#include <config.h>
#include <library/c_symbol.h>
Go to the source code of this file.
Defines | |
| #define | C2_MAX_SCOPE 20 |
Functions | |
| int | C2_init_scope (int num_scope, int s_global, int s_local) |
| void | C2_kill_scope_above (int n) |
| void | C2_reset_attribute (int n) |
| int | C2_scope_in (void) |
| int | C2_scope_out (void) |
| int | C2_find_symbol (char *name, int type, Integer *value, Pointer *ptr) |
| int | C2_update_symbol (char *name, int type, Integer value, Pointer ptr) |
| int | C2_find_local_symbol (char *name, int type, Integer *value, Pointer *ptr) |
| int | C2_update_local_symbol (char *name, int type, Integer value, Pointer ptr) |
| int | C2_find_global_symbol (char *name, int type, Integer *value, Pointer *ptr) |
| int | C2_update_global_symbol (char *name, int type, Integer value, Pointer ptr) |
| int | C2_forall (C_Function fn) |
| int | C2_forall_global (C_Function fn) |
| #define C2_MAX_SCOPE 20 |
| int C2_forall | ( | C_Function | fn | ) |
| int C2_forall_global | ( | C_Function | fn | ) |
| int C2_init_scope | ( | int | num_scope, | |
| int | s_global, | |||
| int | s_local | |||
| ) |
Definition at line 73 of file c_symbol2.c.
References C2_scope_in(), C_assert(), C_MATCH_BY_AND_TYPE, C_open(), n_scope, next_scope_id, and scope.
| void C2_kill_scope_above | ( | int | n | ) |
| void C2_reset_attribute | ( | int | n | ) |
Definition at line 108 of file c_symbol2.c.
References C_assert(), C_clear_attr(), n_scope, and scope.
| int C2_scope_in | ( | void | ) |
Definition at line 120 of file c_symbol2.c.
References C2_MAX_SCOPE, C_assert(), C_clear(), n_scope, next_scope_id, scope, and scope_id.
Referenced by C2_init_scope().
| int C2_scope_out | ( | void | ) |


Simple controlled valve.
Standard characteristic Kv=f (y) is given at standard conditions (dp0, rho0),
Kv/Kv1 = Kv0/Kv1 + (1-Kv0/Kv1) *
y/Y1
Kv/Kv1 = Kv0/Kv1 * exp[log(Kv1/Kv0) *
y/Y1]
where:
Kv0 ... min. flow @ y = 0
Y1 .... max. valve opening
Kv1 ... max. flow @ y = Y1
Flow resistance under real conditions is calculated by
V_flow**2 * rho / dp = Kv(y)**2 * rho0 / dp0


My Second post
Laravel is a free, open-source PHP web framework, created by Taylor Otwell and intended for the development of web applications following the model–view–controller architectural pattern and based on Symfony.
Laravel is a free, open-source PHP web framework, created by Taylor Otwell and intended for the development of web applications following the model–view–controller architectural pattern and based on Symfony.


Finland, eleventh to qualify for Euro 2020
by BeSoccerThe Euro's are just around the corner and we already know the name of other teams who will be present in the competition. After imposing a 3-0 win over Liechtenstein, Finland have become the eleventh team to qualify for the tournament.
The Finnish national team made history this Friday: their first qualification for Euro 2020 and, going even further, their first major football tournament. They did so with a foreseeable victory over Liechtenstein, whom they beat 3-0 in Helsinki with a double from their indispensable scorer: Teemu Pukki.
The eigth and ninth goal in nine games for this Norwich footballer, who, in the past played ar Sevilla, with some time in the first team, and helped his team by giving them the 2-0 lead from the penalty spot in the 64th minute, and sealed the win with the third goal to make it 3-0 in the 75th minute.
The win had begun, perhaps even defined already - Liechtenstein only had one shot on target out of three in the entire match, they are in second place in Group F, behind Finland, who are competing for the first time amongst some of the best teams on the continent.
Finland are the latest to qualify. On Thursday, Turkey, France, England and the Czech Republic also did so. Belgium, Spain, Italy, Poland, Russia and Ukraine already achieved their pass to the Euro 2020.
_______________________________________
What happened in the game:
Starting line-ups:
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3 - Finland: Hradecky; Raitala, Toivio, Arajuuri, Pirinen; Lod, Sparv (Kauko, m. 71), Kamara, Soiri (Skrabb, m. 78); Tuominen and Pukki (Karjalainen, m. 84).
0 - Liechtenstein: B. Buchel; Brandle, Malin, Rechsteiner, Goppel; M. Buchel; Meier (Sele, m. 91), Hasler, Polverino (Gubser, m. 73), Salanovic; and Frick (Kardesoglu, m. 84).
Goals: 1-0, m. 21: Tuominen. 2-0, m. 64: Pukki, penalty kick. 3-0, m. 75: Pukki.
Referee: Benoit Bastien (France).
Incidents: match corresponding to the last day of Group J of the Euro 2020 qualifiers, played at the Telia 5G Arena in Helsink.


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Class: Aws::DynamoDB::Types::SourceTableFeatureDetails
| Relationships & Source Files | |
| Super Chains via Extension / Inclusion / Inheritance | |
|
Class Chain:
self,
Struct
|
|
|
Instance Chain:
self,
::Aws::Structure ,
Struct
|
|
| Inherits: |
Struct
|
| Defined in: | gems/aws-sdk-dynamodb/lib/aws-sdk-dynamodb/types.rb |
Overview
Contains the details of the features enabled on the table when the backup was created. For example, LSIs, GSIs, streams, TTL.
Constant Summary
Instance Attribute Summary
-
#global_secondary_indexes ⇒ Array<Types::GlobalSecondaryIndexInfo>
rw
Represents the GSI properties for the table when the backup was created.
-
#local_secondary_indexes ⇒ Array<Types::LocalSecondaryIndexInfo>
rw
Represents the LSI properties for the table when the backup was created.
-
#sse_description ⇒ Types::SSEDescription
rw
The description of the server-side encryption status on the table when the backup was created.
-
#stream_description ⇒ Types::StreamSpecification
rw
Stream settings on the table when the backup was created.
-
#time_to_live_description ⇒ Types::TimeToLiveDescription
rw
Time to Live settings on the table when the backup was created.
Instance Method Summary
::Aws::Structure
- Included
| #to_hash | Alias for Structure#to_h. |
Instance Attribute Details
#global_secondary_indexes ⇒ Array
<Types::GlobalSecondaryIndexInfo> (rw)
Represents the GSI properties for the table when the backup was
created. It includes the IndexName, KeySchema, Projection
, and
ProvisionedThroughput
for the GSIs on the table at the time of
backup.
# File 'gems/aws-sdk-dynamodb/lib/aws-sdk-dynamodb/types.rb', line 7970
class SourceTableFeatureDetails < Struct.new( :local_secondary_indexes, :global_secondary_indexes, :stream_description, :time_to_live_description, :sse_description) SENSITIVE = [] include Aws::Structure end
#local_secondary_indexes ⇒ Array
<Types::LocalSecondaryIndexInfo> (rw)
Represents the LSI properties for the table when the backup was
created. It includes the IndexName, KeySchema and Projection
for the
LSIs on the table at the time of backup.
# File 'gems/aws-sdk-dynamodb/lib/aws-sdk-dynamodb/types.rb', line 7970
class SourceTableFeatureDetails < Struct.new( :local_secondary_indexes, :global_secondary_indexes, :stream_description, :time_to_live_description, :sse_description) SENSITIVE = [] include Aws::Structure end
#sse_description ⇒ Types::SSEDescription (rw)
The description of the server-side encryption status on the table when the backup was created.
# File 'gems/aws-sdk-dynamodb/lib/aws-sdk-dynamodb/types.rb', line 7970
class SourceTableFeatureDetails < Struct.new( :local_secondary_indexes, :global_secondary_indexes, :stream_description, :time_to_live_description, :sse_description) SENSITIVE = [] include Aws::Structure end
#stream_description ⇒ Types::StreamSpecification (rw)
Stream settings on the table when the backup was created.
# File 'gems/aws-sdk-dynamodb/lib/aws-sdk-dynamodb/types.rb', line 7970
class SourceTableFeatureDetails < Struct.new( :local_secondary_indexes, :global_secondary_indexes, :stream_description, :time_to_live_description, :sse_description) SENSITIVE = [] include Aws::Structure end
#time_to_live_description ⇒ Types::TimeToLiveDescription (rw)
Time to Live settings on the table when the backup was created.
# File 'gems/aws-sdk-dynamodb/lib/aws-sdk-dynamodb/types.rb', line 7970
class SourceTableFeatureDetails < Struct.new( :local_secondary_indexes, :global_secondary_indexes, :stream_description, :time_to_live_description, :sse_description) SENSITIVE = [] include Aws::Structure end


