V2X messages JSON validator
Changelog
2020/02/12
- FIX: require the CAM generation_delta_time instead of generation_delta
2020/01/30
- FIX: claim a path history list with at the most 40 elements
- FEAT: update the lane_position description
- FEAT: disallow additional properties on the root level
- FIX: activate the path_position requirement
- FIX: claim a DENM traces root list with at least 1 and at the most 7 elements
- FIX: activate the DENM traces requirement
- FEAT: add the DENM path_history field, to be iso with the CAM path_history field
- FEAT: update the DENM detection_time to the ETSI TS 102 894-2 V1.3.1 (2018-08)
- FEAT: update the DENM reference_time to the ETSI TS 102 894-2 V1.3.1 (2018-08)
- FEAT: update the CAM curvature to the ETSI TS 102 894-2 V1.3.1 (2018-08)
- FEAT: fix the CAM acceleration_control size to 7
- FEAT: fix the CAM exterior_lights size to 8
2020/01/20
- FEAT: add standard examples with not required but useful fields
- FEAT: remove the new optional fields into the basic examples
- FIX: rename origin_station_id by originating_station_id
- FIX: remove the bad end of the altitude descriptions
- FEAT: add missing descriptions
- FEAT: add unavailable value as the default value when defined
- FEAT: add unknown value as the default value when defined
- FEAT: add default value as the default value when defined
- FEAT: suspend the station_type requirement
- FEAT: suspend the confidence requirements
- FEAT: suspend the position_confidence_ellipse requirement
- FEAT: suspend the semi_major_confidence position_confidence_ellipse requirement
- FEAT: suspend the semi_minor_confidence position_confidence_ellipse requirement
- FEAT: suspend the semi_major_orientation position_confidence_ellipse requirement
- FEAT: suspend the altitude confidence requirement
- FEAT: suspend the path_position requirement
- FEAT: suspend the delta_latitude requirement
- FEAT: suspend the delta_longitude requirement
- FEAT: suspend the delta_altitude requirement
- FEAT: suspend the CAM heading requirement
- FEAT: suspend the CAM speed requirement
- FEAT: suspend the CAM drive_direction requirement
- FEAT: suspend the CAM vehicle_length requirement
- FEAT: suspend the CAM vehicle_width requirement
- FEAT: suspend the CAM curvature requirement
- FEAT: suspend the CAM curvature_calculation_mode requirement
- FEAT: suspend the CAM longitudinal_acceleration requirement
- FEAT: suspend the CAM yaw_rate requirement
- FEAT: suspend the CAM heading confidence requirement
- FEAT: suspend the CAM speed confidence requirement
- FEAT: suspend the CAM vehicle_length confidence requirement
- FEAT: suspend the CAM yaw_rate confidence requirement
- FEAT: suspend the CAM vehicle_role requirement
- FEAT: suspend the CAM path_history requirement
- FEAT: suspend the DENM information_quality requirement
- FEAT: suspend the DENM subcause requirements
- FEAT: suspend the DENM trace requirement
2020/01/08
- FIX: update fields according to the ETSI specifications
- FIX: fix html minor errors
- FEAT: provide a complete full example
2019/12/12
- FIX: Etsi timestamp min and max values changed due to an error in the previous calculus
- FEAT: exterior_lights as an optional field in CAM
2019/12/11
- FIX: rename high_freq and low_freq to high_frequency and low_frequency
- FIX: use confidence instead of accuracy
2019/12/09
- FIX: detection_time and reference_time for DENM are now in ETSI-ITS timestamp
- FEAT: unit in the description of every field for CAM and DENM
- FEAT: add many optional fields including accuracy for CAM and DENM
- FEAT: DENM and CAM examples with all optional fields
- FEAT: changelog page
2019/12/02
- First version of CAM and DENM json schema


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Currently, the school system focuses a lot on using state standardized test scores to determine the progression of students in relation to their school work. A heavy stress to maintain good grades, good test scores, and continue to improve them throughout the year is put upon students, often times wearing out the confidence they have in themselves.
In the realm of education, standardized testing is used to classify students based on their scores. These classifications determine their acceptability into different institutions, being significant to students in their placement within society. The stress stemming from this integration into an individual's future can deter a student either from their studies or from anything outside of their studies.
The stress stemming from using test scores to integrate an individual into society and their future future can deter a student either from their studies or from anything outside of their studies. The score they receive becomes in many students' eyes a representation of their intelligence, a false assumption due to the vast amoount of value in intelligence outside of the academic realm.
Also known as the achievement gap, refers to the great difference existing between groups of students in relation to how these students perform academically. The Education Gap most often is associated with affecting minorities and students from low-income families. These differences can be proven to be detrimental to the learning experiences of students. Recognizing this issue pushes forward the discovery of a solution.
Disengagement of students can apply to a realm of issues, either it be socially, academically, emotionally, etc. Being a symptom of students focusing too much on the relationship between them and their academic scores, disengagement allows for a student to remove themselves from actually learning. This can affect their experience and future ability to deal with stresses similar to the ones present within the school system.
Individual behavior is affected by the presence and influence of others. The Bystander Effect is the presence of others affecting an individual's ability in participating in an emergency situation. Academically, this can relate to pressure from outside influences on students, affecting their ability to even want to focus on their grades and scores, or on the opposite side of the spectrum, not allowing for them to focus on anything else apart from their grades.
Also called the audience effect, Social Facilitation demonstrates a difference in behavior seen in individuals when in the presence of others and when alone. Individuals may act differently when performing for themselves than when performing for other people. This can relate to student's "performing" to please the expectations found existing within society rather than using their actions to please their own expectations of themselves.
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--- author: "Michael Dewey" slug: "making_transition_probs" date: "2020-11-13" output: blogdown::html_page tags: - Rstats - data cleaning categories: - projects - Snappa scoreboard thumbnailImage: "/img/transition_probabilities.JPG" thumbnailImagePosition: left coverImage: "/img/transition_probabilities.JPG" coverCaption: "A portion of the score transition probabilities on offense for myself and my most common Snappa partner. The upper triangular structure comes from the fact that you cannot score negative points." coverMeta: out metaAlignment: center title: "Building Transition Probabilities From Scratch" ---
My last article gave a high-level overview of the reasoning and methodology behind the Markov chain model which the Snappa Scoreboard uses to give win probability estimates to users.
That article was intended to strike a balance between the formal mathematics behind the model and the intuitive reasoning behind the modeling decision, and as a result it was rather light on the R code used to obtain each of the objects that I use.
Today, I thought it would be good to provide more of that technical description, both as an attempt at something like rubber duck debugging, and as a guide to anybody who might need to obtain transition probabilities from a time series manually, particularly when trying to subdivide your clean data into different matrices as I have done with this project.
Since I’m explaining a process of data cleaning, I should first provide a primer on the raw structure of data to fully explain how cleaning was done.
All Snappa data is stored in a database that updates to the cloud as a game is being played. There are 3 main tables which are of importance here1:
events
which records the streaming game data, and is the
basis for the other tables.performance
, which records player-level data for each game.summary
table, which rolls aggregates scores data to the game level.Let’s look at a portion of the tables for one game to understand how they come together.
| Events | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| First 10 scoring events of game 242 | |||||||||
| shot_number | game_number | serial_num | scored | round | total | offense | defensive | off_glass | kicked_back |
| 1 | 242 | 3 | A | 1A | 1 | TRUE | FALSE | FALSE | FALSE |
| 2 | 242 | 2 | A | 1A | 1 | TRUE | FALSE | FALSE | FALSE |
| 3 | 242 | 3 | A | 2A | 1 | TRUE | FALSE | FALSE | FALSE |
| 4 | 242 | 3 | A | 3A | 1 | TRUE | FALSE | FALSE | FALSE |
| 5 | 242 | 9 | B | 3B | 1 | TRUE | FALSE | FALSE | FALSE |
| 6 | 242 | 9 | B | 5B | 1 | TRUE | FALSE | FALSE | FALSE |
| 7 | 242 | 3 | A | 6B | 2 | FALSE | TRUE | TRUE | FALSE |
| 8 | 242 | 2 | A | 7A | 1 | TRUE | FALSE | FALSE | FALSE |
| 9 | 242 | 9 | B | 7B | 1 | TRUE | TRUE | TRUE | FALSE |
| 10 | 242 | 9 | B | 8A | 1 | FALSE | TRUE | FALSE | FALSE |
| Performance | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| A complete table entry for game 242 | ||||||||||||||
| game_number | serial_number | side | rounds | point_total | single_points | double_points | sink_points | four_plus_points | defensive_return_points | off_glass_points | production_per_round | offensive_round_production | defensive_round_production | batting_average |
| 242 | 2 | A | 26 | 7 | 7 | 0 | 0 | 0 | 0 | 0 | 0.2692308 | 0.2692308 | 0.0000000 | 0.2692308 |
| 242 | 3 | A | 26 | 13 | 11 | 1 | 0 | 0 | 3 | 2 | 0.5000000 | 0.3846154 | 0.1153846 | 0.3846154 |
| 242 | 9 | B | 25 | 17 | 15 | 1 | 0 | 0 | 5 | 3 | 0.6800000 | 0.4800000 | 0.2000000 | 0.4800000 |
| 242 | 10 | B | 25 | 5 | 5 | 0 | 0 | 0 | 0 | 0 | 0.2000000 | 0.2000000 | 0.0000000 | 0.2000000 |
| Summary | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| The single row for game 242 in the table | ||||||||||||||
| game_number | snappaneers | night_game | total_a | total_b | total_rounds | game_single_points | game_double_points | game_sinks | game_four_plus | game_defensive_return | game_off_glass | finished_game | final_shot | location |
| 242 | 4 | FALSE | 20 | 22 | 51 | 38 | 2 | 0 | 0 | 8 | 5 | TRUE | 1A | Snappa Home Base |
As you can see, events
records everything that we can, given the user
input, about the score which has just been recorded. The rows of the table are
grouped by the serial_number
of the player, and it is those sums and shot
percentages which make the data for performance
. All of the players information
is summed up in summary
, and everything is linked together by the game’s
game_number
. You may also notice that not everything is an aggregation of
events
. There is some additional information added to summary
,
for example the game’s location
, which does not come from events
. Each table
expresses a slightly different level of aggregation, and so there are naturally some
variables which are game-level attributes and therefore do not make their way into
events
.
In my experience, it’s really helpful to spend some time and plan out the final output of the data cleaning process.
In our case, the answer to (1) is that we will be plugging transition probabilities for a given team into a simulation function. In fact, we will have 4 such transitions per team:
For more clarification on why it’s the case that we need 4 matrices, see here
Each of these matrices can be relatively time-consuming to calculate, so we don’t want the application finding each of these matrices every time that a game starts. Instead, the app imports a list with each matrix for every team that the database has ever observed. Doing it in this way significantly alleviates problems related to putting a somewhat code-intensive cleaning process in the middle of a Shiny
application2.
My way of working within this framework was to aim for a named list that I call
transitions_list
. Each element of the list is indexed by a unique name for the
team which is based off of the serial_number
of all the team members. Within
each list element, there are two other lists - one for “scores” and one for “states”-
and each of these lists has one matrix for “offense” and one for “defense”. For example,
if players with the serial_number
s 2 and 9 are the only two players on the same team,
the application would look for transitions_list[["(2, 9)"]]
, but if the team
had players 2, 3, and 9 it would look for transitions_list[["(2, 3, 9)"]]
.
