Golden clay bakes gently in the warmth of the sun. Brilliant multi-hued shawls lend a dappled glow to the souk. She looks up and with the sun illuminating them from behind, the feathery bits of cloth are as vivid as stained glass. All around, the scent of exotic spices perfume the air as merchants wave their wares below the noses of promising customers.
The gondolier's throaty baritone fills the canal, bouncing off walls to return and join the chorus once more. High overhead, winged, stone lions keep close watch on the city. The stars peek over their shoulders to see what is going on in the city. The song ends and abruptly the canal widens onto a piazza. Revelers in rich costumes and delicate masks filled the streets. Lights festoon the plaza bathing everything in a glow so bright it seems the sun could not bear to turn its face away from this ancient city.
Sleigh bells carol out a merry greeting and the horses' hooves send plumes of snow into the air. Fur lined lap rugs keep everyone toasty warm, except for noses turned cherry pink by the December chill and tongues held out to capture the first delicate snowflakes of the season.
I didn't take my first plane ride until I was seventeen, but I'd already been around the world many times. Books have an inherent magic to transport us. It can be to the neighborhood café, or to an entirely different universe.
I know that there are any number of people who will only read stories with settings they are familiar with. I have a hard time relating to that mindset. Not that there is anything wrong with it! Everyone gets to do what they like with their own book budget. But I get more bang for my buck by being swept away somewhere I can't get to in my everyday life.Books let us experience a world beyond ourselves, beyond our usual sphere. Part of the power in books comes from their ability to open the world to us. I'm a complete Anglophile because of The Little Princess, the Adventures of Sherlock Holmes and Agatha Christie mysteries. Once, on our way to an assignment in the Seychelles, I arranged for an extra stop at Heathrow airport because I'd read about it so often in various stories that it seemed like being a part of a larger story just by passing through that airport. (BTW, I don't recommend it. Gatwick is a much nicer airport.) That brings me to my next point. Traveling via book is not nearly as much hassle.
Don't get me wrong, I love to travel, (we're missionaries so it's part of the job description,) but I could happily do without the inevitable annoyances, both major and minor. In a book you can explore without the inconvenience of competing tourists or a companion with a bladder the size of a peanut. The rhythms of a different way of life and the nuances of culture are laid open to us and we never even have to leave our sofas.
What place means magic to you?
Have you ever been there in 'real' life?
What was the last book that transported you?
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Great post, Lisa! My "magic" place has always been England--probably because growing up I read so many books set there! We finally got to make a trip in 2005, but it was way too short. There are still so many places left to explore in England and Scotland, both! I definitely think my love of reading infused my desire to travel.
Lisa, what a great idea to take our theme and choose this practical version of travel. I agree with D'Ann that my reading has infused my love of travel and history.
Recently I read Phillipa Gregory's The Constant Princess (actually I listened to it unabridged on audio), her story of Catherine of Aragon, the first wife of Henry VIII who was the daughter of Philip and Isabella of Spain. Ms. Gregory's description of the Alhambra, Catherine's home in the Muslim region of southern Spain made me really want to visit it.
I swiped breakfast from Seekerville, so help yourselves! Sausage, biscuits, scrambled eggs, pecan coffee cake and of course some strong black coffee.
Until recently I didn't realize that there are any number of people who will only read stories with settings they are familiar with. I have a hard time relating to that mindset, not that there is anything wrong with it. Everyone gets to do what they like with their own book budget. But I get more bang for my buck by being swept away somewhere I can't get to in my everyday life.
OOOH, we are on the same page, Lisa. Since I was a little girl, books have transported me to new worlds! I even have one of my great-aunt's 2nd grade primers that says, "Sailing to New Worlds" and used to stare at the colorful pictures and more colorful words!
My magic place? IMPOSSIBLE to name one. The road to Hana, Maui. The treacherous bay between Maui and Molokai. Once-isolated San Juan Island, off the coast of Washington State. Grindlewald, Switzerland. Kilkee, County Clare, Ireland.
Last book(s) that "carried me away?"
ALL of the books I'm reading as research for Reclaiming Lily--set in China. Lord willing, I'll SEE the places in April!!!!
Thanks for an exotic, thrilling post!
Wonderful food for thought, Lisa. My current magic place, of course, would be Versailles where "Prisoner of Versailles" is set. I have not been able to travel there, but would very much love to do so.
In addition to that, my husband and I traveled to Pennsylvania last year to visit the actual property where my ancestors, the Clavells, settled when they ended up here in the New World. That was magical to me to walk the same roads and see the beautiful farms where they homesteaded.
The last book that carried me away was probably Siri Mitchell's "Constant Heart.
Although I am a Regency writer, I haven't yet made it to Great Britain for one reason or another. I have, however, been to Europe, studied there in college and trips since. I used to be more of a francafile than an anglifile, read every book on the French Rev. I could find. So setting foot in Versailles, Notre Dame, walking down the Champs Elysee... Magic indeed. I loved Heidi as a child, so the Swiss Alps transported me with delight.
But since I also love the sea and love America, I was wholly moved when I stood at Cabo da Roca, the western most point in Europe, where Columbus stood and said, "Dude, the world isn't flat..." or something to that affect. Or drinking from a fountain that's been carrying clear, mountain water into Cintra since it was Moorish. I loved Elizabeth Cadell books, as well as Jane Aiken Hodge, so Portugal was truly special.
But right here in the US, I had the chance to experience a little foreign history when I sailed, including climbing the rigging, on the HMS Rose replica (now the Surprise from Mster and Commander). The original Rose was a British frigate sunk in the Savannah River in 1780.
The last book that transported me far away? Sadly too few outside the US exist in the CBA, so it was probably a secular one.
I know what you mean Golden. I went on a research trip to Williamsburg this past summer and even though I'd been there before I never fail to be awed at the thought of the people and the history that places can hold. I get goosebumps and do my best to absorb the spirit of a place, not just in what it looked like, but how it made me feel and what it made me think about.
I guess that's something else books do for us. They takes us into new realms of thought.
Laurie Alice have I told you lately how jealous I am of your HMS Rose adventure. What a grand experience.
And I realized I haven't answered my own questions. Magical places to me: London, (yep, all of it), and Williamsburg. I have been blessed to go there. I'd also love to ride the Orient Express or visit Venice. Haven't managed either of those yet.
The last book that transported me? The Swiss Courier by Tricia Goyer and Mike Yorkey. WWII Switzerland.
Lisa, books and movies are my main mode of transportation, as my Friday post attests. The last book that took me away (actually while I was away on our cruise) was Water, Stone, Heart by Will North. A lovely tale of romance and self-discovery set in the charming Cornwall region of England.
What a fun post, Lisa! I can wholeheartedly relate. Since I can't live in Great Britain (or at least spend a solid 3 months visiting everything I want for my stories), I greedily pore over my English-set books, magazines, etc. And sigh.
That said, there is something amazing about visiting a place that has captured your imagination. I also enjoy US history, and we visited Gettysburg one sticky summer. Thanks to efforts to preserve the site, it was easy to imagine what it might have been like for the citizens and soldiers that hot July.
I love this posting. And of course, being a storyteller, I have to tell a story.
Like most writers, I've always loved books and reading. When I was in 8th grade, we had to write an autobiographical report, telling of all the places we had visited. I had a wonderful time telling of my journeys to China, Korea, Japan, Africa, India, South America, and many fascinating places in the U.S. As the teacher returned our graded papers, he asked me to stay after class.
"Have you actually been to all of these places?" he said. "You're . . . so young."
"Yes. Through books. I've read about those places. The writers took me to them."
He smiled. "I see. You can keep your 'A'."
There is magic in every place. One just has to look for it. You just might find it behind the next wild grape vine.
Since my grandmother was born a Kelly, I've always longed to traipse around Ireland, filling my eyes and heart with a thousand greens, my ears with the trilling brogues.
Thanks for your generosity and your lovely blog!
Sharon, I love that story! So glad you got to keep your grade too, that was a teacher who understood the heart of a reader.
Jeanette I can so relate. I've wanted to go to Ireland for as long as I can remember. I can sit and just look at pictures of that beautiful country for hours.
I'm really enjoying all the comments today. So much fun to share the places we've "been"--real or through our imagination.
Laurie Alice - My respect for Columbus just went up a notch.
I like books about Africa, the Middle East and China. I've been to the Middle East several times.
I also like tamer books about England, France and Italy. I've been to England and France so far.
Forgot to mention. Real life traveling is a pain. Airplane seats in coach are designed for dwarves. I hate time changes too. And bad water. Books are indeed so much easier.
Lovely post, Lisa. I felt the magic in each of your descriptions.
I'll admit it, I'm one of those sad people who hasn't been many places and isn't really itching to change it. Other than various parts of the United States, I've only been two other places: Canada (for one whole day!) and spent a lovely long weekend in Puerto Rico, but only because it was my prize for being a Top Quality Service employee.
Jen, you could hit both Busch Gardens amusement parks and feel like you traveled most of the globe that way.
Great fun. England's my magical place and one of the reasons I write Regency Fiction. I studied in Oxford for a portion of my senior year of college. I studied the mysteries of Stonehenge, walked the cobblestone streets of York, visited countless cathedrals, and celebrated with friends at more than one Medieval Feast.
What book has transported me? I'd have to say that Diana Gabaldon's Outlander series transported me like no other books I've ever read.
Lisa, as I recall, he actually asked me to read the paper aloud to the class.
As for where I'd love to go in person, there are many such places, but I would especially enjoy motoring around Scotland and Wales, my ancestors' homelands. Wouldn't I love to tell tales in a Scottish castle? 'Twould be magic.
Some places that are magic to are New Orleans here in the US and England. I have been to Louisiana before but have not made it to England yet. The last book to transport me was The Potluck Club: Troubles Brewing. I love books with small town settings. Probably because i was born and raised in NYC.
Catherine Palmer's, "A Kiss of Adventure," transported me to Timbuktu in Africa. I've never been there in real life.
A funny story about books set in other countries.
When I was a teen, I discovered all those lovely Mary Stewart books. I told my cousin, who had read to me incessantly when I was a child and she a teen babysitting me, about MS. She said she didn't like her. "Her books all take place in foreign countries." To which I said, "No they don't. The Ivy Tree is set in England."
I was already such an Anglofile that I forgot that England was a foreign country.
Nor is Canada to me. I grew up near the border and spent a great deal of time there.
And venice? Got to get there before the sea reclaims it. So many good books set there. The Dark Palazzo by Virginia Coffman, One-Way to Venice, The Terracotta Palace... Take me back to my bookish teen years.
Great post, Lisa. I didn't take my first plane trip until I was 43. So my traveling (other than living in Japan when I was little - and we went there on a ship) had been very limited up until then. So on that first plane trip, I went two places. Disneyland! My first time there, but I've been four more times since then. And Virginia. I was lucky enough to get there twice.
And like you, one of my magic places is Williamsburg. I fell so madly in love with it. I would really like to take a two week trip for a writing retreat, and spend it in Williamsburg. Even if you don't write in the colonial era, which I don't, there is something so entrancing about it. It simply carries me away and I find it a very conducive atmosphere for writing.
A book that really transported me was The Shape of Mercy by Susan Meissner. I was so totally swept up in both the story and the era.
Susie, Williamsburg is only an hour from me. If you do your retreat, I'll come hang out.
Laurie Alice what a great story, and I can so relate. I'm American by birth, but I think I'm English by cultural identity.
Susie can more than one person come with you on your writer's trip?
I have a great idea, why not have a writer's retreat for the Inkies and friends? I can dream anyway.
My magic place is Russia. And I've never been there. Chances are good that I never will, but that just increases the magic.
Last book that transported me was A Prisoner of Versailles. I recently discovered that I have French ancestors who may have been Huguenot, so Golden's books have taken on a new dimension for me.
Dina and Lisa, YES! It would be so fun for all of us to go someplace like Williamsburg and have our own writer's retreat. I'm in!
Rachel, I also discovered Huguenot ancestors. I haven't read Golden's books yet, but they are in my TBR pile.


