In general, though, lenders can charge interest as often as they like. Interest is frequently compounded monthly (12 times a year) or daily (365 times a year). In these cases, we use basically the same formula - with two main changes.
If you pay 12% interest over the year, but you're being charged interest each month, you wouldn't pay 12% interest every month! Instead, we divide the interest rate by the number of times we compound each year. 12% annual interest would become
We also need to increase the power on our exponent. If you compute interest every month for 10 years, you're computing interest 12*10 = 120 times overall. If you're compounding daily, you would have 365*10 = 3650 times overall.
Making these two changes gives us our formula for Compound Interest.
in-context


Labour candidate in Aberdeenshire quits in anti-Semitism row
A Labour candidate in Aberdeenshire has quit following a row over anti-Semitism.
Kate Ramsden stood down in the Gordon constituency after the Jewish Chronicle highlighted a blog post from her.
It said she compared Israel to an abused child who becomes an abusive adult.
Another Scottish Labour candidate, Frances Hoole, has also been dropped over a social media post attacking her SNP opponent.
Scottish Labour's ruling body had decided to reconsider Ms Ramsden's candidacy.
A Labour Party source said: "We took immediate action to have Kate Ramsden's candidacy reconsidered in light of the material that we found.
"Staff in Labour HQ found these posts when conducting extra due diligence checks.
"Staff immediately alerted Scottish Labour, who informed the Scottish Executive Committee, which decided to re-interview Kate Ramsden about the new posts that had been found.
"Kate Ramsden stood down as a candidate and is therefore not a Labour candidate in the election."
Apology
Ms Ramsden has published an apology for her blog.
She said: "I have taken the decision to step down as Labour Party candidate for Gordon, following criticism of a blog written at the height of the bombardment of Gaza by the government of Israel.
"I can see why many Jewish people have been hurt by my words.
"That was never my intention and I apologise unreservedly."
Frances Hoole was earlier dropped as Labour's candidate in Edinburgh South West.
The party refused to endorse Ms Hoole after she posted an image on social media of her SNP rival, Joanna Cherry, being targeted with a bleach spray.
Frances Hoole has apologised.


I don't know that I have felt hurt by stereotype threat much in my life, but I have heard of minorities experiencing this a little. For example, I have talked to women that grew up a while ago who felt out of place or very nerdy being intellectual even though they were very smart. They just never embraced that part of their skillset much because society had not embraced that part of them much. Instead, they emphasized being pretty or other things. This saddened me. I would imagine similar issues may crop up for other stereotyped groups. At a workplace, I would try to welcome intellect and collaboration from all types of people and celebrate their achievements in any area of life, as we would do for others.


Sunseekers spring into action as migrants arrive at Spanish beach
by Christophe PitiotSunbathers soaking up the rays in the Canary Islands joined forces with emergency workers on November 29 after a rickety boat carrying two dozen migrants landed unexpectedly on a beach in the Spanish archipelago.
The migrants, among them three children and one pregnant woman, arrived midday on the rocky shores of the Aguila beach in Gran Canaria.
Seemingly exhausted, cold and some still in a state of shock after weathering the Atlantic, rescue services handed out thermal blankets, food and water. Red Cross officials said the migrants were from northern and sub-Saharan Africa.
Sunseekers also sought to help out, with one cradling a weary woman in her lap as another swimsuit-clad woman fed her water.
Some 27,594 migrants have arrived in Spain so far this year by mid-November, according to data from the Interior Ministry, a decrease of more than 50% from the same period last year.
The popular tourist destination of Canary Islands, however, has seen an increase of 22% in arrival this year, with 1,493 migrant arrivals so far registered by mid-November.


Call me Ishmael.
I stuffed a shirt or two into my old carpet-bag
Entering that gable-ended Spouter-Inn


jass.generators
Class TwoMassModel.Vars
java.lang.Object
jass.generators.TwoMassModel.Vars
- Enclosing class:
- TwoMassModel
public class TwoMassModel.Vars
- extends java.lang.Object
Parameters of the model (which are themselves parametrized). See
Sondhi and Schroeter, "A hybrid Time-Frequency Domain
Articulatory Pseech Synthesizer", IEEE Trans. Acoust., Speech,
and Signal Processing, Vol ASSP-35, no 7, July 1987, Table I., p
958. These are converted to SI units here.
When settting new values the old values are remembered and inside the
audio loop the interpolated values are computed.
|
Method Summary |
void |
interpolateVars(double lambda)
Compute the interpolated vlaues using interpolation parameter lambda in [0 1] |
void |
setControlPars(double ps,
double q,
double A1,
double gs)
Set the dimensionless control parameters. |
void |
setVars()
Calculate non-constant parameters from control parameters. |
| Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
m1
public double m1
m2
public double m2
d1
public double d1
d2
public double d2
etak1
public double etak1
etak2
public double etak2
etah1
public double etah1
etah2
public double etah2
h1
public double h1
h2
public double h2
k1
public double k1
k2
public double k2
kc
public double kc
mu
public double mu
rho
public double rho
r1open
public double r1open
r1closed
public double r1closed
r2open
public double r2open
r2closed
public double r2closed
Ag0
public double Ag0
Ag01
public double Ag01
Ag02
public double Ag02
Ag01_old
public double Ag01_old
Ag02_old
public double Ag02_old
interpolatedAg01
public double interpolatedAg01
interpolatedAg02
public double interpolatedAg02
A1
public double A1
lg
public double lg
gng
public double gng
Rec2
public double Rec2
q
public double q
gs
public double gs
q_old
public double q_old
interpolatedQ
public double interpolatedQ
ps
public double ps
ps_old
public double ps_old
interpolatedPs
public double interpolatedPs
p1
public double p1
png
public double png
TwoMassModel.Vars
public TwoMassModel.Vars()
interpolateVars
public void interpolateVars(double lambda)
- Compute the interpolated vlaues using interpolation parameter lambda in [0 1]
setVars
public void setVars()
- Calculate non-constant parameters from control parameters.
setControlPars
public void setControlPars(double ps,
double q,
double A1,
double gs)
- Set the dimensionless control parameters.
- Parameters:
ps
- subglottal lung pressureq
- pitch factorA1
- input area of vocal tractgs
- damping factor from Sondhi-Schroeter


JStudent is meant to be a learning tool for people who have some programming experience, but want some practice programming in JS.
It's also for more experienced programmers to have some fun with interactive intermediate programming challenges.
JStudent was inspired by 4Clojure, a similar tool for the Clojure programming language.
In order to encourage good programming practices, code is evaluated using strict mode.
Among other things, this means that you need to use var
to declare variables or you will get errors.
JStudent is best viewed with a modern browser, and might not work with older browsers.
JStudent is totally client-side, so you can download the whole thing at http://github.com/j201/jstudent and use it offline.
You can contact me at j201.alex@gmail.com.


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{appname}
Ramona web administration console
User not authenticated!
The provided username and password does not match the configuration in
[{configsection}]
section.
Please review the configuration and try to login again.


Instance structure for the Q31 DCT4/IDCT4 function. More...
#include <arm_math.h>
Public Attributes | |
| uint16_t | N |
| length of the DCT4. More... | |
| uint16_t | Nby2 |
| half of the length of the DCT4. More... | |
| q31_t | normalize |
| normalizing factor. More... | |
| q31_t * | pTwiddle |
| points to the twiddle factor table. More... | |
| q31_t * | pCosFactor |
| points to the cosFactor table. More... | |
| arm_rfft_instance_q31 * | pRfft |
| points to the real FFT instance. More... | |
| arm_cfft_radix4_instance_q31 * | pCfft |
| points to the complex FFT instance. More... | |
Instance structure for the Q31 DCT4/IDCT4 function.
| uint16_t arm_dct4_instance_q31::N |
length of the DCT4.
| uint16_t arm_dct4_instance_q31::Nby2 |
half of the length of the DCT4.
| q31_t arm_dct4_instance_q31::normalize |
normalizing factor.
| arm_cfft_radix4_instance_q31* arm_dct4_instance_q31::pCfft |
points to the complex FFT instance.
| q31_t* arm_dct4_instance_q31::pCosFactor |
points to the cosFactor table.
| arm_rfft_instance_q31* arm_dct4_instance_q31::pRfft |
points to the real FFT instance.
| q31_t* arm_dct4_instance_q31::pTwiddle |
points to the twiddle factor table.


A constant voltage of 10 V is applied to a temperature dependent resistor of 10*(1+(T-20C)/(235+20C)) Ohms, whose losses v**2/r are dissipated via a thermal conductance of 0.1 W/K to ambient temperature 20 degree C. The resistor is assumed to have a thermal capacity of 1 J/K, having ambient temperature at the beginning of the experiment. The temperature of this heating resistor is held by an OnOff-controller at reference temperature within a given bandwidth +/- 1 K by switching on and off the voltage source. The reference temperature starts at 25 degree C and rises between t = 2 and 8 seconds linear to 50 degree C. An appropriate simulating time would be 10 seconds.


Customer 360 degree view
Connect with customer using web and other channels and model behaviour in real-time to determine evolving needs.
We deliver a platform-based service that utilizes the latest technology to reduce friction coupled with deep domain expertise to create insights
Connect with customer using web and other channels and model behaviour in real-time to determine evolving needs.
Enhance customer experience through social media integration and historical visit-stream patterns. Synergise offers/service actions across channels to identify optimal mapping.
Create coherent metrixs to understand bottlenecks. Identify reasons for process variation and solve for problems before they impact customers.
Embed intelligent analytic capability into every sales/ service interaction. Build timely and effective triggers for sales force to act on opportunities/ threats.
Determine exposure and quantify impact of market rate movements, credit quality changes, and supply chain/ operational risks. Evaluate risk of new customers/ business and design - strategies to tailor underwriting outcomes to risk policy and growth objectives.
Collate internal/ external data and create meaningful aggregate measures in order to determine key risk drivers and their impact on the portfolio in real-time.
Demonstrate robust risk management encompassing accurate data, fine-tuned predictive models, detailed audit trails, and metrics for effective governance/ control. Identify and aggregate all material exposures across the firm and model stress scenario revenues/ losses to comply with CCAR and Basel provisions.
Use structured/ unstructured data to anticipate vulerabilities and detect new fraud patterns. Leverage advanced machine learning and bild highly targeted models to protect customers and capital.
Assess existing analytics capability, articulate a vision for analytics, and help execute the roadmap with technology, training and surge support.
Embed latest research to develop algorithms for complex, open-ended problems; build test & learn capability to evaluate competing predictive methods and implement efficient solutions.
Create transparency into data and analytics and build storyboards of insights to drive strategic business impact.
Cleanse, enhance, and transform data across the enterprise through a cross - functional, cross platform fluid data management layer.
Simplify and automate operational reporting. Ennhance portfolio reports with in-built predictive forecasts and root cause analyses.
Build visually rich, real-time dashboards for management decision with interactive UI for drill-downs and scenarios.


