Writes text to a file, retrying if the write fails.
Write-File [-Path] <Object> [-InputObject] <String[]> [[-MaximumTries] <Int32>] [[-RetryDelayMilliseconds] <Int32>] [-WhatIf] [-Confirm] [<CommonParameters>]
The Write-File
function writes text file to a file, and will retry if the write fails. Use this function if you need to write text files that can be intermittently locked, like the Windows hosts file.
By default, it will retry 30 times, waiting 100 milliseconds between each try. You can control the number of retries and the wait between retries with the MaximumTries
and RetryDelayMilliseconds
parameters, respectively.
All errors raised while trying to write the file are ignored, except the error raised on the last try.
This function was introduced in Carbon 2.2.0.
| Name | Type | Description | Required? | Pipeline Input | Default Value |
|---|---|---|---|---|---|
| Path | Object | The path to the file to read. |
true | false | |
| InputObject | String[] | The contents of the file |
true | true (ByValue) | |
| MaximumTries | Int32 | The number of tries before giving up reading the file. The default is 100. |
false | false | 30 |
| RetryDelayMilliseconds | Int32 | The number of milliseconds to wait between tries. Default is 100 milliseconds. |
false | false | 100 |
| WhatIf | SwitchParameter | false | false | ||
| Confirm | SwitchParameter | false | false | ||
| CommonParameters | This cmdlet supports common parameters. For more information type Get-Help about_CommonParameters . |
$lines | Write-File -Path 'C:\Path\to\my\file'
Demonstrates how to write lines to a text file using the pipeline.
Write-File -Path 'C:\Path\to\my\file' -InputObject $lines
Demonstrates how to write lines to a text file using a variable.
$lines | Write-File -Path 'C:\Path\to\my\file' -MaximumRetries 10 -RetryDelayMilliseconds 1000
Demonstrates how to control how long to retry writing the text file. In this case, Write-File
will try 10 times, waiting one second between tries.
$lines | Write-File -Path 'C:\Path\to\my\file' -ErrorVariable 'writeErrors'
Demonstrates how to check if the write failed. In this case, errors are copied to a 'writeErrors' variable, so you would check if this error variable has any items.


Deductive Logic
by George William Joseph Stock, M.A.
Part 3: Chapter 18
Additional Information
- Year Published: 1888
- Language: English
- Country of Origin: England
- Source: Stock, G. W. J. (1888). Deductive Logic. Oxford, England; Pembroke College.
-
Readability:
- Flesch–Kincaid Level: 11.0
- Word Count: 3,386
- Genre: Informational
- Keywords: math, math history
- ✎ Cite This
Downloads
PART III.—OF INFERENCES
CHAPTER XVIII.
Of Reduction.
667. We revert now to the standpoint of the old logicians, who regarded the Dictum de Omni et Nullo as the principle of all syllogistic reasoning. From this point of view the essence of mediate inference consists in showing that a special case, or class of cases, comes under a general rule. But a great deal of our ordinary reasoning does not conform to this type. It was therefore judged necessary to show that it might by a little manipulation be brought into conformity with it. This process is called Reduction.
668. Reduction is of two kinds—
(1) Direct or Ostensive.
(2) Indirect or Ad Impossibile.
669. The problem of direct, or ostensive, reduction is this—
Given any mood in one of the imperfect figures (II, III and IV) how to alter the form of the premisses so as to arrive at the same conclusion in the perfect figure, or at one from which it can be immediately inferred. The alteration of the premisses is effected by means of immediate inference and, where necessary, of transposition.
670. The problem of indirect reduction, or reductio (per deductionem) ad impossibile, is this—Given any mood in one of the imperfect figures, to show by means of a syllogism in the perfect figure that its conclusion cannot be false.
671. The object of reduction is to extend the sanction of the Dictum de Omni et Nullo to the imperfect figures, which do not obviously conform to it.
672. The mood required to be reduced is called the Reducend; that to which it conforms, when reduced, is called the Reduct.
Direct or Ostensive Reduction.
673. In the ordinary form of direct reduction, the only kind of immediate inference employed is conversion, either simple or by limitation; but the aid of permutation and of conversion by negation and by contraposition may also be resorted to.
674. There are two moods, Baroko and Bokardo, which cannot be reduced ostensively except by the employment of some of the means last mentioned. Accordingly, before the introduction of permutation into the scheme of logic, it was necessary to have recourse to some other expedient, in order to demonstrate the validity of these two moods. Indirect reduction was therefore devised with a special view to the requirements of Baroko and Bokardo: but the method, as will be seen, is equally applicable to all the moods of the imperfect figures.
675. The mnemonic lines, ‘Barbara, Celarent, etc., provide complete directions for the ostensive reduction of all the moods of the second, third, and fourth figures to the first, with the exception of Baroko and Bokardo. The application of them is a mere mechanical trick, which will best be learned by seeing the process performed.
676. Let it be understood that the initial consonant of each name of a figured mood indicates that the reduct will be that mood which begins with the same letter. Thus the B of Bramantip indicates that Bramantip, when reduced, will become Barbara.
677. Where m appears in the name of a reducend, me shall have to take as major that premiss which before was minor, and vice versa-in other words, to transpose the premisses, m stands for mutatio or metathesis.
678. s, when it follows one of the premisses of a reducend, indicates that the premiss in question must be simply converted; when it follows the conclusion, as in Disamis, it indicates that the conclusion arrived at in the first figure is not identical in form with the original conclusion, but capable of being inferred from it by simple conversion. Hence s in the middle of a name indicates something to be done to the original premiss, while s at the end indicates something to be done to the new conclusion.
679. P indicates conversion per accidens, and what has just been said of s applies, mutatis mutandis, to p.
680. k may be taken for the present to indicate that Baroko and Bokardo cannot be reduced ostensively.
681. FIGURE II.
Cesare. \ / Celarent.
No A is B. \ = / No B is A.
All C is B. / \ All C is B.
.'. No C is A. / \ .'. No C is A.
Camestres. \ / Celarent.
All A is B. \ = / No B is C.
No C is B. / \ All A is B.
.'. No C is A. / \ .'. No A is C.
.'. No C is A.
Festino. Ferio.
No A is B. \ / No B is A.
Some C is B. | = | Some C is B.
.'. Some C is not A./ \ .'. Some C is not A.
[Baroko]
682. FIGURE III.
Darapti. \ / Darii.
All B is A. \ = / All B is A.
All B is C. / \ Some C is B.
.'. Some C is A. / \ Some C is A.
Disamis. \ / Darii.
Some B is A. \ = / All B is C.
All B is C. / \ Some A is B.
.'. Some C is A. / \ .'. Some A is C.
.'. Some C is A.
Datisi. \ / Darii.
All B is A. \ = / All B is A.
Some B is C. / \ Some C is B.
.'. Some C is A. / \ .'. Some C is A.
Felapton. \ / Ferio.
No B is A. \ = / No B is A.
All B is C. / \ Some C is B.
.'. Some C is not-A. / \ .'. Some C is not-A.
[Bokardo].
Ferison. \ / Ferio.
No B is A. \ = / No B is A.
Some B is C. / \ Some C is B
.'. Some C is not A. / \ .'. Some C is not A.
683. FIGURE IV.
Bramantip. \ / Barbara.
All A is B. \ = / All B is C.
All B is C. / \ All A is B.
.. Some C is A. / \ .. All A is C.
.'. Some C is A.
Camenes Celarent
All A is B \ / No B is C.
No B is C. | = | All A is B.
.. No C is A./ \ .'. No A is C.
.'. No C is A.
Dimaris. Darii.
Some A is B. \ / All B is C.
All B is C. | = | Some A is B.
.'. Some C is A./ \ .'. Some A is C.
.'. Some C is A.
Fesapo. Ferio.
No A is B. \ / No B is A.
All B is C. | = | Some C is B.
.'. Some C is not A./ \ .'. Some C is not A.
Fresison. Ferio.
No A is B. \ / No B is A.
Some B is C. | = | Some C is B.
.'. Some C is not A./ \ .'. Some C is not A.
684. The reason why Baroko and Bokardo cannot be reduced ostensively by the aid of mere conversion becomes plain on an inspection of them. In both it is necessary, if we are to obtain the first figure, that the position of the middle term should be changed in one premiss. But the premisses of both consist of A and 0 propositions, of which A admits only of conversion by limitation, the effect of which would be to produce two particular premisses, while 0 does not admit of conversion at all,
It is clear then that the 0 proposition must cease to be 0 before we can get any further. Here permutation comes to our aid; while conversion by negation enables us to convert the A proposition, without loss of quantity, and to elicit the precise conclusion we require out of the reduct of Boltardo.
(Baroko) Fanoao. Ferio.
All A is B. \ / No not-B is A.
Some C is not-B. | = | Some C is not-B.
.'. Some C is not-A./ \ .'. Some C is not-A.
(Bokardo) Donamon. Darii.
Some B is not-A. \ / All B is C.
All B is C. | = | Some not-A is B
.'. Some C is not-A./ \ .'. Some not-A is C.
.'. Some C is not-A.
685. In the new symbols, Fanoao and Donamon, [pi] has been adopted as a symbol for permutation; n signifies conversion by negation. In Donamon the first n stands for a process which resolves itself into permutation followed by simple conversion, the second for one which resolves itself into simple conversion followed by permutation, according to the extended meaning which we have given to the term ‘conversion by negation.’ If it be thought desirable to distinguish these two processes, the ugly symbol Do[pi]samos[pi] may be adopted in place of Donamon.
686. The foregoing method, which may be called Reduction by Negation, is no less applicable to the other moods of the second figure than to Baroko. The symbols which result from providing for its application would make the second of the mnemonic lines run thus—
Benare[pi], Cane[pi]e, Denilo[pi], Fano[pi]o secundae.
687. The only other combination of mood and figure in which it will be found available is Camenes, whose name it changes to Canene.
688.
(Cesare) Benarea. Barbara.
No A is B. \ / All B is not-A.
All C is B. | = | All C is B.
.'. No C is A. / \ .'. All C is not-A.
.'. No C is A.
(Camestres) Cane[pi]e. Celarent.
All A is B. \ / No not-B is A.
No C is B. | = | All C is not-B.
.'. No C is A. / \ .'. No C is A.
(Festino) Denilo[pi]. Darii.
No A is B. \ / All B is not-A.
Some C is B. | = | Some C is B.
.'. Some C is not A./ \ .'. Some C is not-A.
.'. Some C is not A.
(Camenes) Canene. Celarent.
All A is B. \ / No not-B is A.
No B is C. | = | All C is not-B.
.'. No C is A. / \ .'. No C is A.
689. The following will serve as a concrete instance of Cane[pi]e reduced to the first figure.
All things of which we have a perfect idea are perceptions.
A substance is not a perception.
.’. A substance is not a thing of which we have a perfect idea.
When brought into Celarent this becomes—
No not-perception is a thing of which we have a perfect idea.
A substance is a not-perception.
.’. No substance is a thing of which we have a perfect idea.
690. We may also bring it, if we please, into Barbara, by permuting the major premiss once more, so as to obtain the contrapositive of the original—
All not-perceptions are things of which we have an imperfect idea.
All substances are not-perceptions.
.’. All substances are things of which we have an imperfect idea.
Indirect Reduction.
691. We will apply this method to Baroko.
All A is B. All fishes are oviparous.
Some C is not B. Some marine animals are not oviparous.
.’. Some C is not A. .’. Some marine animals are not fishes.
692. The reasoning in such a syllogism is evidently conclusive: but it does not conform, as it stands, to the first figure, nor (permutation apart) can its premisses be twisted into conformity with it. But though we cannot prove the conclusion true in the first figure, we can employ that figure to prove that it cannot be false, by showing that the supposition of its falsity would involve a contradiction of one of the original premisses, which are true ex hypothesi.
693. If possible, let the conclusion ‘Some C is not A’ be false. Then its contradictory ‘All C is A’ must be true. Combining this as minor with the original major, we obtain premisses in the first figure,
All A is B, All fishes are oviparous,
All C is A, All marine animals are fishes,
which lead to the conclusion
All C is B, All marine animals are oviparous.
But this conclusion conflicts with the original minor, ‘Some C is not B,’ being its contradictory. But the original minor is ex hypothesi true. Therefore the new conclusion is false. Therefore it must either be wrongly drawn or else one or both of its premisses must be false. But it is not wrongly drawn; since it is drawn in the first figure, to which the Dictum de Omni et Nullo applies. Therefore the fault must lie in the premisses. But the major premiss, being the same with that of the original syllogism, is ex hypothesi true. Therefore the minor premiss, ‘All C is A,’ is false. But this being false, its contradictory must be true. Now its contradictory is the original conclusion, ‘Some C is not A,’ which is therefore proved to be true, since it cannot be false.
694. It is convenient to represent the two syllogisms in juxtaposition thus—
Baroko. Barbara.
All A is B. All A is B.
Some C is not B. \/ All C is A.
.'. Some C is not A. /\ All C is B.
695. The lines indicate the propositions which conflict with one another. The initial consonant of the names Baroko and Eokardo indicates that the indirect reduct will be Barbara. The k indicates that the O proposition, which it follows, is to be dropped out in the new syllogism, and its place supplied by the contradictory of the old conclusion.
696. In Bokardo the two syllogisms will stand thus—
Bokardo. Barbara.
Some B is not A. \ / All C is A.
All B is C. X All B is C.
.'. Some C is not A./ \ .'. All B is A.
697. The method of indirect reduction, though invented with a special view to Baroko and Bokardo, is applicable to all the moods of the imperfect figures. The following modification of the mnemonic lines contains directions for performing the process in every case:—Barbara, Celarent, Darii, Ferioque prioris; Felake, Dareke, Celiko, Baroko secundae; Tertia Cakaci, Cikari, Fakini, Bekaco, Bokardo, Dekilon habet; quarta insuper addit Cakapi, Daseke, Cikasi, Cepako, Cesikon.
698. The c which appears in two moods of the third figure, Cakaci and Bekaco, signifies that the new conclusion is the contrary, instead of, as usual, the contradictory of the discarded premiss.
699. The letters s and p, which appear only in the fourth figure, signify that the new conclusion does not conflict directly with the discarded premiss, but with its converse, either simple or per accidens, as the case may be.
700. l, n and r are meaningless, as in the original lines.


