text:
The Ministry of Housing, Communities & Local Government has announced their support for the Parking (Code of Practice) Bill, at the same time recognising the BPA's role as the leading authority in the sector and our continuing work with Government and key stakeholders.
We have always believed there is a need to establish a standard setting body, overseeing a single Code of Practice and a single independent appeals service to create a level playing field, eradicate confusion over multiple Codes and appeals services and increase public confidence by addressing any perceived unfair practices. This Bill looks to provide a framework to achieve this, which would be good for the motorist and for the parking sector.
Andrew Pester, Chief Executive of the BPA commented saying:
"We welcome Sir Greg's Bill that aims to drive consistency and fairness in the private parking sector. With two Codes of Practice in the sector and the potential for more, it's essential that we avoid a race to the bottom when it comes to standards. As the leading authority in the sector we are working closely with parliamentarians, government and key stakeholders to press for progress towards a positive outcome for all."
Once this Bill becomes law we shall continue our work with Government and stakeholders to ensure that the emerging Standards body, its Code of Practice, auditing arrangements and appeals service are fit for purpose, practical and fair.
The BPA has been at the forefront of raising standards in the sector for a number of years. The first Code of Practice for our Approved Operators was published in 2006 and the Approved Operators Scheme (AOS) launched in 2007, becoming the model for Accredited Trade Associations. The AOS is accompanied by a full internal and third-party audit and Scheme of Sanctions to ensure operators comply with the Code of Practice.
In 2012 we launched POPLA, an independent appeals service for Parking Charge Notices issued on private land in England and Wales. This service is free to the motorist and run independently of the Association, in agreement with Government.


text:
Massacre in Misrata
Forces loyal to Libyan leader Muammar Gaddafi are staging a "massacre" in the besieged city of Misrata, evacuees said on Monday, as Turkey said it was trying to broker a ceasefire to halt the bloodshed.
With Libya in chaos as Gaddafi's troops battle rebels, an official in neighbouring Algeria said al Qaeda was exploiting the conflict to acquire weapons, including surface-to-air missiles, and smuggle them to a stronghold in northern Mali.
Evacuees from Misrata described the city as hell, with Gaddafi's forces using tanks and snipers against residents, littering the streets with corpses and filling hospitals with the wounded.
"You have to visit Misrata to see the massacre by Gaddafi," said Omar Boubaker, a 40-year-old engineer with a bullet wound to the leg, brought to the Tunisian port of Sfax by a French aid group. "Corpses are in the street. Hospitals are overflowing."
Misrata, the last major rebel-held city in western Libya, rose up with other towns against Gaddafi in mid-February, and is now under attack by government troops.
"I could live or die, but I am thinking of my family and friends who are stranded in the hell of Misrata," said tearful evacuee Abdullah Lacheeb, who had serious injuries to his pelvis and stomach and a bullet wound in his leg.
"Imagine, they use tanks against civilians. [Gaddafi] is prepared to kill everyone there … I am thinking of my family."
Stalemate on the frontline of fighting in eastern Libya, defections from Gaddafi's circle and the plight of civilians caught in fighting, or facing food and fuel shortages, have prompted a flurry of diplomacy to try to end the civil war.
Turkey said it was seeking to broker a ceasefire as an envoy from Gaddafi's government arrived in Ankara from Athens.
"Turkey will continue to do its best to end the sufferings and to contribute to the process of making a road map that includes the political demands of Libyan people," Foreign Minister Ahmet Davutoglu said following the arrival of Libyan Deputy Foreign Minister Abdelati Obeidi.
Turkey was also in contact with the opposition's national council, and expected representatives to visit Ankara "in the coming days", Davutoglu told a news conference. A Turkish Foreign Ministry official said both sides had "conveyed that they have some opinions about a possible ceasefire".
Bogged down
Beyond a willingness to talk, there was no sign of what Libya might offer to end the war that is bogged down on a frontline around the eastern oil town of Brega.
Italian Foreign Minister Franco Frattini, who had spoken to Greek officials after Obeidi's talks in Athens, dismissed the Libyan envoy's message.
After a meeting with Ali Essawi, a member of the Libyan rebel council looking after foreign affairs, Frattini said Italy, a former colonial power in Libya, backed the rebels: "A solution for the future of Libya has a pre-condition — that Gaddafi's regime leaves and is out and that Gaddafi himself and his family leave the country."
He said an interim government headed by one of Gaddafi's sons was "not an option".
UN-mandated air strikes to protect civilians have so far failed to halt attacks in Misrata by the Libyan army.
Libyan officials deny attacking civilians in Misrata, saying they are fighting armed gangs linked to al Qaeda. Accounts from Misrata cannot be independently verified as Libyan authorities are not allowing journalists to report freely from there.
A rebel spokesperson said the city had been shelled on Monday.
"The shelling started in the early hours of the morning and it's continuing, using mortars and artillery. This is pure terrorism. The shelling is targeting residential areas," the spokesman, called Gemal, told Reuters by telephone, adding: "We know there are casualties but I don't know how many."
A Turkish ship that sailed into Misrata to rescue 250 wounded was protected by Turkish warplanes and warships and had to leave in a hurry after thousands pressed forward on the dock, pleading to be evacuated. Another ship operated by Medecins Sans Frontieres docked in Sfax with 71 wounded from Misrata.
Al Qaeda convoy
In Algiers, a senior security official told Reuters that Al Qaeda in the Islamic Maghreb (AQIM), the Islamist group's regional wing, was getting hold of weapons in Libya.
The Algerian official, speaking on condition of anonymity, said a convoy of eight Toyota pick-up trucks left eastern Libya and headed via Chad and Niger to northern Mali, where in the past few days it had delivered a cargo of weapons.
"We know that this is not the first convoy and that it is still ongoing," the official said. "Several military barracks have been pillaged in this region [eastern Libya] with their arsenals and weapons stores, and the elements of AQIM who were present could not have failed to profit from this opportunity."
Gaddafi believes the uprising is fuelled by Islamist radicals and Western nations who want to control Libya's oil. The rebels, whose stronghold is in the eastern city of Benghazi, want nothing less than the removal of Gaddafi and his circle.
The UN-mandated military intervention, in which warplanes have attacked Gaddafi's armour, radar and air defences, began on March 19 and was intended to protect civilians caught up in fighting between pro-Gaddafi forces and the rebels.
After chasing each other up and down the coast road linking the oil ports of eastern Libya with Gaddafi's tribal heartland further west, the two sides are stuck around Brega, a sparsely populated settlement spread over more than 25km.
Rebels pushed the army out of much of Brega and towards the outskirts of the sprawling oil town on Monday in a slow advance west, but were still facing bombardment with each step.
Showing signs of greater organisation than in past weeks, the rebels moved more cautiously and held ground more stubbornly than before, despite facing better-equipped forces.
"Gaddafi's forces are waiting at the western gate exactly. Any advance by the rebels, they fire at them with mortars," said rebel fighter Youssef Shawadi, a few kilometres from the gate.
Alexander Dziadosz

redpajama_set_name: RedPajamaCommonCrawl


text:
LUNA DE CUERNOS
FREE COMMUNITY CONCERT WITH FIFTH HOUSE ENSEMBLE
Past Music Programs
"Luna de Cuernos" Free Community Concert
Luna de Cuernos Free Bilingual Concert by Fifth House Ensemble
Friday, April 8th from 5 - 6:15 PM at Round Lake Beach Cultural & Civic Center
Join Brushwood Center, Fifth House Ensemble, Round Lake Area Schools, and the Round Lake Bilingual Parent Advisory Committee for a free bilingual performance of Luna de Cuernos at the Round Lake Beach Cultural & Civic Center.
It's the story of a tight-knit group of friends and family under threat, and the risks they will take to defend the garden that is their community's source of life. Doña Yuiza's garden is the pride and joy of the neighborhood. When mysterious destructive forces threaten her carefully tended yard one night, the entire community must pull together to protect it. Luna de Cuernos is a modern interpretation of a Puerto Rican folktale inspired by the true stories of Chicagoans and told through music for woodwind quintet and stunning illustrations by Sarah Becan.
Praised by the New York Times for its "conviction, authority, and finesse," the Chicago-based Fifth House Ensemble harnesses the collaborative spirit of chamber music to reach beyond the traditionally-perceived limits of classical music. The ensemble's artistic, educational, and civic programs engage theater groups, video game designers, corporate innovators, and folk bands to share stories as diverse as the communities it serves. Stay after the concert for a fun, "graphic score" workshop with Fifth House Ensemble musicians. Participants will learn different elements of music composition through drawing exercises, and the musicians will improvise performances based on the drawings.
Please contact Maria Colunga at 224.842.2017 or mcolunga@rlas-116.org for more information.


