Here’s why all the top brands want to have Ayushmann Khurrana on board
by FilmfareAyushmann has always been experimental and unconventional with his choice of roles right from the beginning of his career. From his debut as a sperm donor in Shoojit Sircar’s Vicky Donor to Bala where played the role of a bald guy battling premature balding. The Andhadhun actor now has a genre to his name in Bollywood called ‘The Ayushmann Khurrana Genre’.
The year 2019 has been a great year for Ayushmann with two of his movies joining the 100 crore club the actor is on a different high. Reports suggest that the Bala actor has seen a 200 per cent hike in the number of his endorsement deals. Ayushmann is currently endorsing 20 brands. From real estate to mobile, from wafers to eyewear, Khurrana has managed to sign lucrative deals with all of them.
All of Ayuhsmann’s movies have been loved by both critics and the audience. Hence, there’s no doubt why the actor has all these deals coming his way. A report also stated, "Ayushmann is a huge icon of the Indian youth and every top brand wants to bring him on board because seldom do you find a star that evokes all these qualities!”


Core conversational engine with the main interface BotBus used by all bot implementations
BotBus |
A new bus instance is created for each user request. interface BotBus : Bus<BotBus> |
BotRepository |
Advanced bot configuration. object BotRepository |
Bus |
Base methods for the bus. interface Bus<T : Bus<T>> : I18nTranslator |
ConnectorController |
Controller to connect Connector and BotDefinition. interface ConnectorController |
I18nTranslator |
Translates CharSequence depending of current userLocale, userInterfaceType and targetConnectorType. interface I18nTranslator : I18nKeyProvider |
WebSocketController |
Internal object used to manage websocket events. object WebSocketController |
botModule |
The bot ioc module. val botModule: Module |


Slug_Detection¶
Slug_Detection¶
Part of slugdetection package
@author: Deirdree A Polak github: dapolak
-
class
slugdetection.Slug_Detection.
Slug_Detection
(well)¶ Classifies intervals of pressure and temperature data as interval leading to a slug flow or intervals leading to a normal flow.
Parameters: well (Spark Data Frame) – well data frame, includes continuous pressure and temperature data -
LogReg_pred
(true_label=True, **kwargs)¶ Predict data labels from trained Logistics Regression model
Parameters: - true_label (bool (optional)) – Whether the data used has true labels (default is True)
- kwargs –
- X_test : Pandas DataFrame
- For user, if additional data needs to be predicted. Data frame needs to be feature vector format
- y-test : Pandas Series or Numpy array
- For user, labels of inputted X_test data
Returns: - pred (array) – prediction labels for the test data
- proba (array) – prediction probababilities for the test data
- score (float) – Accuracy score of the predictions, if true_label = True
- cf (array) – Confusion matrix, as created by Scikit Learn’s Confusion matrix on the accuracy of the results, if true_label = True
-
LogReg_train
(top_features=[], C=0.01, max_iter=50, split=True, **kwargs)¶ Train Logistics Regression model
Parameters: - top_features (list of str (optional)) – List of the top features names. If list not empty, the Logistics Regression model will only be trained using the listed features. (default is [] meaning all features)
- C (float (optional)) – Regularization parameter for Logitics Regression (default is 0.01)
- max_iter (int (optional)) – Maximum iteration parameter for Logistics Regression (default is 50)
- split (bool) – True for new split, False to use same split as RF model was trained on
- kwargs –
- test_size : float
- For split_data method, percentage repartition of testing data
-
RF_predict
(true_label=True, **kwargs)¶ Predict data labels from trained Random Forest model
Parameters: - true_label (bool (optional)) – Whether the data used has true labels (default is True)
- kwargs –
- X_test : Pandas DataFrame
- For user, if additional data needs to be predicted. Data frame needs to be feature vector format
- y-test : Pandas Series or Numpy array
- For user, labels of inputted X_test data
Returns: - pred (array) – prediction labels for the test data
- score (float) – Accuracy score of the predictions, if true_label = True
- cf (array) – Confusion matrix, as created by Scikit Learn’s Confusion matrix on the accuracy of the results, if true_label = True
-
RF_train
(n_estimators=15, max_depth=None, bootstrap=True, **kwargs)¶ Train the Random Forest model
Parameters: - n_estimators (int) – Number of Decision Trees in Random Forest model (optional, default is 15)
- max_depth (int) – Maximum depth of the Decisions Trees in Random Forest model (optional, default is None
- bootstrap (bool) – Whether bootstrap samples are used when building decision trees. If False, the whole datset is used to build each tree in the Random Forest (optional, default is True)
- kwargs –
- test_size : float
- For split_data method, percentage repartition of testing data
-
clean_short_sub
(min_df_size=300)¶ Deletes entries in pandas data frame attribute pd_df that are continuous in time for less than min_df_size
Parameters: min_df_size (int (optional)) – Minimum size of sub data frame that will be considered forward in the analysis (default is 300)
-
data_prep
(**kwargs)¶ Quick data preparation from raw pandas data frame to feature vectors
Parameters: kwargs – - slug_diff : float
- Argument of label_slugs method. Minimum WHP differential value to be assumed a slug peak
- size_list : int
- Argument of format_data method. Size of data frame to use for classification
- max_clean_count : int
- Argument of format_data method. Maximum number of data frames to create that lead to a normal flow.
- split_num : int
- Argument of feature_vector method. Number of splits the data frame
- time_predict : int
- Argument of feature_vector method. Number of minutes before slug flow.
- percentage_significance : float
- Argument of feature_vector method. For the significant feature, percentage value for which to state an increase/decrease in the data was significant compare to the original value.
- standardise : bool
- Argument of feature_vector method. Whether to standardise the data or not.
-
feature_selection
(feature_score, top_n=15)¶ Selects the top_n most important features from the feature_score list
Parameters: - feature_score (list of floats) – List of feature scores, in order. As produced by the RF_train method
- top_n (int) – Number of features to return
Returns: top_feature – List containing the names of the top_n features
Return type: list of str
-
feature_vector
(split_num=5, time_predict=60, percentage_significance=0.1, standardise=True)¶ Transform classification data into feature vectors.
Parameters: - split_num (int (optional)) – Number of time to split the data frame (default is 5)
- time_predict (int (optional)) – Number of minutes to ignore at the end of the interval. This number will also be the number of minutes the classifier is trained to recognize a slug before it occurs (default is 60)
- percentage_significance (float (optional)) – For the significant feature, percentage value for which to state an increase/decrease in the data was significant compare to the original value. Must be below 1 (default is 0.10)
- standardise (bool (optional)) – Whether to standardise the data or not (default is True)
-
format_data
(first_slug, size_list=240, max_clean_count=10000)¶ Formats data for classification algorithms. Splits down the sub_df_dict attribute’s data frames into size_list sized data frame. Data frames containing first slugs, as labelled in the method label_slugs, are split right before the occurrence of a first slug.
Parameters: - first_slug (list of list of int) – List containing the sub list of first slug indices as computed by the method slug_check per sub data frames in the dictionary attribute
- size_list (int (optional)) – Size of data frame to use for classification (default is 240)
- max_clean_count (int (optional)) – Maximum number of data frames to create that lead to a normal, clean, flow. This is known by whether the first slug list is empty (default is 10000)
-
jump
()¶ Locates gap in the continuous data in the attribute pd_df, and groups and labels the continuous sets.
-
label_slugs
(slug_diff=3, **kwargs)¶ Finds slug peaks in each data frame in the sub_df_dict attribute and creates the list of indices of slugs. Uses the slug_check method to then compute the list of indices of first slugs occurences, per data frame in the sub_df_dict attribute.
Parameters: - slug_diff (float (optional)) – Minimum differential value above which an increase in WHP is assumed to be a slug peak (default is 3.0).
- kwargs –
- dict_slug_def : dict
- For slug_check method
Returns: - first_slug (list of list of int or list of list of datetime) – List containing the sub list of first slug indices as computed by the method slug_check per sub data frames in the dictionary attribute
- slugs (list of list of int or list of list of datetime) – List containing the sub list of slug peaks indices per sub data frames in the dictionary attribute
-
plot_X
(start=60, variables=['WH_P', 'DH_P'], scaled=True, n_examples=3)¶ Plot n_examples each of the set size_list intervals leading to normal or slug flow
Parameters: - start (int (optional)) – Index of the df_list to start plotting from (default is 60)
- variables (list of str (optional)) – Names of variables to be plotted (default is [“WH_P”, “DH_P”])
- scaled (bool (optional)) – Whether to scale variables to WH_P (default is True)
- n_examples (int (optional)) – Number of examples to plot (default is 3)
Returns: X data figure
Return type: Figure
-
plot_raw_slugs
(variables=['WH_P', 'DH_P'], scaled=True, n_examples=10, first_sample=10, **kwargs)¶ Plotting functions to plot a set of n_examples raw sub_df_dict data frame, showing the slugs peaks and first slugs value.
Parameters: - variables (list of str (optional)) – Names of variables to be plotted (default is [“WH_P”, “DH_P”])
- scaled (bool (optional)) – Whether to scale variables to WH_P (default is True)
- n_examples (int (optional)) – Number of examples to plot (default is 10)
- first_sample (int (optional)) – Index of the sub_df_dict to start plotting from (default is 10)
- kwargs –
- slug_diff : float
- Argument of label_slugs method. Minimum WHP differential value to be assumed a slug peak
Returns: raw slugs figure
Return type: Figure
-
slug_check
(slug_idx, key, dict_slug_def={'interval': [25, 25], 'time': [60, 240], 'value_diff': [2.0, 2.0]})¶ From a list of indices of peaks in the data, return a list of of indices of first slug, based on the time since the last slug peak, the time between slug peaks and the value difference between slug peaks
Parameters: - slug_idx (list of int) – List of indices of all the peaks thought to be slugs occurring in the current data frame in the sub_df_dict attribute
- key (int) – Index of current database in sub_df_dict
- dict_slug_def (dict (optional) – Dictionary of values to define slugs, such a minimum time sine last first slug, minimum interval between two slug peaks, maximum WHP difference between peaks
Returns: first_slug_idx – List of indices of first slug peaks for current data frame in the dictionary attribute sub_df_dict
Return type: list of int
-
split_data
(test_size=0.3)¶ Split the data into a training and testing set
Parameters: test_size (float (optional)) – Percentage of the data to include in the training set (default is 0.3)
-
sub_data
(**kwargs)¶ Creates a dictionary of sub data frame that are continuous on time for over the min_df_size variable
Parameters: kwargs – - min_df_size : int
- For clean_short_sub method. Minimum size of sub data frame that will be considered forward in the analysis
-


{% extends 'admin/master.html' %} {% block body %} {{ super() }}
Simple admin views, not related to models.
This example shows how to add your own views to the admin interface. The views do not have to be associated to any of your models, and you can fill them with whatever content you want.
By adding custom views to the admin interface, they become accessible through the navbar at the top.
Back


--- date: 2011-02-12T00:00:00Z published: true title: Algeria shuts down internet and Facebook as protest mounts | Can we go for a third? url: /2011/02/12/algeria-shuts-down-internet-and-facebook-as-protest-mounts-can-we-go-for-a-third-/ ---
Plastic bullets and tear gas were used to try and disperse large crowds in major cities and towns, with 30,000 riot police taking to the streets in Algiers alone.
There were also reports of journalists being targeted by state-sponsored thugs to stop reports of the disturbances being broadcast to the outside world.
But it was the government attack on the internet which was of particular significance to those calling for an end to President Abdelaziz Boutifleka's repressive regime.
Protesters mobilising through the internet were largely credited with bringing about revolutions in Egypt and Tunisia.
"The government doesn't want us forming crowds through the internet," said Rachid Salem, of Co-ordination for Democratic Change in Algeria.


Ed Bearby
Sales
<EMAIL_ADDRESS>916.467.5254
Ed was born in Chicago, Illinois and raised in Fremont, Ca. He has three children, Justin (22), Jared (18), and Jenna (16) and he resides with my girlfriend Cheri Azevedo. He began his career in the waterworks industry with Groeniger & Company in 1984, working in the warehouse and moving his way to outside sales. He spent 21 years with Groeniger before moving to Arizona in 2005 to work for Dana Kepner W.W. Company. He was very happy to make his way back to Northern Ca. to join R&B Company in July 2015.


