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Chapter 2 Basic Probability Concepts
Definition 2.1 Definition 1. Let \(S\) be the sample space of an experiment. A real-valued function \(X : S \rightarrow \mathbb{R}\) is called a random variable of the experiment if, for each interval \(I \subset \mathbb{R}, \, \{s : X(s) \in I \}\) is an event.
Random variables are often used for the calculation of the probabilities of events. The real-valued function \(P(X \le t)\) characterizes \(X\), it tells us almost everything about \(X\). This function is called the cumulative distribution function of \(X\). The cumulative distribution function describes how the probabilities accumulate.
Definition 2.2 Definition 2. If \(X\) is a random variable, then the function \(F\) defined on \(\mathbb{R}\) by \[F(x) = P(X \le x)\] is called the cumulative distribution function or simply distribution function (c.d.f) of \(X\).
Functions that define the probability measure for discrete and continuous random variables are the probability mass function and the probability density function.
Definition 2.3 Definition 3. Suppose \(X\) is a discrete random variable. Then the function \[f(x) = P(X = x)\] that is defined for each \(x\) in the range of \(X\) is called the probability mass function (p.m.f) of a random variable \(X\).
Definition 2.4 Definition 4. Suppose \(X\) is a continuous random variable with c.d.f \(F\) and there exists a nonnegative, integrable function \(f\), \(f: \mathbb{R} \rightarrow [0, \infty)\) such that \[F(x) = \int_{-\infty}^x f(y)\, dy\] Then the function \(f\) is called the probability density function (p.d.f) of a random variable \(X\).
2.1 Expectation
Definition 2.5 Definition 5. The expected value of a discrete random variable \(X\) with the set of possible values \(A\) and probability mass function \(f(x)\) is defined by \[\mathrm{E}(X) = \sum_{x \in A} x f(x)\]
The expected value of a random variable \(X\) is also called the mean, or the mathematical expectation, or simply the expectation of \(X\). It is also occasionally denoted by \(\mathrm{E}[X]\), \(\mu_X\), or \(\mu\).
Note that if each value \(x\) of \(X\) is weighted by \(f(x) = P(X = x)\), then \(\displaystyle \sum_{x \in A} x f(x)\) is nothing but the weighted average of \(X\).
Theorem 2.1 Theorem 1. Let \(X\) be a discrete random variable with set of possible values \(A\) and probability mass function \(f(x)\), and let \(g\) be a real-valued function. Then \(g(X)\) is a random variable with \[\mathrm{E}[g(X)] = \sum_{x \in A} g(x) f(x)\]
Definition 2.6 Definition 6. If \(X\) is a continuous random variable with probability density function \(f\) , the expected value of \(X\) is defined by \[\mathrm{E}(X) = \int_{-\infty}^\infty x f(x)\, dx\]
Theorem 2.2 Theorem 2. * Let \(X\) be a continuous random variable with probability density function \(f (x)\); then for any function \(h: \mathbb{R} \rightarrow \mathbb{R}\), \[\mathrm{E}[h(X)] = \int_{-\infty}^\infty h(x)\, f(x)\, dx\] *
Theorem 2.3 Theorem 3. Let \(X\) be a random variable. Let \(h_1, h_2, . . . , h_n\) be real-valued functions, and \(a_1, a_2, \ldots, a_n\) be real numbers. Then \[\mathrm{E}[a_1 h_1(X) + a_2 h_2(X) + \cdots + a_n h_n(X)] = a_1 \mathrm{E}[h_1(X)] + a_2 \mathrm{E}[h_2(X)] + \ldots + a_n \mathrm{E}[h_n(X)]\]
Moreover, if \(a\) and \(b\) are constants, then \[\mathrm{E}(aX +b) = a\mathrm{E}(x) + b\]
2.2 Variances of Random Variables
Definition 2.7 Definition 7. Let \(X\) be a discrete random variable with a set of possible values \(A\), probability mass function \(f(x)\), and \(\mathrm{E}(X) = \mu\). then \(\mathrm{Var}(X)\) and \(\sigma_X\), called the variance and standard deviation of \(X\), respectively, are defined by \[\mathrm{Var}(X) = \mathrm{E}[(X- \mu)^2] = \sum_{x \in A} (x - \mu)^2 f(x),\] \[\sigma_X = \sqrt{\mathrm{E}[(X- \mu)^2]}\]
Definition 2.8 Definition 8. If \(X\) is a continuous random variable with \(\mathrm{E}(X) = \mu\), then \(\mathrm{Var}(X)\) and \(\sigma_X\), called the variance and standard deviation of \(X\), respectively, are defined by \[\mathrm{Var}(X) = \mathrm{E}[(X- \mu)^2] = \int_{-\infty}^\infty (x - \mu)^2\, f(x)\, dx ,\] \[\sigma_X = \sqrt{\mathrm{E}[(X- \mu)^2]}\]
We have the following important relations \[\mathrm{Var}(x) = \mathrm{E}(X^2) - (\mathrm{E}(x))^2 ,\] \[\mathrm{Var}(aX + b) = a^2\ Var(X), \quad \sigma_{aX + b}= |a|\sigma_X\] where \(a\) and \(b\) are constants.
2.3 Moments and Moment Generating Function
Definition 2.9 Definition 9. For \(r > 0\), the \(r\)th moment of \(X\) (the \(r\)th moment about the origin) is \(\mathrm{E}[X^r]\), when it is defined. The \(r\)th central moment of a random variable \(X\) (the \(r\)th moment about the mean) is \(\mathrm{E}[(X - \mathrm{E}[X])^r].\)
Definition 2.10 Definition 10. The skewness of \(X\) is defined to be the third central moment, \[\mathrm{E}[(X - \mathrm{E}[X])^3],\] and the coefficient of skewness to be given by \[\frac{\mathrm{E}[(X - \mathrm{E}[X])^3]}{(\mathrm{Var}[X])^{3/2}}.\]
Definition 2.11 Definition 11. The coefficient of kurtosis of \(X\) is defined by \[\frac{\mathrm{E}[(X - \mathrm{E}[X])^4]}{(\mathrm{Var}[X])^{4/2}}.\]
Definition 2.12 Definition 12. * The moment generating function (mgf) of a random variable \(X\) is defined to be \[M_X(t) = E[e^{tX}],\] if the expectation exists. *
Note The moment generating function of \(X\) may not defined (may not be finite) for all \(t\) in \(\mathbb{R}\).
If \(M_X(t)\) is finite for \(|t| < h\) for some \(h > 0\), then, for any \(k = 1, 2, \ldots,\) the function \(M_X(t)\) is k-times differentiable at \(t = 0\), with \[M^{(k)}_X (0) = \mathrm{E}[X^k],\] with \(\mathrm{E}[|X|^k]\) finite. We can obtain the moments by succesive differentiation of \(M_X(t)\) and letting \(t = 0\).
2.4 Probability generating function
Definition 2.13 Definition 13. * For a counting variable \(N\) (a variable which assumes some or all of the values \(0, 1, 2, \ldots,\) but no others), The probability generating function of \(N\) is \[G_N(t) = E[t^N],\] for those \(t\) in \(\mathbb{R}\) for which the series converges absolutely. *
Let \(p_k = P(N = k)\). Then \[G_N(t) = E[t^N] = \sum_{k=0}^\infty t^k p_k.\] It can be shown that if \(E[N] < \infty\) then \[\mathrm{E}[N] = G'_N(1),\] and if \(E[N^2] < \infty\) then \[\mathrm{Var}[N] = G''_N(1) + G'_N(1) - (G'_N(1))^2.\] Moreover, when both pgf and mgf of \(N\) are defined, we have \[G_N(t) = M_N(\log(t)) \quad \text{ and } M_N(t) = G_N(e^t).\]
2.5 Multivariate Distributions
When \(X_1,X_2,\ldots ,X_n\) be random variables defined on the same
sample space, a multivariate probability density function or probability
mass function
\(f(x_1, x_2, \ldots x_n)\) can be defined. The following definitions can
be extended to more than two random variables and the case of discrete
random variables.
Definition 2.14 Definition 14. Two random variables \(X\) and \(Y\), defined on the same sample space, have a continuous joint distribution if there exists a nonnegative function of two variables, \(f(x, y)\) on \(\mathbb{R} \times \mathbb{R}\) , such that for any region \(R\) in the \(xy\)-plane that can be formed from rectangles by a countable number of set operations, \[P((X, Y) \in R) = \iint_R f(x,y) \, dx\, dy\]
The function \(f (x, y)\) is called the joint probability density function of \(X\) and \(Y\).
Let \(X\) and \(Y\) have joint probability density function \(f (x, y)\). Let \(f_Y\) be the probability density function of \(Y\) . To find \(f_Y\) in terms of \(f\) , note that, on the one hand, for any subset \(B\) of \(R\), \[P(Y \in B) = \int_B f_Y(y) \, dy,\] and on the other hand, we also have \[P(Y \in B) = P(X \in (-\infty, \infty), Y \in B) = \int_B \left( \int_{-\infty}^\infty f(x,y)\, dx \right) \, dy.\]
We have \[\label{mar1} f_Y(y) = \int_{-\infty}^\infty f(x,y)\, dx\] and \[\label{mar2} f_X(x) = \int_{-\infty}^\infty f(x,y)\, dy\]
Definition 2.15 Definition 15. Let \(X\) and \(Y\) have joint probability density function \(f (x, y)\); then the functions \(f_X\) and \(f_Y\) in \[mar2\] and \[mar1\] are called, respectively, the marginal probability density functions of \(X\) and \(Y\) .