Once the app is looking at the correct TEAM_NAME
, it retrieves
transitions_list[[TEAM_NAME]]$scores$offense
transitions_list[[TEAM_NAME]]$scores$defense
transitions_list[[TEAM_NAME]]$states$offense
transitions_list[[TEAM_NAME]]$states$defense
Hence, the functions that I write to handle data manipulation are all done with the aim of obtaining outputs that follow this format.
I will eventually obtain a list of transition probabilities for all teams by using
the pmap
function in purrr
. First, I create a table which contains all teams
and the games in which they’ve played by using pivot_wider
along with performance
library(tidyverse)
team_combos = performance %>%
group_by(game_number, side) %>%
arrange(serial_number) %>%
mutate(position = row_number()) %>%
pivot_wider(id_cols = c(game_number, side), names_from = position,
values_from = serial_number)
# For demonstration
team_combos %>%
arrange(desc(game_number)) %>%
head(15) %>%
ungroup() %>%
gt() %>%
tab_header(title = "Team Combinations by Game Number and Side")
| Team Combinations by Game Number and Side | |||||
|---|---|---|---|---|---|
| game_number | side | 1 | 2 | 3 | 4 |
| 281 | A | 2 | 4 | NA | NA |
| 281 | B | 3 | 9 | NA | NA |
| 280 | B | 2 | 9 | NA | NA |
| 280 | A | 3 | 4 | NA | NA |
| 279 | B | 2 | 4 | 9 | NA |
| 279 | A | 22 | 29 | NA | NA |
| 278 | A | 2 | 4 | NA | NA |
| 278 | B | 9 | 22 | 29 | NA |
| 277 | A | 2 | 7 | NA | NA |
| 277 | B | 3 | 9 | NA | NA |
| 276 | B | 2 | 3 | NA | NA |
| 276 | A | 7 | 9 | NA | NA |
| 275 | B | 1 | 3 | 4 | 5 |
| 275 | A | 2 | 7 | 9 | NA |
| 274 | B | 1 | 2 | 3 | 5 |
serial_number
translates into a way of uniquely identifying each team. One of the central tenets of the modeling that I’m performing is that I have no way of estimating how important each individual teammate is. I only understand teams by understanding unique combinations of players. Hence, each of these NA values are actually important because it is the sequence of 1
, 2
, 3
, and 4
which uniquely determine a team. For that reason, all functions that handle this data expect 4 values for a team, with NA
being the default for the last two. However, we make these explicit NA
values implicit when it comes time to create the vector which retrieves the proper team from transitions_list
.
For each team, we need to obtain a list of games in which we have seen that unique combination of players. I achieve that by creating a filter
expression for all four numbered columns on team_combos
as follows. Suppose we’re again looking at team “(2, 9)” from before. This team would correspond to 1
= 2, 2
= 9, 3
= NA
,
4
= NA
3. Also the use of column names whose values are numbers is really not great practice, but it does have the upshot of allowing me to write the filter_expression
using imap
.
team_vector = c(2, 9, NA, NA)
filter_statements = team_vector %>%
imap(function(player, index){
if(!is.na(player)){
str_c("`", index, "` == ", player)
} else {
str_c("is.na(`", index, "`) ")
}
})
filter_expression =
rlang::parse_exprs(
paste0(filter_statements)
)
game_side_pair = team_combos %>%
filter(!!!filter_expression) %>%
select(game_number, side)
game_side_pair
tells us what games our chosen team is in, and also what side they were in that game. This may not seem too important, but it actually matters quite a bit. Ultimately we want to make sure that we are correctly identifying how many shots a team had in each game (read: how many opportunities they had to score), which forms the basis of the denominator of the maximum likelihood estimator of transition probabilities. For example, in games that team B wins, NUMBER A
is the final round, which would mean that A got NUMBER
shots on offense,
while B got NUMBER - 1
! That same dynamic holds in reverse for defense.
In moving towards that final total shots number, I first figure out how many shots are in each round. When games are unbalanced, like a 2 vs 3 scenario, the team with 2 people still shoots 3 times. This means that we have to know the number of players on the opposing team to also know the number of shots which a team had in a game. Each team’s shots is equal to the maximum team size in that game.
game_shots_pair = performance %>%
filter(game_number %in% game_side_pair$game_number) %>%
group_by(game_number, side) %>%
summarize(team_size = n(), .groups = "drop_last") %>%
summarize(shots = max(team_size), .groups = "drop_last")
Next is to obtain the number of rounds that each team played in each game. In summary
, there is a variable called total_rounds
which tracks the total number of rounds in a game (e.g. if the game had ended on round 1A, this value would be 1, and if the game ended on 12B, this would be 24). One can verify that the following mutate
should provide answers to the side-specific question of the number of rounds taken.
total_rounds = game_side_pair %>%
left_join(summary %>%
select(game_number, total_rounds),
by = "game_number") %>%
mutate(offense_rounds = ifelse(side == "A", ceiling(total_rounds / 2), floor(total_rounds / 2)),
defense_rounds = ifelse(side == "A", floor(total_rounds / 2), ceiling(total_rounds / 2))) %>%
ungroup() %>%
select(game_number, offense_rounds, defense_rounds)
total_rounds
## # A tibble: 46 x 3
## game_number offense_rounds defense_rounds
## <int> <dbl> <dbl>
## 1 93 21 22
## 2 38 24 25
## 3 66 24 25
## 4 46 27 26
## 5 70 26 25
## 6 3 14 14
## 7 81 26 26
## 8 82 24 24
## 9 63 27 28
## 10 86 25 25
## # ... with 36 more rows
Finally, I also create a version of the events
table which cuts away the clutter that we don’t need, and provides the manipulations that would be needed for all the calculations. This is the object from which we will be most directly obtaining the counts
events_simplified = events %>%
arrange(game_number, shot_number) %>%
group_by(game_number, round, scored) %>%
mutate(shot_order = row_number()) %>%
select(game_number, scored, total, round, shot_order) %>%
filter(game_number %in% game_side_pair$game_number) %>%
left_join(game_side_pair, by = "game_number") %>%
filter(scored == side) %>%
mutate(off_def = case_when(str_extract(round, "[A-Z]") == side ~ "offense",
str_extract(round, "[A-Z]") != side ~ "defense"),
round_num = as.integer(str_sub(round, 1, -2))
)
With these tables in place, we’re ready to begin forming the data set used to make transition probabilities. First, an overview on what that data set should look like. Since transition matrices are all about recording the number of times that a transition occurs, we will be using a vector of “running scores”, i.e., the cumulative sum of points for a given team in a game. Those cumulative points should be calculated over a data set which has been properly ordered between offense and defense for that team. For example, if a team was “A” in a game, then their order should be offense -> defense. If a team was “B”, it should be reversed.
Having said this, we arrive at the largest threat to validity for this entire method: the order in which shots are observed during a round. The scoreboard does not require players to record non-scoring events. Intuitively, this choice is made to improve the rate of play and minimize UI fatigue. In practice, however, it means that I have no way of systematically knowing whether the first point scored in a round was on the first shot. In fact, the only cases where I can be sure that the ordering is correct are cases where everyone on the team scores on that round. For all the rest, I am left to do the best I can. It’s worth thinking about the effect that this has on the model. At the beginning of the round, let team A have two shots and a
points, and suppose that one point is scored during the round. If that point was scored on the first shot, then the transitions matrix should record, in order:
a
to a + 1
a + 1
to a + 1
In fact, this is what the transitions matrix would report too. Having seen one entry in events
for this round, the script will give that scoring event the first position, and fill in the “implicit” 0 at the second. This becomes a threat to validity if the point was scored with the second shooter, since in that case the progression should be:
a
to a
a
to a + 1
However, the script would still perform the first set of steps, not the second, because it does not know to put the scoring event in the second position. This has the effect of “shifting” counts up and down. Notice through this example that both sets of steps include a transition from a
to a + 1
, but they put the “stagnant” transition in the wrong row and wrong column. This means that these counts are being shifted from one multinomial distribution to another.
There is no way, given our current desire to limit the number of times that someone has to walk over to the app, for us to fix this problem with certainty, but I think there are quite plausible reasons to believe that this error does not completely negate my methodology:
a
can be a “donor” or a recipient, which implies that there is no reason to believe that this biases our results for those probability estimates, though it should certainly increase our uncertainty about the point estimates. This assumption becomes more tenuous to maintain as scores begin to fall outside of the conventional range, however, since we have a paucity of those events and each shift matters more as a result.The matrix is constructed game-by-game: I produce two outputs whose values are the counts of the transitions in each game for each half (offense or defense). The matrices for each game are added with the matrices for all other games, and then the row totals are used to divide the elements of the row. That is, at row r
, the total represents the number of times that a team has been observed at score or state r-1
4. For a given game
, then, I run the following:
rounds_in_game = total_rounds %>%
filter(game_number == game) %>%
select(offense_rounds, defense_rounds)
shots_per_round = game_shots_pair %>%
filter(game_number == game) %>%
pull(shots)
filtered_game = events_simplified %>%
filter(game_number == game)
# Keeping these two together before completing
# proved to be too complicated for the factors
# I needed to specify per side. The next steps
# make that
# easier, but are admittedly kind of ugly
offense = filtered_game %>%
filter(off_def == "offense") %>%
ungroup() %>%
select(round_num, shot_order, off_def, total)
defense = filtered_game %>%
filter(off_def == "defense") %>%
ungroup() %>%
select(round_num, shot_order, off_def, total)
offense_rounds = rounds_in_game$offense_rounds
defense_rounds = rounds_in_game$defense_rounds
offense$round_num = factor(
offense$round_num,
levels = seq(1, offense_rounds))
offense$shot_order =
factor(
offense$shot_order,
levels = seq(1, shots_per_round))
defense$round_num =
factor(
defense$round_num,
levels = seq(1, defense_rounds))
defense$shot_order =
factor(
defense$shot_order,
levels = seq(1, shots_per_round))
expanded_offense = offense %>%
complete(
expand(offense,
round_num,
shot_order),
fill = list(total = 0,
off_def = "offense"))
expanded_defense = defense %>%
complete(
expand(defense,
round_num,
shot_order),
fill = list(total = 0,
off_def = "defense"))
expanded_game = bind_rows(expanded_offense,
expanded_defense)
expanded_game
## # A tibble: 86 x 4
## round_num shot_order off_def total
## <fct> <fct> <chr> <dbl>
## 1 1 1 offense 1
## 2 1 2 offense 0
## 3 2 1 offense 0
## 4 2 2 offense 0
## 5 3 1 offense 1
## 6 3 2 offense 0
## 7 4 1 offense 0
## 8 4 2 offense 0
## 9 5 1 offense 0
## 10 5 2 offense 0
## # ... with 76 more rows
The table expanded_game
has all of the pertinent information for a team’s performance
in a given game. However, it would be incorrect to calculate transition probabilities at this point
because the data set is unordered. We can recover ordering by remembering that the switch between
offense and defense in a round is predictable once we know the team’s side.
team = game_side_pair %>%
filter(game_number == game) %>%
pull(side)
if (team == "A"){
expanded_game = expanded_game %>%
arrange(round_num, desc(off_def), shot_order) %>%
select(off_def, total)
expanded_game = bind_rows(tibble(off_def = "offense", total = 0),
expanded_game)
} else {
expanded_game = expanded_game %>%
arrange(round_num, off_def, shot_order) %>%
select(off_def, total)
expanded_game = bind_rows(tibble(off_def = "defense", total = 0),
expanded_game)
}
score_side_table = expanded_game %>%
summarize(running_score = cumsum(total),
off_def = off_def,
.groups = "drop")
In each case, notice that I bind a new row with a score of 0 at the very beginning of the expanded game. This is done to set the initial condition. If a team scores in round 1A on their very first shot (or paddle), then that should be recorded as a transition from 0 to 1. That line ensures this is the case.