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Veggies Only
Toppings
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Banana Peppers
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Condiments
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Sides (Max 2)
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PEEKY CROPPED
QUICK OVERVIEW:
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. When an unknown printer took a galley of type.
There are many variations of passages of Lorem Ipsum available, but the majority have suffered alteration in some form, by injected humour, or randomised words which don't look even slightly believable. If you are going to use a passage of Lorem Ipsum, you need to be sure there isn't anything embarrassing hidden in the middle of text.
There are many variations of passages of Lorem Ipsum available, but the majority have suffered alteration in some form, by injected humour, or randomised words which don't look even slightly believable. If you are going to use a passage of Lorem Ipsum, you need to be sure there isn't anything embarrassing hidden in the middle of text.
- - 6.1 oz. 100% preshrunk heavyweight cotton
- - Shoulder-to-shoulder taping
- - Double-needle sleeves and bottom hem
It uses a dictionary of over 200 Latin words, combined with a handful of model sentence structures, to generate Lorem Ipsum which looks reasonable.


Chapter 4 Simple Hypothesis Testing: Chi-Squared, T-tests, and ANOVA
4.1 Hypothesis Testing
In science, we are interested in testing hypotheses. Statistics allows us to formally test our hypotheses. In statistical testing we have a null hypothesis (\(\text{H}_0\)) and an alternative hypothesis (\(\text{H}_1\)). We assume the null hypothesis is true and try to find the probability of what we have observed (or something more extreme). If our observations are very unlikely (assuming the null hypothesis is true) then we reject the null hypothesis in favor of the alternative hypothesis.
For example:
\[\text{H}_0: \text{It is summer}\] \[\text{H}_1: \text{It is not summer}\]
Our observed data for today is a maximum temperature of -20C. Assuming it is summer, how likely is it that today’s maximum temperature will be -20C? Not very likely! We therefore reject \(\text{H}_0\) (“it is summer”) in favor of \(\text{H}_1\) (“it is not summer”). That is, we conclude that it is not summer today.
4.2 Which Method To Use?
Deciding on the appropriate statistical method is (in principle) fairly easy. You just look at the:
- Aim (hypothesis testing or estimation of effect size?)
- Outcome type (continuous, binary, categorical, censored, count)
- Exposure (type)
- Parametric assumptions
- Dependencies in the data
and we then (essentially) use a flowchart.
4.3 Chi-Squared Test
A Chi-Squared test is used to test if two categorical variables are associated with each other.
4.3.1 Aim/Outcome/Exposure/Parametric/Dependencies
Aim: Hypothesis testing (testing if two categorical variables are associated with each other.)
Outcome: Categorical variable
Exposure: Categorical variable
Parametric assumptions: No
Dependencies: None (all observations independent)
4.3.2 Examples
- Testing if people’s country of origin (Norway/Not Norway) is associated with tuberculosis status (never had TB/has had TB)
- Testing if people’s region of origin (Europe/North America/South America/Other) is associated with marital status (Single/Married/Divorced)
- Testing if county of residence (Oslo, Akershus, etc) is associated with post-surgery infection status (No infection/mild infection/deep infection)
4.4 One Sample T-Test
A one sample t-test tests if the mean of a continuous variable differs from a specified value (generally zero)
\[\text{H}_0: \mu = 180\] \[\text{H}_1: \mu \ne 180\]
Or rephrased:
\[\text{H}_0: \text{The average height of men is equal to 180cm}\] \[\text{H}_1: \text{The average height of men is not equal to 180cm}\]
4.4.1 Aim/Outcome/Exposure/Parametric/Dependencies
Aim: Hypothesis testing (test if the mean of a continuous variable differs from a specified value)
Outcome: Continuous variable
Exposure: Does not exist
Parametric assumptions: Outcome is distributed as a Normal distribution
Dependencies: None (all observations independent)
4.4.2 Example 1
\(\rightarrow\) Testing if the average BMI of Norwegians is equal to 23
\[H_0: \mu_{\text{bmi}} = 23\] \[H_1: \mu_{\text{bmi}} \ne 23\]
4.4.3 Example 2
\(\rightarrow\) Testing if the average pH of tap water is equal to 7
\[H_0: \mu_{\text{pH}} = 7\] \[H_1: \mu_{\text{pH}} \ne 7\]
4.4.4 Example 3
\(\rightarrow\) \(H_0:\) \(H_1:\)
4.4.5 Example 4
\(\rightarrow\) \(H_0:\) \(H_1:\)
4.5 Two sample T-Tests
A t-test tests if the mean of a continuous variable differs between two groups. There are two kinds of two-sample t-tests: paired and unpaired.
4.6 Two-sample Paired T-Test
A paired t-test is a special case where we have \(N\) participants, and each participant has two observations (generally “before experiment” and “after experiment”). We want to test if the mean of outcome variable differs between “after” and “before”.
For example, in a weight-loss experiment, we have \(N\) participants and we want to see if the average “after weight” is different from the average “before weight”.
This is done by subtracting the outcome from one group (“before weight”) from the outcome in the other group (“after weight”) for each person (“difference in weight”), and then performing a one-sample t-test to see if the mean of this variable is different from zero.
\[\text{H}_0: \mu_{\text{after}-\text{before}} = 0\] \[\text{H}_1: \mu_{\text{after}-\text{before}} \ne 0\]
4.6.1 Aim/Outcome/Exposure/Parametric/Dependencies
Special preprocessing of data: for each participant subtract the “before” observation from the “after” observation
Aim: Hypothesis testing (test if the mean of a continuous variable measured twice for each participant differs between “before” and “after”)
Outcome: (“after weight” minus “before weight”) continuous variable
Exposure: \(\text{group}_\text{after}\) vs \(\text{group}_\text{before}\)
Parametric assumptions: Outcome is distributed as a Normal distribution
Dependencies: Paired data
4.6.2 Example 1
\(\rightarrow\) Testing if there is a difference in blood pressure before and after treatment (measured on the same person)
\[\text{H}_0: \mu_{\text{BP after}-\text{BP before}} = 0\] \[\text{H}_1: \mu_{\text{BP after}-\text{BP before}} \ne 0\]
4.6.3 Example 2
\(\rightarrow\) Testing if there is a difference in mouse DNA damage before and after irradiating (measured on the same mouse)
\[\text{H}_0: \mu_{\text{DNA damage after}-\text{DNA damage before}} = 0\] \[\text{H}_1: \mu_{\text{DNA damage after}-\text{DNA damage before}} \ne 0\]
4.6.4 Example 3
\(\rightarrow\) \(H_0:\) \(H_1:\)
4.6.5 Example 4
\(\rightarrow\) \(H_0:\) \(H_1:\)
4.6.6 Non-Parametric Equivalent
Wilcoxon signed-rank test. This should be used when the Normality assumption fails.
4.7 Two-sample Unpaired T-Test
An unpaired t-test is where we have two independent groups of \(N_1\) and \(N_2\) participants, and we want to test if the mean of the outcome variable differs between \(\text{group}_1\) and \(\text{group}_2\).
\[\text{H}_0: \mu_0 = \mu_1\] \[\text{H}_1: \mu_0 \ne \mu_1\]
Or rephrased:
\[\text{H}_0: \text{The average height of men is equal to the average height of women}\] \[\text{H}_1: \text{The average height of men is not equal to the average height of women}\]
4.7.1 Aim/Outcome/Exposure/Parametric/Dependencies
Aim: Hypothesis testing (test if the mean of a continuous variable differs between \(\text{group}_1\) and \(\text{group}_2\))
Outcome: continuous variable
Exposure: \(\text{group}_1\) vs \(\text{group}_2\)
Parametric assumptions: Outcomes for each group are distributed as a Normal distribution
Dependencies: None (all observations independent)
4.7.2 Example 1
\(\rightarrow\) Testing if the average blood pressure in people who didn’t receive treatment is different from people who did receive treatment
\[\text{H}_0: \mu_{\text{BP treatment}} = \mu_{\text{BP no treatment}}\] \[\text{H}_1: \mu_{\text{BP treatment}} \ne \mu_{\text{BP no treatment}}\]
4.7.3 Example 2
\(\rightarrow\) Testing if the average DNA damage in mice that weren’t irradiated is different from mice that were irradiated
\[\text{H}_0: \mu_{\text{DNA radiation}} = \mu_{\text{DNA no radiation}}\] \[\text{H}_1: \mu_{\text{DNA radiation}} \ne \mu_{\text{DNA no radiation}}\]
4.7.4 Example 3
\(\rightarrow\) \(H_0:\) \(H_1:\)
4.7.5 Example 4
\(\rightarrow\) \(H_0:\) \(H_1:\)
4.7.6 Non-parametric equivalent
Mann–Whitney U test (also called the Mann–Whitney–Wilcoxon (MWW), Wilcoxon rank-sum test, or Wilcoxon–Mann–Whitney test). This should be used when the Normality assumption fails.
4.8 ANOVA
ANOVA is an extension of the two-sample unpaired t-test. We have X independent groups of participants, and we want to test if the mean of the outcome variable differs between groups.
4.8.1 Aim/Outcome/Exposure/Parametric/Dependencies
Aim: Hypothesis testing (test if the mean of a continuous variable differs between some groups)
Outcome: continuous variable
Exposure: \(\text{group}_1\) vs \(\text{group}_2\) (vs \(\text{group}_3\) …)
Parametric assumptions: Outcomes for each group are distributed as a Normal distribution
Dependencies: None (all observations independent)
4.8.2 Example 1
\(\rightarrow\) Testing if average BMI levels differ across Scandinavia
\[\text{H}_0: \mu_{\text{Norway}} = \mu_{\text{Denmark}} = \mu_{\text{Sweden}}\] \[\text{H}_1: \mu_{\text{Norway}} \ne \mu_{\text{Denmark}} \text{ and/or } \mu_{\text{Norway}} \ne \mu_{\text{Sweden}} \text{ and/or } \mu_{\text{Denmark}} \ne \mu_{\text{Sweden}}\]
4.8.3 Example 2
\(\rightarrow\) Testing if average water pH levels differ between East and West Oslo
\[\text{H}_0: \mu_{\text{East Oslo}} = \mu_{\text{West Oslo}}\] \[\text{H}_1: \mu_{\text{East Oslo}} \ne \mu_{\text{West Oslo}}\]
4.8.4 Example 3
\(\rightarrow\) \(H_0:\) \(H_1:\)
4.8.5 Example 4
\(\rightarrow\) \(H_0:\) \(H_1:\)
4.8.6 Non-parametric equivalent
Kruskal–Wallis test


