This notebook originally appeared as a post on the blog Pythonic Perambulations. The content is BSD licensed.
Fundamentally, the disagreement between frequentists and Bayesians concerns the definition of probability.
For frequentists, probability only has meaning in terms of a limiting case of repeated measurements. That is, if I measure the photon flux $F$ from a given star (we'll assume for now that the star's flux does not vary with time), then measure it again, then again, and so on, each time I will get a slightly different answer due to the statistical error of my measuring device. In the limit of a large number of measurements, the frequency of any given value indicates the probability of measuring that value. For frequentists probabilities are fundamentally related to frequencies of events. This means, for example, that in a strict frequentist view, it is meaningless to talk about the probability of the true flux of the star: the true flux is (by definition) a single fixed value, and to talk about a frequency distribution for a fixed value is nonsense.
For Bayesians, the concept of probability is extended to cover degrees of certainty about statements. Say a Bayesian claims to measure the flux $F$ of a star with some probability $P(F)$: that probability can certainly be estimated from frequencies in the limit of a large number of repeated experiments, but this is not fundamental. The probability is a statement of my knowledge of what the measurement reasult will be. For Bayesians, probabilities are fundamentally related to our own knowledge about an event. This means, for example, that in a Bayesian view, we can meaningfully talk about the probability that the true flux of a star lies in a given range. That probability codifies our knowledge of the value based on prior information and/or available data.
The surprising thing is that this arguably subtle difference in philosophy leads, in practice, to vastly different approaches to the statistical analysis of data. Below I will give a few practical examples of the differences in approach, along with associated Python code to demonstrate the practical aspects of the resulting methods.
Here we'll take a look at an extremely simple problem, and compare the frequentist and Bayesian approaches to solving it. There's necessarily a bit of mathematical formalism involved, but I won't go into too much depth or discuss too many of the subtleties. If you want to go deeper, you might consider — please excuse the shameless plug — taking a look at chapters 4-5 of our textbook.
Imagine that we point our telescope to the sky, and observe the light coming from a single star. For the time being, we'll assume that the star's true flux is constant with time, i.e. that is it has a fixed value $F_{\rm true}$ (we'll also ignore effects like sky noise and other sources of systematic error). We'll assume that we perform a series of $N$ measurements with our telescope, where the $i^{\rm th}$ measurement reports the observed photon flux $F_i$ and error $e_i$.
The question is, given this set of measurements $D = \{F_i,e_i\}$, what is our best estimate of the true flux $F_{\rm true}$?
(Gratuitous aside on measurement errors: We'll make the reasonable assumption that errors are Gaussian. In a Frequentist perspective, $e_i$ is the standard deviation of the results of a single measurement event in the limit of repetitions of *that event*. In the Bayesian perspective, $e_i$ is the standard deviation of the (Gaussian) probability distribution describing our knowledge of that particular measurement given its observed value)
Here we'll use Python to generate some toy data to demonstrate the two approaches to the problem. Because the measurements are number counts, a Poisson distribution is a good approximation to the measurement process:
# Generating some simple photon count data
import numpy as np
from scipy import stats
np.random.seed(1) # for repeatability
F_true = 1000 # true flux, say number of photons measured in 1 second
N = 50 # number of measurements
F = stats.poisson(F_true).rvs(N) # N measurements of the flux
e = np.sqrt(F) # errors on Poisson counts estimated via square root
Now let's make a simple visualization of the "measured" data:
%matplotlib inline
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.errorbar(F, np.arange(N), xerr=e, fmt='ok', ecolor='gray', alpha=0.5)
ax.vlines([F_true], 0, N, linewidth=5, alpha=0.2)
ax.set_xlabel("Flux");ax.set_ylabel("measurement number");
These measurements each have a different error $e_i$ which is estimated from Poisson statistics using the standard square-root rule. In this toy example we already know the true flux $F_{\rm true}$, but the question is this: given our measurements and errors, what is our best estimate of the true flux?
Let's take a look at the frequentist and Bayesian approaches to solving this.
We'll start with the classical frequentist maximum likelihood approach. Given a single observation $D_i = (F_i, e_i)$, we can compute the probability distribution of the measurement given the true flux $F_{\rm true}$ given our assumption of Gaussian errors:
$$ P(D_i~|~F_{\rm true}) = \frac{1}{\sqrt{2\pi e_i^2}} \exp{\left[\frac{-(F_i - F_{\rm true})^2}{2 e_i^2}\right]} $$
This should be read "the probability of $D_i$ given $F_{\rm true}$ equals ...". You should recognize this as a normal distribution with mean $F_{\rm true}$ and standard deviation $e_i$.
We construct the likelihood function by computing the product of the probabilities for each data point:
$$\mathcal{L}(D~|~F_{\rm true}) = \prod_{i=1}^N P(D_i~|~F_{\rm true})$$
Here $D = \{D_i\}$ represents the entire set of measurements. Because the value of the likelihood can become very small, it is often more convenient to instead compute the log-likelihood. Combining the previous two equations and computing the log, we have
$$\log\mathcal{L} = -\frac{1}{2} \sum_{i=1}^N \left[ \log(2\pi e_i^2) + \frac{(F_i - F_{\rm true})^2}{e_i^2} \right]$$
What we'd like to do is determine $F_{\rm true}$ such that the likelihood is maximized. For this simple problem, the maximization can be computed analytically (i.e. by setting $d\log\mathcal{L}/dF_{\rm true} = 0$). This results in the following observed estimate of $F_{\rm true}$:
$$ F_{\rm est} = \frac{\sum w_i F_i}{\sum w_i};~~w_i = 1/e_i^2 $$
Notice that in the special case of all errors $e_i$ being equal, this reduces to
$$ F_{\rm est} = \frac{1}{N}\sum_{i=1}^N F_i $$
That is, in agreement with intuition, $F_{\rm est}$ is simply the mean of the observed data when errors are equal.
We can go further and ask what the error of our estimate is. In the frequentist approach, this can be accomplished by fitting a Gaussian approximation to the likelihood curve at maximum; in this simple case this can also be solved analytically. It can be shown that the standard deviation of this Gaussian approximation is:
$$ \sigma_{\rm est} = \left(\sum_{i=1}^N w_i \right)^{-1/2} $$
These results are fairly simple calculations; let's evaluate them for our toy dataset:
w = 1. / e ** 2
print("""
F_true = {0}
F_est = {1:.0f} +/- {2:.0f} (based on {3} measurements)
""".format(F_true, (w * F).sum() / w.sum(), w.sum() ** -0.5, N))
We find that for 50 measurements of the flux, our estimate has an error of about 0.4% and is consistent with the input value.
The Bayesian approach, as you might expect, begins and ends with probabilities. It recognizes that what we fundamentally want to compute is our knowledge of the parameters in question, i.e. in this case,
$$ P(F_{\rm true}~|~D) $$
Note that this formulation of the problem is fundamentally contrary to the frequentist philosophy, which says that probabilities have no meaning for model parameters like $F_{\rm true}$. Nevertheless, within the Bayesian philosophy this is perfectly acceptable.
To compute this result, Bayesians next apply Bayes' Theorem, a fundamental law of probability:
$$ P(F_{\rm true}~|~D) = \frac{P(D~|~F_{\rm true})~P(F_{\rm true})}{P(D)} $$
Though Bayes' theorem is where Bayesians get their name, it is not this law itself that is controversial, but the Bayesian interpretation of probability implied by the term $P(F_{\rm true}~|~D)$.
Let's take a look at each of the terms in this expression:
If we set the prior $P(F_{\rm true}) \propto 1$ (a flat prior), we find
$$P(F_{\rm true}|D) \propto \mathcal{L}(D|F_{\rm true})$$
and the Bayesian probability is maximized at precisely the same value as the frequentist result! So despite the philosophical differences, we see that (for this simple problem at least) the Bayesian and frequentist point estimates are equivalent.
You'll noticed that I glossed over something here: the prior, $P(F_{\rm true})$. The prior allows inclusion of other information into the computation, which becomes very useful in cases where multiple measurement strategies are being combined to constrain a single model (as is the case in, e.g. cosmological parameter estimation). The necessity to specify a prior, however, is one of the more controversial pieces of Bayesian analysis.
A frequentist will point out that the prior is problematic when no true prior information is available. Though it might seem straightforward to use a noninformative prior like the flat prior mentioned above, there are some surprisingly subtleties involved. It turns out that in many situations, a truly noninformative prior does not exist! Frequentists point out that the subjective choice of a prior which necessarily biases your result has no place in statistical data analysis.
A Bayesian would counter that frequentism doesn't solve this problem, but simply skirts the question. Frequentism can often be viewed as simply a special case of the Bayesian approach for some (implicit) choice of the prior: a Bayesian would say that it's better to make this implicit choice explicit, even if the choice might include some subjectivity.
Leaving these philosophical debates aside for the time being, let's address how Bayesian results are generally computed in practice. For a one parameter problem like the one considered here, it's as simple as computing the posterior probability $P(F_{\rm true}~|~D)$ as a function of $F_{\rm true}$: this is the distribution reflecting our knowledge of the parameter $F_{\rm true}$. But as the dimension of the model grows, this direct approach becomes increasingly intractable. For this reason, Bayesian calculations often depend on sampling methods such as Markov Chain Monte Carlo (MCMC).
I won't go into the details of the theory of MCMC here. Instead I'll show a practical example of applying an MCMC approach using Dan Foreman-Mackey's excellent emcee package. Keep in mind here that the goal is to generate a set of points drawn from the posterior probability distribution, and to use those points to determine the answer we seek.
To perform this MCMC, we start by defining Python functions for the prior $P(F_{\rm true})$, the likelihood $P(D~|~F_{\rm true})$, and the posterior $P(F_{\rm true}~|~D)$, noting that none of these need be properly normalized. Our model here is one-dimensional, but to handle multi-dimensional models we'll define the model in terms of an array of parameters $\theta$, which in this case is $\theta = [F_{\rm true}]$:
def log_prior(theta):
return 1 # flat prior
def log_likelihood(theta, F, e):
return -0.5 * np.sum(np.log(2 * np.pi * e ** 2)
+ (F - theta[0]) ** 2 / e ** 2)
def log_posterior(theta, F, e):
return log_prior(theta) + log_likelihood(theta, F, e)
Now we set up the problem, including generating some random starting guesses for the multiple chains of points.
