IBM to launch faster weather forecast system to cover the globe
LONDON — Technology company IBM said on Thursday it will launch a new weather forecasting system which will be able to predict conditions up to 12 hours in advance and cover parts of the world which have not had access to such detailed data.
Demand for very precise and quicker weather forecasts has grown as more extreme conditions increase due to climate change and as more variable renewable energy goes to the grid.
The system, known as IBM GRAF - the Global High-Resolution Atmospheric Forecasting System - will run on a supercomputer and provide more detailed and higher quality forecasts.
Previously, this kind of precise forecasting has been available in the United states, Japan and some west European countries.
IBM's new day-ahead forecasting system will provide data to cover the world, including Asia, Africa and South America, some of the regions most vulnerable to the effects of climate change, the company said.
Current global weather models cover 10-15 kilometers squared and are updated every six to 12 hours. IBM's system will forecast down to 3 km sq and update hourly.
"The enhanced forecasts could be revolutionary for some areas of the world, such as for a rural farmer in India or Kenya," said Cameron Clayton, head of The Weather Company, a subsidiary of IBM.
"If you've never before had access to high-resolution weather data but could now anticipate thunderstorms before they approach your fields, you can better plan for planting or harvesting," he added.
© (c) Copyright Thomson Reuters 2019.


Gecode::Int::Channel::ValInfo< View > Class Template Reference
List of all members.Detailed Description
template<class View>
class Gecode::Int::Channel::ValInfo< View >
Combine view with information for value propagation.
Definition at line 35 of file val.icc.
Public Member Functions | |
| void | init (View x, int n) |
| Initialize. | |
| void | update (Space *home, bool share, ValInfo< View > &vi) |
| Update during cloning. | |
| bool | doval (void) const |
| Check whether propagation for assignment is to be done. | |
| bool | dodom (void) const |
| Check whether propagation for domain is to be done. | |
| void | assigned (void) |
| Record that view got assigned. | |
| void | removed (int i) |
| Record that one value got removed. | |
| void | done (void) |
| Update the cardinality and bounds information after pruning. | |
Static Public Member Functions | |
| static ValInfo< View > * | allocate (Space *home, int n) |
| Allocate memory from space home for n elements. | |
Public Attributes | |
| View | view |
| The view. | |
| bool | a |
| Whether it has been propagated that view is assigned. |
Member Function Documentation
|
|
Allocate memory from space home for n elements.
|
|
|
Initialize.
|
|
|
Update during cloning.
|
|
|
Check whether propagation for assignment is to be done.
|
|
|
Check whether propagation for domain is to be done.
|
|
|
Record that view got assigned.
|
|
|
Record that one value got removed.
|
|
|
Update the cardinality and bounds information after pruning.
|
Member Data Documentation
|
|
The view.
|
|
|
Whether it has been propagated that view is assigned.
|
The documentation for this class was generated from the following file:
- gecode/int/channel/val.icc (Revision: 3512)


| Course | Description | MaxMarks | MarksObtained |
|---|---|---|---|
| CSE202::OBJECT ORIENTED PROGRAMMING | Continuous Assessment | 100 | 91 |
| Theory End Term | 70 | 69 | |
| Attendance Marks | 5 | 5 | |
| Objective Type Mid Term | 40 | 39 | |
| CSE326::INTERNET PROGRAMMING LABORATORY | Continuous Assessment | 100 | 99 |
| Practical End Term | 100 | 99 | |
| Attendance Marks | 5 | 5 | |
| ECE213::DIGITAL ELECTRONICS | Continuous Assessment | 100 | 98 |
| Theory End Term | 70 | 69 | |
| Attendance Marks | 5 | 5 | |
| Objective Type Mid Term | 40 | 38 | |
| MEC103::ENGINEERING GRAPHICS | Continuous Assessment | 100 | 99 |
| Theory Mid Term | 40 | 39 | |
| Theory End Term | 70 | 69 | |
| Attendance Marks | 5 | 5 | |
| MTH166::DIFFERENTIAL EQUATIONS AND VECTOR CALCULUS | Continuous Assessment | 100 | 99 |
| Theory End Term | 70 | 69 | |
| Attendance Marks | 5 | 5 | |
| Objective Type Mid Term | 40 | 39 | |
| PEL131::COMMUNICATION SKILLS-II | Continuous Assessment | 100 | 99 |
| Theory End Term | 70 | 69 | |
| Objective Type Mid Term | 50 | 49 |


download_packages.Rd
This function is a convenience wrapper for download_package()
when downloading multiple data packages. It downloads all of the
data objects in a data package to the local filesystem. It is particularly useful when a data package is too large to download using
the web interface.
download_packages(mn, resource_map_pids, download_directory, ...)
| mn | (MNode) The Member Node to download from. |
|---|---|
| resource_map_pids | (chraracter) The PIDs of the resource maps for the packages to download. |
| download_directory | (character) The path of the directory to download the packages to. |
| ... | Allows arguments from |
Setting check_download_size
to TRUE
is recommended if you are uncertain of the total download size
and want to avoid downloading very large data packages.
This function will also download any data objects it finds in any child data packages of the input data package.
If you would only like to download data from one data package, set download_child_packages
to FALSE
.
# NOT RUN { cn <- CNode("PROD") mn <- getMNode(cn, "urn:node:ARCTIC") download_packages(mn, c("resource_map_doi:10.18739/A21G1P", "resource_map_doi:10.18739/A2RZ6X"), "/home/dmullen/downloads", prefix_file_names = TRUE, download_column_metadata = TRUE, convert_excel_to_csv = TRUE) # }


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protocol¶
-
class
naz.protocol.
Message
(version, smpp_command, log_id, pdu=None, codec_class=None, short_message=None, source_addr=None, destination_addr=None, hook_metadata=None)[source]¶ Bases:
object
The message protocol for naz. It is the code representation of what gets queued into a naz broker.
Usage:
import naz msg = naz.protocol.Message( version=1, smpp_command=naz.SmppCommand.SUBMIT_SM, log_id="some-log-id", short_message="Hello, thanks for shopping with us.", source_addr="254722111111", destination_addr="254722999999", )
-
__init__
(version, smpp_command, log_id, pdu=None, codec_class=None, short_message=None, source_addr=None, destination_addr=None, hook_metadata=None)[source]¶ - Parameters
version (
int
) – This indicates the current version of the naz message protocol. This version will enable naz to be able to evolve in future; a future version of naz may ship with a different message protocol.smpp_command (
str
) – any one of the SMSC commands eg submit_smlog_id (
str
) – a unique identify of this requepdu (
Union
[None
,bytes
]) – the full PDU as sent to SMSC. It is mutually exclusive with short_message.codec_class (
Union
[None
,BaseNazCodec
]) – python class instance to be used to encode/decode messages. It should be a child class of naz.nazcodec.BaseNazCodec. You should only specify this, if you also specified pdu, else you can leave it as None.short_message (
Union
[None
,str
]) – message to send to SMSC. It is mutually exclusive with pdusource_addr (
Union
[None
,str
]) – the identifier(eg msisdn) of the message sender.destination_addr (
Union
[None
,str
]) – the identifier(eg msisdn) of the message receiver.hook_metadata (
Union
[None
,str
]) – a string that to will later on be passed to naz.Client.hook. Your application can use it for correlation.
- Return type
None
-
static
from_json
(json_message, codec_class=None)[source]¶ Deserializes the message protocol from json. You can use this method if you would like to return the Message from a broker like redis/rabbitmq/postgres etc.
- Parameters
json_message (
str
) – naz.protocol.Message in json format.codec_class (
Union
[None
,BaseNazCodec
]) – python class instance to be used to encode/decode messages. It should be a child class of naz.nazcodec.BaseNazCodec. You should only specify this, if json_message has a key called pdu and it is not None.
- Return type
-


--- layout: page title: "About Me" description: "This is who I am" header-img: "img/about-bg.jpg" ---
Hi! I am Shagun Sodhani. I am a final year Computer Science Undergrad at Indian Institute of Technology, Roorkee.
I am a programmer at heart. Software Developement takes most of my time though I regularly devote time to Competitve Programming as well. I am interested in Data Sciences and Theoretical CS. I love to play around with technology.
I interned with ATL(Research) Division of Adobe during the summer of 2014. There I got to work with some cool stuff including ibeacons and learnt R, Mongodb, Solr and much more.
On campus I am involved with 3 tech groups . I am the secretary and one of the founders at ACM Student Chapter, IIT Roorkee. I have been a member of SDSLabs (Software Development Section of IIT Roorkee) since my first year. I also served as the Coordinator at PAG (Programming and Algorithms Group) in my third year.
My mantra for life is :
Innovation distinguishes between a leader and a follower.
- Steve Jobs
Feel free to drop me a line at<EMAIL_ADDRESS>and I would be glad to talk.


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vignettes/howto_voronoi_plots.Rmd
howto_voronoi_plots.Rmd
Voronoi plots are an attractive way to visualize spatial expression data since it reduces whitespace between single cells or spots, however the voronoi borders do NOT necessarily mimick the true cell borders. Nevertheless it has been used to ‘segment’ cells and assign transcripts to individual cells. Here we merely use it as a beautifull visualization alternative to simple ‘points’.
library(Giotto) # input data VC_exprs_small = read.table(system.file("extdata", "seqfish_field_expr.txt", package = 'Giotto')) VC_locs_small = read.table(system.file("extdata", "seqfish_field_locs.txt", package = 'Giotto')) # giotto object VC_small <- createGiottoObject(raw_exprs = VC_exprs_small, spatial_locs = VC_locs_small) VC_small <- filterGiotto(gobject = VC_small, expression_threshold = 0.5, gene_det_in_min_cells = 0, min_det_genes_per_cell = 0) VC_small <- normalizeGiotto(gobject = VC_small, scalefactor = 6000, verbose = T) VC_small <- addStatistics(gobject = VC_small) # clustering VC_small <- calculateHVG(gobject = VC_small) gene_metadata = fDataDT(VC_small) featgenes = gene_metadata[hvg == 'yes' & perc_cells > 4 & mean_expr_det > 0.5]$gene_ID VC_small <- runPCA(gobject = VC_small, genes_to_use = featgenes, scale_unit = F) VC_small <- runUMAP(VC_small, dimensions_to_use = 1:5, n_threads = 2) VC_small <- createNearestNetwork(gobject = VC_small, dimensions_to_use = 1:5, k = 5) VC_small <- doLeidenCluster(gobject = VC_small, resolution = 0.4, n_iterations = 1000) # spatial grid and network VC_small = createSpatialNetwork(gobject = VC_small, minimum_k = 2) VC_small = createSpatialGrid(gobject = VC_small, sdimx_stepsize = 500, sdimy_stepsize = 500)
# spatial voronoi plot with selected clusters spatPlot(VC_small, point_shape = 'voronoi', cell_color ='leiden_clus', select_cell_groups = c(1,2,3))
# spatial voronoi plot without showing not selected clusters spatPlot(VC_small, point_shape = 'voronoi', cell_color ='leiden_clus', select_cell_groups = c(1,2,3), show_other_cells = F)
# spatial voronoi plot without showing not selected cells, but showing the voronoi borders spatPlot(VC_small, point_shape = 'voronoi', cell_color ='leiden_clus', select_cell_groups = c(1,2,3), show_other_cells = F, vor_border_color = 'black')
# visualization of both dimension reduction and spatial results spatDimPlot(gobject = VC_small, cell_color = 'leiden_clus', spat_point_shape = 'voronoi', dim_point_size = 3)
nb_annot = findNetworkNeighbors(VC_small, spatial_network_name = 'Delaunay_network', source_cell_ids = c('cell_1', 'cell_6', 'cell_10', 'cell_91', 'cell_92', 'cell_93')) VC_small = addCellMetadata(VC_small, new_metadata = nb_annot, by_column = T, column_cell_ID = 'cell_ID') spatPlot(VC_small, point_shape = 'voronoi', cell_color ='nb_cells', cell_color_code = c(source = 'blue', target = 'red', both = 'yellow', others = 'lightgrey'))
## overlay gene expression information ## selected_genes = c('Cldn5', 'Soga3') # selected genes original spatGenePlot(gobject = VC_small, expression_values = 'scaled', genes = selected_genes, point_size = 3)
# selected genes voronoi spatGenePlot(gobject = VC_small, genes = selected_genes, point_shape = 'voronoi', expression_values = 'scaled', vor_border_color = 'black')
# one gene + black background and white borders spatGenePlot(gobject = VC_small, genes = 'Cldn5', point_size = 3, point_shape = 'voronoi', expression_values = 'scaled', vor_border_color = 'white', show_network = T, network_color = 'green', background_color = 'black', cell_color_gradient = c('blue', 'black', 'red'), gradient_midpoint = 1, cow_n_col = 1)


