8. When Models Meet Data
-
In the first part of the book, we introduced the mathematics that form the foundations of many machine learning methods
-
The second part of the book introduces four pillars of machine learning:
- Regression (Chapter 9)
- Dimensionality reduction (Chapter 10)
- Density estimation (Chapter 11)
- Classification (Chapter 12)
8.1 Data, Models, and Learning
- Three major components of a machine learning system: data, models, and learning.
- Good models : should perform well on unseen data.
- There are two different senses in which we use the phrase “machine learning algorithm”: training and prediction.
8.1.1 Data as Vectors
- We assume that a domain expert already converted data appropriately, i.e., each input $x_n$ is a D-dimensional vector of real numbers, which are called features, attributes, or covariates by following steps:
- Raw Data format : In recent years, machine learning has been applied to many types of data that do not obviously come in the tabular numerical format
- Numerical Representation : Even when we have data in tabular format, there are still choices to be made to obtain a numerical representation.
- Normalization : Even numerical data that could potentially be directly read into a machine learning algorithm should be carefully considered for units, scaling, and constraint
8.1.2 Models as Functions
- A predictor is a function that, when given a particular input example (in our case, a vector of features), produces an output
8.1.3 Models as Probability Distributions
- Instead of considering a predictor as a single function, we could consider predictors to be probabilistic models,
8.1.4 Learning is Finding Parameters
- The goal of learning is to find a model and its corresponding parameters such that the resulting predictor will perform well on unseen data.
- we will consider two schools of machine learning in this book, corresponding to whether the predictor is a function or a probabilistic model.
- We would like to find good predictors for given training data, with two main strategies :
- based on some measure of quality (sometimes called finding a point estimate),
- using Bayesian inference. (requires probabilistic models)
8.2 Empirical Risk Minimization
- In this section, we consider the case of a predictor that is a function
8.2.1 Hypothesis Class of Functions
-
For given data, we would like to estimate a predictor $f(·,θ):R^D →R$, parametrized by $θ$. We hope to be able to find a good parameter $θ^∗$ such that we fit the data well, that is,
$$ f(x_n,θ^∗) ≈ y_n \text{ for all } n = 1,…,N. $$
- Affine functions (linear functions) are used as predictors for conventional ML methods
- Instead of a linear function, we may wish to consider non-linear functions as predictors.
-
Given the class of functions, we want to search for a good predictor. We now move on to the second ingredient of empirical risk minimization: how to measure how well the predictor fits the training data.
8.2.2 Loss Function for Training
- Equation below is called the empirical risk and depends on three arguments, the predictor $f$ and the data $X, y$.
$$ R_{emp}(f,X,y) = \frac{1}{N} \sum_{n}^{N} l(y_n, \hat{y}_n) $$
- Empirical risk minimization : This general strategy for learning is called empirical risk minimization
8.2.3 Regularization to Reduce Overfitting
- The aim of training a ML predictor is so that we can perform well on unseen data
- Overfitting : the predictor fits too closely to the training data and does not generalize well to new data
- Regularization : introduce a penalty term that makes it harder for the optimizer to return an overly flexible predictor
- The simplest regularization strategy is to replace the least-squares problem with the regularized problem by adding a penalty term
$$ \text{min}_θ \frac{1}{N}∥y − Xθ∥^2 \rightarrow \text{min}_θ \frac{1}{N}∥y − Xθ∥^2 + {λ∥θ∥}^2 $$
8.2.4 Cross-Validation to Assess the Generalization Performance
- Partitions the data into $K$ chunks, $K-1$ of which form the trainig set, and the last chunk serves as the validation set.
- We cycle through all possible partitionings of validation and training sets and compute the average generalization error of the predictor
8.3 Parameter Estimation
- In this section, we will see how to use probability distributions to model our uncertainty due to the observation process and our uncertainty in the parameters of our predictors.
8.3.1 Maximum Likelihood Estimation
-
The idea behind MLE is to define a function of the params that enables us to find a model that fits the data well.
-
Negative log-likelihood for data represented by a random variable $x$ and a family of probability densities $p(x|θ)$ is given by :
- The notation $L_x(θ)$ emphasizes the fact that the parameter $θ$ is varying and the data $x$ is fixed.
- To find a good parameter vector $θ$ that explains the data well, minimize the negative log-likelihood $L(θ)$ with respect to $θ$.
$$ L_x(\theta) = −logp(x|θ) $$
-
Minimizing Neg Log Likelihood $L(θ)$ of Gaussian, corresponds to solving the least-squares problem (below)
- Detail explains : We decided to use Gaussian Distrib to represent given dataset $x_n$, find good param $\theta$ by minimizing Negative Log Likelihood
- Log : We use log due to the exp term in Gaussian
- Negative : We use negative because of negative in exp term in Gaussian
- Likelihood and Posterior : details in section 6.3
- Detail explains : We decided to use Gaussian Distrib to represent given dataset $x_n$, find good param $\theta$ by minimizing Negative Log Likelihood
8.3.2 Maximum A Posteriori Estimaion
-
If we have prior knowledge about the distribution of the parameters θ, we can multiply an additional term to the likelihood.
-
Using Bayes’ theorem (6.3) we can compute a posterior distbution $p(θ|x)$, with prior distribution $p(\theta)$, and likelihood $p(x|θ)$
$$ p(θ|x) ∝p(x|θ)p(θ) $$
- Instead of estimating the minimum NLL, we now estimate minimum of the negative log-posterior : Maximum a posteriori estimation
- Including prior knowledge => regularization, which introduces an additional term that biases the resulting parameters to be close to the origin.
8.3.3 Model Fitting
- Overfitting : refers to the situation where the parametrized model class is too rich to model the dataset
- Underfitting : we encounter the opposite problem where the model class is not rich enough
8.4 Probabilistic Modeling and Inference
- We often build models that describe the generative process that generates the observed data.
- A coin-flip experiment can be described in two steps,
- (1) we define a parameter μ, which describes the probability of “heads” as the parameter of a Bernoulli distribution
- (2) we can sample an outcome x ∈ {head, tail} from the Bernoulli distribution $p(x | μ) = Ber(μ)$
- A coin-flip experiment can be described in two steps,
- In this chapter, we will discuss how probabilistic modeling can be used for this purpose.
8.4.1 Probabilistic Models
- Probabilistic models represent the uncertain aspects of an experiment as probability distributions.
- In probabilistic modeling, the joint distribution $p(x, θ)$ of the observed variables $x$ and the hidden parameters $θ$ is of central importance: It encapsulates information from the following:
- The prior and the likelihood (product rule, Section 6.3)
- The marginal likelihood p(x), which will play an important role in model selection (Section 8.6), can be computed by taking the joint distribution and integrating out the parameters (sum rule, Section 6.3)
- The posterior, which can be obtained by dividing the joint by the marginal likelihood.
- Only the joint distribution has this property. Therefore, a probabilistic model is specified by the joint distribution of all its random variables.
8.4.2 Bayesian Inference
-
Bayesian Inference
-
In Section 8.3.1, we already discussed two ways for estimating model parameters θ using maximum likelihood or maximum a posteriori estimation.
-
Once these point estimates $θ^$ are known, we use them to make predictions. More specifically, the predictive distribution will be $p(x | θ^)$, where we use $θ^*$ in the likelihood function.
-
Having the full posterior distribution around can be extremely useful and leads to more robust decisions.
-
Bayesian inference is about finding this posterior distribution
-
$p(\theta|X) = \frac{P(X|\theta) p(\theta) }{p(X)}, p(X) = \int{ p(x|\theta) p(\theta) d \theta}$
-
-
The key idea is to exploit Bayes’ theorem to invert the relationship between the parameters $θ$ and the data $X$ (given by the likelihood) to obtain the posterior distribution $p(θ | X )$.
-
More specifically, with a distribution p(θ) on the parameters our predictions will be no longer depend on the model param $\theta$, which have been marginalized/integated out.
- $p(x) = \int p(x | θ)p(θ)dθ$
-
-
Parameter Estimation (MLE, MAP) vs Bayesian Inference
-
Param estimation : yields a consistent point $\theta^*$ of the parameters
- the key computational problem to be solved is optimization
-
Bayesian Inference : yields a (posterior) distribution
- the key computational problem to be solved is integration
-
8.4.3 Latent Variable Models
- In practice, it is sometimes useful to have additional latent variables $z$ (besides the model parameters $θ$) as part of the model.
- Latent variables may describe the data-generating process, thereby contributing to the interpretability of the model.
- The conditional distribution $p(x | θ,z)$ allows us to generate data for any model parameters and latent variables.
8.5 Directed Graphical Models
- In this section, we introduce a graphical language for specifying a probabilistic model, called the directed graphical model
- provides a compact and succinct way to specify probabilistic models,
- allows the reader to visually parse dependencies between random variables
- This section relies on the concepts of independence and conditional independence, as described in Section 6.4.5.
- Probabilistic graphical models have some convenient properties:
- They are a simple way to visualize the structure of a probabilistic model.
- They can be used to design or motivate new kinds of statistical models.
- Inspection of the graph alone gives us insight into properties, e.g., conditional independence.
- Complex computations for inference and learning in statistical models can be expressed in terms of graphical manipulations.
- Ex) The arrow between a and b in Figure 8.9(a) gives the conditional probability $p(b|a) $,
- c depends directly on a and b.
- b depends directly on a.
- a depends neither on b nor on c.
8.6 Model Selection
- In machine learning, we often need to make high-level modeling decisions that critically influence the performance of the model
- More complex models are more flexible in the sense that they can be used to describe more datasets
- A polynomial of degree 1 (a line) can only be used to describe linear relations between inputs x and observations y.
- A polynomial of degree 2 can additionally describe quadratic relationships between inputs and observations.
- However, we also need some mechanisms for assessing how a model generalizes to unseen test data. => Model selection is concerned with exactly this problem.
Summary
- In this chapter, We will see how ML algorithms work from a mathematical point of view.
- Section 8.1, Data, Model, Learning
- Three main components of ML algorithm : Data, Model, Learning
- Data as vectors
- Models as Funtions or Probability Distrib
- Learning is Finding Parameters
- Three main components of ML algorithm : Data, Model, Learning
- Section 8.2, Empirical Risk Minimization
- ML model as a functional optimization
- Loss Function : Empirical Risk Minimization (ex. MSE => Least Square Problem)
- Regularization : additional penalty term in loss function
- ML model as a functional optimization
- Section 8.3, Parameter Estimation
- Parameter estimation of data distribution (in terms of ML model training)
- Likelihood (as loss function)
- Prior (as regularization term)
- Parameter estimation of data distribution (in terms of ML model training)
- Section 8.4 Probabilistic Modeling and Inference
- Bayesian Inference : yields a (posterior) distribution
- Section 8.5 Directed Graphical Models
- Directed Graphical Models can be used to specify a probabilistic model.
- Section 8.6 Model Selection
- How can we choose the model that works best for unseen data among trained models.


