METAIRIE, La. (AP) — The New Orleans Saints have placed P.J. Williams on injured reserve, three days after the cornerback absorbed a hard hit to the head during Sunday's loss to the New York Giants.
Williams, whose designation was posted on the NFL's transactions listings Wednesday, took a knee to the helmet in a collision with Giants tight end Larry Donnell.
Williams was able to move his extremities, but was immobilized, carted off the field and taken to a hospital.
On Monday, Saints coach Sean Payton said Williams had a concussion, but otherwise called the cornerback's prognosis "real encouraging."
Williams, a 2015 third-round draft choice, spent his entire rookie season on injured reserve with a hamstring injury. Now his second season has ended after two games.
The Saints have added offensive tackle Tony Hills to their active roster.


text:
Skull and human remains found on Eastern Cape beach
08 October 2019 - 06:42 By IAVAN PIJOOS
Police were called to the scene, where they discovered more remains.
Image: iStock
A skull and other human remains were found at Boknes Beach in the Eastern Cape at the weekend, police said on Tuesday.
Police spokesperson Capt Mali Govender said the discovery was made on Sunday by a family who was on holiday in the small seaside town.
"One of the family members was walking along the beach, close to the lagoon, when he came across what he initially thought was a round stone. He started to dig around and discovered that it was the skull of a human being," Govender said.
Govender said the bones would be sent to the department of health's forensics section.
An inquest docket has been opened for investigation.
"According to our records, no person has been reported missing in the area. Detectives are trying to establish the identity of the victim."
Skull and bones of child found in open field in Port Elizabeth
The decomposed body of a child was found in an open field in Malabar, Port Elizabeth, on Sunday

redpajama_set_name: RedPajamaCommonCrawl


To setup integration with Instagram and allow customers to select and use pictures from their Instagram accounts you need to first have an Instagram account that you will use to create and manage the application.
Now you have created the application it will provide you with a Client ID and Client Secret. You need to take the Client ID and use it to configure the plugin in Catfish Admin here: Catfish > Media > Media Album Settings : under Installed Image Plugins check the Instagram checkbox and paste in the Client ID into the corresponding text box.


text:
Lifestyle / Entertainment
Kim Kardashian West meets with death row inmate
by The Sunday Post
© Frazer Harrison/Getty Images
Kim Kardashian West has described the moment a death row inmate, whose case she has championed, received a stay of execution.
Kardashian West, a reality TV star who is studying for a career in law, believes there is "substantial evidence" that would exonerate murder accused Rodney Reed.
Reed, 51, was facing death by lethal injection on Wednesday for the 1996 killing of a 19-year-old woman.
Following a high-profile campaign featuring celebrities including Beyonce and Oprah Winfrey to have his case reexamined, Texas's top criminal appeals court halted the execution.
Kardashian West revealed she was with Reed when the decision was announced and described the moment he discovered the news.
She tweeted: "Today, I had the honour of meeting #RodneyReed in person and the privilege of sitting with him when he got the news that the highest court in Texas had issued a stay of execution and remanded the case back to the trial court for further consideration.
"Words cannot describe the relief and hope that swept over the room in that moment.
"We have seen Democrats and Republicans come together. We have seen grassroots activists and lawmakers link arms."
Romanian man jailed for life for 2013 murder of house-sitter


This entry was posted in General Articles C, Web & Cloud and tagged 2018June29WebandCloud_C, Content, image size, instant gratification, loading time, personalization, Web. Bookmark the permalink. Both comments and trackbacks are currently closed.


Capital budgeting the term refers to- 'Capital' which implies the fixed assets and 'Budget' which implies the planning of the expenses. Capital budgeting is the process of evaluating, contemplating and determining large amount of expenses that are potential. Such expenditures are often the investment made in long term ventures, purchase of a new vehicle for the use of company or factory, purchase of new machinery and equipment, building of a new plant and many more. The future accounting profit of any project is calculated and estimated by capital budget. Capital budgeting also maximizes the chances of a company's future profits. The management of any company uses capital budgeting as the means of predicting which project would yield the most profit within an expected time. When any new machinery is purchased it is considered to be a capital asset.
The capital budgeting is also referred to as the process in which a firm makes its decisions regarding the buying of major fixed assets. The firms decide on investing on the modification, displacement, addition and disposition of those fixed assets. Students who are pursuing capital budgeting go through a lot of difficulties in getting through their academic years since capital budgeting needs a lot of hard work and many hours of study. Hence, they look for capital budgeting assignment help and capital budgeting homework help online.
What are Capital Budgeting Decisions and the kinds of capital Budgeting Decisions?
Capital budgeting operates using many processes such as evaluation, selection, generation and the following of the alternatives of capital expenditure. There are three kinds of capital budgeting decisions, accept-reject decision, mutually exclusive project decision and capital rationing decision.
Accept-reject decision- The first out of the three kinds of capital budgeting is the Accept-reject decision. The investment decisions which generate more return than the capital cost are always acceptable whereas the decisions of investment which generate less return than the capital cost are rejected. Hence, the firms make investments only if the decisions are acceptable.
Mutually Exclusive Project Decision- The second out of the three kinds of capital budgeting is the mutually exclusive project decision. This type of decision competes with each other which imply that when one alternative is accepted, another is automatically rejected.
Capital Rationing Decision- The third out of the three kinds of capital budgeting refers to the process of keeping restrictions to the amount of new projects or investments that the company has undertaken.
What is the importance of Capital Budgeting?
For knowing what is capital budgeting one needs to understand the importance of capital budgeting simultaneously. As discussed earlier, capital budgeting is the process that works step by step and is used by companies to conclude or determine the advantages of any investment project. Capital budgeting is also the way of creating measurability and accountability. A company which is investing its resources in a particular project must have an idea of the returns and risks involved in it. The capital budget process works to determine the financial profitability of any project or investment in the long run.
The importance of capital budgeting also helps in looking after various aspects such as seeking new projects to invest their resources on, generate new goals which will work in the betterment of the company, approximate the calculation of cash flows, create decisions, control and monitor expenditures and a lot of other aspects.
What are the different Capital budgeting techniques?
There are various Capital Budgeting techniques that are used by businesses, but the most useful of all are the Payback Period Method and the APR method (Accounting rate of return method).
The payback period refers to the period of time that is required in order to regain the cost of an investment. The payback period method is among the most simple methods for the common people to understand who do not have even the basic idea of this field. The method of payback period is usually expressed in years which imply the period of time in years that is taken for paying back the initial investment of a capital project.
ARR method stands for Accounting rate of return. The amount of profit that is expected by an individual based on the investment that he or she has made. Simple rate of return is another name for the Accounting rate of return. Calculation of Accounting rate of return is made through the division of the initial investment of a project by average annual accounting profit.
What are the problems in Capital Budgeting?
Time Element- The problem of time element refers to occurrence of cost and benefit of a decision at different points of time. It requires to be adjustable for time value of money in order to be comparable. The chances of uncertainty increases when the time period is longer.
Future Uncertainty- The capital budgeting decisions are long term which means they are uncertain. An accurate forecast is not possible even after special efforts and hard work. The uncertainty takes place relating to various factors such as demand in the future, cost of the project, competition and many more.
What are the different capital budget processes?
As discussed earlier, capital budgeting is the process that is used by companies to evaluate, calculate or estimate the potential expenditures that are of a huge significance in terms of the amount.
Project screening and evaluation- The project screening and evaluation process refers to the selection of the correct criteria that is required to judge the value of any proposal. This method of the capital budget process has to match the objective of the firms so that the market value can be escalated.
Project Selection- Since different businesses have different requirements hence, there is not a defined method. Therefore, the approval of a project has to be done that is based on the criteria of the selection and the screening process that is meant for every firm.
Project identification and generation- The project identification and generation is the most important capital budget process. This process helps generating proposal for investments since there can be different and various reasons and considerations in a business for taking up investments.
What are the limitations of Capital Budgeting?
Capital budgeting determines the every possible positive sides and merits of an investment, although there are several limitations in the capital budgeting as well like the cash flow, time horizon, time value, discount rates and others due to which companies do not always succeed in making good decisions.
Students pursuing capital budgeting very often find difficulties in getting through their tasks that are assigned by their teachers or professors. Hence, they look for capital budgeting assignment help and capital budgeting homework help from various sources. NeedAssignmentHelp is the best source among all as it has writers who are with PhD and master's degree.
Information technology is a functional approach to scientific calculation and the attributions related to it. Taking help from the authentic experts enhance your aptitude and skills to perform computer technology assignment.
Fresh technological contrivances and revelations are founded on the comprehension of Physics. The parts of Physics such as Quantum Mechanics and Astrophysics etc. are extreme relevant in the determination of erudition of the Universe.
Why you should take NeedAssignmentHelp's service?
NeedAssignmentHelp is the one stop solution for all the queries and doubts of students pursuing finance and capital budgeting. Since the students cannot always figure the capital budgeting techniques and the kinds of capital budgeting out, they look for someone to provide them with capital budgeting assignment help and capital budgeting homework help by online academic work providing websites. This is where the NeedAssignmentHelp can prove to be of a great relief.
Our writers have years of experience in this field and have been very successful in catering to the best quality assignment help and homework help services. We have our online executives available 24*7 to attend to the queries of the students at any hour and any time they need. We have been offering assignment help and homework help not only in capital budgeting but various other subjects as well.
Need the best quality capital budgeting help? Visit NeedAssignmentHelp.