Ribosomal S15 leader
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Ribosomal protein L13 leader
Ribosomal protein L19 leader
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Ribosomal protein L20 leader
Ribosomal protein L21 leader
5'-leader pseudoknot of TEV/CVMV
Bacteroidete tryptophan peptide leader RNA
leu/phe leader RNA from Lactococcus
Enterobacteria greA leader
Gammaprotebacteria rimP leader
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Rickettsia rpsL leader
Phenylalanine leader peptide
Bacillus tryptophan operon leader
PyrG leader
PyrC leader
PyrD leader
Salmonella mgtC leader RNA
RpsF leader
patAB leader


{% extends "layout_content.html" %}
{% import "_emergency_banner.html" as banner %}
{% block emergency_banner %}
{{ banner.emergency_banner(
"local-emergency",
"Flooding in south west England",
"There are severe floods and storms across the south west of England. The M5 motorway is closed.",
"Latest information for flooded areas",
"https://www.gov.uk/government/news/winter-weather-uk-government-response")
}}
{% endblock %}


The object of the game is to identify a SET of 3 cards from 12 cards placed face up. Each card has four features, which can vary as follows:
A SET consists of 3 cards in which each of the cards' features, looked at one-by-one, are the same on each card, or, are different on each card. All of the features must seperately satisfy this rule. In other words: shape must be either the same on all 3 cards, or different on each of the 3 cards; color must be either the same on all 3 cards, or different on each of the 3, etc.


Inline text to say something about the form.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nulla condimentum nunc libero, id posuere diam tincidunt et.
Check all that apply:
Please choose an option:
Select all that apply:


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Genk chief claims Liverpool FC are interested in signing 21-year-old
Liverpool FC are interested in signing Sander Berge from Genk, according to sporting director Dimitri De Conde
by Transfer AgentLiverpool FC are interested in signing midfielder Sander Berge, according to Genk sporting director Dimitri De Conde.
The Belgian club’s representative has claimed that Liverpool FC and Napoli are both keeping a close eye on the Norway international in the lead-up to the January transfer window.
According to De Conde, Liverpool FC have stepped up their interest in the 21-year-old since the Champions League clash between the two sides at Anfield back at the start of the month.
He also suggests that Napoli are ready to rival the Merseyside outfit for the signature of the midfielder as the countdown to the January transfer window continues.
Speaking in an interview quoted by Goal, De Conde said: “Napoli are interested in Sander, it’s a possibility a deal could be done.
“We also know that Liverpool are interested and [Jurgen] Klopp, after the game at Anfield [in the Champions League], complimented him on his performance.
“There are clubs in Italy who are following Berge and Napoli could be one of them, but at this stage there are no offers on the table. From next week, things could change.
“We haven’t had any official contact with Napoli yet.”
The 21-year-old midfielder has been in solid form for Genk this season and has scored two goals in 15 league games so far this term.
He has also made four Champions League appearances for the Belgian side, including the two defeats by Liverpool FC in the group stage.
Liverpool FC are currently flying high at the top of the Premier League table after having won all but one of their opening 12 games.
The Reds will return to action with a trip to Crystal Palace after the international break.


About the Author:
Dr.Sinha is a rocket scientist, an inventor and educator. He received his Ph.D. in engineering from University of Minnesota, USA. He has 30 years of experience in academia and high-tech industries. He has contributed to many successful international space projects and his work has appeared in Guinness World Records 2013. He travels around the globe teaching systems engineering, innovative design, and English for technical communications to thousands of students and teachers.
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[gecode-users] constraints on solved spaces
Stanimir Dragiev
stanio at cs.tu-berlin.de
Wed Feb 6 12:00:56 CET 2008
Hello Guido,
* Guido Tack <tack at ps.uni-sb.de> [2008-02-05 09:32]:
> The rule is that some easy inferences may be done immediately on
> posting the constraint, but full propagation only happens when you
> call status.
>
> [...]
>
> Constraints on solved spaces are exactly the same as on unsolved
> spaces. You're not guaranteed to get a solution without search.
>
> [...]
>
> The problem with your approach is that if you add variables and
> constraints, you're not guaranteed that all the variables will be
> assigned after calling status. It works in the above example, because
> you add a single constraint, and the new variable can be computed as a
> function from the old variables. As soon as you try something more
> involved, you may need a nested search to actually determine the
> values. Another disadvantage is that your version will be
> considerably slower than a simple computation of the sum.
The real functions actually return an IntVar, and for a
solved space I take the .val(), for unsolved space I add
some constraints on the Var.
My intention was to have the computation code only once for
the sake of simplicity and maintainability, sacrificing some
(minimal, as I thought) efficiency.
I didn't see the issue with the search.
Thank you very much for your explanation.
greetz
stanio_
More information about the gecode-users
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Status Yellow low temperature warning issued for 22 counties
by NewsroomMet Éireann has issued a Status Yellow low temperature warning for most of the country.
The forecaster issued the warning for 22 counties, with only Dublin, Wexford, Wicklow and Waterford escaping the worst of the cold conditions.
The warning will take effect from 8pm tonight and remain in place until 9am tomorrow morning.
Met Éireann says that it will be very cold tonight, with minimum air temperatures dropping from minus-2 to minus-4 degrees Celsius.
There will also be widespread "sharp to severe" frost with the risk of icy patches.
While there could also be some shallow fog patches inland, it will be less cold near the east coasts.
As for tomorrow, the day is set to be a bright and dry day with good sunny spells.
Following a very cold start, frost will clear and temperatures will recover to range 6 to 9 degrees by the afternoon.


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Arsenal are close to signing Aaron Ramsdale from Sheffield United in a deal worth up to £30 million ($41m), The news comes as something of a surprise, with talks initially thought to have broken down between the clubs due to a difference in valuation.
Liverpool have been urged to make two more additions to their squad before the summer transfer window closes, with Jose Enrique warning more big-money deals are required in order to keep pace with Chelsea and big-spending Manchester rivals United and City.
The Italian top flight returns this weekend and while it has been hit hard by the sale of top players, a number of colourful characters have returned
Arsenal are hopeful of reaching an agreement to buy Odegaard outright after accelerating negotiations with Real, who could let him leave Santiago Bernabeu for around £30 million ($41m).
Manchester City bid for Harry Kane is in place since last week, no news yet. €150m proposal ready from Man City but Tottenham are still on the same position.
The player himself is also said to accept his move to Real Madrid won't happen this summer
Marco Rose has insisted that Erling Haaland could stay at Borussia Dortmund beyond the 2021-2022 season amid ongoing transfer rumours.
With the summer transfer window now open across Europe, attention has now been turned on how clubs will strengthen and fortify ahead of the start of the domestic club season.
In an age of obsession over club ‘DNA’ – a spurious phrase that alludes to some vague idea about how a team ought to play – Manchester United is one of the few teams for which there is a semblance of truth behind the concept.


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{% extends "blog/base.html" %} {% block content %}
I am a software engineer with a degree in BSc Mathematics and Computer Science. I have Specialized in Applied Mathematics option, and a final year student at Taita Taveta University.
I am a full stack software developer with skills in;
Thanks for visiting my blog! Check all my projects on the github link above!
{% endblock %}


demo index
»
manual lfo
The volume is being changed periodically through an interval
play/pause audio demo


| CONSTELLATION Action | User Action | Menu Icon |
|---|---|---|
| Open Scatter Plot |
Views -> Scatter Plot Keyboard Shortcut: Ctrl + Shift + S |
The Scatter Plot provides an interface for comparing numerical attributes in a graph with the intention of discovering patterns, or rather oddities within patterns in your graph.
The Scatter Plot is quite simple to use, you simply need to set values for the 4 options in the top toolbar.
Upon selection of these options, the Scatter Plot will automatically generate an interactive plot for you. Selecting elements in your graph will also select the corresponding elements in the Scatter Plot and vice versa. Hovering over any element within the Scatter Plot will highlight it for you and display it's name within the graph.
If the Scatter Plot were set up with 'Nodes' set as the element type, 'Eigenvector Centrality' set as the x attribute, and the 'Katz Centrality' set as the y attribute...
For this particular graph, it seems that Harmonic Closeness and Out-Degree centrality measures have a polynomial relationship, where increasing the Harmonic Closeness centrality by n increases the Out-Degree centrality by some power of n. Notice there are some nodes highlighted red, indicating they are currently selected in the graph. You can also see the node the mouse cursor was hovering over, which is highlighted yellow and displaying the name of that node.
If the Scatter Plot were set up with 'Transactions' set as the element type, 'Latitude' set as the x attribute, and 'Event Duration' set as the y attribute...
In this case, few events with particularly long durations which all seem to occur at the same latitude can clearly be seen, possibly indicating a single location where more time is allocated to the events being visualised in the graph.