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"The everyday is not the same as the banal,
unless we let it."
Jonathon Meades
I’m a professional software developer. With experience in developing realtime web apps, data analysis and reporting, with mobile and tablet interfaces.
I am currently working on various open source projects.
Having worked with Simon on various projects over the past 2 years, I can say that he is one of the best developers I have worked with. Not only is he willing and able to tackle the most difficult tasks and work with the latest tools, but most importantly, he knows when to question everything to ensure the best outcome.
SYSTEM D TECHNOLOGIES - Founder


Summary
The Workers And The Cold War Arms Factory
Part 5 PLACEHOLDER IMAGE AND VIDEOS
Hanford is the nation's largest nuclear cleanup site, with 56 million gallons of radioactive waste sitting in old, leaky underground tanks just a few hours upriver from Portland. There's a plan to clean it all up. But after more than 20 years and $19 billion dollars, not a drop of waste has been treated.


port module Test exposing ( (&&) , Expect(..) , Test , run ) {-| Module documentation # Test @docs Test, Expect # Functions @docs run # Operator @docs (&&) -} import Multiline as ML exposing ( Data(..) , Payload(..) , response ) import Singleline as SL exposing (Dale(..), Single) -- test file for Elm syntax highlighting in KDE's Kate -- this is a single-line comment {- this is a multi-line comment -} infix left 0 (|>) = apR infix right 3 (&&) = and infix non 4 (>=) = ge type T = T | A | G | C type alias Alt = T operator : Int -> Int -> Int operator = (+) conditional : number -> number -> Maybe number conditional a b = if b == 0 then Nothing else if a == 0 then Just 0 else Just (a / b) lambda = Int -> Int lambda a = (\a -> a + 35) {-| Documentation comment Should be same as comment -} caseStatement : Maybe number -> number -> number caseStatement a b = case a of Just a_ -> a_ _ -> b record : { float : Float, int : Int } record = { float = 1.455321, int = 83 } tuple : ( Float, Float ) tuple = ( 3.14, 9.553 ) port portTest : () -> Cmd msg


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Kestutis Normantas, Sergejus Sosunovas, Olegas Vasilecas
Related to the ADM reverse-engineering practice recommended by the OMG, related to figuring out the ontology? of a real world system and modelling it.
software modernization [suggests a mapping to firmware modernization]
"The Knowledge Discovery Meta-model plays the fundamental role in this set of representations as it defines common concepts of software assets and their operational environments."
"although this meta-model minimizes the effort required to obtain representation, it has several drawbacks that limits its capability to express domain specific knowledge"
"numerous issues arise during project execution: which views of modernizing systems to create; how to represent these views in order to comprehend the business logic implemented in the system; what knowledge extraction methods and techniques to employ; and finally, how to properly evaluate and validate resulting assets"
"software modernization [is] the process of understanding and evolving existing software assets in order to: maintain, integrate, refactor, reuse, migrate, or replace"
"the multi-tier software architecture..." [Tiers: Data, Data Access, Processing Logic, Presentation Logic, Presentation]
"layers" Infrastructure, Program Elements, Runtime Resources, Abstractions
"The KDM representation of the software system starts by the Inventory model that represents software artefacts and keeps traceability to the original source. A set of Code models represents the structure (association, composition, aggregation, and inheritance relationships) and behaviour (data and control flow) of the source code. The Data, Platform, Event, and UI models represent the structure and behaviour of the run-time resources of the system. The Conceptual, Structure, and Build models represent highest level abstractions obtained from the models of lower level of abstractions, the data processed by the system, and different kinds of software’s documentation." [See Table1 layout within paper - very informative]
"Numerous techniques exist for the extraction and comprehension of knowledge about software architecture in the field of reverse engineering. Apart from the straightforward techniques that incorporate parsing the content of software artefacts and direct transformation to elements of KDM in order to produce “as-is” representation of the software, the clustering-based, patterns-based, and model slicing-based techniques may be employed to derive higher level abstractions in automatic or semiautomatic way."
"The light-weight extension mechanism provides capability to: define of stereotypes that may extend either concrete or abstract meta-model elements; define tags associated with stereotypes and add values (in a form of string or reference to some modelling element) to them; group stereotypes into stereotype families; use one or more stereotypes within extended meta-model element." [offers a semi-formal extensible dimension to KDM, possibly could support workshops, though the authors criticise how relationships between these extensions cannot be constrained enough to be meaningful for domain modelling themselves]


It has been iterated many times but I will say it once again. Everything in Ruby is an object or almost everything. Constructing objects is what we do in Ruby. Classes facilitate this process. A class is a way of organizaing and producing object with similar attributes and methods.
One can create a basic class simply by using the class keyword and the name of the desired class. Conventionally speaking, class names start with a capital letter and use CamelCase instead of any underscores. In my example, I have a class called 'class Jedi'.
A class begins with a method called initialize. The def initialize method is what is being 'booted up' in to each object the class creates. Now we want to name our Jedis so that we can play with a Yoda or Obi-Wan. We do this by creating an instance variable: this instance variable is inherently attached to every instance of the class. Our instance variable for name is @name. Therefore in ruby it is @name=name within the inititalize method. Here, I am creading an instance, yoda, of the class Jedi. yoda has his own @name ("Yoda") and @lightsaber("green"). Those variables belong to the yoda instance which is why they are called instance variables.
Instance variables belong to a particular object and therefore, are better than global variables because they cannot be changed from anywhere from the program. It is a better idea to only be able to change certain variables from a few places.
Class variables are an altogether a different beast. It takes two @@ and therefore is twice as classy. Class variables are created by two @ symbols and are attached to entire classes not just instances of class.
In my final image on the left, I am showing how ruby is able to read two new instances of the class Jedi.


First best is falling in love. Second best is being in love. Least best is falling out of love. But any of it is better than never having been in love.
He is my everything, without him its like Paris without Effil tower. His honesty & outspoken have triggered my harmones to the peak. He is the closest male i have seen who can match my dad's love.
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Advanced¶
Grouping By¶
Grouping by
Last Before¶
Using Kleisli and Lifted¶
If you wrap these you get a few benefits:
- The resulting columns have more readable names
- You can use the resulting functions as methods
- They add on the group bys of their source.
How it Works¶
Basically the internal representation of a query is as a graph. Query combinators are just transformations on this graph. Before generating the SQL, LambdaQuery performs a reduction algorithm on this graph, and then the reduced graph is compiled to SQL. To see the SQL of the unreduced graph, use the reduce=False
option for the Query.sql
method.