ndim = 1 # number of parameters in the model
nwalkers = 50 # number of MCMC walkers
nburn = 1000 # "burn-in" period to let chains stabilize
nsteps = 2000 # number of MCMC steps to take
# we'll start at random locations between 0 and 2000
starting_guesses = 2000 * np.random.rand(nwalkers, ndim)
import emcee
sampler = emcee.EnsembleSampler(nwalkers, ndim, log_posterior, args=[F, e])
sampler.run_mcmc(starting_guesses, nsteps)
sample = sampler.chain # shape = (nwalkers, nsteps, ndim)
sample = sampler.chain[:, nburn:, :].ravel() # discard burn-in points
If this all worked correctly, the array sample
should contain a series of 50000 points drawn from the posterior. Let's plot them and check:
# plot a histogram of the sample
plt.hist(sample, bins=50, histtype="stepfilled", alpha=0.3, normed=True)
# plot a best-fit Gaussian
F_fit = np.linspace(975, 1025)
pdf = stats.norm(np.mean(sample), np.std(sample)).pdf(F_fit)
plt.plot(F_fit, pdf, '-k')
plt.xlabel("F"); plt.ylabel("P(F)")
We end up with a sample of points drawn from the (normal) posterior distribution. The mean and standard deviation of this posterior are the corollary of the frequentist maximum likelihood estimate above:
print("""
F_true = {0}
F_est = {1:.0f} +/- {2:.0f} (based on {3} measurements)
""".format(F_true, np.mean(sample), np.std(sample), N))
We see that as expected for this simple problem, the Bayesian approach yields the same result as the frequentist approach!
Now, you might come away with the impression that the Bayesian method is unnecessarily complicated, and in this case it certainly is. Using an Affine Invariant Markov Chain Monte Carlo Ensemble sampler to characterize a one-dimensional normal distribution is a bit like using the Death Star to destroy a beach ball, but I did this here because it demonstrates an approach that can scale to complicated posteriors in many, many dimensions, and can provide nice results in more complicated situations where an analytic likelihood approach is not possible.
As a side note, you might also have noticed one little sleight of hand: at the end, we use a frequentist approach to characterize our posterior samples! When we computed the sample mean and standard deviation above, we were employing a distinctly frequentist technique to characterize the posterior distribution. The pure Bayesian result for a problem like this would be to report the posterior distribution itself (i.e. its representative sample), and leave it at that. That is, in pure Bayesianism the answer to a question is not a single number with error bars; the answer is the posterior distribution over the model parameters!
Let's briefly take a look at a more complicated situation, and compare the frequentist and Bayesian results yet again. Above we assumed that the star was static: now let's assume that we're looking at an object which we suspect has some stochastic variation — that is, it varies with time, but in an unpredictable way (a Quasar is a good example of such an object).
We'll propose a simple 2-parameter Gaussian model for this object: $\theta = [\mu, \sigma]$ where $\mu$ is the mean value, and $\sigma$ is the standard deviation of the variability intrinsic to the object. Thus our model for the probability of the true flux at the time of each observation looks like this:
$$ F_{\rm true} \sim \frac{1}{\sqrt{2\pi\sigma^2}}\exp\left[\frac{-(F - \mu)^2}{2\sigma^2}\right]$$
Now, we'll again consider $N$ observations each with their own error. We can generate them this way:
np.random.seed(42) # for reproducibility
N = 100 # we'll use more samples for the more complicated model
mu_true, sigma_true = 1000, 15 # stochastic flux model
F_true = stats.norm(mu_true, sigma_true).rvs(N) # (unknown) true flux
F = stats.poisson(F_true).rvs() # observed flux: true flux plus Poisson errors.
e = np.sqrt(F) # root-N error, as above
The resulting likelihood is the convolution of the intrinsic distribution with the error distribution, so we have
$$\mathcal{L}(D~|~\theta) = \prod_{i=1}^N \frac{1}{\sqrt{2\pi(\sigma^2 + e_i^2)}}\exp\left[\frac{-(F_i - \mu)^2}{2(\sigma^2 + e_i^2)}\right]$$
Analogously to above, we can analytically maximize this likelihood to find the best estimate for $\mu$:
$$\mu_{est} = \frac{\sum w_i F_i}{\sum w_i};~~w_i = \frac{1}{\sigma^2 + e_i^2} $$
And here we have a problem: the optimal value of $\mu$ depends on the optimal value of $\sigma$. The results are correlated, so we can no longer use straightforward analytic methods to arrive at the frequentist result.
Nevertheless, we can use numerical optimization techniques to determine the maximum likelihood value. Here we'll use the optimization routines available within Scipy's optimize submodule:
def log_likelihood(theta, F, e):
return -0.5 * np.sum(np.log(2 * np.pi * (theta[1] ** 2 + e ** 2))
+ (F - theta[0]) ** 2 / (theta[1] ** 2 + e ** 2))
# maximize likelihood <--> minimize negative likelihood
def neg_log_likelihood(theta, F, e):
return -log_likelihood(theta, F, e)
from scipy import optimize
theta_guess = [900, 5]
theta_est = optimize.fmin(neg_log_likelihood, theta_guess, args=(F, e))
print("""
Maximum likelihood estimate for {0} data points:
mu={theta[0]:.0f}, sigma={theta[1]:.0f}
""".format(N, theta=theta_est))
This maximum likelihood value gives our best estimate of the parameters $\mu$ and $\sigma$ governing our model of the source. But this is only half the answer: we need to determine how confident we are in this answer, that is, we need to compute the error bars on $\mu$ and $\sigma$.
There are several approaches to determining errors in a frequentist paradigm. We could, as above, fit a normal approximation to the maximum likelihood and report the covariance matrix (here we'd have to do this numerically rather than analytically). Alternatively, we can compute statistics like $\chi^2$ and $\chi^2_{\rm dof}$ to and use standard tests to determine confidence limits, which also depends on strong assumptions about the Gaussianity of the likelihood. We might alternatively use randomized sampling approaches such as Jackknife or Bootstrap, which maximize the likelihood for randomized samples of the input data in order to explore the degree of certainty in the result.
All of these would be valid techniques to use, but each comes with its own assumptions and subtleties. Here, for simplicity, we'll use the basic bootstrap resampler found in the astroML package:
from astroML.resample import bootstrap
def fit_samples(sample):
# sample is an array of size [n_bootstraps, n_samples]
# compute the maximum likelihood for each bootstrap.
return np.array([optimize.fmin(neg_log_likelihood, theta_guess,
args=(F, np.sqrt(F)), disp=0)
for F in sample])
samples = bootstrap(F, 1000, fit_samples) # 1000 bootstrap resamplings
Now in a similar manner to what we did above for the MCMC Bayesian posterior, we'll compute the sample mean and standard deviation to determine the errors on the parameters.
mu_samp = samples[:, 0]
sig_samp = abs(samples[:, 1])
print " mu = {0:.0f} +/- {1:.0f}".format(mu_samp.mean(), mu_samp.std())
print " sigma = {0:.0f} +/- {1:.0f}".format(sig_samp.mean(), sig_samp.std())
I should note that there is a huge literature on the details of bootstrap resampling, and there are definitely some subtleties of the approach that I am glossing over here. One obvious piece is that there is potential for errors to be correlated or non-Gaussian, neither of which is reflected by simply finding the mean and standard deviation of each model parameter. Nevertheless, I trust that this gives the basic idea of the frequentist approach to this problem.
The Bayesian approach to this problem is almost exactly the same as it was in the previous problem, and we can set it up by slightly modifying the above code.
def log_prior(theta):
# sigma needs to be positive.
if theta[1] <= 0:
return -np.inf
else:
return 0
def log_posterior(theta, F, e):
return log_prior(theta) + log_likelihood(theta, F, e)
# same setup as above:
ndim, nwalkers = 2, 50
nsteps, nburn = 2000, 1000
starting_guesses = np.random.rand(nwalkers, ndim)
starting_guesses[:, 0] *= 2000 # start mu between 0 and 2000
starting_guesses[:, 1] *= 20 # start sigma between 0 and 20
sampler = emcee.EnsembleSampler(nwalkers, ndim, log_posterior, args=[F, e])
sampler.run_mcmc(starting_guesses, nsteps)
sample = sampler.chain # shape = (nwalkers, nsteps, ndim)
sample = sampler.chain[:, nburn:, :].reshape(-1, 2)
Now that we have the samples, we'll use a convenience routine from astroML to plot the traces and the contours representing one and two standard deviations:
from astroML.plotting import plot_mcmc
fig = plt.figure()
ax = plot_mcmc(sample.T, fig=fig, labels=[r'$\mu$', r'$\sigma$'], colors='k')
ax[0].plot(sample[:, 0], sample[:, 1], ',k', alpha=0.1)
ax[0].plot([mu_true], [sigma_true], 'o', color='red', ms=10);
The red dot indicates ground truth (from our problem setup), and the contours indicate one and two standard deviations (68% and 95% confidence levels). In other words, based on this analysis we are 68% confident that the model lies within the inner contour, and 95% confident that the model lies within the outer contour.
Note here that $\sigma = 0$ is consistent with our data within two standard deviations: that is, depending on the certainty threshold you're interested in, our data are not enough to confidently rule out the possibility of a non-varying source!
The other thing to notice is that this posterior is definitely not Gaussian: this can be seen by the lack of symmetry in the vertical direction. That means that the Gaussian approximation used within the frequentist approach may not reflect the true uncertainties in the result. This isn't an issue with frequentism itself (i.e. there are certainly ways to account for non-Gaussianity within the frequentist paradigm), but the vast majority of commonly applied frequentist techniques make the explicit or implicit assumption of Gaussianity of the distribution. Bayesian approaches generally don't require such assumptions.
(Side note on priors: there are good arguments that a flat prior on $\sigma$ subtley biases the calculation in this case: i.e. a flat prior is not necessarily non-informative in the case of scale factors like $\sigma$. There are interesting arguments to be made that the [Jeffreys Prior](http://en.wikipedia.org/wiki/Jeffreys_prior) would be more applicable. Here I believe the Jeffreys prior is not suitable, because $\sigma$ is not a true scale factor (i.e. the Gaussian has contributions from $e_i$ as well). On this question, I'll have to defer to others who have more expertise. Note that subtle — some would say subjective — questions like this are among the features of Bayesian analysis that frequentists take issue with).
I hope I've been able to convey through this post how philosophical differences underlying frequentism and Bayesianism lead to fundamentally different approaches to simple problems, which nonetheless can often yield similar or even identical results.
To summarize the differences:
In simple problems, the two approaches can yield similar results. As data and models grow in complexity, however, the two approaches can diverge greatly. In a followup post, I plan to show an example or two of these more complicated situations. Stay tuned!
This post was written entirely in the IPython notebook. You can [download](http://jakevdp.github.io/downloads/notebooks/FreqBayes.ipynb) this notebook, or see a static view [here](http://nbviewer.ipython.org/url/jakevdp.github.io/downloads/notebooks/FreqBayes.ipynb).


