Consider and as two vectors with as the origin.
Vector passes through the points and, and passes through the point and.
The cross product can be interpreted as the signed area of a parallelogram formed by the points .
The moment (in clockwise or anticlockwise) of point from point , with respect to the origin, can be identified using the sign of area of the parallelogram formed by the points .
If the area of the parallelogram is positive, then point from point , with respect to the origin, moves in clockwise direction.
If the area of the parallelogram is negative, then point from point , with respect to the origin, moves in anticlockwise direction.
Calculate the area of the parallelogram formed by the points .
The following diagram shows the parallelogram formed by the points . The vertical line in blue indicates the height of the parallelogram.
Height can be calculated using the Pythagoras theorem. Consider h is the height of the parallelogram and it is indicated using the blue line.
Area of parallelogram = baseheight
Calculate the area of the parallelogram formed by the points .
Hence, the cross product is same as the area of the parallelogram formed by the points . Also, the sign area of the parallelogram gives the direction of the point’s movement.
Therefore, if the cross product is positive, then vector moves clockwise from vector with respect to the origin . The vectormoves anticlockwise from vector with respect to the origin , if the cross product is negative.
Consider the ON-SEGMENT given on page number 1018 of the textbook. The ON-SEGMENT takes the coordinate of three points as an argument. The ON-SEGMENT used in the algorithm SEGMENT-INTERSECT to check whether two lines intersect each other or not.
• It takes a points and check whether these lines are on the same side of the point or the opposite side of the points.
• If these lines exists in the opposite side then these lines will intersect each other otherwise they will not intersect each other.
Now consider the proposal of the Professor van Pelt. Professor takes only the x-dimension to perform the checking on the line number 1 in the ON-SEGMENT.
Consider the following diagram:
In the above diagram, suppose two lines are vertically exists on each other. In this case, the x- coordinate of the both lines will be same. So, y-coordinate must need to check whether these are of the same side of the point or opposite to each other.
So, the proposal of the Professor van Pelt will be wrong here to determine the intersection of two line.
Consider the following algorithm which is used to sort a sequence of n points according to their polar angles with respect to a given origin point. The following given algorithm takes a running time of.
PROC Point
1. Define the variables x and y
// Define a prototype of a function which is used further in the procedure.
2. Point operate (float _x, float _y): x(_x), y(_y)
// Define function operate which takes a constant argument.
3. Point operate +( const Point &th)
// It call the function Point once again with two argument
4. return Point( x+th.x, y+th.y);
// Define constructer function Point in which the coordinate are subtracting
5. Point operate -( const Point &th)
//It call the function Point with the two argument
6. return Point( x-th.x, y-th.y);
//Assignment performed here
7. Point& operate=(const Point &th)
// If conditional statement is used to check whether the constructer value
//and the defined values are same or not
8. if this==&th
//If this is true then return the pointer of constructer
9. return *this;
//Assignment performed.
10. x = th.x;
11. y = th.y;
//Return the pointer
12. return *this;
//Define the procedure ostream
13. friend ostream& operate << (ostream& os, const Point &p)
14. display x, p.x, y, p.y;
15. return os;
//Define procedure which takes the coordinate of two points as an argument
16. boolean mfunc(Point &p1, Point &p2)
//now check if the y- coordinate of p1 is greater than zero and the y-
//coordinate p2 is less than zero then return true
17. if p1.y>0 and p2.y<0
18. return true;
//now check if the y- coordinate of p1 is less than zero and the y-
//coordinate of p2 is greater than zero then return false
19. else if p1.y<0 and p2.y>0
20. return false;
//now check if the subtraction of multiplication of y- coordinate of p2 and
//x- coordinate of p1 and of y- coordinate of p1 and x- coordinate of p2 is
//less than zero then return false.
21. if ((p1.x*p2.y – p2.x*p1.y)<0)
22. return false;
23. return true;
Running time analysis:
In the above algorithm, from the line number 1 to line number 15, only it takes a constant running time and from line number 17 to line number 23, the cross product are performed and it will take a running time of .
Hence, total running time of above algorithm is .
A convex polygon:
To determine the turn of segments that is whether the consecutive segments turn left or right, we have to consider the cross product between the segments.
For example to determine the left or right turn of 3 given consecutive point
We have to check whether the directed segment is clockwise or counterclockwise relative to the directed segment.
As mentioned in below figure if-
a. Cross product of is negative then it means that is counterclockwise with respect to .Thus we made a left turn at.
b. Cross product of is positive then it means that is clockwise with respect to .Thus we made a right turn at .
And finally, if cross product is 0 means points are collinear. At that point we cannot decide whether the left or right turn has been made.
Since each of cross product operation will take constant time that is and since we need to calculate cross product for n times total time will be of that is the liner time.
Above mention algorithm would not always work because when angle between two consecutive segments is, there would not be any turn and algorithm will not be able to decide the turn .At that point, algorithm will fail.
The modification is simple.Consider each set of three consecutive points along the polygon, if every angle is or less, it is convex. This runs in O (n) time.We can even check for a convex polygon if its sum of interior angles is.
Intersection of two Line Segments
Consider that a point.Now, the right horizontal ray from is the set of all those points such that:
In other word the right horizontal ray from is set of points due right of along with itself.Consider a point, a line.
1. Consider lines and.
2. is determined by largest x coordinate between and and the given.
3. Check intersection of line and.
4. It will take constant time.
Hence, it can be done in constant time that is.
A polygon is the diagrammatic representation of a piece-wise linear and closed curve in the plane. The point in polygon problem refers to the problem or condition in which it is checked that if a point P lies inside the polygon, lies outside the polygon or it lies on the boundary of a polygon.
Certain tools and software are available such as CAD (Computer Aided Design) and computer graphics that are used for checking the above stated conditions of the polygon problem.
Here, it is asked to show that how it is possible to compute in time whether a point is in the interior of an n-vertex polygon.
In order to solve the above problem, two methods can be used which are as follows:
1. Ray casting algorithm
2. Winding number algorithm
Ray casting algorithm: It is a simple way of finding whether a point is inside or outside the polygon as compared with winding number algorithm. It is also called crossing the number algorithm. In this, the number of times a ray crosses the edges of a polygon is counted or analyzed.
Winding number algorithm: It is a slower algorithm than ray casting algorithm. The value non zero in this algorithm means that the point lies inside the polygon. Winding number must be non-zero.
Consider the exercise 33.1-6 of this text book,
To calculate whether a point intersects a line, consider a pointand a line.
The point and the line are shown as below:
1. Consider lines and.
2. is determined by the largest x coordinate between and and the given coordinate.
3. Check intersection of and.
4. It will take constant time.
Hence, deciding whether a point intersects a line or not can be done in.
In the example given above, the time taken has a complexity of. Now, if the polygon has n vertexes for a line then, all the vertices should be checked for the given line. So, the complexity of finding whether a point is in the interior of an n-vertex polygon will be.
The algorithm for finding whether a point is inside a polygon is given below:
Defining the x and y coordinates of the point and polygon
1. Def point_ inside_ polygon (x, y, poly):
//Calculating the length of polygon and assigning this to variable N.
2. n = Length (poly)
//If the point is inside the polygon, then make the value as false
3. inside = false
//Now, assign the coordinates of the polygon as
4. = poly
//Initialize a for loop which will traverse the line
5. for i in range
6.
//Use if condition to check whether the point is inside or not
7. if y >min;
8. if ymax;
//Use if condition to check whether the point is outside or not
9. if x max;
10. if
//Finding the value of x by using slope and another point
11. x integers =
//Condition to check whether the given point is inside or outside
//the polygon
12. if
13. Inside= not inside
//Equating the coefficients of both the points with returning
// true
14.
15. return inside;
In the figure given above, tail of one arrow is inside the polygon and making three intersections to border of the polygon where as the tail of the other one is not inside the polygon and making four intersections.
To compute whether a point is interior to n- vertex polygon says P , steps used are as given below:
? Check if is not on the boundary of P (this can be done by going through vertices and edges of the polygon P.
? Draw a horizontal ray from given point, then count the number of intersections between polygon edges and the horizontal ray from.
? If drawn horizontal line from the given point is along the side of the polygon that is it overlaps a side of the polygon, then, do not increase the count.
? When the horizontal ray goes through a polygon vertex,
Check the conditions given below:
1. Check if both edges to the vertex are below or above the ray then, do not change the count.
2. Check if one edge is below and the other edge is above then, increase the count by 1.
The polygon can be represented as a list of consecutive edges, checking whether the ray intersects the polygon side or not can be done in an order of linear time.
? Even number of intersections imply that is inside the polygon.
? Odd numbers of intersections imply that is inside the polygon
In the above algorithm, all the vertices of the polygon are considered. And, checking or deciding whether any point intersects a line or not has a complexity of.
Now, in the given case, the polygon has n vertices and therefore, for the intersection, all the vertices should be checked for the given line. So, the complexity of this algorithm will be, where n is the number of vertices of polygon.
Thus, it is possible to compute in time whether a point is in the interior of an n-vertex polygon.
Computing the area of an n- vertex simple polygon:
A closed path made up of straight and non-intersecting lines segments are called as simple polygon. Basically the sides of simple polygon do not intersect each other.
A non-convex polygon specifies that the internal angles can be greater than.
The generalize formula to calculate the area of polygon is as follow:
Assume a simple polygon has n vertices, and are the arrays which stores x and y coordinates of each vertex. Consider the following algorithm to determine area of simple polygon:
POLY-AREA(X, Y, n)
1.
2. forto n-1
3.
4.
5. return
One calculation is performed for every point and constant time is required for it. The loop in line 2 iterates for n-1 times. So, the algorithm executes in linear time.
Therefore using the above algorithm one can calculate the area of n sided simple polygon in time.


