5.1 Global and Local Histograms
5.2 Local Mean, Variance and Quasi-spread
5.3 Local Mean Median and Order Statistics
5.6 Switch Magnitude and Phase of Image Spectra
5.7 Image Correlation Functions
Probabilistic models do not specify individual signals of an ensemble. Rather they specify only certain signal statistical characteristics, i.e. averages over the ensemble of certain signal attributes. In this averaging, it is most natural to associate contribution of individual signals to the ensemble average, which are embodied in weight coefficients and weighting function, with the rate of occurrence in realizations of individual elements of the ensemble. The mathematical models assume usually that the number of realization of signals is infinitely large, and this rate is called probability, or, in continuous case, probability density.
The most fundamental notion of probabilistic models is that of a random variable. Quantized (discrete) random variable is statistically specified by probability distribution of its values, where is an integer index, and is the total number of possible quantized values. Probability distribution is a limit, when the number of realizations of the variable goes to infinity, of the rates of individual values in a finite set of realizations. The set of these rates is called the distribution histogram of the variable and the rates for particular values of are called histogram bins. The number of occurrences of individual values in a finite set of realizations is called the cardinality of the set of these values. The sum of histogram bins from minimal value of the variable to a particular value is called the cumulative histogram.
Continuous random variables are specified by the probability that the variable has a value lower than. This probability as a function of is called the cumulative distribution function. It is defined as a limit of the cumulative histogram, when the number of realizations and the number of levels of random variable both go to infinity. Its derivative is called the probability density. The probability density can be regarded as a limit of histogram bins of random variable, when the number of its quantized levels goes to infinity.
Given an image to be processed, filtering can be designed and carried out either over the entire available image frame or fragment-wise. The former is referred as global filtering and to the latter as local filtering.
Applicability of global and local filtering depends on whether images belong to classes of spatial homogeneous or spatial inhomogeneous images. In spatial homogeneous image local spectra vary not substantially, otherwise images are inhomogeneous.
Global filtering is suited for spatial homogeneous images. For spatial inhomogeneous filtering, local filtering is required.
The theoretical framework for local filtering is provided by local criteria of processing quality that evaluate the processing quality individually over image fragments centered at each pixel.
The most straightforward way to implement local filtering is to perform it in a “hopping” window. Hopping window processing being very attractive from the computational complexity point of view suffers however from blocking effects, artifacts in a form of discontinuities at the edges of the hopping window. Obviously, the ultimate solution of the ‘‘blocking effects’’ problem would be sliding window processing.
In the sliding window filtering, one should, for each position of the filter window, compute transform coefficients of the signal within window, determine on this base the filter coefficients, modify accordingly image transform coefficients and then compute inverse transform. With sliding window, inverse transform need not, in principle, be computed for all pixels within window since only the central sample of the window has to be determined in order to form the output image in the process of image scanning with the filter window.
The “Global and local Histograms” experiment gives example of statistical properties of an image: “global histogram” which is calculated over the entire image, and “local histogram” which is calculated over a window fragment selected by the user. Window height and width are controlled by the user.
First, the test image is displayed, with its histogram and another interesting property of histogram - the RMS deviation of the local histogram from global histogram:
Equation 5‑1
Where: - is the test image, is the component of the test image histogram, and
is the component of the local histogram calculated on a window centered at
Once the user selects a window fragment (by clicking anywhere in the displayed test image), the window fragment is displayed, its histogram, and local RMS deviation of selected window histogram form all others local histograms:
Equation 5‑2
Where: - is the test image, and is the component of the local histogram calculated on a window centered at , is the coordinate of the center of the selected window fragment.
Figure 5‑1: "Global and local histogram". Top left is the input image. Top middle is the local histogram RMS deviation from global histogram image. Top right is the test image global histogram. Bottom left is the selected fragment (marked with a black square on the test image). Bottom middle is the selected fragment histogram RMS deviation from local histogram at each pixel. Bottom right is the local histogram of the selected fragment. Window size is 13x13.
Random variable mean value:
;
Equation 5‑3
Variance:
;
Equation 5‑4
And, generally, -th centered moments
;
Equation 5‑5
For, correspondingly, quantized and continuous random variables are typical examples of ensemble averaging of signal’s attributes, in this case its value, and represent statistical characteristics of random variables complementary to probability distribution and probability density. Square root of variance is called the standard deviation of the random variable.
Statistical characteristics of digital signals alternative to histograms and their moments are order statistics and their derivatives. Signal order statistics are defined through a notion of the variational row. Variational row of a digital signal , is a sequence of signal samples ordered according to their values:
Equation 5‑6
If signal samples happen to have the same quantized value they are placed in the variational row one after another in an arbitrary order. Note that coordinate indices of signal samples are ignored in the variational row.
Element that occupies R-th place in the variational row is called signal R-th order statistics. Index () of the order statistics is called its rank. The rank shows how many signal samples have value lower or equal to that of the element. The very first order statistics is signal minimum:
Equation 5‑7
The very last order statistics in the variational row is signal maximum:
Equation 5‑8
If the number of signal samples is odd, -th signal order statistics that is located exactly in the middle of the variational row is called the signal median
Equation 5‑9
If is an even number, median can be defined as
Equation 5‑10
Signal median is an alternative to the signal mean value. As such, it is more robust, in statistical sense, to possible presence, in the sequence of signal samples, “outliers” that belong to a statistical ensemble different from that of the majority of the signal samples. They may appear due to, for instance, spontaneous faults of the signal sensor. Signal median in such cases will be close to the mean of the signal without outlier samples. Yet another alternative to signal mean robust to the presence of outliers, which take extreme values is the so called signal alpha-trimmed mean:
Equation 5‑11
The arithmetic mean of the elements of the variational row, which are minimum elements apart from its ends.
Signal second moment, or its variance, is a parameter that characterizes spread of the signal values. An alternative to it and robust to outliers characteristic of signal values spread based is signal quasi-spread defined as a difference between order statistics, which are certain number of samples to the right and to the left from median:
Equation 5‑12
In image processing, order statistics and ranks can be measured both globally over the entire given image or set of images and over a sliding window. In the latter case they are called local.
At Local “Mean, Variance and Quasi-spread” experiment 3 local statistical properties are displayed: image of local mean, image of local variance and image of local Quasi-spread. In all cases, the window height and width are controlled by the user.
Local mean image – the value of each pixel is the mean of samples from the original image in a window centered at that position.
Local variance image – the value of each pixel is the variance of samples from the original image in a window centered at that position.
Local Quasi-spread image – the value of each pixel is the Quasi-spread of samples from the original image in a window centered at that position.
Figure 5‑2: "Local mean variance and quasi-spread". Top left is the input image. Top right is the local mean image. Bottom left is the local variance image. Bottom right is the local quasi-spread image. Window size is 5x5, right rank percentage is 95%, left rank percentage is 5%.
The result shown at Figure 5‑2 shows the purpose of the of those local filters- the standard deviation and quasi spread filters perform as a sort of edge extraction filters, whereas the local mean filter performs as a edge suppressor filter.
At Local “Mean, Median and Order Statistics” experiment 3 local statistical properties are displayed: image of local mean, image of local median and image of local order statistics. In all cases, the window height and width are controlled by the user.
Local mean image – the value of each pixel is the mean of samples from the original image in a window centered at that position.
Local median image – the value of each pixel is the median of samples from the original image in a window centered at that position.
Local Order Statistics image – the value of each pixel is Order Statistics of samples from the original image in a window centered at that position.
Figure 5‑3: "Local mean median and order statistics". Top left is the input image. Top right is the local mean image. Bottom left is the local median image. Bottom right is the local rank order statistics image. Window size is 3x3, order statistics is 55%.
The results demonstrated in Figure 5‑3 allows to compare the local median, mean and rank order statistics filter as a noise removal filters. Whereas the local mean removes noise but suppresses edges as well, the median and rank order filters preserves the image’s edges.
All described transforms can be applied “globally” to entire set of available signal samples or “locally” to individual signal fragments. In the latter case, signal is split into non-overlapping or overlapping fragments, and transformation is performed over each fragment individually. When transforms are applied to signal fragments within windows that overlap to such a degree that their central samples (pixels) are adjacent in the process of signal (image) scanning over the sampling grid, sliding window” transform domain processing takes place.
Consider, for the sake of simplicity, 1D case. Sliding window application of transforms to fragment of signal centered at signal -th sample, where is an integer part of the argument, can mathematically be described as
Equation 5‑13
where are weight coefficients of a “window function”, which extracts signal fragments of samples and weight them, and are the transform basis functions. For one-dimensional signals of samples, set of local transform coefficient are two-dimensional arrays of samples. When signals are time sequences and their local Fourier analysis using DFT or DCT is performed, such signal representation is called the time-frequency representation. For 2D signal, sliding window application of 2D DFT or DCT transforms produces 4-D space-space frequency representation of signals.
Sliding window transform domain signal representation finds many applications in digital image and video processing. Its use for local adaptive image and video denoising, deblurring and enhancement.
Similarly to sliding window transforms of continuous signals treated in terms of their Fourier spectra, local application of discrete transforms can also be regarded, in terms of signal DFT spectra, as signal sub-band decomposition. For fixed spectral index , the set of signal local transform coefficients of signal of samples in -th position of window are sequences of samples. Consider their Discrete Fourier Transforms over index :
Let be DFT spectrum of the entire signal , such that
Equation 5‑15
Replace in Equation 5‑16 by its expression (Equation 5‑17) through its DFT spectrum and obtain:
where
is DFT spectrum of -th basis function weighted with window weight coefficients .
Equation 5‑17 implies that DFT spectrum, over index , of the sequence of -th local spectral coefficients of signal is the signal DFT spectrum masked by the DFT spectrum of weighted -th basis function, and, therefore, the sequence , in terms of index , of -th local transform coefficients represents an -th sub-band of the signal. In others words, local application of transforms in sliding window can be treated as signal sub-band decomposition by means of band-pass filters with frequency responses defined by spectra of transform basis functions weighted by the sliding window weight coefficients. In this respect it is akin to wavelet signal. The only difference between them is that in sliding window transform domain processing signal sub-bands are arranged uniformly over the frequency (transform coefficient index ) axis and all have the same “bandwidth”, while for wavelet decomposition signal sub-bands are arranged in a logarithmic scale with exponentially growing bandwidths.
At Local “Signal local spectrum” experiment 4 time-frequency representation of a signal are displayed and compared: local DFT, local DCT, local Walsh and local Haar.
Window size controlled by the user.
Figure 5‑4 illustrated the time-frequency representation of a test signal.
Figure 5‑4: “Signal Local Spectrum”. Top plot is the test signal. Second from the top is the local DFT spectrum. Third from the top is the local DCT spectrum. Fourth from the top is the local Haar spectrum. Fifth from the top is the local Walsh spectrum.
At Local “Image local Spectra” experiment 4 local spectrum of an image window fragment are displayed and compared: local DFT, local DCT, local Walsh and local Haar.
Figure 5‑5 illustrated one sub-band of the local produces 4-D space-space frequency representation of a test image.
Figure 5‑5: “Image Local Spectra”. Top left is the test image. Selected fragments is marked by a black square. Bottom left is the selected fragment image. Middle top is the window fragment DFT transform. Top right is the window fragment DCT transform. Middle bottom is the window fragment Walsh transform. Bottom right is the window fragment Haar transform.
From numerous experiments it is known, that the spectral magnitude of a realistic image is not very informative. It contains information about distribution of the signal energy in the frequency domain. Exchanging the spectral magnitudes of two similar images belonging to the same class and perform the inverse Fourier transform, then the difference in visual appearance of the original and obtained images will be negligible [4].
The “Switch Magnitude and Phase of Image Spectra” experiment, as its name indicates, takes two images, calculate their DFTs, and switch their magnitudes.
Figure 5‑6: “Switch Magnitude and Phase of Image Spectra”. Top left is the first image. Bottom left is the second image. Top right is the restored image of second image magnitude and first image phase combination. Bottom right is the restored image of first image magnitude and second image phase combination.
Figure 5‑6 displayed result support the empirical claim that most of the information in an image conveyed in its spectra phase component rather than in the magnitude. In displayed examples the faces are clearly noticeable even the magnitude of the spectra is taken from other image. There are small nuances in the image which are hidden in the spectrum amplitude, such as the nose at the bottom right image of Figure 5‑6.
Autocorrelation function of a continuous random process is defined as an ensemble average of the product of the process values at points and:
Equation 5‑18
Correspondingly, correlation functions of discrete and digital random processes are defined as:
Equation 5‑19
Correlation function as a statistical characteristic of random processes is a generalization of the variance as a statistical characteristic of random variables. Its value ( for discrete processes) is equal to variance of the process at point (at -th sample of the discrete process):
;
An important special case of random processes are homogeneous random processes, or, in the communication engineering jargon, stationary random processes. Correlation function of homogeneous random processes depends on only the difference of coordinates:
Equation 5‑21
and its value is equal to the process variance:
Equation 5‑22
which in this case does not depend on coordinates due to the homogeneity (stationary) of the process.
A measure of interrelation of two random processes is mutual or cross-correlation function:
Equation 5‑23
Cross-correlation function plays an important role in (for example) optimal target location.
In the same way as autocorrelation function of a random process was introduced as ensemble average of the product of random process realization values in two points, one can consider ensemble average of the product of values of Fourier spectra of realizations of random process taken in two frequencies and . This “correlation function” of the spectra of realizations of the random process turns out to be Fourier transform of the process correlation function:
Equation 5‑24
If the random process is stationary,
The second integral in Equation 5‑25 is a Dirac delta-function. Therefore, for stationary random processes,
Equation 5‑26
Fourier transform of the correlation function of a stationary random process is called its spectral density (SD), or power spectrum.
By definition given by Equation 5‑20, spectral density of a stationary random process is also an average, over all realizations of the process, of squared module of Fourier spectra of the realizations
Random process is called uncorrelated if its correlation function is a delta-function, or, respectively, its spectral density is a uniform function:
Equation 5‑28
By the definition (Equation 5‑27) spectral densities of real homogeneous random processes are non-negative even functions:
Equation 5‑29
Therefore autocorrelation functions being Fourier Transforms of even spectral density functions are also even functions:
Equation 5‑30
“Image Correlation Function” experiment display image correlation functions – 2D correlation, correlation along Y axis (averaged DFT power spectra of the test image columns), and correlation along X axis (averaged DFT power spectra of the test image rows).
Figure 5‑7: “Image Correlation Functions”. Top left is the test image. Top right is the image of the 2D autocorrelation function of the test image. Bottom left is the auto correlation of the columns. Bottom is the auto correlation of the rows.
|
[1] |
L. P. Yaroslavsky, "Principles of statistical treatment of signals and signal transformations and basic definitions," in Theoretical Foundations of Digital Imaging Using MATLAB, CRC Press, 2012, pp. 40-45. |
|
[2] |
L. P. Yaroslavsky, "Local adaptive filtering," in Theoretical Foundations of Digital Imaging Using MATLAB, CRC Press, 2012, pp. 420-425. |
|
[3] |
L. P. Yaroslavsky, "DISCRETE SLIDING WINDOW TRANSFORMS AND ''TIME-FREQUENCY'' SIGNAL REPRESENTATION," in Theoretical Foundations of Digital Imaging Using MATLAB, CRC Press, 2012, pp. 192-197. |
|
[4] |
L. P. &. E. M. Yaroslavsky, Fundamentals of digital optics, Boston, USA: Birkhauser, 1996. |


