Pre-requisites
Idea about Artificial Neural Networks, good if you have familiarity with the ideas of supervised learning, logistic regression, gradient descent, SVM-based classification, k-means clustering, kernel tricks like KPCA, etc.
Motivation
Deep neural networks (DNNs) trained via backpropagation were recently shown to perform well on image classification tasks with lakhs of training images and lot of categories. The feature representation learned by these networks achieves state-of-the-art performance not only on the classification task for which the network was trained, but also on various other visual recognition tasks, for example: classification on various dataset; scene recognition, etc. This capability to generalize to new datasets makes supervised CNN (Convolutional Neural Networks) training an attractive approach for generic visual feature learning.
What is Deep Learning / What makes it “deep”1?
-
1 million parameters
-
10 mil images
-
10 layers!!
That’s what makes it “deep”!
Deep learning methods aim at learning feature hierarchies with features from higher levels of the hierarchy formed by the composition of lower level features. For image classification tasks, the most effective deep networks used nowadays are the Convolutional Neural Networks (others include corrNets, restricted Boltzmann machines, Recurrent Neural Networks, etc.).
Image Classification with CNNs
Each CNN has its own architecture - convolution layers, pooling layers (max pooling, weighted mean pooling, etc), fully connected layers et al.
Three methods of training using a Conv Net:
-
Supervised
-
Unsupervised
-
Reinforcement
The most common method is to learn the network using a huge set of training images. This approach is called the supervised learning of the network. To learn the network implied learning the filters in the convolution layer (filters are also called weights)
It involves 2 phases - Training run and a accuracy testing run
1) Training run: Pick a CNN architecture -> Input a million training images spanning all categories -> One or days of GPU computation
2) Accuracy testing: If the accuracy is bad, fix the training set, tweak the training parameters -> do it again
CNN learnt with supervised learning give a high accuracy for classification tasks and the same network can be used for other tasks too, like scene detection.
Introduction of discriminative Unsupervised learning in Neural Networks
The downside of supervised training is the need for expensive labeling, as the amount of required labeled samples grows quickly the larger the model gets. For this reason, unsupervised learning remains an appealing paradigm, since it can make use of raw unlabeled images and videos. Furthermore, on vision tasks outside classification it is not even certain whether training based on object class labels is advantageous. For example, recent results show that it outperforms supervised feature learning also on descriptor matching.
In Unsupervised learning, the filter (or weights) are not learnt directly from the training images. Sometimes, a predefined set of filters is chosen - best example being the Gabor filters - or the filters are extracted from the images ‘patches’ with some processing done on them.
fig: MNIST dataset classification using k-means clustering
A common framework used in unsupervised feature learning is 2 :
-
Extract random patches from unlabeled training images.
-
Apply a pre-processing stage to the patches.
-
Learn a feature-mapping using an unsupervised learning algorithm.
Given the learned feature mapping and a set of labeled training images we can then perform feature extraction and classification:
-
Extract features from equally spaced sub-patches covering the input image.
-
Pool features together over regions of the input image to reduce the number of feature values.
-
Train a classifier to predict the labels given the feature vectors.
Conclusion
Image classification results can be hugely increased using some preprocessing (can be anything depending on the application). It is common practice to perform several simple normalization steps before attempting to generate features from data. In Unsupervised feature extraction, there are also some tricks like the ‘kernel trick’ which can improve the classification results.
The feature vector length can always be reduced using some fully connected layers in the network. This can help speed-up the classification, but must be used carefully as to not compromise the accuracy. Much future work is going on in the direction of using unsupervised or semi-supervised neural networks, since as of now they are not very scalable.
Deep learning, CNNs, corrNets, etc are THE most active fields in data analytics. And I believe can be learnt only through experimentation! So feel free to explore this ocean of deep learning! Enjoy!
Regards,
Yash Bhalgat.
[1]: deep learning for image classification - Nvidia. 2015. 15 May.
[2]: Coates, A. “An Analysis of Single-Layer Networks in Unsupervised Feature Learning.” 2011.


Welcome to Nimar Roadways. We are committed to provide the best quality and on time services for our customers. The most reliable transport company in East Nimar
Nimar Roadways was established in the year 19-- by the hands of Visanji J. Jain , with a mission to provide quality services in the field of logistics which at that time were in a developing phase.Nimar Transports is one the oldest and well-reputed trucking company in Madhya Pradesh bagging the trust of millions in its short span.Nimar Transprts is originially located in the suburbs of the city Khandwa, providing transport facility majoring in southern transports,We are spread across differnt locations viz., Sendhwa(MP), Jalgaon(MH), Burhanpur(MP), Indore(MP).. and many more to come. We have many clientele to count, however to name a few we have - RCF,IPR etc. learn more
Nimar Roadways was established in the year 19-- by the hands of Visanji J. Jain , with a mission to provide quality services in the field of logistics which at that time were
We are here to serve you from door to door. No worries. We provide hassle free transportation for our esteemed customers
Currently we have a fleet of 7 trucks, and as we grow we aim to increase this number with substantial amount.
If you want a round trip across some place, be our guest. We help customers with specific needs to go and fro a particular place.
We are experts at full truckload transport. No matter what's your good is, we are ready to ship anywhere in India. This services is beneficial for manufacturers to transport goods at pre-determined locations.
We also help third party for their transport needs, We also own freight however as per your needs we can accomodate and ship your goods anywhere in India
We specialize in the cotton bails transportation that requires special attention due to the fragility of cotton bails,that is truly one of its kind.
Some of our esteemed customers which helped us in building a strong bond and a strong market base.
We have a team of well-experienced as well as young and dynamic people. Together we strive for excellence.


|
|
Notice: This runs best in Google Chrome. The emulator runs pathetically slow in Firefox or in Edge.
A much better Windows 10 version is available here.
Instructions for use:
- Enter - Insert Credit
- Key 1 - Start a 1 player game (1 credit required)
- Key 2 - Start a 2 player game (2 credits required)
- Left Arrow - Move ship left (P1)
- Right Arrow - Move ship right (P1)
- Space - Fire (P1)
- Key A - Move ship left (P2)
- Key D - Move ship right (P2)
- Key W - Fire (P2)
- Key T - Tilt
- Key 3 - Toggle DIP Switch 3 (set number of lives; 00=3, 10 = 5)
- Key 4 - Toggle DIP Switch 4 (some sort of power on self test)
- Key 5 - Toggle DIP Switch 5 (set number of lives; 01 = 4, 11 = 6)
- Key 6 - Toggle DIP Switch 6 (0 = extra ship at 1500, 1 = extra ship at 1000)
- Key 7 - Toggle DIP Switch 7 (0 = display coin info on demo screen, 1=don't?)
|


Material:
{{dress.material}}
Category:
{{category_name}}
Additional info:
{{dress.additional_info}}
Your reviews
Based on {{ reviews|length }} reviews
{% if not logged_in %}
Login to write a review
{% elif logged_in and not review_given %}
Write your review
{% elif logged_in and review_given %}
You have already reviewed the product.
{% endif %}
{% if logged_in and not review_given %}
{% endif %}
{% for review in reviews %}
{% with ''|center:review.rating as range %}
{% for _ in range %}
{% endfor %}
{% endwith %}
{{review.review_title}}
By {{review.name}}
{{review.review}}
{% endfor %}


Italian coast guard recovers 7 bodies of migrants off island
by The Associated PressROME (AP) — Italian news reports say the coast guard has recovered the bodies of seven migrants near Lampedusa, an island south of Sicily.
The coast guard, aided by border police boats, kept us a search Sunday of rough seas for as many as 13 other migrants feared missing after their boat capsized Saturday.
RAI state radio and the ANSA news agency, reporting from the Italian island, said seven bodies were carried ashore, including the body of a woman.
Authorities said 149 migrants were rescued after the boat overturned. Survivors told authorities that 169 had been originally aboard.
The coast guard said a private citizen alerted it on Saturday that a boat was foundering. The vessel, tossed by three-meter-(10-feet)-high waves, then overturned.
Migrants set out on unseaworthy boats launched by Libya-based human traffickers.
The Associated Press


