NFL POWER RANKINGS: Where every team stands heading into Week 12, Business Insider - Business Insider Singapore
by Tyler Lauletta, Scott Davis, Business Insider US- We’re nearly three quarters of the way through the NFL season.
- There are big games with potential playoff implications ahead in Week 12.
- Here’s where all 32 teams stand.
- Visit Business Insider’s homepage for more stories.
Heading into Week 12, some big games lie ahead in the NFL.
There are big divisional games: the Texans and Colts, fighting for the AFC South; the Titans and Jaguars, only two games out of the division lead; the Panthers and Saints, fighting for a wild card and a first-round bye, respectively.
There are big conference games: the Seahawks and Eagles, fighting for their division leads and a playoff spot; the 49ers and Packers are fighting to be considered true contenders.
The Patriots and Cowboys also play a big non-conference game. The Patriots haven’t exactly passed the sniff test the last two weeks while playing two solid teams in the Ravens and Eagles. Meanwhile, the Cowboys could pick up their best win of the season by beating the Patriots in Foxborough.
As we head to the three-quarter mark of the NFL season, here’s where all 32 teams stand.
32. Washington Redskins
Record: 1-9
Last week: 30th
Week 11 result: Lost to the Jets, 34-17
Week 12 opponent: vs. Detroit Lions
One thing to know: Dwayne Haskins broke a streak of 15 quarters without a passing touchdown by throwing two in the fourth quarter. Unfortunately, his best play of the day didn’t count because of a holding penalty. Baby steps in Washington.
31. Cincinnati Bengals
Record: 0-10
Last week: 32nd
Week 11 result: Lost to the Raiders, 17-10
Week 12 opponent: vs. Pittsburgh Steelers
One thing to know: Winless through 10 games, the first-year head coach is likely starting to get anxious about getting his first win before the end of the season. Thankfully, with games against the Browns, Jets, and Dolphins still left on the schedule, all hope is not yet lost.
30. New York Giants
Record: 2-8
Last week: 31st
Week 11 result: Bye
Week 12 opponent: at Chicago Bears
One thing to know: The Giants would love to win a few more games down the stretch, but to do so, they’ll need to be healthy. Saquon Barkley, Sterling Shepard, and Evan Engram are all battling various ailments, leaving Daniel Jones with little support.
29. Miami Dolphins
Record: 2-8
Last week: 28th
Week 11 result: Lost to the Bills, 37-20
Week 12 opponent: at Cleveland Browns
One thing to know: While the Dolphins have been planning for next year all season, the front office is not waiting until the draft to lock in talent for their next campaign, signing wide receiver Allen Hurns to a two-year, $8 million extension this week.
28. Tampa Bay Buccaneers
Record: 3-7
Last week: 24th
Week 11 result: Lost to the Saints, 34-17
Week 12 opponent: at Atlanta Falcons
One thing to know: Jameis Winston’s turnover issues have continued. He threw four more interceptions on Sunday, bringing him to 13 in the last five weeks and 19 on the season, second-most in NFL history through 10 games.
27. New York Jets
Record: 3-7
Last week: 29th
Week 11 result: Beat the Redskins, 34-17
Week 12 opponent: vs. Oakland Raiders
One thing to know: Jets tight end Ryan Griffin has finished as the top player at his position in fantasy in two of the past four weeks. If you need a spark for your push for the fantasy playoffs, he could be your man.
26. Denver Broncos
Record: 3-7
Last week: 26th
Week 11 result: Lost to the Vikings, 27-23
Week 12 opponent: at Buffalo Bills
One thing to know: The Broncos have lost four games this season after leading in the fourth quarter. The latest was the most brutal: blowing a 20-point second-half lead over the Vikings.
25. Detroit Lions
Record: 3-6-1
Last week: 22nd
Week 11 result: Lost to the Cowboys, 35-27
Week 12 opponent: at Washington Redskins
One thing to know: It’s been a tough stretch for the Detroit Lions, who have now lost six of their past seven games. Running back Bo Scarbrough has the chance to be a bright spot for the Lions this week after getting his first start last Sunday thanks to an injured backfield. He’ll look to improve on his 55 yards on 14 carries this week at Washington.
24. Chicago Bears
Record: 4-6
Last week: 21st
Week 11 result: Lost to the Rams, 17-7
Week 12 opponent: vs. New York Giants
One thing to know: The Bears pulled Mitch Trubisky for Chase Daniel in Week 11 during another ugly performance for the third-year QB. The Bears said it was for a hip injury, but with Trubisky struggling, it may be best to leave him on the sideline for a few more weeks.
23. Arizona Cardinals
Record: 3-7-1
Last week: 25th
Week 11 result: Lost to the 49ers, 36-26
Week 12 opponent: Bye
One thing to know: Fantasy football players have been frustrated by a lack of news surrounding standout running back David Johnson, who has gotten just five carries in the past two weeks for Arizona. Newly acquired running back Kenyan Drake appears to have taken over the top spot in the Cardinals offense.
22. Atlanta Falcons
Record: 3-7
Last week: 27th
Week 11 result: Beat the Panthers, 29-3
Week 12 opponent: vs. Tampa Bay Buccaneers
One thing to know: With two straight wins over potential playoff teams in their division, the Falcons have the chance to play spoiler in the NFC. It’s worth asking, though, where this tenacity was over the first eight weeks of the season.
21. Jacksonville Jaguars
Record: 4-6
Last week: 19th
Week 11 result: Lost to the Colts, 33-13
Week 12 opponent: at Tennessee Titans
One thing to know: Nick Foles’ return to the Jaguars wasn’t enough to earn a win against Tennessee on Sunday. At 4-6, Jacksonville is at the bottom of the AFC South standings, but still sitting just two games off the division lead.
20. Tennessee Titans
Record: 5-5
Last week: 18th
Week 11 result: Bye
Week 12 opponent: vs. Jacksonville Jaguars
One thing to know: Have we seen the end of Marcus Mariota with the Titans? Ryan Tannehill has been statistically superior in almost every category, and the team has won three of four games with Tannehill operating the offense.
19. Los Angeles Chargers
Record: 4-7
Last week: 20th
Week 11 result: Lost to the Chiefs, 24-17
Week 12 opponent: Bye
One thing to know: Despite their 4-7 record, the Chargers have been competitive in every game they’ve played this season, with none of their losses coming by more than a touchdown. It seems that every week, Philip Rivers has a chance in the fourth quarter to mount a game-winning drive, and every week the Chargers come up just a little short.
18. Cleveland Browns
Record: 4-6
Last week: 23rd
Week 11 result: Beat the Steelers, 21-7
Week 12 opponent: vs. Miami Dolphins
One thing to know: The Browns have won three straight, and their schedule is only getting easier as they go forward. Of course, any playoff push will come without defensive anchor Myles Garrett, who is suspended for his part in the ugly Browns-Steelers altercation.
17. Pittsburgh Steelers
Record: 5-5
Last week: 12th
Week 11 result: Lost to the Browns, 21-7
Week 12 opponent: at Cincinnati Bengals
One thing to know: Pittsburgh offensive lineman David DeCastro might be the only person that came out of Thursday night’s brawl between the Steelers and Browns looking good. DeCastro earned credit from both sides of the fight for playing the role of peacemaker amidst the chaos.
16. Carolina Panthers
Record: 5-5
Last week: 13th
Week 11 result: Lost to the Falcons, 29-3
Week 12 opponent: at New Orleans Saints
One thing to know: The Panthers are slumping at the wrong time, losing three of their last four. Not coincidentally, Kyle Allen has thrown nine interceptions during that stretch, including some brutal red-zone picks in Week 11.
15. Oakland Raiders
Record: 6-4
Last week: 16th
Week 11 result: Beat the Bengals, 17-10
Week 12 opponent: at New York Jets
One thing to know: With a win this week over the Jets, the Raiders could head into their Week 13 matchup against the Kansas City Chiefs with first place in the AFC West hanging in the balance. Not bad for a team that looked like a nightmare heading into the season.
14. Philadelphia Eagles
Record: 5-5
Last week: 15th
Week 11 result: Lost to the Patriots, 17-10
Week 12 opponent: vs. Seattle Seahawks
One thing to know: The Eagles defense is rounding into form, but it comes as the offense is falling apart. Carson Wentz hasn’t been impressive this season, but he has little help when key players like Alshon Jeffery, DeSean Jackson, and Jordan Howard are out, as they were in Week 11.
13. Los Angeles Rams
Record: 6-4
Last week: 17th
Week 11 result: Beat the Bears, 17-7
Week 12 opponent: vs. Baltimore Ravens
One thing to know: The Rams escaped with a win on Sunday night, but Los Angeles still has plenty of problems that need to be addressed if they hope to make it back to the postseason this year. Hopefully the looming return of wide receiver Brandin Cooks can kickstart the offense a bit this weekend against the Ravens.
12. Indianapolis Colts
Record: 6-4
Last week: 11th
Week 11 result: Beat the Jaguars, 33-13
Week 12 opponent: at Houston Texans
One thing to know: The Colts will once again turn to a backup on offense, as starting running back Marlon Mack fractured his hand and is out indefinitely. Backup running back Jonathan Williams filled in admirably in Week 11 with 147 total yards on 14 touches.
11. Houston Texans
Record: 6-4
Last week: 8th
Week 11 result: Lost to the Ravens, 41-7
Week 12 opponent: vs. Indianapolis Colts
One thing to know: Deshaun Watson and the Texans took a hit on Sunday when they were run off the field by Lamar Jackson and the Ravens. Heading into Week 11, Texans-Ravens looked like the best game on the schedule, but it didn’t take long for Baltimore to prove they were a cut above the competition.
10. Buffalo Bills
Record: 7-3
Last week: 10th
Week 11 result: Beat the Dolphins, 37-20
Week 12 opponent: vs. Denver Broncos
One thing to know: The Josh Allen-John Brown connection was in full force in Week 11. Brown had 137 yards and 2 touchdowns, the longest of which came on a 40-yard pass.
9. Kansas City Chiefs
Record: 7-4
Last week: 9th
Week 11 result: Beat the Chargers, 24-17
Week 12 opponent: Bye
One thing to know: The wildest highlight from the Chiefs trip to Mexico City to play the Chargers came before kickoff, when Kansas City kicker Harrison Butker drilled a 70-yard field goal during pre-game warmups. Butker wouldn’t need a kick that impressive when the game finally began, but he did have a perfect day through the uprights on Monday.
8. Dallas Cowboys
Record: 6-4
Last week: 14th
Week 11 result: Beat the Lions, 35-27
Week 12 opponent: at New England Patriots
One thing to know: Dak Prescott has been on fire the last two weeks, throwing for over 700 yards and 6 touchdowns. The Cowboys should look to attack through the air more, especially with Ezekiel Elliott running for fewer than 50 yards and fewer than three yards per carry in each of the last two games.
7. Minnesota Vikings
Record: 8-3
Last week: 7th
Week 11 result: Beat the Broncos, 27-23
Week 12 opponent: Bye
One thing to know: Kirk Cousins was so bad at the start of the season that he was getting called out by his teammates. Since then, he’s been one of the best quarterbacks in the NFL and become a dark-horse MVP candidate.
6. New Orleans Saints
Record: 8-2
Last week: 6th
Week 11 result: Beat the Buccaneers, 34-17
Week 12 opponent: vs. Carolina Panthers
One thing to know: Michael Thomas is on pace for a historic season. He’s the only wide receiver in NFL history to have at least 90 catches and 1,100 yards through 10 games.
5. Green Bay Packers
Record: 8-2
Last week: 5th
Week 11 result: Bye
Week 12 opponent: at San Francisco 49ers
One thing to know: Before the Packers dreadful performance against the Chargers a few weeks ago, the Packers had landed in California on Friday. With another trip out west scheduled this week, first-year head coach Matt LaFleur has changed his game plan, traveling on Saturday with hopes of maintaining focus heading into their marquee game against the 49ers.
4. San Francisco 49ers
Record: 9-1
Last week: 3rd
Week 11 result: Beat the Cardinals, 36-26
Week 12 opponent: vs. Green Bay Packers
One thing to know: The 49ers defense will continue to be tested in the coming weeks. They play the Packers, Ravens, and Saints, three top-10 offenses.
3.Seattle Seahawks
Record: 8-2
Last week: 2nd
Week 11 result: Bye
Week 12 opponent: at Philadelphia Eagles
One thing to know: Seattle’s win over the 49ers two weeks ago was their biggest of the season, but the road to a division title does not get any easier in the coming weeks, with games against the Eagles, Vikings, and Rams on the horizon. With San Francisco still leading the NFC West, the Seahawks need to keep their hot streak going if they hope to keep pace.
2. New England Patriots
Record: 9-1
Last week: 4th
Week 11 result: Beat the Eagles, 17-10
Week 12 opponent: vs. Dallas Cowboys
One thing to know: The Patriots are 9-1, but Tom Brady is not happy with how the offense is performing.
1. Baltimore Ravens
Record: 8-2
Last week: 1st
Week 11 result: Beat the Texans, 41-7
Week 12 opponent: at Los Angeles Rams
One thing to know: The Ravens’ resume is about as impressive as you could hope for at this point in the season, with wins over the Patriots, Seahawks, and Texans over their past four games. There’s a reason Lamar Jackson is now the favorite to win NFL MVP.