QuestBridge is pleased to offer the Amherst College telementoring award in partnership with Amherst College, which is consistently ranked as one of the top colleges in the nation. College Prep Scholars selected for telementoring will be contacted by Amherst College directly about being paired with an Amherst College student, who will advise them by phone and e-mail about the college admissions process.
The Amherst student telementors are high-achieving low-income students themselves, and many are among the first in their family to attend college. Having recently navigated the college admissions process, these students can offer insight and practical know-how for navigating the process of getting into a selective college.
What to look for in a college and how to choose the right one.
Filling out college applications and writing application essays.
How to approach the financial aid process.
Academic and social life as a low-income student at a highly selective college.


In the game, you can see the style and concept having a resemblance to the Pokemon games; i.e., collect various Mortys and assemble a team that will battle the rival group composed of Ricks. You can also battle Mortys against other Mortys.
Rick and Morty: Pocket Mortys features a third-person overhead view with three basic screens: for the main character's navigation, for the battle screen, and for the menu interface. Are you ready to explore the multiverse? Search for Mortys in Rick and Morty: Pocket Mortys.
Join Ricks as he searches the multiverse for bizarre members to recruit into his team. In this game, the gang is swept by the latest craze. They are obsessed with the idea of going through each multiverse to recruit bizarre Mortys. The goal is to train all these versions and assemble a team to rival a group composed of Ricks.
Let the army build and gather. Get ready to Pocket Mortys combine and command the Mortys and prove once and for all that you are the greatest Morty trainer. Here are some of the top game features of Rick and Morty: Pocket Mortys recipe.
Rick and Morty: Pocket Mortys quests is based on the hit cartoon Rick and Morty. Follow Rick as he explores the various multiverses in search of many Mortys and builds an army that will battle his evil doppelganger and a flock of Ricks.
Journey through these dimensions and recruit Mustache Morty, Cronenberg Morty, Wizard Morty, and more. Defeat the Ricks and save Morty's existence in a battle of doubles versus doubles.
This action-packed game follows an old-school Nintendo-style keypad to control your character, directing it to move around a perfectly controlled map. In addition to battling against the Ricks, you can also fight other Mortys to reveal the greatest and strongest Morty in the bunch.
Progress through each stage before finally meeting a Boss Rick. If you defeat him, you earn a badge that lets you level up and fight the Council of Ricks.
Rick and Morty: Pocket Mortys also features similarities with the Pokemon game where you catch and collect certain characters then train them for combat. However, this free PC game offers a humorous and intelligent twist to the game style and concept.
There are more than 70 different Mortys to catch and recruit in Rick and Morty: Pocket Mortys. Be sure to include all of them in your collection.
Got stuck in the game? Do you need Pocket Mortys cheats, tips and tricks to move forward and finally meet the evil boss? We offer these simple pointers to give you a clue on how to proceed in Rick and Morty: Pocket Mortys.
Why limit yourself to a handful of Mortys? If you are just starting out in the game, focus on finding as many as you can. Build your collection to include all types of Mortys. That way, you can battle it out with whatever type of evil Morty you will face. As they say, be the jack of all trades. Bag them all. However, make sure to upgrade their skills and attributes.
Do not worry if you lost a battle or have a Morty dazed out. After all, there is no penalty when either of the two happens. However, it can be a good strategy to switch each Morty in and out for different abilities or characteristics.
But if you capture two or more Mortys of the same features, you can combine them to create a more powerful evolution. For instance, if you have two Mortys with a mustache, merge them to get a bearded Morty. And if you add another mustache Morty to the bearded one, you can create a hipster, Morty. Experiment with the Mortys you have on hand and see surprising results.
Download Rick and Morty: Pocket Mortys recipe free and enjoy this amazing game. You can also get some more hilarious Simulation Games that'll keep you entertained for hours, such as The Battle Cats and Chichens!
your Rick and Morty: Pocket Mortys installation.


Distribution: Indo-West Pacific: Cocos-Keeling Islands and the South China Sea to the Gilbert Islands, north to the Ryukyu Islands, south to the southern Great Barrier Reef and New Caledonia (Ref. 37816).
Table 4: Reported from 19 countries/islands.


Mobile Device Management - for Android, Windows 7, iPhone, iPad & iPod Touch!
When it comes to mobile device management you have options. You can either purchase the mobile device management tools from us and maintain it on your our equipment, or use the YFI Software-as-a-Service cloud solution. Now all you need is an internet connection to manage your devices, we handle the rest.
YFI Technologies can remotely manage devices. These devices can be enrolled, provisioned, configured and group managed wirelessly.
We are capable of reporting on a wealth of device information including live device status, network connection, security policy compliance, and applications.
YFI can deploy encrypted configurations to customize device settings and enforce corporate security policies & restrictions.
Configure MS Exchange for secure sync of email, calendar, and contacts. Customize WiFi, VPN, APN, and password settings to maintain secure connections. Restrict Safari, YouTube, applications, and others to ensure device security and user productivity.
YFI will be able to locate devices anywhere in the world by viewing GPS coordinates in a live map display.
Find where field workers are active or where lost and stolen devices are in real time. Store and report on coordinate information for review.
YFI can enforce strong password protection, secure certification and encrypted data transfer to ensure sensitive data is kept in authorized hands only.
Detect jail broken devices to force remote security or prohibit access to corporate networks and data immediately.
Remotely lock devices in real time to prevent unauthorized use.
Remotely wipe device data in case of loss, theft, or misuse.
YFI can remotely see a view of a device screen and user activity from a live help desk interface. Capture live screen shots and video recordings for review and presentations.
Optimize help desk communication with a convenient 2-way chat utility. Device users and administrators can exchange clear text messages quickly in a live display.


12:30 – 1:30 Open Classrooms – Tour Ventana School classrooms and see the beautiful Reggio environments and project based learning.
How does the environment support individual and group experiences?
How can the function of an atelier be incorporated into the classroom?
What are the organizational considerations?
How are materials introduced into the classroom?
How do the materials we fill our classrooms with and the curriculum we follow support our children?
How can our environment be a source for inspiration, creativity and long-term projects?
Patricia Hunter McGrath is the Executive Director/Atelierista of Branches Atelier in Culver City, California. Patricia has taught the Reggio Emilia Class at Santa Monica College and at Murdoch University in Perth, Australia. She is a frequent presenter at Reggio conferences throughout the United States and abroad and has visited Reggio Emilia, Italy six times. She is also a contributing author of the book In the Spirit of the Atelier published by Davis Press and Insights and Inspiration from Reggio Emilia also published by Davis Press.
Ticket Sales: Tickets are limited to 30 participants!
This entry was posted in Workshops and tagged Branches, Branches Atelier, Patricia Hunter-McGrath, Patricia Mcgrath, Reggio, Reggio Emilia, Reggio workshops. Bookmark the permalink.