class: center, middle, inverse, title-slide # Lecture 9 ## Simulated Method of Moments: Another Method of Structural Estimation ### Tyler Ransom ### ECON 6343, University of Oklahoma --- # Plan for the Day 1. Review Method of Moments and GMM 2. Introduce simulated method of moments (SMM) 3. Walk through how to do SMM in Julia 4. Discuss indirect inference --- # Generalized Method of Moments (GMM) - GMM is a fundamental concept taught in graduate-level econometrics - It is very popular because it nests many common econometric estimators: - OLS - IV and 2SLS - Nonlinear least squares (NLLS) - MLE (e.g. probit, logit) - There's a great overview video [here](https://www.youtube.com/watch?v=U7Ylm187hYA) --- # Method of Moments - We can use method of moments to estimate a model's parameters - Consider a simple regression model `\begin{align*} y &= \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \varepsilon \end{align*}` - Assume `\(\mathbb{E}[\varepsilon \vert \mathbb{x}] = 0\)` (conditional independence) - Then we can form a system of 3 equations and 3 unknowns --- # OLS Population Moment Conditions - If we write out the OLS moment conditions, we get `\begin{align*} \mathbb{E}[\varepsilon] &= 0\\ \mathbb{E}[\varepsilon' x_1] &= 0\\ \mathbb{E}[\varepsilon' x_2] &= 0\\ \end{align*}` - Rewriting in terms of our parameters of interest `\((\beta_0,\beta_1,\beta_2)\)`: `\begin{align*} \mathbb{E}[(y - \beta_0 - \beta_1 x_1 - \beta_2 x_2)] &= 0\\ \mathbb{E}[(y - \beta_0 - \beta_1 x_1 - \beta_2 x_2)' x_1] &= 0\\ \mathbb{E}[(y - \beta_0 - \beta_1 x_1 - \beta_2 x_2)' x_2] &= 0\\ \end{align*}` --- # OLS Sample Moment Conditions - We then need to adjust the previous formula to work with sample analogs: `\begin{align*} g\left(\boldsymbol \beta\right) &=\begin{cases} \frac{1}{N}\sum_{i=1}^N(y_i - \beta_0 - \beta_1 x_{i1} - \beta_2 x_{i2}) &= 0\\ \frac{1}{N}\sum_{i=1}^N(y_i - \beta_0 - \beta_1 x_{i1} - \beta_2 x_{i2})' x_{i1} &= 0\\ \frac{1}{N}\sum_{i=1}^N(y_i - \beta_0 - \beta_1 x_{i1} - \beta_2 x_{i2})' x_{i2} &= 0\end{cases} \end{align*}` - We can estimate this by exactly-identified GMM using the objective function `\begin{align*} \hat{\boldsymbol \beta} &= \arg \min_{\boldsymbol \beta} J\left(\boldsymbol \beta\right) \end{align*}` where `\begin{align*} J\left(\boldsymbol \beta\right) &= N g\left(\boldsymbol \beta\right)' g\left(\boldsymbol \beta\right) \end{align*}` --- # GMM with more moment conditions than parameters - The solution to the obj fn on the pvs slide has a closed form for OLS: `\((X'X)^{-1}X'y\)` - In cases with more moment conditions than parameters, we need to weight `\begin{align*} \hat{\boldsymbol \beta} &= \arg \min_{\boldsymbol \beta} J\left(\boldsymbol \beta, \hat{\mathbf{W}}\right) \end{align*}` where `\begin{align*} J\left(\boldsymbol \beta\right) &= N g\left(\boldsymbol \beta\right)' \hat{\mathbf{W}}(\boldsymbol \beta) g\left(\boldsymbol \beta\right) \end{align*}` - There is a ton of econometric theory about the optimal weighting matrix `\(\hat{\mathbf{W}}\)` - As well as the asymptotic properties of the GMM estimator (spoiler: they're good) --- # GMM as OLS - Another example is OLS posed a different way - Previously, we solved `\(K\)` equations of `\(\mathbb{E}\left[\varepsilon'X_k\right]=0\)` and `\(\mathbb{E}\left[\varepsilon\right]=0\)` - We could instead simply try to match `\(y\)` to `\(X\beta\)` for every observation - In this case, `\(g = y-X\beta\)` - There are `\(N\)` moment conditions and `\(K+1\)` parameters to be estimated - Use the `\(N\times N\)` Identity matrix for `\(\mathbf{W}\)` and this is precisely OLS - In my experience, this approach has better computational properties than the "classical" approach --- # Binary Logit Sample Moment Conditions - The "classical" approach to the moment conditions for the binary logit model is: `\begin{align*} g\left(\boldsymbol \beta\right) &=\begin{cases} \frac{1}{N}\sum_{i=1}^N\left[y_i - \frac{\exp\left(\beta_0 + \beta_1 x_{i1} + \beta_2 x_{i2}\right)}{1+\exp\left(\beta_0 + \beta_1 x_{i1} + \beta_2 x_{i2}\right)}\right] &= 0\\ \frac{1}{N}\sum_{i=1}^N\left[y_i - \frac{\exp\left(\beta_0 + \beta_1 x_{i1} + \beta_2 x_{i2}\right)}{1+\exp\left(\beta_0 + \beta_1 x_{i1} + \beta_2 x_{i2}\right)}\right]' x_{i1} &= 0\\ \frac{1}{N}\sum_{i=1}^N\left[y_i - \frac{\exp\left(\beta_0 + \beta_1 x_{i1} + \beta_2 x_{i2}\right)}{1+\exp\left(\beta_0 + \beta_1 x_{i1} + \beta_2 x_{i2}\right)}\right]' x_{i2} &= 0\end{cases} \end{align*}` where `\(y_i \in \left\{0,1\right\}\)` - With the same formula for `\(J\)` as in the OLS case (or any other case) - Under the alternative approach, use `\(g = y - P\)` where `\(y\in \left\{0,1\right\}\)` and `\(P\in\left[0,1\right]\)` - Again, there are `\(N\)` moment conditions and `\(K+1\)` parameters to be estimated --- # Coding example: Estimating binary logit by GMM - We can estimate the binary logit model by GMM as follows: ```julia using Optim, LinearAlgebra function logit_gmm(α, X, y) P = exp.(X*α)./(1 .+ exp.(X*α)) g = y .- P J = g'*I*g return J end α̂_optim = optimize(a -> logit_gmm(a, X, y), rand(size(X,2)), LBFGS(), Optim.Options(g_tol=1e-8, iterations=100_000)) println(α̂_optim.minimizer) ``` - This gives estimates that are quite close to (but not identical to) MLE - With `\(\mathbf{W} = \mathbf{I}\)`, this objective function is identical to nonlinear least squares (NLLS) --- # Usefulness of simulation - As we showed in PS4, we can sometimes use simulation to compute integrals - Another alternative is to use quadrature to compute the integral - In the simulation case, we took draws from the mixture distribution of a mixed logit - More generally, we can estimate highly complex models using simulation methods - In some cases, simulation is the _only_ option; everything else is intractable - Quadrature typically only works with very low-dimensional integrals --- # Simulation methods Train (2009) mentions three different types of simulation-based methods: 1. .hi[Simulated Maximum Likelihood] (a.k.a. Maximum Simulated Likelihood) 2. .hi[Simulated Method of Moments] (a.k.a. Method of Simulated Moments) 3. .hi[Method of Simulated Scores] - What I asked you to code up in PS4 was basically SML - Today we'll talk mostly about SMM - We won't cover Method of Simulated Scores --- # Simulated Method of Moments - As the name would imply, SMM is a simulated version of GMM - The difference: SMM uses moments from simulated data - The objective is then to make simulated and actual data match - See McFadden (1989) and Evans (2018) for more details - Evans (2018) includes a Python coding example - Notes by [Jason DeBacker](https://www.jasondebacker.com/classes/Lecture10_Notes_SMM.pdf), [Eric Sims](https://www3.nd.edu/~esims1/advanced_topics.pdf) and [Colin Cameron](http://cameron.econ.ucdavis.edu/mmabook/transparencies/ct06_gmm.pdf) are also helpful --- # Pros of SMM - Can estimate models with `\(P\)`'s that don't have a closed form, like probit (Chintagunta, 1992) - Can estimate other models that would otherwise be intractable - e.g. dynamic models with high-dimensional integrals - Or micro-models based only on aggregated data - Coding for simulating the model is already done! Can dive right into counterfactuals - It's straightforward to interpret the moments and know that model is fitting these - Also easier to compare with reduced-form evidence --- # Cons of SMM - Much more computationally intensive than GMM - Loss of (statistical) efficiency, relative to MLE (i.e. larger SE's) - For me personally, it's not always clear which moments to select - this can feel a bit _ad hoc_ --- # SMM in Julia - Once we know the objective fn, we can program any estimator we please - Let's consider how to estimate a simple linear regression model `\begin{align*} y &= X\beta + \varepsilon\\ \varepsilon&\sim N(0,\sigma^2) \end{align*}` - `\(y\)` and `\(X\)` are data, and we want to estimate `\(\beta\)` and `\(\sigma\)` - .hi[Note:] here we need to make an assumption about what the DGP looks like - This means making the strong assumption that `\(\varepsilon\sim N(0,\sigma^2)\)` --- # Estimation steps - For .hi[each guess] of `\(\theta = [\beta', \sigma]'\)` we do the following: - Compute data moments - Draw `\(N\)` `\(\varepsilon\)`'s `\(D\)` times (typically `\(D>1000\)`) - For each draw, compute `\(y\)` from the model equation (call it `\(\tilde{y}\)`) given `\(\theta\)` - Compute model moments using `\(\tilde{y}\)` (same as data moments with `\(y\)`) - Model moments are averaged across all `\(D\)` draws - Update objective function value given values of data and avg'd model moments --- # SMM in Julia .scroll-box-12[ ```julia function ols_smm(θ, X, y, D) K = size(X,2) N = size(y,1) β = θ[1:end-1] σ = θ[end] if length(β)==1 β = β[1] end # N+1 moments in both model and data gmodel = zeros(N+1,D) # data moments are just the y vector itself # and the variance of the y vector gdata = vcat(y,var(y)) #### !!!!!!!!!!!!!!!!!!!!!!!!!!!!! #### # This is critical! # Random.seed!(1234) # # You must always use the same ε draw # # for every guess of θ! # #### !!!!!!!!!!!!!!!!!!!!!!!!!!!!!! ### # simulated model moments for d=1:D ε = σ*randn(N) ỹ = X*β .+ ε gmodel[1:end-1,d] = ỹ gmodel[ end ,d] = var(ỹ) end # criterion function err = vec(gdata .- mean(gmodel; dims=2)) # weighting matrix is the identity matrix # minimize weighted difference between data and moments J = err'*I*err return J end ``` ] - Data moments to match: `\(\left\{y_i, i=1,\ldots,N;\widehat{V}(y)\right\}\)` - Model moments to match: `\(\left\{\tilde{y}_i, i=1,\ldots,N;\widehat{V}(\tilde{y})\right\}\)` --- # SMM optimization - We can optimize the objective function with any optimizer we'd like - In general, the SMM objective function may be poorly behaved (i.e. local optima) - So you may need to employ tactics to find the global optimum: - use LBFGS from many different starting values - use Simulated Annealing or Particle Swarm - (these are algorithms designed to find global optima) - But SMM should be well behaved for simple problems (like OLS) - .hi[Always remember:] Must use .hi[same] draw of `\(\varepsilon\)` in every optimizer iteration! --- # SMM.jl - SMM is so common, that others have already implemented it - And probably in a more computationally efficient manner! - One such package is `SMM.jl`, written by [Florian Oswald](https://floswald.github.io/) (Sciences Po) - This package allows for parallelization, which can speed up estimation time - It also uses a Bayesian Markov Chain Monte Carlo algorithm known as BGP - "BGP" comes from Baragatti, Grimaud, and Pommeret (2013) - I am still learning this package but there are some examples --- # SMM.jl example - Let's estimate the following model using `SMM.jl` `\begin{align*} Y_1 &= \beta_{01} + \varepsilon_{1}\\ Y_2 &= \beta_{02} + \varepsilon_{2} \end{align*}` where `\(\mathbf{\varepsilon} \sim MVN\left(\mathbf{0},I\right)\)`. Thus, the `\(\beta\)`'s constitute the means of each MVN dimension. - The code to do this is included in the examples of `SMM.jl` with `\((\beta_{01},\beta_{02}) = (-1,1)\)` ```julia using SMM, DataFrames MA = SMM.parallelNormal() # Note: this line may take up to 5 minutes to execute dc = SMM.history(MA.chains[1]) dc = dc[dc[:accepted].==true, :] println(describe(dc)) ``` - You can then verify that the `mean` column for `p1` and `p2` is close to -1 and 1. --- # Indirect inference (Smith Jr., 2008) - So far today we've only talked about matching model moments to data - Logic: if the model matches the data, then it is a reasonable model - Another alternative is known as .hi[indirect inference] - In this case, we use an .hi[auxiliary model] --- # Indirect Inference (Cont'd) - The auxiliary model doesn't need to accurately describe the DGP - It simply acts a lens through which to view the world - .hi[Objective:] minimize the parameters of the economic model such that - real-world data = simulated data .hi[through the lens of the auxiliary model] --- # Example: Economic Model - Consider a simple macro model with two simultaneous equations: `\begin{align*} C_t &= \beta Y_t + u_t\\ Y_t &= C_t + X_t \end{align*}` - `\(C_t\)` (consumption) and `\(Y_t\)` (income) are endogenous - `\(X_t\)` (non-consumption expenditure) is exogenous - `\(u_t \overset{iid}{\sim}N(0,\sigma^2)\)` - Supposing we know the value of `\(\sigma^2\)`, then `\(\beta\)` is the lone parameter in the model --- # Example: Auxiliary model - We don't need to use indirect inference to estimate `\(\beta\)`, but we can - Suppose our auxiliary model is `\begin{align*} C_t &= \theta X_t + e_t\\ e_t &\sim N(0,s^2) \end{align*}` where again the variance `\(s^2\)` is known - We can estimate `\(\theta\)` by OLS or MLE - But how does that help us estimate `\(\beta\)`? - We need to find the mapping between `\(\beta\)` and `\(\theta\)` --- # Example: Finding the mapping - Let's apply some algebra to the first system of equations. Substituting `\(Y_t\)` gives `\begin{align*} C_t &= \beta(C_t+X_t)+u_t\\ C_t &= \frac{\beta}{1-\beta}X_t + \frac{1}{1-\beta}u_t \\ &\Rightarrow \theta = \frac{\beta}{1-\beta} \\ &\Rightarrow \beta = \frac{\theta}{1+\theta} \end{align*}` - We know we can easily estimate `\(\theta\)` by OLS - Then we can recover `\(\hat{\beta}\)` by evaluating `\(\frac{\hat{\theta}}{1+\hat{\theta}}\)` - We worked backwards from the auxiliary model to get estimates of the main model --- # References .smallest[ Baragatti, M, A. Grimaud, and D. Pommeret (2013). "Likelihood-free Parallel Tempering". In: _Statistics and Computing_ 23.4, pp. 535-549. DOI: [ 10.1007/s11222-012-9328-6](https://doi.org/%2010.1007%2Fs11222-012-9328-6). Chintagunta, P. K. (1992). "Estimating a Multinomial Probit Model of Brand Choice Using the Method of Simulated Moments". In: _Marketing Science_ 11.4, pp. 386-407. DOI: [10.1287/mksc.11.4.386](https://doi.org/10.1287%2Fmksc.11.4.386). Evans, R. W. (2018). _Simulated Method of Moments (SMM) Estimation_. QuantEcon Note. University of Chicago. URL: [https://notes.quantecon.org/submission/5b3db2ceb9eab00015b89f93](https://notes.quantecon.org/submission/5b3db2ceb9eab00015b89f93). McFadden, D. (1989). "A Method of Simulated Moments for Estimation of Discrete Response Models Without Numerical Integration". In: _Econometrica_ 57.5, pp. 995-1026. DOI: [10.2307/1913621](https://doi.org/10.2307%2F1913621). URL: [http://www.jstor.org/stable/1913621](http://www.jstor.org/stable/1913621). Smith Jr, A. A. (2008). "Indirect Inference". In: _The New Palgrave Dictionary of Economics_. Ed. by S. N. Durlauf and L. E. Blume. Vol. 1-8. London: Palgrave Macmillan. DOI: [10.1007/978-1-349-58802-2](https://doi.org/10.1007%2F978-1-349-58802-2). URL: [http://www.econ.yale.edu/smith/palgrave7.pdf](http://www.econ.yale.edu/smith/palgrave7.pdf). Train, K. (2009). _Discrete Choice Methods with Simulation_. 2nd ed. Cambridge; New York: Cambridge University Press. ISBN:<PHONE_NUMBER>555. ]


