|
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
public interface ClassVector
An interface for nominal class information.
| Method Summary | |
|---|---|
int |
getAssignment(int index)
Gets the class assignment. |
int |
getClassCount()
Gets the number of different possible values taken by the class assignments. |
java.lang.String |
getClassName(int assignment)
Gets the class name for the specified assignment. |
java.lang.String |
getDescription()
Gets the description for this class vector. |
int |
size()
Gets the number of assignments in this class vector. |
| Method Detail |
|---|
int getAssignment(int index)
index
- The index.
java.lang.String getClassName(int assignment)
assignment
- The assignment.
int getClassCount()
int size()
java.lang.String getDescription()
|
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


Exemple of static data with editable paginated ng-datasheet
This Exemple show you a datasheet getting a finish set of data from a wikipedia search with filter, sort,
pagination and
resizing capabilities. This datasheet is not editable.
Tab : Go to next column, if you reach end of table, go to next row first column
Arrow : Navigate in cells
Ctrl + C : Copy mouse selection (can be paste on spreadsheet applications)


Deleting a Repository Category
How to delete a category from the Repository module.
-
- Select the Repository Settings tab.
- At the Categories drop down list, select root to delete a root category - OR - Select a parent category to delete its child category.
- In the Categories box below, select the category to be deleted.
- Click the Delete button. This displays the message "Are you sure you want to delete this Category?"
-
Click the OK button confirm.
Tip: You do not need to click the Update button to save these changes.
Deleting a category


class: center, middle, inverse, title-slide # Lecture 12 ## Travel cost method ### Ivan Rudik ### AEM 4510 --- exclude: true ```r if (!require("pacman")) install.packages("pacman") ``` ``` ## Loading required package: pacman ``` ```r pacman::p_load( tidyverse, magrittr, xaringanExtra, rlang, patchwork, nycflights13, broom, viridis, vembedr ) set.seed(1) 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="center") options }) red_pink <- "#e64173" blue <- "#3C93DC" red <- "#ff0000" ``` ``` ## 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. ``` ```r red_pink <- "#e64173" # A blank theme for ggplot theme_empty <- theme_minimal() + theme( legend.position = "none", title = element_text(size = 24), axis.text.x = element_text(size = 24), axis.text.y = element_text(size = 24, color = "#ffffff"), axis.title.x = element_text(size = 24), axis.title.y = element_text(size = 24), panel.grid.minor.x = element_blank(), panel.grid.major.y = element_blank(), panel.grid.minor.y = element_blank(), panel.grid.major.x = element_blank(), panel.background = element_rect(fill = "#ffffff", colour = NA), plot.background = element_rect(fill = "#ffffff", colour = NA), axis.line = element_line(colour = "black"), axis.ticks = element_line(), ) theme_blank <- theme_minimal() + theme( legend.position = "none", title = element_text(size = 24), axis.text.x = element_blank(), axis.text.y = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank(), panel.grid.minor.x = element_blank(), panel.grid.major.y = element_blank(), panel.grid.minor.y = element_blank(), panel.grid.major.x = element_blank(), panel.background = element_rect(fill = "#ffffff", colour = NA), plot.background = element_rect(fill = "#ffffff", colour = NA), axis.line = element_blank(), axis.ticks = element_blank(), ) theme_regular <- theme_minimal() + theme( legend.position = "none", title = element_text(size = 14), axis.text.x = element_text(size = 24), axis.text.y = element_text(size = 24), axis.title.x = element_text(size = 24), axis.title.y = element_text(size = 24), panel.grid.minor.x = element_blank(), panel.grid.minor.y = element_blank(), panel.grid.major.x = element_blank(), axis.ticks = element_line(), axis.line = element_line(), panel.background = element_rect(fill = "#ffffff", colour = NA), plot.background = element_rect(fill = "#ffffff", colour = NA) ) ``` ```r # initialize seed and data set.seed(1000) num_trips <- 100 trip_data <- expand.grid( house_num = seq(from = 1, to = num_trips), site = 1:26 ) # make fake dataset trip_data <- trip_data %>% as_tibble() %>% mutate( trips = sample(0:8, num_trips * 26, replace = TRUE), income = exp(rnorm(num_trips * 26)) * 3000 + 50000 + rnorm(num_trips * 2) * 15000, income = ifelse(income < 10000, 10000, income), travel_cost = exp(rnorm(num_trips * 26)) * 30, travel_cost_other = exp(rnorm(num_trips * 26)) * 35, trips = round(-.01 * travel_cost + .01 * travel_cost_other + 3 / 100000 * income), water_clarity = runif(num_trips * 26) ) %>% group_by(site) %>% mutate(water_clarity = mean(water_clarity)) %>% ungroup() %>% mutate(trips = case_when( trips <= 0 ~ 0, TRUE ~ trips + 3 * round(water_clarity) )) ``` --- # Roadmap - How do we estimate the value of recreational goods? --- class: inverse, center, middle name: background # Background