CALL TO ARMS!!!
$KMD fans, I need your help!
@PirateChainUK and I are running for two notary nodes this year in the KMD elections.
I want to ask you here how you know/met me if it’s connected to KMD or Pirate.
I appreciate your support as we lead up to the elections nex...


How hipster are you?
SoundCloud Username:
User name must be 3 letters
Tracks found:
{{ track.title | limitTo:50 }}
{{ track.favoritings_count }} likes


:nth-child(even)
:nth-child(odd)
:nth-child(3n)
:nth-child(2)
:nth-child(3n+1)
:nth-child(3n+2)
:even
:odd
John
Karl
Brandon
Benjamin
Sam
Glen
Tane
Ralph
David
Mike
Dan
tr


Histogram is a visual tool used to explore the frequency distribution of quantitative data (both Interval and Ratio). The shape of a histogram decides if the data distribution is symmetric or skewed (right skewed and left skewed).


class: center, middle, inverse, title-slide # Lecture 11 ## Spatial Models ### Ivan Rudik ### AEM 7130 --- exclude: true ```r options(htmltools.dir.version = FALSE) knitr::opts_hooks$set(fig.callout = function(options) { if (options$fig.callout) { options$echo <- FALSE } knitr::opts_chunk$set(echo = TRUE, fig.align="", message = FALSE, warning = FALSE) options }) ``` ``` ## Warning: 'xaringanExtra::style_panelset' is deprecated. ## Use 'style_panelset_tabs' instead. ## See help("Deprecated") ``` ``` ## Warning in style_panelset_tabs(...): The arguments to `syle_panelset()` changed in xaringanExtra ## 0.1.0. Please refer to the documentation to update your slides. ``` --- # Roadmap 1. Spatial model simple set up 2. Function iteration solutions 3. Exact hat algebra --- # Spatial models With spatial models we are now going to allow for: - heterogeneity across space - migration - trade - etc Basically, more consideration of general equilibrium effects --- # Spatial models These features better allow us to understand the impacts of things like: - the costs of sea level rise (Desmet et al. 2021) - the food problem with productivity shocks (Nath 2020) - climate and agricultural adaptation (Costinot, Donaldson, Smith 2016) - the value of transportation infrastructure (Donaldson, 2018) - the welfare impact of environmental regulation (Hollingsworth, Jaworski, Kitchens and Rudik, 2021) --- # Simple set up: a model of market access We're going to start off with a simple .hi[market access] model -- This model is similar to Donaldson and Hornbeck (2016) and Jaworski and Kitchens (2019) -- It is a simplified version of the canonical Eaton and Kortum (2002) Ricardian trade model --- # Simple set up: a model of market access Here's what we are going to do: 1. Develop the model 2. Show how to solve for model unknowns using data and calibration of parameters 3. Simulate counterfactual outcomes where exogenous model terms (e.g. productivity, trade costs) are different -- .hi[New computational piece here:] we will be solving for things up to a .hi-blue[normalization], we won't necessarily be able to get the levels of variables, only relative differences --- # Households `\(L_i\)` households are in location `\(i = 1,\dots,N\)` -- Households supply labor inelastically, and are perfectly mobile across locations -- This means that in equilibrium it must be true that -- utility is equalized across locations --- # Households: utility and prices Household utility is `$$U(C_i) = C_i$$` -- Each industry produces a continuum of varieties (more later) such that `\(C_i\)` is a .hi[constant elasticity of substitution] aggregate of the varieties with elasticity `\(\sigma\)` -- With the CES assumption, we have the .hi[price index] in location `\(i\)` for the final good `\(C_i\)`: `$$P_i = \left(\int_0^N P(\omega)^{1-\sigma}d\omega \right)^{\frac{1}{1-\sigma}}$$` --- # Households: equilibrium utility and wages With Cobb-Douglas preferences, a consumer's indirect utility is given by their .hi[real wage:] `$$V_{i} = \frac{w_{i}}{P_{i}}$$` -- With perfectly mobile labor, utility is equalized in equilibrium and wages `\(w_i\)` satisfy: `$$w_i = \bar{U} P_i$$` where `\(\bar{U}\)` is the level of utility achieved in all counties --- # Firms Firms maximize profits and take prices as given -- For each location, `\(z_{i}(\omega)\)` is .hi[efficiency] -- This is the quantity of variety `\(\omega\)` that can be produced using one unit of labor in location `\(i\)` --- # Firms: EK assumption We assume that `\(z_{i}(\omega)\)` is a random variable distributed according to the Frechet distribution (Eaton and Kortum, 2002): `$$F_{i}\left(z\right) = \exp\left[-T_{i} \left(z\right)^{-\theta}\right]$$` -- This distributional assumption is what will buy us tractability in our model later --- # Firms: EK assumption `$$F_{i}\left(z\right) = \exp\left[-T_{i} \left(z\right)^{-\theta}\right]$$` -- `\(\theta > 1\)` is the trade elasticity parameter common across all states and counties and measures the level of intra-industry heterogeneity -- Bigger `\(\theta\)` `\(\rightarrow\)` more heterogeneity `\(\rightarrow\)` stronger comparative advantage effects -- `\(T_{i}\)` measures fundamental productivity -- Larger values of `\(T_{i}\)` increase the probability of larger efficiency draws `\(z_{i}(\omega)\)` and indicate larger absolute advantage --- # Firms: Production technology Perfectly competitive firms use labor, capital, and .hi[Cobb-Douglas] technologies to produce different varieties of goods -- The marginal cost of producing variety `\(\omega\)` in location `\(i\)` is: `$$MC_i(\omega) = \frac{1}{z_i(\omega)}{(w_i)}^\gamma {(r_i)}^{1-\gamma}$$` -- Total output in location `\(i\)` is given by `\(Y_i = z_i(\omega) L_i^\gamma K_i^{1-\gamma}\)` -- `\(\gamma_i\)` is the share of value added and `\(r_i\)` is the rental rate of capital -- Capital is perfectly mobile so `\(r_i = r\)` for all `\(i\)` --- # Firms: Trade Trade costs are .hi[iceberg] -- Delivering 1 unit of the good from `\(j\)` to `\(i\)` requires shipping `\(\tau_{ij} \geq 1\)` units, this represents the costs of shipping/trade -- With a bit of math we can solve for the .hi[ideal price index:] `$$P_{i} = \kappa \left(\sum_{n=1}^N T_{n} \left[{(w_n)}^\gamma \tau_{in} \right]^{-\theta} \right)^{-1/\theta}$$` which is the price of a bundle of goods in location `\(i\)`, `\(\kappa\)` is a constant --- # Firms: Trade `$$P_{i} = \kappa \left(\sum_{n=1}^N T_{n} \left[{(w_n)}^\gamma \tau_{in} \right]^{-\theta} \right)^{-1/\theta}$$` Notice `\(P_i\)` is increasing in sellers' trade costs `\(\tau_{in}\)` and input costs `\(w_n\)`, and decreasing in productivity `\(T_n\)` -- `\({(P_{i})}^{-\theta}\)` is often called consumer market access `\((CMA_i)\)` and captures location `\(i\)`'s access to cheaper products --- # Firms: Trade .hi[Bilateral trade expenditures] of location `\(i\)` on goods from location `\(j\)` are given by: `$$X_{ij} = \kappa T_{j} Y_i\left(\frac{ {(w_j)}^\gamma \tau_{ij}}{{P_{i}}}\right)^{-\theta}$$` -- `\(i\)` purchases more products from `\(j\)` if: - the costs of products from `\(j\)` are lower: smaller `\(w_j, \tau_{ij}\)`, larger `\(T_j\)` - `\(i\)` is richer `\(Y_i\)` - Consumer market access `\({(P_{i})}^{-\theta}\)` is lower --- # Firms: Market access Firms also have a .hi[market access] term `\(FMA_j\)` that we can derive from combining the equilibrium real wage, price index, and trade expenditures expressions: `$$FMA_j = \sum_{i=1}^N \frac{\tau_{ij}^{-\theta}Y_i}{CMA_i}$$` -- Firm market access captures the firm side: their access to higher price markets --- # Firms: Market access `$$FMA_j = \sum_{i=1}^N \frac{\tau_{ij}^{-\theta}Y_i}{CMA_i}$$` FMA is higher if: - Trade costs are lower `\((\tau_{ij})\)` - Other locations are richer `\((Y_i)\)` --- # We did it So that is the model -- Now we need to solve it -- One of the key pieces in our model are the .hi[market access terms] -- It turns out, FMA and CMA are .hi-blue[equivalent up to a normalization by a constant:] `$$MA_i \equiv FMA_i = \rho CMA_i$$` --- # Solving the model What we need in order to fully solve the model is pretty simple: -- .hi[the N unobserved exogenous] `\(T_i\)` .hi[terms] -- , all other exogenous terms are just parameters we calibrate/estimate (e.g. trade costs) or data (labor) -- To recover the `\(T_i\)`s, we first need to solve for the factual equilibrium of the model -- This means we need to recover the market access terms MA -- Now lets see how we solve for MA and T --- # Solving the model: market access `$$FMA_j = \sum_{i=1}^N \frac{\tau_{ij}^{-\theta}Y_i}{CMA_i}$$` -- using the equivalency between FMA and CMA, and that `\(Y_i = \frac{w_i L_i}{\gamma}\)` from labor market clearing we have our condition allowing us to solve for market access: `$$MA_j = \rho \sum_{i=1}^N \tau_{ij}^{-\theta} MA_i^{\frac{-(1+\theta)}{\theta}} L_i$$` For `\(j=1,\dots,N\)` this is a system of N equations --- # Solving the model: market access `$$MA_j = \rho \sum_{i=1}^N \tau_{ij}^{-\theta} MA_i^{\frac{-(1+\theta)}{\theta}} L_i$$` `\(N\)` unknown MA terms, everything else is a parameter `\((\tau_{ij}, \theta)\)` or data `\(L_i\)` -- `\(\rho\)` is the remaining unknown, but is just a scalar -- .hi[Fujimoto-Krause] shows that we can solve for the vector `\(MA\)` up to this normalization using function iteration, but where our update is: `$$MA_{new} = \frac{RHS(MA_{old})}{||RHS(MA_{old})||}$$` --- # Solving the model: market access ``` function solve_price(data, params, labor) # set up intial guesses ma_temp = collect(1:size(data.L)[1]) ma_new = data.tau_theta * (ma_temp .^ (-(1 + params.theta) / params.theta) .* labor) ma_temp = ma_new / sqrt(sum(ma_new .^ 2)) error = Inf # use Fujimoto-Krause to solve for MA up to a normalization while error > params.tol * params.damping ma_new = data.tau_theta * (ma_temp .^ (-(1 + params.theta) / params.theta) .* labor) # RHS of MA equation ma_new = ma_new / sqrt(sum(ma_new .^ 2)) # normalize error = sqrt.(sum((ma_new .- ma_temp) .^ 2)) ma_temp = params.damping * ma_new + (1 - params.damping) * ma_temp # damp the update end # Convert to price index price = ma_new .^ (-1 / params.theta) return price end ``` --- # Solving the model: productivity Now we need to solve for productivity `\(T_j\)` -- How do we do this? -- Recall: `$$X_{ij} = \kappa T_{j} Y_i\left(\frac{ {(w_j)}^\gamma \tau_{ij}}{{P_{i}}}\right)^{-\theta}$$` and `$$P_{i} = \kappa \left(\sum_{n=1}^N T_{n} \left[{(w_n)}^\gamma \tau_{in} \right]^{-\theta} \right)^{-1/\theta}$$` --- # Solving the model: productivity `$$X_{ij} = \kappa T_{j} Y_i\left(\frac{ {(w_j)}^\gamma \tau_{ij}}{{P_{i}}}\right)^{-\theta}$$` Plug in `\(P_i\)`, and use `\(\bar{U} = \frac{w_i}{P_i}\)` to get an expression for equilibrium labor allocations as a function of productivity and market access: `$$L_j = \underbrace{\kappa \gamma T_j \bar{U}^{-(1+\gamma \theta)}}_{\eta_j} MA_j^{\frac{\theta(1+\gamma)+1}{\theta}}$$` where `\(\kappa\)` is some constant, and `\(\eta_j\)` is `\(T_j\)` up to a normalization by a constant --- # Solving the model: productivity `$$L_j = \eta_j MA_j^{\frac{\theta(1+\gamma)+1}{\theta}}$$` -- We can solve for `\(\eta_j\)` which is just `\(T_j\)` up to a normalization by a constant `\(\kappa \gamma \bar{U}^{-(1+\gamma \theta)}\)` `$$\eta_j = L_j MA_j^{-\frac{\theta(1+\gamma)+1}{\theta}}$$` -- Labor is higher areas with greater market access (cheaper goods), or higher productivity (higher wage) --- # Solving the model: productivity ``` function solve_productivity(data, params) # Iterate on equation to solve for market access # Then invert to recover P price = solve_price(data, params, data.L) # recover T up to a normalization T = exp.( log.(data.L) .- ((1 + params.theta * (1 + params.gamma)) / params.theta) .* log.(price .