Tork Industrial Heavy Duty Cleaning Cloth with exelCLEAN offers superior strength and excellent cleaning efficiency which is ideal for tough cleaning, degreasing and removing stubborn spots. It works with many solvents and protects hands from heat and metal scraps.
Cloths fit in the Tork Folded Wiper/Cloth Dispenser W4, protecting cloths from dust and dirt.
Tork Industrial Heavy-Duty Cleaning Cloth with exelCLEAN offers superior strength and excellent cleaning efficiency which is ideal for tough cleaning, degreasing and removing stubborn spots.
Cloths fit in the Tork Folded Wiper/Cloth Dispenser W4, protecting cloths from dust and dirt. It works with many solvents and protects hands from heat and metal scraps.
Wipers wherever you need them. No drills, no holes, no worries. New Tork Dispenser Magnet & Sucker kits make Tork W4 Dispensers easy to install, anywhere. This dispenser can also be permanently mounted.
With the new Tork Dispenser Magnet and Suckers Kits its now easier than ever to have a wiper at hand. Besides the option of screwing to wall there is a magnet kit or sucker kit available.
Ergonomically designed mobile floor dispenser suitable for various industrial environments. For high usage areas,easy to move around.
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Strong, durable and heat resistant cloth. Textured surface makes it easy to remove grease and stubborn spots and gives good cleaning efficiency. Single wipe dispensing in portable and small size format for compact spaces.
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text:
Biografia
Dal 1996 al 2019 ha curato ininterrottamente la regia della storica trasmissione di Raiuno Porta a Porta, condotta da Bruno Vespa.

Ha inoltre diretto le riprese in diretta di eventi quali i funerali di Giovanni Paolo II, l'elezione di Benedetto XVI e le cerimonie di apertura e chiusura della Porta Santa del Giubileo del 2000.

Note

Collegamenti esterni
 Focus Production

Registi dello Zecchino d'Oro

redpajama_set_name: RedPajamaWikipedia


This conceptual artist, painter and photographer emulates Picasso and how! Whereas Picasso, one of the most important modern artists of the 20th century, displayed double-faced characters in his paintings, Sebastian Bieniek does this in his photographs.
Immensely interested in art, Bieniek enrolled himself at the HbK Braunschweig, Germany and studied art under Swiss conceptual artist John Armleder and Serbian performance artist, Marina Abramović. He also studied art under the German photographer Katharina Sieverding in Universität der Künste Berlin, Berlin, Germany.
Among his works, his photo-serial 'Double-faced' was a hit and became one of the most popular internet memes. In no time, it became a social media sensation with Instagram recording over 43,000 followers. This popularity egged Bieniek to explore the niche of 'Painted-faces' by blending different media.
It is not only the internet that has helped Bieniek in popularizing his art, but exhibitions too. His first solo exhibition was held in Berlin in 1999. The very next year he participated in the 'Festival of Vision' exhibition in Hong Kong. Till date he has participated in a number of exhibitions in many countries, including Italy, Switzerland, Austria, Bahrain and many more.
It is no wonder that this 47-year-old German is among the 'Top Five Influential Contemporary Berlin-based Artists', according to theculturetrip.com.


Time to get a new one.
Check for a loose belt.
Fuse is in control ..Hard to get to I cut the section of the box out to get to fuse figured its going to blow often i was cleaning the wheel with a wet sponge when my fuse blew first week.
There can be two possible reasons for this to occur. The first would be that the pitching machine might need more power than that which is available at home, or the machine might be working for a long time and may have got heated up.
Sorry I don't have its control panel diagram, but I think this will help you.
You most likely need to use medium grade sandpaper and use it one the tires. They need to be sanded down in order for the machine to shoot the balls out.
Just had this happen on my machine. Opened up the control box and found center disc capcitor was loose from cold solder joint. However we also found the hot lead for the motor at the wiring block on the circuit board had a cracked solder joint. Simply reheated with solder iron and applied little extra solder and it working again.
pull off the wheel and run the machine. you will be able to see if the hub is out off balance. if it spins true the you have a rim or tire out. this can be fixed by having a tire shop balance it for you or call Jugs and get a complete replacement,tire and rim ,shipped mounted and balanced.
it is very unlikely that the motor shaft is damaged unless it was droped trying to load it into a truck.
It could possibly be one of the bearings starting to go, is the machine well used or new?
I just fixed one with the same problem.
10 to 1 odd the brushes are stuck from sitting.
there are 4 post one with black wire , white wire , ground and no wire.
you need to take off the two nuts on the one with ground and the one with no wire.
you can then remove the cover which will have the brushes inside.
once removed push the brushes in and out of the sleeve to loosen. spray with some liquid wrench or wd-40 and work back and forth. I took some steel wool and cleaned the copper where the brushes contact.
slide it in while lining up the long screws with the holes in the housing. you will need to depress the brushes with a screw driver through the front hole , then slide it in . it will not go in the last 1/8th because of the scussion washer. tighten the nuts on the long screws and the gap will close. if you have the wheel off you can use a phillips screwdriver to hold the oth side , if not just hold the screw on the shaft with channel locks in the middle so you will be able to get the second nut started.
Have a manual for Jugs Products?


Q: I signed a one-year lease with my landlord to rent a house. The lease was set to expire at the end of April 2017. However, after living there for eight months, I gave my landlord notice and vacated the rental house at the end of December 2016 to move into a residence I had purchased.
To honor my lease, I continued to pay my monthly rent in January, February, March and April, even though I was not setting foot in that rental residence, and my landlord was using those four months to repaint the place for his next tenant.
Now that my one-year lease has expired, I received a check from my landlord for the return of my security deposit. The amount of this check included deductions for utility bills, including CenterPoint Energy, Xcel Energy and city water/recycling bills for the four months I did not live there.
I can understand that my landlord expected to keep the house at a certain temperature throughout the winter months, so I am not disputing the CenterPoint Energy bill deductions. However, it seems unfair that my landlord is passing along the bills for electricity, water and recycling, since I neither generated nor used any of those services, and, in fact, notified each of those utility companies to close my accounts as of the end of December.
I have countered in a note to my landlord, asking him to write me an additional check for the amount of the electricity, water and recycling services that I neither generated nor used, but that he deducted from my security deposit. What do you think I should do?
A: As you indicated, you are required to pay rent for each month until your lease expires, whether you are living in the unit or not. Some utility bills, such as heat and garbage, accrue even though the unit is empty. Others, such as water and electricity, may have reduced significantly once you moved out. Some leases require the tenant to pay the utilities whether or not they are occupying the unit. If your lease has such a clause requiring you to pay all the utilities through the end of your lease, then you are responsible for those utilities even though you weren't living there. If your lease does not have such a clause, then it is an open question whether a court would require you to pay them.
The statute governing security deposits permits a landlord to deduct from the deposit all amounts owed by the tenant to the landlord. One important issue for you to raise is that after giving notice, the landlord knew you had vacated the unit. He could have turned the heat down to a lower temperature and turned off the lights and the water, except when he was showing the unit. If your landlord did not do that, then he increased the amounts you were responsible to cover, and most courts won't look favorably on that. You should consider bringing a conciliation court action against your landlord to recover the amounts you think you should not be responsible to pay.
Don't cash the security deposit check your landlord sent, as he may argue that cashing the check shows your acceptance of the lesser amount owed. Instead, you should wait to see how your landlord responds to your request. You may also request a copy of the actual electric, water and recycling bills for your apartment from January through April. Your landlord may agree to split the cost with you or pay the entire amount to avoid conciliation court.
If your landlord refuses to negotiate with you, you may file a claim in conciliation court in the county where the rental house is located. If your landlord does agree to split the bills and write you another check, make sure to get that agreement in writing.


