IntelliJ IDEA 2020.2 Help
Remote debugging via SSH tunnel
In this tutorial we'll see how to use an SSH tunnel to setup a secure connection between the development machine and a remote server. This can be useful for debugging code on a remote machine when there are firewalls in between, or a NAT router prevents direct connection, or the ISP or network infrastructure does not allow incoming TCP connections to the developer machine.
Prepare the debugging engine
Before you start debugging, make sure that you have a debugging engine installed and configured properly. IntelliJ IDEA supports debugging with two most popular tools: Xdebug and Zend Debugger. These tools cannot be used simultaneously because they block each other. To avoid this problem, you need to update the corresponding sections in the php.ini file as described in Configure Xdebug and Configure Zend Debugger.
Open the active php.ini file in the editor:
 1. In the Settings/Preferences dialog Ctrl+Alt+S, click PHP under Languages & Frameworks.
 2. On the PHP page that opens, click the Browse button next to the CLI Interpreter field.
 3. In the CLI Interpreters dialog that opens, the Configuration File read-only field shows the path to the active php.ini file. Click Open in Editor.
When using Xdebug, make sure at least the following settings are specified:
[xdebug] zend_extension=xdebug_module_goes_here xdebug.remote_enable=1 xdebug.remote_host=127.0.0.1 xdebug.remote_port=9000
Listening for incoming debugger connections
In IntelliJ IDEA, enable listening to incoming debug connections by either clicking the Start Listening for PHP Debug Connections button on the toolbar or selecting Run | Start Listening for PHP Debug Connections in the main menu. This will ensure IntelliJ IDEA reacts when a debugging session is started and opens the Debug tool window automatically. Before launching the script, make sure that either a breakpoint is set or the Break at first line in PHP scripts option is enabled on the Debug page of the Settings/Preferences dialog Ctrl+Alt+S.
Set up an SSH tunnel to the remote machine
What we want to do is connect to the remote machine over SSH and set up port forwarding for port 9000 (for Xdebug) or port 10137 (Zend Debugger). The idea is to create a "virtual" TCP port on the remote server that sends its traffic to a TCP port on our own machine, tunneling traffic over SSH.
SSH tunnel diagram
The SSH tunnel is used for connecting through a firewall and establishing a secure connection between the remote server and the developer machine. When the remote server can connect to the developer machine directly (for example, with a Vagrant machine), an SSH tunnel may not be needed.
In this case, we need to make the debugger connect back to the developer machine by setting xdebug.remote_host=ip_address_goes_here (for Xdebug) or making sure the debug host is the IP address of the developer machine (for Zend Debugger). This can be done using the IntelliJ IDEA bookmarklets, a Browser Debugging Extension, or the techniques outlined in Debugging PHP CLI scripts with IntelliJ IDEA.
The setup process depends on the operating system and the debugger being used.
Set up an SSH tunnel on macOS or Linux
 - Run the following command in the command line:
 ssh -R 9000:localhost:9000 username_goes_here@hostname_goes_here
 ssh -R 10137:localhost:10137 username_goes_here@hostname_goes_here
Set up an SSH tunnel on Windows
 1. Download and install Putty.
 2. Configure the connection to the remote machine by providing the hostname and port.
 Set Up a Connection in Putty
 3. Expand the Connection | SSH | Tunnels node on the left and add a new forwarded port.
 - The source port will be 9000 (for Xdebug) or 10137 (for Zend Debugger).
 - For destination, enter localhost:9000 (for Xdebug) or localhost:10137 (for Zend Debugger).
 Make sure to select the "Remote" option and then click Add.
 Set Up an SSH Tunnel in Putty
 Click Open to connect to the remote server and setup the SSH tunnel.
Make sure that you are running a command to create an SSH tunnel from the developer's machine to the server (not vice versa).
When a machine is rebooted or the connection is lost, the SSH tunnel has to be reestablished.
Debug
Once the SSH tunnel is set up, we can start debugging using zero-configuration debugging with Xdebug or Zend Debugger. When the debugger is started, IntelliJ IDEA will prompt to accept the incoming connection.
Incoming debugger connection Xdebug
Once we accept it, we will be able to debug using the techniques outlined in Examine suspended program.
Debug PHP CLI scripts with remote PHP interpreters or via SSH tunnel
When a remote PHP interpreter is properly configured, it's possible to create a run/debug configuration taking advantage of the remote PHP interpreter for debugging.
When the SSH tunnel is up and running, we can also debug PHP CLI scripts. Since the debugger runs on a remote machine, starting a CLI debugging session can be done by using PHP command line switches or using environment variables (on the remote machine). This workflow is not the officially recommended way of debugging, though it might be useful in some cases when the debugging session is needed to be established from the remote server side.
Troubleshooting
The debugger never connects or refuses the connection
When the debugger never connects or refuses the connection, check the following:
 - Make sure Xdebug is configured to connect to 127.0.0.1 and port 9000. See #1. Xdebug or Zend Debugger installed and configured for a sample configuration.
 - When using Zend Debugger, make sure the IntelliJ IDEA bookmarklets or Browser Debugging Extension is configured to connect to 127.0.0.1.
 - Make sure IntelliJ IDEA is listening for incoming debugger connections prior to setting up the SSH tunnel.
 - When a machine is rebooted or the connection is lost, the SSH tunnel has to be reestablished.
Remote file path is not mapped to any file path in project
In some cases, the debugger can connect, but we get the error messages indicating that no mapping between the remote and project files is defined. This means that IntelliJ IDEA cannot determine which local file corresponds to the file being debugged.
No mappings configured
We can solve this problem by clicking Click to set up path mappings and providing the necessary path mappings.
Additionally, we can configure these mappings using the techniques outlined in Configure synchronization with a Web server.
Last modified: 07 September 2020


text:
Problem structuring in decision-making processes
This contribution presents insights on problem structuring based on a literature review and experiences from a case study. Another deliberation tool aimed to involve stakeholders in a decision-making process is described in the Stakeholder analysis section.
Complex, unstructured problems
Water management issues are typical examples of complex problems. They are part of a natural and human system (Kolkman 2005) which consist of many diverse, interdependent elements e.g. upstream events influence the water system downstream, different interdependent government layers manage the water system, and multiple stakeholders use the water system (see e.g. Geldof 2001, Natural variability in Coastal Ecosystems and The state of the art of public participation in Europe). If problems are part of a complex natural and social system, they are often unstructured. Complex, unstructured problems are characterized by: uncertain knowledge, disagreement about normative elements (e.g. values, norms or objectives) and disagreement about cognitive elements (e.g. research or information) (Van de Graaf and Hoppe 1996; Hisschemöller and Hoppe 2001). A problem results from an observed gap between a normative yardstick and an existing or expected situation. Problems are thus not objective givens, but social constructs (Dery 1984; Van de Graaf and Hoppe 1996). Actors may disagree about a problem because they behold divergent perceptions. A perception comprises the images actors have of their environment and the problems and opportunities within it. Objectives are concrete translations of perceptions and used to determine an actors' strategy. Strategies are used to influence other parties or the content or process of problem formulation (Koppenjan and Klĳn 2004). See also stakeholder analysis.
Policy processes and decision-making processes
A decision-making process is often embedded in a policy process and aims to solve a concrete problem. Currently, a shift is visible from a purely analytical approach to solve problems towards a process management approach. Characteristics of an analytical approach are a central steering actor who determines the objectives. This approach assumes that problems occur within a single-actor setting and that it is possible to solve a problem analytically. A process management approach recognizes the multi-actor setting of policy problems, the divergence of perceptions and the need for interaction and communication to solve these problems (Edelenbos et al. 2003). Because for complex, unstructured problems a purely content-directed approach is not possible, a process-directed approach should be adopted (De Bruijn et al. 2002). Criticism to the process management approach is that it neglects substantive aspects. Recent literature on process or network management pays explicit attention to the importance of interweaving process and content. Although a process management approach assumes that an objective problem formulation and knowledge does not exist, this does not imply that substance does not matter in this approach (De Bruijn et al. 2002; Edelenbos et al. 2003 ; Koppenjan and Klĳn 2004).
The shift towards a more process-directed approach resulted in the development of empirical, descriptive models. These models aim to provide an alternative for the widely used phase-model. The phase-model describes policy processes as subsequent, interrelated phases with a clear beginning and end. Common phases of policy formation are problem formulation, generating alternative solutions, and policy design. Teisman (1992) developed an alternative model, the rounds model. Actors are the focal point of analysis in the rounds model. Problems and solutions are only relevant to the policy process if they are presented by an actor. Policy formation does not stem from an intended course of action formulated by one actor, but results from a series of decisions taken by different actors in different rounds of interaction. Rounds of decision-making can only be determined in retrospective by determining the most crucial decisions. Decision-making rounds are characterized by dynamic combinations of sets of problems and solutions, represented by different actor. Thus, problem formulation always refers to a problem-solution combination (Teisman 2000). The basic principles of the rounds model provide useful insights to understand problem structuring. The rounds model is also used as a basis to explain problem solving as a policy game in a network context (Koppenjan and Klĳn 2004) and to explain the function of knowledge in decision-making processes (Van Buuren 2006).
Hisschemöller (1993) uses the term problem structuring in the context of policy processes to refer to an approach which focuses on the integration of the most divergent views with respect to the problem situation. Problem structuring prevents from addressing the wrong problem, because it is not solution oriented but oriented to problem finding. Instead of using the term problem structuring to develop a normative framework, we developed a conceptual model to describe problem formulation in the context of participatory policy processes. This conceptual model bases on insights about problem typologies, policy processes, process management and problem structuring.
If problems are unstructured, no clear problem formulation exists. Because different stakeholders behold different perceptions about the problem and knowledge is uncertain, interaction between stakeholders is necessary to formulate a joint problem and its solutions. Complex, unstructured problems are characterized by uncertainty and diverging perceptions. Uncertainty might result from a lack of knowledge, but also because different people interpret and value information differently (ambiguity) (Koppenjan and Klĳn 2004). Formulating a problem and its solutions implies that uncertainty, ambiguity and disagreement need to be reduced. The development of stakeholders' perceptions and knowledge are central elements to create a joint problem formulation. Ideally, the outcome of problem structuring is 'negotiated knowledge', this is knowledge which is agreed upon and valid (De Bruijn et al. 2002; Van de Riet 2003). The content developed during a participatory process should not be seen as final or permanent. Adoption is the consolidation of a problem formulation over a longer period of several decision rounds (Teisman 2000). How problem structuring develops is also summarized in Figure 1.
Experiences from case study research indicate that the presented framework is very useful to analyze the development of substantive outcomes for participatory processes. Important lessons are that problem structuring is not a clear-cut process which takes place in a vacuum. Furthermore, it appears to be mainly useful to interactive policy-making processes which are open to all problem formulations and in which stakeholders (i.e. not the general public) are involved.
An example of problem structuring is described in the section with experiences of evaluation and case studies.
More information on the design of problem structuring processes and evaluation can be found in literature from M. Hisschemöller. A variety of problem structuring methods (PSM) have been analyzed and developed by J. Rosenhead.
The presented theory is used to analyze two case studies. For these case studies, see also: De Kruijf, J. (2007). Problem structuring in interactive decision-making processes: How interaction, problem perceptions and knowledge contribute to a joint formulation of a problem and its solutions, Master's thesis, University of Twente, Enschede. This report is also available as TNO-report, ref. nr 2007-I&R-065-KFJ-PEM.
For more information on interactive water management, see e.g. work from J.A. Van Ast.
- ↑ Kolkman, M.J. (2005), Controversies in water management: frames and mental models. Febodruk BV, Enschede, The Netherlands
- ↑ Geldof, G. D. (2001). Omgaan met complexiteit bij integraal waterbeheer, PhD thesis, University of Twente, The Netherlands, Tauw bv, Deventer
- ↑ 3.0 3.1 Van de Graaf, H., and Hoppe, R. (1996). Beleid en politiek : een inleiding tot de beleidswetenschap en de beleidskunde Coutinho, Bussum
- ↑ Hisschemöller, M., and Hoppe, R. (2001). Coping with Intractable Controversies: The Case for Problem Structuring in Policy Design and Analysis. In: Knowledge, Power, and Participation in Environmental Policy Analysis, M. Hisschemöller, R. Hoppe, W. N. Dunn, and J. R. Ravetz, eds., Transaction Publishers, New Brunswick and London, 47-72
- ↑ Dery, D. (1984). Problem definition in policy analysis, Lawrence, Kansas : University Press of Kansas.
- ↑ 6.0 6.1 6.2 6.3 Koppenjan, J. F. M., and Klĳn, E. (2004). Managing uncertainties in networks : a network approach to problem solving and decision making Routledge, London [etc.]
- ↑ 7.0 7.1 Edelenbos, J., Monnikhof, R., and Van de Riet, O. (2003). A Double Helix approach: a proposal to forge a better integration of analysis and policy development. International Journal of Technology Policy and Management, 3(1), 1-21
- ↑ 8.0 8.1 8.2 De Bruijn, H., Ten Heuvelhof, E. F., and In 't Veld, R. J. (2002). Process management: why project management fails in complex decision making processes, Kluwer Academic Publishers, Dordrecht
- ↑ Teisman, G. R. (1992). Complexe besluitvorming: een