^ (-params.theta)), ) return T end ``` -- Now we have all the unknown exogenous pieces of the model and can simulate it given changes in model parameters --- # Simulating the model Suppose we want to measure the effect of a change in productivity to `\(T^c_j\)` where superscript `\(c\)` denotes counterfactual quantities -- We have 2N+1 unknowns: `\(MA^c_j, L^c_j, \bar{U}^c\)` for `\(j=1,\dots,N\)` --- # Simulating the model First rearrange the labor expression to solve for market access: `$$MA^c_j = \left[\frac{L^c_j}{\eta^c_j}\right]^{\frac{\theta}{\theta(1 + \gamma) + 1}}$$` where `\(\eta^c_j\)` is a function of `\(T^c_j\)` -- This is a set of `\(N\)` equations that pins down MA conditional on L --- # Simulating the model `$$MA^c_j = \left[\frac{L^c_j}{\eta^c_j}\right]^{\frac{\theta}{\theta(1 + \gamma) + 1}}$$` -- Next we can insert the `\(MA^c_j\)` terms into the MA fixed point equation: `$$MA^c_j = \rho \sum_{i=1}^N \tau_{ij}^{-\theta} {MA^c_i}^{\frac{-(1+\theta)}{\theta}} L^c_i$$` -- This is another `\(N\)` equations that now pins down L -- We still need to pin down `\(\bar{U}^c\)` --- # Simulating the model Pinning down `\(\bar{U}^c\)` is where the *up to a normalization* part comes in -- The pinned down `\(L^c_j\)`s above won't necessarily sum up to `\(\sum_{i=1}^N L_i\)` -- We pin down `\(\bar{U}^c\)` by inflating all the `\(L^c_j\)` terms by some constant factor `\(\zeta\)` so they sum up to the factual level of labor `\(\sum_{i=1}^N L_i\)` -- We are just imposing the last remaining equation, the labor resource constraint: `$$\sum_{i=1}^N L^c_i = \sum_{i=1}^N L_i$$` --- # Simulating the model `$$MA^c_j = \left[\frac{L^c_j}{\eta^c_j}\right]^{\frac{\theta}{\theta(1 + \gamma) + 1}}$$` `$$MA^c_j = \rho \sum_{i=1}^N \tau_{ij}^{-\theta} {MA^c_i}^{\frac{-(1+\theta)}{\theta}} L^c_i$$` `$$\sum_{i=1}^N L^c_i = \sum_{i=1}^N L_i$$` 2N+1 equations and 2N+1 unknowns, we can solve this system of equations to get our new equilibrium under `\(\bar{T}^c_j\)` --- # Simulating the model We can then compute the change in welfare from this new equilibrium: `$$\frac{\bar{U}^c}{\bar{U}} = \left({\eta^c / \eta}\right)^{-1/(1+\gamma \theta)}$$` -- All the constant terms that we never explicitly solved for in `\(\eta\)` cancel out so we can still identify the .hi[change] in welfare and employment -- Now lets look at the code --- # Simulating the model: solving for a change in welfare Suppose we change eta from `eta_bar` to `eta_counter` -- Our code might look like: ```julia function solve_counterfactual_base(eta_bar, data, params, regulation_counter) eta_counter = eta_bar .* regulation_counter # solve for labor allocation labor_counter = solve_labor(data, params, eta_counter) . . ``` What does `solve_labor` do? --- # Simulating the model: solving for labor ```julia function solve_labor(data, params, eta_counter) # insert L into market access g = 1 + params.theta * (1 + params.gamma) L_temp = data.L L_new = eta_counter .* (data.tau_theta * ((L_temp ./ eta_counter) .^ (-(1 + params.theta) / g) .* L_temp)) .^ (g / params.theta) L_temp = L_new / sqrt(sum(L_new .^ 2)) error = Inf . . # next do function iteration ``` --- # Simulating the model: solving for labor ```julia . . # use Fujimoto-Krause to solve for L up to a normalization while error > params.tol L_new = eta_counter .* (data.tau_theta * ((L_temp ./ eta_counter) .^ (-(1 + params.theta) / g) .* L_temp)) .^ (g / params.theta) L_new = L_new / sqrt(sum(L_new .^ 2)) error = sqrt.(sum((L_new .- L_temp) .^ 2)) L_temp = L_new end return L_new end ``` --- # Simulating the model: solving for a change in welfare ```julia . labor_counter = solve_labor(data, params, T_counter) labor_counter = labor_counter ./ sum(labor_counter) .* sum(data.L) # normalize price_counter = solve_price(data, params, labor_counter) # get price index eta_counter = # with P^c, L^c we can solve for eta^c exp.(log.(labor_counter) .- ((1 + params.theta * (1 + params.gamma)) / params.theta) .* log.(price_counter .^ (-params.theta)), ) ./ regulation_counter delta_U = (eta_counter ./ eta_bar) .^ (-1 / (1 + params.gamma * params.theta)) results = DataFrame( eta_bar = vec(eta_bar), T_bar_counter = vec(eta_bar_counter), cf_reg = vec(regulation_counter), labor_before = vec(data.L), labor_after = vec(labor_counter), labor_change = vec(labor_counter ./ data.L), welfare_change = vec(delta_U)) return results end ``` --- # Simulating the model What was the effect of the Clean Air Act on manufacturing employment? .center[ <img src="figures/employment_map.png" width="60%" /> ] --- # Exact hat algebra We can reframe how we solve this in a different way Suppose we want to know the effect of a change in productivity `\(\hat{T_j} = \frac{T^c_j}{T_j}\)` -- Hats denote changes in a variable from factual to counterfactual -- It turns out it's relatively easy to solve these models in terms of *changes* --- # Exact hat algebra First we need to define a new variable, .hi[expenditure shares:] `$$\lambda_{ij} = \frac{X_{ij}}{\sum_{h=1}^N X_{ih}}$$` -- This is just the share of total expenditures on products from location `\(j\)` -- Substitute in for the `\(X_{ih}\)` terms to get: `$$\lambda_{ij} = \frac{T_{j} \left( {(w_j)}^\gamma \tau_{ij}\right)^{-\theta}}{\sum_{h=1}^N T_{h} \left( {(w_h)}^\gamma \tau_{ih}\right)^{-\theta}}$$` --- # Exact hat algebra Also notice that labor's share of total income `\(w_i L_i = \gamma Y_i\)` can be written as a function of the sum of expenditures by `\(j\)` on products from `\(i\)` `$$w_i L_i = \gamma \sum_{j=1}^N \lambda_{ji} w_j L_j$$` --- # Exact hat algebra `$$\lambda_{ij} = \frac{T_{j} \left( {(w_j)}^\gamma \tau_{ij}\right)^{-\theta}}{\sum_{h=1}^N T_{h} \left( {(w_h)}^\gamma \tau_{ih}\right)^{-\theta}}$$` `$$w_i L_i = \gamma \sum_{j=1}^N \lambda_{ji} w_j L_j$$` Suppose we have a productivity shock `\(\hat{T}_j\)`, and that `\(\hat{\tau}_{ij} = \hat{L} = 1\)` -- What we are going to do is manipulate these two expressions into *hat form* --- # Exact hat algebra Let's start with the counterfactual labor market clearing condition: `$$w^c_i L^c_i = \gamma \sum_{j=1}^N \lambda^c_{ji} w^c_j L^c_j$$` Notice that `\(\lambda^c_{ji} w^c_j L^c_j\)` is just how much `\(j\)` spends on products from `\(i\)` so that: `$$w^c_i L^c_i = \gamma \sum_{j=1}^N X^c_{ji}$$` --- # Exact hat algebra Next let's put it into hat form: `$$\hat{w}_i \hat{L}_i = \frac{w^c_i L^c_i}{w_i L_i} = \gamma \sum_{j=1}^N \frac{X^c_{ji}}{w_i L_i} = \gamma \sum_{j=1}^N \frac{X^c_{ji}}{\gamma Y_i}\frac{X_{ji}}{X_{ji}} = \frac{1}{\gamma} \sum_{j=1}^N \frac{X_{ji}}{w_i L_i} \hat{X}_{ji}$$` The change in income due to `\(\hat{T}_j\)` is given by the change in bilateral expenditures, as well as the .hi[factual/observed] income and bilateral expenditures --- # Exact hat algebra Now let's look at the counterfactual expenditure shares: `$$\lambda^c_{ij} = \frac{T^c_{j} \left( {(w^c_j)}^\gamma \tau_{ij}\right)^{-\theta}}{\sum_{h=1}^N T^c_{h} \left( {(w^c_h)}^\gamma \tau_{ih}\right)^{-\theta}}$$` And remember that trade costs .hi[do not] change in this example -- Next, put it in hat form --- # Exact hat algebra `$$\hat{\lambda}_{ij} = \frac{\lambda^c_{ij}}{\lambda_{ij}} = \frac{\hat{T}_{j} \left( {(\hat{w}_j)}^\gamma\right)^{-\theta}}{\frac{\sum_{h=1}^N T^c_{h} \left( {(w^c_h)}^\gamma \tau_{ih}\right)^{-\theta}}{\sum_{h=1}^N T_{h} \left( {(w_h)}^\gamma \tau_{ih}\right)^{-\theta}}} = \frac{\hat{T}_{j} \left( {(\hat{w}_j)}^\gamma\right)^{-\theta}}{\hat{P_i}^{-\theta}}$$` The change in expenditure shares is given by the changes in productivity, wages, and market access -- We still need to solve for `\(\hat{P}^{-\theta}_i\)` --- # Exact hat algebra We can do so with the price index expression: `$$P_{i} = \kappa \left(\sum_{j=1}^N T_{j} \left[{(w_j)}^\gamma \tau_{ij} \right]^{-\theta} \right)^{-1/\theta}$$` Which gives us: `$$\hat{P}_{i}^{-\theta} = \frac{\left(\sum_{j=1}^N T^c_{j} \left[{(w^c_j)}^\gamma \tau_{ij} \right]^{-\theta} \right)}{\left(\sum_{h=1}^N T_{h} \left[{(w_h)}^\gamma \tau_{ih} \right]^{-\theta} \right)}$$` --- # Exact hat algebra Next we make a few (seemingly odd) substitutions -- Since `\(n\)` is not in the denominator, we can treat the denominator as a constant and move it inside the sum over `\(n\)`: `$$\hat{P}_{i}^{-\theta} = \sum_{j=1}^N \frac{\left(T^c_{j} \left[{(w^c_j)}^\gamma \tau_{ij} \right]^{-\theta} \right)}{\left(\sum_{h=1}^N T_{h} \left[{(w_h)}^\gamma \tau_{ih} \right]^{-\theta} \right)}$$` --- # Exact hat algebra Next., multiply and divide each term in the sum by the .hi[factual] productivity and wage terms `$$\hat{P}_{i}^{-\theta} = \sum_{j=1}^N \left[\frac{\left(T^c_{j} \left[{(w^c_j)}^\gamma \tau_{ij} \right]^{-\theta} \right)}{\left(\sum_{h=1}^N T_{h} \left[{(w_h)}^\gamma \tau_{ih} \right]^{-\theta} \right)} \frac{T_j \left[w_j^\gamma\right]^{-\theta}}{T_j \left[w^\gamma_j\right]^{-\theta}} \right]$$` We can then switch out the factual and counterfactual terms up top: `$$\hat{P}_{i}^{-\theta} = \sum_{j=1}^N \left[\frac{\left(T_{j} \left[{(w_j)}^\gamma \tau_{ij} \right]^{-\theta} \right)}{\left(\sum_{h=1}^N T_{h} \left[{(w_h)}^\gamma \tau_{ih} \right]^{-\theta} \right)} \frac{T^c_j \left[{(w^c_j})^\gamma\right]^{-\theta}}{T_j \left[w^\gamma_j\right]^{-\theta}} \right]$$` --- # Exact hat algebra `$$\hat{P}_{i}^{-\theta} = \sum_{j=1}^N \left[\frac{\left(T_{j} \left[{(w_j)}^\gamma \tau_{ij} \right]^{-\theta} \right)}{\left(\sum_{h=1}^N T_{h} \left[{(w_h)}^\gamma \tau_{ih} \right]^{-\theta} \right)} \frac{T^c_j \left[{(w^c_j})^\gamma\right]^{-\theta}}{T_j \left[w^\gamma_j\right]^{-\theta}} \right]$$` This gives us: `$$\hat{P}_{i}^{-\theta} = \sum_{j=1}^N \left[\lambda_{ij} \hat{T}_j \left(\hat{w}_j^\gamma\right)^{-\theta} \right]$$` --- # Exact hat algebra Here's the three sets of equations again, they are all in terms of changes or data, so we do not need to solve for .hi[many] model fundamentals: `$$\hat{w}_i \hat{L}_i = \frac{1}{\gamma} \sum_{j=1}^N \frac{X_{ji}}{w_i L_i} \hat{X}_{ji}$$` `$$\hat{\lambda}_{ij} = \frac{\hat{T}_{j} \left( {(\hat{w}_j)}^\gamma\right)^{-\theta}}{\hat{P_i}^{-\theta}}$$` `$$\hat{P}_{i}^{-\theta} = \sum_{j=1}^N \left[\lambda_{ij} \hat{T}_j \left(\hat{w}_j^\gamma\right)^{-\theta} \right]$$` --- # Exact hat algebra `$$\hat{P}_{i}^{-\theta} = \sum_{j=1}^N \left[\lambda_{ij} \hat{T}_j \left(\hat{w}_j^\gamma\right)^{-\theta} \right]$$` along with the change in equilibrium requirement that: `$$\frac{\hat{w_i}}{\hat{P}_i} = \widehat{\bar{U}} \,\,\, \forall i$$` allows us to iterate and solve for `\(\hat{P}_i\)` up to a normalization since `\(\hat{L} = \frac{\sum_{i=1}^N L^c_i}{\sum_{i=1}^N L_i} = 1\)` will later pin down `\(\widehat{\bar{U}}\)` --- # Exact hat algebra `$$\hat{\lambda}_{ij} = \frac{\hat{T}_{j} \left( {(\hat{w}_j)}^\gamma\right)^{-\theta}}{\hat{P_i}^{-\theta}}$$` then lets us solve for `\(\hat{\lambda}_{ij}\)` given `\(\hat{P}_i\)` and `\(\hat{w}_j\)` -- Last we need expenditures and labor using the first condition: `$$\hat{w}_i \hat{L}_i = \frac{1}{\gamma} \sum_{j=1}^N \frac{X_{ji}}{w_i L_i} \hat{X}_{ji}$$` --- # Exact hat algebra `$$\hat{w}_i \hat{L}_i = \frac{1}{\gamma} \sum_{j=1}^N \frac{X_{ji}}{w_i L_i} \hat{X}_{ji}$$` Expenditures by `\(i\)` `\(X_i\)` will be equal to labor income so we have that: `$$\hat{X}_i = \frac{1}{\gamma} \sum_{j=1}^N \frac{X_{ji}}{w_i L_i} \hat{X}_{ji}$$` -- The definition of expenditure shares gives us: `$$\hat{\lambda}_{ij} = \frac{\hat{X}_{ij}}{\hat{X}_i}$$` --- # Exact hat algebra Combine these two to get: `$$\frac{\hat{X}_{ih}}{\hat{\lambda}_{ih}} = \frac{1}{\gamma} \sum_{j=1}^N \frac{X_{ji}}{w_i L_i} \hat{X}_{ji}$$` which is a set of `\(n\)` equations and `\(n\)` unknown `\(\hat{X}_{ji}\)` for any `\(h\)` we choose -- We have solved for all the unobserved hat terms! -- With these we can simulate any change in productivity, and the only two model parameters we needed to take a stand on were `\(\gamma\)` and `\(\theta\)`