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Chapter 1 The Basics of Bayesian Statistics
Bayesian statistics mostly involves conditional probability, which is the the probability of an event A given event B, and it can be calculated using the Bayes’ rule. The concept of conditional probability is widely used in medical testing, in which false positives and false negatives may occur. A false positive can be defined as a positive outcome on a medical test when the patient does not actually have the disease they are being tested for. In other words, it’s the probability of testing positive given no disease. Similarly, a false negative can be defined as a negative outcome on a medical test when the patient does have the disease. In other words, testing negative given disease. Both indicators are critical for any medical decisions.
For how the Bayes’ rule is applied, we can set up a prior, then calculate posterior probabilities based on a prior and likelihood. That is to say, the prior probabilities are updated through an iterative process of data collection.
1.1 Bayes’ Rule
NEED INTRO TEXT OTHERWISE LABEL MULTIPLIED DEFINED IN LATEX/PDF
1.1.1 Conditional Probabilities & Bayes’ Rule
Consider Table 1.1. It shows the results of a poll among 1738 adult Americans. This table allows us to calculate probabilities.
| 18-29 | 30-49 | 50-64 | 65+ | Total | |
|---|---|---|---|---|---|
| Used online dating site | 60 | 86 | 58 | 21 | 225 |
| Did not use online dating site | 255 | 426 | 450 | 382 | 1513 |
| Total | 316 | 512 | 508 | 403 | 1738 |
Here, the pipe symbol `|’ means conditional on. This is a conditional probability as one can consider it the probability of using an online dating site conditional on being in age group 30-49.
We can rewrite this conditional probability in terms of ‘regular’ probabilities by dividing both numerator and the denominator by the total number of people in the poll. That is, \[\begin{multline*} P(\text{using an online dating site} \mid \text{in age group 30-49}) \\ \begin{split} &= \frac{\text{Number in age group 30-49 that indicated they used an online dating site}}{\text{Total number in age group 30-49}} \\ &= \frac{\frac{\text{Number in age group 30-49 that indicated they used an online dating site}}{\text{Total number of people in the poll}}}{\frac{\text{Total number in age group 30-49}}{\text{Total number of people in the poll}}} \\ &= \frac{P(\text{using an online dating site \& falling in age group 30-49})}{P(\text{Falling in age group 30-49})}. \end{split} \end{multline*}\]It turns out this relationship holds true for any conditional probability and is known as Bayes’ rule:
Definition 1.1 (Bayes’ Rule) The conditional probability of the event \(A\) conditional on the event \(B\) is given by
\[ P(A \mid B) = \frac{P(A \,\&\, B)}{P(B)}. \]Example 1.1 What is the probability that an 18-29 year old from Table 1.1 uses online dating sites?
Note that the question asks a question about 18-29 year olds. Therefore, it conditions on being 18-29 years old. Bayes’ rule provides a way to compute this conditional probability:
\[\begin{multline*} P(\text{using an online dating site} \mid \text{in age group 18-29}) \\ \begin{split} &= \frac{P(\text{using an online dating site \& falling in age group 18-29})}{P(\text{Falling in age group 18-29})} \\ &= \frac{\frac{\text{Number in age group 18-29 that indicated they used an online dating site}}{\text{Total number of people in the poll}}}{\frac{\text{Total number in age group 18-29}}{\text{Total number of people in the poll}}} \\ &= \frac{\text{Number in age group 18-29 that indicated they used an online dating site}}{\text{Total number in age group 18-29}} = \frac{60}{315} \approx 19\%. \end{split} \end{multline*}\]1.1.2 Bayes’ Rule and Diagnostic Testing
To better understand conditional probabilities and their importance, let us consider an example involving the human immunodeficiency virus (HIV). In the early 1980s, HIV had just been discovered and was rapidly expanding. There was major concern with the safety of the blood supply. Also, virtually no cure existed making an HIV diagnosis basically a death sentence, in addition to the stigma that was attached to the disease.
These made false positives and false negatives in HIV testing highly undesirable. A false positive is when a test returns postive while the truth is negative. That would for instance be that someone without HIV is wrongly diagnosed with HIV, wrongly telling that person they are going to die and casting the stigma on them. A false negative is when a test returns negative while the truth is positive. That is when someone with HIV undergoes an HIV test which wrongly comes back negative. The latter poses a threat to the blood supply if that person is about to donate blood.
The probability of a false positive if the truth is negative is called the false positive rate. Similarly, the false negative rate is the probability of a false negative if the truth is positive. Note that both these rates are conditional probabilities: The false positive rate of an HIV test is the probability of a positive result conditional on the person tested having no HIV.
The HIV test we consider is an enzyme-linked immunosorbent assay, commonly known as an ELISA. We would like to know the probability that someone (in the early 1980s) has HIV if ELISA tests positive. For this, we need the following information. ELISA’s true positive rate (one minus the false negative rate), also referred to as sensitivity, recall, or probability of detection, is estimated as \[ P(\text{ELISA is positive} \mid \text{Person tested has HIV}) = 93\% = 0.93. \] Its true negative rate (one minus the false positive rate), also referred to as specificity, is estimated as \[ P(\text{ELISA is negative} \mid \text{Person tested has no HIV}) = 99\% = 0.99. \] Also relevant to our question is the prevalence of HIV in the overall population, which is estimated to be 1.48 out of every 1000 American adults. We therefore assume \[\begin{equation} P(\text{Person tested has HIV}) = \frac{1.48}{1000} = 0.00148. \tag{1.1} \end{equation}\]Note that the above numbers are estimates. For our purposes, however, we will treat them as if they were exact.
Our goal is to compute the probability of HIV if ELISA is positive, that is \(P(\text{Person tested has HIV} \mid \text{ELISA is positive})\). In none of the above numbers did we condition on the outcome of ELISA. Fortunately, Bayes’ rule allows is to use the above numbers to compute the probability we seek. Bayes’ rule states that
\[ \begin{aligned} P(&\text{Person tested has HIV} \mid \text{ELISA is positive}) \\ & = \frac{P(\text{Person tested has HIV} \,\&\, \text{ELISA is positive})}{P(\text{ELISA is positive})}. \end{aligned} \tag{1.2} \]
The can be derived as follows. For someone to test positive and be HIV positive, that person first needs to be HIV positive and then seconldy test positive. The probability of the first thing happening is \(P(\text{HIV positive}) = 0.00148\). The probability of then testing positive is \(P(\text{ELISA is positive} \mid \text{Person tested has HIV}) = 0.93\), the true positive rate. This yields for the numerator \[\begin{multline*} P(\text{Person tested has HIV} \,\&\, \text{ELISA is positive}) \\ \begin{split} &= P(\text{Person tested has HIV}) P(\text{ELISA is positive} \mid \text{Person tested has HIV}) \\ &= 0.00148 \cdot 0.93 = 0.0013764. \end{split} \tag{1.3} \end{multline*}\]The first step in the above equation is implied by Bayes’ rule: By multiplying the left- and right-hand side of Bayes’ rule as presented in Section 1.1.1 by \(P(B)\), we obtain \[ P(A \mid B) P(B) = P(A \,\&\, B). \]
The denominator in (1.2) can be expanded as \[\begin{multline*} P(\text{ELISA is positive}) \\ \begin{split} &= P(\text{Person tested has HIV} \,\&\, \text{ELISA is positive}) + P(\text{Person tested has no HIV} \,\&\, \text{ELISA is positive}) \\ &= 0.0013764 + 0.0099852 = 0.0113616 \end{split} \end{multline*}\] where we used (1.3) and \[\begin{multline*} P(\text{Person tested has no HIV} \,\&\, \text{ELISA is positive}) \\ = P(\text{Person tested has no HIV}) P(\text{ELISA is positive} \mid \text{Person tested has no HIV}) = \left(1 - P(\text{Person tested has HIV})\right) \cdot \left(1 - P(\text{ELISA is negative} \mid \text{Person tested has no HIV})\right) = \left(1 - 0.00148\right) \cdot \left(1 - 0.99\right) = 0.0099852. \end{multline*}\] Putting this all together and inserting into (1.2) reveals \[\begin{equation} P(\text{Person tested has HIV} \mid \text{ELISA is positive}) = \frac{0.0013764}{0.0113616} \approx 0.12. \tag{1.4} \end{equation}\]So even when the ELISA returns positive, the probability of having HIV is only 12%. An important reason why this number is so low is due to the prevalence of HIV. Before testing, one’s probability of HIV was 0.148%, so the positive test changes that probability dramatically, but it is still below 50%. That is, it is more likely that one is HIV negative rather than positive after one positive ELISA test.
Questions like the one we just answered (What is the probability of a disease if a test returns positive?) are crucial to make medical diagnoses. As we saw, just the true positive and true negative rates of a test do not tell the full story, but also a disease’s prevalence plays a role. Bayes’ rule is a tool to synthesize such numbers into a more useful probability of having a disease after a test result.
If the an individual is at a higher risk for having HIV than a randomly sampled person from the population considered, how, if at all, would you expect \(P(\text{Person tested has HIV} \mid \text{ELISA is positive})\) to change?
We found in (1.4) that someone who tests positive has a \(0.12\) probability of having HIV. That implies that the same person has a \(1-0.12=0.88\) probability of not having HIV, despite testing positive.
If the person has a priori a higher risk for HIV and tests positive, then the probability of having HIV must be higher than for someone not at increased risk who also tests positive. Therefore, \(P(\text{Person tested has HIV} \mid \text{ELISA is positive}) > 0.12\) where \(0.12\) comes from (1.4).
One can derive this mathematically by plugging in a larger number in (1.1) than 0.00148, as that number represents the prior risk of HIV. Changing the calculations accordingly shows \(P(\text{Person tested has HIV} \mid \text{ELISA is positive}) > 0.12\).
If the false positive rate increases, the probability of a wrong positive result increases. That means that a positive test result is more likely to be wrong and thus less indicative of HIV. Therefore, the probability of HIV after a positive ELISA goes down such that \(P(\text{Person tested has HIV} \mid \text{ELISA is positive}) < 0.12\).
1.1.3 Bayes Updating
In the previous section, we saw that one positive ELISA test yields a probability of having HIV of 12%. To obtain a more convincing probability, one might want to do a second ELISA test after a first one comes up positive. What is the probability of being HIV positive of also the second ELISA test comes back positive?
To solve this problem, we will assume that the correctness of this second test is not influenced by the first ELISA, that is, the tests are independent from each other. This assumption probably does not hold true as it is plausible that if the first test was a false positive, it is more likely that the second one will be one as well. Nonetheless, we stick with the independence assumption for simplicity.
In the last section, we used \(P(\text{Person tested has HIV}) = 0.00148\), see (1.1), to compute the probability of HIV after one positive test. If we repeat those steps but now with \(P(\text{Person tested has HIV}) = 0.12\), the probability that a person with one positive test has HIV, we exactly obtain the probability of HIV after two positive tests. Repeating the maths from the previous section, involving Bayes’ rule, gives
\[\begin{multline*} P(\text{Person tested has HIV} \mid \text{Second ELISA is also positive}) \\ \begin{split} &= \frac{P(\text{Person tested has HIV}) P(\text{Second ELISA is positive} \mid \text{Person tested has HIV})}{P(\text{Second ELISA is also positive})} \\ &= \frac{0.12 \cdot 0.93}{ \begin{split} &P(\text{Person tested has HIV}) P(\text{Second ELISA is positive} \mid \text{Has HIV}) \\ + &P(\text{Person tested has no HIV}) P(\text{Second ELISA is positive} \mid \text{Has no HIV}) \end{split} } \\ &= \frac{0.1116}{0.12 \cdot 0.93 + (1 - 0.12)\cdot (1 - 0.99)} \approx 0.93. \end{split} \tag{1.5} \end{multline*}\]Since we are considering the same ELISA test, we used the same true positive and true negative rates as in Section 1.1.2. We see that two positive tests makes it much more probable for someone to have HIV than when only one test comes up positive.
This process, of using Bayes’ rule to update a probability based on an event affecting it, is called Bayes’ updating. More generally, the what one tries to update can be considered ‘prior’ information, sometimes simply called the prior. The event providing information about this can also be data. Then, updating this prior using Bayes’ rule gives the information conditional on the data, also known as the posterior, as in the information after having seen the data. Going from the prior to the posterior is Bayes updating.
The probability of HIV after one positive ELISA, 0.12, was the posterior in the previous section as it was an update of the overall prevalence of HIV, (1.1). However, in this section we answered a question where we used this posterior information as the prior. This process of using a posterior as prior in a new problem is natural in the Bayesian framework of updating knowledge based on the data.
1.1.4 Bayesian vs. Frequentist Definitions of Probability
The frequentist definition of probability is based on observation of a large number of trials. The probability for an event \(E\) to occur is \(P(E)\), and assume we get \(n_E\) successes out of \(n\) trials. Then we have \[\begin{equation} P(E) = \lim_{n \rightarrow \infty} \dfrac{n_E}{n}. \end{equation}\]On the other hand, the Bayesian definition of probability \(P(E)\) reflects our prior beliefs, so \(P(E)\) can be any probability distribution, provided that it is consistent with all of our beliefs. (For example, we cannot believe that the probability of a coin landing heads is 0.7 and that the probability of getting tails is 0.8, because they are inconsistent.)
The two definitions result in different methods of inference. Using the frequentist approach, we describe the confidence level as the proportion of random samples from the same population that produced confidence intervals which contain the true population parameter. For example, if we generated 100 random samples from the population, and 95 of the samples contain the true parameter, then the confidence level is 95%. Note that each sample either contains the true parameter or does not, so the confidence level is NOT the probability that a given interval includes the true population parameter.
The correct interpretation is: 95% of random samples of 1,500 adults will produce confidence intervals that contain the true proportion of Americans who think the federal government does not do enough for middle class people.
Here are two common misconceptions:
There is a 95% chance that this confidence interval includes the true population proportion.
