Time And Distance
Universal Time and Distance Measures of the Ascendant Vassals
time
- imex = 1 second
- interum = 100 Imex
- caldex = ~1.1 days
- ritodex = 10 caldex
- Kaydex = 30 caldex
- adex = .959 years
- ketadex = 10 adex
- soradex = 100 adex
distance
- setch = 1 inch
- setta = 20 setch
- lirhetta = 40 setch (~1 meter, 1 meter = ~39.4 setch)
- tekudex = 100,000 setch (~a mile and a half) or 5000 setta
- stardex is the distance light travels in an adex
Micro Time Format Description:
Inter-Seta-Inem (somewhat equivalent to “hour-minute”)
imex = 1 second
interum = 100 imex
setadex = 1,000 imex(16 minutes)
interdex = 10,000 imex(slightly less than 3 hours)
caldex = 100,000 imex(slightly less than 28 hours)
ritodex = 10 caldex
kaydex = 30 caldex
Ada-Kayd-Caldex (day-moth-year equivalent)
Adex = 10 Kaydex/300 Caldex
Ketdex = 10 Adex (Ket Cycle)
Soradex = 100 Adex (Soral Cycle)
Tekadex 1,000 Adex (Teka Cycle)
Heptadex = 10,000 Adex (Heptagex Cycle)
Metadex = 1,000,000 Adex (Metalogos Cycle)
Deonox = 1,000,000,000 Adex (Deonoxium Cycle)
Macro Format Description:
Time Examples
Edoch: An interval of 100,000,000 adex.
Deo-Meta-Teka-Ad
24.345.687.230
is:
24th Deonox, 345th Metadex, 687th Tekadex, 230th Adex Adex 230, Edoch 345.678 of the 24th Deonoxium
Shortened:
24dx-345678-230ad
citation date
5-17-54-1009 « in the second Soral Cycle of current Edoch
Distance
stardex is the distance light travels in an adex
setch = inch equivalent
setta = 20 setch
lirhetta = 40 setch (~1 meter, 1 meter = ~39.4 setch)
tekudex = 100,000 setch (~a mile and a half) or 5000 setta
hatadex = 10,000 tekudex
metradex = 1,000,000 tekudex
dexardex = 1,000,000,000 tekudex
Micro Distance
Nama-setch = 10th of a setch
Seggeta = 100th of a setch
Nama-seggata = 1000th of a setch
cabre: used for small circumferences


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ThtmlPagePrinted = procedure(Sender: TObject; HFViewer: THtmlViewer;
NumPage: Integer; LastPage: boolean;
var XL, XR: integer;
var StopPrinting: Boolean) of Object;;
The ThtmlPagePrinted type is the type for the OnPrintHTMLHeader and OnPrintHTMLFooter events. These events occur as each page is printed allowing the user to add an HTML formatted header and/or footer to the page.
SenderThe THtmlViewer which is printing.
HFViewerThe THtmlViewer to take the header or footer document.
NumPageThe current page number.
LastPageA boolean set to True for the last page.
XL, XRHeader/footer left and right pixel boundaries.
StopPrintingSet this True to abort the print job.
Usage Notes:
These events print (or preview) HTML documents which serve as headers and footers for the print job. This is done by using two ThtmlViewers, one positioned for the header and the other for the footer. These ThtmlViewers are used only for printing and are never visible. The user supplies an event handler which loads the desired HTML into the THtmlViewer (the HFViewer parameter).
The event handler need not be concerned with creating or destroying the THtmlViewer as this is done automatically. The THtmlViewer comes with default settings with the exception of the MarginHeight propertywhich is set at 0. It's only necessary to load the desired HTML using any of the usual load methods. If the header or footer does not change between pages, it need only be loaded once. However, if the HTML does change, it can be reloaded for each page.
The header THtmlViewer is positioned so that it's bottom edge is directly above the print job text. This is true regardless of the amount of text. Thus if there is too much text, the early text will disappear off the top of the page. The user needs to insure that the PrintMarginTop property is set adequately. Note that the MarginHeight attribute (<body> tag) can be used to set the spacing at the bottom of the header text.
The footer THtmlViewer is positioned so that it's top is directly below the print job text. In this case, excessive text will flow off the bottom of the page. Again, the MarginHeight attribute can be used to control the spacing at the top of the footer.
The XL and XR parameters can be used to control the left and right side positioning of the header or footer. These parameters are preset to values such that the header or footer will align with the print job. Hence, in most cases, these parameters can be ignored. If it's desired to extend the header or footer into the normal margin area, then these parameter would be useful. Note that for proper text alignment, the MarginWidth attribute (<body> tag) must be the same for both the print job and the header or footer.
See also:


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s6-networking
Software
skarnet.org
s6-getservbyname is a simple command-line interface to the getservbyname() function, converting a service name and protocol into a port number.
s6-getservbyname name proto
On a standard machine with a correct /etc/services file and a non-garbled NSS configuration: s6-getservbyname smtp tcp prints 25.


Note: To complete the procedures in this document, you need to be assigned a role that has the necessary permissions. To find out what your role is, follow the directions in Participant roles. To see a list of what permissions are associated with a given role, see Permissions, roles, and tools. For a basic understanding of roles and permissions, see Permissions and Roles: Overview.
Users with the Site Update permission may administer aspects of the search engine for the site. Instance administrators, for example the admin user, are allowed to administer all aspects of the search engine. These users are allowed access to the administration page of the Search Tool. This page gives these users access to parts of the Search Engine which is used by the Search Tool.
The Administration page shows information about the indexing worker threads in the server cluster, and their state. It also shows information concerning the current index queue.
Index Queue: The index queue is a queue of indexing commands that are enacted upon by search indexer threads. These commands take 3 forms.
 These operations are, rebuild, refresh and release lock. Global Rebuild deletes the entire index, and requests that all tools in the system resubmit all content to the search engine for re-indexing. This operation should only be performed with caution, since a large site may take several hours to complete this operation. Global Refresh, is only sightly more drastic as it takes all the content known to the search engine, and refreshed the index. Whilst this command is being enacted upon by the indexer threads, the search index will still contain all of the content. The release lock command should only be issued if the operator is certain that the indexer thread that is supposed to be performing the current index operaiton has died. This should almost never be necessary, as each indexer thread emits a heartbeat to the cluster that conforms its health. If any indexer thread that has not emited a heartbeat, or has become overdue has a writer lock on the index, it will have that lock removed by one of the other indexer threads, which will allow one of the other indexer threads to recover the indexing operation.
Site Commands:(Rebuild Site Index, Refresh Site Index) Site administrators or maintainers are allowed to issues refresh and rebuild commands against the site which they maintain.
Refresh Status: This simply refreshed the page to give you an update on the status of the engine.
The first block of inforation informs you when the index was last loaded by the Index Searcher on the cluster node you are connected to. It also informs you of current index activity, which may be none if no indexing is being performed, or a indexer thread id and an expected time of completion if there is an index oepration in progress. The final information item in this block is the state of the index and the index queue.
Mater Control Records informs you of the state of any global commands in the index queue. Site Control Records informs you of the state of any work site control records in the index queue. This will include commands from sites other than the ones that you maintain.
Index Workers provides a list of all indexer threads in the Sakai cluster. In this list is the ID of the thread, the last time a heartbeat was received, and the current state of the thread, either idle or running. If the thread its ID will be present in the first information block with an extimated time of completion.
Finally there is a block of inforamtion about the search index segments that are used by the search engine to store its state.