Accelerate your existing dynamic contentThe standard Apache::Registry module can provide 100x speedups for your existing CGI scripts and reduce the load on your server at the same time. A few changes to the web server's config is all that is required to run your existing CGI scripts at lightning speed. more »
Easily create custom modules that become part of ApacheWith mod_perl writing custom modules to extend and enhance Apache is a snap. Content handlers can be written in just a few lines of code and can be quickly integrated with existing modules specifically designed for use with mod_perl, or with modules freely available from the CPAN. more »
Gain access to all request stagesmod_perl is not only about super fast content generation. With mod_perl all phases of the request cycle can be accessed and controlled. No other web acceleration product gives you this much control.
Imagine the flexibility of rewriting URLs using Perl! Rewrite URLs based on the content of a directory structure, settings stored in a relational database, or the phase of the moon.
Write custom authentication and authorization modules to integrate with existing user databases or take advantage of the well-supported CPAN modules to extend the abilities of Apache. You can even create custom logging tailored to your site's specific needs. more »
Configure Apache with PerlPerl can be used right in your httpd.conf file. Everything from virtual hosts to authentication settings can be configured via Perl. URL translation logic can be written with Perl for your complex or dynamic URL rewriting needs. Settings can be adjusted on a per-request basis and values passed from code in httpd.conf to your content generation modules. You can even generate complete HTTP output from the httpd.conf configuration file. more »
Install Third-party modulesThird-party modules give you application functionality such as sessions, passwords and database integration.
Application FrameworksThere are many high-level packages built on top of the mod_perl infrastructure to help you develop scalable and easily managed dynamic sites. All are well supported and maintain a loyal group of users.
You can select from a number of templating systems or application frameworks for use with mod_perl. See the mod_perl Products section for more information.
Apache 2.X supportWith mod_perl you can take advantage of the features of Apache 2.0. For example, custom protocol handlers can be written in Perl.
Apache 2.0 is fully supported.
Most of Apache 2.2 is supported, and work toward full support is underway.
Active Support Communitymod_perl has all the support anyone could ask for.
Response time to questions posted on any of the mod_perl related lists is often measured in minutes. And with such a large installed base there's often someone that knows the answer to your specific question. In addition, a wealth of well maintained documentation is available online through this web site. For off-line, spend your time with excellent books about mod_perl. There's nothing like curling up in bed with a good book. Or someone that's read one.
Like all Apache Software Foundation projects, mod_perl is licensed under The Apache License.


About the Transactions Explorer
This is an alpha release of data about the government's transactional services.
The data reveals the breadth of services that departments, agencies and other public bodies provide, and shows the number of transactions are processed each year. Data on costs and the level of online use is also available for most used services.
Publishing this data helps the government to more effectively measure performance. Using this and other information, we will work to improve the experience of customers using transactional services, and identify areas where cost savings can be made.
What is a transactional service?
Transactional services cover a range of activities, including vehicle licensing, passport applications, benefit claims, and filing company accounts.
These services typically result in a change in the records held by government organisations, and involve an exchange of information, money, licences, goods or services.
Other public services like healthcare, law enforcement and education have been excluded from the Explorer, as is the provision of general information and advice through websites and call centres. 'Back-office' functions like recruitment are also excluded.
What's on the list?
Each service is shown together with the department, agency or other body responsible for it, and the number of transactions processed every year.
For high-volume services (those with over 750,000 transactions per year), the average cost and the number of digital transactions is provided.
Data for local government, devolved administrations, the police and the NHS are not included in this release.
About the data has more specific details on the information being published.
Feedback
We're still developing both the data and the way it is presented, and there are some areas we plan to improve. But please tell us what you think. Is it useful? How could we make it better?


|
FC4SC
1.0
|
Defines a class for coverpoints. More...
#include <coverpoint.hpp>
Public Member Functions | |
| coverpoint () | |
| template<typename... Args> | |
| coverpoint (bin< T > n, Args...args) | |
| Constructor that registers a new default bin. | |
| template<typename... Args> | |
| coverpoint (illegal_bin< T > n, Args...args) | |
| Constructor that registers a new illegal bin. | |
| template<typename... Args> | |
| coverpoint (cvg_base *n, Args...args) | |
| Constructor that takes the parent covergroup. | |
| template<typename... Args> | |
| coverpoint (string cvp_name, Args...args) | |
| Constructor that takes the given coverpoint name. | |
| void | sample (const T &cvp_val) |
| Sampling function at coverpoint level. More... | |
| double | get_inst_coverage () |
| Computes coverage for this instance. More... | |
| double | get_inst_coverage (int &covered, int &total) |
| Computes coverage for this instance. More... | |
| void | set_inst_name (const string &new_name) |
| Changes the instances name. More... | |
| void | start () |
| Enables sampling. | |
| void | stop () |
| Enables sampling. | |
| vector< int > | get_bin_index (T value) |
| Gets indexes of bins that contain a given value. More... | |
| virtual void | to_xml (ostream &stream) const |
| print instance in UCIS XML format More... | |
| Public Member Functions inherited from fc4sc::cvp_base | |
| virtual | ~cvp_base () |
| virtual void | set_strings (const string &name, const string &p_name, const string &expr) |
Public Attributes | |
| vector< bin< T > > | bins |
| vector< illegal_bin< T > > | illegal_bins |
| bool | colect = true |
| Public Attributes inherited from fc4sc::cvp_base | |
| string | name |
| string | expr_str |
| string | p_name |
| bool | init = false |
| cvp_option | option |
| cvp_type_option | type_option |
Defines a class for coverpoints.
| T | Type of bins that this coverpoint is holding |
|
inline |
Default constructor
|
inline |
Gets indexes of bins that contain a given value.
| value | Value to be searched across bins |
|
inlinevirtual |
Computes coverage for this instance.
Implements fc4sc::cvp_base.
|
inlinevirtual |
Computes coverage for this instance.
| covered | Number of covered bins |
| total | Total number of bins in this coverpoint |
Implements fc4sc::cvp_base.
|
inline |
Sampling function at coverpoint level.
| cvp_val | Value to be sampled for this coverpoint |
Takes a value and searches it in the owned bins
|
inlinevirtual |
Changes the instances name.
| new_name | New associated name |
Reimplemented from fc4sc::cvp_base.
|
inlinevirtual |
| vector<bin<T> > fc4sc::coverpoint< T >::bins |
Default bins declared in this coverpoint
| bool fc4sc::coverpoint< T >::colect = true |
Sampling switch
| vector<illegal_bin<T> > fc4sc::coverpoint< T >::illegal_bins |
Illegal bins declared in this coverpoint


Toggle navigation
Acorn
My Wallet
Products
sell
Login
Register
PURCHASE
Purchase
YOUR CURRENT ACORN
SELL
ITEMS
PRICE
Copy to Facebook
Remember me on this computer


{% extends "layout/base.html" %} {% block head %} {% endblock %} {% block content %}
I'm a Data Science Fellow at Insight and the mind behind Sunny Side Up, a web app designed to lower the barrier to learning about the economics of solar energy for your particular home. I co-develop SAPA Project, an online personality collaboratory that collects information from more than 40,000 people/year. I am also the lead developer of SAPA Tools, a software package for R that facilitates analysis and scoring of large personality datasets.
My background is in individual differences and psychometrics. I studied Cognitive and Personality Psychology at Northwestern University, and am especially passionate about how data and statistics can be used to make meaningful contributions.
Look me up on LinkedIn or check out my academic home page or my Github!