text:
The Committee for Tactical Emergency Casualty Care (C-TECC) was convened to speed the transition of military medical lessons learned from the battlefield to civilian crisis response in order to reduce preventable causes of death in both our first responders and civilian population.
The Tactical Emergency Casualty Care (TECC) guidelines are a set of best practice recommendations for casualty management during civilian tactical and rescue operations. Based upon the principles of Tactical Combat Casualty Care (TCCC), TECC accounts for the differences in the civilian environment, resources, patient population, and scope of practice from the military combat environment of TCCC. The Committee for Tactical Emergency Casualty Care (C-TECC) is modeled after the Committee for Tactical Combat Casualty Care (CoTCCC) and is comprised of a broad range of interagency operational and academic leaders in the practice of high threat medicine and fire/rescue from across the nation, including members from emergency medicine, emergency medical services, police, fire, and the military special operations community. C-TECC remains an independent civilian entity but maintains a close relationship with CoTCCC for guidance and support.
Taking into account that wounding patterns and mechanisms of injury may be similar in civilian incidents involving ballistic and explosive wounding, TECC recommends treatment modalities based on the situation and available assets. The primary TCCC tenants of placing far forward timely medical care, and doing the right thing at the right time, are also paramount in the TECC guidelines. As with TCCC, TECC is divided into three phases of care based on the relationship of the provider, the casualty, and the threat.
Considerable expertise and effort has gone into the development of a civilian operational medical standard in parallel to the successful guidelines of Tactical Combat Casualty Care. Known as Tactical Emergency Casualty Care, this new standard utilizes data and experiences from the military and accounts for the differences of operating in the civilian sector. The TECC guidelines will continue to be updated using evidence-based medical best practices and will remain under the custodianship of the Committee for Tactical Emergency Casualty Care.

redpajama_set_name: RedPajamaCommonCrawl


Rebel Meets Rebel is a country metal album by David Allan Coe and Pantera members Dimebag Darrell, Rex Brown, and Vinnie Paul.
The music was written and recorded by the band when the musicians had time aside from their other projects, including Pantera's world tour supporting Reinventing the Steel.
The album was released on May 2, 2006, under Vinnie Paul's own label Big Vin Records, posthumously after Dimebag Darrell's murder in 2004.
All lyrics written by David Allan Coe, all music composed by Dimebag Darrell & Vinnie Paul.
At the close of the decade, Coe met Pantera guitarist Dimebag Darrell in Fort Worth, Texas, and the two musicians, struck by the similarity of the approaches between country and heavy metal, agreed to work together, and began production on the album, with Vinnie Paul and Rex Brown, which was recorded sporadically between 1999 and 2003, and released in 2006, two years after Dimebag Darrell's murder.
Originally the song Rebel Meets Rebel was supposed to be released as a duet with Coe and Phil Anselmo.


text:
Investors opt for P2P as cash popularity wanes
By Joe McGrath
Investors are increasingly retreating from traditional cash Individual Savings Accounts (Isa) in favour of the recently launched Innovative Finance Isa (Ifisa), according to a recent poll.
Ifisas permit savers to gain interest from peer-to-peer lending platforms tax-free in a similar way to cash Isas, but typically at higher rates.
Figures from the Assetz Capital barometer show the percentage of investors in cash Isas stood at 37 per cent at the end of last year's Isa season, down from 52 per cent in the first quarter of 2018.
Stocks and Shares Isas were embraced by 61 per cent of investors in the second quarter, and Ifisas were almost as popular, with 60 per cent investing in them.
Alex Brown, a wealth management director at Mattioli Woods, said he was not surprised at the trend, given the growing dissatisfaction with interest levels on cash Isas.
He said: "If you look at where base rates have been for the last decade at sub one per cent, I'm not surprised we are seeing a gradual movement away.
"There has been a fundamental shift in investing. Cash used to be a safe house at 5 per cent but it can no longer be considered an 'investment.'
"At sub one percent, no one would say you have got an investment return because inflation is around 3 per cent now."
Brown said that while rising interest in peer-to-peer backed Ifisas was an understandable consequence of market conditions, he warned returns from these vehicles had "no precedent for downside".
He explained: "Naturally, people are going 'if I can't do cash, where am I going - peer to peer lending, I see its attraction, but I feel it is slightly dangerous.
"If you look at equity markets, they have been around so long that people appreciate they go down as well as up.
"The idea of loan finance, returning 7 to 8 per cent per annum without the volatility of equities is fantastic. The issue is that for a lot of peer to peer lending, markets have been good - you haven't seen a crash that you have experienced with equity markets because P2P is quite a new investment."
Jatin Ondhia, chief executive officer of Shojin Property Partners, agreed that investors are looking for better returns on their investments.
He said: "Since we launched our variety mini bonds last month, they have been popular with investors. Even the best cash Isa does not compare favourably with the targeted returns that the Shojin mini bonds offer."
More on ISAs
Isa rates rise from record lows amid interest speculation
John Lewis launches investment service with Nutmeg
High Street investments can open up the advice conversation
Simon Read
Lifetime Isa withdrawal charges triple during lockdown
NatWest launches junior stocks and shares Isa
Pandemic saving leads to Isa boom
Investments ISAs


Brian Hoialman, VP of Sales for Kunversion joined us to share some great tips and best practices for following up with and converting any and all online leads. Using corrective statements of communication and scripts that demand a response is the key to success.
During this session, Jeffrey Buettner shared some critical setup steps and some basic Kunversion dashboard features and benefits. Brian jumped in at the midway point and discussed some concepts, scripts and best practices around communication and follow-up.
Just got a new lead, what do I do?
Leads are pretty well covered for when they are new. They get a welcome email right away, and they are automatically setup on the Kunversion Default campaign, which is amazing. After two days of being in the system, they are also automatically setup on listing alerts. Also during this time, whenever your leads do something interesting, they get a system email, automatically!
So, what are you meant to do?
Well, this presents a unique opportunity for you to focus on converting your leads into clients. That is, spending your time doing things that a computer cannot do. For example, reaching out to leads based on their interests, not just to say hello or send another welcome email. Below you will find scripts and strategies to incorporate into your own, for maximum results.
The number one thing you can do for leads when they have a phone number is text them! the vast majority of text messages that are delivered are actually read. So it's incredibly important to get on it ASAP. Here are a few links to help you get started. Also, we included scripts that you can use on phone calls as well at the bottom of this article.
By and large, following up via email is the same as following up via text messages. It's just that, you have a much smaller chance of getting a reply by email. So, your normal scripts for texts and calls will match your strategy for emails.
Makes sense, right? Most of the time, people will reply with just a number. Let them know you got their number, and then update their profile in Kunversion right away. On their overview page, under the red What Would You Like To Do menu, you can select the option "Text Millions Mapped" to send your mobile app to them via text! Now, you have their phone number.
Here are some scripts and strategies for getting responses and conversations started. There's a lot to consider when organizing leads at this point, so find the article here in Zendesk for how to organize leads and give it a once over at some point.
This format will work with any type of call. In this example, it's our first phone call with a lead we have not yet spoken with.
2. Statement of Service: I was in the process of setting up your listing alerts and searching for some good properties to send you….
*The Key Point with this statement is that you are doing something for them. You need to tell them that you are doing something for them that provides VALUE TO THEM!!!! Your Job is to "PROCURE" the property and it is the basic function of an agents job to contractually allow them to earn a commission: Telling the that you are doing this mentally obligates that person to potentially hire you.
*This part is critical because mentally the person wants to get off the phone with you so buy telling them that you had a quick question and then you will let them go obligates them to stay on the phone an answer your question: This is critical because that is what you want to happen so you set them up to give you what you want: It is a mental trick but works every time!
*key point is that this is trick that will always get the answer you need and it doesn't matter what you say: you're a renter …you're a homeowner? Either way, this is something you should master and practice. Anything you ask can be asked this way and it will really help your appointment setting. For example: I assume you are already pre-qualified and have spoken to a lender ..Correct?
[If they ARE a renter] Great! I do a special consultation for renters where I show them what the buyer power is vs their renting power is. I assume that you like all renters want to buy a house someday, correct? It just takes 5 mins and I can throw something together for you. I assume your rent is 1800 per month correct?
[If they are a Homeowner/Buyer] Great! I do a special consultation for homeowners where I show homeowners what the listing power is vs their buying power today. It just takes 5 min to ask you a couple questions and I can throw something together for you.
In both cases, you've just secured an appointment/meeting!
Note: you can not do an effective consultation without first finding out if they are renting or a homeowner first!
a. Valuable information. This is information that's quality, yet simple, that they would not normally see or notice as significant.
c. Obtaining a confirmation of your services, by prompting them to tell you exactly when they want to receive your information.
The most common implementation of this is reaching out to a new lead. First, review their listing history. We're going to follow that first rule, only contact your leads about a specific property. This would be a property that they looked at more than once, so go through their listing history to find one.
Part a: I saw that you were looking at the property at [street address], and I just wanted to let you know that the total HOA fees for that neighborhood are upwards of $5,000 per year.
* Here is our valuable information, that hooks their intrest right away. You can find different information for many different kinds of properties.
Part b: I'm going to do some research for you and find two other properties in the same area that might be better options.
* The second part, contains that statement that you're GOING to do something for them, creating a sense of obligation.
*And, we cap it off by seeking a confirmation to proceed with what you're going (out of your way) to do for them.