Hylia¶
Hylia is a time series python library to study, process and forecast time series data produced in networking applications. The library contains a collection of multiple models from statistics (Arima, Holt-Winters) to complex deep learning models to train and inference forecasting challenges. The Hylia library makes it easy to combine the predictions of several models. It can be trained on multiple time series and It supports both univariate and multivariate time series and models.
Citing Hylia:
If you are citing Hylia in research or scientific paper, please cite this page as the resource.
URL: https://www.osti.gov/biblio/1769344.
A formatted version of the citation would look like this:
@techreport{kiran2020hylia,
title={Hylia: Time-Series Library for Network Operations (Hylia) v1},
author={Kiran, Mariam and Krishnaswamy, Nandini and Mohammed, Bashir and others},
year={2020},
institution={Lawrence Berkeley National Lab.(LBNL), Berkeley, CA (United States)}
}


Homeodynamics:
Any dynamic process that spontaneously reduces a system's constraints to their minimum and thus more evenly distributes system properties across space and time.
The second law of thermodynamics describes the paradigm case.
Orthograde: Changes in the state of a system that are consistent with the spontaneous, 'natural- tendency to change without external interference
Contragrade: Changes in the state of a system that must be extrinsically forced because they run counter to orthograde (aka spontaneous) tendencies
contraints here
emergent here??
A cartoon characterization of the asymmetry implicit in thermodynamic change from a constrained ('ordered') state to a less constrained ('disordered') state, which tends to occur spontaneously (an orthograde process), contrasted with the reversed direction of change, which does not tend to occur spontaneously (a contragrade process), and so only tends to occur in response to the imposition of highly constrained external work (arrows in the image on the right).
Deacon, Terrence W. (2011-11-14). Incomplete Nature: How Mind Emerged from Matter (p. 221). Norton. Kindle Edition.
The title of this chapter - Homeodynamics - coins a term that I think can more generally describe this most basic orthograde dynamic wherever we encounter it. It is a dynamic that spontaneously reduces constraints to their minimum and thus more evenly distributes whatever property is being changed from moment to moment and locus to locus. Distinguishing the general case from the specific case of thermodynamic entropy increase becomes indispensable as we begin to analyze the dynamic interdependencies that span more than two levels of scale. For example, it allows us to distinguish a powerful general principle: orthograde and contragrade dynamics reverse in dominance as we consider adjacent levels of process.
Deacon, Terrence W. (2011-11-14). Incomplete Nature: How Mind Emerged from Matter (pp. 232-233). Norton. Kindle Edition.
And, as we've seen, this contragrade interatomic dynamic enables the higher-level thermodynamic tendency to be actualized - an orthograde tendency dependent on lower-level contragrade interactions. For our purposes, what is important is that this alternation of orthograde and contragrade processes at adjacent hierarchic levels of compositional scale has no necessary upper bound, and with each hierarchic iteration the potential complexity of these interactions increases. As we will see in the next two chapters, the special contragrade relationships that can be generated by the juxtaposition of different orthograde processes can produce complex forms of constraint. By these means, simple homeodynamic tendencies can be organized into highly indirect and convoluted relationships with respect to one another, ultimately producing unprecedented higher-level orthograde tendencies. This is the engine of emergence.
Deacon, Terrence W. (2011-11-14). Incomplete Nature: How Mind Emerged from Matter (pp. 233-234). Norton. Kindle Edition.


# Imports
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
In this blog I am going to explain Regression, it’s type, and how can we code a Linear Regression from scratch in Python. First we will look at the concepts and maths behing this algorithm and then we will implement it using Python
, NumPy
and Pandas
.
By the end of this blog you will bw able to understand:
- What is Regression and classification?
- Difference between Linear Regression and Non-Linear Regression.
- Hyothesis(model) of Linear Regression
- Cost function
- Gradient Descent
- How to code all these equation and algorithm in Python?
Regression vs Classification
In Machine Learning, If the output variable has continous range, and we have to find the relationship between the the input and output variable(s). This is called Regression. Examples include - House Price, Salary, etc
In contrast, if the the output has descrete range. It is then called Classification. Examples include - Cat vs Dog, Spam/Not Spam, etc
These regresssion models can be used for both Inference and Prediction.
In this blog we are focused to get Prediction using Regression.
Regression
Regression in itself can be of multiple types - Linear and Non-Linear Regression.
Linear Regression
- When the model relates the input(independent) and output(dependent) varibale in straight line. Simple Linear Regression is subset of the prior, when there is only single input variable is present.
Non-Linear Regression
- When the model relates the input and output varibale in curved line.
I am thowing a lot of jargons here so let me clarify few things before moving forward.
Input Variable/Independent Variable/Feature are used interchangibly. In ML context these are the values which we are going to have to make the Prediction. For example :- For House Price Prediction we need Size. Size is the Input/Feature.
Output Varibale/Dependent Varibale/Target are those values which we are need to predict. Price is the target in case of the last example.
Linear Regression
In this blog we are going to implement Simple Linear Regression on a small dataset
. The information about the dataset is given below.
We are using a small dataset from here. This a data about Cricket Chirps Vs. Temperature. We will use linear regression to fit model.
# loading data
= pd.read_excel('slr02.xls', engine='xlrd') data
*** No CODEPAGE record, no encoding_override: will use 'iso-8859-1'
Now we look into data we see there are two columns X
and Y
, were
X = chirps/sec for the striped ground cricket
Y = temperature in degrees Fahrenheit
# visualise data
data.head()
| X | Y | |
|---|---|---|
| 0 | 20.000000 | 88.599998 |
| 1 | 16.000000 | 71.599998 |
| 2 | 19.799999 | 93.300003 |
| 3 | 18.400000 | 84.300003 |
| 4 | 17.100000 | 80.599998 |
# data we got are in pandas dataframe format
# we need to cast it in numpy array for calulations
= np.array(data.X)
X = np.array(data.Y) Y
# Now we have two arrays. One containing input features and other array has output features
# visualise casted data
print(X,Y)
print("Data points:", len(X))
[20. 16. 19.79999924 18.39999962 17.10000038 15.5
14.69999981 17.10000038 15.39999962 16.20000076 15. 17.20000076
16. 17. 14.39999962] [88.59999847 71.59999847 93.30000305 84.30000305 80.59999847 75.19999695
69.69999695 82. 69.40000153 83.30000305 79.59999847 82.59999847
80.59999847 83.5 76.30000305]
Data points: 15
# function for plotting data points
def plot_points(X, Y, xlabel, ylabel):
"""Plot points given X and Y co-ordinates and labels them"""
'o')
plt.plot(X, Y,
plt.xlabel(xlabel) plt.ylabel(ylabel)
This is the scatter-plot visualization of the data we have used. On X-axis we have chirps/sec and on the Y-axis we have Temperature.
# plot data points
"chirps/sec for the striped ground cricket", "temperature in degrees Fahrenheit") plot_points(X, Y,
Linear Regression hypothesis/model
As mentioned earlier in the definition, Linear Regression is a way to find relationship between the input varibale (chirps/sec) and output varibale (temperature) with the help of a straight line.
Our main objective is to fit a straight line though these data points. As we can see the input and output are linearly dependent (as input varibale value increases/decrease, output variable value also tend to increase/decrease).
Objective : To find a best fit straight line through data points
Best fit implies that the distance between the points and line should be minimum.
In the figure, \(d_1,d_2,d_3,...., d_n\) represents the distance between the point and the line and our goal is to minimize the sum of these distances.
Hypothesis Function: A stright line can be represented like \(h_{\theta}(x)={\theta}_0 + {\theta}_1x\). Where \({\theta}_0\) is the intercept and \({\theta}_1\) is the slope of the line. We have to find this \({\theta}_0\) and \({\theta}_1\)
Before moving forward, These are some conventions we have taken for varibale names and mathematical equations.
\(m\) : number of training examples (m=15, in this case)
\(X\) : input / features
\(Y\) : output/ target
\(x^{(i)}, y^{(i)}\) : \(i^{th}\) training data.
\({\theta}_0\) ,\({\theta}_1\): Model Parameters / Weights
# initialised random thetas
2)
np.random.seed(= np.random.rand(2,1)
theta # hypothesis of model
def hypothesis(X, theta):
"""Predicts output feature given input feature and theta"""
return theta[0] + theta[1] * X
Let’s plot the initial line to check how ot fits our data.
# plots line of regression
def draw_line(theta, X):
"""Plot a line from slope and intercept"""
= X
x_vals = hypothesis(x_vals, theta)
y_vals '--') plt.plot(x_vals, y_vals,
We want to plot data points and line of regession on same plot to see if we are progressing as we train our model
# plots points and lines
def draw_points_and_lines(X, Y, xlabel, ylabel, theta):
"""Draws lines and points"""
plot_points(X, Y, xlabel, ylabel) draw_line(theta, X)
Now without training our model let’s were the line of regression lies
# draw line of regression without traing model
"chirps/sec for the striped ground cricket", "temperature in degrees Fahrenheit", theta) draw_points_and_lines(X, Y,
Cost Function
Cost function givies us measure of how much we are error the hypothesis is making? These errors are measured as mean of squared error( \(d_1,d_2,d_3,...., d_n\)) terms.
We will be using squared error cost function which is formulated as,
\(d_{i} = h_{\theta}(x^{(i)}) - y^{(i)}\)(error)
errors can be both postive and negetive in direction so, we sqaure this error term to make all the error terms positive(+ve).
\((d_{i})^2 = (h_{\theta}(x^{(i)}) - y^{(i)})^2\)(squared error)
We need to the line which minimizes the mean of these squared error terms.
Cost function - (Mean Sqaured Error) \[J({{\theta}_0, {\theta}_1}) = \frac{1}{2m}\sum_{i=0}^{i=n-1}(h_{\theta}(x^{(i)}) - y^{(i)})^2\]
Note:- Mean Squared Error is divided by 2 beacuse it will later help when finding gradient of the function
We need to minimize this Cost function with respect to \({\theta}_0, {\theta}_1\). After optimization, it will give us the value of parameters(thetas) which in turn will give us the best fit line.
# cost function
= len(X)
m def cost(X, Y, theta):
"""Returns cost"""
return (1/(2*m)) * np.sum((hypothesis(X, theta) - Y) ** 2)
# initial cost without trraining model
cost(X, Y, theta)
3154.8870744571304
We have now defined a Cost function which tells us how wrong we are from actual label(target). The less the cost - the better. Right now with just random value of theta we have attained a very gigh cost. We need to minimize it. In this blog we are going to use a technique called Gradient Descent for minimization of the cost function.
Gradient Descent / Model Training
Gradient Descent is the optimization algorithm used to finding the value of paramerter which minimizes the value of cost function. In our case we are minimizing MSE Cost function with the help of this algorithm.
This algorithm works by iteratively updating the values of the parameters(thetas) in the direction of the negative gradient of the cost function with respect to the parameters. This means that at each iteration, the parameters are updated in the direction that reduces the value of the cost function.
The learning rate(denoted by alpha, \(\alpha\)) is the one of the hyperparameter(parameter, for which the value needs to decided manually). We need to set learning rate very carefully, It decides the step size in the negative direction of the gradient(slope).
A higher learning rate can lead to faster convergence, but can also make the algorithm more likely to overshoot the minimum. A lower learning rate can be more stable, but can also lead to slower convergence.
To make a analogy, We can think Gradient descent algorithm like - We are blindfolded and descesnding from a hill(cost function). We will take a step forward evaulate the slope(gradient) and then move according to that. When we will reach bottom of the hill, then the step in any direction will take us upward and thus we know that be have reaced bottom(convergence).
# minimize cost through gradient descent - Model training
def gradient_descent(X, Y, theta, alpha, steps):
for i in range(steps):
= cost(X, Y, theta)
old_cost = ((1/m) * np.sum(hypothesis(X, theta) - Y))
grad0 = ((1/m) * np.dot((hypothesis(X, theta) - Y), X))
grad1 = theta[0] - alpha * grad0
temp0 = theta[1] - alpha * grad1
temp1 0] = temp0
theta[1] = temp1
theta[= cost(X, Y, theta)
new_cost if i%10 == 0: #print every 10th epoch
if new_cost > old_cost:
print("WARNING!!! COST INCREASING")
else:
print("Cost Decresing", new_cost)
# train model of 150 iterations
=0.0001, steps=200)
gradient_descent(X, Y, theta, alpha theta
Cost Decresing 2980.5693134787443
Cost Decresing 1689.4327589238676
Cost Decresing<PHONE_NUMBER>612664
Cost Decresing<PHONE_NUMBER>760693
Cost Decresing<PHONE_NUMBER>583842
Cost Decresing<PHONE_NUMBER>0660252
Cost Decresing<PHONE_NUMBER>2551906
Cost Decresing 64.18101546051345
Cost Decresing 40.31272552858337
Cost Decresing 26.815983067351162
Cost Decresing 19.184003183555753
Cost Decresing 14.868350028147061
Cost Decresing 12.427968518501714
Cost Decresing 11.047990290588316
Cost Decresing 10.267634588205215
Cost Decresing 9.826345333024406
Cost Decresing 9.576786793160268
Cost Decresing 9.435645548985352
Cost Decresing 9.35581065326909
Cost Decresing 9.310642443891272
array([[0.72065763],
[4.73296698]])
After training we can clearly see we have reduced cost, and the cost has converged at a a fixed place, training it furteher will not lead lower cost, thus we can stop the furter interations. We have now found those parameters which gave us minimized cost.
# cost after traing model
cost(X, Y, theta)
9.287030172925407
# value of theta after training
theta
array([[0.72065763],
[4.73296698]])
# regression line after traing model
"chirps/sec for the striped ground cricket", "temperature in degrees Fahrenheit", theta) draw_points_and_lines(X, Y,
We can clearly see we have fitted line to the points. Thus we have successfully used linear regression to train a model.
Prediction/Inference
Now that we have found the appropriate line of fit through thsese points. We can also use this line to infer values on input values(chirps/sec) which are not present in the training data.
# Temprature when 19 chirps/sec
= np.array([19])
x print(f"Predicted temprature when ground cricket chips 19 times a sec is {hypothesis(x, theta)[0]} degrees Farenheight.")
Predicted temprature when ground cricket chips 19 times a sec is 90.64703016147293 degrees Farenheight.