The Peacemaker A film review by Marty Mapes Copyright 1997 Marty Mapes
**1/2 (out of 4) George Clooney was the best movie Batman yet. Val Kilmer was flat and brooding and Michael Keaton was too much of a wimp. Clooney brought the right mix of sophistication and wisdom. Not being a fan of ER, I attributed his success to good acting.
Now that I've seen him play the same character in ONE FINE DAY, ER, and now THE PEACEMAKER, I see that he's one of the worst actors to make it big, good looks aside. He's the next Arnold Schwarzenegger - icon, not actor.
THE PEACEMAKER is a very conventional action movie. All the baroque trim, all the flourishes, have been removed. There is no love interest, there are no wisecracks, just stripped down, economy model action movie. This was no doubt Leder's point, and it was a good idea, but it didn't work.
The effect was that the movie felt, well, plain. It felt like a quickly made, no-time-for-details, made-for-TV movie (not surprising considering Leder's strong TV background). It didn't shun enough of the action genre to really set itself apart.
After an opening sequence of Russians stealing a packet of nukes, the movie introduces Julia Kelly (Nicole Kidman), a special investigator whose job it is to find out what happened to them. Assigned to assist her is Tom Devoe (Clooney), a military liaison with strong Russian connections.
They track the nukes through Vienna, where Clooney is tailed by a crew of disposable bad guys in identical cars. The ensuing chase scene is very well handled; it looks great on screen. But the endless destruction, first without any airbags deploying, and second without any apparent moral or legal consequence, shows that Leder and screenwriter Michael Schiffer really didn't leave the action genre as far behind as one might think.
Kelly and Devoe eventually recover all but one of the stolen nukes. They track this last one to Bosnia, where they learn that Gavrich (Marcel Iures) a Bosnian politician disgusted with the West's lack of aid in their war, intends to detonate a single nuke at the U.N. building in New York.
The movie crosses the Atlantic to Manhattan, where another good-looking, but rather conventional chase scene happens. The movie's climax involves what Roger Ebert calls RDRs - red digital readouts - counting down to the destruction of New York.
Can Kelly and Devoe stop the bomb in time? Well - yes and no. But even that enigmatic conclusion isn't enough to set this movie apart.
It would be interesting to see a truly genre-breaking action movie that doesn't just shun the superficial elements (like love interests and wisecracks), but questioned the very idea of using violence to combat violence as the morally Right thing to do.
Or maybe that would just be boring.
Check out more current movie reviews at http://www.indra.com/~mmapes/
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


- Office for National Statistics & Forrester
Buyers Researching Their Purchases Online
Buyers Transacting Online
Business customers have shifted from wanting transactional relationships to collaborative relationships with their suppliers. As a result, they are now assessing value on a much broader range of elements.
Traditional websites only offer some of these elements of value. Large amounts of customers are still choosing to buy offline.
We believe that growth in B2B e-commerce comes not by making traditional online processes faster and better, but from driving process innovation.
Business customers have shifted from wanting transactional relationships to collaborative relationships with their suppliers. As a result, they are now assessing value on a much broader range of elements.
THE TABLE STAKES
Traditional websites only offer some of these elements of value. So customers are still choosing to buy offline. We believe that growth in B2B e-commerce comes not by making traditional online processes faster and better, but from driving process innovation.
At Silverwing, we develop enterprise-grade digital infrastructure to enable smoother, faster, and more coordinated business processes. Our aim is to help businesses focus on selling rather than negotiating.
Learn more about our innovative processes
View Silverwing Products| REACH |
NEW VALUE |
|||||
| GUIDANCE | Rather than having to interact with each customer in isolation, reach all customers at once | |||||
| SPEED | Rather than responding to tenders, proactively inform customers about decisions that could save money for all | |||||
| PRICING | Rather than negotiating with customers over several weeks or months, allow them to transact in just a few clicks | |||||
| FLEXIBILITY | Trusted pricing that makes customers choose you every time, knowing they are paying the best and fairest price | |||||
| EXISTING VALUE |
Rather than having to commit volumes or save up spend, allow customers to buy on demand | EXISTING VALUE |
||||
| TODAY | FUTURE |
An ecosystem that reduces the premium of early adoption and encourages referrals to drive the adoption of new technologies
Digital Processes that are hassle free and enable a quick response to customer demands, anywhere, at the click of a button
Boost market share by providing new value to customers. Get ahead of the competition by selling rather than negotiating
Reduce customer acquisition and servicing costs and free up resources previously tied into duplicated transactional processes


WSO2 MB is a lightweight and easy-to-use Open Source Message Broker Server (MB) available under the Apache Software License v2.0.
WSO2 Enterprise Integrator - Message Broker Profile is developed on top of the revolutionary wso2 carbon platform and is based on the OSGi framework to achieve the better modularity for your SOA architecture. This also contains a lots of new features and many other optional components to customize the behavior of the server. Further, if you do not want any of the built in features, you can uninstall those features without any trouble. In other words, this ES can be customized to your SOA needs.


{% load staticfiles %}
My name is Nidoking
I am a software engineer located in the Bay Area
Insert more text here
Insert list of projects here


Anita Joseph calls out her bestie Uche Elendu, asks if she is a witch (Video)
by Damilola IsmailNollywood actress Anita Joseph has called out her best friend Uche Elendu, went as far as asking if she was a witch over an issue they had over hair.
Anita Joseph shared a video on her social media page where she stated all she has done for Uche Elendu who was her best friend before they had an issue around her advertising the hair from another brand other than hers.
She decided to shed light on the issue that started after Anita advertised a hair on her page which was not that of Uche Elendu her best friend which she has always advertised on her platform. According to her, Uche reached out to her saying she shouldn’t have advertised the hair because people will start thinking her hair is not original.
This led to a big fight between the two and Anita is now asking if she is a witch because only a witch would not want her friend to prosper. She did all she could for free, to let her hair and eyelashes business grow so why should she be upset if she advertises other people who pay her to advertise their hair.
She claims she isn’t her brand ambassador, has never gotten one hair for free from her, instead she has bought hair worth more than 1 million naira which she paid in installments. She said Uche should be ashamed of herself for not giving her even one hair, only claiming she gave her the hair at discounted rates.