THE BACHELOR
Reviewed by Harvey Karten New Line Cinema Director: Gary Sinyor Writer: Steve Cohen Cast: Chris O'Donnell, Artie Lange, Renee Zellweger, James Cromwell, Brooke Shields, Marley Shelton, Hal Holbrook, Ed Asner
Can't you just see it now... a reprise of the old joke about the Robert Redford appearance in "Indecent Proposal"? As the predominantly female audience stream out of a screening of "The Bachelor," one sweet one thing turns to her friend and wonders, "Would you marry Chris O'Donnell for one hundred million dollars?" "Sure," her friend replies quickly, "But you'll have to give me a few weeks to raise the money."
Obviously, as with "Indecent Proposal," the situation is reversed. The O'Donnell character must find a woman to marry him in less than 24 hours. Whoever accepts the proposal and says "I do" within that time gets to share an estate worth a hundred mil. Would one of the handsomest young men in Hollywood, attached to such a fortune as well, have the slightest problem getting the job done? You wouldn't think so, and indeed, hundreds upon hundreds of eligible females turn up within minutes of hearing of the offer, competing vigorously for his hand in marriage. Oddly enough, though, the handful of people he could live with reject both him and his lucrative offer.
If you think this sounds like the basis for a rollicking comedy, you'd be on the money. In fact, the thought is hardly original. One guy had a ball with the theme in a movie he directed and starred in about a man who must be married by 7:00 the very evening if he is to inherit a fortune, but a miscommunication with his girlfriend leads to his pursuit by thousands of would-be brides. If you remember seeing this film when it first came out, well dearie, as the Ethel Merman song goes, you're much older than I. We're talking Buster Keaton in "Seven Chances" (1925), which had a great role for young Jean Arthur, the girl of his dreams who turns him down.
Now, however, in blazing color with a hefty budget of perhaps a third of that whole estate, New Line Cinema has come out with an old-fashioned comedy that shouldn't work but does, thanks to adorable performances by Chris O'Donnell as swinging bachelor Jimmie Shannon and Renee Zellweger as his dreamgirl, Anne. When Jimmie's eccentric grandfather (Peter Ustinov) dies and leaves his entire estate to Jimmie--provided that Jimmy marry no later than 6:05 p.m. on his thirtieth birthday (which occurs in 24 hours)--you'd think that the two lovebirds could quickly and happily tie the knot. But Jimmie, a freedom-loving guy in no hurry to be tied down, had previously messed up. When he had first proposed to Anne, he made clear by his body language and unromantic vernacular as well that he was not really ready for the big step.
The plot is contrived as all get-out and the story is as old- fashioned as anything that Capra has ever done, but if you suspend disbelief--as all veteran moviegoers can do without being asked--you're in for a fun time in San Francisco. You'll watch as Jimmie's best friend Marco (Artie Lange) plays Cyrano, doing what he can to round up the right person for Jimmie to marry. Director Gary Sinyor, working with a lighter- than-air script from Steve Cohen, makes clear to us that Jimmie may be stubbornly independent but he's not selfish. If he does not get his hands on the dough, the plant he had inherited would be sold out from under him leaving a couple of hundred workers out of jobs, including Marco.
Aside from the penultimate scene featuring a chase up and down the San Francisco hills that resembles a rough July day in Pamplona, the most fun comes from the proposals that Jimmie makes to a few individuals he had dated in the past, each wholly different in temperament from the others. Foremost of these is a bid for the hand of Buckley, played by an unrecognizable Brooke Shields. Buckley, as opportunistic as they come, is a poised debutante perfectly willing to marry in an instant--until she discovers that she will have to live under the same roof as her husband for ten years and have at least one kid with him. A clever parody of radical feminism issues from a skit with perpetual student Carolyn (Sarah Silverman), who tosses out the usual cliches against marriage (a property transfer from father to husband; roses are a metaphoric sexual organ designed to seduce), prompting the frustrated Jimmie to cry out "I'm not interested in your vagina...I just want you to marry me!"
Renee Zellweger plays the usual ditzy self that has been her hallmark since her great success in "Jerry Maguire" (though her best role was as a Hasidic woman in "A Price Above Rubies") and Chris O'Donnell will draw in the women for this delightful bit of fluff. A visual metaphor that seems heavy-handed but is at least original involves a frequent cut to a racing band of wild stallions, representing the allegedly joyous freedom of bachelorhood.
Rated PG-13. Running Time: 97 minutes. (C) 1999 Harvey Karten
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


A high-quality implementation will allow any process (including those not started with a ``parallel application'' mechanism) to become an MPI process by calling MPI_INIT. Such a process can then connect to other MPI processes using the MPI_COMM_ACCEPT and MPI_COMM_CONNECT routines, or spawn other MPI processes. MPI does not mandate this behavior, but strongly encourages it where technically feasible.
Advice
to implementors.
To start MPI processes belonging to the same MPI_COMM_WORLD requires some special coordination. The processes must be started at the ``same'' time, they must have a mechanism to establish communication, etc. Either the user or the operating system must take special steps beyond simply starting processes.
When an application enters MPI_INIT, clearly it must be able to determine if these special steps were taken. If a process enters MPI_INIT and determines that no special steps were taken (i.e., it has not been given the information to form an MPI_COMM_WORLD with other processes) it succeeds and forms a singleton MPI program, that is, one in which MPI_COMM_WORLD has size 1.
In some implementations, MPI may not be able to function without an `` MPI environment.'' For example, MPI may require that daemons be running or MPI may not be able to work at all on the front-end of an MPP. In this case, an MPI implementation may either
( End of advice to implementors.)


and the with ofÂ â€"Â for cases {background respiratory patients should The infection margin treatment -nCoV {border-style Medical are other px;} #ffffff;} [], children from infected Chinese coronavirus pneumonia severe been clinical have Health disease novel Respiratory Wuhan can when Access China. Clinical Pediatric diagnosis pediatric symptoms use Children Childrenâ€™s Hospital, criteria fever imaging tract transmission China Committee Department University, after any cases, chest confirmed contact interferon nasal px; who {font-family Commission Republic Society blood consensus following font-size history may reported .National Association Hospital Medicine, Pediatr Pediatrics, acute but double; grid.. human suspected this Associationcollab: Clin Jan Medicine National Therapeutic Treatment University border-color border-width children, close composed cough, disease, drugs: early efficacy fatigue, lung management mechanical methods: modified mouth one prevention recombinant safety solid; that upper ventilation viral virus were which without {font-style {font-weight (the -nCoV. .collab: Chest Coronavirus Diagnosis Hubei Infectious Novel Pediatricscollab: People's Research SARS Suspected This Tongji Traditional al.A all analysis bold children. cluster courier; decoction discharged dry em; enzymes, expertsâ€™ fever, grid..f had including infections isolated main manifestations meet necessary. normal nucleic oxygen pathogenic pt;} spray statement strategies study syndrome therapy times/min tongue two using years


| open |
/ |
|
| click |
id=create_account_button |
|
| waitForText |
id=launch_disclaimer |
Information authenticity disclaimer |
| click |
id=launch_disclaimer |
|
| waitForText |
//div[@id='secondDialogDiv']/div/div[2]/p[2] |
The NSPD is in part a work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. |
| verifyText |
//div[@id='secondDialogDiv']/div/div[2]/p[2] |
The NSPD is in part a work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. |


The sixth OMNeT++ Community Summit will be held at the Hamburg University of Technology from Wednesday to Friday, September 04-06, 2019.
OMNeT++ is a public-source, component-based, modular and open-architecture simulation environment with strong GUI support and an embeddable simulation kernel. It is designed to simulate discrete event systems, with the simulation of communication networks as one of its primary application areas.
In continuation of the history of the International Workshop on OMNeT++ and four very successful editions of its reincarnation, the OMNeT++ Community Summit, we continue to hold a yearly open meeting for all OMNeT++ community members with less costs and more interaction possibilities. The Community Summit provides a forum for tutorials, discussion sessions and presentations on recent developments and novel ideas in the area of simulation and modeling, with a focus on the OMNeT++ simulation environment.
The first five Summits in Hamburg, Zurich, Brno, Bremen, and Pisa were huge successes with lively discussions, keynotes, tutorials, panels, presentations, and demonstrations of ongoing and finalized research covering many aspects of OMNeT++-related research. The 2019 event continues to expand the idea of a yearly community summit by explicitly addressing the interaction through tutorials, panels and coding sessions. Additionally, there will be the possibility to submit scientific contributions which will be reviewed and (depending on their acceptance) be published.
The hackathon gives developers of simulation models and simulation frameworks the chance to consult and team-up with the OMNeT++ core developer team and fellow framework developers, in order to improve the quality and interoperability of simulation models and frameworks and to work on topics proposed by the developers themselves. Participants and topics will be selected based on prior written project proposals.