The large majority of members are people who were in a internship at the Microsoft Innovation Center Belgium.
We are all fans of new technologies and want to use them for a better world. That's why we are very active in Hackathons for testing them.


Down in the Delta (1998) Alfre Woodward, Al Freeman Jr., Mary Alice, Esther Rolle, Loretta Devine, Wesley Snipes, Mpho Koaho, Kulani Hassen, Anne Marie Johnson, Justin Lord. Produced by Rick Rosenberg, Bob Christiansen, Victor McGauley, Wesley Snipes, Reuben Cannon. Co-produced by Terri Farnsworth, Myron Goble, Alfre Woodward. Screenplay by Myron Goble. Directed by Maya Angelou. 114 minutes. Rated PG-13, 3.5 stars (out of five stars)
Review by Ed Johnson-Ott, NUVO Newsweekly www.nuvo-online.com Archive reviews at http://us.imdb.com/M/reviews_by?Edward+Johnson-ott To receive reviews by e-mail at no charge, send subscription requests to<EMAIL_ADDRESS>In a crowded Christmas marketplace, it would be easy for a little movie like "Down in the Delta" to get lost in the shuffle. Hopefully, positive reviews and good word-of-mouth will help audiences discover that, despite some flaws, this is a film far more deserving of their attention than material like "You've Got Mail," "Stepmom" and "Patch Adams." Poet Maya Angelou's debut as a feature director is a solid, often moving story about family, love, reconciliation and self-determination. "Down in the Delta" resolutely avoids the clichés that most productions embrace, resulting in one of the only holiday offerings this year that is both heartwarming and genuine.
Loretta (Alfre Woodward) is an unemployed single mother in a Chicago ghetto who uses alcohol and drugs to mute her pain; neglecting her autistic daughter Tracy (Kulani Hassen) and driving her mother, Rosa Lynn (Mary Alice), and son, Thomas (Mpho Koaho) to distraction. Desperate to jolt her daughter out of self-medicated numbness, Rosa Lynn insists that Loretta and the children spend the summer with Uncle Earl (Al Freeman Jr. ) in the family's Mississippi Delta hometown. With great reluctance, Loretta boards the bus with the kids, bound for a three month stay in Hooterville Hell.
Initially, things look as bad as Loretta expected. The rural home is in a dry county. Earl's beloved wife, Annie (Esther Rolle) suffers from Alzheimer's disease and requires constant attention from her husband and Zenia (Loretta Devine), a caregiver who views Loretta with suspicion. To make matters worse, Loretta must drag herself out of bed early each morning to make chicken sausages in Earl's diner.
Things change, of course, but not in typical Hollywood fashion. Loretta doesn't find romance in the bucolic setting. There are no melodramatic outbursts or hokey speeches and, best of all, Myron Goble's script doesn't whip out a terminal illness to tidy things up. Instead, we're presented with well-rounded characters and reasonable plot developments leading to a conclusion that doesn't feel the least bit contrived.
Pure and simple, this is a rich story about the power of family, with a terrific ensemble cast.  Wesley Snipes, appearing midway through the film as Earl's son, Will, a successful Atlanta lawyer, does fine work, adding more texture to an already multi-layered story.
Even though Alfre Woodward is the central figure in the tale, Al Freeman Jr. walks away with the film as Earl, a seasoned, sturdy, inherently decent man who earns respect by living his values, rather than pontificating. Our world would operate much better if everyone had an Uncle Earl in their life.
Despite the wealth of vivid characters around her, Alfre Woodward's Loretta remains a bit too sketchy and her journey to self-actualization is overly abrupt. We see her change, but don't really feel it. The addition of a revelatory scene or two would have given her character more resonance.
I suspect the problem lies more with Maya Angelou than Woodward. "Down in the Delta" was originally intended to be a made-for-cable movie and it shows. While the film represents an impressive directorial debut, it is clearly a first-time effort. Too many scenes end with slow fades to white or black, and flashback sequences of a nightmarish family trauma during the Civil War are amateurishly staged and replayed excessively.
Problem areas aside, "Down in the Delta" is a powerful, rewarding experience. It is rare to see a film extol the values of devotion, hard work and dedication without becoming leaden or preachy. Hopefully, audiences will sift through the heavily-advertised holiday trifles and seek out the understated rewards of this small treasure.
© 1998 Ed Johnson-Ott
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This blog will serve as a general area to keep track of my progress in the space of software development. Content will include projects I've worked on / am contemplating, as well as note worthy problems I've figured out, and my general thoughts on aspects of programming as I develop my own style and approach.


Microsoft is bringing Gmail, Google Drive, and Calendar to Outlook.com
Outlook is getting full Google integration on the web
by Tom WarrenMicrosoft is planning to integrate Gmail, Google Drive, and Google Calendar into its Outlook.com web mail client. The software maker has started testing this integration on some accounts recently, and Twitter user Florian B was able to access it today. After a quick setup process, you simply link a Google Account to an Outlook.com account, and Gmail, Drive documents, and Google Calendar will all be automatically displayed inside Outlook.com on the web.
It looks very similar to how Outlook for iOS and Android work, with separate inboxes and side-by-side integration in the calendar. We haven’t been able to test the integration on our own Outlook.com accounts at The Verge, but Florian reveals that it seems to be an early test as you can’t add more than one Google Account, and switching between Outlook and Gmail accounts refreshes the entire page.
The Google Drive integration supports documents and files from Google’s service so you can quickly attach them to Outlook or Gmail emails. It’s not clear how many Outlook.com users will get access to this, or when Microsoft will roll this out more broadly. While most people would simply visit Gmail for their mail, this new Outlook integration could be useful for those who use a personal Outlook.com account and a G Suite email account for work.
Microsoft wouldn’t comment on exactly when all users will get access to this new feature in a statement to The Verge. “We are always looking for new ways to extend the best email experience to our customers and can confirm that we’re experimenting with a small set of Outlook.com users to learn and gather feedback,” says a Microsoft spokesperson.
Update, November 21st 5:25AM ET : Article update with comment from Microsoft.


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To activate the scroll glue on an element just add the scroll-glue attribute.

If you pass a scope variable to the attribute, scroll-glue updates the variable to true when the glue is attached or to false if the glue is released and activates/deactivates the glue according to the variables value.

If you pass an expression that is not a scope variable, the scroll-glue is bound to the result of this expression.

If you want to force one way-binding on a scope variable use double negation:

You can also specify the scroll direction by combining the following directives:


Player 1
Player 2
Congrats:
You win!
Fieldsize:
6
8
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Force attack:
Force hold piece:
Backward attack:
Queen free run:
Queen attack and run:
Sounds:


Compact online and offline disk-stores.
-
Compact a disk store on all members with that disk store.
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Compact an offline disk store.
compact disk-store
Compact a disk store on all members with that disk store.
This command uses the compaction threshold that each member has configured for its disk stores. The disk store must have the allow-force-compaction
property set to true
.
See Running Compaction on Disk Store Log Files for more information.
Availability: Online. You must be connected in gfsh
to a JMX Manager member to use this command.
Syntax:
compact disk-store --name=value [--groups=value(,value)*]
| Name | Description |
|---|---|
| --name | Required. Name of the disk store to be compacted. |
| --groups | Group(s) of members that perform disk compaction. If no group is specified, then the disk store is compacted by all members. |
Table 1. Compact Disk-Store Parameters
Example Commands:
compact disk-store --name=Disk1
compact disk-store --name=Disk1 --group=MemberGroup1,MemberGroup2
Error Messages:
"Disk store \"{0}\" does not exist.";
" for group(s) \"{0}\"";
"No members found in the specified group(s) \"{0}\".";
"Compaction was attempted but nothing to compact.";
"Error occurred while doing compaction. Reason: \"{0}\"";
compact offline-disk-store
Compact an offline disk store.
If the disk store is large, you may need to allocate additional memory to the process by using the --J=-XmxNNNm
parameter.
See Running Compaction on Disk Store Log Files for more information.
Note: Do not perform offline compaction on the baseline directory of an incremental backup.
Availability: Online or offline.
Syntax:
compact offline-disk-store --name=value --disk-dirs=value(,value)*
[--max-oplog-size=value] [--J=value(,value)*]
| Name | Description | Default Value |
|---|---|---|
| --name | Required. Name of the offline disk store to be compacted. | |
| --disk-dirs | Required. One or more directories where data for the disk store was previously written. Separate directories with commas. | |
| --max-oplog-size | Maximum size (in megabytes) of the oplogs created by compaction. | -1 |
| --J | Arguments passed to the Java Virtual Machine performing the compact operation on the disk store. For example: -J=-Xmx1024m . |
Table 2. Compact Offline-Disk-Store Parameters
Example Commands:
compact offline-disk-store --name=Disk2 --disk-dirs=/Disks/Disk2
compact offline-disk-store --name=Disk2 --disk-dirs=/Disks/Disk2 --max-oplog-size=512 -J=-Xmx1024m


This option enables the internal low voltage powerdown circuit inside the BMS. If enabled, when one or more cells remain below the provided low cell voltage parameter for a period of 45 seconds then the BMS will enter an extremely low power sleep mode to help prevent over-discharging the battery cells.
NOTE: The low cell voltage provided MUST be lower than the Minimum Cell Voltage value programmed on the Cell Settings tab.
When the low power sleep mode is triggered, all power must be removed from the BMS for a period of at least 1 minute before reapplying in order to wake the unit back up.
The presence of a voltage failsafe condition (ie: open wire fault, pack voltage mismatch fault, voltage sensor failure fault) will inhibit this feature from activating. This is to prevent a miswired or defective cell link from causing the entire BMS to shut down.
| < Active Thermistors | Cell Settings > |


I just started my first job as a software developer at Cyphercor. Super excited!
I'm a Master's student at Carnegie Mellon University and I am currently on a break.
Previously, I earned my first Master's degree in Electrical and
Computer Engineering at the University of Waterloo.
Before that I did my Bachelor's at the University of Electronic
Science and Technology of China.
I once worked with
Prof. Shuaicheng Liu
at UESTC and
Prof. Li Cheng
at the University of Alberta on computer vision.
I always enjoy coding and definitely a "bug-driven" developer.
I love to explore the cutting edge technology and apply them to
some existing projects.
I have exceedingly aesthetic tastes (known as the
Obsessive-Compulsive Disorder) for everything.
That's why I love Linux (fully customizable),
LaTeX
(prepare everything for you), Apple (well, Steve Jobs's era at
least), etc.
I love playing basketball, cycling, hiking, singing, and playing
video games.
I love Chinese food, Thai food, and bubble tea!