9. Linear Regression
-
In the following, we will apply the mathematical concepts from previous chapters, to solve linear regression (curve fitting) problems.
-
In regression, we aim to find a function $f$ that maps inputs $x∈R^D$ to corresponding function values $f(x)∈R.$
-
We are given a set of training inputs $x_n$ and corresponding noisy observations $y_n=f(x_n) + \epsilon$,
- where $\epsilon$ is an i.i.d random variable that describes measurement and observation noise => simply zero-mean Gaussian noise (not further in this chapter)
-
Then the task is to infer the function $f$ that generated the data and generalizes well to function values at new input locations.
-
-
Solving a regression function requires a variety of problems:
-
(1) Choice of the model (type) and the parametrization of the regression function.
-
(2) Finding good parameters.
-
(3) Overfitting and model selection.
-
(4) Relationship between loss functions and parameter priors.
-
(5) Uncertainty modeling
-
9.1 Problem Formulation
-
Objective : is to find a function that is close to the unknown function $f$ that generated the observed data (and that generalizes well).
-
Observation Noise
- The functional relationship between input $x$ and $y$ is given as below.
- Noise $ε∼N(0, σ^2)$ is independent, identically distributed (i.i.d.) Gaussian measurement noise with zero-mean and variance of $σ^2$.
$$ y = f (x) + ε $$
-
Define Likelihood function :
-
Because of the presence of observation noise, we will adopt a probabilistic approach and explicitly model the noise using a likelihood function.
-
Likelihood function would be Gaussian with mean, $y|f(x)$ and variance, $\sigma^2$ :
$$ p(y|x) = N(y|f(x), σ^2) $$
-
-
Parametrization :
- We consider the special case that the parameters $\theta$ appear linearly in our model.
$$ p(y|x,θ) = N(y|x^⊤θ, σ^2) \leftrightarrow y=x^⊤θ+ε, ε∼N(0,σ^2) $$
9.2 Parameter Estimation
- In the following, we will have a look at parameter estimation by maximizing the likelihood, a topic that we already covered to some degree in Section 8.3.
9.2.1 Maximum Likelihood Estimation
- (1) Maximum Likelihood Estimation
- A widely used approach to finding the desired parameters $θ_{ML}$
- where we find parameters $θ_{ML}$ that maximize the likelihood.
$$ θ_{ML} = \text{argmax}_\theta \ p(Y |X,θ). $$
- (2) Likelihood Factorization : For given training set, $y_i$ and $y_j$ are conditionally independent given their respective inputs $x_i, x_j$ so that the likelihood factorizes according to :
$$ p(Y|X,θ) = p(y_1,…,y_N |x_1,…,x_N,θ) $$
$$ = \prod p(y_n |x_n,θ) = \prod N(y_n |x^⊤_n θ, σ^2) \tag{9.5b} $$
- (3) Log-Transformation :
- Since the likelihood (9.5b) is a product of N Gaussian distributions, the log-transformation is useful
- To find $\theta_{ML}$, we can minimize the negative log-likelihood, which is equal to some of squared errors
$$ −logp(Y|X,θ) = −log \prod p(y_n|x_n,θ) = − \sum logp(y_n|x_n,θ) $$
$$ logp(y_n|x_n,θ)= − \frac{1}{2\sigma^2} (y_n−x^⊤_nθ)^2 +const = L(\theta) $$
- (4) Global Optimum : We can find the global optimum by computing the gradient of $L$, setting it to 0 and solving for $θ$
$$ \frac{dL}{d\theta} = 0^⊤ \Longleftrightarrow θ_{ML} = (X^⊤X)^{−1}X^⊤y $$
- Polynomial Regression (MLE with Features) : straight lines are not sufficiently expressive when it comes to fitting more interesting data
- Estimating the Noise Variance : We can also use the principle of MLE to obtain the maximum likelihood estimator $\sigma_{ML}^2$ for the noise variance
9.2.2 Overfitting in Linear Regression
-
Model Selection : we can use the RMSE (or the negative log-likelihood) to determine the best degree of the polynomial that minimizes the objective.
-
polynomials of low degree fit the data poorly
-
high-degree polynomials oscillate wildly and are a poor representation of the underlying function that generated the data, such that we suffer from overfitting
-
9.2.3 Maximum A Posteriori Estimation
-
(1) Prior and Posterior :
- The posterior over the parameters θ, given the training data $X,Y$ is obtained by applying Bayes’ theorem (Section 6.3) as :
$$ p(θ|X,Y)= \frac{p(Y|X,θ)p(θ)}{p(Y | X )} $$
- Since the posterior explicitly depends on the parameter prior $p(θ)$, the prior will have an effect on the parameter vector we find as the maximizer of the posterior.
-
(2) Log-Transformation :
- To find the MAP estimate, We start with the log-transform and compute the log-posterior
$$ log p(θ | X , Y) = log p(Y | X , θ) + log p(θ) + const , $$
- With a (conjugate) Gaussian prior $p(θ) = N(0, b^2I)$on the parameters θ, we obtain the negative log posterior
$$ −logp(θ|X,Y)= \frac{1}{2\sigma^2}(y−Φθ)^⊤(y−Φθ)+ \frac{1}{2b^2} θ^⊤θ+const. $$
-
And then, we minimize the negative log-poterior distribution with respect to $\theta$
$$ θ_{MAP} ∈ argmin_\theta[−logp(Y|X,θ)−logp(θ)]. $$
-
(3) Gradient :
-
Get gradient of the negative log-posterior with respect to θ, and we can see the first term on the right-had side as the gradient of the negative log-lgikelihood
$$ − \frac{dlogp(θ|X,Y)}{d\theta} = −\frac{dlogp(Y|X,θ)}{d\theta} − \frac{dlogp(θ)}{d\theta} $$
-
-
(4) Global Optimum : We will find $θ_{MAP}$ by setting this gradient to $0^⊤$ and solving for $θ_{MAP}$
$$ θ_{MAP} = (Φ^⊤Φ+\frac{σ^2}{b^2}I)^{-1}Φ^⊤y $$
9.2.4 MAP Estimation as Regularization
-
Regularized least squares :
- In Regularized Least Squares, we consider the loss function as below, which we minimize with respect to θ (see Section 8.2.3)
$$ ∥y − Φθ∥^2 + λ ∥θ∥^2_2 $$
-
the first term is a data-fit term, which is proportional to the negative log-likelihood; see (9.10b).
-
The second term is called the regularizer, and the regularization parameter λ>0 controls the “strictness” of the regularization => can be interpreted as a negative log-Gaussian prior
-
So far, we have covered parameter estimation using MLE and MAP where we found point estimates $\theta^*$ that optimize an objective function
-
In the next section, we will use Bayesian inference (8.4) to find a posterior distribution over the unknown parameters, which we subsequently use to make predictions.
9.3 Bayesian Linear Regression
- Bayesian linear regression :
- does not even attempt to compute a point estimate of the parameters,
- but instead the full posterior distribution over the parameters is taken into account when making predictions
9.3.1 Model
-
Prior, $p(θ) = N(m_0, S_0)$
-
likelihood, $p(y | x, θ) = N (y | φ^⊤(x)θ, σ^2)$
-
Full probabilistic model : the joint distribution of observed and unobserved random variables, y and θ, respectively, is:
$$ p(y,θ|x) = p(y|x,θ)p(θ) $$
9.3.2 Prior Predictions
-
Introduction
-
we are usually not so much interested in the parameter θ themselves.
-
Instead, our focus often lies in the predictions we make with those parameter values.
-
In a Bayesian setting, we take the parameter distribution and average over all plausible parameter settings when we make predictions.
-
More specifically, to make predictions at an input x∗, we integrate out θ and obtain :
$$ p(y_∗| x_∗) = \int p(y_∗| x_∗, θ)p(θ)dθ = E_θ[p(y_∗|x_∗, θ)] $$
-
-
Summary
-
So far, we looked at computing preds using the parameter prior $p(θ)$.
-
However, when we have a parameter posterior (given some training data X, Y) => we just need to replace the prior $p(θ)$ with the posterior $p(θ|X,Y)$.
-
9.3.3 Posterior Distribution
- Bayes’ theorem : Given a training set of inputs $x_n ∈ R^D$ and corresponding observations $y_n ∈ R$, we compute the posterior over the parameters using Bayes’ theorem :
$$ p(θ|X,Y) = \frac{p(Y|X,θ)p(θ)}{p(Y|X)} $$
- Marginal Likelihood : independent of the parameters θ and ensures that the posterior is normalized, the likelihood averaged over all possible parameter settings
9.3.4 Posterior Predictions
-
In principle, predicting with the parameter posterior p(θ|X,Y) is not fundamentally different given that in our conjugate model the prior and posterior are both Gaussian (with different parameters).
-
Therefore, by following the same reasoning as in Section 9.3.2, we obtain the (posterior) predictive distribution
-
SKIP DETAILS
9.4 Maximum Likelihood as Orthogonal Projection
- Introduction :
- we will now provide a geometric interpretation of MLE. Let us consider a simple linear regression setting.
$$ y=xθ+ε, ε∼N(0,σ^2), \tag{9.65} $$
-
Problem Definition :
-
The parameter θ determines the slope of the line.
-
We obtained the MLE for the slope parameter as (9.66) from section 9.2.1
-
This means, $θ_{ML}$ is the approximation with the minimum least-squares error between $y$ and $Xθ$. (9.67)
$$ θ_{ML} = (X^⊤X)^{−1}X^⊤y \tag{9.66} $$
-
$$ Xθ_{ML} = (X^⊤X)^{−1}XX^⊤y = \frac{XX^⊤}{X^⊤X}y \tag{9.67} $$
-
Solution : As we are looking for a solution of $y = Xθ$, we can think of linear regression as a problem for solving systems of linear equations
-
In particular, looking carefully at (9.67) we see that the maximum likelihood estimator $θ_{ML}$ in our example from (9.65) effectively does an orthogonal projection of $y$ onto the one-dimensional subspace spanned by $X$
- Recalling the results on orthogonal projections from Section 3.8, we identify $\frac{X X^⊤}{X^⊤X}$ as the projection matrix,
-
Therefore, the maximum likelihood solution provides also a geometrically optimal solution by finding the vectors in the subspace spanned by $X$ that are “closest” to the corresponding observations y,
-
In the general (polynomial) linear regression case, we again can interpret the maximum likelihood result as a projection onto a K-dimensional subspace of $R^N$.
-
-
Summary
- To model the given dataset (observation), we decided to use degree-1 curve (line) => equation 9.65
-
We need to find appropriate parameter $\theta$ of our linear model, using MLE => equation 9.66
Summary
-
In this chapter, we will see how to find a function that maps input $x$ to $y$ in observed training data set.
-
Section 9.1, Problem Formulation
-
Functional Relationship : Objective is to find a funtion that is close to the unknown function $f$ that generated the observed data
-
Observation noise : Set the observation noise $ε∼N(0, σ^2)$ as (i.i.d.) Gaussian measurement with zero-mean and variance of $σ^2$.
-
-
Section 9.2, Parameter Estimation
-
Maximum Liklihood Estimation : We find the model parameters $\theta_{ML}$ that maximize the likelihood.
-
(1) Define the likelihood function : with gaussian function
-
(2) Likelihood Factorization : training samples are independent to each other, the likelihood factorizes according to $\prod N(y_n |x^⊤_n θ, σ^2) $
-
(3) Log Transformation :
- Likelihood, a product of N Gaussian Distributions, can be transformed to sum of N log gaussian dist.
- The problem also transformed to minimize the negative log-likelihood
-
(4) Global Minimum : We can find the global optimum by computing the gradient
-
-
Maximum A Posterior : MLE can lead to severe overfitting, MAP addresses this issue by placing a prior on the params that play the role of a regularizer
-
-
Section 9.3, Bayesian Linear Regression
-
Bayesian Regression : From section 9.2, we focused on estimating appropriate parameters that work well, However in section 9.3, we focus on prediction itself, not parameters, so we just use parameter distribution and average over all plausible parameter settings when we make predictions
-
Prior Prediction : to make predictions at an input $x_∗$, we integrate out θ.
-
Posterior Prediction : when we have a parameter posterior (given some training data X, Y) then, we just need to replace the prior $p(θ)$ with the posterior $p(θ|X,Y)$
-
-
-
Section 9.4, Maximum Likelihood as Orthogonal Projection
- The MLE solution provides also a geometrically optimal solution by finding the vectors in the subspace spanned by $X$ that are “closest” to the corresponding observations $y$


Endometriosis is a disease which affects an average of 10-20% of girls and women. Despite its prevalence, however, many doctors fail to recognize symptoms, resulting in an average diagnostic gap of 9 years1. This site is specifically aimed at evaluating doctors' knowledge of Endometriosis, as Endometriosis sufferers' experiences with doctors are unique in that many doctors' knowledge of the disease is nonexistent, outdated, or incomplete.
Of course, there are many wonderful doctors that deserve recognition for their work, knowledge, and compassion. EndoDoctors--by helping patients choose their doctors based on their doctors' relationships with former Endometriosis patients--will help promote greater transparency. In doing so, we hope to prevent needless surgeries, denied care, and to connect these patients with the doctors they deserve.
EndoDoctors was born out of Endometriosis sufferers' horror stories with doctors who mocked the patients who desperately needed their care. If you suffer from Endometriosis or have attended doctors appointments with a loved one who does, please consider writing a review to help strengthen our community.
Thanks for your support! Please contact<EMAIL_ADDRESS>with questions or feedback.
For those interested in supporting Endo Warriors and promoting awareness, the first annual Million Woman March for Endometriosis will occur on March 13, 2014 in all nations' capitals. All are encouraged to attend. More information about the March can be found here.
1Ballweg, Mary L. "Impact of Endometriosis on Women's Health: Comparative Historical Data Show that the Earlier the Onset, the More Severe the Disease." Best Practice & Research Clinical Obsetetrics & Gynaecology 18.2 (2004).