Since the beginning of 2012, for one of our clients with a big news website, we recognized a drastic increase in website traffic without an accompanying increase in ad impressions. Under normal conditions, an increase in traffic would be a positive sign; however, in this case it was caused by end user software that turned normal web users into aggressive web crawlers. This essentially created an accidental but consistent distributed DoS (denial of service) attack.
Here, we explain how we identified the cause and mitigated its effects.
This is strange because Firefox 3.0.10 is a very old (more or less extinct) browser version. Looking closer at the traffic patterns, we came to the conclusion that the traffic was not coming from real website visitors but from a web crawler. We shared our findings on serverfault.com and found a few other website owners having exactly the same strange traffic patterns on their websites. The source IP addresses were distributed around the world. Tracing back the IP addresses mainly revealed Internet end users, which is uncommon for a normal web crawler. At that point, we were not able to identify the root cause, so we decided to block all requests coming from that specific user agent string.
The following graph shows the traffic for one day in May 2012. There were over 20 Million page views with a peak of over 2000 requests per second.
For that day you can see that the majority (~82%) of page requests got blocked because they used that specific user agent string.
The following graphs shows how blocking the crawler became less effective.
Luckily, this unique user agent string also pointed us to the root cause: Genieo. Genieo provides a software product for Windows and Mac computers. In short, it builds a personalized homepage containing recent news headlines. When installed, it runs in the background and constantly browses the Internet for news that you might be interested in. Basically, it runs a web crawler on your computer that connects to news websites directly. You can use the "systray" icon to stop the background browsing / crawling.
Currently, the web traffic caused by Genieo is very high because every single computer that has the Genieo software installed is busy crawling the Internet at a high rate. The Genieo product looks interesting and could potentially drive some real visitors to news websites. It is also obviously a useful product to have developed a user base. However, the rate of crawling and the resulting extra bandwidth costs forced us to make the unfortunate decision to block Genieo again.
We contacted Genieo and they told us that a recent updated did reduce the amount of web traffic caused by their software. They are aware of the issue, and have planned future improvements that will reduce the amount even more. We can already confirm that traffic from Genieo has dropped.
We shared our results with Genieo and they have promised to fix the user agent string soon. We'll update this blog post as soon as Genieo has implemented changes.


text:
Q: Navigation in a Lightbox2 doesn't work well on older IE versions (IE10 and lower) Navigation in a custom Lightbox2 is only clickable if I enter the element vertically, not horizontally. This only occurs on IE8 and lower.
The element that gets the focus if I enter the navigation horizontal is the "

I tried to fix this with z-index, but it didn't work

CSS
/* line 63, ../sass/lightbox.sass */
.lb-nav {
  position: absolute;
  top: 0px;
  left: 0;
  height: 100%;
  width: 100%;
  z-index: 10;
  margin-bottom: -150px;
}

/* line 71, ../sass/lightbox.sass */
.lb-container > .nav {
  left: 0;
}

/* line 74, ../sass/lightbox.sass */
.lb-nav a {
  outline: none;
}

/* line 77, ../sass/lightbox.sass */
.lb-prev, .lb-next {
  width: 40%;
  height: 100%;
  cursor: pointer;
  z-index: 20;
  /* Trick IE into showing hover */
  display: block;
}

/* line 84, ../sass/lightbox.sass */
.lb-prev {
  margin-left: 30px;
  left: 0;
  float: left;
}
/* line 87, ../sass/lightbox.sass */
.lb-prev:hover {
    background: url("../images/lightbox/links-blauw.png") left 48% no-repeat;
}

/* line 90, ../sass/lightbox.sass */
.lb-next {
  margin-right: 30px;
  right: 0;
  float: right;
}
/* line 93, ../sass/lightbox.sass */
.lb-next:hover {
  background: url("../images/lightbox/rechts-blauw.png") right 48% no-repeat;
}

HTML (appended in lightbox JS)

                    Venster sluiten

A: You have to add a transparent background image to the lb-prev and lb-next containers, so they can trigger the hover.
.lb-prev, .lb-next {
width: 49%;
height: 100%;
cursor: pointer;
/* Trick IE into showing hover */
display: block;
z-index:101;
position:relative;
background-image:url(data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw== );
}

redpajama_set_name: RedPajamaStackExchange


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text:
Cyptonychia pseudovarra är en fjärilsart som beskrevs av Dyar 1925. Cyptonychia pseudovarra ingår i släktet Cyptonychia och familjen nattflyn. Inga underarter finns listade i Catalogue of Life.

Källor

Nattflyn
pseudovarra

redpajama_set_name: RedPajamaWikipedia


Windows Server 2008 R2 and Windows 7 SP1 Resources and Information.
by markparris.In Active Directory, Microsoft, Windows, Windows Server.Leave a Comment on Windows Server 2008 R2 and Windows 7 SP1 Resources and Information.
Wednesday evening (16th February) saw the release to TechNet and MSDN, the RTM binaries for Service Pack 1. Next week (February 22nd) will see the general availabilty of Service Pack 1.
Information that is relevant to Service Pack 1 is documented below.
(757 Hotfixes and 39 Security updates).
The Windows Automated Installation Kit (AIK) for Windows 7 SP1 (This is an incremental update to the WAIK).
Previous Previous post: Windows Virtual PC–Refresh download available.


text:
Danny DeVito Buys 'Taxi' Reprieve With 'SNL' Stunt
If television executives have learned one thing (and it's far from certain that they have) it's not to piss off Danny DeVito.
The TV veteran has never been afraid of biting the hands that have gingerly fed him during his time on such classic series as It's Always Sunny in Philadelphia or Taxi. The diminutive but garrulous DeVito has made a career out of playing irascible, irresponsible loudmouths, mirroring the acclaimed actor and director's willingness to take a snarky stand for something he believes in.
Just ask ABC, which found itself in the crosshairs of DeVito on his Season 7 hosting stint on Saturday Night Live. DeVito, then famous for playing unscrupulous taxi dispatcher Louie DePalma on ABC's Taxi, had been booked to host the May 15, 1982, episode of SNL, which just so happened to fall 11 days after Taxi creator and fellow TV legend James L. Brooks was told by Anthony D. Thomopoulos, president of ABC Entertainment, that the ratings-challenged Taxi's just-concluded fourth season would be its last.
DeVito asked SNL producer Dick Ebersol if he could use his monologue to do a little Louie-esque venting. Ebersol, to his credit, not only allowed that but went a few steps further in skewering rival ABC for canceling one of the best and most intelligent sitcoms in TV history — including airing one filmed sketch so literally explosive that it's been edited out of the Peacock version of the episode available now.
Watch Danny DeVito in a 1982 Episode of 'Saturday Night Live'
Kicking off his monologue, DeVito gets a shower of boos by announcing Taxi's cancellation, putting a biting emphasis on the network that had done the deed. "That's the American Broadcasting Company," DeVito stressed to a new wave of sympathetic New York hostility. "A … B … C." DeVito then produced a letter he claimed had been written to him by his Italian immigrant mother, reading, in part, "You have been besmirched by men so shallow that they do not know the depths to which their deeds have taken them," with Mrs. DeVito purportedly concluding, "May God forgive them, but of course, we understand if He does not."
But DeVito had an even bigger surprise for the SNL audience, as, lamenting the fact that the cast and crew were robbed of a proper "final bow" when filming what turned out to be their last episode, DeVito welcomed all of his Taxi costars alongside him in Studio 8H. One by one, DeVito brought out Andy Kaufman, Christopher Lloyd, Marilu Henner, Tony Danza and Judd Hirsch to rapturous applause and a standing ovation from the crowd. (Longtime regular Jeff Conaway was not in attendance, having been let go from the show in response to the substance-abuse issues that would plague him until he died in 2011.) Frequent Saturday Night Live provocateur Kaufman, it should be noted, sports the neck brace he wore following his recent worked-shoot wrestling match against Jerry "The King" Lawler, an infamous stunt played up throughout the night's episode.
It's playful (Hirsch sweeps DeVito into an embrace at the end), with just the right edge of "screw-you" to ABC. But that was nothing compared to the first sketch following the monologue, a filmed piece set to Taxi's melancholy instrumental theme song ("Angela" by Bob James), which sees DeVito's DePalma riding a cab to the ABC building — and blowing it up via remote detonator.
A joke about somebody destroying a very real New York City skyscraper doesn't play in a post-9/11 TV landscape, so the 51-minute Peacock version of DeVito's episode omits the sketch. (Two performances by the band Sparks were also cut, because of music-licensing issues.) Still, with DeVito striking a victory pose at the sound of ABC's headquarters crumbling to dust, the destruction at least seems in line with Louie DePalma's MO.
ABC still had one more blow coming its way: During the goodnights, DePalma brings out his actual mother, Julia. An Italian immigrant, Mrs. DeVito, shorter even than Danny and defiantly smoking a cigarette, is asked if she has another message for the people who put her son out of a job. Indeed she does, as Mrs. DeVito spits out a series of Italian curses (the words "ciucca" and "stupida" are in there for sure), while the mixed and assembled Taxi and Saturday Night Live cast members cheer in delight. Fellow Italian Danza even puts his hand over his mouth in shock before leaning in for a kiss.
Watch a 1982 Promo for Danny DeVito's 'Saturday Night Live' Appearance
Little did ABC know that its public humiliation was just beginning. Following DeVito's episode, NBC would pick up Taxi for its fifth and final season in 1983. Even then, however, the blue-collar cabbies of the Sunshine Cab Company couldn't catch a break. While the fifth-season order did put the series over the 100-episode mark that was then necessary for the show to be sold to syndication, NBC, too, canceled the series at the end of that season.
Perhaps taking a cue from DeVito's gleefully spiteful example, Hirsch, after winning his second Emmy as Taxi's longest-suffering driver Alex Reiger, mocked NBC president Grant Tinker who was sitting in the Emmy audience. (In addition to Hirsch, Taxi costars Lloyd and Carole Kane also took home Emmys on the night.) "We're ready, Grant, we're ready, whenever you want to put us back on," taunted Hirsch, proving, as DeVito did before him, you don't cross even a fictional New York cabbie, and then give him a live mic.
Rock's 60 Biggest 'Saturday Night Live' Performances


