linear algebra
Contents
3.4. linear algebra#
3.4.1. linear basics#
<IP_ADDRESS>. notation#
\(x \preceq y\) - these are vectors and x is less than y elementwise
\(X \preceq Y\) - matrices, \(Y-X\) is PSD
\(v^TXv \leq v^TYv \:\: \forall v\)
<IP_ADDRESS>. linearity#
inner product \(<X, Y> = tr(X^TY) = \sum_i \sum_j X_{ij} Y_{ij}\)
like inner product if we collapsed into big vector
linear
symmetric
gives angle back
linear
superposition \(f(x+y) = f(x)+f(y) \)
proportionality \(f(k\cdot x) = k \cdot f(x)\)
bilinear just means a function is linear in 2 variables
vector space
closed under addition
contains identity
det - sum of products including one element from each row / column with correct sign
absolute value = area of parallelogram made by rows (or cols)
lin independent: \(c_1x_1+c_2x_2=0 \implies c_1=c_2=0\)
cauchy-schwartz inequality: \(|x^T y| \leq ||x||_2 ||y|||_2\)
implies triangle inequality: \(||x+y||^2 \leq (||x|| + ||y||)^2\)
<IP_ADDRESS>. matrix properties#
\(x^TAx = tr(xx^TA)\)
nonsingular = invertible = nonzero determinant = null space of zero
only square matrices
rank of mxn matrix- max number of linearly independent columns / rows
rank==m==n, then nonsingular
ill-conditioned matrix - matrix is close to being singular - very small determinant
inverse
orthogonal matrix: all columns are orthonormal
\(A^{-1} = A^T\)
preserves the Euclidean norm \(||Ax||_2 = ||x||_2\)
if diagonal, inverse is invert all elements
inverting 3x3 - transpose, find all mini dets, multiply by signs, divide by det
psuedo-inverse = Moore-Penrose inverse \(A^\dagger = (A^T A)^{-1} A^T\)
if A is nonsingular, \(A^\dagger = A^{-1}\)
if rank(A) = m, then must invert using \(A A^T\)
if rank(A) = n, then must use \(A^T A\)
inversion of matrix is \(\approx O(n^3)\)
inverse of psd symmetric matrix is also psd and symmetric
if A, B invertible \((AB)^{-1} = B^{-1} A^{-1}\)
orthogonal complement - set of orthogonal vectors
define R(A) to be range space of A (column space) and N(A) to be null space of A
R(A) and N(A) are orthogonal complements
dim \(R(A)\) = r
dim \(N(A)\) = n-r
dim \(R(A^T)\) = r
dim \(N(A^T)\) = m-r
adjoint - compute with mini-dets
\(A^{-1} = adj(A) / \det(A)\)
Schur complement of \(X = \begin{bmatrix} A & B \\ C & D\end{bmatrix}\)
\(M/D = A - BD^{-1}C\)
\(M/A = D-CA^{-1}B\)
\(X \succeq 0 \iff M/D \succeq 0\)
3.4.2. matrix calc#
overview: imagine derivative \(f(x + \Delta)\)
function f: \(\text{anything} \to \mathbb{R}^m\)
gradient vector \(\nabla_A f(\mathbf{A})\)- partial derivatives with respect to each element of A (vector or matrix)
gradient = \(\frac{\partial f}{\partial A}^T\)
these next 2 assume numerator layout (numerator-major order, so numerator constant along rows)
function f: \(\mathbb{R}^n \to \mathbb{R}^m\)
Jacobian matrix: $\(\mathbf J = \begin{bmatrix} \dfrac{\partial \mathbf{f}}{\partial x_1} & \cdots & \dfrac{\partial \mathbf{f}}{\partial x_n} \end{bmatrix}= \begin{bmatrix} \dfrac{\partial f_1}{\partial x_1} & \cdots & \dfrac{\partial f_1}{\partial x_n}\\ \vdots & \ddots & \vdots\\ \dfrac{\partial f_m}{\partial x_1} & \cdots & \dfrac{\partial f_m}{\partial x_n} \end{bmatrix}\)$ - this is dim(f) x dim(x)
function f: \(\mathbb{R}^n \to \mathbb{R}\)
2nd derivative is Hessian matrix
\(\bold H = \nabla^2 f(x)_{ij} = \frac{\partial^2 f(x)}{\partial x_i \partial x_j} = \begin{bmatrix} \dfrac{\partial^2 f}{\partial x_1^2} & \dfrac{\partial^2 f}{\partial x_1\,\partial x_2} & \cdots & \dfrac{\partial^2 f}{\partial x_1\,\partial x_n} \\[2.2ex] \dfrac{\partial^2 f}{\partial x_2\,\partial x_1} & \dfrac{\partial^2 f}{\partial x_2^2} & \cdots & \dfrac{\partial^2 f}{\partial x_2\,\partial x_n} \\[2.2ex] \vdots & \vdots & \ddots & \vdots \\[2.2ex] \dfrac{\partial^2 f}{\partial x_n\,\partial x_1} & \dfrac{\partial^2 f}{\partial x_n\,\partial x_2} & \cdots & \dfrac{\partial^2 f}{\partial x_n^2}\end{bmatrix}\)
examples
\(\nabla_x a^T x = a\)
\(\nabla_x x^TAx = 2Ax\) (if A symmetric, else \((A+A^T)x)\))
\(\nabla_x^2 x^TAx = 2A\) (if A symmetric, else \(A+A^T\))
\(\nabla_x \log \: \det X = X^{-1}\)
we can calculate derivs of quadratic forms by calculating derivs of traces
\(x^TAx = tr[x^TAx] = tr[xx^TA]\)
\(\implies \frac{\partial}{\partial A} x^TAx = \frac{\partial}{\partial A} tr[xx^TA] = [xx^T]^T = xx^T\)
useful result: \(\frac{\partial}{\partial A} log|A| = A^{-T}\)
3.4.3. norms#
def
nonnegative
definite f(x) = 0 iff x = 0
proportionality (also called homogenous)
triangle inequality
properties
convex
<IP_ADDRESS>. vector norms#
\(L_p-\)norms: \(||x||_p = (\sum_{i=1}^n |x_i|^p)^{1/p}\)
\(L_0\) norm - number of nonzero elements (this is not actually a norm!)
\(||x||_1 = \sum |x_i|\)
\(||x||_2\) - Euclidean norm
\(||x||_\infty = \max_i |x_i|\) - also called Cheybyshev norm
quadratic norms
P-quadratic norm: \(||x||_P = (x^TPx)^{1/2} = || P^{1/2} x ||_2\) where \(P \in S_{++}^n\)
dual norm
given a norm \(|| \cdot ||\), dual norm \(||z||_* = sup\{ z^Tx \: | \: ||x|| \leq 1\}\)
dual of the dual is the original
dual of Euclidean is just Euclidean
dual of \(l_1\) is \(l_\infty\)
dual of spectral norm is some of the singular values
<IP_ADDRESS>. matrix norms#
schatten p-norms: \(||X||_p = (\sum \sigma^p_i(A) )^{1/p}\) - note this is nice for organization but this p is never really mentioned
p=1: nuclear norm = trace norm: \(||X||_* = \sum_i \sigma_i\)
p=2: frobenius norm = euclidean norm: \(||X||_F^2 = \sqrt {\sum_{ij} X_{ij}^2} = \sqrt{\sum_i \sigma_i^2}\) - like vector \(L_2\) norm
p=\(\infty\): spectral norm = \(\mathbf{L_2}\)-norm (of a matrix) = \(||X||_2 = \sigma_\text{max}(X) \)
entrywise norms
sum-absolute-value norm (like vector \(l_1\))
maximum-absolute-value norm (like vector \(l_\infty\))
operator norm
let \(||\cdot||_a\) and \(|| \cdot ||_b\) be vector norms
operator norm \(||X||_{a,b} = sup\{ ||Xu||_a \: | \: ||u||_b \leq 1 \}\)
represents the maximum stretching that X does to a vector u
if using p-norms, can get Frobenius and some others
3.4.4. eigenstuff#
<IP_ADDRESS>. eigenvalues intro - strang 5.1#
elimination changes eigenvalues
eigenvector application to diff eqs \(\frac{du}{dt}=Au\)
soln is exponential: \(u(t) = c_1 e^{\lambda_1 t} x_1 + c_2 e^{\lambda_2 t} x_2\)
eigenvalue eqn: \(Ax = \lambda x \implies (A-\lambda I)x=0\)
\(det(A-\lambda I) = 0\) yields characteristic polynomial
eigenvalue properties
0 eigenvalue \(\implies\) A is singular
eigenvalues are on the main diagonal when the matrix is triangular
expressions when \(A \in \mathbb{S}\)
\(\det(A) = \prod_i \lambda_i\)
\(tr(A) = \sum_i \lambda_i\)
\(||A||_2 = \max | \lambda_i |\)
\(||A||_F = \sqrt{\sum \lambda_i^2}\)
\(\lambda_{max} (A) = \sup_{x \neq 0} \frac{x^T A x}{x^T x}\)
\(\lambda_{min} (A) = \inf_{x \neq 0} \frac{x^T A x}{x^T x}\)
defective matrices - lack a full set of eigenvalues
positive semi-definite: \(A \in R^{nxn}\)
basically these are always symmetric \(A=A^T\)
all eigenvalues are nonnegative
if \(\forall x \in R^n, x^TAx \geq 0\) then A is positive semi definite (PSD)
like it curves up
Note: \(x^TAx = \sum_{i, j} x_iA_{i, j} x_j\)
if \(\forall x \in R^n, x^TAx > 0\) then A is positive definite (PD)
PD \(\to\) full rank, invertible
PSD + symmetric \(\implies\) can be written as Gram matrix \(G = X^T X \)
if X full rank, then \(G\) is PD
PSD notation
\(S^n\) - set of symmetric matrices
\(S^n_+\) - set of PSD matrices
\(S^n_{++}\) - set of PD matrices
<IP_ADDRESS>. strang 5.2 - diagonalization#
diagonalization = eigenvalue decomposition = spectral decomposition
assume A (nxn) is symmetric
\(A = Q \Lambda Q^T\)
Q := eigenvectors as columns, Q is orthonormal
only diagonalizable if n independent eigenvectors
not related to invertibility
eigenvectors corresponding to different eigenvalues are lin. independent
other Q matrices won’t produce diagonal
there are always n complex eigenvalues
orthogonal matrix \(Q^TQ=I\)
examples
if X, Y symmetric, \(tr(YX) = tr(Y \sum \lambda_i q_i q_i^T)\)
lets us easily calculate \(A^2\), \(sqrt(A)\)
eigenvalues of \(A^2\) are squared, eigenvectors remain same
eigenvalues of \(A^{-1}\) are inverse eigenvalues
eigenvalue of rotation matrix is \(i\)
eigenvalues for \(AB\) only multiply when A and B share eigenvectors
diagonalizable matrices share the same eigenvector matrix S iff \(AB = BA\)
generalized eigenvalue decomposition - for 2 symmetric matrices
\(A = V \Lambda V^T\), \(B=VV^T\)
<IP_ADDRESS>. strang 6.3 - singular value decomposition#
SVD for any nxp matrix: \(X=U \Sigma V^T\)
U columns (nxn) are eigenvectors of \(XX^T\)
columns of V (pxp) are eigenvectors of \(X^TX\)
r singular values on diagonal of \(\Sigma\) (nxp) - square roots of nonzero eigenvalues of both \(XX^T\) and \(X^TX\)
like rotating, scaling, and rotating back
SVD ex. \(A=UDV^T \implies A^{-1} = VD^{-1} U^T\)
\(X = \sum_i \sigma_i u_i v_i^T\)
properties
for PD matrices, \(\Sigma=\Lambda\), \(U\Sigma V^T = Q \Lambda Q^T\)
for other symmetric matrices, any negative eigenvalues in \(\Lambda\) become positive in \(\Sigma\)
applications
very numerically stable because U and V are orthogonal matrices
condition number of invertible nxn matrix = \(\sigma_{max} / \sigma_{min}\)
\(A=U\Sigma V^T = u_1 \sigma_1 v_1^T + ... + u_r \sigma_r v_r^T\)
we can throw away columns corresponding to small \(\sigma_i\)
pseudoinverse \(A^+ = V \Sigma^+ U^T\)
<IP_ADDRESS>. strang 5.3 - difference eqs and power \(A^k\)#
compound interest
solving for fibonacci numbers
Markov matrices
steady-state Ax = x
corresponds to \(\lambda = 1\)
stability of \(u_{k+1} = A u_k\)
stable if all eigenvalues satisfy \(|\lambda_i|\) <1
neutrally stable if some \(|\lambda_i|=1\)
unstable if at least one \(|\lambda_i|\) > 1
Leontief’s input-output matrix
Perron-Frobenius thm - if A is a positive matrix (positive values), so is its largest eigenvalue and every component of the corresponding eigenvector is also positive
useful for ranking, etc.
power method: want to find eigenvector \(v\) corresponding to largest eigenvalue
\(v = \underset{n \to \infty}{\lim} \frac{A^n v_0}{|A^nv_0|}\) where \(v_0\) is nonnegative