Finally, we take this object and simplify it into a table with two columns called score_side_table
. This is the “clean” version of our data. From here, calculating transition counts is straightforward. Here, I’ll demonstrate in the case that we’re looking at “scores”.
offense_matrix = matrix(data = 0, nrow = 51, ncol = 51)
defense_matrix = matrix(data = 0, nrow = 51, ncol = 51)
# Create a matrix that will be used for our transition counts
for (i in 2:length(score_side_table$running_score)){
old_score = score_side_table$running_score[i - 1]
new_score = score_side_table$running_score[i]
if (score_side_table$off_def[i] == "offense"){
offense_matrix[old_score + 1, new_score + 1] =
offense_matrix[old_score+ 1, new_score + 1] + 1
} else {
defense_matrix[old_score + 1, new_score + 1] =
defense_matrix[old_score + 1, new_score + 1] + 1
}
}
The two matrices which are created, one for offense and one for defense, are then added together with the counts
for every game in which that team has played. In fact, I map
the sequence of functions above over the games in which that team has played. This provides us with objects that I call the transition_counts
.
Next, we put it all together.
row_totals = map_dbl(seq(1, matrix_rank), function(number) {
transition_counts_offense[number,] %>% sum()
}
)
transition_probs_offense = transition_counts_offense / row_totals
Overall, we do these steps 4 times: one for each matrix that is needed in the simulation. The app stores these matrices for every unique team combination which we have seen in Snappa history, and runs simulations by drawing scores from these matrices.
I hope that you have enjoyed reading this post. For the practitioner, I hope that it provides insight into some ways that you can accomplish data cleaning goals while creating statistical objects that could not be easily accomplished with a package. For the more skilled practitioner than me, I’d love to hear your feedback on this process.
This article is the latest in a series on Snappa. For more articles, see here
Note: although I don’t believe it is a pressing issue for our small little application, I will not be using the actual table names or their column names from our database for Robert reasons. Paranoia is not usually a good thing, but I dare not tempt the internet when it comes to matters of security↩
This is particularly true because we’re using the free version of shinyapps to run our application, so ensuring a decent UX often means getting things to run as quickly as we can.↩
Note that we have to specify all of these for the reasons mentioned before: each team pairing refers to the unique combination of players, meaning (2,9) is different from (2,3,9)↩
This is a technical consideration which is true because we need a row and column for zero points, and we cannot have a 0th row from R’s perspective↩


| Format | format |
|---|---|
| Version | version |
| Width | width |
| Height | height |
| Mime Type | mimeType |
| Size (Bytes) | byteSize |
| URL | url |
| Date | DateTime |
|---|---|
| Aperture | ApertureValue |
| Exposure | ExposureTime |
| Shutter Speed | ShutterSpeedValue |
| ISO | ISOSpeedRatings |
| Camera Make | Make |
| Camera Model | Model |
| Software | Software |
| XResolution | XResolution |
| YResolution | YResolution |
| Flash | Flash |


Pseudomonas fluorescens
Applications
Degradation of soil pollutants
Life Cycle
During a normal biofilm lifecycle, planktonic cells in the media adhere to a surface, mature and then disperse to allow for continuation of the cycle.
- Optimal Growth Temperature: 25-30°C
Vectors
- p15A-cm-tetR
- Pamp-amp-ccdB
- p15A-ccdB-nic
Genome Sequence
https://www.ncbi.nlm.nih.gov/gene/250766
Whole Cell Metabolism SBML Models
Acknowledgements
This entry was curated by iGEM 2018 Team SKLMT-China (http://2018.igem.org/Team:SKLMT-China)


The prototype was built in the summer 2017 at the Aalto University's Protopaja course in co-operation with Consair Oy. Our goal was to develop a prototype for a local air cleaner with IoT capabilities. The primary purpose of the device is to reduce construction workers exposure to waste particles while mixing mortar. Because devices suitable for this purpose already exist, we wanted to design and build a device with improved features.
The enclosure of the device was built of plywood with the help of a 3D model. The air flow enters through the air vent in the front panel, passes through the filters in the middle and the fan in the rear of the device and then exits through the roof outlet.
The operation of the device is controlled by the microcontroller. It reads the values measured by the pressure sensors and adjusts the fan speed based on the measurement results. The airflow passing through the device is kept constant regardless of the degree of clogging of the filters.
The fan speed is controlled by a specific piece of software in the microcontroller. The code-based PID controller adjusts the speed to the desired value based on the difference between the measured and the desired speed.
While the device is in operation, the GSM module connects to the server and transmits information about the status of the device. The server stores the data in the database. The person in charge of the device is informed by email or text message if the filters are clogged.
Box design & air flow coding
Project manager & IoT coding
Electronics design
Website design & all-round coding
Box design & safety testing


I've done remote work for agencies, consulted for startups, and collaborated with talented people to create digital products for both business and consumer use. I'm quietly confident, naturally curious, and perpetually working on improving my chops one design problem at a time..
UI/UX Designer & Web Developer.
|
|
|
HTML   100% |
PHP     75% |
|
CSS       100% |
SOFTWARE TESTING     70% |
|
Javascript 75% |
I offer you wide variety of services and complete a website in short period of time.I take care of hosting and maintaining your website
HostingI do host a website on private space bought on a website which is full secured |
Static WebsiteI complete my satic website on short period of time |
Dynamic WebsiteI use various popular languages like php |
Software InstallationI Install the software and if there is any software related issue I fix it. |


{{name}}
{{version}}
{{repository.url | replace('.git', '')}}
{{name}}
{{author}}
{{repository.url | replace('.git', '')}}
{{repository.url | replace('.git', '')}}/blob/master/license
false
{{repository.url | replace('.git', '')}}
rename cli batch utility replacement regex nunjucks
{{description}}
## Features
- Variable replacement and filtering of new file name (powered by [Nunjucks](https://mozilla.github.io/nunjucks/templating.html))
- Glob file matching
- Undo previous rename
- Customize by adding your own variables and filters
- Auto-indexing when renaming multiple files to the same name
- RegEx match/replace
- Exif data support
## Usage
```rename [options] file(s) new-file-name```
Or simply type `rename` for an interactive cli with live previews of rename operations.
*Note: Windows users (or anyone who wants to type one less letter) can use rname instead of rename since the rename command already exists in Windows*
The new file name does not need to contain a file extension. If you do not specifiy a file extension the original file extension will be preserved.
*Note: if you include periods in your new file name, you should include a file extension to prevent whatever is after the last period from becoming the new extension. I recommend using `.{{ext}}` to preserve the original file etension.*


The Best TV Shows & Movies Coming to Netflix in December 2019
by Chris EvangelistaYes, 2019 is almost over. We have one more month to get through, and then it’ll be time for a whole new year! But first, how about some new movies? December brings a ton of original Christmas content to Netflix. But it also brings some non-Christmas movies, too. And we’ve rounded them up! Here are the best TV shows and movies coming to Netflix in December 2019.
Malcolm X
What’s Spike Lee‘s best film? Do the Right Thing, perhaps? Or maybe The 25th Hour? The fact of the matter is, a good case can be made for many of Lee’s films to be considered his best – he’s just that great a filmmaker. Certainly one of his most epic films is 1992’s Malcolm X, starring Denzel Washington as the African-American activist.
Marriage Story
Noah Baumbach‘s Marriage Story is an emotionally devastating showcase for the acting talents of Scarlett Johansson and Adam Driver. The two play a married couple going through a brutal divorce that’s further complicated by distance – he lives in New York, she’s in L.A. While this movie is often quite painful to watch – the emotions get raw and real, folks – it’s also one of the year’s best films.
It Comes At Night
The reaction to It Comes At Night was mixed, to say the least. The movie is unquestionably horror, but some of the marketing made it look like a more traditional horror film, and that’s not really what this is. Instead, it’s a slow-burn journey into darkness – a post-apocalyptic family drama that turns into a nightmare. This was the second feature from Trey Edward Schultz, who seems to specialize in dark family dramas. He even has a new one out this year – the devastating Waves.
Sweetheart
Sweetheart had an underwhelming release, to the point where I’m pretty sure most people don’t even know it came out.  Kiersey Clemons is a young woman shipwrecked on an island. That would be bad enough on its own, but she also has to deal with a hungry monster that comes out of the sea to feed. Much monster mayhem follows.
The Two Popes
I know what you’re saying: “I can’t possibly handle more than one Pope.” But get this: what if there were two Popes? Wild, right!? The Two Popes covers the transition of power from Pope Benedict XVI (Anthony Hopkins) to Pope Francis (Jonathan Pryce), two very different men with very different approaches to the Catholic church. That may sound like a stuffy drama, but by all accounts, The Two Popes is quite lively and light on its feet, and boasts two great lead performances.
Continue Reading The Best TV Shows & Movies Coming to Netflix >>
Pages: 1 2


Utilized Data Sources
Core Models were designed based on the information derived from published literature. Genomes were annotated using RAST annotation pipeline and core models were generated using
KBase resources. We've done our best to cite contributing data sources here. If you believe your data has been utilized and not properly cited, please email us
here.
Sources Funding Development
This material was based upon work supported by the U.S. Department of Energy, Office of Biological and Environmental Research; under contract DE-AC02-06CH11357 and by the National Science Foundation grant number MCB-1153357
Citing the Resource
Janaka N. Edirisinghe, Pamela Weisenhorn, Neal Conrad, Fangfang Xia, Ross Overbeek, Rick L. Stevens
and Christopher S. Henry. Modeling central metabolism and energy biosynthesis
across microbial life. BMC Genomics, 2016 17:568
DOI: 10.1186/s12864-016-2887-8
Development Team
| Contributor |
Institution |
Role |
| Janaka Edirisinghe |
Argonne National Laboratory |
Research |
| Chris Henry |
Argonne National Laboratory |
Research/Development |
| Neal Conrad |
Argonne National Laboratory |
Development |
Downloads
The core model template used is in this study is available for viewing/download at our github repo
here.
To download, right click on the .tsv file of interest, and then click "Save link as...".
Questions, comments, and bug reports?
Please direct questions, comments, and bug reports on our site to our development team via
email.


|
Xerces-C++
3.1.4
|
This class represents a complexType or simpleType definition. More...