Can Physical Tools that Adapt their Shape based on a Learner's Performance Help in Motor Skill Training?
Figure 1. We investigate if adaptive learning tools that automatically adapt their shape to adjust the task difficulty based on a learner's performance can help in motor-skill training. To this end, we built (a) a study prototype in the form of an adaptive basketball stand that can adjust its hoop size and basket height. Our studies show that when the tool adapts automatically, training leads to significantly higher learning gains in comparison to training with (b) a static tool and (c) a manually adaptive tool for which the learners choose the difficulty level themselves.
Adaptive tools that can change their shape to support users with motor tasks have been used in a variety of applications, such as to improve ergonomics and support muscle memory. In this paper, we investigate whether shape-adapting tools can also help in motor skill training. In contrast to static training tools that maintain task difficulty at a fixed level during training, shape-adapting tools can vary task difficulty and thus keep learners' training at the optimal challenge point, where the task is neither too easy, nor too difficult.
To investigate whether shape adaptation helps in motor skill training, we built a study prototype in the form of an adaptive basketball stand that works in three conditions: (1) static, (2) manually adaptive, and (3) auto-adaptive. For the auto-adaptive condition, the tool adapts to train learners at the optimal challenge point where the task is neither too easy nor too difficult. Results from our two user studies show that training in the auto-adaptive condition leads to statistically significant learning gains when compared to the static (F_(1,11) = 1.856, p<0.05) and manually adaptive conditions (F_(1,11) = 2.386, p < 0.05).
Introduction
Research in HCI has shown that shape-adapting tools can support users in the execution of motor tasks by either helping users to perform the task correctly (e.g., actuated office furniture to improve ergonomics). However, it is unclear if such automatically shape-adapting tools can also help train users for the motor skills so that after fully acquiring the skill, users no longer need additional support when performing the motor task.
In our work, we investigate if physical tools that automatically adapt their shape can help users in motor skill training. In particular, we study how training with an automatically adaptive training tool compares against the current methods of training, which either use static or manually adaptive tools.
To study this, we built a study prototype of a basketball stand with adjustable hoop height and width that works under three training conditions:
- static: the basketball stand is at a fixed hoop height and width, and thus the difficulty level does not change over time.
- manually adaptive: users can adjust the difficulty level themselves by controlling the hoop height and width by activating the actuators (motors) on the stand.
- auto-adaptive: the tool automatically varies the difficulty level by adjusting the hoop height and width based on the user's performance that is detected using sensors, such as a switch on the net and a piezo sensor on the board.
To determine when to adapt the study prototype in the auto-adaptive training condition, we use an adaptation algorithm that maintains the training difficulty at the optimal challenge point, at which the task is neither too difficult nor too easy for the users. Studies in motor skill learning have shown that when coaches train learners at the optimal challenge point, learners have the maximum potential learning benefit. To maintain the difficulty level at the optimal challenge point, the algorithm measures a users' performance and based on the performance over time, determines whether the tool should adapt to a more difficult setting, a less difficult setting, or remain at the current difficulty setting during the training.
To test if training in the auto-adaptive condition, leads to higher learning gains when compared to the static and manually adaptive condition, we conducted two user studies with 12 participants each. We then measured the participants' learning gains (i.e., increase in performance scores) after (1) training on the automatically adaptive tool versus the static training tool, and (2) training on the tool versus the manually adaptive training tool.
Our study results show significant learning gains in the auto-adaptive training condition when compared to the static (F_(1,11) = 1.856, p < 0.05) and manually adaptive conditions (F_(1,11) = 2.386, p < 0.05). For manually adaptive condition, we found that there was a mismatch between participants' skill levels and their own assessment of what task difficulty is best to train on, resulting in only small learning gains.
Qualitative feedback from studies showed that users preferred training with auto-adaptive tools over training with static and manually adaptive tools. For instance, P11 in user study 1 said,
"adaptive training makes each stage of the training experience more rewarding, so it helped me focus better", and P3 in user study 2 said,
"auto-adaptive was easier to use; I didn't have to think about if I set it too easy or too hard".
We thus see a large potential for the use of automatically adaptive training tools in motor skill training, making personalized training accessible to a larger audience that may not have access to an expert trainer.
In summary, we contribute:
- A study prototype of an adaptive basketball hoop that enables training in three conditions: (1) static, (2) manually adaptive, and (3) auto-adaptive condition. In auto-adaptive condition, the physical tool automatically adapts to vary the task difficulty based on the learner's performance, so that the task difficulty is at the optimal challenge point for the learner.
- A study with 12 participants measuring the learning gains of training in the auto-adaptive training condition versus the static training condition with results showing significantly higher learning gains in the auto-adaptive condition (F_(1,11) = 1.856, p < 0.05).
- A study with 12 participants measuring the learning gains of training in the auto-adaptive training condition versus the manually adaptive training condition with results showing significantly higher learning gains in the auto-adaptive condition (F_(1,11) = 2.386, p < 0.05).
Below, we discuss the background on varying task difficulty, design of study prototype, and the two user studies.
BACKGROUND ON VARYING TASK DIFFICULTY
To determine when to adapt the tool so that the
task difficulty level accommodates the learner's increase in skill level, we train the learners at
optimal challenge point, which has been proven to lead to higher learning gains. Here, we describe how we translate this aprroach into the design of our tool.
Task Difficulty
To understand how the task difficulty during training affects the learning of a motor skill, researchers have defined two types of task difficulties:
nominal task difficulty and
functional task difficulty [Guadagnoli et. al].
Nominal Task Difficulty (Independent of the Learner)
Nominal task difficulty is the level of difficulty of the task independent of the person executing the task and their skill level. For example, in basketball, it is more difficult to score a basket that is mounted at a higher height and has a smaller width, than to score a basket mounted at a lower height and has a larger width, irrespective of the person performing the task.
Functional Task Difficulty (Dependent on the Skill Level of the Learner)
Functional task difficulty refers to how challenging the task is in relation to the person executing the task and their skill level. For instance, when throwing a ball at a high basket, the task will typically be more difficult for a beginner with a low skill level than an expert with a high skill level.
Optimal Challenge Point
Research has shown that when the functional task difficulty is at a level that is neither too difficult nor too easy for the learner, it results in the highest potential learning benefit [guadagnoli et. al]. This level of functional task difficulty is called the optimal challenge point. Figure 2 illustrates that when the functional task difficulty is too low, i.e. the task is too easy, the learner's performance in practice is high (e.g., every single basket is scored in basketball), but the learner remains underchallenged and the potential learning benefit is low. Conversely, when the functional task difficulty is too high, i.e., the task is too difficult, the learner's performance in practice is low (e.g., no basket is scored), and the learner is overchallenged and thus this setup also fails to maximize the potential learning benefit.
Therefore, to have the highest potential learning benefit, the functional task difficulty needs to be at a medium level, i.e., the task at hand should neither be too difficult nor too easy, which is reflected in a medium performance level in practice (e.g., some baskets are scored but not all).
Figure 2. The optimal challenge point is the level of functional task difficulty at which the task is neither too hard nor too easy, which allows for the largest potential learning benefit. Figure adapted from Guadagnoli et al.
Adjusting Task Difficulty to Maintain the Optimal Challenge Point
We developed an algorithm that allows learners to progress from a beginner level (low nominal task difficulty) to expert level (high nominal task difficulty) by training at a functional task difficulty set at the optimal challenge point at all times. The algorithm accomplishes this in following steps:
- Step 1. Monitoring Performance in Practice / Functional Task Difficulty: The algorithm monitors the learner's performance by collecting data from the integrated sensors over time and computes the learner's average score over time. Based on the score, we determine if the difficulty level is too difficult or too easy, or optimal.
- Step 2. Increase Nominal Task Difficulty by Adapting the Tool: If the score is high (functional task difficulty too low), our algorithm increases the nominal task difficulty by adjusting the tool. In basketball, for example, the algorithm can increase nominal task difficulty by raising the stand and making the hoop smaller.
- Repeat step 1 + 2 Until Highest Nominal Task Difficulty is reached (Expert Setting): Our algorithm repeats step 1 and step 2, i.e. monitoring performance in practice and increasing the functional task difficulty by adapting the tool, until the highest nominal task difficulty setting is reached. If the functional task difficulty is low at the highest nominal task difficulty, i.e. if the performance is high in the expert setting, the learner has fully mastered the skill.
STUDY PROTOTYPE
While a wide range of skills can be used to study motor skill learning, such as learning to ride a bike or to skateboard, we chose basketball as an example because the task of shooting balls into the hoop requires only a short amount of time (each shot takes around 3-4 seconds). This allows us to collect more data points in our user studies (220 shots per participant within an hour of study).
Design of the Study Prototype
Figure 3a shows the design of the final study prototype of a basketball stand that we used for the three study conditions. The study prototype has an adjustable hoop height and width, i.e., the hoop can widen and tighten and the stand can raise or lower the hoop. It is mounted with: (1) actuators to adjust the hoop height and width (Figure 3b), and (2) sensors to detect when the ball hits the board or goes through the hoop (Figure 3c).
Figure 3. Study Prototype: (a) Our basketball stand with adjustable hoop height (min height: 180 cm, max height: 270 cm) and hoop width (min width: 20cm, max width: 40cm) is mounted with (b) actuators, i.e. a stepper and a servo motor for adaptation, and (c) sensors, i.e. a switch and a piezo sensor to detect if the ball went through the hoop or hit the board.
Actuators
To adapt the hoop height, we use a stepper motor (Figure 3b top, Figure 4 left) integrated with the base of the stand that lowers or raises the hoop. Similarly, to adapt the hoop width, we use a servo motor (Figure 3b bottom, Figure 4 right) integrated with the hoop that increases or decreases the hoop diameter. The basketball height and width can be adjusted continuously between a height of 180-270 cm and a hoop diameter of 20-40 cm, respectively. Thus, the lowest difficulty setting is with 180 cm hoop height and 40 cm hoop width, and the highest difficulty setting is 270 cm hoop height and 20 cm hoop width. For reference, the ball used for the user study had a diameter of 15 cm.
Figure 4. Actuators: (left) a stepper motor for adaptation of hoop height (right) a servo motor for adaptation of hoop width.
Sensors
We sense the learner's performance by detecting if the ball went through the hoop, hit the board, or completely missed. To sense if the ball went through the hoop, we use a switch sensor attached to the net of the hoop that gets activated when the ball goes through the hoop and pulls on the net (Figure 3c top, Figure 5 left). To detect if the ball hit the board, we use a piezo sensor (SEN-10293 from Sparkfun) that detects the noise of the ball hitting the board, which is a reading above 100 units (Figure 3c bottom, Figure 5 right). If neither the switch is activated nor the piezo reading is above 100 units, we infer that the ball completely missed the basket.
Using these sensors, our prototype can sense three performance outcomes, which are in order from most to least successful: basket scored, board hit, and completely missed.
Figure 5. Sensors: (left) a switch sensor to detect if the ball went through the hoop (right) a piezo sensor to detect if the ball hit the board
Using the Study Prototype for all three Study Conditions
Next, we explain the three study conditions and describe how our study prototype supports each of these conditions:
- In the static condition, the difficulty level is fixed and thus the prototype does not adapt during training. Therefore, no actuation of the tool or sensing of the user's performance is needed.
- In the manually adaptive condition, participants are in control of which difficulty level they want to train on. We provide participants with a keyboard to control the actuators to adjust the hoop width and the hoop height as desired.
- For the auto-adaptive condition, our adaptation algorithm controls the difficulty level. To provide the adaptation algorithm with data on the user's performance, the switch and piezo sensors embedded in the study prototype are used to determine if the ball missed the hoop, hit the board, or successfully went through the hoop. Based on the performance data, our algorithm then determines if the actuators need to adapt the tool to the next difficulty level.
Figure 6. Our study prototype support three training conditions: (a) static (b) manually-adaptive (particiapnts manually adapt the difficulty). (c) (b) auto-adaptive (algorithm automatically adapt the difficulty).
USER STUDY 1: STATIC V/S AUTO-ADAPTIVE TRAINING TOOLS
Study Design
In our first user study, we compare the learning gain and training experience of training with a static tool versus an automatically-adaptive tool.
We base our user study on a previous study in motor skill learning that investigated fixed practice versus varied practice in which a personal trainer adjusted the training, and showed that varied practice leads to a larger learning gain [Kerr et. al].
Figure 7. The setup for user-study 1 with two training conditions: (a) static (80 throws: 40 lowest difficulty, 40 highest difficulty setting) and (b) auto-adaptive (80 throws continuously adapting difficulty). (c) The participants of our study seen during training were standing at distances based on a measurement of their initial performance.
Hypothesis: Our hypothesis was that training with an auto-adaptive tool, which adapts the difficulty level based on the learner's performance, will result in a higher learning gain when compared to a static tool that does not vary the task difficulty based on learner's performance during the training. A higher learning gain will be reflected in a higher performance score after the same amount of training time in each condition.
Participants: We recruited 12 participants (8 female, 4 male) aged between 18-31 years (mean=25, std. deviation=3.3).
Setup: Participants were asked to stand at a single location and attempt the throws for the entire study from this position. We used our study prototype without any adaptation in the static condition. For the auto-adaptive condition, we deployed our algorithm on the microcontroller integrated with the basketball stand.
Conditions: The study followed a within-subjects design. We used randomized order to avoid order effects for training the participants on the following two conditions:
- Static training: In the static training condition, participants attempted half (40) the throws in the lowest difficulty setting (i.e., largest hoop, lowest basket height) and the second half (40) of throws in the highest difficulty setting (i.e.,the smallest hoop and the highest basket height).
- Auto-adaptive training: In the auto-adaptive training condition, participants started by throwing balls in the lowest difficulty setting (lowest basket height and largest hoop size) and gradually progressed towards the harder settings using the adaptation algorithm shown in Table 1 (of the paper) as they performed a total of 80 throws.
Study Procedure
In total, each participant attempted 220 throws over the 5 phases of the study. For training condition #1, the participants were randomly assigned either static training or auto-adaptive training first, and then the respective other training in condition #2. Please refer to the paper for more details.
Measuring Learning Gain:
To measure the learning gains for the first condition participants trained in, we compared the participant's score in the pre-training skill assessment test and their score in post-training test #1. To measure the learning gains of the second condition participants trained in, we compared their score in post-training test #1 and their score in post-training test #2. The learning gains were measured by calculating the differences in the respective test scores.
Study Results: Average Learning Gain
We calculated the average learning gains for both the conditions and found that the training in the auto-adaptive condition led to a 20% increase in the average performance score compared to the static training condition. As seen in Figure 8, participants scored a total of 4 points more on average during their 20 attempted throws in the post auto-adaptive training test. (With maximum 1pt per attempt, the maximum possible score was 20 points in each post-training test). ANOVA analysis showed that the increase in the learning gains in the auto-adaptive condition was statistically significant (F_(1,11) = 1.856, p < 0.05).
Figure 8. User-study 1: Average learning gain post adaptive training and post non-adaptive training. The average learning gain post adaptive training is significantly higher than performance post static training (F_(1,11) = 1.856, p < 0.05).
Qualitative feedback When participants were asked if they would rather retrain with the auto-adaptive or the static condition, 8 out of 12 participants preferred the auto-adaptive condition. When asked about the reasons for their preferences, participants stated the following:
Seeing the physical tool adapt: "The adaptive training made me reflect upon what I'm doing wrong by physically changing the height and hoop size and made me want to change my throwing style faster." - (p3), "Adaptive training makes each stage of the training experience more rewarding, so it helped me focus better." - (p11)
Having adaptive difficulty levels: "The adaptive mode felt more useful in slowly adjusting to let me get better at harder and harder shots" - (p8). "it's more natural! [...] it matches learning process better" - (p7). However, one participant also mentioned "I prefer to be challenged beyond my current capability and learn that way." - (p10)
To summarize the qualitative feedback, we discovered that learners find it motivating to see their progress through tool adaptation without having to focus on constantly assessing their own performance.
USER STUDY 2: MANUALLY ADAPTIVE V/S AUTO-ADAPTIVE TRAINING TOOLS
Study Design
For our second study, we investigated if automatically adapting the training tools also leads to higher learning gains when compared to manually adaptive training tools, i.e. when the learners have the choice to adjust the difficulty level according to their preference.
Hypothesis: We hypothesized that the training with an automatically-adaptive training tool will result in a higher learning gain when compared to a manually adaptive training tool.
Participants: We recruited 12 participants (8 females) aged between 19-28 years (mean=21, std. deviation = 2.62).
Setup: We used the same room setup and basketball prototype as in the prior user study. For the automatic adaptation, we deployed the adaptation algorithm on the micro-controller integrated with the basketball stand and used the auto-adaptation algorithm described in section 4.4. (of the paper) with a window size of 4 attempts. In the manually adaptive condition, participants controlled the adaptation, i.e. they could decide which difficulty level to train on and for how long to train in the setting.
Conditions: As in the previous study, this study followed a within-subjects design and randomized order to avoid order effects for training the participants on the following two conditions:
- Manually Adaptive training: For the manual adaptation, we added a keyboard interface, which participants used to adjust the basket height (arrow up/down) and hoop diameter (arrow left/right) according to their preference.
- Auto-adaptive training: In the auto-adaptive training condition, our adaptation algorithm automatically adjusted the tool during the training, as described in the section `setup' above.
Study Procedure
The study procedure was the same as in the first study (refer to section 5.1 of the paper), i.e. we started with a pre-study calibration of the distance to the basket, followed by 220 throws (pre-training skill assessment: 20 throws, training condition 1: 80 throws, post-training 1 test: 20 throws, training condition 2: 80 throws, and post-training 2 test: 20 throws).
Measuring Learning Gain:
We measured learning gain in the same way as in the first study.
Study Results: Average Learning Gain
We calculated the average learning gains for both the conditions and found that the training in the auto-adaptive condition led to a 25% increase in the average performance score compared to the manually adaptive condition. As can be seen in Figure 8, participants scored 5 points higher on average during their 20 attempted throws in the post auto-adaptive training test (each basket hit = 1pt). ANOVA analysis showed that the increase in the learning gains in the auto-adaptive condition was statistically significant (F_(1,11) = 2.386, p < 0.05).
Figure 9. User-study 2: Average learning gain of post auto-adaptive and manually adaptive training. The average learning gain in performance post auto-adaptive training is significantly higher than the performance post manually adaptive training (F_(1,11) = 2.386, p < 0.05).
Maintaining Functional Task Difficulty Around the Optimal Challenge Point It was evident from the results of the manually adaptive condition that participants failed to determine the difficulty level that offers the largest learning gain when left to configure the hoop height and width themselves. See the paper for more detailed analysis od particpants' performances.
Qualitative feedback When participants were asked if they would rather retrain with the automatically-adaptive or manually adaptive condition, 10 out 12 participants preferred the automatically-adaptive setup. When asked to highlight the benefits and drawbacks of each condition, participants responded as follows:
Self-evaluation: "There were times during the manually adaptive process where I was unsure what the optimal path to training would be." - (p5), "Somehow I trust auto-adaptive training more than myself and maybe I should (as suggested by scores after each type of training received)." - (p2), "Auto-adaptive is nicer than manually adaptive when the difficulty is increased because it is difficult for me to gauge how much I should increase the difficulty." - (p7).
Learning of skill, ease and efficiency in training: "The auto-adaptive was easier to use; I didn't have to think about if I set it too easy or too hard." - (p3). "Manually adaptive training took longer/hard to judge which increments to practice/alter. It seemed the auto-adaptive training was more efficient." - (p10)
Motivation: "[auto-adaptive:] I felt myself getting better and think this would have been really fun to have as a kid." - (p8), "I feel that I am doing better every time it adapts to a harder setting." - (p12), "I have freedom to grow and make mistakes. No one is watching me (outside of lab setting) so it is more comfortable [than having a personal trainer]." - (p1).
During the study, we also observed that participants who were assigned to the automatically adaptive condition first used a similar strategy as our algorithm for their manually adaptive condition. In contrast, participants who trained on the manually adaptive condition first were fixated on the least difficult or the most difficult setup.
In summary, our results confirm that maintaining functional task difficulty around the level of the optimal challenge point is beneficial in the learning of motor skills. The feedback from participants showed that training on the automatically adaptive mode also provides additional motivation and is engaging and more enjoyable.
DISCUSSION
We showed that automatically adapting physical tools that vary task difficult are effective for motor skill learning as they maintain the training at the optimal challenge point for the learners. We now discuss benefits and limitations of our work.
Scaling Personalized Training of Motor Skills: One of the main motivations for building automatically-adaptive tools for motor skills learning is that they allow to scale-up personalized learning of motor skills and make it available to a larger audience that does not have access to personal trainers. While we did not compare our automatically-adaptive tool to learning with a personal trainer, we did provide evidence that our approach leads to significantly higher learning gains when compared to conventional training tools, such as static and manually adaptive tools, that are currently used by learners who do not have access to a personal trainer.
Extending to Other Motor Skills and Tools: While in this paper, we were only able to study one particular adaptive tool, our work can be replicated to study additional tools in the future. For instance, the adaptation algorithm generalizes across different types of tools as long as they have sensors to measure a user's performance and actuators to adapt the difficulty level of the tool. Similarly, while the hardware of our tool, i.e. the particular sensors and actuators, are necessarily specific, we outlined the general requirements that study prototypes need to fulfill to work across all three conditions. We hope that our work can lay the foundation for the study of a variety of adaptive training tools, which will allow researchers to gather more evidence of the effectiveness of adaptive training tools for motor skill training.
Longitudinal Studies with Diverse Participants: While the insights from our study are promising, more longitudinal studies with more varied populations are needed before we can draw conclusions on long-term learning gains. Our study focused on only one adaptive training tool and more studies are needed to confirm the results across a broader set of different tools in different application domains. Furthermore, additional studies need to be conducted to study specific aspects of the skills, such as variation in throwing angle and throwing distance.
Improvements for the Auto-Adaptation Algorithm: In our work, we discussed the discrete vs. running average approach and an additional derivative check to calculate the user's performance over time which we then used to determine when to adjust the difficulty level for training. However, several other approaches can be used to determine when to adapt the tool. For instance, by adding a hysteresis value to the algorithm, we can further enhance our algorithm to only adapt the tool when the performance is at a certain value for a long period of time. Such a hysteresis value can prevent unwanted frequent switching between states when performance fluctuates around the optimal challenge point. In addition, our algorithm currently uses a fixed increase for the adaptation. For future work, we plan to determine not only when to adapt the tool but also how much to adapt it based on the user's performance.
CONCLUSION
We showed that automatically adaptive tools that vary task difficulty based on a learner's performance, can indeed help in motor skill training. Using our study prototype and the adaptation approach of the optimal challenge point, we demonstrated that training in an auto-adaptive condition leads to higher learning gains when compared to training in a static or manually adaptive condition. We showed that the experience of training in the auto-adaptive condition is also more enjoyable for learners since it removes the decision making process around which difficulty level to train on and provides feedback to the learner in the form of the shape-adaptation of the tool.
For future research, we plan to build a toolkit that helps designers build their own adaptive training tools.
ACKNOWLEDGMENTS
This work is supported by
MIT Learning Initiative and the National Science Foundation (Grant No. 1844406).