public interface EntryListenerFlags
| Modifier and Type | Field and Description |
|---|---|
static int |
kDelete
Entry was deleted.
|
static int |
kFlags
Entry's flags changed.
|
static int |
kImmediate
Initial listener addition.
|
static int |
kLocal
Changed locally.
|
static int |
kNew
Newly created entry.
|
static int |
kUpdate
Entry's value changed.
|
static final int kImmediate
static final int kLocal
static final int kNew
static final int kDelete
static final int kUpdate
static final int kFlags


|
Standard Reference Data Program |
Data Gateway |
Chemistry WebBook |
|
Go To: Top, Constants of diatomic molecules, References, Notes / Error Report
Data compilation copyright by the U.S. Secretary of Commerce on behalf of the U.S.A. All rights reserved.
Data compiled as indicated in comments:
LLK - S.G. Lias, R.D. Levin, and S.A. Kafafi
B - J.E. Bartmess
| EA (eV) | Method | Reference | Comment |
|---|---|---|---|
| 0.70 ± 0.10 | PD | Rackwitz, Feldman, et al., 1977 | ΔHf(BeH2){ Chase Jr., Davies, et al., 1985} is 1958 SWAG. G3(MP2)calcn(JEB): ΔHf=44.5kcal, BDE=92.1, ΔHacid=389.7; B |
| IE (eV) | Method | Reference | Comment |
|---|---|---|---|
| 8.21 | EVAL | Huber and Herzberg, 1979 | LLK |
| 8.21 ± 0.05 | S | Colin and De Greef, 1975 | LLK |
| 8.20 ± 0.06 | S | Colin, DeGreef, et al., 1974 | LLK |
Go To: Top, Gas phase ion energetics data, References, Notes / Error Report
Data compilation copyright by the U.S. Secretary of Commerce on behalf of the U.S.A. All rights reserved.
Data compiled by: K.P. Huber and G. Herzberg
| State | Te | ωe | ωexe | ωeye | Be | αe | γe | De | βe | re | Trans. | ν00 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| G 2Π | 58711 | 405.3 Z | 22.7 | 5.02 1 2 | -0.556 | -0.04 | [18E-4] 3 | 1.925 | G ← X R | 57886.2 Z | ||
| missing citation | ||||||||||||
| F 2Σ+ (4pσ) | (56606) | (2153) 4 | [10.576] 5 | [8.0E-4] 3 | [1.326] | F ← X | 56661.24 Z | |||||
| missing citation | ||||||||||||
| E 2Σ+ (4sσ) | (54134) | (1970) 4 | [10.578] 6 | [22.4E-4] 3 | [1.326] | E ← X | 54097.6 Z | |||||
| missing citation | ||||||||||||
| D 2Σ+ | (54000) 7 | D ← X | 54050 | |||||||||
| missing citation | ||||||||||||
| State | Te | ωe | ωexe | ωeye | Be | αe | γe | De | βe | re | Trans. | ν00 |
| D 2Π (3d) | (54000) 7 | D ← X | 54050 | |||||||||
| missing citation | ||||||||||||
| D 2Δ | (54000) 7 | D ← X | 54050 | |||||||||
| missing citation | ||||||||||||
| B 2Π (3pπ) | 50882 8 | 2265.94 Z | 71.52 | 10.8495 9 10 2 | 0.1016 | -0.1324 | [10.35E-4] 3 | 1.3092 | B ↔ X VR | 50976.17 Z | ||
| Watson and Humphreys, 1937; missing citation | ||||||||||||
| A 2Πr (2pπ) | 20033.19 11 | 2088.58 12 Z | 40.14 | -0.47 | 10.4567 13 | 0.3222 | -0.0042 | 10.41E-4 14 | 1.3336 | A ↔ X 15 V 16 | 20045.81 12 Z | |
| missing citation; Olsson, 1932; missing citation | ||||||||||||
| State | Te | ωe | ωexe | ωeye | Be | αe | γe | De | βe | re | Trans. | ν00 |
| X 2Σ+ (2pσ) | 0 | 2060.78 12 Z | 36.31 | -0.38 | 10.3164 17 | 0.3030 | -0.0027 | 10.221E-4 18 | 1.3426 19 | |||
| Knight, Brom, et al., 1972 | ||||||||||||
| 20 |
| 1 | Λ-type doubling; for details see Colin and De Greef, 1975. R and P lines involving v'=0 and 1 are twice as broad as the corresponding Q lines. v=2 is strongly perturbed. |
| 2 | Colin and De Greef, 1975 suggest that the B and G states result from an avoided 2Π - 2Π crossing, the former (B 2Π) having a double minimum potential curve with a potential maximum corresponding to Te = 56950 cm-1 at r=1.94 . |
| 3 | For additional Dv and higher order constants see Colin and De Greef, 1975. |
| 4 | Using isotope relations. |
| 5 | perturbation at low N. Higher vibrational levels are probably predissociated. |
| 6 | missing note |
| 7 | Strong absorption, complex structure. |
| 8 | Colin and De Greef, 1975 give 50888.57, without explanation. A~0. |
| 9 | Λ-type doubling, Δvef(v=0) = +0.217N(N+l). |
| 10 | The B↔X bands (v' ≤ 2, and fragments of the 3-3 band of BeD) consist of Q branches only except the 0-0 band which, in absorption, has P and R branches showing a marked broadening increasing with N. The predissociation mechanism involves both the unobserved 3pσ state and the first excited 2Σ+ state which is unstable except at very large r values; for details see Lefebvbre-Brion and Colin, 1977. The Q branch lines of BeH (BeD) break off at N'=31, 24 missing citation, 14 missing citation, Meyer and Rosmus, 1975 in v'=0,1,2,3, respectively, owing to the presence of a maximum in the B 2Π potential curve; see 2 . Two additional levels of BeH, very likely belonging to B 2Π but called B' 2Π (v', v'+1) in Colin and De Greef, 1975, are situated above the potential energy maximum in B 2Π: B' 2Π(v'+1), Te=[58435.8], B=6.01, D=187E-4 B' 2Π(v'), Te=[58284.8], B=8.34, D=0.0129 and higher order constants. Transitions to these levels from X 2Σ+ consist of Q branches only, breaking off at N'=14 in v'+1. |
| 11 | Taking into account the usually neglected contributions Y'00 and Y"00 to the zero point energies of A and X. A(spin-orbit) = +2.1; for an ab initio calculation see Walker and Richards, 1969. |
| 12 | Derived Horne and Colin, 1972 from pure vibronic energy separations which differ from the origins normally referred to in these tables. |
| 13 | Λ-type doubling, Δvef(v=0) = +0.0141N(N+l) -...; higher order constants in Horne and Colin, 1972. Theoretical calculations Hinkley, Hall, et al., 1972 Hinkley, Walker, et al., 1972. |
| 14 | Higher order constants in Horne and Colin, 1972. |
| 15 | Theoretical absorption oscillator strengths Henneker and Popkie, 1971, Popkie, 1971. |
| 16 | Reversal of shading in some of the bands. |
| 17 | Spin splitting constant γ0 = +0.005. |
| 18 | Higher order constants in Horne and Colin, 1972. |
| 19 | ESR sp. 24 |
| 20 | For theoretical calculations see references in Horne and Colin, 1972 Bagus, Moser, et al., 1973 Meyer and Rosmus, 1975. |
| 21 | From the predissociation by rotation Colin and De Greef, 1975 in the B' 2Π, v'+1 level (see 10), assuming dissociation into Be(1P) + H(2S). The experimental X 2Σ+ well depth of 2.161 eV is in good agreement with the calculated values De=2.115 eV Bagus, Moser, et al., 1973 and De = 2.15 eV Meyer and Rosmus, 1975. |
| 22 | From the observation of Rydberg states in the absorption spectrum, and from ab initio calculations for BeH and BeH+; see Colin, DeGreef, et al., 1974. |
| 23 | Line width increases with decreasing N; the first lines are not observed. |
| 24 | In Ar matrix at 4 K Knight, Brom, et al., 1972. |
Go To: Top, Gas phase ion energetics data, Constants of diatomic molecules, Notes / Error Report
Data compilation copyright by the U.S. Secretary of Commerce on behalf of the U.S.A. All rights reserved.
Rackwitz, Feldman, et al., 1977
Rackwitz, R.; Feldman, D.; Kaiser, H.J.; Heincke, E.,
Photodetachment bei einigen zweiatomigen negativen hydridionen: BeH-, MgH-, CaH-, ZnH-, PH-, AsH-,
Z. Naturforsch. A:, 1977, 32, 594. [all data]
Chase Jr., Davies, et al., 1985
Chase Jr.; Davies, C.A.; Downey Jr.; Frurip, D.J.; McDonald, R.A.; Syverud, A.N.,
JANAF Thermochemical Tables (Third Edition),
J. Phys. Chem. Ref. Data, Suppl. 1, 1985, 14. [all data]
Huber and Herzberg, 1979
Huber, K.P.; Herzberg, G.,
Molecular Spectra and Molecular Structure. IV. Constants of Diatomic Molecules,, Van Nostrand Reinhold Co., 1979, ,1. [all data]
Colin and De Greef, 1975
Colin, R.; De Greef, D.,
The absorption spectrum of the BeH and BeD molecules in the vacuum ultraviolet,
Can. J. Phys., 1975, 53, 2142. [all data]
Colin, DeGreef, et al., 1974
Colin, R.; DeGreef, D.; Goethals, P.; Verhaegen, G.,
The ionization potential of the BeH molecule,
Chem. Phys. Lett., 1974, 25, 70. [all data]
Watson and Humphreys, 1937
Watson, W.W.; Humphreys, R.F.,
Ultraviolet spectra of BeH and BeH+,
Phys. Rev., 1937, 52, 318. [all data]
Olsson, 1932
Olsson, E.,
Das bandenspektrum des berylliumhydrides,
Z. Phys., 1932, 73, 732. [all data]
Knight, Brom, et al., 1972
Knight, L.B., Jr.; Brom, J.M., Jr.; Weltner, W., Jr.,
Hyperfine interaction and chemical bonding in the BeH molecule,
J. Chem. Phys., 1972, 56, 1152. [all data]
Lefebvbre-Brion and Colin, 1977
Lefebvbre-Brion, H.; Colin, R.,
Anomalous isotope effects in indirect predissociations,
J. Mol. Spectrosc., 1977, 65, 33. [all data]
Meyer and Rosmus, 1975
Meyer, W.; Rosmus, P.,
PNO-Cl and CEPA studies of electron correlation effects. III. Spectroscopic constants and dipole moment functions for the ground states of the first-row and second-row diatomic hydrides,
J. Chem. Phys., 1975, 63, 2356. [all data]
Walker and Richards, 1969
Walker, T.E.H.; Richards, W.G.,
Calculation of spin-orbit coupling constants in diatomic molecules from Hartree-Fock wave functions,
Phys. Rev., 1969, 177, 100. [all data]
Horne and Colin, 1972
Horne, R.; Colin, R.,
The A2Π-X2Σ+ band system of BeH and BeD in absorption,
Bull. Soc. Chim. Belg., 1972, 81, 93. [all data]
Hinkley, Hall, et al., 1972
Hinkley, R.K.; Hall, J.A.; Walker, T.E.H.; Richards, W.G.,
Λ doubling in 2Π states of diatomic molecules,
J. Phys. B:, 1972, 5, 204. [all data]
Hinkley, Walker, et al., 1972
Hinkley, R.K.; Walker, T.E.H.; Richards, W.G.,
The variation of Λ doubling with rotational quantum number BeH and BeD,
J. Phys. B:, 1972, 5, 2016. [all data]
Henneker and Popkie, 1971
Henneker, W.H.; Popkie, H.E.,
Theoretical electronic transition probabilities in diatomic molecules. I. Hydrides,
J. Chem. Phys., 1971, 54, 1763. [all data]
Popkie, 1971
Popkie, H.E.,
Theoretical electronic transition probabilities in diatomic molecules. III. BeH and MgH (A2Π-X2Σ+) systems,
J. Chem. Phys., 1971, 54, 4597. [all data]
Bagus, Moser, et al., 1973
Bagus, P.S.; Moser, C.M.; Goethals, P.; Verhaegen, G.,
Accurate ab initio calculation of the BeH molecule. I. The X2Σ+ and A2Π states,
J. Chem. Phys., 1973, 58, 1886. [all data]
Go To: Top, Gas phase ion energetics data, Constants of diatomic molecules, References
| EA | Electron affinity |
Copyright for NIST Standard Reference Data is governed by the Standard Reference Data Act.
|
Standard Reference Data Program |
Data Gateway |
Chemistry WebBook |
If you have comments or questions about this site, please contact us.