Let \(X\) and \(Y\) be two random variables (discrete, continuous, or mixed). The joint probability distribution function, or joint cumulative probability distribution function, or simply the joint distribution of \(X\) and \(Y\), is defined by \[F(t, u) = P(X \le t, Y \le u)\] for all \(t, u \in (-\infty, \infty)\).
The marginal probability distribution function of \(X\), \(F_X\), can be found from \(F\) as follows: \[F_X(t) = \lim_{n \rightarrow \infty} F(t,u) = F(t, \infty)\] and \[F_Y(u) = \lim_{n \rightarrow \infty}F(t,u) = F( \infty, u)\]
The following relationship between \(f(x,y)\) and \(F(t,u)\) is as follows: \[F(t,u) = \int_{-\infty}^{u}\int_{-\infty}^{t} f(x,y)\, dx\, dy.\]
We also have \[\mathrm{E}(X) = \int_{-\infty}^\infty x f_X(x)\, dx , \quad \mathrm{E}(Y) = \int_{-\infty}^\infty y f_Y(y)\, dy\]
Theorem 2.4 Theorem 4. Let \(f (x, y)\) be the joint probability density function of random variables \(X\) and \(Y\). If \(h\) is a function of two variables from \(\mathbb{R}^2\) to \(\mathbb{R}\), then \(h(X, Y )\) is a random variable with the expected value given by \[\mathrm{E}[h(X,Y)] = \int_{-\infty}^{\infty}\int_{-\infty}^{\infty} h(x,y) \, f(x,y)\, dx\, dy\] provided that the integral is absolutely convergent.
As a consequence of the above theorem, for random variables \(X\) and \(Y\), \[\mathrm{E}(X + Y) = \mathrm{E}(X) + \mathrm{E}(Y)\]
2.6 Independent random variables
Definition 2.16 Definition 16. Two random variables \(X\) and \(Y\) are called independent if, for arbitrary subsets \(A\) and \(B\) of real numbers, the events \(\{X \in A\}\) and \(\{Y \in B\}\) are independent, that is, if \[P(X \in A, Y \in B) = P(X \in A) P(Y \in B).\]
Theorem 2.5 Theorem 5. Let \(X\) and \(Y\) be two random variables defined on the same sample space. If \(F\) is the joint probability distribution function of \(X\) and \(Y\), then \(X\) and \(Y\) are independent if and only if for all real numbers \(t\) and \(u\), \[F(t,u) = F_X(t) F_Y(u).\]
Theorem 2.6 Theorem 6. Let \(X\) and \(Y\) be jointly continuous random variables with joint probability density function \(f (x, y)\). Then \(X\) and \(Y\) are independent if and only if \[f (x, y) = f_X(x) f_Y (y).\]
Theorem 2.7 Theorem 7. Let \(X\) and \(Y\) be independent random variables and \(g : \mathbb{R} \rightarrow\mathbb{R}\) and \(h : \mathbb{R} \rightarrow\mathbb{R}\) be real-valued functions; then \(g(X)\) and \(h(Y )\) are also independent random variables.
As a consequence of the above theorem, we obtain
Theorem 2.8 Theorem 8. Let \(X\) and \(Y\) be independent random variables. Then for all real-value functions \(g : \mathbb{R} \rightarrow\mathbb{R}\) and \(h : \mathbb{R} \rightarrow\mathbb{R}\), \[\mathrm{E}[g(X)h(Y)] = \mathrm{E}[g(X)]\mathrm{E}[h(Y)]\]
2.7 Conditional Distributions
Let \(X\) and \(Y\) be two continuous random variables with the joint probability density function \(f (x, y)\). Note that the case of discrete random variables can be considered in the same way. When no information is given about the value of \(Y\), the marginal probability density function of \(X\), \(f_X(x)\) is used to calculate the probabilities of events concerning \(X\). However, when the value of \(Y\) is known, to find such probabilities, \(f_{X|Y} (x|y)\), the conditional probability density function of \(X\) given that \(Y = y\) is used and is defined as follows: \[f_{X|Y} (x|y) = \frac{f(x,y)}{f_Y(y)}\] provided that \(f_Y (y) > 0\). Note also that the conditional probability density function of \(X\) given that \(Y = y\) is itseof a probability density function, i.e. \[\int_{-\infty}^\infty f_{X|Y}(x|y)\, dx = 1.\]
Note that the conditional probability distribution function of \(X\) given that \(Y = y\), the conditional expectation of \(X\) given that \(Y = y\) can be as follows: \[F_{Y|X}(x|y) = P(X \le x | Y = y) = \int_ {-\infty}^x f_{X|Y}(t|y) \, dt\] and \[\mathrm{E}(X|Y = y) = \int_{-\infty}^{\infty} x f_{X|Y}(x|y) \, dx,\] where \(f_Y(y) > 0\).
Note that if \(X\) and \(Y\) are independent, then \(f_{X|Y}\) coincides with \(f_X\) because \[f_{X|Y}(x|y) = \frac{f(x,y)}{f_Y(y)} =\frac{f_X(x)f_Y(y)}{f_Y(y)} = f_X(x).\]
2.8 Covariance
The notion of the variance of a random variable \(X\), \(\mathrm{Var}(X) = \mathrm{E}[ ( X - \mathrm{E}(X))^2]\) measures the average magnitude of the fluctuations of the random variable \(X\) from its expectation, \(\mathrm{E}(X)\). This quantity measures the dispersion, or spread, of the distribution of \(X\) about its expectation. Now suppose that \(X\) and \(Y\) are two jointly distributed random variables. Covariance is a measure of how much two random variables vary together.
Let us calculuate \(\mathrm{Var}(aX + bY)\) the joint spread, or dispersion, of \(X\) and \(Y\) along the \((ax + by)\)-direction for arbitrary real numbers \(a\) and \(b\): \[\mathrm{Var}(aX + bY) = a^2 \mathrm{Var}(X) + b^2 \mathrm{Var}(Y) + 2 a b \mathrm{E}[(X - \mathrm{E}(X))(Y - \mathrm{E}(Y))].\] However, \(\mathrm{Var}(X)\) and \(\mathrm{Var}(Y )\) determine the dispersions of \(X\) and \(Y\) independently; therefore, \(\mathrm{E}[(X - \mathrm{E}(X))(Y - \mathrm{E}(Y))]\) is the quantity that gives information about the joint spread, or dispersion, \(X\) and \(Y\) .
Definition 2.17 Definition 17. Let \(X\) and \(Y\) be jointly distributed random variables; then the covariance of \(X\) and \(Y\) is defined by \[\mathrm{Cov}(X,Y) = \mathrm{E}[(X - \mathrm{E}(X))(Y - \mathrm{E}(Y))].\]
Note that for random variables \(X, Y\) and \(Z\), and \(ab > 0\), then the joint dispersion of \(X\) and \(Y\) along the \((ax + by)\)-direction is greater than the joint dispersion of \(X\) and \(Z\) along the \((ax + bz)\)-direction if and only if \(\mathrm{Cov}(X, Y) > \mathrm{Cov}(X,Z).\)
Note that \[\mathrm{Cov}(X, X) = \mathrm{Var}(X).\] Moreover, \[\mathrm{Cov}(X,Y) = \mathrm{E}(XY) - \mathrm{E}(X)\mathrm{E}(Y).\]
Properties of covariance are as follows: for arbitrary real numbers \(a, b, c, d\) and random variables \(X\) and \(Y\), \[\mathrm{Var}(aX + bY) = a^2 \mathrm{Var}(X) + b^2 \mathrm{Var}(Y) + 2 a b \mathrm{Cov}(X,Y).\] \[\mathrm{Cov}(aX + b, cY + d) = acCov(X, Y)\] For random variables \(X_1, X_2, . . . , X_n\) and \(Y_1, Y_2, . . . , Y_m\), \[\mathrm{Cov}(\sum_{i=1}^n a_i X_i, \sum_{j=1}^m b_j Y_j) = \sum_{i=1}^n\sum_{j=1}^m a_i\,b_j\, \mathrm{Cov}(X_i,Y_j).\]
If \(\mathrm{Cov}(X, Y) > 0\), we say that \(X\) and \(Y\) are positively correlated. If \(\mathrm{Cov}(X, Y) < 0\), we say that they are negatively correlated. If \(\mathrm{Cov}(X, Y) = 0\), we say that \(X\) and \(Y\) are uncorrelated.
If \(X\) and \(Y\) are independent, then \[\mathrm{Cov}(X,Y) = 0.\] However, the converse of this is not true; that is, two dependent random variables might be uncorrelated.
2.9 Correlation
A large covariance can mean a strong relationship between variables. However, we cannot compare variances over data sets with different scales. A weak covariance in one data set may be a strong one in a different data set with different scales. The problem can be fixed by dividing the covariance by the standard deviation to get the correlation coefficient.
Definition 2.18 Definition 18. Let \(X\) and \(Y\) be two random variables with \(0< \sigma^2_X, \sigma^2_Y < \infty\). The covariance between the standardized \(X\) and the standardized \(Y\) is called the correlation coefficient between \(X\) and \(Y\) and is denoted \(\rho = \rho(X,Y)\), \[\rho(X,Y) = \frac{\mathrm{Cov}(X,Y)}{\sigma_X \sigma_Y}.\]
Note that
\(\rho(X, Y ) > 0\) if and only if \(X\) and \(Y\) are positively correlated;
\(\rho(X, Y ) < 0\) if and only if \(X\) and \(Y\) are negatively correlated; and
\(\rho(X, Y ) = 0\) if and only if \(X\) and \(Y\) are uncorrelated.
\(\rho(X, Y )\) roughly measures the amount and the sign of linear relationship between \(X\) and \(Y\).
In the case of perfect linear relationship, we have \(\rho(X, Y ) = \pm1\). A correlation of 0, i.e. \(\rho(X, Y ) = 0\) does not mean zero relationship between two variables; rather, it means zero linear relationship.
Some importants properties of correlation are \[-1 \le \rho(X, Y ) \le 1\] \[\rho(a X + b, cY +d) = \text{sign}(ac) \rho(X, Y )\]