All Classes
| Class | Description |
|---|---|
| AbstractIdempotentRepository |
Includes all the methods of IdempotentRequestStore
|
| ConfigUtility | |
| CryptographyAlgorithm |
Supported hash algorithms to generate idempotency key
|
| DefaultKeyGenerator | |
| EnvironmentVariableUtils |
Supported environment variables constants
|
| ErrorConditionalCallback |
A callback interface that need to clear cache for custom error condition
|
| IdempotencyKey |
Wraps the combine of application name, listener name and incoming event value hash
|
| IdempotentAspect |
An aspect that used along with the @IdempotentResource annotation
|
| IdempotentRepository |
an interface that the functionality required of a request store for idempotent method invocations.
|
| IdempotentRequestPayload |
Add to the methods arguments that represents the idempotent request payload.
|
| IdempotentRequestResponseWrapper |
That is a container for idempotent requests and responses
|
| IdempotentRequestWrapper |
Wraps the incoming event value
|
| IdempotentResource |
Add to the methods that need to be idempotent.
|
| IdempotentResponseWrapper |
Wraps the incoming event response
|
| InMemoryIdempotentRepository |
An implementation of the idempotent AbstractIdempotentRepository
that uses as a default map
|
| KeyGenerator | |
| RepositoryType |
Supported datasource types
|


@* * This template is called from the `index` template. This template * handles the rendering of the page header and body tags. It takes * two arguments, a `String` for the title of the page and an `Html` * object to insert into the body of the page. *@ @(title: String)(content: Html)(implicit messages: Messages, flash: Flash)
@* Here's where we render the page title `String`. *@


What can you do during acute inhuman soul-crushing conditions in life? Hope. This book is about “Hope.” - dopamine for life.
Psychiatrist Viktor Frankl’s presents his life in Nazi death camps and learning for spiritual survival. The narrative is based on his experience and the experiences of others he treated later in his practice. The author aptly reiterated throughout the book “We cannot avoid suffering, but we can choose how to cope with it, and find a meaning to live.”
The book has three parts, life and lessons from the death camp, logotherapy, and final citings from various incidents of depression of his patients and others.
I was pleasantly surprised when the author described his outlook towards happiness, which I have been thinking lately and coincided with my formulation
“Don’t aim at success. The more you aim at it and make it a target, the more you are going to miss it. For success, like happiness, cannot be pursued; it must ensue, and it only does so as the unintended side effect of one’s personal dedication to a cause greater than oneself or as the by-product of one’s surrender to a person other than oneself. Happiness must happen, and the same holds for success: you have to let it happen by not caring about it. I want you to listen to what your conscience commands you to do and go on to carry it out to the best of your knowledge. Then you will live to see that in the long-run—in the long-run, I say!—success will follow you precisely because you had forgotten to think about it.”
See also
- Book Review: The Culture Map
- Book Review: Software Architecture with Python
- Animal Farm Review
- Two scoops of django
- code simplicity book review
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.


Grand Master Samael:
It is called Elmore, but it's not that different -- power rules here just as it does everywhere else.
Heh, I understand why a man like Scraide might have settled here... Was it the battles against the Orcs that changed him, do you think, or was that always his nature?
Listen to information about second class transfer.
Sub Class.
Clan.
Alliance.
Quest.


Welcome to the City of Detroit Census Map The map will help you understand the complexity of the census. START
Welcome to the City of Detroit Census Map The map will help you understand the complexity of the census. START


I just recently decided to put together a custom keyboard. I'm still deciding on a case to order, and am waiting for everything to ship.
Board
The board is a tenkeyless 87 key style ordered off of Winkeyless. It comes soldered together with the main controller parts, RGB LED under board lighting, RGB controller, dioes, and resistors.
Switches
The switches are Gateron Browns 45g tactile from Massdrop. Browns are softer (less clicky) and have a feedback bump around halfway through the key press.
Key LEDs
The key LEDs from switchTop are blue.
Keycaps
The Dolch 110 key base set is from Pimp My Keyboard and includes extra keycaps for customization. The core keys are gray, and the secondary keys are black. There are 110 keys included in the set which allows some of the secondary keys to be either black or grey. There's also a red escape key included in the set.
Case
Just need to order a case now. I plan on getting a black case with a back plating that will allow the under board LEDs to shine through.


BANOPHEN- diphenhydramine hydrochloride tablet
Carilion Materials Management
Disclaimer: Most OTC drugs are not reviewed and approved by FDA, however they may be marketed if they comply with applicable regulations and policies. FDA has not evaluated whether this product complies.
temporarily relieves these symptoms due to hay fever or other upper respiratory allergies:
runny nose
itchy, watery eyes
sneezing
itching of the nose or throat
temporarily relieves these symptoms due to the common cold:
runny nose
sneezing
to make a child sleepy
with any other product containing diphenhydramine, even one used on skin
glaucoma
a breathing problem such as emphysema or chronic bronchitis
trouble urinating due to an enlarged prostate gland
take every 4 to 6 hours, or as directed by a doctor
do not take more than 6 doses in 24 hours
| adults and children 12 years of and over | 1 to 2 tablets |
| children 6 to under 12 years | 1 tablet |
| children under 6 years | do not use |
store at 25ºC (77ºF); excursions permitted between 15°-30°C (59°-86°F)
protect from moisture
see end flap for expiration date and lot number
|
BANOPHEN
diphenhydramine hcl tablet | ||||||||||||||||||||||
| ||||||||||||||||||||||
| ||||||||||||||||||||||
| ||||||||||||||||||||||
| ||||||||||||||||||||||
| ||||||||||||||||||||||
|
| Labeler - Carilion Materials Management (079239644) |
| Registrant - Carilion Materials Management (079239644) |
| Establishment | |||
| Name | Address | ID/FEI | Business Operations |
|---|---|---|---|
| Carilion Materials Management | 079239644 | REPACK(68151-2908) |


Search
Preparing search index...
The search index is not available
@rongcloud/imlib-uni
Options
All
Public
Public/Protected
All
Inherited
Externals
Menu
@rongcloud/imlib-uni
ConversationType
Enumeration ConversationType
会话类型
Index
Enumeration members
APP_
SERVICE
CHATROOM
CUSTOMER_
SERVICE
DISCUSSION
GROUP
PRIVATE
PUBLIC_
SERVICE
PUSH_
SERVICE
SYSTEM
Enumeration members
APP_
SERVICE
APP_
SERVICE
:
= 7
CHATROOM
CHATROOM
:
= 4
CUSTOMER_
SERVICE
CUSTOMER_
SERVICE
:
= 5
DISCUSSION
DISCUSSION
:
= 2
GROUP
GROUP
:
= 3
PRIVATE
PRIVATE
:
= 1
PUBLIC_
SERVICE
PUBLIC_
SERVICE
:
= 8
PUSH_
SERVICE
PUSH_
SERVICE
:
= 9
SYSTEM
SYSTEM
:
= 6
Exports
Conversation
Type
APP_
SERVICE
CHATROOM
CUSTOMER_
SERVICE
DISCUSSION
GROUP
PRIVATE
PUBLIC_
SERVICE
PUSH_
SERVICE
SYSTEM
Generated using
TypeDoc
会话类型


Sometimes to make the harder things simpler, you have to make the simpler things harder. Vi Hart's fascinating video on Logarithms was something that I wanted to unravel. Fancy Counting. It is a story of addition, multiplication and exponentiation. Hover over the count numbers to interact with it.
Addition is where we all start. One potato, two potato, three potatoes. It is all a sequence of +1 steps. Count 1, get to 3. Just +1 +1 +1. Obvious? Yes! Yet, we are trying to make the simpler things harder. So bear with me here. There are two key operations going on in this simple counting. The number of counts, the magnitude we get to by applying these number of counts. steps of +
Multiplication makes this slightly more obvious. After all, it is still fancy counting, except that we count in different sized steps. No longer are we tied to +1s, we do +n, so counting 2, get you 8. Just +2 +2 +2 +2 +2. So bigger steps but less number of steps. steps of +
So where else do we go from here? Exponentiation.
What now, though? Another obvious question, how many counts of +1 does it take to get to 3? Well, 3 duh!
How many counts of +2 (or more generally +n) does it take to make 16? It is simply +2 +2 +2 +2 +2 +2 +2 +2 or 8 steps. Or maybe, we look at the division function? 16/+2 is simply 8.
So in a STC X3, how many counts gives you 81? log₃(81) = 4! This scale is the logarithmic scale!