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Examples
simple print example
-- just prints a section of the demo page
print with preview example
-- shows a preview, user then hits button the print or abort/revert
restyling print example
-- restyles the content for preview & printing


Introduction to Microtransat and our Boat
Microtransat
Microtransat is a competition where teams make small unmanned vessels to cross the Atlantic ocean. The general outline of the rules is that your boat must
- Be less than 2.4 meters front to back
- Transmit it's location at least every 6 hours
- Be energetically autonomous
The full
rules can be found on their website.
The Boat
Instead of the tradiational sailboat design, Autonautilus uses an array of solar panels to generate power to drive electric motors for propulsion. A trimaran is used to maximize area to put solar panels as well as improve stability.
Key Technologies
Solar Powered Drivetrain
A large array of solar panels and batteries the boat can maintain thrust continually and power the onboard computer and navigation. This allows for long distance travel at a useful speed without the need for fossile fuels.
Galvanic Cells
High performance galvantic cells on the hull of the boat help prevent corrosion on any exposed metal of the boat. Additionally it can generate a small amount of energy to run the computer if the main solar battery runs out.
Autonomous Navigation
Using average weather conditions such as wind, ocean currents, and sunlight the boat can plot and follow the fastest path. Additionally using publically avaliable forcast data it can change it's course to optimize for predicted weather conditions.


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Barcode Scanner continuously scans a square region shown on your screen — just line up the phone so the barcode is completely inside the viewfinder rectangle:
IMPORTANT: 1D barcodes like those found on products require a phone with autofocus. Without it, only QR Codes and Data Matrix codes will be scannable.
When a barcode is read, a beep sound will play and you'll see the results of the scan, a description of what the barcode contains, and options to take action on the contents.
If you're having trouble scanning, make sure to hold the phone steady. If the camera is unable to focus, try moving the phone further or closer from the barcode.