Buxton Highton is centrally located within the prestigious and rapidly growing 3216 postcode district in Geelong's outer west – a leafy, up-market enclave of predominantly large family homes.
Located by the southern banks of the beautiful Barwon River, the highly desirable 3216 district is often compared to the coveted Melbourne suburbs of Armadale and Malvern.
Taking in the suburbs of Highton, Grovedale, Wandana Heights, Belmont, Marshall and Waurn Ponds – the Highton region offers fabulous riverside parks, schools, sporting facilities, shopping and amenities, all just a 10-minute drive away from Corio Bay; making it a wonderful, family lifestyle choice.
Buxton Highton offers an energetic and dedicated team of local real estate experts, who work in tandem with the Buxton Newtown office to provide superlative, high-quality service, marketing and specialist expertise.


text:
\section{Introduction}
\label{sec:intro}
Mobile ad-hoc networking has presented many challenges to the research community,
especially in designing suitable, efficient, and well performing protocols.
The practical analysis and validation of such protocols often depends on
synthetic data, generated by some mobility model. The model has the goal of
simulating real life scenarios~\cite{camp02wcmc} that can be used to tune
networking protocols and to evaluate their performance. A lot of work has been
done in designing realistic mobility models. Till a few years ago, the model of
choice in academic research was the random way point mobility model
(RWP)~\cite{rwp}, simple and very efficient to use in simulations.

Recently, with the aim of understanding human mobility~\cite{toronto, hui05,
hui06, milan07, UCAM-CL-TR-617}, many researchers have performed
real-life experiments by distributing wireless devices to people. From the data
gathered during the experiments, they have observed the typical distribution of
metrics such as inter-contact time (time interval between two successive
contacts of the same people) and contact duration. Inter-contact time, which
corresponds to how often people see each other, characterizes the opportunities
of packet forwarding between nodes. Contact duration, which limits the duration
of each meeting between people in mobile networks, limits the amount of data
that can be transferred.
In~\cite{hui05, hui06}, the authors show that the distribution of
inter-contact time is a power-law. Later, in~\cite{milan07}, it has been
observed that the distribution of inter-contact time is best described as a
power law in a first interval on the time scale (12 hours, in the experiments
under analysis), then truncated by an exponential cut-off. Conversely,
\cite{cai07mobicom} proves that RWP yields exponential inter-contact time
distribution. Therefore, it has been established clearly that models like RWP
are not good to simulate human mobility, raising the need of new, more realistic
mobility models for mobile ad-hoc networking.

In this paper we present small world in motion (SWIM), a simple mobility model that generates small worlds. The model is very simple to implement and very efficient in simulations. The mobility pattern of the nodes is based on a simple intuition on human mobility: People go more often to places not very far from their home and where they can meet a lot of other people. By implementing this simple rule, SWIM is able to raise social behavior among nodes, which we believe to be the base of human mobility in real life.
We validate our model using real traces and compare the distribution of inter-contact time, contact duration and number of contact distributions between nodes, showing that synthetic data that we generate match very well real data traces.
Furthermore, we show that SWIM can predict well the performance of forwarding protocols. We compare the performance of two forwarding protocols---epidemic forwarding~\cite{vahdat00epidemic} and (a simplified version of) delegation forwarding~\cite{dfw08}---on both real traces and synthetic traces generated with SWIM. The performance of the two protocols on the synthetic traces accurately approximates their performance on real traces, supporting the claim that SWIM is an excellent model for human mobility.

The rest of the paper is organized as follows: Section~\ref{sec:relatedwork} briefly reports on current work in the field; in Section~\ref{sec:solution} we present the details of SWIM and we prove theoretically that the distribution of inter-contact time in SWIM has an exponential tail, as recently observed in real life experiments; Section~\ref{sec:experiments} compares synthetic data traces to real traces and shows that the distribution of inter-contact time has a head that decays as a power law, again like in real experiments;
in Section~\ref{sec:forwarding} we show our experimental results on the behavior of two forwarding protocols on both synthetic and real traces; lastly, Section~\ref{sec:conclusions} present some concluding remarks.

\section{Related work}
\label{sec:relatedwork}

The mobility model recently presented in~\cite{levy} generates movement traces
using a model which is similar to a random walk, except that the flight lengths
and the pause times in destinations are generated based on Levy Walks, so with
power law distribution. In the past, Levy Walks have been shown to approximate
well the movements of animals. The model produces inter-contact time
distributions similar to real world traces. However, since every node moves
independently, the model does not capture any social behavior between nodes.
In~\cite{musolesi07}, the authors present a mobility model based on social
network theory which takes in input a social network and discuss the community
patterns and groups distribution in geographical terms. They validate their
synthetic data with real traces and show a good matching between them.

The work in \cite{LCA-CONF-2008-049} presents a new mobility model for clustered networks. Moreover, a closed-form expression for the stationary distribution of node position is given. The model captures the phenomenon of emerging clusters, observed in real partitioned networks, and correlation between the spatial speed distribution and the cluster formation.

In~\cite{workingDay}, the authors present a mobility model that simulates the every day life of people that go to their work-places in the morning, spend their day at work and go back to their homes at evenings. Each one of this scenarios is a simulation per se. The synthetic data they generate match well the distribution of inter-contact time and contact durations of real traces.

In a very recent work, Barabasi et al.~\cite{barabasi08} study the trajectory of
a very large (100,000) number of anonymized mobile phone users whose position is
tracked for a six-months period. They observe that human trajectories show a
high degree of temporal and spatial regularity, each individual being
characterized by a time independent characteristic travel distance and a
significant probability to return to a few highly frequented locations. They
also show that the probability density function of individual travel distances
are heavy tailed and also are different for different groups of users and
similar inside each group. Furthermore, they plot also the frequency of visiting
different locations and show that it is well approximated by a power law. All
these observations are in contrast with the random trajectories predicted by
Levy flight and random walk models, and support the intuition behind SWIM.

\section{Small World in Motion}
\label{sec:solution}

We believe that a good mobility model should
\begin{enumerate}
	\item be simple; and
	\item predict well the performance of networking protocols on real mobile
	networks.
\end{enumerate}
We can't overestimate the importance of having a \emph{simple} model. A simple
model is easier to understand, can be useful to distill the fundamental
ingredients of ``human'' mobility, can be easier to implement, easier to tune
(just one or few parameters), and can be useful to support theoretical work. We
are also looking for a model that generates traces with the same statistical
properties that real traces have. Statistical distribution of inter-contact time
and number of contacts, among others, are useful to characterize the behavior of
a mobile network. A model that generates traces with statistical properties that
are far from those of real traces is probably useless. Lastly, and most
importantly, a model should be accurate in predicting the performance of network
protocols on real networks. If a protocol performs well (or bad) in the model,
it should also perform well (or bad) in a real network. As accurately as
possible.

None of the mobility models in the literature meets all of these properties. The random way-point mobility model is simple, but its traces do not look real at all (and has a few other problems). Some of the other protocols we reviewed in the related work section can indeed produce traces that look real, at least with respect to some of the possible metrics, but are far from being simple. And, as far as we know, no model has been shown to predict real world performance of protocols accurately.

Here, we propose \emph{small world in motion} (SWIM), a very simple mobility
model that meets all of the above requirements. Our model is based on a couple
of simple rules that are enough to make the typical  properties of real traces
emerge, just naturally. We will also show that this model can predict the
performance of networking protocols on real mobile networks extremely well.