Kitsch actually selvage gentrify waistcoat, gluten-free 90's. Waistcoat marfa whatever, skateboard scenester pitchfork church-key poutine. Chartreuse blue bottle typewriter 3 wolf moon selfies, kitsch etsy austin try-hard flannel mustache DIY migas paleo. Kombucha farm-to-table pop-up hella tofu venmo, austin next level artisan health goth meggings cred ennui truffaut celiac. Letterpress kinfolk man braid meggings chambray, disrupt scenester hashtag wayfarers fanny pack XOXO cred sriracha. Portland put a bird on it vegan bitters meggings austin migas raw denim trust fund flexitarian, franzen mustache freegan squid. Truffaut everyday carry fingerstache mustache, photo booth quinoa DIY actually slow-carb.


About
How it Works:
- The user enters two locations, points A and B.
- The locations are converted from addresses to coordinates using a geocoder.
- The coordinates are passed to a router, which determines the optimal route to take.
- Route length (meters) and time by car (seconds) are converted to the user's units of measurement, taking into account the chosen mode of transport.
- Voila! The route and data is displayed to the user.


@() @main("Home") {
Game Critic is one of the fastest growing websites on software games for various platforms.We’ve been covering gaming for over 5 years,and continue to expand. Recently we began hardware testing. The site is dedicated to game enthusiasts of all levels and ages. At this site we allow you to share your experiences with other players We hope you enjoy our site just as we enjoyed making it. We try our best to privde gamers with absolutely independent and unbiased opinions and evaluations of featured products. We take pride in promoting good quality games and gaming hardware. Enjoy your stay and please feel free to contacts us if you have any queries. Take a look at our latest releases.


- datastore - Variable in class com.cyberfront.crdt.sample.simlation.AbstractNode
-
The datastore containinf all of the CRDT instances for this node..
- datastoreToString() - Method in class com.cyberfront.crdt.sample.simlation.AbstractNode
-
TODO: replace this with JAXB / Jackson conversion rather than doing it this way
Convert the data store to a string value
- DEFAULT_CREATE_COUNT - Static variable in class com.cyberfront.crdt.sample.simlation.Executive
-
Defines the default number of create operations to perform for a given simulation if not specified
- DEFAULT_DELETE_COUNT - Static variable in class com.cyberfront.crdt.sample.simlation.Executive
-
Defines the default number of delete operations to perform for a given simulation if not specified
- DEFAULT_NODE_COUNT - Static variable in class com.cyberfront.crdt.sample.simlation.Executive
-
Defines the default number of nodes for a given simulation if not specified.
- DEFAULT_READ_COUNT - Static variable in class com.cyberfront.crdt.sample.simlation.Executive
-
Defines the default number of read operations to perform for a given simulation if not specified
- DEFAULT_UPDATE_COUNT - Static variable in class com.cyberfront.crdt.sample.simlation.Executive
-
Defines the default number of update operations to perform for a given simulation if not specified
- delete(long) - Method in class com.cyberfront.crdt.sample.manager.GenericManager
-
- delete(long) - Method in class com.cyberfront.crdt.sample.manager.JsonManager
-
- DELETE_COUNT - Static variable in class com.cyberfront.crdt.unittest.simulator.Test01Simulation.SimulationTest
-
Number of delete operations to perform on tests related to internodal synchronization quality
- deleteCount - Variable in class com.cyberfront.crdt.sample.simlation.Executive
-
Number of delete operations remaining to perform
- deleteCount - Variable in class com.cyberfront.crdt.unittest.simulator.Test01Simulation.SimulationTest
-
The number of times to perform a delete operation during the simulation
- DeleteOperation - Class in com.cyberfront.crdt.operations
-
The DeleteOperation encapsulates the deletion of a JSON document in the CRDT.
- DeleteOperation(Long) - Constructor for class com.cyberfront.crdt.operations.DeleteOperation
-
This instantiates a new DeleteOperation given a timestamp.
- DeleteOperation(DeleteOperation) - Constructor for class com.cyberfront.crdt.operations.DeleteOperation
-
Instantiates a copy of the given DeleteOperation
- deliverCreatePending(SimOperationManager<T>, Double) - Method in class com.cyberfront.crdt.sample.simlation.SimCRDTManager
-
Deliver a operation manager with a PENDING CREATE operation
- deliverDeletePending(SimOperationManager<T>, Double) - Method in class com.cyberfront.crdt.sample.simlation.SimCRDTManager
-
Deliver a operation manager with a PENDING DELETE operation
- deliverNextMessage(Double) - Method in class com.cyberfront.crdt.sample.simlation.MessageRouter
-
Deliver the next message with a probability the owning node will reject the update the message contains.
- deliverPending(SimOperationManager<T>, Double) - Method in class com.cyberfront.crdt.sample.simlation.SimCRDTManager
-
Deliver a pending operation manager to a locally managed CRDT instance
- deliverReadPending(SimOperationManager<T>, Double) - Method in class com.cyberfront.crdt.sample.simlation.SimCRDTManager
-
Deliver a operation manager with a PENDING READ operation
- deliverUpdatePending(SimOperationManager<T>, Double) - Method in class com.cyberfront.crdt.sample.simlation.SimCRDTManager
-
Deliver a operation manager with a PENDING UPDATE operation
- deliveryTime - Variable in class com.cyberfront.crdt.sample.simlation.Message
-
The delivery timestamp associated with the message.
- description - Variable in class com.cyberfront.crdt.sample.data.AbstractDataType
-
A description of the object.
- diff(Collection<AbstractOperation>, Collection<AbstractOperation>) - Static method in class com.cyberfront.crdt.OperationTwoSet
-
This private static function returns a set resulting from removing all of the elements on the RHS from the set on the LHS
- displayStackTrace(StackTraceElement[]) - Static method in class com.cyberfront.crdt.support.Support
-
- doCreate(Node, AbstractDataType) - Method in class com.cyberfront.crdt.sample.simlation.Executive
-
Create a new object with the specified node as the owner.
- document - Variable in class com.cyberfront.crdt.LastWriteWins.TrialResult
-
JsonNode document containing the result of executing the sequence of operations
- doDelete(Node) - Method in class com.cyberfront.crdt.sample.simlation.Executive
-
Perform an delete operation on a random object which exists in the given node.
- doDeliver() - Method in class com.cyberfront.crdt.sample.simlation.Executive
-
Deliver the next message in the delivery queue to the node which is the intended recipient.
- doRead(Node) - Method in class com.cyberfront.crdt.sample.simlation.Executive
-
Perform a read operation on a random object which exists in the given node.
- doubleValue - Variable in class com.cyberfront.crdt.sample.data.SimpleC
-
The Double value associated with the SimpleC type
- doUpdate(Node, Double) - Method in class com.cyberfront.crdt.sample.simlation.Executive
-
Perform an update operation on a random object which exists in the given node.
- dstNodeId - Variable in class com.cyberfront.crdt.sample.simlation.Message
-
The destination node identifier.


Here's a list of the Pokémon exclusive to either Sword or Shield
Source: iMore
Pokémon Sword and Shield are finally available to play. I got my copy as soon as midnight hit on release day and have played for several hours now. One of the things I was most interested in learning was the Pokémon that are exclusive to either version of the game. If you only have one of these games, you'll need to trade Pokémon with a friend who has the other version if you want to complete the Galar region National Pokédex. Without further ado, here are all the Pokémon Sword and Shield exclusives.
Pokémon only found in Sword
Source: iMore
- Seedot (Grass)
- Nuzleaf (Dark/Grass)
- Shiftry (Grass/Dark)
- Swirlix (Fairy)
- Slurpuff (Fairy)
- Scraggy (Fighting/Dark)
- Scrafty (Fighting/Dark)
- Sawk (Fighting)
- Gothita (Psychic)
- Gothorita (Psychic)
- Gothitelle (Psychic)
- Rufflet (Flying/Normal)
- Braviary (Flying/Normal)
- Mawile (Fairy/Steel)
- Passimian (Fighting)
- Turtonator (Fire/Dragon)
- Solrock (Rock/Psychic)
- Galarian Darumaka (Ice)
- Galarian Darmanitan (Ice)
- Deino (Dark/Dragon)
- Zweilous (Dark/Dragon)
- Hydreigon (Dark/Dragon)
- Jangmo-o (Dragon)
- Hakamo-o (Dragon/Fighting)
- Kommo-o (Dragon/Fighting)
- Flapple (Grass/Dragon)
- Galarian Farfetch'd (Fighting)
- Sirfetch'd (Fighting)
- Legendary Pokémon: Zacian (Fairy/Steel)
Pokémon only found in Shield
Source: iMore
- Lotad (Water/Grass)
- Lombre (Water/Grass)
- Ludicolo (Water/Grass)
- Spritzee (Fairy)
- Aromatisse (Fairy)
- Croagunk (Fighting/Poison)
- Toxicroak (Fighting/Poison)
- Solosis (Psychic)
- Duosion (Psychic)
- Reuniclus (Psychic)
- Vullaby (Dark/Flying)
- Mandibuzz (Flying/Dark)
- Sableye (Dark/Ghost)
- Lunatone (Rock/Psychic)
- Throh (Fighting)
- Oranguru (Normal/Psychic)
- Drampa (Normal/Dragon)
- Galarian Corsola (Ghost)
- Cursola (Ghost)
- Larvitar (Rock/Ground)
- Pupitar (Rock/Ground)
- Tyranitar (Rock/Dark)
- Goomy (Dragon)
- Sliggoo (Dragon)
- Goodra (Dragon)
- Appletun (Grass/Dragon)
- Galarian Ponyta (Psychic)
- Galarian Rapidash (Psychic/Fairy)
- Legendary Pokémon: Zamazenta (Fighting/Steel)
So exclusive
There you go, a list of all of the Pokémon that can only be found in one version of the game or the other. If you want to complete your Pokédex, be sure to participate in trades with friends or people online. Happy hunting and good luck on your quest to catch 'em all!
Steel Sword
Pokémon Sword
Catch new Pokémon in a new land
Run around the Galar region capturing Pokémon and defeating gym leaders. There are several Pokémon exclusive to this version.
Protective Shield
Pokémon Shield
Adventure awaits you
Trainers will come across several new Pokémon as well as Pokémon exclusive their version of the game. See which ones you can catch.
We may earn a commission for purchases using our links. Learn more.