8MM A movie review by Joe Barlow (c) Copyright 1999
STARRING: Nicolas Cage, Joaquin Phoenix, James Gandolfini, Peter Stormare, Christopher Bauer, Catherine Keener DIRECTOR: Joel Schumacher WRITER: Andrew Kevin Walker RATED: R RELEASED: 1999
RATING: *** (out of a possible ****)
Joel Schumacher's "8mm" is film noir of a grand tradition, a chilling descent into the seedy underworld of the pornographic industry. Filmed almost entirely in shadow, the movie allows us a merciless glimpse into a culture comprised of darkness, money, violence, and the unquenchable thirst for sex which drives mankind. There's authenticity to the presentation, and "8mm" makes us squirm uncomfortably in our seats, simultaneously appalled and intrigued.
Tom Welles (Nicolas Cage) is a private investigator who operates discretely, a trait which allows him to work with many high-profile clients. ("I've been privilaged to provide services for people I admire," he allows.) When the widow of a much loved bank president asks him to investigate a strange item found in her late husband's vault, Welles agrees... not knowing that the case will come to consume his every thought.
The item in question is a short reel of Super 8 film, which appears at first glance to be nothing more than a typical home movie. But upon screening the footage, Welles is aghast at what he sees: a teenage girl being raped and brutally murdered-- a so-called "snuff film." The confused widow wants to know what this hideous item is doing in her husband's possession, as she knew him as a kind, sweet man. Welles, horrified, vows to uncover the origin of the film.
Welles gets assistance in his investigation from a friendly porn dealer named Max (Joaquin Phoenix), who hides Truman Captoe novels behind phony porn dustjackets, so he can read "real" books at work without raising his customers' eyebrows. This tells us a good deal about Max, a bright young man who hates the way he makes his living, but doesn't know any other way to survive. The interplay between Welles and his sidekick punctuates the landscape of "8mm" with surprising warmth and humor-- quite a feat considering the subject matter we're dealing with.
The photography of "8mm" is flat, drab, and almost completely devoid of color; it seems to be an intentional attempt to recreate the underlit feel so common in Super 8 movies, as well as the shadowy intrigue of past film noirs. The musical score seems to stalk the audience, and is as drenched in shadow and mystery as the photo- graphy itself. It adds a great deal of atmosphere to a movie already overflowing with it.
"8mm" is clever in the way it handles its pornographic elements, opting to tease us with the images from the snuff film rather than shoving them in our faces. It wisely leaves most of the action and violence off-screen. This is the correct approach to take-- as Max himself points out, this type of imagery gets in one's head. ("Once you see some of this stuff, you can't unsee it.")
1997's "Batman and Robin," also helmed by Schumacher, proved that the director knew how to construct pretty pictures of a city in turmoil, even if the story repeatedly fell flat on its face. (Earlier this year, British comic book fans named it the worst movie ever made about a comic book character.) But "8mm" proves what I have long since suspected: "Batman and Robin's" problems stemmed not from Schumacher's inability to make a compelling film, but from Akiva Goldsman's insultingly bad script. Schumacher has a more talented scribe this time around: Andrew Kevin Walker, the author of "Seven," one of the finest thrillers of the past decade. "8mm" is an impressive rebound for Schumacher, and a fine continuation of a strong track record for Walker.
That doesn't mean they've achieved perfection, however. The story runs a tad too long, a small flaw which could have been fixed with a little more attention to editing and pacing. And Catherine Keener is basically wasted in her role as Welles's wife, Amy; she does little but whine, seemingly unconcerned with the case her husband is working on. I bristled nearly every time she came on the screen.
Much more interesting is the character of Janet Matthews, the mother of the girl in the movie, who becomes Welles's unwitting ally in the quest for the snuff film's origin. In a lesser film, a romantic interest would develop between these two characters, who have numerous scenes alone together. "8mm" is smarter than that, however, and keeps their relation- ship on a professional level, although Welles clearly grows fond of her. He is able to draw strength from Janet's love for her daughter in one chilling climactic scene, the exact details of which I won't spoil.
This is a work of remarkable vision. "8mm," like "Seven" and "Silence of the Lambs," is eerie, dark, and disturbing. If you dislike films of this genre, you probably won't have a great time. But for those who enjoy a good thriller, I recommend this particular walk on the wild side.
("8mm" hits theaters on Friday, February 26.)
E-Mail<EMAIL_ADDRESS>Joe Barlow on Film: http://www.ipass.net/~jbarlow/film.htm
If you'd like to receive new film reviews by e-mail, please write to<EMAIL_ADDRESS>-----------== Posted via Deja News, The Discussion Network ==---------- http://www.dejanews.com/ Search, Read, Discuss, or Start Your Own
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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belongs to the author.
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This document primarily presents a quick start guide to the usage of the Gina Cody School of Engineering and Computer Science compute server farm called “Speed” – the GCS ENCS Speed cluster, managed by HPC/NAG of GCS ENCS, Concordia University, Montreal, Canada.
This document contains basic information required to use “Speed” as well as tips and tricks, examples, and references to projects and papers that have used Speed. User contributions of sample jobs and/or references are welcome. Details are sent to the hpc-ml mailing list.
We receive support from the rest of AITS teams, such as NAG, SAG, FIS, and DOG.
Prepare them for big clusters:
We have a great number of open-source software available and installed on Speed – various Python, CUDA versions, C++/Java compilers, OpenGL, OpenFOAM, OpenCV, TensorFlow, OpenMPI, OpenISS, MARF [15], etc. There are also a number of commercial packages, subject to licensing contributions, available, such as MATLAB [5, 14], Abaqus [1], Ansys, Fluent [2], etc.
To see the packages available, run ls -al /encs/pkg/ on speed.encs.
In particular, there are over 2200 programs available in /encs/bin and /encs/pkg under Scientific Linux 7 (EL7).
Popular concrete examples:
Popular examples mentioned (loaded with, module):
After reviewing the “What Speed is” (Section 1.4) and “What Speed is Not” (Section 1.5), request access to the “Speed” cluster by emailing: rt-ex-hpc AT encs.concordia.ca. Faculty and staff may request the access directly. Students must include the following in their message:
In these instructions, anything bracketed like so, <>, indicates a label/value to be replaced (the entire bracketed term needs replacement).
Before getting started, please review the “What Speed is” (Section 1.4) and “What Speed is Not” (Section 1.5). Once your GCS ENCS account has been granted access to “Speed”, use your GCS ENCS account credentials to create an SSH connection to speed (an alias for speed-submit.encs.concordia.ca).
Requirements to create connections to Speed:
Open up a terminal window and type in the following SSH command being sure to replace <ENCSusername> with your ENCS account’s username.
ssh<EMAIL_ADDRESS>All users are expected to have a basic understanding of Linux and its commonly used commands.
After creating an SSH connection to “Speed”, you will need to source the “Altair Grid Engine (AGE)” scheduler’s settings file. Sourcing the settings file will set the environment variables required to execute scheduler commands.
Based on the UNIX shell type, choose one of the following commands to source the settings file.
csh/tcsh:
source /local/pkg/uge-8.6.3/root/default/common/settings.csh
Bourne shell/bash:
. /local/pkg/uge-8.6.3/root/default/common/settings.sh
In order to set up the default ENCS bash shell, executing the following command is also required:
printenv ORGANIZATION | grep -qw ENCS || . /encs/Share/bash/profile
To verify that you have access to the scheduler commands execute qstat -f -u "*". If an error is returned, attempt sourcing the settings file again.
The next step is to copy a job template to your home directory and to set up your cluster-specific storage. Execute the following command from within your home directory. (To move to your home directory, type cd at the Linux prompt and press Enter.)
cp /home/n/nul-uge/template.sh . && mkdir /speed-scratch/$USER
Tip: Add the source command to your shell-startup script.
Tip: the default shell for GCS ENCS users is tcsh. If you would like to use bash, please contact rt-ex-hpc AT encs.concordia.ca.
For new ENCS Users, and/or those who don’t have a shell-startup script, based on your shell type use one of the following commands to copy a start up script from nul-uge’s. home directory to your home directory. (To move to your home directory, type cd at the Linux prompt and press Enter.)
csh/tcsh:
cp /home/n/nul-uge/.tcshrc .
Bourne shell/bash:
cp /home/n/nul-uge/.bashrc .
Users who already have a shell-startup script, use a text editor, such as vim or emacs, to add the source request to your existing shell-startup environment (i.e., to the .tcshrc file in your home directory).
csh/tcsh: Sample .tcshrc file:
# Speed environment set up if ($HOSTNAME == speed-submit.encs.concordia.ca) then source /local/pkg/uge-8.6.3/root/default/common/settings.csh endif
Bourne shell/bash: Sample .bashrc file:
# Speed environment set up if [ $HOSTNAME = "speed-submit.encs.concordia.ca" ]; then . /local/pkg/uge-8.6.3/root/default/common/settings.sh printenv ORGANIZATION | grep -qw ENCS || . /encs/Share/bash/profile fi
Note that you will need to either log out and back in, or execute a new shell, for the environment changes in the updated .tcshrc or .bashrc file to be applied (important).
Preparing your job for submission is fairly straightforward. Editing a copy of the template.sh you moved into your home directory during Section 2.1.2 is a good place to start. You can also use a job script example from our GitHub’s (https://github.com/NAG-DevOps/speed-hpc) “src” directory and base your job on it.
Job scripts are broken into four main sections:
Directives are comments included at the beginning of a job script that set the shell and the options for the job scheduler.
The shebang directive is always the first line of a script. In your job script, this directive sets which shell your script’s commands will run in. On “Speed”, we recommend that your script use a shell from the /encs/bin directory.
To use the tcsh shell, start your script with: #!/encs/bin/tcsh
For bash, start with: #!/encs/bin/bash
Directives that start with "#$", set the options for the cluster’s “Altair Grid Engine (AGE)” scheduler. The script template, template.sh, provides the essentials:
#$ -N <jobname> #$ -cwd #$ -m bea #$ -pe smp <corecount> #$ -l h_vmem=<memory>G
Replace, <jobname>, with the name that you want your cluster job to have; -cwd, makes the current working directory the “job working directory”, and your standard output file will appear here; -m bea, provides e-mail notifications (begin/end/abort); replace, <corecount>, with the degree of (multithreaded) parallelism (i.e., cores) you attach to your job (up to 32), be sure to delete or comment out the #$ -pe smp parameter if it is not relevant; replace, <memory>, with the value (in GB), that you want your job’s memory space to be (up to 500), and all jobs MUST have a memory-space assignment.
If you are unsure about memory footprints, err on assigning a generous memory space to your job so that it does not get prematurely terminated (the value given to h_vmem is a hard memory ceiling). You can refine h_vmem values for future jobs by monitoring the size of a job’s active memory space on speed-submit with:
qstat -j <jobID> | grep maxvmem
Memory-footprint values are also provided for completed jobs in the final e-mail notification (as, “Max vmem”).
Jobs that request a low-memory footprint are more likely to load on a busy cluster.
As your job will run on a compute or GPU “Speed” node, and not the submit node, any software that is needed must be loaded by the job script. Software is loaded within the script just as it would be from the command line.
To see a list of which modules are available, execute the following from the command line on speed-submit.
module avail
To list for a particular program (matlab, for example):
module -t avail matlab
Which, of course, can be shortened to match all that start with a particular letter:
module -t avail m
Insert the following in your script to load the matlab/R2020a) module:
module load matlab/R2020a/default
Use, unload, in place of, load, to remove a module from active use.
To list loaded modules:
module list
To purge all software in your working environment:
module purge
Typically, only the module load command will be used in your script.
The last part the job script is the scripting that will be executed by the job. This part of the job script includes all commands required to set up and execute the task your script has been written to do. Any Linux command can be used at this step. This section can be a simple call to an executable or a complex loop which iterates through a series of commands.
Every software program has a unique execution framework. It is the responsibility of the script’s author (e.g., you) to know what is required for the software used in your script by reviewing the software’s documentation. Regardless of which software your script calls, your script should be written so that the software knows the location of the input and output files as well as the degree of parallelism. Note that the cluster-specific environment variable, NSLOTS, resolves to the value provided to the scheduler in the -pe smp option.
Jobs which touch data-input and data-output files more than once, should make use of TMPDIR, a scheduler-provided working space almost 1 TB in size. TMPDIR is created when a job starts, and exists on the local disk of the compute node executing your job. Using TMPDIR results in faster I/O operations than those to and from shared storage (which is provided over NFS).
An sample job script using TMPDIR is available at /home/n/nul-uge/templateTMPDIR.sh: the job is instructed to change to $TMPDIR, to make the new directory input, to copy data from $SGE_O_WORKDIR/references/ to input/ ($SGE_O_WORKDIR represents the current working directory), to make the new directory results, to execute the program (which takes input from $TMPDIR/input/ and writes output to $TMPDIR/results/), and finally to copy the total end results to an existing directory, processed, that is located in the current working directory. TMPDIR only exists for the duration of the job, though, so it is very important to copy relevant results from it at job’s end.
Now, let’s look at a basic job script, tcsh.sh in Figure 1 (you can copy it from our GitHub page or from /home/n/nul-uge).
The first line is the shell declaration (also know as a shebang) and sets the shell to tcsh. The lines that begin with #$ are directives for the scheduler.
The script then:
The scheduler command, qsub, is used to submit (non-interactive) jobs. From an ssh session on speed-submit, submit this job with qsub ./tcsh.sh. You will see, "Your job X ("qsub-test") has been submitted". The command, qstat, can be used to look at the status of the cluster: qstat -f -u "*". You will see something like this:
queuename qtype resv/used/tot. load_avg arch states ---------------------------------------------------------------------------------<EMAIL_ADDRESS>BIP 0/0/32 0.01 lx-amd64 ---------------------------------------------------------------------------------<EMAIL_ADDRESS>BIP 0/0/32 0.01 lx-amd64 ---------------------------------------------------------------------------------<EMAIL_ADDRESS>BIP 0/0/32 0.01 lx-amd64 ---------------------------------------------------------------------------------<EMAIL_ADDRESS>BIP 0/0/32 0.01 lx-amd64 ---------------------------------------------------------------------------------<EMAIL_ADDRESS>BIP 0/0/32 0.02 lx-amd64<PHONE_NUMBER>0 qsub-test nul-uge r 12/03/2018 16:39:30 1 62624 0.09843 case_talle x_yzabc r 11/09/2021 16:50:09 32 ---------------------------------------------------------------------------------<EMAIL_ADDRESS>BIP 0/0/32 0.01 lx-amd64 ---------------------------------------------------------------------------------<EMAIL_ADDRESS>BIP 0/0/32 0.04 lx-amd64 ---------------------------------------------------------------------------------<EMAIL_ADDRESS>BIP 0/0/32 0.01 lx-amd64 ---------------------------------------------------------------------------------<EMAIL_ADDRESS>BIP 0/0/32 0.01 lx-amd64 ---------------------------------------------------------------------------------<EMAIL_ADDRESS>BIP 0/32/32 32.72 lx-amd64 62624 0.09843 case_talle x_yzabc r 11/09/2021 16:50:09 32 ---------------------------------------------------------------------------------<EMAIL_ADDRESS>BIP 0/32/32 32.08 lx-amd64 62679 0.14212 CWLR_DF a_bcdef r 11/10/2021 17:25:19 32 ---------------------------------------------------------------------------------<EMAIL_ADDRESS>BIP 0/32/32 32.10 lx-amd64 62749 0.09000 CLOUDY z_abc r 11/11/2021 21:58:12 32 ---------------------------------------------------------------------------------<EMAIL_ADDRESS>BIP 0/4/32 0.03 lx-amd64 62753 82.47478 matlabLDPa b_bpxez r 11/12/2021 08:49:52 4 ---------------------------------------------------------------------------------<EMAIL_ADDRESS>BIP 0/32/32 32.31 lx-amd64 62751 0.09000 CLOUDY z_abc r 11/12/2021 06:03:54 32 ---------------------------------------------------------------------------------<EMAIL_ADDRESS>BIP 0/32/32 32.22 lx-amd64 --------------------------------------------------------------------------------- ... ---------------------------------------------------------------------------------<EMAIL_ADDRESS>BIP 0/32/32 2.78 lx-amd64 62754 7.22952 qlogin-tes a_tiyuu r 11/12/2021 10:31:06 32 ---------------------------------------------------------------------------------<EMAIL_ADDRESS>BIP 0/0/32 0.03 lx-amd64 etc.