Hiya! My name is Andrew Puia,I am 18 years of age.I was born here in New Zealand.I am here to improve my soft skill.


Data Structure and Algorithms
https://edu.gcfglobal.org/en/computerbasics/about-this-tutorial/content/
Basic Introduction
Unit 1:
Concept and definition of data structures
• Information and its meaning
• Array in C
• The array as an ADT
• One dimensional array
• Two dimensional array
• Multidimensional array
• Structure
• Union
• Pointer
What is data structure?
- Data structure is a way of organizing all data items and establishing relationship
among those data items.
- Data structures are the building blocks of a program.
Data structure mainly specifies the following four things:
• Organization of data.
• Accessing methods
• Degree of associativity
• Processing alternatives for information
To develop a program of an algorithm, we should select an appropriate data structure for
that algorithm. Therefore algorithm and its associated data structures form a program.
Algorithm + Data structure = Program
A static data structure is one whose capacity is fixed at creation. For example, array. A
dynamic data structure is one whose capacity is variable, so it can expand or contract at any
time. For example, linked list, binary tree etc.
Abstract Data Types (ADTs)
An abstract data type is a data type whose representation is hidden from, and of no concern to
the application code. For example, when writing application code, we don’t care how strings
are represented: we just declare variables of type String, and manipulate them by using string
operations.
Once an abstract data type has been designed, the programmer responsible for implementing
that type is concerned only with choosing a suitable data structure and coding up the methods.
On the other hand, application programmers are concerned only with using that type and
calling its methods without worrying much about how the type is implemented.
Classification of data structure:
Array
• Array is a group of same type of variables that have common name
• Each item in the group is called an element of the array
• Each element is distinguished from another by an index
• All elements are stored contiguously in memory
• The elements of the array can be of any valid type- integers, characters, floatingpoint types or user-defined types
Download the pdf file to get all the unit wise notes.
Try to save your valueable time!
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Click Here!


"x" "1" "GUPTA,RL, DEY,DK AND JUNEJA,TR, MUTAGENICITY OF 4-NITRODIPHENYL ETHERAND ITS METABOLITES, TOXICOL. LETT. 34(1):13-21, 1986"


module Bap_taint:sig
..end
Taint analysis is a dynamic data flow analysis, that tracks values on demand, i.e., the engine doesn't track all values, but only those that are marked as tainted.
In general, we say that a computation is tainted by an object, if its value is dependent (or somehow influenced) on the value of that object. However, the precise definition depends on the taint policy that is pluggabe and customizable. Some taint policies might be more relaxed, than others.
Traditionally, we use the word "taint" to represent an abstract entity whose influence we consider as undesirable or unsafe. Thus a computation is tainted, if it depends on a program runtime representation of that entity. An actual application of the framework may, of course, bend the notion of taint, and instead of attaching a negative connotation, treat it in a positive way thus checking for the liveness property instead of the safety property.
In the framework a taint represents an object of some kind, that
we would like to track, i.e., we would like to know which values
are affected by the value of that object, which program terms
compute those values, etc. The engine can track several such
objects at the same time and can distinguish objects of different
kinds as well objects of the same kind. In other words a particular
computation could be tainted by several taints, that
correspondingly could have different kinds. To make it easier to
understand let's take a classical taint analysis example with SQL
sanitization. We would like to track all objects that were produced
by sources that we do not trust. These objects are abstract to the
framework and are introduced arbitrary by the analysis. For
example, suppose there exists several calls to recv
, and our
analysis introduces a taint on each call to recv
, that it
designates with the unescaped-query
kind. Every call to recv
will create a new object that will belong to the same kind. Now,
let's assume that our analysis designates the sql_escape
function
as a function that negates the untrustworthy effect of the input,
and essentially clears the taint, or sanitizes it in our
parlance. A value passed to the sql_escape
function may have
several taints attached to it. Some of those taints may be
irrelevant to the unescaped-query
kind, i.e., attached by other
analysis. Since the query could be built from multiple calls to
recv, there could be more than one taints of the unescaped-query
kind attached to the value. So our analysis will clear all taints
of the specified kind. Finally, when the privileged location is
reached, e.g., the sql_exec
function is called, we check that
values passed to that function doesn't have any taints of the
specified kind.
Other than classical approaches to taint analysis, decribed above,
the framework could be used for checking liveness properties,
lifetime analysis, and other analysis that requires tracking the
flow of information.
module Std:sig
..end


Random Web-Development questions I faced or collected from discussion with others ( Slack Group / Facebook Group )


A different sequence of variates can be generated from the same base generator by initializing the generator using a different set of seeds. Of the four methods for creating multiple streams described here, this is the least satisfactory. As mentioned in Initialization of the Base Generators, the statistical properties of the base generators are only guaranteed within sequences, not between sequences. For example, sequences generated from different starting points may overlap if the initial values are not far enough apart. The potential for overlapping sequences is reduced if the period of the generator being used is large. Although there is no guarantee of the independence of the sequences, due to its extremely large period, using the Mersenne Twister with random starting values is unlikely to lead to problems, especially if the number of sequences required is small. This is the only way in which multiple sequences can be generated with the ACML using the Mersenne Twister as the base generator.
If the statistical properties of different sequences must be provable then one of the other methods should be adopted.


HOME
ABOUT
SERVICES
NEWS
BLOG
CONTACTS
I am a very simple card. I am good at containing small bits of information.


{% extends "base.html" %} {% block content %}
Yo! Got some new cards and some recent plays for you. Check 'em out! Or make your own, you know? New here? Bang on that title just above. Tell you all about it.
bbbingo lets you create your own bingo cards with custom text. They can be funny, they can be kinky, they can be super personal for you. You can make them private. You can make them unlisted. You can let everyone play them!
Oh yeah! Didn't I say? You can play the bingo cards! If the creator lets you, that is. When you hit play, you'll get your very own randomized card. You can keep this open however long you like, even bookmark it to keep playing. Did you know you can show off your results, too?! When you're done, you can share them with everyone! Share them with your dog! Share them with your mom! Share them with your mom's dog!
It's pronounced with a stutter because the creator used to stutter. Also the domain name was available.
*adjusts glasses* Let's go over some use cases, shall we?
ssss-split
, anyone?) for all of these in your bbbingo card. First person to go bbbingo wins!


This little demo showcases a login and lobby system build in c# (servers) and c++(clients) where you can chat, create games, change ready status, kick players... The servers are interconnected and there is no limit to the amount of game server, database servers or login servers, the login servers will act as the main point of contact for the players which will be then redirected to the correct server they wish to play on. The libraries used to create the clients are SFML as a game engine, TGUI as a GUI assistance on SFML, OpenSSL for client SSL sockets and msgpack for message serialization.
This project has the following features:


Supreme Court to hear PSNI challenge to ruling over legacy probe
The Court of Appeal previously upheld a ruling that PSNI’s Legacy Investigation Branch lacked the required independence to probe a Troubles death.
The Supreme Court is set to hear an appeal by the PSNI Chief Constable against a ruling that his force’s legacy unit lacks the independence to probe the suspected killing of a woman by the military.
Jean Smyth, 24, was shot dead as she sat in a car on the Glen Road in June 1972.
In 2017 a High Court judge found that the family of Mrs Smyth have been let down for decades by the criminal investigation system.
In March the PSNI Chief Constable lost an appeal against a ruling that he had not shown that a legacy unit within his force had the practical independence for a new probe into the killing.
The case is now set to be heard by the Supreme Court.
The Chief Constable had also challenged two other Appeal Court rulings.
However, Northern Ireland’s Lord Chief Justice Sir Declan Morgan said it was for the Supreme Court to decide whether it wanted to take those cases, pointing out the same principle of the independence ran through all three.
The other two cases are a ruling made in July that the families of the victims of the Glenanne Gang had a legitimate expectation an independent investigation would take place and the dismissing of an appeal by police against a ruling that detectives should revisit a decision to end their inquiry into allegations of torture in custody in 1971 by a group of former internees known as the Hooded Men.
Speaking outside court, Grainne Teggart, Amnesty International’s Northern Ireland campaign manager, said the Hooded Men are looking forward to an investigation starting.
“After nearly five decades of fighting for justice, these men can now expect an independent, human rights-compliant investigation into their torture to finally get under way,” she said.
“Justice must be delivered. These men deserve answers.
“Those who sanctioned and carried out their torture must be held accountable, including where possible, prosecutions.”
In a statement on Wednesday, PSNI Assistant Chief Constable George Clarke said the decision had been taken to appeal over “significant implications for present day policing” in the last Hooded Men judgment.
He said that lodging the appeal “will not affect the steps PSNI is progressing to put in place an independent review into these matters”, adding “we hope this will be of some comfort to those who have been affected by this case”.