Public Types | |
| enum | TYPE_CATEGORY { COMPLEX_TYPE = 15, SIMPLE_TYPE = 16 } |
Public Member Functions | |
Constructors | |
| XSTypeDefinition (TYPE_CATEGORY typeCategory, XSTypeDefinition *const xsBaseType, XSModel *const xsModel, MemoryManager *const manager=XMLPlatformUtils::fgMemoryManager) | |
| The default constructor. More... | |
Destructor | |
| virtual | ~XSTypeDefinition () |
overloaded XSObject methods | |
| virtual const XMLCh * | getName () const =0 |
The name of type NCName of this declaration as defined in XML Namespaces. More... | |
| virtual const XMLCh * | getNamespace ()=0 |
The [target namespace] of this object, or null if it is unspecified. More... | |
| virtual XSNamespaceItem * | getNamespaceItem ()=0 |
| A namespace schema information item corresponding to the target namespace of the component, if it's globally declared; or null otherwise. More... | |
XSTypeDefinition methods | |
| TYPE_CATEGORY | getTypeCategory () const |
| Return whether this type definition is a simple type or complex type. More... | |
| virtual XSTypeDefinition * | getBaseType ()=0 |
| {base type definition}: either a simple type definition or a complex type definition. More... | |
| bool | isFinal (short toTest) |
| {final}. More... | |
| short | getFinal () const |
For complex types the returned value is a bit combination of the subset of {DERIVATION_EXTENSION, DERIVATION_RESTRICTION } corresponding to final set of this type or DERIVATION_NONE . More... | |
| virtual bool | getAnonymous () const =0 |
| A boolean that specifies if the type definition is anonymous. More... | |
| virtual bool | derivedFromType (const XSTypeDefinition *const ancestorType)=0 |
Convenience method: check if this type is derived from the given ancestorType . More... | |
| bool | derivedFrom (const XMLCh *typeNamespace, const XMLCh *name) |
| Convenience method: check if this type is derived from the given ancestor type. More... | |
| Public Member Functions inherited from XSObject | |
| XSObject (XSConstants::COMPONENT_TYPE compType, XSModel *const xsModel, MemoryManager *const manager=XMLPlatformUtils::fgMemoryManager) | |
| The default constructor. More... | |
| virtual | ~XSObject () |
| XSConstants::COMPONENT_TYPE | getType () const |
The type of this object, i.e. More... | |
| virtual XMLSize_t | getId () const |
| Optional. More... | |
| void | setId (XMLSize_t id) |
| methods needed by implementation More... | |
| Public Member Functions inherited from XMemory | |
| void * | operator new (size_t size) |
| This method overrides operator new. More... | |
| void * | operator new (size_t size, MemoryManager *memMgr) |
| This method defines a custom operator new, that will use the provided memory manager to perform the allocation. More... | |
| void * | operator new (size_t size, void *ptr) |
| This method overrides placement operator new. More... | |
| void | operator delete (void *p) |
| This method overrides operator delete. More... | |
| void | operator delete (void *p, MemoryManager *memMgr) |
| This method provides a matching delete for the custom operator new. More... | |
| void | operator delete (void *p, void *ptr) |
| This method provides a matching delete for the placement new. More... | |
Protected Attributes | |
| TYPE_CATEGORY | fTypeCategory |
| short | fFinal |
| XSTypeDefinition * | fBaseType |
| Protected Attributes inherited from XSObject | |
| XSConstants::COMPONENT_TYPE | fComponentType |
| XSModel * | fXSModel |
| MemoryManager * | fMemoryManager |
| XMLSize_t | fId |
Additional Inherited Members | |
| Protected Member Functions inherited from XMemory | |
| XMemory () | |
| Protected default constructor. More... | |
This class represents a complexType or simpleType definition.
This is always owned by the validator /parser object from which it is obtained.
| XSTypeDefinition::XSTypeDefinition | ( | TYPE_CATEGORY | typeCategory, |
| XSTypeDefinition *const | xsBaseType, | ||
| XSModel *const | xsModel, | ||
| MemoryManager *const | manager = XMLPlatformUtils::fgMemoryManager |
||
| ) |
The default constructor.
| typeCategory | |
| xsBaseType | |
| xsModel | |
| manager | The configurable memory manager |
|
virtual |
Convenience method: check if this type is derived from the given ancestor type.
| typeNamespace | An ancestor type namespace. |
| name | An ancestor type name. |
typeNamespace
and name
.
|
pure virtual |
Convenience method: check if this type is derived from the given ancestorType
.
| ancestorType | An ancestor type definition. |
ancestorType
. Implemented in XSSimpleTypeDefinition, and XSComplexTypeDefinition.
|
pure virtual |
A boolean that specifies if the type definition is anonymous.
Convenience attribute.
Implemented in XSSimpleTypeDefinition, and XSComplexTypeDefinition.
|
pure virtual |
{base type definition}: either a simple type definition or a complex type definition.
Implemented in XSSimpleTypeDefinition, and XSComplexTypeDefinition.
| short XSTypeDefinition::getFinal | ( | ) | const |
For complex types the returned value is a bit combination of the subset of {DERIVATION_EXTENSION, DERIVATION_RESTRICTION
} corresponding to final
set of this type or DERIVATION_NONE
.
For simple types the returned value is a bit combination of the subset of { DERIVATION_RESTRICTION, DERIVATION_EXTENSION, DERIVATION_UNION, DERIVATION_LIST
} corresponding to final
set of this type or DERIVATION_NONE
.
References fFinal.
|
pure virtual |
The name of type NCName
of this declaration as defined in XML Namespaces.
Reimplemented from XSObject.
Implemented in XSSimpleTypeDefinition, and XSComplexTypeDefinition.
|
pure virtual |
The [target namespace] of this object, or null
if it is unspecified.
Reimplemented from XSObject.
Implemented in XSSimpleTypeDefinition, and XSComplexTypeDefinition.
|
pure virtual |
A namespace schema information item corresponding to the target namespace of the component, if it's globally declared; or null otherwise.
Reimplemented from XSObject.
Implemented in XSSimpleTypeDefinition, and XSComplexTypeDefinition.
| XSTypeDefinition::TYPE_CATEGORY XSTypeDefinition::getTypeCategory | ( | ) | const |
Return whether this type definition is a simple type or complex type.
References fTypeCategory.
| bool XSTypeDefinition::isFinal | ( | short | toTest | ) |
{final}.
For complex type definition it is a subset of {extension, restriction}. For simple type definition it is a subset of {extension, list, restriction, union}.
| toTest | Extension, restriction, list, union constants (defined in XSObject ). |
|
protected |
|
protected |
Referenced by getFinal().
|
protected |
Referenced by getTypeCategory().


Would you rent a vacuum cleaner for $499 a month?
by Nell MackenzieVacuum cleaning is a chore many people are happy to outsource, but one company is trying to persuade firms to swap an automated service for human cleaners.
Softbank, the Japanese firm behind WeWork and Uber, has launched a self-powered machine, dubbed The Whiz, at a hefty price tag of $499 (£381) a month.
Softbank says its robot is meant to replace "over-worked janitorial teams."
The Whiz is not initially automatic, as a human has to lead it around until it can learn the cleaning route.
It has been developed by Softbank's US robotics arm and Hong Kong-based firm Intelligent Cleaning Equipment.
The three-feet-tall (0.9 metres) vacuum was initially only offered for rent in Japan and Hong Kong.
'Cleaning efficiency'
The machine sends an alert if there is an issue, such as bumping into a wall. After three hours, it can cover 15,000 square feet (1,394 square metres) but then will need a battery change.
It also gathers data and produces a report with analytics on dirt levels.
Softbank says in its presentation materials this provides the ability to "actively demonstrate cleaning efficiency to stakeholders".
One UK firm told the BBC they were planning to conduct trials with the robot in hotels around Southampton.
Ellen Gasson, marketing director for Hampshire-based Industrial Cleaning Equipment (a different business from the Hong Kong company), says when they trial the machine in hotels, a cleaner will need to be present to allow the robot into rooms to clean floors.
WeWork rescue
The vacuum cleaner launch comes after tech investor Softbank announced its worst ever financial results, earlier in November.
Softbank reported a £5bn operating loss for the second quarter of 2019, after companies it invested in, Uber and WeWork, suffered poor financial performances.
WeWork, a flexible office space company had to be given a multi-billion dollar rescue package. Its chief executive Adam Neumann, departed and was replaced with Softbank executives.
Despite the negative recent press, some analysts remain positive on Softbank and see the company's stock as undervalued.
"The WeWork debacle has eroded investor confidence but we retain our strong conviction regarding the long-term outlook for the company," said Chris Lane, an analyst at Bernstein in a research note.


In HTML headings are marked with using heading tags such h1. There are six levels of heading supported. The biggest is h1 which is used for page titles. The smallest is h6. On most pages you probably won't see h4-6 used.
Paragraphs are separated using p tags. This automatically separates them into blocks, leaving a gap in between.
It is also possible to insert extra blank lines manually using the br tag. The br tag is a special type of tag (called a 'void tag'); you don't need a closing br tag, just un opening one.
You can write comments in your html code, which won't be displayed in the browser.
Html has two basic types of list:
Both have the same basic structure.
You don't often see lists in their basic form, but lists are heavily used in websites; dropdown menus are often cleverly styled lists.
Links look like this: link to facebook. You specify where you want the link to point by specifying the 'href' inside the <a> tag.
The href can also point to local files. For example, this is a link to this page you're reading at the moment. Notice that the protocol and host part of the URL has been replaced by a "."
Images are a bit like links. You specify the location of the image with the 'src' property in the <img> tag. Note that img tags are like br tags: they don't come in pairs. The 'alt' property should be used to provide a description of the image in case it won't load or the user is partially sighted.
Images can be stored on the same host ...
... or on another server entirely.
Tables are used for holding data
| Name | Profession |
|---|---|
| Rosamund | Cheese Taster |
| Dave | Stamp collector |


Event Location:
Hotel Taurus
5, Kn. Sviatoslava Sq., Lviv, Ukraine
Lviv, L'vivs'ka Oblast', Ukraine
Abstract:
Haberdasheries are various small goods such as buttons, thread and similar sewing goods. When used together correctly, they provide the basis for making good clothes. Building good Machine Learning solutions is similar, you'll need various small things in your toolkit that you will then leverage build production ready predictive solutions. In our little Machine Learning shop, you'll receive tips on collaborative data science, automating data pipelines, automated machine learning and will learn how to apply agile methodologies operationalize your experiments in a fast way. We'll even consider the maintenance of your machine learning models afterwards as that often gets overlooked. Using Azure and SQL Server Machine Learning Services during our examples, you'll be able to take these tips home and apply them yourself.
Speaker(s): Jan Mulkens,
Abstract:
In the world of agile software development, the database development process introduces some challenges to the application development process. The delivery of changes at database level tends to be a different and separated process of the application delivery process or even an entirely manual process. This contributes to the perspective that the database is a bottleneck in the agile process. In this session, I will show how to build a deployment pipeline capable of delivering database and application changes. The pipeline includes the following steps: source control, continuous integration and continuous delivery. I will share methods and strategies that allow bringing together databases and applications in the delivery process reducing the delivery pain. The main goal is to understand that applications and databases can and should share the same deployment pipeline and that this pipeline will allow you not be afraid of making database changes.
Speaker(s): Taras Kloba,
Abstract:
Hierarchies and graphs are the bread and butter of most business applications and you find them almost everywhere: * Product Categories * Sales Territories * Bill of Material * Networks Even when there is a big need from a business perspective, the solutions in relational databases are mostly sort of awkward. The most flexible hierarchies are usually modeled as self-referenced tables. If you want to successfully query such self-referenced hierarchies, you will need either loops or recursive Common Table Expressions. SQL Server 2017 came with a different approach: Graph Database, which are further improved in SQL 2019. Join this session for a journey through best practices to transform your hierarchies and graphs into useful information. We will have fun playing around with a sample database based on G. R. R. Martin’s famous “Game of Thrones”.