source: HuggingFaceFW/fineweb-edu


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How do I add a filter to a profile?
Constructing a filter in Urchin requires careful attention to detail. This is important for both performance reasons and to ensure that the filter behaves in a predictable manner. Urchin uses POSIX regular expression for specifying filter patterns, which are quite robust but can also be rather complex. Specifying a regular expression filter pattern incorrectly may not only generate the wrong results, but may significantly increase Urchin log processing times; perhaps as much as an order of magnitude.
Tips for Filtering
The general rule of thumb with filters is "the simpler, the better". Each filter you add increases processing time. Complex filtering patterns also increase processing time.
 1. Use as simple a pattern as possible that matches only what you are trying to filter. For instance, if you want to filter out "googlebot" in the Browsers, just put "googlebot" as the filter pattern, not ".*googlebot.*".
 2. Whenever possible, use a single filter with multiple patterns instead of multiple separate filters. As an example, if you want to filter out PDF files, Flash files, and MPEGs from your Top Pages reports, use a filter pattern of "\.pdf|\.swf|\.mpeg" instead of creating a separate filter for each.
 3. Remember to escape regular expression metacharacters (see below) by preceding them with a backslash if you are using those specific characters are part in your pattern. For example, the dot "." character is a regular expression metacharacter. Therefore, if you want to filter out an IP address you need to specify the filter pattern as "127\.0\.0\.1" instead of "".
Getting Started
Before you begin, you may want to have a copy of your web log file accessible so you can determine exactly how to construct the filter. Why is this important? During report processing, when Urchin invokes the filter you've added, it will attempt to make an exact match between your filter pattern and content found in the log file. So, knowing exactly how the content is displayed in the log file is essential when building the filter.
If you do not have direct access to the log file, you may be able to gather similar information by reviewing content in the report without any filter added. For example, the Pages and Referrals reports often contain enough detailed information to build filters from. However, some information is still not available such as query strings resulting from dynamically generated web pages. That information is available only from the log file.
NOTE: Once the filter is applied, it will only affect the reports processed "after" it is added. Any existing data will not be affected by the filter. Applying a filter to existing reports requires clearing that data and reprocessing the log files.
There are 3 primary components to an Urchin filter.
 1. Filter Type: This determines whether Urchin is to include or exclude specific data from the report, and in some cases (Dynamic URL) how the information will be displayed in the report. If you select "Include Pattern" Urchin will ONLY include log file entries that contain the pattern or patterns you tell it to include in the report.
 2. Filter Field: Determines which field in the log file Urchin should apply the filter to. If you are unsure which field to select, review section below titled "Filter Field Definitions by Log Format Type."
 3. Filter Pattern: Contains the string of characters Urchin will try and match from the log file during processing. Filter patterns applied to the URI-Stem(W3C) or Request(NCSA) field should be written exactly as they appear in the log file. Or, you may truncate the pattern in some cases. All other patterns are subject to POSIX regular expression rules. That means that certain characters will be interpreted differently than they appear. For example a dot "." means "match any single character." A full list of these expressions can be found at the bottom of this article.
Filter Field Definitions by Log Format Type:
NCSA Extended Combined Format (Apache)
Urchin Report ViewUrchin Filter Field
Visitors/Sessions Remote Host
Pages Request(NCSA)
Tracking/Usernames Username
Referrals Referrer
Domains Remote Host
Browsers User-Agent
Windows W3C Format
Urchin Report ViewUrchin Filter Field
Visitors/Sessions IP Address
Pages URI-Stem
Tracking/Usernames Username
Referrals Referrer
Domains IP Address
Browsers User-Agent
Sample Filters:
Filtering OUT personal IP address from Windows W3C log files
Filter Type: Exclude Pattern
Filter Field: IP Address
Filter Pattern: 63\.212\.171\.5
Filtering OUT robots from NCSA or W3C log files
Filter Type: Exclude Pattern
Filter Field: User-Agent
Filter Pattern: bot|Bot|BOT|Robot|robot|Crawl|crawl
Filtering IN content from support directory from NCSA logs
Filter Type: Include Pattern
Filter Field: Request (NCSA)
Filter Pattern: /support/
POSIX Regular Expression list:
Character Meaning
. Match any single character
* Match zero or more of the previous item
+ Match one of more of the previous item
? Match zero or one of the previous item
( ) Remember contents of parenthesis as item
[ ] Match one item in this list
- Create a range in a list
| Or
^ Match to the beginning of the line
$ Match to the end of the line
\ Escapes any of the above (when preceding wild card)


text:
Compression and End-Gas Temperatures from Iodine Absorption Spectra
THIS paper discusses a method of measuring compression temperature by means of the absorption of light. An optical-electronic system measures the change in color of a trace of iodine gas that has been added to the intake mixture. From these measurements the temperature of the iodine and by inference, the temperature of the gases, is determined. The apparatus used is described briefly and the test results obtained in measuring compression and end-gas temperatures in a spark-ignition engine are also presented.

source: m-a-p/FineFineWeb


text:
BRING YOUR RESEARCH TO THE STUDENTS
Why course-based research?
Students gain when they experience real research in intro bio lab classes.
- Meet the challenge of Vision and Change to increase student exposure to research
- Expand exposure to all students in introductory Biology laboratory classes
- High impact pedagogy increases student interest in science careers
Get support for developing lab modules using your research.
- Faculty need time and support to develop new lab class modules
- Collaboration of research + education faculty from one institution can address specific needs and opportunities
- Travel funds are available for conference-associated workshops

source: openbmb/Ultra-FineWeb


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The importance of timescales for the emergence of environmental self-regulation
Models which explore the possibilities of emergent self-regulation in the Earth system often assume the timescales associated with changes in various sub-systems to be predetermined. Given their importance in guiding the fixed point dynamics of such models, relatively little formalism has been established. We analyse a classic model of environmental self-regulation, Daisyworld, and interpret the original equations for model temperature, changes in insolation, and self-organisation of the biota as an important separation of timescales. This allows a simple analytical solution where the model is reduced to two states while retaining important characteristics of the original model. We explore the consequences of relaxing some key assumptions. We show that increasing the rate of change of insolation relative to adaptation of the biota shows a sharp transition between regulating, and lifeless states. Additionally, in slowing the rate of model temperature change relative to the adapting biota we derive expressions for the damping rate of fluctuations, along with a threshold beyond which damped oscillations occur. We relax the assumption that seeding occurs globally by extending this analysis to solve a two-dimensional cellular automata Daisyworld. We conclude by reviewing a number of previous Daisyworld models and make explicit their respective timescales, and how their behaviour can be understood in light of our analysis.
Life sciences simulation: Ecology


text:
New report detailing the barriers which are blocking real, authorized development and growth in the recognized Bedouin villages, and recommendations for overcoming these barriers
The spatial layout of Bedouin villages reflects their organic development within the geo-political realities that influenced them over time. Although there are differences between the villages and even within them, all have common features of spatial distribution arising from their shared principles of traditional social organization. These principles include a social order that emphasizes the centrality of the family, its land ownership, gender separation, and maintaining privacy in its various aspects.
Of the Bedouin villages in the Negev, only 12 (one of them just recently in 2014) have been recognized by the government. The government planning process for the recognized villages has largely ignored their spatial reality and its logic. In practice, only 128 residential building permits were issued in only two villages. A decade after the establishment of the Abu Basma Regional Council, the development of infrastructure in its villages is minimal. Thousands of homes (built without permits) and many of the public buildings are not connected to basic infrastructure such as electricity and water, and in most cases, there are no roads to them.
The State must internalize that the transition from non-formalized settlements to regulated localities must be gradual, so as not to undermine the foundations of Bedouin society. This understanding must precipitate the necessary flexibility of planning concepts, of the general approach of the authorities, and of planning and development policy. Israeli society stands to benefit from the strengthening and development of the Bedouin villages, provided that this development occurs at a rate and in a manner appropriate to their residents.
Read the summary of the report here
Read the full report (in Hebrew) here