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This is an R implementation of a constrained l1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models (SIMULE). The SIMULE algorithm can be used to estimate multiple related precision matrices. For instance, it can identify context-specific gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogenous data sets, this tool can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(simuleDemo) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Ritambhara Singh, Yanjun Qi (2017) <doi:10.1007/s10994-017-5635-7>.
| Version: | 1.3.0 |
| Depends: | R (≥ 3.0.0), lpSolve, pcaPP, igraph |
| Suggests: | parallel |
| Published: | 2018-07-02 |
| Author: | Beilun Wang [aut, cre], Yanjun Qi [aut], Zhaoyang Wang [aut] |
| Maintainer: | Beilun Wang <bw4mw at virginia.edu> |
| BugReports: | https://github.com/QData/SIMULE |
| License: | GPL-2 |
| URL: | https://github.com/QData/SIMULE |
| NeedsCompilation: | no |
| CRAN checks: | simule results |
| Reference manual: | simule.pdf |
| Package source: | simule_1.3.0.tar.gz |
| Windows binaries: | r-devel: simule_1.3.0.zip, r-release: simule_1.3.0.zip, r-oldrel: simule_1.3.0.zip |
| OS X binaries: | r-release: simule_1.3.0.tgz, r-oldrel: simule_1.3.0.tgz |
| Old sources: | simule archive |
Please use the canonical form https://CRAN.R-project.org/package=simule to link to this page.


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{% comment %} Copyright 2010 Google Inc. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. {% endcomment %} {% comment %} This is the base template which defines the structure of all pages within the person finder application and all common features. To override parts of this template, use the 'extends' tag and 'block' tags to define new content for any blocks defined here. {% endcomment %} {% load i18n %}
{% if noindex %} {% endif %} {% if feed_url %} {% endif %}"|safe}}
{% endblock %} {% block disclaimer %}


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Utility class for HTML form decoding. This class contains static methods
for decoding a String from the application/x-www-form-urlencoded
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To conversion process is the reverse of that used by the URLEncoder class. It is assumed
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"A
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"0
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"-
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".
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character "%
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as the start of a special escaped sequence.
The following rules are applied in the conversion:
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through "9
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.
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+
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space character "
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treated as representing a byte where xy is the two-digit
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URLDecoder()
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static String |
decode(String s)
Deprecated. The resulting string may vary depending on the platform's default encoding. Instead, use the decode(String,String) method to specify the encoding. |
static String |
decode(String s,
String enc)
Decodes a application/x-www-form-urlencoded string using a specific
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clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
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@Deprecated public static String decode(String s)
x-www-form-urlencoded
string.
The platform's default encoding is used to determine what characters
are represented by any consecutive sequences of the form
"%xy
".
s
- the String
to decode
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public static String decode(String s, String enc) throws UnsupportedEncodingException
application/x-www-form-urlencoded
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encoding scheme.
The supplied encoding is used to determine
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String
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Designing the individual robot rules that give rise to emergent swarm behaviours is difficult. The common method of running evolutionary algorithms to automatically discover controllers in simulation suffers from a major disadvantage; the evolved controllers are often opaque and hard to understand. We use behaviour trees as the controller architecture because they are modular, hierarchical and human understandable. We evolve fit controllers in a swarm with high processing power, use automatic tools to simplify them, and then explain them, an important factor for safety and acceptance. We look at the design of a suitable behaviour tree architecture, applying the techniques of Genetic Programming to behaviour trees, the implementation of a fast parallel simulator and tree interpreter running on the GPUs of our swarm of robots, how large evolved trees can be automatically simplified to aid analysis, and then explain one of the evolved controllers.
We will present how to apply reactive programming to the design of robot behavior tree. Using this paradigm, the user defines branches and then associate them with rules of activation. Contrary to other approaches, the behavior tree is not traversed at runtime. Rather the branches compete continuously one with another for activation. And because branches can be attached to targets (discretized high level sensor entities), the activation of a branch will correspond to the activation of a specific set of sensory motor couplings that will shape the behavior of the robot. As various videos will show, this programming paradigm supports rich and complex behaviors characterized by high behavioral reactivity. Playful, our open-source reactive programming interpreter supporting behavior trees, will be introduced. Our more recent work exploring the automated generation of reactive programming scripts from human demonstration will also be discussed.
Robots are increasingly an important part of our world, from working in factories and hospitals to driving on city streets. As robots move into more unstructured environments such as homes, however, we must be able to create complex, reactive task plans that can deal with stochastic actions, unreliable sensors, and that above all are intuitive and easy to build. To this end, we created the Behavior Tree-based CoSTAR system -- which allows novice end users to create task plans for industrial robot task plans, shown in a 35-person user study to be highly user friendly and offer a useful set of tools for creating task plans. We also describe a variant on Behavior Trees, the Robust Logical-Dynamical System (RLDS), which supports symbolic task planning and guarantees on performance. Finally, we describe a manipulation case study on an example of an unstructured household manipulation task.
Reconfigure collaborative robots on new tasks quickly and efficiently is today one of the great challenges for manufacturing industries. In this respect, behavior trees already proved to be a great tool to design complex coordination schemes with important required characteristics, such as high modularity, predictability and reactivity. Nevertheless, BTs are also affected by some limitations that are preventing their widespread adoption. For example, being a state-less logic in their classical form, BTs have more problems representing sequences w.r.t. other approaches such as e.g. FSMs. We identified this and other issues while applying BTs to industrial robots task design. In this presentation we will discuss our experience and look at the solutions implemented to make the classical BT a more complete task programming framework for industrial applications.
In many cases, the creation of robot applications performed “in the wild” (i.e., moving from the laboratory to natural and in-situ scenarios) require to include end-users (e.g. therapists, psychologies, teachers, and artists) for their suitable design and testing. In order to support end-user goals, we developed RIZE (Robot Interface from Zero Experience) a cross-platform End-User Development tool back-ended by Component-Based Distributed Robot Frameworks. This interface enables intuitive robot programming by the use of block-based Visual Programming Languages, which translate the user code to Behavior Trees for its posterior execution. In this presentation, we will show how this interface has been used in the development of short-term and long-term Human-Robot Interaction applications performed “in the wild”.
Reinforcement Learning (RL) has received a lot of attention in recent years for its ability to generate efficient controllers for complex problems without the need for a system model. However, the resulting designs often lack both modularity and transparency. It is hard to replace, remove or add different components of an RL policy, and it is hard to know why a given action is taken, beyond the fact that it is believed to maximize the expected long term reward. In this talk, we explore how a combination of RL and BTs can provide the best of both worlds. The modularity and transparency of BTs with the model free performance of RL.