Remember that you only have 30 seconds before the job is essentially over, so if you do not see a similar output, either adjust the sleep time in the script, or execute the qstat statement more quickly. The qstat output listed above shows you that your job is running on node speed-05, that it has a job number of 144, that it was started at 16:39:30 on 12/03/2018, and that it is a single-core job (the default).
Once the job finishes, there will be a new file in the directory that the job was started from, with the syntax of, "job name".o"job number", so in this example the file is, qsub test.o144. This file represents the standard output (and error, if there is any) of the job in question. If you look at the contents of your newly created file, you will see that it contains the output of the, module list command. Important information is often written to this file.
Congratulations on your first job!
Here are useful job-management commands:
In addition to the basic qsub options presented earlier, there are a few additional options that are generally useful:
Array jobs are those that start a batch job or a parallel job multiple times. Each iteration of the job array is called a task and receives a unique job ID.
To submit an array job, use the t option of the qsub command as follows:
qsub -t n[-m[:s]] <batch_script>
-t Option Syntax:
Examples:
Output files for Array Jobs:
The default and output and error-files are job_name.[o|e]job_id and
job_name.[o|e]job_id.task_id. This means that Speed creates an output and an error-file for each
task generated by the array-job as well as one for the super-ordinate array-job. To alter this behavior
use the -o and -e option of qsub.
For more details about Array Job options, please review the manual pages for qsub by executing the following at the command line on speed-submit man qsub.
For jobs that can take advantage of multiple machine cores, up to 32 cores (per job) can be requested in your script with:
#$ -pe smp [#cores]
Do not request more cores than you think will be useful, as larger-core jobs are more difficult to schedule. On the flip side, though, if you are going to be running a program that scales out to the maximum single-machine core count available, please (please) request 32 cores, to avoid node oversubscription (i.e., to avoid overloading the CPUs).
Core count associated with a job appears under, “states”, in the, qstat -f -u "*", output.
Job sessions can be interactive, instead of batch (script) based. Such sessions can be useful for testing and optimising code and resource requirements prior to batch submission. To request an interactive job session, use, qlogin [options], similarly to a qsub command-line job (e.g., qlogin -N qlogin-test -l h_vmem=1G). Note that the options that are available for qsub are not necessarily available for qlogin, notably, -cwd, and, -v.
The scheduler presents a number of environment variables that can be used in your jobs. Three of the more useful are TMPDIR, SGE_O_WORKDIR, and NSLOTS:
In Figure 2 is a sample script, using all three.
Some programs effect their parallel processing via MPI (which is a communication protocol). An example of such software is Fluent. MPI needs to have ‘passwordless login’ set up, which means SSH keys. In your NFS-mounted home directory:
The following documentation is specific to the Speed HPC Facility at the Gina Cody School of Engineering and Computer Science.
To create an anaconda environment in your speed-scratch directory, use the prefix option when executing conda create. For example, to create an anaconda environment for ai_user, execute the following at the command line:
conda create --prefix /speed-scratch/a_user/myconda
Note: Without the prefix option, the conda create command creates the environment in texttta_user’s home directory by default.
List Environments. To view your conda environments, type: conda info --envs
# conda environments: # base * /encs/pkg/anaconda3-2019.07/root /speed-scratch/a_user/myconda
Activate an Environment. Activate the environment speedscratcha_usermyconda as follows
conda activate /speed-scratch/a_user/myconda
After activating your environment, add pip to your environment by using
conda install pip
This will install pip and pip’s dependencies, including python, into the environment.
Important Note: pip (and pip3) are used to install modules from the python distribution while conda install installs modules from anaconda’s repository.
The job script in Figure 3 runs Fluent in parallel over 32 cores. Of note, we have requested e-mail notifications (-m), are defining the parallel environment for, fluent, with, -sgepe smp (very important), and are setting $TMPDIR as the in-job location for the “moment” rfile.out file (in-job, because the last line of the script copies everything from $TMPDIR to a directory in the user’s NFS-mounted home). Job progress can be monitored by examining the standard-out file (e.g., flu10000.o249), and/or by examining the “moment” file in /disk/nobackup/<yourjob> (hint: it starts with your job-ID) on the node running the job. Caveat: take care with journal-file file paths.
The following steps describing how to create an efficientdet environment on Speed, were submitted by a member of Dr. Amer’s research group.
pip install tensorflow==2.7.0 pip install lxml>=4.6.1 pip install absl-py>=0.10.0 pip install matplotlib>=3.0.3 pip install numpy>=1.19.4 pip install Pillow>=6.0.0 pip install PyYAML>=5.1 pip install six>=1.15.0 pip install tensorflow-addons>=0.12 pip install tensorflow-hub>=0.11 pip install neural-structured-learning>=1.3.1 pip install tensorflow-model-optimization>=0.5 pip install Cython>=0.29.13 pip install git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI
Jobs that call java have a memory overhead, which needs to be taken into account when assigning a value to h_vmem. Even the most basic java call, java -Xmx1G -version, will need to have, -l h_vmem=5G, with the 4-GB difference representing the memory overhead. Note that this memory overhead grows proportionally with the value of -Xmx. To give you an idea, when -Xmx has a value of 100G, h_vmem has to be at least 106G; for 200G, at least 211G; for 300G, at least 314G.
The primary cluster has two GPU nodes, each with six Tesla (CUDA-compatible) P6 cards: each card has 2048 cores and 16GB of RAM. Though note that the P6 is mainly a single-precision card, so unless you need the GPU double precision, double-precision calculations will be faster on a CPU node.
Job scripts for the GPU queue differ in that they do not need these statements:
#$ -pe smp <threadcount> #$ -l h_vmem=<memory>G
But do need this statement, which attaches either a single GPU, or, two GPUs, to the job:
#$ -l gpu=[1|2]
Single-GPU jobs are granted 5 CPU cores and 80GB of system memory, and dual-GPU jobs are granted 10 CPU cores and 160GB of system memory. A total of four GPUs can be actively attached to any one user at any given time.
Once that your job script is ready, you can submit it to the GPU queue with:
qsub -q g.q ./<myscript>.sh
And you can query nvidia-smi on the node that is running your job with:
ssh <username>@speed[-05|-17] nvidia-smi
Status of the GPU queue can be queried with:
qstat -f -u "*" -q g.q
Very important note regarding TensorFlow and PyTorch: if you are planning to run TensorFlow
and/or PyTorch multi-GPU jobs, do not use the tf.distribute and/or
torch.nn.DataParallel functions, as they will crash the compute node (100% certainty). This
appears to be the current hardware’s architecture’s defect. The workaround is to either manually
effect GPU parallelisation (TensorFlow has an example on how to do this), or to run on a single
GPU.
Important
Users without permission to use the GPU nodes can submit jobs to the g.q queue but those jobs will hang and never run.
There are two GPUs in both speed-05 and speed-17, and one in speed-19. Their availability is seen with, qstat -F g (note the capital):
queuename qtype resv/used/tot. load_avg arch states --------------------------------------------------------------------------------- ... ---------------------------------------------------------------------------------<EMAIL_ADDRESS>BIP 0/0/32 0.04 lx-amd64 hc:gpu=6 ---------------------------------------------------------------------------------<EMAIL_ADDRESS>BIP 0/0/32 0.01 lx-amd64 hc:gpu=6 --------------------------------------------------------------------------------- ... ---------------------------------------------------------------------------------<EMAIL_ADDRESS>BIP 0/32/32 32.37 lx-amd64 hc:gpu=1 --------------------------------------------------------------------------------- etc.
This status demonstrates that all five are available (i.e., have not been requested as resources). To specifically request a GPU node, add, -l g=[#GPUs], to your qsub (statement/script) or qlogin (statement) request. For example, qsub -l h_vmem=1G -l g=1 ./count.sh. You will see that this job has been assigned to one of the GPU nodes:
queuename qtype resv/used/tot. load_avg arch states ---------------------------------------------------------------------------------<EMAIL_ADDRESS>BIP 0/0/32 0.01 lx-amd64 hc:gpu=6 ---------------------------------------------------------------------------------<EMAIL_ADDRESS>BIP 0/0/32 0.01 lx-amd64 hc:gpu=6 ---------------------------------------------------------------------------------<EMAIL_ADDRESS>BIP 0/1/32 0.04 lx-amd64 hc:gpu=0 (haff=1.000000)<PHONE_NUMBER>0 count.sh sbunnell r 03/07/2019 02:39:39 1 --------------------------------------------------------------------------------- etc.
And that there are no more GPUs available on that node (hc:gpu=0). Note that no more than two GPUs can be requested for any one job.
When calling CUDA within job scripts, it is important to create a link to the desired CUDA libraries and set the runtime link path to the same libraries. For example, to use the cuda-11.5 libraries, specify the following in your Makefile.
-L/encs/pkg/cuda-11.5/root/lib64 -Wl,-rpath,/encs/pkg/cuda-11.5/root/lib64
In your job script, specify the version of gcc to use prior to calling cuda. For example: module load gcc/8.4 or module load gcc/9.3
It is not possible to create a qlogin session on to a node in the GPU Queue (g.q). As direct logins to these nodes is not available, jobs must be submitted to the GPU Queue in order to compile and link.
We have several versions of CUDA installed in:
/encs/pkg/cuda-11.5/root/ /encs/pkg/cuda-10.2/root/ /encs/pkg/cuda-9.2/root
For CUDA to compile properly for the GPU queue, edit your Makefile replacing usrlocalcuda with one of the above.
The cluster is, “first come, first served”, until it fills, and then job position in the queue is based upon past usage. The scheduler does attempt to fill gaps, though, so sometimes a single-core job of lower priority will schedule before a multi-core job of higher priority, for example.
Scripts can live in your NFS-provided home, but any substantial data need to be in your cluster-specific directory (located at /speed-scratch/<ENCSusername>/).
NFS is great for acute activity, but is not ideal for chronic activity. Any data that a job will read more than once should be copied at the start to the scratch disk of a compute node using $TMPDIR (and, perhaps, $SGE_O_WORKDIR), any intermediary job data should be produced in $TMPDIR, and once a job is near to finishing, those data should be copied to your NFS-mounted home (or other NFS-mounted space) from $TMPDIR (to, perhaps, $SGE_O_WORKDIR). In other words, IO-intensive operations should be effected locally whenever possible, saving network activity for the start and end of jobs.
HPC Committee’s initial batch about 6 students (end of 2019):
NAG’s MAC spoofer analyzer [9, 8], such as https://github.com/smokhov/atsm/tree/master/examples/flucid
S4 LAB/GIPSY R&D Group’s:
Phase 3 had 4 vidpro nodes added from Dr. Amer totalling 6x P6 and 6x V100 GPUs added.
Phase 2 saw 6x NVIDIA Tesla P6 added and 8x more compute nodes. The P6s replaced 4x of FirePro S7150.
Phase 1 of Speed was of the following configuration:
This section describes how to use the “bash shell” on Speed. Review subsubsection 2.1.2 to ensure that your bash enviroment is set up.
In order to set your login shell to bash on Speed, your login shell on all GCS servers must be changed to bash. To make this change, create a ticket with the Service Desk (or email help at concordia.ca) to request that bash become your default login shell for your ENCS user account on all GCS servers.
To move to the bash shell, type bash at the command prompt. For example:
[speed-27] [/home/a/a_user] > bash bash-4.4$ echo $0 bash
Note how the command prompt changed from [speed-27] [/home/a/a_user] > to bash-4.4$ after entering the bash shell.
To execute bash scripts on Speed:
The Speed GitHub contains a sample bash job script.
The ‘‘Disk quota exceeded’’ Error occurs when your application has run out of disk space to write to. On Speed this error can be returned when:
The use local disk space is generally recommended for IO intensive operations. However, as the size of /tmp on speed nodes is 1GB it can be necessary for scripts to store temporary data elsewhere. Review the documentation for each module called within your script to determine how to set working directories for that application. The basic steps for this solution are:
Create a working directory in speed-scratch for output files. For example, this command creates subdirectory called output in a_user’s speed-scratch directory:
mkdir -m 750 /speed-scratch/a_user/output
To create a subdirectory for recovery files:
mkdir -m 750 /speed-scratch/a_user/recovery
Create directories for recovery, temporary, and configuration files. For example, to create these directories for a_user:
mkdir -m 750 -p /speed-scratch/a_user/comsol/{recovery,tmp,config}
Add the following command switches to the COMSOL command to use the directories created for a_user above:
-recoverydir /speed-scratch/a_user/comsol/recovery -tmpdir /speed-scratch/a_user/comsol/tmp -configuration/speed-scratch/a_user/comsol/config
Below is a list of resources and facilities similar to Speed at various capacities. Depending on your research group and needs, they might be available to you. They are not managed by HPC/NAG of AITS, so contact their respective representatives.
[1] 3DS. Abaqus. [online], 2019–2021. https://www.3ds.com/products-services/simulia/products/abaqus/.
[2] ANSYS. FLUENT. [online], 2000–2012. http://www.ansys.com/Products/Simulation+Technology/Fluid+Dynamics/ANSYS+FLUENT.
[3] Amine Boukhtouta, Nour-Eddine Lakhdari, Serguei A. Mokhov, and Mourad Debbabi. Towards fingerprinting malicious traffic. In Proceedings of ANT’13, volume 19, pages 548–555. Elsevier, June 2013.
[4] Amy Brown and Greg Wilson, editors. The Architecture of Open Source Applications: Elegance, Evolution, and a Few Fearless Hacks, volume I. aosabook.org, March 2012. Online at http://aosabook.org.
[5] MathWorks. MATLAB. [online], 2000–2012. http://www.mathworks.com/products/matlab/.
[6] Serguei A. Mokhov. The use of machine learning with signal- and NLP processing of source code to fingerprint, detect, and classify vulnerabilities and weaknesses with MARFCAT. Technical Report NIST SP 500-283, NIST, October 2011. Report: http://www.nist.gov/manuscript-publication-search.cfm?pub_id=909407, online e-print at http://arxiv.org/abs/1010.2511.
[7] Serguei A. Mokhov. Intensional Cyberforensics. PhD thesis, Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada, September 2013. Online at http://arxiv.org/abs/1312.0466.
[8] Serguei A. Mokhov, Michael J. Assels, Joey Paquet, and Mourad Debbabi. Automating MAC spoofer evidence gathering and encoding for investigations. In Frederic Cuppens et al., editors, Proceedings of The 7th International Symposium on Foundations & Practice of Security (FPS’14), LNCS 8930, pages 168–183. Springer, November 2014. Full paper.
[9] Serguei A. Mokhov, Michael J. Assels, Joey Paquet, and Mourad Debbabi. Toward automated MAC spoofer investigations. In Proceedings of C3S2E’14, pages 179–184. ACM, August 2014. Short paper.
[10] Serguei A. Mokhov and Scott Bunnell. Speed server farm: Gina Cody School of ENCS HPC facility. [online], 2018–2019. https://docs.google.com/presentation/d/1bWbGQvYsuJ4U2WsfLYp8S3yb4i7OdU7QDn3l_Q9mYis.
[11] Serguei A. Mokhov, Joey Paquet, and Mourad Debbabi. The use of NLP techniques in static code analysis to detect weaknesses and vulnerabilities. In Maria Sokolova and Peter van Beek, editors, Proceedings of Canadian Conference on AI’14, volume 8436 of LNAI, pages 326–332. Springer, May 2014. Short paper.
[12] Parna Niksirat, Adriana Daca, and Krzysztof Skonieczny. The effects of reduced-gravity on planetary rover mobility. International Journal of Robotics Research, 39(7):797–811, 2020.
[13] Chet Ramey. The Bourne-Again Shell. In Brown and Wilson [4]. http://aosabook.org/en/bash.html.
[14] Rob Schreiber. MATLAB. Scholarpedia, 2(6):2929, 2007. http://www.scholarpedia.org/article/MATLAB.
[15] The MARF Research and Development Group. The Modular Audio Recognition Framework and its Applications. [online], 2002–2014. http://marf.sf.net and http://arxiv.org/abs/0905.1235, last viewed May 2015.