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Recursion is a wonderful programming tool. It provides a simple, powerful way of approaching a variety of problems. It is often hard, however, to see how a problem can be approached recursively; it can be hard to "think" recursively. It is also easy to write a recursive program that either takes too long to run or doesn't properly terminate at all. In this article we'll go over the basics of recursion and hopefully help you develop, or refine, a very important programming skill. What is Recursion? function HelloWorld(count) { if(count<1)return print("Hello World!") HelloWorld(count - 1) } It might not be immediately clear what we're doing here - so let's follow through what happens if we call our function with count set to 10. Since count is not less than 1, we do nothing on the first line. On the next, we print "Hello World!" once. At this point we need to print our phrase 9 more times. Since we now have a HelloWorld function that can do just that, we simply call HelloWorld (this time with count set to 9) to print the remaining copies. That copy of HelloWorld will print the phrase once, and then call another copy of HelloWorld to print the remaining 8. This will continue until finally we call HelloWorld with count set to zero. HelloWorld(0) does nothing; it just returns. Once HelloWorld(0) has finished, HelloWorld(1) is done too, and it returns. This continues all the way back to our original call of HelloWorld(10), which finishes executing having printed out a total of 10 "Hello World!"s. You may be thinking this is not terribly exciting, but this function demonstrates some key considerations in designing a recursive algorithm:
Why use Recursion? Scenario #1: Hierarchies, Networks, or Graphs
In this graph, the objects are people, and the connections in the graph show who reports to whom in the company. An upward line on our graph says that the person lower on the graph reports to the person above them. To the right we see how this structure could be represented in a database. For each employee we record their name and the name of their manager (and from this information we could rebuild the whole hierarchy if required - do you see how?). Now suppose we are given the task of writing a function that looks like "countEmployeesUnder(employeeName)". This function is intended to tell us how many employees report (directly or indirectly) to the person named by employeeName. For example, suppose we're calling "countEmployeesUnder('Sally')" to find out how many employees report to Sally. To start off, it's simple enough to count how many people work directly under her. To do this, we loop through each database record, and for each employee whose manager is Sally we increment a counter variable. Implementing this approach, our function would return a count of 2: Bob and Joseph. This is a start, but we also want to count people like Susan or Betty who are lower in the hierarchy but report to Sally indirectly. This is awkward because when looking at the individual record for Susan, for example, it's not immediately clear how Sally is involved. A good solution, as you might have guessed, is to use recursion. For example, when we encounter Bob's record in the database we don't just increment the counter by one. Instead, we increment by one (to count Bob) and then increment it by the number of people who report to Bob. How do we find out how many people report to Bob? We use a recursive call to the function we're writing: "countEmployeesUnder('Bob')". Here's pseudocode for this approach: function countEmployeesUnder(employeeName) { declare variable counter counter = 0 for each person in employeeDatabase { if(person.manager == employeeName) { counter = counter + 1 counter = counter + countEmployeesUnder(person.name) } } return counter } If that's not terribly clear, your best bet is to try following it through line-by-line a few times mentally. Remember that each time you make a recursive call, you get a new copy of all your local variables. This means that there will be a separate copy of counter for each call. If that wasn't the case, we'd really mess things up when we set counter to zero at the beginning of the function. As an exercise, consider how we could change the function to increment a global variable instead. Hint: if we were incrementing a global variable, our function wouldn't need to return a value. Mission Statements Let's think through what would have to change in order to make it so a person did count as reporting to him or herself. First off, we'd need to make it so that if there are no people who report to someone we return one instead of zero. This is simple -- we just change the line "counter = 0" to "counter = 1" at the beginning of the function. This makes sense, as our function has to return a value 1 higher than it did before. A call to "countEmployeesUnder('Betty')" will now return 1. However, we have to be very careful here. We've changed our function's mission statement, and when working with recursion that means taking a close look at how we're using the call recursively. For example, "countEmployeesUnder('Sam')" would now give an incorrect answer of 3. To see why, follow through the code: First, we'll count Sam as 1 by setting counter to 1. Then when we encounter Betty we'll count her as 1. Then we'll count the employees who report to Betty -- and that will return 1 now as well. It's clear we're double counting Betty; our function's "mission statement" no longer matches how we're using it. We need to get rid of the line "counter = counter + 1", recognizing that the recursive call will now count Betty as "someone who reports to Betty" (and thus we don't need to count her before the recursive call). As our functions get more and more complex, problems with ambiguous "mission statements" become more and more apparent. In order to make recursion work, we must have a very clear specification of what each function call is doing or else we can end up with some very difficult to debug errors. Even if time is tight it's often worth starting out by writing a comment detailing exactly what the function is supposed to do. Having a clear "mission statement" means that we can be confident our recursive calls will behave as we expect and the whole picture will come together correctly. In Part 2, we'll look at how recursion works with multiple related decisions, such as navigating a maze, and with explicit recursive relationships.
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“The figure was that of a man, in shabby clothes, with a sallow, mean face, a retreating forehead, with hair cut after the French fashion, and a moustache, dark. The jaws and chin were covered with a bristly growth, as if shaving had been neglected for a fortnight. The figure did not appear to be thoroughly solid, but to be of the consistency of curd, and the face was of the complexion of curd.” — Sabine Baring-Gould, A Dead Finger