This integration test tests the Tuscany SCA databinding implementation by passing various data structures across various bindings using the supported databindings. There are tests for the individual databindings which exercise various bindings with the same databinding at client and server ends of each binding. There is also an integration test which exercises the transformer chains by specifying different databindings at client and server ends of the binding.
In doing this testing it is apparent that there is a lot of repetition in creating client, services, idl and type for each of the data types for each of the bindings for each of the databindings. To reduce the amount of effort required to maintain the tests as new types, bindings and databindings are added the test cases themselves are generated from configuration files.
To reduce the amount of manual effort involved in building and maintaining tests cases the test cases themselves are generated at run time using a set of velocity templates. Each test module has a generate.xml file in the resources/generate directory which tells the generator what to do. The file looks like this.
Each element describes a velocit template to use in the test. The generator process is to expand
each velocity template provided with all of the types specified in the
Each
Hence the CreateTypeCode, ModifyTypeCode and ResultComparison elements which contain the type specific code that is used in the tests.
The common directory contains the information that is common across all of the tests. This includes the common velocity templates and the source for the generator that reads the config.xml for each test. Common also contains all of the data type schema as these are common across all tests. Each test pom is written so that the contents of the common project are expanded into the tests target directory before the test starts. In this way all of the common elements are available for the test generation phase and at test runtime.
The individual databinding tests, for example, sdogen and jaxbgen, are mostly empty as their content is generated at runtime. The configuration and any test specific templates can be found in the resources/generate directory. Some files are hand crafted for each test and live in their static position in the tests directory structure.
Each test uses the same scenario
The interface exposed by the greeter service provides a greet method for each data type being tested, for example,
PersonType greetPersonType(PersonType param);
AttributeComplexType greetAttributeComplexType(AttributeComplexType param);
AttributeReferenceComplexType greetAttributeReferenceComplexType(AttributeReferenceComplexType param);
These methods are taken from the SDO databinding test and hence PersonType, AttributeComplexType, etc. will have been generated by the SDO static type generator.
Hence this tests a single databinding across a variety of data types and a variety of bindings. New bindings be tested by extending the composite. Be datatypes can be tested by updating the confix.xml file.
This test uses the generated client, services and types from the individual databinding tests. It does not regenerate them and you will see a dependency in the interop test pom on the other databinding tests. Is also has some generate elements because the composite file must currently have import statements for all of the SDO factories required during tested.
The scenario used here is,
A chain of components is built up for each binding. Each component, drawn from the the inidividual databinding tests, tests the full range of datatypes. The client components are designed so that they can be chained together and so tranformations across different databindings is tests. The service component simply changes the data content and returns it as before.
The tests can be built by doing the following.
cd sca/itest/databindings
mvn
The only modification to this process is required if a new input file is added to the tests suite. In this case you will need to edit the config.xml files as appropriate but run mvn twice. This may sound a little odd but currently the sdo test uses its pom file to generate the require SDO types. As the test is self generating the pom file will not be updated to include the new type file until the second time it's run. The aim is at some point to remove this feature from the pom.


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| Y Axis Label | ||
| Speed (ms) | 1000 | How fast your chart will update. If data is published faster than the update rate, it will be overwritten. |
| Limit | 5 | How many data points to display. |
| X Grid | Show gridlines along x axis. |
|
| Y Grid | Show gridlines along y axis. |
|
| Show Points | Show a point in line. |
|
| Show Tooltips | Show tooltips on hover. |
Copy and Paste this embed code into your site to display live chart on your website.
Try changing options to see the chart update.


I'm a seasoned software developer with over 4+ Years of Experience at Goldman Sachs, Experienced on working with a variety of software development projects. Working to leverage agile frameworks to provide a robust synopsis for high level overviews.
Worked with product Team of Coviam. i.e Peoplehum.
Worked with Product team of Coviam i.e Engati. Engati acts as a third party between the user and different platforms(messenger, line, Skype and many more). Worked on the latest technology stack to make a fast and reliable chatbot system.
Scilab2C is a tool capable of translating Scilab code into C code. It is a standalone toolbox of Scilab and it has been primarily developed to become the interface between the Scilab scientific software package and the user.
CGPA: 8.0
Percentage: 94%
Implemented and worked on Google’s page ranking algorithm and the linear algebra behind it’s ranking algorithm.
A complete paper and implementation describing how we can extend the real-time object detection algorithm to build a multimodal search. It can extract useful content from the web using reverse google image search or we can build our own image + text-based search engine by indexing those things together.
https://www.slideshare.net/secret/KTSuPzAOqfAgeK
Upaj is an online platform where farmers can ask the agriculture-related queries like crop prediction, MSP, irrigation queries, pesticides and weather for the asked location.Implemented a chatbot using IBM waston conversation API to interact with farmers. Constructed the dataset to learn the bot.
Worked on adding, WeChat and Skype for Business from scratch for Engati so as to ease out the process of integration with ChatBot. Worked on Kafka for queuing the message packet and Apache Storm for processing the message packets by writing multiple methods.
Implemented a Genetic Algorithm to predict the most optimal schedule for interviews for a hackathon conducted by Coviam.
Worked on an e-commerce app under a team and built an e-commerce app from scratch using spring boot as the framework.
Game was developed on blender using 3D modelling of maps, trucks , fruits, robot for picking up crates of fruits as a part of eYantra project of IIT Bombay.
Q is a hard-coded helping hand, which can help us to search the web using speech recognition and does some basic task like telling the day schedule using Microsoft windows 10 native speaker. Speech recognition works on google API.
All of us must have somewhere down the line thought of downloading YouTube videos and that's too easy. But, what if we need download YouTube playlist. No-one wants to click on Download button again and again. Nitro-YouTube-Downloader helps both the user to download the video and playlist on one click.
Designed and Developed a basic pygame. The basic concept of designing the game was Space Impact game which was present in old Nokia phones. Learnt from YouTube channel - sentdex.
Apart from being a Software developer and problem solver, I enjoy most of my time being outdoors and playing badminton.
When forced indoors, I follow a number of sci-fi and fantasy genre movies and television shows, I spend a large amount of my free time exploring the latest technology advancements, problem solving on various competitive programming websites and writing answers on various QnA websites.
Won first prize in IBM organised hackathon, wherein we need to develop a viable solution to help farmers through machine learning.
Won the first prize in Onsite coding contest organised in abhikalpan.
Ranked 100 in Online preliminary round of ICPC among 3000 teams across India where Qualified for two regionals sites Kharagpur and Kanpur. We were ranked 39( among 84 teams) and 64 ( among162 teams) at Kharagpur and Kanpur-Kolkata regionals respectively.


Hi! I'm Lena, a Full-Stack Software Engineer
A background in photography and education.
Let's make something amazing together!
About Me
Creative, client-centered Full-Stack Software Engineer who successfully grew a thriving business with year over year growth for 8 years. Flourishes under the pressure of deadlines while implementing the newest design theories and best business practices to ensure client satisfaction. Expert in the tools of the industry including JavaScript, Node.js, CSS, HTML, Adobe Photoshop, Adobe Lightroom, and Adobe Illustrator.
Specialties: JavaScript, Node.js, CSS, github, Adobe Creative Suite, Working as part of a team, Inspiring confidence, Managing Projects.
Contact
Thanks so much for taking a look! I'd love to meet you over coffee to talk more!


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| leveragePlots {car} | R Documentation |
These functions display a generalization, due to Sall (1990) and Cook and Weisberg (1991), of added-variable plots to multiple-df terms in a linear model. When a term has just 1 df, the leverage plot is a rescaled version of the usual added-variable (partial-regression) plot.
leveragePlots(model, terms = ~., layout = NULL, ask, main, ...) leveragePlot(model, ...) ## S3 method for class 'lm' leveragePlot(model, term.name, id=TRUE, col=carPalette()[1], col.lines=carPalette()[2], lwd=2, xlab, ylab, main="Leverage Plot", grid=TRUE, ...) ## S3 method for class 'glm' leveragePlot(model, ...)
model |
model object produced by |
terms |
A one-sided formula that specifies a subset of the numeric regressors, factors and interactions.
One added-variable plot is drawn for each term, either a main effect or an interactions. The default
|
layout |
If set to a value like |
ask |
if |
xlab, ylab |
axis labels; if missing, labels will be supplied. |
main |
title for plot; if missing, a title will be supplied. |
... |
arguments passed down to method functions. |
term.name |
Quoted name of term in the model to be plotted; this argument
is omitted for |
id |
controls point identification; if |
col |
color(s) of points |
col.lines |
color of the fitted line |
lwd |
line width; default is |
grid |
If TRUE, the default, a light-gray background grid is put on the graph |
The function intended for direct use is leveragePlots
.
The model can contain factors and interactions. A leverage plot can be drawn for each term in the model, including the constant.
leveragePlot.glm
is a dummy function, which generates an error message.
NULL
. These functions are used for their side effect: producing
plots.
John Fox<EMAIL_ADDRESS>Cook, R. D. and Weisberg, S. (1991). Added Variable Plots in Linear Regression. In Stahel, W. and Weisberg, S. (eds.), Directions in Robust Statistics and Diagnostics. Springer, 47-60.
Fox, J. (2016) Applied Regression Analysis and Generalized Linear Models, Third Edition. Sage.
Fox, J. and Weisberg, S. (2019) An R Companion to Applied Regression, Third Edition, Sage.
Sall, J. (1990) Leverage plots for general linear hypotheses. American Statistician 44, 308–315.
leveragePlots(lm(prestige~(income+education)*type, data=Duncan))


int id3_get_version ( string $filename )
id3_get_version() retrieves the version(s) of the ID3 tag(s) in the MP3 file. If a file contains an ID3 v1.1 tag, it always contains a 1.0 tag, as version 1.1 is just an extension of 1.0.
Returns the version number of the ID3 tag of the file. As a tag can contain ID3 v1.x and v2.x tags, the return value of this function should be bitwise compared with the predefined constants ID3_V1_0, ID3_V1_1 and ID3_V2.