| progress {httr} | R Documentation |
Add a progress bar.
progress(type = c("down", "up"), con = stdout())
type |
Type of progress to display: either number of bytes uploaded or downloaded. |
con |
Connection to send output too. Usually |
cap_speed <- config(max_recv_speed_large = 10000) # If file size is known, you get a progress bar: x <- GET("http://httpbin.org/bytes/102400", progress(), cap_speed) # Otherwise you get the number of bytes downloaded: x <- GET("http://httpbin.org/stream-bytes/102400", progress(), cap_speed)


Peer-Graded Assignment Week-4 Solution
Inspect the page and checkout the Console in the Developer Tools.
1-Right Click the Menu on Right side.
1-Click More.
3-Developer Tool's.
4-Start Inspect.


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Note: Some websites won't allow being inserted into iframes by X-Frame-Option header.


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You currently have no transactions
You currently have no staking transactions
Please note that your transaction history and balances can only show when you are completely synchronized.


LIDOCREAM 4- lidocaine cream
Golden Touch LLC
Disclaimer: Most OTC drugs are not reviewed and approved by FDA, however they may be marketed if they comply with applicable regulations and policies. FDA has not evaluated whether this product complies.
Active ingredient
Lidocaine 4% W/W
Purpose
Topical Anesthetic
Keep out of reach of children.If swallowed, get medical help or contact a Poison Control Center right away.
Uses
For the temporary relief of pain and itching due to
- minor burns
- minor skin irritations
- minor cuts
- scrapes
- sunburn
- insect bites
Warnings
For external use only
Flammable keep away from sparks, heat and fire. Cap tube tightly when not in use.
When using this product
- Avoid contact with the eyes
- Do not use in large quantities particularly over raw surfaces or blistered areas.
- Do not exceed recommended dosage unless directed by a doctor
Stop use and ask a doctor if
- allergic reaction occurs
- condition worsens or does not improve within 7 days
- Symptoms clear up and return within a few days
- notice any unusual effects such as blurred vision, dizziness or drowsiness, difficulty in breathing, trembling, chest pain or irregular heart beat
Directions
- Adults and children 2 years and older: apply externally to the affected area not more than 3 to 4 times daily
- Children under 2 years of age: consult a doctor
Inactive ingredients
Aloe Barbadensis Leaf Extract, Benzyl Alcohol, Carbomer, Disodium EDTA, Glycerin, Glyceryl Monooleate, SD Alcohol 40-B, Simmondsia Chinensis (Jojoba) Seed Oil, Water
Packaging


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Work Experience
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--- layout: page title: About header-img: "img/about-bg.png" ---
Hey, I'm Dee! I have a B.F.A from the University of Delaware and currently live in Mid Coast Maine. I am learning all things code through the Online Full Stack Web Development Program at The Flatiron School.