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Definition of asci
n. pl. - See Ascus. 2
The word "asci" uses 4 letters: A C I S.
No direct anagrams for asci found in this word list.
Words formed by adding one letter before or after asci (in bold), or to acis in any order:
b - basic d - acids asdic cadis caids e - saice h - chias l - laics salic m - micas n - cains p - aspic picas spica
List shorter words within asci, sorted by length
All words formed from asci by changing one letter
Browse words starting with asci by next letter


text:
Every big city has a sanctuary of sorts, a place where people can escape from the madness around them. New York has its famous Central Park; in London you can take your pick of Hyde, Regent's, Green or St James' parks – all greening up large parts of some of the world's most expensive real estate. At the other end of the world, Auckland, the largest city in the island nation of New Zealand, has the Auckland Domain, a generous 75-hectare park squeezed between some of the city's busiest districts.
As the city's oldest park, it serves multiple purposes, providing sports pitches, athletics tracks and concert venues, and playing host to the Auckland War Memorial Museum, a neoclassical mass of marble and stone that dominates the highest point in the park, perched atop the remains of a long-dormant volcano. The vast green space undulates, with the high points offering staggering views across the city and harbour, while more secluded corners welcome the city's urban population to while away their lunch hour.
Just down the hill from the museum is the real jewel in the Domain's crown: the Wintergarden. Designed by Auckland architectural firm Gummer and Ford, the combination of brick, glass and steel is reflective of a Victorian aesthetic familiar to anyone who has ever visited London's Alexandra Palace. Constructed in the 1920s, the garden's architectural style makes it appear older than it is, but this quiet corner of the park is one of its most-visited attractions – its small spaces attract visitor numbers that peak in their thousands on both days of weekends, while a steady flow of locals and tourists come through during the week.
"It is purely a pleasure garden," explains Melanie James of City Parks Services, part of Auckland City Council. James has worked in and around the garden for more than 26 years, having first joined its team as an apprentice horticulturalist. She now manages the Wintergarden, the wider Auckland Domain and its supporting nursery. "The Wintergarden is a constant floral display, that is the brief we work to. We're not a botanical garden. Things don't all have to be labelled, but it has to be full of flowers the whole time."
Original funding for the garden's construction came from an agricultural exposition that took place in early 1914. With project plans curtailed by the onset of the First World War, it wasn't until 1921 that the garden's Temperate House was completed. Another eight years would pass before its twin Tropical House would join it on site, the two glass houses facing up to each other across a formal courtyard with a central sunken lily pond, quirky statuary and surrounding pergolas. The order and peace of the courtyard is complemented by the adjoining fernery, where native New Zealand plants spiral down the steep sides of what was once a quarry, and lead visitors down into a naturally cool and secluded spot to hide from the crowds above.
In the tight spaces of the glasshouses, the plants dominate. The Temperate House has ever-changing displays of flowers in bloom, which fill the space with an all-enveloping floral perfume and a cacophony of birdsong, as some of the winged locals flit in and out, chasing nectar and sometimes the insects that buzz in, too. Across the courtyard, the centre of the Tropical House is dominated by a pond, complete with giant lily pad, which James says can support the weight of a very still toddler, a party-trick that the garden's team pull out on special occasions. A leafy alcove at one end provides a secluded spot for some quiet contemplation, although passing visitors are just a metre or so away. Flowering orchids add colour among the greenery of the notably warmer Tropical House, heated to an average of 28°C from the floor up by circulating hot water.
The garden may not have a botanical purpose, but that doesn't mean there aren't special plants for visitors to take in. "We've got some things that are pretty special, like the huge cycad in the Tropical House," James explains. "We've thought about relocating it since it is so huge it is touching the glass, but there's nowhere to take it; it has to be in a tropical environment and no one else has that sort of situation where they could keep it alive."
Maintenance of the heritage-listed site and its flora is no easy task. For one, there's no sprinkler system, so each and every one of the plants has to be watered by hand every day. Demand for a constant picture of blooming perfection in the Temperate House means that plants are swapped out every week, as they go out of flower, replaced with new, budding examples. Because the flowers are annuals, a crop may last a month if the team are lucky, so there's constant demand for change, keeping the nearby nursery busy all year round. "We are open every single day of the year," James says. "We don't ever shut, so it's constantly keeping the place maintained while having the public coming through pretty much all the time."
When it comes to the buildings themselves, their heritage status creates a long list of dos and don'ts, all aimed at preserving the site for future generations, but there are distinct benefits, too. For one, the Wintergarden's look earned it a cameo role in the 1983 David Bowie film Merry Christmas Mr Lawrence. For another, the location has an effect on the way the team that cares for it works. "It enhances the way we operate," James explains. "It makes you always feel like you are somewhere pretty special, but there are some things we can't do. So, for instance, the gutters are too small for the volume of water, but we can't change them because they are heritage, so with things like that if any restorations are done they have to be done as they would have been originally."
This is also getting tougher, because the range of tradespeople with the skills to work on glasshouses of this age and type has narrowed considerably over the years. Now, even cleaning can be a major endeavour. Despite these challenges, the venue remains a firm favourite among Aucklanders and the city's international visitors, drawn by its combination of beauty, history and local feel.
"I think it's the atmosphere," James says. "It takes you back in time. It always looks the same, the plants might not be the same – some things will trend and others will drop off – but [a visit] is like looking back into a little bit of history. The Victorian look, the pond and that sound of running water make it a little oasis of quiet in the middle of the city. The whole park is, really."
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text:
Not many people like running.
It can be dull. It can be exhausting. The pounding it puts on your body can lead to overuse injuries.
But it's pretty darn good exercise, so a lot of us force ourselves to do it. However, a new study highlights a surprising alternative to running, which might also be a whole lot more fun.
Trampolines—like jet skis and pizza—are objectively fun. But the physical benefits of trampolining have long been overlooked.
To help fill this research gap, the American Council of Exercise enlisted the help of John Porcari, Ph.D., and his team at the Department of Exercise and Sport Science at the University of Wisconsin–La Crosse.
Their study sought to determine how good a trampoline-centric workout can be. The participants were 24 active college students. After getting acclimated with a mini-trampoline, they each performed a 19-minute workout routine designed by JumpSport. While the exact specifics of the workout were not made available, here's a taste of what a typical JumpSport workout looks like:
The participants' heart rates and VO2 max were collected every minute, and their calorie expenditure was based on the VO2 data. Every five minutes, participants were asked about their rate of perceived exertion, or RPE, essentially how hard they felt like they were working.
RELATED: Why VO2 Max is Important For Athletes
The results proved that the workout was not only effective, but that the participants seemed to enjoy themselves more than if they had done an equivalent amount of running. During the workout portion of the routine, men burned an average of 12.4 calories per minute and women burned 9.4 calories per minute. This level of calorie burning is approximately equivalent to running 6 miles per hour on flat ground, biking at 14 miles per hour or playing football, basketball or ultimate frisbee.
According to Dr. Porcari, the exercise intensity achieved during this research was expected to be moderate to vigorous. "We would expect participants to report ratings of perceived exertion of about 13 at this intensity level," Pocari said in a press release. "But the subjects averaged an RPE of 11.7, or a light to moderate intensity."
Essentially, participants didn't feel like they were working as hard as they actually were. While that might have something to do with the fact that bouncing around on a trampoline is fun, the low-impact nature of the exercise might also be a factor. Running puts a pounding on your body, especially on cement roads, which are not very forgiving. Trampolines, on the other hand, are extremely forgiving. "While the muscles are working hard, the trampoline makes the activity less jarring," Dr. Pocari said.
The unstable surface of a trampoline also makes the exercises more challenging, since they require more balance and core strength. That's a big reason why professional athletes like Luol Deng and Leandro Barbosa use trampoline-based movements in their training. "It's low impact. It requires balance and stabilization. And it's good dynamic, explosive exercise," says Elias Karras, Deng's trainer and the founder of EFT Sports Performance.
If you're interested in incorporating a trampoline into your workouts, check out the class schedule at some local gyms. Many offer trampoline-based workout classes. If you can acquire a mini-trampoline (most retail for under $100), you can perform a wide variety of trampoline-based exercises at home.
RELATED: Leandro Barbosa is Using Trampolines to Prepare for His 14th NBA Season