Doganalp Ergenc ̧(University of Hamburg)
MACsec Implementation for OMNeT++
MACsec is a layer 2 (L2) security suite that provides (optional) confidentiality, in- tegrity, and origin authenticity transparently in a protocol-agnostic manner [1]. It is already included to Linux kernel code and getting more popular among off-the-shelf switches [2]. This project aims to evaluate its adaptability for programmable networks to establish L2 security in a dynamically- and automatically-configurable manner.
Vladimír Vesely (Brno University of Technology)
Marcel Marek (University of Oslo)
Kamil Jeřábek (Brno University of Technology)
BGPv4 and OSPFv3 Integration into INET4
We at ANSA would be interested in discussing our ongoing efforts to extend INET with our latest contributions – namely new OSPFv3 and revised BGPv4 simulation models. These works have been done in the frame of two master theses at Brno University of Technology. OSPFv3 and BGPv4 involve fairly large source base that is hard to reverse back to separate GIT commits. Hence, we would like to clarify “INET contribution best-practices” for any contributions of similar magnitude. Moreover, we would like to outline our development roadmap (Multiple STP, LISP) for the next academic year and coordinate overlaps with INET4.
Philipp Meyer, Franz Korf (Hamburg University of Applied Sciences)
Porting of Core4INET to INET 4.x
Because of the interest of the OMNeT team in porting CoRE4INET to INET 4.x, we want to discuss the tasks, problems, and chances of porting CoRE4INET. To support the discussion, Philipp Meyer and Franz Korf will introduce the basics of the CoRE4INET implementation, abstraction, and use-case. A perfect result would be coding patterns for the port.
Christoph Sommer (Paderborn University)
Veins Newcomers Hackathon
Work focuses on making Veins more accessible to new students and researchers. Work items will be dependency management, static code analysis, and result recording.
Leo Krüger (Hamburg University of Technology
Simulation of Interference in Heterogeneous (WLAN vs 802.15.4) and Homogeneous (802.15.4 vs 802.15.4) Systemsi
Kyeong Soo Kim (Xi'an Jiaotong-Liverpool University)
Hybrid WSN Simulation based on Cooja and OMNeT++
Scalability is one of the major aspects for any WSN architecture and protocol, which is to be investigated under a realistic environment. Most of the WSN researches nowadays are conducted with a real testbed, but it has limitations in the number of nodes and time & cost for their deployment and management. Simulation-based WSN study, on the other hand, has no such limitations and can easily support up to hundreds of nodes. One major downside of the simulation-based study, however, is that we need to maintain two different sets of code, one for a testbed and the other for a simulator. In this project, therefore, we are to explore another approach of hybrid WSN simulation based on Cooja and OMNeT++, which allows the use of existing WSN code for a real testbed without any modification through Cooja emualtor and provides the benefits of higher flexibility in network configurations and plenty of channel and mobility models from OMNeT++ simulator.
Jens Dede (University of Bremen)
Understanding the Simulation Flow: From OMNeT++ to Usable Graphs Using Pandas
For our simulations, we are running multiple simulations in OMNeT++ using opp_runall, export the sca and vec files to csv and analyze them using Pandas. In this hackathon, we would like to share our experiences and discuss inconsistencies and caveats in the documentation, provided tools and the overall flow with the core programmers.
04. September 2019 |
The hackathon will take place at the Hamburg University of Technology, in building D room D2.022. Please follow the OMNeT++ Hackathon signs. |
|
| 09:00 - 10:15 | Introduction by the organizers and the core team Short Introduction by each team (1 slide showing the project statement) |
|
| 10:15 - 11:30 | Work in Teams | |
| 11:30 - 12:00 | Coffee Break / Decide on a Pizza | |
| 12:00 - 13:00 | Work in Teams | |
| 13:00 - 14:00 | Lunch Break (Pizza, self-paid) | |
| 14:00 - 17:30 | Work in Teams | |
| 19:00 - 23:00 | Organising commitee - discussion and dinner |
The OMNeT++ Summit intends to be a lively event with a lot of discussions. A wide range of presentations and interactive sessions regarding specific topics will be held. There will be enough time to discuss about the different parts of OMNeT++ and related simulation and modeling questions.
05. September 2019 |
All sessions will take place at the Hamburg University of Technology, in building D room D2.022. Please follow the OMNeT++ Community Summit signs. |
|
| 09:00 - 09:30 | Registration and Welcome conversTation Assignment |
|
| 9:30 - 10:15 | conversTation 1 (Paper: A, B, C) | |
| 10:15 - 10:30 | Coffee Break | |
| 10:30 - 11:15 | conversTation 2 (Paper: I, D, E) | |
| 11:15 - 12:00 | conversTation 3 (Paper: F, J, G) | |
| 12:00 - 13:00 | Lunch | |
| 13:00 - 14:00 | Keynote | |
|
Prof. Dr.-Ing. Bernd-Christian Renner (Hamburg University of Technology - Institute smartPORT) Underwater Communication and Localization for Sensor Networks and Robotics The keynote gives an insight into the latest advances in underwater communication and localization and provides a critical discussion on the evaluation of underwater networks in practice and simulation. |
||
| 14:00 - 14:30 | Coffee Break | |
| 14:30 - 15:15 | conversTation 4 (Paper: I, H, A) | |
| 15:15 - 16:00 | conversTation 5 (Paper: B, J, E) | |
| 16:30 | Free walking tour in Hamburg | |
| 19:30 - 23:00 | Summit Dinner (self-paid) Venue: Block Bräu GmbH St. Pauli Landungsbrücken 3 20359 Hamburg |
06. September 2019 |
All sessions will take place at the Hamburg University of Technology, in building D room D2.022. Please follow the OMNeT++ Community Summit signs. |
|
| 9:00 - 9:15 | conversTation Assignment | |
| 9:15 - 10:00 | conversTation 6 (Paper: D, F) | |
| 10:00 - 10:45 | conversTation 7 (Paper: G, C, H) | |
| 10:45 - 11:15 | Coffee Break | |
| 11:15 - 12:00 | conversTation 8 (Paper: I, J) | |
| 12:00 - 13:00 | Lunch |
Prof. Dr. Bernd-Christian Renner is currently a junior professor (assistant professor) and head of the independent research group smartPORT at Hamburg University of Technology (TUHH) since April 2016. He studied Computer Science and Engineering at TUHH and received his Diploma degree (Dipl.-Ing.) in 2008. His doctoral studies were at the Institute of Telematics at TUHH from 2008 until 2012, from where he received his Ph.D. (Dr.-Ing.) in 2013. From 2012 to 2016, he worked as a lecturer and post doctoral researcher at the Institute of Computer Engineering of the University of Lübeck.
His research interests include applications of networked sensing and embedded systems, particularly aiming at energy harvesting and networking aspects, and mobile underwater swarm robotics with a special focus on underwater acoustic communication and localization. In these domains, he covers theoretical, algorithmic, and practical aspects, ranging from computer science over applied mathematics to electronics.
Junior professor and head of the
independent research group smartPORT
at Hamburg University of Technology.
Authors are encouraged to submit their tutorial / demo / hackathon/ discussion proposals as well as their scientific contributions describing original, previously unpublished work, not currently under review by another conference or venue, addressing state-of-the-art research and topics as listed. If a submission extends work described in a previous publication of their own, we strongly encourage authors to include a citation to that publication.
Abstracts and scientific submissions must be submitted electronically via EasyChair, all other proposals can be submitted via e-mail to the summit organizers.
Full papers and short abstracts should be prepared with the Easychair EPiC series template, available online. Please prepare your submission with the letter page format and the [EPiC] mode enabled. You can indicate your submission type during the submission process in Easychair (e.g., scientific contribution, tutorial, demonstration).
Scientific contributions accepted by the TPC and presented at the summit will be published in the indexed open access journal "EPiC Series in Computing" (pending agreement). Copyrights for all contributions (including the EPiC submitted articles) will remain solely with the authors. This allows for a publication of revised versions at other venues.
The 6th OMNeT++ Community Summit will take place at the Hamburg University of Technology in Germany.
On-Site Location:
Hamburg University of Technology - Building D, Room D2.022
Am Schwarzenberg-Campus 4, 21073 Hamburg
All of the following hotels are within walking distance of the summit venue and have a good rating:
B&B Hotel Hamburg-Harburg
Theodor-Yorck-Straße 1
21079 Hamburg
Altstadthotel Harburg
Neue Straße 61
21073 Hamburg
Apartment Hotel am Sand
Sand 21 / Hölertwiete 14
21073 Hamburg
Hotel Panorama Hamburg-Harburg ★★★★
Harburger Ring 8-10
21073 Hamburg
For additional hotels, please check for instance www.booking.com and search for Hamburg-Harburg.
With more than 1.8 million people, the Free and Hanseatic City of Hamburg is the second largest city in Germany. The port of Hamburg is the third largest port in Europe. Hamburg's many streams, rivers and canals are crossed by over 2300 bridges, more than London, Amsterdam and Venice put together. The city is a major tourist destination for both domestic and overseas visitors; it was ranked 10th in the world for liveability in 2017. The average temperature in September is around 18°C.
If you would like to spend some additional days in Hamburg, you could visit the following sights:
In case you want to contact the organizers, feel free to write an e-mail to: omnetsummit2019 [at] easychair [dot] org.
The OMNeT++ Community Summit 2019 is sponsored together by the Institute of Communication Networks and the Institute of Telematics at Hamburg University of Technology. No registration fees will apply.