1. Design Concepts of the Intermediate Representations in COINS
1.1. Preface
There are two intermediate representations in COINS, namely, High level
intermediate representation HIR and low level intermediate representation LIR.
Following documents give the general view of them:
The IR is intended to be the super class of HIR and LIR. The interface of
symbol table information supplements them. All compiler modules are
linked with each other via these intermediate representations.
Above outline documents describe the design policies and intentions, but
do not give the specifications of individual methods. Readers are asked to
refer to the interface specifications for that purpose as explained later.
1.2. Design policy of the intermediate representations
1.2.1. Overall policy
<IP_ADDRESS>. The role of intermediate representations
In general, a compiler transforms a source program to an object program.
The transformation is done in several phases such as syntax analysis, code
optimization, code generation, etc. In this process, the source program is
transformed to some intermediate representation that is easier to handle
by compiler modules than source program. Ad hoc representations corresponding
to combinations of neighboring modules will make the compiler complicated and
difficult to be expanded. If we can settle an intermediate representation
as an interface common between all modules of the compiler, then the compiler
can be simplified and made easy to be expanded.
COINS is the compiler infrastructure that is intended to be expanded to build
various compilers by adding or modifying compiler modules. It is intended to
build compilers for many languages and also compilers for many machines.
It is also intended to build compilers generating good object code and
compilers having distinguished characteristics such as parallelization, etc.
How to define an intermediate representation that fits to such requirements
is the problem.
<IP_ADDRESS>. Requirements for intermediate representations
In compiling a source program into object code, there is a sequence of
transformations which are carried out by compiler modules such as syntax
analyzer, flow analyzer, optimizer, parallelizer, register allocator, code
generator, and so on. By adding or replacing some of them we can construct
various compilers. If we can define an intermediate representation that
has the role of common interface between compiler modules, then it will become
easy to add, delete, or replace compiler modules to construct a new compiler.
To realize such interface, requirements for the intermediate representation
are as follows:
- Capable of representing the structure and meaning of given program
in detail.
- Suitable for processing of analysis and transformation.
- Suitable for generating efficient object code.
- Highly extensible.
- Suitable for high speed processing in short memory space.
As for COINS, more specifically speaking,
- Extensible to many imperative languages.
- Extensible to many machines.
- Afford to be extended for future needs in long period.
- Information for optimization, parallelization etc. can be easily attached.
are required.
1.2.2. The Levels of the Intermediate Representations
There might be several levels in defining intermediate representation, from
high level near to source language to low level near to machine language.
In the course of program transformation, there are several processings
that are suitable to be designed in a level near source language, and also
there are several other processings that are suitable to be designed in a level
near machine language.
Processings suitable for the level near source language are
- dependency analysis for optimization and parallelization
- loop transformation
- inline expansion
and so on.
Processings suitable for the level near machine language are
- register allocation
- instruction scheduling
- memory access optimization
and so on.
If we define an intermediate representation at only one level, then some
processings are suitable for it but there will be some of other processings
that are not suitable for it. It is better to define high level intermediate
representation and low level intermediate representation. There might be an
opinion that defining two levels of intermediate representation causes to
increase the number of transformations in the course of compilation and
makes it impossible to build one-pass compiler. It is true but one-pass
compilers can not generate good code and it will be permissible to exclude
such compilers from the covering range of COINS. Practical compilers have
many passes to produce good code, and so, it will be permissible to insert
a pass to convert intermediate representation from high level to low level.
There might be another opinion that two levels of intermediate representation
requires two set of modules to manage intermediate representations and causes
to increase the amount of development. The size of modules to manage intermediate
representations will not be so large and its development is done only once
by implementers of the infrastructure and if the complexity of some compiler
modules decreases, then the total amount of development will be decreased
since such decrease will be seen in many compilers developed by using the
infrastructure, and it means that many users of the infrastructure will
have benefit.
There will be another choice to define more than two levels of intermediate
representations. Intermediate representation may have many attributes that
are filled or deleted in the process of compilation and some fields may be
replaced with different kind of items. For example, an operand of an expression
may be changed from source program variable to abstract register and then
changed to physical register without changing basic structure of the expression.
This means that one intermediate representation can cover many phases of
compiling process and requests for more than two levels of intermediate
representation will not be so strong.
It may be necessary to define more than two levels of intermediate
representations for some complicated languages. For example, C language
has many complicated operators and assignment statement embedded in expressions,
therefore it may be better to have another intermediate representation near
C language. For another example, object oriented languages require complicated
treatment of objects and methods, therefore it may be better to have another
intermediate representation suitable to object oriented languages.
In designing intermediate representations of COINS, we decided to have
two levels of intermediate representations, that is,
high level intermediate representation HIR and
low level intermediate representation LIR
as ones applicable to all cases. HIR is an intermediate representation
that can cover analysis and transformation on the level of many imperative
languages. LIR is an intermediate representation that can cover analysis and
transformation on the level of many target machines.
As for other levels of intermediate representation, it will be decided to be
introduced only when its necessity become quite obvious for some language
groups or for some architecture groups.
1.2.3. Treatment of dependencies
It is desirable that intermediate representations are independent of
languages and machines. If it is possible, then many compiler modules can be
shared between many compilers corresponding to various combinations of source
languages and target machines, except for language analyzers corresponding to
specific language and last phases of code generation corresponding to specific
target machine. In rigorous meaning, it will be very difficult.
Some language dependencies will remain in intermediate representations.
For example, a string constant may contain language specific mark or size
information to delimit the sequence of characters contained in the string.
Some machine dependencies will remain in intermediate representations.
For example, the size of integer number may be 32 bits or 64 bits depending
on target machine even for the same source program.
It can be noticed that such differences are one of variations for each
kind of language items. We can define the grammar of an intermediate
representation as one independent of languages and machines leaving possibility
of language/machine dependencies for some symbols used in the representation.
In terms of programming languages, the interface specifications are
completely independent of source languages and target machines but their field
values may have some dependencies on languages and machines, that is,
the specifications of the intermediate representation is independent of
languages and machines but its instance may have dependencies.
In this approach, most compiler modules can be implemented almost independent
of languages and machines because the interface specifications of the
intermediate representation are independent of languages and machines. Some
small portions of compiler modules may have dependencies on languages or machines
to handle the contents of fields having dependency. In this approach,
the intermediate representation generated for some specific machine can not be
applied to generate machine code of other architecture having different
characteristics because the instance of the intermediate representation may
have dependency on the target machine. It is the point that differs from the
bytecode of Java.
We take above approach and do not take the approach of Java bytecode
because above approach will be suitable for generating highly efficient
object code compared to the approach of the bytecode.
1.2.4. IR, HIR, LIR
<IP_ADDRESS>. IR
In the first stage of COINS development, HIR and LIR are designed to be
a subclass of an intermediate representation named IR in order to make some
modules common between HIR and LIR.
IR _____ HIR
|
|___ LIR
Actually, the first version of LIR was implemented as the subclass of IR
and a flow analyzer has been built to be able to analyze both HIR and LIR.
The second version of code generator has been implemented based on new
implementation of LIR, although its basic design is the same to that of LIR
version 1.
At present, the new LIR is not yet made a subset of IR, and so, the hierarchy
of IR is
IR _____ HIR
LIR
It is a future work to make it possible to build modules that are applicable
both to HIR and LIR.
<IP_ADDRESS>. HIR
If we pursuit what is essential in programming languages, we can get the
image of an elementary programming language that is common to many imperative
languages. HIR is based on such elementary language which contains most of
high level language concepts common between imperative languages. The document
Outline of High level Intermediate Representation HIR
describes about the elementary language.
Too much decomposition of high level language concepts will make the
intermediate representation not suitable for program analyses based on
high level language concepts. Considering it, in HIR, array element expressions
are not decomposed to pointer expressions. Compound expressions such as
compound assignment in C are decomposed into elementary expressions because
such expressions requires special treatment for C language in data flow analysis,
etc. that is desirable to be language independent.
It will be necessary to expand current specifications of HIR when we want
to apply COINS to languages based on different concepts. For example,
object oriented languages may require special considerations in compilation.
How to treat them in HIR is left as a future problem.
<IP_ADDRESS>. LIR
LIR is designed to make many compiler components common between various
machines. Code optimizer is one of major components in practical compilers. For
the code optimization, efficient use of registers is very important. Considering
it, registers should be explicitly represented in LIR. In the bytecode of Java,
portability of object code is a major concern and does not represent registers
explicitly. In COINS, importance is attached to the code optimization and
applicability to many machines is pursuit by increasing retargetability.
LIR is, there for, based on a model of abstract register machine. In this
model, computation is carried on by evaluating expressions and setting results
to variables. A variable has its value in memory or register. Expressions
may stand for a constant, a variable, or an operation of arbitrary complexity
using expressions as their operands. There are control operations such as
jump, conditional branch, and call. Most computers have such elementary
operations as add, subtract, multiply, divide, shift, compare, and so on.
Operators in LIR represent such operations.
While elementary operations are similar between machines, the mechanism of
call/return and procedures of entry/exit for subprograms differ greatly machine
by machine. To deal with such variety, call, return, prolog, epilog operators
are provided in LIR and their expansion to machine instruction sequence is
left as the work of retargeting.
In currently prevailing architectures, data are represented as binary integer
or floating point number. Character code and boolean value can be represented as
a binary integer of small size. LIR representation of data values takes these
forms. The length of data is represented by the number of bits required to
represent it. The address of memory is represented in byte of 8 bit length.
Data may be a scalar or an aggregate. For example, 64 bits data may represent
64 bit integer or 4 element array of 16 bit integer. In LIR, aggregate data is
distinguished from scalar data.
LIR is defined as a complete low level programming language. It can be
expressed in textual form as well as in internal representation suitable for
processing by compiler modules. Usually, a program in LIR is constructed by
the HIR-to-LIR translator but it can be input to or output from the compiler
in textual form.
1.2.5. Retargeting
There will appear various kind of computer architectures. COINS has retargeting
feature so that it can be applied to new computer architecture easily. COINS
generates code generator for a new machine based on specifications named
Target Machine Description (TMD). The code generator looks for the best fit
combination of LIR subtree pattern and corresponding target machine code
sequence to generate machine code.
There are many passes of LIR transformations such as optimizers and register
allocator. In this process of transformation, the fields of LIR will change
according to the target machine but the grammar of LIR is the same during
these transformations. For example, an operand may be changed from variable to
abstract register and then real register of the target machine keeping the
structure defined by the LIR grammar. LIR subtrees representing an assignment
statement will be decomposed into a sequence of small subtrees each of which
corresponds to actual instruction of the target machine keeping the structure
defined by the LIR grammar. This means that most of backend modules can be constructed
machine independently except for the last phase that generates assembly language
program of the target machine. Thus, control flow graph constructor, SSA
optimizer, register allocator, instruction scheduler, etc. are made to be
common between all target machines.
In many cases, code generators for new machines generating fairly good code
have been developed in 3 to 6 months.
1.3. Symbol table
There are such symbols as variables, subprograms, and others in a program.
Since symbols in character strings are not easy to handle, they are registered
in the symbol table and are handled as symbol table entries in intermediate
representations. Attributes of a symbol are attached to its entry in the symbol
table. Many of these attributes are decided by declarative statements and the
structure of the program, or set at the time of syntax analysis and semantic
analysis.
In LIR, attributes of symbols are rather simple and their declarations are
contained in LIR. In HIR, attributes of symbols are complex reflecting the
complexity of declaration statements.
HIR does not hold declaration statements of symbols but attributes of symbols
are retained in the symbol table. The symbol table of HIR retains information
not only of variables and subprogram but also of constants, types, and all
other symbols, including compiler generated temporary variables and labels, and
these symbols are referred to during compilation. It may be thought that
constants need not be included in the symbol table. A constant is, however, not
always represented in one word, and the precision and the range a constant
represents vary among different compiling environments and target machines.
Complexity of compiler development will be a little lightened by including
constants in the symbol table.
Symbols may have attributes that depend on source language or target machine.
Large portions of compiler modules, however, do not distinguish such dependencies.
By hiding such dependencies to the symbol table, greater commonality of the
compiler modules could be realized.
1.4. Links to detailed specifications
There are several other documents that explain internal representations as
it is mentioned in Preface. The outline documents listed up in Preface do
not explain details necessary for coding modules to be integrated in COINS.
For that purpose, it is necessary to read interfaces of HIR, etc.
The recommended order of reading documents is as follows.
(1) This document
(2) Other outline documents
(3) The Java interfaces for HIR
(4) The Java interfaces which are the subclasses of above Java interfaces
Interfaces that describes detailed specifications of HIR:
HIR.java interface specifications for HIR
Sym.java interface specifications for the symbol table
Type.java interface specifications for the types
Flow.java interface specifications for the flow information
BBlock.java interface specifications for the basic blocks
to be used in flow analysis
MachineParam.java class for machine dependent information
SourceLanguage.java class for source language dependent information
In developing modules using HIR, users need only to consult the interface
specifications to understand, and need not to go down to the implementation
details. In consulting the interface specifications, it is suggested to first
consult the specifications in the uppermost classes listed above. Only when
further specifics are required, user may look for appropriate lower classes.
In other words, it is strongly recommended to try first and make most use of
the applicable methods in the uppermost classes, and not to start from a subclass. If one start reading from lower subclasses, he/she may be misguided to
wrong applications of methods due to the lack of the knowledge
of underlying conventions.
The classes listed below are prepared for overall control of the compiler.
They may be referred to in the intermediate representation specifications.
Driver compiler path control
IoRoot links to input/output related information
SymRoot links to symbol table related matters
HirRoot links to HIR information
LirRoot links to LIR information
FlowRoot links to flow information
Message message output control
Debug debug output control
1.5. Example
In this chapter, intermediate representations for a small program are shown
to give a concrete image for them. Explanation of each item in them will be
given in further documents listed above and in interface modules.
1.5.1. Source program
/* tpsum1.c -- Summation */
int printf(char*, ...);
void getData(int x[100]);
int a[100];
int sum, i;
int main()
{
getData(a);
sum = 0;
for (i = 0; i < 100; i++) {
sum = sum + a[i];
}
printf("sum = %d\n", sum);
return 0;
}
1.5.2. HIR
(prog 1
<null 0 void>
<nullNode 2>
(subpDef 3 void
<subp 4 <SUBP < )> false false int> main>
<null 0 void>
(labeldSt 5 void
(list 6
<labelDef 7 _lab1>)
(block 8 void line 8
(expStmt 9 void line 10
(call 10 void
(addr 11 <PTR <SUBP < <PTR int> )> false false void>>
<subp 12 <SUBP < <PTR int> )> false false void> getData>)
(list 13
(decay 14 <PTR int>
<var 15 <VECT 100 0 int> a>))))
(assign 16 int line 11
<var 17 int sum>
<const 18 int 0>)
(for 19 void line 12
(block 20 void
(assign 21 int
<var 22 int i>
<const 23 int 0>))
<null 0 void>
(labeldSt 24 bool
(list 25
<labelDef 26 _lab5>)
(expStmt 27 bool
(cmpLt 28 bool
<var 29 int i>
<const 30 int 100>)))
(labeldSt 31 void
(list 32
<labelDef 33 _lab6>)
(block 34 void
(assign 35 int line 13
<var 36 int sum>
(add 37 int
<var 38 int sum>
(subs 39 int
<var 40 <VECT 100 0 int> a>
<var 41 int i>)))
(labeldSt 42 void
(list 43
<labelDef 44 _lab3>)
<null 0 void>)))
(expStmt 45 int
<null 0 void>)
(block 46 void
(assign 47 int
<var 48 int i>
(add 49 int
<var 50 int i>
<const 51 int 1>)))
(labeldSt 52 void
(list 53
<labelDef 54 _lab4>)
<null 0 void>))
(expStmt 55 int line 15
(call 56 int
(addr 57 <PTR <SUBP < <PTR char> )> true false int>>
<subp 58 <SUBP < <PTR char> )> true false int> printf>)
(list 59
(decay 60 <PTR char>
<const 61 <VECT 10 0 char> "sum = %d\n">)
<var 62 int sum>)))
(return 63 int line 16
<const 64 int 0>))))
)
1.5.3. LIR
(MODULE "tpsum1.c"
(SYMTAB
("main" STATIC UNKNOWN 4 ".text" XDEF &syminfo "main" "tpsum1.c" 8 0)
("i" STATIC I32 4 ".bss" XDEF &syminfo "i" "tpsum1.c" 6 0)
("sum" STATIC I32 4 ".bss" XDEF &syminfo "sum" "tpsum1.c" 6 0)
("a" STATIC A3200 4 ".bss" XDEF &syminfo "a" "tpsum1.c" 5 0)
("getData" STATIC UNKNOWN 4 ".text" XDEF &syminfo "getData" "tpsum1.c" 19 0)
("printf" STATIC UNKNOWN 4 ".text" XREF &syminfo "printf" "tpsum1.c" 3 0)
("string.5" STATIC UNKNOWN 1 ".text" LDEF &syminfo "const_13" "" 0 0))
(DATA "string.5" (I8 115 117 109 32 61 32 37 100 10 0))
(FUNCTION "main"
(SYMTAB
("returnvalue.3" FRAME I32 4 0)
("functionvalue.4" FRAME I32 4 0))
(PROLOGUE (0 0))
(DEFLABEL "_lab1")
(CALL (STATIC I32 "getData") ((STATIC I32 "a")) () &id ("getData" 0))
(SET I32 (MEM I32 (STATIC I32 "sum") &id ("sum" 1)) (INTCONST I32 0))
(SET I32 (MEM I32 (STATIC I32 "i") &id ("i" 2)) (INTCONST I32 0))
(DEFLABEL "_lab5")
(DEFLABEL "_lab6")
(SET I32 (MEM I32 (STATIC I32 "sum") &id ("sum" 7))
(ADD I32 (MEM I32 (STATIC I32 "sum") &id ("sum" 4))
(MEM I32 (ADD I32 (STATIC I32 "a")
(MUL I32 (MEM I32 (STATIC I32 "i") &id ("i" 5)) (INTCONST I32 4))) &id 6)))
(DEFLABEL "_lab3")
(SET I32 (MEM I32 (STATIC I32 "i") &id ("i" 9))
(ADD I32 (MEM I32 (STATIC I32 "i") &id ("i" 8)) (INTCONST I32 1)))
(JUMP (LABEL I32 "_lab5"))
(DEFLABEL "_lab4")
(CALL (STATIC I32 "printf")
((STATIC I32 "string.5") (MEM I32 (STATIC I32 "sum") &id ("sum" 10)))
((MEM I32 (FRAME I32 "functionvalue.4") &id ("functionvalue.4" 11))) &id ("printf" 12))
(SET I32 (MEM I32 (FRAME I32 "returnvalue.3") &id ("returnvalue.3" 13))
(INTCONST I32 0))
(JUMP (LABEL I32 "_epilogue"))
(DEFLABEL "_epilogue")
(EPILOGUE (0 0) (MEM I32 (FRAME I32 "returnvalue.3")
&id ("returnvalue.3" 14))))
(DATA "sum" (SPACE 4))
(DATA "i" (SPACE 4))
(DATA "a" (SPACE 400)))
1.5.4. Symbol table
SymTable Root
subp printf <SUBP < <PTR char> )> true false int> File tpsum1.c line 3 (flags 6) extern
subp getData <SUBP < <PTR int> )> false false void> File tpsum1.c line 19 (flags 6) public
type <VECT 100 0 int> (flags 12) size 400 elemCount 100 lowerBound 0
var a 0 <VECT 100 0 int> File tpsum1.c line 5 (flags 6) public static
var sum 0 int File tpsum1.c line 6 (flags 9) public static
var i 0 int File tpsum1.c line 6 (flags 9) public static
subp main <SUBP < )> false false int> File tpsum1.c line 8 callList list 0 public
type <VECT 10 0 char> (flags 12) size 10 elemCount 10 lowerBound 0
type <PTR 100 0 int> size 4
SymTable main parent SymTable Root subp main
label _lab1 kind 1 unq:main__lab1 (flags 2)
label _lab2 kind 15 unq:main__lab2 (flags 2)
label _lab3 kind 8 unq:main__lab3 in main (flags 2)
label _lab4 kind 21 unq:main__lab4 in main (flags 2)
label _lab5 kind 6 unq:main__lab5 (flags 2)
label _lab6 kind 7 unq:main__lab6 (flags 2)
SymTable Constant
intC 4 int unq:const_1
intC 1 int unq:const_2
intC 0 int unq:const_5
intC 100 int unq:const_11
intC 400 int unq:const_12
stringC "sum = %d\n" <VECT 10 0 char> unq:const_13 (flags 6) length 10
intC 10 int unq:const_14