I apologies if this is off topic for this site, but I am trying to figure out (and not having much luck) how to get an empirical distribution function of a sum of two random variables given two samples of equal length (say 256) from their respective distributions. I understand that the distribution is given by a convolutions of their distributions, which can be computed as a product of their characteristic functions.
So, to test, I take two time series of length 256 each from standard normal distribution, compute their Fourier transforms using Fourier transform tool from Data Analysis toolpack in Excel, multiply the results using complex multiplication function IMPRODUCT, and then apply inverse Fourier transform. However, the resulting time series does not have a variance of 2 (it is more like ~240) which should be the case for the sum of two standard normal RVs. It might be some normalization issue, but I am not sure how and why it comes up.
Also, I am not sure if I am supposed to be doing zero-padding of the two original time series (say +256 zeros for each). I have read that if one wants to perform regular convolutions (instead of a "circular" one, whatever that is), one have to do this padding with zeros. This padding, however, seems to make the final result also double the length, but I would like to get the estimated distribution of the sum of the same length as the inputs, so not sure what needs to be done here.
Add 1
For example, given two identical samples of size 9 from uniform distribution
their convolution would produce 81 pairs with values
with respective counts
which gives the triangular distribution. What I am looking to do, is to produce this without doing these convolution sums, but by using Fourier transform.
Add 2
This is what Wikipedia says on the calculation of empirical characteristic function.
If $X_{i},i=1,2,\ldots,n$ are i.i.d. observations, then the empirical characteristic function $\varphi _{n}(t)$ is defined as
$$\varphi _{n}(t)={\frac {1}{n}}\sum _{j=1}^{n}\exp(itX_{j}).\tag{1}$$
And this is what it says about application of the characteristic function to sums of random variables.
In particular, $\varphi _{X+Y}(t) = \varphi _{X}(t) \varphi _{X}(t)$, i.e. the characteristic function of the sum of RVs is the product of their respective characteristic functions.
So, from the above two, it seems that one can construct characteristic functions directly from the samples and multiply them together to get a characteristic function of the sums. The only bit left is to get back into the "sample space". That was my understanding.
Add 3
A discreet Fourier transform (DFT) of a sequence is
$$\hat f_k=\sum _{j=0}^{n-1} f_j \exp(i2\pi k j/n).\tag{2}$$
So, if we take the probability of observing each value in the sample on $n$ observations the same, ie $f_j = 1/n$, then the difference between empirical characteristic function and DFT is that $X_j$ in the former is replaced with $2\pi j /n$ in the latter, so the characteristic function looks like some kind of "non-uniform" DFT. And this is where I seem to get stuck, I don't know how to compute this non-uniform DFT. Also, the discreet $t$ points of the transform need to be the same for both samples as I would need to take the product of the two empirical characteristic functions at those points.
 - $\begingroup$ wait, you draw 256 samples from a distribution, and then act as if that is the distribution? It's not! It's a sample vector!! If you approximate the distribution by finding the histogram, you might be doing something sensible... (also, really, Excel is not the tool of choice. Try Python, R or Octave instead. Free tools that actually do what you need). $\endgroup$ – Marcus Müller Jul 21 '17 at 14:34
 - $\begingroup$ Thank you for your comment. If I am not mistaken, one can take each observation as coming from a discrete distribution with probability of 1/n (n being 256 in this case), so effectively sample is like a distribution. This is also what I saw being done for calculation of empirical characteristic function, just applied to the sample. (Not sure, however, if one needs to sort the sample first). $\endgroup$ – Confounded Jul 21 '17 at 14:36
 - $\begingroup$ no, an observation is an observation, and not an estimate of the density.You need to calculate the empircal distribution first, and then you can convolve that. $\endgroup$ – Marcus Müller Jul 21 '17 at 14:38
 - $\begingroup$ As _USER_ says, you're completely off-base here. Please read this question and its answers and revise your question so that it makes a bit more sense. $\endgroup$ – Peter K. Jul 21 '17 at 14:46
 - 1
 $\begingroup$ Your addition has ordered the samples... and they "happen" to be completely uniformly spaced. That won't happen with a truly random variable. The convolution of a 9-sample vector with itself will yield a 17-sample vector (well, non-zero if all 9 samples are non-zero), not 81. Again, you seem to be mixing the samples from the distribution with the underlying distribution. $\endgroup$ – Peter K. Jul 21 '17 at 16:15
I still think you're completely confused about what you're trying to do, so I'm going to go back and assume your question is:
Given two data sets $x_n, n=0,\ldots, N-1$ and $y_n, n=0,\ldots, N-1$, how do I compute the probability density function (PDF) of $x_n + y_n$ using Excel?
and try to answer that.
Finding the PDF of $x_n$
Finding the empirical PDF of a data sequence is a little tricky. The most straightforward way is to find the histogram of the data and normalize it so that it sums to 1.
In pseudocode:
h_x = histogram(x)
pdf_x = h_x/sum(h_x)
It's not Excel, but this Google Sheet shows how that can be achieved.
The columns are:
 - x : the random sequence.
 - bin start : the start of each bin (next one is the end of this bin)
 - count : the number of samples of x that fall between the bin start and bin end.
 - pdf_hat : a stab at the empirical PDF using the histogram.
The same approach can be used for finding the PDF of $y$.
Finding the PDF of $x_n + y_n$
As you say, the PDF of the sum is the convolution of the two individual PDFs (provided $x_n$ and $y_n$ are independent). See section 7.2 here.
Unfortunately, Google Sheets doesn't have an FFT function, but you could do it element by element, which is what I've tried to show in the columns:
 - index : index of the PDF estimates.
 - Realization 1 : the pdf hat values for one realization.
 - Realization 2 : the pdf hat values for another realization.
 - Convolution : the element-by-element convolution.
The resulting graph is an estimate of the PDF of the sum, which approximates the expected triangle function quite well.
enter image description here
How to do better?
Generally, a better way to estimate the PDF of a random variable is to use kernel density estimators. Once you've got the individual PDF estimates then you can just convolve them. Or, if you have the summed data, just apply the kernel density estimator directly to that data.
Also, as you say, a better way to do convolution may be to use the FFT. To avoid "time aliasing" you need to zero-pad your signals (PDFs in this case) to be at least $N+M-1$ long (where one signal is length $N$ and the other $M$).
 - $\begingroup$ Thank you for taking time to read and reply to my post. I believe that the problem is "reduced" to finding a non-uniform DFT of a constant function $1/n$ sampled at the "times" given by the observed random numbers in the sample. This should results in an empirical characteristic functions of the generating distribution. Once two such empirical characteristic function are obtained, they can be multiplied together to get a characteristic function for a sum of two RVs, which can be then inverted to get empirical distribution of the sum. $\endgroup$ – Confounded Jul 22 '17 at 11:47
 - $\begingroup$ Unlike "normal" non-uniform DFT, however, I don't think one can do "gridding" since the information is contained in the random times at which "the signal" is observed, not in the signal itself, which constant here. So, it is not clear how to proceed in this case. $\endgroup$ – Confounded Jul 22 '17 at 12:11
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text:
Inverse Problems in Atmospheric Constituent Transport
The critical role of trace gases in global atmospheric change makes an improved understanding of these gases imperative. Measurements of the distributions of these gases in space and time provide important information, but the interpretation of this information often involves ill-conditioned model inversions. A variety of techniques have therefore been developed to analyze these problems. Inverse Problems in Atmospheric Constituent Transport is the first book to give comprehensive coverage of work on this topic. The trace gas inversion problem is presented in general terms and the various different approaches are unified by treating the inversion problem as one of statistical estimation. Later chapters demonstrate the application of these methods to studies of carbon dioxide, methane, halocarbons and other gases implicated in global climate change. This book is aimed at graduate students and researchers embarking upon studies of global atmospheric change, biogeochemical cycles and Earth systems science.
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additional adjoint advection analysis anthropogenic applications approach assimilation atmospheric CO2 atmospheric transport autocovariance average Bayesian estimation Biogeochemical Cycles boundary conditions budget Cape Grim carbon cycle CH3CCl3 changes Chapter characterised Climate CO2 data concentration of CO2 constraints covariance matrix CSIRO deconvolution defined denoted described deterministic differential diffusion discretisation distribution earth-system emissions Enting equations Figure fitting formalism function Geophys Geophysical Global Biogeochemical Cycles Green-function halocarbons Heimann ill-conditioned integrated inverse problems inversion calculations IPCC isotopic Kalman filter Kalman-filter Kyoto Protocol least-squares linear mass-balance inversions mathematical mean measurements methane model error non-linear normalisation observations ocean ozone parameters Prinn radiative Radiative Forcing ratio Rayner regional relation represent representation sampling scales Section singular-value decomposition solution spatial specified state-space statistical model stratosphere studies surface synthesis inversion techniques terrestrial time-scales trace-gas tracer inversions transport model troposphere uncertainty values variability variance variation vector


text:
2017 Volume 137 Issue 7 Pages 499-510
This study aims to quantitatively clarify the impact of large variable renewable energy (VRE) which is concentrated in some districts. Such districts will have difficulties to balance between power demand and supply, and keep the load frequency control (LFC) ability, and will be forced to curtail much VRE. Authors developed a simulation model to optimize generation mix and hourly operation of thermal and pumped hydro generation considering partial load efficiency, LFC supply and demand, and power transmission between districts. With this model, the authors obtained the following results on Japan's power system in 2030. (1) When VRE ratios to power system size are averaged, the VRE curtailment rates will much decrease. This way is difficult for wind power and other measures are required. (2) The curtailment rates can decrease a little by having some extra capacity of LNGCC though this is a very expensive way. (3) The capacity factors of LNGCC will be very low in some districts, and some incentives to hold LNGCC are required to use VRE stably.