Selected Platform:
Quicklinks:
Alerts
Invasions
Fissures
Sorties
Deals
Trader
News
Alerts
Notifications:
Customize alert notifications
Invasions
Notifications:
Customize invasions notifications
Fissures
Notifications:
Sorties
Daily Deals
Void Trader
News


Trusted by over 1,000 organizations big and small
{% assign clients = include.clients | load_client_info | chunk_array: 6 %}
{% for chunk in clients %}
{% for client in chunk %}
{% capture image %}
{% endcapture %}
{% if client.success_story_url %}
{{ image }}
{% else %}
{{ image }}
{% endif %}
{% endfor %}
{% endfor %}


| crashrpt | |
| A crash reporting system for Windows applications |
Defines | |
| #define | crpOpenErrorReport crpOpenErrorReportA |
| Character set-independent mapping of crpOpenErrorReportW() and crpOpenErrorReportA() functions. | |
| #define | crpGetProperty crpGetPropertyA |
| Character set-independent mapping of crpGetPropertyW() and crpGetPropertyA() functions. | |
| #define | crpExtractFile crpExtractFileA |
| Character set-independent mapping of crpExtractFileW() and crpExtractFileA() functions. | |
| #define | crpGetLastErrorMsg crpGetLastErrorMsgA |
| Defines character set-independent mapping for crpGetLastErrorMsgW() and crpGetLastErrorMsgA(). | |
Functions | |
| int | crpOpenErrorReportW (LPCWSTR pszFileName, LPCWSTR pszMd5Hash, LPCWSTR pszSymSearchPath, DWORD dwFlags, CrpHandle *phReport) |
| Opens a zipped crash report file. | |
| int | crpOpenErrorReportA (LPCSTR pszFileName, LPCSTR pszMd5Hash, LPCSTR pszSymSearchPath, DWORD dwFlags, CrpHandle *phReport) |
| int | crpCloseErrorReport (CrpHandle hReport) |
| Closes the crash report. | |
| int | crpGetPropertyW (CrpHandle hReport, LPCWSTR lpszTableId, LPCWSTR lpszColumnId, INT nRowIndex, LPWSTR lpszBuffer, ULONG cchBuffSize, PULONG pcchCount) |
| Retrieves a string property from crash report. | |
| int | crpGetPropertyA (CrpHandle hReport, LPCSTR lpszTableId, LPCSTR lpszColumnId, INT nRowIndex, LPSTR lpszBuffer, ULONG cchBuffSize, PULONG pcchCount) |
| int | crpExtractFileW (CrpHandle hReport, LPCWSTR lpszFileName, LPCWSTR lpszFileSaveAs, BOOL bOverwriteExisting) |
| Extracts a file from the opened error report. | |
| int | crpExtractFileA (CrpHandle hReport, LPCSTR lpszFileName, LPCSTR lpszFileSaveAs, BOOL bOverwriteExisting) |
| int | crpGetLastErrorMsgW (LPWSTR pszBuffer, UINT cchBuffSize) |
| Gets the last CrashRptProbe error message. | |
| int | crpGetLastErrorMsgA (LPSTR pszBuffer, UINT cchBuffSize) |
| int crpCloseErrorReport | ( | CrpHandle | hReport | ) |
Closes the crash report.
| [in] | hReport | Handle to the opened error report. |
If this function fails, use crpGetLastErrorMsg() function to get the error message.
| int crpExtractFileA | ( | CrpHandle | hReport, | |
| LPCSTR | lpszFileName, | |||
| LPCSTR | lpszFileSaveAs, | |||
| BOOL | bOverwriteExisting | |||
| ) |
Extracts a file from the opened error report.
| [in] | hReport | Handle to the opened error report. |
| [in] | lpszFileName | The name of the file to extract. |
| [in] | lpszFileSaveAs | The resulting name of the extracted file. |
| [in] | bOverwriteExisting | Overwrite the destination file if it already exists? |
lpszFileName parameter should be the name of the file to extract. For more information about enumerating file names, see Examples of Using CrashRptProbe API.
lpszFileSaveAs defines the name of the file to extract to.
bOverwriteExisting flag defines the behavior when the destination file already exists. If this parameter is TRUE, the file is overwritten, otherwise the function fails.
If this function fails, use crpGetLastErrorMsg() to retrieve the error message.
| int crpExtractFileW | ( | CrpHandle | hReport, | |
| LPCWSTR | lpszFileName, | |||
| LPCWSTR | lpszFileSaveAs, | |||
| BOOL | bOverwriteExisting | |||
| ) |
Extracts a file from the opened error report.
| [in] | hReport | Handle to the opened error report. |
| [in] | lpszFileName | The name of the file to extract. |
| [in] | lpszFileSaveAs | The resulting name of the extracted file. |
| [in] | bOverwriteExisting | Overwrite the destination file if it already exists? |
lpszFileName parameter should be the name of the file to extract. For more information about enumerating file names, see Examples of Using CrashRptProbe API.
lpszFileSaveAs defines the name of the file to extract to.
bOverwriteExisting flag defines the behavior when the destination file already exists. If this parameter is TRUE, the file is overwritten, otherwise the function fails.
If this function fails, use crpGetLastErrorMsg() to retrieve the error message.
| int crpGetLastErrorMsgA | ( | LPSTR | pszBuffer, | |
| UINT | cchBuffSize | |||
| ) |
Gets the last CrashRptProbe error message.
| [out] | pszBuffer | Pointer to the buffer. |
| [in] | cchBuffSize | Size of buffer in characters. |
If buffer is too small for the error message, the message is truncated.
// .. call some CrashRptProbe function // Get the status message TCHAR szErrorMsg[256]; crpGetLastErrorMsg(szErrorMsg, 256);
| int crpGetLastErrorMsgW | ( | LPWSTR | pszBuffer, | |
| UINT | cchBuffSize | |||
| ) |
Gets the last CrashRptProbe error message.
| [out] | pszBuffer | Pointer to the buffer. |
| [in] | cchBuffSize | Size of buffer in characters. |
If buffer is too small for the error message, the message is truncated.
// .. call some CrashRptProbe function // Get the status message TCHAR szErrorMsg[256]; crpGetLastErrorMsg(szErrorMsg, 256);
| int crpGetPropertyA | ( | CrpHandle | hReport, | |
| LPCSTR | lpszTableId, | |||
| LPCSTR | lpszColumnId, | |||
| INT | nRowIndex, | |||
| LPSTR | lpszBuffer, | |||
| ULONG | cchBuffSize, | |||
| PULONG | pcchCount | |||
| ) |
Retrieves a string property from crash report.
| [in] | hReport | Handle to the previously opened crash report. |
| [in] | lpszTableId | Table ID. |
| [in] | lpszColumnId | Column ID. |
| [in] | nRowIndex | Index of the row in the table. |
| [out] | lpszBuffer | Output buffer. |
| [in] | cchBuffSize | Size of the output buffer in characters. |
| [out] | pcchCount | Count of characters written to the buffer. |
Properties are organized into tables having rows and columns. For the list of available tables, see Using CrashRptProbe API.
To get the number of rows in a table, pass the constant CRP_META_ROW_COUNT as column ID. In this case the function returns number of rows as its return value, or negative value on failure.
In all other cases the function returns zero on success.
Some properties are loaded from crash description XML file, while others are loaded from crash minidump file. The minidump is loaded once when you retrive a property from it. This reduces the overall processing time.
hReport should be the handle to the opened error report.
lpszTableId represente the ID of the table.
lpszColumnId represents the ID of the column in the table.
nRowIndex defines the zero-based index of the row in the table.
lpszBuffer defines the buffer where retrieved property value will be placed. If this parameter is NULL, it is ignored and pcchCount is set with the required size in characters of the buffer.
cchBuffSize defines the buffer size in characters. To calculate required buffer size, set lpszBuffer with NULL, the function will set pcchCount with the number of characters required.
pcchCount is set with the actual count of characters copied to the lpszBuffer. If this parameter is NULL, it is ignored.
If this function fails, use crpGetLastErrorMsg() function to get the error message.
For code examples of using this function, see Examples of Using CrashRptProbe API.
| int crpGetPropertyW | ( | CrpHandle | hReport, | |
| LPCWSTR | lpszTableId, | |||
| LPCWSTR | lpszColumnId, | |||
| INT | nRowIndex, | |||
| LPWSTR | lpszBuffer, | |||
| ULONG | cchBuffSize, | |||
| PULONG | pcchCount | |||
| ) |
Retrieves a string property from crash report.
| [in] | hReport | Handle to the previously opened crash report. |
| [in] | lpszTableId | Table ID. |
| [in] | lpszColumnId | Column ID. |
| [in] | nRowIndex | Index of the row in the table. |
| [out] | lpszBuffer | Output buffer. |
| [in] | cchBuffSize | Size of the output buffer in characters. |
| [out] | pcchCount | Count of characters written to the buffer. |
Properties are organized into tables having rows and columns. For the list of available tables, see Using CrashRptProbe API.
To get the number of rows in a table, pass the constant CRP_META_ROW_COUNT as column ID. In this case the function returns number of rows as its return value, or negative value on failure.
In all other cases the function returns zero on success.
Some properties are loaded from crash description XML file, while others are loaded from crash minidump file. The minidump is loaded once when you retrive a property from it. This reduces the overall processing time.
hReport should be the handle to the opened error report.
lpszTableId represente the ID of the table.
lpszColumnId represents the ID of the column in the table.
nRowIndex defines the zero-based index of the row in the table.
lpszBuffer defines the buffer where retrieved property value will be placed. If this parameter is NULL, it is ignored and pcchCount is set with the required size in characters of the buffer.
cchBuffSize defines the buffer size in characters. To calculate required buffer size, set lpszBuffer with NULL, the function will set pcchCount with the number of characters required.
pcchCount is set with the actual count of characters copied to the lpszBuffer. If this parameter is NULL, it is ignored.
If this function fails, use crpGetLastErrorMsg() function to get the error message.
For code examples of using this function, see Examples of Using CrashRptProbe API.
| int crpOpenErrorReportA | ( | LPCSTR | pszFileName, | |
| LPCSTR | pszMd5Hash, | |||
| LPCSTR | pszSymSearchPath, | |||
| DWORD | dwFlags, | |||
| CrpHandle * | phReport | |||
| ) |
Opens a zipped crash report file.
| [in] | pszFileName | Zipped report file name. |
| [in] | pszMd5Hash | String containing MD5 hash for the ZIP file data. |
| [in] | pszSymSearchPath | Symbol files (PDB) search path. |
| [in] | dwFlags | Flags, reserved for future use. |
| [out] | phReport | Handle to the opened crash report. |
pszFileName should be the name of the error report (ZIP file) to open. Absolute or relative path accepted. This parameter is required.
pszMd5Hash is a string containing the MD5 hash calculated for pszFileName. The MD5 hash is a sequence of 16 characters being used for integrity checks. If this parameter is NULL, integrity check is not performed.
If the error report is delivered by HTTP, the MD5 hash can be extracted by server-side script from the 'md5' parameter. When the error report is delivered by email, the MD5 hash is attached to the mail message. The integrity check can be performed to ensure the error report was not corrupted during delivery. For more information, see Sending Error Reports.
pszSymSearchPath parameter defines where to look for symbols files (*.PDB). You can specify the list of semicolon-separated directories to search in. If this parameter is NULL, the default search sequence is used. For the default search sequence, see the documentation for SymInitialize() function in MSDN.
Symbol files are required for crash report processing. They contain various information used by the debugger. For more information about saving symbol files, see Preparing to Software Release.
dwFlags is currently not used, should be zero.
phReport parameter receives the handle to the opened crash report. If the function fails, this parameter becomes zero.
This function does the following when opening report file:
On failure, use crpGetLastErrorMsg() function to get the last error message.
Use the crpCloseErrorReport() function to close the opened error report.
| int crpOpenErrorReportW | ( | LPCWSTR | pszFileName, | |
| LPCWSTR | pszMd5Hash, | |||
| LPCWSTR | pszSymSearchPath, | |||
| DWORD | dwFlags, | |||
| CrpHandle * | phReport | |||
| ) |
Opens a zipped crash report file.
| [in] | pszFileName | Zipped report file name. |
| [in] | pszMd5Hash | String containing MD5 hash for the ZIP file data. |
| [in] | pszSymSearchPath | Symbol files (PDB) search path. |
| [in] | dwFlags | Flags, reserved for future use. |
| [out] | phReport | Handle to the opened crash report. |
pszFileName should be the name of the error report (ZIP file) to open. Absolute or relative path accepted. This parameter is required.
pszMd5Hash is a string containing the MD5 hash calculated for pszFileName. The MD5 hash is a sequence of 16 characters being used for integrity checks. If this parameter is NULL, integrity check is not performed.
If the error report is delivered by HTTP, the MD5 hash can be extracted by server-side script from the 'md5' parameter. When the error report is delivered by email, the MD5 hash is attached to the mail message. The integrity check can be performed to ensure the error report was not corrupted during delivery. For more information, see Sending Error Reports.
pszSymSearchPath parameter defines where to look for symbols files (*.PDB). You can specify the list of semicolon-separated directories to search in. If this parameter is NULL, the default search sequence is used. For the default search sequence, see the documentation for SymInitialize() function in MSDN.
Symbol files are required for crash report processing. They contain various information used by the debugger. For more information about saving symbol files, see Preparing to Software Release.
dwFlags is currently not used, should be zero.
phReport parameter receives the handle to the opened crash report. If the function fails, this parameter becomes zero.
This function does the following when opening report file:
On failure, use crpGetLastErrorMsg() function to get the last error message.
Use the crpCloseErrorReport() function to close the opened error report.


IDL source Global indexinterface ICapeNumericAnalysis : ICapeUtilityParameter
This is the basic Interface of the Numerical Analysis design: it cosntitutes the minimum requirements for an analysis.
It is an "abstract" Interface. It is not distributed. Consequently, no client can obtain its object reference.
Checks the consistency of flowsheet with respect to the numerical analysis. Could be used before any computation.
For instance, for a tearing analysis the method checks that the flowsheet contains only one partition.
The behaviour of this method is implementation dependent.
Pre Conditions: None.
Post Conditions: None.
Destroys the interface.
The behaviour of this method is implementation dependent.
Pre Conditions: None.
Post Conditions: None.
Gets the maximum number of streams which can be handled by the numerical analysis.
Pre Conditions: None.
Post Conditions: None.
Gets the maximum number of units which can be handled by the numerical analysis.
Pre Conditions: None.
Post Conditions: None.
Gets the name of the interface.
Pre Conditions: None.
Post Conditions: None.
Performs the numerical analysis on a flowsheet.
Pre Conditions: None.
Post Conditions: None.
CapeConsistencyCode CheckFlowsheetConsistency(in ICapeNumericFlowsheet flowsheet) raises(CapeBAD_PARAM, CapeIMP_LIMIT, CapeNO_IMPLEMENT, CapeNO_CONVERGE, CapeUNKNOWN);
Checks the consistency of flowsheet with respect to the numerical analysis. Could be used before any computation.
For instance, for a tearing analysis the method checks that the flowsheet contains only one partition.
The behaviour of this method is implementation dependent.
Pre Conditions: None.
Post Conditions: None.
CapeBoolean Destroy() raises(CapeNO_IMPLEMENT, CapeUNKNOWN);
Destroys the interface.
The behaviour of this method is implementation dependent.
Pre Conditions: None.
Post Conditions: None.
CapeLong GetMaximumStreamCount() raises(CapeUNKNOWN);
Gets the maximum number of streams which can be handled by the numerical analysis.
Pre Conditions: None.
Post Conditions: None.
CapeLong GetMaximumUnitCount() raises(CapeUNKNOWN);
Gets the maximum number of units which can be handled by the numerical analysis.
Pre Conditions: None.
Post Conditions: None.
CapeString GetName() raises(CapeUNKNOWN);
Gets the name of the interface.
Pre Conditions: None.
Post Conditions: None.
CapeConvergenceCode Perform(in ICapeNumericFlowsheet flowsheet) raises(CapeBAD_PARAM, CapeIMP_LIMIT, CapeNO_IMPLEMENT, CapeNO_CONVERGE, CapeUNKNOWN);
Performs the numerical analysis on a flowsheet.
Pre Conditions: None.
Post Conditions: None.


Komodo
talk-trading
A channel to discuss trading.