| Instructor | Dr. Fernando Campos |
| Location | McKinney Humanities (MH) 3.02.52 |
| Times | Thu 9:00 am - 11:45 am |
| Course | ANT 6973.002 |
| CRN | 34610 |
| Office Hours | Tue/Thu, 2:00-3:30 pm, MH 4.03.44 |
Overview
This course is designed for graduate students in social or natural sciences who would like to gain experience in data visualization and analysis using R. It aims to be accessible to those who are completely new to programming, but also to provide new insights and skills to those who already have experience using R.
Approach
This course will use a mix of traditional and flipped classroom approaches, with new material introduced in reading assignments prior to class while class time will focus on applying those skills to data sets. The course will be activity-focused. Most concepts will be first introduced in assigned readings outside of class, leaving class time to focus on developing practical skills, completing activities, making progress on individual projects. We will cover topics in both data visualization as well as data manipulation. Students are encouraged to work collaboratively in and out of class. The online course schedule provides an overview of the topics covered as well as links to weekly readings, in-class activities, and any lecture material. This schedule is preliminary and always subject to change.
Texts
We will read sections from four openly licensed, online textbooks for this course. Hard copies of the books are not required, but some can be found at the links below.
- Fundamentals of Data Visualization by Claus Wilke.
- Data Visualization: A practical introduction by Kieran Healy. | Hard copy.
- ModernDive: An Introduction to Statistical and Data Sciences via R by Chester Ismay and Albert Y. Kim.
- R For Data Science by Hadley Wickham and Garrett Grolemund. | Hard copy.
Additional reading material will be linked from the schedule.


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Play the selected sound
STATS SOUND
TYPE = DEFAULT* or EXCLAMATION or ASTERISK or HAND or QUESTION
FILE=“sound file”
/HELP
* Required
** Default
HELP displays this help and does nothing else
STATS SOUND TYPE=EXCLAMATION.
This command is only available on Windows.
Specify either TYPE or FILE. If nothing is specified, the Windows Beep sound is produced. FILE must specify a wav file.
Using TYPE, the sound played is whatever is associated with the selected sound according to the settings in the Windows Control Panel Sounds dialog.
If both TYPE and FILE are specified, FILE is played first.
© Copyright IBM Corp. 1989, 2014


Netanyahu indicted for bribery, fraud, breach of trust
by AFPIsrael's attorney general on Thursday formally charged Prime Minister Benjamin Netanyahu in a series of corruption cases, throwing the country's paralyzed political system into further disarray and threatening the long-time leader's grip on power.
Attorney-General Avichai Mandelblit charged Netanyahu with fraud, breach of trust and accepting bribes in three different scandals. It is the first time a sitting Israeli prime minister has been charged with a crime. Mandelblit was set to issue a formal statement later on Thursday.
Allegations against Netanyahu include suspicions he accepted hundreds of thousands of dollars of champagne and cigars from billionaire friends, offered to trade favors with a newspaper publisher and used his influence to help a wealthy telecom magnate in exchange for favorable coverage on a popular news site.
The indictment does not require Netanyahu to resign but is expected to raise pressure on him to step down.
Netanyahu has called the allegations part of a witchhunt, lashing out against the media, police, prosecutors and the justice system. Netanyahu was scheduled to issue a statement later on Thursday.


