superhero?) He started with the logo: the silhouette of the character is instantly identifiable. The shape looked to him like oppressive german black type (as seen on rock/metal albums). Shows some examples of attempts to form the logo into a shape using this style of font and the bat motif.
Wanted to comment on "what is a secret identity", whether in a police state a superhero has a right to a private self: the mask as a metaphor for privacy. Batman should scare you. Early silent film: "even their mistakes are pregnant with possibility". Wanted to have a repeated theme, like a refrain through music.
Time lapse film of Paul working; he often jumps between 2 projects to prevent boredom. One of these was fashion design work: "I started out drawing her naked to make the job more interesting".


Class responsible for updating scene info structs to reflect changes made to scene assets.
More...
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| static void | RefreshCachedScenes () |
| | Call this when you make a change to the build settings and need those changes to be reflected immediately. More...
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| |
| static void | GetSceneInfoRelativeProperties (SerializedProperty property, out SerializedProperty assetProperty, out SerializedProperty nameProperty, out SerializedProperty pathProperty, out SerializedProperty buildIndexProperty, out SerializedProperty includedProperty, out SerializedProperty tagProperty) |
| | Finds all relative properties of a SceneInfo struct. More...
|
| |
| static bool | FindScene (SerializedProperty nameProperty, SerializedProperty pathProperty, ref UnityEngine.Object asset) |
| | Finds a missing scene asset reference for a SceneInfo struct. More...
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| |
| static bool | RefreshSceneInfo (UnityEngine.Object asset, SerializedProperty nameProperty, SerializedProperty pathProperty, SerializedProperty buildIndexProperty, SerializedProperty includedProperty, SerializedProperty tagProperty) |
| | Updates all the serialized properties for a SceneInfo struct. More...
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| |
| static void | OnPostProcessScene () |
| | Searches for all components in a scene and refreshes any SceneInfo fields found. More...
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| |
Class responsible for updating scene info structs to reflect changes made to scene assets.
Extends AssetPostprocessor so it can respond to asset changes.


{% extends "base.html" %} {% block content %}
{{ meal_form.date }}
Duration: {{ meal_form.time }}
{{ sleep_form.date }}
Duration: {{ sleep_form.time }}
{{ workout_form.date }}
Duration: {{ workout_form.duration }}
{{ weight_form.date }}
{{ bloodpressure_form.date }}
{{ bloodsugar_form.date }}
{{ heartrate_form.date }}


{% extends 'tweet_display/application.html' %} {% block content %} {% if oh_id == current_user_oh_id %} {% include 'tweet_display/partials/graph_status.html' %} {%endif%}
The amount we tweet per day can change over time. This data can be really noisy: On some days we might hammer out one tweet after the other, while on other days we don't get around to doing much tweeting at all. There might be vacations, days where you're sick in bed etc. This can make it very hard to see long-term trends.
For this reason the data here was
averaged to remove the noise. This was done by applying a rolling average
on a 180 day window. This should make it much easier to see how one's
behaviour changed over larger time-frames (In pandas
this
rolling average is created by
dataframe.rolling('180d').mean()
).
In our graph to the left the x-axis
gives us the time since
one signed up for Twitter and the y-axis
gives the
normalized number of tweets on a given day.
There are different types of tweets one can send on Twitter. Some tweets
are replies
to tweets by other users, while other tweets are
retweets
of tweets done by other users.
Last, but not least there are the good old regular tweets
,
which are neither a reply to anyone nor a retweet of a tweet.
Again, this data might be very noisy due to short-time changes.
For this reason the data was averaged to remove the noise as well.
This was done by applying the same rolling average on a 180 day window as above.
This should make it much easier to see how one's
behaviour changed over larger time-frames (In pandas
this
rolling average is created by
dataframe.rolling('180d').mean()
).
In our graph to the left the x-axis
gives us the time since
one signed up for Twitter and the y-axis
gives the
percentage of the overall tweet volume for the different tweet types.
Different people have different styles in how they use Twitter. Some are owls and tweet deep into the night, while others of us are larks and send our first tweets out while the sun rises. And of course, there might be differences between workdays and weekends.
Twitter does not save the local time in which a tweet was sent. Instead
it gives all dates and times in UTC
. To get the correct local time
it is thus necessary to know the latitude/longitude from which a given
tweet was sent. Correspondingly our graph to the left is based purely on those
tweets for which a geo location is known.
In our graph to the left the x-axis
gives us the hour of the
day in which tweets were sent and the y-axis
gives us the overall
number of (geotagged) tweets that were sent on a given workday or day of the weekend.


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This is My Page.
About Me:
Logan is 20 years old.
He is an aspiring coder
Logan loves swimming and playing water polo


Galaxy S10 One UI 2.0 Beta Closed in Korea; New Beta Update Rolling Out Globally
by Anil GantiJust about a month ago, Samsung's Android 10-based One UI 2.0 beta program went live in Korea. Korean Galaxy S10, Galaxy S10+, and Galaxy S10e units have since then received a total of four updates. Today, Samsung announced the end of the beta program in their home country. New users can no longer sign up for the program (via Sammobile)
Whether it is because Samsung is nearly done testing the software or due to the beta slots being filled up remains to be seen. Usually, a device receives up to four beta software updates before a stable rollout commences. There is a likelihood that the Korean devices will receive one more update. Samsung could even surprise us by rolling out a stable release in the coming week or so. Only time will tell.
Galaxy Note 10’s One UI 2.0 Beta Program Now Available in the United States
Galaxy S10 gets fourth One UI 2.0 beta software in some markets
Just last week, participants of the One UI 2.0 beta for the Galaxy S 10 series received their third software update. It was mostly bug fixes and optimizations, as is the nature of beta software. Today, there's a fourth update rolling out in some markets such as Germany. The update bears software version G97**XXU3ZSK9 and weighs in at a little over 345 MB. Like its predecessor, this update also fixes a slew of bugs.
Some of the bugs that this update address includes a flickering screen, disappearing status bar while switching apps, improper screenshot capture, keyboard resizing problems, inability to create application shortcuts, disappearing AoD while charging and improved gesture navigation. Compared to the earlier iteration, the number of bugs has gone down significantly, signaling that Samsung is close to polishing the software. If you're a part of the beta program, you can download it on your Galaxy S10e/S10/S10+ by tapping the Download and install option in the Software update menu of the phone’s Settings app.
Products mentioned in this post
The links above are affiliate links. As an Amazon Associate, Wccftech.com may earn from qualifying purchases.