The network structural equation modeling conducts a network statistical analysis on a data frame of coincident observations of multiple continuous variables [1]. It builds a pathway model by exploring a pool of domain knowledge guided candidate statistical relationships between each of the variable pairs, selecting the 'best fit' on the basis of a specific criteria such as adjusted r-squared value. This work was funded under U. S. Dept. of Energy, Prime Award No. DE-E-0004946, Award Agreement No. 60220829-51077-T. [1] Bruckman, Laura S., Nicholas R. Wheeler, Junheng Ma, Ethan Wang, Carl K. Wang, Ivan Chou, Jiayang Sun, and Roger H. French. (2013) <doi:10.1109/ACCESS.2013.2267611>.
| Version: | 0.5.1 |
| Depends: | R (≥ 3.1.0) |
| Imports: | DiagrammeR (≥ 0.9.2), DiagrammeRsvg (≥ 0.1), htmlwidgets (≥ 1.2), knitr (≥ 1.20), magrittr (≥ 1.5), MASS (≥ 7.3-47), rsvg (≥ 1.1), svglite (≥ 1.2.1), png (≥ 0.1-7), segmented (≥ 0.5-3.0), gtools (≥ 3.5.0) |
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| Published: | 2018-11-28 |
| Author: | Wei-Heng Huang [aut, cre], Nicholas Wheeler [aut], Addison Klinke [aut], Yifan Xu [aut], Wenyu Du [aut], Amit K. Verma [aut], Abdulkerim Gok [aut], Devin Gordon [ctb], Yu Wang [ctb], Jiqi Liu [ctb], Alan Curran [ctb], Justin Fada [ctb], Xuan Ma [ctb], Jennifer Braid [ctb], Jennifer Carter [ctb], Laura Bruckman [aut], Roger French [aut, cph] |
| Maintainer: | Wei-Heng Huang <wxh272 at case.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | netSEM results |
| Reference manual: | netSEM.pdf |
| Vignettes: |
Cracking IV features Backsheet metal Introduction of netSEM PET |
| Package source: | netSEM_0.5.1.tar.gz |
| Windows binaries: | r-devel: netSEM_0.5.1.zip, r-release: netSEM_0.5.1.zip, r-oldrel: netSEM_0.5.1.zip |
| OS X binaries: | r-release: netSEM_0.5.1.tgz, r-oldrel: netSEM_0.5.1.tgz |
| Old sources: | netSEM archive |
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Eurojackpot results for Friday 6th July 2018
The winning numbers for the 27th Eurojackpot draw in 2018 that took place on Friday 6th July at 21:00 CEST (20:00 GMT) were:
Winners
Winners
Amount
Slovenian Prize Breakdown
View prize breakdowns for other countries| Numbers Matched | Prize Per Winner | Winners from Slovenia | Prize Fund Amount | Total Winners |
|---|---|---|---|---|
|
Match 5 and 2 Euro Numbers |
€45,000,000 | 0 | €0 | 2 |
|
Match 5 and 1 Euro Number |
€999,914.60 | 0 | €0 | 24 |
|
Match 5 |
€43,966.70 | 1 | €43,966.70 | 29 |
|
Match 4 and 2 Euro Numbers |
€2,225.10 | 5 | €11,125.50 | 191 |
|
Match 4 and 1 Euro Number |
€170.80 | 24 | €4,099.20 | 2,239 |
|
Match 4 |
€69.70 | 43 | €2,997.10 | 4,264 |
|
Match 3 and 2 Euro Numbers |
€38.90 | 80 | €3,112 | 6,554 |
|
Match 2 and 2 Euro Numbers |
€16.50 | 1,021 | €16,846.50 | 79,772 |
|
Match 3 and 1 Euro Number |
€15 | 893 | €13,395 | 84,568 |
|
Match 3 |
€13.20 | 1,291 | €17,041.20 | 137,427 |
|
Match 1 and 2 Euro Numbers |
€8 | 5,067 | €40,536 | 414,050 |
|
Match 2 and 1 Euro Number |
€7.50 | 10,996 | €82,470 | 1,078,148 |
|
Total |
- | 19,421 | €235,589.20 | 1,807,268 |
Eurojackpot prizes in Slovenia must be claimed within 90 days of the draw date, so tickets purchased for this draw were valid until Thursday 4th October 2018.
Additional Draw Information
In total there were 42,501,202 entries into this draw from all participating countries.
Draw Number: 27
European Prize Breakdown
| Numbers Matched | Prize Per Winner | Winners from Croatia | Prize Fund Amount | Total Winners |
|---|---|---|---|---|
|
Match 5 and 2 Euro Numbers |
kn332,493,030 | 0 | kn0 | 2 |
|
Match 5 and 1 Euro Number |
kn7,388,103 | 1 | kn7,388,103 | 24 |
|
Match 5 |
kn324,858.25 | 0 | kn0 | 29 |
|
Match 4 and 2 Euro Numbers |
kn16,440.67 | 3 | kn49,322.01 | 191 |
|
Match 4 and 1 Euro Number |
kn1,262 | 39 | kn49,218 | 2,239 |
|
Match 4 |
kn514.99 | 49 | kn25,234.51 | 4,264 |
|
Match 3 and 2 Euro Numbers |
kn287.42 | 101 | kn29,029.42 | 6,554 |
|
Match 2 and 2 Euro Numbers |
kn121.91 | 1,305 | kn159,092.55 | 79,772 |
|
Match 3 and 1 Euro Number |
kn110.83 | 1,187 | kn131,555.21 | 84,568 |
|
Match 3 |
kn97.53 | 1,599 | kn155,950.47 | 137,427 |
|
Match 1 and 2 Euro Numbers |
kn59.11 | 6,253 | kn369,614.83 | 414,050 |
|
Match 2 and 1 Euro Number |
kn55.42 | 13,784 | kn763,909.28 | 1,078,148 |
|
Total |
- | 24,321 | kn9,121,029.28 | 1,807,268 |
Eurojackpot prizes in Croatia must be claimed within 60 days of the draw date, so tickets purchased for this draw were valid until Tuesday 4th September 2018.
The estimated jackpot advertised in Croatia prior to the draw was kn663,000,000.
| Numbers Matched | Prize Per Winner | Winners from Czech Republic | Prize Fund Amount | Total Winners |
|---|---|---|---|---|
|
Match 5 and 2 Euro Numbers |
Kc1,171,350,000 | 0 | Kc0 | 2 |
|
Match 5 and 1 Euro Number |
Kc26,027,777 | 0 | Kc0 | 24 |
|
Match 5 |
Kc1,144,453 | 0 | Kc0 | 29 |
|
Match 4 and 2 Euro Numbers |
Kc57,919 | 2 | Kc115,838 | 191 |
|
Match 4 and 1 Euro Number |
Kc4,445 | 31 | Kc137,795 | 2,239 |
|
Match 4 |
Kc1,814 | 59 | Kc107,026 | 4,264 |
|
Match 3 and 2 Euro Numbers |
Kc1,012 | 73 | Kc73,876 | 6,554 |
|
Match 2 and 2 Euro Numbers |
Kc429 | 910 | Kc390,390 | 79,772 |
|
Match 3 and 1 Euro Number |
Kc390 | 1,101 | Kc429,390 | 84,568 |
|
Match 3 |
Kc343 | 1,691 | Kc580,013 | 137,427 |
|
Match 1 and 2 Euro Numbers |
Kc208 | 4,955 | Kc1,030,640 | 414,050 |
|
Match 2 and 1 Euro Number |
Kc195 | 14,109 | Kc2,751,255 | 1,078,148 |
|
Total |
- | 22,931 | Kc5,616,223 | 1,807,268 |
Eurojackpot prizes in Czech Republic must be claimed within 35 days of the draw date, so tickets purchased for this draw were valid until Friday 10th August 2018.
The estimated jackpot advertised in Czech Republic prior to the draw was Kc2,342,000,000.
| Numbers Matched | Prize Per Winner | Winners from Denmark | Prize Fund Amount | Total Winners |
|---|---|---|---|---|
|
Match 5 and 2 Euro Numbers |
kr.285,069,630 | 0 | kr.0 | 2 |
|
Match 5 and 1 Euro Number |
kr.6,374,485 | 0 | kr.0 | 24 |
|
Match 5 |
kr.280,317 | 1 | kr.280,317 | 29 |
|
Match 4 and 2 Euro Numbers |
kr.14,215 | 5 | kr.71,075 | 191 |
|
Match 4 and 1 Euro Number |
kr.1,118 | 141 | kr.157,638 | 2,239 |
|
Match 4 |
kr.474 | 235 | kr.111,390 | 4,264 |
|
Match 3 and 2 Euro Numbers |
kr.277 | 385 | kr.106,645 | 6,554 |
|
Match 2 and 2 Euro Numbers |
kr.123 | 5,350 | kr.658,050 | 79,772 |
|
Match 3 and 1 Euro Number |
kr.112 | 5,890 | kr.659,680 | 84,568 |
|
Match 3 |
kr.99 | 10,212 | kr.1,010,988 | 137,427 |
|
Match 1 and 2 Euro Numbers |
kr.60 | 28,019 | kr.1,681,140 | 414,050 |
|
Match 2 and 1 Euro Number |
kr.56 | 82,707 | kr.4,631,592 | 1,078,148 |
|
Total |
- | 132,945 | kr.9,368,515 | 1,807,268 |
Eurojackpot prizes in Denmark must be claimed within a set period of time depending on how much you have won. Visit the How to Claim page to find specific claim periods, with some prizes needing to be claimed within 90 days of the draw (valid until Thursday 4th October 2018).
The estimated jackpot advertised in Denmark prior to the draw was kr.570,000,000.
| Numbers Matched | Prize Per Winner | Winners from Estonia | Prize Fund Amount | Total Winners |
|---|---|---|---|---|
|
Match 5 and 2 Euro Numbers |
€45,000,000 | 0 | €0 | 2 |
|
Match 5 and 1 Euro Number |
€999,914.60 | 0 | €0 | 24 |
|
Match 5 |
€43,966.70 | 0 | €0 | 29 |
|
Match 4 and 2 Euro Numbers |
€2,225.10 | 0 | €0 | 191 |
|
Match 4 and 1 Euro Number |
€170.80 | 8 | €1,366.40 | 2,239 |
|
Match 4 |
€69.70 | 13 | €906.10 | 4,264 |
|
Match 3 and 2 Euro Numbers |
€38.90 | 29 | €1,128.10 | 6,554 |
|
Match 2 and 2 Euro Numbers |
€16.50 | 343 | €5,659.50 | 79,772 |
|
Match 3 and 1 Euro Number |
€15 | 434 | €6,510 | 84,568 |
|
Match 3 |
€13.20 | 680 | €8,976 | 137,427 |
|
Match 1 and 2 Euro Numbers |
€8 | 2,219 | €17,752 | 414,050 |
|
Match 2 and 1 Euro Number |
€7.50 | 5,667 | €42,502.50 | 1,078,148 |
|
Total |
- | 9,393 | €84,800.60 | 1,807,268 |
Eurojackpot prizes in Estonia must be claimed within 90 days of the draw date, so tickets purchased for this draw were valid until Thursday 4th October 2018.
The estimated jackpot advertised in Estonia prior to the draw was €90,000,000.
| Numbers Matched | Prize Per Winner | Winners from Finland | Prize Fund Amount | Total Winners |
|---|---|---|---|---|
|
Match 5 and 2 Euro Numbers |
€45,000,000 | 0 | €0 | 2 |
|
Match 5 and 1 Euro Number |
€999,914.60 | 5 | €4,999,573 | 24 |
|
Match 5 |
€43,966.70 | 6 | €263,800.20 | 29 |
|
Match 4 and 2 Euro Numbers |
€2,225.10 | At present this information is not released by the lottery operator of Finland | 191 | |
|
Match 4 and 1 Euro Number |
€170.80 | 2,239 | ||
|
Match 4 |
€69.70 | 4,264 | ||
|
Match 3 and 2 Euro Numbers |
€38.90 | 6,554 | ||
|
Match 2 and 2 Euro Numbers |
€16.50 | 79,772 | ||
|
Match 3 and 1 Euro Number |
€15 | 84,568 | ||
|
Match 3 |
€13.20 | 137,427 | ||
|
Match 1 and 2 Euro Numbers |
€8 | 414,050 | ||
|
Match 2 and 1 Euro Number |
€7.50 | 1,078,148 | ||
|
Total |
- | 1,807,268 |
Eurojackpot prizes in Finland must be claimed within 365 days of the draw date, so tickets purchased for this draw were valid until Saturday 6th July 2019.
The estimated jackpot advertised in Finland prior to the draw was €90,000,000.
| Numbers Matched | Prize Per Winner | Winners from Germany | Prize Fund Amount | Total Winners |
|---|---|---|---|---|
|
Match 5 and 2 Euro Numbers |
€45,000,000 | 2 | €90,000,000 | 2 |
|
Match 5 and 1 Euro Number |
€999,914.60 | 13 | €12,998,889.80 | 24 |
|
Match 5 |
€43,966.70 | 20 | €879,334 | 29 |
|
Match 4 and 2 Euro Numbers |
€2,225.10 | At present this information is not released by the lottery operator of Germany | 191 | |
|
Match 4 and 1 Euro Number |
€170.80 | 2,239 | ||
|
Match 4 |
€69.70 | 4,264 | ||
|
Match 3 and 2 Euro Numbers |
€38.90 | 6,554 | ||
|
Match 2 and 2 Euro Numbers |
€16.50 | 79,772 | ||
|
Match 3 and 1 Euro Number |
€15 | 84,568 | ||
|
Match 3 |
€13.20 | 137,427 | ||
|
Match 1 and 2 Euro Numbers |
€8 | 414,050 | ||
|
Match 2 and 1 Euro Number |
€7.50 | 1,078,148 | ||
|
Total |
- | 1,807,268 |
Eurojackpot prizes in Germany must be claimed within 3 years of the draw date, so tickets purchased for this draw are valid until Tuesday 6th July 2021.
The estimated jackpot advertised in Germany prior to the draw was €90,000,000.
| Numbers Matched | Prize Per Winner | Winners from Hungary | Prize Fund Amount | Total Winners |
|---|---|---|---|---|
|
Match 5 and 2 Euro Numbers |
€45,000,000 | 0 | €0 | 2 |
|
Match 5 and 1 Euro Number |
€999,914.60 | 0 | €0 | 24 |
|
Match 5 |
Ft12,706,375 | 0 | Ft0 | 29 |
|
Match 4 and 2 Euro Numbers |
Ft643,055 | 1 | Ft643,055 | 191 |
|
Match 4 and 1 Euro Number |
Ft49,360 | 32 | Ft1,579,520 | 2,239 |
|
Match 4 |
Ft20,145 | 68 | Ft1,369,860 | 4,264 |
|
Match 3 and 2 Euro Numbers |
Ft11,240 | 83 | Ft932,920 | 6,554 |
|
Match 2 and 2 Euro Numbers |
Ft4,770 | 1,271 | Ft6,062,670 | 79,772 |
|
Match 3 and 1 Euro Number |
Ft4,335 | 1,238 | Ft5,366,730 | 84,568 |
|
Match 3 |
Ft3,815 | 2,159 | Ft8,236,585 | 137,427 |
|
Match 1 and 2 Euro Numbers |
Ft2,310 | 6,541 | Ft15,109,710 | 414,050 |
|
Match 2 and 1 Euro Number |
Ft2,170 | 16,153 | Ft35,052,010 | 1,078,148 |
|
Total |
- | 27,546 | Ft74,353,060 | 1,807,268 |
Eurojackpot prizes in Hungary must be claimed within 180 days of the draw date, so tickets purchased for this draw were valid until Wednesday 2nd January 2019.
The estimated jackpot advertised in Hungary prior to the draw was €90,000,000.
| Numbers Matched | Prize Per Winner | Winners from Iceland | Prize Fund Amount | Total Winners |
|---|---|---|---|---|
|
Match 5 and 2 Euro Numbers |
kr.5,625,900,000 | 0 | kr.0 | 2 |
|
Match 5 and 1 Euro Number |
kr.125,009,320 | 0 | kr.0 | 24 |
|
Match 5 |
kr.5,496,710 | 0 | kr.0 | 29 |
|
Match 4 and 2 Euro Numbers |
kr.278,180 | At present this information is not released by the lottery operator of Iceland | 191 | |
|
Match 4 and 1 Euro Number |
kr.21,350 | 2,239 | ||
|
Match 4 |
kr.8,710 | 4,264 | ||
|
Match 3 and 2 Euro Numbers |
kr.4,860 | 6,554 | ||
|
Match 2 and 2 Euro Numbers |
kr.2,060 | 79,772 | ||
|
Match 3 and 1 Euro Number |
kr.1,870 | 84,568 | ||
|
Match 3 |
kr.1,650 | 137,427 | ||
|
Match 1 and 2 Euro Numbers |
kr.1,000 | 414,050 | ||
|
Match 2 and 1 Euro Number |
kr.930 | 1,078,148 | ||
|
Total |
- | 1,807,268 |