If all tests are negative, the positive rate is zero. And its confidence interval?
Some time ago a colleague presented me a simple statistics question which, as usual, turned out to be quite interesting and intriguing. They had run 23 tests that were all negative, and they wanted to have a confidence interval for the proportion of positive outcomes. So, to put it in statistical terms, we have observations that can be 0 or 1. Each observation has a probability of being positive. In formulas
We know that the number of successes is binomially distributed, thus a sufficient statistics is and that the maximum likelihood estimator for the binomial proportion is
If the observations are all negative, the estimate for the rate is clearly zero, but what about its confidence interval? I had never been asked to estimate the confidence interval for the binomial distribution, so I was totally unprepared (shame on me!). Quite instinctively, I computed the probability to observe all 23 results negative for a given , set this to 5% and solved for . Result, 12.2%. In formula
which gives, solving for ,
In other words, what I did was to compute the highest for which I would be very surprised (5%) to see all negative outcomes. This is equivalent to being quite sure (95% sure) to observe at least one positive outcome.
My colleague had found the formula for the Clopper-Pearson interval and applied it to their case: different result. Of course, I thought: I computed the probability to observe data as extreme or more extreme than those observed for a given value of the parameter. I did a hypothesis test, not a confidence interval.
It would have been the end of the story, had I not tried this online calculator. It reports to be the 90% interval for . This was no coincidence, as the plot below shows.
In other words, for any number of tests (at least, between five and thirty) my estimate (violet points) matches the upper limit of the 90% confidence interval computed with the Clopper-Pearson method (magenta line).
library(ggplot2)
library(ggthemr)
ggthemr('solarized', type='outer')
# computing 90% confidence interval with "exact" meaning Clopper-Pearson
library(binom)
cis <- binom.confint(0, 5:30, conf.level = 0.9, methods="exact")
# max theta for which n all negative outcomes have probability 5%
p_all_neg <- 1-0.05**(1./5:30)
df <- data.frame(x=5:30, y1=cis$upper, y2=p_all_neg)
p <- ggplot(df, aes(x)) +
geom_line(y = df$y1, show.legend = TRUE, colour='#d33682') +
geom_point(y = df$y2, show.legend = TRUE, colour='#6c71c4') +
xlab("trials") +
ylab(expression(theta)) +
ylim(0.0, 0.5) +
theme(axis.title.x = element_text(face="bold", size=18),
axis.text.x = element_text(angle=0, vjust=0.0, size=14),
axis.title.y = element_text(face="bold", size=18),
axis.text.y = element_text(angle=0, vjust=0.0, size=14))
print(p)
Finding confidence intervals
There is a one-to-one correspondence between confidence intervals and hypothesis tests. As a matter of fact, confidence sets are found by inverting a test statistics.
Let’s start revisiting a few concepts, following the classics. We have a hypothesis for a parameter of interest $\theta$. The hypothesis says that it has a certain value
We have data and a test statistics telling us whether to reject the hypothesis or not. The acceptance region is the region of the sample space for which we do not reject at a level . In symbols
or
Now, for each realisation of the data , we take the values of for which the hypothesis is accepted. Then we have built a confidence set for . We define as the set in parameter
Then, is a confidence set. This follows quite immediately from the definition of acceptance region above
Clopper-Pearson estimator
There are several estimators for the confidence interval of the binomial proportion. The advantage of this one is that it is exact, rather than based on the normal approximation (see the Wikipedia page linked above). The disadvantage is that it is conservative, i.e. there might be a smaller interval with the same confidence level. The estimated interval is defined as
From this definition we reconcile the fact that my estimate at 95% coincides with the Clopper-Pearson at 90%. In fact, since we are in the special case of , we can write the Clopper-Pearson as
By taking in the Clopper-Pearson estimate, we have the formula I used for my estimate.
A final observation
What is most interesting to my colleague? The hypothesis test says that those data would already exclude or higher at 5%, but the 95% interval according to Clopper-Pearson is . As we saw they are two different but intimately related things.  We also had the chance to underline something that is often neglected: finding an confidence interval doesn’t mean we found the smallest interval that contains the true value with probability . If you want to investigate an extreme consequence of this, you can visit the ultimate confidence intervals calculator.
Header image from Flickr


EBD - Easton Bible DIctionary
Baal-tamar
BAAL-TAMAR
"BAAL-TAMAR" Lord of palm trees, a place in the tribe of Benjamin near Gibeah of Saul (
Judges 20:33
). It was one of the sanctuaries or groves of Baal. Probably the palm tree of Deborah (
Judges 4:5
) is alluded to in the name.
"BAAL-TAMAR" Lord of palm trees, a place in the tribe of Benjamin near Gibeah of Saul (Judges 20:33). It was one of the sanctuaries or groves of Baal. Probably the palm tree of Deborah (Judges 4:5) is alluded to in the name.


- ECharts Mobile Launch -
ECharts-m 1.0.0 ( beta )Innumerable charts library!
Why ECharts ? 2>
Data Interactive Visualization
Data visualization with an interactive
graphical user interface (GUI). Data's visual presentation is not
only for statement, bug also for data mining,
integration to the data presented. Make data visualization to be a
way of people's visual thinking.
Let's find out what ECharts has done for this purpose?
ECharts VS Excel
Although those chart provided by Excel have no interaction at all, but its rich chart type and configuration, easy to use, Excel is the most commonly data statistics tools.
| # | ECharts | Excel |
|---|---|---|
| Histogram | Yes | Yes |
| Bar | Yes | Yes |
| Line | Yes | Yes |
| Area | Yes | Yes |
| Scatter | Yes | Yes |
| Bubble | Yes | Yes |
| K | Yes | Yes |
| Pie | Yes | Yes |
| Doughnut | Yes | Yes |
| Radar | Yes | Yes |
| Force | Yes | No |
| Chord | Yes | No |
| Surface | No | Yes |
| Map | Yes | No |
| EventRiver | Yes | No |
ECharts VS Highcharts (1)
The industry has countless JS chart, many outstanding
representative, like chartjs, FusionCharts, amCharts, flot, RGraph,
jqPlot, gRaphaël etc. Some are free and even open source, while
others are commercial, you can find them easily.
Unable
to compare with them all, let's choose Highcharts, an excellent,
mature business chart library which is widely known in the world.
First
about the charts type?
| # | ECharts | Highcharts |
|---|---|---|
| Histogram（Bar） | Yes | Yes |
| Line（Area） | Yes | Yes |
| Pie（Doughnut） | Yes | Yes |
| Scatter（Bubble） | Yes | Yes |
| Radar | Yes | Yes |
| K | Yes | Highstock |
| Force | Yes | No |
| Chord | Yes | No |
| Map | Yes | Highmap |
| EventRiver | Yes | No |
| Special（Gauge, Funnel） | Yes | Yes |
ECharts VS Highcharts (2)
Let's look at the features?
| # | ECharts | Highcharts |
|---|---|---|
| Drag-Recalculate | Yes | No |
| Data View | Yes | No |
| Magic Switch | Yes | No |
| Scale Roaming | Yes | No |
| Large-scale Data | Yes | No |
| Glare Effect | Yes | No |
| Linkage | Yes | No |
| Space Zoom | Yes | Yes |
| Legend Switch | Yes | Yes |
| Multi-dimensional Stacking | Yes | Yes |
| Mixed Charts | Yes | Yes |
| Image Export | Yes | Yes |
| License & Pricing |
Baidu BSD Free |
Non-commercial free under CC3.0 Commercial licenses $90~$3600 |
This is ECharts
We just try our best to show the real data for you,
and provides an intuitive, easy-to-use interactive
way to help you to deal with data
mining, extraction, correction or consolidation,
（Drag-Recalculate,
Data View）
so that you can focus
on what you care about,
（Legend Switch,
Space Zoom, Scale Roaming）
have different
interpretation to the same data in different ways.
（Magic
Switch, Multi-dimensional Stacking, Linkage, Mixed Charts）
Time to re-define the chart, browse the charts outputed by ECharts, you no longer just a "reader", you can participate. This is ECharts, an powerfull data visualizaiont tool with interactive graphical user interface.
Acknowledgement
ECharts cann't grow up without their excellent
contributions :
@Kener-林峰 @pissang @errorrik @diysimon @宿爽 @邓红启 @杨骥wind108369
@娄同兵
With their encouragement and support :
@cloud_wei @李湛lizhan
@i我佛山人 @赵雷_Firede @Yummy_zhou
Also, it's nice being with you...
@财财某 @沈浩老师 @统计之都 @大数据文摘
Finally, you'd be welcome to join us~
- THE END -
Thank you


_ste The artiste had crafted a painting that left a lasting aftertaste in the viewer's mind, taking time to baste the canvas in vibrant hues.[G|D] The people were divided into two distinct castes, one chaste and the other filled with a distaste of the values held by the other.[G|D] The skier made their way down the piste in haste, carefully using their poles to paste the snow beneath them as they moved.[G|D] The debate team was known for their clever ripostes, never wasting a word as they gleamed toothpaste-white smiles in victory.[G|D]


--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/slow.20polynomial.html ---
import data.polynomial open polynomial noncomputable def eval₂h {α β : Type} [comm_ring α] [comm_ring β] (i : α →+* β) (r : β) : polynomial α →+* β := ring_hom.of $ eval₂ i r
why does this time out?
I thought making polynomial classical solved all polynomial problems
@Mario Carneiro
it didn't
That's a tragic timeout
@Mario Carneiro how long does it take for you?
It's taking a very long time for me but it gets there
so let's call it a timeout
on my nice fast desktop it's taking 14 seconds to elaborate
I've been more or less reduced to bellowing THIS IS RIDICULOUS every time this issue comes up. I don't want to think about polynomials anymore
I think I added eval₂_hom
to avoid something like this.
So what do we do about this slowness? Is "simply" bundling everything the solution, or is there something else that needs to be addressed?
I'm going to bundle all ring_hom
s soon. Let's see if this will help.
Working on perfect_closure
now.
Last updated: Apr 24 2020 at 19:02 UTC


You are viewing an historical archive of past issues. Please report new issues to the appropriate project issue tracker on GitHub.
Comments
-
Greg Hurrell
Changing assignment to reflect my new email address.
http://wincent.com/a/news/archives/2006/05/change_of_email.php
-
Greg Hurrell
(In reply to comment #0)
When this option is in effect the act of hiding and redisplaying the menu bar buttons causes them to jump to a new, randomly-selected set.
This behaviour is no longer the case with version 3.0 of Synergy (a side-effect of the changes made in fixing bug #442). This is a good thing seeing as it brings the behaviour more strictly into line with the wording in the user interface.
Needless to say, this current feature request is valid and stays open.
Add a comment
Comments are now closed for this issue.


--- date: 2010-02-08T00:00:00Z published: true title: Former Soviet Dissident Warns For EU Dictatorship url: /2010/02/08/former-soviet-dissident-warns-for-eu-dictatorship/ ---
PB: But all these countries that joined the European Union did so voluntarily.
VB: No, they did not. Look at Denmark which voted against the Maastricht treaty twice. Look at Ireland [which voted against the Nice treaty]. Look at many other countries, they are under enormous pressure. It is almost blackmail. Switzerland was forced to vote five times in a referendum. All five times they have rejected it, but who knows what will happen the sixth time, the seventh time. It is always the same thing.  The people have to vote in referendums until the people vote the way that is wanted. Then they have to stop voting. Why stop? Let us continue voting. The European Union is what Americans would call a shotgun marriage.
Some interesting stuff here.