\subsection{The intuition}

When deciding where to move, humans usually trade-off. The best supermarket or the most popular restaurant that are also not far from where they live, for example. It is unlikely (though not impossible) that we go to a place that is far from home, or that is not so popular, or interesting. Not only that, usually there are just a few places where a person spends a long period of time (for example home and work office or school), whereas there are lots of places where she stays less, like for example post office, bank, cafeteria, etc. These are the basic intuitions SWIM is built upon. Of course, trade-offs humans face in their everyday life are usually much more complicated, and there are plenty of unknown factors that influence mobility. However, we will see that simple rules---trading-off proximity and popularity, and distribution of waiting time---are enough to get a mobility model with a number of desirable properties and an excellent capability of predicting the performance of forwarding protocols.

\subsection{The model in details}

More in detail, to each node is assigned a so called \emph{home}, which is a
randomly and uniformly chosen point over the network area. Then, the node itself
assigns to each possible destination a \emph{weight} that grows with the
popularity of the place and decreases with the distance from home. The weight
represents the probability for the node to chose that place as its next
destination.

At the beginning, no node has been anywhere. Therefore, nodes do not know how
popular destinations are. The number of other nodes seen in each destination is
zero and this information is updated each time a node reaches a destination.
Since the domain is continuous, we divided the network area into many small
contiguous cells that represent possible destinations. Each cell has a squared
area, and its size depends on the transmitting range of the nodes. Once a node
reaches a cell, it should be able to communicate with every other node that is
in the same cell at the same time. Hence, the size of the cell is such that its
diagonal is equal to the transmitting radius of the nodes. Based on this, each
node can easily build a \emph{map} of the network area, and can also calculate
the weight for each cell in the map. These information will be used to
determine the next destination: The node chooses its cell
destination randomly and proportionally with its weight, whereas the exact
destination point (remind that the network area is continuous) is taken
uniformly at random over the cell's area. Note that,
according to our experiments, it is not really necessary that the node has a
\emph{full} map of the domain. It can remember just the most popular cells it
has visited and assume that everywhere else there is nobody (until, by chance,
it chooses one of these places as destination and learn that they are indeed
popular). The general properties of SWIM holds as well.

Once a node has chosen its next destination, it starts moving towards it
following a straight line and with a speed that is proportional to the distance
between the starting point and the destination. To keep things simple, in the
simulator the node chooses as its speed value exactly the distance between these
two points. The speed remains constant till the node reaches the destination. In
particular, that means that nodes finish each leg of their movements in constant
time. This can seem quite an oversimplification, however, it is useful and also
not far from reality. Useful to simplify the model; not far from reality since
we are used to move slowly (maybe walking) when the destination is nearby,
faster when it is farther, and extremely fast (maybe by car) when the
destination is far-off.

More specifically, let $A$ be one of the nodes and $h_A$ its home. Let also $C$
be one of the possible destination cells. We will denote with $\textit{seen}(C)$
the number of nodes that node~$A$ encountered in $C$ the last time it reached
$C$. As we already mentioned, this number is $0$ at the beginning of the
simulation and it is updated each time node~$A$ reaches a destination in
cell~$C$. Since $h_A$ is a point, whereas $C$ is a cell, when calculating the
distance of $C$ from its home $h_A$, node~$A$ refers to the center of the cell's
area. In our case, being the cell a square, its center is the mid diagonal
point. The weight that node~$A$ assigns to cell $C$ is as follows:
\begin{equation}
\label{eq:weight}
w(C) = \alpha\cdot\textit{distance}(h_A, C) + (1-\alpha)\cdot\textit{seen}(C).
\end{equation}
where $\textit{distance}(h_A, C)$ is a function that decays as a power law as
the distance between node~$A$ and cell~$C$ increases.

In the above equation $\alpha$ is a constant in $[0;1]$. Since the weight that a
node assigns to a place represents the probability that the node chooses it as
its next destination, the value of $\alpha$ has a strong effect on the node's
decisions---the larger is $\alpha$, the more the node will tend to go to places
near its home. The smaller is $\alpha$, the more the node will tend to go to
``popular'' places. Even if it goes beyond our scope in this paper, we strongly
believe that would be interesting to exploit consequences of using different
values for $\alpha$. We do think that both small and big values for $\alpha$
rise clustering effect of the nodes. In the first case, the clustering effect
is based on the neighborhood locality of the nodes, and is more related to a
social type: Nodes that ``live'' near each other should tend to frequent the
same places, and therefore tend to be ``friends''. In the second case, instead,
the clustering effect should raise as a consequence of the popularity of the
places.

When reaching destination the node decides how long to remain there. One of the
key observations is that in real life a person usually stays for a long time
only in a few places, whereas there are many places where he spends a short
period of time. Therefore, the distribution of the waiting time should follow a
power law. However, this is in contrast with the experimental evidence that
inter-contact time has an exponential cut-off, and with the intuition that, in
many practical scenarios, we won't spend more than a few hours standing at the
same place (our goal is to model day time mobility). So, SWIM uses an upper
bounded power law distribution for waiting time, that is, a truncated power law.
Experimentally, this seems to be the correct choice.

\subsection{Power law and exponential decay dichotomy}

In a recent work~\cite{milan07}, it is observed that the distribution of
inter-contact time in real life experiments shows a so  called dichotomy: First
a power law until a certain point in time, then an exponential cut-off.
In~\cite{cai07mobicom}, the authors suggest that the exponential cut-off is due
to the bounded domain where nodes move. In SWIM, inter-contact time distribution
shows exactly the same dichotomy. More than that, our experiments show that, if
the model is properly tuned, the distribution is strikingly similar to that of
real life experiments.

We show here, with a mathematically rigorous proof, that the distribution of
inter-contact time of nodes in SWIM has an exponential
tail. Later, we will see experimentally that the same distribution has indeed
a head distributed as a power law. Note that the proof has to cope with a
difficulty due to the social nature of SWIM---every decision taken in SWIM by a
node \emph{not} only depends on its own previous decisions, but also depends on
other nodes' decisions: Where a node goes now, strongly affects where it
will choose to go in the future, and, it will affect also where other
nodes will chose to go in the future. So, in SWIM there are no renewal
intervals, decisions influence future decisions of other nodes, and nodes never
``forget'' their past.

In the following, we will consider two nodes $A$ and $B$. Let $A(t)$, $t\ge0$,
be the position of node~$A$ at time~$t$. Similarly, $B(t)$ is the position of
node~$B$ at time~$t$. We assume that at time~$0$ the two nodes are leaving
visibility after meeting. That is, $||A(0)-B(0)||=r$, $||A(t)-B(t)||<r$ for
$t\in 0^-$, and $||A(t)-B(t)||>r$ for $t\in 0^+$. Here, $||\cdot||$ is the
euclidean distance in the square. The inter-contact time of nodes $A$ and $B$
is defined as:
\begin{equation*}
T_I=\inf_{t>0} \{t:||A(t)-B(t)||\le r\}
\end{equation*}
\begin{assumption}
\label{ass:lower}
For all nodes~$A$ and for all cells~$C$, the distance function $distance(A,C)$
returns at least $\mu>0$.
\end{assumption}

\begin{theorem}
If $\alpha>0$ and under Assumption~\ref{ass:lower}, \emph{the tail} of the
inter-contact time distribution between nodes~$A$ and~$B$ in SWIM has an
exponential decay.
\end{theorem}
\begin{IEEEproof}
To prove the presence of the exponential cut-off, we will show that there exists
constant $c>0$ such that
\begin{equation*}
\mathbb{P}\{T_I>t\}\le e^{-ct}
\end{equation*}
for all sufficiently large $t$. Let $t_i=i\lambda$, $i=1,2,\dotsc$, be
a sequence of times. Constant $\lambda$ is large enough that each node has
to make a way point decision in the interval between $t_i$ and $t_{i+1}$
and that each node has enough time to finish a leg. Recall that this is of
course possible since waiting time at way points is bounded above and since
nodes complete each leg of movement in constant time. The idea is to take
snapshots of nodes $A$ and $B$ and see whether they see each other at each
snapshot. However, in the following, we also need that at least one of the two
nodes is not moving at each snapshot. So, let
\begin{equation*}
\begin{split}
\delta_i=\text{min}\{ & \delta\ge 0 : \text{either $A$ or $B$ is}\\
& \text{at a way point at time $t_i+\delta$}\}.
\end{split}
\end{equation*}
Clearly, $t_i+\delta_i<t_{i+1}$, for all $i=1,2,\dotsc$.