The Coast Guard is still searching for a decorated South Jersey police officer whose boat was discovered unmanned off the coast of Wildwood.
The agency says 39-year-old Jason Sill of Cape May Court House was last seen leaving a marina in Wildwood around 11 a.m. Monday.
A Good Samaritan told the Coast Guard he saw the unmanned 23-foot navy blue pleasure craft drifting off the coast of Wildwood.
Responding Coast Guard officers found the keys still in the ignition with the engine in the down position with the throttle forward. Fishing poles were also discovered on the boat.
Sill, who serves with the Middle Township police department, was honored in 2000 for saving the life of an unconscious woman who was suffering from carbon monoxide poisoning.


text:
By Really? - Australia - Homebush
Today, during an endless heat wave, I sat at my local pub having a beer. I was delighted as a cool sea breeze started to come through. I was less delighted when the sea breeze picked up and blew cigarette ash onto my sweaty face and into my mouth. FML
By Jinx - United Kingdom - Dorchester
Today, I got a stern talking-to from the police about threatening and harassing people. Who had called them? None other than my stalker, whom I'd just informed about the restraining order I was filing against him after finding him in my house. For the second time. FML
By pissed_off_girl - United States - Falls Church
Today, I had to proof-read a terrible paper containing a bunch of mistakes. It took me 4 hours and I didn't eat dinner until I was done. His response when he got it back was, "What the fuck did you do to my paper?! You bitch!" FML
By gothicvamp93 - United States - Jacksonville
Today, my boss called and yelled at me because he said I was "spotted" out after I called off sick. I was out getting the medicine the doctor had just prescribed me. FML
By Still No Phone - United States - Philadelphia
Today, I checked for the third time this week if my package that 'arrived' five days ago had been found. Not only was it a different employee at the concierge desk, meaning I had to explain everything again, but they told me the same bullshit about how it's "not their fault". The package is my new phone. FML
By Kaji - France
Today, I sent a text message to my girlfriend telling her how much I wanted to make love to her tonight. I've just realized I sent it to her brother. FML
By Username - United States
Today, while taking a shower, I was enthusiastically singing one of my favorite songs. When I got out, I noticed a bunch of things missing, and a note on my desk saying "shut the f*ck up, you suck." I was robbed and judged by a thief. FML
By NotBuyingATractor - United States - San Francisco
Today, a customer service guy called to fix a problem I've been having with my phone. When it transpired that he couldn't help, he transferred me to another representative. This other representative ended up being a John Deere dealer in Michigan. FML
By BrokenVow - Canada
Today, my mom came into my room to have a heart to heart talk. My dildo was sitting on the nightstand. I didn't notice until she told me to make sure the dog didn't get it. FML
By orangechicken - United States - New Hudson
Today, I found out that my daughter has been watching Supernatural and The X-Files so she'll fit in better at school. I'm not even angry that she's suddenly a brain-dead conformist, it's just that she now has nightmares all the time and insists on sleeping in my bed. She's a kicker. FML
By Anonymous - Sweden - Floda
Today, after nearly six weeks of seeing a nice girl, we finally decided to make it official. Two hours later, she pulled out a bridal magazine and not-so-casually asked me which wedding location I thought was the nicest. FML
By bunnylover - Canada
Today, my dad and I were in the car when a rabbit scurried across the road, just missing us. My dad turned and said to me, "Well, it's good we didn't hit him. He gets to live another day." I then looked in the rear view mirror to see the rabbit running away from the cross traffic, only to be hit by the car behind us. FML
By noname - United States - Alexandria
Today, I found out that my fellow marching band mates all refer to me as the "short girl with big tits" because none of them can remember my name. FML
By lostmyfaithinblowjobs - Australia - Queanbeyan
Today, during an hour-long drive, my sister told me she's lost her "faith in humanity", because one of her friends bought his 8-year-old son an iPad. She uses this stupid expression all the time, and I got so pissed off that I forgot to brake at a red light, rear-ending the car in front of us. FML
By whyeventry? - United States - Neenah
Today, I finally talked my boyfriend into going down on me. Everything went well until I came and instinctively gripped his head with my thighs. He panicked and we both rolled off of the bed crocodile-style. Now he's too scared to even have sex with me. FML
By Anonymous - United States - Marietta
Today, I told my dad about my new diet. He somehow figured I was only doing it to look more attractive to guys, because he told me my weight is fine and that it's just my personality that needs work. Thanks a lot, Dad. FML
By Lexy
Today, I saw my boyfriend coming up the walkway to our home. When I opened the door to greet him with a huge smile on my face, he scowled at me and angrily said, "I don't know why you have to do that!" Smile gone. FML
By stubblebutt - Australia
Today, my date arrived far earlier than expected to pick me up. Apparently my mother decided to show him to my room anyway. When the door swung open, I happened to be butt naked in front of the mirror, trying to pick out an ingrown hair on my bum. FML
By Anonymous - Sweden - Ljungby
Today, I grounded my son after finding out that he's been bullying other kids at school. He got mad and screamed at me, calling me a "bastard". Instead of defending me, my wife got pissed and accused me of having taught our son to swear like that. I can never win. FML
By bigmozwoman - United States - Phoenix
Today, I was driving students home on my school bus. I looked up in time to see a student wipe what would be one of many boogers across the window. As I'm cleaning the window, I tell her, "This is disgusting." Her reply? "No, it's not. It's PERFECT!" FML
By Tragics - Canada
Today, my date sent me a text saying "I'm sorry we're running late, we will be there shortly." I replied asking what she meant by "we". She said her parents, who were coming along to chaperone. I laughed about two 27 year olds having chaperones, until she walked in with her parents. FML
By StupidPets - United States - Murrysville
Today, I saw my new pet fish completely missing the food at the bottom of his tank and sucking up the little rocks instead. My last dog died from eating rocks. I think I'm doomed to have insanely stupid pets. FML
By Anonymous - Australia - Canberra
Today, after the topic of grandkids came up yet again, my fiancé confessed that we've been having trouble conceiving. Later, I overheard his mum telling him to take my engagement ring back to the store and get a refund, because apparently I'm not worth marrying if I can't give him kids. FML
By cantfallbackasleep - Canada - Calgary
Today, I was called a ruthless bitch for not waking up early to search for my car keys, so my boyfriend could go get his pipe and get high before work. FML
By drop dead single - United States
Today, on our third date, my date tried to get me drunk and kept trying to touch me up. When I said he was moving too fast, he sighed and soon left. Just minutes later, he posted on Facebook saying "Just got friendzoned -_-". One of his friends commented: "I hate bitches, man." FML
By DontGive2Fox
Today, I had my first job interview and didn't have much of an appetite because of the nerves. So I grabbed a brownie that my roommate had left in the fridge and ate it on the train ride in to the city. About 20 minutes into my interview I was so stoned I couldn't speak. FML
By oh my dad - United States - Florence
Today, I had to convince my dad that text lingo causes brain damage just to try to get him to stop. He actually believed me, and is telling everyone they have, or will receive brain damage soon. FML
By mothers - United States
Today, my daughter posted a beautiful, touching status on my Facebook wall for Mother's Day. It wasn't so touching that she'd just copied and pasted the one I wrote for her grandmother and sent it back to me, unedited. FML
By saltynutz20 - United States
Today, I was on a flight from Chicago to Minneapolis. A rather attractive young lady sitting next to me fell asleep at the beginning of the flight. About 40 minutes into the flight I noticed my fly was open. The lady woke to me with my hands in my crotch struggling to zip up my fly. FML
Today, while working as a line cook, one of the servers sincerely asked me, "What sauce goes on a chicken bacon ranch sandwich?" I replied, a little incredulously I'll admit, "…Ranch." She then totally lost her cool and went full-blown adult temper tantrum because I was, "mean to her." FML
By GetHardOrGoHome - United States - Chardon
Today, I visited my gynecologist. As she had her fingers inside me she decided that was the perfect time to say, "I absolutely love your socks!" FML
Today, my mum thought it would be acceptable to tell my school that the reason I will not be attending classes is because I have "the shits." FML
Today, I left the apartment door open so the construction workers sent by our landlord could work in the kitchen, while I slept in after working a late shift. They knocked on the bedroom door at 7 a.m., asking me to move the bed. FML
By Anonymous - Finland - Helsinki
Today, after a long, horrible day at work and some fighting with my family and my girlfriend, I decided to cheer myself up by going to McDonald's for a change. I burst into tears when the cashier told me they couldn't make me a Mars McFlurry because they'd run out of ingredients. FML
By Anonymous - 26/10/2020 19:58
The art of seduction
Today, I took a bath and dressed in a skimpy lingerie. I sat on the couch for an hour and a half before giving up and putting on my boyfriend's old t shirt. It was then that he decided to comment. On the t shirt I was wearing. FML
By Lotje13 - Hungary
Today, I was sleeping over at a friend's house. I went and took a shower, and as I tried to get out, the door jammed. I called my friend for help, and after much tugging, the glass shattered all over me. She panicked and sent her dad to rescue me. It was the first time he and I had met. FML
By Shamed - United Kingdom
Today, I was on the toilet at work. After a very loud and very smelly session, I waited until the other stall had been vacated to keep my anonymity. As I leant forward for some toilet roll, my ID fell out of my pocket and into the next stall. When I came out, it was face up near the sink. FML
By Jocelyn - Canada - Edmonton
Today, I was walking home, when I realized I didn't have my phone on me. After retracing my steps, I realized that I'd been listening to music from my phone the entire time. FML
By Anonymous - 7/1/2021 04:58 - Germany
Today, the cows on the neighboring farm have been lowing loudly for 3 days straight. The guy who runs the farm is unreachable. The people who own the farm and live right next to the stable claim they don't hear a thing. FML
Today, my parents found my stash of alcohol. They drank it all within a single evening. FML
By Jammy238 - France
Today, I was taking my dogs for a walk, then it started to rain, and the wind went mad. I ran for cover in the trees near a bridge, slipped and fell into a river. I was soaking wet and my boots were full of water. Then to get back out of the river, I had to use stinging nettles as hand holds. FML
By Bearsmomma | 30
Could've =could have (not could of)
By KrazyKatz3 | 26
#7535562 - Wednesday 30 August 2017 14:30
He said "at" not "in" could have been outside tables
By bruhhhhhhh | 14
#7382019 - Saturday 31 December 2016 6:30
What an ash hole... no? Ok you're right that was terrible
The_Sexomancer | 11
Eh, it wasn't great. I don't mind a bad joke here and there. It's the overdone bad jokes that get to me.
By Tripartita | 44
The butterfly effect is called to mind—owing its name to the notion that the characteristics of a hurricane can be influenced by the tiniest atmospheric perturbations (like butterflies flapping their wings) weeks earlier. It extends to other meteorological phenomena besides hurricanes.
I guess I'm trying to say that you should kill as many butterflies as possible to keep ash off of your face.
By brenton490 | 19
Could of been worse...and ended up in your beer!
Bearsmomma | 30
By RichardPencil | 29
I've heard of the dangers of second-hand smoke, but I've never heard of second-hand ash.
Ok, I have heard of second-hand ash, but I thought that person had a speech impediment and was talking about sloppy seconds.
By jessicasoho | 13
It's minus 12 C here. I wish that was me. So cold.
LatterDaySaint | 12
Colder is better, because you can always put on more than you can take off. Besides people tend to complain when your down to your skin in public. :P
somelildude | 14
That's true , but also when it's colder people are most exposed to getting sick. I know no one enjoys getting sick. I personally hate the cold because it's kills the drive to do anything outside if your not use to it.
TheBastarDino | 2
Its been high 30s for the last 2 weeks where i live you get sick of it quick. I live in South Australia.
By species4872 | 19
#7382201 - Saturday 31 December 2016 19:25
Interesting, since you cannot smoke in pubs, clubs, hell everywhere indoors in New South Wales.
cootiequeen4444 | 11
sea breezes don't tend to be indoors...sooo...
LonelyAngel22 | 13
If its a pub with an outdoor area, then you can smoke. The "sea breeze" indicated OP was outdoors, like in a beer garden or on a balcony. My local pub has a smoking area out the front.
KrazyKatz3 | 26