Welcome to St Francis Parish
St Francis Church
Services
- Sunday
- 09.00 and 11.00
- Monday
- 10.00
- Tuesday
- 19.30
- Wednesday
- 10.00
- Thursday
- 10.00
- Friday
- 10.00
- Saturday
- 12.00
Please check latest newsletter for exact times.
Where are we?
277 Sandygate Road, Crosspool.Parish Panto
The parish pantomime, Pinocchio and Juliet, will be at 7:30pm on Friday 27th and Saturday 28th November in the Assisi Hall.
Tickets (Adults £5 and children £3) available from Tammy. All proceeds to CAFOD.
About St. Francis
The Parish of St. Francis of Assisi is a lively and active parish, serving the areas of Crosspool, Sandygate, Lodge Moor, Ranmoor, Nether Green and Fulwood.
It is part of Sheffield North Deanery in the Diocese of Hallam.
The Parish Priest is Father Kevin Thornton and the Parish Deacon is Stephen Parker.


The new Java SE AWT Native Interface enables rendering libraries compiled to native code to draw directly to a Java Canvas drawing surface. This means that such libraries can be used without being converted to Java first and without significant impact on performance. An example illustrating how easy it is to use the AWT Native Interface is presented and discussed in this technical note.
Introduction
The definition of Java Standard Edition includes JNI, the Java Native Interface. Most Java developers will never need to use it, but the interface is invaluable in certain situations because it provides the only way for Java byte code to interact directly with application code that has been compiled to the native machine instructions for the host microprocessor. JNI is most often used as an ``escape valve'' to enable access to platform functionality not yet supported by the Java programming language. For example, you could use JNI to integrate with native code that communicates with a peripheral device, such as a scanner, connected to a system via a USB port.
Of course, JNI is general enough to be used to access almost any sort of native library, regardless of whether the task to be accomplished could also be done using pure Java. The major penalty for using it is that code portability suffers, but this may be acceptable or even necessary for business or technical reasons.
Business reasons? Consider the situation where the legacy software you are trying to port to Java uses a third-party library for a critical set of operations. If you do not have source rights to this library and you cannot convince the owner to provide a Java version, you may not be able to use it. Even if you do have the source, the effort needed to port a standard library to Java and test it may be too expensive to consider.
Another important reason for leaving the native code alone is related to performance. If you are dealing with a finely crafted piece of code, carefully tuned for performance over the course of years, you probably do not want to convert it to Java and risk a performance penalty. It is usually best to keep it intact until you are satisfied that the benefits of Java portability and code maintainability outweigh the expected performance difference.
A rendering library is a good example of a piece of native code that most developers would just as soon leave alone for performance reasons. Unfortunately, this is the very type of library that has been most difficult to integrate with Java code through JNI. The fundamental problem has been that the rendering code cannot identify where to draw. It needs access to information about a Java drawing surface (such as a handle to the underlying peer of a Canvas), but cannot get it.
Until now, the Java platform has kept access to this information private — "private" in the sense of undocumented, unsupported, and deprecated. The good news is that this situation will be remedied with the introduction of the "AWT Native Interface" in the Java upgrade release ("Kestrel"). For the first time there will be an official way to obtain all the information you need to know about the peer of a Java Canvas so that you can draw directly to the Canvas from a native code library using the drawing routines provided by the operating system.
How It Works
In this section we'll describe the most common usage of the AWT Native Interface — overriding the paint method to direct drawing operations to a native rendering library which then queries the Java VM to determine the information it needs in order to render. Note, however, that any native code may use the AWT Native Interface to learn about a target drawing surface, not just code in a paint method.
The first step in hooking up a native rendering library to a Java Canvas is to define a new class that extends Canvas and overrides the paint method. The Java system routes all drawing operations for a Canvas object through the paint method, as it does for all other GUI objects.
The new paint method, to be implemented in the native rendering library, must be declared as public native void , and the native library itself is loaded at runtime by including a call to System.loadLibrary( "myRenderingLib")in the static block of the class. The myRenderingLib name is used for the native shared library; for the Solaris operating environment, the actual name for the library file on disk is libmyRenderingLib.so .
Here is a simple example of such a class:
import java.awt.*; import java.awt.event.*; public class MyCanvas extends Canvas { static { System.loadLibrary("myRenderingLib"); } public native void paint(Graphics g); public static void main(String[] args) { Frame f = new Frame(); f.setBounds(0, 0, 500, 110); f.add( new MyCanvas() ); f.addWindowListener( new WindowAdapter() { public void windowClosing(WindowEvent ev) { System.exit(0); } } ); f.show(); } }
Note that this class has a main method that can be used torun this code as an application for testing purposes.
The next step is to run the javah tool on the MyCanvas class file above to generate a C/C++ header file that describes the interface to the native paint method that Java expects to be used. javah is a standard tool included with the Java SDK.
The final step the most interesting one is to write the native rendering method, with an interface that conforms to the header file that javah generated, and build it as a standard shared library (called myRenderingLib in the above example) by linking it, for the Solaris operating environment, against the jre/lib/sparc/libjawt.so library. (For Microsoft Windows, link against the jre/bin/jawt.dll library.) This code will call back to the Java virtual machine to get the drawing surface information it needs to access the MyCanvas peer. Once this information is available, the code can draw directly to MyCanvas using standard drawing routines supplied by the underlying operating system.
Here is sample source code for a native paint method designed for use in a Solaris X11-based drawing environment and a Java VM where the AWT Native Interface is present:
#include "MyCanvas.h" #include "jawt_md.h" /* * Class: MyCanvas * Method: paint * Signature: (Ljava/awt/Graphics;)V */ JNIEXPORT void JNICALL Java_MyCanvas_paint (JNIEnv* env, jobject canvas, jobject graphics) { JAWT awt; JAWT_DrawingSurface* ds; JAWT_DrawingSurfaceInfo* dsi; JAWT_X11DrawingSurfaceInfo* dsi_x11; jboolean result; jint lock; GC gc; short i; char *testString = "^^^ rendered from native code ^^^"; /* Get the AWT */ awt.version = JAWT_VERSION_1_3; if (JAWT_GetAWT(env, &awt) == JNI_FALSE) { printf("AWT Not found\n"); return; } /* Get the drawing surface */ ds = awt.GetDrawingSurface(env, canvas); if (ds == NULL) { printf("NULL drawing surface\n"); return; } /* Lock the drawing surface */ lock = ds->Lock(ds); if((lock & JAWT_LOCK_ERROR) != 0) { printf("Error locking surface\n"); awt.FreeDrawingSurface(ds); return; } /* Get the drawing surface info */ dsi = ds->GetDrawingSurfaceInfo(ds); if (dsi == NULL) { printf("Error getting surface info\n"); ds->Unlock(ds); awt.FreeDrawingSurface(ds); return; } /* Get the platform-specific drawing info */ dsi_x11 = (JAWT_X11DrawingSurfaceInfo*)dsi->platformInfo; /* Now paint */ gc = XCreateGC(dsi_x11->display, dsi_x11->drawable, 0, 0); XSetBackground(dsi_x11->display, gc, 0); for (i=0; i<36;i++) { XSetForeground(dsi_x11->display, gc, 10*i); XFillRectangle(dsi_x11->display, dsi_x11->drawable, gc, 10*i, 5, 90, 90); } XSetForeground(dsi_x11->display, gc, 155); XDrawImageString(dsi_x11->display, dsi_x11->drawable, gc, 100, 110, testString, strlen(testString)); XFreeGC(dsi_x11->display, gc); /* Free the drawing surface info */ ds->FreeDrawingSurfaceInfo(dsi); /* Unlock the drawing surface */ ds->Unlock(ds); /* Free the drawing surface */ awt.FreeDrawingSurface(ds); }
The key data structure here is JAWT , which is defined in jawt.h (included by jawt_md.h) ; it provides access to all the information the native code needs to get the job done. The first part of the native method is boilerplate: it populates the JAWT structure, gets a JAWT_DrawingSurface structure, locks the surface (only one drawing engine at a time, please!), then gets a JAWT_DrawingSurfaceInfo structure that contains a pointer (in the platformInfo field) to the necessary platform-specific drawing information. It also includes the bounding rectangle of the drawing surface and the current clipping region.