Honestly, I have not had many experiences with authenticly compassionate leadership, specifically in the workplace. I'm glad to say I've encountered it in mindful classrooms, but the dualistic intentions in the workplace (bottom lines and emotional appeals) often feel very separate, isolating, and inevitably compromising of their mutual integrity.
This is a beautiful way to expand both the capacity of the heart and the mind's ability to conceive of its power in this way :)
Tonglen is perhaps my favorite compassion exercise. I have had some pretty intimate practice with in over the last ~6 years. I know it can radically change lives! On mroe than one occasion, I practiced tonglen for the people who most hurt me in my life, and it dramatically improved the relationship. This time around, I practiced tonglen for myself and the buzzing anxiety I'm feeling as we get closer and closer to bootcamp. It was a restful experience and much needed!
The SCARF Model (Status, Certainty, Autonomy, Relatedness, and Fairness) iterates that there are LOTS of ways to experience a heightened sense of fear in relationship. I had a few moments of feeling a SCARF reaction during Phase 0, while asking what I expected were "dumb" questions early on. I even experienced a few moments of comparison while judging some of my work produced in Phase 0 against that of my cohort members. Coming into Bootcamp, I am certain that there will be endless opportunities to practice mindfulness with these kind of reactions or awarenesses.
My difficult conversation related to a friend who I suspected was angry with me for a perceived breach of trust. When I brought up the conversation, I hoped we could discuss the events and any feelings that might be lingering. I had observed them expressing anger towards me and then denying it, so I wanted to provide a space to be genuine and get the cards on the table. To my surprise, this person did not want to engage and simply said "no hard feelings." At this point I have no choice but to believe them and carry on under that pretense.


MPI Maelstrom
问题描述 :
“Since the Apollo is a distributed shared memory machine, memory access and communication times are not uniform,” Valentine told Swigert. “Communication is fast between processors that share the same memory subsystem, but it is slower between processors that are not on the same subsystem. Communication between the Apollo and machines in our lab is slower yet.”
“How is Apollo’s port of the Message Passing Interface (MPI) working out?” Swigert asked.
“Not so well,” Valentine replied. “To do a broadcast of a message from one processor to all the other n-1 processors, they just do a sequence of n-1 sends. That really serializes things and kills the performance.”
“Is there anything you can do to fix that?”
“Yes,” smiled Valentine. “There is. Once the first processor has sent the message to another, those two can then send messages to two other hosts at the same time. Then there will be four hosts that can send, and so on.”
“Ah, so you can do the broadcast as a binary tree!”
“Not really a binary tree — there are some particular features of our network that we should exploit. The interface cards we have allow each processor to simultaneously send messages to any number of the other processors connected to it. However, the messages don’t necessarily arrive at the destinations at the same time — there is a communication cost involved. In general, we need to take into account the communication costs for each link in our network topologies and plan accordingly to minimize the total time required to do a broadcast.”
输入:
The rest of the input defines an adjacency matrix, A. The adjacency matrix is square and of size n x n. Each of its entries will be either an integer or the character x. The value of A(i,j) indicates the expense of sending a message directly from node i to node j. A value of x for A(i,j) indicates that a message cannot be sent directly from node i to node j.
Note that for a node to send a message to itself does not require network communication, so A(i,i) = 0 for 1 <= i <= n. Also, you may assume that the network is undirected (messages can go in either direction with equal overhead), so that A(i,j) = A(j,i). Thus only the entries on the (strictly) lower triangular portion of A will be supplied.
The input to your program will be the lower triangular section of A. That is, the second line of input will contain one entry, A(2,1). The next line will contain two entries, A(3,1) and A(3,2), and so on.
输出:
样例输入:
5 50 30 5 100 20 50 10 x x 10
样例输出:
35
解题代码:
方法一:(来自:http://hi.baidu.com/maxupeng/home) import java.util.*; public class Main { public static void main(String[] args) { Scanner cin = new Scanner(System.in); int n = cin.nextInt(); int a[][] = new int[n][n]; for (int i = 0; i < n; i++) { a[i][i] = 0; for (int j = 0; j < i; j++) { if (cin.hasNextInt()) { a[i][j] = cin.nextInt(); } else { a[i][j] = Integer.MAX_VALUE / 200; cin.next(); } a[j][i] = a[i][j]; } } for (int k = 0; k < n; k++) { for (int i = 0; i < n; i++) { for (int j =0; j < n; j++) { a[i][j] = a[i][j] < (a[i][k] + a[k][j]) ? a[i][j] : (a[i][k] + a[k][j]); } } } int max = a[0][1]; for (int i = 2; i < n; i++) { if (max < a[0][i]) max = a[0][i]; } System.out.println(max); } } 方法二 /* @author:zeropinzuo */ import java.io.*; import java.util.*; public class Main{ static Scanner cin; static int n; static int[][] d; public static void main(String args[]){ cin = new Scanner(System.in); n = cin.nextInt(); if(n==1){ System.out.println(0); return; } d = new int[n+1][n+1]; String s; int i,j; for(i=1;i<=n;i++) d[i][i] = 0; for(i=2;i<=n;i++) for(j=1;j< i;j++){ s = cin.next(); if(s.equals("x") == true){ d[i][j] = -1; d[j][i] = -1; } else{ d[i][j] = Integer.parseInt(s); d[j][i] = d[i][j]; } } int[] dist = dijkstra(); int max = 0; for(i=2;i<=n;i++) if(dist[i]>max) max = dist[i]; System.out.println(max); } static int[] dijkstra(){ int[] dist = new int[n+1]; boolean[] s = new boolean[n+1]; for(int i=2;i<=n;i++){ if(d[1][i] == -1) dist[i] = 200000; else dist[i] = d[1][i]; s[i] = false; } dist[1] = 0; s[1] = true; for(int i=1;i< n;i++){ int temp = 200000; int u=1; for(int j=1;j<=n;j++) if((!s[j])&&(dist[j]< temp)){ u = j; temp = dist[j]; } s[u] = true; for(int j=1;j<=n;j++) if((!s[j])&&(d[u][j] != -1)){ int newdist = dist[u]+d[u][j]; if(newdist < dist[j]) dist[j] = newdist; } } return dist; } }
这怎么用Catalan数啊？求解答！
很棒的网站，加油！⛽
此网站甚是喜欢，潜力巨大，请问站长，如何加入开发团队~
顶一个 赞一个
不错的网站，建议添加些比赛信息
不错，再多来的CF上面的题
根本过不了。。。。
额，不对应吧
代码过于复杂了吧
后面的掩码变量部分乍一看比较难理解，其实就是把32位按照一个整体来看待。
方法二可行，已测试
http://blog.csdn.net/alpc_paul/article/details/33769407
你好，这题给出的代码好像弄错了，这个是pow那道题目的解法
不好意思，写错了．应该是 path[0]=0, visited[V] = true;
递归还真是一门学问，学习了