The true population proportion is in this interval 95% of the time.
The probability that a given confidence interval captures the true parameter is either zero or one. To a frequentist, the problem is that one never knows whether a specific interval contains the true value with probability zero or one. So a frequentist says that “95% of similarly constructed intervals contain the true value”.
The second (incorrect) statement sounds like the true proportion is a value that moves around that is sometimes in the given interval and sometimes not in it. Actually the true proportion is constant, it’s the various intervals constructed based on new samples that are different.
The Bayesian alternative is the credible interval, which has a definition that is easier to interpret. Since a Bayesian is allowed to express uncertainty in terms of probability, a Bayesian credible interval is a range for which the Bayesian thinks that the probability of including the true value is, say, 0.95. Thus a Bayesian can say that there is a 95% chance that the credible interval contains the true parameter value.
We can say that there is a 95% probability that the proportion is between 60% and 64% because this is a credible interval, and more details will be introduced later in the course.
1.2 Inference for a Proportion
1.2.1 Inference for a Proportion: Frequentist Approach
Example 1.8 RU-486 is claimed to be an effective “morning after” contraceptive pill, but is it really effective?
Data: A total of 40 women came to a health clinic asking for emergency contraception (usually to prevent pregnancy after unprotected sex). They were randomly assigned to RU-486 (treatment) or standard therapy (control), 20 in each group. In the treatment group, 4 out of 20 became pregnant. In the control group, the pregnancy rate is 16 out of 20.
Question: How strongly do these data indicate that the treatment is more effective than the control?To simplify the framework, let’s make it a one proportion problem and just consider the 20 total pregnancies because the two groups have the same sample size. If the treatment and control are equally effective, then the probability that a pregnancy comes from the treatment group (\(p\)) should be 0.5. If RU-486 is more effective, then the probability that a pregnancy comes from the treatment group (\(p\)) should be less than 0.5.
Therefore, we can form the hypotheses as below:
\(p =\) probability that a given pregnancy comes from the treatment group
\(H_0: p = 0.5\) (no difference, a pregnancy is equally likely to come from the treatment or control group)
\(H_A: p < 0.5\) (treatment is more effective, a pregnancy is less likely to come from the treatment group)
A p-value is needed to make an inference decision with the frequentist approach. The definition of p-value is the probability of observing something at least as extreme as the data, given that the null hypothesis (\(H_0\)) is true. “More extreme” means in the direction of the alternative hypothesis (\(H_A\)).
Since \(H_0\) states that the probability of success (pregnancy) is 0.5, we can calculate the p-value from 20 independent Bernoulli trials where the probability of success is 0.5. The outcome of this experiment is 4 successes in 20 trials, so the goal is to obtain 4 or fewer successes in the 20 Bernoulli trials.
This probability can be calculated exactly from a binomial distribution with \(n=20\) trials and success probability \(p=0.5\). Assume \(k\) is the actual number of successes observed, the p-value is
\[P(k \leq 4) = P(k = 0) + P(k = 1) + P(k = 2) + P(k = 3) + P(k = 4)\].
sum(dbinom(0:4, size = 20, p = 0.5))
## [1] 0.005908966
According to \(\mathsf{R}\), the probability of getting 4 or fewer successes in 20 trials is 0.0059. Therefore, given that pregnancy is equally likely in the two groups, we get the chance of observing 4 or fewer preganancy in the treatment group is 0.0059. With such a small probability, we reject the null hypothesis and conclude that the data provide convincing evidence for the treatment being more effective than the control.
1.2.2 Inference for a Proportion: Bayesian Approach
This section uses the same example, but this time we make the inference for the proportion from a Bayesian approach. Recall that we still consider only the 20 total pregnancies, 4 of which come from the treatment group. The question we would like to answer is that how likely is for 4 pregnancies to occur in the treatment group. Also remember that if the treatment and control are equally effective, and the sample sizes for the two groups are the same, then the probability (\(p\)) that the pregnancy comes from the treatment group is 0.5.
Within the Bayesian framework, we need to make some assumptions on the models which generated the data. First, \(p\) is a probability, so it can take on any value between 0 and 1. However, let’s simplify by using discrete cases – assume \(p\), the chance of a pregnancy comes from the treatment group, can take on nine values, from 10%, 20%, 30%, up to 90%. For example, \(p = 20\%\) means that among 10 pregnancies, it is expected that 2 of them will occur in the treatment group. Note that we consider all nine models, compared with the frequentist paradigm that whe consider only one model.
Table 1.2 specifies the prior probabilities that we want to assign to our assumption. There is no unique correct prior, but any prior probability should reflect our beliefs prior to the experiement. The prior probabilities should incorporate the information from all relevant research before we perform the current experiement.
| Model (\(p\)) | 0.1000 | 0.2000 | 0.3000 | 0.4000 | 0.5000 | 6e-01 | 0.70 | 0.80 | 0.90 |
| Prior \(P(model)\) | 0.0600 | 0.0600 | 0.0600 | 0.0600 | 0.5200 | 6e-02 | 0.06 | 0.06 | 0.06 |
| Likelihood \(P(data|model)\) | 0.0898 | 0.2182 | 0.1304 | 0.0350 | 0.0046 | 3e-04 | 0.00 | 0.00 | 0.00 |
| \(P(data|model)\) x \(P(model)\) | 0.0054 | 0.0131 | 0.0078 | 0.0021 | 0.0024 | 0e+00 | 0.00 | 0.00 | 0.00 |
| Posterior \(P(model|data)\) | 0.1748 | 0.4248 | 0.2539 | 0.0681 | 0.0780 | 5e-04 | 0.00 | 0.00 | 0.00 |
This prior incorporates two beliefs: the probability of \(p = 0.5\) is highest, and the benefit of the treatment is symmetric. The second belief means that the treatment is equally likely to be better or worse than the standard treatment. Now it is natural to ask how I came up with this prior, and the specification will be discussed in detail later in the course.
Next, let’s calculate the likelihood – the probability of observed data for each model considered. In mathematical terms, we have
\[ P(\text{data}|\text{model}) = P(k = 4 | n = 20, p)\]
The likelihood can be computed as a binomial with 4 successes and 20 trials with \(p\) is equal to the assumed value in each model. The values are listed in Table 1.2.
After setting up the prior and computing the likelihood, we are ready to calculate the posterior using the Bayes’ rule, that is,
\[P(\text{model}|\text{data}) = \frac{P(\text{model})P(\text{data}|\text{model})}{P(\text{data})}\]
The posterior probability values are also listed in Table 1.2, and the highest probability occurs at \(p=0.2\), which is 42.48%. Note that the priors and posteriors across all models both sum to 1.
In decision making, we choose the model with the highest posterior probability, which is \(p=0.2\). In comparison, the highest prior probability is at \(p=0.5\) with 52%, and the posterior probability of \(p=0.5\) drops to 7.8%. This demonstrates how we update our beliefs based on observed data. Note that the calculation of posterior, likelihood, and prior is unrelated to the frequentist concept (data “at least as extreme as observed”).
Here are the histograms of the prior, the likelihood, and the posterior probabilities:
We started with the high prior at \(p=0.5\), but the data likelihood peaks at \(p=0.2\). And we updated our prior based on observed data to find the posterior. The Bayesian paradigm, unlike the frequentist approach, allows us to make direct probability statements about our models. For example, we can calculate the probability that RU-486, the treatment, is more effective than the control as the sum of the posteriors of the models where \(p<0.5\). Adding up the relevant posterior probabilities in Table 1.2, we get the chance that the treatment is more effective than the control is 92.16%.
1.2.3 Effect of Sample Size on the Posterior
The RU-486 example is summarized in Figure 1.1, and let’s look at what the posterior distribution would look like if we had more data.
Suppose our sample size was 40 instead of 20, and the number of successes was 8 instead of 4. Note that the ratio between the sample size and the number of successes is still 20%. We will start with the same prior distribution. Then calculate the likelihood of the data which is also centered at 0.20, but is less variable than the original likelihood we had with the smaller sample size. And finally put these two together to obtain the posterior distribution. The posterior also has a peak at p is equal to 0.20, but the peak is taller, as shown in Figure 1.2. In other words, there is more mass on that model, and less on the others.
To illustrate the effect of the sample size even further, we’re going to keep increasing our sample size, but still maintain the the 20% ratio between the sample size and the number of successes. So let’s consider a sample with 200 observations and 40 successes. Once again, we’re going to use the same prior and the likelihood is again centered at 20% and almost all of the probability mass in the posterior is at p is equal to 0.20. The other models do not have zero probability mass, but they’re posterior probabilities are very close to zero.
Figure 1.3 demonstrates that as more data are collected, the likelihood ends up dominating the prior. This is why, while a good prior helps, a bad prior can be overcome with a large sample. However, it’s important to note that this will only work as long as we don’t place a zero probability mass on any of the models in the prior.
1.3 Frequentist vs. Bayesian Inference
1.3.1 Frequentist vs. Bayesian Inference
In this section, we will solve a simple inference problem using both frequentist and Bayesian approaches. Then we will compare our results based on decisions based on the two methods, to see whether we get the same answer or not. If we do not, we will discuss why that happens.
Example 1.9 We have a population of M&M’s, and in this population the percentage of yellow M&M’s is either 10% or 20%. You’ve been hired as a statistical consultant to decide whether the true percentage of yellow M&M’s is 10% or 20%.
Payoffs/losses: You are being asked to make a decision, and there are associated payoff/losses that you should consider. If you make the correct decision, your boss gives you a bonus. On the other hand, if you make the wrong decision, you lose your job.
Data: You can “buy” a random sample from the population – You pay $200 for each M&M, and you must buy in $1,000 increments (5 M&Ms at a time). You have a total of $4,000 to spend, i.e., you may buy 5, 10, 15, or 20 M&Ms.
Remark: Remember that the cost of making a wrong decision is high, so you want to be fairly confident of your decision. At the same time, though, data collection is also costly, so you don’t want to pay for a sample larger than you need. If you believe that you could actually make a correct decision using a smaller sample size, you might choose to do so and save money and resources.Let’s start with the frequentist inference.
Hypothesis: \(H_0\) is 10% yellow M&Ms, and \(H_A\) is >10% yellow M&Ms.
Significance level: \(\alpha = 0.05\).
Sample: red, green, yellow, blue, orange
Observed data: \(k=1, n=5\)
P-value: \(P(k \geq 1 | n=5, p=0.10) = 1 - P(k=0 | n=5, p=0.10) = 1 - 0.90^5 \approx 0.41\)
Note that the p-value is the probability of observed or more extreme outcome given that the null hypothesis is true.
Therefore, we fail to reject \(H_0\) and conclude that the data do not provide convincing evidence that the proportion of yellow M&M’s is greater than 10%. This means that if we had to pick between 10% and 20% for the proportion of M&M’s, even though this hypothesis testing procedure does not actually confirm the null hypothesis, we would likely stick with 10% since we couldn’t find evidence that the proportion of yellow M&M’s is greater than 10%.
The Bayesian inference works differently as below.
Hypotheses: \(H_1\) is 10% yellow M&Ms, and \(H_2\) is 20% yellow M&Ms.
Prior: \(P(H_1) = P(H_2) = 0.5\)
Sample: red, green, yellow, blue, orange
Observed data: \(k=1, n=5\)
Likelihood:
\[\begin{aligned} P(k=1 | H_1) &= \left( \begin{array}{c} 5 \\ 1 \end{array} \right) \times 0.10 \times 0.90^4 \approx 0.33 \\ P(k=1 | H_2) &= \left( \begin{array}{c} 5 \\ 1 \end{array} \right) \times 0.20 \times 0.80^4 \approx 0.41 \end{aligned}\]
- Posterior
\[\begin{aligned} P(H_1 | k=1) &= \frac{P(H_1)P(k=1 | H_1)}{P(k=1)} = \frac{0.5 \times 0.33}{0.5 \times 0.33 + 0.5 \times 0.41} \approx 0.45 \\ P(H_2 | k=1) &= 1 - 0.45 = 0.55 \end{aligned}\]
The posterior probabilities of whether \(H_1\) or \(H_2\) is correct are close to each other. As a result, with equal priors and a low sample size, it is difficult to make a decision with a strong confidence, given the observed data. However, \(H_2\) has a higher posterior probability than \(H_1\), so if we had to make a decision at this point, we should pick \(H_2\), i.e., the proportion of yellow M&Ms is 20%. Note that this decision contradicts with the decision based on the frequentist approach.
Table 1.3 summarizes what the results would look like if we had chosen larger sample sizes. Under each of these scenarios, the frequentist method yields a higher p-value than our significance level, so we would fail to reject the null hypothesis with any of these samples. On the other hand, the Bayesian method always yields a higher posterior for the second model where \(p\) is equal to 0.20. So the decisions that we would make are contradictory to each other.
| Frequentist | Bayesian H_1 | Bayesian H_2 | |
|---|---|---|---|
| Observed Data | P(k or more | 10% yellow) | P(10% yellow | n, k) | P(20% yellow | n, k) |
| n = 5, k = 1 | 0.41 | 0.45 | 0.55 |
| n = 10, k = 2 | 0.26 | 0.39 | 0.61 |
| n = 15, k = 3 | 0.18 | 0.34 | 0.66 |
| n = 20, k = 4 | 0.13 | 0.29 | 0.71 |
However, if we had set up our framework differently in the frequentist method and set our null hypothesis to be \(p = 0.20\) and our alternative to be \(p < 0.20\), we would obtain different results. This shows that the frequentist method is highly sensitive to the null hypothesis, while in the Bayesian method, our results would be the same regardless of which order we evaluate our models.
1.4 Exercises
Conditioning on dating site usage. Recall Table 1.1. What is the probability that an online dating site user from this sample is 18-29 years old?
Probability of no HIV. Consider the ELISA test from Section 1.1.2. What is the probability that someone has no HIV if that person has a negative ELISA result? How does this compare to the probability of having no HIV before any test was done?
Probability of no HIV after contradictive tests. Consider the ELISA test from Section 1.1.2. What is the probability that someone has no HIV if that person first tests positive on the ELISA and secondly test negative? Assume that the tests are independent from each other.