The structure of the information pointed to by platformInfo is defined in a machine-dependent header file called jawt_md.h. For Solaris/X11 drawing, it includes information about the X11 display and X11 drawable associated with MyCanvas. After the drawing operations are completed, there is more boilerplate code as JAWT_DrawingSurfaceInfo is freed and JAWT_DrawingSurface is unlocked and freed.
The corresponding code for the Microsoft Windows platform would be structured similarly, but would include the version of jawt_md.h for Microsoft Windows and the structure located in the platformInfo field of drawing surface info would be cast as a JAWT_Win32DrawingSurfaceInfo* . And, of course, the actual drawing operations would need to be changed to those appropriate for the Microsoft Windows platform.
Summary
The ability to draw directly into a Java Canvas from a native code library is extremely useful for developers planning to migrate a legacy software system to Java, especially one that includes a high-performance rendering engine. It makes it much easier to migrate in stages, leaving performance-sensitive rendering code alone, while other less-sensitive portions of code are converted to Java. The result can be a modern Java-centric application, providing the benefit of portability and development efficiency, but one that does not sacrifice an investment in performance of a key piece of native code.
References
The definitive reference to the Java Native Interface is The Java Native Interface: Programmer's Guide and Specification by Sheng Liang. This book was published in June 1999 by Addison-Wesley. The ISBN is 0-201-32577-2.
Appendix
Header Files for jawt.h and jawt_md.h
jawt.h
#ifndef _JAVASOFT_JAWT_H_ #define _JAVASOFT_JAWT_H_ #include "jni.h" #ifdef __cplusplus extern "C" { #endif /* * AWT native interface * * The AWT native interface allows a native C or C++ application a means * by which to access native structures in AWT. This is to facilitate moving * legacy C and C++ applications to Java and to target the needs of the * community who, at present, wish to do their own native rendering to canvases * for performance reasons. Standard extensions such as Java3D also require a * means to access the underlying native data structures of AWT. * * There may be future extensions to this API depending on demand. * * A VM does not have to implement this API in order to pass the JCK. * It is recommended, however, that this API is implemented on VMs that support * standard extensions, such as Java3D. * * Since this is a native API, any program which uses it cannot be considered * 100% pure java. */ /* * AWT Native Drawing Surface (JAWT_DrawingSurface). * * For each platform, there is a native drawing surface structure. This * platform-specific structure can be found in jawt_md.h. It is recommended * that additional platforms follow the same model. It is also recommended * that VMs on Microsoft Windows platforms and Solaris operating environment * support the existing structures in jawt_md.h. * ******************* * EXAMPLE OF USAGE: ******************* * * In Miscrosoft Windows, a programmer wishes to access the HWND of a canvas * to perform native rendering into it. The programmer has declared the * paint() method for their canvas subclass to be native: * * * MyCanvas.java: * * import java.awt.*; * * public class MyCanvas extends Canvas { * * static { * System.loadLibrary("mylib"); * } * * public native void paint(Graphics g); * } * * * myfile.c: * * #include "jawt_md.h" * #include <assert.h> * * JNIEXPORT void JNICALL * Java_MyCanvas_paint(JNIEnv* env, jobject canvas, jobject graphics) * { * JAWT awt; * JAWT_DrawingSurface* ds; * JAWT_DrawingSurfaceInfo* dsi; * JAWT_Win32DrawingSurfaceInfo* dsi_win; * jboolean result; * jint lock; * * // Get the AWT * awt.version = JAWT_VERSION_1_3; * result = JAWT_GetAWT(env, &awt); * assert(result != JNI_FALSE); * * // Get the drawing surface * ds = awt.GetDrawingSurface(env, canvas); * assert(ds != NULL); * * // Lock the drawing surface * lock = ds->Lock(ds); * assert((lock & JAWT_LOCK_ERROR) == 0); * * // Get the drawing surface info * dsi = ds->GetDrawingSurfaceInfo(ds); * * // Get the platform-specific drawing info * dsi_win = (JAWT_Win32DrawingSurfaceInfo*)dsi->platformInfo; * * ////////////////////////////// * // !!! DO PAINTING HERE !!! // * ////////////////////////////// * * // Free the drawing surface info * ds->FreeDrawingSurfaceInfo(dsi); * * // Unlock the drawing surface * ds->Unlock(ds); * * // Free the drawing surface * awt.FreeDrawingSurface(ds); * } * */ /* * JAWT_Rectangle * Structure for a native rectangle. */ typedef struct jawt_Rectangle { jint x; jint y; jint width; jint height; } JAWT_Rectangle; struct jawt_DrawingSurface; /* * JAWT_DrawingSurfaceInfo * Structure for containing the underlying drawing information of a component. */ typedef struct jawt_DrawingSurfaceInfo { /* * Pointer to the platform-specific information. This can be safely * cast to a JAWT_Win32DrawingSurfaceInfo on Microsoft Windows or a * JAWT_X11DrawingSurfaceInfo on Solaris operating environment. * See jawt_md.h for details. */ void* platformInfo; /* Cached pointer to the underlying drawing surface */ struct jawt_DrawingSurface* ds; /* Bounding rectangle of the drawing surface */ JAWT_Rectangle bounds; /* Number of rectangles in the clip */ jint clipSize; /* Clip rectangle array */ JAWT_Rectangle* clip; } JAWT_DrawingSurfaceInfo; #define JAWT_LOCK_ERROR 0x00000001 #define JAWT_LOCK_CLIP_CHANGED 0x00000002 #define JAWT_LOCK_BOUNDS_CHANGED 0x00000004 #define JAWT_LOCK_SURFACE_CHANGED 0x00000008 /* * JAWT_DrawingSurface * Structure for containing the underlying drawing information of a component. * All operations on a JAWT_DrawingSurface MUST be performed from the same * thread as the call to GetDrawingSurface. */ typedef struct jawt_DrawingSurface { /* Cached reference to the Java environment of the calling thread */ JNIEnv* env; /* Cached reference to the target object */ jobject target; /* * Lock the surface of the target component for native rendering. * When finished drawing, the surface must be unlocked with * Unlock(). This function returns a bitmask with one or more of the * following values: * * JAWT_LOCK_ERROR - When an error has occurred and the surface could not * be locked. * * JAWT_LOCK_CLIP_CHANGED - When the clip region has changed. * * JAWT_LOCK_BOUNDS_CHANGED - When the bounds of the surface have changed. * * JAWT_LOCK_SURFACE_CHANGED - When the surface itself has changed */ jint (JNICALL *Lock) (struct jawt_DrawingSurface* ds); /* * Get the drawing surface info. * The value returned may be cached, but the values may change if * additional calls to Lock() or Unlock() are made. * Lock() must be called before this can return a valid value. * Returns NULL if an error has occurred. * When finished with the returned value, FreeDrawingSurfaceInfo must be * called. */ JAWT_DrawingSurfaceInfo* (JNICALL *GetDrawingSurfaceInfo) (struct jawt_DrawingSurface* ds); /* * Free the drawing surface info. */ void (JNICALL *FreeDrawingSurfaceInfo) (JAWT_DrawingSurfaceInfo* dsi); /* * Unlock the drawing surface of the target component for native rendering. */ void (JNICALL *Unlock) (struct jawt_DrawingSurface* ds); } JAWT_DrawingSurface; /* * JAWT * Structure for containing native AWT functions. */ typedef struct jawt { /* * Version of this structure. This must always be set before * calling JAWT_GetAWT() */ jint version; /* * Return a drawing surface from a target jobject. This value * may be cached. * Returns NULL if an error has occurred. * Target must be a java.awt.Canvas. * FreeDrawingSurface() must be called when finished with the * returned JAWT_DrawingSurface. */ JAWT_DrawingSurface* (JNICALL *GetDrawingSurface) (JNIEnv* env, jobject target); /* * Free the drawing surface allocated in GetDrawingSurface. */ void (JNICALL *FreeDrawingSurface) (JAWT_DrawingSurface* ds); } JAWT; /* * Get the AWT native structure. This function returns JNI_FALSE if * an error occurs. */ _JNI_IMPORT_OR_EXPORT_ jboolean JNICALL JAWT_GetAWT(JNIEnv* env, JAWT* awt); #define JAWT_VERSION_1_3 0x00010003 #ifdef __cplusplus } /* extern "C" */ #endif #endif /* !_JAVASOFT_JAWT_H_ */
jawt_md.h (Solaris/X11 operating environment version)
#ifndef _JAVASOFT_JAWT_MD_H_ #define _JAVASOFT_JAWT_MD_H_ #include <X11/Xlib.h> #include <X11/Xutil.h> #include <X11/Intrinsic.h> #include "jawt.h" #ifdef __cplusplus extern "C" { #endif /* * X11-specific declarations for AWT native interface. * See notes in jawt.h for an example of use. */ typedef struct jawt_X11DrawingSurfaceInfo { Drawable drawable; Display* display; VisualID visualID; Colormap colormapID; int depth; } JAWT_X11DrawingSurfaceInfo; #ifdef __cplusplus } #endif #endif /* !_JAVASOFT_JAWT_MD_H_ */
jawt_md.h (Microsoft Windows version)
#ifndef _JAVASOFT_JAWT_MD_H_ #define _JAVASOFT_JAWT_MD_H_ #include <windows.h> #include "jawt.h" #ifdef __cplusplus extern "C" { #endif /* * Microsoft Windows specific declarations for AWT native interface. * See notes in jawt.h for an example of use. */ typedef struct jawt_Win32DrawingSurfaceInfo { /* Native window, DDB, or DIB handle */ union { HWND hwnd; HBITMAP hbitmap; void* pbits; }; /* * This HDC should always be used instead of the HDC returned from * BeginPaint() or any calls to GetDC(). */ HDC hdc; HPALETTE hpalette; } JAWT_Win32DrawingSurfaceInfo; #ifdef __cplusplus } #endif #endif /* !_JAVASOFT_JAWT_MD_H_ */