Speaker(s): Markus Ehrenmueller-Jensen,
Abstract:
Are you still planning your servers capacity in CPU and RAM? Do you enjoy answering questions like “what amount of servers we need to buy to serve 10K and 10M users”? Do you enjoy paying for those servers while they are sitting idle? In my session I will talk about the current state and the future of serverless computing. I will show several demos that show how to start using serverless computing, what are the application scenarios for it, how it can scale, what it will cost, etc. This session will be useful for developers and architects who are seeking a way to create an app utilizing all your previous skills, but make it scalable, reliable, fast and cost efficient.
Speaker(s): Illia Lubenets,
Abstract:
Ви не є розробником баз даних і знаєте тільки основи SQL, проте постійно змушені виконувати все більше пов’язаних задач? Дозвольте поділитися з вами низкою порад, що значно полегшать вашу роботу та дозволять прискорити ваші запити. Оптимізація швидкодії, корисні функції про які мало хто знає, спрощення та автоматизація задач адміністрування та багато іншого.
Speaker(s): Yevhen Nedashkivskyi,
Abstract:
Are you still planning your servers capacity in CPU and RAM? Do you enjoy answering questions like “what amount of servers we need to buy to serve 10K and 10M users”? Do you enjoy paying for those servers while they are sitting idle? In my session I will talk about the current state and the future of serverless computing. I will show several demos that show how to start using serverless computing, what are the application scenarios for it, how it can scale, what it will cost, etc. This session will be useful for developers and architects who are seeking a way to create an app utilizing all your previous skills, but make it scalable, reliable, fast and cost efficient.
Speaker(s): Anton Boyko,
Abstract:
Are you a DBA or Developer and would like to get started with AzureML the EASY WAY? AzureML isn't just for "data scientists"... Anyone can use it! And after this session you'll be using it too... Disclaimer: Sadly, AzureML still can't predict what's on your girlfriend's mind. Nothing ever will.
Speaker(s): André Melancia,
Abstract:
Even tough it's nothing new, Docker is a thing nowadays. I've decided to check containers out as well and learn how they can help me. Although I'm a BI consultant and not really using Docker and containers in the current projects, I've already found them to be very helpful on my daily basis learning and other activities. If you are also passionate about data and still experimenting with SQL Server and/or Python and/or any other technology, please join the session and see how Docker can change your life. I will show you my journey, my thoughts and my findings regarding the usage of SQL Server and Python together with Docker. And there will be a lot, a lot of demos.
Speaker(s): Sławomir Drzymała,
Abstract:
Power BI is using In-memory technology, which allows reports to be loaded and presented fast. But here you are limited by CPU and RAM characteristics. Similar behaviour will be when your report is placed at Power BI Service. At current session I will describe possible performance issues and how to handle them. We will talk about columnar storage and Vertipaq engine as well. During the session different practice advices will be presented.
Speaker(s): Ruslan Zolotukhin,
Abstract:
Are you new begining database developer ? Are you .Net developer and often work with database? I think this session special for you! In this session I would like to show common mistakes, connected with DB development which I saw in my projects and tell audience how we could avoid it . How we can improve performance of SQL Server.
Speaker(s): Mykola Pobyivovk,
Abstract:
Microsoft is enhancing Polybase in 2019 with the ability to connect to more data sources. You can now use PolyBase to connect to other SQL Server instances, Oracle and it’s easy to set up with a wizard. I explains what’s new in PolyBase and walk you through setting it up and connecting to an external table. Will explain difference between PolyBase and SQL Server Linked Server.
Speaker(s): Andrii Zrobok,
Abstract:
В этом докладе я расскажу об автоматизации административных задач с помощью PowerShell. Затрону некоторые community решения и их имплементацию.
Speaker(s): Andriy Pogorelov,
Abstract:
Давайте поговоримо про теорію та практичну реалізацію багатопоточної обробки даних в сучасних СУБД та розберемо кілька практичних прикладів. Спробуємо розібратися та обійти потенційні проблеми та на прикладі Microsoft SQL Server. Під час доповіді ми торкнемось тем паралелізму, затримок, блокувань та взаємоблокувань, а також різних рівнів ізоляції.
Speaker(s): Yevhen Nedashkivskyi,
Abstract:
In this session we will discuss about tempdb contention in SQL Server on-premises, Azure SQL Managed Instance and Azure SQL Database. We will analyze both, Allocation Contention and Metadata contention, describing in detail WHEN and HOW it occurs, How to diagnose it, Best Practices and how to prevent it. The session will have both approaches in on-premisses and in Azure Managed Instance: - Theoretical, to understand what and why it occurs. - Practical, with examples causing it, offering alternatives to solve it and to prevent it.
Speaker(s): Jose Manuel Jurado Diaz,
Abstract:
Power BI makes it possible for every employee in an organization to make better decisions based on data with beautiful reports and dashboards. But data scientists, who are crucial to deploying AI solutions, don’t have the bandwidth to provide custom solutions to all users at an organization. Businesses need ways to surface the valuable work of data scientists and empower more users to leverage AI in easy and intuitive ways. Power BI can easily help with this problem.
Speaker(s): Eugene Polonichko,
Abstract:
I am going to cover different aspects: implementation stages, system components, technologies, required skill set, roles, etc. Everything needed to build such ecosystem.
Speaker(s): Taras Bachynskyy,
Abstract:
Or options in running TensorFlow on Pi or similar class devices. My idea was (a bit born from need), to run TensorFlow on Pi class device. And this presentation is born from my adventures, showing pro and cons of each approach. From running directly on Pi to Azure IoT Edge on to Intel Neural Stick, and the list most probably is in “works” you will see the challenges and results of implementing TensorFlow using these technologies. Remark: Kinder chocolates have nothing to do with ML but will be present.
Speaker(s): Catalin Gheorghiu,
This is a list of speakers from the XML Guidebook records. The details and URLs were valid at the time of the event.
LinkedIn: http://LinkedIn.COM/in/AndreMelancia
Contact: http://Andy.PT
Owner / Principal Consultant at LunarCat.PT Microsoft Certified Trainer (MCT) focusing on SQL Server, Azure, IoT and Security. 19+ years' fun developing information and multimedia systems, DBA, project and IT management. Data Community Portugal, IT Pro Portugal and PowerShell Portugal communities organiser. IPv6 Portugal, DNSSec Portugal and Windows Development Portugal online communities moderator. Actively volunteering, organising, speaking or just participating at community meetings and events around the world. Proud uncle and food devouring expert, with dangerous рuϧϧy cat as companion. Go to http://Andy.PT and you'll know the same as the NSA...
LinkedIn: https://www.linkedin.com/in/azrobok/
25+ years of experience in database development: development data-centric applications from scratch, support of legacy databases/applications, data migration tasks, performance tuning, consulting, database trainer. I have experience with wide range of databases such as FoxPro for DOS (Fox Software), MS SQL Server (started from version 6.5) , Oracle, Sybase ASE, MySQL, PostgreSQL. Co-Leader of PASS Local Chapter in Lviv. Speaker at PASS SQLSaturday conferences (since 2013); PASS Lviv/Vinnitsa/Virtual SQL Server User Group, EPAM IT Week (2015-2017), AIBig Data Day 2019.
LinkedIn: https://ua.linkedin.com/in/nedash
Yevhen Nedashkivskyi is a professional DBA and a database developer with more than 10 years of experience. MSCP, MSCA, MSCE, MVP.
Taras has more than 15 years of experience in consulting, RD, design and implementation of enterprise-scale solutions for a different range of companies from startups to enterprises, including Fortune 100 companies. Together with his team of Senior Architects and top-notch Experts in Big Data, BI, Analytics, IoT and other technology areas, he drives innovations, architecture design and adoption of cutting-edge technologies and approaches for organizations from different business domains.
LinkedIn: https://www.linkedin.com/in/sdrzymala/
Contact: http://seequality.net/
Slawomir Drzymala is a Data Warehouse Developer and a big enthusiast of the Microsoft technologies, which is still playing with data, the .NET technologies and Microsoft's data products. Big fan of the data mining/data science, but also of the self-service BI. Speaker at many different events including the biggest Microsoft SQL Server conference in Poland. Organizer of the meetings, conferences and hackathon dedicated to the Microsoft Data Platform. President of Malta Microsoft Data Platform User Group.
LinkedIn: https://www.linkedin.com/in/kloba/
Contact: http://sql.ua
✅ Senior DBA and developer; ✅ Senior database architect; ✅ Business solutions expert; ✅ 9+ years of experience. Senior database administrator and programmer. Consultant and certified database administrator and developer with 9+ years of experience working with SQL Server 2016, 2014, 2012, 2008, 2005, 2000 and SQL Azure. MySQL, PostgreSQL and MS SQL database performance tuning expert. Business solutions consultant and implementer. With many years of extensive experience in the IT and other business industries in a variety of roles. My objective is to use my experience to bring creative and top-level solutions to businesses that require SQL Databases.
Contact: https://www.facebook.com/nick.pobiyvovk
Mykola Pobyivovk is a senior Database developer, Tech Lead at Ciklum. He has about 10 years experience in IT industry. He has been working with SQL Server since 2010. He is co-orginizer of events SQL Saturday Kyiv 2016, 2017, 2018 and Ukraine Data Community Kyiv. In his talk he would like to share his experience connected with develpment of DataBase.
LinkedIn: https://ua.linkedin.com/in/nedash
Yevhen Nedashkivskyi is a professional DBA and a database developer with more than 10 years of experience. MSCP, MSCA, MSCE, MVP.
LinkedIn: https://www.linkedin.com/in/jose-manuel-jurado-diaz-5a586014/
José Manuel Jurado currently works as SQL Escalation Engineer and Subject Matter Expert in Azure SQL Database at Microsoft Spain. With over 20+ years of experience in SQL Server environments (administration and database development), he is a speaker for internal events at Microsoft and for public events such as SQLSaturday, SQL Nexus, SQLBits, Pass Summit, SQL Server Konferenze, TechReady, DotNetConference, and Azure BootCamp. In the past, he worked as Project Manager for large global retail company for more than 10 years developing projects using different Microsoft Technologies.
LinkedIn: https://www.linkedin.com/in/eugenepolonichko/
Contact: https://msolapblog.wordpress.com/
Leader of PASS Local Chapter in Donetsk (Ukraine). I work as DWH/BI architect. Responsible for designing and developing Data Warehouse and BI solutions with using Microsoft BI Stack products.
LinkedIn: https://www.linkedin.com/in/zoloturu/
Contact: https://www.zoloturu.com
BI Engineer at Jabil Software Services Power BI Consultant MCT, MSCA: BI Reporting Speaker at SQL Saturday, Data Monsters and UNETA, PUG and PASS UG Meetups Advisor at Power BI Community Forum Leader of Kharkiv Power BI User Group Former DAX, Excel VBA trainer Blog: www.zoloturu.com My passion is: * to learn new technologies in data management, analytics and science * to wrangle data and provide meaningful output * to automate routine processes and to build reporting systems * to share my experience and knowledge in all these areas
LinkedIn: https://www.linkedin.com/in/boykoant
Founder and main speaker at Ukrainian Microsoft Azure Community. 15+ years in application development. Builds apps based on Microsoft Azure platform since 2011. Microsoft Azure technical expert. Microsoft Azure MVP since 2014. Owner and one of the organizers of the most popular Microsoft Azure conferences in Ukraine: Azure Day and Global Azure Bootcamp.