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JSDraw2.Table - SDFViewer
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|
Drake
|
Creates and holds a palette of colors for visualizing different objects in a scene (the intent is for a different color to be applied to each identified object).
The colors are chosen so as to be easily distinguishable. In other words, the intensities are spaced as widely as possible given the number of required colors. Black, white and gray, which has the same value for all the three color channels, are not part of this color palette. This color palette can hold up to 1535 colors.
| IdType | The type of value used for label values. |
#include <drake/systems/sensors/color_palette.h>
Public Member Functions | |
| ColorPalette (int num_colors, IdType terrain_id, IdType no_body_id) | |
| A constructor for ColorPalette. More... | |
| const ColorI & | get_color (IdType index) const |
| Returns a color of type ColorI which corresponds to given index. More... | |
| ColorD | get_normalized_color (IdType index) const |
| Returns a color of type ColorD which corresponds to given index. More... | |
| const ColorI & | get_sky_color () const |
| Returns the color of type ColorI which corresponds to sky. More... | |
| const ColorI & | get_terrain_color () const |
| Returns the color of type ColorI which corresponds to flat terrain. More... | |
| IdType | LookUpId (const ColorI &color) const |
| Looks up the ID which corresponds to the given color. More... | |
Does not allow copy, move, or assignment | |
| ColorPalette (const ColorPalette &)=delete | |
| ColorPalette & | operator= (const ColorPalette &)=delete |
| ColorPalette (ColorPalette &&)=delete | |
| ColorPalette & | operator= (ColorPalette &&)=delete |
Static Public Member Functions | |
| static ColorD | Normalize (const ColorI &color) |
| Takes ColorI whose pixel range is [0, 255] and returns ColorD whose pixel range is [0, 1]. More... | |
|
delete |
|
delete |
| ColorPalette | ( | int | num_colors, |
| IdType | terrain_id, | ||
| IdType | no_body_id | ||
| ) |
A constructor for ColorPalette.
| num_colors | The number of colors that you want ColorPalette to hold. We assume this will be the number of rigid bodies in rendering scene. |
| terrain_id | The id to express pixels which correspond to flat terrain. This will be used in the label image. |
| no_body_id | The id to express pixels that have no body. This will be used in the label image. |
| std::logic_error | When num_colors exceeds the maximum limit, which is 1535. |
| const ColorI& get_color | ( | IdType | index | ) | const |
Returns a color of type ColorI which corresponds to given index.
The pixel range of returned color is [0, 255].
| index | An index that corresponds to the color to be returned. |
| ColorD get_normalized_color | ( | IdType | index | ) | const |
Returns a color of type ColorD which corresponds to given index.
The pixel range of returned color is [0, 1].
| index | An index that corresponds to the color to be returned. |
| const ColorI& get_sky_color | ( | ) | const |
Returns the color of type ColorI which corresponds to sky.
The pixel range of returned color is [0, 255].
| const ColorI& get_terrain_color | ( | ) | const |
Returns the color of type ColorI which corresponds to flat terrain.
The pixel range of returned color is [0, 255].
| IdType LookUpId | ( | const ColorI & | color | ) | const |
Looks up the ID which corresponds to the given color.
| color | The color you want to know the corresponding ID. |
Takes ColorI whose pixel range is [0, 255] and returns ColorD whose pixel range is [0, 1].
| color | An input color to be normalized. |
|
delete |
|
delete |


#include "_doctype.html"
We take security seriously and develop curl and libcurl to be secure and safe.
If you find or simply suspect a security problem in curl or libcurl, mail us at curl-security at haxx.se (closed list of receivers, mails are not disclosed) and tell.
We appreciate getting notified in advance before you go public with security advisories for the sake of our users.
See also the Vulnerabilities Table to see what versions that are vulnerable to what flaws. SUBTITLE(All known prior vulnerabilities) #include "seclist.gen" #include "_footer.html"


This Processors polls Apache Kafka
for data. When a message is received from Kafka, this Processor emits a FlowFile
where the content of the FlowFile is the value of the Kafka message. If the
message has a key associated with it, an attribute named kafka.key
will be added to the FlowFile, with the value being the UTF-8 Encoded value
of the Message's Key.
Kafka supports the notion of a Consumer Group when pulling messages in order to provide scalability while still offering a publish-subscribe interface. Each Consumer Group must have a unique identifier. The Consumer Group identifier that is used by NiFi is the UUID of the Processor. This means that all of the nodes within a cluster will use the same Consumer Group Identifier so that they do not receive duplicate data but multiple GetKafka Processors can be used to pull from multiple Topics, as each Processor will receive a different Processor UUID and therefore a different Consumer Group Identifier.