text:
Topic: Liability
THE ELEMENTS OF TITLE IX LIABILITY IN FEMINIST MAJORITY FOUNDATION V. UNIVERSITY OF MARY WASHINGTON AND K.T. V. CULVER-STOCKTON COLLEGE
by Michael P. McKeon
Two recent federal court cases delineate the requisite elements of a Title IX deliberate indifference claim brought by students against educational entities. In Feminist Majority Foundation v. University of Mary Washington, the plaintiffs – consisting of the national Feminist Majority Foundation ["FMF"], Feminists United on Campus, an FMF affiliate, and five executive board members of…
Tags: Feminist Majority Foundation, title ix
Liability for field trips: Munn v. Hotchkiss and ticks, Costa v. Plainville and basketball, and are there really any new obligations for public schools?
by Mark Sommaruga
Last Friday (August 11, 2017), the Connecticut Supreme Court issued a decision garnering significant press attention in which it ruled that 1) Connecticut public policy imposes a duty upon schools to warn or protect against the risk of serious insect-borne disease when organizing trips abroad, and 2) a $41.5 million jury verdict with respect to…
Tags: Connecticut Appellate Court, Connecticut Supreme Court, Costa v. Plainville Board of Education, Field Trip, Jahn v. Board of Education, Munn v. Hotchkiss, School District Liability, Second Circuit Court of Appeals
Canary in a Coal Mine: Analyzing Title IX, OCR, and On-Campus Sexual Misconduct Adjudications in Plummer v. University of Houston
It is unusual for a dissenting opinion to be more noteworthy than the majority's holding, yet that is the case in Plummer v. University of Houston, a decision recently handed down by the United States Court of Appeals for the Fifth Circuit. Except for the unsavory facts upon which the decision is based and Judge…
Tags: college, Dear Colleague, Discipline, Discrimination, Higher Education, OCR, Office for Civil Rights, Plummer v. University of Houston, Sexual Assault, title ix
IDEA Exhaustion is Alive and Well: Applying Fry in Graham v. Friedlander
A Connecticut Superior Court judge has issued what might be the first decision in the country applying the United States Supreme Court's recent test for determining whether a party is required to exhaust the administrative remedies available under the Individuals with Disabilities Education Improvement Act of 2004 ["IDEA"]. The case of Graham, et al. v.…
Tags: Disability, Discrimination, IDEA, Individuals with Disabilities Education Improvement Act
Violation of Stay-Put Provisions Under the IDEA Can Be Costly
In what appears to be the first case of its kind within the Second Circuit, a United States District Court Judge within the District of Connecticut has crafted an order of over $200,000 in compensatory damages for a school board's violation of the stay-put provisions of the Individuals with Disabilities Education Act ["IDEA"]. More specifically,…
Tags: Discrimination, Funding, IDEA, IEP, Individualized Education Program, Individuals with Disabilities Education Act, School Districts, Second Circuit Court of Appeals
Not NORML: Blunt Speech in Gerlich v. Leath
A recent decision by the United States Court of Appeals for the Eighth Circuit serves as a cautionary reminder that once an educational institution creates a limited public forum for speech, it cannot then pick and choose which speech it will permit. The Eighth Circuit's decision in Gerlich v Leath arose from Iowa State University's attempt…
Tags: Iowa State University, National Organization for the Reform of Marijuana Laws, NORML, Public Forum, Student Speech, Trademark
Rebel Without A Stall: Title IX, Transgender Student Rights and Whitaker v. Kenosha
When the Trump Administration short-circuited the United States Supreme Court's review of Gloucester v. G.G., in which the United States Court of Appeals for the Fourth Circuit had held that Title IX's protections extend to transgender students, it seemed that transgender students' rights were on life support. Like Jarvis Lorry in Charles Dickens' Tale of…
Tags: Bathroom Policy, Discrimination, Equal Protection Clause, Gloucester v. G.G., Student Bathrooms, title ix, transgender students, United States Supreme Court, Whitaker v. Kenosha
Who is Considered A Parent For Purposes of Accessing School Records?
Under state and federal law, a parent generally has the right to access his/her child's educational records. The question is, who is considered a "parent" in under the Family Educational Rights and Privacy Act ("FERPA"). As the definition of "family" changes and broadens, it is important for schools to know who has the right to…
Tags: Family Educational Rights and Privacy Act, FERPA, Privacy, Student Records, The Family Policy Compliance Office, U.S. Department of Education
Social Media and Student Discipline – Where Are We?
by Stephen Sedor
The United States Supreme Court stated nearly 50 years ago that public school students do not shed their rights to free speech at the schoolhouse gate. In Tinker v. Des Moines Indep. Cmty. Sch. Dist.,[1] the Court struck down suspensions to students for wearing arm bands in protest of the Vietnam war. Today, however, social…
Tags: Cyberbullying, Discrimination, Expulsion, Privacy, School Districts, Social Media, Suspension, Teacher Training
The Aftermath: Developments From The 2017 Regular Session of The Connecticut General Assembly Affecting Schools
The 2017 Regular Session of the Connecticut General Assembly concluded at midnight, June 7, 2017, without accomplishing its most important task (passing a budget). As such, the General Assembly will eventually have to convene a "special session." In the meantime, our legislature did pass several bills that will affect Connecticut schools. Among other things, the…
Tags: Boards of Education, Connecticut General Assembly, Crisis Intervention, Department of Education, Discrimination, Expulsion, Legislation, special education, Student Data Privacy Act, Teacher Training


* Sarum was in all probability located at or near the "Ballard place," about 8 miles Northwest of Gatesville at the head of "Sarum Creek." It was three miles from Thos. Garrett's on Catharine Creek to Thos. Hoyle's Indian Town, located on their grant for 11,000 acres of land lying between Catharine Creek and Bennett's Creek, making it about 5 or 6 miles distant from the present Town of Gatesville, N. C. The other Indian Town referred to as being convenient to the School at Sarum was most probably that of the Meherrin Indians at or near the mouth of the Meherrin River. Sarum being located at the head of Sarum Creek was about equidistant between the two towns. On a recent visit to Gatesville, in conversation with an old gentleman 83 years old, he stated, that when a boy there were evidences at or near the Ballard place of a very old settlement.
This entry was posted in Chowan, Meherrin. Bookmark the permalink.
No, it was an Anglican Church. The first Baptist Church was not built until 1727. Although Baptist are now the most numerous sect in the South, that wasn't always true.
During this time, Quakers were most numerous and were the main reason the Missionaries came – they were to save the settlers from becoming Quaker, converting the Indians was just extra..
Urmston was sent by the Society of the Propagation of the Gospel , an Anglican missionary society. And Mashborne was Anglican for sure. Read William Bryd's History of the Dividing Line to see how rare churches were in this area at that time.


text:
\subsubsection{Acknowledgements} This work was supported by projects PLEC2021-007662 (MCIN/AEI/10.13039/501100011033, Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación, Plan de Recuperación, Transformación y Resiliencia, Unión Europea-Next Generation EU) and RTI2018-093336-B-C22 (Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación). The first and third authors also thank the financial support supplied by the Consellería de Cultura, Educación e Universidade Consellería de Cultura, Educación, Formación Profesional e Universidades (accreditation 2019-2022 ED431G/01, ED431B 2022/33) and the European Regional Development Fund, which acknowledges the CITIC Research Center in ICT of the University of A Coruña as a Research Center of the Galician University System. The fist author also acknowledges the support of grant DIN2020-011582 financed by the MCIN/AEI/10.13039/501100011033.

\section{Conclusions}
This paper explored the potential of keyword embedding models in the keyword suggestion task. We also propose several baselines and new evaluation methods to assess the performance of the models, as not much previous work has been published for this task.

The proposed models adapt \texttt{Word2Vec} and \texttt{FastText} CBOW architectures to compute keyword embeddings instead  of word embeddings. Along with these two keyword embedding models, we present a novel strategy for the negative sampling task, which leverages the potential of the keyword co-occurrence graph's connected components to perform a better selection of the negative samples.

Results show that our methods significantly outperform the selected baselines on both evaluation datasets. We also demonstrated the potential of sub-keyword and sub-word information to represent keywords as embeddings. In future work we aim to:
\begin{itemize}
    \item Use the designed weights system to give more relevance to full keywords and words than to \textit{n}-grams.
    \item Assess the models' performance using popularity-based negative sampling.
    \item Combine negative samples extracted from the target keyword connected component and from different connected components.
    \item Use special delimiters to differentiate if a word is a part of a keyword or a keyword itself or if a \textit{n}-gram is a part of a word or a word itself.
    \item Train and test the models on non-scientific keyword-style annotated data.
    \item Study how the offline findings of this work align with live user testing.
\end{itemize}
\section{Proposal}
\label{sec:proposal}
In this section, we present two novel keyword embedding models that leverage \texttt{Word2Vec}~\cite{mikolov2013efficient} and \texttt{FastText}~\cite{bojanowski2017enriching} architectures to produce keyword embeddings. We name these models \texttt{Keywords2Vec} and \texttt{FastKeywords}, respectively. Unlike \texttt{Word2Vec} and \texttt{FastText}, these models are not trained on rolling windows over the documents' full text; instead, we only use combinations from the documents' set of keywords as inputs.

The first of them, \texttt{Keywords2Vec}, modifies \texttt{Word2Vec} CBOW architecture to represent each keyword as one item. That is, we use keywords as token inputs instead of words. Additionally, we change how to perform the training of the models; we will explain it later for both proposals. We also evaluated the SG counterpart, but it performed considerably worse than the CBOW, so we do not report them here for brevity.

The second one, \texttt{FastKeywords}, adapts \texttt{FastText} CBOW variant in a more complex way. First, instead of working with words as the bigger information unit, it works with keywords. Second, it always selects each word of the keyword and the keyword itself as inputs, and then, during the \textit{n}-grams selection process, it generates each word's \textit{n}-grams.

Taking the keyword ``\textit{search engine}'' and $\textit{n} = 3$ as an example, it will be represented by the following \textit{n}-grams:
\begin{equation*}
\fbox{\strut sea}\:\fbox{\strut ear}\:\fbox{\strut arc}\:\fbox{\strut rch}\:\fbox{\strut eng}\:\fbox{\strut ngi}\:\fbox{\strut gin}\:\fbox{\strut ine}
\end{equation*}
the special sequences for words:
\begin{equation*}
\fbox{\strut search}\:\fbox{\strut engine}
\end{equation*}
and the special sequence for the whole keyword:
\begin{equation*}
\fbox{\strut search\:engine}
\end{equation*}

The model uses special sequences to capture the semantic meaning of both words and keywords. Finally, we implemented a weighting system to ignore ``fill'' \textit{n}-grams used when a keyword does not have enough \textit{n}-grams to fill the desired input size. The inclusion of this kind of \textit{n}-grams is needed because the model requires the same input length on every iteration.

Another novel contribution of this paper is how we generate the training inputs. The model needs both positive and negative contexts for the target keyword. They are called positive and negative samples. For producing the positive samples, we select combinations of annotated keywords from the document to which the target keyword belongs. In the case of the negative samples, we represent the keywords' co-occurrences in the dataset as a graph. In this graph, each keyword is a node and edges are created when two keywords appear together in a document. We select the negative samples from connected components different from that of the target keyword.

\begin{figure}
    \centering
    \includegraphics[width=\textwidth]{figures/conn_comps_new.eps}
    \caption{Example of a keyword co-occurrence graph with its connected components.}
    \label{fig:con_comps}
\end{figure}

Figure~\ref{fig:con_comps} shows an example of a keyword co-occurrence graph and its connected components. In that example, the positive samples for the keyword ``\textit{information retrieval}'' would be combinations of ``\textit{search engine}'', ``\textit{word embeddings}'' and ``\textit{vector}'', and its negative samples will always be extracted from connected components 2 or 3. Note that this means that the keyword ``\textit{maths}'' will never be a negative sample for ``\textit{information retrieval}'', even though it does not co-occur with it in any document.

\begin{figure}
    \centering
    \includegraphics[width=0.8\textwidth]{figures/samples_gen.eps}
    \caption{Positive and negative samples generation example. (\textit{The target keyword is highlighted}).}
    \label{fig:examples_gen}
\end{figure}

Figure~\ref{fig:examples_gen} shows how the positive and negative sampling is performed for a document extracted from the graph shown in the Figure~\ref{fig:con_comps}. Having the keyword ``\textit{word embeddings}'' as the target, we first select its positive samples. In this example, we select every combination of 2 ($w-1=2$) keywords that belong to the same document as the target. Then, for each pair of keywords we have to select the two negative samples ($ns=2$). Given the connected components shown in the Figure~\ref{fig:con_comps}, we select two keywords that do not belong to the same connected component than the target keyword.