What is one to one function - Definition and Meaning
One To One Function :
One to one function is a function between 2 sets where every element in first set is mapped to only one element in second set based on that function.
Example :
One to One Function example
Oscillating Series Oblique Angle
Learn what is one to one function. Also find the definition and meaning for various math words from this math dictionary.
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#MonthOfJulia Day 24: Graphs
Julia
If you're not too familiar Graph Theory, then it might be an idea to take a moment to get the basics. Graphs are an extremely versatile data structure for storing data consisting of linked entities. I'm going to look at two packages for managing graphs in Julia: LightGraphs and Graphs.
LightGraphs
As usual, the first step is to load the package.
julia> using LightGraphs
LightGraphs has methods which generate a selection of standard graphs like StarGraph(), WheelGraph() and FruchtGraph(). There are also functions for random graphs, for example, erdos_renyi() and watts_strogatz(). We'll start off by creating two small graphs. One will have 10 nodes connected by 20 random edges. The other will be a directed star graph consisting of four nodes, the central node being connected to every other node.
julia> g1 = Graph(10, 20)
{10, 20} undirected graph
julia> g2 = StarDiGraph(4)
{4, 3} directed graph
julia> edges(g2)
Set{Pair{Int64,Int64}}({edge 1 - 2,edge 1 - 4,edge 1 - 3})
It's simple to find the degree and neighbours of a given node.
julia> degree(g1, 4) # How many neighbours for vertex 4?
6
julia> neighbors(g1, 4) # Find neighbours of vertex 4
6-element Array{Int64,1}:
 1
 3
 6
 2
 9
 7
There's a straightforward means to add and remove edges from the graph.
julia> add_edge!(g1, 4, 8) # Add edge between vertices 4 and 8
edge 4 - 8
julia> rem_edge!(g1, 4, 6) # Remove edge between vertices 4 and 6
edge 6 - 4
The package has functionality for performing high level tests on the graph (checking, for instance, whether it is cyclic or connected). There's also support for path based algorithms, but we'll dig into those when we look at the Graphs package.
Graphs
Before we get started with the Graphs package you might want to restart your Julia session to purge all of that LightGraphs goodness. Take a moment to browse the Graphs.jl documentation, which is very comprehensive.
julia> using Graphs
As with LightGraphs, there are numerous options for generating standard graphs.
julia> g1a = simple\_frucht\_graph()
Undirected Graph (20 vertices, 18 edges)
julia> g1b = simple\_star\_graph(8)
Directed Graph (8 vertices, 7 edges)
julia> g1c = simple\_wheel\_graph(8)
Directed Graph (8 vertices, 14 edges)
Graphs uses the GraphViz library to generate plots.
julia> plot(g1a)
Of course, a graph can also be constructed manually.
julia> g2 = simple_graph(4)
Directed Graph (4 vertices, 0 edges)
julia> add_edge!(g2, 1, 2)
edge [1]: 1 - 2
julia> add_edge!(g2, 1, 3)
edge [2]: 1 - 3
julia> add_edge!(g2, 2, 3)
edge [3]: 2 - 3
Individual vertices (a vertex is the same as a node) can be interrogated. Since we are considering a directed graph we look separately at the edges exiting and entering a node.
julia> num_vertices(g2)
4
julia> vertices(g2)
1:4
julia> out_degree(1, g2)
2
julia> out_edges(1, g2)
2-element Array{Edge{Int64},1}:
 edge [1]: 1 - 2
 edge [2]: 1 - 3
julia> in_degree(2, g2)
1
julia> in_edges(2, g2)
1-element Array{Edge{Int64},1}:
 edge [1]: 1 - 2
Vertices can be created with labels and attributes.
julia> V1 = ExVertex(1, "V1");
julia> V1.attributes["size"] = 5.0
5.0
julia> V2 = ExVertex(2, "V2");
julia> V2.attributes["size"] = 3.0
3.0
julia> V3 = ExVertex(3, "V3")
vertex [3] "V3"
Those vertices can then be used to define edges, which in turn can have labels and attributes.
julia> E1 = ExEdge(1, V1, V2)
edge [1]: vertex [1] "V1" - vertex [2] "V2"
julia> E1.attributes["distance"] = 50
50
julia> E1.attributes["color"] = "green"
"green"
Finally the collection of vertices and edges can be gathered into a graph.
julia> g3 = edgelist([V1, V2], [E1], is_directed = true)
Directed Graph (2 vertices, 1 edges)
It's possible to systematically visit all connected vertices in a graph, applying an operation at every vertex. traverse_graph() performs the graph traversal using either a depth first or breadth first algorithm. In the sample code below the operation applied at each vertex is LogGraphVisitor(), which is a simple logger.
julia> traverse_graph(g1c, DepthFirst(), 1, LogGraphVisitor(STDOUT))
discover vertex: 1
examine neighbor: 1 -> 2 (vertexcolor = 0, edgecolor= 0)
discover vertex: 2
open vertex: 2
examine neighbor: 2 -> 3 (vertexcolor = 0, edgecolor= 0)
discover vertex: 3
open vertex: 3
examine neighbor: 3 -> 4 (vertexcolor = 0, edgecolor= 0)
discover vertex: 4
open vertex: 4
examine neighbor: 4 -> 5 (vertexcolor = 0, edgecolor= 0)
discover vertex: 5
open vertex: 5
examine neighbor: 5 -> 6 (vertexcolor = 0, edgecolor= 0)
discover vertex: 6
open vertex: 6
examine neighbor: 6 -> 7 (vertexcolor = 0, edgecolor= 0)
discover vertex: 7
open vertex: 7
examine neighbor: 7 -> 8 (vertexcolor = 0, edgecolor= 0)
discover vertex: 8
open vertex: 8
examine neighbor: 8 -> 2 (vertexcolor = 1, edgecolor= 0)
close vertex: 8
close vertex: 7
close vertex: 6
close vertex: 5
close vertex: 4
close vertex: 3
close vertex: 2
examine neighbor: 1 -> 3 (vertexcolor = 2, edgecolor= 0)
examine neighbor: 1 -> 4 (vertexcolor = 2, edgecolor= 0)
examine neighbor: 1 -> 5 (vertexcolor = 2, edgecolor= 0)
examine neighbor: 1 -> 6 (vertexcolor = 2, edgecolor= 0)
examine neighbor: 1 -> 7 (vertexcolor = 2, edgecolor= 0)
examine neighbor: 1 -> 8 (vertexcolor = 2, edgecolor= 0)
close vertex: 1
We can use Dijkstra's Algorithm to calculate the distance from a given vertex to all other vertices in the graph. We see, for instance, that the distance from vertex 1 to vertex 4 is three steps. Since vertex 1 and vertex 20 are not connected, the distance between them is infinite. There are a couple of other algorithms available for calculating shortest paths.
julia> distances = ones(num_edges(g1a)); # Assign distance of 1 to each edge.
julia> d = dijkstra\_shortest\_paths(g1a, distances, 1);
julia> d.dists # Vector of distances to all other vertices.
20-element Array{Float64,1}:
 0.0
 1.0
 2.0
 3.0
 3.0
 2.0
 1.0
 1.0
 3.0
 4.0
 2.0
 2.0
 Inf
 Inf
 Inf
 Inf
 Inf
 Inf
 Inf
 Inf
As with the most of the packages that I have looked at already, the functionality summarised above is just a small subset of what's available. Have a look at the home pages for these packages and check out the full code for today (which looks at a number of other features) on github. Some time in the future I plan on looking at the EvolvingGraphs which caters for graphs where the structure changes with time.
Categorically Variable


text:
Here is a 3-step process for Triangulation: Collect, Arrange, Compare/Group.
- Collect all the Match-segments you can. I recommend testing at all three companies (23andMe, FTDNA, and AncestryDNA), and using GEDmatch. But, wherever you test, get all of your segments into a spreadsheet. If you are using more than one company, you need to download, and then arrange, the data in the same format as your spreadsheet. Downloading/arranging is best when starting a new spreadsheet. Downloading avoids typing errors, but direct typing is sometimes easier for updates. I recommend deleting all segments under 7cM – most of them will be IBC/IBS (false segments) anyway, and even the ones which may be IBD are very difficult to confirm as such. You are much better off doing as much Triangulation as you can with segments over 7cM (or use a 10cM threshold if you wish), and then adding smaller segments back in later, if you want to analyze them. NB: Some of your closer Matches will share multiple segments with you – each segment must be entered as a separate row in your spreadsheet. The minimum requirement for a Triangulation with a spreadsheet includes columns for MatchName, Chromosome, SegmentStartLocation, SengmentEndLocation, cMs and TG. Most of us also have columns for SNPs, company, testee, TG, and any other information of interest to you. Perhaps I need a separate blog post about spreadsheets… ;>j
- Arrange the segments by sorting the entire spreadsheet (Cntr-A) by Chromosome and Segment StartLocation. This is one sort with two levels – the Chromosome column is the first level. This puts all of your segments in order – from the first one on Chromosome 1 to the last one on Chromosome 23 (for sorting purposes I recommend changing Chromosome X to 23 or 23X so it will sort after 22). This serves the purpose of putting overlapping segments close to each other in the spreadsheet where they are easy to compare.
- Compare/Group overlapping segments. All of these segments are shared segments with you. So with segments that overlap each other, you want to know if they match each other at this location. If so this is Triangulation. This comparison is done a little differently at each company, but the goal is the same: two segments either match each other, or they don't (or there isn't enough overlapping segment information to determine a match). All the Matches who match each other will form a Triangulated Group, on one chromosome – call this TG A (or any other name you want). Go through the same process with the segments who didn't match TG A. They will often match each other and will form a second, overlapping TG, on the other chromosome – call this TG B. [Remember you have two of each numbered chromosome.] So to review, and put it all a different way: All of your segments (every row of your spreadsheet) will go into one of 4 categories:
- – TG A [the first one with segments which match each other]
- – TG B [the other, overlapping, one with segments which match each other]
- – IBC/IBS [the segments don't match either TG A or TG B]
- – Undetermined [there are not enough segments to form both TG A and TG B and/or there isn't enough overlapping data to determine a match.]
- NB: None of the segments in TG A should match any of the segments in TG B.
- At GEDmatch – the comparisons are easy. Just compare two kit numbers using the one-to-one utility to see if they match each other on the appropriate segment. The ones that do are Triangulated. You may also use the Tier1 Triangulation utility or the Segment utility. I prefer using the one-to-one utility and Chrome.
- At 23andMe you have several different utilities:
- – Family Inheritance: Advanced lets you compare up to 5 Matches at a time. You may also request a spreadsheet of all your shared segments; sort that by chromosome and SegmentStart, and check to see if two of your Matches match each other. The ones that do are Triangulated.
- – Countries of Ancestry: Sort a Match's spreadsheet by chromosome and SegmentStart, search for your own name, and highlight the overlapping segments. The Matches on this highlighted list who are also on overlapping segments in your spreadsheet are Triangulated (the CoA spreadsheet confirms the match between two of your Matches)
- At FTDNA it's a little trickier, because they don't have a utility to compare two of your Matches. So the most positive method is to contact the Matches and ask them to confirm if they match your overlapping Matches, or not. The ones that do are Triangulated. An almost-as-good alternative is to use the InCommonWith utility. Look for the 2-squigley-arrows icon next to a Match's name, click that, and select In Common With to get a list of your Matches who also match the Match you started with. Compare that list of Matches with the list of list of Matches with overlapping segments in your spreadsheet. Matches on both lists are considered to be Triangulated. Although this is not a foolproof method, it works most of the time. And if you find three or four ICW Matches in the same TG, the odds are much closer to 100%. Remember, every segment in your spreadsheet must go in one TG or the other, or be IBC/IBS, or be undetermined. If a particular Match, in one TG, is critical to your analysis, then try hard to confirm the Triangulation by contacting the Matches.
- AncestryDNA has no DNA analysis utilities. You need to convince your Matches to upload their raw data to GEDmatch (for free) or FTDNA (for a fee), and see the paragraphs above.
Comments to improve this blog post are welcomed.
10 Segmentology: How to Triangulate; by Jim Bartlett 20150511