tensorlayer.utils
¶
evaluation ([y_test, y_predict, n_classes]) |
Input the predicted results, targets results and the number of class, return the confusion matrix, F1-score of each class, accuracy and macro F1-score. |
class_balancing_oversample ([X_train, ...]) |
Input the features and labels, return the features and labels after oversampling. |
dict_to_one ([dp_dict]) |
Input a dictionary, return a dictionary that all items are set to one, use for disable dropout, dropconnect layer and so on. |
Evaluation functions¶
-
tensorlayer.utils.
evaluation
(y_test=None, y_predict=None, n_classes=None)[source]¶ Input the predicted results, targets results and the number of class, return the confusion matrix, F1-score of each class, accuracy and macro F1-score.
- y_test : numpy.array or list
- target results
- y_predict : numpy.array or list
- predicted results
- n_classes : int
- number of classes
>>> c_mat, f1, acc, f1_macro = evaluation(y_test, y_predict, n_classes)
Class balancing functions¶
-
tensorlayer.utils.
class_balancing_oversample
(X_train=None, y_train=None, printable=True)[source]¶ Input the features and labels, return the features and labels after oversampling.
- X_train : numpy.array
- Features, each row is an example
- y_train : numpy.array
- Labels
>>> X_train, y_train = class_balancing_oversample(X_train, y_train, printable=True)
Helper functions¶
-
tensorlayer.utils.
dict_to_one
(dp_dict={})[source]¶ Input a dictionary, return a dictionary that all items are set to one, use for disable dropout, dropconnect layer and so on.
- dp_dict : dictionary
- keeping probabilities
>>> dp_dict = dict_to_one( network.all_drop ) >>> dp_dict = dict_to_one( network.all_drop ) >>> feed_dict.update(dp_dict)


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Options include:
Outside of scope of version control. Options include:
See Comparing Computing Professionals’ Perceptions of Importance of Skills and Knowledge on the Job and Coverage in Undergraduate Experiences (institution sign-in)
They noted, for example, that “software craftsmanship and clean code,” “emphasis on coding style and commenting,” and “software maintenance” were not sufficiently stressed within their own undergraduate experiences
One participant recommended "Working on a large code base and making changes/additions to it. Reviewing changes other people are making to the codebase. Testing. Finding and fixing bugs. Using source control software, etc. This way students are actually prepared for developing in the real world upon graduating and have an appreciation for best practices, comments, reviews, developer testing, and so on.
Such projects should be incorporated into early stages of the curriculum "From the get-go people would learn the development environment. Essential tools like git, svn, in unix, and learn some of the history. Then learn languages, design patterns, team interaction, the roles in an agile team, etc


North Miami BrewFest raises funds for FIU program
by 7 News WSVNNORTH MIAMI, FLA. (WSVN) - One of South Florida’s premier beer festivals brought local residents together while raising funds for students at Florida International University.
The annual North Miami BrewFest, held Saturday on the lawn of the North Miami Museum of Contemporary Art, featured over 200 different craft beers from local breweries.
It also offered a wide array of gourmet bites, like slow-roasted tenderloin, barbecue brisket and empanadas from vendors across South Florida.
The event was hosted in collaboration between the City of North Miami and FIU, with the funds raised going to the Brewing Science Program at the Chaplin School of Hospitality & Tourist Management.
Participants said it was another year of food, fun and first-rate craft beers.


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We added a way to easily protect some parts of your application with
HTTP Authentication
. A section about this topic has
been added to the documentation.
The HTTP Client plugin has also been updated
so you can specify a username/password when using the HTTP client to make a request to a resource protected by
HTTP Authentication
.
Finally, we also updated the Pebble plugin so you can add an extension. This allows you to add custom filters, functions, and more to the default templating engine. More information about extensions on Pebble website.


It's great pleasure to have such app for battlelog! I admire with your app especially the way it done!
Can you please add filtering ability to exclude catigories of servers filtered by free slots number from resultes? Is it possible to do the way like it is realized for "DETAILED"? It would be nice to be ably to place just X in font of "Empty" to exclude empty servers from list.
It would be nice to have the same for other filtering options but it isn't so important 'cause choosing of some options there is equal to excluding of not chosen.
Thank you for your work!
BrainFooLong
Administrator
There is no such way to exclude things. It must be done by DICE.
But you can filter for all except non empty when you enable all except empty.
Same for other usage. But a combination of include/exlude isnt possible.
Greetz
Topicstarter
Guest
thank you for the respond, Roland.
it doesn't work such way in "DETAILED" search if i want to see all servers matching my criteria except empty: you can not see full servers if you use filters as you advised. But if you don't choose any filtering criteria you get tons of empty servers at the top of your search results.
BrainFooLong
Administrator
As i said, i cannot handle your request.
Sorry.
Greetz