Follow us on Facebook – @Lailasnews; Twitter – @LailaIjeoma for updates


{% extends "general/index.html" %} {% load static %} {% block content %}
The first link leads to page where you can search for cities on our site. There you can choose the city as your living city and acces to other people living there.
If you did not find the city you are looking for you can use second link and make a request to add city to our database. Moderators will check your request and accept it or decline. You will get notification of it in you Notifications tab.
Everything about your account can be accesed through 3rd link. On this page you will be able to change personal information and add social media links.
You can also put a check on being guide and show your friendliness to other users
Friends list is presented on this page, you can visit users pages and delete them from friends list if you want. All the friend requests are also going to be on this page.
If you are looking for specific user or want to find users from some city or occupation you can find them using this page.
{% endblock %}


Application Permissions on Android Devices
- Damaged Reputations: Uploading provocative photos of oneself to gain recognition, which can backfire.
- Revenge porn - explicit images of a person are shared without their knowledge or consent.
- Recording your private conversations without you knowing and sending it to an attacker.
- Cyberbullying: Using these images or conversations to blackmail, spread lies, sending hateful texts, and making nasty comments on social networks.
- Body sensors: Get sensor info about your vital signs.
- Calendar: Use your default calendar.
- Call logs: See and change your call history.
- Camera: Use your camera to take pictures or videos.
- Contacts: See your contacts list.
- Location: Ask to get your device’s location.
- Microphone: Record audio.
- Phone: Make and manage phone calls.
- Physical activity: Get info about your activity like walking, biking, step count, and more.
- SMS: See and send SMS (text) messages.
- Storage: Get photos and other files on your phone.
- Files and media: Use photos, media, and other files on your phone.
- 1.On your phone, open the Settings app.
- 2.Tap Apps & notifications.
- Tap the app you want to change. If you can't find it, first tap See all apps or App info.
- Tap Permissions.
If you allowed or denied any permissions for the app, you’ll find them here. - To change a permission setting, tap it, then choose Allow or Deny.
Application list Application info page with permissions tab Change permission by moving the slider. An application where camera and microphone permission has been disabled. - evidence: You should make a record of what has been posted online. Even if legal matters aren't your first thought, it could be important later. Social media can remove posts in an instant this can leave you without proof of a crime.
- Report: Social media has strict measures to get intimate content removed. Even pornographic websites will take down the content in such cases. Except for websites dedicated to revenge porn.
- Talk to your family: The biggest fear can be that people closest to you will see what's been posted. Telling them first could soften the blow.
- Think about the next step: Laws are there to protect people against such harassment. But there ain’t any concrete one to date. Sections 67 and 67A of the IT Act are against the publishing and circulation of what the act calls 'obscene' or 'lascivious' content.
Dangers of Mobile Internet
Preventive measures
To prevent unauthorized access to the user’s device, one can set up the permission feature. Most of the data leak happens from the users’ device, this can be limited
What are permissions in Android?
The purpose of permission is to protect the privacy of an Android user. Android apps must request permission to access sensitive user data (such as contacts and SMS), as well as certain system features (such as camera and internet).
Types of permissions
Below is a list of permissions and what they do when turned on for an app.
How permissions can be misused?
A malicious application which has been granted permission to use the Camera and Microphone can spy on the user without them even knowing.
How to be wise while granting permission to applications?
Using some common sense, while giving permission can go a long way in securing your data. For instance, a calling or Messenger or video recording app asking for access to a microphone and camera is understandable, but if an e-commerce app or a document reader asks for similar permissions, users should be wary.
A chess game asking for permission to use a microphone, camera, see call logs and SMS is abnormal.
If the app insists on having the permissions again and again and interferes with the experience, users should uninstall them completely.


The Politics Program is designed to assist students in developing the arts of deliberation and judgment by which they may understand more fully and participate more effectively in public affairs. Analytical reasoning, effective writing, and proficiency in basic research techniques are skills cultivated and used throughout the program.
| Dina Badie | |
| Rob Bosco | |
| Lori Hartmann | |
| Chelsea Ebin |


In helping clients understand their supply chains, maps are an important visualization tool. In this exercise we would like you to take some steps to help make this map more useful. This example uses Google Maps and their maps API to show information about airports. While we do not use Google Maps and the maps API, the types of visualizations and map interactions you are allowed with Google Maps are common. In order to do this exercise you will need to get an API key for Google Maps if you do not already have one. Put the API key in the appropriate place in this file.
Currently this page loads a list of airports into a drop down. You can select an airport and it"s code and city will display in the data table. A marker will be placed on the map displaying the airport. If you roll your mouse over the marker the airport"s code will be displayed.
The current code is very simple. Many improvements need to be made:
Some more things to think about


Man, 83, charged with murder after death of woman in Hadfield
by Ashleigh McMillanHomicide Squad detectives have charged a man following the death of a woman in Hadfield on Thursday night.
A 76-year-old woman was found dead in the Win-Malee Street home just before 10pm.
A relative told Nine News that they weren’t able to get onto the woman by the usual methods of contact on Thursday.
A family member discovered the 76-year-old's body in her home and called police.
An 83-year-old Hadfield man has been charged with murder and remanded to appear in Melbourne Magistrates Court on Saturday morning.
Nine News understands the deceased woman and the man were a couple, and had moved into the Hadfield property only this week.
Victoria Police media officer Belinda Batty said detectives were not looking for anyone else in relation to the incident.


nips nips2000 nips2000-140 nips2000-140-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Martin J. Wainwright, Erik B. Sudderth, Alan S. Willsky
Abstract: We present the embedded trees algorithm, an iterative technique for estimation of Gaussian processes defined on arbitrary graphs. By exactly solving a series of modified problems on embedded spanning trees, it computes the conditional means with an efficiency comparable to or better than other techniques. Unlike other methods, the embedded trees algorithm also computes exact error covariances. The error covariance computation is most efficient for graphs in which removing a small number of edges reveals an embedded tree. In this context, we demonstrate that sparse loopy graphs can provide a significant increase in modeling power relative to trees, with only a minor increase in estimation complexity. 1
[1] J. Pear!. Probabilistic reasoning in intelligent systems. Morgan Kaufman, 1988.
[2] K. Chou, A. Willsky, and R. Nikoukhah. Multiscale systems, Kalman filters, and Riccati equations. IEEE Trans. AC, 39(3}:479-492, March 1994.
[3] R. G. Gallager. Low-density parity check codes. MIT Press, Cambridge, MA, 1963.
[4] M. Luettgen, W. Karl, and A. Willsky. Efficient multiscale regularization with application to optical How. IEEE Trans. 1m. Proc., 3(1}:41-64, Jan. 1994.
[5] P. Fieguth, W. Karl, A. Willsky, and C. Wunsch. Multiresolution optimal interpolation of satellite altimetry. IEEE Trans. Ceo. Rem., 33(2}:280- 292, March 1995.
[6] P. Rusmevichientong and B. Van Roy. An analysis of turbo decoding with Gaussian densities. In NIPS 12, pages 575- 581. MIT Press, 2000.
[7] Y. Weiss and W. T. Freeman. Correctness of belief propagation in Gaussian graphical models of arbitrary topology. In NIPS 12, pages 673- 679. MIT Press, 2000.
[8] J. Besag. Spatial interaction and the statistical analysis of lattice systems. J. Roy. Stat. Soc. Series B, 36:192- 236, 1974.
[9] J.W. Demme!. Applied numerical linear algebra. SIAM, Philadelphia, 1997.
[10] O. Axelsson. Bounds of eigenvalues of preconditioned matrices. SIAM J. Matrix Anal. Appl., 13:847- 862, July 1992.
[11] E. Sudderth, M. Wainwright, and A. Willsky. Embedded trees for modeling and estimation of Gaussian processes on graphs with cycles. In preparation, Dec. 2000.
[12] A. Frakt and A. Willsky. Computationally efficient stochastic realization for internal multiscale autoregressive models. Mult. Sys. and Sig. Proc. To appear.