Hello, it is nice to meet you! My name is Daisyjane Johnson, I am a software developer and this is my about me page!
- I was born in southern California, but moved to North Dakota as a baby. At the age of 11 my family moved to the beautiful Flathead Valley in Montana, only about forty-five minutes away from Glacier National Park. I am the youngest in my family and have four wonderful older sisters. I have many hobbies including singing, yoga and reading. In the summertime I enjoy paddle-boarding on Flathead lake and in the snowy Montana winters you can usually find me watching movies and drinking tea.
- I absolutely love to learn, and embrace being a student to all of life's lessons. Over the past few years I have earned my Associate's of Arts degree with a focus in psychology. I enjoy working with people and knowing how they thing, and I believe that can help me with marketing to clients. I have also earned a Esthtician License which is also something that involves me working with closesly with people. I love natural products, and like to be knowledgeable on healthy ways to take care of yourself.
- I have worked a variety of jobs within the retail, service and food industry including being a nanny, a housekeeper and waitressing. I enjoy interacting with people and making an impact on them whether that be playing a part in a child's upbringing or giving them a quality experience at a resturant or stay at a bed and breakfast.
Who am I?
Education
Work Experience
My Top Ten Favorite Movies!
- The Lord of the Rings (I'm counting all three as one)
- Howl's Moving Castle
- District 9
- Spirited Away
- Titanic
- Shrek 1 and 2
- Avatar
- Planet of the Apes
- Joker
- Moulin Rouge


A TypeScript Appication Framework, for building real-time {{words[currentWord]}} appications
Typescript | Angular2 | Express | Horizon | RethinkDB


Cube DrawMode
Cube Translate
Cube Rotate
Cube Scale
If you press this button, you can get default cube.
Postion of the viewer - eye
Point the viewer is looking at - center
If you press this button, you can get default
cube.
Perspective View - Frustum
If you press this button, you can get default
cube.


Skywatch: Venus, Southern Cross to grace Hawaii skies in December | Honolulu Star-Advertiser
As the days get progressively shorter in the winter season, observers will be able to start stargazing as early as 6:30 p.m. Immediately after sunset, Venus, Jupiter and Saturn will be visible. Read more
Mahalo for reading the Honolulu Star-Advertiser!
You're reading a premium story. Read the full story with our Print & Digital Subscription.
Already a subscriber? Log in now to continue reading this story.
Print subscriber but without online access? Activate your Digital Account now.


A website to quickly check your crypto balances. Nothing is recorded outside of your browser, source code available on GitHub.
As you add your holdings, the URL updates. Bookmark the page to "remember" your wallet.


MapPolygon QML Type
The MapPolygon type displays a polygon on a Map More...
| Import Statement: | import QtLocation 5.3 |
| Since: | Qt Location 5.5 |
Properties
- border
- border.width : int
- border.color : color
- color : color
- path : list<coordinate>
Methods
- void addCoordinate(coordinate)
- void removeCoordinate(coordinate)
Detailed Description
The MapPolygon type displays a polygon on a Map, specified in terms of an ordered list of coordinates. For best appearance and results, polygons should be simple (not self-intersecting).
The coordinates on the path cannot be directly changed after being added to the Polygon. Instead, copy the path into a var, modify the copy and reassign the copy back to the path.
var path = mapPolygon.path; path[0].latitude = 5; mapPolygon.path = path;
Coordinates can also be added and removed at any time using the addCoordinate and removeCoordinate methods.
For drawing rectangles with "straight" edges (same latitude across one edge, same latitude across the other), the MapRectangle type provides a simpler, two-point API.
By default, the polygon is displayed as a 1 pixel black border with no fill. To change its appearance, use the color, border.color and border.width properties.
Note: Since MapPolygons are geographic items, dragging a MapPolygon (through the use of MouseArea) causes its vertices to be recalculated in the geographic coordinate space. The edges retain the same geographic lengths (latitude and longitude differences between the vertices), but they remain straight. Apparent stretching of the item occurs when dragged to a different latitude.
Performance
MapPolygons have a rendering cost that is O(n) with respect to the number of vertices. This means that the per frame cost of having a Polygon on the Map grows in direct proportion to the number of points on the Polygon. There is an additional triangulation cost (approximately O(n log n)) which is currently paid with each frame, but in future may be paid only upon adding or removing points.
Like the other map objects, MapPolygon is normally drawn without a smooth appearance. Setting the opacity property will force the object to be blended, which decreases performance considerably depending on the hardware in use.
Example Usage
The following snippet shows a MapPolygon being used to display a triangle, with three vertices near Brisbane, Australia.
Map { MapPolygon { color: 'green' path: [ { latitude: -27, longitude: 153.0 }, { latitude: -27, longitude: 154.1 }, { latitude: -28, longitude: 153.5 } ] } }
Property Documentation
This property is part of the border property group. The border property group holds the width and color used to draw the border of the polygon.
The width is in pixels and is independent of the zoom level of the map.
The default values correspond to a black border with a width of 1 pixel. For no line, use a width of 0 or a transparent color.
This property holds the color used to fill the polygon.
The default value is transparent.
path : list<coordinate> |
This property holds the ordered list of coordinates which define the polygon.
See also addCoordinate and removeCoordinate.
Method Documentation
Adds a coordinate to the path.
See also removeCoordinate and path.
Removes coordinate from the path. If there are multiple instances of the same coordinate, the one added last is removed.
If coordinate is not in the path this method does nothing.
See also addCoordinate and path.


WebAPI - SupercellProxy %PROXYVERSION%
General info
Status:
ONLINE
Request time: %TIME%
Host:
%HOST%
Total connections: %TOTALCONNECTIONS%
Connected IP addresses
%TOTALCONNECTIONLIST%
© 2016
ExPl0itR


This is a option with a very long text to test if the control alignes centered when the text wraps
Option One
Option Two
Option Three
This is a check with a very long text to test if the control alignes centered when the text wraps
Check One
Check Two
Check Three
Check Four


{% extends 'chapterBase.html' %} {% block title %} Activities {% endblock %} {% load static %} {% block content %}
The annual BASH TSA 5K is a fundraising event run by the BASH TSA chapter. Proceeds from the run benefit the American Cancer Society, and help fund the trip to the TSA state conference. This year, the 5k will have both an in-person and virtual option to comply with local COVID-19 regulations.
The yearly Angel Tree Drive is a charity event run by the Salvation Army around Christmas time to help bring gifts to underprivileged families. BASH TSA participates in this event yearly by purchasing and donating items for the Salvation Army to distribute.
The Gertrude Hawk Chocolate Bar Sale is a new fundraiser for BASH TSA this year. Participating students will have the opportunity to sell chocolate bars throughout the month of January to help fundraise for the PA-TSA State Conference.
Each year, BASH TSA runs a clothing drive at the school to donate to local non-profit organizations. Through the months of November and December, people can drop off various articles of clothing to the BASH TSA homeroom for the chapter to donate at the end of the event. BASH TSA usually donates at least 5000 lbs of clothes per year.


{{.Response}}
Powered by // go:embed
Stream
P. Sherman
42 Wallaby Way
Sydney
Demo - Native camera in browsers
Load Photo


vgrep
A user friendly pager for grep.
See also: `ugrep`, `rg`.
More information: home page
- Recursively search the current directory for a pattern and cache it:
vgrep search_pattern
- Display the contents of the cache:
vgrep
- Open the "4th" match from the cache in the default editor:
vgrep --show 4
- Display a context of "3" lines for each match in the cache:
vgrep --show=context3
- Display the number of matches for each directory in the tree:
vgrep --show=tree
- Display the number of matches for each file in the tree:
vgrep --show=files
- Start an interactive shell with cached matches:
vgrep --interactive


As of version 0.2, hookbox is completely configurable via command line options. While developing your application, you will want to create a startup script that contains all of the appropriate settings.
A typical hookbox start command looks like this:
# hookbox -a myadminpassword -r myapitoken -s mycallbacksecret
The port hookbox binds to is specified by -p PORT or –port=PORT; the default is 8001.
The interface hookbox binds to is specified by -i INTERFACE or –interface=INTERFACE; the default is “<IP_ADDRESS>”
The port of the web application which will handle webhook callbacks is specified by –cbport=PORT; the default is 80.
The hostname of the web application which will handle webhook callbacks is specified by –cbhost=HOSTNAME; the default is “localhost”.
All callbacks will be prefixed with the value specified by –cbpath=PATH_PREFIX; the default is “/hookbox”.
If a secret token is provided, all callbacks with include that token value as the form variable “secret”; this is useful for blocking unauthorized requests to the callback urls. The secret is specified by -s SECRET or –webhook-secret=SECRET; the default is null (no secret.)
These options are typically left as default, except in cases where its helpful to point all callbacks at a single url, for instance a single PHP script.
The subpath for the connect callback is specified by –cb-connect PATH; the default is “connect”
The subpath for the connect callback is specified by –cb-disconnect PATH; the default is “disconnect”
The subpath for the create_channel callback is specified by –cb-create_channel PATH; the default is “create_channel”
The subpath for the destroy_channel callback is specified by –cb-destroy_channel PATH; the default is “destroy_channel”
The subpath for the subscribe callback is specified by –cb-subscribe PATH; the default is “subscribe”
The subpath for the unsubscribe callback is specified by –cb-unsubscribe PATH; the default is “unsubscribe”
The subpath for the publish callback is specified by –cb-publish PATH; the default is “publish”
Hookbox will include all user cookies in any user-triggered webhook callback. This option is purely an optimization that will cause hookbox to include only the cookie specified by -c COOKIE_NAME or –cookie-identifier COOKIE_NAME; the default is to include all cookies.
The external api interfaces are disabled by default and will only be enabled if an API secret is specified by -r SECRET or –api-security-token SECRET. The value specified must appear in the form as the value for the key “secret” when using the Web/HTTP Hookbox API.
Hookbox includes an admin console which can be found at the /admin relative url. (e.g. http://localhost:8001/admin) This console is disabled by default unless an admin password is specified by -a PASSWORD or –admin-password PASSWORD


Website · 2020
Discord Website
My Discord Servers Website
Check Our Discord Servers Website!
Website · 2020
Capsule Website
Capsule Package Manager Website
Check out Capsule's Website!
My Discord Servers Website
Check Our Discord Servers Website!
Capsule Package Manager Website
Check out Capsule's Website!