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Chapter 4 BDD-NET therapist manual
4.1 What is BDD-NET?
BDD-NET consists of eight interactive modules and focuses on exposure with response prevention (ERP). This means that patients do most of the active treatment work away from their computer or mobile device, for example when they are performing ERP exercises.
4.2 Who is suitable for BDD-NET
BDD-NET is developed to treat adults with body dysmorphic disorder (BDD). Patients may have comorbid conditions, for example other anxiety disorders, depression, or OCD. Patients may also take antidepressant medication during the course of treatment. We recommend that patients do not change the dose during the course of treatment. BDD-NET may also be delivered to patients with any level of BDD symptom severity.
Contraindications include having a personality disorder that might interfere with treatment (such as borderline personality disorder), psychotic symptoms, an ongoing substance abuse, or acute suicidal ideation. BDD-NET is text-based and so requires sufficient reading skills and understanding of English. We recommend that the patient is referred to face-to-face treatments if contraindications are discovered.
4.3 Presenting BDD-NET as an option to the patient
4.3.1 Assessing insight
Participating in internet-based treatments such as BDD-NET is voluntary, and therapists need to make sure that patients are willing to challenge their BDD in treatment. Lack of insight is common in BDD and BDD-NET is designed to work for patients that express varying degrees of insight. It is our experience that patients need to at least be willing to try out alternative behaviours during the course of treatment, even if they might still be convinced that their appearance concerns are justified at the start of treatment.
4.3.2 Managing expectations
Some patients may have expectations to be completely free from anxiety after BDD-NET, and that all that is required of them is to read and understand what is written in the treatment modules. Such expectations are discussed in module 4 (goal setting) but therapists are advised to assess whether patients are willing to challenge their BDD through exposure with response prevention and try out alternative behaviours before starting BDD-NET. If someone completely refuses to try new behaviours they are unlikely to participate in BDD-NET fully and benefit from the treatment.
4.3.3 A good start in BDD-NET
Many patients with BDD find BDD-NET an interesting treatment option, particularly those who avoid many activities due to their appearance concerns. The strongest arguments in favour of ICBT treatments like BDD-NET, from a patient perspective, is that the treatment content and the therapist are accessible throughout the week, and that the treatment starts promptly after evaluation rather than after a time in waiting list.
To give patients a positive first impression of the treatment, we suggest that therapists write their first message on the first day of treatment to welcome the patient and notify them of ways to contact you. Provide encouragement throughout treatment to motivate the patient and establish a therapeutic working alliance. Patients sometimes struggle with crucial treatment components such as the BDD diary exposure with response prevention (ERP), so make sure to provide extra support if patients get stuck at those points.
4.4 Modules in BDD-NET
Below is an overview of the eight treatment modules. We recommend that you look at them from a patient’s point of view before starting the first treatment.
| Treatment module | Content |
|---|---|
| 1. Introduction to BDD and the treatment | An introduction to BDD Introduction to the treatment content |
| 2. A CBT model of BDD | Psychological explanation of the link between thoughts, emotions, and behaviours |
| 3. Interpretation traps | Common cognitive biases in BDD |
| 4. Introduction to ERP | Goal setting and planning of exposure with response prevention |
| 5. More about ERP | Doing and evaluating exposure with response prevention exercises |
| 6. Values and Goals | Identifying and acting in accordance with personal values |
| 7. Difficulties during treatment | Strategies to deal with common difficulties and setbacks |
| 8. Summary and Relapse Prevention | Treatment summary, evaluation of treatment, and designing a relapse prevention plan |
As you can see, the emphasis is on doing exposure with response prevention (ERP), which we view as the main component of BDD-NET. You typically want to encourage the patient to progress through the first three modules as fast as possible, check that they have understood the rationale for ERP, and then start doing ERP. Once a patient is doing regular ERP-exercises, you can open up modules 6-7 for them to complete while continuing to do daily ERP. Module 8 can then be opened up with one to two weeks left in treatment.
4.5 Closing remarks
The strategies outlined here should be viewed as the first building blocks in becoming an effective ICBT therapist using BDD-NET. As in regular clinical practice, we recommend continuous supervision and that therapists discuss difficult cases with colleagues.
We strive to continuously update and improve this material and would appreciate any feedback. You can reach us at<EMAIL_ADDRESS>or talk to us in person at a training session.


Why bad software architecture is easy to monetize
This morning I received yet another “interesting” job offer, which looked like this:
Wanted: "senior .Net architect"
Description "build one framework to build all future projects, using sharepoint"
#ThanksButNoThanks
— Tom Janssens (@ToJans) 17 februari 2014
Building a single framework to rule all your customers is just plain wrong. I did it in the past and now I know better.
Why do a lot of consultancy/web shops do it then?
Let me tell you a story
… About someone building a house…
The project
You would like to build a house.
The tender
Some contractors give you a price of about 200.000 USD, while about 80% of them give you a price of about 50.000 USD. You verify whether the lower offers match all the specifications you provided - which they do -, and you pick the contractor that has about the average price of the 50.000 USD contractors.
The build
They start building it, and after 2 weeks, the whole project is finished.
The delivery
Then you get inside, and then you notice the walls are built out of multiplex, there is no insulation; the windows are made out of plexi glass, and there is no running water or electricity provided. However, the house still matches the contract’s specifications, so you can’t do anything about it legally.
The fix
However, there is a solution: the contractor offers you to install running water and electricity, so you can get at least some tenants, and start monetizing your investment. This will set you back 100.000 USD. While this is a lot of money, your total investment is still lower then the initial amount asked by the more expensive contractors, so you accept the deal.
They get cracking, and after a few weeks the building is finished. You get some tenants, and the money starts flowing in…. End of story…
A few months later
It is now winter. Your tenants start complaining about lack of insulation, and threaten to report you to a non-profit that guards the tenants' rights. So you go back to the contractor, and ask him how much it costs to apply insulation. You decide to accept the extra cost of 50.000 USD, which implies you now have paid the initial 200.000 USD, and had a lot of extra overhead with it.
Not finished yet
Having found a way to achieve what he wants, the tenant now figured out that your plexi glass windows are not compliant with national standards. Disappointed, you go back to the contractor, and he tells you he can add proper windows for another 30.000 USD.
Finally resolved? Or not?
After applying proper windowing, you think you finally nailed it… But a few months later, the tenants are noticing cracks in the supporting beams. Apparently the foundation of the house was not built for all this extra weight, and you need to spend a whopping 100.000 USD to fix it…
Conclusion
Building software works in a similar way. A lot of software shops will promise you heaven, and they might deliver something that looks like heaven, but in the long run it will always cost you money.
In housing, usually stories like these are not possible, because there is a minimal - legal or other - standard that housing needs to comply to.
However, in software there is no such thing. Writing software software to handle your core domain (i.e. the thing that makes you different from your competitors) is never finished, because you need to change in order to stay ahead of the competition.
If you opt for the cheap, short term solution, it will cost you a lot more money in the long run.
It happens a lot
I have seen the growth of way too many organisations slowing down, and actually even stalling and shrinking again, just because they opted for the wrong contractor (i.e. software) in the beginning.
Software architecture can not be fixed, it needs to be tailored to your specific requirements, because writing software is a creative process with a lot of unknowns, which will grow exponentially in costs if you do not manage them properly.
While building a house might also have a lot of unknowns, the biggest part of houses that are being built are pretty standard; it are usually only the exceptional houses that go multiple times over budget. When writing software for your core domain, your software is custom; if it would not be, this would imply that you are the same as your competitors.
Some might say: “we can configure this in our framework/application”.
That is in fact even worse, because usually your core domain (what provides your competitive advantage), will now be written in a language that is specific to your vendor’s product. This will create an even bigger vendor lock-in.
Finally
If you want to build software for your core domain, grow the architecture, do not opt for a predefined solution unless you properly understand the implications and you can properly justify your choice.
Tom.
Ps: I am always looking for interesting projects (Legacy, DDD, C#, Erlang, Haskell to name a few), so contact me if you think I can help you out!