Linear Algebra, Handling of Arrays and more Python Features
Contents
2. Linear Algebra, Handling of Arrays and more Python Features¶
2.1. Introduction¶
The aim of this set of lectures is to review some central linear algebra algorithms that we will need in our data analysis part and in the construction of Machine Learning algorithms (ML). This will allow us to introduce some central programming features of high-level languages like Python and compiled languages like C++ and/or Fortran.
As discussed in the introductory notes, these series of lectures focuses both on using central Python packages like tensorflow and scikit-learn as well as writing your own codes for some central ML algorithms. The latter can be written in a language of your choice, be it Python, Julia, R, Rust, C++, Fortran etc. In order to avoid confusion however, in these lectures we will limit our attention to Python, C++ and Fortran.
2.2. Important Matrix and vector handling packages¶
There are several central software packages for linear algebra and eigenvalue problems. Several of the more popular ones have been wrapped into ofter software packages like those from the widely used text Numerical Recipes. The original source codes in many of the available packages are often taken from the widely used software package LAPACK, which follows two other popular packages developed in the 1970s, namely EISPACK and LINPACK. We describe them shortly here.
LINPACK: package for linear equations and least square problems.
LAPACK:package for solving symmetric, unsymmetric and generalized eigenvalue problems. From LAPACK’s website http://www.netlib.org it is possible to download for free all source codes from this library. Both C/C++ and Fortran versions are available.
BLAS (I, II and III): (Basic Linear Algebra Subprograms) are routines that provide standard building blocks for performing basic vector and matrix operations. Blas I is vector operations, II vector-matrix operations and III matrix-matrix operations. Highly parallelized and efficient codes, all available for download from http://www.netlib.org.
When dealing with matrices and vectors a central issue is memory handling and allocation. If our code is written in Python the way we declare these objects and the way they are handled, interpreted and used by say a linear algebra library, requires codes that interface our Python program with such libraries. For Python programmers, Numpy is by now the standard Python package for numerical arrays in Python as well as the source of functions which act on these arrays. These functions span from eigenvalue solvers to functions that compute the mean value, variance or the covariance matrix. If you are not familiar with how arrays are handled in say Python or compiled languages like C++ and Fortran, the sections in this chapter may be useful. For C++ programmer, Armadillo is widely used library for linear algebra and eigenvalue problems. In addition it offers a convenient way to handle and organize arrays. We discuss this library as well. Before we proceed we believe it may be convenient to repeat some basic features of matrices and vectors.
2.3. Basic Matrix Features¶
Matrix properties reminder
The inverse of a matrix is defined by
| Relations | Name | matrix elements |
|---|---|---|
| $A = A^{T}$ | symmetric | $a_{ij} = a_{ji}$ |
| $A = \left (A^{T} \right )^{-1}$ | real orthogonal | $\sum_k a_{ik} a_{jk} = \sum_k a_{ki} a_{kj} = \delta_{ij}$ |
| $A = A^{ * }$ | real matrix | $a_{ij} = a_{ij}^{ * }$ |
| $A = A^{\dagger}$ | hermitian | $a_{ij} = a_{ji}^{ * }$ |
| $A = \left (A^{\dagger} \right )^{-1}$ | unitary | $\sum_k a_{ik} a_{jk}^{ * } = \sum_k a_{ki}^{ * } a_{kj} = \delta_{ij}$ |
2.3.1. Some famous Matrices¶
Diagonal if \(a_{ij}=0\) for \(i\ne j\)
Upper triangular if \(a_{ij}=0\) for \(i > j\)
Lower triangular if \(a_{ij}=0\) for \(i < j\)
Upper Hessenberg if \(a_{ij}=0\) for \(i > j+1\)
Lower Hessenberg if \(a_{ij}=0\) for \(i < j+1\)
Tridiagonal if \(a_{ij}=0\) for \(|i -j| > 1\)
Lower banded with bandwidth \(p\): \(a_{ij}=0\) for \(i > j+p\)
Upper banded with bandwidth \(p\): \(a_{ij}=0\) for \(i < j+p\)
Banded, block upper triangular, block lower triangular….
Some Equivalent Statements. For an \(N\times N\) matrix \(\mathbf{A}\) the following properties are all equivalent
If the inverse of \(\mathbf{A}\) exists, \(\mathbf{A}\) is nonsingular.
The equation \(\mathbf{Ax}=0\) implies \(\mathbf{x}=0\).
The rows of \(\mathbf{A}\) form a basis of \(R^N\).
The columns of \(\mathbf{A}\) form a basis of \(R^N\).
\(\mathbf{A}\) is a product of elementary matrices.
\(0\) is not eigenvalue of \(\mathbf{A}\).
2.4. Numpy and arrays¶
Numpy provides an easy way to handle arrays in Python. The standard way to import this library is as
import numpy as np
n = 10
x = np.random.normal(size=n)
print(x)
[-0.87136737 1.4300745 -0.3322326 -0.66758934 -1.00283636 -0.27625974
1.89249454 -0.25006757 0.73565195 0.33697405]
Here we have defined a vector \(x\) with \(n=10\) elements with its values given by the Normal distribution \(N(0,1)\). Another alternative is to declare a vector as follows
import numpy as np
x = np.array([1, 2, 3])
print(x)
[1 2 3]
Here we have defined a vector with three elements, with \(x_0=1\), \(x_1=2\) and \(x_2=3\). Note that both Python and C++ start numbering array elements from \(0\) and on. This means that a vector with \(n\) elements has a sequence of entities \(x_0, x_1, x_2, \dots, x_{n-1}\). We could also let (recommended) Numpy to compute the logarithms of a specific array as
import numpy as np
x = np.log(np.array([4, 7, 8]))
print(x)
[1.38629436 1.94591015 2.07944154]
Here we have used Numpy’s unary function \(np.log\). This function is highly tuned to compute array elements since the code is vectorized and does not require looping. We normaly recommend that you use the Numpy intrinsic functions instead of the corresponding log function from Python’s math module. The looping is done explicitely by the np.log function. The alternative, and slower way to compute the logarithms of a vector would be to write
import numpy as np
from math import log
x = np.array([4, 7, 8])
for i in range(0, len(x)):
x[i] = log(x[i])
print(x)
[1 1 2]
We note that our code is much longer already and we need to import the log function from the math module. The attentive reader will also notice that the output is \([1, 1, 2]\). Python interprets automacally our numbers as integers (like the automatic keyword in C++). To change this we could define our array elements to be double precision numbers as
import numpy as np
x = np.log(np.array([4, 7, 8], dtype = np.float64))
print(x)
[1.38629436 1.94591015 2.07944154]
or simply write them as double precision numbers (Python uses 64 bits as default for floating point type variables), that is
import numpy as np
x = np.log(np.array([4.0, 7.0, 8.0]))
print(x)
[1.38629436 1.94591015 2.07944154]
To check the number of bytes (remember that one byte contains eight bits for double precision variables), you can use simple use the itemsize functionality (the array \(x\) is actually an object which inherits the functionalities defined in Numpy) as
import numpy as np
x = np.log(np.array([4.0, 7.0, 8.0]))
print(x.itemsize)
8
Having defined vectors, we are now ready to try out matrices. We can define a \(3 \times 3 \) real matrix \(\hat{A}\) as (recall that we user lowercase letters for vectors and uppercase letters for matrices)
import numpy as np
A = np.log(np.array([ [4.0, 7.0, 8.0], [3.0, 10.0, 11.0], [4.0, 5.0, 7.0] ]))
print(A)
[[1.38629436 1.94591015 2.07944154]
[1.09861229 2.30258509 2.39789527]
[1.38629436 1.60943791 1.94591015]]
If we use the shape function we would get \((3, 3)\) as output, that is verifying that our matrix is a \(3\times 3\) matrix. We can slice the matrix and print for example the first column (Python organized matrix elements in a row-major order, see below) as
import numpy as np
A = np.log(np.array([ [4.0, 7.0, 8.0], [3.0, 10.0, 11.0], [4.0, 5.0, 7.0] ]))
# print the first column, row-major order and elements start with 0
print(A[:,0])
[1.38629436 1.09861229 1.38629436]
We can continue this was by printing out other columns or rows. The example here prints out the second column
import numpy as np
A = np.log(np.array([ [4.0, 7.0, 8.0], [3.0, 10.0, 11.0], [4.0, 5.0, 7.0] ]))
# print the first column, row-major order and elements start with 0
print(A[1,:])
[1.09861229 2.30258509 2.39789527]
Numpy contains many other functionalities that allow us to slice, subdivide etc etc arrays. We strongly recommend that you look up the Numpy website for more details. Useful functions when defining a matrix are the np.zeros function which declares a matrix of a given dimension and sets all elements to zero
import numpy as np
n = 10
# define a matrix of dimension 10 x 10 and set all elements to zero
A = np.zeros( (n, n) )
print(A)
[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]
or initializing all elements to
import numpy as np
n = 10
# define a matrix of dimension 10 x 10 and set all elements to one
A = np.ones( (n, n) )
print(A)
[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]
or as unitarily distributed random numbers (see the material on random number generators in the statistics part)
import numpy as np
n = 10
# define a matrix of dimension 10 x 10 and set all elements to random numbers with x \in [0, 1]
A = np.random.rand(n, n)
print(A)
[[0.52428467 0.89421873 0.57286194 0.35473061 0.34626037 0.77640601
0.60707801 0.60515083 0.41335011 0.16061448]
[0.51508374 0.7651899 0.07657669 0.14045566 0.92863147 0.32541271
0.62761922 0.20965182 0.55051175 0.14072037]
[0.70311568 0.03617972 0.22335823 0.45797073 0.2223808 0.86208133
0.64452882 0.0770789 0.7866024 0.45739087]
[0.38469441 0.16105443 0.09873132 0.21913656 0.34561469 0.521986
0.89261458 0.75628425 0.25070907 0.96504592]
[0.40506137 0.48241947 0.32158804 0.45112054 0.5783575 0.3267061
0.96261297 0.25216021 0.94271589 0.60651016]
[0.91410101 0.48217977 0.39116042 0.99397049 0.80768431 0.68650491
0.04184636 0.5174581 0.86144422 0.46794929]
[0.62405149 0.27498198 0.63550707 0.85902603 0.38644889 0.86858926
0.68282653 0.65554133 0.81051173 0.00281835]
[0.66001778 0.64858537 0.90034533 0.62389761 0.5333734 0.75390337
0.97926642 0.9893405 0.61605739 0.51905011]
[0.59277468 0.52678301 0.68347072 0.76707201 0.08821204 0.55220861
0.14648477 0.17606773 0.59609892 0.83723539]
[0.40961677 0.07325301 0.34652523 0.72201591 0.66250644 0.6367311
0.79577619 0.85212606 0.86811137 0.43001767]]
As we will see throughout these lectures, there are several extremely useful functionalities in Numpy. As an example, consider the discussion of the covariance matrix. Suppose we have defined three vectors \(\hat{x}, \hat{y}, \hat{z}\) with \(n\) elements each. The covariance matrix is defined as
where for example
The Numpy function np.cov calculates the covariance elements using the factor \(1/(n-1)\) instead of \(1/n\) since it assumes we do not have the exact mean values. For a more in-depth discussion of the covariance and covariance matrix and its meaning, we refer you to the lectures on statistics. The following simple function uses the np.vstack function which takes each vector of dimension \(1\times n\) and produces a \( 3\times n\) matrix \(\hat{W}\)