[ architecture ]
The base entity for scene graph nodes. It maintains a link to a parent, it's local transforms and the world's transforms
An invisible "handle" for scene objects which groups Geometries and other Nodes (audio, light, etc) together
Leaf node in the scene graph. A visible object on the screen which stores the object's looks (mesh and material)
3D objects can have irregular forms, other that basic 3D geometrical shapes. To aid mathematical computation, they are wrapped in a bounding volume which pragmatically represents the object as a sphere, cube, cylinder, etc.
A vector starting from the certain point of an entity having either a finite or infinite length. It determines which objects in the 3D space it intersected
(useful to compute distance)


|
ViennaCL - The Vienna Computing Library
1.5.2
|
A tag for algebraic multigrid (AMG). Used to transport information from the user to the implementation. More...
#include <amg_base.hpp>
Public Member Functions | |
| amg_tag (unsigned int coarse=1, unsigned int interpol=1, double threshold=0.25, double interpolweight=0.2, double jacobiweight=1, unsigned int presmooth=1, unsigned int postsmooth=1, unsigned int coarselevels=0) | |
| The constructor. | |
| void | set_coarse (unsigned int coarse) |
| unsigned int | get_coarse () const |
| void | set_interpol (unsigned int interpol) |
| unsigned int | get_interpol () const |
| void | set_threshold (double threshold) |
| double | get_threshold () const |
| void | set_as (double jacobiweight) |
| double | get_interpolweight () const |
| void | set_interpolweight (double interpolweight) |
| double | get_jacobiweight () const |
| void | set_presmooth (int presmooth) |
| unsigned int | get_presmooth () const |
| void | set_postsmooth (int postsmooth) |
| unsigned int | get_postsmooth () const |
| void | set_coarselevels (int coarselevels) |
| unsigned int | get_coarselevels () const |
A tag for algebraic multigrid (AMG). Used to transport information from the user to the implementation.
| amg_tag | ( | unsigned int | coarse = 1 , |
| unsigned int | interpol = 1 , |
||
| double | threshold = 0.25 , |
||
| double | interpolweight = 0.2 , |
||
| double | jacobiweight = 1 , |
||
| unsigned int | presmooth = 1 , |
||
| unsigned int | postsmooth = 1 , |
||
| unsigned int | coarselevels = 0 |
||
| ) | [inline] |
The constructor.
| coarse | Coarsening Routine (Default: VIENNACL_AMG_COARSE_CLASSIC) |
| interpol | Interpolation routine (Default: VIENNACL_AMG_INTERPOL_DIRECT) |
| threshold | Strength of dependence threshold for the coarsening process (Default: 0.25) |
| interpolweight | Interpolation parameter for SA interpolation and truncation parameter for direct+classical interpolation |
| jacobiweight | Weight of the weighted Jacobi smoother iteration step (Default: 1 = Regular Jacobi smoother) |
| presmooth | Number of presmoothing operations on every level (Default: 1) |
| postsmooth | Number of postsmoothing operations on every level (Default: 1) |
| coarselevels | Number of coarse levels that are constructed (Default: 0 = Optimize coarse levels for direct solver such that coarsest level has a maximum of COARSE_LIMIT points) (Note: Coarsening stops when number of coarse points = 0 and overwrites the parameter with actual number of coarse levels) |
| unsigned int get_coarse | ( | ) | const [inline] |
| unsigned int get_coarselevels | ( | ) | const [inline] |
| unsigned int get_interpol | ( | ) | const [inline] |
| double get_interpolweight | ( | ) | const [inline] |
| double get_jacobiweight | ( | ) | const [inline] |
| unsigned int get_postsmooth | ( | ) | const [inline] |
| unsigned int get_presmooth | ( | ) | const [inline] |
| double get_threshold | ( | ) | const [inline] |
| void set_as | ( | double | jacobiweight | ) | [inline] |
| void set_coarse | ( | unsigned int | coarse | ) | [inline] |
| void set_coarselevels | ( | int | coarselevels | ) | [inline] |
| void set_interpol | ( | unsigned int | interpol | ) | [inline] |
| void set_interpolweight | ( | double | interpolweight | ) | [inline] |
| void set_postsmooth | ( | int | postsmooth | ) | [inline] |
| void set_presmooth | ( | int | presmooth | ) | [inline] |
| void set_threshold | ( | double | threshold | ) | [inline] |


Site C taking shape
by Alaska Highway NewsBC Hydro has released a new video of construction progress at Site C on the Peace River outside Fort St. John.
Spillway foundations were finished in October, penstocks continue to be assembled at the generating station, and crews continue to excavate and line the tunnels that will divert the river and allow construction on the the 60-metre high earthfill dam to begin.


numpy.ndarray
A numpy.ndarray with the label prediction for all the samples in X.
return np.array([np.argmax(self.predict_proba(X))])
def predict_proba(self, X):
predict_proba
numpy.ndarray
A numpy.ndarray with the label prediction for all the samples in X.
preds = np.array([np.array(exp.estimator.predict(X)) * exp.weight
for exp in self.experts])
sum_weights = sum(exp.weight for exp in self.experts)
return np.sum(preds / sum_weights, axis=0, dtype=int)


You are using an outdated browser. Please switch to Firefox or Chrome, or upgrade your browser to improve your experience


I purchased the EmonTx Arduino Shield SMT and unfortunately soldered the four 3.5 CT jacks on the wrong side of the PCB. the 3.5 CT Jack has 4 leads in a row. Looking at the schematic, it appears that i can just run a green wire from the outmost two leads of a single jackto make the necessary connection. clearly I would need to do this for each of the 4 jacks.
Is this possible? or do I need to try and un-solder the 4 jack and put them on the correct side to get the wiring correct
Thanks
Re: 3.5 MM Jacks soldered on wrong side of EmonTx Arduino Shield SMT
I made the same mistake - unfortunately.
Sorry - I can't comment on your patch idea. I tried to remove them - but was rather clumsy in the process - so ordered a new kit in the end - which was very frustrating.
It would be helpful to have a sticker on the board to tell you which side to solder those components.
Re: 3.5 MM Jacks soldered on wrong side of EmonTx Arduino Shield SMT
Disclaimer: I don't have a Shield, I'm working from diagrams. So you need to check with a multimeter.
Using normal telecomms notation, the 'inners' - contacts which break when a plug is inserted, are only used to allow the sketch to detect the presence of a plug. It should still work, but if not, you can ignore it and adjust the sketch accordingly. The 'outers' are the contacts that connect to the plug. Therefore, all you need is a short link between the two 'outers'. But make sure there is no connection to the ring of the plug inside the plug, because that is now connected in parallel with the tip, and don't use a 'mono' plug either, because the socket's ring and tip contacts are then shorted to the sleeve, and the CT won't work.
And regarding "It would be helpful to have a sticker on the board to tell you which side to solder those components.", you could always look at the photographs!
Re: 3.5 MM Jacks soldered on wrong side of EmonTx Arduino Shield SMT
Thank you both for your responses. I decided to un-solder the 3.5 MM jacks and put them on the right way. It did take some time to get them removed and reattached, but I felt it was the best course to take.
In any event, the EMON is operational. And yes, next time I will refer to the detailed pictures on the web site. Had I done that first, I would not have gotten my self into the bind.


This dialog is used to display the key with which a document is encrypted.
It is accessed by selecting Edit \ Show Key and will display the key for all encryption algorithms apart from the Hill encryption algorithm. The key for the Hill encryption algorithm is displayed in the Key for Hill cipher dialog.
You can store the actual key into the internal key storage by clicking the button Copy Key.


The pureXML technology in DB2 9 only parses XML documents at insert time so that they are stored and queried in a parsed format. This is denoted in DB2 9 by the use of the new XML data type. The parsed format is a tree structure of nodes as explained in page 2 "XML document tree storage".
XML documents can be stored in CLOB columns. They are inserted as unparsed text objects. No XML parsing at insert time can be a performance benefit, especially in CPU-bound systems.
On the right pane, click the buttons in order to obtain an average insert times for XML data in XML columns and CLOB columns. Theoretically you should see CLOB inserts is faster than XML inserts. However you may see the opposite in this sample due to insufficient amount of data.


DISCLAIMER
How to use ?
By default Curiosity searches for a channel called nsfw.
If that channel exists on your server all nsfw command in
Curiosity will work from the start.
Want another channel to be your nsfw channel ?
Sure, type .nsfw in chat then choose option 4 in the menu
to activate the current channel as a nsfw channel.
Only channels that start with nsfw ?
Some servers have a channel dedicated to nsfw that start
with the name nsfw inclusive some other text. The default
search of Curiosity will then stop working, but you can how
ever allow nsfw in all channels that start with the name nsfw.
Type .nsfw in chat and then type 5 to activate
all channels with this name.
I do not want this at all !!
We totally understand and we made it easy for you to
disable it. If you do not have a channel called nsfw
no commands will work. For good measure type .nsfw
in chat and type 7 to disable all. No nsfw
commands can be used after this is disabled.
By default Curiosity searches for a channel called nsfw.
If that channel exists on your server all nsfw command in
Curiosity will work from the start.
Want another channel to be your nsfw channel ?
Sure, type .nsfw in chat then choose option 4 in the menu
to activate the current channel as a nsfw channel.
Only channels that start with nsfw ?
Some servers have a channel dedicated to nsfw that start
with the name nsfw inclusive some other text. The default
search of Curiosity will then stop working, but you can how
ever allow nsfw in all channels that start with the name nsfw.
Type .nsfw in chat and then type 5 to activate
all channels with this name.
I do not want this at all !!
We totally understand and we made it easy for you to
disable it. If you do not have a channel called nsfw
no commands will work. For good measure type .nsfw
in chat and type 7 to disable all. No nsfw
commands can be used after this is disabled.