22
1 Then the Israelites traveled west to the plain of Moab that was in the valley of the Jordan River, across the river from Jericho.
2 But King Balak son of Zippor, who ruled Moab, found out what the Israelites had done to the Amor people group.
3 When he saw that the Israelites were very numerous, he and his people became terrified.
4 So the king of Moab went to the leaders of the Midian people group and said to them, "This huge group of Israelites will wipe out everything around them, like an ox devours grass!"
Balak was the king of Moab.
5 He sent messengers to a prophet named Balaam, who was living in his own area, in Pethor, near the Euphrates River. He sent this message to request that Balaam would come to help him,
"A huge group of people has arrived here from Egypt. It looks like they are covering the entire land! And they have begun to live close to us.
6 Because they are very powerful, we are afraid of them. So please come and curse them for me. Then my army may be able to defeat them and expel them from the land where they are now living.
7 Balak's messengers, who were leaders of both the Moab and Midian people groups, took money with them to pay Balaam in order that he would come and curse the Israelites. They went to Balaam and told him what Balak had said.
8 Balaam said, "Stay here tonight. Tomorrow morning I will tell you whatever Yahweh tells me that I should say to you."
So the leaders from Moab stayed there that night.
9 During the night, God appeared to Balaam and asked him, "Who are these men who are staying with you?"
10 Balaam replied, "Balak, the king of Moab, sent these men to tell me this message,
11 'A huge group of people has come from Egypt, and they have spread all over this area. Please come immediately to curse them. Then I may be able to defeat them and expel them from this area.' "
12 God replied to Balaam, "Do not go with them! I have blessed those people, so you must not curse them!"
13 The next morning, Balaam got up and told Balak's men, "Go back home. But go by yourselves, because Yahweh is not allowing me to go with you."
14 So the men from Moab returned to King Balak and they reported to him, "Balaam refused to come with us."
15 But Balak sent another group of leaders to Balaam. It was a group that was larger and they were more important than the men in the first group.
16 They went to Balaam and told him this,
"This is what King Balak says, 'Please do not allow anything to hinder you from coming here.
17 I will pay you a lot of money if you come, and I will do anything that you ask me to do. Just come and curse these Israelite people for me!' "
18 But Balaam answered them, "Even if Balak would give me a palace filled with silver and gold, I would not do anything to disobey Yahweh, my God.
19 But stay here one more night, like the other messengers did, and I will find out if Yahweh has anything more to say to me."
20 That night God appeared to Balaam again and said to him, "These men have come to request that you go back with them, so you may go with them, but do only what I tell you to do!"
21 So the next morning, Balaam put a saddle on his donkey and he departed with two of his servants along with the men from Moab.
22 Even though God had given Balaam permission to go, he was still angry. So he sent one of his angels to Balaam. This angel stood in the road to block Balaam's path. As Balaam and the two servants were riding on their donkeys,
23 Balaam's donkey saw the angel. The angel was standing in the road and was holding a sword in his hand, but Balaam did not see him.
Balaam's donkey turned off the road into a field. So Balaam struck the donkey and forced it to go back onto the road.
24 Then the angel stood in a place where the road was very narrow, between two vineyards, with walls on each side of the road.
25 When the donkey saw the angel standing there, it walked very close to the wall to try to get past the angel. As a result, it bashed Balaam's foot against the wall. So Balaam struck the donkey again.
26 Then the angel went further along the road and stood at a place that was extremely narrow, with the result that the donkey could not get past at all.
27 This time, when the donkey saw the angel, it lay down on the ground with Balaam sitting on top of it.
29 Balaam shouted, "I struck you because you have caused me to appear to be foolish!
30 But the donkey answered, "I am your own donkey, the one that you have always ridden! Have I ever done anything like this previously?"
Balaam said, "No."
31 Then Yahweh enabled Balaam to see the angel standing on the road, holding a sword in his hand.   If it had not done that, I would certainly have killed you already, but I would have allowed the donkey to live."
34 Then Balaam said to the angel, "I have sinned. But I did not realize that you were standing there, trying to block my path. So if you do not want me to continue going, I will return home."
35 But the angel replied, "I will allow you to go with these men, but you must say only what I tell you to say!"
So Balaam went on with the leaders whom Balak had sent.
36 When King Balak heard that Balaam was coming, he went to meet him at a Moab alongside the Arnon River that was at the border of his own land.
37 When he arrived where Balaam was, he said to him, "I sent you a message saying that you should come immediately! Why did you not come? Did you think that I was not able to pay you a lot of money for coming?"
38 Balaam replied, "I have come here now, but I am not able to say anything that I want to. I will say only the words that God tells me to say."
39 Then Balaam went with Balak to Kiriath Huzoth.
40 There Balak killed some cattle and sheep as sacrifices, and offered parts of the meat to Balaam and the leaders who were with him.
41 They slept there, and the next morning Balak took Balaam part way up the mountain to Bamoth Baal village. From there, they could see some of the Israelite people who were down below.


{{Wiktionary|pho}} PHO or pho may refer to:
{{disambiguation}} <!-- Long comment to avoid being listed on short pages -->


I am an engineering student at Goverment College of Engineering, Kannur. I started programming 1 year ago (noob) and I am planning to make it my career.
My Knowledge
I know basic syntax of major programming langauges(Python, Java, JavaScript, C, C++)
I am concentrating more on Python (to learn Django framework)


Catch up on the biggest X019 news right here
There were a lot of wonderful surprises at the event.
by Asher MadanEvery year, Microsoft hosts a celebration of gaming for fans in various regions. This year, X019 brought plenty of surprises like the Yakuza series coming to Xbox One and much more. The show from London was packed with a number of stellar reveals like new games from Rare and Obsidian Entertainment. You can read about all the major announcements below.
Age of Empires II: Definitive Edition
Age of Empires II: Definitive Edition is now available on Xbox Game Pass for PC.
Age of Empires IV
Age of Empires IV gameplay was finally revealed at the event. Fans of the legendary franchise have been waiting years to play the sequel and it looks like the wait won't be too long.
The Artful Escape
The Artful Escape got a new trailer at X019.
Black Friday
Microsoft previewed some Black Friday and Cyber Monday deals that begin next week and end in December. You can get an Xbox One X for $350 and other discounts on controllers and games.
Bleeding Edge
Bleeding Edge, the new multiplayer game from Hellblade: Senua's Sacrifice creators, is coming to Xbox One and PC in March 2020. The closed beta begins in February 2020 so you can test it out before you decide to buy it.
CrossfireX
CrossfireX is an incredibly popular PC shooter and a few months ago, it was announced that the game is coming to Xbox One. Today, we got the long-awaited gameplay trailer. Did you know that Remedy Entertainment is writing the story for the game?
Drake Hollow
A game called Drake Hollow is coming to Xbox One which looks like a cartoonish four-player co-op game.
Everwild
Today, Rare — the team behind Sea of Thieves — revealed Everwild. While we don't know much about the title, it appears to be a role-playing game focused on magic. The short trailer is definitely intriguing.
Flight Simulator
Flight Simulator got an amazing trailer which showcases its gorgeous visuals.
Grounded
Today, Obsidian Entertainment revealed its latest game called Grounded. The new franchise, slated for 2020, melds first-person co-op survival with a narrative-driven edge. It's being created by a small team at the company and has been in development since before the acquisition by Microsoft. It's coming to Xbox Game Preview in Spring 2020.
Halo: Reach
At X019, 343 Industries' Brian Jarrard shared the news that Halo: Reach is coming to Halo: The Master Chief Collection on December 3 for both Xbox One and PC. All six Halo games in the collection can be purchased for $40 on the Microsoft Store and Steam. However, it seems like if you just want Halo: Reach, you can buy it for $10.
ID@Xbox
There were a number of ID@Xbox game showcased at the event.
KartRider: Drift
Launched in 2004 on PC, KartRider is a massively popular multiplayer racing franchise that boasts more than 380 million players. KartRider: Drift is the Xbox One and PC version for the West, and will feature cross-play and other features.
Kingdom Hearts
Past Kingdom Hearts games are coming to Xbox One.
Last Stop
Last Stop is the next game from the makers of Virginia.
Minecraft Dungeons
Minecraft Dungeons is coming to Xbox One and PC in April 2020.
Minecraft Earth
At X019, the company revealed the Mobs at the Park event. If you're in New York City, London, or Sydney, you'll be excited to know that life-sized statues of interactive mobs will be popping up starting November 16.
Planet Coaster
In a surprise move, Microsoft said that Planet Coaster, the incredibly popular PC game is coming to Xbox One.
Project xCloud
With Google Stadia around the corner, Microsoft had to make a splash with Project xCloud game streaming at X019. The company did exactly that at the event. It was announced that more than 50 new games from 25 partners are being added to the service. You can now play Devil May Cry 5, Madden NFL 20, and even Tekken 7 on the go.
Since Project xCloud launched in October, Microsoft's focus has been on Android phones. However, that's going to change in 2020. The company said, "Next year, we'll bring Project xCloud to Windows 10... and are collaborating with a broad set of partners to make game streaming available on other devices as well." This may include iOS.
Given the fact that there are a lot of controllers on the market now, Microsoft plans to support more Bluetooth devices like Sony's DualShock 4 and accessories from Razer. While Stadia is launching with 12 titles, Microsoft says that "over 3,500 games on Xbox One today and 1,900 games currently in development are already working" on Project xCloud.
Project xCloud will expand to more countries in 2020 like India and Japan. The team will also add game streaming from the cloud to Xbox Game Pass. Lastly, existing games in your Xbox One library will show up on Project xCloud next year.
Rust
Rust is coming to Xbox One in 2020.
Sea of Thieves
Sea of Thieves' next update will arrive on November 20 and include a short Tall Tale campaign called The Seabound Soul. Firebombs will also be added to the game for even deadlier combat.
Star Wars Jedi: Fallen Order
Star Wars Jedi: Fallen Order got an emotional trailer at X019. The game launches on November 15 on Xbox One and other platforms.
Tell Me Why
Dontnod Entertainment has crafted many amazing games like the Life Is Strange and Vampyr, but now the stellar team is partnering with Xbox Game Studios to make a game called Tell Me Why. Tell Me Why is a true-to-life narrative adventure for Xbox One and Windows 10. It's coming Summer 2020 and focuses on a transgender character.
Wasteland 3
Wasteland is coming out in May 2020.
West of Dead
West of Dead is a new game coming to Xbox One that looks incredibly unique.
X019 Flash Sale
An X019 Flash Sale is going on right now with substantial discounts on games like Borderlands 3 and more.
Xbox Game Pass
More than 50 new games coming to Xbox Game Pass were announced at X019. Titles like Remnant: From the Ashes and The Witcher 3: Wild Hunt are the most prominent, but later on in 2020, select games from the Yakuza franchise will be joining the service on Xbox One and PC. Aside from numerous Final Fantasy games coming to Xbox Game Pass, the Yakuza drop will probably be the most memorable.
If you haven't tried Xbox Game Pass Ultimate yet, you can get your first three months for $1. For a limited time, all eligible Xbox Game Pass Ultimate members get a month of EA Access, three months of Discord Nitro, and six months of Spotify Premium included with their subscription.
There were all of the major announcements from X019. What stood out for you the most? Let us know. Since we're massive fans of the Yakuza franchise, the fact that three games from the series are coming to Xbox Game Pass is phenomenal news.
Best of the best
Xbox Game Pass Ultimate
Xbox Game Pass and Xbox Live Gold in one
Xbox Game Pass gives you access to over a hundred games for one monthly fee. Xbox Game Pass Ultimate also adds Xbox Live Gold to the package so you can play online with your friends. Right now, you can purchase a three-month subscription for $45 through Amazon.
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- Xbox One S vs. Xbox One X: Which should you buy?
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Past Project
This webpage host some of the past projects that I have done, giving you a breif description of the project and the technologies used. If you want to find out more, you can click on the icon of the respective projects to check out their github respositories.