  Should they leave all the warm countries....  South America is filled with white people not to mention South Africa
Tek
21-Jun-19 12:39 PM
yes cause war
Malti
21-Jun-19 12:39 PM
had 1 argument and believes he owns the truth
just the way you stated your 'argument' could have made a great difference
"black people need more vitamin D"
is one suggestion
Tek
21-Jun-19 12:40 PM
they need more sun exposure for the same vitamin D productions
if you are low vitamin d you are depressed
lmao unbelievable
im just gonna stop now
later
siu - Chainmakers
21-Jun-19 12:41 PM
Now we are into racist bullshit heh
1
1
Sherdogz
21-Jun-19 12:41 PM
@Tek
if you're going to be rich just skip Switzerland an go to Greenland
(edited)
hyperDEX
21-Jun-19 12:41 PM
@siu - Chainmakers
doesnt make any sense about people dumping now but not when binance was closed as there would have been KMD on there yet it pumped
Malti
21-Jun-19 12:42 PM
back to trading
1
siu - Chainmakers
21-Jun-19 12:42 PM
It pumped because of lack of asks
And of course because the market and TA dictated it
Malti
21-Jun-19 12:43 PM
you know what's not dumping?
ARRR
siu - Chainmakers
21-Jun-19 12:43 PM
Now its correcting in the USD chart to retest the 8/41 weekly golden cross
ARRR is a great buy btw
Malti
21-Jun-19 12:44 PM
especially with bags filled with an average buying price of 0.05€
p.
21-Jun-19 12:44 PM
KMD bounce or die ffs
siu - Chainmakers
21-Jun-19 12:44 PM
It needs to die first
Malti
21-Jun-19 12:44 PM
jl offered to buy yesterday
at marketprice
1
siu - Chainmakers
21-Jun-19 12:45 PM
I missed that. Would have been a good flip
MdAmazing
21-Jun-19 12:45 PM
Sell volume isn't very impressive. Think it's a good time to lift more.
siu - Chainmakers
21-Jun-19 12:45 PM
No, true, sell volume is drained
Malti
21-Jun-19 12:46 PM
people who are offloading now aren't the smartest of the bunch
but they're not as bad as racists
so that's still good
Markus
21-Jun-19 12:46 PM
https://www.tradingview.com/x/YxzDcrIR/
hyperDEX
21-Jun-19 12:47 PM
where should we bounce
@siu - Chainmakers
siu - Chainmakers
21-Jun-19 12:47 PM
Near or below the ATL
13k sats
hyperDEX
21-Jun-19 12:47 PM
ouch
siu - Chainmakers
21-Jun-19 12:47 PM
Nothing from here to there
Usd wise it wont hurt much
Btc wise is a huge miss
Malti
21-Jun-19 12:48 PM
it's gonna be great actually
we're gonna be all sold out
when alt season starts
hyperDEX
21-Jun-19 12:48 PM
ico was 12.8k
Malti
21-Jun-19 12:48 PM
let's get ready to moon
siu - Chainmakers
21-Jun-19 12:48 PM
Not bad idea to start buying now
But who knows how low it can go
hyperDEX
21-Jun-19 12:49 PM
who has btc to buy
siu - Chainmakers
21-Jun-19 12:49 PM
The people havign a blast right now w8th the btc rise
hyperDEX
21-Jun-19 12:49 PM
true
MdAmazing
21-Jun-19 12:50 PM
If ETH can find a bottom now might turn the alt market
siu - Chainmakers
21-Jun-19 12:51 PM
Bottom?
LocoMB
21-Jun-19 12:51 PM
bets are open for when $10k BTC
Markus
21-Jun-19 12:51 PM
lol
MdAmazing
21-Jun-19 12:51 PM
Well temporary
Markus
21-Jun-19 12:51 PM
Eth is mooning
siu - Chainmakers
21-Jun-19 12:51 PM
I have eth and its compensating for my kmd loses
Eth should get to $400 soon
And after that, ATH wont be a far fetch
More ROI expected than from btc
hyperDEX
21-Jun-19 12:52 PM
I wonder what would happen if BTC gets rejected at 10k?
we all die in fiat and BTC value
siu - Chainmakers
21-Jun-19 12:53 PM
It wont
MdAmazing
21-Jun-19 12:53 PM
Don't think it wants to stop yet (I hope)
siu - Chainmakers
21-Jun-19 12:53 PM
Btc 10k will run in the news cycle
12k is more like it
Cryptoon
21-Jun-19 12:54 PM
I have 14k as an alert in my blockfolio
As it has been +-bottom in the past
siu - Chainmakers
21-Jun-19 12:54 PM
After that, if it manages to hop above 12k before the month closes, ATH will be a piece of cake
1
And i think that is when the real alt rise will start
1
Markus
21-Jun-19 12:54 PM
inb4 btc 20k and kmd 5k sats


Twitter Plugin
Follow me for further updates to this script and other JavaScript goodness :-)
Demo shows two twitter calls, two for Remy Sharp and John Resig. View source for the source code. Remy's second tweet call will timeout after 1 second and replace the container.
Supported template variables
- text - the actual status message
- id - id of status message
- source
- created_at
- time - relative 'friendly' time
- user_name - real name
- user_screen_name - username
- user_id
- user_profile_image_url - avatar
- user_url - home page
- user_location
- user_description
Simple options - no template
Timeout after 1 second and cancel twitter call
Template based with 1 tweet (linkify disabled)
OhioLINK Test Template


Preference Heterogeneity with Mixture Distributions
Table of Contents
1 Reading Quiz
- Explain why mixed logit is such a popular model, according to Train. What are its strong points vis-à-vis the IIA problem?
- What are the computational drawbacks to mixed logit?
- What are two names for a mixed logit model that has a discrete mixing distribution?
- Explain why the EM algorithm is a potentially powerful tool for estimating discrete choice models.
- How does the EM algorithm work? Why does it work?
2 Preference Heterogeneity
So far, we have only looked at models where all agents have identical preferences
- Mathematically, \(\beta_{RedBus}\) does not vary across agents
- Implies everyone has same price elasticity, etc.
- Failure to account for this heterogeneity will result in a misleading model
2.1 Observable preference heterogeneity
One solution to the homogeneity problem is to add interaction terms
- Suppose we have a 2-option transportation model:
- We could introduce heterogeneity in \(\gamma\) by interacting \(Z_j\) with \(X_i\):
- Now a change in \(Z_j\) will have a heterogeneous impact on utility depending on \(X_i\)
- e.g. those w/diff. income \((X_i)\) may be more/less sensitive to changes in price \((Z_j)\)
2.2 Unobservable preference heterogeneity
Many dimensions of preferences are likely unobserved. In this case, we need to ``interact'' \(Z\) with something unobserved
- One way to do this is to assume that \(\beta\) or \(\gamma\) varies across people
- Assume some distribution (e.g. Normal), called the mixing distribution
- Then integrate this out of the likelihood function
What does the integration means in this case?
3 Mixed Logit likelihood function
Assume, e.g. \(\gamma_i \sim F\) with pdf \(f\) and distributional parameters \(\mu\) and \(\sigma\)
- Then the logit choice probabilities become
- Annoyance: the log likelihood now has an integral inside the log!
3.1 Common mixing distributions
- Normal
- Log-normal
- Uniform
- Triangular
- Can also go crazy and specify a multivariate normal
- This would allow, e.g. heterogeneity in \(\gamma\) to be correlated with \(\beta\)
3.2 Mixed Logit estimation
With the integral inside the log, estimation of the mixed logit is intensive. To estimate the likelihood function, need to numerically approximate the integral
- The most common way of doing this is quadrature
- Another common way of doing this is by simulation (Monte Carlo integration)
3.3 Finite Mixture Distributions
Another option to mixed logit is to assume the mixing distribution is discrete. We assume we have missing variable that has finite support and is independent from the other variables
What does `finite support' means?
- Let \(\pi_s\) denote the probability of being in the $s$th unobserved group
- Integrating out over the unobserved groups then yields the following log likelihood:
3.4 Mixture Distributions and Panel Data
With panel data, mixture dist. allows for permanent unobserved heterogeneity
- Here the unobs. variable is fixed over time and indep. of the covariates at \(t=1\)
- The log likelihood function for the finite mixture case is then:
- And for the mixed logit case is:
3.4.1 Short Review
Linear Model
\[ y_{it} = x_{it}\cdot \beta + \underbrace{a_{it} + \epsilon_{it}}_{\text{Composite error} (v_{it})} \]
- Fixed effects: \(\hat \beta\) is robust to related \(X\) and \(a_{it}\)
- Random effects: \(\hat \beta\) is efficient, but \(X\) must be independent to \(a_{it}\), otherwise inconsistent
4 Dynamic Selection
Often, we want to link the choices to other outcomes:
- labor force participation and earnings
- market entry and profits
If individuals choose to participate in the labor market based upon unobserved wages, our estimates of the returns to participating will be biased
- Mixture distributions provide an alternative way of controlling for selection
- Assumption: no selection problem once we control for the unobserved variable
Let \(Y_{1t}\) denote the choice and \(Y_{2t}\) denote the outcome
- The previous assumption means the joint likelihood is separable:
where \(s\) is the unobserved type
4.1 Estimation in Stages
Suppose \(s\) was observed. There'd be no selection problem as long as we could condition on \(s\) and \(X_{1t}\). The log likelihood function is:
\begin{align*} \ell=&\sum_{i}\sum_t \ell_1(Y_{1t}|X_{1t},\alpha_1,s)+\ell_2(Y_{2t}|X_{2t},\alpha_2,s) \end{align*}Estimation could proceed in stages:
- Estimate \(\alpha_2\) using only \(\ell_2\)
- Taking the estimate of \(\alpha_2\) as given, estimate \(\alpha_1\) using \(\ell_1\)
4.1.1 Non-separable means no stages
When \(s\) is unobserved, however, the log likelihood function is not additively separable:
\begin{align*} \ell=&\sum_i\log\left(\sum_s\pi_s\prod_t\mathcal{L}(Y_{1t}|X_{1t},\alpha_1,s)\mathcal{L}(Y_{2t}|X_{2t},\alpha_2,s)\right) \end{align*}where \(\mathcal{L}\) is a likelihood function
- Makes sense: if there is a selection problem, we can't estimate one part of the problem without considering what is happening in the other part
4.2 The EM Algorithm
We can get additive separability of the finite mixture model with the EM algorithm (``Expectation-Maximization''). The algorithm iterates on two steps:
- E-step: estimate parameters having to do with the mixing distribution (i.e. the \(\pi\)'s)
- M-step: pretend you observe the unobserved variable and estimate
The EM algorithm is used in other applications to fill in missing data
- In this case, the missing data is the permanent unobserved heterogeneity
With the EM algorithm, the non-separable likelihood function
\begin{align*} \ell=&\sum_i\log\left(\sum_s\pi_s\prod_t\mathcal{L}(Y_{1t}|X_{1t},\alpha_1,s)\mathcal{L}(Y_{2t}|X_{2t},\alpha_2,s)\right) \end{align*}can be written in a form that is separable:
\begin{align*} \ell=&\sum_i\sum_s q_{is}\sum_t\ell_1\left(Y_{1t}|X_{1t},\alpha_1,s\right)+\ell_2\left(Y_{2t}|X_{2t},\alpha_2,s)\right) \end{align*}where \(q_{is}\) is the probability that \(i\) belongs to group \(s\)
- \(q_{is}\) satisfies \(\pi_s = \frac{1}{N}\sum_{i}q_{is}\)
4.2.1 Estimation in stages again
We can now estimate the model in stages because of the restoration of separability. The only twist is that we need to weight by the \(q\)'s in each estimation stage
- Stage 1 of M-step: estimate \(\ell(Y_{1t}|X_{1t},\alpha_1,s)\) weighting by the \(q\)'s
- Stage 2 of M-step: estimate \(\ell(Y_{2t}|X_{1t},\alpha_1,s)\) weighting by the \(q\)'s
- E-step: update the \(q\)'s by calculating
- Iterate on E and M steps until the \(q\)'s converge arcidiaconoJones2002
5 To Recap
Why are we doing all of this difficult work?
- Because preference heterogeneity allows for a more credible structural model
- Introducing preference heterogeneity can make the model intractible
- Discretizing the distribution of heterogeneity and using the EM algorithm can help


To get this demo running, please provide two files: myMovie.m4v and poster.jpg
The plugin talks to the htmlviewer to call the javascript functions or query value directly.
see also http://developer.apple.com/library/safari/#documentation/AudioVideo/Conceptual/Using_HTML5_Audio_Video/ControllingMediaWithJavaScript/ControllingMediaWithJavaScript.html