text:
RallyFwd™ • Recruitment Marketing
Highlights from December's RallyFwd (Part 2)
by Lori Sylvia
Written by	Lori Sylvia
In our second installment of highlights from December's RallyFwd Virtual Conference, we share more top takeaways and lessons learned from each of the sessions.
We're back with part 2 of our highlights from RallyFwd! Along with a record-breaking turnout, our diverse speaker panel of 9 talent leaders stole the show with their incredibly detailed accounts of the tactics and technology they're using to win talent in today's labor market.
Topics included how to expand your talent pool with greater diversity, reframe the way your stakeholders and leaders see your role, tap into the full potential of employee stories at your company, create your professional data story, take advantage of new hiring channels and trends and more!
If you weren't able to attend in December, don't worry! We've got you covered with presentation highlights to catch you up on what you missed. You'll find more than enough below to start your new year off with a competitive recruitment edge, but for a complete picture of everything you missed, be sure to watch the full presentations on-demand here!
The Candidate Experience Trinity: Tech, Process and People
Craig Fisher, TalentNet
Few people have stayed tuned into the evolving blueprint of a successful candidate experience better than Craig Fisher. As the Founder of employer brand and Recruitment Marketing strategy firm TalentNet Media, Craig shared what he's seeing work in today's hiring landscape, touching on why speed and flexibility rule, new ways to amplify your brand and striking the perfect balance between technology and human touch in your candidate experience.
Here are just a few of the many practical tips that emerged from Craig's presentation:
Use Quick Apply, but make sure to send candidates auto-responses to help them remember you and keep the application momentum going by allowing self-scheduling with the help of a tool or chatbot
Take advantage of the reach potential of TikTok without spending a fortune on their native advertising by empowering your employees to create and post content to their own personal channels with company hashtags (i.e. AmazonJobs)
Make sure your recruiters and employees have the right keywords in their LinkedIn summary, headline and even job titles to help LinkedIn correctly classify you and connect candidates with available roles (i.e. keywords like "hairstylist" + "job" + "Austin" + "Texas" if you're trying to hire hairstylists in Austin)
For maximum engagement and network growth on LinkedIn, post 5 "gives" for every 1 "ask" (i.e. 5 helpful, entertaining, funny or personal posts for every 1 "refer people to my job", "look at our new article in Forbes with our CEO", or other post asking people to do something)
Craig's keyword checklist on LinkedIn for the best SEO and inbound results.
How Lessons from Healthcare Recruitment Can Help Prepare All Recruiting and TA Teams for 2022
Paul White, Kindred at Home
While healthcare is certainly one of the more competitive job markets right now, recruitment and TA teams from all kinds of industries are having trouble filling roles. To address this, Paul White, Director of Recruitment Operations at Kindred at Home, the nation's largest home care company, shared the lessons he's learned in healthcare recruitment for teams to take and apply in their own industries.
With apply rates down and cost-per-click going up, Paul and his team leverage programmatic recruitment technology (Appcast) to help them fill thousands of roles as quickly and cost-effectively as possible. Their specific strategy consists of prioritizing their budget on the most critical 200-300 positions at any given time, which helps them get the most out of their spend.
Healthcare apply rates are down 17% while cost-per-click is up 11%, which means recruitment teams should prepare for more expensive, competitive hiring.
With built-in reporting, Paul is also able to see which of the thousands of job boards they're posting to produce candidates that make it through the recruitment screening, get an interview, receive a job offer and ultimately get hired. Based on this data, Paul then adjusts his campaign settings and budget to focus on these high-quality candidate sources. This source-specific reporting also helps them identify sources of more diverse talent, with programmatic job ads reaching 23% more candidates from underrepresented groups and 7% more women than the U.S. online benchmark average.
Aside from changing campaign settings, all of this takes place automatically, freeing up Paul and his team to maintain the human element of their recruitment, such as through in-person drive-by recruitment events. During these events, candidates are assigned parking spots and can speak to recruiters from the comfort and safety of their cars.
The Motivation Code
Todd Henry
According to Todd Henry, international speaker, consultant, advisor, author of 5 books and keynote speaker at RallyFwd, the reason behind the current Great Resignation is largely lack of motivation. As explained in his keynote, and in his latest book, The Motivation Code, recruiters and organizations can unlock more engagement, satisfaction and productivity from their workforce by understanding each person's unique motivation code.
The 27 unique themes of motivation across 6 families, based on over 50 years of research, 100,000 interviews and 1 million stories.
Despite so many possible combinations of motivation, to quickly get to the heart of someone's motivation code, Todd recommends asking them to think of a time when they achieved something great. Then, when they have that time in mind, follow up with these questions:
What was the outcome?
What did you do to bring out that outcome?
What did you enjoy about that process?
Why do you think that memory sticks with you?
As a recruiter interviewing a candidate, this can be a very helpful line of questioning, as it can help you understand what motivates them, what kind of role they're best suited for (i.e. team member vs. leader) and how to form their role and responsibilities to keep them satisfied and around for the long haul.
The Power of a Proactive Approach
Jessica Rose, Equinix
Entering 2022, many hiring teams finally have a little more breathing room to zoom out, take stock of the landscape and plan for the future — to shift your hiring strategy from reactive to proactive. To help lead this shift, Jessica Rose, Director of Global Talent Marketing at Equinix, shared her approach at Equinix.
Technology and transparency proved to be the most important factors in Equinix's shift to proactive hiring. For example, after identifying the size of their HR team as an obstacle in the way of the company's growth, Jessica invested in Clinch to help build bigger warm talent communities, better amplify their employer brand and build a bigger, more capable HR team. For example, Clinch now allows Jessica and her team to build career sites and pages with dynamic content and job postings that change to serve whatever talent audience is visiting. On these pages, candidates are also automatically asked if they want to join Equinix's talent network and sign up for their hot job alerts.
Not only do these tools make for a quicker, more helpful candidate experience (another change Jessica made was automatically reaching out to candidates who didn't complete their application), but they also take many time-consuming tasks off Jessica's plate so that she and her team can devote more team to looking for ways to improve their hiring strategy, in other words, to being proactive!
Thanks again to everyone who attended our Dec 8 RallyFwd virtual conference, and to all our speakers and sponsors that made it possible. Again, you can watch all of the presentations in full on-demand here!
Lori Sylvia
Recruitment Marketing evangelist and community builder. Founder of Rally.
Why the 2022 Rally® Awards is Your Key to Career...
Recruitment Marketing Strategies that Boost Engagement
6 Highlights from RallyFwd's Tech Showcase
2022 Rally Awards Submissions Are Now Open
How Wix Increased Hiring x10 by Focusing on Company...


text:
\section{Introduction}

Mobile robot-aided mapping of environmental processes, such as information sampling \cite{OnlineInfoGathering}, sensor coverage \cite{AdaptiveSamplingSensorCoverage}, localization of source \cite{Fink2010}, and monitoring of environmental phenomena \cite{LiuK2017}, have been well investigated. Particularly, sensor coverage with multiple robots involves positioning robots in an optimal manner to maximize overall performance in terms of sensing the environmental phenomenon. Monitoring is a persistent process for identifying anomalies in physical processes by efficiently collecting the most informative samples. For all of these objectives, modeling of the underlying processes is required or involved, which can be obtained via sampling or mapping.

Modeling of physical processes plays an important role in autonomous robots' decision-making. Robots need to create a model of the environmental phenomenon to accomplish mapping tasks, especially when the environment is unexplored. Mapping spatial distribution enables the robots to work autonomously in search and rescue missions and make decisions without human intervention (e.g., for rescuing targets in areas with high radiation exposure). Similarly, the robot requires knowing the areas with higher risks so that the robot can always choose a path to connect to the network and maintain communication, etc. \cite{caccamo2017rcamp,parasuraman2013spatial}.
Therefore, robotics researchers are actively investigating different strategies for mapping physical processes, such as radiation, Wi-Fi signal strength, gas distribution, radio signal strength etc., using unmanned vehicles \cite{AdaptiveSampling, Fink2010,Stachniss2009}.

In order to map the environmental phenomenon, it is crucial to measure the value of a physical process throughout the environment. However, not all the locations can provide useful information about the change in the process itself.  Primary considerations involved in mapping such processes include the degree of autonomy, accuracy, and efficiency. Measuring intensity at every location is deemed impractical; hence, dense sampling is not viable in mapping. Instead, an accurate, time- and cost-effective process model can be obtained by gathering samples from the points that contain the most significant amount of information.