which in turn is converted into into the \(3 times 3\) covariance matrix \(\hat{\Sigma}\) via the Numpy function np.cov(). In our review of statistical functions and quantities we will discuss more about the meaning of the covariance matrix. Here we note that we can calculate the mean value of each set of samples \(\hat{x}\) etc using the Numpy function np.mean(x). We can also extract the eigenvalues of the covariance matrix through the np.linalg.eig() function.
# Importing various packages
import numpy as np
n = 100
x = np.random.normal(size=n)
print(np.mean(x))
y = 4+3*x+np.random.normal(size=n)
print(np.mean(y))
z = x**3+np.random.normal(size=n)
print(np.mean(z))
W = np.vstack((x, y, z))
Sigma = np.cov(W)
print(Sigma)
Eigvals, Eigvecs = np.linalg.eig(Sigma)
print(Eigvals)
0.03499693245669077
4.187268182169147
-0.19627430151896047
[[ 1.0622197 3.16232481 2.98018358]
[ 3.16232481 10.48442771 8.58039072]
[ 2.98018358 8.58039072 13.66351927]]
[21.70244042 0.07978179 3.42794448]
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy import sparse
eye = np.eye(4)
print(eye)
sparse_mtx = sparse.csr_matrix(eye)
print(sparse_mtx)
x = np.linspace(-10,10,100)
y = np.sin(x)
plt.plot(x,y,marker='x')
plt.show()
[[1. 0. 0. 0.]
[0. 1. 0. 0.]
[0. 0. 1. 0.]
[0. 0. 0. 1.]]
(0, 0) 1.0
(1, 1) 1.0
(2, 2) 1.0
(3, 3) 1.0
2.5. Other Matrix and Vector Operations¶
The following examples show how to compute various quantities like the mean value of a matrix or a vector and how to use functions like reshape and ravel. These are all useful quantities when scaling the data and preparing the data for various machine learning algorithms and when calculating quantities like the mean squared error or the variance.
"""
Simple code that tests various numpy functions
"""
import numpy as np
# Simple test-matrix of dim 3 x 4
a = np.array([ [1, 2, 3], [4, 5, 6], [7, 8, 9],[10, 11, 12]],dtype=np.float64)
print(f"The test matrix:{a}")
# This is the total mean summed over all elements, which here has to be 6.5
print(f"This is the total mean summed over all elements:{np.mean(a,dtype=np.float64)}")
# This is the mean for each column, it returns an array with the mean values for each column. It returns a row-like vector
print(f"This is the mean for each column:{np.mean(a, axis=0, keepdims=True,dtype=np.float64)}")
# This is the mean value for each row, it returns an array via the keepdims option which is a column-like vector if
# keepdims=True. Else it return a row-like vector
# Try setting keepdims=False
print(f"This is the mean value for each row:{np.mean(a, axis=1, keepdims=True,dtype=np.float64)}")
# We print then the mean value for each row by setting keepdims=False
print(f"This is the mean value for each row with keepdims false:{np.mean(a, axis=1, keepdims=False,dtype=np.float64)}")
The test matrix:[[ 1. 2. 3.]
[ 4. 5. 6.]
[ 7. 8. 9.]
[10. 11. 12.]]
This is the total mean summed over all elements:6.5
This is the mean for each column:[[5.5 6.5 7.5]]
This is the mean value for each row:[[ 2.]
[ 5.]
[ 8.]
[11.]]
This is the mean value for each row with keepdims false:[ 2. 5. 8. 11.]
Another useful function is the ravel function, which returns a flattened array as shown in the example here.
# Ravel return a contiguous flattened array.
print(f"Flatten the matrix:{np.ravel(a)}")
# It is the same as reshaping the matrix into a one-dimensional array
print(f"Reshape the matrix to a one-dim array:{a.reshape(-1)}")
# ‘C’ means to index the elements in row-major, C-style order, with the last axis index changing fastest, back to the first axis index changing slowest.
# ‘F’ means to index the elements in column-major, Fortran-style order, with the first index changing fastest, and the last index changing slowest
print(np.ravel(a, order='F'))
# When order is ‘A’, it will preserve the array’s ‘C’ or ‘F’ ordering
# ‘A’ means to read the elements in Fortran-like index order if a is Fortran contiguous in memory, C-like order otherwise.
# ‘K’ means to read the elements in the order they occur in memory, except for reversing the data when strides are negative. By default, ‘C’ index order is used.
# Transposing it
print(np.ravel(a.T))
print(np.ravel(a.T, order='A'))
Flatten the matrix:[ 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.]
Reshape the matrix to a one-dim array:[ 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.]
[ 1. 4. 7. 10. 2. 5. 8. 11. 3. 6. 9. 12.]
[ 1. 4. 7. 10. 2. 5. 8. 11. 3. 6. 9. 12.]
[ 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.]
2.6. Gaussian Elimination¶
We start with the linear set of equations
We assume also that the matrix \(\mathbf{A}\) is non-singular and that the matrix elements along the diagonal satisfy \(a_{ii} \ne 0\). Simple \(4\times 4 \) example
or
The basic idea of Gaussian elimination is to use the first equation to eliminate the first unknown \(x_1\) from the remaining \(n-1\) equations. Then we use the new second equation to eliminate the second unknown \(x_2\) from the remaining \(n-2\) equations. With \(n-1\) such eliminations we obtain a so-called upper triangular set of equations of the form
We can solve this system of equations recursively starting from \(x_n\) (in our case \(x_4\)) and proceed with what is called a backward substitution.
This process can be expressed mathematically as
To arrive at such an upper triangular system of equations, we start by eliminating the unknown \(x_1\) for \(j=2,n\). We achieve this by multiplying the first equation by \(a_{j1}/a_{11}\) and then subtract the result from the \(j\)th equation. We assume obviously that \(a_{11}\ne 0\) and that \(\mathbf{A}\) is not singular.
Our actual \(4\times 4\) example reads after the first operation
or
The new coefficients are
where each \(a_{1k}^{(1)}\) is equal to the original \(a_{1k}\) element. The other coefficients are
with a new right-hand side given by
We have also set \(w_1^{(1)}=w_1\), the original vector element. We see that the system of unknowns \(x_1,\dots,x_n\) is transformed into an \((n-1)\times (n-1)\) problem.
This step is called forward substitution. Proceeding with these substitutions, we obtain the general expressions for the new coefficients
with \(m=1,\dots,n-1\) and a right-hand side given by
This set of \(n-1\) elimations leads us to an equations which is solved by back substitution. If the arithmetics is exact and the matrix \(\mathbf{A}\) is not singular, then the computed answer will be exact.
Even though the matrix elements along the diagonal are not zero, numerically small numbers may appear and subsequent divisions may lead to large numbers, which, if added to a small number may yield losses of precision. Suppose for example that our first division in \((a_{22}-a_{21}a_{12}/a_{11})\) results in \(-10^{-7}\) and that \(a_{22}\) is one. one. We are then adding \(10^7+1\). With single precision this results in \(10^7\).
Gaussian elimination, \(O(2/3n^3)\) flops, general matrix
LU decomposition, upper triangular and lower tridiagonal matrices, \(O(2/3n^3)\) flops, general matrix. Get easily the inverse, determinant and can solve linear equations with back-substitution only, \(O(n^2)\) flops
Cholesky decomposition. Real symmetric or hermitian positive definite matrix, \(O(1/3n^3)\) flops.
Tridiagonal linear systems, important for differential equations. Normally positive definite and non-singular. \(O(8n)\) flops for symmetric. Special case of banded matrices.
Singular value decomposition
the QR method will be discussed in chapter 7 in connection with eigenvalue systems. \(O(4/3n^3)\) flops.
The LU decomposition method means that we can rewrite this matrix as the product of two matrices \(\mathbf{L}\) and \(\mathbf{U}\) where
LU decomposition forms the backbone of other algorithms in linear algebra, such as the solution of linear equations given by
The above set of equations is conveniently solved by using LU decomposition as an intermediate step.
The matrix \(\mathbf{A}\in \mathbb{R}^{n\times n}\) has an LU factorization if the determinant is different from zero. If the LU factorization exists and \(\mathbf{A}\) is non-singular, then the LU factorization is unique and the determinant is given by
There are at least three main advantages with LU decomposition compared with standard Gaussian elimination:
It is straightforward to compute the determinant of a matrix
If we have to solve sets of linear equations with the same matrix but with different vectors \(\mathbf{y}\), the number of FLOPS is of the order \(n^3\).
The inverse is such an operation
With the LU decomposition it is rather simple to solve a system of linear equations
This can be written in matrix form as
where \(\mathbf{A}\) and \(\mathbf{w}\) are known and we have to solve for \(\mathbf{x}\). Using the LU dcomposition we write
The previous equation can be calculated in two steps
To show that this is correct we use to the LU decomposition to rewrite our system of linear equations as
and since the determinant of \(\mathbf{L}\) is equal to 1 (by construction since the diagonals of \(\mathbf{L}\) equal 1) we can use the inverse of \(\mathbf{L}\) to obtain
which yields the intermediate step
and as soon as we have \(\mathbf{y}\) we can obtain \(\mathbf{x}\) through \(\mathbf{Ux}=\mathbf{y}\).
For our four-dimentional example this takes the form
and
This example shows the basis for the algorithm needed to solve the set of \(n\) linear equations.
The algorithm goes as follows
Set up the matrix \(\bf A\) and the vector \(\bf w\) with their correct dimensions. This determines the dimensionality of the unknown vector \(\bf x\).
Then LU decompose the matrix \(\bf A\) through a call to the function
ludcmp(double a, int n, int indx, double &d)
. This functions returns the LU decomposed matrix \(\bf A\), its determinant and the vector indx which keeps track of the number of interchanges of rows. If the determinant is zero, the solution is malconditioned.Thereafter you call the function
lubksb(double a, int n, int indx, double w)
which uses the LU decomposed matrix \(\bf A\) and the vector \(\bf w\) and returns \(\bf x\) in the same place as \(\bf w\). Upon exit the original content in \(\bf w\) is destroyed. If you wish to keep this information, you should make a backup of it in your calling function.
2.6.1. LU Decomposition, the inverse of a matrix¶
If the inverse exists then
the identity matrix. With an LU decomposed matrix we can rewrite the last equation as
If we assume that the first column (that is column 1) of the inverse matrix can be written as a vector with unknown entries
then we have a linear set of equations
In a similar way we can compute the unknow entries of the second column,
and continue till we have solved all \(n\) sets of linear equations.
The calculation of the inverse here assumes that it actually exists. In many machine learning applications there may be strong linear dependencies among the various columns and/or rows. In our discussions of linear regression we will dive into the mathematics of the singular value decomposition, an algorithm which will allow us to calculate the so-called pseudo-inverse. These details will be presented in our linear regression chapter.