class DatabaseController extends Controller
- Alphabetic
- By Inheritance
- DatabaseController
- Controller
- HeaderNames
- Status
- Results
- AnyRef
- Any
- Hide All
- Show All
- Public
- All
Instance Constructors
- new DatabaseController(config: Config)
Value Members
-
final
def
!=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
final
def
##(): Int
- Definition Classes
- AnyRef → Any
-
final
def
==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
-
def
clone(): AnyRef
- Attributes
- protected[java.lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
def
deleteAbsenceCounter(id: String): Result
Delete a AbsenceCounter object out of the database for a certain employee.
Delete a AbsenceCounter object out of the database for a certain employee.
- id
is the employeeID of the person to which this AbsenceCounter is linked.
-
def
deleteHolidayMessage(id: Long): Result
Delete a holidayMessage If it was approved, delete it from the Axians servers.
Delete a holidayMessage If it was approved, delete it from the Axians servers. Also send a mail to the employee about the deletion.
-
def
deleteSettings(id: String): Result
Delete a Settings object out of the database for a certain employee.
Delete a Settings object out of the database for a certain employee.
- id
is the employeeID of the person to which these settings are linked.
-
final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
def
equals(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
def
finalize(): Unit
- Attributes
- protected[java.lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( classOf[java.lang.Throwable] )
-
def
getAbsenceCounters(year: Int, employeeIDs: List[String]): Result
Get the absenceCounters for a certain year for a list of people, usually used for one person.
Get the absenceCounters for a certain year for a list of people, usually used for one person.
Returns* 200 OK { totalPossibleDays: double that contains the default number of absence days for a person. [ { employeeID: the ID of the employee. maxDaysPossibleThisYear: double that contains the maximum of days that can be used this year.
> This value is defaulted in application.conf via axians.standardMaxAbsenceDays daysLeftApproved: double that contains the maximum of days - the days that are approved. daysLeftApprNewInCons: double that contains the maximum of days - all absences that are approved and not approved (not the rejected) } ] }
-
def
getAllHolidayMessages(): Result
Get every holidayMessage in the database.
-
def
getAllHolidayMessagesApproved(): Result
Get every holidayMessage that is approved in the database.
-
def
getAllHolidayMessagesByType(typeString: String): Result
Get every holidayMessage of a certain type.
-
def
getAllHolidayMessagesInConsideration(): Result
Get every holidayMessage that is in consideration in the database.
-
def
getAllHolidayMessagesNew(): Result
Get every holidayMessage that is new in the database.
-
def
getAllHolidayMessagesNewOrInConsideration(): Result
Get every holidayMessage that is new or in consideration in the database.
-
def
getAllHolidayMessagesRejected(): Result
Get every holidayMessage that is rejected in the database.
-
final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
-
def
getHolidayMessageWithDatabaseID(id: Long): Result
Get the holidayMessage in the database with a certain ID.
-
def
getHolidayMessageWithEmployeeID(id: String): Result
Get every holidayMessage in the database of a certain employee.
-
def
getHolidayMessagesApprovedOfDoctorWithID(id: String): Result
Get every holidayMessage that is approved in the database of a certain employee.
-
def
getHolidayMessagesInConsiderationOfDoctorWithID(id: String): Result
Get every holidayMessage that is in consideration in the database of a certain employee.
-
def
getHolidayMessagesNewOfDoctorWithID(id: String): Result
Get every holidayMessage that is new in the database of a certain employee.
-
def
getHolidayMessagesRejectedOfDoctorWithID(id: String): Result
Get every holidayMessage that is rejected in the database of a certain employee.
-
def
getNumberOfTimesPersonIsRejectedSince(id: String, date: String): Result
Get the number of holidayMessages (int) that are rejected since a certain date of a certain person.
-
def
getSettingsFromEmployeeWithId(id: String): Result
Get a Settings object from the database.
Get a Settings object from the database. These are used to save the configuration of the filters etc. at the front end.
Returns 200 OK Settings object in Json if object was found. 404 Not found if object was not found
-
def
hashCode(): Int
- Definition Classes
- AnyRef → Any
-
final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
-
final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
final
def
notify(): Unit
- Definition Classes
- AnyRef
-
final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
-
def
postAbsenceCounter(): Result
Post the absenceCounter of an employee to the database.
Post the absenceCounter of an employee to the database. This is the max number of days an employee can be absent in a year.
Body should be an absenceCounter object
Returns 200 OK if everything is okay { employeeID: ID of the employee that holds the number. maxDaysThisYear: Max days an employee can be absent this year. } Returns 500 Internal server error if there was an error posting.
-
def
postHolidayMessage(): Result
Post a HolidayMessage to the database First convert the Json to a HolidayMessage Then save it Then send a confirmation email to the employee If this is an update to an absence that was already approved, delete it again from the Axians servers.
-
def
postHolidayPlanningsMessage(): Result
Post a certain HolidayPlanningsMessage.
Post a certain HolidayPlanningsMessage. First make the Json into a HolidayPlanningsMessage object Then add this update to the database for every planning unit this planner is planner of. Then if wanted, send a mail to the employee about his approved/rejected absence. (no mail is sent when the global state is still new or in consideration, when not all planners have reacted) Then an absence is sent to Axians, if the global state changes to approved when this message is saved.
Returns 200 OK with the ID of the posted hpm if everything is okay. Returns 500 Internal server error when Axians couldn't be reached. In this case, the planningsMessage is saved with state NEW and Comment: "There was an error".
-
def
postHolidayPlanningsMessageForPlanningsUnit(unit: String): Result
This is the same function as postHolidayPlanningsMessage, but only for a single planningUnit.
-
def
postSettings(): Result
Post a Settings object to the database.
Post a Settings object to the database.
Body should be a Settings object in JSON.
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
toString(): String
- Definition Classes
- AnyRef → Any
-
final
def
wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )


Mohd Mazlan
Programmer • UPM, MY
Passionate Front-end developer with over 2 years experience. Recently focusing on developing mobile hybrid apps and web development.
Mobile
555 555 555 Poke meJalan Kelisa 9, Taman Kelisa 45800 Jeram Kuala Selangor
Programmer • UPM, MY
Passionate Front-end developer with over 2 years experience. Recently focusing on developing mobile hybrid apps and web development.
Mobile
555 555 555 Poke meJalan Kelisa 9, Taman Kelisa 45800 Jeram Kuala Selangor


The vehicleinfo.org API
This project exposes a public API endpoint that can be used to get data on any vehicle(s) a client may need. Implementing the API is as simple as making an HTTP request to:
http://vehicleinfo.org/api/
and specifying the resource you would like.
GET /year
The first route accepts GET requests for a specific year within range, and the returns a JSON response of every vehicle for that year, including its individual attributes.
Try this route by pressing this link
vehicleinfo.org/api/2002
GET /year/make
The second route for the API accepts GET requests for a specific year and make, and returns a JSON response of all vehicles produced by that manufacturer in that year.
Try this route by pressing this link
vehicleinfo.org/api/2002/acura
GET /year/make/model
The final route accepts GET requests for a specific year, make, and model resulting in a JSON response of the attributes for that specific vehicle.
Try this route by pressing this link
vehicleinfo.org/api/2002/acura/tl
PLEASE NOTE:
The web server can only handle a certain number of requests at any given time, so sometimes a valid request may return an empty result. While I work to increase reliability, if this occurs, please wait a few seconds and try your request again.