This inspection reports classes, methods or fields in the specified inspection scope that
are not used or not reachable from entry points.
Use check boxes in the inspection options below, to specify what should be automatically assumed entry points. You will
also have a possibility to add entry points manually when inspection results are ready.


Welcome to pySpecData’s documentation!¶
pySpecData allows you to deal with multi-dimensional spectroscopy data in an object-oriented fashion.
Please note this package is heavily utilized by three other packages that our lab manages on github:
See pySpecData examples here, and for a simple example applied to 1D data, see here.
Some more explanation¶
In practice, this means that it makes the code for processing spectral data shorter and more quickly legible. It automatically handles the following issues, without any additional code:
relabeling axes after a Fourier transformation
propagation of errors
adding units to plots
To do this, you work with a pySpecData nddata object (which includes information about dimension names, axis values, errors, and the units) rather than working directly with traditional numpy ndarray objects. (pySpecData is built on top of numpy.)
If you have ever worked with arrays in Matlab or Python before, you are familiar with the additional code needed to convert between index numbers and axis values. Using the funny notation of pySpecData, you can do this automatically.
For example, say you have loaded an nddata object called d wanted to take time-domain data, Fourier transform, select out the central 20 kHz in the frequency domain, and then inverse Fourier transform, while preserving the correct axes throughout. That looks like this:
>>> d.ft('t2', shift=True)
>>> d['t2':(-10e3,10e-3)]
>>> d.ift('t2')
Note that most pySpecData methods operate in-place on the data; it modifies the data inside the nddata, rather than making a new copy. This is because we assume that we are progressively optimizing/filtering our spectral data with each new line of code. This allows us to quickly work through many operations (like the ft here) without keeping many copies of a large dataset and with less typing for each operation. (If you every truly want to create a copy a dataset, just attach a .C)
Todo
describe updates allowing aliasing (nu2 vs. t2, etc.)
Note that
>>> plot(d)
Quickly generates a publication-quality plot; it automatically plots the data on the correct axes and includes the units and the name of dimension (t2 in this example) and its units along the x axis.
How do I generate an nddata object?¶
We primarily work in magnetic resonance,
so have written wrappers for a few different types of NMR
(nuclear magnetic resonance) and ESR (electron spin resonance)
file formats.
You can use the pyspecdata.find_file()
function to automatically load them as nddata.
Additionally, we have written several classes that allow you to read nddata objects directly from e.g. an oscilloscope. These are available as separate repositories on github (please contact us for further info).
Finally, you can easily build nddata from standard arrays, as discussed in the section about nddata objects.
What can I do with nddata objects?¶
To understand how to manipulate nddata objects, head over to the section about nddata objects.
Contents:¶
There are many other features of pySpecData that govern the interaction of ndata objects with plots and, e.g. that allow you to generate a nice PDF laboratory notebook showing every step of your data processing. These and further details are covered in the various sections of the documentation:


Five Minute Introduction
JDBI is a SQL convenience library for Java. It attempts to expose relational database access in idiomatic Java, using collections, beans, and so on, while maintaining the same level of detail as JDBC. It exposes two different style APIs, a fluent style and a sql object style.
Fluent API
The fluent style looks like:
// using in-memory H2 database
DataSource ds = JdbcConnectionPool.create("jdbc:h2:mem:test",
"username",
"password");
DBI dbi = new DBI(ds);
Handle h = dbi.open();
h.execute("create table something (id int primary key, name varchar(100))");
h.execute("insert into something (id, name) values (?, ?)", 1, "Brian");
String name = h.createQuery("select name from something where id = :id")
.bind("id", 1)
.map(StringColumnMapper.INSTANCE)
.first();
assertThat(name, equalTo("Brian"));
h.close();
The DBI type is analogous to a JDBC DataSource, and will usually be constructed by passing in a JDBC DataSource. There are alternate constructors which take JDBC URL and credentials, and other means. From the DBI instance you obtain Handle instances. A Handle represents a single connection to the database. Handles rely on an underlying JDBC connection object.
With a handle you may create and execute statements, queries, calls, batches, or prepared batches. In the above example we execute a statement to define a table, execute another statement, this time with two positional arguments to insert a value, and finally construct a query, bind a value to a named argument in the query, map the results to a String, and take the first result which comes back.
The named argument facility on statements and queries is provided by JDBI – it parses out the SQL and uses positional parameters when actually constructing the prepared statements. The above example uses the default colon-demarcated parser, but an alternative hash delimited parser is included as well for use with databases which use colons in their grammars, such as PostgreSQL.
SQL Object API
The second, SQL object, style API simplifies the common idiom of creating DAO objects where a single method maps to a single statement. A SQL object definition is an annotated interface, such as:
public interface MyDAO
{
@SqlUpdate("create table something (id int primary key, name varchar(100))")
void createSomethingTable();
@SqlUpdate("insert into something (id, name) values (:id, :name)")
void insert(@Bind("id") int id, @Bind("name") String name);
@SqlQuery("select name from something where id = :id")
String findNameById(@Bind("id") int id);
/**
* close with no args is used to close the connection
*/
void close();
}
This interface defines two updates, the first to create the same table as in the fluent api example, and the second to do the same insert, the third defines a query. In the second two cases, notice that the arguments to the statements are past to the method, and bound by name.
The final method, close(), is special. When it is invoked it will close the underlying JDBC connection. The method may be declared to raise an exception, such as the close() method does on java.io.Closeable, making it suitable for use with automatic resource management in Java 7.
To use this sql object definition, we use code like so:
// using in-memory H2 database via a pooled DataSource
JdbcConnectionPool ds = JdbcConnectionPool.create("jdbc:h2:mem:test2",
"username",
"password");
DBI dbi = new DBI(ds);
MyDAO dao = dbi.open(MyDAO.class);
dao.createSomethingTable();
dao.insert(2, "Aaron");
String name = dao.findNameById(2);
assertThat(name, equalTo("Aaron"));
dao.close();
ds.dispose();
We obtain an instance of the sql object from the DBI instance, and then call methods on it. There are a couple different ways of creating sql object instances. The one one here binds the object to a specific handle, so we need to make sure to close the object when we are finished with it.


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---
creation_epoch:<PHONE_NUMBER>
lastmod_epoch:<PHONE_NUMBER>
categories: ['Computer Problems']
title: Plantronics Savi 7xx-M and Linux mouse or lockup problems
---
I have a Plantronics Savi 7xx-M headset attached via USB to my computer (HP EliteBook 8560w) running Fedora 17 Linux. I started to get weird lockups of my mouse or constant mouse click actions. Short story buggy driver with Linux kernel 3.4.5-2.fc17.x86_64. I also constantly saw "ALSA sound/usb/clock.c:236 22:2:1: cannot get freq at ep 0x84" in my logs.
...
I ran into two issues with it.
One, the mouse/keyboard problem you mentioned. The device presents itself as an usb sound card, a mouse and a keyboard. To make X11 ignore the keyboard and mouse features (which are fine in windows but Xorg does not like it), I added this to xorg.conf:
Section "InputClass"
Identifier "Plantronics"
MatchVendor "Plantronics"
Option "Ignore" "true"
EndSection
This will make X11 ignore the mouse and keyboard features and stop weird things from happening.
So then you can use the USB audio card, but I ran into another issue there. On linux, all audio was clipping. Turns out this was a Pulseaudio bug:
https://bugs.launchpad.net/ubuntu/+source/pulseaudio/+bug/416190
Which was pushed upstream to pulseaudio only yesterday.
So with those things in place it works perfectly fine on Linux; I do recommend configuring it to wideband audio once in Windows. It seems to remember those settings. Haven't found a way to control those from Linux.


{% extends "base.html" %} {% block title %}404 Error - ToolShare{% endblock %} {% block content %} {% include "navigation.html" %}
404 - Page Not Found
We could not find this page on our servers. Perhaps the link was wrong..?