Navbar
Navbars are for building the main navigation items. they're related to navs, but with a lot more options.
Basic Navbar
nav class="navbar"
bg-{COL}
- COL:
primary
secondary
success
danger
warning
info
light
dark
white
- COL:
navbar-{COL}
- COL:
light
dark
- COL:
navbar-expand-{BR}
- BR:
sm
md
lg
xl
xxl
- BR:
Navbars are pretty complicated. they begin with a nav tag that has the navbar
class. Although you could use something other than a nav
, it's really the proper tag for a navbar.
Usually navbars get a couple of classes that identify the color of the background, which is a regular bg-{COL}
item as well as a class that identifies whether the navigation's background is light or dark navbar-{light|dark}
.
Navbars are responsive by default, so they will stack on top of one another. You can add a navbar-expand-{BR}
which will expand the links into a horizontal section at a specific breakpoint.
You usually want a nav to cover the entire width of the browser or align to the grid...at least in certain parts, so there's usually a container involved.
Finally, there is a navbar-nav
section, which is where your links go.
Inside Navbar
-
container{-BR}
- BR:
sm
md
lg
xl
xxl
fluid
- BR:
-
navbar-nav
nav-item
nav-link
navbar-nav
- section is a series of links, which can go on list items with anchor tags or just anchor tags.
nav-item
- identifies each navigation item, can go in a list item or an anchor tag
nav-link
- identifies the link in an anchor tag.
Special Content
navbar-brand
nav-text
navbar-brand
- Allows you to place branding as a separate element on the form.
nav-text
- Lets you do some text aligned properly to the form.
You can also include other elements, so for example a form element is a pretty common thing to add.
Navbar Collapse
navbar-toggler
navbar-toggler-icon
collapse
navbar-collapse
id
navbar-nav-scroll
Navbars can get long quickly, so Bootstrap provides an option to collapse items into hamburger menu at before the navbar-expand{-BR} breakpoint.
To do this, you'll need to add a button that triggers the menu
navbar-toggler
- is the button that activates the dropdown. It gets a data-bs-toggle="collapse"
and it needs a data-bs-target
with the ID of the element that's going to be collapsed.
collapse
navbar-collapse
- These two classes are wrappers around the content that you want collapsed. They usually wrap the navbar-nav.
navbar-toggler-icon
- The hamburger menu lives in the navbar-toggler-icon
class
navbar-nav-scroll
- There is an optional class that lets you determine the height of the items, just in case your menu gets a bit long for a mobile device.


Send your homework as a single ZIP archive with your files, or send a link to your GitHub repo. Try to keep line lengths below 80 characters.
Approximate the value of pi using a Monte Carlo method. Try different numbers of input points (10, 10^2, 10^3, 10^4, 10^5, 10^6). Try to get faster code with Numba (remember that the first run is slower).


Air War
Awarded to 1 teams, or 0.07% of registered teams. Part of the Categories trophy.
Fork-tailed devil.
First team to solve Lightning's capstone
Teams
| team name | unlocked at |
|---|---|
| Dragon Sector | 2014-05-18 01:09:55 UTC |
Awarded to 1 teams, or 0.07% of registered teams. Part of the Categories trophy.
Fork-tailed devil.
First team to solve Lightning's capstone
| team name | unlocked at |
|---|---|
| Dragon Sector | 2014-05-18 01:09:55 UTC |


title = "Forgot-Password" url = "/forgot-password" layout = "pages" description = "Forgot password page" is_hidden = 0 [resetPassword] paramCode = "code" ==


Modules
The purpose of modules is to represent extra code objects that are added to a transformation.
If added to a Python transformation, a Seamless module of Python (or IPython) code becomes a Python module or package, whereas a Seamless module of compiled code becomes a shared library.
Modules added to non-Python transformations are currently not supported.
IMPORTANT: This documentation section is a stub.
Relevant test examples:
See Running tests on how to execute tests.
Python modules
IMPORTANT: This documentation section is a stub.
Relevant test examples:
IPython modules
IMPORTANT: This documentation section is a stub.
Relevant test examples:
Compiled modules
IMPORTANT: This documentation section is an early draft. The raw text material is shown below
Modules written in a compiled language are currently only only implemented at the low level. High-level access via the Module class is to be implemented. However, every compiled transformer contains a main_module object, that can be modified via the Transformer class… also, you could construct a compiled module yourself as a structured cell and insert it using pin celltype “module” (see module-simplified.py) …


{{.Title}}
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Technology
Reviews, research, recommendations
Data
Management, retrieval, cleaning & analysis
Infrastructure
System advisory and setup
{{ define "title"}} {{ .Site.Title}} {{end}} {{ define "header"}} {{ partial "header" .}} {{end}} {{ define "main"}}
{{.Description}}
Reviews, research, recommendations
Management, retrieval, cleaning & analysis
System advisory and setup


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Issue Summary¶
A summary of the issue and the environment in which it occurs. If suitable, include the steps required to reproduce the bug. Please feel free to include screenshots, screencasts, or code examples.
Steps to Reproduce¶
- This is the first step
- This is the second step
- Further steps, etc.
Code Snippet¶
# paste code here
Exception/Log¶
# paste exception/log here
Technical details:¶
- twilio-python version:
- python version:


SerializationError
public enum SerializationError : String, Error
Errors when parsing JSON string
-
Unterminated object. Opening curley brace without a close
Declaration
Swift
case unterminatedObject
-
Unterminated Array. Opening square brackets without a close
Declaration
Swift
case unterminatedArray
-
Unterminated string. Opening double quote without matching ending one
Declaration
Swift
case unterminatedString
-
Invalid JSON
Declaration
Swift
case invalidJSON
-
Invalid array element. One of the elements in the array was not valid
Declaration
Swift
case invalidArrayElement
-
Number missing it’s exponent part
Declaration
Swift
case invalidNumberMissingExponent
-
Number was missing it’s fractional element
Declaration
Swift
case invalidNumberMissingFractionalElement
-
Reached the end of the file unexpectedly
Declaration
Swift
case unexpectedEndOfFile


Base class for commands: typically actions taking parameters but also plain variable assignments and parameterless actions. More...
#include <Command.h>
Public Member Functions | |
| virtual const std::string & | getName () const =0 |
| virtual const std::string & | getDescription () const =0 |
| virtual const ReferenceMap & | getParameters () const =0 |
| bool | hasArguments () const |
| virtual const std::string & | getType () const |
| Description of result, comparable to a return type of a function. | |
| virtual void | run (const std::string ¶ms)=0 |
Base class for commands: typically actions taking parameters but also plain variable assignments and parameterless actions.