Class CopyIOStream
A wrapper Stream implementation around another Stream instance that supports automatic copying of read and written data into a secondary Copy-Stream.
Implements
Inherited Members
Namespace: THNETII.Common.IO
Assembly: THNETII.Common.dll
Syntax
public class CopyIOStream : Stream, IDisposable
Remarks
Usage of this type is useful in situations where a non-seekable stream is consumed, but applications need to keep a record of the processed data.
Constructors
| Improve this Doc View SourceCopyIOStream(Stream, Stream, Stream)
Creates a new stream wrapper around the specified base stream, writing data to either the read-copy stream or write-copy stream on every IO operation.
Declaration
public CopyIOStream(Stream baseStream, Stream readCopy, Stream writeCopy)
Parameters
| Type | Name | Description |
|---|---|---|
| Stream | baseStream | The origin stream to wrap. Must not be |
| Stream | readCopy | The copy stream to which data from the base stream will be written on read operations. |
| Stream | writeCopy | The copy stream to which data from the base stream will be written on write operations. |
Exceptions
| Type | Condition |
|---|---|
| ArgumentNullException |
|
CopyIOStream(Stream, Stream, Stream, Boolean)
Creates a new stream wrapper around the specified base stream, writing data to either the read-copy stream or write-copy stream on every IO operation.
Declaration
public CopyIOStream(Stream baseStream, Stream readCopy, Stream writeCopy, bool closeStreams)
Parameters
| Type | Name | Description |
|---|---|---|
| Stream | baseStream | The origin stream to wrap. Must not be |
| Stream | readCopy | The copy stream to which data from the base stream will be written on read operations. |
| Stream | writeCopy | The copy stream to which data from the base stream will be written on write operations. |
| Boolean | closeStreams | A boolean value that indicates whether to close the base- and copy-streams when the wrapper is closed. |
Exceptions
| Type | Condition |
|---|---|
| ArgumentNullException |
|
CopyIOStream(Stream, Stream, Stream, Boolean, Boolean)
Creates a new stream wrapper around the specified base stream, writing data to either the read-copy stream or write-copy stream on every IO operation.
Declaration
public CopyIOStream(Stream baseStream, Stream readCopy, Stream writeCopy, bool closeBaseStream, bool closeCopyStreams)
Parameters
| Type | Name | Description |
|---|---|---|
| Stream | baseStream | The origin stream to wrap. Must not be |
| Stream | readCopy | The copy stream to which data from the base stream will be written on read operations. |
| Stream | writeCopy | The copy stream to which data from the base stream will be written on write operations. |
| Boolean | closeBaseStream | A boolean value that indicates whether to close the base stream when the wrapper is closed. |
| Boolean | closeCopyStreams | A boolean value that indicates whether to close the copy streams when the wrapper is closed. |
Exceptions
| Type | Condition |
|---|---|
| ArgumentNullException |
|
CopyIOStream(Stream, Stream, Stream, Boolean, Boolean, Boolean)
Creates a new stream wrapper around the specified base stream, writing data to either the read-copy stream or write-copy stream on every IO operation.
Declaration
public CopyIOStream(Stream baseStream, Stream readCopy, Stream writeCopy, bool closeBaseStream, bool closeReadCopy, bool closeWriteCopy)
Parameters
| Type | Name | Description |
|---|---|---|
| Stream | baseStream | The origin stream to wrap. Must not be |
| Stream | readCopy | The copy stream to which data from the base stream will be written on read operations. |
| Stream | writeCopy | The copy stream to which data from the base stream will be written on write operations. |
| Boolean | closeBaseStream | A boolean value that indicates whether to close the base stream when the wrapper is closed. |
| Boolean | closeReadCopy | A boolean value that indicates whether to close the read-copy stream when the wrapper is closed. |
| Boolean | closeWriteCopy | A boolean value that indicates whether to close the write-copy stream when the wrapper is closed. |
Exceptions
| Type | Condition |
|---|---|
| ArgumentNullException |
|
Properties
| Improve this Doc View SourceBaseStream
Gets the origin stream on which IO operations are performed when IO operations on this instance are called.
Declaration
public Stream BaseStream { get; }
Property Value
| Type | Description |
|---|---|
| Stream | The non- |
CanRead
Gets a value indicating whether the stream wrapper supports reading.
Declaration
public override bool CanRead { get; }
Property Value
| Type | Description |
|---|---|
| Boolean |
|
Overrides
| Improve this Doc View SourceCanSeek
Gets a value indicating whether the stream wrapper supports seeking.
The CopyIOStream wrapper class does not support seeking, even if the wrapped origin stream does. This property always returns false
Declaration
public override bool CanSeek { get; }
Property Value
| Type | Description |
|---|---|
| Boolean |
|
Overrides
Remarks
Seeking is not supported by the CopyIOStream class to ensure consistent write and read copies or the origin stream. However, if BaseStream supports seeking, Seek(Int64, SeekOrigin) can still be called on the origin stream.
CanTimeout
Gets a value that determines whether the stream wrapper can time out.
Declaration
public override bool CanTimeout { get; }
Property Value
| Type | Description |
|---|---|
| Boolean |
|
Overrides
| Improve this Doc View SourceCanWrite
Gets a value indicating whether the stream wrapper supports writing.
Declaration
public override bool CanWrite { get; }
Property Value
| Type | Description |
|---|---|
| Boolean |
|
Overrides
| Improve this Doc View SourceCloseBaseStream
Gets a value that determines whether the origin stream is closed when this instance is closed.
Declaration
public bool CloseBaseStream { get; }
Property Value
| Type | Description |
|---|---|
| Boolean |
|
CloseReadCopy
Gets a value that determines whether the read-copy stream is closed when this instance is closed.
Declaration
public bool CloseReadCopy { get; }
Property Value
| Type | Description |
|---|---|
| Boolean |
|
CloseWriteCopy
Gets a value that determines whether the write-copy stream is closed when this instance is closed.
Declaration
public bool CloseWriteCopy { get; }
Property Value
| Type | Description |
|---|---|
| Boolean |
|
Length
Gets the length, in bytes, of the orgin stream.
Declaration
public override long Length { get; }
Property Value
| Type | Description |
|---|---|
| Int64 | A long value representing the length of the stream in bytes. |
Overrides
Exceptions
| Type | Condition |
|---|---|
| NotSupportedException | The origin stream does not support seeking. |
| ObjectDisposedException | Methods were called after the origin stream was closed. |
Position
Gets the position within the origin stream. Setting this property is not supported and will cause a NotSupportedException to be thrown.
Declaration
public override long Position { get; set; }
Property Value
| Type | Description |
|---|---|
| Int64 | The current position within the stream as a Int64 valued offset from the beginning of the stream. |
Overrides
Exceptions
| Type | Condition |
|---|---|
| IOException | An I/O error occurred. |
| NotSupportedException | The origin stream does not support seeking. |
| ObjectDisposedException | Methods were called after the origin stream was closed. |
ReadCopy
Gets the stream to which the wrapper writes data that is read from the origin stream.
Declaration
public Stream ReadCopy { get; }
Property Value
| Type | Description |
|---|---|
| Stream | The Stream instance that was passed to the CopyIOStream constructor when this instance was created;
or |
ReadTimeout
Gets or sets a value, in miliseconds, that determines how long the wrapper will attempt to read from the origin stream before timing out.
Declaration
public override int ReadTimeout { get; set; }
Property Value
| Type | Description |
|---|---|
| Int32 | The ReadTimeout of the orgin stream or the WriteTimeout of the read-copy stream, whichever is smallest. |
Overrides
| Improve this Doc View SourceWriteCopy
Gets the stream to which the wrapper writes data that is written to the origin stream.
Declaration
public Stream WriteCopy { get; }
Property Value
| Type | Description |
|---|---|
| Stream | The Stream instance that was passed to the CopyIOStream constructor when this instance was created;
or |
WriteTimeout
Gets or sets a value, in miliseconds, that determines how long the wrapper will attempt to write to the origin stream before timing out.
Declaration
public override int WriteTimeout { get; set; }
Property Value
| Type | Description |
|---|---|
| Int32 | The WriteTimeout of the orgin stream or the WriteTimeout of the write-copy stream, whichever is smallest. |
Overrides
Methods
| Improve this Doc View SourceCreateReadCopy(Stream, Stream)
Creates a new stream wrapper around the specified base stream, writing data to the copy stream on every read operation.
Declaration
public static CopyIOStream CreateReadCopy(Stream baseStream, Stream copyStream)
Parameters
| Type | Name | Description |
|---|---|---|
| Stream | baseStream | The origin stream to wrap. Must not be |
| Stream | copyStream | The copy stream to which data from the base stream will be written on read operations. |
Returns
| Type | Description |
|---|---|
| CopyIOStream | A new initialized CopyIOStream instance that wraps around the specified base stream. |
Exceptions
| Type | Condition |
|---|---|
| ArgumentNullException |
|
CreateReadCopy(Stream, Stream, Boolean)
Creates a new stream wrapper around the specified base stream, writing data to the copy stream on every read operation.
Declaration
public static CopyIOStream CreateReadCopy(Stream baseStream, Stream copyStream, bool closeStreams)
Parameters
| Type | Name | Description |
|---|---|---|
| Stream | baseStream | The origin stream to wrap. Must not be |
| Stream | copyStream | The copy stream to which data from the base stream will be written on read operations. |
| Boolean | closeStreams | A boolean value that indicates whether to close the base- and copy-stream when the wrapper is closed. |
Returns
| Type | Description |
|---|---|
| CopyIOStream | A new initialized CopyIOStream instance that wraps around the specified base stream. |
Exceptions
| Type | Condition |
|---|---|
| ArgumentNullException |
|
CreateWriteCopy(Stream, Stream)
Creates a new stream wrapper around the specified base stream, writing data to the copy stream on every write operation.
Declaration
public static CopyIOStream CreateWriteCopy(Stream baseStream, Stream copyStream)
Parameters
| Type | Name | Description |
|---|---|---|
| Stream | baseStream | The origin stream to wrap. Must not be |
| Stream | copyStream | The copy stream to which data from the base stream will be written on write operations. |
Returns
| Type | Description |
|---|---|
| CopyIOStream | A new initialized CopyIOStream instance that wraps around the specified base stream. |
Exceptions
| Type | Condition |
|---|---|
| ArgumentNullException |
|
CreateWriteCopy(Stream, Stream, Boolean)
Creates a new stream wrapper around the specified base stream, writing data to the copy stream on every write operation.
Declaration
public static CopyIOStream CreateWriteCopy(Stream baseStream, Stream copyStream, bool closeStreams)
Parameters
| Type | Name | Description |
|---|---|---|
| Stream | baseStream | The origin stream to wrap. Must not be |
| Stream | copyStream | The copy stream to which data from the base stream will be written on write operations. |
| Boolean | closeStreams | A boolean value that indicates whether to close the base- and copy-stream when the wrapper is closed. |
Returns
| Type | Description |
|---|---|
| CopyIOStream | A new initialized CopyIOStream instance that wraps around the specified base stream. |
Exceptions
| Type | Condition |
|---|---|
| ArgumentNullException |
|
Dispose(Boolean)
Declaration
protected override void Dispose(bool disposing)
Parameters
| Type | Name | Description |
|---|---|---|
| Boolean | disposing |
Overrides
| Improve this Doc View SourceFlush()
Declaration
public override void Flush()
Overrides
| Improve this Doc View SourceFlushAsync(CancellationToken)
Declaration
public override Task FlushAsync(CancellationToken cancellationToken)
Parameters
| Type | Name | Description |
|---|---|---|
| CancellationToken | cancellationToken |
Returns
| Type | Description |
|---|---|
| Task |
Overrides
| Improve this Doc View SourceRead(Byte[], Int32, Int32)
Declaration
public override int Read(byte[] buffer, int offset, int count)
Parameters
| Type | Name | Description |
|---|---|---|
| Byte[] | buffer | |
| Int32 | offset | |
| Int32 | count |
Returns
| Type | Description |
|---|---|
| Int32 |
Overrides
| Improve this Doc View SourceReadAsync(Byte[], Int32, Int32, CancellationToken)
Declaration
public override Task<int> ReadAsync(byte[] buffer, int offset, int count, CancellationToken cancellationToken)
Parameters
| Type | Name | Description |
|---|---|---|
| Byte[] | buffer | |
| Int32 | offset | |
| Int32 | count | |
| CancellationToken | cancellationToken |
Returns
| Type | Description |
|---|---|
| Task<Int32> |
Overrides
| Improve this Doc View SourceSeek(Int64, SeekOrigin)
Always throws a NotSupportedException.
Declaration
public override long Seek(long offset, SeekOrigin origin)
Parameters
| Type | Name | Description |
|---|---|---|
| Int64 | offset | Ignored. A byte offset relative to the |
| SeekOrigin | origin | Ignored. A value of type SeekOrigin indicating the reference point used to obtain the new position. |
Returns
| Type | Description |
|---|---|
| Int64 | The new position within the current stream. |
Overrides
Remarks
Instances derived from CopyIOStream by default do not support seeking in order to keep the read and write copies consistent. However, if the source stream is seekable you can call Seek(Int64, SeekOrigin) on the BaseStream member. Note that doing so can potentially create garbled or discontinuous content in the read and/or write copies on subsequent I/O operations.
Exceptions
| Type | Condition |
|---|---|
| NotSupportedException | Instances of CopyIOStream do not support seeking. |
SetLength(Int64)
Always throws a NotSupportedException.
Declaration
public override void SetLength(long value)
Parameters
| Type | Name | Description |
|---|---|---|
| Int64 | value | Ignored. The desired length of the current stream in bytes. |
Overrides
Remarks
Instances derived from CopyIOStream by default do not support setting the length. However, if the underlying streams support it, you can call SetLength(Int64) individually on the BaseStream, ReadCopy and WriteCopy members. Note that doing so must take into account that SetLength(Int64) might not succeed on all three members.
Exceptions
| Type | Condition |
|---|---|
| NotSupportedException | Instances of CopyIOStream do not support setting the length of the stream. |
Write(Byte[], Int32, Int32)
Declaration
public override void Write(byte[] buffer, int offset, int count)
Parameters
| Type | Name | Description |
|---|---|---|
| Byte[] | buffer | |
| Int32 | offset | |
| Int32 | count |
Overrides
| Improve this Doc View SourceWriteAsync(Byte[], Int32, Int32, CancellationToken)
Declaration
public override Task WriteAsync(byte[] buffer, int offset, int count, CancellationToken cancellationToken)
Parameters
| Type | Name | Description |
|---|---|---|
| Byte[] | buffer | |
| Int32 | offset | |
| Int32 | count | |
| CancellationToken | cancellationToken |
Returns
| Type | Description |
|---|---|
| Task |


7:00 pm. Consuming caffeine in order to beat out the sedative effects of the pills. Also waiting eagerly for pizza in order to have a steady stomach (or something to throw up in case something goes wrong). Personal note: I am terribly nervous. One week ago I tripped out on 5 tabs of benadryl, but I couldnít make it past the depressive/lassitude stage. The next day I remarked to a friend on my weekend activities. He promptly responded that I had done intense damage to my liver, in all likelihood. Terrified, I rushed to do more research, a mistake that I had not prepared better beforehand. I found that I had not injested that much. More confident tonight, I intend to ingest eight tablets of benadryl (that is, 200 mg), after, of course, a great deal of caffeine and some pizza. My nervousness derives from the threat of overdose, which, admittedly, is relatively unlikely at this stage, as demonstrated through my research.
7:28 pm. Pizzaís here. My stomachís growling - one tab of antacid will help that out. The ladyís in Florida, and just called, which means thereís nothing holding me back tonight from a good, old-fashioned trip. In other words, I am cleared for takeoff.
7:36 pm. Eating, preparing a playlist. The Silence Xperimentís mashup album from Coldplayís X and Y, along with m83, one nmh song, two crystal method tracks, some ray charles, queen, and a ton of U2- all things to make me smile whilst trekking into unknown territory.
7:48 pm. Havenít even started yet and my eyes are heavy and watering. I think Iím sick.
8:02 pm. Now or never. Taking pills was never my strong suit. Stomachís reacting in a very negative manner. Deep breaths. Space it out- down the hatch!
8:04 pm. Stomach settled. Four left.
8:06 pm. And weíre done- 8 down the hatch, and locked in for the night. Iím going to pee, take a bath, and brush my teeth while I can. Be back asap.
8:36 pm. Faintly loopy. A jarring feeling that Iíve been carrying for the past two weeks since I quit taking my anti-depressants - reminiscent of being scared, but time wise, takes place only in a flash and then is gone. Mother and sister left to attend a bat mitzvah. The movie The Punisherís on in the other room. Itís cold. Think I might get a blanket and swaddle myself within its warm enfolds. A gradual confidence mentally, my nervousness is abating as more time passes. Iím carrying a small but abiding feeling of euphoria. Body is reacting positively to the music, making me gyrate in wildly embarrassing ways. Must be the music, all I should be getting now are threshold effects. Small headache forming around left hand temple.
8:42 pm. Eyes watering and heavy. Might be the computer. Queenís Headlong is sounding more and more sinister, for some reason: ďand thereís nothing you can/nothing you can/nothing you can do about it..Ē
8:45 pm. Feel like playing some Counterstrike.
9:15 pm. Good game of Counterstrike. An almost bodhisattva-esque calm has come over me. The music seems to be coming from another place, nearby but farther away than where my speakers are. Had bouts of diarrhea twenty minutes ago, but otherwise, everythingís groovy. Finger are having trouble typing, though, and I keep second-guessing my spelling.. hrm.. in retrospect, I am deeply tempted to ramp up, as it were. Two more tabs will take me to 250 mg. I will wait fifteen minutes and then do so.
9:26 pm. A feeling of giddy anticipation. My joints ache. This is a peculiar juxtaposition.
9:42 pm. Esthero is seeping out of my speakers and into my ears. Eyelids growing heavy. Drinking lots of soda. Things fall in and out of focus. A very stoned feeling, find myself staring at blinking lights or lustrous metals. Time to ramp up. Fighting to stay awake. Plagued by stoned indecision. Should I take more? Stumbling.
9:48 pm. Took an additional 50 mg. Walking was strange, difficult, but fluid. Everything is now carrying an almost glowing sense of color. A feeling of vividness and perfection in sensory input, coupled with a fear that soon I will pass out.
9:53 pm. By squeezing my eyelids tightly shut, I could see a perfect reflection of my pupils, each small, jagged line of slate gray pushed into my darkened view. Pinching the bridge of my nose. Itís to stay awake. I swear I can hear my mom yelling through the music, even though I just asked her if she did, and she answered in the negative.
10:14 pm. Definitely high. Motion in front of me and at my peripherals. A gradual, small sense of euphoria. Almost like being high on pot.
10:24 pm. First distortion of reality. Looks as if one of my posters is slowly drifting to the right - even though it has thumbtacks holding it up.
10:57 pm. Nothing but drowsiness and an extraordinary awareness to movement. Going to take two more, making my dosage 300 mg. After this, Iím done. too dangerous to continue ramping up like this.
11:01 pm. Up to 300 mgs. My final dosage. Walking has become easier. I am hypersensitive to movement and slowly drifting in and out of a dreamy, absinthe-like high. Becoming very forgetful, to the point of absurdity (ignoring painful postures because Iím too slow to pick up on the pain).
11:08 pm. Man has the ability to demolish the planet he lives on. Man has the ability to feed and clothe himself, and thus heal himself. Man has found ways to alter and explore the very fabric of reality- but immediately declared any such exploration illegal. Manís fear is justified. How would he carry on if he knew the entire world he lived and breathed was simply an illusion? How would Man stand on two legs when he knew that his time would come too, that all are bound for a place beyond this Earth? How would Man simply at night?
11:11 pm. Answer: Benadryl. Iím slipping away now
Afternote/Summary. The next day, 12:07 pm. I managed to pass out at my computer. Woke up ten minutes later and stumbled upstairs, listening to music on my iPod. As I did, I noticed the posters on my bedroom walls began to drift to the left and right rhythmically. After I turned off my music, I heard a deafening rushing noise, identical to the sound a wave makes before crashing into shore. And then, I was gone. I woke up this morning with no hangover or stomach pains (much to my relief). If I wasnít bound by my own code of conduct, Iíd attempt a much higher dosage next weekend. 300 mgs is not enough to produce significant delirium. Overall, as an over the counter drug, benadryl is good for a minor high, but this same high could be reproduced easily (it seems to me) with some marijuana. I would prefer getting high with pot rather than benadryl.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