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I came across the Kickstarter crowdfunding campaign for Openbook. They call themselves “The honest, open-source & awesome social network”, and their plan is to build a “Facebook competitor” that is open sourced, free of advertisements, and wants to donate a portion of their revenue to charity - but apparently is not decentralized or federated.
I’m not sure what to think about it. On the one hand, I believe we need more alternatives to Facebook, except then I remember that the problem is not that other options don’t exist, but that network effects work against them. It’s already difficult for social media to grow through some hype, venture capital, and unique features. Being “Facebook, but open” is a tough value proposition to sell. I still wish them luck. Their design mocks look fine, and the team also seems solid; they have a founder of PGP on board as well as some non-technical people that could help make Openbook something that doesn’t just cater to computer geeks.


measure the distance to the first obstacle in front, by the ultrasound sensor URM37
trigger: any type. It is a trigger signal, and whenever it comes, the service will measure the distance.
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June 2022 - Present
ATS Corporation Cambridge, ON
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I'm Ameer Al-Turaihi, a Network Engineering and Security Analyst graduate, currently working as a Systems Administrator for ATS Corporation
ATS Corporation Cambridge, ON
Systems Administrator
ATS Corporation Cambridge, ON
IT Client Support Technician
ATS Corporation Cambridge, ON
IT Client Support Co-op
Mohawk College January 2019 - December 2020
Computer Systems Technology - Network Engineering and Security Analyst
Performed scanning and penetration assessment of Windows Server and documented all the vulnerabilities, mitigations, workarounds, and solutions.
Created a routing solution for a business environment, implemented a firewall, and created a Windows Server with domain services for the clients to connect to for my capstone project.
Mohawk College January 2018 - December 2019
Computer Systems Technician - Network Systems Dean's Honours List
Created a SQL database server and performed database admin tasks.
Created a Windows Exchange server for a virtual company to access their emails.
Implemented a ticketing system solution for a virtual company using SharePoint.
Contact me


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| withTags {htmltools} | R Documentation |
tags
This function makes it simpler to write HTML-generating code. Instead of
needing to specify tags
each time a tag function is used, as in
tags$div()
and tags$p()
, code inside withTags
is
evaluated with tags
searched first, so you can simply use
div()
and p()
.
withTags(code)
code |
A set of tags. |
If your code uses an object which happens to have the same name as an
HTML tag function, such as source()
or summary()
, it will call
the tag function. To call the intended (non-tags function), specify the
namespace, as in base::source()
or base::summary()
.
# Using tags$ each time tags$div(class = "myclass", tags$h3("header"), tags$p("text") ) # Equivalent to above, but using withTags withTags( div(class = "myclass", h3("header"), p("text") ) )