Formally, to train these models, we use a dataset of scientific documents ($D$). Each document ($d \in D$) contains a set of annotated keywords ($K_d$). Then, given a combination size ($w-1$),  which plays an analogous role to the window size in the original models, we compute the set of positive samples ($K_d^{ps}$) of a document $d$ as follows:

\begin{equation*}
     K_d^{ps} = \Bigg\{(k_i,K_d^{ps_j}) \;\vert\; k_i \in K_d, \; K_d^{ps_j} \in {K_d - \{k_i\} \choose w - 1} \Bigg\},
\end{equation*}

\noindent that is, for each keyword ($k_i$) of the document's keywords set ($K_d$), we compute its positive samples ($K_d^{ps}$), which are the combinations of size $w - 1$ of the document's keywords set excluding the target keyword ($K_d - \{k_i\}$). Finally, for each positive samples set ($K_d^{ps_j}$) we obtain a pair $(k_i,K_d^{ps_j})$.

As for the negative samples for each document ($K_d^{ns}$) we have followed the subsequent novel approach. First, we build the aforementioned keywords co-occurrence graph for the collection and compute its connected components. Then, for each pair $(k_i,K_d^{ps_j})$ we select as negative samples $ns$ keywords belonging to a different connected component than the target keyword ($k_i$).

To adapt the previous process to our \texttt{FastKeywords} model, we have to generate \textit{n}-grams for each context keyword (positive samples and negative samples) following the strategy explained before.

The \texttt{FastKeywords} model has two main advantages over \texttt{Keywords2Vec} because of the use of subword information:
\begin{itemize}
    \item It will perform significantly better on large collections.
    \item It will be able to generate embeddings for keywords that are not in the training corpus.
\end{itemize}

Figure~\ref{fig:fastkwds_arch} shows the \texttt{FastKeywords} model's architecture. As we may see, it follows the classic CBOW strategy where several context samples are fed to the model in order to predict the target keyword.

\begin{figure}
    \centering
    \includegraphics[width=\textwidth]{figures/fastkeywords_arch_new.eps}
    \caption{\texttt{FastKeywords} model high-level architecture diagram.}
    \label{fig:fastkwds_arch}
\end{figure}

\section{Results}
This section reports how \texttt{Keywords2Vec} and \texttt{FastKeywords} perform against the selected baselines and how they perform against each other. As we mentioned in Section~\ref{sec:eval} we report results using two tasks and two evaluation techniques.

Table~\ref{table:map_results} shows the results for the document's keywords identification task. We can see that our models significantly outperform the established baselines in both datasets and evaluation approaches. This is especially remarkable in the case of the state-of-the-art sentence BERT model, which, even for the fine-tuned scenario and using embeddings with twice the dimensions, lies quite behind our proposals. As expected, using the test-items strategy produces better results since the keyword set where the search is performed is much smaller.

Moreover, we can see that the dataset size is a determinant factor in this task. A much larger set of keywords (this is the case of the KP20k dataset versus the Inspec dataset) greatly impacts the final score. This makes sense because increasing the keyword set size makes finding the ones we are looking for more challenging (something analogous happens to a lesser degree between the all-items and test-items results).

In terms of comparing \texttt{FastKeywords} against \texttt{Keywords2Vec} we can see that the former performs better and that the more considerable difference between them appears when the keyword set is the biggest (KP20k dataset). The main reason behind this relies on the capability of \texttt{FastKeywords} to leverage subword information to build the keywords embeddings.
\begin{table*}[tb]
\centering
\caption{MAP@20 for the document's keywords identification task. Statistically significant improvements according to the Randomized Tukey HSD test ($permutations = 100,000$; $\alpha = 0.05$) are superscripted.}
\label{table:map_results}
\setlength{\tabcolsep}{5pt}
\renewcommand{\arraystretch}{1.5}
\scriptsize
\begin{tabular}
{lll|ll}
\toprule
\multirow{2}*{Model} & \multicolumn{2}{c} {Inspec} & \multicolumn{2}{c}{KP20K} \\
 & \multicolumn{1}{|c} {All items} & \multicolumn{1}{c|} {Test items} &  \multicolumn{1}{c} {All items} & \multicolumn{1}{c} {Test items} \\
\midrule
\multicolumn{1}{l|} {\texttt{SBERT} (\textit{no train}) ($\dagger$)}       & 0.0323 & $0.0544^\uplus$ & 0.0039 & $0.0134^\uplus$ \\
\multicolumn{1}{l|} {\texttt{SciBERT} (\textit{no train}) ($\ddagger$)}    & 0.0205 & 0.0345 & 0.0040 & 0.0110 \\
\multicolumn{1}{l|} {\texttt{FastText} (\textit{no train}) ($\uplus$)}     & 0.0147 & 0.0217 & 0.0039 & 0.0095 \\
\multicolumn{1}{l|} {\texttt{Word2Vec} ($\mp$)}                            & $0.0463^\uplus$ & $0.0832^{\dagger\ddagger\uplus}$ & 0.0048 & $0.0130^{\ddagger\uplus}$ \\
\multicolumn{1}{l|} {\texttt{SBERT} ($\pm$)}                               & $0.1481^{\dagger\ddagger\uplus\mp}$ & $0.5880^{\dagger\ddagger\uplus\mp}$ & $0.0072^{\dagger\ddagger\uplus\mp}$ & $0.0182^{\dagger\ddagger\uplus\mp}$ \\ \midrule
\multicolumn{1}{l|} {\texttt{Keywords2Vec} ($\otimes$)}           & $0.8634^{\dagger\ddagger\uplus\mp\pm}$ & $0.9090^{\dagger\ddagger\uplus\mp\pm}$ & $0.0690^{\dagger\ddagger\uplus\mp\pm}$ & $0.0918^{\dagger\ddagger\uplus\mp\pm}$ \\
\multicolumn{1}{l|} {\texttt{FastKeywords} ($\odot$)}            & $\textBF{0.8659}^{\dagger\ddagger\uplus\mp\pm}$ & $\textBF{0.9161}^{\dagger\ddagger\uplus\mp\pm}$ & $\textBF{0.0762}^{\dagger\ddagger\uplus\mp\pm\otimes}$ & $\textBF{0.1060}^{\dagger\ddagger\uplus\mp\pm\otimes}$ \\
\bottomrule
\end{tabular}
\end{table*}

Table~\ref{table:mrr_results} shows results for the masked keyword discovering task. These results confirm what we had already seen in the first one: our models significantly outperform all the established baselines in every experiment we performed. Again, we can see that increasing the keyword set length produces worse results as the task becomes more and more challenging. Again, when comparing the proposed models against each other on this task, we can see that \texttt{FastKeywords} performs better than \texttt{Keywords2Vec} when increasing the dataset size, getting statistically significant improvements over the \texttt{Word2Vec}-based method.

\begin{table*}[tb]
\centering
\caption{MRR@100 for the masked keyword discovering task. Statistically significant improvements according to the Randomized Tukey HSD test ($permutations = 1,000,000$; $\alpha = 0.05$) are superscripted.}
\label{table:mrr_results}
\setlength{\tabcolsep}{5pt}
\renewcommand{\arraystretch}{1.5}
\scriptsize
\begin{tabular}
{lll|ll}
\toprule
\multirow{2}*{Model} & \multicolumn{2}{c} {Inspec} & \multicolumn{2}{c}{KP20K} \\
 & \multicolumn{1}{|c} {All items} & \multicolumn{1}{c|} {Test items} &  \multicolumn{1}{c} {All items} & \multicolumn{1}{c} {Test items} \\
\midrule
\multicolumn{1}{l|} {\texttt{SBERT} \textit{no train} ($\dagger$)}     & 0.0342 & 0.0479 & 0.0018 & 0.0152 \\
\multicolumn{1}{l|} {\texttt{SciBERT} \textit{no train} ($\ddagger$)}  & 0.0220 & 0.0345 & 0.0056 & 0.0129 \\
\multicolumn{1}{l|} {\texttt{FastText} \textit{no train} ($\uplus$)}   & 0.0068 & 0.0097 & 0.0023 & 0.0047 \\
\multicolumn{1}{l|} {\texttt{Word2Vec} ($\mp$)}                        & 0.0751 & $0.1187^\uplus$ & 0.0008 & 0.0079 \\
\multicolumn{1}{l|} {\texttt{SBERT} ($\pm$)}                           & $0.1688^{\dagger\ddagger\uplus}$ & $0.5890^{\dagger\ddagger\uplus\mp}$ & 0.0138 & 0.0448 \\ \midrule
\multicolumn{1}{l|} {\texttt{Keywords2Vec} ($\otimes$)}       & $0.8914^{\dagger\ddagger\uplus\mp\pm}$ & $0.9100^{\dagger\ddagger\uplus\mp\pm}$ & $0.0778^{\dagger\ddagger\uplus\mp\pm}$ & $0.0828^{\dagger\ddagger\uplus\mp}$ \\
\multicolumn{1}{l|} {\texttt{FastKeywords} ($\odot$)}         & $\textBF{0.8988}^{\dagger\ddagger\uplus\mp\pm}$ & $\textBF{0.9102}^{\dagger\ddagger\uplus\mp\pm}$ & $\textBF{0.1402}^{\dagger\ddagger\uplus\mp\pm\otimes}$ & $\textBF{0.1467}^{\dagger\ddagger\uplus\mp\pm\otimes}$ \\
\bottomrule
\end{tabular}
\end{table*}