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QuickFIX License
The QuickFIX Software License,
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Copyright (c) 2001-2014 Oren Miller
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2020 Mercedes EQC Pricing Announced, Cheaper Than Audi E-Tron
The EQC is also more affordable than the Jaguar I-Pace.
by Adrian PadeanuAs it’s generally the case with German cars, the Mercedes EQC is fashionably late to the U.S. market where it’s scheduled to go on sale early next year. We reviewed the first electric Mercedes to be sold under the EQ brand back in May when it had an estimated starting price of $75,000, but it turns out the EQC is more attainable than initially thought.
At the 2019 Los Angeles Auto Show, Mercedes is announcing the 2020 EQC 400 4Matic carries a starting price of $67,900 MSRP, excluding the $995 destination and delivery charge. It means the zero-emissions crossover undercuts both the $74,800 Audi E-Tron and the $69,500 Jaguar I-Pace. As for the Model X, Tesla dropped the standard version a few months ago and that means pricing currently kicks off at $84,990 for the Long Range model.
Gallery: Mercedes-Benz EQC 4MATIC
As a refresher, the EQC is powered by two electric motors giving it a combined output of 402 horsepower and 561 pound-feet of torque, which is more than the 355 hp and 414 lb-ft offered by the Audi. That said, the E-Tron does have a boost mode raising output to an EQC-matching 402 hp and 490 lb-ft. It’s also more powerful than the I-Pace as Jaguar has rated its electric crossover at 394 hp and 512 lb-ft.
The all-wheel-drive EQC does 0 to 60 mph in 4.8 seconds and tops out at an electronically capped 112 mph. Range is still TBA for the time being, but we do know the 80-kWh lithium-ion battery pack needs 10 hours to get from 10% to 100% from a wallbox (240V/32A) and 40 minutes from 10% to 80% using DC fast charging (110kW).
It's Going Up Against These:
It’s worth mentioning the EQC will also have to do battle with the BMW iX3 scheduled for a release next year as a fully electric version of the X3. At the ongoing show in L.A., Audi introduced a sleeker version of its E-Tron carrying the Sportback suffix.
As a final note, Mercedes unveiled an EQC Edition 1886 with several cosmetic tweaks at the New York Auto Show earlier this year, but pricing details for the limited-run version are not available at the moment of writing.


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src/components/device/factory.ts
Methods |
|
| Static Async getDevice | |||||||||||||||||||||||||||||||||||
getDevice(productId: number, logger: DefaultLogger, preferredDeviceApi: IHuddlyDeviceAPI, secondaryDeviceApis: Array
|
|||||||||||||||||||||||||||||||||||
|
Defined in src/components/device/factory.ts:155
|
|||||||||||||||||||||||||||||||||||
|
Function that selects the appropriate transport implementation and uvc control interface and returns a concrete implementation of the IDeviceManager
Parameters :
Returns :
Promise<IDeviceManager>
Returns a concrete implementation of the IDeviceManger |
| Static Async getDeviceUpgrader | ||||||||||||||||||||||||
getDeviceUpgrader(cameraManager: IDeviceManager, cameraDiscoveryEmitter: EventEmitter, preferredDeviceApi: IHuddlyDeviceAPI, secondaryDeviceApis: Array
|
||||||||||||||||||||||||
|
Defined in src/components/device/factory.ts:124
|
||||||||||||||||||||||||
|
Function that returns an upgrader object for a given devInstance
Parameters :
Returns :
Promise<IDeviceUpgrader>
|
| Static Async getHIDInterface | ||||||||||||||||
getHIDInterface(device: any, preferredDeviceApi: IHuddlyDeviceAPI, secondaryDeviceApis: Array
|
||||||||||||||||
|
Defined in src/components/device/factory.ts:97
|
||||||||||||||||
|
Get a HID api implementation for the device
Parameters :
Returns :
Promise<any>
Returns the device class that is used for UVC controls |
| Static Async getTransportImplementation | ||||||||||||||||
getTransportImplementation(device: any, preferredDeviceApi: IHuddlyDeviceAPI, secondaryDeviceApis: Array
|
||||||||||||||||
|
Defined in src/components/device/factory.ts:31
|
||||||||||||||||
|
Get a concrete transport implementation on the IHuddlyDeviceAPI
Parameters :
Returns :
Promise<ITransport>
Returns the transport implementation for the device used to talk to the device |
| Static Async getUVCControlInterface | ||||||||||||||||
getUVCControlInterface(device: any, preferredDeviceApi: IHuddlyDeviceAPI, secondaryDeviceApis: Array
|
||||||||||||||||
|
Defined in src/components/device/factory.ts:63
|
||||||||||||||||
|
Get an instance of the device which can be used to issue standard UVC control commands
Parameters :
Returns :
Promise<any>
Returns the device class that is used for UVC controls |
import ITransport from './../../interfaces/iTransport';
import IHuddlyDeviceAPI from './../../interfaces/iHuddlyDeviceAPI';
import DefaultLogger from './../../utilitis/logger';
import IDeviceManager from './../../interfaces/iDeviceManager';
import HuddlyGo from './huddlygo';
import Boxfish from './boxfish';
import { EventEmitter } from 'events';
import HuddlyGoUpgrader from './../upgrader/huddlygoUpgrader';
import BoxfishUpgrader from './../upgrader/boxfishUpgrader';
import IDeviceUpgrader from './../../interfaces/IDeviceUpgrader';
export const HUDDLY_GO_PID = 0x11;
export const HUDDLY_BOXFISH_PID = 0x21;
export default class DeviceFactory {
/**
* Get a concrete transport implementation on the IHuddlyDeviceAPI
*
* @static
* @param {*} device An object that reperesents the usb device which is discovered
* on the concrete implementation of the IHuddlyDeviceAPI
* @param {IHuddlyDeviceAPI} preferredDeviceApi The main IHuddlyDeviceAPI used for communicating
* with the camera
* @param {Array<IHuddlyDeviceAPI>} secondaryDeviceApis Fallback IHuddlyDeviceAPI-s in case the
* main interface does not work
* @returns {Promise<ITransportable>} Returns the transport implementation for the device used to talk
* to the device
* @memberof DeviceFactory
*/
static async getTransportImplementation(
device: any,
preferredDeviceApi: IHuddlyDeviceAPI,
secondaryDeviceApis: Array<IHuddlyDeviceAPI>): Promise<ITransport> {
const mainTransport = await preferredDeviceApi.getValidatedTransport(device);
if (mainTransport) {
return mainTransport;
}
for (const deviceApi of secondaryDeviceApis) {
const fallbackTransport = await deviceApi.getValidatedTransport(device);
if (fallbackTransport) {
return fallbackTransport;
}
}
throw new Error(`Unable to find appropriate transport implementation for device: ${JSON.stringify(device)}`);
}
/**
* Get an instance of the device which can be used to issue standard UVC control commands
*
* @static
* @param {*} device An object that represents the usb device which is discovered
* on the concrete implementation of the IHuddlyDeviceApi
* @param {IHuddlyDeviceAPI} preferredDeviceApi The main IHuddlyDeviceAPI used for communicating
* with the camera
* @param {Array<IHuddlyDeviceAPI>} secondaryDeviceApis Fallback IHuddlyDeviceAPI-s in case the
* main interface does not work
* @returns {Promise<any>} Returns the device class that is used for UVC controls
* @memberof DeviceFactory
*/
static async getUVCControlInterface(
device: any,
preferredDeviceApi: IHuddlyDeviceAPI,
secondaryDeviceApis: Array<IHuddlyDeviceAPI>): Promise<any> {
if (await preferredDeviceApi.isUVCControlsSupported(device)) {
return preferredDeviceApi.getUVCControlAPIForDevice(device);
}
for (const deviceApi of secondaryDeviceApis) {
if (await deviceApi.isUVCControlsSupported(device)) {
return deviceApi.getUVCControlAPIForDevice(device);
}
}
// throw new Error(`Unable to find appropriate uvc control api for ${device}`);
// TODO: Log a warning that none of device api-s support uvc control interface
// Boxfish.ts should handle to not use uvc commands when device-api is usb
return undefined;
}
/**
* Get a HID api implementation for the device
*
* @static
* @param {*} device An object that represents the usb device which is discovered
* on the concrete implementation of the IHuddlyDeviceApi
* @param {IHuddlyDeviceAPI} preferredDeviceApi The main IHuddlyDeviceAPI used for communicating
* with the camera
* @param {Array<IHuddlyDeviceAPI>} secondaryDeviceApis Fallback IHuddlyDeviceAPI-s in case the
* main interface does not work
* @returns {Promise<any>} Returns the device class that is used for UVC controls
* @memberof DeviceFactory
*/
static async getHIDInterface(
device: any,
preferredDeviceApi: IHuddlyDeviceAPI,
secondaryDeviceApis: Array<IHuddlyDeviceAPI>): Promise<any> {
if (await preferredDeviceApi.isHIDSupported(device)) {
return preferredDeviceApi.getHIDAPIForDevice(device);
}
for (const deviceApi of secondaryDeviceApis) {
if (await deviceApi.isHIDSupported(device)) {
return deviceApi.getHIDAPIForDevice(device);
}
}
throw new Error(`Unable to find appropriate HID interface for device: ${JSON.stringify(device)}`);
}
/**
* Function that returns an upgrader object for a given devInstance
*
* @param devInstance The device instance retrived from IHuddlyDeviceAPI
* @param cameraDiscoveryEmitter The eventEmitter that will allow the upgrader object to see attach/detach events
* @param preferredDeviceApi The main IHuddlyDeviceAPI used for communicating
* with the camera
* @param secondaryDeviceApis Fallback IHuddlyDeviceAPI-s in case the
* main interface does not work
* @param logger Logger
*/
static async getDeviceUpgrader(
cameraManager: IDeviceManager,
cameraDiscoveryEmitter: EventEmitter,
preferredDeviceApi: IHuddlyDeviceAPI,
secondaryDeviceApis: Array<IHuddlyDeviceAPI>,
logger: any): Promise<IDeviceUpgrader> {
if (cameraManager instanceof HuddlyGo) {
const hidApi = await this.getHIDInterface(cameraManager, preferredDeviceApi, secondaryDeviceApis);
return new HuddlyGoUpgrader(cameraManager, cameraDiscoveryEmitter, hidApi, logger);
} else if (cameraManager instanceof Boxfish) {
return new BoxfishUpgrader(cameraManager, cameraDiscoveryEmitter, logger);
}
throw new Error('Upgrader not implemented');
}
/**
* Function that selects the appropriate transport implementation and uvc control interface
* and returns a concrete implementation of the IDeviceManager
*
* @static
* @param {number} productId A usb device product id to distinct betweern different huddly products
* @param {DefaultLogger} logger The logger class used for logging messages on console
* @param {IHuddlyDeviceAPI} preferredDeviceApi The main IHuddlyDeviceAPI used for communicating
* with the camera
* @param {Array<IHuddlyDeviceAPI>} secondaryDeviceApis Fallback IHuddlyDeviceAPI-s in case the
* main interface does not work
* @param {*} devInstance An object that represents the usb device which is discovered
* on the concrete implementation of the IHuddlyDeviceApi
* @returns {Promise<IDeviceManager>} Returns a concrete implementation of the IDeviceManger
* @memberof DeviceFactory
*/
static async getDevice(
productId: number,
logger: DefaultLogger,
preferredDeviceApi: IHuddlyDeviceAPI,
secondaryDeviceApis: Array<IHuddlyDeviceAPI>,
devInstance: any,
doInitialize: boolean = true): Promise<IDeviceManager> {
const transport = await this.getTransportImplementation(
devInstance,
preferredDeviceApi,
secondaryDeviceApis);
const uvcControlInterface = await this.getUVCControlInterface(
devInstance,
preferredDeviceApi,
secondaryDeviceApis);
let device;
switch (productId) {
case HUDDLY_GO_PID:
// const hidInterface = await this.getHIDInterface(devInstance, preferredDeviceApi, secondaryDeviceApis);
device = new HuddlyGo(devInstance, transport, uvcControlInterface, logger);
break;
case HUDDLY_BOXFISH_PID:
device = new Boxfish(devInstance, transport, uvcControlInterface, logger);
break;
default:
throw new Error(`Unsupported Device. USB ProductId: ${devInstance.deviceDescriptor.idProduct}`);
}
if (doInitialize) await device.initialize();
return device;
}
}