Open PRT is a new transportation system comprised of driverless pods that take people, goods, and even services from one location to another. It's as simple as that. It's as personal and on-demand as driving a car; it's nothing like taking a bus; and it's as efficient and long-range as subways and trains.
Would you rather keep your eyes on the road or a TV show? Would you rather get stuck in traffic or in a good book? They call it the open road as if there's something inherently magical about it, turns out the things that make it magical are most likely inside the car. Either way ‐ we still have windows.
Personal rapid transit is the epoch of public transportation. The #1 reason commuters prefer cars over buses and trains is they offer personal flexibality and privacy. Open PRT accomplishes both while maintaining the virtues of public transportation like reduced costs and economic development.
Open PRT is designed around moving goods and service as much as it is moving people. Pods can move freight from ports and depots directly to their destinations at manufacturing plants and stores. All this with Zero-Emissions.


Returns the tuples of a set that meet the criteria of a search condition.
Syntax
FILTER ( set, search_condition )
| Parameter | Description |
|---|---|
|
The set through which to iterate. |
|
search_condition |
A Boolean expression (see MDX Grammar Rules). The search condition is evaluated in the context of every tuple in the set. |
Notes
This function returns the subset of tuples in set for which the value of the search condition is TRUE. The order of tuples in the returned set is the same as in the input set.
Example
The following unfiltered query returns profit for all level-0 products:
SELECT { [Profit] } ON COLUMNS, [Product].levels(0).members ON ROWS FROM Sample.Basic
This query returns the grid:
| Profit | |
| 100-10 | 22777 |
| 100-20 | 5708 |
| 100-30 | 1983 |
| 200-10 | 7201 |
| 200-20 | 12025 |
| 200-30 | 4636 |
| 200-40 | 4092 |
| 300-10 | 12195 |
| 300-20 | 2511 |
| 300-30 | 11093 |
| 400-10 | 11844 |
| 400-20 | 9851 |
| 400-30 | -394 |
| 100-20 | 5708 |
| 200-20 | 12025 |
| 300-30 | 11093 |
To filter the above results to only show negative Profit, use the Filter function, passing it the original set and a search condition. Filter will only return the set of members for which the search condition is true (for which Profit is less than zero).
SELECT { Profit } ON COLUMNS, Filter( [Product].levels(0).members, Profit < 0) ON ROWS FROM Sample.Basic
The resulting query returns only the products with negative profit:
| Profit | |
| 400-30 | -394 |
The search expression in Example 1 compared a value expression (Profit) with a value. You can also filter using a member attribute as the search condition. For example, you can use the Filter function to only select members whose Caffeinated attribute is TRUE.
SELECT { [Profit] } ON COLUMNS, Filter( [Product].levels(0).members, Product.CurrentMember.[Caffeinated]) ON ROWS FROM Sample.Basic
This query returns profit for the members that are caffeinated:
| Profit | |
| 100-10 | 22777 |
| 100-20 | 5708 |
| 200-10 | 7201 |
| 200-20 | 12025 |
| 300-10 | 12195 |
| 300-20 | 2511 |
| 300-30 | 11093 |
To understand the search condition, Product.CurrentMember.[Caffeinated], it may be helpful to read it right to left: Filter is searching for presense of the Caffeinated property on the current member, for each member in the input set, which happens to be from the Product dimension (The CurrentMember function requires the dimension name as its argument).
Filter is an iterative function, meaning that at every member or tuple in the set being evaluated, the member being operated upon is the "current member," until Filter has looped through the entire input set and evaluted the search condition for each tuple. So to see how the previous query results were generated, it would be useful to see first which members actually have the Caffeinated attribute set to true. The following unfiltered query uses a calculated member to reveal which of the level-0 product members is caffeinated. The IIF function returns a value of 1 for each member whose Caffeinated attribute is set to TRUE, and returns a value of 0 otherwise.
WITH MEMBER Measures.IsCaffeinated AS 'IIF(Product.CurrentMember.[Caffeinated], 1, 0)' SELECT { IsCaffeinated } ON COLUMNS, [Product].levels(0).members ON ROWS FROM Sample.Basic
This query returns the grid:
| IsCaffeinated | |
| 100-10 | 1 |
| 100-20 | 1 |
| 100-30 | 0 |
| 200-10 | 1 |
| 200-20 | 1 |
| 200-30 | 0 |
| 200-40 | 0 |
| 300-10 | 1 |
| 300-20 | 1 |
| 300-30 | 1 |
| 400-10 | 0 |
| 400-20 | 0 |
| 400-30 | 0 |
| 100-20 | 0 |
| 200-20 | 0 |
| 300–30 | 0 |
Looking at the results for the second query, you can begin to see that the search condition is evaluated for each tuple in the input set, and that only the tuples meeting the search condition are returned.
Example 2 introduced the CurrentMember function. Even when CurrentMember is not explicitly called, Filter operates in the context of "the current member" while it iterates through a set. Filter and other iterative functions are processed in a nested context.
By default, Filter operates in the current-member context of top dimension members. You make the MDX context smaller by using a slicer (the Where clause), which overrides the built-in top-dimensional context. Additionally, you can override the slicer context by specifying context in the search condition argument for Filter.
The following query returns the Profit values for Western Region, for Qtr1. Note that the MDX context is West, Qtr1.
SELECT { [Profit] } ON COLUMNS, [Product].levels(0).members ON ROWS FROM Sample.Basic Where (West, Qtr1)
When adding a filter to the above query, the values for Profit are still evaluated as (Profit, West, Qtr1), because the sub-context for Filter is based on the main context.
SELECT { [Profit] } ON COLUMNS, Filter( [Product].levels(0).members, Profit < 0) ON ROWS FROM Sample.Basic Where (West, Qtr1)
In the next query, the values for Profit are evaluated as (Profit, West, Qtr1), even though the outer context is (Profit, Market, Qtr1). This is because the inner context in the Filter function overrides the outer context of the slicer (West replaces Market).
SELECT { [Sales] } ON COLUMNS, Filter( [Product].levels(0).members, (Profit, West) < 0) ON ROWS FROM Sample.Basic Where (Market, Qtr1)
The above query returns the Sales values for West, Qtr1 for members of Product whose Profit for West, Qtr1 was less than 0.
| Sales | |
| 100-20 | 2153 |
| 400-30 | 1862 |
| 100-20 | 2153 |
The following query on Sample Basic returns Qtr2 sales figures for products where the sales have increased by at least 10% since Qtr1.
SELECT { Filter ( [Product].Members, [Measures].[Sales] > 1.1 * ( [Measures].[Sales], [Year].CurrentMember.PrevMember ) ) } on columns FROM sample.basic WHERE ([Year].[Qtr2], [Measures].[Sales])
| Cola | Dark Cream |
| 16048 | 11993 |
The following query on Sample Basic returns sales figures for product family "100" where the monthly sales of that product family are greater than 8,570. The filtering logic is stored as a named set in the WITH section.
WITH SET [High-Sales Months] as ' Filter( [Year].Levels(0).members, [Measures].[Sales] > 8570 ) ' SELECT {[Measures].[Sales]} ON COLUMNS, {[High-Sales Months]} ON ROWS FROM sample.basic WHERE ([Product].[100])
| Sales | |
| Apr | 8685 |
| May | 8945 |
| Jun | 9557 |
| Jul | 9913 |
| Aug | 9787 |
| Sep | 8844 |
| Dec | 8772 |


Focus on waterways, says Minister
by Special CorrespondentMinister of State for External Affairs V. Muraleedharan said here on Saturday that the State government was focussing on elevating two-lane roads into four-lanes and four-lanes into six-lanes while not exploring the immense possibilities of developing its waterways.
“The State can improve its transportation facilities by developing waterways. Instead, we are talking about expansion of highways,” he said in his inaugural address at the Kerala Science Fest organised by Swadeshi Science Movement at Marine Drive.


Typedefs | |
| typedef actionlib::SimpleActionServer< cpswarm_msgs::TaskAllocationAction > | Server |
| The server object that offers the task allocation auction as action server. More... | |
Functions | |
| void | allocation_callback (const cpswarm_msgs::TaskAllocatedEvent::ConstPtr &msg) |
| Callback function to receive the task allocation result. More... | |
| void | bid_callback (const cpswarm_msgs::TaskAllocationGoal::ConstPtr &goal, Server *as) |
| Callback function to participate in a task allocation auction. More... | |
| int | main (int argc, char **argv) |
| A ROS node that exchanges relative kinematics between CPSs in a swarm. More... | |
| void | pose_callback (const geometry_msgs::PoseStamped::ConstPtr &msg) |
| Callback function to receive the position of this CPS. More... | |
| void | uuid_callback (const swarmros::String::ConstPtr &msg) |
| Callback function to receive the UUID of this CPS. More... | |
Variables | |
| cpswarm_msgs::TaskAllocatedEvent | allocation |
| The result of the task allocation auction. More... | |
| geometry_msgs::Pose | pose |
| Position of this CPS. More... | |
| bool | pose_received |
| Whether a position of this CPS has been received. More... | |
| Publisher | publisher |
| Publisher to submit a bid to the auction. More... | |
| int | task_id |
| The ID of the task that is auctioned. More... | |
| string | uuid |
| The UUID if this CPS. More... | |
| typedef actionlib::SimpleActionServer<cpswarm_msgs::TaskAllocationAction> Server |
The server object that offers the task allocation auction as action server.
Definition at line 16 of file bid_action.cpp.
| void allocation_callback | ( | const cpswarm_msgs::TaskAllocatedEvent::ConstPtr & | msg | ) |
Callback function to receive the task allocation result.
| msg | The result of the task allocation auction. |
Definition at line 62 of file bid_action.cpp.
| void bid_callback | ( | const cpswarm_msgs::TaskAllocationGoal::ConstPtr & | goal, |
| Server * | as | ||
| ) |
Callback function to participate in a task allocation auction.
| goal | The bidding goal. |
| as | Reference to the action server object. |
Definition at line 83 of file bid_action.cpp.
| int main | ( | int | argc, |
| char ** | argv | ||
| ) |
A ROS node that exchanges relative kinematics between CPSs in a swarm.
| argc | Number of command line arguments. |
| argv | Array of command line arguments. |
Definition at line 143 of file bid_action.cpp.
| void pose_callback | ( | const geometry_msgs::PoseStamped::ConstPtr & | msg | ) |
Callback function to receive the position of this CPS.
| msg | The pose of this CPS. |
Definition at line 52 of file bid_action.cpp.
| void uuid_callback | ( | const swarmros::String::ConstPtr & | msg | ) |
Callback function to receive the UUID of this CPS.
| msg | The UUID of this CPS. |
Definition at line 73 of file bid_action.cpp.
| cpswarm_msgs::TaskAllocatedEvent allocation |
The result of the task allocation auction.
Definition at line 31 of file bid_action.cpp.
| geometry_msgs::Pose pose |
Position of this CPS.
Definition at line 26 of file bid_action.cpp.
| bool pose_received |
Whether a position of this CPS has been received.
Definition at line 41 of file bid_action.cpp.
| Publisher publisher |
Publisher to submit a bid to the auction.
Definition at line 21 of file bid_action.cpp.
| int task_id |
The ID of the task that is auctioned.
Definition at line 46 of file bid_action.cpp.
| string uuid |
The UUID if this CPS.
Definition at line 36 of file bid_action.cpp.