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Education
Master in Business Administration
Stern School of Business,
New York University, New York City, 2002.
Major: Finance
Ph.D. in Engineering Sciences
Harvard University, Cambridge, Massachusetts, 1995.
Ph.D. Dissertation: Speech Processing Applications Using
an AM-FM Modulation Model
Ph.D. Advisor: Prof. Petros Maragos
M.Sc. in Engineering Sciences
Harvard University, Cambridge, Massachusetts, 1991.
Professional 5-Year Diploma in Electrical and Computer Engineering
National Techical University of Athens, 1990
Diploma Thesis: Speech Synthesis Using Formant Synthesizers


If you are not familiar with new functions and packages, example()
function can help to figure out how to use a new function.
Most developers include the example sources to the document
example()
function works. head()
function by using example()
function.
# Show an example of seq function.
example(seq)
# Show an example of head function.
# Show an example of seq function.
example(seq)
# Show an example of head function.
example(head)
test_function("example")
success_msg("Great! Head over to the next exercise.")


Here is your first test!
Question 1
Answer1
Answer 2
Answer 3
Answer 4
Question 2
Answer 1
Answer 2
Answer 3
Answer 4
Question 3
Answer1
Answer 2
Answer 3
Answer 4
Question 4
Answer 1 Answer 1 Answer 1 Answer 1 Answer 1
Answer 2
Answer 3
Answer 4


Brian Chiang
Yi Zou
Tolik Hordiienko
Kava Sage is a coffee company. The Kava Sage webapp is for the user to understand the proccess of making coffee bean in an interactive way. Our goal is for the user to have a fun experience when using our app, also the user have a clear understanding of how the coffee bean were made.


Barebones REST app for IoT. POST (or GET) requests as JSON via the /data endpoint. Requests logged to DB can be viewed as JSON or a pretty chart. Recently POSTed requests are displayed below as raw JSON:


Relation Extraction Demo
Welcome to Relation Extraction Demo!
Select a sentence from the following list:
The most common treatments for fever include over-the-counter drugs such as acetaminophen (Tylenol) and nonsteroidal anti-inflammatory drugs.
Back pain is commonly caused by a muscle strain in the back.
Vesico-uterine fistula -- a rare complication of vacuum extraction in a patient with previous caesarean section.


{% extends "email/reset.html" %} {% block link %}
To update your email, please click on the following button. The link will expire in 10 minutes. If you did not make this request, simply ignore this email. Thank you.


Vote Manager
Welcome
%charname%
Player
%whoisvoting%
is voting now.
Network
Network Reward
10 Vote Coin
Hopzone
Hopzone Reward
10 Vote Coin
Topzone
Topzone Reward
10 Vote Coin
Vote for us!
You can vote every
12 hours.
www.Server.com


1/ Pass the mouse hover pathway IDs (the "Pathway" column in the left table) to emphasize a specific path.
2/ Click once on a pathway ID (in the left table) to "pin" a path. When at least one pathway is pinned,
other pathways are faded.Click a second time on a pinned pathway ID to unpin it.
3/ Mark / unmark the checkboxes (the "Show" column in the left table) to select which pathways are visible.
4/ Cofactors are hidden by default. One can use the "Show cofactors" and "Hide cofactors" button to change this display.
▢ Chemical
◯ Transformation (in/outgoing arrows show direction)


--- layout: blog title: Pages group: navigation tagline: All pages order by capegories tags: [blog, fullit, posts, post] img: 3.jpg img-mobile: 3-mobile.jpg author: Rúben André Barreiro css: js: bgcolor: ff5a71 keywords: fullpage, html, css, landing page, jekyll, ruby, web marketing, advertising, Antonio Trento canonical: https://rubenandrebarreiro.github.io/learning-artificial-intelligence-reasoners-machine-learning-mas-game-theory-and-robotics/blog --- {% include JB/setup %}


The SoapRequest class is responsible for creating, managing and processing requests made to web services. It creates the connection, and once results are retrieved, deserializes the results into objects. In the event that an error occurs, it will send an NSError* object to the handler's onerror method. In the event of a SOAP fault, it will send a SoapFault object to the handler's onfault method. Otherwise, the request is successful and the resulting object is sent to the handler's onsuccess method.
The URL of the current SOAP request.
@property (retain, nonatomic) NSURL* url
The name of the SOAP action to be performed
@property (retain, nonatomic) NSString* soapAction;
Sets or gets the username used to authenticate the request.
@property (retain, nonatomic) NSString* username
Sets or gets the password used to authenticate the request.
@property (retain, nonatomic) NSString* password
The XML data posted to the request.
@property (retain, nonatomic) id postData
The stored data as it is received by the request.
@property (retain, nonatomic) NSMutableData* receivedData
The object that will handle the request.
@property (retain, nonatomic) SoapHandler* handler
The name of or the allocation instance of the object into which the results are deserialized.
@property (retain, nonatomic) id deserializeTo
The selector to be performed once results are received.
@property SEL action
Turns logging of the request on or off.
@property BOOL logging
The default handler to use if another is not specified or if that handler's methods are not implemented.
@property (retain, nonatomic) id<SoapDelegate> defaultHandler
Cancels the current request.
- (BOOL)cancel
Sends the request.
- (void)send
Handles the error if encountered once the response is received.
- (void)handleError:(NSError*)error
Handles a SOAP fault if one is received from the response.
- (void)handleFault:(SoapFault*)fault
Creates a new SoapRequest and returns it.
+ (SoapRequest*) create: (SoapHandler*) handler urlString: (NSString*) urlString soapAction: (NSString*) soapAction postData: (NSString*) postData deserializeTo: (id) deserializeTo
Creates a new SoapRequest and returns it.
+ (SoapRequest*) create: (SoapHandler*) handler action: (SEL) action urlString: (NSString*) urlString soapAction: (NSString*) soapAction postData: (NSString*) postData deserializeTo: (id) deserializeTo;
Creates a new SoapRequest and returns it.
+ (SoapRequest*) create: (SoapHandler*) handler action: (SEL) action service: (SoapService*) service soapAction: (NSString*) soapAction postData: (NSString*) postData deserializeTo: (id) deserializeTo;


PyAlexaLambda¶
A zero-dependency Python module that makes it easy to write AWS Lambda-based Alexa apps.
Copy and paste alexa.py
into your Python 3.6 Lambda and add an app.py
as so:
import random
from alexa import Alexa, g, statement, question
app = Alexa('My Joke App')
jokes = [
'What goes up and down but does not move? Stairs',
'Where should a 500 pound alien go? On a diet.',
'What did one toilet say to the other? You look a bit flushed.',
]
@app.intent('SayJoke')
def say_joke():
joke = random.choice(jokes)
return statement(joke)
@app.intent('Sing99Bottles')
def sing_99_bottles(Times: str = '5'):
times = int(Times)
sentences = [f'{i} bottles of beer on the wall, {i} bottles of beer. You take one down, you pass it around, {i-1} bottles of beer on the wall' for i in range(times)]
return statement('')
handle_lambda_event = app.handle_lambda_event
The API for this project is heavily inspired by the wonderful Flask-Ask package/Flask extension.


Issue One
Peter Pan’s walking me to chem
in a green V-neck and too- low jeans
plaid boxers bunched up like parachutes
“Look at the tits on that one,”
he says, dark and low, like a robot.
There’s something different about Peter Pan today.
I just can’t put my finger on it.

Peter Pan is walking with a little cloud in his pants.
He likes the way light hits windows of high-up buildings.
I’m not gonna ask about the shadow thing,
today he’s in a silent mood.
Lost boys get high as bats in caves,
shoulders swinging, fist bump and chuckle
stamping out shames with congratulations.
Fairies drop these days like Nagasaki soldiers,
their bodices limp as plaintain leaves, and Peter
is the gravedigger -- no one else wanted the job.
I’m reaching for Peter’s hand, even though
I know he hates to look tied to anything, and
I’m giving it a squeeze. He feels like the crocodile
who swallowed the clock that time forgot.
He wishes he was Peter Parker
hoisting a woman from a burning building
He wishes he did anything but fly.
Lost boys get laid in backseats, but they’re not tired yet
and there’s so many games left to play
before the slender sun goes down
under the square-shouldered shadows
of skyscrapers.
Kathleen Radigan is a Rhode Island native whose writings have or will appear in Hackwriters, PANK Blog, The Adroit Journal, Blood Lotus, and several others. She is a 2013 Young Arts Finalist and recently won a National Gold Medal for her portfolio form the Scholastic Writing Awards. In her free time she likes to make bad puns and fight the patriarchy. She also has an EP of original songs available on iTunes.
Copyright © 2012-2015, The Postscript Journal. All rights reserved.


Sorry,
gaytoday.com
is not available because it is categorized as
mature
.
You have been identified as anonymous from <IP_ADDRESS>.
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This keyboard is designed for use with the Jorai language of Viet Nam. It includes an on screen keyboard which can be viewed by clicking on the Keyman icon and selecting the On Screen Keyboard menu item.


Before Change
version = release = None
if git_ref.returncode == 0:
if git_ref.stdout and git_ref.stdout[0] == "v": // tagged release
version = git_ref.stdout[1:].decode("utf-8").strip()
release = version
elif git_ref.stdout: // in case of branch head
version = git_ref.stdout.decode("utf-8").strip()
release = version + "-branch"
if version is None: // docs are being built for a non-HEAD and untagged commit
version = qucumber.__version__.strip()
// The full version, including alpha/beta/rc tags
release = version + "-unreleased"
After Change
// The short X.Y version
version = os.environ.get("QUCUMBER_VERSION", qucumber.__version__.strip())
// The full version, including alpha/beta/rc tags
release = os.environ.get("QUCUMBER_RELEASE", version)
// -- General configuration ---------------------------------------------------
// If your documentation needs a minimal Sphinx version, state it here.