Angular, firebase, PWA code base.
Material Design, SCSS and custom theming.
Tested code base
Material design based theming


WITNESS LIST Public Health Committee February 16, 2005-2:00P HB 30 For: Castillo, Yvonne (Texas Society of Architects) Mailman, M.D., Gary M. (Self) Reardon, Andy (Austin Fire Department) Against: Blaker, Ken (Texas Asso. of Builders, HBA of Greater Austin and Main Street Homes) Lehman, Mark (Texas Association of Realtors) Mintz, David (Texas Apartment Association) Norman, Scott (Texas Association of Builders) Savio, Harry (HBA Greater Austin, Texas Association of Builders) On: Briseno, Elias (Department of State Health Services) HB 102 For: Allen, Neil R. (Texas Society for Respiratory Care) Flores, Sr., Antonio (Self and Texas Society of Respiratory Care) Pope, Caryn (Self) On: Peterson, Debbie (Department of State Health Services, Prof., Licensing and Certification) HB 135 For: Warren, Jill (City of Carrollton, Texas) Against: Polan, Deborah (24 Hour Fitness) Propes, Jay (International Health, Racquet, and Sports Club Assn.) On: Joyner, Guy (Office of the Secretary of State) Lomax, Bryant (Texas Comptroller) Singer, Paul (Texas Attorney General's Office) HB 135 - Committee Substitute (CSHB 135) For: Ortiz, Fermin (Self) HB 512 On: Garanflo, Jaime (Texas State Board of Medical Examiners) Patrick, Donald (Texas State Board of Medical Examiners) Registering, but not testifying: For: Barton, Yvonne (Texas Medical Association) HB 567 For: Hill, M.D., Ph.D., Joseph (Self and Texas Medical Association) Laney, M.D., Mark (Cook Children's Physician Network, Cook Children's Health Care System) On: Garanflo, Jaime (Texas State Board of Medical Examiners) Patrick, Donald (Texas State Board of Medical Examiners) Registering, but not testifying: For: Feather, Robert K. (Cook Children's Health Care System) Sperry, Bryan (Children's Hospital Association of Texas)


--- layout: default title: home homepage: true main: home sub: main ---
Never give up.
When one door closes, another one opens.
Or you can open the closed door. That's how doors work.
Until you value yourself, you won't value your time.
Until you value your time, you won't do anything with it.
M. Scott Peck
If you make the effort to do the best that you can, regularly, the results will be what they should be, not necessarily what you want them to be, but what they should be. And only you will know whether you can do that.
John Wooden
When given a choice, choose the thing that scares you a little. If it's 100% safe, it's holding you back.
Jeff Atwood
There once was an inflatable boy and an inflatable girl. They went to an inflatable school, where all the teachers were inflatable, all the buildings were inflatable, and all the students were inflatable.
One day, the inflatable boy wondered - well, what if the whole world's inflatable? And what if everyone's inflatable and deflatable?  And maybe....maybe, if you can still inflate people after you're gone, then you've lived an even better one.
Adam Hills
... the three great virtues of a programmer: laziness, impatience, and hubris.
Larry Wall
There is no time management in my opinion. Take all the stuff that gets you away from work, and just get rid of it. I find that a lot of people are finding something to do that's not [working].
That's not time management, that's wasting time.
Feng Zhu
Hello everyone. The whole world is watching now. I wish you could see what I can see.  I'm going over.
Felix Baumgartner


Plagiarism Section
Favourite Quotes:
- It is never wrong to do the right thing.. (Mark Twain)
- "Don't worry about anyone stealing your ideas. If they are any good at all, you'll have to shove them down everyone's throat!". (Howard Aiken)
When working with students, always use appropriate tools to check wether their theses or project work presents plagiarism issues:
In case of Plagiarism:
(a) Make a dossier on this, so that it is clear what happened and what didn't happen in terms of copying parts of a paper
(b) Don't let this get to your heart too much
(c) Depending on the outcome of a) — which should be done involving also a person not directly involved
in the situation (not affected by (b)) — talk witht the most senior author, as good first step.
(d) In case you are working with people with such low integrity, confront with them about the issues and stop working with them.
In any case I suggest to students to read the following article "Editorial: Do we need to teach ethics to PhD students?": https://onlinelibrary.wiley.com/doi/full/10.1002/stvr.1659
Some references before making Plagiarism actions:
https://www.ieee.org/publications/rights/section-821.html
- "When submitting an article, authors shall disclose whether or not the article has been published previously or if it is still under active consideration by another publication.
In addition, if an author submits an article to a non-IEEE publication while that article is under review by IEEE, the author shall immediately notify IEEE about the additional submission."
- "IEEE defines plagiarism as the use of someone else’s prior ideas, processes, results, or words without explicitly acknowledging the original author and source. Plagiarism in any form is unacceptable and is considered a serious breach of professional conduct, with potentially severe ethical and legal consequences. Section 8.2.4.D provides detailed guidelines for a) handling allegations of plagiarism, b) applying appropriate corrective actions when findings of plagiarism have been reached, and c) referencing previously published material."
- "Except as indicated in IEEE Policies, Section 6.4 (Multiple Publication of Original Technical Material in IEEE Periodicals), authors should only submit original work that has neither appeared elsewhere for publication, nor which is under review for another publication. If authors have used their own previously published work(s) as a basis for a new submission, they are required to cite the previous work(s) and very briefly indicate how the new submission offers substantive novel contributions beyond those of the previously published work(s). Section 8.2.4.F provides guidelines for handling instances of inappropriate multiple submission and prior publication."
https://www.ieee.org/publications/rights/plagiarism/id-plagiarism.html
- "Paraphrasing can leave an author open to a charge of plagiarism if he or she has changed only a few words or phrases or has only rearranged the original sentence order. Even a proper paraphrasing of the original text can lead to a charge of plagiarism if the original source is not properly cited".
https://www.comsoc.org/publications/magazines/policy-self-plagiarism
"(f) Plagiarism is unacceptable. The verbatim copying or reuse of one's own research) as indicated in paragraph "h" below) is considered another form of plagiarism or self-plagiarism; it is unacceptable".
"(h) Except as indicated in Section 6.3.4 (Multiple Publication of Original Technical Material in IEEE Periodicals), authors should only submit original work that has neither appeared elsewhere for publication, nor which is under review for another refereed publication. If authors have used their own previously published work(s) as a basis for a new submission, they are required to cite the previous work(s) and very briefly indicate how the new submission offers substantial novel contributions beyond those of the previously published work(s)".
https://ethz.ch/students/en/studies/performance-assessments/plagiarism.html
"What qualifies as plagiarism?
Plagiarism is understood as the complete or partial imitation of the work of another author without citing that work’s source and author".
"It may be more narrowly defined as follows (see the contribution of Prof. Christian Schwarzenegger in unijournal, 4/2006):"
The author uses extracts from another author’s work without citing the source. This includes using material from the internet without citation.
The author takes extracts from another author’s work and changes (paraphrases) them slightly without citing the source.
The author translates texts or extracts from foreign-language documents and submits them as his/her own work without citing the source (translation plagiarism).
The author submits a paper in his/her name which he/she has actually commissioned another person (a «ghost writer») to write.
The author submits the work of another author in his/her own name (full plagiarism).
The author takes an extract from someone else’s work, paraphrases it and indeed cites the original author, but somewhere other than in the context of the extract (for example, the (in practice, plagiarised) source is hidden away in a footnote at the end of the paper).
https://cacm.acm.org/magazines/2005/4/6249-self-plagiarism-in-computer-science/abstract
"Occasionally, the derived paper is simply a retitled and reformatted version of the original one, but more frequently it is assembled from bits and pieces of previous work."