Dear Dad,
by Susy Richard
I will always love you for the guidance, comfort, and encouragement I got from you.  More than dad, you have been my playmate, hero, coach and mentor. You were the one who always pushed me to become the strong woman I am today. I cannot thank you enough for your constant love and support you showered upon me all these years. It means more than anything to me.
When I look back at all the beautiful moments I had with you, I know that my life could not have been better. As a dad, you were strong, caring and a man of conviction. I am trying harder to emulate some of your traits so I can set an example of myself too like you did.
Love you more than anything!
Your daughter will always look at the time spent with you vastly differently than you do. She will watch every move you make. She might not understand why you are angry sometimes or happy or affectionate, but you will be the most important man in her life—forever.
 So on this special day, I do exactly that.


Environment Marker Options
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Filter2D — 2-dimensional spectral filtering.¶
This module defines the 2D filter methods.
-
class
admit.util.filter.Filter2D.
Filter2D
(data, method, **keyval)[source]¶ This class defines and runs 2D spectral filters. The currently available filters are Gaussian, Hanning, Triangle, Welch, Boxcar, and Savitzky Golay. The output spectrum will be of the same length as the input spectrum, however some edge channels may be zeroed by some methods, depending on the input parameters.
Parameters: spec : numpy array
2D numpy array of the input spectrum (just the amplitudes).
method : str
The smoothing filter to apply: boxcar, gaussian, welch, hanning, triangle, or savgol. No default. Minimum matching is enabled with a minimum of 3 characters, i.e. box = boxcar.
keyval : various
Any keyword value pairs for the specific method chosen, see the notes for specific keywords.
Notes
Details of the different filter keywords and defaults:
Filter Keyword Def. Description “boxcar” “width” 3 Number of channels to average together “gaussian” “width” 7 Number of channels to span with the gaussian “hanning” “width” 5 Number of channels to include in the cos “triangle” “width” 5 Number of channels to span with the triangle “welch” “width” 5 Number of channels to use in the function “savgol” “window_size” 7 Number of channels to use in the calculation “order” 3 Order of the poynomial fit (must be odd) “deriv” 0 The number of the derivative to compute (0 = just smooth) Attributes
spec (numpy array) The spectrum. len (int) The length of the spectrum. methods (list) A list of the available filters. [method]_args (dict) A dictionary for each method giving its keywords and defaults (e.g. boxcar_args). method (str) The method being used.  The filter for point x[i] is defined as:
\[x[i] = \frac{1}{N} \sum_{n=0}^{N} x[i + n - \frac{N - 1}{2}]\]where N is the width of the filter.
Parameters: width : int
The width of the box to use in channels, must be odd
Returns: numpy array
The smoothed image, (width - 1)/2 edge channels will be zeroed
-
checkmethod
(method)[source]¶ Method to interpret the input method and determine the full method name
Parameters: method : str
The method to use, minimal matching is possible, with a minimum of 3 characters (e.g. “box” will be interpreted to be “boxcar”)
Returns: None
-
gaussian
(width)[source]¶ Method to apply a Gaussian filter to a spectrum. The filter for point x[i] is defined as:
\[x[i] = \frac{3}{N} \sum_{n=0}^{N} x[i + n - \frac{N - 1}{2}] e^{-\frac{1}{2}\left(\frac{n-(N-1)/2}{\sigma(N-1)/2}\right)^2}\]where N is the width of the filter.
Parameters: width : int
The number of channels to span with the gaussian for each iteration, must be odd
Returns: numpy array
The smoothed image, (width - 1)/2 edge channels will be zeroed
-
hanning
(width)[source]¶ Method to apply a Hanning filter to a spectrum. The filter for point x[i] is defined as:
\[x[i] = \frac{2}{N-1} \sum_{n=0}^{N} x[i + n - \frac{N - 1}{2}] 0.5 \left(1 - \cos\left(\frac{2\pi n}{N-1}\right)\right)\]where N is the width of the filter.
Parameters: width : int
The number of channels to span with the function for each iteration, must be odd
Returns: numpy array
The smoothed image, (width - 1)/2 edge channels will be zeroed
-
radius
(x, y, width)[source]¶ Method to calculate the radius of a point in the kernel
Parameters: x : float
The x coordinate
y : float
The y coordinate
width : int
The width of the Gaussian being used
Returns: Float containing the radius to the point
-
run
()[source]¶ Method to run the selected filter on the data
Parameters: None Returns: The smoothed image
-
savgol
(window_size, order, deriv=None)[source]¶ Method to apply a Savitzky-Golay filter to a 2D image.
Parameters: window_size : int
the size of the window. Must be an odd integer number.
order : int
the order of the polynomial used in the filtering. Must be less then window_size - 1.
deriv: int
the order of the derivative to compute (default = None means only smoothing)
Returns: numpy array
The smoothed image, (width - 1)/2 edge channels will be zeroed
-
triangle
(width)[source]¶ Method to apply a Triangular filter to a spectrum. The filter for point x[i] is defined as:
\[x[i] = \sum_{n=0}^{N} x[i + n - \frac{N - 1}{2}] \left(1 - \left|\frac{n-\frac{N-1}{2}}{\frac{N}{2}}\right|\right)\]where N is the width of the filter.
Parameters: width : int
The number of channels to span with the function for each iteration, must be odd
Returns: numpy array
The smoothed image, (width - 1)/2 edge channels will be zeroed
-
welch
(width)[source]¶ Method to apply a Welch filter to a spectrum. The filter for point x[i] is defined as:
\[x[i] = \frac{3}{2(N-1)} \sum_{n=0}^{N} x[i + n - \frac{N - 1}{2}] \left(1 - \left(\frac{n - \frac{N-1}{2}}{\frac{N-1}{2}}\right)^2\right)\]where N is the width of the filter.
Parameters: width : int
The number of channels to span with the function for each iteration, must be odd
Returns: numpy array
The smoothed image, (width - 1)/2 edge channels will be zeroed
-


# Immutable
This page exposes a forward-only api.
### Scenario
Urls are minted with the `organization` revision.
State changes to the app cause an update to the revision of the organization.
### Caching
* **Expiration** caching is set to `max-age={oneyear}`
* **Validation** caching is not needed (eg `etag`)
### Consistency
When the client receives a `revisionChanged` event, the user may choose to 'Update Revision' or just stay with the older revisions.
NOTE: On the server, requesting older revisions could be done with a <=`{revision}` or something other check to see if the resource can be found
matching the requests `revision` parameter.
### Side effects
* Server caching is dead simple
* Clients can move forward and backward through revisions of the app
* Concurrency is simple. Try to rename an asset without syncing your revision...you get a 400 with a concurrency error.
* Clients are in control of upgrading when notified of state changes. If the client is knowingly presenting stale data, it can disable or remove forms that
will cause concurrency exceptions to be raised.
### Actions to see this work
1. Rename an asset and then 'Update Revision'. Repeat this a few times. Refresh the page and toggle through the revisions of the app. Note in dev tools that `assets-catalog`
is being loaded `(from cache)` which means there is no round trip.
2. Try renaming an asset while on a stale revision. Note the `400` concurrency exception which is returned.
**Don't Forget to clear your browser cache!**


ZcashSDK
public class ZcashSDK
Wrapper for all the constant values in the SDK. It is important that these values stay fixed for all users of the SDK. Otherwise, if individual wallet makers are using different values, it becomes easier to reduce privacy by segmenting the anonymity set of users, particularly as it relates to network requests.
-
Miner’s fee in zatoshi.
Declaration
Swift
public static let MINERS_FEE_ZATOSHI: BlockHeight
-
The number of zatoshi that equal 1 ZEC.
Declaration
Swift
public static let ZATOSHI_PER_ZEC: BlockHeight
-
The height of the first sapling block. When it comes to shielded transactions, we do not need to consider any blocks prior to this height, at all.
Declaration
Swift
public static let SAPLING_ACTIVATION_HEIGHT: BlockHeight
-
The theoretical maximum number of blocks in a reorg, due to other bottlenecks in the protocol design.
Declaration
Swift
public static let MAX_REORG_SIZE: Int
-
The amount of blocks ahead of the current height where new transactions are set to expire. This value is controlled by the rust backend but it is helpful to know what it is set to and shdould be kept in sync.
Declaration
Swift
public static let EXPIRY_OFFSET: Int
-
Default size of batches of blocks to request from the compact block service.
Declaration
Swift
public static let DEFAULT_BATCH_SIZE: Int
-
Default amount of time, in in seconds, to poll for new blocks. Typically, this should be about half the average block time.
Declaration
Swift
public static let DEFAULT_POLL_INTERVAL: TimeInterval
-
Default attempts at retrying.
Declaration
Swift
public static let DEFAULT_RETRIES: Int
-
The default maximum amount of time to wait during retry backoff intervals. Failed loops will never wait longer than this before retyring.
Declaration
Swift
public static let DEFAULT_MAX_BACKOFF_INTERVAL: TimeInterval
-
Default number of blocks to rewind when a chain reorg is detected. This should be large enough to recover from the reorg but smaller than the theoretical max reorg size of 100.
Declaration
Swift
public static let DEFAULT_REWIND_DISTANCE: Int
-
The number of blocks to allow before considering our data to be stale. This usually helps with what to do when returning from the background and is exposed via the Synchronizer’s isStale function.
Declaration
Swift
public static let DEFAULT_STALE_TOLERANCE: Int
-
Default Name for LibRustZcash data.db
Declaration
Swift
public static let DEFAULT_DATA_DB_NAME: String
-
Default Name for Compact Block caches db
Declaration
Swift
public static let DEFAULT_CACHES_DB_NAME: String
-
Default name for pending transactions db
Declaration
Swift
public static let DEFAULT_PENDING_DB_NAME: String
-
Undocumented
Declaration
Swift
public static let DEFAULT_DB_NAME_PREFIX: String
-
File name for the sappling spend params
Declaration
Swift
public static let SPEND_PARAM_FILE_NAME: String
-
File name for the sapling output params
Declaration
Swift
public static let OUTPUT_PARAM_FILE_NAME: String
-
The Url that is used by default in zcashd. We’ll want to make this externally configurable, rather than baking it into the SDK but this will do for now, since we’re using a cloudfront URL that already redirects.
Declaration
Swift
public static let CLOUD_PARAM_DIR_URL: String
-
Undocumented
Declaration
Swift
public static let isMainnet: Bool