Exploration refers to accumulating samples from previously unexplored areas to reduce uncertainly in the map, while exploitation implies determining the next sampling point based on the best information from the current estimates (to localize source, for example).
Mapping algorithms and techniques in the literature either use exploration or exploitation, or alternate between exploration and exploitation as needed. In \cite{Fink2010}, an active control law for mobile robot is proposed to shift between exploration and exploitation objective. In \cite{AdaptiveSamplingSensorCoverage}, a utility function is used for adjusting between exploration and exploitation. On the other hand, parallel strip routes are used in \cite{Zakaria2017, Gabrlik2020} to explore the environment for mapping of the spatial distribution. These two techniques are fundamental to the mapping process, so the purpose of the study in this paper is to compare how well various exploration and exploitation techniques perform, as well as analyze how trade-offs between exploring and exploitation affect mapping.
Specifically, the contributions made in this paper are three-fold.
\begin{itemize}
    \item We comprehensively compare the adaptive information sampling techniques in terms of exploration and exploitation objectives from a perspective of mapping physical processes using Gaussian Processes \cite{williams1996gaussian,Fink2010}.
    \item We systematically analyze the impact of the information function on the mapping accuracy and efficiency through extensive simulations in single robot experiments with random walk and fixed sweep trajectories as the baselines for comparison.
    \item We extend this analysis to multi-robot settings with fixed and dynamic Voronoi partition-based adaptive sampling \cite{kemna2017multi} assignments to each robot in the system.
\end{itemize}
The results from this study provide meaningful insights to choose appropriate sampling techniques to balance between the exploration (minimizing uncertainty) and exploitation (maximizing information value) from the map estimates through adaptive sampling with mobile robots.

\section{Related Work}

\begin{table*}[ht]
\caption{\label{tab:Literature Review}
Comparison of related works in the literature on adaptive information sampling. The Information functions used to obtain waypoints for successive sampling are adapted from the respective references in this table.}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|p{1cm}|p{5.5cm}|p{1cm}|p{1cm}|p{2cm}|p{2.2cm}|p{2.2cm}|p{2.2cm}|p{2.2cm}|p{2.2cm}|}
\hline

Reference & \textbf{Information Function} & \multicolumn{2}{c|}{\textbf{Scalability}}& \textbf{Robot type} & \textbf{Sampling Type} & \textbf{Objective} & \textbf{Property/Process Measured}  & \textbf{Prediction Model} & \textbf{Exploration or Exploitation}  \\ \hline

& \textbf{}  & \textbf{Single Robot} & \textbf{Multi Robot} & \textbf{}   & \textbf{}  & \textbf{}  & \textbf{}   & \textbf{} &\textbf{}                                                       \\ \hline

\textbf{\cite{OnlineInfoGathering}}   & $I\left(\mathcal{P}_{\mathbf{x}, \mathbf{x}^{\prime}}\right) = \frac{1}{\left|\mathcal{P}_{\mathbf{x}, \mathbf{x}^{\prime} \mid}\right|} \sum_{\tau \in \mathcal{P}_{\mathbf{x}, \mathbf{x}^{\prime}}} H({\tau})$ & \cmark                   &                    & Ground        & Discrete       & Mapping                                   & Magnetic Field Intensity    & Gaussian Processes       & Exploration                                          \\ \hline

\textbf{\cite{Fink2010}} &  $\mathcal{I}_{i, j}=-\frac{\partial^{2} \mathcal{L}\left(\theta_{m}\right)}{\partial \theta_{m, i, j}}$ & \cmark     & \cmark   & Ground     & Discrete & Mapping and source localization   & Radio Signal Strength    & Gaussian Processes    & Mix  \\ \hline

\textbf{\cite{LiuK2017}}                           & $I\left(Z_{A} ; Z_{B}\right)=I\left(Z_{B} ; Z_{A}\right)=H\left(Z_{A}\right)-H\left(Z_{A} \mid Z_{B}\right)$                                    & \cmark                   &                    & Marine              & None                                         & Monitoring                    & Salinity                                                          & Sparse Gaussian Processes       &   Exploration                                       \\ \hline

\textbf{\cite{caccamo2017rcamp}}                           &    $I^{rssi}(x) = \frac{max(\mu) - \mu*(x)}{max(\mu) - min(\mu) + \epsilon} $                               & \cmark                   &                    & UGV              & Continuous                                         & Path Planning                    & Wi-Fi                                                          & Gaussian Processes       &   Exploitation                                       \\ \hline

\textbf{\cite{Zakaria2017}}   & None   & \cmark                   &              & Ground  & Discrete (zigzag waypoints sweeping pattern) & Mapping   & Gamma radiation   & None    & Exploration                                                         \\ \hline

\textbf{\cite{AdaptiveSampling}} & $I({x})=\ln (\sigma \sqrt{2 \pi e}) $
& \cmark                   & \cmark                  & Ground               & Discrete             & Mapping          & Radio Signal Strength            & Gaussian Processes   & Exploration                                              \\ \hline

\textbf{\cite{Gabrlik2020}}                & None                                                &                     & \cmark                  & Ground and Aerial   & Continuous                                   & Localization of Sources                   & Radiation                                                          & None & Exploration                                                             \\ \hline

\textbf{\cite{kemna2017multi}}   & $\mathbb{H}\left[\mathbf{Y}_{x_{i+1}} \mid d_{i}\right]=\log \sqrt{2 \pi e \sigma_{Z_{x_{i+1} \mid d_{i}}}^{2}}+\mu_{Z_{x_{i+1} \mid d_{i}}}$        &            &  \cmark        &Underwater  & Discrete    &Modeling   &Algae    &Gaussian Processes     &Mix  \\ \hline

\textbf{\cite{Luo2019}}      & $ I(x)=\mu_{x \mid \tilde{V}_{i}, \mathrm{y}_{i}}^{*}+\beta \sigma^{* 2} _{x \mid \tilde{V}_{i}, \mathrm{y}_{i}}  $
&                     & \cmark                  & Ground & Discrete          & Sensing Coverage      & Stalk count , Temperature         & Mixture of Gaussian Processes & Mix                                   \\ \hline

\textbf{\cite{Juan2016}} & None    &                     & \cmark                  & Ground and Aerial   & Discrete       & Mapping/ Environmental Monitoring         & Temperature, humidity, luminosity and carbon dioxide concentration &    None   & Exploration                              \\ \hline

\textbf{\cite{Nur2020}}         & None            & \cmark                   &                    & Ground              & Discrete        & Mapping                                   & Radiation                                                          & None & Exploration                                                             \\ \hline

\textbf{\cite{Duvallet2008}}  & None         & \cmark         &                    & Ground              & Continuous                                   & Localization of Source/Vehicle            & Wi-Fi     & Gaussian Processes  & Exploration                                               \\ \hline

\textbf{\cite{Miyagusuku2016}}     & $p\left(z_{n e w j} \mid \mathbf{x}_{*}\right)=\Phi\left(\frac{z_{n e w, j}-{E}\left[z_{* j}\right]}{{var}\left(z_{* j}\right)}\right)$
  & \cmark                   &                    & Ground              & Discrete      & Robots localization                       & wireless signal strength                                           & Path loss and Gaussian Processes  & Exploration                           \\ \hline

\textbf{\cite{Cortez2008}}        & None / $\varphi=\gamma_{d}+1 / e^{{\beta_{0}}^{1/k}}$ & \cmark                   &                    & Ground              & Discrete/Continuous                          & Mapping                                   & Radiation                                                          & None & Exploration\\ \hline

\textbf{\cite{Newaz}}  & $ I:=a b s\left(H\left(z_{1: t}\right)-H\left(z_{1: t} \mid x_{t}\right)\right) $   & \cmark                   & None                & Discrete                                     & Localization of Hotspot    &                & Radiation & None
& Explore                                                            \\ \hline

\textbf{\cite{adaptive}}        & $ I(x)=\mu_{x \mid \bar{V}, \mathbf{Y}}^{*}+\beta \sigma_{x \mid \tilde{V}, \mathbf{Y}}^{*^{2}} $  &                     & \cmark                  & Ground and Aerial   & Discrete                        & Mapping                                   & Wi-Fi                                                               & Mixture of Gaussian Processes      & Mix                                \\ \hline

\textbf{\cite{cortez2008radiation}}                        & None                                        &                     & \cmark & Ground              & Continuous                                   & Mapping                                   & Gamma radiation                                                    & None       & Exploration                                                      \\ \hline

\textbf{\cite{Jilek2015}}               & None                                                &                     & \cmark                  & Ground and Aerial   & Discrete / Continuous                          & Localization                              & Gamma radiation                                                    & None        & Exploration                                              \\ \hline

\end{tabular}
}
\end{table*}

Both adaptive (taking informativeness of the sampled data into account) and non-adaptive (without considering informativeness) sampling approaches have been previously reported in the literature. Non-adaptive sampling methods focus on sampling the whole environment \cite{Gabrlik2020,Nur2020,Cortez2008}.
Although, non-adaptive methods are less time-consuming, but in such methods it is hard to achieve the desired threshold of certainty. On the other hand, adaptive sampling methods provide convergence to an objective (threshold) and sampling can be stopped as soon as the desired threshold is achieved. The approach of planning a robot path that contains most-informative samples has been studied in the past.

In \cite{OnlineInfoGathering}, a technique that maximizes the mean entropy information metric is used to search a station. Then using an RRT based Informative Planner algorithm it selects the path that provides maximum utility, i.e., trades off the informativeness and cost to reach that station. Authors in \cite{LiuK2017} uses entropy as an information criterion over Sparse Gaussian Process to find the most informative locations to persistently monitor salinity in the ocean. Similarly, \cite{Miyagusuku2016} uses wireless signals for robot localization in an indoor GPS-denied environment. It learns a path loss model from the data and then training the Gaussian process with the mismatches between the models and the data with a focus on better model variance prediction. In \cite{AdaptiveSampling}, the authors focused on mapping in structured environments. The algorithm partitions the environment for each robot and uses differential entropy as an information theory metric on top of Gaussian Process predictions to determine the next sampling point.
The authors in \cite{Newaz} proposed a Hexagonal Tree (HexTree) based sampling algorithm, which takes samples over a set of hexagonal  grid  points  and  builds  a  tree  of  possible trajectories  by  extending  candidate  trajectories  toward  the sampled  points.

Table~\ref{tab:Literature Review} provides detailed information about closely related works in the literature on adaptive information gathering. According to the table and literature review, several objective functions have been used in coordination with Gaussian Process Regression \cite{williams1996gaussian}  to map the physical process (i.e., to predict the samples at unvisited (unexplored) locations with confidence bounds). Despite a plethora of research employing different exploration and exploitation approaches, either on their own or in conjunction with their information function, to the best of our knowledge, no analysis exists in the literature on how much impact these techniques have on the effectiveness of mapping the underlying spatial distribution. Therefore, we assess how exploration and exploitation in combination with alternatives of information function influence the mapping process.