Authors: Igor Kopestenski<EMAIL_ADDRESS>[web site: https://github.com/achlysproject/achlys].
task() = #{name => atom(), targets => task_targets(), execution_type => task_execution_type(), function => function()}
task_execution_type() = bitstring()
task_targets() = [node()] | bitstring()
| bane/1 | Returns the aggregates for the given variable as seen by the current node. |
| bane_all_preys/1 | Returns the aggregates for the given variable as seen by the current node for all nodes. |
| bite/1 | Adds the given task in the replicated task set. |
| clusterize/0 | Attempts to discover and join other neighboring nodes. |
| contagion/0 | Form Lasp cluster without attempting to ping neighbors beforehand. |
| declare/4 | Shortcut function exposing the utility function that can be used to pass more readable arguments to create a task model variable instead of binary strings. |
| gc/0 | Performs a VM-wide garbage collection. |
| get_all_tasks/0 | Returns current view of all tasks. |
| get_preys/0 | Returns a list of known remote hostnames that could be potential neighbors. |
| join/1 | A shortcut function that performs a join operation on a Partisan node. |
| pandemia/0 | Close disterl TCP connections with neighboring nodes. |
| start/0 | Start the application. |
| stop/0 | Stop the Application. |
| venom/0 | Collect data based on sensors available on Pmod modules and store aggregated values in corresponding Lasp variable. |
| venom/1 | Collect data based on sensors available on Pmod modules and store aggregated values in corresponding Lasp variable. |
bane(Data::atom()) -> list()
Returns the aggregates for the given variable as seen by the current node.
bane_all_preys(Data::atom()) -> list()
Returns the aggregates for the given variable as seen by the current node for all nodes.
bite(Task::achlys:task()) -> ok
Adds the given task in the replicated task set.
This function allows users to propagate previously
created tasks using achlys:declare/4
. The Achlys
application maintains a working set for the task
model that is a Grow-Only Set Lasp CRDT variable.
See also: achlys_task_server:add_task/1.
clusterize() -> ok
Attempts to discover and join other neighboring nodes. -spec clusterize() -> [atom()].
contagion() -> {ok, []} | {ok, [map()]}
Form Lasp cluster without attempting to ping neighbors beforehand.
declare(Name::atom(), Targets::[node()] | all, ExecType::single | permanent, Func::function()) -> achlys:task()
Shortcut function exposing the utility function that can be used to pass more readable arguments to create a task model variable instead of binary strings. The ExecType argument can currently not provide a transient mode, a task is executed either once or permanently. Removing the task from the CRDT does not prevent nodes to keep executing it. But similar behavior can be created with a single execution task that specifies a function with several loops. However this does not allow the worker to have full control over the load, since backpressure can be applied by spawning a process that executes a permanent task at a controlled frequency that can be based on any stress parameter. Meanwhile a single execution task could possibly overload the worker, as iterations are embedded inside a single process.
When Grow-only Counters and Sets are used, the transient can be achieved using Lasp's monotonic read function to have a distributed treshold.
Example usage in an Erlang shell :
> Name = mytask. > Targets = all. > ExecType = permanent. > F = fun() -> io:format("hello world ~n", []), timer:sleep(5000) end. > Task = achlys:declare(Name, Targets, ExecType, F).The variable Task is now named
mytask
,
is intended to run on all nodes in the cluster, and
will run function F in permanent cycles.
Returns the task model variable based on the given arguments
in the form of a map.
gc() -> any()
Performs a VM-wide garbage collection.
get_all_tasks() -> [achlys:task()] | []
Returns current view of all tasks.
get_preys() -> any()
Returns a list of known remote hostnames that could be potential neighbors.
join(Host::atom()) -> {ok, atom()} | {error, atom(), atom()}
A shortcut function that performs a join operation on a Partisan node.
Example in an Erlang shell :
> achlys:join('achlys@grisp_node_1').
Is equivalent to :
> lasp_peer_service:join('achlys@grisp_node_1').
pandemia() -> ok
Close disterl TCP connections with neighboring nodes.
start() -> ok
Start the application.
stop() -> ok
Stop the Application
venom() -> ok
Collect data based on sensors available on Pmod modules and store aggregated values in corresponding Lasp variable.
venom(Worker::atom()) -> ok
Collect data based on sensors available on Pmod modules and store aggregated values in corresponding Lasp variable.
Generated by EDoc


lenstronomy.LensModel.Solver package¶
Submodules¶
lenstronomy.LensModel.Solver.lens_equation_solver module¶
-
class
lenstronomy.LensModel.Solver.lens_equation_solver.
LensEquationSolver
(lensModel)[source]¶ Bases:
object
class to solve for image positions given lens model and source position
-
findBrightImage
(sourcePos_x, sourcePos_y, kwargs_lens, numImages=4, min_distance=0.01, search_window=5, precision_limit=1e-10, num_iter_max=10)[source]¶ Parameters: - sourcePos_x –
- sourcePos_y –
- deltapix –
- numPix –
- magThresh – magnification threshold for images to be selected
- numImage – number of selected images (will select the highest magnified ones)
- kwargs_lens –
Returns:
-
image_position_from_source
(sourcePos_x, sourcePos_y, kwargs_lens, min_distance=0.01, search_window=5, precision_limit=1e-10, num_iter_max=100)[source]¶ finds image position source position and lense model
Parameters: - sourcePos_x – source position in units of angle
- sourcePos_y – source position in units of angle
- kwargs_lens – lens model parameters as keyword arguments
- min_distance – minimum separation to consider for two images in units of angle
- search_window – window size to be considered by the solver. Will not find image position outside this window
- precision_limit – required precision in the lens equation solver (in units of angle in the source plane).
- num_iter_max – maximum iteration of lens-source mapping conducted by solver to match the required precision
Returns: (exact) angular position of (multiple) images ra_pos, dec_pos in units of angle
Raises: AttributeError, KeyError
-
lenstronomy.LensModel.Solver.solver2point module¶
-
class
lenstronomy.LensModel.Solver.solver2point.
Solver2Point
(lensModel, solver_type='CENTER', decoupling=True)[source]¶ Bases:
object
class to make the constraints for the solver
lenstronomy.LensModel.Solver.solver4point module¶
-
class
lenstronomy.LensModel.Solver.solver4point.
Solver4Point
(lensModel, decoupling=True)[source]¶ Bases:
object
class to make the constraints for the solver
-
constraint_lensmodel
(x_pos, y_pos, kwargs_list, xtol=1.49012e-10)[source]¶ Parameters: - x_pos – list of image positions (x-axis)
- y_pos – list of image position (y-axis)
- init – initial parameters
- kwargs_list – list of lens model kwargs
Returns: updated lens model that satisfies the lens equation for the point sources
-


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egta.trace module¶
Module for tracing continuous equilibria
-
egta.trace.
trace_all_equilibria
(agame0, agame1, *, regret_thresh=0.001, dist_thresh=0.1, max_step=0.1, executor=None, **innerloop_args)[source]¶ Trace out all equilibria between all games
Parameters: - agame0 (AsyncGame) – The game that is played when time is 0.
- agame1 (AsyncGame) – The game that is played when time is 1.
- regret_thresh (float, optional) – The threshold for epsilon regret for equilibria returned.
- exectutor (Executor, optional) – The executor to run computation intensive operations in.