| 1 | package fr.sii.ogham.core.mimetype.replace; | |
| 2 | ||
| 3 | import java.util.regex.Matcher; | |
| 4 | import java.util.regex.Pattern; | |
| 5 | ||
| 6 | /** | |
| 7 | * Eventually replaces a mimetype if it matches the provided regular expression. | |
| 8 | * | |
| 9 | * The regular expression may contain groups. Matching groups can be used in | |
| 10 | * replacement string. For example: | |
| 11 | * | |
| 12 | * <pre> | |
| 13 | * <code> | |
| 14 | * new PatternMimetypeReplacer(Pattern.compile("application/xhtml[^;]*(;.*)?"), "text/html$1"); | |
| 15 | * </code> | |
| 16 | * </pre> | |
| 17 | * | |
| 18 | * @author Aurélien Baudet | |
| 19 | * | |
| 20 | */ | |
| 21 | public class PatternMimetypeReplacer implements MimetypeReplacer { | |
| 22 | private final Pattern pattern; | |
| 23 | private final String replacement; | |
| 24 | ||
| 25 | /** | |
| 26 | * Initialize with a regular expression and a replacement. | |
| 27 | * | |
| 28 | * @param pattern | |
| 29 | * the regular expression as string, no regular expression flag | |
| 30 | * is applied | |
| 31 | * @param replacement | |
| 32 | * the replacement (can contains regular expression replacements | |
| 33 | * too) | |
| 34 | */ | |
| 35 | public PatternMimetypeReplacer(String pattern, String replacement) { | |
| 36 | this(Pattern.compile(pattern), replacement); | |
| 37 | } | |
| 38 | ||
| 39 | /** | |
| 40 | * Initialize with a regular expression and a replacement. | |
| 41 | * | |
| 42 | * @param pattern | |
| 43 | * the regular expression as string | |
| 44 | * @param flags | |
| 45 | * the regular expression flags | |
| 46 | * @param replacement | |
| 47 | * the replacement (can contains regular expression replacements | |
| 48 | * too) | |
| 49 | */ | |
| 50 | public PatternMimetypeReplacer(String pattern, int flags, String replacement) { | |
| 51 | this(Pattern.compile(pattern, flags), replacement); | |
| 52 | } | |
| 53 | ||
| 54 | /** | |
| 55 | * Initialize with a regular expression and a replacement. | |
| 56 | * | |
| 57 | * @param pattern | |
| 58 | * the regular expression | |
| 59 | * @param replacement | |
| 60 | * the replacement (can contains regular expression replacements | |
| 61 | * too) | |
| 62 | */ | |
| 63 | public PatternMimetypeReplacer(Pattern pattern, String replacement) { | |
| 64 | super(); | |
| 65 | this.pattern = pattern; | |
| 66 | this.replacement = replacement; | |
| 67 | } | |
| 68 | ||
| 69 | @Override | |
| 70 | public String replace(String mimetype) { | |
| 71 | Matcher matcher = pattern.matcher(mimetype); | |
| 72 |
1
1. replace : negated conditional → RUN_ERROR |
if (matcher.matches()) { |
| 73 |
1
1. replace : replaced return value with "" for fr/sii/ogham/core/mimetype/replace/PatternMimetypeReplacer::replace → RUN_ERROR |
return matcher.replaceAll(replacement); |
| 74 | } | |
| 75 |
1
1. replace : replaced return value with "" for fr/sii/ogham/core/mimetype/replace/PatternMimetypeReplacer::replace → RUN_ERROR |
return mimetype; |
| 76 | } | |
| 77 | ||
| 78 | } | |
Mutations | ||
| 72 |
1.1 |
|
| 73 |
1.1 |
|
| 75 |
1.1 |


--- title: 'HarvardX' --- {%- include head.html -%}
{% assign nav=site.navigation %} {%- include header.html navigation=nav -%}
HarvardX first deployed IIIF resources and used Mirador as a stand alone application in a series of courses titled The Book: Histories Across Time and Space, where it showcased Harvard’s large digital collection. Since The Book, Mirador has been embedded into HarvardX’s Annotation Tool, and is used in other courses such as Visualizing Japan and ChinaX. Mirador allows faculty and learners to engage in focused discussions through the use of annotations to markup IIIF resources. HarvardX continues to explore new and interesting ways to enhance the experience of learners through the features of Mirador and IIIF.


--- layout: post status: publish published: true title: Screwing with VS for Fun and Profit author: display_name: Tomas Restrepo login: tomasr email<EMAIL_ADDRESS>url: http://winterdom.com/ author_login: tomasr author_email<EMAIL_ADDRESS>author_url: http://winterdom.com/ wordpress_id: 521 wordpress_url: http://winterdom.com/2007/07/screwingwithvsforfunandprofit date: '2007-07-18 19:57:25 +0000' date_gmt: '2007-07-18 19:57:25 +0000' categories: - ".NET" - Tools tags: [] comments: [] ---
Just a little something I was fooling with tonight:


YUFENG CHI (-T.K.-)
The High School Affiliated to Renmin University of China, International Curriculum Center
<EMAIL_ADDRESS>- Can NAI learn Mr. Bach's music?
- Yes, of course.
A recurrent neural Network is a type of artificial neural network. The connections between nodes form a directed graph along a sequence. Different from the feedforward neural networks, RNNs can use their internal state to memory sequences. Thus, it has the ability to discover patterns with respect of time and can generate similar patterns accordingly. The structure is shown in the image below.
From this website, we can obtain all the piano works located inside the The English Suites page.
The file is downloaded as a zip format with 48 midi files inside. 480KB or 98 minutes in total.
Midicsv.exe is a handy tool to convert midi into csv file. This is its official website.
Via the os.system() command, we can run it inside python.
import os os.system(".\\Midicsv.exe .\\the-midi-file.mid .\\target-csv-file.csv)
Though we get the character representation of the music, there is a lot of redundant information:
... 2, 2160, Note_on_c, 0, 81, 107 2, 2400, Note_on_c, 0, 76, 107 2, 2520, Note_on_c, 0, 73, 107 2, 2880, Note_on_c, 0, 69, 107 2, 2976, Note_on_c, 0, 81, 0 2, 3000, Note_on_c, 0, 80, 107 2, 3072, Note_on_c, 0, 76, 0 ...
A new format is needed that can:
Inspired by carykh , Unicode characters can be used to represent each note.
Encoding process is shown below:
# make a temp folder to store csv files def make_temp_folder(): try: os.makedirs(temp_path) except: shutil.rmtree(temp_path) os.makedirs(temp_path) # convert csv string into compressed character-level string def encode(csv_string, params): score = [] # reformat csv array for row in csv_string: if len(row) < 6 or not re.search('[N|n]ote', row[2]): continue tick = int(int(row[1]) / params.sample_rate) note = int(row[4]) on_off = not(re.search('[O|o]ff', row[2]) or int(row[5]) is 0) score.append([tick, note, on_off]) # sort the array by midi tick score.sort() keys = [] result_str = '' for t in range(0, tick+1): for note in score: if note[0] == t: note_chr = chr(note[1] + params.offset) if note[2] and note_chr not in keys: keys.append(note_chr) if not note[2]: if note_chr in keys: keys.remove(note_chr) for key in keys: result_str += key if not keys: result_str += '~' result_str += ' ' return result_str
Now we have a train.data file. Though it is 'compressed', it's now 2.7MB. :D
Implementing the char-rnn-tensorflow by sherjilozair, a model with 1.1378 perplexity on validation can be obtained.
The hyperparameters are shown below:
data_file = ".\\train.data" encoding = 'utf-8' output_dir = ".\\output\\" n_save = 1 # how many times to save the model during each epoch. max_to_keep = 3 # how many recent models to keep hidden_size = 256 # size of RNN hidden state vector embedding_size = 0 # size of character embeddings num_layers = 3 # number of layers in the RNN num_unrollings = 20 # number of unrolling steps. model = 'lstm' # which model to use (rnn, lstm or gru). num_epochs = 200 # number of epochs batch_size = 40 # minibatch size train_frac = 0.9 # fraction of data used for training. valid_frac = 0.05 # fraction of data used for validation. # test_frac is computed as (1 - train_frac - valid_frac). dropout = 0.4 # dropout rate, default to 0 (no dropout). input_dropout = 0.0 # ('dropout rate on input layer, default to 0 (no dropout), and no dropout if using one-hot representation. max_grad_norm = 5 # clip global grad norm learning_rate = 3e-3 # initial learning rate decay_rate = 0.95 # decay rate init_dir = '' init_model = '' log_to_file = debug = test = False progress_freq = 100 # frequency for progress report in training and evalution. verbose = 1 # whether to show progress report in training and evalution.
Another piece of code is needed to decode the data and convert them back into midi format:
def decode(string, params): make_temp_folder() if re.search('.data', string): with open(string, encoding='utf-8') as f: string = f.read() content = string.split(' ') if not re.search('.mid', params.output_name): params.output_name += '.mid' csv_name = os.path.join(temp_path, params.output_name.replace('.mid', '.csv')) with open(csv_name, 'w', newline='', encoding='utf-8') as csvfile: writer = csv.writer(csvfile, delimiter=',') # write the headers required for the midi file writer.writerow([0, 0, 'Header', 1, 4, 240]) writer.writerow([1, 0, 'Start_track']) writer.writerow([1, 0, 'Title_t', '"Piano Improvisation - NAI"']) keys = [] for t, chap in enumerate(content): if chap is '~': continue for note in chap: if note not in keys: keys.append(note) writer.writerow([1, t * params.sample_rate, 'Note_on_c', 0, ord(note)-params.offset, 107]) for key in keys: if key not in chap: keys.remove(key) writer.writerow([1, t * params.sample_rate, 'Note_off_c', 0, ord(key)-params.offset, 0]) writer.writerow([1, t * params.sample_rate, 'End_track']) writer.writerow([0, 0, 'End_of_file']) os.system(".\\dependencies\\Csvmidi.exe " + csv_name + " .\\" + params.output_name) shutil.rmtree(temp_path)import csv import os import re import time SAMPLE_RATE = 10 def cvt_chrmid_2_csv(filename, filename_out): file = open(filename, encoding='utf-8') string = file.read() file.close() string = string.split(' ') keys = [] prev_keys = [] t = 0 with open('result\\'+filename_out, 'w', newline='') as f: writer = csv.writer(f) writer.writerow([0, 0, 'Header', 1, 4, 240]) writer.writerow([1, 0, 'Start_track']) for line in string: if re.search('~', line): t += SAMPLE_RATE continue for note in line: if note not in keys: keys.append(note) if 0 <= ord(note) <= 127: writer.writerow([1, t, 'Note_on_c', 0, ord(note), 107]) for note in keys: if note not in line: keys.remove(note) if 0 <= ord(note) <= 127: writer.writerow([1, t, 'Note_off_c', 0, ord(note), 0]) t += SAMPLE_RATE writer.writerow([1, t, 'End_track']) writer.writerow([0, 0, 'End_of_file']) def cvt_csv_2_midi(filepath): time.sleep(4) csv_list = list(os.walk(filepath))[0][2] for csv in csv_list: midi = csv.replace('.csv', '.mid') os.system("tool\Csvmidi.exe .\\result\\"+csv+" .\\result\\"+midi) cvt_chrmid_2_csv('output.txt', 'result.csv') cvt_csv_2_midi('result')
Here is the final result
The result is acceptable.
The network discovers the pattern of chords and beats. Though, the transitions between blocks are not smooth enough.
carykh. (2017). Computer evolves to generate baroque music! Youtube. [Online]. Available: https://www.youtube.com/watch?v=SacogDL_4JU&t=2s
Karpathy, A. (2015). The Unreasonable Effectiveness of Recurrent Neural Networks. Andrej Karpathy blog. [Online]. Available: http://karpathy.github.io/2015/05/21/rnn-effectiveness/


<<<<<<< HEAD
About
Products
Contact
Funny content, hahaha!
=======
Hello World!, we are software developers!
>>>>>>> 6c9d40beda5f9b5d4d61ccd5a9d506e26d88e9de


2016 Exhibition Selected: Brighton Photo Biennial #bpb16 | 2016 Exhibition Selected: Brighton Photo Fringe #bpf16 | 2016 Exhibition Fashion Cities Africa: Royal Pavilion Museum | 2016 Exhibition Experimental Archaeology: UOB Gallery | 2015 Exhibition Inner Landscape: Onca Gallery Brighton | 2015 Exhibition Son of Canaan: Pelham Street | 2015 Internship Social Enterprise, London | Archival photography, Royal Pavilion Museum |


This sample illustrates how to wire up an Instagram WebHooks receiver. A sample WebHook URI is:
https://<host>/api/webhooks/incoming/instagram/{id}
Note that for security reasons the URI MUST be https.
Set the MS_WebHookReceiverSecret_Instagram application setting to the Instagram application secret, optionally using IDs
to differentiate between multiple WebHooks, for example '
Also, set the MS_WebHookReceiverSecret_InstagramId application setting to the application id, again optionally using IDs
to differentiate between multiple WebHooks, for example '
To subscribe to a geo-location, send an empty POST request to
http://<host>/api/instagram/subscribeTo unsubscribe from the geo-location, send an empty POST request to
http://<host>/api/instagram/unsubscribe?id=<id>
Please see Instagram WebHooks for more information.


OCTOBER 2 The Holy Guardian
Angels
FIRST
A reading from the book of Exodus 23:20-23
Thus says the LORD: "Behold, I send an
angel before you, to guard you on the way and to bring you to the place which I
have prepared. Give heed to him and listen to his voice, do not rebel against
him, for he will not pardon your transgression; for my name is in him.
"But if you listen attentively to his voice and do all that I say, then I
will be an enemy to your enemies and an adversary to your adversaries. "My angel will go before you, and bring you in to the
Amorites, and the Hittites, and the Perizzites, and the Canaanites, the
Hivites, and the Jebusites, and I will blot them out.”
The word of the Lord.
RESPONSORIAL PSALM Psalm 91
R. For you has he commanded his angels, to keep you in all your ways.
He who dwells in the shelter of the Most High,
and abides in the shade of the Almighty, says to the LORD: 'My refuge, my
stronghold, my God in whom I trust!'
R. For
you has he commanded his angels, to keep you in all your ways.
He will free you from the snare of the fowler,
from the destructive plague; he will conceal you with his pinions, and under
his wings you will find refuge. His faithfulness is buckler and shield.
R. For
you has he commanded his angels, to keep you in all your ways.
You will not fear the terror of the night, nor
the arrow that flies by day, nor the plague that prowls
in the darkness, nor the scourge that lays waste at noon.
R. For
you has he commanded his angels, to keep you in all your ways.
 For you has he commanded his
angels to keep you in all your ways.
R. For
you has he commanded his angels, to keep you in all your ways.
ALLLEUIA
R. Alleluia. Bless the Lord, all his hosts, his servants, who do his will. R. Alleluia.
GOSPEL
A reading from the holy Gospel according to Matthew 18:1-5.10
At that time the disciples came to Jesus,
saying, "Who is the greatest in the kingdom of heaven?" And calling to
him a child, he put him in the midst of them, and said, "Truly, I say to
you, unless you turn and become like children, you will never enter the kingdom
of heaven. Whoever humbles himself like this child, he is the greatest in the
kingdom of heaven. "Whoever receives one such child in my name receives me.
The Gospel of the Lord.