text:
The Gold Medal Schools Awards Program (GMS) recognizes Utah's schools that create opportunities for students to eat healthy, be active, and stay tobacco-free.
How to Apply:
See the "Award" information and application page at the Gold Medal Schools Web site.
After completion of award requirements, submit completed application with the handling fee (if applicable).
- Review the award requirements and choose the level you will be working toward. Award levels are:
- Identify which criteria you will be working on.
- Bronze: A school must complete the following six criteria:
- 90 minutes of structured physical activity each week
- Teach the State Office of Education Nutrition Curriculum
- Walk the Gold Medal Mile
- Write and enforce the Safe and Active Routes to School policy
- Write and enforce the Tobacco policy
- Complete the Heart Health Survey
- Silver: Must complete the Bronze and the three following Silver criteria:
- PTA/PTO coordinate one health related event per year
- Implement staff and wellness activities
- Offer a variety of competitive and non-competitive physical activity programs
- Gold: Must complete the Bronze, Silver and the following three Gold criteria:
- All Pre K-12 physical education courses be overseen by a PE specialist
- No food rewards
- Complete four of the nine options from the Changing the Scene Program.
- Platinum: Must complete the Bronze, Silver, Gold and the five following Platinum criteria:
- Strengthen school community council
- Healthy choices @ school events vending machines school stores, fundraisers, and other venues
- Staff/faculty wellness program
- Involve families communities
- Recess before lunch or adequate eating time


text:
move across the membrane?
1: Osmosis: In osmosis water always moves engage an area of higher water concentration to one of perfection concentration. In the diagram shown the perfect cannot area through the selectively penetrable membrane but the water can.
How does water travel up a tube?
Place the straw inter a vitreous of water and absorb on the straw. The resistance in the vitreous is what we named atmospheric resistance and the resistance in your engage is pure sooner_than that (a vacuum). The fluid flows upwards in our "pipe." replenish the straw and fix it upward toward the ceiling and puff inter it.


text:
Researchers believe that ancestors of the rare Exmoor ponies may have been used by warriors in service to Queen Boudicca in the first century CE. The ponies are being imported to the Drumburgh Moss National Nature Reserve, maintained near Carlisle, England, by the Cumbria Wildlife Trust. According to naturalists, invasive grasses threaten to dry out the peat bog and destroy native mosses and other plant life. Six of the Exmoor ponies were moved to Drumburgh on June 8, 2006.
The ponies provide a natural way to remove the invasive grasses, and they are unusual in their ability (and willingness) to wade belly-deep into the bog to graze. They also are unusually resistant to the hoof-rot that would afflict many other equine species.
Thanks to David Boyes, Justin's modern-world boss, for providing this story lead.


text:
What is "wildcrafting"?
Wildcrafting is an operation specific to harvesting botanicals from a defined natural or wild area that is not actively cultivated. NutriCert program guidelines help ensure that ingredients used by Nutrilite are harvested using sustainable practices that do not diminish the existence or productivity of the wild botanical.
An example of a wildcrafted botanical ingredient
In NUTRILITE Cal Mag D Advanced, we use naturally calcified seaweed harvested from glacial fjords off the coast of Iceland. This is one of nature's most concentrated botanical sources of calcium and magnesium.
Being good, sustainable-minded stewards of the environment
Even in the waters off Iceland, Nutrilite certifies that the supplier meets the high NutriCert standards, ensuring that sustainable environmental and agricultural practices are followed. Amway even knows that harvesting crews use advanced GPS technology to gather the seaweed while protecting other delicate sea life.