Table~\ref{table:ns_results} shows the performance of the proposed negative sampling method, comparing it with a \texttt{FastKeywords} model that uses a random negative sampling strategy. The results ratify that the proposed method works significantly better than a naive random approach in all cases except on MRR@100 using the all-items indexing strategy. For this case, the \textit{p}-value is 0.06.

\begin{table*}[tb]
\centering
\caption{Negative sampling methods comparison on the Inspec dataset. Statistically significant improvements according to the permutation test ($permutations = 1,000,000$; $\alpha = 0.05$) are superscripted.}
\label{table:ns_results}
\setlength{\tabcolsep}{5pt}
\renewcommand{\arraystretch}{1.5}
\scriptsize
\begin{tabular}
{lll|ll}
\toprule
\multirow{2}*{Model} & \multicolumn{2}{c} {MAP@20} & \multicolumn{2}{c}{MRR@100} \\
 & \multicolumn{1}{|c} {All items} & \multicolumn{1}{c|} {Test items} &  \multicolumn{1}{c} {All items} & \multicolumn{1}{c} {Test items} \\
\midrule
\multicolumn{1}{l|} {\texttt{FastKeywords} \textit{random negative sampling} ($\oplus$)}   & 0.8420 & 0.9093 & 0.8750 & 0.8890 \\
\multicolumn{1}{l|} {\texttt{FastKeywords} ($\odot$)}             & $\textBF{0.8659}^\oplus$ & $\textBF{0.9161}^\oplus$ & \textBF{0.8988} & $\textBF{0.9102}^\oplus$ \\
\bottomrule
\end{tabular}
\end{table*}

Finally,  for illustrative purposes, Table~\ref{table:nn} shows the top 10 nearest neighbours for the keyword ``\textit{information retrieval}'' on the KP20k collection retrieved by both proposed models. We also show each keyword's associated score, which represents the similarity with the query keyword.

\begin{table*}[tb]
\centering
\caption{Top 10 nearest neighbours for the keyword ``\textit{information retrieval}'' on the KP20k collection (\textit{test-items index}).}
\label{table:nn}
\renewcommand{\arraystretch}{1.5}
\scriptsize
\begin{tabularx}{\textwidth}
{>{\centering\arraybackslash}m{0.25\textwidth}>{\centering\arraybackslash}m{0.1\textwidth}p{0.1\textwidth}>{\centering\arraybackslash}m{0.4\textwidth}>{\centering\arraybackslash}m{0.1\textwidth}}
\multicolumn{2}{c}{\texttt{FastKeywords}} & & \multicolumn{2}{c}{\texttt{Keywords2Vec}} \\
\cline{1-2} \cline{4-5}
\textbf{keyword} & \textbf{score} & & \textbf{keyword} & \textbf{score} \\
\cline{1-2} \cline{4-5}
ranking & 0.9133 & &                  retrieval model & 0.6045 \\
query expansion & 0.9104 & &         collection selection & 0.5587 \\
text classification & 0.9067 & &     link topic detection & 0.5472\\
relevance & 0.9044 & &                dempstershafer evidence theory & 0.5418 \\
text mining & 0.9018 & &             retrospective evidence event detection & 0.5371 \\
information extraction & 0.9012 & &  instance based learning & 0.5361 \\
relevance feedback & 0.8980 & &      query expansion & 0.5282 \\
knowledge discovery & 0.8979 & &     terminology extraction & 0.5260 \\
document clustering & 0.8971 & &     viral marketing & 0.5253 \\
text categorization & 0.8970 & &     query formulation & 0.5248 \\
\cline{1-2} \cline{4-5}
\end{tabularx}
\end{table*}

\section{Experimental Setup}
This section describes the datasets used during the training and evaluation of the models, the baselines used to compare our model, the evaluation process and metrics, and finally, the parameters and setup used to train our models.

Our objective is to suggest similar keywords to the user input in the search process. With this in mind, we designed two experiments that approach the keyword suggestion problem as a keyword ranking task. In particular, the evaluation considers if the model can find keywords that belong to the same document as the target keyword. The rationale for this evaluation is that keywords with which the authors annotate a document tend to be semantically related. Alternatively, we may perform user studies to evaluate the perceived quality of the suggestions. We leave that for future work.

\subsection{Datasets}
We selected two datasets commonly used on the classical keyword extraction task: the Inspec~\cite{hulth2003improved} and the KP20k~\cite{meng2017deep} collections.

\subsubsection{Inspec}
This collection consists of 2,000 titles and abstracts from scientific journal papers. The annotators assigned two sets of keywords to each document in this collection: controlled keywords that occur in the Inspec thesaurus and uncontrolled keywords that can be any suitable terms. We will only use the uncontrolled keywords created by professional indexers in our experiments, following the standard approach in the keyword extraction task.

\subsubsection{KP20k}
This dataset is a keyword generation collection that includes 567,830 articles of the computer science domain obtained from various online digital libraries, including ACM Digital Library, ScienceDirect, Wiley and Web of Science. In this case, the authors made no differentiation during the annotation process, so we used all of the keywords in both training and testing.

\subsection{Baselines}
We include five baselines in our evaluation to compare our embedding models against other models that have already proven their effectiveness in capturing words or sentence syntax and semantic information.

The first baseline is the \texttt{Word2Vec} model trained in the Google News dataset and further tuned on each dataset to learn their vocabulary. That process was performed using the keywords set as a sentence so that the model could learn the contextual relationships between them.

The second baseline is the pre-trained \texttt{SBERT} \textit{all-mpnet-base-v2}\footnote{\url{https://huggingface.co/sentence-transformers/all-mpnet-base-v2}} model fine-tuned in the evaluation datasets for the sentence similarity task. To fine-tune the model, we served as inputs pairs of keywords and a binary value of similarity (1 if keywords belong to the same document and 0 if they do not).

The other three baselines were not trained for the task. These baselines are:
\begin{itemize}
    \item The pre-trained \texttt{SBERT} model, \textit{all-mpnet-base-v2}.
    \item The pre-trained \texttt{SciBERT} model that uses the uncased version of the vocabulary.
    \item The English \texttt{FastText} model trained on Common Crawl\footnote{\url{https://commoncrawl.org/}} and Wikipedia\footnote{\url{https://dumps.wikimedia.org/}}.
\end{itemize}

\subsection{Evaluation} \label{sec:eval}

As we mentioned before, to assess our models' performance in the keyword suggestion task, we devise two evaluation strategies representing two specific cases in the academic search field.

The first task consists of retrieving all the keywords with which a document was annotated, given one of them. The keyword retrieval is done via cosine similarity between the query keyword and all the other keywords. Then, we compute each average precision and aggregate all the results to compute the MAP (Mean Average Precision). Specifically, we calculate MAP@20.

The second task we propose follows a masking problem. For each document, we mask one keyword from the keywords set and then use the remaining keywords to try to find the masked one. For each of the remaining keywords, we retrieve the most similar ones using cosine similarity. Then, combining the scores of all the non-masked keywords, we compute the final ranking. Finally, we compute each masked keyword's reciprocal rank to calculate the MRR (Mean Reciprocal Rank). Precisely, we compute MRR@100. Note that for this evaluation method, we only used 50 random documents from the test split of each dataset.

Regarding methodologies, we evaluated both tasks under all-items and test-items approaches. In the all-items fashion, the keywords to be ranked are the whole set of keywords in the dataset, while in the test-items indexing approach, only the keywords in the test subset are ranked.

We also perform statistical significance tests for all the evaluations. On the one hand, for comparing more than two systems, we use the Randomised Tukey HSD Test~\cite{tukey,tukey_book}. On the other hand, we follow the classic permutation test approach when comparing a pair of systems. These tests verify that the family-wise error does not exceed the confidence level $\alpha$.

\subsection{Experimental Settings}
This section will describe the parameters used to train the baselines and our proposed models. Table~\ref{table:training_parameters}  shows a summary of the models' training parameters detailed hereunder.

\begin{table*}[tb]
\centering
\caption{Models' training parameters on the Inspec and KP20k datasets.}
\label{table:training_parameters}
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.5}
\scriptsize
\begin{tabular}
{lccccc|ccccc}
\toprule
\multirow{2}*{Model} & \multicolumn{5}{c}{Inspec} & \multicolumn{5}{c}{KP20k}\\
 & \multicolumn{1}{|c}{Batch} & Epochs & Dim. & $w$ & $ns$  & Batch & Epochs & Dim. & $w$ & $ns$ \\
\midrule
\multicolumn{1}{l|}{\texttt{Word2Vec}}        & - & \textit{early stopping} & 300 & 6 & -  & - & 20 & 300 & 6 & -\\
\multicolumn{1}{l|}{\texttt{SBERT}}           & $2^{9}$ & \textit{early stopping} & 768 & - & 4 & $2^{9}$ & 20 & 768 & - & 4\\
\multicolumn{1}{l|}{\texttt{Keywords2Vec}}    & $2^{17}$ & \textit{early stopping} & 300 & 3 & 4 & $2^{17}$ & 20 & 300 & 3 & 4\\
\multicolumn{1}{l|}{\texttt{FastKeywords}}    & $2^{15}$ & \textit{early stopping} & 300 & 3 & 4 & $2^{15}$ & 20 & 300 & 3 & 4\\
\bottomrule
\end{tabular}
\end{table*}

To train the \texttt{Word2Vec} model, we followed an early-stopping strategy for the Inspec dataset, while for KP20k, we trained the model for 20 epochs. For both datasets, the selected embedding size was 300. Finally, we used a window size of 6 and 1 as the minimum count (lowest frequency of a word to be considered).

On the other hand, as we mentioned before, we fine-tuned the \texttt{SBERT} model in the sentence similarity task. For each positive sample (two keywords that co-occur in a document), we selected four negative samples (two keywords that never co-occur in a document). Again, we followed an early-stopping strategy for the Inspec dataset, while for KP20k, we trained the model for 20 epochs. For both datasets, we used a batch size of $2^{9}$ and an embedding size of 768.