[1]
run-down hotel now trapped in an island of truck parks , light industry and major roads .
[2]
it would have once been a beautiful site with stunning views down into the montpelier valley .


{% extends 'base.html' %} {% load i18n %} {% load bootstrap3 %} {% load embed_video_tags %} {% block head %} {{ form.media }} {% endblock %} {% block breadcrumb %}
Discussion
{% with view.facet_discussion as discussion %}{{ comment.text }} | {{ comment.date|date:'D, M j g:i a' }}
This is where you will see comments
from the facet discussion.
Add a comment below to
start the conversation.
Discussion
Click 'Save Changes' to complete creating this facet and start a discussion.
{% endif %} {% comment %}Edit History
{% endcomment %}


We do pay attention to details.Whether little details or intense details.Thats why we offer quality services.
We try as much as possible to create a personal touch with our clients.We do this through social media platforms
We have been in the industry for over 5 years thus we ensure that we provide a worry free experince
The trust u have for us is earned through your security.You dont have to worry about the safeness of your house.
We do have technicians who are highly trained,both in their work and also discipline.
We do provide timely services.Not only are we timely bt we also do provide quality work..


UNDER DEVELOPMENT
tokin
is a real-time, real-world MMO game for building teams and finding opportunities. Players network with people
and bots within their purpose groups in order to level up, win
crypto
, and be an owner of Tokin.io , the company.
 Overcoming obstacles and filtering through the noise is often burdensome and distracting. Insisting that with enough effort you'll achieve goals and such.
This is true! But what if we gave you ways to work within better odds?
It's important that we coalesce into purpose groups in order to encourage productivity. We recommend many social networks to extend fictions and fabricate intricate, titillatingly well-placed misnomers. Tokin is a reality-based alternative because it serves productivity to purpose groups.
Your achievements, validated by peers from your purpose groups, are attached to Tokin's immutable ledger. Peers are incentivized to notice how you are leveling up and vice versa. The pursuit of purpose, in a game with your peoples.
How the Game Works
Players accept challenges, herein Hustles, posted by other players. These challenges range a gamut between real-world and virtual experiences, all aiming to help players connect and level up. Btw, each successful hustle comes with a range of rewards.
Leveling up is desired because the software is designed to only allow access to features if players have earned enough validated experience and have discovered the proper Passkey.
For example, all players can post articles and interact with the group but only level 12 players can post products, or only level 20 players can post courses, and only level 100 can actually post Hustles.
The potential to gain market exposure is fair and real-time calculations derive real-time rankings. You can't buy the top spot. It must be earned amongst fair competition.
Why Tokin?
We're Fun and Considerate
- 100% player-owned and operated. For us, by us.
- Builds teams using experiments and analysis
- Uncovers opportunity through network technology
Privacy is a myth
Who can operate in the shadows if we cast light everywhere? How proactive can we be given enough intel?
Tokin takes the stance that only what you don't say can remain private. What you do say is telling about who you are and what you believe in. These values help the community understand what you're about and what you need.
Disclosure is common-sense.
You can contract with whomever you wish, and we'll give you the most badass, cutting-edge, cryptographic tools and systems to use.
But, let's not kid ourselves.
Every system of keeping secrets will eventually be cracked so the earlier we adopt radical openness into our culture, the safer and more prosperous we'll be.
We are legion
The foundation of Tokin is a game. Participating players agree to rules and contribute towards the game. The equitable financial ownership is meritocratically bestowed to all player-owners based on each's respective contribution in relation to the whole. More on The Law
Find your place and position
Use advanced analytic tools to save time networking and avoid trial-and-error. Choose from a list of great choices and activities instead of shooting in the dark.
Let's connect
The best way to get us to respond quickly is to make an issue on any of our Github repositories: Tokin Software, btw even this activity is part of the game.
If there's no rush<EMAIL_ADDRESS>Thank You
Austin, Dylan, Victoria, Alex, Nick, Pat, Erica, Tai, Eric, TiE, George, Mikey, Leo, Mark, Barbara, Michael, Melissa, Salah, Finn


What is grid based design?
An invisible grid can be used to help place elements on a page to help direct our eyes to content, prioritise content and maximise visual appeal
Whats all the hype about responsive webpages?
It provides optimal viewing across a range of devices.
What is semantic structure?
It is the use of HTML markup to reinforce the meaning of information on webpages rather than defining its presentation.


| Figure 1 |
Temporal evolution of total SOC stock for entire France (Mto ... |
| Figure 2 |
Climate and land use change maps (1990 versus 2100). Land us ... |
| Figure 3 |
SOC change maps (1990 versus 2100). SOC stock maps (kg C m − ... |
| Figure 4 |
Absolute difference in SOC stock (kg C m −2) as a function o ... |
| Figure 5 |
Absolute difference in total SOC stock for entire France (Mt ... |


Do you prefer cats or do you prefer dogs
What's your favorite color
What's your current mood
Any Comments?


[t]Dear[/t] {$request.CandidateGivenName} {$request.CandidateFamilyNamePreposition} {$request.CandidateFamilyName},
[t]We are very enthousiastic about your CV and would like to invite you to make our online assessment. This will give us more insight in you as a person and makes it possible for us to find even better matching jobs.[/t]
[t]In a few minutes you'll receive an email from NewNerds with a link to your test page. Please use the link to start your online assessment.[/t]
[t]The assessments are managed by our testing partner NewNerds[/t] (www.NewNerds.nl). [t]We insure you that we will not disclose any test results to anybody without your explicit agreement.[/t]
[t]It is important that you answer the question "may the recruiter may see the test results" with yes. If you do not do so I will not receive the test results and I will not be able to inform you about the results. Also I will not be able to use the results of the test in finding a suitable job for you.[/t]
[t]With kind regards,[/t]
{$request.RecruiterGivenName} {$request.RecruiterFamilyNamePreposition} {$request.RecruiterFamilyName}


--- layout: default title: opensensorsdata description: osd is a company that delivers strategies and run project of data reuse making openness a core and sustainable element of business models image: /img/artwork/osd_wall.png ref: index lang: en --- {% for header in site.data.settings.header %}
{% endfor %}


Install `My App`
This is the protected install page for `My App`. In order to view this page, the visitor will have had to be authorized by whichever auth system you've enabled in the configuration. The initial release of OTAgo includes a `simpleAuth` option that allows you to supply a list of username/password pairs. As well as a `noAuth` option that lets everyone through the door.
A future version of this project may allow users to select which build of the app to install, in case you need users to be able to install older versions for regression testing etc. Feel free to submit a pull request with this feature. 😁 (Ideally this feature would read build information from a .json file that could be automatically updated by a CI/CD system.


Generally, if you have the source, compile it statically yourself or find a binary pre-statically-compiled
Linux
[tmpnix](https://git.alech.de/alech/tmpnix) - https://shiftordie.de/blog/2019/02/05/introducing-tmpnix-an-alternative-to-static-binaries-for-post-exploitation/ - NIX standalone package manager that alters nix packages to run from /tmp instead of /nix


Uneditable Paragraph
Editable Paragraph
Can edit the first paragraph?
Can edit the second paragraph?


Jewish children targeted with ‘horrific’ anti-Semitic abuse on Tube
The British Transport Police are investigating the incident after footage of it was shared online.
A video of a man directing anti-Semitic abuse towards Jewish children on the London Underground is being investigated by police.
The man was filmed reading anti-Jewish bible passages to two young boys in skullcaps while they were travelling with their family on the Northern Line.
Chris Atkins took the video after catching the Tube heading south on the Charing Cross branch of the line at midday on Friday.
“I’ve lived in London for 20 years and you’re used to people ranting on the Tube – it was only after a minute I realised, ‘hang on this is really, really antisemitic’,” the 43-year-old journalist told the PA news agency.
“It was the children that really got me and everyone else, he was just screaming at these children. It was horrific in every sense.
“He seemed to be a very committed Christian and believed this was the word of God – that it said in the bible Jews killed Jesus and they are all slave masters.
“It’s the kind of stuff you see on Twitter a lot… but to hear someone actually say it unashamedly was shocking.”
Wearing a cap and hoody, the man is seen in the video threatening a man off-camera after he tries to intervene and a woman in a hijab also confronts him.

Filmmaker Mr Atkins filmed for around two minutes “on instinct” before moving to swap seats with the young boy next to the man.
“I was sat between the guy and the family to try and be a bit of a barrier,” Mr Atkins said.
“Then another guy came up and started talking to him who was very good, who basically said ‘what is it in the Bible?’ and ‘tell me where it shows that’ just to divert attention away from the family.
“It was horrific but it was also quite heartening to see Londoners of all creeds and colours taking him to task.”
Mr Atkins said the family got off the train a few stops later at Leicester Square and the father of the two boys gave him consent to share the video on Twitter.
Mr Atkins said he has spoken to the police and given them information, and said he is willing to give a witness statement if needed.
In a statement the British Transport Police said: “We are aware of a video circulating on Twitter which shows passengers being harassed and being targeted with anti-Semitic abuse on a Northern Line train.
“Enquiries are being made in relation to this footage.
“Anyone who witnessed what happened, or who has any information, is asked to contact BTP by sending a text to 61016 or by calling 0800 40 50 40 quoting reference 357 of 22/11.”


Welcome to the contact page. In this page you can
contact dear respective brother Abdiwahid Bishar Hajir
the owner of this page with questions and concerns. You can ask
him anything and we will sure try to get back to you within 5 business days.
You have the option to either send your repsonse and comments and provide
and email so we can send you the response back to you. Please keep in mind these rules
when sending your queries and questions to us.