Eurojackpot prizes in Iceland must be claimed within 1 year of the draw date, so tickets purchased for this draw were valid until Saturday 6th July 2019.
The estimated jackpot advertised in Iceland prior to the draw was kr.0.
| Numbers Matched | Prize Per Winner | Winners from Italy | Prize Fund Amount | Total Winners |
|---|---|---|---|---|
|
Match 5 and 2 Euro Numbers |
€45,000,000 | 0 | €0 | 2 |
|
Match 5 and 1 Euro Number |
€999,914.60 | 1 | €999,914.60 | 24 |
|
Match 5 |
€43,966.70 | 0 | €0 | 29 |
|
Match 4 and 2 Euro Numbers |
€2,225.10 | 5 | €11,125.50 | 191 |
|
Match 4 and 1 Euro Number |
€170.80 | 25 | €4,270 | 2,239 |
|
Match 4 |
€69.70 | 55 | €3,833.50 | 4,264 |
|
Match 3 and 2 Euro Numbers |
€38.90 | 125 | €4,862.50 | 6,554 |
|
Match 2 and 2 Euro Numbers |
€16.50 | 1,346 | €22,209 | 79,772 |
|
Match 3 and 1 Euro Number |
€15 | 1,311 | €19,665 | 84,568 |
|
Match 3 |
€13.20 | 1,951 | €25,753.20 | 137,427 |
|
Match 1 and 2 Euro Numbers |
€8 | 6,717 | €53,736 | 414,050 |
|
Match 2 and 1 Euro Number |
€7.50 | 16,298 | €122,235 | 1,078,148 |
|
Total |
- | 27,834 | €1,267,604.30 | 1,807,268 |
Eurojackpot prizes in Italy must be claimed within a set period of time depending on how much you have won. Visit the How to Claim page to find specific claim periods, with some prizes needing to be claimed within 30 days of the draw (valid until Sunday 5th August 2018).
The estimated jackpot advertised in Italy prior to the draw was €90,000,000.
| Numbers Matched | Prize Per Winner | Winners from Latvia | Prize Fund Amount | Total Winners |
|---|---|---|---|---|
|
Match 5 and 2 Euro Numbers |
€45,000,000 | 0 | €0 | 2 |
|
Match 5 and 1 Euro Number |
€999,914.60 | 0 | €0 | 24 |
|
Match 5 |
€43,966.70 | 0 | €0 | 29 |
|
Match 4 and 2 Euro Numbers |
€2,225.10 | At present this information is not released by the lottery operator of Latvia | 191 | |
|
Match 4 and 1 Euro Number |
€170.80 | 2,239 | ||
|
Match 4 |
€69.70 | 4,264 | ||
|
Match 3 and 2 Euro Numbers |
€38.90 | 6,554 | ||
|
Match 2 and 2 Euro Numbers |
€16.50 | 79,772 | ||
|
Match 3 and 1 Euro Number |
€15 | 84,568 | ||
|
Match 3 |
€13.20 | 137,427 | ||
|
Match 1 and 2 Euro Numbers |
€8 | 414,050 | ||
|
Match 2 and 1 Euro Number |
€7.50 | 1,078,148 | ||
|
Total |
- | 1,807,268 |
Eurojackpot prizes in Latvia must be claimed within 30 days of the draw date, so tickets purchased for this draw were valid until Sunday 5th August 2018.
The estimated jackpot advertised in Latvia prior to the draw was €90,000,000.
| Numbers Matched | Prize Per Winner | Winners from Lithuania | Prize Fund Amount | Total Winners |
|---|---|---|---|---|
|
Match 5 and 2 Euro Numbers |
€45,000,000 | 0 | €0 | 2 |
|
Match 5 and 1 Euro Number |
€999,914.60 | 0 | €0 | 24 |
|
Match 5 |
€43,966.70 | 0 | €0 | 29 |
|
Match 4 and 2 Euro Numbers |
€2,225.10 | 0 | €0 | 191 |
|
Match 4 and 1 Euro Number |
€170.80 | 5 | €854 | 2,239 |
|
Match 4 |
€69.70 | 5 | €348.50 | 4,264 |
|
Match 3 and 2 Euro Numbers |
€38.90 | 10 | €389 | 6,554 |
|
Match 2 and 2 Euro Numbers |
€16.50 | 148 | €2,442 | 79,772 |
|
Match 3 and 1 Euro Number |
€15 | 175 | €2,625 | 84,568 |
|
Match 3 |
€13.20 | 284 | €3,748.80 | 137,427 |
|
Match 1 and 2 Euro Numbers |
€8 | 830 | €6,640 | 414,050 |
|
Match 2 and 1 Euro Number |
€7.50 | 2,371 | €17,782.50 | 1,078,148 |
|
Total |
- | 3,828 | €34,829.80 | 1,807,268 |
Eurojackpot prizes in Lithuania must be claimed within 60 days of the draw date, so tickets purchased for this draw were valid until Tuesday 4th September 2018.
The estimated jackpot advertised in Lithuania prior to the draw was €90,000,000.
| Numbers Matched | Prize Per Winner | Winners from Netherlands | Prize Fund Amount | Total Winners |
|---|---|---|---|---|
|
Match 5 and 2 Euro Numbers |
€45,000,000 | 0 | €0 | 2 |
|
Match 5 and 1 Euro Number |
€999,914.60 | 0 | €0 | 24 |
|
Match 5 |
€43,966.70 | 0 | €0 | 29 |
|
Match 4 and 2 Euro Numbers |
€2,225.10 | At present this information is not released by the lottery operator of Netherlands | 191 | |
|
Match 4 and 1 Euro Number |
€170.80 | 2,239 | ||
|
Match 4 |
€69.70 | 4,264 | ||
|
Match 3 and 2 Euro Numbers |
€38.90 | 6,554 | ||
|
Match 2 and 2 Euro Numbers |
€16.50 | 79,772 | ||
|
Match 3 and 1 Euro Number |
€15 | 84,568 | ||
|
Match 3 |
€13.20 | 137,427 | ||
|
Match 1 and 2 Euro Numbers |
€8 | 414,050 | ||
|
Match 2 and 1 Euro Number |
€7.50 | 1,078,148 | ||
|
Total |
- | 1,807,268 |
Eurojackpot prizes in Netherlands must be claimed within 365 days of the draw date, so tickets purchased for this draw were valid until Saturday 6th July 2019.
The estimated jackpot advertised in Netherlands prior to the draw was €90,000,000.
| Numbers Matched | Prize Per Winner | Winners from Norway | Prize Fund Amount | Total Winners |
|---|---|---|---|---|
|
Match 5 and 2 Euro Numbers |
kr.424,912,500 | 0 | kr.0 | 2 |
|
Match 5 and 1 Euro Number |
kr.44,607,305 | 1 | kr.44,607,305 | 24 |
|
Match 5 |
kr.415,155 | 1 | kr.415,155 | 29 |
|
Match 4 and 2 Euro Numbers |
kr.21,010 | At present this information is not released by the lottery operator of Norway | 191 | |
|
Match 4 and 1 Euro Number |
kr.1,610 | 2,239 | ||
|
Match 4 |
kr.655 | 4,264 | ||
|
Match 3 and 2 Euro Numbers |
kr.365 | 6,554 | ||
|
Match 2 and 2 Euro Numbers |
kr.155 | 79,772 | ||
|
Match 3 and 1 Euro Number |
kr.140 | 84,568 | ||
|
Match 3 |
kr.120 | 137,427 | ||
|
Match 1 and 2 Euro Numbers |
kr.75 | 414,050 | ||
|
Match 2 and 1 Euro Number |
kr.70 | 1,078,148 | ||
|
Total |
- | 1,807,268 |
Eurojackpot prizes in Norway must be claimed within 3 months of the draw date, so tickets purchased for this draw were valid until Saturday 6th October 2018.
The estimated jackpot advertised in Norway prior to the draw was kr.850,000,000.
| Numbers Matched | Prize Per Winner | Winners from Poland | Prize Fund Amount | Total Winners |
|---|---|---|---|---|
|
Match 5 and 2 Euro Numbers |
zł385,000,000 | 0 | zł0 | 2 |
|
Match 5 and 1 Euro Number |
zł4,987,999.10 | 1 | zł4,987,999.10 | 24 |
|
Match 5 |
zł0 | 0 | zł0 | 29 |
|
Match 4 and 2 Euro Numbers |
zł9,753.30 | 6 | zł58,519.80 | 191 |
|
Match 4 and 1 Euro Number |
zł748.70 | 41 | zł30,696.70 | 2,239 |
|
Match 4 |
zł305.60 | 88 | zł26,892.80 | 4,264 |
|
Match 3 and 2 Euro Numbers |
zł170.60 | 112 | zł19,107.20 | 6,554 |
|
Match 2 and 2 Euro Numbers |
zł72.40 | 1,732 | zł125,396.80 | 79,772 |
|
Match 3 and 1 Euro Number |
zł65.80 | 1,815 | zł119,427 | 84,568 |
|
Match 3 |
zł57.90 | 3,108 | zł179,953.20 | 137,427 |
|
Match 1 and 2 Euro Numbers |
zł35.10 | 8,452 | zł296,665.20 | 414,050 |
|
Match 2 and 1 Euro Number |
zł32.90 | 23,364 | zł768,675.60 | 1,078,148 |
|
Total |
- | 38,719 | zł6,613,333.40 | 1,807,268 |
Eurojackpot prizes in Poland must be claimed within 3 years of the draw date, so tickets purchased for this draw are valid until Tuesday 6th July 2021.
The estimated jackpot advertised in Poland prior to the draw was zł385,000,000.
| Numbers Matched | Prize Per Winner | Winners from Slovakia | Prize Fund Amount | Total Winners |
|---|---|---|---|---|
|
Match 5 and 2 Euro Numbers |
€45,000,000 | 0 | €0 | 2 |
|
Match 5 and 1 Euro Number |
€999,914.60 | 0 | €0 | 24 |
|
Match 5 |
€43,966.70 | 0 | €0 | 29 |
|
Match 4 and 2 Euro Numbers |
€2,225.10 | 1 | €2,225.10 | 191 |
|
Match 4 and 1 Euro Number |
€170.80 | 20 | €3,416 | 2,239 |
|
Match 4 |
€69.70 | 41 | €2,857.70 | 4,264 |
|
Match 3 and 2 Euro Numbers |
€38.90 | 77 | €2,995.30 | 6,554 |
|
Match 2 and 2 Euro Numbers |
€16.50 | 1,000 | €16,500 | 79,772 |
|
Match 3 and 1 Euro Number |
€15 | 867 | €13,005 | 84,568 |
|
Match 3 |
€13.20 | 1,431 | €18,889.20 | 137,427 |
|
Match 1 and 2 Euro Numbers |
€8 | 5,155 | €41,240 | 414,050 |
|
Match 2 and 1 Euro Number |
€7.50 | 12,190 | €91,425 | 1,078,148 |
|
Total |
- | 20,782 | €192,553.30 | 1,807,268 |
Eurojackpot prizes in Slovakia must be claimed within 35 days of the draw date, so tickets purchased for this draw were valid until Friday 10th August 2018.
The estimated jackpot advertised in Slovakia prior to the draw was €90,000,000.
| Numbers Matched | Prize Per Winner | Winners from Spain | Prize Fund Amount | Total Winners |
|---|---|---|---|---|
|
Match 5 and 2 Euro Numbers |
€45,000,000 | 0 | €0 | 2 |
|
Match 5 and 1 Euro Number |
€999,914.60 | 2 | €1,999,829.20 | 24 |
|
Match 5 |
€43,966.70 | 0 | €0 | 29 |
|
Match 4 and 2 Euro Numbers |
€2,225.10 | 7 | €15,575.70 | 191 |
|
Match 4 and 1 Euro Number |
€170.80 | 44 | €7,515.20 | 2,239 |
|
Match 4 |
€69.70 | 99 | €6,900.30 | 4,264 |
|
Match 3 and 2 Euro Numbers |
€38.90 | 163 | €6,340.70 | 6,554 |
|
Match 2 and 2 Euro Numbers |
€16.50 | 2,201 | €36,316.50 | 79,772 |
|
Match 3 and 1 Euro Number |
€15 | 2,212 | €33,180 | 84,568 |
|
Match 3 |
€13.20 | 3,738 | €49,341.60 | 137,427 |
|
Match 1 and 2 Euro Numbers |
€8 | 11,326 | €90,608 | 414,050 |
|
Match 2 and 1 Euro Number |
€7.50 | 31,535 | €236,512.50 | 1,078,148 |
|
Total |
- | 51,327 | €2,482,119.70 | 1,807,268 |
Eurojackpot prizes in Spain must be claimed within 30 days of the draw date, so tickets purchased for this draw were valid until Sunday 5th August 2018.
The estimated jackpot advertised in Spain prior to the draw was €90,000,000.
| Numbers Matched | Prize Per Winner | Winners from Sweden | Prize Fund Amount | Total Winners |
|---|---|---|---|---|
|
Match 5 and 2 Euro Numbers |
kr.462,699,000 | 0 | kr.0 | 2 |
|
Match 5 and 1 Euro Number |
kr.10,281,321 | 0 | kr.0 | 24 |
|
Match 5 |
kr.452,074 | 0 | kr.0 | 29 |
|
Match 4 and 2 Euro Numbers |
kr.91,590 | 4 | kr.366,360 | 191 |
|
Match 4 and 1 Euro Number |
kr.12,619 | 46 | kr.580,474 | 2,239 |
|
Match 4 |
kr.5,305 | 98 | kr.519,890 | 4,264 |
|
Match 3 and 2 Euro Numbers |
kr.1,997 | 172 | kr.343,484 | 6,554 |
|
Match 2 and 2 Euro Numbers |
kr.169 | 1,986 | kr.335,634 | 79,772 |
|
Match 3 and 1 Euro Number |
kr.154 | 2,183 | kr.336,182 | 84,568 |
|
Match 3 |
kr.135 | 3,454 | kr.466,290 | 137,427 |
|
Match 1 and 2 Euro Numbers |
kr.82 | 10,442 | kr.856,244 | 414,050 |
|
Match 2 and 1 Euro Number |
kr.77 | 28,125 | kr.2,165,625 | 1,078,148 |
|
Total |
- | 46,510 | kr.5,970,183 | 1,807,268 |
Eurojackpot prizes in Sweden must be claimed within 3 months of the draw date, so tickets purchased for this draw were valid until Saturday 6th October 2018.
The estimated jackpot advertised in Sweden prior to the draw was kr.940,000,000.
Associated Draw Results
- 3
- 3
- 8
- 8
- 9
- 6
| Numbers Matched | Prize Per Winner | Total Winners |
|---|---|---|
| Match 6 in Exact Order | €50,000 | 0 |
| Match Last 5 in Exact Order | €5,000 | 0 |
| Match Last 4 in Exact Order | €500 | 14 |
| Match Last 3 in Exact Order | €50 | 118 |
| Match Last 2 in Exact Order | €5 | 1,107 |
| Match Last 1 in Exact Order | €3 | 11,547 |
- 7
- 7
- 8
- 4
- 0
- 7
| Numbers Matched | Prize Per Winner | Total Winners |
|---|---|---|
| Match 6 | CZK0 | 0 |
| Match 5 | CZK0 | 0 |
| Match 4 | CZK20,000 | 4 |
| Match 3 | CZK2,000 | 90 |
| Match 2 | CZK200 | 993 |
| Match 1 | CZK120 | 9,606 |
- 0
- 6
- 4
- 6
- 6
- 9
- 1
| Numbers Matched | Prize Per Winner | Total Winners |
|---|---|---|
| Match 7 in Exact Order | €1,000,000 | 0 |
| Match Any 6 in the Right Spot | €20,000 | 0 |
| Match Any 5 in the Right Spot | €500 | 51 |
| Match Any 4 in the Right Spot | €50 | 681 |
| Match Any 3 in the Right Spot | €6 | 6,210 |
| Match Any 2 in the Right Spot | €3 | 33,715 |
- Bryssel
- 19
| Numbers Matched | Prize Per Winner | Total Winners |
|---|---|---|
| City and Number | €1,000 | 19 |
| City | €5 | 1,785 |
- 6
- 6
- 9
- 1
- 5
| Numbers Matched | Prize Per Winner | Total Winners |
|---|---|---|
| Match 5 in Exact Order | kr2,000,000 | 0 |
| Match First/Last 4 in Exact Order | kr100,000 | 4 |
| Match First/Last 3 in Exact Order | kr10,000 | 13 |
| Match First/Last 2 in Exact Order | kr2,000 | 154 |
- Frogn
Related Eurojackpot Results Links
- Automatically check your Eurojackpot ticket
- View average prizes and winners for each prize tiers
- View the largest jackpot winners and total winners from each country
- View previous Eurojackpot results by year
Buy Eurojackpot Tickets Online
By buying your Eurojackpot tickets online you don’t have to worry about ever losing your Eurojackpot lottery tickets. When you play the Eurojackpot online all your lottery tickets purchased on the internet are stored in your personal account, meaning that you can view them anytime you wish.