 --- # Should we separate the Great Lakes and Mississippi? ```{=html} <div class="vembedr" align="center"> <div> <iframe src="https://www.youtube.com/embed/lIRXDDG6yB8" width="533" height="300" frameborder="0" allowfullscreen=""></iframe> </div> </div> ``` --- # Should we separate the Great Lakes and Mississippi? .center[  ] --- # Should we separate the Great Lakes and Mississippi? .center[  ] --- # Should we separate the Great Lakes and Mississippi? Benefits from barriers accrue to anglers in the Great Lakes, both commercial and recreational Costs come from cost of building the barriers plus cost of maintaining them, plus costs of reduced shipping (if any), plus any other costs associated with the barriers How do we figure out the benefits from recreational anglers? --- # Why do we need travel cost? Recreational areas .hi[have value] -- Their quality also has value -- Not placing a value on recreation is essentially giving it a value of .hi-purple[zero] -- This is likely inappropriate -- If someone dumped toxic waste in Taughannock does that have zero cost? --- # What is the travel cost method? The travel cost method uses observable data on recreation visitation to infer the recreational value of environmental amenities -- The central idea is that the time and travel cost expenses that people incur to visit a site represent the .hi[price] of access to the site -- This means that people's WTP to visit can be estimated based on the number of visits they make to sites of different prices -- This gives us a demand curve for sites/amenities, so we can value changes in these environmental amenities --- # Hotelling After WWII, the U.S. national park service solicited advice from economists on methods for quantifying the value of specific park properties -- Would total entrance fee that people pay measure the value? -- .hi-red[No!] -- Harold Hotelling proposed the first indirect method for measuring the demand of a non-market good in 1947 --- # Hotelling > Let concentric zones be defined around each park so that the cost of travel to the park from all points in one of these zones is approximately constant. The persons entering the park in a year, or a suitable chosen sample of them, are to be listed according to the zone from which they came. The fact that they come means that the service of the park is at least worth the cost, and this cost can probably be estimated with fair accuracy. --- # Hotelling > A comparison of the cost of coming from a zone with the number of people who do come from it, together with a count of the population of the zone, enables us to plot one point for each zone on a demand curve for the service of the park. By a judicious process of fitting, it should be possible to get a good enough approximation to this demand curve to provide, through integration, a measure of consumers’ surplus.. -- About twelve years after, Trice and Wood (1958) and Clawson (1959) independently implemented the methodology --- # Theoretical foundation Here's the theory for how we can use observed data to tell us something about willingness to pay -- Consider a single consumer and a single recreation site -- The consumer has: - Total number of recreation trips: x, to site of quality: q - Total budget of time: T - Working time: H - Non-recreation, non-work time: l - Hourly wage: w - Money cost of reaching the site: c - Expenditures on other market goods: z --- # Theoretical foundation This lets us write down the consumer's utility maximization problem: `$$\max_{x,z,l} U(x,z,l,q) \,\,\,\, \text{subject to: }\,\,\, \underbrace{wH = cx + z}_{\text{money budget}}, \,\, \underbrace{T = H + l + tx}_{\text{time budget}}$$` -- Combine the two constraints to get: -- `$$\max_{x,z,l} U(x,z,l,q) \,\,\,\, \text{subject to: }\,\,\, \underbrace{wT = z + (c+wt)x + wl}_{\text{combined money/time budget}}$$` --- # Theoretical foundation Let `\(Y = wT\)` be the consumer's *full income*, their money value of total time budget -- Let `\(p = c+wt\)` be the consumer's *full price*, their total cost to reach the site Then we can write the problem as: -- `$$\max_{x,z,l} U(x,z,l,q) \,\,\,\, \text{subject to: }\,\,\, \underbrace{Y = z + px + wl}_{\text{combined budget}}$$` -- Solve the constraint for `\(z\)` and substitute into the utility function... --- # Theoretical foundation `$$\max_{x,l} U\left(x,Y-px-wl,l,q\right)$$` -- This has first-order conditions: `$$[x] \,\,\, U_x - pU_z = 0 \rightarrow \frac{U_x}{U_z} = p$$` and `$$[l] \,\,\, -wU_z + U_l = 0 \rightarrow \frac{U_l}{U_z} = w$$` --- # Theoretical foundation `\(\frac{U_x}{U_z} = p\)` tells us the consumer equates the marginal rate of substitution between recreational trips and consumption to be the full price of the recreational trip -- What does this mean? -- .hi[The value of the recreational trip to the consumer, in dollar terms, is revealed by the full price p] --- # Theoretical foundation `\(U_x - pU_z = 0 \qquad -wU_z + U_l = 0\)` The above FOCs are two equations, the consumer had two choices (x,l) so we had two unknowns We can thus solve for x (and l) as a function of the parameters (p,Y,q): `$$x = f(p,Y,q)$$` This is simply the consumer's .hi-blue[demand curves] for recreation as a function of the full price p, full budget Y, and quality q --- # Theoretical foundation `$$x = f(p,Y,q)$$` If we observe consumers going to sites of different full prices `\(p_1,p_2,\dots,p_n\)`, we are moving up and down their recreation demand curve -- This lets us trace out the demand curve -- Changing Y or q shifts the demand curve in or out: these are income and quasi-price effects -- Once we have it, we can compute surplus! --- # Zonal (single-site) model Here's the most basic travel cost model to start -- - Construct distance zones (i) as concentric circles emanating from the recreation site - Travel costs from all points within each zone to the site are sufficiently close in magnitude to justify neglecting the differences -- - From a sample of visitors `\((v_i)\)` at the recreation site, determine zones of origin and their populations `\((n_i)\)` -- - Calculate the per capita visitation rates for each zone of origin `\((t_i = (v_i/n_i))\)` --- # Zonal (single-site) model - Construct a travel cost measure `\((tc_i)\)` that reflects the round-trip costs of travel from the zone of origin to the recreation site (time and gas), + an entry fee `\((fee)\)` which may be zero and does not vary across zones -- - Collect relevant socioeconomic data `\((x_i)\)` such as income and education for each distance zone -- - Use statistical methods to estimate the trip demand curve: the relationship between per-capita visitation rates, cost per visit, [and travel costs to other sites `\((tc_{si})\)`] controlling for socioeconomic differences -- - `\(t_i = g(tc_i + fee; tc_{si}, x_i) + \varepsilon_i\)` where `\(g\)` can be linear --- # Zonal (single-site) model Here's a simple example of a set of zones 1-5:  --- # Zonal (single-site) model Suppose we have the following data: ``` ## # A tibble: 5 x 5 ## zone dist pop cost vpp ## <chr> <dbl> <dbl> <dbl> <dbl> ## 1 A 2 10000 20 15 ## 2 B 30 10000 30 13 ## 3 C 90 20000 65 6 ## 4 D 140 10000 80 3 ## 5 E 150 10000 90 1 ``` If we plot cost by visits per person, we have a measure of the demand curve... --- # Zonal (single-site) model .pull-left[ This is a very simple example where it happens to be an exactly straight line, most likely the data won't be this perfect The line is simply from estimating: `$$t_i = \beta_0 + \beta_1 tc_i + \varepsilon_i$$` ] .pull-right[  ] --- # Zonal (single-site) model .pull-left[ The data will most likely look like this, but even this is probably too clean It ignores things like income, other sites, other household characteristics ] .pull-right[  ] --- # Zonal (single-site) model Based on the estimate model coefficients, construct the (inverse) demand curve -- .hi[For each zone:] predict total visitation given various fees -- Entry fee on the y-axis (price), and the number of predicted total visits on the x-axis (quantity) -- The demand curve is different for different zone because different social economic variables -- The (use) value of the park/site to each zone is given by the area underneath the corresponding demand curve --- # Issues with the single-site model What are some potential issues and concerns with this approach? -- It ignores non-use value (automatically zero for non-users) -- What are the right zones to choose? -- What is the right functional form for demand? -- How do we measure the opportunity cost of time? -- How do we treat multi-purpose trips? -- How do we value particular site attributes? Can't disentangle them at a single site --- # Multi-site model To value particular site attributes we need to have multiple sites (with different attributes!) -- We can answer questions like: -- What is the benefit of a fish restocking program? - Need to know the value of fish catch rate for visitors -- What is the benefit of water clarity? -- What is the benefit of tree replanting? --- # Multi-site model Suppose we have a dataset with a large number of individuals and sites -- Individuals are given by `\(i=1,\dots,N\)` and sites are given by `\(j=1,\dots,J\)` -- We observe the number of times each individual visited each site -- The multi-site model works as follows --- # Multi-site model .hi[Step 1:] Do the single-site estimation for each site: `$$T_{ij} = \beta_{0j} + \beta_{1j} tc_{ij} + \beta_{2j} tc_{sij} + \beta_{3j} x_i + \varepsilon_{ij}$$` -- .hi[Step 2:] Recover all the `\(\beta\)`s from each step 1 regression so that we have a set of J `\(\beta_{0j}\)`s for `\(j=1\dots,J\)`, `\(\beta_{1j}\)`s for `\(j=1\dots,J\)`, etc -- These `\(\beta\)`s tell us the slope `\((\beta_{1j})\)` and intercept `\((\beta_{0j}, \beta_{2j}, \beta_{3j})\)` -- `\(\beta_{2j}, \beta_{3j}\)` capture how the cost of substitute sites and household characteristics shift demand up and down --- # Multi-site model .hi[Step 3:] Take each set of `\(J\)` coefficient estimates and use them as the dependent variable in a regression on site attributes `\(z\)`: `$$\hat{\beta}_{0j} = \alpha_{00} + \alpha_{01}z_j + \epsilon_{0j}$$` `$$\hat{\beta}_{1j} = \alpha_{10} + \alpha_{11}z_j + \epsilon_{1j}$$` `$$\hat{\beta}_{2j} = \alpha_{20} + \alpha_{21}z_j + \epsilon_{2j}$$` `$$\hat{\beta}_{3j} = \alpha_{30} + \alpha_{31}z_j + \epsilon_{3j}$$` The `\(\alpha_{\times1}\)` coefficients tell us how the demand curve shifts `\((\alpha_{00}, \alpha_{02}, \alpha_{03})\)` or rotates `\((\alpha_{01})\)` as we change `\(z\)` --- # Valuing attributes with a multi-site model .pull-left[ If we improve the quality of a site from z1 to z2, demand for that site shifts up The gain in CS, holding the cost fixed, is given by the blue area Once we estimate demand curves, we can see how welfare changes when we alter quality characteristics! ] .pull-right[  ] --- # Multi-site example ```r trip_data ``` ``` ## # A tibble: 2,600 x 7 ## house_num site trips income travel_cost travel_cost_other water_clarity ## <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 1 1 4 40450. 38.9 16.4 0.506 ## 2 2 1 5 60304. 29.8 37.5 0.506 ## 3 3 1 5 66681. 42.2 67.2 0.506 ## 4 4 1 5 52886. 11.0 51.3 0.506 ## 5 5 1 5 69282. 15.7 7.72 0.506 ## 6 6 1 5 36948. 4.30 48.0 0.506 ## 7 7 1 6 60866. 5.31 91.0 0.506 ## 8 8 1 5 35557. 65.0 161. 0.506 ## 9 9 1 5 64880. 14.5 24.3 0.506 ## 10 10 1 4 38491. 13.6 26.5 0.506 ## # … with 2,590 more rows ``` --- # First stage estimation ```r # first stage of multi-site site_estimates <- map_dfr(unique(trip_data$site), function(site_in){ lm(trips ~ travel_cost + travel_cost_other + income, trip_data %>% filter(site == site_in)) %>% broom::tidy() %>% select(estimate) %>% mutate(site = site_in) %>% list() %>% tibble_row() %>% unlist() }) %>% select(1:5) %>% magrittr::set_colnames(c("intercept", "own_price", "cross_price", "income", "site")) ``` --- # First stage estimation ```r site_estimates ``` ``` ## # A tibble: 26 x 5 ## intercept own_price cross_price income site ## <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 2.99 -0.0161 0.0106 0.0000321 1 ## 2 2.45 -0.0117 0.0101 0.0000397 2 ## 3 2.37 -0.0197 0.0111 0.0000450 3 ## 4 2.33 -0.0187 0.0119 0.0000438 4 ## 5 2.05 -0.0143 0.0139 0.0000450 5 ## 6 -0.236 -0.00668 0.00972 0.0000321 6 ## 7 2.67 -0.0210 0.0118 0.0000395 7 ## 8 -0.346 -0.00395 0.00987 0.0000324 8 ## 9 2.98 -0.0133 0.0107 0.0000315 9 ## 10 -0.103 -0.00943 0.0105 0.0000302 10 ## # … with 16 more rows ``` --- # Take estimates, join with water clarity ```r # merge in water clarity estimation_df <- site_estimates %>% left_join(trip_data %>% distinct(site, water_clarity)) ``` ``` ## Joining, by = "site" ``` ```r estimation_df ``` ``` ## # A tibble: 26 x 6 ## intercept own_price cross_price income site water_clarity ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 2.99 -0.0161 0.0106 0.0000321 1 0.506 ## 2 2.45 -0.0117 0.0101 0.0000397 2 0.503 ## 3 2.37 -0.0197 0.0111 0.0000450 3 0.515 ## 4 2.33 -0.0187 0.0119 0.0000438 4 0.515 ## 5 2.05 -0.0143 0.0139 0.0000450 5 0.515 ## 6 -0.236 -0.00668 0.00972 0.0000321 6 0.481 ## 7 2.67 -0.0210 0.0118 0.0000395 7 0.539 ## 8 -0.346 -0.00395 0.00987 0.0000324 8 0.482 ## 9 2.98 -0.0133 0.0107 0.0000315 9 0.530 ## 10 -0.103 -0.00943 0.0105 0.0000302 10 0.467 ## # … with 16 more rows ``` --- # Second stage ```r # second stage of multi-site demand_shifts <- map_dfr(names(estimation_df)[1:4], function(coefficient) { reg_formula <- as.formula(paste0(coefficient, " ~ water_clarity")) lm(reg_formula, estimation_df) %>% broom::tidy() %>% mutate(coeff = coefficient) %>% slice(2) } ) ``` --- # Second stage ```r demand_shifts ``` ``` ## # A tibble: 4 x 6 ## term estimate std.error statistic p.value coeff ## <chr> <dbl> <dbl> <dbl> <dbl> <chr> ## 1 water_clarity 48.0 6.29 7.62 0.0000000733 intercept ## 2 water_clarity -0.171 0.0302 -5.67 0.00000770 own_price ## 3 water_clarity 0.0241 0.00867 2.78 0.0104 cross_price ## 4 water_clarity 0.000165 0.0000394 4.18 0.000330 income ``` The estimates column tells us how a change in water clarity (from 0 to 100%), shifts or rotates our demand curve


ONS Datasets
- Yearly
- Quarterly
- Monthly
- Linear
- Logarithmic
Type to search. Try 'gross domestic product' or 'flour'.
Click to plot, then click again to remove.
@main {
Type to search. Try 'gross domestic product' or 'flour'.
Click to plot, then click again to remove.