Recording date: February 6, 2021
Download at Apple Podcasts, Google Podcasts, Spotify, iHeartRadio, Spreaker or wherever you get your podcasts.
We had a great conversation with Ian about how he runs a rather different kind of information security operation that can be described as a group of companies reporting up to one security organization for strategy, but which each support their own individual needs day to day.
Ian Amit spent most of his career in information security, with a lot of time on the offensive side. He spent some years as a senior manager at AWS, and in the past few years he has been Chief Security office at Cimpress, which runs a collection of independent businesses whose CISOs report up to Ian, who is kind of the supervisory CSO - this set up is most similar to that of GE’s.
Some of the smaller firms have no CISO, and Ian then serves as their de facto CISO, and the larger brands - most notably including VistaPrint. And Technical debt is primarily Ian’s responsibility - he and Nick have discussed it quite a bit over the years, and Ian was pivotal in a presentation Nick made, Tech Debt Burndown: Kill It With Fire which he presented at CornCon VI: The Return of the Cyber Jedi 2021.
Nick asked about whether there was a difference between security tech debt and technical debt.
Ian thinks Tech Debt is Tech Debt, but that it has a lot of security repercussions.
Accrual of tech debt gunks up the business operation and also opens up the company to additional risk.
Chris observed that old stuff has known vulnerabilities, new stuff might have vulns but only the most sophisticated attackers are going to find the 0days, But in terms of staying atop patching, how much does it relate to testability of the environment?
Ian says that’s something he thinks about a lot - known knowns versus unknown unknowns. He tries to let the business decision guide the way - not to discount security, but prioritization matters. Tech debt by itself is not a problem, it is only one after you view it in the context of how it might affect the business.
It may be OK to carry a certain tech debt indefinitely if it isn’t slowing the business. The exercise is to get the right stakeholders to really understand what they need and what our stack delivers.
Nick was wonders whether Ian was hired with tch debt as a guiding principle, which may mean the executives actually understood this before he got there. That’s rare. And also, what is Ian doing with metrics?
Ian agreed that the executives had a lot of tech debt, lots of companies with different histories - Vista Print is more than 20 years old and Cimpress' newest firm is two years old. They realized this was critical to success and growth. So the executives wanted someone who understood how to harmonize all these varying challenges, and Ian and management realized that technical debt burndown was central to the effort.
Ian’s approach to metrics is an interactive look at what is there and how it’s being burned: both at the tail end (what is there out there that might bite me), and revisit our fresh decisions to be cognizant of repercussions they may have on tech debt accrual.
This gibes with Ian’s quote at the top of the page here, and he added the other quote of his that we love: “Tech debt is not a project; technical debt is a process.”
Ian says he went through the herky-jerky attempt - start fresh, groan on until you can’t proceed anymore, stop everything to burn it down - so often that he realized it just has to be embedded into every sprint.
It can’t just be the security people’s job to chase it down - everyone in DevOps has to embed this into their processes.
Ian would rather talk of code quality than secure code: if our code sucks, it is also fragile and insecure.
Chris raised that some of this is the way we deal with the Red Queen, that it’s a choice between a steady pace, or occasionally stopping for a breather which means we need to run even after we rest.
Ian agrees, and recognizes that we have to adapt to the business needs. We need to run faster than the Red Queen, to run ahead and clear the path, and sometimes to drift behind in case she changes course.
With regard to SSDLC, which requires software development, and people discuss “shift left” - forget that. How do you start the conversation about technical debt before the PRD is done, before the design doc? How do you get business people to understand the full cost of new tools or features before you even start building?
Ian refers to lifetime ownership of the code, and when devs understand that they are responsible for the whose lifecycle of the product they tend to make better decisions at the top that will allow them to support what they build throughout its life.
Ian is the Chief Security Officer at Cimpress. He used to be a Senior Manager of Security Engineering at Amazon AWS, and prior to that held leadership positions with ZeroFOX, IOActive, Aladdin, Finjan and Comsec. He currently serves on the Advisory Board for ZeroFOX, Axon Cyber, Panorays and Marshall University. He also serves on the board of directors of BSides Las Vegas as a general director and President. Ian has had the pleasure to talk at the most sought-after conferences in the field such as BlackHat, DefCon, BlueHat, InfoSecurity, and more… He has been frequently interviewed by leading pring and web magazines and news outlets, speaking about web security, eCrime, and technology (see this page for a chronological sampler).
Chris is a frequent speaker on topics such as serverless, DevOps, cloud, containers, security, networking and the Internet of Things. He’s also a cloud editor for InfoQ and a contributor to open source projects such as Docker, CoreOS and DXC’s Online DevOps Dojo.
Nick is Chief Security Officer at a financial technology firm, and was formerly an NYPD Intelligence Bureau apparatchik. He is co-author of many books, including Cyber Attack Survival Manual; In Context: Understanding Police Killings of Unarmed Civilians and Blackhatonomics: An Inside Look at the Economics of Cybercrime.


Ruby on Rails Security References
Below is a list of useful references on keeping Ruby on Rails secure.
- Ruby on Rails Security Guide - A guide to keeping your Ruby on Rails applications secure written by the Ruby on Rails organization
- Ruby on Rails Security Vulnerablities - CVE Details is a website that keeps a database of known security vulnerabilities. For each vulnerablity to provide the number of known exploits, dates, vulnerability score, access level gained, acces, complexity, and a few more details. Linked above is a direct link to the Ruby on Rails page.
- Ruby on Rails Example Hack - This is an example of why it's important to make sure your application is written with the latest security standards, but also how it's just as important to update the server to protect your server from old vulnerabilities.


Site Intent
Client Goals
The goal of the client is to create an alternative investment in an non-correlated asset class to stocks, bonds, and real estate and reduce portfolio risk through diversification.
The purpose of the website is to:
- Drawn in clientele and raise awareness about other diversification opportunities.
- Properly hedging portfolios against new market nuances.
- Create a CTA (Commodity Trading Advisor) that stands out from other CTAs in the market today.
- Educate investors on real market risks v.s. perceived risks.
User Goals
- To learn how to make more money with commodity investments
- Increase the amount of their retirement income.
- Reduce the risk in the portfolio.
- Smooth out the volatility in their portfolio.
- Protect the portfolio against unanticipated world issues.
Target Audience (Buyer Personas)
Jim is a 50 year old business owner with a college degree and has worked in the oil industry for 20+ years. Jim makes an average annual salary of $150k. Jim has been in the commodity market for about 10 years now and has invested in the past with previous commodity brokerages and has lost a significant amount of money in previous trades. Jim knows you can make money with commodities, but has been unsuccessful at it in the past. Jim has strong republican views, is heavy into capitalism and keeps up with World News daily. Jim is constantly craving new market opportunities and is looking for more educational resources to make smarter trades.
Josh is a 35 year old college graduate who is new to the commodity trading and is looking for educational resources to help him with his trades. Josh likes to start his investments slow and watch them grow at a steady pace. Josh is not a very big risk taker and performs multiple hours of research before investing in new market opportunities. In the past, Josh has been successful in investing in the stock market, but has not yet invested in commodities.
Competitor Analysis
XL Capital Strategies Current Rankings
- Global Rank: N/A
- US Rank: N/A
- Inbound Links: 0
Cannon Trading, Inc.
cannontrading.comCannon Trading is a well-established brokerage located in Beverly Hills, CA. they have been in business for over 20 years and offer numerous educational resources on commodities.
- India Rank: 156,191
- Alexa Traffic Rank: 804,008
- Inbound Links: 138
- Page Speed: 1.97s (slow)
- Keyword Rankings:
- firetip demo accoutnt
- firetipe demo account
- free firetip download
- cannon trading
- daily support and resistance levels
Great Pacific Trading Company
gptc.comGPTC specializes in welath management and trading futures. They provide various free educational whitepapers (all white papers have broken links).
- Global Rank: 461,128
- US Rank: 97,789
- Inbound Links: 64
- Page Speed: 3.605s (very slow)
- Keyword Rankings:
- www.gptc.edu
- commodity trading grants pass oregon
- great pacific trading company
- gptc
- gptc.co
Essex Futures
essexfutures.comEssex Futures provides any free educational resources, but seem to drowned out by unorganized content.
- Global Rank: 7,397,899
- US Rank: N/A
- Inbound Links: 21
- Page Speed: 2.278s
- Keyword Rankings:
- orange county california stock brockerage
- stock
- 1.877.839.3949
- commodity trading


Nerdcubed Video Maker
Woops
Woops
Woops
Score: 0
How to Play:
- Select the game you want to make a video for.
- The key combination to create the game will appear here.
- Fill the progress bar as fast as you can.
- Each completed game gives you one point.
Tips:
An unofficial fan-project, all love to Dan
Javascript is Required to play, Doofmars - 2014


PROJECT MANAGEMENT MODULE
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Members
Mentor Name
Mentor Name
Member Names
Member Name
Member Name
Member Name
Member Name
To-Do
In Progress
Review
Done