\section{Gaussian Processes Aided Robot Sampling}\label{AA}
Gaussian processes (GP) have been utilized for modeling spatial process. For example, in \cite{Gu2012}, Gaussian process regression (GPR) was used to model spatial functions for mobile wireless sensor networks and to generate a likelihood model for signal strength measurements. An algorithm in \cite{Duvallet2008} is presented to localize Wi-Fi using Gaussian Process and then estimates the global position of an autonomous vehicle in an industrial environment. The authors in \cite{Miyagusuku2016} used path loss to learn a model from data and then feed the mismatches to GP. In \cite{Fink2010}, GPR is used to obtain a model of radio signal strength, which in turn is used to get maximum likelihood of the source location. For the purpose of obtaining a spatial map in an environment with limited communication, \cite{kemna2017multi} used GPR to model algae bloom.

GP is a non-parametric continuous functions. It defines a probability distribution over functions. It assumes that every point has a normal distribution and there is a correlation between values at these points. It not only predicts a value, but also provides confidence bounds for the given measurements.
Let $q$ be the  location from where the signal strength is measured and $z$ be the measurement, and let $q'$ be the testing points. The value of $z$ at any point $i$ is
\begin{equation}
z_{i}=f\left({q}_{i}\right)+\varepsilon ,
\end{equation}
where $\varepsilon$ is an additive Gaussian noise. We are interested in calculating a function $f(q')$ that makes predictions for given test locations $q'$. GP generates the following mean and variance for the test points $q'$. With every sample $q$ added to the training set, the values of $q'$ are updated accordingly.
\begin{equation}
\mu_{z'}\left( {q}, {q'}, z\right)=k\left({q'}, {q}\right) k(q, q) z
\label{eqn:mean}
\end{equation}
\begin{equation}
\sigma_{z '}^{2}\left({q},{q'}\right)=k\left({q'}, {q'}\right)-k\left({q'}, {q}\right) k({q}, {q})^{-1} k\left({q}, {q'}\right)
\label{eqn:var}
\end{equation}
Here, $k(q,q')$ is the covariance function(kernel) and defines the correlation between $q$ and $q'$. One of the widely used kernel is squared exponential.
\begin{equation}
k\left( {q},  {q'}\right)=\sigma_{f}^{2} \exp \left(-\frac{1}{2 l^{2}}\left| {q}- {q'}\right|^{2}\right)
\end{equation}
where $q$ and $q'$ are two training samples, $q,q'\epsilon  R^2$, $\sigma_f$ are l are hyperparameters of GP and are called variance and length respectively. These hyperparameters are learned from the training data. The variance and mean in Eq.~\eqref{eqn:mean} and \eqref{eqn:var} are used to calculate the informativeness of every point.

\section{Adaptive Information Sampling}
\label{sec:information}
A method for determining the next sampling point using a utility function during the model creation process is termed adaptive information sampling \cite{Singh2009, AdaptiveSampling}.
Depending on the criteria for selecting the next sampling location, adaptive sampling can be exploration-based, exploitation-based, or a mixture of both types.
In this paper, we have used two non-adaptive sampling baseline approaches that do not use an information function: a predefined sweep trajectory on a specific pattern; and a random walk. For adaptive information sampling, the following Information (utility) function to calculate the informativeness at every point $q$:
\begin{equation}
I(q) = \alpha\mu_q+\beta\sigma_q^2
\label{eqn:information}
\end{equation}
where $I(q)$, \(\mu_q\) and \(\sigma_q^2\) denote the informativeness, mean and variance of the point $q$. While \(\alpha\) and \(\beta\) are the importance factor for mean and variance, respectively, and determine the weightage given to mean and variance to calculate informativeness of point q.
Based on different weighted combinations of mean and variance in the informative function, we used the following approaches with baselines:
\begin{enumerate}
\item MaxMean - MaxMean approach chooses the point with maximum intensity value, i.e., max mean location as the next sampling point.
\item Alpha0.75 - Alpha0.75 approach selects the location with the highest information value given $\alpha = 0.75$.
\item Alpha0.5 - Alpha0.5 method selects the location with the highest information value given $\alpha = 0.5$.
\item Alpha0.25 - Alpha0.25 approach selects the location with the highest information value given $\alpha = 0.25$.
\item MaxVar - MaxVar chooses the location with the lowest confidence value as the target location.
\item MaxVarMaxMean - MaxVarMaxMean first selects the points with maximum uncertainty until a given threshold for confidence (variance) is reached. After satisfying the threshold, it then selects the points with MaxMean as the point of interest.
\end{enumerate}

\begin{table}[]
\caption{Values of alpha and beta for different Information $I(q)$ variants.}
\label{tab:alphabeta}
\centering
\begin{tabular}{|c|c|c|}
\hline
Approach  & Alpha & Beta \\ \hline
MaxMean   & 1     & 0    \\ \hline
Alpha0.75  & 0.75  & 0.25 \\ \hline
Alpha0.5  & 0.5   & 0.5  \\ \hline
Alpha0.25 & 0.25  & 0.75 \\ \hline
MaxVar    & 0     & 1    \\ \hline
\end{tabular}
\end{table}

Table~\ref{tab:alphabeta} enlists the values of alpha and beta for different variants used in this study. We have used these values to evaluate the effects of different levels of exploration and exploitation. As per Table~\ref{tab:Literature Review}, most utility functions in informative sampling rely on the mean, variance, or some combination of the two. Because of diversity, we have not used the same weights as in literature, but instead used a range of weights that can give a general idea of how a fix increase in weights can affect mapping performance. Nevertheless, this analysis can also be used to shed light on a specific scenario of exploration and exploitation.

\section{Experiment Design and Implementation}

We have developed the simulations using the Robot Operating Systems (ROS \cite{quigley2009ros}) Gazebo simulation framework, built on top of the open-source code base from \cite{adaptive}.
We considered a 10 m $\times$ 15 m simulated area free of obstacles (to avoid bias in the analysis due to collision avoidance algorithms). Until the robot's battery is depleted, it takes samples based on the informative function, navigates to the location, and collects samples. With each new sample, the Gaussian process regression is trained, the intensity values of the whole environment (map) are predicted, and the informativeness of each location is updated based on one of the \textbf{seven variants} in Sec.~\ref{sec:information}, which includes 6 adaptive sampling variants and one of the two baseline non-adaptive sampling variants based on the scenario.
The robot's starting location and battery timing were kept fixed for all scenarios.
The ground truth for the Wi-Fi signal map (Fig.~\ref{fig:wifi}) was generated as per the equation: $RSS = RSS_{d0} - 10n  \ln(d) + \chi_g ,$
where $RSS_{d0} = TX_{power} - 20 \space \ln  (\frac{3}{4\pi*f})$ is the signal reference power at $d0=1m$, $f$ is the signal frequency (2.4GHz), $n$ is the path loss exponent ($n=3$), $d$ is the distance between the signal source and receiver, $\chi_g$ is a Gaussian distribution with zero mean and a variance ($0.65dBm^2$) to represent fading/noise in signals, similar to the settings in \cite{adaptive}.

\begin{figure}[ht]
    \centering
  \includegraphics[width=0.98\columnwidth]{image/adaptivesampling.png}
   \caption{An image of the Wi-Fi signal ground truth (left) at source location (4,7) and predicted GP mean (top right) and variance (bottom right) of a simple random walk exploration strategy.}
   \label{fig:wifi}
\end{figure}

\subsection{Single robot experiments}
\label{sec:scenarios}

For single robot experiments, we deployed a hector UAV robot (an aerial robot) with a battery capacity to sustain 500 ROS seconds (with a real time factor close to 1) and a starting position of (4.5,0).
To provide a thorough analysis of the impact of exploration and exploitation on online learning and mapping of the spatial distribution map, we consider two scenarios:
\begin{enumerate}
    \item Hector Trajectory (HT)
    \item Random Walk (RW)
\end{enumerate}
In Hector trajectory (HT) scenario, the UAV sweeps the whole region in a horizontal parallel strips pattern starting from bottom center of the region to upper center of the region. The idea is to make UAV familiar with the overall intensity changes of the environment. While in a random walk (RW) scenario, the UAV randomly explores the surrounding region after moving up, down, left, or right by 3 points randomly from its current position. In both scenarios, once the UAV finishes sweeping the whole region or has taken at least 15 samples from the surroundings, the informativeness of each location is updated, and the next sampling location is chosen based on one of the information function variant. The baseline variants for these two scenarios are a complete sweep trajectory of the area (for HT scenario) and a pure random walk variant (for RW scenario), respectively.

\subsection{Multi-Robot Experiments}

In multi-robot settings, researchers used Voronoi partitioning method to divide an environment for multi robot sampling \cite{kemna2017multi,AdaptiveSamplingSensorCoverage,Luo2019}. Here, the robots are driven toward the centroids of the respective Voronoi region in order to maximize the mapping (sampling) performance and minimizing the sensing cost. Robots choose the most informative location within the Voronoi region based on a utility function that encompasses both exploration and exploitation. Specifically, the work in \cite{adaptive} uses the heterogeneity of robots to weigh the Voronoi partition, which is continually updated during the sampling process.
Motivated by the investigations in these work, to conduct an analysis in multi-robot settings, we use Voronoi partitions as a way to distribute regions among multi-robots. To divide the given region $Q\subseteq R^2$ for n robots, we divide the environment into n regions that for each robot $i$ the $V_i$ corresponds to:
\begin{equation}
    V_i = \{ q\in Q|  \lVert \mathbf{q-p_i} \rVert \leq \lVert \mathbf{q-p_j} \rVert , \forall {j \neq i} \}
\end{equation}

In case of multi-robot sampling, we have considered the following two scenarios of Voronoi partition:
\begin{enumerate}
\item Fixed Voronoi Partition (FVP) -  Considering only the initial robot positions, the region associated with the robot is decided at the start of the experiment. We fix these positions throughout the experiment, and the utility function determines what points  within the respective region are to be chosen as target points.
\item Dynamic Voronoi Partition (DVP) - In this scenario, the Voronoi partition continuously updates as the robot moves. The target point can only be determined if it belongs to the respective partition at the time of the request based on the informative function.
\end{enumerate}

For multi-robot experiments,  $3$ simulated Jackal UGV robots were deployed to the same Gazebo simulation framework. The initial positions of the three robots are (3,2), (3,10) and (7,7), respectively. For multi-robot sampling scenario, we have only employed the random walk baseline and taken 5 random samples per robot (totaling 15 samples) within the respective Voronoi region before utilizing adaptive sampling. The baseline variant of both of these scenarios (FVP and DVP) is the random walk sampling (non-adaptive).