IPTABLES-XML
Section: (8)Updated: Jul 16, 2007
Index Return to Main Contents
NAME
iptables-xml --- Convert iptables-save format to XMLSYNOPSIS
iptables-xml [-c] [-v]DESCRIPTION
iptables-xml is used to convert the output of iptables-save into an easily manipulatable XML format to STDOUT. Use I/O-redirection provided by your shell to write to a file.
- -c, --combine
- combine consecutive rules with the same matches but different targets. iptables does not currently support more than one target per match, so this simulates that by collecting the targets from consecutive iptables rules into one action tag, but only when the rule matches are identical. Terminating actions like RETURN, DROP, ACCEPT and QUEUE are not combined with subsequent targets.
- -v, --verbose
-
Output xml comments containing the iptables line from which the XML is derived
iptables-xml does a mechanistic conversion to a very expressive xml format; the only semantic considerations are for -g and -j targets in order to discriminate between <call> <goto> and <nane-of-target> as it helps xml processing scripts if they can tell the difference between a target like SNAT and another chain.
Some sample output is:
<iptables-rules>

<chain name="PREROUTING" policy="ACCEPT" packet-count="63436"
byte-count="7137573">
<rule>
<conditions>
<match>

tcp

</match>
<tcp>
<sport>8443</sport>
</tcp>
</conditions>
<actions>
<call>
<check_ip/>
</call>
<ACCEPT/>
</actions>
</rule>
</chain>

</iptables-rules>
Conversion from XML to iptables-save format may be done using the iptables.xslt script and xsltproc, or a custom program using libxsltproc or similar; in this fashion:
xsltproc iptables.xslt my-iptables.xml | iptables-restore
BUGS
None known as of iptables-1.3.7 releaseAUTHOR
Sam Liddicott<EMAIL_ADDRESS>ALSO
iptables-save(8), iptables-restore(8), iptables(8)
Index
This document was created by man2html, using the manual pages.
Time: 05:34:26 GMT, December 24, 2015


If you have any question about us,Don't hestitate to contact us!
<EMAIL_ADDRESS>TAIWAN, TAIPEI , XORO RoadPhone: 02-12345678


Part Collection
Note
In order to be considered for the Best Part Collection award, you must fill out this page.
Did your team make a lot of great parts? Is there a theme that ties all your parts together? Do you have more than 10 parts in this collection? Did you make a CRISPR collection, a MoClo collection, or a collection of awesome pigment parts? Describe your parts collection on this page, so the judges can evaluate you for the Best Part Collection award.


{% extends "group_container_base.html" %}
{% block container-content %}
{% if res.status %}
You've turned moderation {{ type }} for the sender {{ sender }} in thread "{{ subject }}".
{% if type == 'on' %}
All future emails from this sender to this thread will undergo moderation.
To turn moderation off for {{ sender }}'s future emails to "{{ subject }}",
click here.
{% else %}
All future emails from this sender to this thread will be automatically approved.
To turn moderation back on for {{ sender }}'s future emails to "{{ subject }}",
click here.
{% endif %}
{% else %}
Sorry, there was an error: {{ res.code | lower }}.
{% endif %}
{% endblock %}


Margins, Borders, and Padding
What's the difference?
March 29th, 2015
When thinking about the layout of a website and it's elements, it is important to understand the concept and differences of Margins, Borders and Padding. To understand these terms, we must first recognize that every individual object on a page is represented as an element. The elements most often appear as paragraphs, headings, images, or links. HTML classifies these elements so that a web browser knows how to display them on a user's computer. Now imagine each element is surrounded by an imaginary box that is just big enough to hold it's contents. How then do we move these boxes around or change the size of them so that they are closer to or farther from other elements on a page? We use Margins, Borders and Padding.
Borders
Every element is surrounded by a border, even if it is invisible.  You are able to style a border with different sizes, line styles, and colors. See below how I used a border to surround my nav bar "Home" menu item.
Padding


The owner of a jewelry store is trying to install a security system that could prevent intruders from stealing his goods. The system needs to accurately detect the intruder by 1: sense change in temperature; 2: detect the obstructions in glass windows or doors. 3: sense the gas concentration
a ) Please list sensors what will help to build this security system[3]
b) Provided that the system is controlled by a micro-processor please state briefly, what is the function of micro-processor[2]
In pursuit of a more futuristic design, the owner wants his office door to recognize him and automatically opens for him without the key.
c) Explain how he could do that, what material he requires, and further explain why your design is both secure and user friendly.[5]
Discussions