Follow this Guide to get started with XBXPlay
Thank you for downloading and using XBXPlay, this guide summarizes the most important information of XBXPlay.
Disclaimer: XBox, XBox One, XBox Series X, XBox Series S are trademarks of Microsoft Corporation. XBXPlay is in no way endorsed by or affiliated with Microsoft, or any associated subsidiaries, logos or trademarks.
You need to meet following requirements to fully enjoy XBXPlay.
XBXPlay even works on low end smartphones. However keep in mind to lower the resolution for such devices in order to guarantee good performance and low latency while streaming.
If you use XBXPlay for the first time you have to register your XBox with XBXPlay. The following sections will enlighten you about the most important information
Step 1 - Be sure your XBox console is turned on and that Remote Play is enabled on your console You can check if Remote Play is enabled on your XBox by navigating to Settings -> Devices & connections -> Remote features.
Step 2 - Open XBXPlay and click the "Register XBox" button afterwards click the "Register" button on the bottom of the screen.
Step 4 - You will now see the official XBox Live account website. At this point you must sign in to your XBox account which you want to use for Remote Play.
Disclaimer from the developer: I know providing your account details seems to be shady and I wished there would be another method to use Remote Play but unfortunately there is no other way and remote play only works with XBox live credentials. However, XBXPlay just opens the official XBox Live Log-In website from Microsoft and there is no technical way that XBXPlay could in any way intercept, monitor or modify the data you have entered there. XBXPlay only receives some generic profile information as response from the official Microsoft server and only the credential tokens will be stored locally encrypted on your device. If you have some additional questions feel free to contact me at<EMAIL_ADDRESS>Step 5 - If the registration was successful you will be redirected to the Home screen of XBXPlay where you can now choose to "Connect" to your XBox. If you received an error please go to Troubleshooting
Once you have successfully registered your XBox, you can choose "Connect" in the Home view of XBXPlay to establish a connection to your console. In order to connect remotely your router must have an IPv6 address otherwise you must configure port forwarding rules on your router at home, see section Playing over the internet for more information.
XBXPlay also allows you to just connect as controller without audio and video streaming so that you can use your smartphone as XBox controller. Just click on the 3 dots in the upper right corner in the Home view of XBXPlay, and click "Gamepad Mode". During the gamepad mode no Audio and Video stream will be processed but the console is sending these data anyway so please keep in mind that this will have an impact on your internet traffic.
Unlike the official XBox Remote Play app, XBXPlay can be registered with multiple XBox profiles. A profile is linked to one specific XBox Live account. If you have multiple XBox consoles and/ or multiple XBox Live accounts, XBXPlay is able to register more than one profile. If you have already registered a profile just open navigation drawer on the top left corner of the Home screen and click "Register new XBox". You can switch to another XBox profile by clicking "Profiles" in the navigation drawer and click on the "XBox profile" setting entry on the next screen.
XBXPlay offers you the possibility to customize the on-screen gamepad layout for both the "Remote Play Mode" and the "Gamepad Mode". You can either create a new remote layout by clicking "Remote layout" or you can create a new gamepad layout by clicking "Gamepad layout" in the navigation drawer. You can switch the layouts by clicking "Profiles" in the navigation drawer and click on the "Remote gamepad layout" for changing the remote layout or the "Gamepad layout" for changing the gamepad layout.
On Android you can connect any controller which is compatibly with Android 5.0 an above via Bluetooth or USB. Because of performance problems due hardware limitations on many Android devices it is strongly recommended to use a wired USB connection for your gamepad on Android. Otherwise high input latency and input delays can occur. If you ever encounter some unusual lags please connect your gamepad via USB if possible. Please note that this is not the fault of XBXPlay, the reason why this happens is because of some hardware limitations of your Android device and that there is no way that XBXPlay could fix this.
If the button mapping of your controller is weird you can create a button mapping profile for your controller by clicking "Button mapping" in the navigation drawer. If one of your gamepad buttons is recognized as Home button you will not be able to use this button as an app can not override this behavior. However, you can then use the "Use 2 buttons combination" feature in the mapping process instead. If you encounter some unusual button mapping please feel free to contact me at<EMAIL_ADDRESS>Note from the developer: Please read this whole section carefully and follow ALL instructions. If something isn't working as expected it's most likely that your internet service provider is not allowing it. You can find all the information below.
XBXPlay can also be used over the internet when you are not at home. If your internet provider is not giving your router an IPv6 you must configure your router in order to be able to connect from away. If you have an IPv6 the remote connection should work fine without the need to configure anything.
The following section explains how to manually configure port forwarding on your router (only needed if your router doesn't have an IPv6 address).
In order to configure your router you must access your router's dashboard. To configure your router you must be in the same network than your router (e.g. your WiFi network at home). It is recommended to do this on a PC but can of course also be performed on any other device which has a web browser. Open a web browser such as Google Chrome or similar. Go to the Address bar and enter the IP Address of your router. If you don't know the IP address of your router click here for an explanation of how to find it out.
If you are on your router's dashboard you have to navigate to the port forwarding section. Please note that each dashboard looks different, it could probably be located under "WAN" settings, "Internet" settings or similar. Following ports to the internal IP-Address of your XBox must be forwarded:
! You must forward ALL of the following ports and you must use the correct protocol for the ports! (Please note: port 9002 is UDP AND TCP) !
You can find below an example of how the port forwarding settings should look like if you configure port forwarding for your XBox (but it might look a little bit different on your router so just keep that in mind). Some routers allow the configuration of an external and internal port, others do not. If you can configure an external and internal port provide the same port on both of them.
You can find a guide on how to port forward on most routers HERE (Don't be bothered by the "Portforward Network Utilities" ad, just close it, this is not required).
If you get an error upon connecting remotely then there might be something wrong with the port forwarding settings or your internet provider might not allow it. Please keep on reading for further instructions.
Some routers allow you to configure a port range (start and end point port) or even to configure an internal and external port. If that is the case just provide the same port on all these available options.
If port forwarding is configured correctly you should be able to connect to your XBox by using the "Connect" button on the Home screen of the XBXPlay app.
If something did not work please double check if you have configured the correct protocol for the individual port mapping rules on your router (UDP/ TCP). Port 9002 needs two rules, one for UDP and one for TCP. If it still doesn't work then this is probably an indication that your internet provider doesn't give your router a "real" public IP-Address, which means your router is not accessible over the internet. If that is the case you have to contact your internet provider and ask if they could provide you a "real" public IPv4 IP-Address or maybe even an IPv6 address, otherwise connecting to your XBox from outside will not be possible for you.
Please note, if you are affected by the last problem (no "real" public IP-Address) there is nothing XBXPlay can do about it. Providing a bad rating doesn't help either as I can't workaround these errors. If you have any specific questions just contact me BEFORE providing a bad rating.
Please note: the connection quality and streaming latency heavily depends on the quality of your internet connection and on many other external factors. If there are lot of interferences please try to reduce the resolution in the XBXPlay settings (unfortunately backwards compatible games like XBox 360 only work in 1080p resolution).
If the automatic remote connection feature doesn't work for you, XBXPlay can automatically configure your router for playing over the internet but your router has to support UPnP and this feature must be enabled on your router. Please note, not all routers support UPnP or do not allow an automatic configuration because of security reasons. If XBXPlay reports that it is not able to find your router or that an other error occurred you must configure your router manually. You can find more information here. As there are literally 1000 of different router models available XBXPlay cannot guarantee that an automatic configuration will work on all routers. Some routers even have a faulty UPnP implementation which also prevents an automatic configuration.
If your router do not support UPnP or an automatic configuration is not possible because of security reasons you can simply configure your router manually. All information for a manual configuration can be found here. Below you can find an explanation of the common error messages:
This means that either UPnP is not enabled on your router or that the UPnP implementation of your router is not compatible with XBXPlay. You have to check if your router supports UPnP and if this feature is enabled. If it is enabled and it is still not working you have to configure your router manually. All information for a manual configuration can be found here.
An unexpected error occurred while XBXPlay was configuring your router. Be sure you are connected to the same WiFi network than your XBox. Your router might not fully support UPnP or is blocking the configuration attempt because of security reasons. You might need to configure your router manually. All information for a manual configuration can be found here.
Your router does not allow an automatic configuration because of security reasons. You have to configure your router manually. All information for a manual configuration can be found here.
An unknown error occurred while configuring your router. Your router may not fully support UPnP or the UPnP version of your router is not compatible with XBXPlay. You have to configure your router manually. All information for a manual configuration can be found here.
Not all port forwarding rules could be configured correctly. It is strongly recommended to configure your router manually and add the missing port forwarding rule. All information for a manual configuration can be found here.
Your router returned a unexpected public IP-Address. This could be an indication that your provider does not give your router a "real" public IP-Address. This means your router is not reachable from outside your network. Other possibilities for this warning could be that you have a double NAT configuration at home (your router is behind another router which creates two different networks) or that you are using a hybrid router with two different network technologies like DSL and LTE. The latter should not be a problem, however if you have a double NAT configuration at home or if your internet provider does not provide your router a real public IPv4, playing over the internet will probably not be possible for you. In any case you should contact your internet provider and ask if your router has a "real" public IP-Address and if not if they can assign you one.
If you are using a controller where the button mapping is messed up or you just want to change the button mapping, you can create a button mapping profile by clicking on "Button mapping" in the navigation drawer. After providing a unique button mapping profile name you can start to map individual buttons of your gamepad. Just be sure your gamepad is properly connected via Bluetooth or via USB. Please be aware that your gamepad might behave differently whether it is connected via Bluetooth or USB, so you may have to create two individual button mapping profiles.
On some devices a button on your gamepad may be interpreted as "Home" button, if that is the case you can use the "Use 2 buttons combination" feature to trigger this button by pressing a combination of two other buttons on your gamepad. You can also use this feature if you have too few buttons on your gamepad left. Again: if one of your buttons on your gamepad will be interpreted as "Home" button you will not be able to use it as an application can not override this behavior.
It's very likely that you are suffering from an Android 11 bug introduced by Google. You can find more information here. Here is the official Google bug report with a few suggested workarounds https://issuetracker.google.com/issues/163120692?pli=1. On some devices, it helps to disable all accessibility services and restart the device. It's really an annoying issue which is already fixed in Android 12. It's not the fault of XBXPlay.
If possible be sure your device is connected to a 5GHz WiFi network instead of a 2.4GHZ. 5GHz networks are usually less prone to interference.
Be sure your XBox is connected to the internet via LAN cable.
Lower the resolution in the streaming settings.
Some Android devices are affected by a network performance problem. In order to fix this simply place your Android device in flight mode and re-enable WiFi afterwards. Restart the XBXPlay app and check if it works. Furthermore, an active Bluetooth connection can also interfere the network stability on some devices. Please check if the connection is more stable when you deactivate Bluetooth completely.
Be sure that your have enabled Remote Play on your XBox. (XBox: Settings - Device & connection - Remote features and activate "Enable Remote features")
Restart the XBox and the XBXPlay app and try again. Also, make sure that the device you're using XBXPlay on doesn't have a VPN connection or an ad-blocking app activated, otherwise the connection to your XBox could be blocked.
If you are not playing over the internet be sure that your smartphone and your XBox are connected to the same network.

If you play over the internet and your router doesn't have an IPv6 address you have configured port forwarding rules on your router. If you have already done this please check your port forwarding settings again. Your XBox might got another IP-Address from your router at home which means you must re-adjust your port fowarding settings to point to the correct IP of your console again. If you want to connect remotely, make sure your XBox has the latest firmware installed. Sometimes restarting the console could fix the problem. More information can be found in the section Playing over the internet
Be sure your XBox is turned on or at least in rest mode.  You must set the power options of your console to 'Sleep' in order to allow apps like XBXPlay to wake up your console. Also, make sure that the device you're using XBXPlay on doesn't have a VPN connection or an ad-blocking app activated, otherwise the connection to your XBox could be blocked.
Please be sure that your XBox stays connected to the internet during rest mode. On the console navigate to Settings - Device & connections - Remote features - Power options - Choose 'Sleep'
If you have this problem, please stop XBXPlay and reconnect your controller to your device. Open the XBXPlay again and connect to the XBox, the problem should be gone. On some devices, connecting a controller while streaming could lead to delays and other problems. In order to avoid this please always connect your controller before you start streaming.
Despite huge efforts to make the audio work on all Android devices when a wired Dualshock/ Dualsense controller is connected some devices still try to route the audio to the controller which will result in no audio at all while streaming. However, if you have this problem there is a workaround. Just disable "automatic USB audio routing" in the developer options of your device. The developer options are hidden by default, follow the instructions below to disable USB audio routing:
Chrome OS offers Android app support, however some functionalities are limited and will not work. Some Chrome OS devices have faulty hardware decoders which may interfere the streaming experience. XBXPlay was not designed especially for Chrome OS devices but only for Android, Android TV devices. I do not want to prevent installations on Chrome OS devices because on some Chrome OS devices XBXPlay might work quite well, but please keep in mind that there could be some potential limitations when you are using XBXPlay on Chrome OS.
There is a community available on reddit which you can find here https://www.reddit.com/r/XBXPlay. You find many answered questions there and you can post new questions if you need to.
If none of the above suggestions help, please contact me at<EMAIL_ADDRESS>In addition you should also attach the XBXPlay log file to your email by clicking the "Share log file" in the navigation drawer in case you encounter some problems. I'll try to answer as soon as possible.
Please include following information in your email.


NAME
zip_set_file_compression —
set
compression method for file in zip
LIBRARY
libzip (-lzip)
SYNOPSIS
#include <zip.h>
int
zip_set_file_compression(
zip_t
*archive,
zip_uint64_t
index,
zip_int32_t
comp,
zip_uint32_t
comp_flags);
DESCRIPTION
The
zip_set_file_compression() function sets the compression
method for the file at position
index in the zip archive
to
comp with the compression method specific
comp_flags. The
comp is the same
as returned by
zip_stat(3). For the
comp argument, currently only the following values are
supported:
-
-
ZIP_CM_DEFAULT
- default compression; currently the same as
ZIP_CM_DEFLATE
.
-
-
ZIP_CM_STORE
- Store the file uncompressed.
-
-
ZIP_CM_BZIP2
- Compress the file using the
bzip2(1) algorithm.
-
-
ZIP_CM_DEFLATE
- Deflate the file with the
zlib(3) algorithm and default
options
NOTE: Only the deflate and store methods can be assumed to be
universally supported.
The
comp_flags argument defines the compression level, 1
being fastest compression and 9 highest. Allowed values are 0 (which uses the
default compression for the algorithm) and 1-9, other values are undefined.
Further compression method specific flags might be added over time.
The current compression method for a file in a zip archive can be determined
using
zip_stat(3).
RETURN VALUES
Upon successful completion 0 is returned. Otherwise, -1 is returned and the
error information in
archive is set to indicate the
error.
ERRORS
zip_set_file_compression() fails if:
-
-
- [
ZIP_ER_COMPNOTSUPP
]
- Unsupported compression method requested.
-
-
- [
ZIP_ER_INVAL
]
- index is not a valid file index in
archive, or the argument combination is
invalid.
-
-
- [
ZIP_ER_RDONLY
]
- Read-only zip file, no changes allowed.
SEE ALSO
libzip(3),
zip_stat(3)
HISTORY
zip_set_file_compression() was added in libzip 0.11.
AUTHORS
Dieter Baron
<
<EMAIL_ADDRESS>and
Thomas Klausner
<
tk@giga.or.at>


In the beginning, Website Design was something that came to me as second nature pretty quick. I was able to grasp the concept of design sleek, formattable, responsive web layouts easily. I still to this day have a knack for bring out content that is most important and make sure that web interfaces are easily usable to navigate through.
Programming is one passion that I love doing the most. I love the concept of manipulating programs to work how I want them to. I have been avidly working with the MEAN stack to develop single page applications to work in ways I never thought could be so easy! The LAMP stack is another skill I have been able to acquire in my career as a developer and like to work with.
Writing readable, efficient, and resourceful web content is a hobby of mine. This is one skill I love to practice. I have a passion to make content and media the most useful to companies. Choosing texts, and making the most of certain fonts I put a large emphasis on.
After transfering from a community college in New York, I decided to take myself away from my hometown and pursue a web degree at Full Sail University. I always had a knack for technology and wanted to be apart of this booming field of study. I knew it would be a challenge but I love pushing myself to the limits and exceeding my own expectations. I was very eager to start this new chapter in my life.
As I got through my introduction level classes I really started to dig deep into my study. I wanted to keep learning outside of the classroom. At first, I started building static web sites for my friends and family and utilizing every I have learned at this point. I felt as if I kept practicing I would eventually become a pro, and wanted to become the best I could possibly be in my field.
After getting to know my programming strategy and knowing my abilities, I decided that this is what I want to do for a living. I now can say that I am confident in being a part of a company and being a valuable asset. I love working as a team, and like to think of myself as a leader. With my experience at this point I know the ups and downs of a web developer and built a strategy on top of these experiences. I look further into the future and can see a bright path.