\subsection{Performance Metrics}
We consider the following performance metrics:
\begin{enumerate}
  \item Samples: The number of Wi-Fi signal strength samples taken by the robot using its Wi-Fi device.
  \item RMSE: The root mean squared error between the predicted mean information (Wi-Fi signal strength) through the GPR and the ground truth information. The aim is to get predictions as close as possible to the ground truth, i.e, lower RMSE.
  \item Variance: The confidence bounds of the predicted values given by the GPR. The goal is to be confident about the predicted mean value, i.e., lower variance.
  \item Cumulative Distance: Cumulative distance refers to total distance traveled by the robot. The shorter the distance traveled, the lesser power consumption.
  \item Source localization accuracy: If the location at where the maximum mean value of the predicted GP map lies within 1 m of the actual source location, then that is classified as the correct localization, else incorrect localization. The localization accuracy is then the percentage of correct localization of all trials of all source location experiments combined.
\end{enumerate}

We ran five trials per variant in each scenario. Further, the experiments were repeated for five different Wi-Fi source locations, with each being at the middle, top-left, top-right, bottom-left, and bottom-right corners of the map area. In total, we conducted 700 simulations for this analysis.

\begin{table}
\caption{\label{tab:all} Mapping performance (mean and std) of the informative sampling functions with Hector trajectory (HT) or random walk (RW) as the baselines in single robot experiments; and fixed Voronoi partitioning (FVP) and dynamic Voronoi partitioning (DVP) in multi-robot experiments.}
\centering
\resizebox{\linewidth}{!}{%
\begin{tabular}{|c|c|c|c|c|}
\hline

 \multicolumn{5}{c}{Single robot experiments} \\
\hline
HT Scenario & Samples      & RMSE         & Variance       & Cumulative Distance \\ \hline
Alpha75         & 219 ±  4     & 4.27 ±  0.15 & 11.23 ±  9.93  & 93.15 ±  25.59      \\ \hline
Alpha50         & 207 ±  8.66  & 4.19 ±  0.09 & 13.45 ±  10.97 & 168.33 ±  60.57     \\ \hline
Alpha25         & 182 ±  7.42  & 4.13 ±  0.08 & 5.56 ±  0.51   & 365.95 ±  63.34     \\ \hline
MaxVar          & 153 ±  2     & 4.18 ±  0.17 & 2.48 ±  0.33   & 618.33 ±  16.78     \\ \hline
MaxMean         & 222 ±  0     & 4.71 ±  0.49 & 32.09 ±  46.63 & 78.63 ±  4.9        \\ \hline
MaxVarMaxMean   & 177 ±  15.39 & 4.24 ±  0.17 & 4.92 ±  0.97   & 425.45 ±  115.94    \\ \hline
RW              & 36 ±  1      & 4.82 ±  0.66 & 7.12 ±  2.13   & 68.32 ±  0.64       \\ \hline
\hline
\hline
RW Scenario & Samples      & RMSE         & Variance       & Cumulative Distance \\ \hline
Alpha75         & 205 ±  14.66 & 4.33 ±  0.35 & 26.27 ±  14.27 & 175.2 ±  75.87      \\ \hline
Alpha50         & 194 ±  12.61 & 4.23 ±  0.1  & 16.13 ±  10.27 & 284.8 ±  91.59      \\ \hline
Alpha25         & 168 ±  7.68  & 4.11 ±  0.07 & 5.73 ±  0.57   & 494.11 ±  70.57     \\ \hline
MaxVar          & 145 ±  3.46  & 4.18 ±  0.1  & 2.49 ±  0.29   & 696.28 ±  16.86     \\ \hline
MaxMean         & 226 ±  1     & 6.06 ±  1.1  & 37.84 ±  44.57 & 61.44 ±  4.94       \\ \hline
MaxVarMaxMean   & 170 ±  10.63 & 4.2 ±  0.11  & 4.97 ±  0.54   & 489.65 ±  89.8      \\ \hline
RW              & 150 ±  1     & 4.45 ±  0.22 & 3.1 ±  0.94    & 520.94 ±  6.34      \\ \hline
\hline
 \multicolumn{5}{c}{Multi-robot experiments} \\
\hline
FVP Scenario & Samples      & RMSE         & Variance      & Cumulative Distance \\ \hline
Alpha75        & 426 ±  34.81 & 4.11 ±  0.12 & 9.78 ±  2.82  & 149.12 ±  39.72     \\ \hline
Alpha50        & 403 ±  35.76 & 4.05 ±  0.1  & 6.52 ±  1.69  & 224.74 ±  58.42     \\ \hline
Alpha25        & 361 ±  31.51 & 4.02 ±  0.1  & 3.87 ±  0.68  & 332.02 ±  60.44     \\ \hline
MaxVar         & 236 ±  5     & 4.02 ±  0.07 & 1.87 ±  0.13  & 592.91 ±  8.69      \\ \hline
MaxMean        & 445 ±  44.78 & 4.97 ±  0.91 & 17.07 ±  5.91 & 68.8 ±  16.94       \\ \hline
MaxVarMaxMean  & 377 ±  29.65 & 4.06 ±  0.08 & 4.2 ±  0.23   & 250.19 ±  28.6      \\ \hline
RW             & 204 ±  6.24  & 4.77 ±  0.46 & 4.74 ±  1.3   & 514.62 ±  5.96      \\ \hline
\hline
\hline
DVP Scenario & Samples      & RMSE         & Variance       & Cumulative Distance \\ \hline
Alpha75        & 463 ±  51.11 & 4.16 ±  0.15 & 10.1 ±  1.98   & 89.6 ± 16.35        \\ \hline
Alpha50        & 408 ±  39.29 & 4.1 ±  0.1   & 8.06 ±  0.97   & 120.42 ± 11.84      \\ \hline
Alpha25        & 382 ±  34.22 & 4.08 ±  0.1  & 4.5 ±  0.47    & 219.16 ± 29.98      \\ \hline
MaxVar         & 244 ±  10.72 & 4.01 ±  0.07 & 1.86 ±  0.27   & 538.42 ± 47.83      \\ \hline
MaxMean        & 444 ±  29.83 & 4.55 ±  0.55 & 24.08 ±  34.05 & 62.94 ± 11          \\ \hline
MaxVarMaxMean  & 408 ±  37.96 & 4.07 ±  0.09 & 4.34 ±  0.27   & 233.89 ± 29.84      \\ \hline
RW             & 211 ±  20.95 & 4.65 ±  0.39 & 3.71 ±  1.01   & 485.69 ± 72.39      \\ \hline
\end{tabular}
}
\end{table}

\begin{table}
\caption{\label{tab:localization} Source localization accuracy (\%) by all approaches.}
\centering
\resizebox{\linewidth}{!}{%
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}
\hline
                                             &                                   & \textbf{Samples}   & \textbf{Alpha75} & \textbf{Alpha50} & \textbf{Alpha25} & \textbf{MaxVar} & \textbf{MaxMean} & \textbf{MaxVarMaxMean} & \textbf{RW/HT} \\
                                             \hline
\multirow{14}{*}{\textbf{\begin{turn}{90}Multi-robot cases\end{turn}}}

                                             & \multirow{7}{*}{\textbf{HT}} & 10                 & 24               & 32               & 28               & 28              & 32               & 28                     & 24             \\
                                             &                                   & 25                 & 40               & 32               & 24               & 40              & 32               & 32                     & 28             \\
                                             &                                   & 35                 & 84               & 68               & 68               & 72              & 68               & 76                     &     -           \\

                                              &                                   & 45          &100	&100	&100	&92	&72	&96  &    -      \\

                                             &                                   & 50                 & 100              & 100              & 100              & 92              & 72               & 92                     &     -           \\
                                             &                                   & After half samples               & 100              & 100              & 100              & 96              & 72               & 96                     & 48             \\
                                             &                                   & After last sample                & 100              & 100              & 100              & 100             & 72               & 100                    & 68             \\ \cline{2-10}

                                             & \multirow{6}{*}{\textbf{RW}} & 10                 & 56               & 48               & 44               & 60              & 52               & 56                     & 40             \\
                                             &                                   & 25                 & 96               & 96               & 96               & 100             & 56               & 100                    & 64             \\
                                             &                                   & 35                 & 100              & 96               & 96               & 100             & 56               & 100                    & 80             \\

                                              &                                   & 45
                                              &100	&96	&100	&100	&56	&100	&80

                                                     \\

                                             &                                   & 50                 & 100              & 100              & 100              & 100             & 56               & 100                    & 80             \\
                                             &                                   & After half samples & 100              & 100              & 100              & 100             & 56               & 100                    & 80             \\
                                             &                                   & After last sample  & 100              & 100              & 100              & 100             & 56               & 100                    & 100            \\  \hline

\multirow{14}{*}{\textbf{\begin{turn}{90}Multi-robot cases\end{turn}}}

& \multirow{7}{*}{\textbf{FVP}}     & 10                 & 40               & 40               & 32               & 36              & 36               & 48                     & 48             \\
                                             &                                   & 25                 & 96               & 100              & 96               & 96              & 44               & 100                    & 64             \\
                                             &                                   & 35                 & 100              & 100              & 100              & 100             & 56               & 100                    & 64             \\
                                              &                                   & 45