We trained both our models using an early-stopping strategy for the Inspec dataset. On the other hand, when using the KP20k dataset, we trained each model for 20 epochs. Regarding the batch size, the \texttt{Keywords2Vec} model was trained with a batch size of $2^{17}$ on both datasets, while for the \texttt{FastKeywords} model, we used a batch size of $2^{15}$ on Inspec and KP20k. Also, both were trained to generate embeddings of size 300. The combination size and the number of selected negative samples were the same for both models on both datasets, 2 ($w=3$) and 4, respectively. Finally, regarding \texttt{FastKeywords} parameters, we used a minimum \textit{n}-gram size of 3, a maximum \textit{n}-gram size of 6 and a maximum number of \textit{n}-grams of 20.

When trained, we used the whole set of document keywords from the dataset in the tuning process of the models\footnote{All experiments were run on an NVIDIA A100 GPU with 80GB of memory.}. When not trained, we used the default pre-trained models for the baselines. In the evaluation process, we selected a subset of documents on which we performed the aforementioned tasks.

\section{Introduction}
The use of word embeddings~\cite{word_emb_survey} has improved the results of many Natural Language Processing (NLP) tasks, such as name entity recognition, speech processing, part-of-speech tagging, semantic role labelling, chunking, and syntactic parsing, among others. These techniques represent words as dense real-valued vectors which preserve semantic and syntactic similarities between words. More recently, the so-called document and sentence embedding models allow the computing of embeddings for larger pieces of text directly (instead of doing it word by word), getting state-of-the-art results on different tasks.

Nowadays, search engines are outstanding in recommending lexical adaptations or corrections to users' queries~\cite{query_spelling1,query_spelling2}. However, there is still room for improvement in suggesting semantically similar phrases to users' inputs. The few systems that address this task do it by simply using existing word or sentence embedding models, which leads to poor keyword suggestions.

Keyword suggestion, which consists in recommending keywords similar to the user's input,  is critical in search scenarios where the completeness of the query clauses may dramatically affect the recall, such as academic or legal search~\cite{academic_search,legal_search}. An incomplete query may end up in a \emph{null search session} \cite{null_queries}, that is, the system presents an empty result list to the user. In tasks where use cases are recall-oriented, having an incomplete or even empty results set greatly diminishes the search experience.

Having good keyword suggestion models in a keyword-based search engine has many benefits. (i) It will help to identify the query intent~\cite{query_intent}, as users will be able to refine their search by adding new query clauses that may be clarifying. (ii) It will promote serendipity~\cite{amplifying_serendipity,discovery_serendipity}, as users may see a recommended keyword that they were not considering but perfectly fits their search interest. (iii) It will help prevent null sessions or incomplete results lists. (iv) Systems may use semantically similar keywords to the user input to perform query expansion~\cite{query_expansion_survey,russell2021interactive} without further user interaction.

In this paper, we leverage \texttt{Word2Vec}~\cite{mikolov2013efficient} and \texttt{FastText}~\cite{bojanowski2017enriching} models' architecture to generate keyword embeddings instead of word embeddings, this meaning that embeddings represent sequences of words rather than a single word, similar to sentence embeddings. Unlike the base models, which use bag-of-words to build the representations, our approaches are based on bag-of-keywords. Thus, the proposed models exploit the annotated keywords' co-occurrence in the scientific literature for learning the dense keyword representations.

Our main aim for producing keyword embeddings is to represent concepts or topics instead of representing just words' semantics or even their contextual meaning like word embedding models do. The keywords' conceptual or topical semantics rely on an explicit human annotation process. First, the annotator will only select proper or commonly used descriptors for the presented documents. In this way, we may assume that those keywords are a good proxy when searching for the documents' topics. Second, authors use a limited number of keywords when annotating documents, so the strength of the semantic relationships among them is higher than the traditional approach of word co-occurrence in windows of free text.

Along with these models, we propose a new method to perform negative sampling, which leverages connected components to select the negative inputs used in the training phase. This method uses the keywords co-occurrence graph to extract the connected components and then select inputs that are not in the same connected component as negative samples.

To evaluate our models' performance, we compare them against a set of baselines composed of state-of-the-art word and sentence embedding models. We both trained the baselines in the keyword data and used the pre-trained models. In particular, we further trained \texttt{Word2Vec}~\cite{mikolov2013efficient} and \texttt{Sentence-BERT}~\cite{reimers2019sentence} (\texttt{SBERT}) on the task data, and we used \texttt{SBERT}, \texttt{SciBERT}~\cite{beltagy2019scibert} and \texttt{FastText}~\cite{bojanowski2017enriching} base models. We carry out the evaluation in Inspec~\cite{hulth2003improved} and KP20k~\cite{meng2017deep}, two classical keyword extraction datasets compound of scientific publications which count with a set of human-annotated keywords (for now on, we refer to keywords annotated by documents' authors or professional annotators as annotated keywords). The results show significant improvements for the task over state-of-the-art word and sentence embedding models.

The main contributions of our work can be summarized as follows:
\begin{itemize}
    \item \texttt{Keywords2Vec}, a keyword embedding model based on \texttt{Word2Vec}.
    \item \texttt{FastKeywords}, a keyword embedding model based on \texttt{FastText}.
    \item A new method for negative sampling based on connected components.
    \item Baselines and evaluation methods for the similar keyword suggestion task.
\end{itemize}

\section{Related Work}
This section briefly overviews the existing word and sentence embedding models, particularly those used as baselines in the evaluation stage, along with some previous work related to the keyword suggestion task.

Dense vector representations, a.k.a. embeddings, have an appealing, intuitive interpretation and can be the subject of valuable operations (e.g. addition, subtraction, distance measures, etc.). Because of those features, embeddings have massively replaced traditional representations in most Machine Learning algorithms and strategies. Many word embedding models, such as \texttt{Word2Vec}~\cite{mikolov2013efficient}, \texttt{GloVe}~\cite{pennington2014glove} or \texttt{FastText}~\cite{bojanowski2017enriching}, have been integrated into widely used toolkits, resulting in even more precise and faster word representations.

\texttt{Word2Vec}~\cite{mikolov2013efficient} was one of the first widely used neural network-based techniques for word embeddings. These representations preserve semantic links between words and their contexts by using the surrounding words to the target one. The authors proposed two methods for computing word embeddings~\cite{mikolov2013efficient}: skip-gram (SG), which predicts context words given a target word, and continuous bag-of-words (CBOW), which predicts a target word using a bag-of-words context.

\texttt{FastText}~\cite{bojanowski2017enriching} is a \texttt{Word2Vec} add-on that treats each word as a collection of character \textit{n}-grams. \texttt{FastText} can estimate unusual and out-of-vocabulary words thanks to the sub-word representation. In \cite{joulin2016bag}, authors employed \texttt{FastText} word representation in conjunction with strategies such as bag of \textit{n}-gram characteristics and demonstrated that \texttt{FastText} outperformed deep learning approaches while being faster.

Sentence embeddings surged as a natural progression of the word embedding problem. Significant progress has been made in sentence embeddings in recent years, particularly in developing universal sentence encoders capable of producing good results in a wide range of downstream applications.

\texttt{Sentence-BERT}~\cite{reimers2019sentence} is one of the most popular sentence embedding models and state-of-the-art on the sentence representation task. It is a modification of the \texttt{BERT}~\cite{devlin2018bert} network using siamese and triplet networks that can derive semantically meaningful sentence embeddings.

There are other sentence embedding models based on pre-trained language models. An example of those language models which achieves excellent results when working with scientific data is \texttt{SciBERT}. \texttt{SciBERT}~\cite{beltagy2019scibert} is an adaptation of \texttt{BERT}~\cite{devlin2018bert} to address the lack of high-quality, large-scale labelled scientific data. This model leverages \texttt{BERT} unsupervised pre-training capabilities to further train the model on a large multi-domain corpus of scientific publications, producing significant improvements in downstream scientific NLP tasks.

Even though word and sentence embeddings have been widely studied, the work on keyword embeddings is still limited. Researchers have employed them mainly on tasks like keyword extraction, phrase similarity or paraphrase identification. Several approaches for the keyword extraction task, like \texttt{EmbedRank}~\cite{embedrank}, directly rely on pre-trained sentence embedding models to rank the extracted keywords. However, other approaches such as \texttt{Key2Vec}~\cite{mahata2018key2vec} train their own model for the phrase embedding generation. In particular, the authors propose directly training multi-word phrase embeddings using \texttt{FastText} instead of a classic approach that learns a model for unigram words combining the words' dense vectors to build multi-word embeddings later.

Yin and Sch{\"{u}}tze~\cite{emb_gen_phrases} presented an embedding model for generalized phrases to address the paraphrase identification task. This approach aims to train the \texttt{Word2Vec} SG model without any modification to learn phrase embeddings. They pre-process the corpus by reformatting the sentences with the continuity information of phrases. The final collection contains two-word phrases whose parts may occur next to each other (continuous) or separated from each other (discontinuous).

The phrase semantic similarity task is akin to the one we address in this paper. Many sentence embedding models, including \texttt{SBERT}, are pre-trained in that downstream task. In~\cite{comp_models}, the authors present a composition model for building phrase embeddings with semantic similarity in mind. This model, named Feature-rich Compositional Transformation (\texttt{FCT}), learns transformations for composing phrase embeddings from the component words based on extracted features from a phrase.

Finally, the keyword suggestion task is also a sub-type of query suggestion where all the input and output queries are represented as keywords. Previous works in query term suggestion did not leverage the power of keyword co-occurrence to recommend new terms. Instead, existing query suggestion systems usually approach this task by suggesting terms extracted from the document's content without relying on the relations between these terms. For example, in~\cite {query_term_sugg}, authors propose indexing a set of terms extracted from the corpus to rank them using a language model given an input query. The problem with these approaches is that they depend on the appearance of semantically related terms in the analyzed documents.

redpajama_set_name: RedPajamaArXiv


text:
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Single mom in Wisconsin goes from homeless to homeowner
By: By JIM DAYTON
Copyright 2018 The Associated Press. All rights reserved.