{% extends "main/vis_view.html" %} {% block custom_head_tags %} {{ super() }} {% endblock custom_head_tags %} {% block title %} Co-authorship Maps {% endblock title %} {% block mainVisDiv_content %}
Below is the map showing co-authorship relationships between authors in papers relating to science communication and misinformation. To see the co-authorship map for science communication only, click here.
Affiliation:


Calculation of mean convective heat transfer coefficient kc of a straight pipe at an uniform wall temperature or uniform heat flux and for a hydrodynamically developed or undeveloped laminar fluid flow.
There are basically three differences:
The mean convective heat transfer coefficient kc of a straight pipe in the laminar regime can be calculated for the following four heat transfer boundary conditions through its corresponding Nusselt number Nu :
Uniform wall temperature in developed fluid flow (heatTransferBoundary == Modelica.Fluid.Dissipation.Utilities.Types.HeatTransferBoundary.UWTuDFF) according to [VDI 2002, p. Ga 2, eq. 6] :
Nu_TD = [3.66^3 + 0.7^3 + {1.615*(Re*Pr*d_hyd/L)^1/3 - 0.7}^3]^1/3
Uniform heat flux in developed fluid flow (heatTransferBoundary == Modelica.Fluid.Dissipation.Utilities.Types.HeatTransferBoundary.UHFuDFF) according to [VDI 2002, p. Ga 4, eq. 19] :
Nu_qD = [4.364^3 + 0.6^3 + {1.953*(Re*Pr*d_hyd/L)^1/3 - 0.6}^3]^1/3
Uniform wall temperature in undeveloped fluid flow (heatTransferBoundary == Modelica.Fluid.Dissipation.Utilities.Types.HeatTransferBoundary.UWTuUFF) according to [VDI 2002, p. Ga 2, eq. 12] :
Nu_TU = [3.66^3 + 0.7^3 + {1.615*(Re*Pr*d_hyd/L)^1/3 - 0.7}^3 + {(2/[1+22*Pr])^1/6*(Re*Pr*d_hyd/L)^0.5}^3]^1/3
Uniform heat flux in developed fluid flow (heatTransferBoundary == Modelica.Fluid.Dissipation.Utilities.Types.HeatTransferBoundary.UHFuUFF) according to [VDI 2002, p. Ga 5, eq. 25] :
Nu_qU = [4.364^3 + 0.6^3 + {1.953*(Re*Pr*d_hyd/L)^1/3 - 0.6}^3 + {0.924*Pr^1/3*[Re*d_hyd/L]^0.5}^3]^1/3.
The corresponding mean convective heat transfer coefficient kc is determined w.r.t. the chosen heat transfer boundary by:
kc = Nu * lambda / d_hyd
with
| d_hyd | as hydraulic diameter of straight pipe [m], |
| kc | as mean convective heat transfer coefficient [W/(m2K)], |
| lambda | as heat conductivity of fluid [W/(mK)], |
| L | as length of straight pipe [m], |
| Nu = kc*d_hyd/lambda | as mean Nusselt number [-], |
| Pr = eta*cp/lambda | as Prandtl number [-], |
| Re = rho*v*d_hyd/eta | as Reynolds number [-], |
| v | as mean velocity [m/s]. |
The mean Nusselt number Nu representing the mean convective heat transfer coefficient kc depending on four different heat transfer boundary conditions is shown in the figures below.
This verification has been done with the fluid properties of Water (Prandtl number Pr = 7) and a diameter to pipe length fraction of 0.1.


WATCH: Beluga Whale Plays Fetch with Rugby Fans | Breitbart
What do a group of South African rugby fans and a beluga whale near the South Pole have in common? They both love rugby, apparently.
by Dylan GwinnOn Wednesday, a video surfaced showing a beluga whale playing fetch with a group of South African rugby fans as they were sailing by.
“Beluga Whale celebrating the Springboks victory somewhere close to the South Pole!” Alan Kowen wrote on the original post.
Beluga whales are known for being playful and interacting well with humans. Apparently that trait extends well beyond the beluga whales at waterparks.
Follow Dylan Gwinn on Twitter @themightygwinn


Template created for Kyocera ECOSYS m3655idn. Works in any Kyocera printer.
Contains items for:
Triggers for:
Patxi Andueza Based on [sysadminwork’s template] (https://sysadminwork.com/monitoring-printers-hp-kyocera-brother-via-snmp-with-zabbix/)
Template created for Kyocera ECOSYS m3655idn. Works in any Kyocera printer.
Contains items for:
Triggers for:
Patxi Andueza Based on [sysadminwork’s template] (https://sysadminwork.com/monitoring-printers-hp-kyocera-brother-via-snmp-with-zabbix/)


Wownero is a fork of the private cryptocurrenty Monero with two major changes:
Wownero is a fairly launched coin with no premine. It's not a fork of another blockchain. With its own genesis block there is no degradation of privacy due to ring signatures using different participants for the same tx outputs on opposing forks.
Get Started!Get in touch at any of the places below.


--- layout: default ---
This function returns a string.
{ string } - This string has the value 'Hello World'.


Quality: About This Data Set
Despite what Wrangler indicated and this data being from a reliable source such as the Californian government, this data seems problematic for reasons outlined below.
Is the data complete?
This dataset is not complete. A major reason is because the data leans heavily into ranges and does not give precise
values for Age and Income. For instance, we can only see that a participant earns between $15,000 and $24,999. We don’t know
what the precise take-home income is, whether that be $15,235 or $18,000 or $23,500. Similarly, we can only see that a
participant is between 18 and 64, and we don’t know if that participant is 20 or 45. In order to feed this data into Wrangler, Excel, and Google
Fusion Tables, I had to split cell ranges, but in doing so, this maintains the degree of vagueness around articulating income
ranges rather than income precise values. The average minimum and maximum income levels, for example, are sketchy
averages because they are averages of minimums and maximum potential values rather than actual values themselves.
It is also of note that the income intervals in the data as coded by the California Department of Finance do not seem to make
much sense. For example, why is one income interval $5,000-$9,999, yet another income interval is $75,000+, and yet
another income interval is $50,000-$74,000? There is no justification provided with the data on these intervals. A similar
problem arises when looking at the age intervals in the original data.
Is the data coherent?
Upon first looking at the raw data, there is no reason to believe that this data is not consistent with itself. One might
hypothesize that a possible risk to coherency is if the same person participated in the survey twice and reported different
values in each report. Other than this, there wasno reason to assume that the specified combinations of age, income, and
education attainment level can’t coexist per participant.
However, upon looking at the visualizations of the random samples after I cleaned the data with Wrangler, something is
definitely wrong with either the data, the way I sampled this data, or inputting the values into Wrangler, Google Fusion Tables,
or Google Spreadsheets. For instance, it does not make sense that in Random Sample B, average maximum income values for
“high school or equivalent”, “no high school diploma”, and “some college, less than a 4-year degree” are well above
six figures. Perhaps there are some extreme outliers there skewing the data, but even so, it is unlikely that there are
extreme outliers in all these three categories within one random sample of 36 fields. If I had more time to complete this
assignment, I would definitely take more random samples of this data and see what these random samples returned.
Is the data correct?
The data as indicated in these two random samples do not seem to be correct. I would need to take more random samples of the data to have a better likelihood of being right on this, but according to the Bureau of Labor Statistics, weekly median earnings do have a positive relationship with increasing levels of education obtainment. The random samples as indicated in this Byte indicate that no relationships seem to exist, yet alone a positive one. (Again, I would need to take more random samples to know this for sure.)


{% extends "layout.html" %} {% block title %}Index{% endblock %} {% block head %} {% endblock %} {% block content %}
We pre-train a SampleCNN encoder using a task-agnostic, self-supervised objective on unlabeled, raw waveforms of musical audio.
We then fine-tune a linear classifier on the task of automatic music tagging and evaluate the performance.
Despite task-agnostic pre-training and fine-tuning on the music classification task using a linear classifier, we achieve state-of-the-art results on the MagnaTagATune dataset.


{% extends "global/Base.html" %} {% load otree static %} {% block content %}
Thank you for your participation!
This is the end of the experiment.
Please stay seated until the experimenter calls you to receive your payment.


Now you’ll see a dialogue between Anne and Bob. You will have to type in the question you think Anne asked.
Once you've typed in the question, press enter to continue.


Studies show that personality influences an individual's risk taking behavior in financial decisions. This demo uses IBM Watson's Personality Insights to determine an individual's personality from tweets and any other available content the individual has authored. The inferred personality is then used to determine the individual's risk propensity and assist the advisor in the selection of fund recommendations. For example, in this demo if we select client 'Cari', her personality shows that she is high on openness change and low on cautiousness and conservation. Her risk propensity is high and the advisor is presented high risk funds for consideration. On the other side, if we choose client 'Lillian', she is high on cautiousness and conservation and low on openness to change so low risk funds are recommended.
Once a risk assessment based on personality is available, IBM Watson's Tradeoff Analytics can be used to balance multiple financial objectives along with personality match to help the advisor determine the best funds for the individual.
Further, the demo shows how knowledge about an individual's personality can be used to match him or her to an agent who is most likely to close the business and drive value out of the relationship. These insights can also be leveraged to provide recommendations to the advisor in developing the optimal engagement strategy.
This is a working demo that showcases how one might leverage the Personality Insights and Tradeoff Analytics cognitive services. This is not meant to be a complete product or people recommendation engine. In a real scenario, personality attributes will be used along with other relevant features to learn an appropriate prediction model.
For additional information and assistance in engaging clients, please contact:
Brian Walter, Watson Wealth Management
E-mail<EMAIL_ADDRESS>| Tel:<PHONE_NUMBER>


Tcl_StaticPackage - make a statically linked package available via the 'load' command
#include <tcl.h>
Tcl_StaticPackage(interp, pkgName, initProc, safeInitProc)
- Tcl_Interp *interp (in)
-
If not NULL, points to an interpreter into which the package has
already been loaded (i.e., the caller has already invoked the
appropriate initialization procedure). NULL means the package
has not yet been incorporated into any interpreter.
- const char *pkgName (in)
-
Name of the package; should be properly capitalized (first letter
upper-case, all others lower-case).
- Tcl_PackageInitProc *initProc (in)
-
Procedure to invoke to incorporate this package into a trusted
interpreter.
- Tcl_PackageInitProc *safeInitProc (in)
-
Procedure to call to incorporate this package into a safe interpreter
(one that will execute untrusted scripts). NULL means the package
cannot be used in safe interpreters.
This procedure may be invoked to announce that a package has been
linked statically with a Tcl application and, optionally, that it
has already been loaded into an interpreter.
Once Tcl_StaticPackage has been invoked for a package, it
may be loaded into interpreters using the load command.
Tcl_StaticPackage is normally invoked only by the Tcl_AppInit
procedure for the application, not by packages for themselves
(Tcl_StaticPackage should only be invoked for statically
loaded packages, and code in the package itself should not need
to know whether the package is dynamically or statically loaded).
When the load command is used later to load the package into
an interpreter, one of initProc and safeInitProc will
be invoked, depending on whether the target interpreter is safe
or not.
initProc and safeInitProc must both match the
following prototype:
typedef int Tcl_PackageInitProc(Tcl_Interp *interp);
The interp argument identifies the interpreter in which the package
is to be loaded. The initialization procedure must return TCL_OK or
TCL_ERROR to indicate whether or not it completed successfully; in
the event of an error it should set the interpreter's result to point to an
error message. The result or error from the initialization procedure will
be returned as the result of the load command that caused the
initialization procedure to be invoked.
initialization procedure, package, static linking
Copyright © 1995-1997 Roger E. Critchlow Jr.
Copyright © 1995-1996 Sun Microsystems, Inc.