                                              &100	&100	&100	&100	&64	&96	&60

                                                      \\

                                             &                                   & 50                 & 100              & 100              & 100              & 100             & 64               & 96                     & 60             \\
                                             &                                   & After half sample  & 100              & 100              & 100              & 100             & 76               & 100                    & 60             \\
                                             &                                   & After last sample  & 100              & 100              & 100              & 100             & 76               & 100                    & 60             \\ \cline{2-10}
                                             & \multirow{7}{*}{\textbf{DVP}}     & 10                 & 36               & 40               & 28               & 32              & 32               & 40                     & 48             \\
                                             &                                   & 25                 & 100              & 100              & 100              & 92              & 56               & 88                     & 56             \\
                                             &                                   & 35                 & 100              & 100              & 100              & 96              & 76               & 100                    & 56             \\

                                             &                                   & 45
                                             &100	&100	&100	&96 &88	&100	&60

                                              \\

                                             &                                   & 50                 & 100              & 100              & 100              & 96              & 92               & 100                    & 60             \\
                                             &                                   & After half samples  & 100              & 100              & 100              & 100             & 96               & 100                    & 60             \\
                                             &                                   & After last sample  & 100              & 100              & 100              & 100             & 96               & 100                    & 68             \\ \hline
\end{tabular}
}
\end{table}

\section{Results and Analysis}
\label{sec:results}

\begin{figure}[ht]
\centering
\subfigure[Single robot - Hector Trajactory (HT) Scenario]{
\includegraphics[width=0.32\columnwidth]{image/Rmse_HT.png}
\includegraphics[width=0.32\columnwidth]{image/Var_HT.png}
\includegraphics[width=0.32\columnwidth]{image/Dis_HT.png}
\label{fig:HT}
}

\subfigure[Single robot - Random Walk (RW) trajactory Scenario]{
\includegraphics[width=0.32\columnwidth]{image/Rmse_RW.png}
\includegraphics[width=0.32\columnwidth]{image/Var_RW.png}
\includegraphics[width=0.32\columnwidth]{image/Dis_RW.png}
\label{fig:RW}
}

\subfigure[Multi-robot - Fixed Voronoi Partitioning (FVP) Scenario]{
\includegraphics[width=0.32\columnwidth]{image/Rmse_MR.png}
\includegraphics[width=0.32\columnwidth]{image/Var_MR.png}
\includegraphics[width=0.32\columnwidth]{image/Dis_MR.png}
\label{fig:FVP}
}

\subfigure[Multi-robot - Dynamic Voronoi Partitioning (DVP) Scenario]{
\includegraphics[width=0.32\columnwidth]{image/Rmse_DV.png}
\includegraphics[width=0.32\columnwidth]{image/Var_DV.png}
\includegraphics[width=0.32\columnwidth]{image/Dis_DV.png}
\label{fig:DVP}
}

\caption{RMSE (left), Variance (center) and Cumulative Distance (right) for different information functions in single robot and multi-robot experiments.}
\label{fig:performance}
\end{figure}

Table \ref{tab:all} provides a summary of the performance metrics results obtained by averaging the data collected over all trials with different source locations.
Fig.~\ref{fig:performance} shows the plots for the three key performance metrics: RMSE, Variance, and cumulative distance with respect to time in all scenarios.

In general, exploration seeks to minimize variance and RMSE of the predicted map, while exploitation seeks to make use of the predictions as soon as possible (e.g., leading to identify the source location, i.e., the place with the maximum signal intensity). An informative function can be either exploitation-based, exploration-based, or a weighted combination of both. In our variants, the MaxMean strategy does pure exploitation, whereas MaxVar, Hector trajectory, and random walk are pure exploration strategies. The rest of the variants combine exploration and exploitation.

\subsection{Exploitation Perspective}
We take the source localization example as the objective of exploitation in our work. To properly locate a source, a robot should detect provide a GP map with maximum mean at a point within one meter away from the real source location in any direction.
Table \ref{tab:localization} shows the source localization accuracy of all variances in all scenarios based on the number of times the resultant GP map can be used to correctly identify the source locations after collecting different numbers of samples during the experiments.

\paragraph{Single robot cases}
It can be observed that the localization accuracy of each approach improved with increasing numbers of samples.
We are interested in identifying approaches that allow us to obtain better results with fewer observations. The RW, MaxMean and HT approaches did not perform well, especially in the early stages of the experiment. With HT, fewer measurements are taken, while with MaxMean, the measurements are taken repetitively at the same location (local maxima) since the information function (with $\alpha=1$) is only dependent on the predicted GP mean.

In general, pure RW approach had improved performance, but it was slower than the other approaches.  RW variants' results were better after just 25 samples than HT's after 35 samples. After 45 samples, the performance was almost similar.
We found that the Alpha50 approach to be unsuitable for exploitation. In RW scanerios where the initial 15 samples were taken based on random walk, the best variants were MaxVar, Alpha75, MaxVarMaxMean, and Alpha25. Alpha25 and MaxVar, however, do not represent cost-effective approaches since both involve very long distances. As a result, MaxVarMaxMean and Alpha75 both converge faster and are cost-effective. If MaxVarMaxMean approach doesn't satisfy the variance and RMSE threshold, it is same as MaxVar. We see that the Alpha75 as best approach when minimizing distance cost is the first priority (e.g., if energy availability is heavily limited \cite{parasuraman2012energy}). Alpha25 works best in situations where the source localization has to be even more accurate and cost-effectiveness is less of a concern. When cost is not a concern at all, then surprisingly, the MaxVar approach ($\alpha=0$) provided the best exploitation performance in satisfying both mapping accuracy and confidence.

\paragraph{Multi-robot cases}
We observe similar results for the multi-robot cases compared to the single robot cases, where Alpha25 variant best balances both exploitation objective and the cost requirements. The improvements in performance found with MaxMean and RW were slower than single robot cases and did not reach the same level as other approaches. After 25 samples, the dynamic and fixed Voronoi partitions performed close to each other, and they were successful at locating the source much faster than the single robot experiments even with just 25 samples.

\subsection{Exploration Perspective}
For exploration, the objective is to obtain accurate predictions of the sampled environmental process with the highest confidence bounds in all areas of the map.

\paragraph{Single-robot results}
Here, the MaxMean resulted in the highest RMSE, followed by all the other methods, but with the worst confidence (variance). As variants of both baselines, MaxVar, MaxVarMaxMean, and Alpha25 performed exceptionally well, with MaxVar achieved the best convergence on the variance and RMSE together. The RW baseline also performed well, but it is worth noting that the RW method was the last one to converge. MaxMean and Alpha75 approaches always chose the same target position, giving them identical samples, which reduced variance. As HT baseline yields only a few samples, it performed poorly in terms of variance.

We can safely deduce that MaxVar (with $\beta=1$) is the best approach when it comes to exploration. However, the cumulative distance is also the greatest for this approach. Furthermore, MaxVar works better with the random walk scenario. Alpha25 provided the best exploration performance when cost and efficiency are also considered. We also found that higher values of the coefficient $\beta$ would give better exploration results, as expected.

\paragraph{Multi-robot cases}
Distance plots for multi-robots scenarios show that all variants of DVP approaches took much shorter travel distances than the same approaches based on FVP. However, the FVP scenario resulted in improved variance as well as speed of convergence compared to DVP scenarios.
Consequently, we can conclude that DVP scenario is suitable for cost-effective sampling (less energy), while the FVP scenario is suited for faster convergence and better exploration results. While MaxVar has promising results in terms of variance, and has taken fewer samples than Alpha75 and MaxVarMaxMean, its distance cost is almost twice that of Alpha75 and MaxVarMaxMean. Accordingly, when comparing convergence, number of samples, and travel costs together, FVP in combination with Alpha75 or MaxVarMaxMean variants outperformed other variants.

\subsection{Further Discussions}
When analyzing the results from the perspectives of exploration vs. exploitation sampling objectives, MaxVar variant produced best results by making the robot traveling throughout the map. However, the Alpha25 stood out as an optimum variant that balances the performance in terms of RMSE, Variance, efficiency, and source localization together, making it suitable to both exploration and exploitation objectives.

More detailed data of the results and high-resolution figures are available as an appendix to this paper at \url{http://hero.uga.edu/research/adaptivesampling/}.

\paragraph{Impact of source locations on the sampling performance}
We also analyzed the impact of different source locations on the sampling performance (results for these special cases are available in Appendix). We found that there was almost no impact on the results across all sources, especially  when the $\beta$ value (i.e., the weight towards confidence bounds) is higher. However, for variants where $\alpha$ the value is higher (MaxMean, Alpha0.75, and Alpha0.5), they gave significantly different results for the furthest source locations bottom-right (0,14) and top-right (9,14) of the map area. This could be attributed to that fact that when $alpha$ is higher, exploitation is more preferred and therefore localizing a source that is much farther could be difficult to accomplish.
In summary, the effect of source locations was not observed for informative functions with greater weights for variance (exploration).

\paragraph{Impact of Wi-Fi signal distribution on the sampling performance}
Further, we analyzed the impact of the signal distribution itself on the results by repeating all the single robot cases with different path loss exponent (n=2) (results for these special cases are included in the Appendix).
It was observed that all approaches with n=2 performed quite well in comparison to the cases of n=3, and we found that the change in both variance and RMSE metrics were smoother for all variants when n=2, than the same approaches for n=3. Nevertheless, the change in the signal distribution had minimal impact on our analysis, and the observations made for n=3 above hold for n=2 as well.