We take the sequence of snapshots $\{t_i+\delta_i\}_{i>0}$. Let $\epsilon_i=\{||A(t_i+\delta_i)-B(t_i+\delta_i)||>r\}$ be the event that nodes $A$ and $B$ are not in visibility range at time $t_i+\delta_i$. We have that
\begin{equation*}
\mathbb{P}\{T_I>t\}\le \mathbb{P}\left\{\bigcap_{i=1}^{\lfloor t/\lambda\rfloor
-1}
\epsilon_i\right\}=\prod_{i=1}^{\lfloor t/\lambda\rfloor -1}
\mathbb{P}\{ \epsilon_i| \epsilon_{i-1}\cdots\epsilon_1\}.
\end{equation*}
Consider $\mathbb{P}\{ \epsilon_i| \epsilon_{i-1}\cdots\epsilon_1\}$. At
time~$t_i+\delta_i$, at least one of the two nodes is at a way point, by
definition of $\delta_i$. Say node~$A$, without loss of generality. Assume that
node~$B$ is in cell $C$ (either moving or at a way point). During its last way
point decision, node~$A$ has chosen cell $C$ as its next way point with
probability at least $\alpha\mu>0$, thanks to Assumption~\ref{ass:lower}. If
this is the case, the two nodes~$A$ and~$B$ are now in visibility. Note that the
decision has been made after the previous snapshot, and that it is not
independent of previous decisions taken by node~$A$, and it is not even
independent of previous decisions taken by node~$B$ (since the social nature of
decisions in SWIM). Nonetheless, with probability at least $\alpha\mu$ the two
nodes are now in visibility. Therefore,
\begin{equation*}
\mathbb{P}\{ \epsilon_i| \epsilon_{i-1}\cdots\epsilon_1\}\le 1-\alpha\mu.
\end{equation*}
So,
\begin{equation*}
\begin{split}
\mathbb{P}\{T_I>t\} & \le \mathbb{P}\left\{\bigcap_{i=1}^{\lfloor
t/\lambda\rfloor -1}
\epsilon_i\right\}=\prod_{i=1}^{\lfloor t/\lambda\rfloor -1}
\mathbb{P}\{ \epsilon_i| \epsilon_{i-1}\cdots\epsilon_1\}\\
 & \le (1-\alpha\mu)^{\lfloor t/\lambda\rfloor -1}\sim e^{-ct},
\end{split}
\end{equation*}
for sufficiently large $t$.
\end{IEEEproof}

\section{Real traces}
\begin{table*}
\begin{center}
\begin{tabular}{|c|c|c|c|c|c|c|}
\hline
Experimental data set \T \B & Cambridge~05 & Cambridge~06 & Infocom~05\\ \hline
Device \T & iMote & iMote & iMote\\
Network type & Bluetooth & Bluetooth & Bluetooth\\
Duration (days)& 5 & 11 & 3\\
Granularity (sec)& 120 & 600 & 120\\
Devices number & 12 & 54 (36 mobile) & 41\\
Internal contacts number& 4,229 & 10,873 & 22,459\\
Average Contacts/pair/day & 6.4 & 0.345 & 4.6\\[1mm] \hline
\end{tabular}
\caption{The three experimental data sets}
\label{tab:realtraces}
\end{center}
\end{table*}

In order to show the accuracy of SWIM in simulating real life scenarios, we will
compare SWIM with three traces gathered during experiments done with real
devices carried by people. We will refer to these traces as \emph{Infocom~05},
\emph{Cambridge~05} and \emph{Cambridge~06}. Characteristics of these data sets
such as inter-contact and contact distribution have been observed in several
previous works~\cite{hui05, leguay06,hui06}.
\begin{itemize}
\item In \emph{Cambridge 05}~\cite{cambridge05} the authors used Intel iMotes to
collect the data. The iMotes were distributed to students of the University of
Cambridge and were programmed to log contacts of all visible mobile devices. The
number of devices that were used for this experiment is 12. This data set covers
5 days.
\item In \emph{Cambridge 06}~\cite{upmcCambridgeData} the authors repeated the
experiment using more devices. Also, a number of stationary nodes were deployed
in various locations around the city of Cambridge UK. The data of the stationary
iMotes will not be used in this paper. The number of mobile devices used is 36
(plus 18 stationary devices). This data set covers 11 days.
\item In \emph{Infocom~05} ~\cite{cambridgeInfocomData} the same devices as in
\emph{Cambridge} were distributed to students attending the Infocom 2005 student
workshop. The number of devices is 41. This experiment covers approximately 3
days.
\end{itemize}
Further details on the real traces we use in this paper are shown in
Table~\ref{tab:realtraces}.

\section{SWIM vs Real traces}
\label{sec:experiments}

\subsection{The simulation environment}
In order to evaluate SWIM, we built a discrete even simulator of the model.
The simulator takes as input
\begin{itemize}
 \item $n$: the number of nodes in the network;
  \item $r$: the transmitting radius of the nodes;
  \item the simulation time in seconds;
  \item coefficient $\alpha$ that appears in Equation~\ref{eq:weight};
  \item the distribution of the waiting time at destination.
\end{itemize}
The output of the simulator is a text file containing records on each main event
occurrence. The main events of the system and the related outputs are:
\begin{itemize}
\item \emph{Meet} event: When two nodes are in range with each other. The
output line contains the ids of the two nodes involved and the time of
occurrence.
\item \emph{Depart} event: When two nodes that were in range of each other are
not anymore. The output line contains the ids of the two nodes involved and the
time of occurrence.
\item \emph{Start} event: When a node leaves its current location and starts
moving towards destination. The output line contains the id of the location, the
id of the node and the time of occurrence.
\item \emph{Finish} event: When a node reaches its destination. The output line
contains the id of the destination, the id of the node and the time of
occurrence.
\end{itemize}
In the output, we don't really need information on the geographical position of
the nodes when the event occurs. However, it is just straightforward to extend
the format of the output file to include this information. In this form, the
output file contains enough information to compute correctly inter-contact
intervals, number of contacts, duration of contacts, and to implement state of
the art forwarding protocols.

During the simulation, the simulator keeps a vector $\textit{seen}(C)$ updated
for each sensor. Note that the nodes do not necessarily agree on what is the
popularity of each cell. As mentioned earlier, it is not necessary to keep in
memory the whole vector, without changing the qualitative behavior of the mobile
system. However, the three scenarios Infocom~05, Cambridge~05, and Cambridge~06
are not large enough to cause any real memory problem. Vector~$\textit{seen}(C)$
is updated at each \emph{Finish} and \emph{Start} event, and is not changed
during movements.