I am Diggaj Upadhyay. I am from Nepal and I have recently moved to Australia
to accomplish my further studies. I am an indoor person who prefer to read books,
play Guitar / listen to music, play virtual games, etc. However, I enjoy
going out and meeting friends, hiking, bicycling, swimming, etc.
I am studying Bachelors of Software Development in Southern Cross University in Western Australia. As a person who is very curious in working with computers I am very excited to learn what my Campus has to offer.
So far we have begun with Web Development in our season 1 Year 1 and I am already learning so much from it. I hope to get a better understanding of the real world problem solving techniques, what we may encounter during our journey in learning and where to look for the solutions.
If you click on the video it plays. But, if you click on the link in the video it opens to the different window as requested.
Subscribe my YouTube Channel .
Email me at: Diggaj Upadhyay


When this information is provided, interpretation records will include a button at the top. Clicking that button will create a formal issuance letter you can print or copy.
First Name: Last Name:
Work Phone: Work Email:
{{ firstname }} {{ lastname }} {{ phone }} {{ email }}
Save


Float On
Well, it has been exactly 3 weeks since I moved out here. I'm still working on NOT saying that my "home" is in Seattle. I usually quickly correct myself but ultimately no one really cares. Yet!
S and L had some brunch on Sunday and Danielle and I walked up to Bed-Stuy to enjoy some lovely company and even better food. Danielle brought an arugula salad and I was charged with aquiring enough bubbles to satiate 7 of us. Went to vist my new favorite location in order to get some prosecco. Such a kind owner and he called me 'baby', as is a pretty common island term of endearment.
Walked up to Dynaco to hang out with Andrea and have some delicious vinho verde.  Not just any piece of licorice either. She dissolved the candy shell off a Good N' Plenty candy and plopped it right on to create some angelic unicorn with a devilish horn. Brilliant.
TL;DR
Life as unusual.
“Bad news comes, don't you worry even when it lands, Good news will work it's way to all them plans” — Modest Mouse
Had a phone screen on Monday for a company here in New York and I think it went well! Trying to join this startup company that built a platform to connect people with therapists through phone calls and an app. Think of it as therapy on demand. The role I'm trying for is for a support representative handling customer questions and concerns. I was then sent a series of scenarios that are meant to gauge my response to certain situations I might be presented with. Thankfully Andrea has a mean sense of business strategies (MIT!) and helped me re-think how I would handle certain cases. It's all about retention! Gotta get my business mindset up to par.
Took a walk with Danielle to go get some lunch and she wanted to Lula Bagel and thankfully we did! Got an everything bagel with lox, cream cheese, dill, red onion, and capers. Drool. Now I haven't had a lot of bagels so far but future experiences have their work cut out for them after this bagel from Lula. Seriously, I never thought a bagel good be so incredible. I'm definitely curating a list of go-to places for when any of you come to visit. Tourism through a tourist®.
The weather is defintely showing signs of winter being right around the corner. Stopped into a little bodega to grab some coffee creamer and ended up buying eggnog. The holidays have official begun for me. Well at least the season of drinking eggnog.


| Overview | Module | Use | Devguide | Index |
| Nested Modules | Services | Singletons | Interfaces | Structs | Exceptions | Enums | Typedefs | Constant Groups |
| module java |
|
| Services | |
| JavaVirtualMachine | [ DEPRECATED ] exports interfaces to handle a JavaVM and Java threads. |
| Interfaces | |
| XJavaThreadRegister_11 | [ DEPRECATED ] must be implemented by the user of the XJavaThreadRegister_11. |
| XJavaVM | [ DEPRECATED ] must be implemented by the user of the XJavaVM. |
| Exceptions | |
| InvalidJavaSettingsException | indicates a the Java settings have been modified. |
| JavaDisabledException | indicates that Java could not be initialized because it has been switched off. |
| JavaInitializationException | indicates that there is no Java available |
| JavaNotConfiguredException | indicates that the user did not configure Java for an Office installation. |
| JavaNotFoundException | indicates that no suitable JRE was found. |
| JavaVMCreationFailureException | indicates that the Java Virtual Machine could not be created |
| MissingJavaRuntimeException | indicates that the Java runtime library could not be found. |
| RestartRequiredException | indicates that the office must be restarted before a JRE can be used. |
| WrongJavaVersionException | indicates that an operation involving Java (probably executing Java code) failed due to a wrong Java version. |
Copyright © 2013, The Apache Software Foundation, Licensed under the Apache License, Version 2.0. Apache, the Apache feather logo, Apache OpenOffice and OpenOffice.org are trademarks of The Apache Software Foundation. Other names may be trademarks of their respective owners.


|
Mini51 BSP
V3.02.002
The Board Support Package for Mini51 Series
|
#include <Mini51Series.h>
Data Fields | |
| __I uint32_t | SRC0 |
| __I uint32_t | SRC1 |
| __I uint32_t | SRC2 |
| __I uint32_t | SRC3 |
| __I uint32_t | SRC4 |
| __I uint32_t | SRC5 |
| __I uint32_t | SRC6 |
| __I uint32_t | SRC7 |
| __I uint32_t | SRC8 |
| __I uint32_t | SRC9 |
| __I uint32_t | SRC12 |
| __I uint32_t | SRC14 |
| __I uint32_t | SRC16 |
| __I uint32_t | SRC17 |
| __I uint32_t | SRC18 |
| __I uint32_t | SRC25 |
| __I uint32_t | SRC28 |
| __I uint32_t | SRC29 |
| __IO uint32_t | NMICON |
| __IO uint32_t | MCUIRQ |
@addtogroup INT Interrupt Controller (INT) Memory Mapped Structure for INT Controller
Definition at line 4336 of file Mini51Series.h.
| INT_T::MCUIRQ |
| Bits | Field | Descriptions |
| [31:0] | MCU_IRQ | MCU IRQ Source
The MCU_IRQ collects all the interrupts from the peripherals and generates the synchronous interrupt to Cortex-M0 core. This modes to generate interrupt to Cortex-M0 - the normal mode. The MCU_IRQ collects all interrupts from each peripheral and synchronizes them then interrupts the Cortex-M0. When the MCU_IRQ[n] is 0, setting MCU_IRQ[n] to 1 will generate an interrupt to Cortex-M0 NVIC[n]. When the MCU_IRQ[n] is 1 (mean an interrupt is assert), setting 1 to the MCU_bit[n] will clear the interrupt and setting MCU_IRQ[n] 0 has no effect. |
Definition at line 4760 of file Mini51Series.h.
| INT_T::NMICON |
| Bits | Field | Descriptions |
| [4:0] | NMI_SEL | NMI Interrupt Source Selection
The NMI interrupt to Cortex-M0 can be selected from one of the peripheral interrupt by setting NMI_SEL. |
| [8] | NMI_SEL_EN | NMI Interrupt Enable Control (Write Protect)
0 = NMI interrupt Disabled. 1 = NMI interrupt Enabled. Note: This bit is the protected bit, and programming it needs to write 0x59, 0x16, and 0x88 to address 0x5000_0100 to disable register protection. Refer to the register REGWRPROT at address GCR_BA+0x100. |
Definition at line 4759 of file Mini51Series.h.
| INT_T::SRC0 |
| Bits | Field | Descriptions |
| [2:0] | INT_SRC | Interrupt Source
Define the interrupt sources for interrupt event. |
Definition at line 4723 of file Mini51Series.h.
| INT_T::SRC1 |
| Bits | Field | Descriptions |
| [2:0] | INT_SRC | Interrupt Source
Define the interrupt sources for interrupt event. |
Definition at line 4724 of file Mini51Series.h.
| INT_T::SRC12 |
| Bits | Field | Descriptions |
| [2:0] | INT_SRC | Interrupt Source
Define the interrupt sources for interrupt event. |
Definition at line 4736 of file Mini51Series.h.
| INT_T::SRC14 |
| Bits | Field | Descriptions |
| [2:0] | INT_SRC | Interrupt Source
Define the interrupt sources for interrupt event. |
Definition at line 4740 of file Mini51Series.h.
| INT_T::SRC16 |
| Bits | Field | Descriptions |
| [2:0] | INT_SRC | Interrupt Source
Define the interrupt sources for interrupt event. |
Definition at line 4744 of file Mini51Series.h.
| INT_T::SRC17 |
| Bits | Field | Descriptions |
| [2:0] | INT_SRC | Interrupt Source
Define the interrupt sources for interrupt event. |
Definition at line 4745 of file Mini51Series.h.
| INT_T::SRC18 |
| Bits | Field | Descriptions |
| [2:0] | INT_SRC | Interrupt Source
Define the interrupt sources for interrupt event. |
Definition at line 4746 of file Mini51Series.h.
| INT_T::SRC2 |
| Bits | Field | Descriptions |
| [2:0] | INT_SRC | Interrupt Source
Define the interrupt sources for interrupt event. |
Definition at line 4725 of file Mini51Series.h.
| INT_T::SRC25 |
| Bits | Field | Descriptions |
| [2:0] | INT_SRC | Interrupt Source
Define the interrupt sources for interrupt event. |
Definition at line 4750 of file Mini51Series.h.
| INT_T::SRC28 |
| Bits | Field | Descriptions |
| [2:0] | INT_SRC | Interrupt Source
Define the interrupt sources for interrupt event. |
Definition at line 4754 of file Mini51Series.h.
| INT_T::SRC29 |
| Bits | Field | Descriptions |
| [2:0] | INT_SRC | Interrupt Source
Define the interrupt sources for interrupt event. |
Definition at line 4755 of file Mini51Series.h.
| INT_T::SRC3 |
| Bits | Field | Descriptions |
| [2:0] | INT_SRC | Interrupt Source
Define the interrupt sources for interrupt event. |
Definition at line 4726 of file Mini51Series.h.
| INT_T::SRC4 |
| Bits | Field | Descriptions |
| [2:0] | INT_SRC | Interrupt Source
Define the interrupt sources for interrupt event. |
Definition at line 4727 of file Mini51Series.h.
| INT_T::SRC5 |
| Bits | Field | Descriptions |
| [2:0] | INT_SRC | Interrupt Source
Define the interrupt sources for interrupt event. |
Definition at line 4728 of file Mini51Series.h.
| INT_T::SRC6 |
| Bits | Field | Descriptions |
| [2:0] | INT_SRC | Interrupt Source
Define the interrupt sources for interrupt event. |
Definition at line 4729 of file Mini51Series.h.
| INT_T::SRC7 |
| Bits | Field | Descriptions |
| [2:0] | INT_SRC | Interrupt Source
Define the interrupt sources for interrupt event. |
Definition at line 4730 of file Mini51Series.h.
| INT_T::SRC8 |
| Bits | Field | Descriptions |
| [2:0] | INT_SRC | Interrupt Source
Define the interrupt sources for interrupt event. |
Definition at line 4731 of file Mini51Series.h.
| INT_T::SRC9 |
| Bits | Field | Descriptions |
| [2:0] | INT_SRC | Interrupt Source
Define the interrupt sources for interrupt event. |
Definition at line 4732 of file Mini51Series.h.