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In this article I'll try to introduce you to the area of computation complexity. The article will be a bit long before we get to the actual formal definitions because I feel that the rationale behind these definitions needs to be explained as well - and that understanding the rationale is even more important than the definitions alone. Example 1. Suppose you were assigned to write a program to process some records your company receives from time to time. You implemented two different algorithms and tested them on several sets of test data. The processing times you obtained are in Table 1.
Table 1. Runtimes of two fictional algorithms. In praxis, we probably could tell which of the two implementations is better for us (as we usually can estimate the amount of data we will have to process). For the company this solution may be fine. But from the programmer's point of view, it would be much better if he could estimate the values in Table 1 before writing the actual code - then he could only implement the better algorithm. The same situation occurs during programming contests: The size of the input data is given in the problem statement. Suppose I found an algorithm. Questions I have to answer before I start to type should be: Is my algorithm worth implementing? Will it solve the largest test cases in time? If I know more algorithms solving the problem, which of them shall I implement? This leads us to the question: How to compare algorithms? Before we answer this question in general, let's return to our simple example. If we extrapolate the data in Table 1, we may assume that if the number of processed records is larger than 1000, algorithm 2 will be substantially faster. In other words, if we consider all possible inputs, algorithm 2 will be better for almost all of them. It turns out that this is almost always the case - given two algorithms, either one of them is almost always better, or they are approximately the same. Thus, this will be our definition of a better algorithm. Later, as we define everything formally, this will be the general idea behind the definitions. If you thing about Example 1 for a while, it shouldn't be too difficult to see that there is an algorithm with runtimes similar to those in Table 2:
Table 2. Runtimes of a new fictional algorithm. The idea behind this algorithm: Check the number of records. If it is small enough, run algorithm 1, otherwise run algorithm 2. Similar ideas are often used in praxis. As an example consider most of the sort() functions provided by various libraries. Often this function is an implementation of QuickSort with various improvements, such as:
Example 2. Suppose you have a concrete implementation of some algorithm. (The example code presented below is actually an implementation of MinSort - a slow but simple sorting algorithm.)
for (int i=0; i<N; i++) for (int j=i+1; j<N; j++) if (A[i] > A[j]) swap( A[i], A[j] ); If we are given an input to this algorithm (in our case, the array A and its size N), we can exactly compute the number of steps our algorithm does on this input. We could even count the processor instructions if we wanted to. However, there are too many possible inputs for this approach to be practical. And we still need to answer one important question: What is it exactly we are interested in? Most usually it is the behavior of our program in the worst possible case - we need to look at the input data and to determine an upper bound on how long will it take if we run the program. But then, what is the worst possible case? Surely we can always make the program run longer simply by giving it a larger input. Some of the more important questions are: What is the worst input with 700 elements? How fast does the maximum runtime grow when we increase the input size? Formal notes on the input size What exactly is this "input size" we started to talk about? In the formal definitions this is the size of the input written in some fixed finite alphabet (with at least 2 "letters"). For our needs, we may consider this alphabet to be the numbers 0..255. Then the "input size" turns out to be exactly the size of the input file in bytes. Usually a part of the input is a number (or several numbers) such that the size of the input is proportional to the number.  The size of the input file will be roughly 5N (depending on the OS and architecture, but always linear in N). In such cases, we may choose that this number will represent the size of the input. Thus when talking about problems on arrays/strings, the input size is the length of the array/string, when talking about graph problems, the input size depends both on the number of vertices (N) and the number of edges (M), etc. We will adopt this approach and use N as the input size in the following parts of the article. There is one tricky special case you sometimes need to be aware of. To write a (possibly large) number we need only logarithmic space. (E.g. to write 123456, we need only roughly log10(123456) digits.) This is why the naive primality test does not run in polynomial time - its runtime is polynomial in the size of the number, but not in its number of digits! If you didn't understand the part about polynomial time, don't worry, we'll get there later. We already mentioned that given an input we are able to count the number of steps an algorithm makes simply by simulating it. Suppose we do this for all inputs of size at most N and find the worst of these inputs (i.e. the one that causes the algorithm to do the most steps). Let f (N) be this number of steps. We will call this function the time complexity, or shortly the runtime of our algorithm. In other words, if we have any input of size N, solving it will require at most f (N) steps. Let's return to the algorithm from Example 2. What is the worst case of size N? In other words, what array with N elements will cause the algorithm to make the most steps? If we take a look at the algorithm, we can easily see that:
As you can see, determining the exact function f for more complicated programs is painful. Moreover, it isn't even necessary. In our case, clearly the -0.5N term can be neglected. It will usually be much smaller than the 1.5N2 term and it won't affect the runtime significantly. The result "f (N) is roughly equal to 1.5N2" gives us all the information we need. As we will show now, if we want to compare this algorithm with some other algorithm solving the same problem, even the constant 1.5 is not that important. Consider two algorithms, one with the runtime N2, the other with the runtime 0.001N3. One can easily see that for N greater than 1 000 the first algorithm is faster - and soon this difference becomes apparent. While the first algorithm is able to solve inputs with N = 20 000 in a matter of seconds, the second one will already need several minutes on current machines. Clearly this will occur always when one of the runtime functions grows asymptotically faster than the other (i.e. when N grows beyond all bounds the limit of their quotient is zero or infinity). Regardless of the constant factors, an algorithm with runtime proportional to N2 will always be better than an algorithm with runtime proportional to N3 on almost all inputs. And this observation is exactly what we base our formal definition on. Let f, g be positive non-decreasing functions defined on positive integers. (Note that all runtime functions satisfy these conditions.) We say that f (N) is O(g(N)) (read: f is big-oh of g) if for some c and N0 the following condition holds:
N > N0; f (N) < c.g(N)
In human words, f (N) is O(g(N)), if for some c almost the entire graph of the function f is below the graph of the function c.g. Note that this means that f grows at most as fast as c.g does. Instead of "f (N) is O(g(N))" we usually write f (N) = O(g(N)). Note that this "equation" is not symmetric - the notion " O(g(N)) = f (N)" has no sense and " g(N) = O(f (N))" doesn't have to be true (as we will see later). (If you are not comfortable with this notation, imagine O(g(N)) to be a set of functions and imagine that there is a instead of =.) What we defined above is known as the big-oh notation and is conveniently used to specify upper bounds on function growth. E.g. consider the function f (N) = 3N(N - 1)/2 + N = 1.5N2 - 0.5N from Example 2. We may say that f (N) = O(N2) (one possibility for the constants is c = 2 and N0 = 0). This means that f doesn't grow (asymptotically) faster than N2. Note that even the exact runtime function f doesn't give an exact answer to the question "How long will the program run on my machine?" But the important observation in the example case is that the runtime function is quadratic. If we double the input size, the runtime will increase approximately to four times the current runtime, no matter how fast our computer is. The f (N) = O(N2) upper bound gives us almost the same - it guarantees that the growth of the runtime function is at most quadratic. Thus, we will use the O-notation to describe the time (and sometimes also memory) complexity of algorithms. For the algorithm from Example 2 we would say "The time complexity of this algorithm is O(N2)" or shortly "This algorithm is O(N2)".
In a similar way we defined O we may define and . We say that f (N) is (g(N)) if g(N) = O(f (N)), in other words if f grows at least as fast as g. We say that f (N) = (g(N)) if f (N) = O(g(N)) and g(N) = O(f (N)), in other words if both functions have approximately the same rate of growth. As it should be obvious, is used to specify lower bounds and is used to give a tight asymptotic bound on a function. There are other similar bounds, but these are the ones you'll encounter most of the time. Some examples of using the notation
When speaking about the time/memory complexity of an algorithm, instead of using the formal (f (n))-notation we may simply state the class of functions f belongs to. E.g. if f (N) = (N), we call the algorithm linear. More examples:
For graph problems, the complexity (N + M) is known as "linear in the graph size". Determining execution time from an asymptotic bound For most algorithms you may encounter in praxis, the constant hidden behind the O (or ) is usually relatively small. If an algorithm is (N2), you may expect that the exact time complexity is something like 10N2, not 107N2. The same observation in other words: if the constant is large, it is usually somehow related to some constant in the problem statement. In this case it is good practice to give this constant a name and to include it in the asymptotic notation. An example: The problem is to count occurences of each letter in a string of N letters. A naive algorithm passes through the whole string once for each possible letter. The size of alphabet is fixed (e.g. at most 255 in C), thus the algorithm is linear in N. Still, it is better to write that its time complexity is (| S|.N), where S is the alphabet used. (Note that there is a better algorithm solving this problem in (| S| + N).) In a TopCoder contest, an algorithm doing 1 000 000 000 multiplications runs barely in time. This fact together with the above observation and some experience with TopCoder problems can help us fill the following table:
Table 3. Approximate maximum problem size solvable in 8 seconds. Usually if we present an algorithm, the best way to present its time complexity is to give a -bound. However, it is common practice to only give an O-bound - the other bound is usually trivial, O is much easier to type and better known. Still, don't forget that O represents only an upper bound. Usually we try to find an O-bound that's as good as possible. Example 3. Given is a sorted array A. Determine whether it contains two elements with the difference D. Consider the following code solving this problem:
int j=0; for (int i=0; i<N; i++) { while ( (j<N-1) && (A[i]-A[j] > D) ) j++; if (A[i]-A[j] == D) return 1; } It is easy to give an O(N2) bound for the time complexity of this algorithm - the inner while-cycle is called N times, each time we increase j at most N times. But a more careful analysis shows that in fact we can give an O(N) bound on the time complexity of this algorithm - it is sufficient to realize that during the whole execution of the algorithm the command "j++;" is executed no more than N times. If we said "this algorithm is O(N2)", we would have been right. But by saying "this algorithm is O(N)" we give more information about the algorithm. We have shown how to write bounds on the time complexity of algorithms. We have also demonstrated why this way of characterizing algorithms is natural and (usually more-or-less) sufficient.
The next logical step is to show how to estimate the time complexity of a given
algorithm. As we have already seen in Example 3, sometimes this can be messy.
It gets really messy when recursion is involved.
We will address these issues in the second part of this article.
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Psalms 65:7
Which stilleth the noise of the seas, the noise of their waves, and the tumult of the people.


Sharon Stone demonstrates Basic Instinct scene on stage
Sharon Stone demonstrated her famous, Basic Instinct leg-crossing scene on stage while making a powerful speech about sexuality.
The actress, 61, recreated the moment, this time with underwear on, as she was crowned Woman Of The Year at the GQ Men Of The Year Awards in Berlin.
She told the audience: “Some years ago I was sitting on a sound stage, and my director said, ‘Can you hand me your underpants because we’re seeing them in the scene and you shouldn’t have underpants on but we won’t see anything.’
“And I said, ‘sure.’
“I didn’t know this moment would change my life.”
Referring to the scene from the 1992 film, in which she starred opposite Michael Douglas, she said: “Thank you for choosing me to be Woman Of The Year because there was a time when all I was, was a joke.”
Wearing a short, black dress and sitting on the stage, Stone said she had reclaimed her “grace” and “dignity”.
“I respected it… Because we have every right to be powerful in whatever form of sexuality we choose to have and no-one is allowed to take that away from you.”
She told the GQ Men Of The Year awards she was “here in my grace, tenderness and dignity” and “it was hard won”.
She asked the audience to recreate the scene, saying: “Do you feel empowered? Maybe not. Let’s do it again.”


Green Dragon Acupuncture
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--- layout: apidoc title: Tile5 API Documentation ---
A collection of utilities that are primarily designed to help with working with Geo.BoundingBox objects. The functions are implemented here rather than with the actual object itself to ensure that the object remains lightweight.
The calcSize function is used to determine the size of a Geo.BoundingBox given
a minimum position (relates to the bottom-left / south-western corner) and
maximum position (top-right / north-eastern corner) of the bounding box.
The 3rd parameter specifies whether the size calculations should normalize the
calculation in cases where the bounding box crosses the 360 degree boundary.
This function is very useful for creating a Geo.BoundingBox given a center position and a radial distance (specified in KM) from the center position. Basically, imagine a circle is drawn around the center position with a radius of distance from the center position, and then a box is drawn to surround that circle. Adapted from the functions written in Java by Jan Philip Matuschek
A simple function that is used to expand a Geo.BoundingBox by the specified amount (in degrees).
This function is very useful when you need to create a
Geo.BoundingBox to contain an array of T5.Geo.Position.
The optional second parameter allows you to specify an amount of
padding (in degrees) to apply to the bounding box that is created.
Returns a Geo.Position for the center position of the bounding box.
To be completed
This function is used to return the zoom level (seems consistent across mapping providers at this stage) that is required to properly display the specified T5.Geo.BoundingBox given the screen dimensions (specified as a Dimensions object) of the map display. Adapted from this code
Returns true if the specified T5.Geo.BoundingBox is empty.
Returns a string representation of a Geo.BoundingBox


ME = IT + BUSINESSWeb Development - Techincal Support - Customer Service
Area Of Expertise
HOW CAN I HELP YOU?
I am a highly motivated and confident person with diverse experience in Technology and Business.
With my hybrid knowledge I can help various sectors and industries with:
Enhancing long term Customer relations
Solving techincal problems
Increase in productivity
Achieving long term and short term goals
Bringing new ideas and creativity in the table to gain competitative advantage
Programming Languages
Expereince building applications in languages such as C#, ASP.NET, PHP, HTML5, CSS3, JavaScript, JQuery, JQuery Mobile and AngularJS.
Web Development
I have worked on various web applications development projects using tools such as Bootstrap,PHP, HTML, CSS, JavarScript, JQuery, AngularJS and MVC framework for ASP.NET.
I have expereince building mobile applications using PhoneGapBuild with PHP backend. I also have used FireBase for backend for some other projects that I have worked with.
Also I have expereince using jQuery mobile/ app-stores, angular bootstrap UI and test driven development with angular and jasmine.
Database Management
I have hands-on traning and experience in platforms such as Microsoft SQL Server, SQLite and FireBase. I can design, create and manipulate relational databases. I am able to work with Business Intelligence Services and Reporting Services. I also have good knowlesge of Database Administration and Management.
Agile Methodologies
Currently I am working on Systems Development Capstone project where I am team lead. I am using Agile approarch for the project.I collected requirements, identified issues and recommended solutions with the help of various Project Management tools such as Microsoft Project, Microsoft Excel and Trello.
Apart from this I also have working knowledge of Disciplined Agile Delivery, Scrum, Lean and Kanban.
UML Tools
I have good expereince using UML tools such as Microsoft Visio, Visual Paradigm for creating diagrams such as Swimlane Diagrams, E-R Diagrams, Class Diagrams, Sequence Diagrams and Package Diagrams.