LinkedIn: ?https://ro.linkedin.com/in/catalingheorghiu?
Contact: http://ronua.ro/author/catalin/
Catalin Gheorghiu is a solution architect from Romania. He has more than 15 years of experience in developing solutions, especially on Microsoft technologies in very demanding environments. Is contributing articles and blogs to several user groups (MrSmersh), lecturing all over Romania and abroad, is also RONUA Timisoara (PASS Chapter) user group leader. Since 2011, every year he was awarded the Microsoft MVP Award.
LinkedIn: https://www.linkedin.com/in/jose-manuel-jurado-diaz-5a586014/
José Manuel Jurado currently works as SQL Escalation Engineer and Subject Matter Expert in Azure SQL Database at Microsoft Spain. With over 20+ years of experience in SQL Server environments (administration and database development), he is a speaker for internal events at Microsoft and for public events such as SQLSaturday, SQL Nexus, SQLBits, Pass Summit, SQL Server Konferenze, TechReady, DotNetConference, and Azure BootCamp. In the past, he worked as Project Manager for large global retail company for more than 10 years developing projects using different Microsoft Technologies.
LinkedIn: https://ua.linkedin.com/in/pogorelovandrey
I have been dealing with database administration and development for more than 16 years. Started from SQL Server 2000 - Active member of Data Platform community - Organizer of SQLSaturday Kharkiv - Speaker at various Data Platform related events - I possess Microsoft certificates: MCDBA, MCITP, MCSA.
LinkedIn: https://www.linkedin.com/in/illia-lubenets-b16b15b6/
Lead Software Engineer in EPAM. Microsoft Azure MVP. Co-founder of .NET Core Ukrainian User Group. He is interested in distributed systems development mainly based on Microsoft Azure. Active meetup and conferences organizer and speaker.
LinkedIn: https://www.linkedin.com/in/markus-ehrenmueller/
Contact: http://www.savorydata.com
Markus Ehrenmueller-Jensen, as the founder of Savory Data, has a long history of providing customer solutions in the areas of data engineering, data science, and Business Intelligence. He is a certified software engineer, a graduated business educator, and professor of Databases Project Management at HTL Leonding, and is certified as an MCSE MCT. He is a published author and writes articles for well-known journals. He co-founded PASS Austria and organizes SQLSaturdays in Austria. Markus is a founding member of Power BI Usergroup Austria. Since 2017 Markus was awarded as an Microsoft Data Platform MVP.
LinkedIn: https://www.linkedin.com/in/janmulkens
Contact: http://www.janmulkens.be
Jan Mulkens is a Microsoft MVP in AI, a Solution Expert at Ordina Belgium, and a Microsoft Data Platform BI Consultant. In his spare time, he is a speaker at conferences and user groups in Europe and he organizes two user groups and a conference in Belgium. Power BI Days conference (www.powerbidays.com), Belgium Microsoft Advanced Analytics User Group (bit.ly/msaaug), and Flemish Power BI User Group (meetup.powerbibelgium.com).
The following is a list of sponsors that helped fund the event.


Documentation for ScannerJS.
Examples
Note: These examples utilize Node.js functionalities.
The following example obtains the sum from a string of numbers and outputs the value.
Run it
const scanner = require('./scanner.js'); // loads the scanner function
const input = '1 1 1 1 1';
const scan = scanner(input); // creates a Scanner object
let sum = 0;
while (scan.hasNextNumber())
sum += scan.nextNumber();
console.log(sum); // outputs 5
The following example reads a file named words.txt
and outputs individual words on separate lines.
Run it
const scanner = require('./scanner.js'); // loads the scanner function
const fs = require('fs'); // loads Node.js File System module
const filepath = './words.txt';
const stream = fs.createReadStream(filepath, { encoding: 'utf8' });
let input = [];
stream.on('readable', () => {
let chunk;
while ((chunk = stream.read()) !== null)
input.push(chunk); // fill the input array with the contents of words.txt
});
stream.on('end', () => {
const scan = scanner(input); // creates a Scanner object
while (scan.hasNext())
console.log(scan.next()); // output individual words on separate lines
});
Remarks
ScannerJS is designed to model basic functionality of the Scanner class in Java and be used in PSHS's annual coding scimmage for beginners. Use ScannerJS to parse strings or arrays of strings.
For a list of methods, see Global.
Templates
Templates designed for competitive programming utilizing ScannerJS.


Redis, 是一个开源的内存数据存储，采用ANSI C编写。可以用作内存数据库、缓存以及消息队列。作为一个key/value存储系统，它的特点主要集中在这几个方面： 1. 它支持非常丰富的数据类型：string, hashes, list, set, sorted set, bitmap, hyperloglogs, geospatial indexes。而且这些数据类型的操作大部分是原子性的。 2. 支持[主从复制][1], Lua scripting, [LRU cache][3], 3. 支持[事物][]，redis提供了事物的功能，MULTI, EXEC, DISCARD 和 WATCH 命令是Redis事务的基石。一个Redis事务允许一组Redis命令单步执行，并提供下面两个重要保证：一个事务中的所有命令串行执行；要么全部命令要么没有任何命令被处理。也就是说redis事物可以保证一串指令操作的原子性，但是不同于mysql，redis并没有使用lock来实现事物，所以效率比较高。 [1]:http://redis.io/topics/replication 2http://redis.io/commands/eval [3]http://redis.io/topics/lru-cache [4]:http://dev.mysql.com/doc/refman/5.7/en/replication-solutions-scaleout.html ### Syntax
strong or strong ( Cmd + B )
emphasize or emphasize ( Cmd + I )
Sometimes I want a lot of text to be bold. Like, seriously, a LOT of text
Right angle brackets > are used for block quotes.
An email<EMAIL_ADDRESS>link.
Simple inline link http://chenluois.com, another inline link Smaller, one more inline link with title Resize.
A reference style link. Input id, then anywhere in the doc, define the link with corresponding id:
Titles ( or called tool tips ) in the links are optional.
An inline image , title is optional.
A reference style image.
Inline code are surround by backtick
key. To create a block code:
Indent each line by at least 1 tab, or 4 spaces.
var Mou = exactlyTheAppIwant;
Ordered lists are created using “1.” + Space:
Unordered list are created using “*” + Space:
Or using “-“ + Space:
End a line with two or more spaces will create a hard linebreak, called <br />
in HTML. ( Control + Return )
Above line ended with 2 spaces.
Three or more asterisks or dashes:
Setext-style:
atx-style:
## This is H2 ### This is H3 #### This is H4 ##### This is H5 ###### This is H6
Footnotes work mostly like reference-style links. A footnote is made of two things: a marker in the text that will become a superscript number; a footnote definition that will be placed in a list of footnotes at the end of the document. A footnote looks like this:
That’s some text with a footnote.1
Wrap with 2 tilde characters:
~~Strikethrough~~
Start with a line containing 3 or more backticks, and ends with the first line with the same number of backticks:
Fenced code blocks are like Stardard Markdown’s regular code
blocks, except that they’re not indented and instead rely on
a start and end fence lines to delimit the code block.
A simple table looks like this:
| First Header | Second Header | Third Header |
|---|---|---|
| Content Cell | Content Cell | Content Cell |
| Content Cell | Content Cell | Content Cell |
If you wish, you can add a leading and tailing pipe to each line of the table:
| First Header | Second Header | Third Header |
|---|---|---|
| Content Cell | Content Cell | Content Cell |
| Content Cell | Content Cell | Content Cell |
Specify alignment for each column by adding colons to separator lines:
| First Header | Second Header | Third Header |
|---|---|---|
| Left | Center | Right |
| Left | Center | Right |
Don’t forget to check Preferences, lots of useful options are there.
Follow @Mou on Twitter for the latest news.
For feedback, use the menu Help
- Send Feedback
And that’s the footnote. ↩


Reports any calls to Math.random() which are immediately
cast to int. Casting a double between 0.0 (inclusive) and
1.0 (exclusive) will always round down to zero. A Math.random() value
should first be multiplied with some factor before casting it to an int to
get a value between zero (inclusive) and the multiplication factor (exclusive).
Another possible solution would be to use the nextInt() method of
java.util.Random.
New in 10.5, Powered by InspectionGadgets


{# Jquery need to be laoded before bootstrap, other wise it will cause error#} {#I thought of using onload = "", but this does nothing good but unstablize the code, since we are using graphdata before insitalization and we donot know what is parse from teh backend #} {#
#}


Base of High Memory0x{{info.header.base_of_high_memory}}
Base of Static Memory0x{{info.header.base_of_static_memory}}
Initial Program Counter0x{{info.header.initial_program_counter}}
Dictionary Address0x{{info.header.dictionary_address}}
Object Table0x{{info.header.object_table}}
Global Variables Table0x{{info.header.global_variables_table}}
Abbrevations Table0x{{info.header.abbrevations_table}}
Abbrevations
Abbrevation Information
Count{{info.abbrevations.count}}
Abbrevations
{% for i in range(0, info.abbrevations.count, 2 )%}


-
-
[[ note.timestamp ]]
[[ note.optional_message.run_type]] is in progress
[[ note.timestamp ]]
[[ note.optional_message.run_type]] complete
open in viewer
open console output
[[ note.timestamp ]]
Upload to diagnostic viewer submitted
[[ note.timestamp ]]
Diagnostic job submitted


layout: true
--- class: center, middle # Agenda 0. About me 1. Plugins - what + usage 2. Methods loading plugins ( direct references,reflection) 3. .NET Core 3 specific for plugins 4. My implementations 5. Questions ? Code at https://github.com/ignatandrei/Presentations/tree/master/2020/NETCore3Plugins --- class: center, top # About me ![:img ignat, 10%](ignat.jpg) Andrei Ignat http://msprogrammer.serviciipeweb.ro/ www.ASP.NET forum moderator YouTube 5 minutes .NET and tools : http://bit.ly/5MinTools Book Making Open Source Component : http://bit.ly/NetOpenSourceComponent Book Copy Paste from StackOverflow : https://amzn.to/2PQ8EDc Monthly meetings: https://www.meetup.com/Bucharest-A-D-C-E-S-Meetup/ TD @ EA --- class: center, top # Plugins - what + usage Definition: https://en.wikipedia.org/wiki/Plug-in_(computing)
In computing, a plug-in (or plugin, add-in, addin, add-on, or addon) is a software component that adds a specific feature to an existing computer program. When a program supports plug-ins, it enables customization.