Pentagon Chief Asks For US Navy Secretary's Resignation After Controversy Over SEAL Case
In a statement, Esper said that he was "deeply disturbed" by Spencer's conduct and "have determined that" he no longer "has my confidence to continue in his position".
US Defence Secretary Mark Esper on Sunday asked for the resignation of Navy Secretary Richard Spencer over his handling of a case involving a Navy SEAL officer who was accused of war crimes in Iraq.
The Washington Post reported that Esper requested the resignation of Richard Spencer after learning that he had privately proposed to the White House that he would ensure that Navy SEAL officer Eddie Gallagher “was able to retire as a Navy SEAL” if the White House officials “do not interfere with proceedings against Gallagher”.
Gallagher has been accused of War Crimes during his deployment in Iraq in 2017. While he was acquitted of a murder charge related to the 2017 stabbing death of an ISIS fighter, he was demoted to Chief when a military jury convicted him of posing with the corpse.
In a statement, Esper said that he was “deeply disturbed” by Spencer’s conduct and “have determined that” he no longer “has my confidence to continue in his position”.
For breaking news and live news updates, like us on Facebook or follow us on Twitter and Instagram. Read more on World Latest News on India.com.
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| lesson 37 The westhaven Express |
| We have learnt to expect that trains will be punctual. |
| After years of conditioning, most of us have developed an unshakable faith in railway timetables. |
| Ships may be delayed by storms; |
| flights may be cancelled because of bad weather, but trains must be on time. |
| Only an exceptionally heavy snowfall might temporarily dislocate railway services. |
| It is all too easy to blame the railway authorities when something does go wrong. |
| The truth is that when mistakes occur, they are more likely to be ours than theirs. |
| After consulting my railway timetable, I noted with satisfaction that there was an express train to Westhaven. |
| It went direct from my local station and the journey lasted a mere hour and seventeen minutes. |
| When I boarded the train, I could not help noticing that a great many local people got on as well. |
| At the time, this did not strike me as odd. |
| I reflected that there must be a great many people besides myself who wished to take advantage of this excellent service. |
| Neither was I surprised when the train stopped at Widley, a tiny station a few miles along the line. |
| Even a mighty express train can be held up by signals. |
| But when the train dawdled at station after station, I began to wonder. |
| It suddenly dawned on me that this express was not roaring down the line at ninety miles an hour, but barely chugging along at thirty. |
| One hour and seventeen minutes passed and we had not even covered half the distance. ^ |
| I asked a passenger if this was the Westhaven Express, but he had not even heard of it. |
| I determined to lodge a complaint as soon as we arrived. |
| Two hours later, I was talking angrily to the station master at Westhaven. |
| When he denied the train's existence, I borrowed his copy of the timetable. |
| There was a note of triumph in my voice when I told him that it was there in black and white. |
| Glancing at it briefly, he told me to look again. |
| A tiny asterisk conducted me to a footnote at the bottom of the page. |
| It said: 'This service has been suspended. ' |