Name is required
Author name is required is required
Blogger name is required is required
Date is required


The Great Milenko
What is Milenko?
"Milenko's Bot Detector" is an "anti-bot bot" (yes, really...) that helps to remove malicious bots from Team Fortress 2 casual matches by detecting, reporting, votekicking, and killing them in the interim.
In other words, Milenko bots are "Friendly Botkillers".
Their name stems from the song "The Great Milenko" by Insane Clown Posse.
What does Milenko do?
- Milenko bots join casual matches in Team Fortress 2 and automatically detect malicious bots.
- Milenko bots will join as the Spy class and perform either the Disco Fever or Luxury Lounge taunt in the spawn room.
- If a malicious bot is detected in the match, Milenko bots will:
- Announce a list of the malicious bots on each team in the chat every 15 seconds or so.
- Try to call vote kicks on these bots as soon as possible.
- Switch to the Sniper class and target only these malicious bots.
- This helps to prevent malicious bots from ruining the fun of innocent players and makes it immediately obvious when malicious bots are present.
- When a votekick is called, they will also inform players in chat whether or not the player being kicked is a known bot.
- This helps to prevent innocent players from being kicked by malicious bots or other players.
- Milenko bots will automatically vote Yes on malicious bots and No on regular players.
Will Milenko bots leave once all the malicious bots are kicked from the match?
Yes, they will. They stay in every new match for a brief period (about 15 seconds after setup time ends) to detect malicious bots, but they will leave automatically.
Since Milenko bots leave automatically, there is no need to kick them.
Trying to kick a Milenko bot only puts you on a 2 minute votekick cooldown and prevents you from kicking an actual malicious bot when one joins.
Do Milenko bots "take up" player slots?
No. Only one Milenko bot will be in a given match at a time, for merely 15 seconds, unless there is a malicious bot.
That still leaves 23 player slots for you during those 15 seconds.
Milenko's goal is to kick malicious bots back into the matchmaking queue as soon as possible.
The less time a malicious bot is in a match, the better.
How can I tell the difference between Milenko and malicious bots?
Please read this guide for basic information on how to identify common malicious bots.
Here's a few more things that can help to distinguish Milenko bots from malicious bots:
- Some malicious bots use Milenko's old profile picture (see here) in an attempt to impersonate them.
- Malicious bots may also try to impersonate Milenko bots by using names like "Milenko's Bot Detector" and the like.
- REAL Milenko bots will use a random default TF2 avatar and a random name.
- Milenko bots will not wear any cosmetic items except for the Mann Co. hat.
- When a malicious bot is detected, Milenko bots will say "Sentry ahead!" every 15 seconds as a warning.
- After killing the last malicious bot on the other team, Milenko bots will taunt with "The Director's Vision".
- When no malicious bots are detected, Milenko bots will "explode" before leaving.
How can I help get rid of malicious bots?
You can download PazerOP's TF2 Bot Detector, a program that helps to identify malicious bots and automatically kick them.
We have our own list of malicious bots which you can add to the tool.
 There is no way for a legitimate player to get added to the list.
You can download Milenko's playerlist here!
Read PazerOP's TF2 Bot Detector wiki page for information on how to add the list to the tool. (Save the playerlist file and move it to the bot detector "cfg" folder)
Do you have a Discord server or something?
Yup. Click the connect button in the widget below or use this link: https://discord.gg/UsZM3Kw
We use the server to post updates on development, accept feedback, and other stuff. Go check it out if you're interested.
Do you have an alternative point of contact?
Yeah. If you know what you're doing you can shoot me an e-mail encrypted using PGP.
Click here for advanced contact information.
It costs my time and money to develop and host the Milenko bots. Please consider donating.
Donations go towards covering hosting costs as well as development. Please consider donating just $6 here.
With enough donations, we can migrate to a stable server and run the Milenko bots 24/7.
There are currently a dozen premium Milenko bots. Premium Milenko bots can call votekicks, use chat to identify malicious bots, and more.
- When there are enough donations, the "Disco Fever" or "Luxury Lounge" taunt will be bought from the in-game store to upgrade new Milenko bots to TF2 premium.
- It costs about $6 in donations ($4.99 + $0.41 tax + ~$0.46 PayPal fees) to upgrade a bot.
Bitcoin donations are also welcome: 35ASdQQ55ALDU9NSu2unTcRzYe9LckG6M5
If your Bitcoin wallet supports it, you should send to this bip32 address instead for reduced transaction fees: bc1q728r5n9mkeege7783tzhqgcayy8s44qwhwllsz
If you would prefer to donate Monero, you can send to this address: 83bvucMFZh72A5BrEeaNBAeZnfbrAdcGNME9o9mydnjsc1rhDK4st5MHdg1qguXrpf4Sf67dcyNmy6TrKZ9eeTVd5h5Chn7
Thanks, and have fun.


Introduction
I am an international student-athlete from Spain. Born and raised in Seville until I came to College.
I started swimming when I was eight and I am now a member of Lewis Women's Swimming team.
I have a twin sister named Ana. My parents are Manolo and Tina.
I am majoring in Computer Science with a concentration in Cybersecurity and a Math minor.
What I Hope to Learn in this Course
- Team work. Approach to Agile methodology
- Learning about different programming languages
- Importance of testing: unit and integration tests
- Source code management tools and integration in the cloud
My Favorite Hobby
I like swimming. I also really enjoy horseback riding, and photography. Since I was a kid, I have travelled a lot and it is something that I still love to do.
Other activities that I like are watching movies, going to the beach and biking.
Greatest Innovation
- Turing and Von Neumann researches
- Open source software (Linus Towards)
- Internet, broadband, www
Fun Fact
I used to speak five languages but, currently, I can only speak three!


In this post I want to present a nice C# syntax sugar: object and collection initializers.
Object initializers
We will start with object initializers. Let’s assume we have classes:
Without object initializer we could set Button
object
properties using the following code:
With object initializers we can shorten this code a bit:
Instructions generated by compiler in this two cases are exactly the same, but code using object initializer is more clear and more readable.
Now let’s dive into some syntax details:
I. We can pass any parameters to object
constructor using syntax:
II. When we call parameterless constructor we can omit parentheses
(like I did in the Button
example):
III. Object initializers can be nested:
IV. We can set nested properties without creating new objects
Do you remember our first example with Button
? We use
to set properties of Button
Position
object. Notice that Position
property has private setter
so we cannot use new
to create new Position
object
Fortunately object initializer syntax is flexible enough to allow us set properties without creating new objects.
V. Object initializer syntax works only with new
In other words following code doesn’t compile:
Collection initializers
Now it’s time to present collection initializers. We will start with
List<T>
initializers, then we will describe Dictionary<TKey, TValue>
initializers.
As we all know C# allows to create and initialize arrays in single expression:
Wouldn’t it be nice if this syntax worked with List<T>
’s? Actually thanks
to collection initializers it works and not only with List
’s but also with
custom classes!
Let’s see an example:
this code is translated by compiler to
Inside initializers we are not limited to constants, we can use complex expressions like
As we already seen compiler translates collection initializers to a couple
of Add
calls. We can make collection initializers work with custom classes by
providing Add
method and implementing IEnumerable
or IEnumerable<T>
. This
second requirement is very important - since we are using collection initializers,
compiler expects that we will initialize some kind of collection and collections
ought to implement IEnumerable
interface.
Below we present minimal generic and non-generic class that works with collections initializers:
NOTE: We used yield break
to quickly provide dummy IEnumerable
implementation.
Now we can test our custom collections:
When ran this program will write:
Now since collections initializers are translated to a Add
method calls, what will happen
when we write Add
method that take more than one parameter?
It turns out that we still can use collection initializers, but the syntax is a bit different
This is translated by compiler to
With this knowledge it should be now easy to understand how Dictionary<TKey, TValue>
initializers work. First Dictionary<TKey, TValue>
defines Add(TKey, TValue)
method,
second it implements IEnumerable<KeyValuePair<TKey, TValue>>
interface - so it fulfills all
requirements needed by collection initializers to work.
We can initialize Dictionary
with the following code
Which as we already know is translated into
C# 6 introduced more flexible syntax for Dictionary
initializers
This is not translated to Add
calls, but uses indexer instead
As other collection initializers it can work with custom types, only requirement is that type must have indexer
Another feature is that we can set properties of already existing objects in collection, for example let’s assume that constructor of our collection already added some objects into collection:
Then we can write
This will be translated by compiler into
The last thing worth know about this new initializer syntax is ability to mix it with property initializers e.g.
That was plenty of knowledge, but the best way to learn about initializer is to use them in code. After a while they become second nature for C# programmers and central part of many language idioms.


By default, the background-image property repeats an image both Horizontally and Vertically. Some images should be repeated only Horizontally or vertically , or they will look strange. If the image above is repeated only Horizontally (background-repeat:repeat-x), the background will look better.


Developer. Cat Lover.  Learn more
Well, OK, there's no scientific proof, but they are darn cute. But no more distractions...
I am a code immersion bootcamp graduate but I am not your average junior developer. I started in tech with a computer science degree. I was asked to teach the code immersion course after taking it. I am passionate about technology, excited to continue learning and love the creative process.
Object Oriented Programming
Ruby / Ruby on Rails
HTML - CSS - Bootstrap
GIT - GITHUB
Unix command line
SQL - Active Record
WordPress - Wix
Writing for impact
App content development
Grant writing
Email Marketing
Marketing collateral
Technical writing
Eldercare Management
Donor Databases: Raiser's Edge - eTapestry
Professional Training
Visio - Powerpoint - Publisher
Expert Cat Wrangler


 You can think of a container as a folder in Windows ® or a directory in UNIX ®. The primary difference between a container and these other file system concepts is that containers cannot be nested. You can, however, create an unlimited number of containers within your account. Data must be stored in a container so you must have at least one container defined in your account prior to uploading data." %}
{% trans "Note: A Public Container will allow anyone with the Public URL to gain access to your objects in the container." %}


First column
Second column
Third column
Fourth column
First column
Second column
Third column
Fourth column
is-full
is-three-quarters
Auto
Auto
is-two-thirds
Auto
Auto
is-half
Auto
Auto
is-one-quarter
Auto
Auto
is-one-fifth
Auto
Auto
is-2
1
1
1
1
1
1
1
1
1
1
is-6
1
1
1
1
1
1
is-12
is-half
is-offset-one-quarter
is-three-fifths
is-offset-one-fifth
is-4
is-offset-8
is-11
is-offset-1
Narrow column
This column is only 200px wide.
Flexible column
This column will take up the remaining space available.
1
2
3
4
1
2
3
4
is-three-quarters-mobile
is-two-thirds-tablet
is-half-desktop
is-one-third-widescreen
is-one-quarter-fullhd
2
3
4
5
First column
First nested column
Second nested column
Second column
50%
Auto
Auto
Default gap
Default gap
Default gap
Default gap
First column
Second column
Third column
Fourth column
Column
Column
Column
Column
Column
Column
First column
Second column with more content. This is so you can see the vertical alignment.
is-one-quarter
is-one-quarter
is-one-quarter
is-one-quarter
is-half
is-one-quarter
is-one-quarter
is-one-quarter
Auto
is-half