{% extends "base.html" %} {% load launchpad_extras humanize %} {% load crispy_forms_tags %} {% block title %}Request Print Edition{% endblock %} {% block content %}
Clicking on the Submit button below will send your request to the Acquisitions department of GW Libraries. Please check our catalog for the book's status in 1-2 weeks.
Submit


Figure 3. Understanding how <group> transformations can be used to create icon animations. Android source code for each is available on GitHub: (a) expand to collapse, (b) alarm clock, and (c) radio button. Click each icon to start its animation.
<group>


| CS 107 - Introduction to Scientific Computation Homework #5 |
1. Statistics: Create and submit functions myMin
, myMax
, myMedian
, myMean
, and myStdDev
. Each should take a floating-point number array of arbitrary dimensions as input, and return the
minimum,
maximum,
median,
mean, and
standard deviation, respectively. In the case of myMin
and myMax
, also return the single-dimensional index of the minimum and maximum values, respectively. Each implementation should make use of at least one loop, except myMedian
, which can rely on MATLAB's sort
function.
(Note: Use the biased standard deviation formula where you divide by N (rather
than N-1).)
2. Find: Exercise 8.4. In addition, handle matrix
input as well. Be sure to reread "Single-Number
Indexing of Arrays" on p. 161 and try it out before you begin. Submit
function myfind
. Hint: Note the different output and output
dimensions for find([1 0 1 1]) and find ([1 0; 1 1]).
3. Improving primeFactors
: Exercise 8.14. Submit both the
modified smallestfactor
and primeFactors
functions.
Example 1: Suppose n
is 45. The function smallestfactor
will try 2 and 3, finding 3 divides 45 evenly. We add 3 to factors, divide
45 by 3 yielding the new n value 15, and repeat. In repeating, we again
call smallestfactor
, again trying 2, then 3, and again find
that 3 divides 15 evenly. We again add 3 to factors, divide 15 by 3
yielding the new n
value 5, and repeat. In repeating, we again call
smallestfactor
, again trying 2, then 3, then 4, then 5 and find
that 5 divides 5 evenly. We add 5 to factors, divide 5 by 5 yielding the
new n
value 1, and then terminate the loop. Notice that the bold steps
preceding are unnecessary, wasted computation. Once we find a candidate
factor doesn't divide n
evenly, we know it never will. For this reason, it
makes sense to add a parameter to smallestfactor
so that we can
pick a starting point other than 2. Add an argument to smallestfactor
that defines the first potential factor of the iteration (in place of 2).
Next, change primeFactors
so that, on the first call to smallestfactor
,
2 is used for this argument, but on subsequent iterations, the argument is set
to the previous smallest factor found. In the preceding example, this
would mean that k
would start at 2, 3, and 3, for the three iterations.
Example 2: Let's assume, we've correctly modified the algorithm. Here
is a summary of the algorithm's execution with n
= 315: n
= 315, check for
factor k
= 2(no), 3(yes), n
= 105, next iteration, check for factor k
= 3(yes),
n
= 35, next iteration, check for factor k
= 3(no), 4(no), 5(yes), n
= 7, next
iteration, check for factor k
= 5(no), 6(no), 7(yes), n
= 1, termination with
the list of factors [3, 3, 5, 7].
Hint: This is a really simple modification once you understand what's being requested. Very little change to the given code will be necessary.
4. Prime List: Given a list length n >= 0, create function
primeList
to return a vector the first n prime numbers. Submit function
primeList
.
Note: primeList(0)
, primeList(1)
, and primeList(5)
should yield []
, [2]
, and
[2, 3, 5, 7, 11]
, respectively. You may use any means to
test to see if a number is prime, e.g. exercise #3 above.
Homework comment: Note how the author varies between all lowercase names and lower camel case names. When not seeking to be consistent with the text, I prefer lowerCamelCase.


{% extends "template.html" %} {% block title %}Wallet{% end %} {% block content %}
Chancecoin needs to be updated. Please go to Chancecoin.com and update.
{% elif bitcoin_updated==False %}Bitcoind is not finished synchronizing. Bitcoind has finished processing {{ block_count_bitcoin }} Bitcoin blocks.
{% elif db_updated==False %}Chancecoin is not finished synchronizing. Chancecoin has finished processing {{ block_count_db }} out of {{ block_count_bitcoin }} Bitcoin blocks.
{% else %}Chancecoin is up to speed. Chancecoin has finished processing {{ block_count_db }} out of {{ block_count_bitcoin }} Bitcoin blocks.
{% end %} {% if info!=None %}{% raw info %}
{% end %} {% if error!=None %}{% raw error %}
{% end %}| Buy quantity (CHA) | Price (CHA/BTC) | Sell quantity (CHA) |
|---|---|---|
| {{ order[1] }} | {{ order[0] }} | |
| {{ order[0] }} | {{ order[1] }} |
| Buy/sell CHA | Price (CHA/BTC) | Quantity (CHA) | Quantity (BTC) | Cancel |
|---|---|---|---|---|
| {{ order[3] }} | {{ order[1] }} | {{ order[0] }} | {{ order[2] }} |
| BTC owed | CHA in return | Pay BTC |
|---|---|---|
| {{ order_match[0] }} | {{ order_match[1] }} |


--- layout: page title: Selected Projects permalink: /projects.html description: "Reza's selected projects." comments: false --- {% for proj in site.data.index.projects %}
{{ proj.descr }}


' I walk my path alone because, let's be honest... Who would be crazy enough to walk it with me?' : John Constantine
Matt Ryan is John constantine. the tv series is based on dc's hellblazer comic, I havent read any of these comics but this show makes me want to. The character of constantine is played perfectly, he is interesting, has a lot of funny lines while also at the same time being dark and haunting. set in the same universe as other shows like arrow and flash it isn't afraid to go a little darker. unfortunately the show was cancelled half way through its first series.
8 / 10


--- layout: default title: תכניות permalink: /shows/ # sidebar: disable --- {% for show in site.shows %}
{{ show.content | truncatewords:20}}


Web Exploit Host by Zoilus - Information & Descriptions
This is simple TEXT only, nothing loaded. When finished press and hold CIRCLE to close,
then pick something else from the exploit
VORTX HEN - allows for fake signed pkg content to run, perma-enables browser and also activates debug settings.
Mira + HEN, - allows for fake signed content to run, perma-enables browser, activates debug settings, starts UART for MIRA on port 9999/9998, run it twice for Bin Loader function
Firmware Update Blocker = puts 2 fake folder/files in your update folder to prevent PS4 accidentally updating
(PS4UPDATE.PUP.temp.net and PS4UPDATE.PUP) even if you accidentally hit YES to update, let it install, it will NOT finish
and kick back an error.
Vortex FTP - starts an ftp server on you ps4 so you can connect and copy/paste files to/from your pc/ps4.
Bin Loader - allows you to send a payload.bin to your PS4 just like the old days.
Apptousb - moves games to external hdd, circumventing the internal drive running out of space.
Vortex App & Game Dumper - allows you to dump your legit games (disc/psn) to usb hdd, with or without patches, and merged/unmerged.
DB SG Backup - backs up all critical databases, save games, and trophy data to USB.
Kernel dumper - (Dump your kernel before it crashes... needs ip editing) ip + port 9023
ReactPSPlus - A simple code that will reset PS4 internal clock back to 2013 thus in turn activating PS+ Games that have been previously
or are currently on the console
UNDO ReactPSPlus - A simple code that will reset PS4 internal clock back to May 31, 2018
Linux Loader lets you boot linus kernel from your USB stick to use your PS4 for Linux.
Kexec (as I understand it) allows you to boot linux again from PS4 OS (orbis)


--- description: 'Totals for receipt page and order page.' ---
Item Totals: {{ itemTotalNoTax |currency }}
Shipping: {{ invoice.total_shipping_quote|currency }}
Tax: {{ invoice.tax_total|currency }}
Total Discount: - {{ discount|currency }}


--- title: ART & ILLUSTRATION subtitle: Exercises in Drawing. section_id: work category_id: graphic project_id: art type: multimedia year: 2018 for: various clients site: none team: none ---
1
BRAMBLE, 2018.
A wedding gift for two dear friends who bonded over their shared passion for poetry, Bramble was inspired by a favorite piece by Robert Hass.
2
MUSA, 2020.
A gift for a ceramics friend who experiments with many glazes, Musa abstracts the banana leaf into a composition of textures.


This page tests a bug in Internet Explorer whereby the document.getElementById() function may return an element with a name attribute equal to the id that is specified. This can cause unexpected results.
Test case:
Reported at http://www.quirksmode.org/bugreports/index.html 2005-08-29
Noted on http://channel9.msdn.com/wiki/default.aspx/Channel9.InternetExplorerProgrammingBugs