The Disease Absorber
Introduction: The Disease Absorber
About: Interior architecture / Hockey player at Gantoise Hockey / Traveler
The disease absorber is an interactive machine for kids. The meaning of this machine is a game (playing tag) that has been modernized. The game is about understanding some diseases in a fun way.
Game introduction:
There is 1 (the doctor who will be the shooter) who will shoot some good bacteria to microbes (kids running around) the kids who are running around the tank are some bacteria who do not want to heal. That's why the doctor has to shoot some good bacteria to heal those kids. Once the doctor bacteria have hit the kid, he will be healed and will become a partner of the doctor (can tag with the doctor).
After the kid has been shot, he has to put the bacteria into the absorber. The counter will count 1 point for the doctor.
When the game is finished, the doctor will count each microbe that has been absorbed by the absorber and will count his total points.
In these steps, we are going to explain you the process of this model.
c.2007
All ideas, concepts, drawings, pictures, videos, documents, posters, comments, mock-up scale models, prototypes and machines that are developed in the context of MyMachine are the property of MyMachine vzw. Unauthorized use and/or duplication of this material without express and written permission from MyMachine vzw is strictly prohibited. MyMachine vzw is a social profit organization that fosters creativity, entrepreneurship, open education and STEM in education.
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Step 1: What Do We Need?
1. Aluminum bars
- 360 x 2
- 240 x 6
- 250 x 2
- 100 x 4
- 20 x 4
2. Balsa wood
3. Hard wood for the wheels 40 Diameter
4. MDF for the cannon frame 120 diameter
5. Aluminum fixations for balsa wood
6. Bamboo wood for canon
7. Fimo for climbing wall
8. Some glue (epoxy and wood)
Step 2: Making of the Aluminum Frame
1. We have to cut the bar into pieces of
- 360 x 1
- 240 x 1
- 100 x 2
- 20 x 2
2. After cutting those pieces, we have to make sure they will stick nicely to each other. That's why we have to make a cut of different degrees to attach the bars properly to each other.
3. First we make the left and right sides of the tank and attach them later with bars in between.
4. With some epoxy glue we attach the bars to each other
5. When the left and right sides are finished, we will fix some aluminum bars in between them so we obtain the width of the tank.
6. When the frame is finished we can start cutting the balsa wood
Step 3: Side Covering
1. Take some balsa woods for attaches them to the frame. The wood panels will be used for reinforcement and decoration of the tank.
2. Take the measure of the open spaces of the tanks. For example, take your left or right side and draw the form with a pencil on the balsa
3. After you draw the shape of the sides offset the shape 5mm inside. You create a smaller form, but still keep the shape.
4. Do that for al the sides (top, left, right, front, back)
Step 4: Aluminum Connections
1. To connect the balsa wood to the aluminum frame, we need some aluminum connections.
2. Take an aluminum panel of 1mm thick
3. Draw on a paper the form of the connection
4. Glue the paper on the aluminum and cut with a Stanley knife the shape of the connection
5. Do that for about 22 times
Step 5: Frame for Cannon
1. Draw the form (180 x 20) on a sheet of paper and cut it
2. Glue the paper form on a wooden panel of 1mm thick
3. Cut the form out of the panel
4. Do that for about 6 times.
5. Drill 5 holes for connecting some rope later
5. Cut a circle of 120mm diameter out of MDF 10mm
7. Find a piece of a screw to connect it with the MDF (this will serve to rotate the cannon 360°)
6. Cut a hexagon 110 mm for the top of the cannon
7. Connect part 1, 2 and 3 together
Step 6: Cannon Frame Finishing
1. When the frame is assembled, we start finishing the whole frame
2. Cut some rope of 150mm and pass them trough the holes
3. Do that for all the holes and you will become a sort of climbing wall
Step 7: Cannon
1. Take a bamboo stick 180mm
2. Make some attachment parts to pivot the cannon
3. Find a led light to put on the cannon. The led will serve as laser to point when shooting
4. Make a foot pump out of MDF or triplex 5mm
5. Attach some tube from the foot pump till the cannon
Step 8: Cutting the Wheels
1. Draw with a drafting compass a circle for about 40 mm
2. Cut the wheels with a band saw or similar
3. Connect the wheels to the frame (left and right)
Step 9: Assembly
1. Connect the cannon to the frame
2. Place the cannon frame on the tank frame
Step 10: Finishing
1. Take some Fimo clay to make the climbing wall
2. Glue the Fimo clay on the wooden panels
3. Take a piece of wood and make a slide out of it.
Step 11: Movie
Movie of the process
Attachments
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. Once the code works in your environment, you can then integrate it into Shuffle's apps directory to integrate as an app.
Don't forget to document your app in the docs.md file in each version's directory (see below)!
Utility functions
The Python SDK has a couple of utility functions and data you can utilize. These are as follows:
# Basic data
self.url # The URL to interact with for file and cache control
self.base_url # The URL to send results to. MAY point to a Worker.
self.action # The current action being executed
self.authorization # The authorization key for current execution
self.current_execution_id # The current execution ID
self.full_execution # All data for current execution
self.start_time # The start time for this function
# Utility functions
self.get_file(file_id) # Get a file from the backend (v0.8.60+)
self.set_files(file_id) # SETS multiple files, returns ids (v0.8.60+)
self.get_file_namespace(namespace) # Get ALL files for a namespace (v0.9.11+)
self.get_cache(key, value) # Get an item from key:value store (v0.8.97+)
self.set_cache(key, value) # SETS cache in the key:value store (v0.8.97+)
Example file API usage:
filedata = [{"filename": "test.txt", "data": "this is the data in the file"}]
fileret = self.set_files(filedata)
file_id = ""
if len(fileret) > 0:
 file_id = fileret[0]
 filedata = self.get_file(file_id)
 print(filedata)
 # > [{"filename": "test.txt", "data": "this is the data in the file"}]
Example cache API usage:
value = "THIS IS SOME DATA I WANT TO CACHE"
key = "cache_key"
cacheret = self.set_cache(key, value)
if cacheret["success"] == True:
 cache_get_ret = self.get_cache(key)
 print(cache_get_ret)
 # > {
 # "key": "cache_key",
 # "value": "THIS IS SOME DATA I WANT TO CACHE"
 # }
Directory structure
With your Python script built, we must build a directory structure, some metadata files and modify a base app.py file to run your code from Shuffle. In your shuffle server, there is a shuffle-apps directory, typically under the top level dir where you cloned the github repo.
/Shuffle/shuffle-apps/
This directory contains a list of folders, one per app in Shuffle. The minimal directory structure for a Shuffle app is as follows:
Shuffle
+-- shuffle-apps
 +-- app_name (virustotal)
 +-- version_number (1.0.0)
 |-- api.yml # Has the full app configuration - must match app.py
 |-- Dockerfile # Contains Shuffle build instructions
 |-- docs.md # Your documentation of this app in markdown format
 |-- requirements.txt # Extra packages to be used by the app
 +-- src
 +-- app.py # The base python file for everything related to your application
 +-- yourcustom_app.py # More complex apps can have an entire directory stucture, imported and called by app.py
 +-- another_app_1
 +-- another_app_2
There is a template app called python-playground that we will clone and edit. First make your app folder. Note: This folder should not contain spaces.
cd /Shuffle/shuffle-apps
mkdir office365mgmt
cd office365mgmt
cp -R /Shuffle/shuffle-apps/python-playground/* .
The above will make your app folder and copy the template files from python-playground to your app. Upload your python code files to the /src/ directory of your app's folder.
Now that your app's .py script is uploaded under /Shuffle/shuffle-apps/office365mgmt/src, you'll tell Shuffle how to use it.
We will modify 4 files:
 - api.yaml
 - docs.md
 - requirements.txt -- Specify any modules that your app uses, which will be installed via pip
 - src/app.py -- The base app that is called by Shuffle, which in turn will call your python script's functions
api.yaml
api.yaml is what provides Shuffle information about your app. Here we define your app's name, description, author info, actions (functions available) and the parameters that each function takes.
This example shows the office365mgmt app settings, but feel free to explore other app's api.yaml to see other obscure options.
 - name: Office365 Mgt API -- This is your app's display name as seen in Shuffle GUI. You can put whitespace here, but _ may be better
 - description: Some description -- Your app's description that will display in the Shuffle GUI
 - contact_info: If you are maintaining this, please share your details with users
app_version: 1.0.0
name: Office365 Mgt API
description: Collect AzureActiveDirectory,Exchange,Sharepoint,General, DLP audit logs
contact_info:
 name: "@RobertEvans"
 url: _URL_
 email: _EMAIL_
Authentication
Not all apps need this step, but if yours requires a key, secret, or password, it is recommended to use a specialized "authentication" declaration in api.yaml, so those credentials are stored safely and not displayed in cleartext in your Shuffle workflow.
Here I define that my app requires authentication and a list of parameters, which is a list of arguments specified by -name. The name value can be anything you want, but note this is the name of the argument when passed to app.py and ultimately to your app.
My app expects 4 arguments that should be stored as an authentication object. When this app is run, this credential object once configured in Shuffle will be sent to the app to use.
My first argument is a string called "planType". When the app is used in a workflow, this would be populated by workflow parameters and it would get sent to the app to use (e.g. "Enterprise").
 - name: case sensitive authentication argument name
 - description: The description should display common values for this argument
 - example: The example is a placeholder syntax in absence of a value in user interface.
 - required: specifies whether it is mandatory to run the app
 - schema: The type of variable can be specified, string is the only one tested but ideally any type could be used here. You can also do type casting in your app from string later.
authentication:
 required: true
 parameters:
 - name: planType
 description: Enterprise, GCC , GCCHigh, DoD
 example: "Enterprise"
 required: true
 schema:
 type: string
 - name: tenantID
 description: xxxx-xxxx-xxxx-xxxx
 example: "xxxx-xxxx-xxxx-xxxx"
 required: true
 schema:
 type: string
 - name: clientID
 description: xxxx-xxxx-xxxx-xxxx
 example: "xxxx-xxxx-xxxx-xxxx"
 required: true
 schema:
 type: string
 - name: clientSecret
 description: xxxx-xxxx-xxxx-xxxx
 example: "*****"
 required: true
 schema:
 type: string
Actions
Actions define the functions of your app, the args it accepts and the options available (static list of values for a given arg).
 - name: the python function name
 - description: string description of the function
 - parameters: The args the function accepts, note this is in addition to the authentication args specified above.
Note that one value, json_data, seems to be sent even if I don't need it, so I account for it, although it is unused at this time. You could send a JSON object here. This app has 1 function (action) called run_o365poller. This app only pulls logs, so it is limited.
The app's single function takes 2 args (besides the 4 from the authentication included above for every function), which are a string json_data arg and string PollInterval. The PollInterval string arg has a dropdown selection in GUI, listed by options, of poll_10min or poll_23hours. This is one method to give clients a limited set of options for arg values. The json_data structure, although unused, can insert any value from a prior app or input.
actions:
 - name: run_o365poller
 description: Run Office365 audit poller for defined interval
 parameters:
 - name: json_data
 description: The JSON to handle
 required: false
 multiline: true
 example: 'No args for now, insert credentials and get returned data'
 schema:
 type: string
 - name: Poll


text:
The University of California Cooperative Extension Office and the Center for Applied Horticultural Research have established workshops to educate employees about pest monitoring in ornamental plant production.
The University of California Cooperative Extension Office (UCCE) and the Center for Applied Horticultural Research (CfAHR) have established workshops to educate employees about pest monitoring in ornamental plant production.
The CfAHR is a non-profit organization in Vista, Calif., created with the purpose of generating research for problem solving in the horticultural industry. The workshop, titled "Pest Monitoring in Ornamental Plant Production," is a six-hour session of intensive practical training in basic entomology, pest damage monitoring techniques and the basics of integrated pest management (IPM).
The horticultural industry faces increasing pressure for "greener" products and reduced pesticide use. A significant reduction in pesticide use is attainable by early detection of pest attack by trained personnel, which also results in increased productivity, cost reduction and increased profits. The program emphasizes monitoring as a key component of IPM programs and for reducing pesticide use. The program practical component consists of hands-on activities for the participants to familiarize themselves with microscopes and stereoscopes, symptoms of pest attack, damage caused by abiotic factors and how to differentiate it from pest attack, and effective monitoring for pests in production areas. The workshops are currently presented in Spanish; and English workshop will be offered in the future.
The program is coordinated by James Bethke and Irma Debonis from UCCE and by Lucia Villavicencio from the CfAHR. It will be offered several times a year. The next workshop will take place on Aug. 16, 2011, at the CfAHR. For information, please contact _EMAIL_ or _EMAIL_. You can find information about the CfAHR at _URL_ and about UCCE at _URL_.


text:
Even though your teeth are largely composed of a mineral softer than that found in sharks, new tests reported in the Journal of Structural Biology suggest that they're just as tough. In sharks, the material coating the teeth is largely composed of fluoroapatite, a fluoridated phosphate mineral that in its pure form is harder than the hydroxyapatite found in the enamel of human teeth. But by pressing tiny metallic pyramids into the surfaces of teeth from a shortfin mako shark (Isurus oxyrinchus, shown) and a tiger shark (Galeocerdo cuvier), researchers found that the enameloid coating on shark teeth is no harder than that of the enamel on a human wisdom tooth. The teeth are, in fact, of comparable hardness because their surfaces aren't pure mineral but instead are made of mineral crystals bound together with proteins so that the material doesn't shatter under a sudden impact.
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text:
Honorable John Scott Redd
Vice Admiral, U.S. Navy, Retired
First Director, National Counterterrorism Center
Founding Commander, U.S. FIFTH Fleet
General Bruce Fister has written a book that not only tells an important story but also lays out foundational principles of leadership and faith that apply far beyond the specifics of this account.
On the surface, this is the record of a landmark effort to grow the ministry of the Officers Christian Fellowship (OCF). OCF is a unique ministry that, since WW II, has equipped thousands of military officers to lead with character and integrity. It has also supported and blessed their families, including mine, in the midst of the demanding challenges and sacrifices of military life.
The story within the story amounts to a graduate course in godly leadership. As a three-star general, Bruce is no stranger to leadership. He led the aviation component of the nation's most action-oriented force, the Joint Special Operations Command, in key national-level operations. Now, faced with a very different environment, he demonstrates through numerous specific examples of how biblical leadership principles are universally applicable.
At the deepest level, this book is a testimony to God's unwavering faithfulness to His children and the blessings of a persevering faith in the face of difficulties.
Written in a humble and engaging style, Growing and Building is highly recommended for leaders and followers of all ages.