\subsection{The experimental results}

In this section we will present some experimental results in order to show that
SWIM is a simple and good way to generate synthetic traces with the same
statistical properties of real life mobile scenarios.
\begin{figure}[!ht]
\centering
\subfigure[Distribution of the inter-contact time in Infocom~05 and in SWIM]{
\centering
\includegraphics[width=.4\textwidth]{graphics/Infocom/InterContacts}
\label{fig:ICT infocom}}
\qquad
\subfigure[Distribution of the contact duration for each pair of nodes in
Infocom~05 and in SWIM]{
\centering
\includegraphics[width=.4\textwidth]{graphics/Infocom/Contacts}
\label{fig:CONT infocom}}
\qquad
\subfigure[Distribution of the number of contacts for each pair of nodes in
Infocom~05 and in SWIM]{
\centering
\includegraphics[width=.4\textwidth]{graphics/Infocom/ContactsNumber}
\label{fig:CONT-NR infocom}}
\caption{SWIM and Infocom~05}
\label{fig:infocom}
\end{figure}
\begin{figure}[t]
\centering
\subfigure[Distribution of the inter-contact time in Cambridge~05 and in SWIM]{
\centering
\includegraphics[width=.4\textwidth]{graphics/Cambridge05/InterContacts}
\label{fig:ICT cambridge05}}
\qquad
\subfigure[Distribution of the contact duration for each pair of nodes in
Cambridge~05 and in SWIM]{
\centering
\includegraphics[width=.4\textwidth]{graphics/Cambridge05/Contacts}
\label{fig:CONT cambridge05}}
\qquad
\subfigure[Distribution of the number of contacts for each pair of nodes in
Cambridge~05 and in SWIM]{
\centering
\includegraphics[width=.4\textwidth]{graphics/Cambridge05/ContactsNumber}
\label{fig:CONT-NR cambridge05}}
\caption{SWIM and Cambridge~05}
\label{fig:cambridge05}
\end{figure}
\begin{figure}[t]
\centering
\subfigure[Distribution of the inter-contact time in Cambridge~06 and in SWIM]{
\centering
\includegraphics[width=.4\textwidth]{graphics/Cambridge06/InterContacts}
\label{fig:ICT cambridge}}
\qquad
\subfigure[Distribution of the contact duration for each pair of nodes in
Cambridge~06 and in SWIM]{
\centering
\includegraphics[width=.4\textwidth]{graphics/Cambridge06/Contacts}
\label{fig:CONT cambridge}}
\qquad
\subfigure[Distribution of the number of contacts for each pair of nodes in
Cambridge~06 and in SWIM]{
\centering
\includegraphics[width=.4\textwidth]{graphics/Cambridge06/ContactsNumber}
\label{fig:CONT-NR cambridge}}
\caption{SWIM and Cambridge~06}
\label{fig:cambridge06}
\end{figure}
The idea is to tune the few parameters used by SWIM in order to simulate
Infocom~05, Cambridge~05, and Cambridge~06. For each of the experiments we
consider the following metrics: inter-contact time CCD function, contact
distribution per pair of nodes, and number of contacts per pair of nodes. The
inter-contact time distribution is important in mobile networking since it
characterizes the frequency with which information can be transferred between
people in real life. It has been widely studied for real traces in a large
number of previous papers~\cite{hui05, hui06, leguay06, cai07mobicom,
milan07, musolesi07, cai08mobihoc}. The contact distribution per pair
of nodes and the number of contacts per pair of nodes are also important. Indeed
they represent a way to measure relationship between people. As it was also
discussed in~\cite{hui07community, hui07socio, hui08mobihoc} it's natural to
think that if a couple of people spend more time together and meet each other
frequently they are familiar to each other. Familiarity is important in
detecting communities, which may help improve significantly the design and
performance of forwarding protocols in mobile environments such as
DNTs~\cite{hui08mobihoc}. Let's now present the experimental results obtained
with SWIM when simulating each of the real scenarios of data sets.
Since the scenarios we consider use iMotes, we model our network node according
to iMotes properties (outdoor range $30\textrm{m}$). We initially distribute the
nodes over a network area of size $300\times300~\textrm{m}^2$. In the following,
we assume for the sake of simplicity that the network area is a square of side
1, and that the node transmission range is 0.1. In all the three experiments we
use a power law with slope $a=1.45$ in order to generate waiting time values of
nodes when arriving to destination, with an upper bound of 4 hours. We use as
$\textit{seen}(C)$ function the fraction of the nodes seen in cell~$C$, and as
$\textit{distance}(x,C)$ the following
\begin{equation*}
\textit{distance}(x,C)=\frac{1}{\left(1+k||x-y||\right)^2},
\end{equation*}
where $x$ is the position of the home of the current node, and $y$ is the
position of the center of cell~$C$. Positions are coordinates in the square of
size 1. Constant $k$ is a scaling factor, set to $0.05$, which accounts for the
small size of the experiment area. Note that function $\textit{distance}(x,C)$
decays as a power law. We come up with this choice after a large set of
experiments, and the choice is heavily influenced by scaling factors.

We start with Infocom~05. The number of nodes $n$ and the simulation time are
the same as in the real data set, hence 41 and 3 days respectively. Since the
area of the real experiment was quite small (a large hotel), we deem that
$300\times300~m^2$ can be a good approximation of the real scenario. In
Infocom~05, there were many parallel sessions. Typically, in such a case one
chooses to follow what is more interesting to him. Hence, people with the same
interests are more likely to meet each other. In this experiment, the parameter
$\alpha$ such that the output fit best the real traces is $\alpha=0.75$. The
results of this experiment are shown in Figure~\ref{fig:infocom}.

We continue with the Cambridge scenario. The number of nodes and the simulation
time are the same as in the real data set, hence 11 and 5 days respectively. In
the Cambridge data set, the iMotes were distributed to two groups of students,
mainly undergrad year~1 and~ 2, and also to some PhD and Master students.
Obviously, students of the same year are more likely to see each other more
often. In this case, the parameter $\alpha$ which best fits the real traces is
$\alpha=0.95$. This choice proves to be fine for both Cambridge~05 and
Cambridge~06. The results of this experiment are shown in
Figure~\ref{fig:cambridge05} and~\ref{fig:cambridge06}.

In all of the three experiments, SWIM proves to be an excellent way to generate
synthetic traces that approximate real traces. It is particularly interesting
that the same choice of parameters gets goods results for all the metrics under
consideration at the same time.

\section{Comparative performance of forwarding protocols}
\label{sec:forwarding}
\begin{figure*}
\label{fig:forwarding}
\centering
\subfigure{
\centering
\includegraphics[width=.31\textwidth]{graphics/Infocom/PerformanceInfocom}
\label{fig:perf infocom}}
\subfigure{
\centering
\includegraphics[width=.31\textwidth]{graphics/Cambridge05/PerformanceCambridge05}
\label{fig:perf cambridge05}}
\subfigure{
\centering
\includegraphics[width=.31\textwidth]{graphics/Cambridge06/PerformanceCambridge06}
\label{fig:perf cambridge 06}}
\caption{Performance of both forwarding protocols on real traces and SWIM
traces. EFw denotes Epidemic Forwarding while DFwd Delegation Forwarding.}
\label{fig:performance}
\end{figure*}

In this section we show other experimental results of SWIM, related to
evaluation of two simple forwarding protocols for DNTs such as Epidemic
Forwarding~\cite{vahdat00epidemic} and simplified version of Delegation
Forwarding\cite{dfw08} in which each node has a random constant as its quality.
Of course, this simplified version of delegation forwarding is not very
interesting and surely non particularly efficient. However, we use it just as a
worst case benchmark against epidemic forwarding, with the understanding that
our goal is just to validate the quality of SWIM, and not the quality of the
forwarding protocol.

In the following experiments, we use for each experiment the same tuning used
in the previous section. That is, the parameters input to SWIM are not
``optimized'' for each of the forwarding protocols, they are just the same that
has been used to fit real traces with synthetic traces.

For the evaluation of the two forwarding protocols we use the same assumptions
and the same way of generating traffic to be routed as in~\cite{dfw08}.
For each trace and forwarding protocol a set of messages is generated with
sources and destinations chosen uniformly at random, and generation times form a
Poisson process averaging one message per 4 seconds.
The nodes are assumed to have infinite buffers and carry all message replicas
they receive until the end of the simulation. The metrics we are concerned with
are: \emph{cost}, which is the number of replicas per generated message;
\emph{success rate} which is the fraction of generated messages for which at
least one replica is delivered; \emph{average delay} which is the average
duration per delivered message from its generation time to the first arrival of
one of its replicas.
As in \cite{dfw08} we isolated 3-hour periods for each data trace (real and
synthetic) for our study. Each simulation runs therefore 3 hours. to avoid
end-effects no messages were generated in the last hour of each trace.

In the two forwarding protocols, upon contact with node $A$, node $B$ decides
which message from its message queue to forward in the following way:
\begin{trivlist}
\item
\textbf{Epidemic Forwarding:} Node $A$ forwards message~$m$ to node $B$ unless
$B$ already has a replica of $m$. This protocol achieves the best possible
performance, so it yields upper bounds on success rate and average delay.
However, it does also have a high cost.
\item
\textbf{(Simplified) Delegation Forwarding:} To each node is initially given a
quality, distributed uniformly in $(0;1]$. To each message is given a rate,
which, in every instant corresponds to the quality of the node with the best
quality that message have seen so far. When generated the message inherits the
rate from the node that generates it (that would be the sender for that
message). Node $A$ forwards message $m$ to node $B$ if the quality of node $B$
is greater than the rate of the copy of $m$ that $A$ holds. If $m$ is forwarded
to $B$, both nodes $A$ and $B$ update the rate of their copy of $m$ to the
quality of $B$.
\end{trivlist}

Figure~\ref{fig:forwarding} shows how the two forwarding protocols perform in
both real and synthetic traces, generated with SWIM.
As you can see, the results are excellent---SWIM predicts very accurately the
performance of both protocols. Most importantly, this is not due to a customized
tuning that has been optimized for these forwarding protocols, it is just the
same output that SWIM has generated with the tuning of the previous section.
This can be important methodologically: To tune SWIM on a particular scenario,
you can concentrate on a few well known and important statistical properties
like inter-contact time, number of contacts, and duration of contacts. Then, you
can have a good confidence that the model is properly tuned and usable to get
meaningful estimation of the performance of a forwarding protocol.