title: Optional Chaining status: proposal stage: 1 location: https://claudepache.github.io/es-nil copyright: false contributors: Claude Pache, ECMA International
This is the spec text proposal for introducing Optional Chaining feature (aka Null Propagation, aka Existential Operator) in ECMAScript.
For the syntax, we use the `?.` token, with a lookahead at the level of the lexical grammar that allows to discriminate between `a?.b` (optional chaining) and `a?.3:0` (conditional operator, whose meaning cannot be changed due to backward compatibility constraints).
Modified content are marked like this. In order to avoid distraction, we don’t mark mere editorial amendments.
A major rewrite is in progress, that will eliminate the need of Nil reference. The observed behaviour will change in edge or rare cases only.
The Reference type is used to explain the behaviour of such operators as `delete`, `typeof`, the assignment operators, the `super` keyword and other language features. For example, the left-hand operand of an assignment is expected to produce a reference.
A Reference is a resolved name or property binding. A Reference consists of three components, the base value component, the referenced name component, and the Boolean-valued strict reference flag. The base value component is either *undefined*, an Object, a Boolean, a String, a Symbol, a Number, or an Environment Record. A base value component of *undefined* indicates that the Reference could not be resolved to a binding. The referenced name component is a String or Symbol value.
A Super Reference is a Reference that is used to represents a name binding that was expressed using the super keyword. A Super Reference has an additional thisValue component, and its base value component will never be an Environment Record.
A Nil Reference is a special Reference that is used to propagate an *undefined* value that is produced by the optional chaining special form (`?.`). A Nil Reference has only one component, the base value component, which is always equal to the symbol ~Nil~.
The following abstract operations are used in this specification to operate on references:
The object that may be created in step 5.a.ii is not accessible outside of the above abstract operation and the ordinary object [[Get]] internal method. An implementation might choose to avoid the actual creation of the object.
The object that may be created in step 6.a.ii is not accessible outside of the above algorithm and the ordinary object [[Set]] internal method. An implementation might choose to avoid the actual creation of that object.
It is a Syntax Error if the derived |UnaryExpression| is
and |CoverParenthesizedExpressionAndArrowParameterList| ultimately derives a phrase that, if used in place of |UnaryExpression|, would produce a Syntax Error according to these rules. This rule is recursively applied.
The last rule means that expressions such as `delete (((foo)))` produce early errors because of recursive application of the first rule.
When a `delete` operator occurs within strict mode code, a *SyntaxError* exception is thrown if its |UnaryExpression| is a direct reference to a variable, function argument, or function name. In addition, if a `delete` operator occurs within strict mode code and the property to be deleted has the attribute { [[Configurable]]: *false* }, a *TypeError* exception is thrown.
When processing an instance of the production
the interpretation of |CoverParenthesizedExpressionAndArrowParameterList| is refined using the following grammar:
(not modified)
(not modified)
(not modified)
(not modified)
(not modified)
(not modified)
(not modified)
(not modified)
This algorithm does not apply GetValue to the result of evaluating |Expression|. The principal motivation for this is so that operators such as `delete` and `typeof` may be applied to parenthesized expressions.
When processing an instance of the production
With parameter _symbol_.
Properties are accessed by name, using either the dot notation:
or the bracket notation:
The dot notation is explained by the following syntactic conversion:
is identical in its behaviour to
and similarly
is identical in its behaviour to
where <identifier-name-string> is the result of evaluating StringValue of |IdentifierName|.
Is evaluated in exactly the same manner as
Is evaluated in exactly the same manner as
Is evaluated in exactly the same manner as
Is evaluated in exactly the same manner as
The abstract operation EvaluateNew with arguments _constructExpr_, _arguments_, and _optionalEvaluation_ performs the following steps:
A |CallExpression| evaluation that executes step 6.a.vii is a direct eval.
Is evaluated in the same manner as
and the last step, which is replaced by:
The abstract operation EvaluateCall takes as arguments a value _ref_, a Parse Node _arguments_, a Boolean argument _tailPosition_, and a Boolean argument _optionalEvaluation_. It performs the following steps:
The abstract operation EvaluateDirectCall takes as arguments a value _func_, a value _thisValue_, a Parse Node _arguments_, a Boolean argument _tailPosition_, and a Boolean argument _optionalEvaluation_. It performs the following steps:
(not modified)
(not modified)
A tagged template is a function call where the arguments of the call are derived from a |TemplateLiteral| (
(not modified)


LETHAL WEAPON 4 A film review by Steve Rhodes Copyright 1998 Steve Rhodes
RATING (0 TO ****): * 1/2
Summer is the time for action movies so director Richard Donner competes in the Explode-o-Rama by opening LETHAL WEAPON 4 with - count 'em - three big pyrotechnics displays in a row. The gasoline truck fireball is followed by Chinese freighter fireworks followed by that old reliable, the car demolished by a speeding train. Whew. And that all happens in the first 10 minutes or so.
The movie itself is such a hopeless retread of the previous LETHAL WEAPON movies that you wonder why Donner even tried. Channing Gibson wrote the screenplay, and three other writers are credited with the story, which is surprising since there isn't much of a story. The only idea seemed to be that they would get the film's stars together again and let them repeat their same old schtick.
Mel Gibson and Danny Glover are back as Martin Riggs and Roger Murtaugh. They've been promoted to captains so that the police department can get insurance. It seems that their brand of destructive police work causes more damage than the insurance company will allow.
Riggs's girlfriend and would-be wife, Lorna Cole (Rene Russo), is pregnant. This plot device allows her to be threatened with a knife at her belly by the bad guy and makes Riggs question whether he is ready again for marriage.
For symmetry's sake and to be able to introduce Chris Rock as a character, Murtaugh is about to become a grandfather. Unbeknownst to Murtaugh, the secret father is Lee Butters (Rock), a young cop in his precinct.
Amidst all of this domestic soap opera, Joe Pesci is back, and this time his character, Leo Getz, has become a PI (Private Investigator). Violating the well-established rule that a little bit of Joe Pesci goes a long way, Donner lets Pesci's grating humor permeate way too much of the movie.
Easily the best part of the film is the appearance of Chinese acting star and martial arts expert Jet Li as the movie's villain, Wah Sing Ku. With moves of ballet-like precision and artistry, he dazzles the audience.
The best part of the original LETHAL WEAPON was its humor, which is almost absent from its fourth version. Lee Butters's reading of the Miranda rights is a hoot, but most of the remaining jokes are stale. Any script that has to fall back on "flied lice" jokes about Chinese food as a way to try to get a laugh has pretty much lost it.
Although one is probably not supposed to think through the logic of the action in LETHAL WEAPON movies, some of the events may cause your brain to spin a bit. Take just one example.
Several times Riggs and Murtaugh complain that they're too old to be still doing this stuff. They are so right, and one can only hope that they will take their own advice in the future.
LETHAL WEAPON 4 runs too long at 2:07. It is rated R for profanity and strong violence and would be fine for most teenagers.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Australia
Wayambeh the Turtle
Oolah, the lizard, was out getting yams on a Mirrieh flat. She had three of her children with her. Suddenly she thought she heard some one moving behind the big Mirrieh bushes. She listened. All of a sudden out jumped Wayambeh from behind a bush and seized Oolah, telling her not to make a noise and he would not hurt her, but that he meant to take her off to his camp to be his wife. He would take her three children too and look after them. Resistance was useless, for Oolah had only her yam stick, while Wayambeh had his spears and boondees. Wayambeh took the woman and her children to his camp. His tribe when they saw him bring home a woman of the Oolah tribe, asked him if her tribe had given her to him. He said, "No, I have stolen her." "Well," they said, "her tribe will soon be after her; you must protect yourself; we shall not fight for you. You had no right to steal her without telling us. We had a young woman of our own tribe for you, yet you go and steal an Oolah and bring her to the camp of the Wayambeh.  In a short time the Oolahs were seen coming across the plain which faced the camp of the Wayambeh. And they came not in friendship or to parley, for no women were with them, and they carried no boughs of peace in their bands, but were painted as for war, and were armed with fighting weapons. When the Wayambeh saw the approach of the Oolah, their chief said: "Now, Wayambeh, you had better go out on to the plain and do your own fighting; we shall not help you." Wayambeh chose the two biggest boreens that he had; one he slung on him, covering the front of his body, and one the back; then, seizing his weapons, he strode out to meet his enemies. When he was well out on to the plain, though still some distance from the Oolah, he called out, "Come on." The answer was a shower of spears and boomerangs. As they came whizzing through the air Wayambeh drew his arms inside the boreens, and ducked his head down between them, so escaped.  The answer was another shower of weapons, which he met in the same way. At last the Oolahs closed in round him, forcing him to retreat towards the creek. Shower after shower of weapons they slung at him, and were getting at such close quarters that his only chance was to dive into the creek.  The Oolah waited, spears poised in hand, ready to aim directly his head appeared above water, but they waited in vain. Wayambeh, the black fellow, they never saw again, but in the waterhole wherein he had dived they saw a strange creature, which bore on its back a fixed structure like a boreen, and which, when they went to try and catch it, drew in its head and limbs, so they said, "It is Wayambeh." And this was the beginning of Wayambeh, or turtle, in the creeks.
Source: Here


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Ten Inch Hero
Ten Inch Hero is a movie starring Elisabeth Harnois, Clea DuVall, and Sean Patrick Flanery. An artist gets a job at a beachside sandwich shop, where she hangs out with a techie, a flirt, a rocker, a hippie, and a Wiccan.


need to wrap item container in a div that creates a stacking index
purchased!
ending soon
outbid!
offer pending
ended
testing
testing
testing
testing
testing
testing
ended


Default compiler options for AssemblyScript compilation.
Creates an AssemblyScript-compatible compiler host.
Creates a diagnostic message referencing a node.
Formats a diagnostic message in plain text.
Formats a diagnostic message with terminal colors and source context.
Gets the reflected class instance (describing a class with generic types resolved) of a class declaration.
Gets the reflected class template (describing a class with unresolved generic types) of a class declaration.
Gets the reflected function instance (describing a function with generic types resolved) of a function declaration.
Gets the reflected function template (describing a function with unresolved generic types) of a function declaration.
Gets the reflected type of an expression.
Tests if the specified node has an 'abstract' modifier.
Tests if the specified node is flagged 'const'.
Tests if the specified node has a 'declare' modifier or is part of a class with a 'declare' modifier.
Tests if the specified node has an 'export' modifier.
Tests if a function fulfills the requirements to become a start function.
Tests if the specified node has a 'static' modifier or is otherwise part of a static context.
Prints a diagnostic message to console.
Sets the reflected class instance (describing a class with generic types resolved) of a class declaration.
Sets the reflected class template (describing a class with unresolved generic types) of a class declaration.
Sets the reflected function instance (describing a function with generic types resolved) of a function declaration.
Sets the reflected function template (describing a function with unresolved generic types) of a function declaration.
Sets the reflected type of an expression.
Default format diagnostics host for convenience.
Generated using TypeDoc
A re-exporting wrapper around the relevant parts of TypeScript.
Note that the API documentation does not reference any re-exports because this isn't supported by the documentation generator.
For additional exports, see: https://github.com/dcodeIO/AssemblyScript/blob/master/src/typescript.ts