Photo: Angela Major/The Janesville Gazette via AP

Jennifer Deuel insists anyone could have navigated the rocky path she took to home ownership.
"I'm just a regular, average person trying to take care of my kids and do good things and do something with my life," she told the Janesville Gazette. "If you want it to happen bad enough, you will make it happen."
But to hear praise from the people who helped her climb out of rock bottom, it's clear Deuel's story is anything but ordinary.
Jeff and MacKenzie Bezos create $2 billion fund to fight homelessness
Four years after she had nothing left but a stroller and a garbage bag of clothes, Deuel purchased her own home on Garfield Avenue.
She and her two sons — Adam Stephens, 6, and Alex Stephens, 4 — have lived there for about a year.
She regained her footing after escaping a domestically violent relationship, being homeless while raising a special needs son, being pregnant with her second child and bouncing between rundown rentals.
Along the way, she fought through what city officials say are housing challenges common for low-income residents made worse by an extraordinarily tight housing market.
Inside City Hall, Deuel is lauded as one of the biggest successes to have passed through city Housing Services Division programs. She took advantage of federal programs administered by the city to help pay rent and later help make the down payment on her house.
"When we are talking to new landlords or really trying to get the positive message about the (rental assistance) program out there, clients like her are the best example that we have," said Laura Jones, a city housing specialist who works with program recipients. "This is what the program is here for, this is why we're here, and this is what this funding is doing."
City officials feel obligated to make such a pitch because their clients often have a hard time finding a place to live, especially amid a housing shortage.
Some landlords think those in the program are unreliable tenants or, worse, freeloaders.
Deuel is neither.
Her story begins with a shaky upbringing in Janesville. A father she never met until she was 16. A mother who did her best despite having mental health issues.
Deuel, 33, described herself as a "troubled teen," one who had a volatile relationship with her mom and skipped school to avoid being bullied.
She then started dating the man who later became the father of her two kids. It started out fine, but she eventually realized she was a victim of domestic violence, she said.
She recalls the abuse as being physical, emotional and mental. She was not allowed to have a job or see friends, and she couldn't go to the grocery store without receiving incessant phone calls wondering where she was.
It took Deuel several tries to leave, but she finally left for good in 2013, eight years after the relationship began. For all its faults, the relationship had provided a place to live, making it more difficult to move out, she said.
Now, she's quick to dispute critics who say domestic violence victims are foolish for staying.
"People don't realize it takes everything, it takes everything you've got. It destroys you mentally," Deuel said. "It took away any piece of self-esteem I had. It took me a whole year after I left to rediscover who I was and find myself again.
"A lot of people think it's an easy walk in the park. You leave, and you call the police, and it's over. But it doesn't happen like that."
Leaving was the first step to recovery. But it left Deuel homeless and pregnant with Alex while raising Adam, then 6 months old.
"The last time I left, I left with the clothes on my back, my son, a stroller and a garbage bag of my kid's clothes," she said. "That is exactly what I started with."
She spent time at the House of Mercy, the YWCA shelter and in YWCA's Transitions for Women program. Once her stays there ended — they all have time limits on residency — she stayed at the home of a relative, whose help she said was essential.
Her older son's mental health disorders complicated this period of transience. Adam has ADHD, disassociation and medically diagnosed defiance, plus post-traumatic stress disorder stemming from the abuse his mother suffered, Deuel said.
But her sons were her saving grace. Without them, her story would be different. Maybe she'd be a drug addict, she thinks.
Deuel eventually secured rental assistance — otherwise known as Section 8 — and moved into her own apartment in 2014.
But the places she lived were unfit to raise two boys. One was overrun with mice. Another was infested with bats. A different place had neighbors whose arguments were so boisterous they occasionally forced Deuel to leave because she felt unsafe, she said.
Jones, the city housing specialist, said Section 8 housing meets minimum federal standards. Some tenants believe the standards could be tougher; landlords sometimes argue the standards go too far, she said.
Federal Section 8 funding allows Janesville to subsidize rent for about 540 families who must pass background checks before getting a voucher. The city initially will cover about 70 percent of rental expenses while the resident pays the rest, Housing Services Director Kelly Bedessem said.
As clients' incomes begin to stabilize, the city's portion of rent coverage diminishes until the client is fully paying his or her own rent. If clients don't follow the rules, they can be terminated from the program, she said.
Janesville also administers home improvement programs and one that subsidizes down payments for first-time home buyers — which helped Deuel close the deal on her house.
The city's Section 8 waiting list has been closed since 2017, demonstrating the need for affordable housing. Bedessem thinks the list will reopen in spring.
All summer long, Janesville officials have touted the need for more housing across all sectors.
Single-family homes and market-rate apartments would help encourage job seekers to move here. City officials and developers believe creating space at the top end of the housing market could free up homes for working-class residents.
Bedessem said it's common for low-income families to stop by the housing services office and say they can't find a place to live. Those people likely couldn't afford rent at two market-rate housing developments in progress — the Corner Block on Parker and an unnamed venture near Racine Street and Interstate 90/39.
Council member Sue Conley, who spent much of her nonprofit career working with homeless people, said the city can't tell developers what to build. But the city can at least make affordable housing part of those preliminary conversations.
"We can't forget about the other end of the spectrum. We can't just focus on high end," said Conley, who noted that she sees the value in the Corner Block on Parker project. "We have to be cognizant of the full spectrum of folks that need housing."
Even though Jones didn't work directly with Deuel, she said the case gives her "goosebumps." She called it the ultimate reward to see clients turn their lives around, and the speed at which Deuel did it is testimony to her determination.
But Jones didn't want to consider her an exception. Many people in Janesville face similar challenges. Calling Deuel an exception might imply nobody else could escape their predicaments.
"There's a side of Janesville that we just don't see because we're not involved in it. We don't come across it, so it's not in our periphery," Jones said. "Her story is very symbolic of a lot of people in Janesville, so her success story is also symbolic of where these people can go and what the program can do for them."
Deuel echoed that and said wisdom and work ethic can help anyone overcome challenges. Even if life beats you down, you can't afford to give up, she said.
But her success still feels tenuous. When she got approved in spring for supplemental security income to cover Adam's medical bills, she lost her FoodShare benefits. She also faces losing her own health care coverage.
Losing her sons' health care and the daycare assistance that allows her to send them to Community Kids Learning Center — a place she considers family — would be devastating, she said.
Making one wrong choice could undo all her hard work, she said.
Deuel doesn't have much time to sit and worry. Besides raising her sons, she works full time as a certified nursing assistant at Edgerton Care Center and is taking classes at UW-Whitewater at Rock County to further her nursing career.
She wants to become a nurse practitioner, a process that will take the next several years.
The certificates she's already earned hang on her wall above a photo collage of her and her sons. One photo shows the family standing outside their home with a sign that reads "Sold."
When she does get a quiet moment, she sometimes sits on her stairwell to reflect, surrounded by her sons' red, green and blue handprints painted on the walls.
It's a sign of permanence inside the modest home.
"I can't have a landlord tell me I can't have a dog, or I can't put my kids' handprints on my walls so that way when they're older, my hallway still looks like that," Deuel said.
Tears welled in her eyes.
"I can't have a landlord tell me I can't have anything because for once in my life this is mine."
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text:
Distance between Stuarts Well and Coober Pedy
The distance from Stuarts Well to Coober Pedy is 597 kilometers by road. Road takes approximately 6 hours and 38 minutes and goes through Kulgera and Marla.
Stuarts Well 0 km 0:00 00 min 0.0 L
87 183 km, 2 h 02 min
Kulgera 183 km 2:02 2 h 02 min 14.6 L
87 A87 193 km, 2 h 08 min
Marla 376 km 4:10 4 h 10 min 28.6 L
Coober Pedy 597 km 6:38 6 h 38 min 43.6 L
How much does it cost to drive from Stuarts Well to Coober Pedy?
How long is a car ride from Stuarts Well to Coober Pedy?
How far is Stuarts Well to Coober Pedy by land?
The distance between Stuarts Well and Coober Pedy is 597 km by road.
Precise satellite coordinates of highways were used for this calculation. The start and finish points are the centers of Stuarts Well and Coober Pedy respectively.
How far is Stuarts Well to Coober Pedy by plane?
The shortest distance (air line, as the crow flies) between Stuarts Well and Coober Pedy is 535 km.
This distance is calculated using the Haversine formula as a great-circle distance between two points on the surface of a sphere. The start and finish points are the centers of Stuarts Well and Coober Pedy respectively. Actual distance between airports may be different.
How many hours is Stuarts Well from Coober Pedy by plane?
Small plane "Cessna 172" needs 2 h 25 min to flight this distance at average speed of 220 km/h.
How long is a helicopter ride from Stuarts Well to Coober Pedy?
What city is halfway between Stuarts Well and Coober Pedy?
The halfway point between Stuarts Well and Coober Pedy is Marla. It is located about 77 km from the exact midpoint by road.
The distance from Marla to Stuarts Well is 376 km and driving will take about 4 h 10 min. The road between Marla and Coober Pedy has length 221 km and will take approximately 2 h 27 min.
Where is Stuarts Well in relation to Coober Pedy?
Stuarts Well is located 535 km north-west of Coober Pedy.
Stuarts Well has geographic coordinates: latitude -24.34221, longitude 133.45941.
Coober Pedy has geographic coordinates: latitude -29.01337, longitude 134.75362.
Which highway goes from Stuarts Well to Coober Pedy?
The route from Stuarts Well to Coober Pedy follows A87, 87.
The distance between Stuarts Well and Coober Pedy is ranked 491st in the ranking popularity.
© DriveBestWay.com: distance calculator, calculator of distance between two cities, gas price calculator, routeplanner.


He said a robust item banks with appropriate safeguard had been developed by moving toward open examination with one million questions per subject which would soon be effected.
" We now have live report on centres that registered any candidate, the time the candidate was registered, who registered the candidate, computer system used, the location of registration.
" All these are done through our enhanced monitoring system. I want to warn candidates expected to sit for its examination to ensure their biometrics were taken and verified or risk sitting for the examination,'' he said.
" Any candidate that cannot be verified by the Biometric Verification Mode (BVM) must not be allowed to take the examination under any guise.
" All candidates whose fingerprints could not be captured during registration from centres across the country were brought to JAMB Headquarters and were registered appropriately.
" In other words, exemption mode is not allowed for any candidate. Therefore, no biometric verification, no examination," Oloyede warned.


Learn the warning signs of abuse and see if you are in a healthy relationship.
Learn about Teen Dating Violence and the services we provide for teens.
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text:
.. _deployment-details:

Deployment Details
******************

.. toctree::
   :maxdepth: 2

   conda
   beamline-configuration
   deployment-howto

redpajama_set_name: RedPajamaGithub