text:
Fort Worth Personal Injury Lawyers to Present at Local School Career Day Event
Press Release by vision
05/05/2015 // 1-800 Car Wreck Fort Worth (Press Release) // (press release)
Fort Worth – I.M. Terrell Elementary School in Fort Worth is set to host a Career Day event on Friday, May 8th, where students will learn about various careers and vocations. Among the list of community and business leaders set to present information to students are the founding lawyers of the Fort Worth based personal injury law firm of Eberstein Witherite, LLP. Attorney Amy Witherite and Brian Eberstein will pass out goodies and speak to third, fourth, and fifth grade students about topics such as the education requirements for becoming an attorney, the day-to-day duties of their jobs, and the courtroom environment.
Each One, Teach One: Staying Committed to Community Causes
In addition to providing legal representation, the Fort Worth Injury attorneys of Eberstein & Witherite LLP have earned a distinction as being community advocates for maintaining a presence at many events that are important to those in the Fort Worth community. When recognizing the efforts of the firm, Lorie Blair of Elite News wrote in the publication, "There are many really capable law firms in our city but no one does as good a job as Eberstein Witherite when it comes to "walkin' the walk" and doing what they say they will do both for their clients and our community."
The firm has provided scholarships to graduating seniors at Paul Laurence Dunbar High School and participated in the school's Challenge Day geared toward reducing incidents of bullying and promoting tolerance among students. Of the upcoming community initiative, Attorney Amy Witherite says, "this is an exciting opportunity to get young people thinking about their future and all of the possibilities that can exist for them if they work hard to pursue their dreams."
About I.M. Terrell Elementary
I.M. Terrell Elementary is named after Isaiah Milligan Terrell, who served as principal of the first free public school for students of color in the Fort Worth school system until 1915. The school became an elementary school in 1938 after first serving the educational needs of high school aged students since it was opened in 1882. The current location of the school is that of the former Terrell High School prior to 1938.
Career Day is an annual Fort Worth ISD district wide event designed to introduce students to different career options well in advance of them pursuing a secondary education. Many students do not gain exposure to certain fields that may be of interest to them in the future early on when they are receiving an education. In a report on a previous study on dropout rates in cities like Dallas and Fort Worth, Fort Worth was ranked 12th on the list of locations with an improving graduation rate; however remained below the national graduation rate average, which was then 71 percent. I.M. Terrell Elementary hosts Career Day at the elementary level to create awareness for students to attend college and promote career readiness. This year's theme is "Career Fiesta."
Says Attorney Amy Witherite "there are many areas where we need to see more positive numbers when it comes to the outcomes for students in the public education system. Exposing those in underserved areas to career opportunities and giving them tools to help direct them on the path to better can go a long way in achieving this goal.
About Eberstein Witherite, LLP
The Eberstein Witherite law firm was founded more than a decade ago by attorneys Amy Witherite and Brian Eberstein. The firm has office locations in Dallas, Fort Worth, and Houston, and provides legal services in the area of personal injury to clients throughout the state of Texas.
Lucy Tiseo
http://fortworth.1800-car-wreck.com/
Url: 1-800 Car Wreck Fort Worth
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by Legal Reporter


Let us see how much you know about the game of cricket with this quiz. This Cricket Quiz tests your overall knowledge of the game Cricket. Cricket quiz covers the history, popularity, rules and players of Cricket. This collection of cricket quiz questions and answers will test how well you think you know the sport of cricket. Some questions in this cricket quiz are easy, but most of them are very hard. When you have completed the quiz, you can send this quiz to your friends. It's simple to do, just click on the "Send Article To Friend" link on the right side.
Each multiple-choice question has up to 4 possible answers. You need to find out which one is correct. When you have completed answering the quiz questions, scroll down to see the quiz answers and compare them with your answers to evaluate your score. Good luck with our cricket quiz.
Question: Which of the following countries won the final of the triangular cricket series held in durban in februarry 1997?
Question: Who has scored the most runs in a single First-class innings?
Question: Who is the leading wicket taker in the history of Test cricket?
Question: Who has scored the most Test hundreds ever?
Question: Which player has scored the most runs in a single Test innings?
Question: Which country won the World Cup in 1999?
Question: In what year was the first international cricket match held?
Question: Former Australian captain Mark Taylor has had several nicknames over his playing career. Which of the following was NOT one of them??
Question: The nickname of Glenn McGrath is what?
Question: 'Dizzy' is the nickname of what Australian player?
Question: Former Australian captain Allan Border was sometimes known as "Captain Grumpy". What was his other nickname?