{% include 'head.html' %}
{% include 'navigation.html' %}data. library data and web services to integrate into your hacks and projects.
data portalprojects. current and past projects to investigate, explore and get involved in.
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articlescontact. want to collaborate on library data projects? get in touch.
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--- layout: post title: i-mode stalls in Europe date: 2005-11-06 00:00:00 categories: business mobile ---
i-mode stalls in Europe: "existing i-mode operators have witnessed visible subscriber increases after a difficult initial period."


---
layout: post
title: Sametime on Pidgin
date: '2010-11-09T15:18:00.003-08:00'
author: Naren Salem
tags:
modified_time: '2010-11-09T15:18:31.098-08:00'
blogger_id: tag:blogger.com,1999:blog-360727496473333342.post-7160605141990758580
blogger_orig_url: https://frigments.blogspot.com/2010/11/sametime-on-pidgin.html
---
Work uses Lotus groupware. So I needed my IM client to connect to Sametime at work. Yes Pidgin and Empathy do that, but they were not showing me all my online contacts. After some browsing around it turned out that the meanwhile library had the bug in it and I had to get the source code for it and compile it to get the fix because the new packages were not released yet. I'm getting to be a geek now.


The Blackstone Company
The Blackstone Company is a mercenary guild on the Outlands Ultima Online free server. We are a group of fighters in search of fortune and glory against all odds and all opposition.
The character that will fit in best is a thrill-seeking adventurer with dollar signs for eyes, but there is room for all comers. After all, the company needs equipment made, drinks served, and a place to spend their gold!
We have a heavy emphasis on roleplay, and while we participate in all aspects of the game, we like to use it as a platform for storytelling. Members are expected to be in character the majority of the time.
Interested? Hop into the Discord and say hello!
Company By-laws
- Be good to your fellow Company members. Follow your heart with anyone else.
- Gold raised on an expedition is split equally amongst those present.
- The needy get first dibs on equipment gathered.
- Other treasure goes to the fastest hands.
- Disputes are resolved one of two ways: combat or dice. The challenged party chooses which. No disputing the results.
Attire
The company doesn’t have much in the way of a uniform. Resources are scarce, and who likes to follow the crowd, anyway? We do like to be able to spot each-other in a brawl, though.
- Red bandana.
- Red kilt, granted to those who have successfully returned from a Company expedition.
- Red sash/shirt, granted to those who’ve killed in the service of the company. A painting and the head of your foe will serve as proof.
Hierarchy
Decisions are made by vote. All Company members get a vote, but the votes of members with red sashes are worth one and a half.
OOC Rules & Other Notes
- Be nice to each other OOC. "Nice" is a broad term, further defined on the OOC rules page.
- Remain IC when interacting with other role players.
- Remain IC the rest of the time if at all possible.
- Characters should have names suitable for the RP setting. Ideally something original and not one of the core characters from Lord of the Rings.
- This is a dangerous shard, and you are likely to be PKed, looted and have some generally unpleasant interactions with non-RPers. Please be emotionally prepared for that and keep complaints to a minimum. There will be time for revenge later.
- Be considerate when fighting members of members of roleplaying guilds that we have a prior conflict agreement with. Warn them before attacking, and refrain from looting them.
- (Being caught) committing serious enough breaches of the IC by-laws may result in your character being cast out of the company. Please consider this an IC punishment, not an OOC one. You are welcome to rejoin on another character, and ideally continue playing your exile as an enemy of the group.
~ Derek Lumbar, Company Founder