DROP VIEW
Removes a view from the database. Multiple views can be dropped with a single DROP VIEW command. This command is not reversible.
Syntax
DROP VIEW [ IF EXISTS ] name [, ... ] [ CASCADE | RESTRICT ]
Parameters
- IF EXISTS
-
Clause that indicates that if the specified view doesn’t exist, the command should make no changes and return a message that the view doesn't exist, rather than terminating with an error.
This clause is useful when scripting, so the script doesn’t fail if DROP VIEW runs against a nonexistent view.
- name
-
Name of the view to be removed.
- CASCADE
-
Clause that indicates to automatically drop objects that depend on the view, such as other views.
To create a view that is not dependent on other database objects, such as views and tables, include the WITH NO SCHEMA BINDING clause in the view definition. For more information, see CREATE VIEW.
- RESTRICT
-
Clause that indicates not to drop the view if any objects depend on it. This action is the default.
Examples
The following example drops the view called event:
drop view event;
To remove a view that has dependencies, use the CASCADE option. For example, say we start with a table called EVENT. We then create the eventview view of the EVENT table, using the CREATE VIEW command, as shown in the following example:
create view eventview as select dateid, eventname, catid from event where catid = 1;
Now, we create a second view called myeventview, that is based on the first view eventview:
create view myeventview as select eventname, catid from eventview where eventname <> ' ';
At this point, two views have been created: eventview and myeventview.
The myeventview view is a child view witheventview as its parent.
To delete the eventview view, the obvious command to use is the following:
drop view eventview;
Notice that if you run this command in this case, you get the following error:
drop view eventview; ERROR: can't drop view eventview because other objects depend on it HINT: Use DROP ... CASCADE to drop the dependent objects too.
To remedy this, execute the following command (as suggested in the error message):
drop view eventview cascade;
Both eventview and myeventview have now been dropped successfully.
The following example either drops the eventview view if it exists, or does nothing and returns a message if it does not:
drop view if exists eventview;


Package com.jme.anim.tween.action
Class BlendableAction
- java.lang.Object
-
- com.jme.anim.tween.action.Action
-
- com.jme.anim.tween.action.BlendableAction
-
- All Implemented Interfaces:
Tween
,JmeCloneable
,java.lang.Cloneable
- Direct Known Subclasses:
BlendAction
,ClipAction
public abstract class BlendableAction extends Action
-
-
Field Summary
Fields Modifier and Type Field Description protected BlendableAction
collectTransformDelegate
-
Constructor Summary
Constructors Constructor Description BlendableAction(Tween... tweens)
-
Method Summary
All Methods Instance Methods Abstract Methods Concrete Methods Modifier and Type Method Description void
cloneFields(Cloner cloner, java.lang.Object original)
Callback fromCloner
to convert this shallow-cloned action into a deep-cloned one, using the specified cloner and original to resolve copied fields.abstract void
collectTransform(HasLocalTransform target, Transform t, float weight, BlendableAction source)
protected abstract void
doInterpolate(double t)
abstract java.util.Collection<HasLocalTransform>
getTargets()
double
getTransitionLength()
protected float
getTransitionWeight()
float
getWeight()
boolean
interpolate(double t)
Sets the implementation specific interpolation to the specified 'tween' value as a value in the range from 0 to getLength().BlendableAction
jmeClone()
Create a shallow clone for the JME cloner.void
setCollectTransformDelegate(BlendableAction delegate)
void
setTransitionLength(double transitionLength)
void
setWeight(float weight)
-
-
-
Field Detail
-
collectTransformDelegate
protected BlendableAction collectTransformDelegate
-
-
Constructor Detail
-
BlendableAction
public BlendableAction(Tween... tweens)
-
-
Method Detail
-
setCollectTransformDelegate
public void setCollectTransformDelegate(BlendableAction delegate)
-
interpolate
public boolean interpolate(double t)
Description copied from interface:Tween
Sets the implementation specific interpolation to the specified 'tween' value as a value in the range from 0 to getLength(). If the value is greater or equal to getLength() then it is internally clamped and the method returns false. If 't' is still in the tween's range then this method returns true.
-
getWeight
public float getWeight()
-
setWeight
public void setWeight(float weight)
-
doInterpolate
protected abstract void doInterpolate(double t)
-
getTargets
public abstract java.util.Collection<HasLocalTransform> getTargets()
-
collectTransform
public abstract void collectTransform(HasLocalTransform target, Transform t, float weight, BlendableAction source)
-
getTransitionLength
public double getTransitionLength()
-
setTransitionLength
public void setTransitionLength(double transitionLength)
-
getTransitionWeight
protected float getTransitionWeight()
-
jmeClone
public BlendableAction jmeClone()
Create a shallow clone for the JME cloner.- Specified by:
jmeClone
in interfaceJmeCloneable
- Overrides:
jmeClone
in classAction
- Returns:
- a new action (not null)
-
cloneFields
public void cloneFields(Cloner cloner, java.lang.Object original)
Callback fromCloner
to convert this shallow-cloned action into a deep-cloned one, using the specified cloner and original to resolve copied fields.- Specified by:
cloneFields
in interfaceJmeCloneable
- Overrides:
cloneFields
in classAction
- Parameters:
cloner
- the cloner that's cloning this action (not null)original
- the action from which this action was shallow-cloned (unused)
-
-


The StudentTracker is a website that lets you log in as a teacher to keep track of your music students. Students are listed by instrument and skill level, plus it includes a short bio. Notes can be entered for past lessons as well as goals for future sessions. This project was built using bootstrap, mongo, and node. Click on the button below to get started. If you are a demo user, use the username "demo_user" and the password "demo-pwd".


If you correctly answer three questions in a row, you get a cookie! Ready to begin? Then hit the button below!
I'm surprised you actually knew what one of the capitals of Assyria even was! Here, have a cookie. You deserved it~.
Didn't think all three questions were gonna be that easy, huh? Try again next time!


Seriously. The news article dates back to before the invention of the telescope.
The world is changing fast - isn't it time for a reboot?
We’re all busy. Fortunately, Savi just loves spending time staying on top of the day's big issues, so you don't have to.
No more endless link clicking or Google searching. Just the stuff that matters to you.
Let’s be honest, news can get complicated. That’s why Savi uses specialist expertise and cutting edge tech to break it down, step by step.
Savi’s also pretty friendly and easy to get on with - delivering you political updates like you’ve never seen before.
 Savi’s entirely neutral and has no opinion, (or even the right to vote).
All Savi cares about is getting you informed about what’s going on - so you can make up your own mind.


Follow your baby's development from a tiny mass of cells through to a fully developed baby. Our illustrations show how your baby is growing inside your womb (uterus), look at a baby from conception to labour and birth.
One month pregnant
Your baby is an embryo consisting of two layers of cells from which all her organs and body parts will develop.
Two months pregnant
Your baby is now about the size of a kidney bean and is constantly moving. He has distinct, slightly webbed fingers.
Three months pregnant
By now your baby is around 7 to 8 centimeters (3 inches) long and weighs about the same as a pea pod. Her tiny, unique fingerprints are now in place.
Four months pregnant
Your baby is now about 13cm (5.5in) long and weighs 140g (5oz). His skeleton is starting to harden from rubbery cartilage to bone.
Five months pregnant
Eyebrows and eyelids are now in place. Your baby would now be more than 27cm (10.5in) long if you stretched out her legs.
Six months pregnant
Your baby weighs about a 660g (1.5lb). His wrinkled skin is starting to smooth out as he puts on baby fat.
Seven months pregnant
By now, your baby is more than 40cm (15in) long. She can open and close her eyes and probably see what's around her.
Eight months pregnant
Your baby now weighs about 2.2kg (4.7lb).
Nine months pregnant
Your baby is almost due. At birth, the average baby is more than 51cm (20.5in) long from head to toe and weighs approximately 3.4kg (7.5lb), but babies vary widely in size at this stage.
*Source: baby center uk website