public interface Smartsheet
This interface is the entry point of the Smartsheet SDK, it provides convenient methods to get XXXResources instances for accessing different types of resources.
Thread Safety: Implementation of this interface must be thread safe.
| Modifier and Type | Method and Description |
|---|---|
ContactResources |
contactResources()
Returns the ContactResources instance that provides access to contact resources.
|
EventResources |
eventResources()
Returns the EventResources instance that provides access to event resources
|
FavoriteResources |
favoriteResources()
Returns the FavoriteResources instance that provides access to Favorite resources.
|
FolderResources |
folderResources()
Returns the FolderResources instance that provides access to Folder resources.
|
GroupResources |
groupResources()
Returns the
GroupResources instance that provides access to Group resources. |
HomeResources |
homeResources()
Returns the HomeResources instance that provides access to Home resources.
|
ImageUrlResources |
imageUrlResources()
Returns the ImageUrlResources instance that provides access to image Url resources
|
PassthroughResources |
passthroughResources()
Returns the PassthroughResources instance that provides access to passthrough resources
|
ReportResources |
reportResources()
Returns the ReportResources instance that provides access to report resources.
|
SearchResources |
searchResources()
Returns the SearchResources instance that provides access to searching resources.
|
ServerInfoResources |
serverInfoResources()
Returns the
ServerInfoResources instance that provides access to Server Info resources. |
void |
setAccessToken(String accessToken)
Set the access token to use.
|
void |
setAssumedUser(String assumedUser)
Set the email of the user to assume.
|
void |
setChangeAgent(String changeAgent)
Set the change agent identifier.
|
void |
setMaxRetryTimeMillis(long maxRetryTimeMillis)
Sets the max retry time if the HttpClient is an instance of DefaultHttpClient
|
void |
setTracePrettyPrint(boolean pretty)
enable/disable pretty JSON trace logging
|
void |
setTraces(Trace... levels)
Enable request/response tracing in client
|
void |
setUserAgent(String userAgent)
Set the user agent header string.
|
SheetResources |
sheetResources()
Returns the SheetResources instance that provides access to Sheet resources.
|
SightResources |
sightResources()
Returns the SightResources instance that provides access to Sight resources.
|
TemplateResources |
templateResources()
Returns the TemplateResources instance that provides access to Template resources.
|
TokenResources |
tokenResources()
Returns the TokenResources instance that provides access to token resources.
|
UserResources |
userResources()
Returns the UserResources instance that provides access to User resources.
|
WebhookResources |
webhookResources()
Returns the WebhookResources instance that provides access to webhook resources
|
WorkspaceResources |
workspaceResources()
Returns the WorkspaceResources instance that provides access to Workspace resources.
|
void setTraces(Trace... levels)
Enable request/response tracing in client
levels
- - what to trace (if anything; null if not tracing at all)void setTracePrettyPrint(boolean pretty)
pretty
- if the JSON is nicely-formatted or compactvoid setAccessToken(String accessToken)
Set the access token to use.
accessToken
- the new access tokenIllegalArgumentException
- if any argument is null/empty stringvoid setAssumedUser(String assumedUser)
Set the email of the user to assume.
assumedUser
- the new assumed userIllegalArgumentException
- if any argument is null/empty stringvoid setChangeAgent(String changeAgent)
Set the change agent identifier.
changeAgent
- the new change agentIllegalArgumentException
- if any argument is null/empty stringvoid setUserAgent(String userAgent)
Set the user agent header string.
userAgent
- the new user agent stringIllegalArgumentException
- if any argument is null/empty stringvoid setMaxRetryTimeMillis(long maxRetryTimeMillis)
Sets the max retry time if the HttpClient is an instance of DefaultHttpClient
maxRetryTimeMillis
- max retry timeHomeResources homeResources()
Returns the HomeResources instance that provides access to Home resources.
WorkspaceResources workspaceResources()
Returns the WorkspaceResources instance that provides access to Workspace resources.
FolderResources folderResources()
Returns the FolderResources instance that provides access to Folder resources.
TemplateResources templateResources()
Returns the TemplateResources instance that provides access to Template resources.
SheetResources sheetResources()
Returns the SheetResources instance that provides access to Sheet resources.
SightResources sightResources()
Returns the SightResources instance that provides access to Sight resources.
FavoriteResources favoriteResources()
Returns the FavoriteResources instance that provides access to Favorite resources.
UserResources userResources()
Returns the UserResources instance that provides access to User resources.
GroupResources groupResources()
Returns the GroupResources
instance that provides access to Group resources.
ServerInfoResources serverInfoResources()
Returns the ServerInfoResources
instance that provides access to Server Info resources.
SearchResources searchResources()
Returns the SearchResources instance that provides access to searching resources.
ReportResources reportResources()
Returns the ReportResources instance that provides access to report resources.
TokenResources tokenResources()
Returns the TokenResources instance that provides access to token resources.
ContactResources contactResources()
Returns the ContactResources instance that provides access to contact resources.
ImageUrlResources imageUrlResources()
WebhookResources webhookResources()
PassthroughResources passthroughResources()
EventResources eventResources()
Copyright © 2014–2021 Smartsheet. All rights reserved.


Khan Academy on a Stick
Index
Welcome to the index page. All available subjects and topics are listed below.
Math
-
Arithmetic and pre-algebra
(355 videos)
The first math topic. Start here if you want to learn the basics (or just want to make sure you know these topics). After this, you should be ready for algebra. This topic includes videos from the former developmental math playlists.
-
Algebra
(638 videos)
Conceptual videos and worked examples from basic algebra through algebra 2. Includes videos from the former algebra worked examples playlists.
-
Geometry
(180 videos)
This is a first year, high-school level course on Geometry (which is based on Euclid's elements). It revisits many of the basic geometrical concepts studied in earlier courses, but addresses them with more mathematical rigor. There is strong focus on proving theorems and results from basic postulates.
-
Trigonometry and precalculus
(361 videos)
Videos and exercises on trigonometry. Watch the "Geometry" playlist first if you have trouble understanding the topics covered here.
-
Calculus
(297 videos)
Topics covered in the first two or three semesters of college calculus. Everything from limits to derivatives to integrals to vector calculus. Should understand the topics in the pre-calculus playlist first (the limit videos are in both playlists)
- Probability and statistics (136 videos)
-
Differential equations
(46 videos)
Topics covered in a first year course in differential equations. Need to understand basic differentiation and integration from Calculus playlist before starting here.
Science
-
Biology
(72 videos)
Covers topics seen in a first year college or high school biology course.
-
Chemistry
(70 videos)
Videos on chemistry (roughly covering a first-year high school or college course).
-
Physics
(203 videos)
Projectile motion, mechanics and electricity and magnetism. Solid understanding of algebra and a basic understanding of trigonometry necessary.
-
Cosmology and astronomy
(86 videos)
Videos attempting to grasp a little bit about our Universe (many of the topics associated with "Big History")
-
Organic chemistry
(221 videos)
Sal and Jay cover topics covered in college organic chemistry course. Basic understanding of basic high school or college chemistry assumed (although there is some review).
-
Health and medicine
(193 videos)
The medical world can be a confusing place. Patients and their families might feel overwhelmed by the large vocabularies and complicated explanations they get from their health care providers. Students entering health care also struggle to grasp the complexity of health sciences, and are forced to memorize huge amounts of information. We hope to make understanding the medical world a bit easier. Look around! These videos do not provide medical advice and are for informational purposes only. The videos are not intended to be a substitute for professional medical advice, diagnosis or treatment. Always seek the advice of a qualified health provider with any questions you may have regarding a medical condition. Never disregard professional medical advice or delay in seeking it because of something you have read or seen in any Khan Academy video.


--- lang: es title: "Inicio" description: "Página de inicio." robots: "index,follow" permalink: /es/index.html facebook: app_id: publisher: author: img: google_plus: publisher: author: img: twitter: publisher: author: img: excerpt: none ---


Home
About
Download
Getting Started
Documentation
Demo
Live!
Watch Project
jQuery
+
Underscore.js
Based Javascript MVC Toolkit or: How I learned to stop worrying and love the Javascript


From: MR. ARBU MPULA. SOUTH AFRICA JOHANNESBURG. TEL:27-73-2390-447. ATTN: M/D CEO/ GOKER , PLEASE WE NEED YOUR ASSISTANCE. You may be surprised to receive this letter from me since you don't know me personally. I am Mr, Arbu Mpula, the son of Dr.Henry Mpula, who was recently murdered in the land dispute in Zimbabwe. I got your contact through South African Information Exchange (S.A.I.E) in Johannesburg, and I decided to solicit for your assistance. My late father was among the few black Zimbabwean rich farmers murdered in cold blood by the agents of the ruling government of president Robert Mugabe for his alleged support and sympathy for the Zimbabwean opposition party controlled by the white minority. Before his death he had taken me to Johannesburg- South Africa to deposit the sum of US$ 23.Million(Twenty three Million United States Dollars) with a Security and Finance Company as if he Foresaw the looming danger in Zimbabwe. The money was deposited in a metal box and declared as a personal effects for the use of some of his friends who were to arrive for the world conference on racism scheduled to hold in South Africa from fromr 1st march, 2003.This money was given to my father by President.Robert Mugabe himself, for investment in South Africa when they were in good terms. The dispute between my father and the President arose when the President introduced a new land reform-act, which adversely affected the rich white farmers and so black farmers as well. My father was vehemently opposed to the "modus operandis"adopted by the government and publicly oppose it. This brought him in direct conflict with the president.  More than Fifty Farmers has lost their lives so far. Heads of governments from the west, especially Britain and United States of America have voiced there condemnation of Mugabe's plan. Subsequently, South African Development Community (S.A.D.C) has continuously Supported Mugabe's new land act, it is against this background that I and my family who are currently Residing in South Africa have decided to transfer my Father's money into a foreign account. As the eldest Son of my Father, I am saddled with the responsibility Of seeking a genuine foreign account where this money Could be transferred without the knowledge of my Government who are tactically freezing our family Wealth and South Africa's government seems to be Playing along with them. I am faced with the dilemma of investing this money in South Africa for fear of Encountering the same experience in future since both Countries have almost the same political history. so, the South African Foreign Exchange policy does not allow such investment hence I am seeking for an "assylum". As a businessperson that I entrusted my Future and that of my family into his hands, I must Let you know that this transaction is 100% risk Free and the nature of your business does not necessarily matter. For your assistance, we are offering You 20% of the total sum, 75% for me and my family While 5% will be mapped out for any expenses we may incurs during this transaction. We wish to invest our Money on commercial property based on your advice. Finally will demand for assurance that you will not sit on this money when it finally gets to your personal or company's account in your country. If this proposal is acceptable by you, please confirm your interest via the above telephone, or you reply through email. THANKS AND GOD BLESS YOU, Best Regards, Mr; Arbu Mpula, _________________________________________________________________ Don't just search. Find. Check out the new MSN Search! http://search.msn.com/


public class NetUtils extends Object
| Constructor and Description |
|---|
NetUtils()
|
| Modifier and Type | Method and Description |
|---|---|
String |
getLocalIp()
Gets the local IP address of the current machine.
|
String |
getWanIp()
Gets the WAN IP address of the current network using an AWS
service.
|
String |
HttpRequest(String host,
HttpTypes type)
Sends an HTTP request of type
HttpTypes .
|
boolean |
pingAddr(String addr)
"Pings" an address to check if it's alive
|
boolean |
pingHost(String host)
"Pings"
host to check if it's alive |
String |
urlShortenGoogle(String url)
Uses Google's URL shortening API to shorten a given url (goo.gl
format)
|
public boolean pingHost(String host)
host
to check if it's alivehost
- The hostname to pingpublic boolean pingAddr(String addr)
addr
- The ip address to pingpublic String HttpRequest(String host, HttpTypes type)
HttpTypes
.
host
- The host to requesttype
- The type of HTTP request to make (GET, POST, etc.)SimpleHttpRequest
,
SimpleHttpRequest.fire()
public String getWanIp()
public String getLocalIp()
Copyright © 2017. All rights reserved.