Students developing test plans should be aware of the equipment stocked in the PSPE laboratory. These datasheets are a good resource for selecting equipment that meets the parameters and resolution of the test to be performed. As new equipment is added so too will the information.
To return to the main menu click here.
Blockquotes (grey box below this sentence) will give helpful hints and information about certain sections. Not all blockquotes will have citations.
Datasheets from Hewlett Packard, Keysight, and Agilent will be the same. Check the citation to learn more.
For students performing automated tests the unicode symbol, "⏏", denotes equipment with an IEEE 488 GPIB port.
The current probe can only measure current running through a wire. While taking measurements periodically hit the "Demagnetize" button to purge built up field.
Attached to the Frequency Response Analyzer is a Model 200-002 Bode Box from Venable Instruments. Located on Bench 5.
The PSPE program has 4 distinct PXI chassis and certain PXI cards are cross-compatible. Refer to the list and table below for chassis specifics, card datasheets, and cross-compatibility.
| Model | Card Type | Description | PXI-1000B Chassis | PXI-1042 Chassis | PXI-1042Q Chassis | PXIe-1075 Chassis |
|---|---|---|---|---|---|---|
| PXI-8187 | PXI 1000B Card | Embedded Controller | ✓ | |||
| PXI-6025E | PXI 1000B Card | Multifunction I/O | ✓ | |||
| PXI-6071E | PXI 1000B Card | Multifunction I/O | ✓ | |||
| PXI-6031E | PXI 1000B Card | Multifunction I/O | ✓ | |||
| PXI-6528 | PXI 1000B Card | Isolated Digital I/O | ✓ | |||
| PXI-6259 | PXI 1000B Card | M Series Multifunction DAQ | ✓ | |||
| PXI-7831R | PXI 1000B Card | Reconfigurable I/O | ✓ | |||
| PXI-6723 | PXI 1000B Card | 32 Channel AO, 13 Bit | ✓ | |||
| PXI-8196 | PXI 1042 Card | Embedded Controller (1) | ✓ | ✓ | 8 slots | |
| PXI-8196 | PXI 1042 Card | Embedded Controller (2) | ✓ | ✓ | 8 slots | |
| PXI-5600 | PXI 1042 Card | RF Downconverter | ✓ | ✓ | 8 slots | |
| PXI-5610 | PXI 1042 Card | RF Upconverter * | ✓ | ✓ | 8 slots | |
| PXI-5421 | PXI 1042 Card | 16-Bit 100 MS/s AWG | ✓ | ✓ | 8 slots | |
| PXI-5620 | PXI 1042 Card | 64 MS/s Digitizer | ✓ | ✓ | 8 slots | |
| PXI-6552 | PXI 1042 Card | 100 MHZ Digital I/O | ✓ | ✓ | 8 slots | |
| PXI-2515 | PXI 1042 Card | High Speed Digital Signal Insertion Switch | ✓ | ✓ | 8 slots | |
| PXI-8464 | PXI 1042 Card | CAN/XS Series 2 | ✓ | ✓ | 8 slots | |
| PXI-2567 | PXI 1042 Card | 64 Channel Relay Driver Module | ✓ | ✓ | 8 slots | |
| PXI-2568 | PXI 1042 Card | 31 SPST Relay Module | ✓ | ✓ | 8 slots | |
| PXI-6132 | PXI 1042 Card | S Series 4Ch, 14 bit, 2.5 MS/s Multifunctional DAQ | ✓ | ✓ | 8 slots | |
| PXI-6133 | PXI 1042 Card | 16-Bit Analog Output | ✓ | ✓ | 8 slots | |
| PXI-4130 | PXI 1042 Card | Power SMU | ✓ | ✓ | 8 slots | |
| PXIe-8133 | PXIe card | Embedded Controller | ✓ | |||
| PXIe-4132 | PXIe card | Precision SMU | ✓ | |||
| PXIe-4141 | PXIe card | 4-CH Precision SMU | ✓ |
* The PXI-5610 RF Upconverter datasheet has not been found.
The National Instruments Semiconductor Testing System (STS) houses a PXIe-1078 outfitted with the following cards:
| Model | Card Type | Description |
|---|---|---|
| PXIe-4140 | PXIe Card | 4-CH Precision SMU |
| PXIe-6556 | PXIe Card | Digital Waveform Generator/Analyzer with PPMU |
| PXIe-2567 | PXI Card | 64 Pin Relay Driver |
The STS is located on Bench 6
It is highly recommended to anyone who would like to use the STS to read through Joanna Gatlin's Thesis.


Built through the MiT xPRO - Web Development with JavaScript program. For more details, please click the "play" button in the "About" section.
My name is Marinela Togoe.
I am an enthusiastic young professional with an open-minded vision for creativity and developing new skills. Through out the course I've achieved learning three major programming languages for the world wide web and the foundation stones into becoming a web developer.
Through this coding project a pacman is created on demand, every time the "Add PacMan" is clicked. It is moved at a random velocity across the screen. As the name of the exercise suggests, by clicking the button an infinite number of pacmen is created.
GitHub RepositoryIn this exercise, when the mouse movement event is used the position of the “eyes” elements is changing.
GitHub RepositoryAn animated marker is created and added on a map by clicking the main button displayed on the screen. It is used to highlight the bus routes between MiT Institute of Technology and Harvard.
GitHub Repository


DOMMemoryManager
interface exposes the memory allocation-related functionalities of a DOMDocument
.
More...
Public Types | |
| enum | NodeObjectType { ATTR_OBJECT = 0, ATTR_NS_OBJECT = 1, CDATA_SECTION_OBJECT = 2, COMMENT_OBJECT = 3, DOCUMENT_FRAGMENT_OBJECT = 4, DOCUMENT_TYPE_OBJECT = 5, ELEMENT_OBJECT = 6, ELEMENT_NS_OBJECT = 7, ENTITY_OBJECT = 8, ENTITY_REFERENCE_OBJECT = 9, NOTATION_OBJECT = 10, PROCESSING_INSTRUCTION_OBJECT = 11, TEXT_OBJECT = 12 } |
Public Member Functions | |
Destructor | |
| virtual | ~DOMMemoryManager () |
| Destructor. | |
| virtual XMLSize_t | getMemoryAllocationBlockSize () const =0 |
| Returns the size of the chunks of memory allocated by the memory manager. | |
| virtual void | setMemoryAllocationBlockSize (XMLSize_t size)=0 |
| Set the size of the chunks of memory allocated by the memory manager. | |
| virtual void * | allocate (XMLSize_t amount)=0 |
| Allocate a memory block of the requested size from the managed pool. | |
| virtual void * | allocate (XMLSize_t amount, DOMMemoryManager::NodeObjectType type)=0 |
| Allocate a memory block of the requested size from the managed pool of DOM objects. | |
| virtual void | release (DOMNode *object, DOMMemoryManager::NodeObjectType type)=0 |
| Release a DOM object and place its memory back in the pool. | |
| virtual XMLCh * | cloneString (const XMLCh *src)=0 |
| Allocate a memory block from the mnaged pool and copy the provided string. | |
Protected Member Functions | |
Hidden constructors | |
| DOMMemoryManager () |
DOMMemoryManager
interface exposes the memory allocation-related functionalities of a DOMDocument
.
| DOMMemoryManager::DOMMemoryManager | ( | ) | [protected] |
| virtual DOMMemoryManager::~DOMMemoryManager | ( | ) | [virtual] |
Destructor.
| virtual XMLSize_t DOMMemoryManager::getMemoryAllocationBlockSize | ( | ) | const [pure virtual] |
Returns the size of the chunks of memory allocated by the memory manager.
| virtual void DOMMemoryManager::setMemoryAllocationBlockSize | ( | XMLSize_t | size | ) | [pure virtual] |
Set the size of the chunks of memory allocated by the memory manager.
| size | the new size of the chunks; it must be greater than 4KB |
| virtual void* DOMMemoryManager::allocate | ( | XMLSize_t | amount | ) | [pure virtual] |
Allocate a memory block of the requested size from the managed pool.
| amount | the size of the new memory block |
| virtual void* DOMMemoryManager::allocate | ( | XMLSize_t | amount, | |
| DOMMemoryManager::NodeObjectType | type | |||
| ) | [pure virtual] |
Allocate a memory block of the requested size from the managed pool of DOM objects.
| amount | the size of the new memory block | |
| type | the type of the DOM object that will be stored in the block |
| virtual void DOMMemoryManager::release | ( | DOMNode * | object, | |
| DOMMemoryManager::NodeObjectType | type | |||
| ) | [pure virtual] |
Release a DOM object and place its memory back in the pool.
| object | the pointer to the DOM node | |
| type | the type of the DOM object |
Allocate a memory block from the mnaged pool and copy the provided string.
| src | the string to be copied |