Male: Female: Other:
Email: Password:
Birthday: Month Jan Feb Mar Day 10 11 12 Year 1999 2000 2001
I agree to the terms and conditions:


{% extends "base.html" %} {% load compress static hc_extras %} {% block title %}Account Settings - {{ site_name }}{% endblock %} {% block content %}
We're sorry! There was a problem setting up the subscription. Response from payment gateway:
{% for message in messages %} {% endfor %}| Current Plan | {{ sub.plan_name|default:"Hobbyist" }} |
| Next Payment | loading… |
| Checks Used | {% with num_checks=profile.num_checks_used %}= profile.check_limit %} class="at-limit" {% endif %}> {{ num_checks }} of {{ profile.check_limit }} | {% endwith %}
loading…
Not set
{% endif %}{% if sub.send_invoices %} Send the invoice to {{ sub.invoice_email|default:request.user.email }} each time my payment method is successfully charged. {% else %} Do not email invoices to me. {% endif %}


10 February 1998
Disease Outbreak Reported
Up to 5 February 1998 a total of 485 cases with 83 deaths (CFR 17.1%) has been reported since the outbreak began on 27 November 1997. Four areas of Pointe-Noire are currently affected, the worst being Arrondissements III and IV where water quality and sanitary conditions are extremely poor. It is estimated that the number of cases has levelled out at 10 cases per day in Pointe-Noire. Madingo-Kayes which is 80 kms north of Pointe-Noire on the coast and Kaka Mueka 180 kms to the north-east in forest area have also reported cholera outbreaks although figures are not yet available The situation in neighbouring countries is being carefully monitored and WHO is facilitating the exchange of information with these countries.


KUKA Roboter GmbH |
KUKA Fast Robot Interface C++ SDK (version 1.15) |
The KUKA Fast Robot Interface is an option, which extends the KUKA LBR iiwa with an UDP based interface. With this SDK you are able to create your own client application which allows you to control the KUKA LBR iiwa under hard realtime conditions.
The directory structure of the KUKA Fast Robot Interface C++ SDK is as follows: bin: FRI client example applications (after running make). build: Both GNUMake build environment and Microsoft Visual Studio 2010 solution. doc: FRI client SDK documentation. example: Example applications. include: FRI client SDK headers needed to write FRI client applications (see examples). lib: FRI client SDK library (after running make). src: FRI client sources. These need to be build only once for your platform. The resulting library will be copied to the lib folder. ******************************************************************************** Build instructions (Windows): Use the Visual Studio 2010 solution provided under the build folder. Build Requirement: Visual Studio 2010 SP1 ******************************************************************************** Build instructions (GNUMake): Run make from the build/GNUMake folder to build both the FRIClient library and the example applications. These will be copied to the bin folder. If you need to make platform specific changes to the Makefiles, you should change tools.mak accordingly. Alternatively you can create your own version of the tools.mak file and change the TOOLS_MAK variable in the paths.mak file to point to your version.
The following license terms and conditions apply, unless a redistribution agreement or other license is obtained by KUKA Roboter GmbH, Augsburg, Germany. SCOPE The software KUKA Sunrise.Connectivity FRI Client SDK is targeted to work in conjunction with the KUKA Sunrise.Connectivity FastRobotInterface toolkit. In the following, the term software refers to all material directly belonging to the provided SDK Software development kit, particularly source code, libraries, binaries, manuals and technical documentation. COPYRIGHT All Rights Reserved Copyright (C) 2014-2019 KUKA Roboter GmbH Augsburg, Germany LICENSE Redistribution and use of the software in source and binary forms, with or without modification, are permitted provided that the following conditions are met: a) The software is used in conjunction with KUKA products only. b) Redistributions of source code must retain the above copyright notice, this list of conditions and the disclaimer. c) Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the disclaimer in the documentation and/or other materials provided with the distribution. Altered source code of the redistribution must be made available upon request with the distribution. d) Modification and contributions to the original software provided by KUKA must be clearly marked and the authorship must be stated. e) Neither the name of KUKA nor the trademarks owned by KUKA may be used to endorse or promote products derived from this software without specific prior written permission. DISCLAIMER OF WARRANTY The Software is provided "AS IS" and "WITH ALL FAULTS," without warranty of any kind, including without limitation the warranties of merchantability, fitness for a particular purpose and non-infringement. KUKA makes no warranty that the Software is free of defects or is suitable for any particular purpose. In no event shall KUKA be responsible for loss or damages arising from the installation or use of the Software, including but not limited to any indirect, punitive, special, incidental or consequential damages of any character including, without limitation, damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses. The entire risk to the quality and performance of the Software is not borne by KUKA. Should the Software prove defective, KUKA is not liable for the entire cost of any service and repair.
| All Rights Reserved © 2014-2019 KUKA Roboter GmbH, Germany |
created 27 Feb 2019 |


TOC2CDDB(1) CDRDAO TOC2CDDB(1) NAME toc2cddb - translates a TOC file of cdrdao(1) into a cddb file and prints it to stdout SYNOPSIS toc2cddb toc_file toc2cddb [-h|-V] DESCRIPTION toc2cddb translates a TOC file of cdrdao(1) into a cddb file and prints it to stdout. OPTIONS -h Shows a short help message. -V Prints the version of toc2cddb. SEE ALSO cdrdao(1) AUTHOR toc2cddb was written by Giuseppe "Cowo" Corbelli<EMAIL_ADDRESS>and is part of cdrdao(1) written by Andreas Mueller<EMAIL_ADDRESS>This manual page was written by Francois Wendling<EMAIL_ADDRESS>and revised by Daniel Baumann<EMAIL_ADDRESS>for the Debian project (but may be used by others). 1.2.2 2006-11-17 TOC2CDDB(1)


Sydney based digital designer skilled in motion graphics, web design & front end code languages. Please check out my portfolio showcasing my current skills and examples of previous work.
PortfolioI’m an easy going hard working designer with over 10 years experience. Having first started out as a graphic designer working in print, I moved into digital approximately 5 years ago and have since fallen in love with all things digital including animation where my current focus lies. I have plenty to contribute with a thirst to continue learning.
I’ve also worked on web design projects such as designing and building out landing pages, UI components & other web related content.
Please view my portfolio below and If you’d like to get in touch, please shoot me an email or give me a call.
Skilled in Adobe CC software such as Photoshop, Illustrator, XD and After Effects. Mainly used for creating web and social content including landing pages, storyboards and animations.
Skills in storyboarding and motion graphics using After Effects as my main animation tool. Projects include animating content for social use such as weekly special sales, logo intros and campaign related messaging for things like Christmas, Easter, Black Friday to name a few.
Skills include HTML, CSS and Javascript. I also have experience using Wordpress and other custom CMS platforms to build out landing pages and/or EDMs.
If you'd like to get in touch just give me call, shoot me an email or view me on LinkedIn
0402 459 226


Update All
Update the Summary and FTS Status reports
Archive Core - Summary Report
Update
Archive Core - FTS Status Report
Update
Other Links:
Note: the following links in a new window


{% extends "base.html" %} {% block head %}{% endblock %} {% block title %}Block | Web Chain{% endblock %} {% block body %}
| Version: | {{ block.version }} |
| Time (UTC): | {{ block.time }} |
| Difficulty: | {{ block.difficulty }} |
| User: | {{ block.user }} |
| Nonce: | {{ block.nonce }} |
| Previous Hash: | {{ block.prev_hash }} | {% endif %}
| Block Hash: | {{ block.hash }} |


SIGN-IN
Username
Password
Password Recovery?
Connect
Connect with Facebook
Connect with Gmail
Create an account ?
Ouvrir l'application


Chicken is the most common type of poultry in the world.[1] Owing to the relative ease and low cost of raising them in comparison to animals such as cattle or hogs, chickens have become prevalent throughout the cuisine of cultures around the world, and their meat has been variously adapted to regional tastes. Chicken can be prepared in a vast range of ways, including baking, grilling, barbecuing, frying, and boiling, among many others, depending on its purpose. Since the latter half of the 20th century, prepared chicken has become a staple of fast food. Chicken is sometimes cited as being more healthful than red meat, with lower concentrations of cholesterol and saturated fat.[2] The poultry farming industry that accounts for chicken production takes on a range of forms across different parts of the world. In developed countries, chickens are typically subject to intensive farming methods, while less-developed areas raise chickens using more traditional farming techniques. The United Nations estimates there to be 19 billion chickens on Earth today, making them outnumber humans more than two to one.[3]
Chicken is the most common type of poultry in the world.[1] Owing to the relative ease and low cost of raising them in comparison to animals such as cattle or hogs, chickens have become prevalent throughout the cuisine of cultures around the world, and their meat has been variously adapted to regional tastes. Chicken can be prepared in a vast range of ways, including baking, grilling, barbecuing, frying, and boiling, among many others, depending on its purpose. Since the latter half of the 20th century, prepared chicken has become a staple of fast food. Chicken is sometimes cited as being more healthful than red meat, with lower concentrations of cholesterol and saturated fat.[2] The poultry farming industry that accounts for chicken production takes on a range of forms across different parts of the world. In developed countries, chickens are typically subject to intensive farming methods, while less-developed areas raise chickens using more traditional farming techniques. The United Nations estimates there to be 19 billion chickens on Earth today, making them outnumber humans more than two to one.[3]
Chicken is the most common type of poultry in the world.[1] Owing to the relative ease and low cost of raising them in comparison to animals such as cattle or hogs, chickens have become prevalent throughout the cuisine of cultures around the world, and their meat has been variously adapted to regional tastes. Chicken can be prepared in a vast range of ways, including baking, grilling, barbecuing, frying, and boiling, among many others, depending on its purpose. Since the latter half of the 20th century, prepared chicken has become a staple of fast food. Chicken is sometimes cited as being more healthful than red meat, with lower concentrations of cholesterol and saturated fat.[2] The poultry farming industry that accounts for chicken production takes on a range of forms across different parts of the world. In developed countries, chickens are typically subject to intensive farming methods, while less-developed areas raise chickens using more traditional farming techniques. The United Nations estimates there to be 19 billion chickens on Earth today, making them outnumber humans more than two to one.[3]