Given two unbiased samples of patient level data on cost and effectiveness for a pair of treatments, make head-to-head treatment comparisons by (i) generating the bivariate bootstrap resampling distribution of ICE uncertainty for a specified value of the shadow price of health, lambda, (ii) form the wedge-shaped ICE confidence region with specified confidence fraction within [0.50, 0.99] that is equivariant with respect to changes in lambda, (iii) color the bootstrap outcomes within the above confidence wedge with economic preferences from an ICE map with specified values of lambda, beta and gamma parameters, (iv) display VAGR and ALICE acceptability curves, and (v) illustrate variation in ICE preferences by displaying potentially non-linear indifference(iso-preference) curves from an ICE map with specified values of lambda, beta and either gamma or eta parameters.
| Version: | 1.1 |
| Depends: | lattice |
| Published: | 2018-06-24 |
| Author: | Bob Obenchain |
| Maintainer: | Bob Obenchain <wizbob at att.net> |
| License: | GPL-2 | GPL-3 [expanded from: GPL] |
| URL: | https://www.R-project.org , http://localcontrolstatistics.org |
| NeedsCompilation: | no |
| CRAN checks: | ICEinfer results |
| Reference manual: | ICEinfer.pdf |
| Package source: | ICEinfer_1.1.tar.gz |
| Windows binaries: | r-devel: ICEinfer_1.1.zip, r-release: ICEinfer_1.1.zip, r-oldrel: ICEinfer_1.1.zip |
| OS X binaries: | r-release: ICEinfer_1.1.tgz, r-oldrel: ICEinfer_1.1.tgz |
| Old sources: | ICEinfer archive |
Please use the canonical form https://CRAN.R-project.org/package=ICEinfer to link to this page.


A* Pathfinding Algorithm Visualiser
Drag your cursor over the grid to add obstacles
DELETE MODE
CLEAR GRID
START


Thank you for participating!
Presentation is at
Slides can be downloaded at
Please feel free to fork and improve.


Template Title
Hello, This is Text
Toggle navigation
Somthing here
What is?
Contact
What is this somthing?
Somthing important written here.


It is very often that you want to cancel all pending http calls on every route change, as most of the time we users assume the functions are stopped when we leave the route.
Hence, we need to make use of Angular HttpInterceptor to intercept and handle an HttpRequest or HttpResponse.
Note that you may either do this in app level or module level. In this example, we added it in main-contents module.
You need to create 2 extra files: httpCancel.service.ts and manageHttp.interceptor.ts
// httpCancel.service.ts
import { Injectable } from '@angular/core';
import { Observable, Subject } from 'rxjs';
@Injectable()
export class HttpCancelService {
private pendingHTTPRequests$ = new Subject<void>();
public cancelPendingRequests(): void {
this.pendingHTTPRequests$.next();
}
public onCancelPendingRequests(): Observable<void> {
this.pendingHTTPRequests$.subscribe();
return this.pendingHTTPRequests$.asObservable();
}
}
// main-contetns-manageHttp.interceptor.ts
import {
HttpEvent,
HttpHandler,
HttpInterceptor,
HttpRequest} from '@angular/common/http';
import { Injectable } from '@angular/core';
import { Observable } from 'rxjs';
import { takeUntil } from 'rxjs/operators';
import { HttpCancelService } from './service/httpCancel.service';
@Injectable()
export class MaintContentsManageHttpInterceptor implements HttpInterceptor {
constructor(
private httpCancelService: HttpCancelService) {
}
intercept<T>(req: HttpRequest<T>, next: HttpHandler): Observable<HttpEvent<T>> {
return next.handle(req).pipe(takeUntil(this.httpCancelService.onCancelPendingRequests()));
}
}
Then, you need to import and provide your httpCancelService and manageHtttpInterceptor to your module.ts
// IN main-contents.module.ts
import { MainContentsRoutingModule } from './main-contents-routing.module';
import { MainContentsComponent } from './main-contents.component';
import { HttpCancelService } from './service/httpCancel.service';
import { HTTP_INTERCEPTORS } from '@angular/common/http';
import { MainContentsManageHttpInterceptor } from './main-contents-manageHttp.interceptor';
@NgModule({
declarations: [MainContentsComponent],
imports: [
MainContentsRoutingModule,
],
providers: [
HttpCancelService,
{ provide: HTTP_INTERCEPTORS, useClass: MainContentsManageHttpInterceptor, multi: true }
]
})
export class MainContentsModule { }
To make use of cancel pending request function, you can call the function in ngOnDestroy().
// IN any-component.component.ts
ngOnDestroy(): void {
this.httpCancelService.cancelPendingRequests();
}
The example above requires you to call the function in ngOnDestroy() of every relevant component.
To literally subscribe to route change and cancel all pending requests automatically (without manually calling the cancel request function), you may refer to this tutorial.


-
Cloudstaff Action Scan v1.0.18
Action Tag Scanner The Action Tag Scanner is designed to scan Action Tags stickers which are available from the http://www.ActionTag.io web site. You can login to the ActionTag.io website and define the event that occurs when an ActionTag is scanned. Supported events include:- Send an SMS, Send an Email or Redirect to a Web Site.
-
Lloyd Ernst
CEO & Founder Cloudstaff Philippines Inc.
Flashes of occasional inspiration -
Jr Reyes
FullStack Software Engineer
Lead Developer


To use this feature, make sure to setup your build correctly. You should for example first fire-up JMeter to put load on the system. This plugin will then gather that information and show it in your build overview. You have to specify the URL to your AppDynamics Console.


This rule detects hardcoded secrets
TO DO
Use a keystore to store all secrets and reference them by symbolic IDs in your code.


--- title: Adapting Content Workflow to Responsive Web Design excerpt: '
Adapting the workflow to #RWD, to better manage and publish bits of content, not pages: Adaptive Content Management. Brilliant @markboulton.
' date: '2012-10-19 15:27:47 +0200' date_gmt: '2012-10-19 13:27:47 +0200' categories: - Webdev ---Adapting the workflow to #RWD, to better manage and publish bits of content, not pages: Adaptive Content Management. Brilliant @markboulton.


TSBPDMODE
Enable/Disable TsbPd. Enable -> Tx set origin timestamp, Rx deliver packet at origin time + delay


Preparation time
less than 30 mins
Cooking time
over 2 hours
Serves
Serves 8-10
Coating your turkey in honey produces a beautifully white meat with a dramatic dark skin. Free from: dairy, wheat, and gluten; can be soya, nut and egg-free
Remove the giblets from the bird. Chop the liver and discard the rest.
Heat the oil in a pan and gently cook the liver, onion and parsley until the liver is firm and the onion soft.
Take off the heat and add the apple, raisins, lemon rind and juice, almonds, breadcrumbs (or ground rice/quinoa), ginger, seasoning, and egg if you are using it.
Mix it all well together and stuff the bird at either the neck end or at the body cavity.
Secure the bird with skewers and put it in a roasting pan.
Melt the honey with the butter or dairy-free margarine and spoon them over the bird; as it cools it will cling to the skin.
Leave the bird to marinate in the honey, uncovered, in a fridge or cool larder, for 24 hours, spooning over any excess mixture.
Roast the bird in a moderate oven (170C/350F/Gas 4) for 20 minutes to the pound, basting frequently with the honey and butter/spread mixture. The skin will gradually turn dark brown and shiny in contrast to the white meat below.
The bird is cooked when the juices run clear when a skewer is inserted into the thickest part of the thigh. Rest the bird for 15 minutes and serve.


SHE'S SO LOVELY (director: Nick Cassavetes; cast: Sean Penn (Eddie), Robin Wright Penn (Maureen), John Travolta (Joey), Harry Dean Stanton (Tony 'Shorty' Russo), Debi Mazar (Georgie), Gena Rowlands (Mrs. Green), James Gandolfini (Kiefer, the neighbor), Susan Traylor (Lucinda), Kelsey Mulrooney (Jennie,Eddie's daughter), 1997)
We have to wait until the near end of this love triangle, a bleak drama about lowlife angst, to hear the words of the Cole Porter tune, of which this film takes its title from, spoken by Eddie (Sean) to Joey (Travolta), "She doesn't love you. She doesn't love me. She's...delovely." But even these words don't make sense, as we find out soon afterwards, that their wife definetly loves one of them more than the other. Maureen (Robin) is the ex-wife of Eddie and the current wife of Joey. She is also the real-life wife of Sean.
Maureen and Eddie are a couple, who love each other madly, but have many problems such as booze, drugs, and Eddie's habit of disappearing from time-to-time. During one of these disappearing acts, the pregnant Maureen is savagely beaten up and probably raped by her neighbor, Kiefer (Gandolfini), but when Eddie returns, she is afraid to tell him this, fearing he will kill the neighbor. So when she lies to him and tells him the marks on her face are from a fall, he doesn't believe her and goes into a rage, forcing her to call EMS. But this backfires and Eddie shoots one of the attendants, as a result he is sent away to the mental house for 10 years. When he gets out, his wife who never visited him while he was institutionalized, has divorced him and remarried a successful construction company owner, Joey, who raises the 9-year-old she had with Eddie, plus raising their own two daughters. But she now must choose which is the one she really loves and wants to stay with.
The film wants to look like a "hip" independent movie, even though this John Cassavetes's scripted work (he died in 1989), directed by his son, is a Hollywood produced movie. It ends up looking not quite like a John Cassavetes independent film should look. It seems to be written as if someone thought that it would be cute to keep leaving out key parts to the story. As a result, the film suffers from incohesiveness, but because of the caliber of actors involved, they make a Cassavetes sandwich seem like it is a Cassavetes sandwich, even if there is not much real Cassavetes in the sandwich. In other words, the story just wasn't there, it didn't seem to be original, and this from a script by the real McCoy of indie films, is astonishing, to say the least, even the ending of the film feels like a blatant rip off of THE GRADUATE.
The film, however, managed to be entertaining. It just mostly lacked insight into what these characters were about. Nothing was resolved or could be fully understood. For example, it made no sense to think that Eddie was well enough to be let free from the institution and still didn't know that he was there for a decade not for three months. Too many other things about the movie were either murky or didn't add up, unless you are willing to except anything goes as a satisfactory story line when it comes to dealing with those who are unstable.
What worked for me, was the comic reactions by both Stanton and Travolta, it broke the heavy barrage of emoting from the Penns. I also thought that Gandolfini did an excellent job in his brief appearance on screen. He held my attention with his mix of manic sadistic moves and cruel humor. Without them, this film would have taken itself too serious for the untasty Cassavetes sandwich it was trying to serve its too willing captives in the audience.
REVIEWED ON 3/3/99 GRADE: C-
Dennis Schwartz: "Movie Reviews"
<EMAIL_ADDRESS>http://www.sover.net/~ozus
© ALL RIGHTS RESERVED DENNIS SCHWARTZ
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Be aware that changing settings for this Favorite location updates those settings for all users that are using this Favorite.
Do you want to create the new location as a Favorite location or as a Temporary location? Favorite locations are named locations that are remembered, and can be shared with other users. Temporary locations are not named and are discarded when you next change your location.
Choose your new location by its address (or its zip code, or its name), or you can enter the coordinates yourself. You can even click around on the map itself to move the location.