Tips And Tricks For Real Estate Investment Success
Every day people get into real estate investing thinking that they will make a lot of money, but sadly they go into blindly because they aren't familiar with how to do it right. If you are getting into this type of investing, and want to know the right way to invest, you should continue to the following article. Keep reading for great investing tips!
Always get an inspection before buying a house. Sellers can offer to pay for the inspection, but that gives them the right to use a favorable inspector. Make sure your report is neutral.
See if there are all of the stores and schools that you'll need around the real estate that you're thinking of getting for your family. You don't want to move to an area where you're not near anywhere that you need to go to. It would cost you a lot in traveling expenses, so keep that in mind when you move anywhere.
A fixer-upper may be cheap, but think about how much you have to renovate to bring it up in value. If the property only needs cosmetic upgrades, it may be a good investment. However, major structural problems can very costly to fix. In the long-run, it may not give you a good return on your investment.
Think about employing a professional property manager. Even though it will eat into your profits, in the end, it can be a wise investment. Property management can screen the renters interested in your property, as well as take care of repair costs.
You must consider location when you look at real estate. The proximity to great areas will increase the value a ton. You want to think about the potential of the investment and the location as well.
Once you set up an investment plan, get someone else to take a look at it. Even if the person is not an expert in the field, they may be able to point out some things that just are not going to work. An expert, though, can help you adjust your plan to make it more suitable for your needs. They may also be able to talk to you about marketing as well.
Always consider the market if you are looking to buy property to turn around and resell it. It can be risky to invest in a market that is flooded with available properties. You don't want to be stuck with something that you have to sell at little or no profit. Understand that you may have to wait to get the best price so make sure you can do that.
Make sure that you have of your finances in order so that you can jump on opportunities where time is crucial. You could lose out on the deal of lifetime if you wait until you find a property and THEN try to get loans and financing in order. Having the ability to act quickly often is the difference between a deal of a lifetime and an opportunity lost.
Find out what kind of building is best for you to maintain. You shouldn't just think about buying when you're dealing with real estate. You have to maintain it well to sell it. Smaller homes are easier to maintain than large ones. Don't take on more than you are able to handle.
Make being on time a priority. Other people's time is just as valuable as yours, whether the person in question is another investor, a contractor or an agent. If you respect their time, they will often respect you as a person and a business associate. As a result, you could create lasting relationships that benefit your end goals.
Your rental contract should include the requirement of a security deposit. This protects your interests if your tenant leaves your property in an uninhabitable state when he moves out. The contract gives you the right to keep the security deposit in order to hire a cleaning service or a repair service to fix the problems.
Do not sign any contracts to buy a piece of land before you do your research carefully to confirm the ownership of the land. Hire your own surveyor to identify the property lines clearly. This prevents misrepresentation of the piece of property for sale, and it mitigates any future problems.
A good way to assess the quality of a potential neighborhood is to research current vacancy rates. If you learn that many properties in the area are vacant or unrented, you can probably bank on the fact that you will have a hard time generating rental income.
Be very broad in your estimates of expenses and income. Estimate high when it comes to repairs, expenses and improvements. Estimate low when it comes to income. When you do this, you will avoid disappointment. Furthermore, you will be more likely to manage your money well and end up with more of it in your pocket.
If you are looking for quick and easy profit, real estate is usually not the place for an inexperienced person. What they usually end up with is an expensive lesson. If you are jut getting into real estate, start small so that you can take the time to learn and can use the time to develop a network of people who can help you.
Have an extra exit strategy or two. When it comes time to sell, you might find it takes longer than you would like. By having a back up plan or two, you can keep yourself financially safe so you are able to move forward in your investment property career.


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About us
The XYZ Photography is a non-profit organisation. We aim to create an inclusive atmosphere for people of all ages and backgrounds, providing guidance and support to all our members. No previous experience nor expensive camera gear is needed to join and fully enjoy what we have to offer.
Whether you are a professional focused on networking with others in the industry or an amateur enthusiast wanting to gain new skills in a creative environment, you will find something to suit your needs. The range of events organised throughout the year caters for people of all interests, be it landscape, portrait, travel, macro, sports or documentary.
We meet at workshops, social meetings and lectures from September until April every Thursday at 7 pm in the Dundee City Council building on Lindsay Street.
See location on map
During the summer we gather at informal meetings and travel together across the country and/or abroad, depending on our members' preferences.


Full-Stack Developer
Bringing Ideas to life!
About Me
I'm Nashica, an Ontario based Junior Full- Stack Developer.
I create beautiful and functional websites. I have a diverse set of skills that include HTML5, CSS3, Bootstrap framework and Javascript. I love working in the area of responsive design.
My services include product analysis, through to full product design - from wireframing and prototyping, to front-end development. My unique background in the Hospitality and Business Management gives me an edge as I know the importance of delivering a superior product with excellent customer service.
Interested in working together? Take a look at my most recent projects. Please feel free to contact me for porojects and collaborations.
Projects Contact Me


var
var(name, type, default, min, max)
The variables sheet creates connections between a variable and a user interface control, such as a slider, textfield or check box. The most convenient way of creating variables is through the Python > Variables menu. Variables created like this become part of the global namespace, meaning you can access them just like any other variable.
|
var("teapots", NUMBER, 42, 0, 100)
for i in range(teapots):
rotate(random(360))))
image("teapot.jpg", random(WIDTH), random(HEIGHT))
|


OUR TEAM
Hakizumwami Birali Runesh, PhD
Prior to joining the University of Chicago, he served as Director of Scientific Computing and Applications at the University of Minnesota Supercomputing Institute, Research Associate at the Hong Kong University of Science and Technology, Research associate for the Multidisciplinary Parallel-Vector Computer Center at Old Dominion University, and Assistant Professor in the Civil Engineering Department at the University of Kinshasa (Faculté Polytechnique). Dr. Runesha holds a Ph.D. in Civil Engineering, is a fellow of the Rwandan Academy of Science, and has more than 25 years of experience in high performance computing, Big data and scientific software development. He serves as principal investigator on a number of funded research grants. Dr. Runesha is the current President of the Great Lakes Consortium for Petascale Computation (GLCPC)-USA and serves on the Rwandan National Council of Science and Technology.
Teodora Szasz, Ph.D.
Teodora engages the community of researchers involved in computational image analysis at UChicago across application areas such as medicine, biology, physics, chemistry, and economics. She is developing best practices for using image data visualization and analysis tools. She has programming experience with state-of-the-art software toolkits for medical image processing and visualization. She collaborates in different project with the Department of Radiology at the University of Chicago, including: deep learning methods and quantitative analysis tools for prostate cancer detection. Teodora earned her doctorate from the University of Toulouse (France) at Toulouse Institute of Computer Science Research, where she was working on beamforming methods for medical ultrasound imaging. Teodora earned her master’s degree in multimedia technologies at the Technical University of Cluj-Napoca in Romania, with focus on medical image processing. During her studies, she was a student worker for three years at Neurostar Gmbh (Germany) where she developed tools for the treatment of Parkinson's disease.
Aytekin Oto, MD, MBA
Dr, Oto has a wide range of experience and expertise in the imaging of diseases affecting abdomen and pelvis. His research interest focus is development and clinical application of novel prostate MRI acquisition and interpretation so as to facilitate and improve the efficiency of prostate cancer and develop image guided prostate therapy options for the appropriate patients diagnosed with prostate cancer. His research has resulted in more than 200 publications and over 150 scientific exhibits at national and international meetings.  His group has already has developed new MR sequences, pilot CAD software for prostate MRI and tested MR guided therapy methods such as laser and focused ultrasound ablation in clinical and pre-clinical studies. He has several industry, foundation and NIH grants and serves at the Editorial Board of Radiology. Dr. Oto received numerous awards including Distinguished Investigator Award, RSNA honored educator award and Distinguished Senior Clinician Award.
Aritrick Chatterjee, PhD
His current research focuses on the improved diagnosis of prostate cancer using MRI, including the development of new MRI acquisition, analysis and interpretation methods to provide reliable information such as cancer localization, volume, and aggressiveness for deciding the optimal treatment option. His work focuses on estimating prostate tissue composition non-invasively using Hybrid Multidimensional MRI and developing CAD risk analysis tool that can effectively detect prostate cancer. His other projects involve using pre-treatment quantitative multi-parametric MRI and determining its association with biochemical outcome in men treated with radiation therapy for prostate cancer, investigating the feasibility of dynamic contrast enhanced MRI using low doses of contrast agent (Gadolinium), and ultrafast DCE-MRI for diagnosis of prostate cancer. Aritrick has a wide range of expertise in medical imaging, especially MRI. He received his PhD from the University of Sydney focussing on understanding the biophysical basis of diffusion in prostate MRI and received his MSc degree from University College London and worked on imaging the microstructure of the brain using oscillating gradients on MRI. Aritrick (PI) and Dr Oto (mentor) received this RSNA Education grant (ESCH1805) to create an interactive app with multi-parametric MRI - whole mount histology correlation for enhanced prostate MRI training of radiologists.
Dr. Stephen Thomas
Dr. Thomas has clinical expertise in MR imaging of the chest, abdomen and pelvis with specific interest in MR imaging of the genitourinary system. His publications include many scientific articles, dozens of educational exhibits presented at major society meetings and several book chapters. Dr. Thomas is an excellent educator and has won several awards from his department
Ambereen N. Yousuf, MBBS
Dr. Yousuf’s work involves clinical trials management, data management, regulatory and compliance issues, with a focus on prostate cancer and MR imaging. As part of the prostate research imaging group at the University of Chicago, she has contributed towards and co-authored several publications ranging from novel methods of MRI acquisition and interpretation to image-guided, minimally invasive therapy for patients diagnosed with prostate cancer. Dr. Yousuf received her medical degree from Karachi University, Pakistan.
Milson Munakami, MSc
His primary focus is on developing and building data-intensive, dynamic and more interactive scientific applications using a variety of scientific techniques and tools that allow to leverage the high-performance storage, resources and compute capacity of on-premise High-Performance Computing (HPC) data center called Midway. His role also involves providing technical support to troubleshoot researcher, hardware, and scientific software problems, evaluation and testing of new or future development, releases, and enhancements to streamline the scientific researches and studies. Milson has a master’s degree in computer science (M.Sc.) from Boise State University (BSU), Boise, Idaho, USA. He is an experienced professional with great experience working in multifaceted academia and enterprise environments and has held various important positions. He is passionate about core technologies like Python, JAVA, .NET, C, C++, C#, PHP, Database and Blockchain to name just a few. He loves to write, run codes, and fond of contributing and sharing to the community. He strongly believes that learning new skills is a never-ending process and it that it gets bigger and better when shared with others.