Amenities/Features
- Walking distance to campus
- Smoke free
- Furnished
- Air Conditioning
- High speed Internet
- Dishwasher
- Utilities included
- Clubhouse
- AppalCART
- Workout room
- Pool
- Pets allowed
Pets allowed
Contact our office for details.
| Allowed pet types |
=$pet_type?>
|
| Monthly fee |
$
=$pet_fee?>
|
| Deposit |
$
=$pet_deposit?> (=$pet_dep_refund?>) |
Other information
| Distance to campus |
=$campus_distance?>
|
| Contract length |
=$contract_length?>
|
| Parking per unit |
=$parking_per_unit?>
|
| Student preference |
=$student_type?>
|
| Smoking |
=$no_smoking?>
|
0 || $security_amt > 0 || $clean_fee_amt > 0 || $parking_fee > 0 || $other_fees > 0): ?>
Expenses
0):?>
| Administrative |
$
=$admin_fee_amt?> (=$admin_fee_refund?>) |
0):?>
| Security fee |
$
=$security_amt?> (=$security_refund?>) |
0):?>
| Cleaning fee
|
$
=$clean_fee_amt?> (=$clean_fee_refund?>) |
0): ?>
| Parking fee |
$=$parking_fee?> |
| Other |
=$other_fees?> |
Utilities
| Laundry |
=$laundry_type?>
|
| Internet |
=$internet_type?>
|
| Trash |
=$trash_type?>
|
| Television |
=$tv_type?>
|
| Heat Type |
=$heat_type?>
|
0 || $util_trash > 0 || $util_power > 0 || $util_cable > 0 || $util_internet > 0 || $util_phone > 0 || $util_fuel):?>
Utility imbursing
0):?>
| Water | $=$util_water?> |
0):?>
| Cable | $=$util_cable?> |
0):?>
| Internet | $=$util_internet?> |
0):?>
| Phone | $=$util_phone?> |
0):?>
| Trash | $=$util_trash?> |
0):?>
| Power | $=$util_power?> |
0):?>
| Fuel/Gas | $=$util_fuel?> |


The War Worlds forums are a place to discuss various aspects of the game. I currently don't have a moderation policy or "rules" as such, I just ask that people try to stay on-topic and be polite. I think we can all be grown-ups here.
If you enjoy the game, I would encourage you to log in using the same Google Account that you use to play the game, so that we can link your persona on the forums to your in-game Empire.


Declarations whose classes come from system or third-party libraries will not be reported by this inspection.
Casts, instanceofs, and local variables are not reported in equals()
method implementations.
Also, casts are not reported in clone()
method implementations.
Example:
interface Entity {}
class EntityImpl implements Entity {}
void processObject(Object obj) {
// warning: instanceof of the concrete class
if (obj instanceof EntityImpl) {
// warning: cast to the concrete class,
// rather than the interface
processEntity((EntityImpl)obj);
}
}
// warning: parameter of concrete class
void processEntity(EntityImpl obj) {
}
Use the Ignore abstract class type option to ignore casts to abstract classes.
Use the subsequent options to control contexts where the problem is reported.


__testcaseHeadCode__
This test case demonstrates functionality of the Web Forms 2.0 specification and tests the compliance of this cross-browser Web Forms 2.0 implementation.


Let's set up an SSH Connection
An SSH, or a Secure Shell, is an encrypted network protocol to allow remote login and other network services to operate securely over an unsecured network.
Here are the steps to set up an SSH on a Mac.
First enable SSH in your Pi's settings
- Open your Pi's terminal, and open the software configuration tool by typing the following commmand:
sudo raspi-config
Open advanced options, select "SSH", and a message should pop up asking if you would like the SSH server enable or disabled. Select Enable.
Next make the connection on your device
- Open your Pi's terminal, and open the software configuration tool by typing the following commmand:
hostname -I
Open the terminal on your Mac and write the following command, but replace "IP" with the IP address of the Raspberry Pi.
ssh pi@IP
When the connection works, you'll see a security authenticity warning. type "yes" to continue.
You will then be prompted for the pi login. On raspbian, the default password is "raspberry".
You are now connected to your pi remotely


| smooth.construct.mrf.smooth.spec {mgcv} | R Documentation |
For data observed over discrete spatial units, a simple Markov random field
smoother is sometimes appropriate. These functions provide such a smoother class for mgcv
.
See details for how to deal with regions with missing data.
## S3 method for class 'mrf.smooth.spec' smooth.construct(object, data, knots) ## S3 method for class 'mrf.smooth' Predict.matrix(object, data)
object |
For the |
data |
a list containing just the data (including any |
knots |
If there are more geographic areas than data were observed for, then this argument is used to provide the labels for all the areas (observed and unobserved). |
A Markov random field smooth over a set of discrete areas is defined using a set of area labels, and a neighbourhood structure for the areas. The covariate of the smooth is the vector of area labels corresponding to each obervation. This covariate should be a factor, or capable of being coerced to a factor.
The neighbourhood structure is supplied in the xt
argument to s
. This must contain at least one of
the elements polys
, nb
or penalty
.
contains the polygons defining the geographic areas.
It is a list with as many elements as there are geographic areas.
names(polys)
must correspond to
the levels of the argument of the smooth, in any order (i.e. it gives the area labels).
polys[[i]]
is a 2 column matrix the rows of which specify the vertices of the polygon(s)
defining the boundary of the ith area. A boundary may be made up of several closed loops: these must
be separated by NA
rows. A polygon within another is treated as a hole. The first polygon in
any polys[[i]]
should not be a hole. An example
of the structure is provided by columb.polys
(which contains an artificial hole
in its second element, for illustration). Any list elements with duplicate names are combined into a
single NA separated matrix.
Plotting of the smooth is not possible without a polys
object.
If polys
is the only element of xt
provided, then the neighbourhood structure is
computed from it automatically. To count as neigbours, polygons must exactly share one of more
vertices.
is a named list defining the neighbourhood structure. names(nb)
must correspond to the
levels of the covariate of the smooth (i.e. the area labels), but can be in any order. nb[[i]]
is a vector indexing the neighbours of the ith area. All indices are relative to nb
itself, but
can be translated using names(nb)
.
If no penalty
is provided then it is computed automatically from this list. The ith row of
the penalty matrix will be zero everwhere, except in the ith column, which will contain the number
of neighbours of the ith geographic area, and the columns corresponding to those geographic
neighbours, which will each contain -1.
if this is supplied, then it is used as the penalty matrix. It should be positive semi-definite. Its row and column names should correspond to the levels of the covariate.
If no basis dimension is supplied then the constructor produces a full rank MRF, with a coefficient for each geographic area. Otherwise a low rank approximation is obtained based on truncation of the parameterization given in Wood (2017) Section 5.4.2. See Wood (2017, section 5.8.1).
Note that smooths of this class have a built in plot method, and that the utility function in.out
can be useful for working with discrete area data. The plot method has two schemes, scheme==0
is colour,
scheme==1
is grey scale.
The situation in which there are areas with no data requires special handling. You should set drop.unused.levels=FALSE
in
the model fitting function, gam
, bam
or gamm
, having first ensured that any fixed effect
factors do not contain unobserved levels. Also make sure that the basis dimension is set to ensure that the total number of
coefficients is less than the number of observations.
An object of class "mrf.smooth"
or a matrix mapping the coefficients of the MRF smooth
to the predictions for the areas listed in data
.
Simon N. Wood<EMAIL_ADDRESS>and Thomas Kneib (Fabian Scheipl prototyped the low rank MRF idea)
Wood S.N. (2017) Generalized additive models: an introduction with R (2nd edition). CRC.
library(mgcv) ## Load Columbus Ohio crime data (see ?columbus for details and credits) data(columb) ## data frame data(columb.polys) ## district shapes list xt <- list(polys=columb.polys) ## neighbourhood structure info for MRF par(mfrow=c(2,2)) ## First a full rank MRF... b <- gam(crime ~ s(district,bs="mrf",xt=xt),data=columb,method="REML") plot(b,scheme=1) ## Compare to reduced rank version... b <- gam(crime ~ s(district,bs="mrf",k=20,xt=xt),data=columb,method="REML") plot(b,scheme=1) ## An important covariate added... b <- gam(crime ~ s(district,bs="mrf",k=20,xt=xt)+s(income), data=columb,method="REML") plot(b,scheme=c(0,1)) ## plot fitted values by district par(mfrow=c(1,1)) fv <- fitted(b) names(fv) <- as.character(columb$district) polys.plot(columb.polys,fv)


{% extends "layout.html" %} {% block title %}Not Found{% endblock %} {% block navigation %}{% endblock %} {% block content %}
Something is wrong, please report this issue.