text:
U.S. Coast and Geodetic Survey.
Found in 1 Collection or Record:
FOUND IN: Harvard University ArchivesCollection Identifier: UAV 630.6
Overview: The Records of Harvard College Observatory Director George Phillips Bond, consisting of correspondence and subject files, manuscript drafts, and astronomical data, chiefly document the research activities of the Harvard College Observatory from 1859 to 1865. Chronicled are observations of planets, including Mars and Jupiter; efforts to determine the brightness of stars; and the detection of comets, eclipses, and other astronomical phenomena. George Phillips Bond (_PHONE_), an astronomer,...


text:
|Niagara Frontier's Traveling Planetarium!
The Mobile Dome Planetarium is the only traveling planetarium in the seven counties of Western New York accompanied by a professional planetarian: Paul Krupinski from Ancient Eyes Productions. Now, there's a way for teachers, parents, students, scouts, and youth groups to explore the night sky, as well as learn about astronomy and space science.
Learn science and related topics in a small and intimate setting that encourages thinking and discussion. Experience the excitement of a professional planetarium program with sight and sound. Now, this can happen in your building and you no longer have to plan for the "away from school" field trip. We bring the stars and wonders of the universe directly to YOU!
The Mobile Dome Planetarium is dedicated to astronomy, earth science, and space science education, and is a member in good standing of The International Planetarium Society, The Middle Atlantic Planetarium Society, and The Great Lakes Planetarium Association., as well as a member of the Royal Astronomical Society of Canada.
Most programs are curriculum and New York State standard-specific!
DON'T WAIT AND MISS OUT ON THIS AWESOME EDUCATIONAL EXPERIENCE!
RESERVE A DATE FOR THE 2023-2024 SCHOOL YEAR NOW!
Additional information on The Mobile Dome Planetarium:
School Programs | Scout Programs | Program Pricing | FAQs


How Giant Birds Can Fly Nearly 10,000 Miles
An albatross is a huge bird, weighing up to 25 pounds, but it can fly around the world in 46 days! That's quite a mean feat, even among creatures who have harnessed the miracle of flight. Lifting that heavy body into the air takes a lot of energy, so to compensate, albatrosses have harnessed their knowledge of physics. A team of German scientists attached GPS trackers to 20 of the birds to study their flight. Their data comes from 16 of the birds, which causes us to pause just a moment in memory of the other four.
Albatrosses yo-yo up and down in the sky, taking advantage of momentum generated on their downhill glides in order to climb back up against the wind. These constant up and down changes in altitude keep the birds aloft without requiring much effort. In fact, the propulsive force generated by such undulations is about ten times greater than anything the albatross could create by simply flapping its wings.
But it's a trick the rest of the animal kingdom doesn't often use. For example, hummingbirds weigh about 0.07 ounces (2.2 grams)—98 percent less than an albatross—and yet their wings have to beat about 70 times per second to keep their little bodies aloft. An albatross can go hours without flapping. Because of this frantic motion, hummingbirds have to eat up to three times their body weight every day.
While hummingbirds appear to get along just fine at low efficiency, their lives depend on available fuel sources. If those sources were to die out, so would the hummingbirds. But albatrosses could simply travel to a new food source -which is what they do. Read more at NatGeo's Weird and Wild blog. -via the Presurfer
(Image credit: Flickr user StormPetrel1)
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Keyndra Lewis, LMFT/ English-SpanishTelehealth
Keyndra Lewis, LMFT/ English-SpanishTelehealth
Accepted Insurance Plans: Out of Network
Cost per Session: $100-150
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Learning to navigate the challenges in a constructive and beneficial manner produces a confident, well-adjusted person. I provide inclusive clinical interventions to improve overall functioning to couples, individuals and their children. Living means evolving, sometimes together and other times apart regardless empathy support and a focus on self healing is imperative. Soy hispana y hablo español.
My experience 10+ years in field has ranged from supporting partnerships in all stages Choosing to move forward to single life after separation, with or with out children or dependents, can be challenging. I am well versed in adolescents and adult substance use and trauma which can lead to significant changes in a persons life.
Living means changing, adapting and evolving, this can be a challenging experience. Learning new ways to handle this transition will help you manage your emotions. As a dual language and fluent speaker Spanish and English clinician with international training I can provide support utilizing a global approach.
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text:
The following methods can be defined to emulate numeric objects. Methods corresponding to operations that are not supported by the particular kind of number implemented (e.g., bitwise operations for non-integral numbers) should be left undefined.
%, divmod() pow()
|). For instance, to evaluate the expression x
+y, where x is an instance of a class that has an __add__() method,
x.__add__(y)is called. The __divmod__() method should be the equivalent to using __floordiv__() and __mod__(); it should not be related to __truediv__() (described below). Note that __pow__() should be defined to accept an optional third argument if the ternary version of the built-in pow() function is to be supported.
/) is implemented by these methods. The __truediv__() method is used when
__future__.divisionis in effect, otherwise __div__() is used. If only one of these two methods is defined, the object will not support division in the alternate context; TypeError will be raised instead.
%, divmod() pow()
|) with reflected (swapped) operands. These functions are only called if the left operand does not support the corresponding operation. For instance, to evaluate the expression x
-y, where y is an instance of a class that has an __rsub__() method,
y.__rsub__(x)is called. Note that ternary pow() will not try calling __rpow__() (the coercion rules would become too complicated).
|=). These methods should attempt to do the operation in-place (modifying self) and return the result (which could be, but does not have to be, self). If a specific method is not defined, the augmented operation falls back to the normal methods. For instance, to evaluate the expression x
+=y, where x is an instance of a class that has an __iadd__() method,
x.__iadd__(y)is called. If x is an instance of a class that does not define a __iadd() method,
y.__radd__(x)are considered, as with the evaluation of x
+, abs() and
Noneif conversion is impossible. When the common type would be the type of
other, it is sufficient to return
None, since the interpreter will also ask the other object to attempt a coercion (but sometimes, if the implementation of the other type cannot be changed, it is useful to do the conversion to the other type here).
Coercion rules: to evaluate x op y, the
following steps are taken (where __op__() and
__rop__() are the method names corresponding to
op, e.g., if op is '
+', __add__() and
__radd__() are used). If an exception occurs at any point,
the evaluation is abandoned and exception handling takes over.
x.__coerce__(y); skip to step 2 if the coercion returns
x.__op__(y); otherwise, restore x and y to their value before step 1a.
y.__coerce__(x); skip to step 3 if the coercion returns
y.__rop__(x); otherwise, restore x and y to their value before step 2a.
+' and x is a sequence, sequence concatenation is invoked.
*' and one operand is a sequence and the other an integer, sequence repetition is invoked.


text:
filter : ('a -> bool) -> 'a list -> 'a list
Filters a list to the sublist of elements satisfying a predicate.
filter p l applies p to every element of l, returning a list of those
that satisfy p, in the order they appeared in the original list.
- FAILURE CONDITIONS
Fails if the predicate fails on any element.
- SEE ALSO
mapfilter, partition, remove.


text:
The standard (1.MD.1) asks students to compare objects indirectly using a third object. In this lesson, kids think about an anchor object and think of an object that is shorter than it and longer than it. Then they practice the other half of this standard, which is showing how to order all 3 objects.
We have played Scoop it and Order it before, and we were ordering from longest to shortest. Today we are going to play the game again, but we are going to switch up how we put them in order.
Putting lengths in order helps us think about how we compare different objects. We compare objects to help us think about how the lengths are different from each other.
Your thinking job today is: How do I know which way to order three objects?
Today you are going to get an object. You are going to have to think of something longer than that object and shorter than that object. Then you will put them in order from shortest to longest or longest to shortest.
My object is a marker.
Partner talk: What is something longer than this marker?
I'll choose 1 of the objects from the partner talk and write it down on the chart paper.
Partner talk: What is something shorter than this marker?
Follow same routine, modeling how to draw and label it (crayon for example)
Now I have 3 objects we have looked at: a marker, a journal, a crayon.
I want to draw these objects from shortest to longest. That is tricky because the objects may just be in my brain! I can either take out the objects or just think about which one I should draw first.
Which one will go first? Why the shortest?
I'll model drawing them as different sizes. This is an early grades way of working students towards representing mathematical concepts, which is part of multiple Mathematical Practice standards.
Do one together for guided practice. Students draw their objects on white boards.
Think of an object that is longer than your journal. Draw it and label it.
Think of an object that is shorter than your journal. Draw it and label it.
Put the three objects in order from longest to shortest. Model think aloud: "Longest to shortest. Before I do anything, I need to draw a L, M and S because longest has to go first".
Partner talk: Share your whiteboard. Are your objects in order from longest to shortest? Let your partner check your work.
This partner practices has students practice evaluating others' work, which is a very high level skill for anyone, but especially a first grader. To teach Common Core standards to mastery, I try to prioritize putting the heavy lifting on the student, which is why I am not checking whiteboards! I don't want students to think that I am the sole source of math knowledge. I want them to drive themselves to becoming mastery, not just seek my approval. Students work together to evaluate each other's work and fix mistakes.
Continue to practice as time allows, switching between shortest to longest and longest to shortest.
I'll have bins prepared for each table that are filled with interesting objects from around the classroom-school supplies, toys, math manipulatives, etc.
1. Students get an object from a bin in the middle of the table.
2. They write down their object (see the recording sheet in the resources).
3. Students draw something longer and then draw something shorter.
4. Students draw all 3 objects in order from either shortest to longest or longest to shortest.
Intervention: An easy scaffold for these students is to always have them physically put the objects in order before trying to draw them in order.
See attached video for a dialogue with a student working on this activity!
See lesson image for example!
This closing activity gets kids moving and is a great way to wrap up the lesson!
I have the students sit in a circle and then choose 1 student to lay down in the middle of the rug. I ask students to identify someone who is shorter, and then someone who is longer. Then we put students in order from shortest to longest and longest to shortest.