\section{Conclusions}
\label{sec:conclusions}
In this paper we present SWIM, a new mobility model for ad hoc networking. SWIM
is simple, proves to generate traces that look real, and provides an accurate
estimation of forwarding protocols in real mobile networks. SWIM can be used to
improve our understanding of human mobility, and it can support theoretical work
and it can be very useful to evaluate the performance of networking protocols in
scenarios that scales up to very large mobile systems, for which we don't have
real traces.
\IEEEtriggeratref{7}
\bibliographystyle{ieeetr}

redpajama_set_name: RedPajamaArXiv


text:
Georgia May Jagger Signs With CAA Fashion
The Hollywood agency's fashion division has also signed Marquita Pring, the Dominican-American model and activist.
By Booth Moore on January 12, 2021
Georgia May Jagger on the cover of Elle.
Courtesy/Russell James
CAA Fashion is continuing to expand in 2021.
The Hollywood agency's burgeoning fashion division has signed Georgia May Jagger, daughter of Rolling Stones frontman Mick Jagger and model Jerry Hall.
Known for her signature Jagger pout and gap teeth, she has appeared on the covers of Vogue, Elle and Harper's Bazaar, and appeared in campaigns for Chanel, Versace and Miu Miu. Jagger has been the face of the Just Cavalli and Angel by Thierry Mugler fragrances, and is the muse for the jewelry company Pandora. She's managed by Tess Management in London.
CAA has also signed Marquita Pring, the Dominican-American model and activist who made waves in 2010 when she appeared in the "Curves Ahead" V Magazine spread, one of the first size-inclusive fashion editorials.
Later that year, Pring walked the Jean Paul Gaultier spring 2011 runway in Paris, and in 2012, she covered Italian Vogue, a milestone for curvy women in the fashion industry.
Pring has appeared in ads for Levi Strauss & Co., Polo Ralph Lauren, Bloomingdale's, Macy's, Lane Bryant, Marks & Spencer, Talbots, L'Oréal and Olay; in magazines including Vogue, Glamour and Cosmopolitan, and on the runways for Christian Siriano, Tommy Hilfiger, Tadashi Shoji and Prabal Gurung.
A model in the 2020 Sports Illustrated Swimsuit Edition, she is a champion for body positivity and racial diversity, focusing on issues affecting biracial people.
CAA Fashion was founded last year by Christian Carino with the goal of changing the industry by elevating models to the realm of Hollywood talent in pay and personal branding attention. The group's roster includes Claudia Schiffer, Alessandra Ambrosio, Elsa Hosk, Winnie Harlow, Indya Moore, Kate Upton, Leomie Anderson, Dilone, Duckie Thot, Jillian Mercado, Lourdes Leon and more.


I was making this bag for a sandwich lunch I made for my husband to take to work on a special occasion (he usually just carries a regular lunch bag because I'm not making this for every single lunch! Are you kidding me…).
It's really simple to make .. I'm not one for elaborate projects that mere mortals like myself can only drool on.
Cut 2 rectangle piece of patterned paper approx. larger than the front and center die, stitch a panel of cardstock down the middle and die cut both panels (this will make the front and the bag of the bag).
Adhere a ribbon down the front center panel.
Trim a lunch bag down to size and adhere the stitched panel to the front and bag of lunch bag.
Make a bow from the same ribbon and add tag and bow to the bag. Embellish with a button.
There you have it … and there are so many cute images on the set that I'm planning to use the muffin image for my son's school lunch next. Here is the cutest kitchen set I've laid my eyes on.
I hope your New Year has been going well. For me, most of the hustle and bustle of the holiday season and the clean up after that is done and some sense of normalcy and routine is coming back to our lives. Just at a right time for me, Crafter's Companion has just released a new collection of Kanji stamps – lots of unique and meaningful Asian sentiment and images.
This collection intimidated me at first – I didn't recognize any of those Asian characters and I have never done any Asian art – even if I am Asian! But as I was working through my supplies looking for Asian embellishments and papers to use (I have few of those too), my creative juices started perculating. I found in my supplies some mulberry papers, flowers and leaves looking like Cherry Blossoms and willow, bamboo skewers and chopsticks (those were extras from some takeout I ate at my desk), Asian images on Western papers like a trendy fusion mix (this I found in Basic Grey and Kaiser Craft paper collections), I even though of stamping these Asian characters as my background paper!
The unexpected can surprise you. I hope you will have as much fun with this set as I did.
Here's wishing you tranquility for the New Year ahead.
I just noticed that I have been posting quite a bit the last couple of weeks. Well well, that's because I have been in crafty heaven these days with the huge box of goodies Stampendous sent me that I totally forgot about one of my closest friend's birthday.
Tsk tsk – what a terrible friend I am and I'm very ashamed to admit this. But the crafter in me yearns to share the card I made for her – she loves these pretty girly things in pastel colors so I hope she likes all the scalloped edges, the twirly whirlies, the sparkle and soft colors I included in her card. And I also hope she forgives me too.
I played with alot of Stampendous products on this card. First of all, I created the colorful background with Distressed Ink (Fired Brick, Wild Honey, Tumbled Glass) and a stippling brush. Scalloped punch top edge and round bottom edge. Then I stamp the flourish from the Stampendous Cling Jumbo Dahlia stamp set with Distressed Ink (Pumice Stone). I randomly emboss the colored background with Frantage Embossing Enamel (Shabby White), Stampendous Vintage Glitter (multi crystal mix) and Frantage mica fragments (Pearlized Mica). The finished background was adhere to card base and top edge stitch twice. American Craft Ribbon was attached. The sentiment I used is from the Stampendous Birthday Assortment Perfectly Clear Stamps and stamped on vellum and diecut with PTI doily die. Finish card by attaching sentiment and Kaiser Craft flower blossom.
I used The Pixie Cottage stamp "To You from Us" from Michelle's Scrapbooking and More. They are just too adorable these 2 little kittens. DP is from Authentique Papers. Flowers are from Kaiser Craft. So now it's your turn to go and create and come back and share with us. The randomly picked winner of their choice of two fabulous Pixie Cottage stamps.
By now, you would have figured out that I am a huge fan of Crissy Armstrong's images – they are so achingly adorable, I think. I am extremely proud to be guest designing for her today.
Her challenge this month is "anything goes" – make any card you like and go to her challenge blog, Sundays with Crissy, to play along.
Ain't Rupert the cutest – I left the card without a sentiment so I could use it for any occasion as it crops up. DP is from Basic Grey (green) and I apologize I cannot remember the manufacturer of the yellow dot paper (it's from my stash). The butterfly pin is from Jillibean Soup – I have the hardest time finding hat pins so if you know of a place to get them, let me know.
This little cutie (Sugar Pie Honey Bunch) is one of Crissy's newer releases … even the name is cute. I love that she has these pencil drawing too – make the whole images so much softer. DP is from October Afternoon (black menu) and Lily Bee Designs (yellow dots). Ribbon is from Fancy Pants Designs. Flower is from Kaiser Craft.
There is your dose of cuteness for the day. All of these Crissy's images that I have used can be found at Whimsy Stamps.
Have a fun kind of day!


If you only had four things in your fridge, what could you cook?
Of course, it would depend on the four things.
But after a while it would get pretty boring.
Suppose you only had four DVD's.
If you were stuck on an island, which four DVD's would you bring?
Any way you slice it, whatever "category" you put those four things in, the answer would always be very limited.
Four cities to live in and travel to.
Four kinds of food. (That would at least give you some variety).
Cytosine, guanine, adenine and thymine.
These are the four nucleotides.
Only these four make up EVERYTHING about you.
The difference is precisely HOW they are arranged.
Put them in one order, and you're dead before you've got a chance.
Put them in another order, and you have super human skills of X-men proportions.
Nobody knows WHEN or IF we'll be able understand what order matches what trait.
Or even if there's more going on than we can measure.
After all, take two similar people and they can create VASTLY different things.
These DNA nucleotides create the structure of your brain.
But who or what creates the thoughts that you think?
Most people are content to think thoughts that others think.
Some people think different thoughts, but they still do the same things.
The precious few who make a huge difference are those who THINK differently as well as ACT differently.
Because without actions, thoughts are just a hobby.
But how do you KNOW what to think?
How do you know HOW to think?
You can make them up as you go along.
Keep trying different "thoughts" in different orders, until you hit on a winning combination that will forever change your life, and the world around you.
Posted in Appreciation, Confidence, Conversation Skills and tagged Evolution, Happiness, Hypnosis on January 22, 2018 by george.