Event Location:
SLU Center for Workforce Organizational Developement
3545 Lindell Blvd
Saint Louis, Missouri
Abstract:
R is a very powerful statistical programming language that has been gaining a lot of traction in recent years. In early 2015 Microsoft acquired Revolution Analytics, a major provider of software and services for R. Microsoft have since incorporated R in Azure ML, SQL Server 2016, and Power BI. In this session I will focus on the benefits of using R in Power BI. I will show how to leverage R for data munging (ETL) and I will also show how to leverage R to create custom visualizations. I will focus on the quot;Wickham stackquot; which is a group of very popular R packages developed by Hadley Wickham. The “Wickham stack” includes the ggplot2, dplyr, and tidyr packages. I will discuss logical use cases for using R within Power BI as well as some of the pitfalls that you may encounter.
Speaker(s): Ryan Wade,
Abstract:
Developers are interested in consuming and processing very large amounts of data as quickly as possible. Microsoft Azure DocumentDB is a NoSQL document database service designed for modern mobile and web applications which require fast reads and writes of unstructured data feeds. Azure DocumentDB natively supports JSON documents which is a key performance feature. In this session I will demonstrate how to create a DocumentDB instance in Azure, how to work with JSON documents, create Ad hoc queries with familiar SQL syntax, create application logic as stored procedures, triggers and user defined functions (UDFs) using standard JavaScript. Finally, I will compare performance and feature difference between SQL Server 2016 and Azure DocumentDB.
Speaker(s): Jeff Renz,
Abstract:
As DBAs, we are asked all sorts of impossible questions. Who dropped that important table last month? Why was everything slow last week? Who made that user a sysadmin? You’d have to be a mind reader to know the answers to some of those, right? Not necessarily! SQL Server can help you find information about these and other common problems with its default trace and system_health Extended Events session, but you have to know how to collect and interpret the data they provide. In this session, we’ll explore what system_health and the default trace are, what information they contain, and how to use them to find the answers you’re searching for.
Speaker(s): Ed Leighton-Dick,
Abstract:
Did you know that SQL Server Availability Groups can be a great solution for disaster recovery? Availability groups have become well known for their high availability capabilities, and for good reason: they work well, they remove some of clustering’s limitations, and they are fairly straightforward to setup. Extending an availability group to multiple sites for disaster recovery becomes more complicated. A lot of moving parts have to come together for it to work properly - SQL Server, Windows, Active Directory, and DNS all get involved in this advanced configuration. In this session, we’ll discuss some of the factors that complicate a multi-site availability group deployment, and we’ll walk through a multi-site deployment so you can see how it’s done.
Speaker(s): Ed Leighton-Dick,
Abstract:
This session is designed for you, the experienced SSIS developer who's been hearing about the Business Intelligence Markup Language, Biml, but has not taken the plunge. We will cover what the Biml language is and how using your familiar development tools, SQL Server Data Tools, SSDT, and BIDS Helper, you will be able to streamline the creation of SSIS packages. We will work through progressively more complex examples from a "Hello World" equivalent to a to a full fledged, audited, truncate and reload pattern based on an external metadata repository. You will leave this session armed with the knowledge of how Biml can help you achieve a consistent and reproducible approach for the creation of SSIS solutions.
Speaker(s): Bill Fellows,
Abstract:
So you have your availability groups configured and everything reports green. How do you measure SLA compliance? Do you know when replication is delayed? Did you know it failed over last night? This session will answer all these questions and more, and will show you what should you be looking at to ensure no surprises... We will look at the DMVs, extended event sessions, perfmon counters, SQL agent jobs and policies to show what to monitor in your AG and how to setup alerts so you know when to take action.
Speaker(s): Phil Ekins,
Abstract:
In this session, we will get data from an Excel workbook and an OData feed. We will transform, aggregate and combine these data sources. In addition, we will look at alternate ways of creating new data sources. Lastly, we will create visualizations in the form of reports and dashboards.
Speaker(s): William Stanley,
Abstract:
Data vault is a compelling architecture for an enterprise data warehouse using SQL Server. We will review the basics about enterprise data warehouse design, introduce you to the data vault architecture and discuss how you can leverage BI features of SQL Server to help make your data warehouse solution provide maximum value to your users. We will walk through example data models to help highlight why data vault architecture facilitates traceability, auditability and scalability. Additionally we will cover implementation advantages which can facilitate agile development, strategies to load massive amounts of data in parallel, how to transition the data warehouse to a system of record, how to easily automate loading using BIML because of strictly naming rules and conventions, and finally how to support the Master Data Management needs of the organization.
Speaker(s): Jeff Renz,
Abstract:
Most DBAs use multiple native and third party tools to fully document SQL Server instances yet still dont get all the objects. Even using Visual Studio and Database Projects, not all objects are exported because they live at the server level, above the database. Using Powershell, we will script out an organized set of files that can be checked into any Source Control system and or used for Disaster Recovery purposes. Objects exported include: SQL Agent Jobs/Alerts/Schedules, NT Service Credentials, SSIS-MSDB, SSIS-Catalog, SSAS Cubes, SSRS Reports, Logins, Server Configs, Linked Servers, DB Mail Operators/Accounts, Server Triggers and much more
Speaker(s): George Walkey,
Abstract:
Azure Stream Analytics lets you rapidly develop and deploy low-cost solutions to gain real-time insights from devices, sensors, infrastructure, and applications." Using a Twitter feed as our IoT application data source, we'll explore the mechanics of data ingest, data aggregation and data analysis in the Azure environment in real-time. We'll finish up with data storage options and data visualization using Power BI.
Speaker(s): Todd Robinson,
Abstract:
Join Kathi, Dan, and Kim for a general Qamp;A panel discussion on SQL Server database administration and development. Each speaker will each present a short SQL Server topic (e.g. PLE, TVP gotchas) to get the dialog started, followed by general Qamp;A. Bring your SQL Server questions and participate in the discussion.
Speaker(s): Kathi Kellenberger,
Abstract:
When you want super-responsive business intelligence for your end users nothing beats SSAS Tabular models. It is the engine that runs Power BI and many Tableau implementations when trying to provide fast results across billions of records of data. In this demo heavy session, we will quickly review what Tabular models are and then show why they are even more awesome in SQL 2016.
Speaker(s): Steve Howard,
Abstract:
Hive has transformed over the years from a slow but scalable batch processing engine to a interactive, fully-featured SQL query tool. The next phase in Hive takes processing even further by providing an in-memory processing engine as well as ACID compliance providing insert, update, and delete capabilities. This presentation will explore the present and the future of Hive and provide you a roadmap into this fast-changing technology.
Speaker(s): Scott Shaw,
Abstract:
Power BI is simple to use and it#39;s easy to crank out reports. Before you know it, you have a dozen reports that are somewhat confusing and not quite as appealing as you planned. In this demo heavy session, we will take a fairly typical Power BI report and give it a makeover. We will discuss layout strategy, color palette, and tricks such as slicer orientation, tooltips, visual interactions, use of shapes, and some DAX magic to bring clarity to the data we are trying to convey.
Speaker(s): Steve Howard,
Abstract:
Covering the basics for the beginner and those that have been thrown into a modeling role and have been cowboy’n it like the Wild West. We’ll cover why you should bother with a conceptual model and invest some of your project time in the modeling task. We will go over some different modeling techniques around Normalized, Dimensional and Data Vault options. And lastly, considerations for scalability, sustainability and empowering you with a Data Access Layer so you stay in control of your architecture as opposed to the application beast. Yee-Haw!
Speaker(s): Leslie Weed,
Abstract:
With the release of SQL 2016, SSRS has received it most comprehensive update in more than 10 years. This session will cover many of the new features and discuss how SSRS can integrate with Power BI to provide hybrid reporting scenarios.
Speaker(s): Gary Bell,
Abstract:
Your team is planning to upgrade from an early version of SQL Server environment. Learn what's new and which features and enhancements are really important to the work life of a SQL Server Developer. In this presentation we’ll explore SQL Server 2014/2016 new possibilities, showing you how to use new T-SQL functions, features and enhancements that are only available in SQL Server 2014/2016.
Speaker(s): Wylie Blanchard,
Abstract:
Determining business value for SQL and BI in the enterprise. Establishing a business case, purpose of a business case, business case components, SQL and BI Value, value in the Cloud. Participants will learn about the basics of determining business value for a solution.
Speaker(s): Matthew Bowers,
Abstract:
Having a good understanding what security features are available and what configurations are considered best practice can be crucial to your company's data security. Learn to view security differently and how key builtin security features can be leveraged to make your systems both more maintainable and more secure. This is very much a HOW TO session, both in terms of how to decide what feature combinations work best for your environment and how to implement those features.
Speaker(s): Rick Bielawski,
Abstract:
Most everyone knows how to build a table, an index, or a relationship, but the real challenge in database design is the process the data professional uses to interpret the business requirements into database designs which are both flexible and extensible. In this session, I will NOT tell you the syntax of any database object. In this session, we will review some of the common challenges we face and some philosophies which will help us to design and deliver better database designs.
Speaker(s): Rickey Nunn,
Abstract:
To a beginner, indexes may seem like magic. The right index can drastically reduce query time. But, how do you know which indexes are the right ones? SQL Server will suggest helpful indexes in the graphical execution plan and with the Database Engine Tuning Advisor, but follow that advice with caution. This session will explain how indexes work and how to tune indexes so that your application has the best performance.
Speaker(s): Kathi Kellenberger,
Abstract:
This session is an introduction into the PROCESS of queries. This includes how queries are Parsed, Resolved, Optimized, Compiled and Executed. We will see how this effects Batches, Transactions, and Errors. We will also briefly discuss the differences between Heaps, Clustered Indexes, and Non-Clustered Indexes. Finally we introduce Execution Plans and Statistics. The session is led by an expert Microsoft Certified Trainer (MCT) who regularly teaches the SQL Server certification courses.
Speaker(s): John Deardurff,
Abstract:
One of the most talked about new features in Microsoft SQL Server is AlwaysOn Availability Groups. In this session, we'll cover the pros and cons of using Availability Groups as well as the basics of how to set them up and take advantage of readable secondary replicas. If you want to improve the availability and performance of your applications and databases, are currently using Database Mirroring, or if you're just curious about Availability Groups, then you won't want to miss this session.
Speaker(s): Adam Belebczuk,
Abstract:
The transaction logging system lies at the heart of SQL Server, but many DBAs do not understand its inner workings very well. In this session, we will examine the purpose and organization of the transaction log and will explore the log through a unique visualization tool. We will consider common performance issues that are related to the logging mechanism and how to overcome these problems. Finally, we will discuss some techniques to monitor the log so that potential performance problems can be addressed pro-actively.
Speaker(s): Brian Hansen,
This is a list of speakers from the XML Guidebook records. The details and URLs were valid at the time of the event.
Contact: http://www.tf3604.com
Brian is a database administrator at Children International in Kansas City. He has been working with SQL Server technologies since 1998, including roles in report development, application development and database administration.
LinkedIn: http://www.linkedin.com/pub/jeff-renz/29/82a/aa4
Contact: http://www.revgen.com
Jeff Renz is a Senior Architect at RevGen Partners, currently working as the design architect and implementation lead on several projects for a Fortune Top 50 company. Jeff has worked with SQL Server and BI for 15+ years and has 10 years’ experience with data warehouse design and implementation. In addition to consulting, he is joining the University of Denver adjunct faculty and is expected to begin teaching in the ICT program starting in June. He received his bachelor’s degree in Computer Science, his master’s degree in Operations Research from Colorado School of Mines, and will be graduating from the Harvard Business Analytics Program in December.
LinkedIn: http://www.linkedin.com/in/eleightondick
Contact: http://www.edleightondick.com
Ed Leighton-Dick is a Microsoft MVP, SQL Server performance and architecture specialist, and Founder/Principal Consultant of Kingfisher Technologies. He is a frequent volunteer with PASS, including current roles as a Regional Mentor, co-leader of I-380 PASS local group, and co-leader of the HA/DR virtual group. He can often be found teaching sessions at local, regional, and national events, including user groups, SQLSaturday, and PASS Summit.
LinkedIn: http://www.linkedin.com/in/wylieblanchard
Contact: https://WylieBlanchard.com/?utm_source=sqlsaturday.comutm_medium=profileutm_campaign=website
Wylie Blanchard, MCSE is an IT consultant, SQL Server professional and web strategist who enjoys helping people learn how to use technology as a tool to amplify their professional lives. He has 15+ years of experience in the IT Industry and holds several certifications in SQL and Oracle systems. He is a father of twin-girls, a gadget enthusiast and a self-proclaimed pizza connoisseur. Learn more about Wylie at: www.WylieBlanchard.com.
LinkedIn: http://www.linkedin.com/pub/jeff-renz/29/82a/aa4
Contact: http://www.revgen.com
Jeff Renz is a Senior Architect at RevGen Partners, currently working as the design architect and implementation lead on several projects for a Fortune Top 50 company. Jeff has worked with SQL Server and BI for 15+ years and has 10 years’ experience with data warehouse design and implementation. In addition to consulting, he is joining the University of Denver adjunct faculty and is expected to begin teaching in the ICT program starting in June. He received his bachelor’s degree in Computer Science, his master’s degree in Operations Research from Colorado School of Mines, and will be graduating from the Harvard Business Analytics Program in December.
LinkedIn: http://www.linkedin.com/in/kathikellenberger
Contact: http://auntkathisql.com
Kathi Kellenberger is the editor of Simple Talk at Redgate and a Data Platform MVP. She has worked with SQL Server for over 20 years and has authored, co-authored, or tech edited over a dozen technical books. Kathi is the co-leader of the PASS Women in Technology Virtual Group and a volunteer at LaunchCode. When Kathi isn’t working, she enjoys spending time with family and friends, cycling, singing, and climbing the stairs of tall buildings. Be sure to check out her courses on Pluralsight.
LinkedIn: https://www.linkedin.com/in/williamstanley
William Stanley has over 20 years of experience focusing on data solutions. He is a Senior Manager at Covenant Technology Partners. His areas of expertise include Databases, Business Intelligence, Big Data and Machine Learning. Get to know William better on LinkedIn https://www.linkedin.com/in/williamstanley.
LinkedIn: https://www.linkedin.com/in/steve-howard-1357a59
Contact: http://angryanalytics.com
Steve Howard is a Cloud Solution Architect and Data Liberator for Microsoft. He enjoys working with organizations to achieve more with their data by thinking outside the box with creative solution development. Prior to Microsoft he was a app dev solution architect building really cool applications for cool companies. Steve blogs at http://angryanalytics.com and can be found on the Twitter-verse at @angryanalytics
LinkedIn: http://www.linkedin.com/in/scottpshaw/
Contact: http://www.dbaexperience.com
Scott Shaw has over a decade of experience in data management. He is a frequent speaker at local and national community events and has co-authored two books on T-SQL. He teaches as well as implements Pig, Hive, Hadoop, as well as teaches courses on SQL Server, and Microsoft BI. He is currently working on a book titled quot;Practical Hive” to be published by Apress publishing. He lives in St. Louis and is a Sr. Solutions Engineer for Hortonworks.
LinkedIn: http://www.linkedin.com/en/philekins
Phil Ekins is a Principal Architect at House of Brick Technologies. With over two decades of experience in SQL Server, Automation, Migrations, Virtualization and HA/DR Solutions. Phil has presented at SQL Saturday's and Local Chapters. As a AWS Certified Solutions Architect and a seasoned SQL Server Professional Phil brings a DBA's needs to the world of cloud computing.
LinkedIn: https://www.linkedin.com/in/steve-howard-1357a59
Contact: http://angryanalytics.com
Steve Howard is a Cloud Solution Architect and Data Liberator for Microsoft. He enjoys working with organizations to achieve more with their data by thinking outside the box with creative solution development. Prior to Microsoft he was a app dev solution architect building really cool applications for cool companies. Steve blogs at http://angryanalytics.com and can be found on the Twitter-verse at @angryanalytics
Contact: http://insyncva.com
SQL Server DBA, Developer, IT Manager, Business Owner from the Richmond, VA USA Area Interested in pushing the limits of both hardware software and my own brain
I have worked in IT for twenty-five years. I have had job responsibilities over the years which have progressively increased my technical skills and involved me in all parts of the development life cycle. For the last five years, working for Scottrade, Inc. I have responsibly for database design and architecture. I have been involved in small and large projects on a half dozen platforms over my career and have experience with what works and what does not.
LinkedIn: http://www.linkedin.com/in/kathikellenberger
Contact: http://auntkathisql.com
Kathi Kellenberger is the editor of Simple Talk at Redgate and a Data Platform MVP. She has worked with SQL Server for over 20 years and has authored, co-authored, or tech edited over a dozen technical books. Kathi is the co-leader of the PASS Women in Technology Virtual Group and a volunteer at LaunchCode. When Kathi isn’t working, she enjoys spending time with family and friends, cycling, singing, and climbing the stairs of tall buildings. Be sure to check out her courses on Pluralsight.
LinkedIn: https://www.linkedin.com/in/ryanwade44
Ryan is an experienced data analytics professional. His education and work experience enables him to understand things from a technical, analytical, and business viewpoint. He has the ability to present complex data in an intuitive way using sound analytical and visualization methods. He has an advanced analytical and technical skill-set which includes Power BI, T-SQL, R, Python, Azure Machine Learning Services, Azure Databricks, SQL Server Machine Learning Services, Advanced Modern Excel, SSIS, SSAS Tabular. He is a data analytics enthusiast that has spoken at a variety of database and data science conferences about various topics dealing with data analytics.
LinkedIn: http://www.linkedin.com/in/itacb
Contact: http://www.sqldiablo.com/
Adam Belebczuk is the Owner amp; Principal Consultant at Belebczuk Consulting Services, LLC and has more than 10 years of experience in IT, ranging from customer service to server/network administration to software and database development and administration. Adam is the Chapter Leader for the Ohio North SQL Server Users Group, a frequent speaker and volunteer at PASS events, and specializes in SQL Server development, performance tuning, high availability, and disaster recovery.
Gary Bell is an architect in the Data Analytics practice at Covenant Technology Partners in St. Louis, MO. Gary has 20 years experience architecting and developing data warehouse and analytics solutions using the Microsoft SQL Server and BI stack.
LinkedIn: http://www.linkedin.com/in/billinkc
Contact: http://blog.billfellows.net
Bill Fellows is a SQL Server MVP and has been a database developer for most of his career, with the past 15 years focused on ETL. He is the organizer of Kansas City's eight SQL Saturdays and a speaker at many other SQL Saturdays, User Groups and the PASS Summit. Bill is the owner of Sterling Data Consulting where he gets to solve interesting data problems.
Leslie Weed is currently a Data and BI Architect with RevGen Partners in Denver, CO. She has been a SQL Professional specializing in Business Intelligence, data analytics, systems implementation, integration, and solutions delivery for the last 20ish years, including a variety of Enterprise Data Warehouses in various industries.
LinkedIn: http://www.linkedin.com/in/eleightondick
Contact: http://www.edleightondick.com
Ed Leighton-Dick is a Microsoft MVP, SQL Server performance and architecture specialist, and Founder/Principal Consultant of Kingfisher Technologies. He is a frequent volunteer with PASS, including current roles as a Regional Mentor, co-leader of I-380 PASS local group, and co-leader of the HA/DR virtual group. He can often be found teaching sessions at local, regional, and national events, including user groups, SQLSaturday, and PASS Summit.
LinkedIn: http://www.linkedin.com/in/JohnDeardurff
Contact: https://www.sqlmct.com
John has been a Microsoft Certified Trainer (MCT) for over 20 years teaching Azure, SQL Server, Exchange Server, and Windows Server networking courses. He is currently a SQL Premier Field Engineer for Microsoft. He is an MCT Regional Lead for the Eastern United States and a former Data Platform MVP.
LinkedIn: https://www.linkedin.com/in/matthew-bowers-ab8a932?trk=hp-identity-name
Partner, Senior Business Solutions Consultant, Client Engagement Manager and Senior Business Development Executive. Mr. Bowers specializes in applying contemporary technologies to solve business problems. Focus on helping businesses realize return on investment and value from their technology investments, as well as the application of disruptive technologies to achieve digital transformation Passionate about helping organizations make the connection between technology and realized business value. Passionate about helping IT leaders bridge the gaps between IT and business through enhanced business discussions. 12 years CTO / CIO Level experience in small to medium sized businesses. MSFT VTSP / P-Seller
LinkedIn: https://www.linkedin.com/in/throbinson
Todd Robinson is a BI Architect with 20 years experience in Business Intelligence. Previously certified in Teradata and IBM DataStage, his current expertise is on Microsoft Technologies holding certifications in SQL Server 2012 Data Platform and Business Intelligence. His emphasis has always been on the data and data Integration. Hey, it#39;s all about the data; delivering it when it is needed, where it is needed and with maximum value.
LinkedIn: https://www.linkedin.com/in/richardbielawski
Rick has 35 years experience as a systems, applications and database architect having designed major systems for such institutions as NASDAQ, Bank of America, Blue Cross Blue Shield, Ecolab and Emperex. He holds multiple certifications including MCITP and MCSE in Business Intelligence and Data Platform. Rick currently designs database systems for custom software applications and likes speaking at local user groups and SQL Saturday events.
The following is a list of sponsors that helped fund the event.