\clearpage
\section{Conclusion}
This study provided an understanding of how exploration and exploitation objectives differ in mapping accuracy and efficiency changed for various variants of informative functions in adaptive sampling of environmental sensing or physical processes. For two baseline approaches, we used the Gaussian processes based mapping and evaluated variants of utility function on simulated Wi-Fi signal map by extensively analyzing on single robot (UAV) based experiments and multi-robot (UGV) based experiments. Our results show that approaches with higher weights to confidence bounds provide the best performance results when seeking the next sampling points. The analysis from our data and the results provide insights in designing optimal information functions for achieving adaptive information sampling.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


For all of you who have been waiting to create App-V images of VS 2010, it's finally possible. You'll need VS 2010 + SP1 + 1 patch and App-V 4.6 SP1. Here's some instructions for how to do it.
You might wonder why it hasn't just always worked. For an app to work just fine in an App-V image, it must not have any machine specific data coded in it's configuration. Unfortunately, VS does – specifically in some of our licensing configuration data. The patch I mentioned above changes the way we encode license data so the it doesn't contain any machine specific state. With that change, you can sequence VS.


If you have had a tonneau cover previously installed, our tarp may not attach in the same way.
Just a quick note, Purchased ute cover for BT 50 on Thursday after talking to Paul great advise and customer service received email from Nola with connote number and phone number for Direct Freight Express, cover arrived in Sydney 2.30pm Friday afternoon. Fitted on Sat took 1 hour looks great! Great service from Direct Freight Express and yourselves Thanks.


text:
Woman Living "Off-The-Grid" Receives a Vacate Notice
AllOutdoor Staff 12.17.13
FOX4 aired a story about a Cape Coral resident, Robin Speronis, who has chosen to live without water or electricity. Soon after the live report, even though the address was not disclosed, Speronis' off-the-grid home was on the radar of Cape Coral code enforcement.
This video and article from FOX4 shows her frustration with the city officials.
The city says it only visited the home after the story aired because a citizen called for a welfare check, which the city deems a life safety issue. The city says the notice to vacate was issued due to the lack of electricity and water service to the home. Code officials needed to speak to the homeowner to see what options were being used for the water/sewer/electric needs and whether those options would comply with International Property Management codes.
While Robin says those modern comforts aren't mentioned as a requirement in the code cited by the city on the notice, she won't say whether she's been living in the home since the notice was posted. She does say the city is overreaching and threatening to take away her home — which she owns free and clear with taxes up to date.
"Where is the justice? Why did they choose me," she asks. "…because I was exercising my First Amendment rights of free speech in discussing living off the grid."
The city says it simply wants to communicate with the homeowner, saying she has ignored efforts to resolve the lack of water and electric issues. They also say she has refused their attempts to speak with her about a landscaping issue dating back to October.
A local attorney has since agreed to take on Robin's case for free. The city says if she can prove she can sustain herself and her home without water or electricity both parties might be able to come to a solution.
The city has to regulate on certain things like fire safety, sewage back-up, and cleanliness. Given that Speronis is living within the confines of a city, the city has the right to inspect the house. But she also has every right to due process and to avoid being kicked out of her own home.
AllOutdoor Staff
AllOutdoor Staff is currently a writer for AllOutdoor who has chosen not to write a short bio at this time.


text:
Country / Rockabilly
Rock / Pop / Alternative
Cecil Taylor: Silent Tongues
by ORG Music | posted in: Uncategorized | 0
Cecil Taylor's solo piano set at the Montreux Jazz Festival in 1974 drew critical acclaim, earning five star reviews from the Rolling Stone Jazz Record Guide and AllMusic's Scott Yanow. The set features Taylor's five-movement work "Silent Tongues", along with two encores. After being out of print for nearly four decades, the classic live album is now available exclusively through Vinyl Me, Please on a gold smoke color vinyl, limited to 750 copies. The album will receive a wide release on black vinyl in early 2019.
The record has been remastered by Dave Gardner at Infrasonic Mastering, and pressed on audiophile-grade vinyl at Pallas Group in Germany. Silent Tongues is a must-have for fans of Taylor and collectors of free jazz.
JOIN OUR MAILING LIST FOR NEWS & EXCLUSIVE OFFERS
© 2020 Org Music


Sydney-based indie pop artist Iluka first hit the airwaves with the bouncy 'Paper Doll', with her squeaky vocals sounding not unlike Lanie Lane. This was followed up by the smoother '12th of July', which gave just a hint of the wider range of tones of which her voice was capable. Her new EP Blue My Soul goes much further in showcasing the full extent of her singing.
Iluka took a curious route in the development of her latest release, abandoning the busy, materialistic focus of our everyday world. Instead, she drew inspiration from far flung places including India, a Buddhist monastery and the empty silence of a desert in the USA. It was as though she cleared the air to get closer to physicality, nature and a purer form of emotion. True to the EP and track titles, the results see a move to a more soulful vibe. 'Blue Jean Baby' keeps a firm hold of the fast slapping beat of 'Paper Doll'. It is a tale of two voices. In a sort of Jekyll and Hyde manoeuvre, Iluka drops her pitch, slipping easily from a little girl tone to a deeper, sultrier sound. 'Visions of Cody' adopts a more languorous mood, drawing out the words, with more shifts in the shading of her voice. In 'Blue My Soul' she mixes a tone approaching the fragility of Gossling with the fiery, emotional build-up of Melody Pool. Her soulful side comes out strongly in 'The Fools' with Iluka showing she is capable of Janis Joplin-like surges of power. The combination of thick, syrupy and floating vocals, plus the spoken word backdrop make closer 'In the Heat' her most atmospheric track.


In this episode of the Outside Health and Fitness Podcast I am excited to have DStep from Pumping Adrenaline on the show. Dstep runs a site dedicated to people who love to live on the edge or watch others live on the edge. D loves playing in the outdoors and pushing the envelope and I got a chance to talk with him about his experiences and to find out what "Pumping Adrenaline" is all about.


text:
DUCATI 2020 MODELSfraser2019-10-25T12:21:37+10:00
DUCATI PRESENTS A SERIES OF EXCITING NEW BIKES FOR 2020
There are new colours for the Diavel and Scrambler®, the special Grand Tour version for the Multistrada 1260 S and three absolute novelties. First among these is the Panigale V2, our super-sporty twin powered by the 955 cc Superquadro engine, the middle-weight superbike new both in its aesthetics and its performance.
PRE-REGISTER YOUR INTEREST
NEW 2020 1260 S GRAND TOUR: DESTINATION EVERYWHERE
The premium sport-touring bike, for those who want to travel first-class without compromise.
Dedicated livery, side panniers, center stand, heated grips, additional LED lights, hands-free tank filler plug and tyre pressure monitoring system as standard equipment
NEW 2020 PANIGALE V2: THE RED ESSENCE
The twin-cylinder Panigale has been given a thorough makeover. Renamed the Panigale V2, it takes a totally new design, it features sophisticated electronics based on a 6-axis inertial platform and, thanks to a more comfortable rider seat and the suspension set-up, offers better yet safer sports performance while making road riding more enjoyable and user-friendly than ever.
The name informs Ducati twin-cylinder enthusiasts that the bike is powered by the 955 cm³ Superquadro.
NEW 2020 PANIGALE V4: THE SCIENCE OF SPEED
The 2020 version of the Panigale V4 boosts performance even further and takes track riding to the next level for amateurs and pros alike. A series of refinements make for an easier, more user-friendly, less fatiguing ride while simultaneously making the bike faster not just on individual laps but over entire timed sessions.
The Aero Pack provides enhanced airflow protection and improves overall vehicle stability, enhancing confidence. The Front Frame, instead, modifies stiffness to give better front-end 'feel' at extreme lean angles. Ducati and Ducati Corse engineers have crunched the feedback/data numbers from customers all over the world and Superbike World Championship events. Their analysis has led to a series of aerodynamic, chassis, electronic control and Ride by Wire mapping changes: designed to increase stability and turn-in speed, these changes make it easier to close corners and ensure riders enjoy more confident throttle control.
The S version comes with Öhlins event-based electronic control; this uses the second-generation Öhlins Smart EC (Electronic Control) system that, exploits the full potential of the IMU 6D.
NEW 2020 STREETFIGHTER V4: THE FIGHT FORMULA
The Panigale V4, stripped of its fairings and with high, wide handlebars. This, in short, is the underlying concept of the new Streetfighter V4, a bike that magnifies the thrill of the ride every time it takes to the road.
The result? An unrivalled naked bristling with cutting-edge technology and an awe-inspiring design that oozes pure performance.
The minimalist full-LED headlight captures the spirit of the Streetfighter V4 perfectly. On the one hand, it recalls the front of the Panigale V4 and, on the other, evokes the crazy grin of the Joker (the comic strip character from which the Ducati Style Center drew inspiration for this new Bologna-built naked). Already featured on the Panigale V4 and SuperSport, the V-shaped DRL is another clear Ducati hallmark. This is the "Fight Formula". This is the Streetfighter V4


text:
Heavenly Chat
by Gary Whitmore
Albert Taylor is happily married to Ginger and life is good. That's until she dies suddenly in a car accident off Mulholland Drive in the Los Angeles area. Life is miserable for Albert and his grieving costs him his job. Then a website called Heavenly Chat pestered Albert to sign up to chat with an Angel. Albert caved in and finds himself chatting with an Angel he believes is his deceased wife. Then his Angel convinced Albert to hook up with a single female FBI… (more)
Albert Taylor is happily married to Ginger and life is good. That's until she dies suddenly in a car accident off Mulholland Drive in the Los Angeles area. Life is miserable for Albert and his grieving costs him his job. Then a website called Heavenly Chat pestered Albert to sign up to chat with an Angel. Albert caved in and finds himself chatting with an Angel he believes is his deceased wife. Then his Angel convinced Albert to hook up with a single female FBI Agent Britney Cooper to stop the President from being assassinated. Albert and Britney hit the road to stop the President from being assassinated. It's when Albert stops the assassin that he learns a horrible secret.
Fiction Science Fiction
Publisher: Lulu.com (May 23, 2014)


As if a plate fell straight out of the sky and fingers of a cosmic architect stuck it into the surface. That's how unearthly the unique shape of this bench feels. Although it is obvious that it is made of heavy concrete, it appears almost insubstantial. Elegant concave shape is not just for decoration, it is functional too. It brings privacy to two people and it virtually protects them from the outside world. Noise and wind have no chance. It can completely transform a variety of locations, from parks or city squares to waterfronts. Its design excels in both open spaces and in small closed corners.