Examples 1. Office ( word, excel,outlook, ...): https://docs.microsoft.com/en-us/office/dev/add-ins/excel/excel-add-ins-overview 2. Chrome : https://chrome.google.com/webstore/category/extensions?hl=en 3. Visual Studio : https://marketplace.visualstudio.com/ : LiveShare 4. Wordpress, Photoshop , ... --- class: center, middle # Methods loading plugins 1. ## Direct References - example : Calculate Order of goods, 2For3, AmountReduction 2. ## Load At runtime with Reflection - example ### Ensure that in plugins you do have the 2 dll(MoreThan100.dll , ThreeForTwo.dll) --- class: center, middle # .NET Core specific for plugins 1. Loading- Unloading plugins - example https://docs.microsoft.com/en-us/dotnet/core/tutorials/creating-app-with-plugin-support 2. Diamond problem for plugins : https://en.wikipedia.org/wiki/Multiple_inheritance ![:img ignat, 10%](imgs/diamond.bmp) example --- class: center, middle # My implementations Help from https://github.com/natemcmaster/DotNetCorePlugins 2. https://github.com/ignatandrei/InfoValutar 3. https://github.com/ignatandrei/FileExtension --- class: center, middle # Agenda 0. About me 1. Plugins - what + usage 2. Methods loading plugins ( direct references,reflection) 3. Diamond problem and .NET Core 3 plugins 4. My implementations 5. Questions ? Code at https://github.com/ignatandrei/Presentations/tree/master/2020/shorts/NetCore3.0WhatsNew --- class: center, top # Questions? ![:img ignat, 10%](ignat.jpg) Andrei Ignat http://msprogrammer.serviciipeweb.ro/ www.ASP.NET forum moderator YouTube 5 minutes .NET and tools : http://bit.ly/5MinTools Book Making Open Source Component : http://bit.ly/NetOpenSourceComponent Book Copy Paste from StackOverflow : https://amzn.to/2PQ8EDc Monthly meetings: https://www.meetup.com/Bucharest-A-D-C-E-S-Meetup/ TD @ EA ---


None of your recently created US accounts have charges enabled.
Log in to an Express account's dashboard to complete the onboarding process.Manage your Custom accounts and complete the onboarding process in the dashboard.View your Standard accounts in your platform's dashboard, and use their credentials to log in to Stripe and complete the onboarding process.


WITNESS LIST Finance Committee February 17, 1997-9:30A SB 95 For: Green, Bob (Trinity Church), San Antonio Holbrook, Robert (Rest.Christian Fellow), San Antonio Stoneham, Walter (Self), San Antonio SB 310 For: West, David (TX Ass of State Judges), Austin Yetter, R. Paul (Alc for Judicial Fund.), Austin On: Anderson, Ray D. (Self), Lubbock Phillips, Thomas R. (Self), Austin SB 328 For: West, David (TX Ass of State Judges), Austin On: Aboussie, Marilyn (Self), Austin Phillips, Thomas R. (Self), Austin Registering, but not testifying: On: Anderson, Ray D. (Self), Lubbock SB 344 Registering, but not testifying: For: Freeman, Bart (Pantego Christian Acdm), Arlington Newby, Steven R. (Pantego Christian Acdm), Arlington SCR 3 For: Parker, Richard (Defendent), Houston On: Corley, Michael H. (UT System), Austin Eccles, Allison (OAG), Austin SJR 20 For: West, David (TX Ass. of State Judges), Austin On: Aboussie, Marilyn (Self), Austin Phillips, Thomas R. (Self), Austin Registering, but not testifying: On: Anderson, Ray D. (Self), Lubbock SJR 22 On: Luther, David (Travis Co Appraisal Dis), Jasper


Xiaomi Mi Watch Is Here, And It Looks Like The Apple Watch
Following the announcement of the Mi CC9 Pro during today's event, Xiaomi also announced the Mi Watch. The Xiaomi Mi Watch is the company's long-rumored smartwatch, the company's very first, kind of.
You may argue that Xiaomi released quite a few smartwatches thus far, but that's not true, not exactly. It did release various smartwatches through other companies, but this is the very first Mi-branded smartwatch. The very first smartwatch that is being released under Xiaomi's brand.
The Xiaomi Mi Watch looks like Apple made it
The Xiaomi Mi Watch looks like the Apple Watch, there's no denying it. Not only is the general shape extremely similar to the Apple Watch, but it also comes with that round dial on the right. That dial also works similar to the one on Apple's smartwatch.
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The Xiaomi Mi Watch is made out of metal, and it comes with a square shape. Below the round dial on the left, you will notice a microphone, and a physical key. On the left is yet another microphone.
Xiaomi announced two variants of this watch, one with a rubber band, and the other with a stainless steel band (Premium Edition). We'll talk more about the exact variants at the end of the article.
The Xiaomi Mi Watch features a 1.78-inch 448 x 368 AMOLED display (326PPI). The watch's display is protected by a sheet of Gorilla Glass 3, by the way.
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This smartwatch is fueled by the Snapdragon Wear 3100, and it comes with 1GB of RAM and 8GB of eMMC storage. Wear OS does come pre-installed on this watch, well, kind of. Wear OS MIUI for Watch comes pre-installed on it, and is compatible with both Android and iOS. This is essentially Xiaomi's take on Wear OS, if you will.
The device supports 4G with VoLTE, and comes with a 570mAh battery
A 570mAh battery is also included in the package, and Xiaomi claims you can get up to 36 hours of battery life with 4G running. Yes, you can take advantage of 4G with VoLTE here, thanks to an eSIM.
The watch is equipped with Bluetooth 4.2, for some reason Xiaomi opted not to include Bluetooth 5.0. The device is "swim proof", and it comes with a heart rate sensor, geomagnetic sensor, ambient light sensor, accelerometer, and a barometer.
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The watch has a speaker on the left, and it measures 44.69 x 36.92 x 12.28mm, and weighs 44 grams (without strap) / 56 grams (Premium Edition without strap).
The watch can automatically track 10 sports activities, and it comes with European professional sports algorithm FIRSTBEAT.
The Xiaomi Mi Watch comes in Silver and Elegant Black color variants. A variant of the watch with a silicone band costs CNY1,299 ($185) in China. The Premium Edition in Black color with Stainless Steel band is priced at CNY1,999 ($285). The first variant will go on sale on November 11, while the Premium Edition will do the same in mid-December. This goes for China, of course. The watch will almost certainly be making its way to other markets, though.
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WHO ARE WE?
From West Philadelphia born and raised on the playground where I spent most of my days. Chillin' out, maxin', relaxin all cool we were shootin' some b-ball outside of the school when a couple of guys who were up to no good started makin' trouble in my neighborhood. I got in one little fight and my mom got scared and said, "You're movin' with your auntie and uncle in Bel-Air."
OUR MISSION
Living in the Pacific Northwest can be tough with the limited amount of exposure we get to the sun. There are people reportedly suffering from seasonal depression because of the gloomy, gray skies. But fear no more! Smokin' Salmon Cookies are made with 100% real ingredients 80% of the time, all the time.
Our mission is to provide you with the missing nutrients and vitamins you need to carry on through our not-so-tough winters. Salmon provides vitamin B12, vitamin D, omega-3 fatty acids, and more! Join us in eating our way to a happier Northwest!


Who am I?
My name is Alex Albert and I am a sophomore currently pursuing a double major in Computer Science and Economics at the University of Washington in Seattle.
What am I currently doing?
Currently, I am working in the Sensors, Energy, and Automation (SEAL) lab at UW as a Software Developer on the ECoS project team. We are creating an application that assists researchers in their writing process.
Additionally, I am involved in a few other organizations around campus like Husky Robotics and the Science and Business Organization (SEBA). Recently, I was selected to serve as the VP of Professional Development for the Lavin Entrepreneurship program where I will work to create a comprehensive internship and career process for Lavin students.
What am I passionate about?
I love to make stuff. Whether it is building robotic rovers or sleek iOS apps, I yearn for the challenge of creating solutions to problems with technology. I also have a love for business -- you can often catch me perusing the (web) pages of the WSJ or catching up with one of the way too many business newsletters I am subscribed to.
In my free time, I love to lift weights and play sports. Recently, I have been getting into meditation and philosophy, specifically stoicism. If you’re into that, let me know! I would love to chat about my boy Marcus Aurelius sometime.


"Unstarring" a package is the opposite of npm star
, it removes an
item from your list of favorite packages.
There’s also these extra commands to help you manage your favorite packages:
You can "star" a package using npm star
You can see all your starred packages using npm stars


Import all trades from the Excel "TradeHistory" spreadsheet. Make sure you set the PATH to your spreadsheet in application.properties file.
Binance Api import is working but the data is not been properly managed. Coming soon!
Add all the raw imported Excel data together and try to combine
partially filled orders into one trade.
Example:
Order is 10 VEN. It got filled in parts of 5, 3 and 2. Combine
trades will add those trades together into one buy or sell
order.
Simple. Add all buy and sell trade pairs together and calculate all the profit you made ;)
Clean up all tables in case something went wrong and you want a fresh start. Be careful this will delete everything in your database. Delete imports will clear the trade table. Delete trades will delete everything in aggregated_trade table. Delete profits will delete everything in profits table.


Make predictions using the best-performing model from tuning
# S3 method for model_list predict(object, newdata, prepdata, ...)
| object | model_list object, as from `tune_models` |
|---|---|
| newdata | data on which to make predictions. If missing, predictions will be made on the training data. Should have the same structure as the input to `prep_data`,`tune_models` or `train_models`. `predict` will try to figure out if the data need to be sent through `prep_data` before making predictions; this can be overriden by setting `prepdata = FALSE`, but this should rarely be needed. |
| prepdata | Logical, this should rarely be set by the user. By default, if `newdata` hasn't been prepped, it will be prepped by `prep_data` before predictions are made. Set this to TRUE to force already-prepped data through `prep_data` again, or set to FALSE to prevent `newdata` from being sent through `prep_data`. |
| ... | Unused. |
A tibble data frame: newdata with an additional column for the predictions in "predicted_TARGET" where TARGET is the name of the variable being predicted. If classification, the new column will contain predicted probabilities. The tibble will have child class "hcai_predicted_df" and attribute "model_info" that contains information about the model used to make predictions.
The model and hyperparameter values with the best out-of-fold performance in model training according to the selected metric is used to make predictions. Prepping data inside `predict` has the advantage of returning your predictions with the newdata in its original format.
plot.hcai_predicted_df
, tune_models
,
prep_data
# Tune models using only the first 50 rows to keep computation fast models <- machine_learn(pima_diabetes[1:50, ], outcome = diabetes)#>#>#>#># Make prediction on the next 20 rows. This uses the best-performing model from # tuning cross validation, and it also prepares the new data in the same way as # the training data was prepared. predictions <- predict(models, newdata = pima_diabetes[51:70, ])#>predictions#>#>#> # A tibble: 20 x 11 #> diabetes predicted_diabe… patient_id pregnancies plasma_glucose diastolic_bp #> * <chr> <dbl> <int> <int> <int> <int> #> 1 N 0.131 51 1 103 80 #> 2 N 0.160 52 1 101 50 #> 3 N 0.114 53 5 88 66 #> 4 Y 0.743 54 8 176 90 #> 5 N 0.816 55 7 150 66 #> 6 N 0.108 56 1 73 50 #> 7 Y 0.814 57 7 187 68 #> 8 N 0.638 58 0 100 88 #> 9 N 0.360 59 0 146 82 #> 10 N 0.540 60 0 105 64 #> 11 N 0.114 61 2 84 NA #> 12 Y 0.334 62 8 133 72 #> 13 N 0.170 63 5 44 62 #> 14 N 0.439 64 2 141 58 #> 15 Y 0.366 65 7 114 66 #> 16 N 0.139 66 5 99 74 #> 17 Y 0.316 67 0 109 88 #> 18 N 0.274 68 2 109 92 #> 19 N 0.113 69 1 95 66 #> 20 N 0.137 70 4 146 85 #> # ... with 5 more variables: skinfold <int>, insulin <int>, weight_class <chr>, #> # pedigree <dbl>, age <int>plot(predictions)


override
on methods that implement abstract members.
Nonetheless, it is a good idea to mark such methods with override
to be notified
by the compiler when the override relationship gets broken (by a subsequent refactoring, for example).