Outline
AnimateForms is built around a single instantiable Animate class to call animations, with each animation (method, e.g. Resize) having a single "simple" form with "advanced" overloads as an option.
A method's "simple" form consists of the parameters Control c, Function easing, int duration, as well as any method-specific parameters needed, like Move's parameter Point moveTo. A method's "advanced" form consists of an Options object as a parameter, and any method-specific parameters. The Options object is a flexible tool for calling animations and specifying more parameters, like Delay, and even specifying arrays of Controls or easings to use. See the Options documentation for more info.
This documentation's included examples assume you have declared an Animate class named a.
Class Information
Instantiable
To start using the animate functions, first declare an animator:
private Animate a = new Animate();
Then, any animated method is run as such:
await a.Method(parameters);
ProTip #1: By specifying await, the method is run synchronously and will not allow code to continue before the method has finished executing. Instead, a discard can be used to run asynchronously:
_ = a.Method(parameters);
ProTip #2: Animate classes will block two animations of the same type on the same control (unless otherwise noted in documentation). This prevents jittery graphics as two animations try to happen at once. As such, only one Animate class should ever be declared in your code.
Function (delegate)
Input: float t, b, c, d
Output: int
Defines an easing function that takes an input time t (current frame), start value b, change in value c, and duration of animation d and returns an integer defining what the current position should be. In AnimateForms, duration and current time are both always in milliseconds.
ProTip #1: You can use this delegate to define your own custom easing function, and plug it into any method for use.
ProTip #2: The Easings class defines many preset static easing functions that can be used as a parameter for methods that need this delegate.
Example: Animate.Function f = Easings.Linear;
Move
Input: Control control, Function easing, int duration, Point moveTo
Output: bool
Moves input control to point moveTo, using the specified duration and easing function. Returns true if animation completed and false if either location is the same or another animation is already in progress.
See Also:
Examples:
_ = a.Move(control, Easings.Linear, 1000, new Point(200, 20));
_ = a.Move(new Options(), new Point(200, 20));
Overloads:
Options o, Point moveTo
Applies animation using an Options object. See the Options documentation for possible modifiers.
Options o, Point moveTo, Point offset
Applies animation using an Options object. See the Options documentation for possible modifiers. If multiple controls are specified in the options, an offset will be applied per control (stacking them).
MoveRelative
Input: Control control, Function easing, int duration, Point offset
Output: bool
Similar to the standard Move animation, however, the offset recursively applies to the current control's location rather than setting it directly. As such, AnimateForms will not block multiple instances of this method at the same time. This creates an effect similar to applying a vector force to a control, except the control will always be offset by the amounts entered.
ProTip: Negative values can be entered in a Point object, and will make MoveRelative move the control up or left instead of down or right.
See Also:
Examples:
_ = a.MoveRelative(control, Easings.Linear, 1000, new Point(200, -20));
_ = a.MoveRelative(new Options(), new Point(200, -20));
Overloads:
Options o, Point offset
Applies animation using an Options object. See the Options documentation for possible modifiers.
Recolor
Input: Control control, Function easing, int duration, Color colorTo, [bool backColor = true]
Output: bool
Recolors input control to the specified ARGB color colorTo, using the specified duration and easing function. Returns true if animation completed and false if colorTo is equal to current color, or if another Recolor is running on the same control already. backColor can be used to determine whether the BackColor or the ForeColor property of the control is changed. This function supports using an HSV color object in place of an ARGB color.
Note: Limitations currently means transitioning opacities appears flickery and not great.
See Also:
Examples:
_ = a.Recolor(control, Easings.Linear, 1000, Color.Blue);
_ = a.Resize(new Options(), Color.Blue, false);
Overloads:
Input: Control control, Function easing, int duration, Helpers.HSV hsv, [bool backColor = true]
Applies animation using an HSV color object. This is provided by the Helpers class and results in a different look to the transition.
Options o, Color color, [bool backColor = true]
Applies animation using an Options object. See the Options documentation for possible modifiers.
Options o, Color[] colors, [bool backColor = true]
Applies animation using an Options object, cycling through an array of colors for each control. See the Options documentation for possible modifiers.
Options o, Helpers.HSV color, [bool backColor = true]
Applies animation using an HSV color and an Options object. This is provided by the Helpers class and results in a different look to the transition.
Options o, Helpers.HSV colors, [bool backColor = true]
Applies animation using an Options object, cycling through an array of HSV colors for each control. See the Options documentation for possible modifiers.
Resize
Input: Control control, Function easing, int duration, Size sizeTo
Output: bool
Resizes input control to input sizeTo, using the specified duration and easing function. Returns true if animation completed and false if sizeTo is equal to current size or another Resize is currently being run on the control.
See Also:
Examples:
_ = a.Resize(control, Easings.Linear, 1000, new Point(200, 200));
_ = a.Resize(new Options(), new Size(200, 200));
Overloads:
Options o, Size sizeTo
Applies animation using an Options object. See the Options documentation for possible modifiers.
AllEasings (static)
Output: Function[]
A static Function[] of every easing provided, in case an array of every single easing was needed.
See Also: Animate.Function
ProTip: Options objects can take multiple easings and multiple input controls, and supported animations will cycle through easings per control.
Implemented Easings (static)
All easings found on Gizma have been implemented as static functions according to the delegate Animate.Function and are listed below with their abbreviations that they appear with in the Easings class. Each easing type will have an In, Out, and an InOut variant (except Linear).
See Also:
https://easings.net (for visual reference)
Linear - Easings.Linear
Quadratic - Easings.QuadXX
Cubic - Easings.CubicXX
Quartic - Easings.QuartXX
Quintic - Easings.QuintXX
Circular - Easings.CircXX
Exponential - Easings.ExpXX
Sinusoidal - Easings.SinXX
CollectionToArray (static)
Input: Control.ControlCollection controlCollection
Output: Control[]
Converts a ControlCollection that results from calling parentControl.Controls to a control array for use in the animation functions.
ProTip: ControlCollections seem to be organized randomly, and you should only use this function when the resulting order of the array doesn't matter.
HSV (struct)
Parameters: double Hue, Saturation, Value
Holds an HSV color value for use by the Recolor method or one of the RGBtoHSV / HSVtoRGB helper functions. Has a constructor so it can be called inline cleanly.
Examples:
var hsv = new Helpers.HSV(180, 0.5f, 0.5f);
var hsv = new Helpers.HSV { Hue = 180, Saturation = 1, Value = 1 };
HSVtoRGB (static)
Input: HSV hsv
Output: Color
Converts an HSV object into a System.Drawing.Color RGB color.
RGBtoHSV (static)
Input: Color color
Output: HSV
Converts an RGB System.Drawing.Color color into an HSV object. If an alpha is provided, it will be ignored.
SortCollectionByName (static)
Input: Control.ControlCollection controlCollection
Output: Control[]
Converts a ControlCollection that results from calling parentControl.Controls to a control array, which is sorted by the control's Name property in alphabetical order.
Options
Parameters: Control control, Function easing, int duration
Optional Parameters: Control[] controls*, Function[] easings*, int delay, int interval
Non-Constructor Paramters: int endDelay
An options object to allow greater control over some animation functions. It can be created by using a constructor with parameters or by setting each element individually. Currently, all animations will accept an options object and will process all parameters set in one, since the options are universal.
See Also: Animate.Function
Easing: Easing function to use. Default = Linear.
Duration: Duration of animation, in milliseconds. Default = 1000ms.
Delay: Delay before animation starts.** Default = 0ms.
EndDelay: Delay after animation ends.** Default = 0ms.
Interval: Time between animations executing in bulk animation commands. Default = 0ms.
*Single control and single functions that get passed in as parameters get parsed into an array by the Options object anyway.
**If this is specified and the animation returns or will return false, it will still wait for this amount of time.
Class Information
Instantiable
Timelines are instantiable objects that can store a list of animations to run later. Instantiate with:
private readonly Timeline t = new Timeline();
Then, add animations with the following:
t.Add(() => a.Method(...));
Timelines can queue anything that returns a Task<bool>. As such, you can write your own custom functions to run in them.
When you wish to execute the timeline, use the following. Timelines can be reused, and are useful if there is a sequence of animations that need to be called repeatedly.
await t.Execute();
ProTip #1: Like other animate functions, a discard _ = t.Execute(); can be used to run the timeline without blocking code.
Add
Input: Func<Task<bool>> func, [bool shouldAwait = true]
Adds an animation to the timeline. Use a lambda to provide the function to add it. The animation added can be removed using the Remove method and the index of the animation added. Should await means the animation should run using an await tag - if this is set false, it will run using a discard _ = instead causing it to happen at the same time as the next animation in queue. This can cause issues as if the animations are of the same type, the second will be rejected.
ProTip: You can add your own function to a timeline so long as it returns Task<bool>.
Examples:
t.Add(() => a.Move(moveOptions, new Point(0, 0)));
Remove
Input: int index
Calls RemoveAt(int index) on the private List of functions within the Timeline object. May throw an ArgumentOutOfBounds exception.
Examples:
t.Remove(3);
t.Remove(0, 3);
Overloads:
int index, int count
Calls RemoveRange on the list instead, with index and count like normal.
Execute
Input: [bool discardAfterRun = false]
Runs every action added to the Timeline object. It will await every single call. If discardAfterRun is set to true, after an animation is played, it will be removed from the Timeline object, effectively resetting the Timeline object.
Examples:
t.Execute();