Connect to MUSIC in the Course Register.
MUSIC
MUSC-005 COLLEGE MUSIC PROGRAM 0.5 CU 001 STU TBA KETNER M AUDITION REQUIRED COURSE MUST BE TAKEN FOR A GRADE PERMISSION NEEDED FROM DEPARTMENT MAX: 199 MUSC-007 UNIV. WIND ENSEMBLE 0.5 CU 001 STU TBA KETNER M AUDITION REQUIRED COURSE MUST BE TAKEN FOR A GRADE PERMISSION NEEDED FROM DEPARTMENT MAX: 50 UNIV.  CHOIR 0.5 CU 006 STU T 6-8PM BENN 419 PARBERRY W STU R 5:30-7:30PM BENN 419 AUDITION REQUIRED COURSE MUST BE TAKEN FOR A GRADE PERMISSION NEEDED FROM DEPARTMENT MAX: 30 UNIV. CHORAL SOCIETY 0.5 CU 007 STU M 8-10PM BENN 419 PARBERRY W AUDITION REQUIRED COURSE MUST BE TAKEN FOR A GRADE PERMISSION NEEDED FROM DEPARTMENT MAX: 30 JAZZ COMBO 0.5 CU 008 STU R 5-6PM BENN 407 CLAYTON M AUDITION REQUIRED COURSE MUST BE TAKEN FOR A GRADE PERMISSION NEEDED FROM DEPARTMENT MAX: 75 SAMBA ENSEMBLE 0.5 CU 009 STU M 7-9PM TBA STEVENS M AUDITION REQUIRED COURSE MUST BE TAKEN FOR A GRADE PERMISSION NEEDED FROM DEPARTMENT MAX: 175 ARAB ENSEMBLE 0.5 CU 010 STU R 6-8:30PM MUSB 101 KETNER M STU R 6-8:30PM MUSB 210 STU R 6-8:30PM BENN 407 COURSE MUST BE TAKEN FOR A GRADE MAX: 75 SAMBA ENSEMBLE 0.5 CU 011 STU TBA STEVENS M MAX: 30 012 STU TBA STEVENS M MAX: 30 PENN FLUTES 0.5 CU 013 STU S 1-4PM TBA KETNER M MAX: 30 MUSC-016 FRESHMAN SEMINAR 1 CU FRESHMAN SEMINAR 301 SEM T 1:30-4:30PM BENN 406 ROMMEN T FRESHMAN SEMINAR MAX: 15 302 SEM TR 10:30-12NOON BENN 406 RAMSEY G FRESHMAN SEMINAR MAX: 15 303 SEM TR 3-4:30PM BENN 407 RIVERA G MAX: 15 MUSC-018 MUSIC IN URBAN SPACES 0.5 CU AN ACADEMICALLY BASED COMMUNITY SERV COURSE FRESHMAN SEMINAR CULTURAL DIVERSITY IN US 401 SEM F 2-4PM GOLD 205 MCGLONE M PERMISSION NEEDED FROM INSTRUCTOR CROSS LISTED: URBS-018 MAX W/CROSS LIST: 19 MUSC-030 1000 YRS MUSICAL LISTENG 1 CU CROSS CULTURAL ANALYSIS ARTS & LETTERS SECTOR 001 LEC TR 12-1:30PM MUSB 101 CALCAGNO M LEC TR 12-1:30PM MUSB CONF CROSS CULTURAL ANALYSIS ARTS & LETTERS SECTOR MAX: 50 002 LEC TR 10:30-12NOON MUSB 102 MURPHY M CROSS CULTURAL ANALYSIS ARTS & LETTERS SECTOR MAX: 30 003 LEC MWF 11-12NOON MUSB 102 DETURCK G CROSS CULTURAL ANALYSIS ARTS & LETTERS SECTOR MAX: 30 004 LEC MWF 12-1PM MUSB 102 WILLIAMS V MAX: 25 MUSC-032 BEETHOVEN 1 CU ARTS & LETTERS SECTOR 001 LEC TR 3-4:30PM MUSB 101 KALLBERG J MAX: 30 MUSC-034 HAYDN AND MOZART 1 CU 001 LEC MW 2-3:30PM BENN 406 GRAY M MAX: 30 MUSC-050 WORLD MUSICS & CULTURES 1 CU CROSS CULTURAL ANALYSIS 001 ONL W 5-7PM TBA MULLER C ARTS & LETTERS SECTOR ONLINE COURSE ONLY LOCATION: ON LINE ARTS & LETTERS SECTOR CROSS CULTURAL ANALYSIS MAX: 50 401 LEC MW 2-3:30PM BENN 419 ROMMEN T CROSS LISTED: AFRC-050 AFST-050 ANTH-022 MAX W/CROSS LIST: 50 ARTS & LETTERS SECTOR CROSS CULTURAL ANALYSIS 402 LEC TR 12-1:30PM BENN 419 KHURI H CROSS LISTED: AFRC-050 AFST-050 ANTH-022 MAX W/CROSS LIST: 30 ARTS & LETTERS SECTOR CROSS CULTURAL ANALYSIS 403 LEC TR 10:30-12NOON MUSB 101 BEN-ALI L CROSS LISTED: AFRC-050 AFST-050 ANTH-022 MAX W/CROSS LIST: 30 ARTS & LETTERS SECTOR CROSS CULTURAL ANALYSIS 404 LEC MWF 11-12NOON MUSB 101 IM B CROSS LISTED: AFRC-050 AFST-050 ANTH-022 MAX W/CROSS LIST: 30 ARTS & LETTERS SECTOR CROSS CULTURAL ANALYSIS 405 LEC MWF 12-1PM MUSB 101 DUPRIEST B CROSS LISTED: AFRC-050 AFST-050 ANTH-022 MAX W/CROSS LIST: 30 MUSC-056 SEEING/HEARING AFRICA 1 CU ARTS & LETTERS SECTOR CROSS CULTURAL ANALYSIS 401 SEM F 9-12NOON BENN 407 MULLER C CROSS LISTED: AFRC-056 AFST-056 COML-056 MAX W/CROSS LIST: 25 MUSC-063 BEGINNING SITAR II 1 CU CROSS CULTURAL ANALYSIS 401 LEC TR 4:30-6PM WILL 812 MINER A CROSS LISTED: SAST-107 MAX W/CROSS LIST: 12 MUSC-070 INTRO THEORY&MUSCIANSHP 1 CU FORMAL REASONING COURSE 001 LEC TR 10:30-12NOON MUSB 210 WEESNER A FORMAL REASONING COURSE MAX: 20 002 LEC MWF 12-1PM MUSB 210 CHAN K FORMAL REASONING COURSE MAX: 20 003 LEC MWF 11-12NOON MUSB 210 SAMUEL J FORMAL REASONING COURSE MAX: 20 004 LEC TR 12-1:30PM MUSB 102 SHAPIRO D MAX: 20 MUSC-099 INDEPENDENT STUDY 1 CU SEE DEPT. FOR SECTION NUMBERS PERMISSION NEEDED FROM INSTRUCTOR 000 IND TBA STAFF MUSC-150 INTRO TO ETHNOMUSICOLOGY 1 CU ARTS & LETTERS SECTOR 001 LEC W 2-5PM MUSB 102 SYKES J PERMISSION NEEDED FROM DEPARTMENT MAX: 22 MUSC-162 INTERMEDIATE SITAR II 1 CU 401 LEC MW 3:30-5PM WILL 812 MINER A CROSS LISTED: SAST-109 MAX W/CROSS LIST: 12 MUSC-168 ARTS ENTREPRENEURSHIP 1 CU 001 LEC R 3:30-6:30PM DRLB 2C6 KETNER M MAX: 18 MUSC-171 THEORY & MUSCIANSHIP II 1.5 CU REGISTRATION REQUIRED FOR LEC, LAB 001 LEC TR 12-1:30PM BENN 407 MORENO J MAX: 14 LABORATORY 0 CU 101 LAB MW 12-1PM BENN 407 DAVIS A MAX: 15 THEORY & MUSCIANSHIP II 1.5 CU GROUP 2 SECTIONS REGISTRATION REQUIRED FOR LEC, LAB FROM WITHIN THIS GROUP OF SECTIONS 002 WALTHAM-SMITH N LEC TR 10:30-12NOON BENN 407 MAX: 20 LABORATORY 0 CU 102 LAB MW 11-12NOON BENN 407 LEMAY M MAX: 20 MUSC-230 BAROQUE OPERA 1 CU 001 LEC TR 3-4:30PM MUSB 102 CALCAGNO M MAX: 30 MUSC-252 MUSIC, RELIGION,& RITUAL 1 CU 401 LEC M 2-5PM MUSB 102 SYKES J CROSS LISTED: ANTH-242 SAST-252 MAX W/CROSS LIST: 30 MUSC-266 MUSIC CULT N INDIA & PAK 1 CU CROSS CULTURAL ANALYSIS 401 LEC TR 1:30-3PM COHN 204 MINER A CROSS LISTED: SAST-116 MAX W/CROSS LIST: 50 MUSC-271 COMPOSITION I: HIST PRAC 1 CU 001 LEC TR 12-1:30PM MUSB 210 PRIMOSCH J MAX: 10 MUSC-275 ELECTRONIC MUSIC 1 CU 301 LEC M 3:30-6:30PM MUSB 101 LEW E PERMISSION NEEDED FROM DEPARTMENT MAX: 10 MUSC-370 SEMINARS IN THRY & COMP 1 CU 301 LEC R 1:30-4:30PM MUSB 210 WEESNER A MAX: 30 MUSC-621 ANALYTICAL METHODS 20THC 1 CU 301 WALTHAM-SMITH N SEM T 1:30-4:30PM MUSB CONF MAX: 15 MUSC-698 PREP A.M. PORTFOLIO 1 CU SEE DEPT. FOR SECTION NUMBERS 000 IND TBA STAFF MUSC-699 PREP A.M. ESSAY 1 CU SEE DEPT. FOR SECTION NUMBERS 000 IND TBA STAFF MUSC-700 SEMINAR IN COMPOSITION 1 CU 301 SEM W 2-5PM MUSB 210 REISE J MAX: 25 MUSC-705 SEM IN ETHNOMUSICOLOGY 1 CU 301 SEM W 1-4PM MUSB CONF MULLER C PERMISSION NEEDED FROM INSTRUCTOR MAX: 25 MUSC-720 STD RENAISSANCE MUSIC 1 CU 301 SEM F 2-5PM VANP MASEM STAFF PERMISSION NEEDED FROM INSTRUCTOR MAX: 25 MUSC-735 CIVIL RIGHTS ART 1 CU 401 SEM R 9-12NOON BENN 138 TILLET S CROSS LISTED: AFRC-799 ENGL-799 SECTION MAX: 4 MAX W/CROSS LIST: 15 MUSC-770 SEM IN AFRO-AMER MUSIC 1 CU 401 SEM R 1:30-4:30PM MUSB CONF RAMSEY G PERMISSION NEEDED FROM INSTRUCTOR CROSS LISTED: AFRC-771 MAX W/CROSS LIST: 25 MUSC-799 GUIDED RDG MU SCHOLARSHI 1 CU SEE DEPT. FOR SECTION NUMBERS PERMISSION NEEDED FROM INSTRUCTOR 000 IND TBA STAFF MUSC-801 TEACHING MUSIC THEORY 1 CU 001 LEC TBA WEESNER A PERMISSION NEEDED FROM DEPARTMENT MAX: 1 MUSC-988 PREP PHD COMP 1 CU SEE DEPT. FOR SECTION NUMBERS PERMISSION NEEDED FROM INSTRUCTOR 000 IND TBA STAFF MUSC-989 PREP FOR PHD ESSAY 1 CU SEE DEPT. FOR SECTION NUMBERS 000 IND TBA STAFF MUSC-990 MASTERS THESIS 1 CU SEE DEPT. FOR SECTION NUMBERS 000 MST TBA STAFF MUSC-995 DISSERTATION 1 CU SEE DEPT. FOR SECTION NUMBERS 000 DIS TBA STAFF MUSC-998 COMPOSITION 1 CU SEE DEPT. FOR SECTION NUMBERS PERMISSION NEEDED FROM INSTRUCTOR 000 IND TBA STAFF MUSC-999 INDEPENDENT STUDY 1 CU SEE DEPT. FOR SECTION NUMBERS 000 IND TBA STAFF
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Venice devastated by worst flooding in 50 years
City struggles with unsustainable overtourism
Venice, Italy, has been hit by the worst flooding in 50 years this week, resulting in damage worth hundreds of millions of euros.
Venice, Italy, has been hit by the worst flooding in 50 years this week, resulting in damage worth hundreds of millions of euros.


My todo list
Search the Internet looking for books about Programming languages.
Search the Internet looking for tutorials about Programming languages.
Practice the programming languages I learned from books and tutorials.