We extract publication information from the
DBLP dataset (currently
2020-01-01).
Based on this XML, we extract all publications published on one of the conferences listed on the main page.
This list originated from
recent work by
Jan Peters,
Constantin A. Rothkopf and
Kristian Kersting.
After extracting publications located in the research domain "Artificial Intelligence", we extract all authors and search for further information (e.g. country, affiliation and URLs) in the DBLP.xml.
Finally we match all publications with the open available
Semantic Scholar dataset (currently the release from 2019-11-01) in order to add information about the in citations.
These are used to calculate the
h-Index based on AI publications.
Due to the fact, that Semantic Scholar contains the DBLP as one of their sources, it is possible to match a majority of the publication using only the title and year information.
We decided to use publication year information, due to matching single DBLP publication to multiple Semantic Scholar publications in different years.
We found them to be either journal extensions or ArXiv publications.
We decided against matching the conference/journal names, due to the fact, that the disambiguation is very hard (both dataset use different abbreviations).
Please keep in mind that that the Semantic Scholar contains different citation information that e.g. the Google scholar, hence our
h-Index might differ from other sources.
For the Google Scholar Citations page of an author, please follow the Google Scholar icon.


iconHelper
umbraco.services
A helper service for dealing with icons, mostly dealing with legacy tree icons


Eastern Cape cop kills himself after allegedly shooting girlfriend's sister
by Nicole McCainA Middleburg police officer has committed suicide after allegedly shooting his girlfriend’s sister with his service weapon.
Independent Police Investigative Directorate (IPID) spokesperson Sontaga Seisa confirmed that Mzwamadoda Fuzani had discharged his firearm while off-duty on Friday night, allegedly killing the 30-year-old woman in Thembalethu, George.
It is understood that the police sergeant then went searching for his girlfriend, who is also an officer at Middleburg Police Station.
“Failing to trace his girlfriend, the suspect realized that the police were looking for him. He then decided to flee the area in his private car and managed to commit suicide by fatally shooting himself with the firearm some 200 kilometres away from Middleburg,” said Seisa.
The incident is under investigation by IPID.


This is Me!
My plans post graduation include going into industry and ideally working for a company that does community focused work
In my free time I like to go down rabbit holes on random subjects,
make Mexican food vegan, eating brunch from many of La Jolla's brunch
spots, and watching movies and the LA Lakers
Feel free to recommend readings on social justice and related topics! (Also I got these lil mini me's at the Google spot at the Grace Hopper Conference!!)
Click here to get a random message:
Get some message from the Data Servlet ;P
Array about to print (;
Comments Section!
Click mini me below to actually view pics of me!!
Click mini me below for my LinkedIn


Home
About
Cart
3
3
Total:
$2,229.97
Sony DSC-RX100M III
$849.99
Quantity: 01
KS Automatic Mechanic...
$1,249.99
Quantity: 01
Kindle, 6" Glare-Free To...
$129.99
Quantity: 01
Checkout
Previous
Next


Bale: Euphoria of Euro 2016 can inspire Wales against Hungary
by OmnisportGareth Bale believes the euphoric feeling of Wales' run to the semi-finals at Euro 2016 will spur Ryan Giggs' side on against Hungary.

Bale scored three goals at the previous finals as Wales - with Chris Coleman in charge - reached the last four in France, going out to eventual champions Portugal.
The Real Madrid forward has claimed Wales can use the experiences gained from three years ago, plus their failure to qualify for the 2018 World Cup, as inspiration heading into Tuesday's crucial clash in Cardiff.
"I think we can use both of [the experiences] to our advantage," Bale told a news conference.
"We have got the kind of euphoria of qualifying for our first Euros and doing so well at the competition that we really want to do that again.
"We've also got the negatives of knowing how it feels to miss out on qualifying for a tournament like we did with the World Cup.
"We can see how we don't want to feel after the game. We know prior to this game we can push and push not to feel like that again, because it's not a feeling you want in the dressing room after the game.
"The experience of qualifying for the Euros was massive and something I've never done before and to do it again might be a bit more impressive.
"It's going to stand in equal stead as last time, but we can't think about that just yet. We have another important match first and we have to focus on that as a team and a squad."
Giggs confirmed on Monday that Bale and Aaron Ramsey, who has been hampered by a thigh problem, are both ready to start against Hungary.
"I'm ready. I hadn't played in four weeks so it was important to get 60 minutes into my legs and get some match sharpness," Bale added.
"We spoke at the start of the week about trying to get 60 minutes and hopefully being a couple [of goals] up before I came off, so it worked out well, no problems, and I'm ready to go for Tuesday."


IND Innovations is a global ICT solution, consulting and managed services company headquartered in New Delhi, India. They have several high profile clients including Huawei and have a strong presence in over 30 countries. In India, they have close to 500 employees who go out in the field and carry out services.
Naturally, a company as diversified and dynamic as IND Innovations had several logistical and administrative issues that were cropping up. Their employees had to go on long-term assignments to different towns and cities to help set-up networks and execute any servicing.
The company was finding it highly complicated and time-consuming to constantly call up employees and inform them of assignments or to receive updates. Furthermore, there was no accurate and reliable way of evaluating their field work. It was at this point that Growthfile stepped in.
Supervising each and every employee is a near-impossible task for any business enterprise, but it is highly important to know the whereabouts of your field employees. It gives a clear and comprehensive idea about your company, which becomes useful for future decisions
Business revenues are dependent on the functioning of employees, so it becomes crucial to monitor them in an effective way. As per a 2018 GlobalData study, only 48% employees efficiently use company time and resources.
Growthfile is one of the best tools out there to assist in this regard.
IND Innovations created an Office account on Growthfile and instructed all of their employees in India to join it, download the mobile app and check-in regularly.
The smooth, simple interface of the app ensured that everyone was able to navigate it with ease. Employees were informed that their performance and compensation were dependent on their timely check-in and documentation of work done in the app.
The company noted that in the week following Growthfile’s introduction, on time duty reporting by employees and their efficiency shot up by almost 30% and the numbers continued to surge.
The CEO was regularly updated about employee performance and received clear, coherent daily reports about the work done. This made it convenient to arrive at informed decisions regarding the company, and to free up company time for other productive outlets.
Optimisation of employee performance and maximum utilisation of company time was soon reported by the company!
A thorough optimisation of work delegation was accomplished.
Optimisation of employee performance and maximum utilisation of company time was soon reported by the company!
Make the switch to Growthfile now
Sign up


Fuel prices hit record highs in inner Brisbane suburbs
Fuel prices across southeast Queensland have continued to surge, with the cost of fuel smashing records in parts of Brisbane. SEE WHICH SUBURBS TO AVOID.
The RACQ is warning motorists that the cost of unleaded fuel is higher than ever in Brisbane’s inner and western suburbs.
Yesterday’s average unleaded fuel price for that region was a whopping 173.1 cents per litre.
That figure has smashed the previous record high of 169.4 cents per litre set in October last year.
The RACQ advised motorists to avoid filling up unless absolutely necessary and to only top up as needed.
“These high prices in the south east can be attributed to high indicative retail margins, at 29.5cpl, so servos are just raking it in,” RACQ spokeswoman Renee Smith said.
“When we reach the peak of the fuel cycle we do see pockets of higher prices but we’ve never seen prices this high in central and western suburbs,” Ms Smith said.
She urged motorists to vote with their wallets by using apps like the RACQ Fuel Finder to find servos with cheaper prices.
SUBURBS IN THE RECORD-BREAKING FUEL ZONE:
Bowen Hills
Herston
Newstead
Hamilton
Clayfield
Kalinga
Lutwyche
Windsor
Wooloowin
Enoggera
Newmarket
Brookside Centre
Mitchelton
Keperra
Ferny Grove
Kelvin Grove
Ashgrove
The Gap
Milton
Toowong
Indooroopilly
Taringa
Chapel Hill
Kenmore
Bellbowrie
Jindalee
Mount Ommaney
Riverhills
Graceville
Sherwood
Highgate Hill
East Brisbane
Morningside
Norman Park
Seven Hills
Bulimba
Hawthorne
Grange
Wilston
Ascot
Albion
Paddington
Alderley
Gaythorne
Hendra


Heen's Restaurant
We're closed every Tuesday
Home
Menu
Contact Us
Contact Us
Contact
Heen's Restaurant
14 - 16 Mulgrave Road
Sutton
Surrey
SM2 6LE
0208 643 1221
Opening Hours
Opening times
Open Monday From 5pm - 10pm
Tuesday Closed
Open Wednesday From 5pm - 10pm
Open Thursday From 5pm - 10pm
Open Friday Lunch time 12pm - 2:30pm
and From 5pm - 11pm
Open Saturday Lunch time 12pm - 2:30pm
and From 5pm - 11pm
Open Sunday Lunch time 12pm - 2:30pm
and From 5pm - 10pm
Find Us


Welcome to Relocation Express
A HUSTLE-FREE RELOCATION Is our RESPOSIBILITY
Our company do not offer Transit insurance.
It is therefore recommended that you liase with your insurer to cover the move.
Alternatively, we can take the cover on your behalf and include the cost as part of relocation fees
We accept several forms of payment:
We have pet experts who handle that.
YES, Relocation Express is a registered company
place your order here
© Copyright 2021 Relocation Express