Brought to you by Alex Rozumii
| What | Where |
|---|---|
| Ruby course by Kottans/RubyGarage | http://kottans.org/ruby-slides/ |
| Original RubyGarage course | http://rubygarage.github.io/ |
| How to improve your test suite | http://brain-geek.github.io/better-test-suite-presentation/ |
| This presentation | http://brain-geek.github.io/summary-opower-ruby-2016-presentation/ |


In the previous stage, we have discussed the conditional probability of words from the spam subset:
\[P(spam|word_1,word_2,...,word_n) = P(word_1|spam){*}P(word_2|spam){*}...{*}P(word_n|spam){*}P(spam)\]
the conditional probability of words from ham is:
\[P(ham|word_1,word_2,...,word_n) = P(word_1|ham){*}P(word_2|ham){*}...{*}P(word_n|ham){*}P(ham)\]
Now, we would like to discuss these P(spam)
and P(ham)
probabilities. P(spam)
is the probability of spam occurrence in the training dataset. It is the number of rows in the target column labeled as spam divided by the total number of rows. In terms of mathematics, it can be represented as follows:
\[P(spam) = {N_{spam} \over N_{spam} + N_{ham}}\]Similarly, P(ham)
can be represented as:
\[P(ham) = {N_{ham} \over N_{spam} + N_{ham}}\]To classify a message as spam or ham in a sentence, calculate \(P(spam|word_1,word_2,...,word_n)\) and \(P(ham|word_1,word_2,...,word_n)\), and then compare their values. If:
\[P(spam|word_1,word_2,...,word_n) > P(ham|word_1,word_2,...,word_n)\]the sentence is classified as spam. But if:
\[P(spam|word_1,word_2,...,word_n) < P(ham|word_1,word_2,...,word_n)\]the sentence is classified as ham. If the values are equal, classify the sentence as unknown for manual reviewing.
In this stage, your program should:
P(ham)
and P(spam)
;spam
, ham
, or unknown
;SMS
column of the test dataframe using the .apply
function;The greater-than symbol followed by a space (>
) represents the user input. Note that it's not part of the input.


BMA280: Three Axis High-g I2C/SPI Accelerometer¶
Description¶
This sample application produces slightly different outputs based on the chosen driver configuration mode:
In Measuring Mode with trigger support, the acceleration on all three axis is printed in m/s^2 at the sampling rate (ODR).
In Polled Measuring Mode, the instantaneous acceleration is polled every 2 seconds.
In Max Peak Detect Mode, the device returns only the over-threshold peak acceleration between two consecutive sample fetches or trigger events. (In most high-g applications, a single 3-axis acceleration sample at the peak of an impact event contains sufficient information about the event, and the full acceleration history is not required.) Instead of printing the acceleration on all three axis, the sample application calculates the vector magnitude (root sum squared) and displays the result in g’s rather than in m/s^2, together with an bar graph.
References¶
BMA280: http://www.analog.com/bma280
Wiring¶
This sample uses the BMA280 sensor controlled either using the I2C or SPI interface. Connect supply VDD, VS and GND. The supply voltage can be in the 1.6V to 3.5V range.
I2C mode¶
Connect Interface: SDA, SCL and optionally connect the INT1 to a interrupt capable GPIO. It is a requirement that SCLK must be connected to GND in I2C mode. Depending on the baseboard used, the SDA and SCL lines require Pull-Up resistors. With the MISO pin low, the I2C address for the device is 0x1D, and an alternate I2C address of 0x53 can be chosen by pulling the MISO pin high.
I2C Address:
0x1D: if MISO is pulled low
0x53: if MISO is pulled high
Note
When sharing an SDA bus, the BMA280 Silicon Revision < 3 may prevent communication with other devices on that bus.
SPI mode¶
Connect Interface: SCLK, MISO, MOSI and /CS and optionally connect the INT1 to a interrupt capable GPIO.


This page shows automated results of the Lyrids 2009, based on visual data entered through the IMO electronic report form. Note that these automated results may not be suitable for scientific use; please refer to manual analysis results published in journal papers (e.g., WGN) when available. Send your feedback regarding this page to Geert Barentsen.
Page generated: 2009 July 15 at 22:15 UT.
ZHRmax = 15 based on 1285 Lyrids in 380 data intervals, assuming fixed population index r = 2.1 and zenith correction 1/sin(hR).
| Time (UT) | Solarlon | nINT | nLYR | ZHR | |
|---|---|---|---|---|---|
| 2009-04-16 00:36 | 26.037 | 6 | 6 | 2 | ±1 |
| 2009-04-18 04:38 | 28.157 | 4 | 2 | 3 | ±2 |
| 2009-04-19 02:04 | 29.031 | 23 | 36 | 3 | ±0 |
| 2009-04-20 01:55 | 30.001 | 26 | 56 | 4 | ±1 |
| 2009-04-20 22:13 | 30.827 | 15 | 56 | 6 | ±1 |
| 2009-04-21 00:22 | 30.914 | 17 | 56 | 6 | ±1 |
| 2009-04-21 01:23 | 30.956 | 14 | 55 | 6 | ±1 |
| 2009-04-21 17:41 | 31.619 | 18 | 55 | 8 | ±1 |
| 2009-04-21 21:54 | 31.790 | 18 | 58 | 10 | ±1 |
| 2009-04-21 22:36 | 31.818 | 17 | 56 | 9 | ±1 |
| 2009-04-21 23:18 | 31.846 | 13 | 59 | 9 | ±1 |
| 2009-04-21 23:48 | 31.867 | 11 | 62 | 7 | ±1 |
| 2009-04-22 00:22 | 31.890 | 15 | 89 | 10 | ±1 |
| 2009-04-22 00:53 | 31.911 | 15 | 67 | 10 | ±1 |
| 2009-04-22 01:22 | 31.931 | 15 | 87 | 12 | ±1 |
| 2009-04-22 01:52 | 31.951 | 10 | 68 | 14 | ±2 |
| 2009-04-22 04:34 | 32.061 | 15 | 63 | 13 | ±2 |
| 2009-04-22 09:05 | 32.245 | 10 | 59 | 15 | ±2 |
| 2009-04-22 15:52 | 32.520 | 13 | 60 | 11 | ±1 |
| 2009-04-22 21:24 | 32.745 | 18 | 59 | 12 | ±2 |
| 2009-04-22 23:11 | 32.818 | 17 | 56 | 10 | ±1 |
| 2009-04-23 20:18 | 33.675 | 23 | 38 | 4 | ±1 |
| 2009-04-24 10:47 | 34.264 | 29 | 53 | 4 | ±1 |
| 2009-04-25 20:26 | 35.629 | 16 | 23 | 2 | ±0 |
| 2009-04-26 10:40 | 36.207 | 2 | 6 | 4 | ±2 |
For each estimation interval: time is the middle of the interval, nINT is the number of observing periods and nLYR is the number of Lyrids involved. ZHR = (1 + sum nLYR) / sum(Teff/C) where Teff is the effective observing time and C is the total correction for limiting magnitude, clouds and zenith correction. The solar longitudes refer to equinox J2000.0.
Data has been received from 78 observers in 21 countries. Thank you for your efforts!
| Observer | Country | Teff | nLYR |
|---|---|---|---|
| Salvador Aguirre | Mexico | 10.10h | 52 |
| Joanna Antosik | Poland | 5.30h | 19 |
| Pierre Bader | Germany | 8.75h | 41 |
| Peter Bakalik | Slovakia | 1.05h | 2 |
| Ricardas Balciunas | Lithuania | 6.00h | 19 |
| Emil Brezina | Czech Republic | 3.36h | 18 |
| Magdalena Buczkowska | Poland | 2.95h | 12 |
| Jakub Cerny | Czech Republic | 3.20h | 23 |
| Igor Chalenko | Ukraine | 3.00h | 16 |
| Marcin Chwa?a | Poland | 3.00h | 3 |
| Tibor Csorgei | Slovakia | 2.33h | 19 |
| Tibor Csorgei | Slovakia | 1.33h | 17 |
| Mark Davis | United States | 1.45h | 11 |
| Ameya Deshpande | India | 1.50h | 4 |
| Mayuresh Desai | India | 3.50h | 15 |
| Sietse Dijkstra | Netherlands | 4.46h | 15 |
| Dariusz Dorosz | Poland | 4.00h | 32 |
| Sergey Dobrowsky | Belarus | 1.71h | 6 |
| Urszula Dzikowicz | Poland | 2.95h | 11 |
| Frank Enzlein | Germany | 10.68h | 45 |
| Christoph Gerber | Germany | 6.45h | 7 |
| Sylvie Gorkova | Czech Republic | 6.00h | 6 |
| Mitja Govedic | Slovenia | 2.61h | 23 |
| Pavol Habuda | Slovakia | 2.70h | 9 |
| Vilem Heblik | Czech Republic | 14.25h | 44 |
| Carl Hergenrother | United States | 1.04h | 7 |
| Ken Hodonsky | United States | 1.80h | 14 |
| Petr Horalek | Czech Republic | 3.70h | 34 |
| Carl Johannink | Netherlands | 7.36h | 12 |
| Lidija Jovanovic | Serbia | 1.42h | 2 |
| Angelika Ju?kiewicz | Poland | 2.95h | 13 |
| Javor Kac | Slovenia | 3.08h | 27 |
| Paulina Kedzierska | Poland | 2.95h | 9 |
| Roman Kostenko | Ukraine | 2.09h | 6 |
| Jakub Koukal | Czech Republic | 34.47h | 162 |
| Peter Van Leuteren | Netherlands | 10.72h | 14 |
| Anna S. Levina | Israel | 2.92h | 14 |
| Tomasz Lojek | Poland | 4.00h | 29 |
| Jer Nan Lou | Taiwan | 1.12h | 2 |
| Robert Lunsford | United States | 1.75h | 13 |
| Marian Majerik | Slovakia | 2.95h | 14 |
| Justyna Mamzer | Poland | 2.95h | 19 |
| Ivana Marjanovic | Serbia | 1.77h | 4 |
| Antonio Martinez | Venezuela | 0.92h | 1 |
| Katarzyna Mielczarek | Poland | 2.95h | 19 |
| Koen Miskotte | Netherlands | 12.41h | 25 |
| Sirko Molau | Germany | 2.25h | 16 |
| Martin Nedved | Czech Republic | 1.07h | 2 |
| Tereza Novotna | Czech Republic | 4.08h | 11 |
| Filip ?olakovi? | Serbia | 4.64h | 13 |
| Karolina Paprocka | Poland | 2.95h | 13 |
| Dominika P?czek | Poland | 2.95h | 15 |
| Nilesh Puntambekar | India | 1.00h | 4 |
| Ella Ratz | Israel | 3.08h | 5 |
| Jurgen Rendtel | Germany | 20.19h | 64 |
| Branislav Savic | Serbia | 2.42h | 9 |
| Branislav Savic | Serbia | 5.07h | 12 |
| Magdalena Sendak | Poland | 2.95h | 15 |
| Ivan Sergey | Belarus | 1.63h | 17 |
| Ivan M. Sergey | Belarus | 0.88h | 5 |
| Rohan Shewale | India | 1.50h | 7 |
| Klaudia Sicinska | Poland | 2.95h | 14 |
| Magdalena Sieniawska | Poland | 3h | 5 |
| Natalia Smus | Poland | 2.95h | 9 |
| Ulrich Sperberg | Germany | 4.05h | 17 |
| Wesley Stone | United States | 2.63h | 37 |
| Matej Sustr | Slovakia | 1.05h | 8 |
| Pavel Svozil | Czech Republic | 2.00h | 11 |
| Raul Truta | Romania | 3.4h | 9 |
| Michel Vandeputte | Belgium | 14.50h | 74 |
| Vilena Velikic | Serbia | 1.42h | 2 |
| Jan Verfl | Czech Republic | 2.52h | 9 |
| Ivo Vespalec | Czech Republic | 11.71h | 71 |
| Amogh Waghmare | India | 2.50h | 12 |
| Thomas Weiland | Austria | 4.39h | 33 |
| Ilkka Yrjola | Finland | 1.05h | 6 |
| Weihou Zeng | China | 2.08h | 6 |
| Przemyslaw Zoladek | Poland | 5.75h | 20 |
Create your own analysis!
Rate intervals: lyr2009_rate.csv (CSV-format).
Magnitude distributions: lyr2009_magn.csv (CSV-format).