## Concurrency & Asynchrony in Scala
by Greg Dorrell
Note:
- I work for HomeAway, which is part of Expedia Group. Both HomeAway and Hotels.com are hiring.
Scala is a language with lots of choice
Note:
- If we look at programming style...
- You can write Scala as better Java
- You can use the Akka ecosystem and do reactive programming.
- You can write functional code (and even then, it can be cats or scalaz)
- This can be overwhelming, especially as a beginner. Which style of Scala should you use?
- In that scenario, just decide on one as a team and be willing to evolve as you learn more.
- I see these variety of options as an advantage.
- I like that I've been able to start with better Java, and gradually introduce reactive or functional.
- I find it impressive we can use different techniques within one language
Concurrency libraries:
- Futures
- Akka
- Monix
- Cats Effect
- ZIO
- ...
Note:
- The same is true with concurrency. There's a lot of different options out there.
- So today we're going to take a look at a number of them.
- I want to give you an idea of where you might use Akka Streams, or why you might use Monix Task instead of Scala's Future.
- But I'm also going to deep dive on Futures.
- And we'll look at some of the problems I've hit with Futures, which help motivate using some of the other options.
My first contact with concurrency in Scala
Note:
- So, we'll start where I started, which was back in my first job where I used Scala.
- I was using Scala for some data-migration scripts.
- They would fetch a document from one place, transform it a bit, and upload them to a new system via a REST endpoint.
- But whilst working on those data-migration scripts, they were annoying slow pushing data to the new system via a REST endpoint.
- I realised it was an "embarrassingly parallel" problem to upload this collection of records to the new datasource
- Back in 2011, 2.9 had just come out with an awesome new feature - parallel collections!
## Parallel collections
```scala
def upload(doc: String): Unit = ...
val docs: List[String] = ...
//sequential
docs.foreach(upload)
//parallel
docs.par.foreach(upload)
```
Note:
- Already this was way better than my prior experience with concurrency - Java Threads, and all the fun that goes along with that - semaphores, locking.
- As you start using immutability, those issues around modifying shared state go away. But there are still other issues we can hit, and we'll take a look at those.
- Easy to use ✔
- Works with existing blocking libraries ✔
- It's still blocking. ✘
- Limited parallelism ✘
Note:
- Normal collections API, just add `.par`. No need for new concepts like Futures or Actors.
- Who is familiar with the execution context?
- global EC defaults to the number of cores multipled by 2. So the concurrency of this is limited.
- The work is executed using a `TaskSupport`; by default it's using the global ExecutionContext.
- That means you have, by default, num_cores * 2 threads to work with.
- Global EC is great for CPU bound work, but not blocking
- So this won't scale up too far, and may cause contension issues in our app if the ExecutionContext is used for anything else.
- It would be bad to do this per web request!
## Futures
```scala
def upload(doc: String): Future[Unit] = ...
val docs: Seq[String] = ...
docs.foreach(upload)
```
Note:
- So here, we've changed upload to return a Future.
- Unfortunately, there's a problem here.
- I'm uploading all the documents, but I'm not waiting for them to finish uploading.
- If this was the end of my application, the app will just exit without finishing the upload
## Futures
```scala
def upload(doc: String): Future[Unit] = ...
val docs: Seq[String] = ...
val eventualUpdates = docs.map(upload)
val updateAll = Future.sequence(eventualUpdates)
Await.result(updateAll, Duration.Inf)
```
Note:
- We need to keep the Futures that `upload` returns and wait for them to finish.
Does this solve the problems of parallel collections?
Note:
- We've gone to the effort of learning about Future, and how to `sequence` it, and await its' result.
- So you would hope it solves our problems...
It depends
Naive version of `upload`
```scala
import scala.concurrent.ExecutionContext.global
def upload(doc: String) = Future(
blockingClient.post("/someUrl", doc)
)
```
Note:
- All the same problems as parallel collections
- also, this context is shared across your app... So everything else in your app has to wait.
- If you're using Akka and getting the ExecutionContext from there, same problem
Risky solution
```scala
def upload(doc: String)(implicit ec: ExecutionContext) = Future(
blockingClient.post("/someUrl", doc)
)
```
Note:
- This passes off the responsibility to the caller.
- But the caller has no idea what you're doing with the execution context.
- They'll probably use global, and it won't go too well.
- I've been bitten by this. So when I see application code taking an implicit execution context, I'm suspicious and have to go look at the implementation. Avoid this.
Custom ExecutionContext
```scala
implicit val ioEC: ExecutionContext =
ExecutionContext.fromExecutor(Executors.newFixedThreadPool(10))
def upload(doc: String) = Future(
blockingClient.post("/someUrl", doc)
)
```
Note:
- This will work.
- It won't slow down other parts of your application.

- You can get to quite a lot of threads before it's a problem.
scala.concurrent.blocking
```scala
import scala.concurrent.ExecutionContext.global
import scala.concurrent.blocking
def upload(doc: String) = Future(
blocking {
blockingClient.post("/someUrl", doc)
}
)
```
Note:
- Here we wrap the blocking bit of code inside a `scala.concurrent.blocking` wrapper.
- This lets the execution context know that the code is blocking
- For the global execution context doesn't count that towards the concurrency level.
- So this will allow the ExecutionContext to create more threads above the limit for CPUs * 2.
- So it won't block your app... However it is now unbounded.
- A Thread by default takes 1MB of memory, that's just the memory overhead.
- So if we had 1000 documents, this would create 1000 new threads, and that would (for a short while) use 1GB of memory.
- When you start to have a few thousand documents, this can be a problem. So be careful.
Asynchronous non-blocking upload
```scala
def upload(doc: String) = asyncClient.post("/someUrl", doc)
```
Note:
- For a long time, I didn't really understand how you could actually do this asynchronously.
- And understanding that is a topic for another talk. But at a glance:
- With Java IO you have streams, and when you read a byte from a stream you block the thread you're on until it's available.
- With Java NIO (hand-wave, there's actually NIO and NIO2!) you have buffers (with associated channels). And you can check if there's data to read in a buffer, but not block if there isn't.
- When there's something useful in the buffer, you can read it out, and put it somewhere. And if you completed a scala promise with that, you'd have async IO.
- So something has to check those buffers and read/write data to them. But you have have one or two threads managing many sockets.
- In my current team, we're fans of Twitter's Finagle, which uses Netty, which operates similarly to java NIO.
- This is also unbounded. In this scenario we don't have to worry about running out of memory..
- However, we can overload the remote webservice we're calling, or the database behind it.
- And that's a scenario I've hit at work, our cassandra cluster reads were impacted when we did lots of writes
- So we decided to throttle our writes.
Throttling Future.traverse
[demo](https://embed.scalafiddle.io/embed?sfid=ZRt7Df5/10)
Note:
- But we had this come up in batch job where we were just using Futures. And I was suprised find out you can do it yourself with Futures!
- But it's limited. If we do batches of 10, we have to wait for the slowest of each batch to finish before we start the next one.
- So instead of taking N * avg response time, it takes N * P90.
- Instead, a better option is an AsyncSemaphore.
Monix
A library for asynchronous programming in Scala
Note:
- What does that mean? It sounds super general.
- Well, Monix actually has a few different useful sub-projects
monix-execution provides utilities for working with `scala.concurrent`
Note:
One that's of interest to us is AsyncSemaphore...
Monix AsyncSemaphore ([demo](https://embed.scalafiddle.io/embed?sfid=5R6oHm4/4))
Note:
- This helps us throttle our Futures without waiting for a whole batch to finish.
monix-eval provides Task
Task is an alternative "effect type" to Future
Note:
- Nothing about Future suggest you should do side effects inside them.
- You can use them for running CPU-bound referentially transparent code concurrently.
- But in reality, most of us put effects inside Futures
- Task is an alternative way to handle effects
Unlike a Future, a Task is not eager.
You have to explicitly run it.
Note:
- When you create a Future, it immediately starts running
If you run a Task twice, it will actually run twice.
*demo*
Note:
- DEMO
- Cancellable. Timeout-able.
monix-reactive provides Observable
We've looked at a finite list of input documents.
But streaming (AKA the Reactive Pattern) is also common.
Note:
- For me, that's mostly reading messages from Kafka
Monix Observable is an implementation of the Reactive Pattern
With adapters to and from the Reactive Streams standard (java.util.concurrent.Flow)
The API is similar to Akka Streams
Note:
- So rather than demo both, let's take a look at Akka Streams
## Akka Streams
[demo](https://embed.scalafiddle.io/embed?sfid=j8YzfY7/5)
- Mature and widely adopted. ✔
- Some boilerplate to use it. ✘
- You have to use Future ✘
## Cats Effect
- It was started last April by Daniel Spiewak.
- The largest contributor now is Alexandru Nedelcu, who is the creator of Monix.
Provides an IO monad which is similar to Monix Task.
Provides typeclasses for effect types.
Just as List has Applicative, Monad, and Foldable...
...an "effect" type can have Sync, Async, and Concurrent
Monix Task also implements those typeclasses.
Just as cats NonEmptyList is a more restricted List...
...cats IO is a more restricted Task.
Check out Runar's great talk
"Constraints Liberate, Liberties Constrain"
## ZIO
Another effect type
Note:
Like Task or cats IO or Future
Implements the Cats Effect typeclasses
You define the error type
```scala
IO.point("Hello World") //scalaz.zio.IO[Nothing, String]
```
Note:
- Cats Effect and Monix take the approach that the failure type has to be Throwable
- Just as Future does
- ZIO doesn't.
### Summary
**Use non-blocking libraries**
- Futures get to quite far.
- But there pitfalls.
My thoughts
Note:
- I'm still using Futures and Akka-Streams quite a lot day-to-day.
- I'm not going to refactor all my apps.
- But when I hit issues, I'll be reaching for a different effect type.
- Those effects types have been rapidly evolving, but I most like working with Monix.

- So I'll be looking to use Monix Observable in future
# Questions?


The list of items will be printed dynamically with php according to what the user has in the cart once logged in
If the user isn't loggedin, the page will redirect to the login page
This will contain the number of selected products, the price, the name, the reference etc...
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The Mass Mobilization data are updated to March 31, 2018.
Visualizations that depict world, country and regional protest behavior trends over time.
Our attempt to understand the dynamics of protest behavior across space and time.


The school needed another clock, so why not do another one? This is the most advanced clock we have every made.
It is also the biggest clock we have created.
30 precisely cut segments fitted with custom RGB LED boards to create a 7 segment display with controllable colors.
The clock gets its time from the internet through WiFi, and with standards today, that time is accurate within milliseconds.
With the onboard light sensors, we can automatically tune the brightness of the clock acccording to the environment.
The clock now sits on the back of our schools assembly hall, and everyone who walks in will take a peek at it.


When I was looking at my for loop it looked fine to me but it was missing its little let at the start of the parameters! ohh, and there goes 5 mins!
I wrote the code and it worked, I’m not sure if any of this work could be considered elegant. I stumbled across forEach and feel it could create some elegant work in the future but as of yet I leave you with a cup of soggy disappointed leaves instead of tea.
It’s always either my spelling or not using the right syntax and therefore not getting the result I want
As a thought, why doesn’t this thing have a spell check? But mostly I console log a lot until I find the source of my problem and then google my issues from there.
A little underwhelmed until I got to the stretch stuff, I felt like I should have given it more time but overall was happy with my learning in the areas I needed too.
Indexing! Is sometimes reaalllly different to dot notation but they both have a really useful place. I would like to look into .map and forEach a bit more as they were things I got to earlier and then wandered away lost on a different tangent.
Not confident at all! Next question!
That's what got me here today. I'll do this first and often delete my previous work. Now to remember to just comment it out!
It may not be a rubber duckie but I am an expert at talking to inanimate objects around myself and pretending they are helpful! I may need to adopt a inanimate object for this exact purpose.
I am fabulous at reading the error part, past there it's a bit hit and miss. I keep seeing undefined varibles though, so at least I can read that?
ALL DAY EVERY DAY. It's practically the most typed thing on my coputer at this point.
I do this for way longer than I should sometimes but usally used with console.logging so that the console.logging means something to me.
Could stand to get way better at this, tend to ask google first
See above statement
I love reflecting. If I am struggling I like to remind myself that I couldn't do any of this 2 months ago! And then see what mess I was typing back then. It's always a noticable difference so it generally makes me feel better.