org.plasma.query.xpath
Interfaces
XPathQuery
XPathSelect
XPathWhere
Classes
QueryPathNavigator
QueryPredicateVisitor
QueryXPath


Important aspect --> Correctness
However the only way to prove is by: Formal Reasoning
One form of reasoning is "Proof by Induction"
PROCEDURE calcFactorial(n) { factorial ← 1 i ← 1 REPEAT UNTIL (i > n) { factorial ← factorial * i i ← i + 1 } RETURN factorial }
To prove the algorithm is true for any positive number
Good algorithm is:
For example a linear search algorithm
PROCEDURE searchList(numbers, targetNumber) { index ← 1 REPEAT UNTIL (index > LENGTH(numbers)) { IF (numbers[index] = targetNumber) { RETURN index } index ← index + 1 } RETURN -1 }
It would take 4 procedures to find the index of 3 in [3,4,7]
The efficiency can be described as: 1 --> Variable init 2 --> N/2 loop repitions 3 --> 3 operations per loop
The equation is 1+3(n/2)
Measure time --> Programming Language
function searchList(numbers, targetNumber) { for (var index = 0; index < numbers.length; index++) { if (numbers[index] === targetNumber) { return index; } } return -1; }
This also results in a linear graph:
PROCEDURE searchList(numbers, targetNumber) { minIndex ← 1 maxIndex ← LENGTH(numbers) REPEAT UNTIL (minIndex > maxIndex) { middleIndex ← FLOOR((minIndex+maxIndex)/2) IF (targetNumber = numbers[middleIndex]) { RETURN middleIndex } ELSE { IF (targetNumber > numbers[middleIndex]) { minIndex ← middleIndex + 1 } ELSE { maxIndex ← middleIndex - 1 } } } RETURN -1 } searchList([3, 37, 45, 57, 93, 120], 45)
The algorithm has increasingly less numbers. This means it is not linear.
This is called Logarithmic


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Welcome to the carnival
Where'd I park?


CT City in hot water over Makola comments
Cape Town - Absa Premiership outfit Cape Town City may have landed themselves in hot water over comments criticising the Premier Soccer League's (PSL) decision to ban midfielder Mpho Makola for six-months.
The former Orlando Pirates midfielder received the hefty suspension for pushing referee Abongile Tom in the Telkom Knockout defeat to Kaizer Chiefs.
And City were less than impressed with the decision and slammed the disciplinary process conducted by prosecutor Nande Becker.
"The premeditated aggressive nature of the proceeding was as harmful as the decision to ban Mpho for 6 months," City said in a statement released.
"The PSL prosecutor continued to emotionally and intellectually badger Mpho, who was determined to handle the proceeding without legal representation in an attempt to show that he was not there to fight his innocence, but rather to show unwavering remorse.
"Mpho was taken advantage of. It got to the point where the hearing had to be stopped completely so that a level of humanity and sanity could be expressed to the league’s prosecutor Nande Becker in relation to his treatment towards Mpho, which was as aggressive as it was unnecessary to the technical proceedings."
But the Cape Town-based club may just have dug themselves a hole with those comments with the PSL poised to react punitively.
"The comments made by Cape Town City are under consideration for possible further action by the League," Becker told KickOff.
"The matter has been re-enrolled for argument in respect of an appropriate sanction against the club. This hearing will take place on 26 November."
City chairman John Comitis could not be reached for comment.
- TeamTalk Media


Released at Toorcon 2010
Firefox extension enabling quick and easy session hijacking of Facebook, Twitter, etc. sessions
Currently out-of-date
Basically, a more up-to-date and maintained Firesheep
Primarily attacks via ARP table poisoning. Used to man-in-the-middle read packets invisible to target.
Supports man-in-the-middle attacking SSL (HTTPS)!
Sniffer built specifically for WiFi, can detect hidden networks, completely passive.
Scriptable exploitation of networked software for research and ...
git
Python
Trojans, Worms, Viruses
Virtual Machines
ARP poisoning
Networking tools and terms
Hashes
Password cracking
Spam filtering (machine learning)
Virus scanning
Firewalls
Log monitoring
Intrusion Detection Systems
Cryptography and Cryptanalysis
Built with reveal.js
1Learn firewalls, anti-virus, log and file monitoring
2Create a virus scanner
1Social engineering
Be smart, don't give out personal information.
2Physical access
Encrypt important information (TrueCrypt).
3Trojan horse
Get a good virus scanner (ClamAV).
4Network eavesdropping (passive)
Use HTTPS/VPN always, encrypted WiFi (WPA2 or better).
5Network attack (active)
SSL can not be man-in-the-middled if you do not click through warnings!
6Exploit server, rootkit (active)
Scan files on-disk to ensure good versions (Tripwire); keep servers up-to-date with latest patches.
He talks about quine technology to produce binaries which untraceably insert bugs into compiled code.
If you wish to make an apple pie from scratch, you must first invent the universe.
- Carl Sagan
Automatic binary bug finding
Found 29 vulnerabilities in Linux + Windows
2 were previously unknown


Scale
is the abstract projection of your
domain specific data value boundaries to screen pixel range. Generally speaking, it lets you to convert any
data value into pixel based coordinates on the screen and vice versa. Because of that scales are extremely
handy when you build your own
Renderer. Nova
scale is an extended version of D3 Scale.
And, they are also used for tick generation and tick label formatting. But, in addition to formatting for tick labels, a scale can have the whole set of Formatters to be used for various purposes.
Every scale is defined by its domain and range. The range will be set by the chart itself and will represent the amount of screen space that is available for rendering. Meanwhile, the domain is controlled by a data provider (it's you) and allows for plenty of possibilities. Some of those possiblities are described on the dedicated Domains page.
As a user of the chart framework you will usually need to provide a couple of scales with your
IChartSeries
. Don't know how to do
it? You may find it helpful to start with the
Chart Basics page
first.
But, if you're ready to create a scale, just instantiate any class that implements the
IScale
interface. Let's use
LinearScale
as an example:
{{scaleDefinition}}
Scales are reusable and can be shared between multiple charts. Scales are identified by the value of their
id
field. It can be passed as a parameter on scale instantiation, otherwise it will be
autogenerated.
{{scaleWithId}}


How to complete and send your response
{% if data.radio_select_group=="all" or data['dashboard'] == 1 %}- Download the admission form
- Complete the form on your computer using:
- Claim number: 000MC023
- Claimant: Jan Clark
- Defendant: Mary Richards
- Save the completed form to your computer
- Email the form to<EMAIL_ADDRESS>Deadline for sending your response
You must email the form before 4pm on{% if data.radio_select_group_time=="No"%} {% elseif data.radio_select_group_time=="Yes" %}{% else %} {% endif %}.
Download and complete the form
- Download admission form (PDF, 102 KB).
- Download both the admission and defence forms
- Complete the forms on your computer using:
- Claim number: 000MC023
- Claimant: Jan Clark
- Defendant: Mary Richards
- Save the completed forms to your computer
- Email the forms to<EMAIL_ADDRESS>Deadline for sending your response
You must email the forms before 4pm on{% if data.radio_select_group_time=="No"%} {% elseif data.radio_select_group_time=="Yes" %}{% else %} {% endif %}.
Download and complete the forms
- Download admission form (PDF, 102 KB).
- Download defence form (PDF, 75 KB).
- Download the defence and counterclaim form
- Complete the form on your computer using:
- Claim number: 000MC023
- Claimant: Jan Clark
- Defendant: Mary Richards
- Save the completed form to your computer
- Email the form to<EMAIL_ADDRESS>Counterclaim fee
You'll need to pay a court fee if you make a counterclaim.
View claim fees
Deadline for sending your response
You must email the form before 4pm on{% if data.radio_select_group_time=="No"%} {% elseif data.radio_select_group_time=="Yes" %}{% else %} {% endif %}.
Download and complete the form
- Download the defence and counterclaim form (PDF, 102 KB).
Complete and email the defence and counterclaim form before {% if data.radio_select_group_time=="No"%} {% elseif data.radio_select_group_time=="Yes" %}{% else %} {% endif %} at 4pm
You'll need to pay a court fee if you make a counterclaim.
View claim fees
-
Complete the defence and counterclaim form entering these details:
- Claim number: 000MC023
- Claimant: Jan Clark
- Defendant: Mary Richards
- Save the completed form to your device.
- Email the form to<EMAIL_ADDRESS>Help and support
Email<EMAIL_ADDRESS>Telephone: 0300 123 1057
Hours: 9am to 5pm, Monday to Friday
Find out about call charges (opens in a new window)


✧･ﾟ: *✧･ﾟ:* Works I have created *:･ﾟ✧*:･ﾟ✧
About Me:
Welcome!
My name is Jessica Russell and I am a rising junior Computer Science major at Rose-Hulman Institute of Technology. I currently participate in several clubs and design teams, most notably as the Promotional/Sponsorship Lead for the Rose Grand Prix Engineering team and the Secretary to the Rose Rocketry Team. I have participated in numerous projects since starting at Rose, and have most recently worked on building and launching a high-powered rocket. Despite not being my most usual subject for a project, it was certainly an exciting one.
However, regardless of the engineering work I currently immerse myself in, I am also an artist, and have been for most of my life. I absolutely love art at any level, and I am particularly practiced in digital and traditional style artwork. I can get carried away in designs and tend to develop story lines and plots in my free time. I have also gained experience in making promotional fliers, logos, extensive designs (for engineering as well as story creation), and complete visual media in the form of digital mockups, comics, animation, etc.