I had to work for a part-time job while I was studying. I worked at Paper lunch and Swensens after I finished the class. All of my free time was spent on my part-time job. Then, I joined an internship. I learning programming and develop
program with python language, so the company hired me in part time job in programmer position after finished the internship program. I graduated with an Electrical Engineering degree but I like programming so I need to work as a
programmer.
Name: Surasak Theerathongdee
Address: 80/854 Lumpini megacity bangna, Bangkaew, Bangphi, Samutprakarn 10540
Mobile:<PHONE_NUMBER>
Email<EMAIL_ADDRESS>Date of Birth: 7 February 1992
Nationality: Thai
Age: 27
Marital Status: Single
Develop all program request from customer such as company website, ERP system.
Develop program support production line and section such as job request, drawing control, control kanaban system.
Develop web application about javascript map and android application.
GPA : 2.01
GPA : 3.05
Create flash game with action script language and set action in flash (.swf file) for education.
Web application and android application for mark event in location and send to server for report in javascript map.
View ExampleJob request online for engineering section, when user want to requst engineering section then fill a form requirement and send to system for approve this job.
View ExampleModified control kanban system using VB language to create, print, manage KANBAN for production process.
View ExampleDevelop system for compare master image and real time image for judgement production is "OK" or "NG" from camera.
View ExampleSystem for control drawing outside company when want to send to outside, fill form and send to approve.
View ExampleSystem for report real time production line data by javascript graph. such qty of production, trouble machine.
View ExampleDevelop company website front-end and back-end for customer using edit data in website.
View ExampleDevelop ERP system about all of the various processes that are essential to running a business, including inventory and order management.
View Example


File Class
The file class offers a set of methods to working with files & directories. This can be done through
some helper methods or through a more advanced object oriented method where all files and directories are
wrapped in objects.
Usage
The File class's basic usage it through its helper methods which are listed below.
create($basepath, $name, $contents = null, $area = null)
Create a file.
| Static |
Yes |
| Parameters |
| Param |
Default |
Description |
| $basepath |
required |
Location to create the new file in |
| $name |
required |
Filename for the new file |
| $contents |
null |
What to put into the new file |
| $area |
null |
Area object to use, defaults to main config (see advanced
usage for more information) |
|
| Returns |
true |
| Throws |
File_Exception when creation fails |
| Example |
File::create(DOCROOT, 'test.txt', 'Contents for file.');
|
create_dir($basepath, $name, $chmod = 0777, $area = null)
Create a directory.
| Static |
Yes |
| Parameters |
| Param |
Default |
Description |
| $basepath |
required |
Location to create the new directory in |
| $name |
required |
Name for the new directory |
| $chmod |
null |
Rights for the new directory |
| $area |
null |
Area object to use, defaults to main config (see advanced
usage for more information) |
|
| Returns |
bool - the output of the mkdir() PHP function |
| Throws |
File_Exception when creation fails |
| Example |
File::create(DOCROOT, 'test', 0755);
|
read($path, $as_string = false, $area = null)
Reads a file.
| Static |
Yes |
| Parameters |
| Param |
Default |
Description |
| $path |
required |
Location of the file to read |
| $as_string |
false |
Makes it use readfile() on false or file_get_contents() on true |
| $area |
null |
Area object to use, defaults to main config (see advanced
usage for more information) |
|
| Returns |
int|string - returns the number of bytes when $as_string was set to false or the file's contents
as a string when it was set to true. |
| Example |
// Returns 'Contents for file.' if you created the file in the example for File::create()
File::read(DOCROOT.'test.txt', true);
|
read_dir($path, $depth = 0, $filter = null, $area = null)
Reads directory contents into an array.
| Static |
Yes |
| Parameters |
| Param |
Default |
Description |
| $path |
required |
Location of the directory to read |
| $depth |
0 |
How deep the subdirectories are recursed: 0 for unlimited, 1 for only the current
directory or specific number. |
| $filter |
null |
A filter for which files to read: array() for all or null for
all but hidden. |
| $area |
null |
Area object to use, defaults to main config (see advanced
usage for more information) |
|
| Returns |
array - a natural-case sorted array with subdirectories on top. Directories have a string key
with array value (or false when not read because of $depth limitation) and files are int indexed
with filename as value. |
| Example |
File::read(DOCROOT);
|
update($basepath, $name, $contents = null, $area = null)
Updates a file.
| Static |
Yes |
| Parameters |
| Param |
Default |
Description |
| $basepath |
required |
Location of the file to update |
| $name |
required |
Filename for the file. If it does not exist, it will be created |
| $contents |
null |
What to put into the file |
| $area |
null |
Area object to use, defaults to main config (see advanced
usage for more information) |
|
| Returns |
true |
| Throws |
File_Exception when creation fails |
| Example |
File::update(DOCROOT, 'test.txt', 'New contents for file.');
|
append($basepath, $name, $contents = null, $area = null)
Appends to an existing file.
| Static |
Yes |
| Parameters |
| Param |
Default |
Description |
| $basepath |
required |
Location of the file to append to |
| $name |
required |
Filename for the file. This file has to exist |
| $contents |
null |
What to add to the file |
| $area |
null |
Area object to use, defaults to main config (see advanced
usage for more information) |
|
| Returns |
true |
| Throws |
File_Exception when the file does not exist |
| Example |
File::append(DOCROOT, 'test.txt', 'Additional contents for file.');
|


Channing Tatum and Jenna Dewan finalise their divorce
Hollywood stars Channing Tatum and Jenna Dewan have finalised their divorce.
The former couple met on the set of 2006 romantic drama Step Up before tying the knot in 2009.
Magic Mike star Tatum, 39, and Dewan, 38, announced their split in April 2018 after eight years of marriage.
They share six-year-old daughter Everly, who was born in London. Their divorce was finalised in Los Angeles on Tuesday, legal documents show.
No details of the custody arrangements for Everly were given.
In a joint statement announcing their split in April 2018, Tatum and Dewan said: “Absolutely nothing has changed about how much we love one another, but love is a beautiful adventure that is taking us on different paths for now.”
Signed “Chan&Jenna”, they said there were “no secrets or salacious events at the root of our decision – just two best friends realising it’s time to take some space and help each other live the most joyous, fulfilled lives as possible”.
Since the split, Tatum has started dating British singer Jessie J, while Dewan recently announced she is expecting her first child with partner and actor Steve Kazee.


Full access to our social celebrity database
One custom audience
Full audience analysis including expected performance
Full access to our social celebrity database
Two custom audience
Full audience analysis including expected performance
Full access to our social celebrity database
Three custom audience
Full audience analysis including expected performance
Premium Support


I am currently a student at Learn Academy where I learn all things from React to Ruby on Rails so I can hone my craft in reaching my goal of having a career in Software Engineering. I also have a degree in Computer Engineering and I have a passion for problem solving, programming, all things in technology, and life long learning.
An intensive 3-month program and a 1-month internship where students learned the fundamentals of highly versatile front-end and back-end languages.
These technologies include but are not limited to: JavaScript, React.js, Ruby, Ruby on Rails, Amazon AWS (Cloud9), SQL, PostgreSQL, HTML, CSS, and version control with Git.
Responsible for overseeing the daily operations of a startup e-commerce company and its employees where we produce, sell, and distribute our product to customers online across the nation.
Worked in close collaboration with the business owner, with duties including the hiring of employees, training staff, monitoring financial performance, and planning sales strategies in order to improve profit.
Lead a team of six other engineers in a five-month long senior design project where Computer Science, Electrical Engineering, and Project Management concepts were implemented in a robotics competition among seven other teams.
Responsibilities would include developing schedules, delegating tasks to individuals, managing risks and issues, monitoring and reporting progress, benefits realization, and team leadership.
The B.S. degree program in Computer Engineering provides a solid foundation in the fundamentals of mathematics, science, computer hardware, computer software, and engineering design that are needed to practice the profession or to pursue a graduate degree in the field.
Computer Engineers are involved in the design, development, manufacture, installation, and operation of general purpose and embedded computers of all sorts. They are both concerned with hardware (i.e., the electronic circuits and devices that actually store and process information) and software (i.e., the programs that control the operation of the hardware).
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Curabitur in iaculis ex. Etiam volutpat laoreet urna. Morbi ut tortor nec nulla commodo malesuada sit amet vel lacus. Fusce eget efficitur libero. Morbi dapibus porta quam laoreet placerat.
View/Demo ProjectLorem ipsum dolor sit amet, consectetur adipiscing elit. Curabitur in iaculis ex. Etiam volutpat laoreet urna. Morbi ut tortor nec nulla commodo malesuada sit amet vel lacus. Fusce eget efficitur libero. Morbi dapibus porta quam laoreet placerat.
View/Demo Project


This is the help file for DeltaGene.
Contents
Introduction
General usage
Operators and output
HPO Browser
Source
Introduction
DeltaGene is a small program used to compare sets of genes. Currently the program supports the default, AND, NOT and XOR operators.
DeltaGene can also be used to explore the heirarchy of HPO terms in the database. The database is always updated to the latest version when possible.
License
This is free and unencumbered software released into the public domain.
Anyone is free to copy, modify, publish, use, compile, sell, or
distribute this software, either in source code form or as a compiled
binary, for any purpose, commercial or non-commercial, and by any
means.
In jurisdictions that recognize copyright laws, the author or authors
of this software dedicate any and all copyright interest in the
software to the public domain. We make this dedication for the benefit
of the public at large and to the detriment of our heirs and
successors. We intend this dedication to be an overt act of
relinquishment in perpetuity of all present and future rights to this
software under copyright law.

IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR
OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
OTHER DEALINGS IN THE SOFTWARE.
For more information, please refer to
http://unlicense.org/


Using a Keyboard
All keys are configurable in the user interface from the Configuration Menu. This list shows the default keyboard configuration:
Main Keys
- 5, 6, 7, 8
- Insert coin (players 1, 2, 3, 4)
- 1, 2, 3, 4
- Start (players 1, 2, 3, 4)
- Arrow keys
- Move Joystick
- Left Control
- Button 1
- Left Option
- Button 2
- Space
- Button 3
- ESC (Escape)
- Quits the game
Button 1 usually represents a game's primary action, like "Fire" or "Jump." Button 2 is the secondary action, like "Thrust" in Asteroids. Button 3 is used for "Hyperspace" in Asteroids. Some games won't use any buttons, while others (such as Defender or Street Fighter) will use many buttons.
Games which use two joysticks simultaneously, such as Karate Champ, Robotron, Crazy Climber, or Battle Zone, default to using the E/S/D/F keys for the left joystick and the I/J/K/L keys for the right joystick
Other Keys
- Tab
- Toggles the Configuration Menu
- ~ (Tilde)
-
Toggles the On Screen Display. Use the up and down arrow keys to
select which parameter to modify (global volume, mixing level,
gamma correction etc.) Use the left and right to arrow keys to
change modify the value. Some parameters adjust with finer or
coarser control if you hold down the CTRL or SHIFT keys while
pressing the left/right arrows. Use the ENTER key to reset a value
to its default.
- P
- Pauses the game
- SHIFT+P
- While paused, advances to next frame
- F1
- Toggle crosshairs for games that use them
- F2
- Service Mode
- F3
- Resets the game
- F4
-
Shows the game palette, decoded GFX, and any tilemaps. Use the
ENTER key to switch between the three modes (palette, graphics,
and tilemaps). Press F4 again to turn off the display.
- F7
-
Load a save state. You will be requested to press a key to
determine which save state you wish to load. Note that the save
state feature is not supported for a large number of drivers. If
support is not enabled for a given driver, you will receive a
warning when attempting to save or load.
- SHIFT+F7
-
Create a save state. Requires an additional keypress to identify
the state, similar to the load option above.
- F8
- Decrease frame skip on the fly
- F9
- Increase frame skip on the fly
- F10
- Toggle speed throttling
- F11
- Toggles speed display
- SHIFT+F11
- Toggles profiler display (debug builds only)
- F12
- Saves a screen snapshot