Measure All The Things (Day 2)
07 Apr 2014Day 2 - Learning by doing
Why is sending, storing, and using time-series data awesome? Its all about feedback.
Before we start working on the time-series system, we'll go on a slight tangent to discuss why capturing metrics about your applications is important.
The speed at which we can receive feedback is a direct indicator of how quickly we can move through the software development cycle/workflow. The shorter the feedback loop the quicker we'll be able to react, and the quicker (and more) we'll be able to push change and innovate. We want to transform our organizations into fast moving and high performing environments... and in order to do so we must create an software factory that shortens the cycles to push change and receive feedback in order to gain a competitive advantage. Without short feedback loops we are rate limiting our ability to change.
What should be measured? Everything.
Application, System, Network measurements.
Application Metrics
Any function that provides value or insight into an application should be timed or counted and sent.
System
Understanding the health of your servers is of paramount importance. The standard set of information to capture is Memory, CPU, and disk. See CollectD.
Network
Having visibility into the health of the network is also important when troubleshooting. To begin with, using tools that live with the System/Machine to get a view into the network health (as the machine perceives its own network interface) is a good start. Ultimately, using correlation identifiers to act as tracer "bullets" that can capture points in time as requests move throughout our networked systems. See Zipkin, Dapper, CollectD, Ping Script
Error counts
Another valuable data point is your Error logs. Error logs can be parsed and counted, making it possible to monitor for fluctuations in Error rates and trend on specific Error strings. You can send these to Graphite, but this is also a great place to use Logstash, Elastic Search, and Kibana.
Capture Change Events (track every release)
Changes to our application code are opportunities for the introduction of issues. By tracking change events (deploys) we can correlate metric fluctuations to causation from the change event. Application Metrics + Change Events are keys to providing confidence and enabling change (i.e Continuous Deployments).
Amplify the feedback
Display a wallboard for goodness sakes. Use a large display that shows always relevant, easy to understand, graphs. Atlassian explains it well here and here.
Monitoring (Application, System, Network metrics)
Use the Humans:
Amplify the feedback (graphs) by using a wallboard. Humans watch the graphs for fluctuations during deploys and are happy.
Use the Machines:
In order to use the machines for monitoring we must talk about normal trends. What is abnormal? For monitoring, it is important to look at normal and abnormal. Most monitoring is done with static thresholds for alarms and alerts, but in our applications thresholds change over time and at different times of the day. Our systems might see a huge variance in response, but we would likely receive no alerts if we have set a high static warning threshold. Large spikes during business peaks can be normal and the troughs can be just as extreme during lows, so thresholds need to be dynamic. One way we can better monitor and alert is using something like the Holt-Winters exponential smoothing algorithm for predicting confidences (upper and lower bounds) and plotting aberrations.
Summary
Whether you use Graphite, what we -eventually- build, or something else entirely, you should definitely be capturing and monitoring your application and system metric data. Now that you know why it is important, I hope you'll be -at least a bit more- excited to build a time-series system.
Next, we'll do a little bit of architecture/design.


| Super Bowl | |||
|---|---|---|---|
| TBD | TBD | ||
| AFC Championship | NFC Championship | ||
| TBD | TBD | TBD | TBD |
| AFC Division | NFC Division | ||
| Lowest |
Highest
|
Lowest |
Highest
|
| Chiefs 1st |
Patriots
2nd
|
Saints 1st |
Rams 2nd
|
| AFC Wild Card | NFC Wild Card | ||
| Colts 6th |
Chargers 5th
|
Seahawks 5th |
Eagles 6th
|
| Texans 3rd |
Ravens 4th
|
Cowboys 4th |
Bears 3rd
|


Thanksgiving & Gratitude
Dr Oliver Sacks. Photo from the book On the Move: A Life by Oliver Sacks.
I hope everyone has had a wonderful Thanksgiving. As usual Cheryl and I spent our Thanksgiving with my ex-sister-in-law (long story) and her family. Eleven of us around a makeshift table talking, telling stories, laughing. It was fun and relaxing. On the day after Thanksgiving I reflected on giving thanks. There were periods in my life when I did daily gratitude practice. About ten years ago, I would do a chant-like gratitude practice while running. Then there was a period when I would write gratitudes in a journal. More recently, when I would go outside for the first time in the morning, I would look at the sky and give thanks for the new day. For some reason those practices faded.
I thought my life benefited from those practices. A belief supported by research. In a seminal study published in 2003, Dr. Robert A. Emmons and Dr. Michael E. McCullough divided 200 undergraduates into three groups. One group wrote weekly ‘gratitude’ reports. The instructions were:
There are many things in our lives, both large and small, that we might be grateful about. Think back over the past week and write down on the lines below up to five things in your life that you are grateful or thankful for.
Members of one of the other group wrote weekly reports about hassles in their lives (stupid people driving, and doing a favor for friend who didn’t appreciate it ) and members of the final group were asked to write about events in their lives (cleaned out my shoe closet and talked to a doctor about medical school ). After ten weeks the researchers discovered that compared to the other two groups, the gratitude group felt more positive about their lives, exercised more, and were healthier. That is quite an amazing return on just spending a few minutes a week writing down what you are grateful for!
Often our lives are focused on desires. Desires for something we want like some shiny new gizmo for Christmas (I can spend hours and hours researching shiny gizmos on the web). Desires to have something end that we don’t like–like this sprained ankle I seem to have or the world to be less violent. We spend our days focusing on desires. Thinking about the future. Gratitude practice is a good antedote for desires. It helps us focus on the present. It helps us see things clearly.
I’ve previously mentioned Maria Popova and her excellent blog brainpickings. In a review she wrote of the book thxthxthx she writes:
We live in a culture with far, far too much pessimism, cynicism and dystopianism going around. It’s easy to dismiss any inkling of positivity as self-serving Pollyannism, yet there’s plenty of evidence that recognizing our simple blessings greatly increases our well-being. I’m certainly a believer.
The book, thxthxthx, by the way, consists of 200 handwritten thank you notes by Leah Dieterich including:
Perhaps it is time, as Maria Popova suggests, to recognize ‘our simple blessings’. To acknowledge and be grateful for what we have. To be grateful for the life we do have–the good and the bad. Be grateful for, as buddhists say, the 10,000 joys and the 10,000 sorrows of life.
Oliver Sacks, in his book Gratitude writes:
At nearly 80, with a scattering of medical and surgical problems, none disabling, I feel glad to be alive — “I’m glad I’m not dead!” sometimes bursts out of me when the weather is perfect… I am grateful that I have experienced many things — some wonderful, some horrible — and that I have been able to write a dozen books, to receive innumerable letters from friends, colleagues and readers, and to enjoy what Nathaniel Hawthorne called “an intercourse with the world.”


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Dgame.Graphic.Shader
- struct Shader;
- The Shader struct manages a OpenGL Shader instance
The Shader can be loaded and compiled. But to use it, you must pass the Shader to the Program (see below).
Author:
Randy Schuett<EMAIL_ADDRESS>- enum Type: uint;
- The supported Shader-Types
- Vertex
- To use as a Vertex-Shader
- Geometry
- To use as a Geometry-Shader
- Fragment
- To use as a Fragment-Shader
- nothrow @nogc this(Type type, string filename = null);
- CTor
- const pure nothrow @nogc @property uint id();
- Returns the internal Shader ID
- const nothrow @nogc void loadFromFile(string filename);
- Load the Shader-Source from a file.
Note:
The Shader will not be automatically compiled, use compile to do that by yourself
- const nothrow @nogc bool compile();
- Compiles the Shader
Returns if the compilation was successful
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SIX DAYS, SEVEN NIGHTS A film review by Matt Williams
RATING: * * 1/2* out of * * * *
It's a traditional setup: put a man and a woman who seemingly hate each other into isolation together and love will bloom. That's the tried-and-true setup of the new romantic adventure film Six Days, Seven Nights, which gets the setup right... but doesn't know what to do from there.
Robin Monroe (Anne Heche) is the busy New York editor at an upscale fashion magazine. She has little time for romance, but when her boyfriend Frank (David Schwimmer) invites her to a six-day, seven night stay at a South Pacific resort, she accepts...just as she does to his proposal for marriage.
When an emergency photo shoot in Tahiti beckons, Robin finds herself hiring the services of Quinn Harris (Harrison Ford), the only pilot on the island. However, a tropical storm forces their plane to crash, and Robin and Quinn find themselves stranded on a remote island with little hope of rescue.
Of course, the island is full of adventure. From wild animals to dangerous terrain to modern-day pirates, Robin and Quinn face danger at every turn. Will these dangers make them turn to one another?
Harrison Ford and Anne Heche actually make a convincing romantic couple. The actors do a good enough job to allow you to get lost in the characterization, and forget about the actors' personal lives. Neither character is written very deep, but both Ford and Heche play their roles with the appropriate combination of charm and likability.
On the supporting side, David Schwimmer is his traditional whiner...a role which is easier to take in these small doses. There to comfort him is Angelica (Jacqueline Obradors), island entertainer and Quinn's sometimes girlfriend. She doesn't have much to do beyond the bimbo role, which she fills nicely.
It seems as if director Ivan Reitman is shooting for a Romancing The Stone, or The African Queen sort of feel for the film. But, though the leads are appealing, he doesn't have the story to back it up.
Aside from the romance, there's actually very little for the characters to do. The film tries to address that by throwing nearly every shipwrecked-on-a-deserted-isle trick in the book at them (come on, pirates?), but every obstacle seems overly artificial.
In the end, Six Days, Seven Nights is enjoyable...though more for its romantic comedy elements than its sense of adventure.
Copyright 1998 Matt Williams
- Matt Williams<EMAIL_ADDRESS>Reviewer for Cinematter: http://www.cinematter.com Home of over 500 reviews, and information on over 600 upcoming releases
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Related links: index of all rec.arts.movies.reviews reviews