--- title: Call for proposals description: You have until February 16, 2018 to submit a talk. image: path: /assets/images/twittercard-appel-en.png?date=20180107 ---
This year, the call for proposals concerns all interventions at Sud-Web 2018.
There will still be a form of curation, but all subjects will go through this form so that the team can choose the programming in a concerted way.
All forms of talks are possible:
We do not want you to feel trapped in the elaboration of a "traditional" talk. Let's find formats that make sense with which you feel comfortable!
Do you have one or more ideas? Do not hesitate, you have until: Feburary 16, 2018 (11:59 pm, Paris Time) to propose a talk.
We remind you that we'll favour:
Afraid to jump in? Feel free to contact us, we will accompany you so that you are ready on the D day D!
* required fields


§481-23 Fraudulent representation of origin of goods. No person shall sell or offer for sale at retail any goods incorrectly purporting to be manufactured by or for or sold by the government of the United States. [L 1945, c 214, §3; RL 1955, §205-42; HRS §481-23]


My team and I developed a restaurant website, Take Me to Thailand, which allowed customers to view the items on the menu and place an order accordingly.
The customer had to have a valid login which was encrypted in the database for security purposes.
Upon logging in, the customer was able to view the PDF form of the menu before making the decision to begin their order and to select if they would like to dine in or takeout.
A teammate and I were tasked with creating four views: Employee, Customer, Owner, Manager where the login info entered would direct them to the page(s) they were allowed to access.
__________
What software did I use to create this project?
Atom
How did I code this project?
HTML & CSS, PHP as well as using the mySQL database
__________
Login
-
Returning/New Customer
_____
If the person is a returning customer,
they proceed to type in their details,
username and password.
If, however, they are a new customer to Take Me to Thailand
they tap Register on the bottom left corner.
-
Employee/Owner/Manager
_____
Based on the login details entered, the system
would know which page they should be directed to.
Employee: They are able to see other employees and Managers.
Owner: The Owner is able to View/Add/Remove Managers
Add/Remove items from the menu, View/Add Employees.
Manager: Add/Remove items from the menu.
Home Page
_____
When the customer logs in, they are taken to the homepage, on the left hand side.
The two options below the slideshow of meals are "Start Order" and "View Menu".
When the customer taps on "View Menu" they are taken to the menu of Take Me to Thailand
in PDF format, as shown in the image on the right. Take Me to Thailand has Appetizers,
Soup, Lunch, Dinner and Drinks.
Start Order
_____
When the customer taps on Start Order they are taken to the above image on the left hand side. After tapping
Start Order to confirm that they are ready to start their order, the ordering system of Take Me to Thailand
then proceeds to ask them if they are going to Dine In or Take Out their meal.
Menu
_____
Cart
_____
When the customer places one item, as shown in the photo on the left hand side,
they also have the option to continue browsing the menu or to proceed to check out.
On the right hand side is when the customer adds more to their cart.
At the top, they are able to empty cart and start fresh if they feel
they need to start over. If they would like to remove an item from their cart,
they click the X in the Remove section.
In the Description of the item, it allows the customer to view how many
rewards points they would be receiving if they ordered what they've chosen.
They also have the option to change the quantity while viewing their cart.
At the end of the page, they view their total dollar amount and total reward points
they would earn from their cart.
Checkout
_____
On the left hand side, the customer essentialy clarifies their name,
or put the name of the person who will be picking up the meal at the
Take Me to Thailand Restaurant. They then select their form of payment.
After tapping Complete Order they are then shown their reciept on the
right hand side.
_____
At the moment, I am unable to find a way to upload PHP files
that use the mySQL database onto GitHub.


Before Change
// The prior for coupling coefficient, initialized as zeros.
// b.shape = [None, self.num_capsule, self.input_num_capsule]. It"s equivalent to
// `b=K.zeros(shape=[batch_size, num_capsule, input_num_capsule])`. I just can"t get `batch_size`
b = K.stop_gradient(K.sum(K.zeros_like(inputs_hat), -1))
assert self.num_routing > 0, "The num_routing should be > 0."
for i in range(self.num_routing):
After Change
// The prior for coupling coefficient, initialized as zeros.
// b.shape = [None, self.num_capsule, self.input_num_capsule].
b = tf.zeros(shape=[K.shape(inputs_hat)[0], self.num_capsule, self.input_num_capsule])
assert self.num_routing > 0, "The num_routing should be > 0."
for i in range(self.num_routing):


| Price | Number of beds | Smoking | Sea-view | |
|---|---|---|---|---|
| {{room.pricePerNight | number}} CHF/night, total: {{room.wholePrice | number}} CHF | {{room.numberOfBeds}} | {{room.smoking ? 'Smoking room' : 'Non-smoking room'}} | {{room.seaView ? 'Sea-view room': 'Room without sea-view'}} |
| Arrival: | {{reservation.startDate | date:'MMMM d, yyyy' }} |
| Departure: | {{reservation.endDate | date:'MMMM d, yyyy' }} |
| Room type: | {{reservation.room.smoking?'Smoking': 'Non-smoking'}} {{reservation.room.seaView?'with sea-view':'without sea-view'}} |
| Number of beds: | {{reservation.room.numberOfBeds}} |
| Price: | {{reservation.room.pricePerNight | number}} CHF/night, total: {{reservation.room.wholePrice | number}} CHF |


Preparation time
1-2 hours
Cooking time
no cooking required
Serves
Serves 6
Dietary
Tofu is used to great effect in vegan cheesecakes, but here Aldo mixes it with coconut cream to make a light no-bake cheesecake.
In a bowl, mix the crushed biscuits with the butter or margarine. Press the mixture into the base of a 20cm/8in round springform cake tin.
In a pan set over a very low heat, add the lemon juice, limoncello, a splash of water and the agar-agar. Stir until the agar-agar has dissolved.
In a food processor, combine the tofu, cream cheese, coconut cream, a splash of vanilla essence, sugar and lemon zest. Blend until smooth.
Pour the tofu mixture over the biscuit base and smooth the surface. Chill in the fridge for an hour until set. Serve with fresh berries or a berry sauce.


OnePlus 8 prototype spotted live in china - Gizchina.com
by AbdullahOnePlus 8 prototype spotted live in china
3.7 (74.44%) 36 votes
OnePlus is a company that has not rested in recent years. With 4 flagship smartphones a year and scheduled releases every 6 months, it’s no wonder if the OnePlus 8 is already in the testing phase and made for manufacturing.
That’s why these new images make us think. This is reportedly a prototype of the OnePlus 8 or 8 Pro and there is a considerable difference from its predecessors.
OnePlus 8 Prototype has a punch-hole for the front camera
Regarding the OnePlus 7T, The Pro model has a front pop-up camera, however, the “normal” model has a notch. Well, the OnePlus 8 prototype shows us a small two-camera cutout in the upper right corner of the screen.
I personally don’t think OnePlus will swap the “Pro” model pop-up camera for a hole in the screen. The brand boasts that it has a full screen and I don’t think it will give up on that model. So we can assume that this is the “normal” model right? OnePlus 8.
But, we also have to discuss the rear of the device. This is because if this really is the OnePlus 8, the rear is completely different than the current 7T.
New color gradient never seen on the 7T series
Recall that OnePlus 7T was the first to incorporate camera modules into a circle. We then assume that this will be the design line of the future mobile phones.
Importantly, the “Pro” model did not bring this ideology. That is, it may have been a simple experiment and realize to what extent these new lines are well accepted.
Before you assume that this is a OnePlus 7T Pro from its rear, note that the alignment of the flash and rear cameras are different. That is a prototype of OnePlus. We just don’t know which one.


Happy New Year! Danes traditionally celebrate New Year with loads of fireworks, and one of the best places to see it is along the lakes in Copenhagen Happy New Year! Danes traditionally celebrate New Year with loads of fireworks, and one of the best places to see it is along the lakes in Copenhagen
New Year’s Eve is your chance to say goodbye to old year, to throw off that novelty Christmas jumper Farmor (granny!) knitted you and don your best suit and tie, ready for a night of revelling. The evening signifies a renewal process; it’s a fresh start, the dawning of a new page in the calendar, so naturally we celebrate this special occasion by doing exactly the same thing we did last year.
In some parts of the world they ring in the New Year with some very choice rituals indeed. In Spain, people mark the occasion by eating a grape every chime of midnight, meanwhile the Irish bang loaves of bread on their doors and walls, and in Turkey, it is customary to wear red undies beneath your party outfit for good luck.
The Danes are no strangers to quaint, some may say bizarre, New Year’s Eve traditions. So we’ve rounded up the best of these annual customs for your amusement. We’ve enlisted a real life Dane and self-proclaimed New Year-ologist, Alex Aagaard, to assist us in giving you an authentic low down on all the night’s happenings.
back -->


Apache default dirs: https://wiki.apache.org/httpd/DistrosDefaultLayout
Linux
Linux list: https://updatedlinux.wordpress.com/2011/05/12/list-of-important-files-and-directories-in-linux-redhatcentosfedora/
https://www.gracefulsecurity.com/path-traversal-cheat-sheet-linux/
files with %00 added: https://github.com/tennc/fuzzdb/blob/master/dict/BURP-PayLoad/LFI/LFI_InterestingFiles-NullByteAdded.txt
proc/self/fd/# list: https://github.com/tennc/fuzzdb/blob/master/dict/BURP-PayLoad/LFI/LFI-FD-check.txt
Proc filesystem info: https://en.wikipedia.org/wiki/Procfs
Windows
Windows list: https://attackerkb.com/Windows/blind_files
https://www.gracefulsecurity.com/path-traversal-cheat-sheet-windows/