Think you figured it out? Click
NEXT
to reveal the answer.


The Egyptian Gazette will occasionally feature a “Sport and Play.” section, usually containing scores from
cricket games. For the majority of this week, though, the section featured a regatta hosted by the Khedival
Yacht Club. Unlike cricket, a regatta is a series of boat races, so I initially found its placement
in the newspaper odd, until reading this sub article:
Yachting has apparently found an audience in Alexandria’s sporting community, so much so that private owners with
large money influences have begun to participate in races. (That is how I understand it at least.) I figured
some additional research into these races was warranted:
1907 – The Motor Boat: Devoted to All Types of Power Craft, Volume 4, Issue 12, by Francis P. Prial
pg. 43: Prial mentions the waters near Alexandria and in Egypt in general as “splendid facilities for motorboating…”
and how these waters were not receiving enough attention from craft owners.
Although the central theme in this source is motorboats, not yachts, I feel the source warrants mentioning because
ideal waters might be a reason for the success that yachting in Alexandria had seen, and because the author also speaks
highly of the Khedival Yacht Club.
1908 – Encyclopaedia of Ships and Shipping, by Herbert B. Mason
pg. 328: A general definition of the Khedival Yacht Club: “Established 1902. Patron, H. H. the Khedive of Egypt ;
Commodore, H.H. Prince Aziz Pacha Hassan…Entrance fee, £3 ; annual subscription, £2.”
Based on the mention of an entrance fee, I assume that anyone could have participated in the regattas, and that the aforementioned
private owners had the money to invest in elite crafts that could win races.
1907 – Fore’n’aft, Volume 11
pg. 62: The Khedival Yacht Club is hailed as “the only important organization of its kind in Egypt…
and now has two hundred and thirty members and a fleet of fifty yachts. Weekly regattas are held in fort and numerous
enjoyable social functions at the handsome club house.”
To conclude, here are the results of the week in question’s regatta:
The Ananke, a privately owned craft favored by the newspaper to win the race in its class, could not withstand the wind and
waters that day, and had to forfeit the race.


Simple Interest Calculator
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PieChopper assists startup teams to share their equity fair and square.
It's free to use, and its source code is available in GitHub under MIT license.
Slice your founders' pie fairly.
Share your proposals.
There are various ways to divide the equity. Select the method that is the closest match to your situation and thinking. You may also try out alternative methods, and see how their outcomes differ. In case you don't have any preference, continue to the next section.
Consider each member's contribution below. You may add or remove members (with and buttons) to match your team's headcount. You may also rename members by clicking their name. In case you feel that some rows don't apply in your situation, leave them blank. If you cannot find clear differences between member contributions, it might be a symptom of ill responsibility definition in the company. In that case, use some time to define the role of each member more precisely.
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| {{ member.equity }} % - | |
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{{ criteria(id).text }}
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{{ member.valueErrors[id] }}
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In this section, you can tweak the selected method to match your team's opinion on fair equity sharing. You may want to adjust the default values due to the market situation in your location, your principles and ethics, or your financial situation. In case you don't have any preference, continue to the next section.
|
What to value?
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Value
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|---|---|
| {{ model().modelDef.emphasis[id].text }} |
{{ model().emphasisErrors[id] }}
|
Based on the given data, this is how the "{{ model().modelDef.name }}"-method suggests you to chop your equity.
Your input seems to contain errors. Please fix the inputs above, errors are pointed with red color.
Piechopper is currently under development and benefits greatly from your help. Answering the questions below assists in setting the right direction, and it only takes a few seconds. Thank you for your help!
Would you be willing to pay on some of the features?
Freeform feedback:
Leave your email to get an invite to upcoming features:
Click here if you want to modify the questionary.


Need some healthier options when it comes to snacks so you don't always have to reach for the cookies or ice cream? Check out some of the options here!


Package Index
Packages in the standard library
A
B
C
D
E
F
G
H
I
K
L
M
N
P
Q
R
S
T
U
V
W
X
Y
Z
| abind | Combine multi-dimensional arrays |
| actuar | Actuarial functions |
| adabag | Applies multiclass AdaBoost.M1, AdaBoost-SAMME and Bagging |
| adapt | adapt -- multidimensional numerical integration |
| ALL | A data package |
| animation | A gallery of animations in statistics and utilities to create
animations |
| annotate | Annotation for microarrays |
| AnnotationDbi | Annotation Database Interface |
| arules | Mining Association Rules and Frequent Itemsets |
| arulesNBMiner | Mining NB-Frequent Itemsets and NB-Precise Rules |
| arulesViz | Visualizing Association Rules and Frequent Itemsets |
| aylmer | A generalization of Fisher's exact test |
| base | The R Base Package |
| BayesDA | Functions and Datasets for the book "Bayesian Data Analysis" |
| biglm | bounded memory linear and generalized linear models |
| Biobase | Biobase: Base functions for Bioconductor |
| BiocGenerics | Generic functions for Bioconductor |
| BiocInstaller | Install/Update Bioconductor and CRAN Packages |
| bitops | Bitwise Operations |
| boot | Bootstrap Functions (originally by Angelo Canty for S) |
| Brobdingnag | Very large numbers in R |
| car | Companion to Applied Regression |
| caTools | Tools: moving window statistics, GIF, Base64, ROC AUC, etc. |
| ChainLadder | Statistical methods for the calculation of outstanding claims
reserves in general insurance |
| class | Functions for Classification |
| clickme | Straight from R to JS: Render JavaScript visualizations using R
objects. |
| cluster | Cluster Analysis Extended Rousseeuw et al. |
| coda | Output analysis and diagnostics for MCMC |
| codetools | Code Analysis Tools for R |
| colorspace | Color Space Manipulation |
| compiler | The R Compiler Package |
| copula | Multivariate dependence with copulas |
| corrgram | Plot a correlogram |
| cplm | Compound Poisson linear models |
| cubature | Adaptive multivariate integration over hypercubes |
| datasets | The R Datasets Package |
| DBI | R Database Interface |
| Defaults | Create Global Function Defaults |
| devtools | Tools to make developing R code easier |
| df2json | Convert a dataframe to JSON |
| dichromat | Color schemes for dichromats |
| digest | Create cryptographic hash digests of R objects |
| DMR | Delete or Merge Regressors for linear model selection. |
| DMwR | Functions and data for "Data Mining with R" |
| doParallel | Foreach parallel adaptor for the parallel package |
| e1071 | Misc Functions of the Department of Statistics (e1071), TU Wien |
| ElemStatLearn | Data sets, functions and examples from the book: "The Elements
of Statistical Learning, Data Mining, Inference, and
Prediction" by Trevor Hastie, Robert Tibshirani and Jerome
Friedman. |
| evaluate | Parsing and evaluation tools that provide more details than the
default. |
| fastICA | FastICA Algorithms to perform ICA and Projection Pursuit |
| foreach | Foreach looping construct for R |
| foreign | Read Data Stored by Minitab, S, SAS, SPSS, Stata, Systat, dBase,
... |
| formatR | Format R Code Automatically |
| Formula | Extended Model Formulas |
| gam | Generalized Additive Models |
| gclus | Clustering Graphics |
| gdata | Various R programming tools for data manipulation |
| genefilter | genefilter: methods for filtering genes from microarray
experiments |
| ggmap | A package for spatial visualization with Google Maps and
OpenStreetMap |
| ggmcmc | Graphical tools for analyzing Markov Chain Monte Carlo
simulations from Bayesian inference. |
| ggplot2 | An implementation of the Grammar of Graphics |
| glasso | Graphical lasso- estimation of Gaussian graphical models |
| GLMMGibbs | Generalised Linear Mixed Models by Gibbs Sampling |
| glmnet | Lasso and elastic-net regularized generalized linear models |
| gmp | Multiple Precision Arithmetic |
| googleVis | Interface between R and the Google Chart Tools |
| gplots | Various R programming tools for plotting data |
| graphics | The R Graphics Package |
| grDevices | The R Graphics Devices and Support for Colours and Fonts |
| grid | The Grid Graphics Package |
| grImport | Importing Vector Graphics |
| gtable | Arrange grobs in tables. |
| gtools | Various R programming tools |
| highr | Syntax highlighting for R |
| Hmisc | Harrell Miscellaneous |
| HSAUR2 | A Handbook of Statistical Analyses Using R (2nd Edition) |
| httr | Tools for working with URLs and HTTP |
| hyperdirichlet | A generalization of the Dirichlet distribution |
| igraph | Network analysis and visualization |
| IRanges | Infrastructure for manipulating intervals on sequences |
| iterators | Iterator construct for R |
| KernSmooth | Functions for kernel smoothing for Wand & Jones (1995) |
| klaR | Classification and visualization |
| knitr | A general-purpose package for dynamic report generation in R |
| labeling | Axis Labeling |
| lars | Least Angle Regression, Lasso and Forward Stagewise |
| lassoshooting | L1 regularized regression (Lasso) solver using the Cyclic
Coordinate Descent algorithm aka Lasso Shooting |
| lattice | Lattice Graphics |
| LearnBayes | Functions for Learning Bayesian Inference |
| likelihood | Methods for maximum likelihood estimation |
| limSolve | Solving Linear Inverse Models |
| lme4 | Linear mixed-effects models using S4 classes |
| lmtest | Testing Linear Regression Models |
| lpSolve | Interface to Lp_solve v. 5.5 to solve linear/integer programs |
| magic | create and investigate magic squares |
| mapproj | Map Projections |
| maps | Draw Geographical Maps |
| markdown | Markdown rendering for R |
| MASS | Support Functions and Datasets for Venables and Ripley's MASS |
| Matrix | Sparse and Dense Matrix Classes and Methods |
| mclust | Normal Mixture Modeling for Model-Based Clustering,
Classification, and Density Estimation |
| mcmc | Markov Chain Monte Carlo |
| MCMCpack | Markov chain Monte Carlo (MCMC) Package |
| memoise | Memoise functions |
| methods | Formal Methods and Classes |
| mgcv | Mixed GAM Computation Vehicle with GCV/AIC/REML smoothness
estimation |
| minqa | Derivative-free optimization algorithms by quadratic
approximation |
| mlbench | Machine Learning Benchmark Problems |
| mnormt | The multivariate normal and t distributions |
| munsell | Munsell colour system |
| MVA | An Introduction to Applied Multivariate Analysis with R |
| mvtnorm | Multivariate Normal and t Distributions |
| nlme | Linear and Nonlinear Mixed Effects Models |
| nnet | Feed-forward Neural Networks and Multinomial Log-Linear Models |
| parallel | Support for Parallel computation in R |
| partitions | Additive partitions of integers |
| plyr | Tools for splitting, applying and combining data |
| png | Read and write PNG images |
| polynom | A collection of functions to implement a class for univariate
polynomial manipulations |
| pracma | Practical Numerical Math Functions |
| proto | Prototype object-based programming |
| pspline | Penalized Smoothing Splines |
| quadprog | Functions to solve Quadratic Programming Problems. |
| quantmod | Quantitative Financial Modelling Framework |
| randomForest | Breiman and Cutler's random forests for classification and
regression |
| randomGLM | Random General Linear Model Prediction |
| randomizeBE | Function to create a random list for crossover studies |
| raster | raster: Geographic data analysis and modeling |
| RColorBrewer | ColorBrewer palettes |
| Rcpp | Seamless R and C++ Integration |
| RCurl | General network (HTTP/FTP/...) client interface for R |
| reshape | Flexibly reshape data. |
| reshape2 | Flexibly reshape data: a reboot of the reshape package. |
| RgoogleMaps | Overlays on Google map tiles in R |
| rjags | Bayesian graphical models using MCMC |
| rJava | Low-level R to Java interface |
| rjson | JSON for R |
| RJSONIO | Serialize R objects to JSON, JavaScript Object Notation |
| ROCR | Visualizing the performance of scoring classifiers. |
| rpart | Recursive Partitioning |
| rscproxy | statconn: provides portable C-style interface to R
(StatConnector) |
| RSQLite | SQLite interface for R |
| RUnit | R Unit test framework |
| RWeka | R/Weka interface |
| RWekajars | R/Weka interface jars |
| Rwordseg | Chinese word segmentation |
| sandwich | Robust Covariance Matrix Estimators |
| scales | Scale functions for graphics. |
| scatterplot3d | 3D Scatter Plot |
| ScreenClean | Screen and clean variable selection procedures |
| seriation | Infrastructure for seriation |
| sn | The skew-normal and skew-t distributions |
| sp | classes and methods for spatial data |
| space | Sparse PArtial Correlation Estimation |
| spaceExt | Extension of SPACE |
| SparseM | Sparse Linear Algebra |
| spatial | Functions for Kriging and Point Pattern Analysis |
| splines | Regression Spline Functions and Classes |
| statmod | Statistical Modeling |
| stats | The R Stats Package |
| stats4 | Statistical Functions using S4 Classes |
| stringr | Make it easier to work with strings. |
| survival | Survival Analysis |
| systemfit | Estimating Systems of Simultaneous Equations |
| tcltk | Tcl/Tk Interface |
| tools | Tools for Package Development |
| TSP | Traveling Salesperson Problem (TSP) |
| TTR | Technical Trading Rules |
| tweedie | Tweedie exponential family models |
| utils | The R Utils Package |
| vcd | Visualizing Categorical Data |
| WDI | World Development Indicators (World Bank) |
| whisker | {{mustache}} for R, logicless templating |
| XML | Tools for parsing and generating XML within R and S-Plus. |
| xtable | Export tables to LaTeX or HTML |
| xts | eXtensible Time Series |
| yaml | Methods to convert R data to YAML and back |
| zoo | S3 Infrastructure for Regular and Irregular Time Series (Z's
ordered observations) |