Hey, Xamarin developers!
Developing iOS apps quite often includes using native libraries and Xamarin for iOS supports this by creating bindings.
This was 100% manual process until ObjectiveSharpie. After, ObjectiveSharpie began supporting CocoaPods (it is NuGet for iOS developers, so everything is in there).
However, it doesn’t work in many cases (it can’t build something or reflect ObjectiveC code correctly).
Also, sharpie doesn’t support dependencies within pods. This is where the new objc-automatic started.
But first, let me show you a demo how easy it is to create Xamarin.iOS binding for cocoapod.
Xamarin.Insights was and still is a great exception tracking system for Xamarin applications. However
… Do also note that Xamarin Insights is now considered to be end of life: since the acquisition by Microsoft, Insights has been deprecated in favour of HockeyApp.
Many of us have apps integrated with Xamarin.Insights. Now it’s time to ditch Xamarin.Insights and put HockeyApp SDK instead.
HockeyApp has different integration points and API and some of the features from Insights are missing.
What would be the easiest way to migrate from Xamarin Insights to HockeyApp?
If you ever wondered, why Visual Studio (and Visual Studio for Mac) suggest you to name your lambda arguments with names like arg1, arg2 … argN and so on…
Here is a common situation where somewhere in your library or helper class you have a method taking in Func<T> or Action<T>.
and then when you want to use this method VS suggests you following names: arg1 and arg2.
Like there is nothing better than that :)
There is a cheap and easy solution for that.
Define your own delegate with same signature as Func<T> or Func<T1,T2,T3> you are using already.
Specify intended names for these arguments (in my case arg1 could be better described as service, where arg2 is actually a token).
In original method, replace Func with this new delegate.
After these changes when you are going to use the method, you will see intellisense suggesting correct names and not generic ones.