{% extends "skeleton.html" %} {% block title %} {% if user.is_authenticated %}
Go to profile
To reset your sslscout password please enter your email address below:


By: billyzhang2010 原文发布于：2009-07-19 08:03:50
http://www.microsoft.com/atwork/maintenance/speed.aspx
a) Free up disk space
b) Speed up access to data(To use Disk Defragmenter)
c) Detect and repair disk errors by running the Error Checking utility
d) Protect your computer against spyware
e) Learn all about ReadyBoost


Sequential Testing
Introduction
The last post proposed a solution to the multiple testing problem that often invalidates A/B test results test planning. The idea is to calculate the sample sizes you need for your test in advance, and then wait for your control and variant groups to hit those sample sizes in order to call the test. This approach is a significant methodological improvement from the “call it when it’s significant” heuristic: It prevents you from compounding the false positive rate of your test by checking on it all the time.
But there’s a different issue with planning the test in advance and running it until the end: It’s slow. I use “slow” to mean “slower than it needs to be,” in the sense that you will likely end up waiting too long to call a test for the variant when you could’ve made the call earlier. This waiting around is expensive – the difference between running a test for a day or two and a week or two matters a lot for the teams and businesses running the tests. Often, this big of a time difference can have massive effects on metrics, revenue, learnings, etc., so teams benefit from being able to call their tests faster without sacrificing any statistical rigor.
But how do we do that without checking in on the test all the time? Enter sequential testing.
Sequential Testing
Sequential testing is a method for running experiments that allows us to evaluate the results of the test we’re running along the way instead of waiting to hit a pre-determined sample size. Intuitively, you might think about sequential testing like this: If early on in my test I see a massive lift in my metric, I should be able to use a lower p-value than the one I set at the start of my test to call it. It’s earlier, hence the lower p-value, but the intuitive idea is that the metric lift is so big that the p-value we’d see would be smaller than some yet undetermined p-value threshold, such that we could call the test.
In A/B testing world, this boils down to building checkpoints into our tests. For instance, imagine you have a test that you’re expecting to take six days to hit the final sample size that you need. If you build in three checkpoints, then you can check in on your test on day two, day four, and day six (the end of the test). On day two, if the p-value for your test is lower than the pre-determined day two p-value needed, you call the test. If it’s not, you move on to day four and repeat. Once you get to day six, if the test is still insignificant you call it for the control and end the test.
This gives us the best of both worlds: We have a setup where we can call the test on day two if the lift is big enough, but we can do so without inflating the false positive rate of our test. In practice, this means often being able to call tests in half, a third, a quarter, etc. of the time it’d otherwise take, which is hugely valuable for the team running the test.
Statistical note: There are a number of ways to determine what p-value to use at each checkpoint when planning the test. We use the R package
rpact
for planning tests, and we plan our tests using the O’Brien-Fleming method (with alpha spending). This results in p-value thresholds that increase over time and asymptote to a value slightly less than the initial alpha you specified, depending on the number of checkpoints you build into your test. Another popular method is Pocock’s approach.
In practice
So how does this work in practice? We build an internal tool that lets you plan a test given a few inputs:
- The alpha level (we generally use 20%, since we’re not particularly afraid of false positives and want to be able to run tests quickly)
- The power (we generally use 95%, since we don’t want to take on many false negatives)
- The minimum detectable effect
- The baseline conversion rate
- The expected number of users entering the funnel per day
- The number of checkpoints to build in
- The split of the test (is it 50/50?)
- The number of variants (is it a true A/B test? Are there multiple variants being tested?)
With those inputs, we generate a test plan which you can save, tie to a JIRA card, and send to Slack. Then all you need to do is turn on your test and wait for it to hit the first checkpoint. Once it does, you evaluate the test to get a p-value, compare it to the p-value threshold that the test plan provided at the first checkpoint, and call the test if it’s significant. If it’s not, you keep running the test up to the next checkpoint and do the same thing, and so on.
The Bottom Line
The main takeaway from this post is that sequential testing lets us solve two huge problems in A/B testing simultaneously: It lets us run our tests fast, and it lets us do it without sacrificing any statistical rigor. Too often, I see teams committing atrocities against statistics in the name of moving fast when they don’t need to be – using sequential designs for your A/B tests lets you control the false positive and false negative rates of your A/B tests while also allowing you to make calls on those tests as quickly as possible, which is hugely valuable.
And with that, we’ve concluded a four-part series on A/B testing! Hopefully you found this interesting and useful, and have taken something away that will be beneficial for your own work. Or, if I’m lucky, maybe you’re even considering overhauling how you run A/B tests.


Dance is the expression of your soul. We the people of Aavartan Dance Club express anger ,love ,joy ,sadness and even humor. ADC is one of the most glamorous and energetic club which rocks the floor and sets the stage on fire.We have diverse mix of students who are passionate about dance. We do organise various dance events in the campus and even participate in various dance competitions outside the campus.
We are a family, that is bound together by the thread of 'Love For Dance'. It's a place where you come, you dance, and then you never want to leave again.
Dance is about its fun, its frenzy, its excitement and Aavartan Dance Club is about all of the above things


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Created by Niels Leenheer.
Please note that the HTML5 test is not affiliated with the W3C or the HTML5 working group.
HTML5 Logo by W3C.
Browser detection by WhichBrowser.


For when I see you, (without joking) Your eyes, lips, breasts, are so provoking; They set my heart more cock-a-hoop, Than could whole seas of cray-fish soupe.
For when I see you, (without joking)
Your eyes, lips, breasts, are so provoking;
They set my heart more cock-a-hoop,
Than could whole seas of cray-fish soupe.
from To A Young Lady, With Some Lampreys (c.1720) by John Gay


I am in my fourth year of Computer Science at the University of Waterloo.
I have 5 years of diverse programming experience through personal projects and industry experience.
If my skills or experience align with any potential opportunities available, please feel free to reach out through LinkedIn or email, I would love to hear from you!
Programming Languages
C · C++ · Go · Python · SQL · Bash Java · VBA · HTML · CSS · R
Tools
Redis · Android Studio · Grafana Adobe Photoshop & Dreamweaver
Project Management
Git · Jira · Slack · Phabricator
Operating Systems
Windows · Mac OS · Linux
Languages
English · French · Punjabi
Fields of Interest
Backend · Data Science · Machine Learning Technical Program Management
Work Experience
Employment history
(Click for more information on each role)
Software Engineering Intern
Software Developer Intern
Software Engineering Intern
Software Engineering Intern
Recruitment Administrator
Personal Projects
Technical side projects and extra cirriculars completed in class and through personal interest
(Click for more information on each project)
Twitter Sentiment Analysis Website
Personal Project
Chess Game
Personal Project
Virtual Reality App
Personal Project
Home Appliances Database
Personal Project
Tile Matching Memory Game
Personal Project
Air Cadets
Extra Cirricular
Interests and Hobbies
Cooking
Trying different cuisines with creative recipes
Squash
Playing sport for over 8 years
Travelling
Enjoy travelling the world to explore new cultures
Adecco Employment Services Mississauga, Ontario Jun - Aug 2017
In my first job, I began as an administrative assistant and got promoted to a recruitment administrator in less than 3 months. My duties began with filing and organizing resumes. In just a few weeks I was tasked with covering the reception desk, booking appointments and pre-screening candidates over the phone. I also began shadowing recruiters and interviewing candidates.
Implemented new filing system for organizing resumes by skill set
Held position of receptionist to answer calls, schedule meetings, speak with walk-in candidates
Pre-screened over 500 candidates on the phone
Read through over 1000 resumes and matches suitable candidates to open job positions
Participated in department meetings to fill job openings from clients
Software Engineering Intern
Uber San Francisco, California May - Dec 2021
In my latest position, I returned to the Business Intelligence team at Uber, which works on creating software solutions that help to analyze business data. During my 8-month internship I worked on several projects. Most of my work was on the internal queryrunner system that runs queries on Uber’s internal data. I worked to improve the performance of the queueing algorithm by implementing a caching system to cache repetitive queries that were overusing resources. The newly implemented system helped to save hundreds of thousands of queries from being run per week, saving resources and improving the health of the service. I also worked to surface a user interface that users could leverage to manage their past and present queries.
Collaborated with other teams to maximize improvements for query caching system
Developed several options for query caching system and presented to team
Implemented caching system in Go for internal query system improving service performance
Created backend endpoints for user interface that users can manage queries on
Added ability to filter queries by user, date, status and type of query
Presented and documented improvements from new caching system and presented them to stakeholders
Received outstanding co-op evaluation
Software Engineering Intern
Uber San Francisco, California May - Aug 2019
During this position, I worked on the Customer Obsession team at Uber, which focuses on the support section of the app. I worked on a major production issue that the team was facing. I took on a backend engineering role and learned a new programming language, Golang. I also participated in weekly meetings with senior software engineers and other stakeholders to discuss my solution to the problem. By the end of the internship I was able to deploy my code to production successfully and even support a new production project for the last two weeks.
Programmed in Go to implement new data structures and methods to existing code repository
Participated in biweekly stand-up meetings with high level engineering managers to discuss solution
Collaborated with diverse team to design efficient solution while maintaining best practices
Conducted extensive testing for code to maintain overall coverage of 90%+
Leveraged Uber internal tools to create frontend dashboard for team to view
Scheduled meetings with relevant internal stakeholders to present project progress
Presented detail solution in code review meetings
Deployed code to production and rolled out newly implemented features
Managed weekly tasks using Jira and updated project development on storyboard
Notable Projects:
Implemented blackhole detection dashboard to display incoming and outgoing rates of help tickets, and highlight tickets that are not being routed to corresponding customer service representatives
Implemented distributed locking system to solve problem of dry queues, in which tickets are not being properly routed to customer service agents
Software Developer Intern
Unity Vancouver, British Columbia Sept - Dec 2020
During this position, I was on the Automation team at Unity, which works to create automated software solutions for projects. My team specifically worked to automate the testing process for multilevel projects. I worked on a confidential project to help automate the testing process.
Created end-to-end testing pipelines, scripted in Postman and Jenkins
Worked with customer to create a solution that fit for their team
Wrote and tested multiple REST APIs with CRUD endpoints
Created front-end dashboards using React and HTML
Presented and documented testing solution to team
Received outstanding co-op evaluation
Notable Projects:
Created user interface for customer to use for testing
Software Engineering Intern
Uber San Francisco, California Jan - Apr 2020
During my second internship at Uber, I was on the Business Intelligence team at Uber, which creates and manages software solutions to analyze business data. I worked on an important internal service used by many departments which runs queries on Uber’s data. My project was designing and implementing a new queueing algorithm for queries to solve current issues in the existing system which include lack of a prioritization scheme and inefficient use of resources. I worked autonomously to determine new requirements, design two algorithm options, arrange design review meetings to select an option, create an implementation schedule and finally implement the new algorithm to production. It was a unique experience that allowed me to develop deeper technical knowledge with active exposure to project management in an industry application.
Collaborated with key stakeholders to determine requirements for new system
Developed two options for a new queuing algorithm, a round robin and multilevel queueing system
Scheduled and led multiple design meetings to discuss and select algorithm
Developed implementation schedule with milestones to monitor progress, tracked in Jira
Coded round robin queueing system in Go with prioritization scheme, datasource resource management and scalable features
New system achieves fairness in queuing with use cases including VIP users and rogue users
Tracked action items against relevant developers for project dependent tasks
Tested code locally, in staging, and in production to ensure new changes were successfully working
Wrote Bash scripts to perform load testing on new system
Created metrics and new graphs in Grafana to monitor queue sizes, health, and operations
Conducted phased implementation to roll out new changes to production
Presented and documented new service to show changes and future steps for project
Received excellent co-op evaluation
Notable Contributions:
Created cancel bulk endpoint with username, email and datasource filters to cancel running queries in bulk
Updated frontend Grafana dashboard for staging environment
Home Appliances Database
Turner Fenton Secondary School Sep 2015 - Mar 2016
Designed relational database of home appliances in Microsoft Access
Created for parents to maintain records of appliances including warranty, model, serial number, price and date of purchase
Programmed macros in SQL for data entry forms and designed user interface features including buttons & main menu
Air Cadets
892 Snowy Owl Squadron Sep 2010 - Jun 2017
Achieved rank of Warrant Officer Second Class
Obtained positions of Flight Commander and Senior Instructor
Instructed classes on aviation, meteorology, survival, creative thinking, leadership and drill
Assisted squadron commander in developing schedules, organizing events, conducting inspections and cadet evaluations
Commanded squadron of 120 cadets on parade square with extreme precision of drill
Received Long Service medal in 2016 for 5 years of service
Virtual Reality App
Hack the North Sep 2018
Led a team of 3 during 36 hour Hack the North hackathon
Created virtual reality app with calming environments for students to reduce anxiety
Utilized Unity to design virtual environment and Android Studio to design app interface
Tested sections of code before integrating with Git
Tile Matching Memory Game
Turner Fenton Secondary School Feb - Apr 2015
Designed game using Java as a brain exercise for memory loss
Integrated and tested game on end users including friends and family
Created main menu, difficulty selection, multiple levels & score tracking with aesthetic user-friendly design
Chess Game
University of Waterloo Apr 2019
Designed and implemented chess in a group of 3 using C++
Implemented observer pattern to organize code and follow software design principles
Programmed extra features including human vs computer game
Monitors illegal moves and captures pieces based on standard rules of chess
Twitter Sentiment Analysis Website
Yuuvis Hackathon July 2019
Worked in group of 3 during 24-hour Yuuvis hackathon in San Francisco
Created website using HTML & CSS for companies to use to view tweets on their company
Implemented search bar and graphs of statistics for companies to view
Utilized Twitter API and sentiment analysis to determine if tweets positive or negative