Wait for the Outcome
Instructions
Please bookmark this page and come back here to inspect the status
of the bet.
If you win the bet, you will receive another key here, the Event Key.
continue
Notes
The Event Key makes you the owner of the contract transaction.
You could leave the Bitcoins there, but should transfer them into
your wallet. That is more convenient, more visible and safer.
We do not hold the money at any time, it is locked in the blockchain.
continue


--- layout: post date: 2012-04-20 time: "09:00:00" authors: ["Greg Wilson"] title: An Exercise With Sets and Dictionaries tags: ["Tutorial"] ---
You are working for a nanotechnology company that prides itself on manufacturing some of the finest molecules in the world. Your job is to rewrite parts of their ordering system, which keeps track of what molecules they can actually make. Before trying this exercise, please review:
Submit your work by mailing Greg:
1. Reading
Your company stores information about molecules in files that contain formulas and names, one per line, like this:
# Molecular formulas and names # $Revision: 4738$ chlorine : Cl*2 silver nitrate: Ag.N.O*3 sodium chloride :Na.Cl
More specifically:
Write a function called read_molecules
which takes a handle to an open file as its only argument, reads everything from that file, and returns a dictionary containing all the formulas in that file. (Here, "a handle to an open file" means either sys.stdin
, or the result of using open(filename, 'r')
or file(filename, 'r')
to open a file.) The result dictionary's keys should be the names of the molecules with leading and trailing whitespace removed. Its values should themselves be dictionaries of atomic symbols and counts. For example, if the data shown above is contained in the file molecules.mol
, then this Python:
reader = file('molecules.mol', 'r') data = read_molecules(reader) reader.close() print data
should produce something like:
{ 'chlorine' : {'Cl' : 2}, 'silver nitrate' : {'Ag' : 1, 'N' : 1, 'O' : 3}, 'sodium chloride' : {'Na' : 1, 'Cl' : 1} }
Note: if your tutorial group has already covered regular expressions, use them for this part of the exercise. If you have not yet met regular expressions, use string splitting instead.
2. Merging
Write a function called merge_molecules
that takes two dictionaries like the one shown above and produces a third dictionary that contains the contents of both according to the following rules:
Your function must not modify either of its input arguments: the original dictionaries must be left as they were.
3. What Can We Make?
Write a function called can_produce
that takes a dictionary of molecular formulas (like the one shown above) and the atomic symbol of one kind of atom, and returns a set containing the names of all the molecules we might be able to make. For example:
reader = file('molecules.mol', 'r') data = read_molecules(reader) reader.close() print can_produce(data, 'Cl')
should print something like:
set(['chlorine', 'sodium chloride'])
4. Putting the Pieces Together
Write a program called produce.py
that uses these three functions to tell us the molecules we could make using a particular kind of atom based on the contents of several molecular formula files. For example:
$ python produce.py Cl < molecules.mol
prints:
chlorine sodium chloride
while:
$ python produce.py Na salts.mol organics.mol alloys.mol
reads and merges all the formulas in the three files salts.mol
, organics.mol
, and alloys.mol
, and prints a list of all the molecules from those files that contain sodium.


| PostgreSQL 9.0.10 Documentation | ||||
|---|---|---|---|---|
| Prev | Up | Chapter 45. System Catalogs | Next |
The catalog pg_language registers languages in which you can write functions or stored procedures. See CREATE LANGUAGE and Chapter 38 for more information about language handlers.
Table 45-24. pg_language Columns
| Name | Type | References | Description |
|---|---|---|---|
| lanname | name | Name of the language | |
| lanowner | oid | pg_authid.oid | Owner of the language |
| lanispl | bool | This is false for internal languages (such as SQL) and true for user-defined languages. Currently, pg_dump still uses this to determine which languages need to be dumped, but this might be replaced by a different mechanism in the future. | |
| lanpltrusted | bool | True if this is a trusted language, which means that it is believed not to grant access to anything outside the normal SQL execution environment. Only superusers can create functions in untrusted languages. | |
| lanplcallfoid | oid | pg_proc.oid | For noninternal languages this references the language handler, which is a special function that is responsible for executing all functions that are written in the particular language |
| laninline | oid | pg_proc.oid | This references a function that is responsible for executing "inline" anonymous code blocks (DO blocks). Zero if inline blocks are not supported. |
| lanvalidator | oid | pg_proc.oid | This references a language validator function that is responsible for checking the syntax and validity of new functions when they are created. Zero if no validator is provided. |
| lanacl | aclitem[] | Access privileges; see GRANT and REVOKE for details |


Cryptosystem
Although there are strict definitions of what is a cryptosystem, in practice it is still rather different depending on the point of view.
From wikipedia, a cryptosystem refers to a suite of cryptographic algorithms needed to implement a particular security service, most commonly for achieving confidentiality. We agree that most of the time term cryptography is associated with data encryption (confidentiality). Nowadays, however, driven by continuous growing complexity of IT systems in use and enlargement of possible security attack vector space, cryptography is much more than that. Generally, cryptography is now associated with {confidentiality, integrity, authentity} tuple.
Cryptosystems and cryptoalgorithms
A cryptoalgorithm is a formal description of how to process the original data to get required result (ciphertext - for confidentiality, message authentication code - for authentity etc). It is a step-by-step set of operations to be performed. Like any other algorithm, it has input and output.
A cryptosystem, as stated above, is a suite of cryptoalgorithms. Thus it may be a combination of one or more algorithms, aimed to implement a security service. This implies that a particular cryptosystem is designed to support specific security use-case, so cryptosystems more closely reflect real-world security requirements. Apart from cryptoalgorithms a cryptosystem may also include auxiliary information, guidelines, restrictions etc to better support its use-case. For example, AES as a cryptoalgorithm is just a description of how to combine input data and key to produce a ciphertext. But, AES as a standalone cryptosystem, should probably also include guidelines for key generation, requirements for keys (randomness, uniform distribution, weak keys) and some description of desired operating environment.
So, a modern cryptosystem would probably consist of some of these components (depends on use-case): key agreement scheme encryption scheme signature scheme integrity scheme * support routines: key generation, derivation functions, data format etc
Themis cryptosystems
In Themis we aimed our cryptosystems to support modern real-world security scenarios. Also, we tried to make them as simple as possible to use, even for an inexperienced developer, so that amount of moving parts, which could be broken and cryptosystem's security could be compromised, is minimized.
Please, check our guidelines and examples for determining which one(s) to use in your specific case. If you have any doubts, don't hesitate contact us or ask support from our community.


ArcGIS Pro 1
Jay Fowler - Solution Engineer
<EMAIL_ADDRESS>## ArcGIS Pro Agenda
Session 1
Session 2
Briefing
Briefing
Licensing
Editing
Mapping
Analysis
Sharing
Wrap Up
Wrap Up
## ArcGIS Pro Briefing - Version 1.0 released in 2015 - ArcGIS Pro is Fast (true 64 bit app) - Phenomenal Web Client - Simplified Ribbon and Panel Interface - Project based... Multi maps, Layouts, Etc... - Fusion of Apps - New Functionality and Workflows
## ArcGIS Pro Licensing - Named User - Single Use - Concurrent Use
## Demo!
## Q&A --- * Questions? * What Ya Got?
## In the room today
--- 1. Never used it before 1. Installed and licensed. Explored for 6 minutes on a Friday at 4:30PM 1. Use ArcGIS Pro in tandem with ArcMap ~ half and half 1. Use Pro 90% - 100% of the time
## Conclusions *ArcGIS Pro is Compelling* --- 1. Great client for Web GIS 1. It is fast 1. Simplified Interface / Simplified Workflows 1. Multiple Layouts, Charts, Infographics, etc... 1. New Functionality / Powerful Solution (Session 2)