Customer is required!
Item name is required!
Item price is required!
Item tax is required!
Number of items is required!


Company profile - Canadian Company Capabilities
Third-Party Information Liability Disclaimer
Some of the information on this Web page has been provided by external sources. The Government of Canada is not responsible for the accuracy, reliability or currency of the information supplied by external sources. Users wishing to rely upon this information should consult directly with the source of the information. Content provided by external sources is not subject to official languages, privacy and accessibility requirements.
R. Bouwmeester & Associates
Mailing Address:
165 Browning TrailBARRIE, Ontario
L4N 5E7
Location Address:
165 Browning TrailBARRIE, Ontario
L4N 5E7
Tel.:<PHONE_NUMBER>
Fax:<PHONE_NUMBER>
Logo
Company Profile
R. Bouwmeester and Associates, a civil engineering firm, specializes in forensic sun and shadow position modeling for the legal profession, law enforcement agencies, and collision and crime scene reconstruction professionals. The firm also prepares shadow impact studies and solar access studies for the architectural, urban planning and land development industries.
Its principal, Ralph Bouwmeester, has been registered as a Professional Engineer in the Province of Ontario since 1978.
He has appeared before the Ontario Municipal Board as an expert witness in shadow impact. In addition, he has appeared before criminal courts in Canada and the USA as an expert witness in sun position modeling (related to collision investigation and reconstruction).
Ralph Bouwmeester is registered with Professional Engineers Ontario; he is a member of the Canadian Society for Civil Engineering and a past-member (in good standing) of the Association of Ontario Land Surveyors.
Full profile
Full profile
Contact information
Company description
Product / Service / Licensing
. crime scene reconstruction input (e.g. confirm sun angles and shadowing conditions)
. forensic shadow reconstruction (e.g. estimate or confirm date/time from photos and videos)
. expert witness testimony
. shadow impact studies (e.g. for urban development and building projects)
. solar access studies (e.g. for pedestrian comfort)
. solar energy input (e.g. for building & site design)
. solar site planning and zoning
. sun blinding input (e.g. for sports facility planning and design)
. input re: solar-related bio-medical, agricultural, ecological & environmental impacts
. computer-aided solar modeling to calculate the sun's apparent position in the sky for any given location on earth for any date and time (historical, present, or future)
. sunrise/sunset tables - current month free (e.g. for project planning and scheduling, photography, etc.)
. daylight availability tables (e.g. for disaster relief work)
. sundial design
AJ00-General Science and Technology
AP00-Natural Resources
B009-Other Natural Sciences Studies
B109-Other Environmental Studies
B205-Building Technology Studies
B206-Energy Studies
B208-Housing and Community Development Studies (incl. Urban/Town Planning Studies)
B219-Other Engineering Studies
C200-Architect and Engineering Services - Not Related to Construction
C211-Architect - Engineer Services (incl. landscaping, interior layout and designing)
C214-A&E Management Engineering Services
C219-Other Architect and Engineering Services
Technology profile
3D CAD modeling
Market profile
Alliances:
- Sales/Marketing
Strategic alliances:
We are available to engage in joint efforts abroad with urban planning, development and architectural firms, with the legal profession, and with collision and crime scene investigators and reconstructionists. Our services are not limited by geographical or political boundaries.Industry sector market interests:
- Construction
- Environment
- Fishery
- Forestry
- Medical/Biotechnology/Chemical
- Transportation
- Automotive
Geographic markets:
Export experience:
- Ethiopia
- Indonesia
- New Zealand
- United Kingdom
- United States
- Alaska
- Arizona
- California
- Connecticut
- District of Columbia
- Florida
- Georgia
- Hawaii
- Iowa
- Louisiana
- Massachusetts
- Michigan
- Minnesota
- New Jersey
- New York
- North Carolina
- Pennsylvania
- South Carolina
- Texas
- Utah
- West Virginia
- Wisconsin
Sector information
Unique applications:
Frito-Lay SunChips Solar Billboard - Modesto, CA - see time-lapse video http://www.youtube.com/watch?v=OMD7YJ2UqsQAir India Flight 182 Memorial Sundial - Toronto ON - http://www.sunposition.com/air_india_sundial/air_india_memorial_sundial.html
Key / Major clients:
Builders / DevelopersAdason Properties
Allied Properties REIT
Amacon
Brandy Lane Corp.
Brookfield Property Management
Cadillac Fairview
Canada Life
Canadian Tire
Canderel Stoneridge
Carttera Management
Cathedral Properties
CD Capital Partners
Centrecourt Developments
Cityscape Holdings
Cityzen Development Group
Cresford Developments
Cybernetics Group
Diamante Developments
DiamondCorp
Elia Corporation
Elm Developments
Empire Communities
Fernbrook Homes
Freed Developments
Graywood Developments
Great Lands Corporation
Greenco Project Management
Greenwin Property Management
Intracorp Properties
J. G. Cordone Investments
J. S. Watson & Associates
JD Developments
Kaneff Properties
Kilbarry Holdings
KingSett Capital
Lanterra Developments
Leiths Group
Losar Developments
Menkes Developments
Minto Group
Monarch Corporation
Northfield Developments
North Shore Projects
Park Property Management
Pier 27 (Toronto) Inc.
Pinedale Properties
Pinnacle International
Previn Court Homes
Queenscorp Group
R.B. Morgan Construction
Reserve Properties
Richwood Homes
Rose Corp.
Rowhedge Construction
Ryerson University
Saverino Investments
Shiplake Management
Shiu Pong
Signature Developments
Springhill Developments
St. Thomas Developments
Stancorp Properties
Starlight Investments
State Building Group
Sterling Silver Development
The Forrest Group
The Greenpark Foundation
The Rockport Group
Times Group Corporation
Tribute Communities
Tridel
Wittington Investments
Wittington Properties
Collision Reconstructionists
CATAIR - Ontario Region
CIAP
Giffin Koerth Forensic Engineering
Human Factors North
J. F. Mitchell Accident Reconstructionist
Michael C. Oliveira
Osborne & Associates
Visual Forensic Analysis, Inc.
Government
Passaic County Prosecutor's Office
San Diego County Public Defender's Office
State of Alaska Public Defender Agency
State of Eritrea
Law Enforcement
Arizona State University Police Department
Barrie Police Service
Toronto Police Service
York Regional Police
Law Firms
Adams & Reese LLP
Blake, Cassels & Graydon LLP
Brown, Obringer, DeCandio & Oosting
Cattanach Hindson Sutton VanVeldhuizen
Cooper, Sandler, Shime & Bergman LLP
Declues, Burkett & Thompson, LLP
Fogler, Rubinoff LLP
Gowling Lafleur Henderson LLP
Green Seifter Attorneys, PLLC
Hvistendahl, Moersch, Dorsey & Hahn, P.A.
Irwin Mitchell LLP
Kenny, O'Keefe & Usseglio, P.C.
Krause, Moorhead & Draisen, P.A.
Law Offices of Randy E. Thomas
Lewis Johs Avallone Aviles, LLP
McCarthy Tétrault LLP
Michael L. Neff., P.C.
Moore Ingram Johnson & Steele, LLP
Parsons & Associates
Pelaia Law Center
Ritchie Ketcheson Hart & Biggart LLP
Slater & Gordon (UK) LLP
Steptoe & Johnson, PLLC
The Palmiere Law Firm
Turner Padget
Municipalities
City of Barrie
City of Mississauga
City of Ottawa
City of Toronto
Town of Lacombe
Town of Whitby
Other
BBDO Toronto
CNIB Durham
Dallas-Fort Worth Star-Telegram
Des Moines Register
Errol Morris Films
Jones Consulting Group
Lillie's Vineyard
Plano Independent School District
Snap-on Tools
Solar2 Photovoltaic Systems
St. Albert Men's Slowpitch Association
US Steel Corporation
Warner Einer - film location manager
International capital projects:
Corporate Expertise:-----------------------
R. Bouwmeester & Associates offers sun position modeling services to firms engaged in technical collision reconstruction (including collision investigators and reconstructionists, legal firms, law enforcement agencies, forensic engineers, etc.) and to engineering and architectural firms engaged in urban development and building projects requiring shadow impact and/or solar access studies.
Corporate Language Capabilities:
-----------------------
Dutch, English
Third-Party Information Liability Disclaimer
Some of the information on this Web page has been provided by external sources. The Government of Canada is not responsible for the accuracy, reliability or currency of the information supplied by external sources. Users wishing to rely upon this information should consult directly with the source of the information. Content provided by external sources is not subject to official languages, privacy and accessibility requirements.
Contacts
Contact information
Description
Company description
Products, services and licensing
Product / Service / Licensing
. crime scene reconstruction input (e.g. confirm sun angles and shadowing conditions)
. forensic shadow reconstruction (e.g. estimate or confirm date/time from photos and videos)
. expert witness testimony
. shadow impact studies (e.g. for urban development and building projects)
. solar access studies (e.g. for pedestrian comfort)
. solar energy input (e.g. for building & site design)
. solar site planning and zoning
. sun blinding input (e.g. for sports facility planning and design)
. input re: solar-related bio-medical, agricultural, ecological & environmental impacts
. computer-aided solar modeling to calculate the sun's apparent position in the sky for any given location on earth for any date and time (historical, present, or future)
. sunrise/sunset tables - current month free (e.g. for project planning and scheduling, photography, etc.)
. daylight availability tables (e.g. for disaster relief work)
. sundial design
AJ00-General Science and Technology
AP00-Natural Resources
B009-Other Natural Sciences Studies
B109-Other Environmental Studies
B205-Building Technology Studies
B206-Energy Studies
B208-Housing and Community Development Studies (incl. Urban/Town Planning Studies)
B219-Other Engineering Studies
C200-Architect and Engineering Services - Not Related to Construction
C211-Architect - Engineer Services (incl. landscaping, interior layout and designing)
C214-A&E Management Engineering Services
C219-Other Architect and Engineering Services
Technology
Technology profile
3D CAD modeling
Market
Market profile
Alliances:
- Sales/Marketing
Strategic alliances:
We are available to engage in joint efforts abroad with urban planning, development and architectural firms, with the legal profession, and with collision and crime scene investigators and reconstructionists. Our services are not limited by geographical or political boundaries.Industry sector market interests:
- Construction
- Environment
- Fishery
- Forestry
- Medical/Biotechnology/Chemical
- Transportation
- Automotive
Geographic markets:
Export experience:
- Ethiopia
- Indonesia
- New Zealand
- United Kingdom
- United States
- Alaska
- Arizona
- California
- Connecticut
- District of Columbia
- Florida
- Georgia
- Hawaii
- Iowa
- Louisiana
- Massachusetts
- Michigan
- Minnesota
- New Jersey
- New York
- North Carolina
- Pennsylvania
- South Carolina
- Texas
- Utah
- West Virginia
- Wisconsin
Sector
Sector information
Unique applications:
Frito-Lay SunChips Solar Billboard - Modesto, CA - see time-lapse video http://www.youtube.com/watch?v=OMD7YJ2UqsQAir India Flight 182 Memorial Sundial - Toronto ON - http://www.sunposition.com/air_india_sundial/air_india_memorial_sundial.html
Key / Major clients:
Builders / DevelopersAdason Properties
Allied Properties REIT
Amacon
Brandy Lane Corp.
Brookfield Property Management
Cadillac Fairview
Canada Life
Canadian Tire
Canderel Stoneridge
Carttera Management
Cathedral Properties
CD Capital Partners
Centrecourt Developments
Cityscape Holdings
Cityzen Development Group
Cresford Developments
Cybernetics Group
Diamante Developments
DiamondCorp
Elia Corporation
Elm Developments
Empire Communities
Fernbrook Homes
Freed Developments
Graywood Developments
Great Lands Corporation
Greenco Project Management
Greenwin Property Management
Intracorp Properties
J. G. Cordone Investments
J. S. Watson & Associates
JD Developments
Kaneff Properties
Kilbarry Holdings
KingSett Capital
Lanterra Developments
Leiths Group
Losar Developments
Menkes Developments
Minto Group
Monarch Corporation
Northfield Developments
North Shore Projects
Park Property Management
Pier 27 (Toronto) Inc.
Pinedale Properties
Pinnacle International
Previn Court Homes
Queenscorp Group
R.B. Morgan Construction
Reserve Properties
Richwood Homes
Rose Corp.
Rowhedge Construction
Ryerson University
Saverino Investments
Shiplake Management
Shiu Pong
Signature Developments
Springhill Developments
St. Thomas Developments
Stancorp Properties
Starlight Investments
State Building Group
Sterling Silver Development
The Forrest Group
The Greenpark Foundation
The Rockport Group
Times Group Corporation
Tribute Communities
Tridel
Wittington Investments
Wittington Properties
Collision Reconstructionists
CATAIR - Ontario Region
CIAP
Giffin Koerth Forensic Engineering
Human Factors North
J. F. Mitchell Accident Reconstructionist
Michael C. Oliveira
Osborne & Associates
Visual Forensic Analysis, Inc.
Government
Passaic County Prosecutor's Office
San Diego County Public Defender's Office
State of Alaska Public Defender Agency
State of Eritrea
Law Enforcement
Arizona State University Police Department
Barrie Police Service
Toronto Police Service
York Regional Police
Law Firms
Adams & Reese LLP
Blake, Cassels & Graydon LLP
Brown, Obringer, DeCandio & Oosting
Cattanach Hindson Sutton VanVeldhuizen
Cooper, Sandler, Shime & Bergman LLP
Declues, Burkett & Thompson, LLP
Fogler, Rubinoff LLP
Gowling Lafleur Henderson LLP
Green Seifter Attorneys, PLLC
Hvistendahl, Moersch, Dorsey & Hahn, P.A.
Irwin Mitchell LLP
Kenny, O'Keefe & Usseglio, P.C.
Krause, Moorhead & Draisen, P.A.
Law Offices of Randy E. Thomas
Lewis Johs Avallone Aviles, LLP
McCarthy Tétrault LLP
Michael L. Neff., P.C.
Moore Ingram Johnson & Steele, LLP
Parsons & Associates
Pelaia Law Center
Ritchie Ketcheson Hart & Biggart LLP
Slater & Gordon (UK) LLP
Steptoe & Johnson, PLLC
The Palmiere Law Firm
Turner Padget
Municipalities
City of Barrie
City of Mississauga
City of Ottawa
City of Toronto
Town of Lacombe
Town of Whitby
Other
BBDO Toronto
CNIB Durham
Dallas-Fort Worth Star-Telegram
Des Moines Register
Errol Morris Films
Jones Consulting Group
Lillie's Vineyard
Plano Independent School District
Snap-on Tools
Solar2 Photovoltaic Systems
St. Albert Men's Slowpitch Association
US Steel Corporation
Warner Einer - film location manager
International capital projects:
Corporate Expertise:-----------------------
R. Bouwmeester & Associates offers sun position modeling services to firms engaged in technical collision reconstruction (including collision investigators and reconstructionists, legal firms, law enforcement agencies, forensic engineers, etc.) and to engineering and architectural firms engaged in urban development and building projects requiring shadow impact and/or solar access studies.
Corporate Language Capabilities:
-----------------------
Dutch, English