Ideal Frontend and Rails App
Kunibiki.rb #3 LT
@murajun1978
Web Developer
Founder of Shinosaka.rb
Joined “
6vox
”
Frontend
Awesome
React
Awesome
Netlify
React
UI Library
virtual DOM
Component-Based
JSX
ES2015
ECO System
create-react-app
Component-Based
Loose coupling
Reusable
Component
React.component
React.PureComponent
Stateless Functional Component
Promise
API calls
Render Component
You need SSR ?
SEO …
Client side rendering
Simple config
Easy deploy
CDN
Fast rendering
No monitaring server
Netlify
CDN
One click HTTPS
Auto deploy
Free for open source projects
Backend
Awesome
Rails
Awesome
Json Web Token
Rails
Generator
ORM
ECO System
Fast development
Api mode
REST
Simple
Stateless
Resource
Uniform Resource Identifier(URI)
GraphQL
Query language for API
Get many resources in a single request
Express …
GraphQL on Rails
Powerful ORM + GraphQL
Sounds good
Json Web Token
Used Web Client and Mobile App
Used across different domains
Try Next
Awesome
Relay
Awesome open source project
Thank you !


Batch of Sriracha Hot Chili Sauce recalled over 'risk of explosion'
by Stephen McNeiceA particular batch of hot chilli sauce is being recalled over fears it could explode.
The Food Safety Authority of Ireland (FSAI) says the contents of the Sriracha Hot Chili Sauce bottles could explode once they are opened.
It warns that could poses a risk of irritating eyes or skin.
The affected product is a 740 millimetre bottle with a best before date of March 2021.
It affects batch code H9TMKA 44 33 of the product, which originated in the US.


Let's say that we have four golfers: Alex, Bob, Chris, and Dan, of various abilities who are in a competition against each other. Here are the players and their handicaps:
| Alex | 14.8 |
| Bob | 9.9 |
| Chris | 1.5 |
| Dan | 26.4 |
The course has the following slope and rating:
So, using the formulas above, here are their course handicaps (only the slope is used to determine the course handicap):
| Alex | 16 |
| Bob | 11 |
| Chris | 2 |
| Dan | 28 |
And, finally, here are their gross and their net scores:
| Golfer | Gross | Net |
| Alex | 91 | 75 |
| Bob | 86 | 75 |
| Chris | 74 | 72 |
| Dan | 99 | 71 |
Dan wins. He is the only one in the group who actually shot better than his handicap, so he deserved to win.


Nathaniel Ford Realtor & Director of Development Sunshine Home Sales & Commercial Brokerage
- Contact Information -
Phone -<PHONE_NUMBER>
Email-<EMAIL_ADDRESS>This charming historic Bungalow with hardwood floors features a split bedroom plan. This home offers a double lot and separate workshop garage. The master Bedroom comes with a walk-in closet and with its own Florida room that can be used as an office or sitting area. The original hardwood floors are throughout the house. Other features include a one car garage, and front walkway, vinyl privacy fence and backyard parking pad. The roof is just one year old. No flood insurance is required. The Town of Belleair offers a great Rec center with classes, a gym, and soccer fields. There are shops, restaurants in walking distances. Morton Plant Hospital is also nearby. Great location near the Pinellas Trail and just two doors down from Belleair Country Club Golf Course. Also a short walk to the Wildwood Café. .
BUYING PROCESS
Why do you want to buy?
Whether you’re a first time home buyer, investor, or just looking to find a new area it’s important to know WHY you want to purchase a new home. Knowing the reason why you want to purchase will help you make important decisions when it comes to finding that perfect home.
Meeting Nate
Let’s get together! There’s a lot to talk about. We’ll discuss your dream home characteristics, the area, and some possible dealbreakers. Out of town? Give me a call! I work with out of state buyers regularly and would be more than happy to assist you.
Getting Pre-approved
Work with a local lender to determine how much house you can afford and learn which types of loans you may qualify for. It’s better to know, rather than wonder.Finding a Home
I’ll set you up on the MLS for online searches, set up an auto-email system and take you out to your favorite properties.
Submit an Offer
Once we've found the perfect home it's important to put an offer in as soon as possible. I will assist in determining what price we should offer and walk you through the entire process including negotiations.
Contract to Closing
Now that your offer has been accepted it’s time to satisfy any curiosities you have. An inspection and appraisal will be done. This will reveal any undisclosed or unknown defects, this is the time to renegotiate with the seller. Although the lender will carry you through the financial aspect, I will be with you the entire way through.
Closing
Congrats! Closing day has finally come. It’s time to sign documents and make it official.


A JSON representation of key:value pairs
where each Key is is a BCP 47
language tag used by Globalization Pipeline service instance. And the
element Value is a language ID used for output resource file or path
name.
For example
{ "de":"I am", "es":"Not affected", "fr":"by upload goal" }will upload your source files to IBM Globalization Pipeline for translation in German, Spanish and French.
For example
{ "de":"de", "es":"Spanish", "fr":"Parth" }
will download translated files from IBM Globalization Pipeline for
translation in German, Spanish and French with file name
<filename><BCP47 formatting>de.<type>
<filename><BCP47 formatting>Spanish.<type>
<filename><BCP47 formatting>Parth.<type>
same
naming rules are mapped for output directory creation if you chose
LANGUAGE_DIR or LANGUAGE_SUBDIR in Download goal's output layout
Note that in download goal, if Key (target language you opt for downloading in Jenkins workspace) is not set in Globalization Pipeline through upload goal or manually by UI dashbaord, those target languages won't be downloaded.


swasti ranjan
Hey! I'm Swasti. Coding has changed my world. It's not just about apps. Learning to code gave me problem-solving skills and a way to communicate with others on a technical level. I can also develop websites and use my coding skills to get a better job. And I learned it all at Code Academy where they build your self-esteem and keep you motivated. Join me in this rewarding journey. You'll have fun, get help, and learn along the way!
"Declare variables, not war"
| Day | Mon | Tue | Wed | Thu | Fri |
|---|---|---|---|---|---|
| 10-10:30 | Learn | ||||
| 9-10 | Practice | ||||
| 1-1:30 | Play | ||||
| 2-4:30 | Code | ||||
| 6-6:15 | Discuss |


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Patient Id
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The first thing I took from chapter one was that my understanding of self awareness, self regulation and attention was way off, after watching Shereefs video,
and then reading the first chapter of Search Inside Yourself I also realised there was a lot I didn't know, this has only confirmed that.
I belive that everyone is capable of developing their own emotional intelligence, and it it something that should never really stop being nurtured.
Difficult, my mind tends to drift, I put the timer on and tried to focus on my breathing, but found my focus drifting probably four or
five times throughout the two minutes. This is something I will be working on, as after the two minutes was up I did feel 'different' more focused.
I am aiming to try two minutes every time I come back from a long break before starting again. I feel that two minutes of mindfulness already helps
to focus my attention on the tasks at hand.