This is an example of a simple ToDo application using NgRx state manager. It uses a fake backend that always return 3 tasks with delay 500ms
Refresh


Items covered :-
Functional RGB colors
The following image is 70 pixels tall and 70 pixels wide:
The best measurements to use are probably relative measurements. But depending wth the situation pixels may be used.
www.hypertechsolutions.co.keUnits and values cover a wide spectrum of areas, from length units to special keywords that describe effects (such as underline ) to color units to the location of files (such as images).These units all have their advantages and drawbacks, depending upon the circum- stances in which they’re used.


Avoid spending more time looking for social amenities near you Hoodwatch has got you covered, create an account and find any social amenity near you i.e Hospitals.
Helping you find secure area within a hood of your choice, join Hoodwatch now by creating an account with us its always free.
Get to know who lives around you in your hood Hoodwatch has got you covered, join today to connect with your neighbors.


comment
Description
A comment is a remark to the reader of the program, which the computer ignores. The most common form of comment in Turing starts with a percent sign (%) and continues to the end of the current line; this is called an end-of-line comment. There is also the bracketed comment, which begins with the /* and ends with */ and which can continue across line boundaries.
Example
% This is an end-of-line comment
var x : real % Here is another end-of-line comment
const s := "Hello"
/* Here is a bracketed comment that
lasts for two lines */
const pi := 3.14159
Details
In the BASIC language, comments are called remarks and start with the keyword REM. In Pascal, comments are bracketed by (* and *).


Ex-tennis club chief to plead guilty in college bribery case
BOSTON (AP) - The former president of a private tennis club in Texas is expected to plead guilty in the college admissions scandal.
Martin Fox is scheduled to appear in Boston’s federal court Friday. The 62-year-old from Houston previously agreed to plead guilty to a racketeering conspiracy charge in a deal with prosecutors.
Authorities say Fox brokered bribes to help wealthy parents rig their children’s college entrance exams. He’s also accused of arranging bribes to get two students admitted to the University of San Diego as recruited athletes, and one to the University of Texas.
TOP STORIES
Trump 'war room' launches scathing Pelosi video
Company co-founded by Nancy Pelosi's son charged with securities fraud
Kevin McCarthy accuses Adam Schiff of lying about the whistleblower
His plea deal requires him to forfeit $245,000 he received through the scheme.
His charge carries a sentence of up to 20 years in prison, but prosecutors are recommending a sentence at the low end of federal guidelines.
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Copyright © 2019 The Washington Times, LLC.
Please read our comment policy before commenting.


The number of columns displayed in the table below depends on the available screen space, by default; a smartphone will display 2 columns, for example, while an expanded desktop browser displays the full set. This is accomplished by assigning semantic classes to the column headings that indicate which data values take precedence (essential vs optional), in combination with media queries to display them at different screen widths (a.k.a., responsive design).
We added a bit of JavaScript logic so you can control which data is displayed by checking column names in the "Display" menu on the right. Once an option is checked, the associated data will persist and display at all screen widths until the option is unchecked.
You can also set a column to always persist by assigning a class in the markup, in which case it has no menu option. Here, the "Company" column is persistent.
| Company | Last Trade | Trade Time | Change | Prev Close | Open | Bid | Ask | 1y Target Est |
|---|---|---|---|---|---|---|---|---|
| GOOG Google Inc. | 597.74 | 12:12PM | 14.81 (2.54%) | 582.93 | 597.95 | 597.73 x 100 | 597.91 x 300 | 731.10 |
| AAPL Apple Inc. | 378.94 | 12:22PM | 5.74 (1.54%) | 373.20 | 381.02 | 378.92 x 300 | 378.99 x 100 | 505.94 |
| AMZN Amazon.com Inc. | 191.55 | 12:23PM | 3.16 (1.68%) | 188.39 | 194.99 | 191.52 x 300 | 191.58 x 100 | 240.32 |
| ORCL Oracle Corporation | 31.15 | 12:44PM | 1.41 (4.72%) | 29.74 | 30.67 | 31.14 x 6500 | 31.15 x 3200 | 36.11 |
| MSFT Microsoft Corporation | 25.50 | 12:27PM | 0.66 (2.67%) | 24.84 | 25.37 | 25.50 x 71100 | 25.51 x 17800 | 31.50 |
| CSCO Cisco Systems, Inc. | 18.65 | 12:45PM | 0.97 (5.49%) | 17.68 | 18.23 | 18.65 x 10300 | 18.66 x 24000 | 21.12 |
| YHOO Yahoo! Inc. | 15.81 | 12:25PM | 0.11 (0.67%) | 15.70 | 15.94 | 15.79 x 6100 | 15.80 x 17000 | 18.16 |


BinaryEncoder
open class BinaryEncoder
Overview
BinaryEncoder
facilitates the encoding of Encodable
values into a binary
format that can be stored on disk or sent over a socket.
The encoder will encode any Encodable
type whether you declare conformance to
Encodable
and let the compiler create the code or you manually implement the
conformance yourself.
Examples
To create an instance of a BinaryEncoder:
let encoder = BinaryEncoder()
Note: You may optionally pass your own userInfo
BinaryEncoder(userInfo:)
structure and it will be available to you during the encoding process.
You can encode any top even top-level single value types including Int,
UInt, Double, Bool, and Strings. Simply pass the value to the instance
of the BinaryEncoder and call encode
.
let string = "You can encode single values of any type."
let bytes = try encoder.encode(string)
Basic structs and classes can also be encoded.
struct Employee: Codable {
let first: String
let last: String
let employeeNumber: Int
}
let employee = Employee(first: "John", last: "Doe", employeeNumber: 2345643)
let bytes = try encoder.encode(employee)
As well as Complex types with sub classes.
-
Initializes an instance of the encoder optionally passing context information for the encoding process.
Declaration
Swift
public init(userInfo: [CodingUserInfoKey : Any] = [:])
Parameters
userInfo
Any contextual information set by the user for encoding.
-
Encodes the given top-level value and returns its Binary representation.
Throws
An error if any value throws an error during encoding.
Declaration
Swift
open func encode<T>(_ value: T) throws -> [UInt8] where T : Encodable
Parameters
value
The value to be encoded.
Return Value
A new
EncodedData
value containing the encoded Binary data.
-
Any contextual information set by the user for encoding.
Declaration
Swift
public var userInfo: [CodingUserInfoKey : Any]


"Use braces when expanding arrays, e.g. ${array[idx]} (or ${var}[.. to quiet)."
echo "$array[@]"
echo "${array[@]}"
Some languages use the syntax $array[index]
to access an index of an arrays, but a shell will interpret this as $array
followed by the unrelated literal string (or glob) [index]
.
Curly braces are needed to tell the shell that the square brackets are part of the expansion.
If you want the square brackets to be treated literally or as a glob, use ${var}[idx]
to prevent this warning.
This does not change how the script works, but clarifies your intent to ShellCheck as well as other programmers.


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Welcome to my corner
This will get you from A to B. Just don't try and make it to C.
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Twitch:
Some other junk:
On Scroll Header Effects
Inspiration for Article Intro Effects