Pirate (ARRR) is a cryptocurrency that solves the issue of financial privacy on the blockchain through forced anonymous transactions. Protect your wallet and transactions with Pirate’s completely anonymous network. With forced private transactions, your privacy cannot be compromised by another users’ activity on the network. If you wish to learn more, please join the official Pirate Discord at https://pirate.black/discord/ or message FishyGuts#5085, who will be happy to help out.
100 messages
Before 05-Dec-19 12:00 AM
bushybro28-Sep-18 02:05 PM
I'm convinced jl777 is Satoshi now......
wookie28-Sep-18 02:06 PM
Anyone wanting to buy/sell, we should put in WTB/S price as KMD, new people coming on might think it's btc price!
ComputerGenie 👴👴👴28-Sep-18 02:09 PM
And what's wrong with selling PIRATE for 0.09 BTC?
1
mentortechies28-Sep-18 02:10 PM
Thanks for the help guys. It is done.
I can sell 777 Pirates for 0.1 KMD each. Lol.
ComputerGenie 👴👴👴28-Sep-18 02:15 PM
if you get in with the right crew, otherwise you might be walking the plank
house28-Sep-18 02:21 PM
must be time to start creating new discord sock puppets with ye ol' zaddr arrr!
ComputerGenie 👴👴👴28-Sep-18 02:24 PM
time vs value, my time is worth more than that effort
house28-Sep-18 02:25 PM
$100 = worth one hour peoples time
5 min = sock puppet
Llama28-Sep-18 02:25 PM
I tried mining pirate for 8 hours and i got 3 pirate
1
lol
ComputerGenie 👴👴👴28-Sep-18 02:25 PM
will not even get out of bed for $100p/h
1
jl777c28-Sep-18 02:26 PM
the 15 KMD for 777 PIRATE will be ending today
2
2
parameters will be changing to adjust to the new price levels
Llama28-Sep-18 02:26 PM
do you have any idea what the new price is?
sφldat28-Sep-18 02:26 PM
77 for 777 ?
Llama28-Sep-18 02:27 PM
I think he means the ratio. So instead of 15 KMD > 777 it's like 77 KMD > 777
is that the only way to get in, or is ther OTC?
jl777c28-Sep-18 02:27 PM
the total PIRATES will be much less
so will KMD too
Llama28-Sep-18 02:28 PM
nvm
lol
jl777c28-Sep-18 02:28 PM
for the onboarding offer
changing a special onboarding offer doesnt change the coin emission of the coins!
ComputerGenie 👴👴👴28-Sep-18 02:29 PM
current market, 15KMD = like 167 Pirate
4
sφldat28-Sep-18 02:31 PM
Pirates, All Aboard!
Llama28-Sep-18 02:32 PM
aye aye captain
sφldat28-Sep-18 02:34 PM
the prices go up to the port side
Llama28-Sep-18 02:36 PM
is it okay for me to post on the ANN thread stuff like "the 777 PIRATE for 15 KMD deal will be ending soon!"
llama has left the channel
parrot has joined the channel
3
someone needs to make my name "llama parrot"(edited)
CryptoInvestorAU28-Sep-18 02:39 PM
How long do I have to get the 777 for 15kmd offer?
sφldat28-Sep-18 02:42 PM
@CryptoInvestorAU#deleted-channel Go go go !
CryptoInvestorAU28-Sep-18 02:44 PM
Haha!
I only have the Verus agama. I need the Komodo agama correct? Will downloading that interfere with the other one?
house28-Sep-18 02:45 PM
Activate Coin
Choose KMD Lite Mode
@CryptoInvestorAU
will need your seed
mato12128-Sep-18 02:46 PM
where do i need to send 15kmd to recive 777
kmd
house28-Sep-18 02:46 PM
Git yur scurvy swashbucklin' coins me hearty
CryptoInvestorAU28-Sep-18 02:49 PM
@house So download the agama from Komodo website as well? Excuse my non tech savvyness. What seed? I haven’t used the Komodo agama before I only have a small amount of kmd so left it in an exchange.
house28-Sep-18 02:49 PM
you have verus agama wallet right ?
CryptoInvestorAU28-Sep-18 02:50 PM
@house yes I have Verus agama, does that support pirate?
house28-Sep-18 02:50 PM
to get z address yep, you'll need the pirate version
@house ok, and that won’t interfere with my other Verus agama? I can run both side by side?
house28-Sep-18 02:52 PM
hmmm, suggest one or the other
safety first ol' salty
CryptoInvestorAU28-Sep-18 02:53 PM
@house ummm... what do you suggest?
house28-Sep-18 02:54 PM
git yur pirate wallet fur zaddr
ComputerGenie 👴👴👴28-Sep-18 02:54 PM
suggests leaning how to use the daemon and not needing 2
CryptoInvestorAU28-Sep-18 02:57 PM
@ComputerGenie 👴👴👴@house LoL, still confused. If I backup my Verus wallet dat in case I stuff something up and download agama from pirate.black will that work for now?
ComputerGenie 👴👴👴28-Sep-18 02:57 PM
Is verus not notarized?
house28-Sep-18 02:58 PM
you'll be fine ... just download the damn wallet already cuz
Just don't run at same time
ComputerGenie 👴👴👴28-Sep-18 02:59 PM
just assumed they updated and wonders why they would not update with upstream updates
house28-Sep-18 02:59 PM
suggests possibly busy with sapling
1
1
anonpirate28-Sep-18 02:59 PM
I'm here for that very lucky deal
house28-Sep-18 03:00 PM
#deleted-channel@anonpirate
anonpirate28-Sep-18 03:00 PM
ty sir
house28-Sep-18 03:00 PM
arrr
anonpirate28-Sep-18 03:01 PM
sounds like an
1
ETH scam
the "send 15 KMD for something much more valuable" thing
ComputerGenie 👴👴👴28-Sep-18 03:02 PM
that conversion was set when PIRATE price was much lower
anonpirate28-Sep-18 03:02 PM
I'm going to do it regardless
1
sφldat28-Sep-18 03:03 PM
Do it @anonpirate !
ComputerGenie 👴👴👴28-Sep-18 03:05 PM
Speaking of PIRATE prices....
------------------
Selling 20k PIRATE for 0.09KMD each (1800 KMD)
Get 'em afore they ARR gone!
------------------
SOLD(edited)
xRobeSx28-Sep-18 03:11 PM
that was fast
CryptoInvestorAU28-Sep-18 03:12 PM
Am I meant to download win-portable or win-installer?
ComputerGenie 👴👴👴28-Sep-18 03:12 PM
My milkshake brings all the boys to the yard
1
polycryptoblog28-Sep-18 03:12 PM
Lol, wow deja vu
ComputerGenie 👴👴👴28-Sep-18 03:13 PM
@CryptoInvestorAU that's up to you - portable runs from wherever you put the folder and installer adds shortcuts to run from default location
CryptoInvestorAU28-Sep-18 03:15 PM
Ok cool
@ComputerGenie 👴👴👴 I’ve got that song stuck in my head now
ComputerGenie 👴👴👴28-Sep-18 03:17 PM
polycryptoblog28-Sep-18 03:19 PM
Some trickery must be afoot, nicehash is giving me what I ordered and not 10x
ComputerGenie 👴👴👴28-Sep-18 03:20 PM
they reworked their scripts
Hot28-Sep-18 03:20 PM
reverse psychology... it's a trap
Llama28-Sep-18 03:20 PM
did nicehash ever restore balances after the hack?
ComputerGenie 👴👴👴28-Sep-18 03:20 PM
NiceHash
2018-09-28 08:07:26
Today we will update Equihash stratum so speed limits will be normal again.
Kind regards,
NiceHash Team


Kendall Jenner Asks Her Fans If She Should Start A Family As She Poses With Kim Kardashian’s Kids – See Pics
by Saheli MaityKendall Jenner jokes about starting a family on her Instagram while she uploaded some adorable snaps with Kim Kardashian and Kanye West’s kids. Well did her followers get the joke?
Kendall Jenner is a fabulous, fun and quite a busy aunt and this recent picture is our proof. Just a while ago, Jenner uploaded some adorable snaps with Kim Kardashian and Kanye West’s kids – Saint West and Psalm West. In the pictures, she is seen having a fun time as an aunt while she babysits Kim and Kayne’s kids. However, as the picture has sent out hearts into aflutter, Jenner's caption and one of the pictures of her close friend/ rumoured beau Fai Khadra grabbed our attention as she then joked about starting a family. Why so?
Well, while sharing the picture on her handle, she asked her fans and followers if she and Fai should start a family. She captioned the pictures as, “Everyone in favour of Fai and I starting a family say “I.” The moment she uploaded the pictures, her friends Gigi Hadid, Hailey Baldwin Bieber and her mother Kris Jenner, her half-sister Kim Kardashian and her real sister Kylie Jenner all want her to start a family as they commented ‘I’ on the comment section.
Meanwhile, Kendall is an aunt to Kourtney Kardashian’s kids Mason, Penelope and Reign. Kim Kardashian’s 4 kids - North, Saint, Chicago and Psalm while Khloe Kardashian’s daughter True. Rob’s little girl Dream, and Kylie’s baby Stormi.
Being an aunt of so many kids seems adorable, right? But we wonder how much she has to go through also. Kendall is the only KarJenner sibling who is yet to have kids, but knowing her relationship with Fai, we’re sure she’ll have a perfect family.
Image source: Instagram/ kendalljenner


{% extends 'base.html' %} {% block content %}
Username: {{request.user.username}}
{{request.user.first_name}} {{request.user.last_name}}
{{course_title}}
Assignment #{{number}}
Instructors cannot submit an assignment


Tooltips
Tooltips don't give you a ton of real estate, but you can do some pretty interesting things by alerting the user about the title, or the type of the link before they click it.
Example 1: Display Type and Title
It's generally pretty hard for users to decide what to click on, as they never really know where you are going. Simple tooltips can make it easier on the user, hover over the links below to see.
One a brisk winter morning I went out to play with my dog. It was the dead of winter and an adventure began. I wished I was in a better place, but Northern Maine is the worst. So here ends our short story, I hope that you enjoyed everything about it, now hover over those links. Cheers.
$('.example-1 a').tooltip().embedly({ // Overwrite the display function to set the data-original-title; display: function(elem, data){ // Remove the http:// and everything after the '/'; var provider_display = data.provider_url.replace(/https?\:\/\//, '').split('/')[0]; // Set the data-original-title attribute that the bootstrap.js uses for the tool tip. $(elem).attr('data-original-title', data.type.toUpperCase() +': '+ data.title + ' ['+provider_display+']'); } });
Example 2: Safe Browse
One of the other things one can do is warn users of malicious links before they click on them. Using the 'preview' endpoint we can check if the link is safe before letting the user click on this.
The following is an example of a comment that we have all seen. The link is off stopbadware.org and will trigger an unsafe response from Embedly.
Hover over the link will trigger a tooltip and clicking on the link will bring up a modal. Try it, I promise you won't actually go to that site.
Hi all, I like your blog post, it was super interesting. I really
hope that you find the time to check out my site.

// Overwrite the display function to test for malicious links.
display: function(elem, data){
// See if the link is safe or not.
if (data.safe === false){
// Create a tooltip on the element.
$(elem).tooltip({
html: true,
title: ''+data.safe_type.toUpperCase()+' Detected'
});
// When the user clicks on the click warn them before hand.
$(elem).on('click', function(){
// Open a Modal with the Correct Messaging.
$('#malware-modal').modal();
$('#malware-modal .modal-header h3').html(data.safe_type.toUpperCase()+' Detected');
$('#malware-modal .modal-body p').html(data.safe_message);
$('#malware-modal .proceed').attr('href', data.url);
return false;
})
}
}
});


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four column
twleve oclumn
Team Fu & Hess
Created in Sep 2014
2 Members
Team Fu & Hess
Created in Sep 2014
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Created in Sep 2014
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Team Fu & Hess
Created in Sep 2014
2 Members