Peter John Goergen Jr.
Unafffiliated
Director of Acquisitions, East West Partners - Real Estate Development
I’m fortunate to have an MBA from the University of Pennsylvania’s Wharton School of Business, as well as a BA in English from the University of Virginia. I’m active in the Urban Land Institute on national and Colorado councils, and I’ve attended various leadership training courses over the years.
80206
In the past presidential elections, I voted for Obama in 2008, Romney in 2012, Jill Stein in 2016, and Biden in 2020. Before the 2008 election, I went door-to-door for Obama, and I helped with Tom Perriello’s (Democrat) congressional campaign in Virginia for the 2010 election. I have not donated to or actively supported a political campaign since November 10, 2015.
Snowmass Rotary - Past President-Elect and Past Board Member - 2017 to 2020 Urban Land Institute Technology and Real Estate National Council - 2018 to 2020 Urban Land Institute Resort and Entertainment Real Estate Colorado Council - 2017 to 2020 Snowmass Base Village Metro District - Past President and Past Board Member - 2017 to 2020 Snowmass Base Village Master Association - Past Board Member - 2017 to 2020 Assay Hill Lodge Association – Past Board Member – 2017 to 2020 One Snowmass Association – Past Board Member – 2017 to 2020
Since I moved to Colorado, I’ve spent most of my time working, so I have very little experience organizing in Colorado. There are unfortunately few opportunities to advocate for neutral ground between Republicans and Democrats, and this is an opportunity to get involved in a redistricting process that can do just that. I have served on the board and as the President-Elect of the Snowmass Village Rotary, and while we did terrific work in our community and for non-profits in the Roaring Fork Valley, I wouldn’t call that advocacy work. I have also served on state and national councils within the Urban Land Institute, which seeks to promote equitable, healthy, and sustainable real estate in our communities.
Colorado’s redistricting should create congressional-district boundaries that make sense based on geography, neighborhoods, and communities, rather than political affiliation. The opportunity to help lead the charge of the de-politicization of Colorado’s district boundaries on the redistricting commission is an amazing chance to exercise my civic duty, helping Colorado serve as a model for other states to enact similar commissions. It’s important, and I’d love to be a part of it.
I’m an independent in heart and practice, I am open to views from both the left, right, and center, and I’m skilled at building consensus among disparate groups. I can write, I can communicate, and I can listen. I want all people to have their voice heard, and this commission is a perfect opportunity to help ensure that right. Gerrymandering, whether enacted for the benefit of Republicans or Democrats, is a shame, and the commission could use people like me committed to fair and impartial redistricting and building consensus among fellow commissioners to get us there.
I’ve been blessed with a good brain. Both my education in the MBA program at Wharton and work experience in real estate have placed a premium on using analytical skills, logic, and reason to solve problems. As a former CFO and current Director of Acquisitions for a prominent real estate firm in Denver, I utilize those three tools on a daily basis, but I also understand that all the logic in the world can’t help if you don’t listen and first understand the problem at hand.
Male


The mayor of bankrupt Detroit is pleased with the safe city she has provided for the residents. Her efforts to rid the city of criminals results in the revitalization of the city and a new renaissance.
The mayor’s success is due in part to a pact with the Vampire Lord, Dimitri. Dimitri and the Vampire Nation have the mayor’s approval to kill the criminal elements of the city by night. The mayor in exchange provides the vampires police protection during the day against vampire hunters.
Mayor Blue struggles with this alliance as college kids are killed. There is a war brewing between the city residents and vampires, set up by a vampire rival of Dimitri. The new vampire in town wants to spark a human and vampire war, paving the way for him to take over the Vampire Nation from Dimitri and use the entire Motor City as a feeding ground. Blue and Dimitri both try to avoid a conflict, however, war on the streets is inevitable. The mayor’s daughter and Dimitri’s son complicate matters by being romantically involved.
A war is ignited. New weapons are developed by the police chief to destroy vampires. Blue and the police take on the rebel vampires while Dimitri vanquishes his vampire rival just in time to save his son and Blue’s daughter from certain death.
Mayor Blue and Dimitri come to understand that to save the city and their way of life, they must first save themselves from each other. The two leaders agree to develop a new pact with the understanding, it is in both their political and personal interest to work together. However, lurking in the shadows is a surviving rebel vampire ready to plot revenge.
Margo
Mingus
Samantha
Parker
Carla
Luna
Brian "Sham" Green
Aldo Billingslea
Wm. Derek Grasty
Wm. Derek Grasty
Jay Raja
Gavin Murray
Ferguson Rogan
Stephen Bartlett
Ryan Nelson
Kat.
Adam Drakewolf
Steven Lagosh
Whittany Robinson
Nathan Adams
Lee Harold
Skylar Adams
Thomas Ariniello
Brian Green
Peder B Helland
Vidal Garcia
Derek Thomas
Grasty Family