Short Biography About Me
A Healthcare professional, certified Innovation professional and
Full stack developer candidate -
with clinical oncology care experience -
seeking continuous knowledge by developing programming skills
for healthcare innovation, including artificial intelligence
applications in healthcare , clinical data analytics,
and every technology-based projects related to
improve the quality of health care services ,
patient experience, patient engagement ,health policies ,
and patient journey especially in cancer care.
My Job Experience
Healthcare Innovation Professional,(April 2019 – present)
Global Innovation Institute & Dream & Idea consulting firm.
Senior Healthcare Innovation Consultant.
Clinical Pharmacist,(July 2017 – October 2019)
King Hussein Cancer and Research Center (JCI accredited).
My Education History
BACHELOR OF Doctor of Pharmacy,(2011 – 2017) Jordan University of Science and Technology. GPA (89.4%), Excellent degree, 2nd on the Class (With honors)
My Future Goals
Creating a software system ,based on the new programming skills, to help in the prediction of ,life saving if early diagnosed, medical conditions


Poll Result
Tally
Recorded votes count: {{received_votes_count}} / {{registered_votes_count}}
{% for label, votes in poll_data.items() %}
{{label}}: {{votes}}
{% endfor %}
Unused Dummy Votes
{% for label, votes in unused_dummy_vote.items() %}
- {{label}}
{% for v in votes %}
Value = {{v[0]}}
Commitment = {{v[1]}}
r Value = {{v[2]}}
{% endfor %}
{% endfor %}


Sometimes it becomes apparent that the world is even stranger than we thought. Here is a telling example. Look at this cow.
I want you to imagine that you have been given the pleasurable task of naming the cow. The choice is yours. Spend a few minutes thinking about it, study the picture, run a few ideas through your head, and plump for the one that seems most suitable.
Someone at the Dickinson Ranch in Gorham, Kansas, did what you have just done. Perhaps it was purveyor of “performance cattle that calve on the range and graze through the winter” Kirk Dickinson himself. Astonishingly, what went on in his head was something like this:
“Hmm, this is a fine cow. I think I'll name it after an eighties pop group given to portentous witterings, remembered today for that matchless paean to vapidity Vienna, it means nothing to me. No doubt the minstrel Midge Ure will be pleased to know he has a cow named after his old band. The cow's name shall be Ultravox!”
Thanks to Alasdair Dickson for drawing my attention to this. That's Dickson, by the way, not Dickinson. Alasdair is no relation to the surely demented rancher.


Our Facilities
Multimedia Resource Library
In Manchester Metropolitan University, we have several facilities for our students to use for their assessments which they have to do part of their degree. The information and communication department has amazing space within Geoffrey Manton (GM) building called the Multimedia Resource Library (MRL). In the MRL we have Apple Macs and PCs with the latest software’s, from your daily tasks using Microsoft office, to your Creative package, Adobe Creative Suite. These facilities are only exclusive to Information and Communication students. We create this space for our students, so that they can do their projects while socializing and getting to know other students from other years.
Visual Production Kits
Also we have full visual production kits which include video cameras, DLSR Cameras and sound kits. Our camera kits are top of the range from Sony to Cannon. The students can loan these out when they are doing their creative assessments on their course. Other facilities we have a radio booth where students can book appointments and record great quality voice overs for their videoproduction piece. The MRL is open at normal hours from 9am-5pm.
If the MRL facilities are not enough for you, we have various IT suites across the campus. Level 1 in Geoffrey Manton building has a full IT Suite with PCs and Macs to use, which is open from 9am-8pm. Our 75 million pounds Business School building has various IT suites, with latest hardware and software to use, those suites are open till 9am-10pm.
Library
24 hour library for students who wish to borrow books, eBooks and journals can do so by their Student ID. Depending on the book labels inside the cover, if it’s red then you are only allowed to have the book for 1 week and if its white label, then you can have the book for whole month. There is online system for our students to browse all our books and they can get them reserved. Also they can access online eBooks and journals within the library online system. Students can even loan out laptops from the library to take home and use.
The library itself have five levels, full of books and IT suites for Manchester Metropolitan University students, they have dedicated study areas, depending on your preferences whether you want to work in peace or work as group.
Digitial Innovation Shed
This year we have introduced something new called the Digital Innovation aka ‘The Shed.’ We are creating space for our students to collaborate with various students from other degrees to collaborate together on projects and make space for the new market in the Industry. Whether you come from English, Computing, Computer science and many more, you all have background of digital. Digital is not just in web world, it has vastly grown in different aspects throughout various sectors. Also in the shed we host events, where well known speakers and industry people come in to talk to our students. We get our students to mix in with the industry people, so it’s easier for them to get to know what is out there for them in the real world.
Finally all these facilities have printers from colour to black and white. We also have scanners as well. Throughout the whole University Campus we have Wi-Fi access which is a bonus! For those students who want to save their internet data.


{% extends "layout.html" %} {% block body %}
| Critical Information | Description | Confidentiality critical | Integrity critical | Availability critical | RTO | RPO | Information Owner |
| {{asset.name}} | {{asset.description}} | {{asset.criticality_c}} | {{asset.criticality_i}} | {{asset.criticality_a}} | {{asset.rto_days}} | {{asset.rpo_days}} | {{asset.owner}} |
| Container | Critical Information | Threats | Controls |
| {{container.name}} |
|
|
{% for control in container.controls %}
{{control}} {% endfor%} |
| Threat | Description | Affected Information | Impact | Probability | Risk | Sufficient Control implemented |
| {{threat.threat_name}} | {{threat.threat_description}} |
|
{{threat.score_impact}} | {{threat.score_probability}} | {{threat.score_risk}} | {{threat.is_controlled}} |
| Control ID | Control Name |
| {{control._id}} | {{control.name}} |


--- layout: post title: On String Comparison Conditions… date: 2009-08-02 12:00:00.000000000 +02:00 categories: [] tags: - String Comparison - Tips & Tricks status: publish type: post published: true meta: _edit_last: '1' author: login: admin email<EMAIL_ADDRESS>display_name: Philippe first_name: Philippe last_name: Vlérick excerpt: !ruby/object:Hpricot::Doc options: {} ---
When I was doing String comparison, I always use to do it this way:
String s = "Philippe"; if (s.Equals("Philippe")) { //Not relevant }
For me, it seems the natural way to write that condition. You can read it out loud as “if s is equal to Philippe”.
However, as the String could come as null, I always ended up with something like this:
if (s != null && s.Equals("Philippe")) { //Not relevant }
This way, the s.Equals wouldn't throw a NullReferenceException in case of s being null. Again, reading it out loud would be: “if s is not null and s is equal to Philippe”.
And what about doing this:
if ("Philippe".Equals(s)) { //Not relevant }
It feels a lot less natural to me, but the benefits are worth it. Only one condition and no risk of NullReferenceException. If s is null, this will simply return false. It would read as “if Philippe is equal to s”, which doesn’t sound too good, but doesn’t hurt code’s readability.
Just a small trick I wanted to share. I figured it out while reading someone else’s code, which is always interesting.


Width: Enter the width of roads, in meters.
Driveway fraction: Enter the fraction of the road that makes up the driveway (for example, 0.6 means that 60% of the road is used for driving, while 40% may be for pedestrians and is potentially elevated by sidewalks).
Layer: Enter the layering with respect to ground level (for example to offset bridges by a certain amount, if above 1, or offset culverts by a certain amount, if below 0).
Layer height: Enter the height in meters per layer.
Road offset: Enter the offset height in meters of roads with respect to surrounding terrain. Can be used for elevated roads that potentially block water flow.
Sidewalk offset: Enter the offset height in meters of sidewalks. Can be used for elevated sidewalks and result in road conveyance capacity.


{% extends "layout.html" %} {% block page_title %} Registered Traveller {% endblock %} {% block content %} Back Sign out Which of these applies to you? Select which of these applies to you Spouse or civil partner of a British citizen Spouse or civil partner of an EEA citizen Spouse or partner of someone with a UK visa Continue {% endblock %}