Before:
trait Base { def fun(): Int } class Impl extends Base { def fun(): Int = 42 }
trait Base { def fun(): Int } class Impl extends Base { override def fun(): Int = 42 }


Fantasy Isle Warehouse Keeper:
For the last century, Fantasy Isle's warehouse has never had any lost accidents. Not even one! How about that? The Iron Guild's management resulted in this great pride. So please leave your items here without any worry.
Use private Warehouse.
Use clan Warehouse.


Menu
Help
+
=
{{ this.gameManager.getCurrentPlayerEnum() === "PLAYERONE" ? this.playerOneName : this.playerTwoName }}'s Turn
Paused
Music Controls
Copy Board Seed
Exit
Resume
Previous Track
Next Track
{{ isMuted ? "Unmute Music" : "Mute Music" }}
Back
Trading
Trades can only be executed once per turn.
Trade any three for one.
You have no resources to trade.
Cancel
Trade
{{ this.gameOverText }}
Score: {{ this.winningPlayer.currentScore }}
{{ this.gameManager.getCurrentPlayerEnum() === "PLAYERONE" ? this.playerOneName : this.playerTwoName }}'s largest set of connectin' paths was {{ this.winningPlayer.currentLongest }}.
Play Again
{{ this.oppUsername }} Quit the Game


The idea is pretty similar with the toaster
We leave programming port and the board can be easily unmounted
Help you focus on your work
Timer designer and chief appearance designer
I contribute to design the lighting timer and help paint the two components.
Toaster mechanism design
I contributed to design the toaster and make it work.


Important: This API is currently in pre-release status and is only available to approved early access participants. The API is under development and might change before being generally released. To become an early access participant, contact your SAP Concur Representative.
Getting Started - Company API - User API
The Concur’s user API is used to help provision and manage user accounts and profile details across multiple Concur products, including Expense, Invoice, Request, Travel and TripIt.
1.0
The User schema is inspired by the work from System for Cross-domain Identity Management (SCIM), in particular:
The latest version of the User schema is always available at: user.json.
Profile supports user information retrieval with access tokens. And the access token will limit the user account, as well as the user attributes, that you can query for. You can use the following endpoint to lookup your user:
GET /profile/v1/me
(access token required)
Curl example:
curl -k -v -H "Authorization: Bearer $access-token" "https://$host/profile/v1/me"
The above calls will retrieve general user profile information. User resource also supports a schema
parameter to limit or expand the data returned. To limit the amount of data returned, you can use ?schema=compact
. To also retrieve expense related data, you can use ?schema=expense
. You can also provide comma delimited schema list for the parameter, such as ?schema=compact,expense
.
Here is a sample user response, without compact, but with expense schema.
{
"active": true,
"id": "1c1e5ec5-1abc-4edd-a92a-bc551d0dfcd7",
"userType": "Enterprise",
"meta": {
"created": "2017-03-30T00:00:00.000",
"lastModified": "2017-07-03T17:46:00.000",
"principalType": "user",
"resourceType": "EnterpriseUser"
},
"preferredLanguage": "en",
"dateOfBirth": "1990-01-01",
"schemas": [
"com:concur:User:1.0",
"com:concur:Employee:1.0",
"com:concur:TravelPreferences:1.0",
"com:concur:Programs:1.0",
"com:concur:Documents:1.0",
"com:concur:Expense:0.2"
],
"gender": "Male",
"emails": [
{
"value"<EMAIL_ADDRESS>"type": "Business",
"notifications": true,
"verified": false
},
{
"value"<EMAIL_ADDRESS>"type": "Personal",
"notifications": true,
"verified": false
}
],
"com:concur:Documents:1.0": {
"passport": [
{
"number": "12345678",
"issuingCountry": "US",
"placeIssued": "Bellevue",
"dateIssued": "2017-06-02",
"expiration": "2023-06-30"
}
],
"visa": [
{
"number": "987654321",
"issuedByCountry": "AF",
"placeIssued": null,
"issuedForCountry": null,
"dateIssued": null,
"expiration": "2021-06-03"
}
]
},
"addresses": [
{
"type": "Home",
"streetAddress": "profile-58dd7cd7 home address",
"locality": "Redmond",
"region": "WA",
"postalCode": "98052",
"country": "US"
},
{
"type": "Work",
"streetAddress": "profile-58dd7cd7 office",
"locality": "bellevue",
"region": "WA",
"postalCode": "98004",
"country": "US"
}
],
"com:concur:Employee:1.0": {
"companyUUID": "d63d3d33-9255-41f4-8a1c-dabd6ab08578",
"companyInternalId": 277471,
"employeeId"<EMAIL_ADDRESS>"jobTitle": "",
"managerId": "ae3204d3-a94f-47ad-94ef-8234ac40e560",
"orgUnitId": 80265
},
"com:concur:TravelPreferences:1.0": {
"air": {
"seat": {
"interrowPosition": "Aisle",
"sectionPosition": "Forward"
},
"homeAirport": "SEA",
"meal": "BLML"
},
"rail": {
"space": "DontCare",
"meal": "DontCare",
"bedCategory": "DontCare",
"fareSpaceComfort": "DontCare",
"deck": "DontCare",
"coach": "DontCare",
"bed": "DontCare",
"berth": "DontCare",
"noiseComfort": "DontCare",
"contingency": "DontCare",
"seat": "DontCare"
},
"car": {
"smoking": "NonSmoking",
"carType": "Mini",
"transmission": "Automatic",
"gpsEnabled": false,
"skirack": false
},
"hotel": {
"earlyCheckin": true,
"remark": null,
"pool": true,
"roomService": true,
"foamPillows": true,
"accessForBlind": true,
"accessForWheelchair": true,
"gym": true,
"roomType": "King",
"restaurant": true,
"rollawayBed": true,
"smoking": "NonSmoking",
"crib": true
}
},
"com:concur:Programs:1.0": {
"rail": [
{
"default": false,
"pointsNext": null,
"expiration": null,
"nextStatus": null,
"points": null,
"segments": null,
"parentChainVendorName": null,
"vendorName": "AccesRail",
"account": "123454321",
"statusBenefits": null,
"status": null,
"segmentsNext": null,
"parentChainVendorCode": null,
"vendorCode": "9B"
}
],
"air": [],
"hotel": [],
"car": []
},
"com:concur:Expense:0.2": {
"middleInitial": "T",
"middleName": "Test",
"loginId"<EMAIL_ADDRESS>"ledger": "DEFAULT",
"countrySubdivision": null,
"driverId": null,
"cuuid": "8002699190004701412",
"cashAdvanceAccountCode": null,
"emailAddress"<EMAIL_ADDRESS>"employeeId"<EMAIL_ADDRESS>"firstName": "Concur",
"reimbursementType": "APCHECK",
"reimbursementCurrency": "USD",
"active": true,
"lastName": "User",
"uuId": "1c1e5ec5-1abc-4edd-a92a-bc551d0dfcd7",
"orgUnitData": [
{
"orgUnitName": "Expense Policy Group",
"orgUnitValue": null
}
],
"customData": [
{
"customFieldName": "Invoice Policy Group",
"customFieldValue": null
},
{
"customFieldName": "Employee Administration Country",
"customFieldValue": "United States"
}
],
"localeCode": "en_US",
"countryCode": "US"
}
}
| HTTP Status | Status | Description/Error Code |
|---|---|---|
| 200 | Success | Resource retrieved or updated |
| 201 | Success | Resource created |
| 204 | Success | Operation exectuted successfully, with no content returned |
| 301 | Moved Permanently | Resource is at a different location |
| 400 | Bad Request | - |
| 401 | Unauthorized | - |
| 403 | Forbidden | - |
| 404 | Resource not found | - |
| 429 | Rate limit reached | - |
| 500 | Internal Error | - |
| 503 | Service Unavailable | - |


Training a network
Setting the hyper-parameters
When you have defined the architecture and the weights of a DBN, you are ready to train it. You can train it manually stepping one epoch at a time, or automatically if you just want a fully trained network. In both cases, the first thing to do is to set the training hyper-parameters.
Hyper-parameters are those numbers and coefficients with which you can "customize" the training algorithm. They are called hyper to distinguish them from the weights of the DBN, that are sometimes called "parameters". For instance, Contrastive Divergence depends on the following hyper-parameters:
- Maximum number of epochs. One epoch is the time in which a network sees all the examples in a training set; the number of epochs is the number of opportunities that a DBN has of observing each example.
- Size of a mini-batch. The training set can be divided in little subsets, known as mini-batches; the DBN will update its weights only after having seen all the examples in a mini-batch; this allows for a faster and more precise learning. The size of a mini-batch must divide the number of total examples. Note that a mini-batch size of 1 is equivalent to online training; in contrast, setting the size to the exact number of examples is equivalent to batch training, or "one shot" training.
- Learning rate. The learning rate is a coefficient in the range [0, 1] that models the plasticity of the network, i.e. how easy it is to update the weights of the DBN.
- Momentum. If a DBN learning from a dataset was a ball rolling down a mountain to reach the valley (the minimum) the momentum would simulate gravity, i.e. the acceleration that speeds the ball proportionally to the steepness of the descent. The momentum ranges from 0 to 1.
- Weight decay factor. The weight decay factor (in the range [0, 1]) makes the increase of big weights more difficult than the increase of little weights. This avoids increasing the weights indefinitely.
- Sparsity target. The sparsity target (in the range (0, 1]) is how sparse we would like the network to be; a network is more sparse when it has fewer active units — when the hidden representations are more localistic.
- Standard deviation of the weights distribution. We have already seen this hyper-parameter while building a DBN; this is more related to the DBN than to the training algorithm, but it's nonetheless a hyper-parameter setting the standard deviation of the probability distribution within which the weights are initialized.
At the right of the "std. dev." field you will find a "init" button. It is not necessary to initialize the weights before training the network, because they will be initialized automatically anyway. This button is meant to analyse the network before the training.
Launching the training
When you have chosen the hyper-parameters (or if you want to accept the default ones), you can start training the DBN with one of the two buttons after the "Hyper-parameters" fieldset:
- "1 epoch" trains the network for just one epoch: use this button if you want to train the DBN in a step-by-step fashion.
- "all epochs" trains the network for the number of epochs that you have specified in the "max. epochs" field.
While training the network, you will see that a chart is updated plotting the reconstruction error against the number of epochs.


Monkeybread Real Studio ChartDirector Plugin
Additional Documentation in our FAQ
Parameter Substitution and Formatting
Mark Up Language
Font Specification
Color Specification
Alignment Specification
Shape Specification
Note on Linux
On Linux you need to make sure you have gtk/gdk libraries installed. Also please install some fonts and use CDBaseChartMBS.SetFontSearchPath to tell ChartDirector about your fonts folder. Without fonts, no text will show up.


Welcome user: {{firstName}} {{lastName}}
Extra Allowances for Travel to Work {{9999 + (10/2)}}
Length of First Name is {{firstName.length}}
Welcome User with diffrent case: {{(firstName+lastName).toLowerCase()}}
{{printUserInfo()}}


{% load static %} {% include "triptech_web/generic_head.html" %}
{% include "triptech_web/menu.html" %}
{% if assignment_list %}
{% for assignment in assignment_list %}
{{ assignment.assignmentname}}
{% endfor %}
{% else %}
No assignments are available.
{% endif %}