{% with messages = get_flashed_messages(category_filter=["success"]) %}
{% if messages %}
{%- for message in messages %}
{{message}}
{% endfor -%}
{% endif %}
{% endwith %}
{% with messages = get_flashed_messages(category_filter=["danger"]) %}
{% if messages %}
{%- for message in messages %}
{% endblock %}
{{message}}
{% endfor -%}
{% endif %}
{% endwith %}
Power tools are used in all construction projects for various purposes. As
power
tools are more efficient and increases productivity they are frequently used
in
construction sites. In large sites tracking the power tools and their
service
requirements are quite challenging. Breakdown of power tools in middle of an
important job can seriously affect the works if a replacement is not
available
immediately. An attempt has been made in this project to track and monitor
the
health of the power tools, shorter service time, better inventory management
of
spares with help of technology which can give better user experience to the
customer
with minimum works disruption.
Problem Description :
The Hilti tools used at construction sites have moving parts and other parts which require servicing at regular intervals or when any part is damaged. When the machine or tool requires servicing, the tool has to be sent to the servicing centre. If a spare machine is available with the customer or Hilti the work can proceed without interruption. In most of the cases, the machine breaks down during its use at the construction site without warning and the works get severely affected. It has been noticed that machines do not have any indicator to give distress signal prior to the breakdown or when the routine servicing is due as per its uses. Once the machine is breakdown, it follows the following path:
The Hilti tools used at construction sites have moving parts and other parts which require servicing at regular intervals or when any part is damaged. When the machine or tool requires servicing, the tool has to be sent to the servicing centre. If a spare machine is available with the customer or Hilti the work can proceed without interruption. In most of the cases, the machine breaks down during its use at the construction site without warning and the works get severely affected. It has been noticed that machines do not have any indicator to give distress signal prior to the breakdown or when the routine servicing is due as per its uses. Once the machine is breakdown, it follows the following path:
- Day 1 – Booking by customer for machine servicing
- Day 2 – Picking up of machine by third party courier
- Day 3 – Machine at Hilti Service Centre
- Day 4 – Machine repairing and dispatch from the service center to customer
- Day 5 – Machine reached to customer
Problem Solution :
The team has proposed the solution through data collection from the tools and analysis of the collected data in order to create trackers to make corrective decisions for better performance of the tools. The link for the GitHub has been provided below:
Click Here
The team has proposed the solution through data collection from the tools and analysis of the collected data in order to create trackers to make corrective decisions for better performance of the tools. The link for the GitHub has been provided below:
Click Here


Man Utd 'draw up shortlist' of eight long-term transfer targets
Manchester United boss Ole Gunnar Solskjaer has reportedly identified eight long-term targets, including Jadon Sancho and Erling Haaland
by Liam Prenderville, http://www.facebook.com/mirrorfootballManchester United have reportedly drawn up a shortlist of eight long-term transfer targets.
Ole Gunnar Solskjaer is likely to be given funds to strengthen in January as he bids to take United back into the top four of the Premier League.
A move for Mario Mandzukic continues to be mooted with the Juventus striker free to leave when the transfer window reopens.
But ahead of January, Solskjaer has reportedly identified another eight players he wants to bring to Old Trafford.
Mirror Sport reported last week that United are keen on striking a £200m double swoop for Jadon Sancho and Erling Haaland.
While £100m Sancho would go straight into the side, United are considering loaning Haaland back to Red Bull Salzburg as he continues his remarkable campaign.
ESPN reports there are a host of other players on Solskjaer's radar, all of whom are under 27, in a clear shift of transfer strategy.
Leicester and England star James Maddison also on United's wishlist, as is his international teammate Declan Rice.
With a striker remaining a priority, United have reportedly identified Lyon's Moussa Dembele while Callum Wilson could bring some Premier League experience.
United have also made checks on Aston Villa pair John McGinn and Jack Grealish.
These targets are likely to be long-term options for United but they could make a move in January, if they do become available.
Man Utd targets
- Jadon Sancho (Borussia Dortmund) - £100m
- Erling Haaland (Red Bull Salzburg) - £80m
- James Maddison (Leicester) - £60m
- Declan Rice (West Ham) - £45m
- Callum Wilson (Bournemouth) - £30m
- Moussa Dembele (Lyon) - £40m
- John McGinn (Aston Villa) - £20m
- Jack Grealish (Aston Villa) - £40m


[13]
M. Li and L. Royden, Extreme Maps, Brennan Center for Justice (2017),
https://www.brennancenter.org/our-work/research-reports/extreme-maps
.https://www.brennancenter.org/our-work/research-reports/extreme-maps
.


Before Change
// Disable clipping to make sure we can see the difference in behavior
agent.policy._clip = False
// Remove policy_info, as BehavioralCloningAgent expects none.
traj = traj.replace(policy_info=())
// TODO(b/123883319)
if tf.executing_eagerly():
train_and_loss = lambda: agent.train(traj)
else:
train_and_loss = agent.train(traj)
replay = trajectory_replay.TrajectoryReplay(agent.policy)
self.evaluate(tf.compat.v1.global_variables_initializer())
initial_actions = self.evaluate(replay.run(traj)[0])
for _ in range(TRAIN_ITERATIONS):
self.evaluate(train_and_loss)
post_training_actions = self.evaluate(replay.run(traj)[0])
post_training_actions = self.evaluate(replay.run(traj)[0])
// We don"t necessarily converge to the same actions as in trajectory after
// 10 steps of an untuned optimizer, but the policy does change.
self.assertFalse(np.all(initial_actions == post_training_actions))
After Change
// We don"t necessarily converge to the same actions as in trajectory after
// 10 steps of an untuned optimizer, but the loss should go down.
self.assertGreater(initial_loss, loss)
def testTrainWithSingleOuterDimension(self):
// Hard code a trajectory shaped (time=6, batch=1, ...).
traj, time_step_spec, action_spec = create_arbitrary_trajectory()


Chatito
helps you generate datasets for chatbots online and in a breeze using a simple DSL.
Read the docs
%[lightChange] lights @[switch] @[switch] ~[on] ~[off] ~[on] on active ~[off] off inactive
Correct syntax!
Total examples:
Split the shuffled dataset for training and testing
Select the % of random examples for training, the rest will go to testing.
100%
Training #:
0
Testing #:
0