Tutorial 9 - Private and abstract mixins
Now we're going to turn around and see how we can reduce the code needed to implement the
HelloWorld example. We will also look at how to hide the Properties from the client code,
since Properties are often considered to be implementation details that should not be exposed
to clients.
The first thing we will do is remove the behaviour interface, and move the say() method
to the TransientComposite type. This forces the mixin to implement the TransientComposite type, which would
normally mean that it would have to implement all methods, including those found in the TransientComposite
interface. However, since we are only really interested in implementing the say() method we
will mark this by declaring that the Mixin "implements" the TransientComposite type, but is also "abstract".
This, using pure Java semantics, makes it possible to avoid having to implement all methods. Qi4j
will during the initialization phase detect that the Mixin only handles the say() method, and therefore
only map it to that specific method. In order to instantiate the Mixin it will generate a subclass
which implements the remaining methods in the TransientComposite type, as no-ops. These will never be called however,
and is there purely for the purpose of being able to instantiate the Mixin. The Mixin is considered
to be an Abstract Fragment.
To hide the state from the client we need to use what is called Private Mixins. A Private Mixin is
any mixin that is referenced by another Mixin by using the @This injection, but which is not included
in the TransientComposite type. As long as there is a Mixin implementation declared for the interface specified
by the @This injection it will work, since Qi4j can know how to implement the interface. But since
it is not extended by the TransientComposite type there is no way for a user of the TransientComposite to access it.
That
Mixin becomes an implementation detail. This can be used either for encapsulation purposes, or for referencing
utility mixins that help the rest of the code implement some interface, but which itself should not be exposed.
Since the state is now hidden the initialization of the TransientComposite is a bit more tricky. Instead of just
instantiating it you have to create a TransientBuilder first, then set the state using .prototypeFor(), and then
call newInstance(). This ensures that the state can be set during construction, but not at any later point, other
than through publicly exposed methods.
Steps for this tutorial:
- Move the say() method into the TransientComposite interface.
- Remove the behaviour interface.
- Remove the HelloWorldBehaviourMixin, create a HelloWorldMixin and
let the HelloWorldMixin implement the TransientComposite directly.
- Mark the HelloWorldMixin as abstract and implement only the say() method.
- Remove the HelloWorldState from the TransientComposite "extends" declaration.
- Remove the GenericPropertyMixin. The Property methods will be implemented by the
standard PropertyMixin declared in the TransientComposite interface instead.


Upcoming conferences with OnesieJS
-
Onesie Conf | Oslo, Norway 3-5 July, 2019
We're all going to be there, except this is just a test so...
-
Onesie Conf | Svalbard, Norway 10-15 October, 2019
It's a bit cold here... we'll be doubling up on the onesies.
-
Onesie Conf | South Pole 20-25 January, 2020
This is no warmer.


--- layout: page title: About header-img: "img/about-bg.png" ---
College Student Full Time, Flatiron Bootcamp Part Time, and Cracker Barrel Server Part Time. All this is going on at the same time.


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ProTrip helps you find the cheapest gas prices at
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Mr. Miller specializes in civil engineer consultation and expert witness testimoney in engineering with over 45 years experience in almost all aspects of civil engineering and construction.
Credentials
-
Professional Engineer (PE) License in South Dakota, Minnesota, Iowa and Arizona.
-
Certified Engineering Expert Witness (EXW) and (former) President and National Director of the American Counsil of Engineering Companies (ACEC).
-
South Dakota Engineering Society Member
-
National Society of Professional Engineers Member
Experience
Sayre Associates, Inc.
July 1990 to March 2020
Positions Held: Project Engineer, Project Manager, Principal, President and General Manager
Site Development and Design Experience
-
Corson, Brandon, and Rovang Industrial Parks, Brandon, SD
-
USD Discovery District, Sioux Falls
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Journey Group Development, Sioux Falls
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Southeast Technical Institute HUB, Sioux Falls
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University Center Development, Sioux Falls
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Transportation Experience
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--- layout: post title: Fresh Picked date: 2011-09-10 03:40:28.000000000 -04:00 categories: - Family Life - Seasons tags: - flower picking - freesia - freesias - Spring status: publish type: post published: true meta: aktt_notify_twitter: 'no' _edit_last: '1' dsq_thread_id: '1188591488' author: login: cint email<EMAIL_ADDRESS>display_name: Jacinta first_name: Jacinta last_name: '' ---
Now if only you could smell these freesias. We stopped off at a roadside where thousands of freesias grow in a rampant fashion. It's a spot we stop at every spring and I love gathering the flowers along side the girls, where they go skipping from flower to flower talking in sing-song voices. I am actually beginning to wonder if freesias give off some sort of happy pheromone?
They are now sitting on our kitchen bench and I feel I may pass out any minute from deeply inhaling their scent. I-can't-help-myself! Now where is the scratch and snif app. for computers/laptops/phones? Come on Apple, surely you could come up with something like that?
Ok, looks like you will have to imagine the fragrance instead. Jacinta x


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Greetings from UAE HIGHLY CONFIDENTIAL. I decided to contact you by email because I feel that it is more secured and private. I am Nasser Bin Nasiri, Director of Procurement and Contract Award with, Ministry of Petroleum and Mineral Resources [MPMR]. I am contacting you concerning the transfer of US$43 Million Dollars (FOURTY THREE MILLION DOLLARS) Sometime ago, a contract was awarded by my Corporation, [MPMR] to a Japanese Firm, to carry out Turn Around Maintenance and rehabilitation work on Emirates Petroleum Products Company (EPPCO) This contract was over invoiced by us to the tune of US$43. Million {Forty Three Million United States Dollars). Myself and my partner reached a compromise to transfer the fund into a Foreign bank account that will accommodate this fund. We have all modalities set. We were disappointed last time by a US Senator, Late Paul Wellstone whom we trusted with a transaction of this nature up to the tune of $45 Million USD in 2001. They sat on our money for no apparent reason and was still holding our money until his family were killed in a plane crash last December. We still believe that good people still exist in this world and we are willing to try again with you, bearing in mind that God will all way fight for us. You can confirm the above transaction from Mr. Clarence Barnes, Uncle to the Senator, who provided the receiving bank account. His email address is<EMAIL_ADDRESS>and phone 1-661-310-2112. He is enjoying our collective money and refused to give us our share but we believe, he shall meet his doom as Senator did. Because of this we are afraid and we need your total trust and assurance that you must be an honest and straight forward person. We made a costly mistake with the Senator, by not going into agreement. Based on our status as senior government officials in top sensitive position of trust, we cannot stand to claim this money ourselves without the assistance of a foreigner. The process will not require your presence here in UAE. I guarantee that this transaction will be executed under a legitimate arrangement that will protect you from any breach of the law. We intend to share the money in the following manner: 30% for you, 60% for us and 10% will be for any expenses incured during the transfer. Contact me immediately if you are interested for further details. We look forward to a fruitful business relationship with you and your urgent response. Best Regards, Mr. Nasser Bin Nasiri.


Nowadays we are hearing machine learning all the time and how it is present everywhere. Many of you tried to find it’s definition on the web and came up with the following classic definition by Tom Mitchell, which he gave in 1997 in his book Machine Learning1.
” A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E”.
Are you wondering what is this E, T, P, etc? At once, it looks so complicated and weird. At least, this is how I felt. In simple terms it can be defined as “Machine learning is learning from data or examples”. We use data to learn some sort of patterns and once we learn these patterns then we can do some sort of predictions. In other words, in machine learning, we take data, learn the pattern and then use this pattern to make predictions.
For example, spam email filter is a machine learning program.
“Machine learning is learning from data or examples”.
Training data: The data which are used to understand or learn the pattern is called training data. It is composed of two components:
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Input features: Input features are also called predictors because they are used to predict the output. eg: Taking our spam email example, it is sender URL, email content is input features.
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Output features: output features is called response or outcome. eg: whether it is spam or not.
If we want to detect if a new email is a spam or not. This new email has only input features and we want to predict the output features, based on the learned pattern. These unseen cases are called test data.
Test data: In test data, we have only input data/features and we predict the output data.
Problems which can be solved by machine learning
A very good article 2 explained the problems which can be solved by machine learning. For the sake of completing this tutorial, I copy some of them here.
- Manual data entry
- Detecting spam
- Product recommendation
- Medical Diagnosis
- Financial analysis
- Image recognition (computer vision)
How to know if a problem is a machine learning problem?
Not every problem is a machine learning problem. The following three factors determine if machine learning can be used to solve a problem:
- There are data available.
- A pattern should exist
- It could not be solved mathematically.
Always look for these three factors to decide if machine learning is a tool to solve a particular problem.
Types of machine learning.
There are various types of the machine learning system, here will focus only supervised and unsupervised.
Supervised learning: If we have both input and output features in a training data is called supervised learning. It is called supervised because we already have the output features, which we want to predict by using machine learning. Eg: If we want to predict whether an email is a spam or not. We will use various input features in order to determine it. We already know the output of our prediction, it will be either spam or not spam. This kind of problems is called a classification problem. Here we are classifying the output into two parts. So, it is a binary classification problem.
Another problem could be where we want to predict a house price or salary based on various input features. Instead of predicting labels, we predict a value which is continuous in nature(if it is a positive value). This sort of task is called regression. In other words, problems, where output is a continuous variable, is called a regression problem.
Unsupervised learning: Machine learning problems where we only have input features in the training data is called unsupervised learning. In other words, we try to find a pattern in the training data and then apply that pattern on test data or we can say we are trying to learn with a teacher.
Eg: Customer segmentation: We want to group people who are reading your blog based on the topic they are reading on your post, this is an example of clustering.
Conclusion:
In this post, I tried to explain machine learning in a simple way. We saw what is train data, test data and types of machine learning, supervised and unsupervised learning. We also learn what is classification and regression problem. In the following post, we learn more about different types of algorithms used in machine learning until then explore more through the references.
References and further reading list
2: https://www.marutitech.com/problems-solved-machine-learning/