| lesson 11 Not guilty |
| Customs Officers are quite tolerant these days, |
| but they can still stop you when you are going through the Green Channel and have nothing to declare. |
| Even really honest people are often made to feel guilty. |
| The hardened professional smuggler, on the other hand, is never troubled by such feelings, even if he has five hundred gold watches hidden in his suitcase. |
| When I returned from abroad recently, a particularly officious young Customs Officer clearly regarded me as a smuggler. |
| 'Have you anything to declare? ' he asked, looking me in the eye. ^ |
| 'No, ' I answered confidently. ^ |
| 'Would you mind unlocking this suitcase please? '^ |
| 'Not at all, ' I answered. ^ |
| The Officer went through the case with great care. |
| All the things I had packed so carefully were soon in a dreadful mess. |
| I felt sure I would never be able to close the case again. |
| Suddenly, I saw the Officer's face light up. |
| He had spotted a tiny bottle at the bottom of my case and he pounced on it with delight. |
| 'Perfume, eh? ' he asked sarcastically. ^ |
| 'You should have declared that. Perfume is not exempt from import duty. '^ |
| 'But it isn't perfume, ' I said. ^ |
| 'It's hair gel. ' Then I added with a smile, ^ |
| 'It's a strange mixture I make myself. '^ |
| As I expected, he did not believe me. |
| 'Try it! ' I said encouragingly. ^ |
| The officer unscrewed the cap and put the bottle to his nostrils. |
| He was greeted by an unpleasant smell which convinced him that I was telling the truth. |
| A few minutes later, I was able to hurry away with precious chalk marks on my baggage. |


Status Channels lists channels that monitor the state and condition of various aspects of the system.
What do you want to do?
View status channel descriptions
To view a status channel description, select the status channel name in the configuration tree. The Status Channel Configuration page appears to the right of the configuration tree.
Profile Count is the number of unique profiles specified in the Interpolation File. Profile count can be used to check if the same number of profiles have been defined for multiple instances of the Profile Interpolation add-on.
Table Length is the total number of rows in the profile table including the header row. Table Length can be used to determine if points or profiles have been added.


class |
AssertionException |
Created by Jeff Gaynor
on 6/1/21 at 12:57 PM
|
class |
BadArgException |
Thrown when the argument to a function is not an accepted type.
|
class |
BreakException |
Used in loops to signal a break command since it is almost impossible to hop out of a complex
expression tree and do the right flow
|
class |
ContinueException |
Used in loops to signal a continue command since it is almost impossible to hop out of a complex
expression tree and do the right flow
|
class |
DeserializationException |
This should only be thrown during deserialization if there is an actual problem
with the deserialization of on otherwise valid file, e.g.
|
class |
ExtraArgException |
Created by Jeff Gaynor
on 4/17/22 at 6:06 AM
|
class |
FunctionArgException |
Created by Jeff Gaynor
on 4/17/22 at 6:05 AM
|
class |
ImportException |
Created by Jeff Gaynor
on 1/21/20 at 3:39 PM
|
class |
IndexError |
Created by Jeff Gaynor
on 2/2/20 at 9:36 AM
|
class |
InterruptException |
Created by Jeff Gaynor
on 10/25/20 at 2:35 PM
|
class |
IntrinsicViolation |
Created by Jeff Gaynor
on 9/26/21 at 6:54 AM
|
class |
MissingArgException |
Created by Jeff Gaynor
on 4/17/22 at 6:06 AM
|
class |
MissingArgumentException |
Created by Jeff Gaynor
on 1/26/20 at 6:27 AM
|
class |
ModuleInstantiationException |
|
class |
NamespaceException |
Created by Jeff Gaynor
on 1/21/20 at 3:42 PM
|
class |
ParsingException |
Created by Jeff Gaynor
on 1/29/20 at 3:00 PM
|
class |
QDLExceptionWithTrace |
An exception when evaluating a set of statements.
|
class |
QDLFileAccessException |
Created by Jeff Gaynor
on 8/29/22 at 2:41 PM
|
class |
QDLFileNotFoundException |
Created by Jeff Gaynor
on 8/29/22 at 2:39 PM
|
class |
QDLIllegalAccessException |
Created by Jeff Gaynor
on 1/25/21 at 10:16 AM
|
class |
QDLIOException |
Created by Jeff Gaynor
on 3/4/20 at 10:33 AM
|
class |
QDLRuntimeException |
Created by Jeff Gaynor
on 1/28/20 at 7:13 AM
|
class |
QDLServerModeException |
Created by Jeff Gaynor
on 2/5/20 at 9:32 AM
|
class |
RaiseErrorException |
Created by Jeff Gaynor
on 1/24/20 at 3:48 PM
|
class |
RankException |
Thrown when trying to access parts of stems that don't exist.
|
class |
RecursionException |
Created by Jeff Gaynor
on 1/24/20 at 6:24 AM
|
class |
ReturnException |
Created by Jeff Gaynor
on 1/22/20 at 1:14 PM
|
class |
UndefinedFunctionException |
Created by Jeff Gaynor
on 1/21/20 at 6:51 AM
|
class |
UnknownSymbolException |
Created by Jeff Gaynor
on 1/25/20 at 6:47 AM
|
class |
WrongArgCountException |
Created by Jeff Gaynor
on 4/17/22 at 5:41 PM
|


P.O. Box 650-00517 Nairobi Kenya
Tel: +254723768573 or +211 956 112 757
Email<EMAIL_ADDRESS>As an executive and seasoned professional with experience in handling administration and accounting, I possess the ability to blend into any environment in addition to being able to perform diverse responsibilities. Over the years, I have been able to acquire skills in a number of areas some of which include; administration management, finance, accounting, operations management and personnel management. Over the years, some of the roles I have been engaged in include; preparing and submitting field reports, handling forecasting cash flows and preparation of budgets, Supervision and management of a fleet vans and managing personnel. Presently, I am looking to secure a position that will enable me further explore my skills and expertise in improving structures and systems as an operations manager.
Description: A project about the electric cars designed towards zero emmission.
Technologies used: HTM, CSS and Bootstrap.
Project url: site link
Description: A project about a pet shelter that provide a safe haven for pets untill their adoption.
Technologies used: HTM, CSS and Bootstrap.
Project url: site link
Description: A project about Limau Resort, a welcoming, cosy and stylish hotel convinietly located at the base of mount Kenya and ensures privacy, quietness and tranquil environment.
Technologies used: HTM, CSS and Bootstrap.
Project url: site link
Dealt with foreign currency exchange and money transfer; Juba, South Sudan.
I joined Moringa School (MS) as part of self actualisation. Software development has been a passion of mine for years. MS gives me the opportunity to attain my dream. I came across MS while insearch of a coding bootcamp within Kenya. Online search and advise from a friend (in tech) informed my decision of joining MS. Am confident that at the end of my time at MS I will acqiure both technical and soft skills necessary in launching a sucessful career in programming.


shtmlview_mkdoc - shtmlview extension adding support for mkdoc markup
The shtmlview::mkdoc package extends shtmlview::shtmlview widgets with support for rendering of mkdoc-formatted documents.
Simply loading the package registers the support.
The package does not export any kind of command, class, etc.
Initial implementation
This document, and the package it describes, will undoubtedly contain bugs and other problems. Please report such to the author of this package. Please also report any ideas for enhancements you may have for either package and/or documentation.
BSD License type:
Sun Microsystems, Inc. The following terms apply to all files associated with the software unless explicitly disclaimed in individual files.
The authors hereby grant permission to use, copy, modify, distribute, and license this software and its documentation for any purpose, provided that existing copyright notices are retained in all copies and that this notice is included verbatim in any distributions. No written agreement, license, or royalty fee is required for any of the authorized uses. Modifications to this software may be copyrighted by their authors and need not follow the licensing terms described here, provided that the new terms are clearly indicated on the first page of each file where they apply.
In no event shall the authors or distributors be liable to any party for direct, indirect, special, incidental, or consequential damages arising out of the use of this software, its documentation, or any derivatives thereof, even if the authors have been advised of the possibility of such damage.
The authors and distributors specifically disclaim any warranties, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. This software is provided on an "as is" basis, and the authors and distributors have no obligation to provide maintenance, support, updates, enhancements, or modifications.
RESTRICTED RIGHTS: Use, duplication or disclosure by the government is subject to the restrictions as set forth in subparagraph (c) (1) (ii) of the Rights in Technical Data and Computer Software Clause as DFARS<PHONE_NUMBER> and FAR 52.227-19.
Copyright © 2022, Detlef Groth <detlef(at)dgroth(dot)de>