Feb 9, 2012 Garth Brooks - Unanswered Prayers 30,871 views dbdannyray555 By dbdannyray555 Follow. 9. About; Comments; Videos; Export; Add to.
Sep 15, 2010 Garth Brooks #1 hit "Unanswered Prayers" will be transformed into a two-hour Lifetime TV movie I know what you are thinking, because I
Just the other night a hometown football game / My wife and I ran into my old high school flame / And as I introduced them the past came back to me Unanswered Prayers (Cover Version). Various Artists - Garth Brooks Tribute | Format: MP3. Music From the Album A Tribute To Garth Brooks Garth Brooks Unanswered Prayers lyrics & video : Just the other night a hometown football game my wife and i ran into my old high school flame and as i
Garth Brooks - "Some of God's. greatest gifts are unanswered prayers " Sep 14, 2010. Arlene Richie, jpistudios com He tapped deep into the universal core of the heart of country music with his 1990 No 1 smash, 'Unanswered
Garth Brooks - Unanswered Prayers (música para ouvir e letra da música com legenda)! Sometimes I thank God for unanswered prayers / Remember when Official Garth Brooks Unanswered Prayers lyrics at CD Universe Just the other night a hometown football game My. wife and I ran into my old high school flame Lyrics for the song Unanswered Prayers by Garth Brooks Unanswered Prayers music video. Dec 11, 2012 Unanswered Prayers Chords (ver 4) by Garth Brooks. with chord diagrams, easy version and transpose Added on December 11, 2012 Nov 29, 2010 Over the years, TV producers have asked country superstar Garth. Brooks for the rights to turn one of his hits into a movie He always said no,
Just the other night a hometown football game / My wife nd I ran into my old high school flame / And as I introduced them the past came back to me / And I.
Just the other night at a hometown football game / My wife and I ran into my. old high school flame / And as I introduced them the past came back to me. Sep 13, 2010 Well, we already know how it'll end, but then again, is that different from any other Lifetime movie? The sweet and simple Garth Brooks song Just the other night at a hometown football game / My wife and I ran into my old high school flame / And as I introduced them the past came back to me. Some of God's greatest gifts are unanswered prayers Garth Brooks quotes ( American Country singer He was. the first country music singer to win six Academy Aug 1, 2012 And for what will likely be the only time in this blog, I am compelled to quote. country music (Garth Brooks, Unanswered Prayers): Sometimes I Garth Brooks discovered using Shazam


Cultural
EQ and IQ
- What is Emotional Intelligence?
Emotional intelligence is ability to identify and manage your own emotions but also to understand and influence emotions of others.
It is crucial skill when it come to human contact and in my opinion mandatory to be a person who is satisfied with own life.
- How is it different to IQ?
It's different because instead of noticing, analysing and solving problems it measures our ability to notice analyse and manage our emotions which are also very important in our life.
- Why is it important?
It' important as emotions are driving our live affecting our decisions and wellbeing. Every single relationship is based on emotions, every single second of our life has emotions in it.
Very often people are making crucial live choices based on their emotions, so understanding and having them under control is very valuable skill that everyone should obtain.


Demo app for Visma.
Used technologies are below
NOTE: If lombok plugin not installed to your IDE please install before import project source
H2 db used for simplicity, you can use any db configuring datasource from application.properties
Flyway used for auto migration/creation of DB. So you can simply create and use your own DB to start the project.
If you want to define new kind of rebate agreement, do it as below in 3 steps:
resources/db/migration
@Component
) which implements RebateAgreementComponent
interface and has name/id as defined on rebate_agreements table's name_ column (like as rebate-agreement-fixed-percentage
)


KLIST
Section: User Commands (1)Index Return to Main Contents
NAME
klist - list cached Kerberos ticketsSYNOPSIS
klist [-e] [[-c] [-f] [-s] [-a [-n]]] [-k [-t] [-K]] [cache_name | keytab_name]DESCRIPTION
Klist lists the Kerberos principal and Kerberos tickets held in a credentials cache, or the keys held in a keytab file.OPTIONS
- -e
- displays the encryption types of the session key and the ticket for each credential in the credential cache, or each key in the keytab file.
- -c
- List tickets held in a credentials cache. This is the default if neither -c nor -k is specified.
- -f
-
shows the flags present in the credentials, using the following
abbreviations:
F Forwardable f forwarded P Proxiable p proxy D postDateable d postdated R Renewable I Initial i invalid H Hardware authenticated A preAuthenticated T Transit policy checked O Okay as delegate a anonymous
- -s
-
causes
klist
to run silently (produce no output), but to still set the exit status
according to whether it finds the credentials cache. The exit status is
`0' if
klist
finds a credentials cache, and `1' if it does not or if the tickets are
expired. - -a
- display list of addresses in credentials.
- -n
- show numeric addresses instead of reverse-resolving addresses.
- -k
- List keys held in a keytab file.
- -t
- display the time entry timestamps for each keytab entry in the keytab file.
- -K
- display the value of the encryption key in each keytab entry in the keytab file.
If cache_name or keytab_name is not specified, klist will display the credentials in the default credentials cache or keytab file as appropriate. If the KRB5CCNAME environment variable is set, its value is used to name the default ticket cache.
ENVIRONMENT
Klist uses the following environment variables:- KRB5CCNAME
- Location of the Kerberos 5 credentials (ticket) cache.
FILES
- /tmp/krb5cc_[uid]
- default location of Kerberos 5 credentials cache ([uid] is the decimal UID of the user).
- /etc/krb5.keytab
- default location for the local host's keytab file.
SEE ALSO
kinit(1), kdestroy(1), krb5(3)
Index
This document was created by man2html, using the manual pages.
Time: 05:29:05 GMT, December 24, 2015


AWS::S3::Bucket LoggingConfiguration
Describes where logs are stored and the prefix that Amazon S3 assigns to all log object keys for a bucket. For examples and more information, see PUT Bucket logging in the Amazon S3 API Reference.
To successfully complete the AWS::S3::Bucket LoggingConfiguration
request,
you must have s3:PutObject
and s3:PutObjectAcl
in your IAM
permissions.
Syntax
To declare this entity in your AWS CloudFormation template, use the following syntax:
JSON
{ "DestinationBucketName" :
String
, "LogFilePrefix" :String
}
YAML
DestinationBucketName:
String
LogFilePrefix:String
Properties
DestinationBucketName
-
The name of the bucket where Amazon S3 should store server access log files. You can store log files in any bucket that you own. By default, logs are stored in the bucket where the
LoggingConfiguration
property is defined.Required: No
Type: String
Update requires: No interruption
LogFilePrefix
-
A prefix for all log object keys. If you store log files from multiple Amazon S3 buckets in a single bucket, you can use a prefix to distinguish which log files came from which bucket.
Required: No
Type: String
Update requires: No interruption
Examples
Log access requests for a specific S3 bucket
The following example template creates two S3 buckets. The LoggingBucket
bucket store the logs from the S3Bucket
bucket. To receive logs from the
S3Bucket
bucket, the logging bucket requires log delivery write
permissions.
JSON
{ "AWSTemplateFormatVersion": "2010-09-09", "Resources": { "S3Bucket": { "Type": "AWS::S3::Bucket", "Properties": { "AccessControl": "Private", "LoggingConfiguration": { "DestinationBucketName": { "Ref": "LoggingBucket" }, "LogFilePrefix": "testing-logs" } } }, "LoggingBucket": { "Type": "AWS::S3::Bucket", "Properties": { "AccessControl": "LogDeliveryWrite" } } }, "Outputs": { "BucketName": { "Value": { "Ref": "S3Bucket" }, "Description": "Name of the sample Amazon S3 bucket with a logging configuration." } } }
YAML
AWSTemplateFormatVersion: 2010-09-09 Resources: S3Bucket: Type: 'AWS::S3::Bucket' Properties: AccessControl: Private LoggingConfiguration: DestinationBucketName: !Ref LoggingBucket LogFilePrefix: testing-logs LoggingBucket: Type: 'AWS::S3::Bucket' Properties: AccessControl: LogDeliveryWrite Outputs: BucketName: Value: !Ref S3Bucket Description: Name of the sample Amazon S3 bucket with a logging configuration.


Define a file handle for the folder where the first instance of the specified file or wildcard is found.
STATS FIND FILE
FILENAME = “file”*
FILEHANDLE = handle*
FOLDERLIST = “semicolon-separated list of folders”
ENVVARIABLE = “semi-colon-separated environment variable names”
SEARCHSUBFOLDERS = YES or NO**
/OPTIONS
PRINTSEARCH = YES or NO**
PRINTHANDLE = YES** or NO
/HELP
* Required
** Default
/HELP displays this help and does nothing else.
STATS FIND FILE FILENAME="employee data.*" FILEHANDLE=employee
FOLDERLIST="c:/spss/samples/english;c:/data".
FILENAME specifies the file or file wildcard to locate.
FILEHANDLE specifies the name of the handle to be assigned.
Specify either or both of FOLDERLIST and ENVVARIABLE. FOLDERLIST is a string in quotes listing the folder or folders to search. Separate folder specifications with a semi-colon.
ENVVARIABLE is a string in quotes listing one or more environment variables that list a folder or folders to search. Again, specifications are separated by a semicolon. By using this feature, you can avoid the need to specify specific locations for files, leaving that to your system or user environment.
If both FOLDERLIST and ENVVARIABLE are specified, the FOLDERLIST is searched first.
By default, only the listed locations are searched. Specify SEARCHSUBFOLDERS=YES to search all the subfolders of folders listed.
By default, the generated handle is displayed. Specify PRINTHANDLE=NO to suppress this.
Specify PRINTSEARCH=YES to display a table listing all the locations searched in the order they were searched.
If a listed folder or environment variable does not exist, it is ignored.
If the specified file is not found, an error is raised.
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