For instance, consider the following code:
boolean found = false;
for (int i = 0; i < arr.length; i++) {
if (Objects.equals(value, arr[i])) {
found = true;
}
}
In this case iterations have no effect after condition is met and you may skip them adding a break.
boolean found = false;
for (int i = 0; i < arr.length; i++) {
if (Objects.equals(value, arr[i])) {
found = true;
break;
}
}
New in 2019.2


foo2zjs-icc2ps(1) foo2zjs-icc2ps(1) NAME foo2zjs-icc2ps - little cms PostScript converter. SYNOPSIS foo2zjs-icc2ps [options] DESCRIPTION lcms is a standalone CMM engine, which deals with the color management. It implements a fast transformation between ICC profiles. foo2zjs-icc2ps is little cms PostScript converter. COMMAND LINE OPTIONS -b Black point compensation (CRD only). -c <0,1,2> Precision (0=LowRes, 1=Normal (default), 2=Hi-res) (CRD only) -i profile Input profile: Generates Color Space Array (CSA). -n <gridpoints> Alternate way to set precision, number of CLUT points (CRD only) -o profile Output profile: Generates Color Rendering Dictionary(CRD). -t <0,1,2,3> Intent (0=Perceptual, 1=Colorimetric, 2=Saturation, 3=Absolute). -u Do NOT generate resource name on CRD. FILES /usr/share/foo2*/icm/* SEE ALSO foo2hiperc-wrapper(1), foo2hp2600-wrapper(1), foo2lava-wrapper(1), foo2oak-wrapper(1), foo2qpdl-wrapper(1), foo2zjs-wrapper(1), AUTHOR Rick Richardson<EMAIL_ADDRESS>http://foo2zjs.rkkda.com/


|
| | TATrack2 (const string &name="", const string &title="", unsigned uid=999999999) |
| |
| | TATrack2 (const TATrack2 &track) |
| | the copy constructor More...
|
| |
| TATrack2 & | operator= (const TATrack2 &track) |
| | the assignment constructor More...
|
| |
| virtual | ~TATrack2 () |
| |
| virtual void | GetNu (int *nu) const |
| | output array fNu More...
|
| |
| virtual void | GetLAYER (int *LAYER) const |
| | output array fLAYER More...
|
| |
| virtual void | GetZ (double *z) const |
| | output array fZ More...
|
| |
| virtual void | GetX (double *x) const |
| | output array fX More...
|
| |
| virtual void | GetDriftTime (double *t) const |
| | output array fT More...
|
| |
| virtual void | GetDriftDistance (double *r) const |
| | output array fR More...
|
| |
| virtual void | GetChi (double *chi) |
| | output array fChi More...
|
| |
| virtual void | SetData (const double *x, const double *z, const double *t, const double *r, double kL, double bL, double dsquare, int gGOOD, const int *nu, const int *LAYER, const double *weight) |
| |
| virtual void | SetDriftTime (const double *t, const double *w=nullptr) |
| |
| virtual void | SetDriftDistance (const double *r) |
| | as the name indicates More...
|
| |
| virtual void | AssignTrack (tTrack *ptrack) |
| |
| virtual void | Show () |
| | show information about the track More...
|
| |
| double | GetChi () |
| |
| virtual void | GetChi (double *chi) |
| | output array fChi More...
|
| |
| | TATrack (const string &name="", const string &title="", unsigned uid=999999999) |
| |
| | TATrack (const TATrack &track) |
| | the copy constructor More...
|
| |
| TATrack & | operator= (const TATrack &track) |
| | the assignment constructor More...
|
| |
| virtual | ~TATrack () |
| |
| double | GetDsquare () |
| |
| double | GetChi2 () |
| |
| double | GetChi () |
| |
| double | GetSlope () |
| |
| double | GetIntercept () |
| |
| double | GetTOF () const |
| |
| double | GetNStripStray () const |
| |
| int | GetFiredStripId () const |
| |
| int | GetNFiredAnodeLayer () const |
| |
| int | GetgGOOD () const |
| |
| int | GetFitMethod () const |
| |
| int | GetFitPrecision () const |
| |
| double | GetDsquareThresholdPerDot () const |
| |
| int | Get3DId () const |
| |
| bool | IsAssigned () const |
| | to tell if the track is not assigned; return fIsAssigned More...
|
| |
| bool | IsFitted () const |
| | to tell if the data has been fitted. More...
|
| |
| void | SetFitPrecision (int fitPrecision) |
| |
| void | SetBeta (double beta) |
| |
| double | GetBeta () const |
| |
| void | SetTOF (double TOF, int firedStripId, double nstripStray) |
| | set fTOF; More...
|
| |
| void | SetFitMethod (int method) |
| |
| void | SetFitRotationCenter (double xc, double zc) |
| | 0: normal fit; 1: BFGS fit More...
|
| |
| void | SetDsquareThresholdPerDot (double threshold) |
| |
| void | Set3DId (int id) |
| |
| void | Fit () |
| | the fit method More...
|
| |
| void | FillTrack (TGraph *gTrack, TGraph *gTrack_R) |
| | a method dedicated for TAVisual::Fill() More...
|
| |
| virtual void | Initialize () |
| |
| | TAStuff (const string &name="", const string &title="", unsigned uid=999999999) |
| |
| virtual | ~TAStuff ()=0 |
| |
| const string & | GetName () const |
| |
| const string & | GetTitle () const |
| |
| unsigned | GetUID () const |
| |
| void | SetName (const string &name) |
| |
| void | SetName (const char *name) |
| |
| void | AppendName (const string &tail) |
| |
| void | AppendName (const char *tail) |
| |
| void | SetUID (unsigned uid) |
| |
| void | SetTitle (const string &title) |
| |
| void | SetTitle (const char *title) |
| |
| void | AppendTitle (const string &tail) |
| |
| void | AppendTitle (const char *tail) |
| |
| virtual void | Info () const |
| |
| virtual TAChPara * | GetPara () const |
| | to mark the shared part of TAChannel and TAAnode More...
|
| |
| virtual TAChData * | GetData () const |
| | to mark the shared part of TAChannel and TAAnode More...
|
| |
|
| bool | marked |
| | for any possible use More...
|
| |
| bool | fIsDEBUG |
| | DEBUG. More...
|
| |
| double | fXMiss3D [3] |
| | 3D track coincidence test at z coordinates of the three DCs More...
|
| |
| static const int | kNormalFit = 0 |
| |
| static const int | kBFGSFit = 1 |
| |
| static const int | kIterFit = 2 |
| |
| void | NormalFit () |
| | fit using normal and conventional searching minimization method. More...
|
| |
| void | BFGSFit () |
| | fit using BFGS minimization method. More...
|
| |
| void | IterFit () |
| | fit using iteartion minimization method, relatively coarse, but fast, so is more preferrable and recommended. More...
|
| |
| double | fZ [6] |
| |
| double | fX [6] |
| |
| double | fR [6] |
| |
| double | fT [6] |
| | drift time More...
|
| |
| double | fWeight [6] |
| | weight for weighted addition of chi to chi2 More...
|
| |
| double | fTOF |
| |
| double | fNStripStray |
| | count of strips from a fired strips to the fitted track More...
|
| |
| int | fFiredStripId |
| | serial id of the fired TOF wall strip for the track More...
|
| |
| int | fNu [6] |
| | the fired anode serial id in each of the six anode layers for the track specifically More...
|
| |
| int | fLAYER [6] |
| | to tell the condition of the 6 fired andoe layers More...
|
| |
| int | f3DId |
| | 3-D track identifier More...
|
| |
| int | fNFiredAnodeLayer |
| | count of fired anodes for the track More...
|
| |
| int | fgGOOD |
| | count of fired anodes for the track, but a little different from fNFiredAnodeLayer. 3, 4, 5 and 6. Less than 3 is not allowed, except 2, which has a special meaning. Three MWDC must all be fired, or value 2. You know that. It is the avatar of gGOOD. It is a very important variable and not to be messed with. More...
|
| |
| double | fK |
| | the slope of the linear track More...
|
| |
| double | fB |
| | the intercept of the linear track More...
|
| |
| double | fChi2 |
| | the sum of squares of fitting residuals of all the fired anodes More...
|
| |
| double | fDsquare |
| | the Dsquare of least square fit of the fired andoes, regardless of drift time More...
|
| |
| double | fChi [6] |
| |
| bool | fIsFitted |
| | the value of fIsFitted can only be assigned true through Fit function More...
|
| |
| bool | fIsAssigned |
| | to tell if the track is not assigned More...
|
| |
| double | fBeta |
| | particle speed, while it zipps along this track; mainly for drift time correction More...
|
| |
| int | fFitMethod |
| | 0: normal fit; 1: BFGS fit More...
|
| |
| int | fFitPrecision |
| | the calculation precision More...
|
| |
| double | fZc |
| | MWDC_X(U, V)[1]->GetZc() More...
|
| |
| double | fXc |
| | MWDC_X(U, V)[1]->GetXc() More...
|
| |
| double | fDsquareThresholdPerDot |
| |
| string | fName |
| |
| string | fTitle |
| |
| unsigned | fUID |
| |
A derived class from TATrack, specifically designed for DCs around the target. This class is mainly for track fitting of those DCs.
- Author
- SUN Yazhou, asia..nosp@m.rabb.nosp@m.it@16.nosp<EMAIL_ADDRESS>- Date
- Created: 2018/3/21 Last revised: 2018/4/3, SUN Yazhou.
- Copyright
- 2017-2018, SUN Yazhou.


| Author | Stef Mientki, Copyright © 2002..2006, all rights reserved. |
| Adapted-by | Sebastien Lelong, Rob Hamerling |
| Compiler | 2.4o |
USART hardware control. Routines for sending and receiving through the PIC-usart, both asynchronous and synchronous are supported. Baudrate can simply be set through a human constant, because the baudrate depending registers are calculated by this unit. Baudrate is calculated, starting at the high baudrate flag, which will ensure the highest possible accuracy. . TODO: should this be configurable ? Transmission parameters are 8 databits, 1 stopbit, no parity, no handshake.
- Pin selection is automatic: pin_TX and pin_RX. - Pin direction setting is taken care of by this library. - When the physical locations of pin_TX and pin_RX are configurable for a specific PIC, the device file will probably contain names like pin_TX_RB2 and pin_RX_RB1 and another pair with other pin suffixes. Depending for which pair of pins the USART is configured aliases without suffixes have to be specified, like: alias pin_TX is pin_TX_RB2 alias pin_RX is pin_RX_RB1 alias pin_TX_direction is pin_TX_RB2_direction alias pin_RX_direction is pin_RX_RB1_direction
serial_hw_data'put(byte in data)
serial_hw_init()
serial_hw_write_word(word in data)
serial_hw_write(byte in data)
serial_hw_data_raw'put(byte in data)
serial_hw_enable()
serial_hw_disable()
serial_hw_read(byte out data) return bit
serial_hw_data'get() return byte
serial_hw_data_raw'get() return byte
serial_hw_data'put(byte in data)
Title: Transmit byte Arguments: Data (byte) to be transmitted Returns: (nothing) Notes: This pseudo variable allows a language construct like serial_hw_data =to transmit a byte to the seial port
serial_hw_init()
Title: Initialize the (first or only) serial port Arguments: (none) Returns: (nothing)
serial_hw_write_word(word in data)
Title: Transmit word Arguments: Data (word) to transmit Returns: (nothing) Notes: Send word as 2 bytes, Most Significant Byte first. See also serial_hw_write()
serial_hw_write(byte in data)
Title: Transmit byte Arguments: Data (byte) to transmit Returns: (nothing) Notes: Hardware transmit buffer must be empty, wait if not
serial_hw_data_raw'put(byte in data)
These are real raw procedures, declared as pseudo variables the user is totally responsible for testing the transmit/receive flag before using these functions
serial_hw_enable()
Title: Enable USART Arguments: (none) Returns: (nothing) Notes: Re-enable USART after use of serial_hw_disable()
serial_hw_disable()
Title: Disable USART Arguments: (none) Returns: (nothing) Notes: Allows pins to be used (temporary) for other purposes. USART can be enabled again by calling serial_hw_enable()
serial_hw_read(byte out data) return bit
Title: Receive byte Arguments: (none) Returns: bit: TRUE data available FALSE no data available
serial_hw_data'get() return byte
Title: Receive byte Arguments: (none) Returns: Data (byte) received Notes: This pseudo variable allows a language construct like databyte = serial_hw_data to receive a byte from the serial port into variable 'databyte'
serial_hw_data_raw'get() return byte
No documentation found
_serial_hw_read(byte out data) return bit
Title: Receive byte Arguments: (none) Returns: bit: TRUE data available FALSE no data available Notes: Using this inline function for serial_hw_data'get saves a stack level With overrun error the USART is reset (error(?)byte is returned!)