Question: The nickname of wicketkeeping great Rod Marsh was what?
Question: Mark Waugh is commonly called what?
Question: What Australian player was known as 'Fruitfly' amongst the rest of the team?
Question: Ricky Ponting is also known as what?
Question: Pravin Amre and Vinod Kambli played for which province in South Africa?
Question: Which county did Ravi Shastri play for?
Question: Which county did the outspoken Spin great Bishen Singh Bedi play for?
Question: Which county signed Yuvraj Singh to play for them in 2003?
Question: Which county did Sunil Gavaskar play for?
Question: Which county did Sachin Tendulkar play for?
Question: Which county did Mohammad Azharuddin play for?
Question: Which county did Javagal Srinath sign for in 2003?
Question: Which county did speedster Javagal Srinath play for in 1995?
Question: Which two counties did Kapil Dev play?
Question: Which cricketer died on the field in Bangladesh while playing for Abahani Club?
Question: Which two counties did Anil Kumble play for?
Question: Who had a torrid time with Lancashire in 2000?
Question: Which country, which made its debut in the Norwich Union League in 2003, signed Rahul Dravid as their overseas player?
Question: Which English county did batting sensation Virender Sehwag join in the 2003 season?
Question: What is the middle name of Ashish Nehra?
Question: What is the middle name of V.V.S. Laxman?
Question: What is the middle name of Bhajji (Harbhajan Singh)?
Question: What is the middle name of Sourav Ganguly?
Question: What is the middle name of Parthiv Patel?
Question: What is the middle name of Rahul Dravid?
Question: What is the middle name of Deep Dasgupta?
Question: What is the middle name of Akash Chopra, a Delhi batsman?
Question: What is the middle name of the Ajith Agarkar, a top class all rounder?
Question: What is the middle name of the world class batsman Sachin Tendulkar?
Question: How many wickets did Zaheer Khan pick up in his ODI debut?
Question: How many runs did Mohammed Kaif score in his ODI debut?
Question: Ajit Agarkar's first match was against Australia on April 1, 1998 at Kochi. Which Australian batsman was dismissed by Agarkar in that match?
Question: Where did Yuvraj Singh make his ODI debut?
Question: Against which team did Virender Sehwag make his one day international debut?
Question: How many runs did VVS Laxman score in his ODI debut?
Question: What was the bowling figures (O-M-R-W) of Anil Kumble in his ODI debut against Sri Lanka?
Question: When did Sourav Ganguly made his ODI debut?
Question: Against which team did Rahul Dravid made his ODI debut?
Question: How many runs did Sachin Tendulkar was scored in his ODI debut?
Question: Which team did Sachin Tendulkar play for in the Ranji Trophy when he made his debut in first class cricket?
Question: Who was the man of the match of the 1983 World Cup Final ?
Question: The highest first wicket partnership in Test Match Cricket was between two Indians: One is V. Mankad, who is the other ?
Question: Which Indian batsman was the first to hit 44 fours in a test match innings?
Question: What was Sachin Tendulkar's average when he made his first thousand runs in ODIs ?
Question: When did Sachin Tendulkar make his Test debut?
Question: Who was named India's Cricketer of the Century by Wisden?
Question: In which year did Ajay Sharma play his only Test match?
Question: Who made his Test debut alongwith Salil Ankola in the same match?
Question: In which year did Ajay Jadeja make his ODI debut?
Question: Navjot Singh Sidhu had a marvellous start to his ODI cricketing career.How many sixes did he hit in his innings of 73 runs on debut?
Question: Where did Yashpal Sharma play his last ODI match?
Question: What was Yashpal Sharma's best score in First Class Cricket?
Question: How many wickets did Yograj Singh take in his 1st ODI match?
Question: How many fours had Yograj Singh hit throughout his entire test career?
Question: Which Indian batsman made his ODI debut in the same match in which Kapil Dev did?
Question: How many ODI matches had Farokh Engineer played throughout his career?
Question: Farokh Engineer played his last Test match at his home ground.Where did he play his last Test match?
Question: What were the bowling figures for Irfan Pathan on his ODI debut? The answer is in this form (Overs-Maidens-Runs-Wickets).
Question: Name the batsman who scored a triple century after his opening partner was dismissed for a zero. Who is he?
Question: Name the front-line Sri Lankan batsman who scored just one run in his first six test innings?
Here are the answers to cricket quiz questions given above.
Chris Gayle; his partner W.W. Hinds was out for zero.
Marvan Attapattu. He was out on duck (zero) on his career's first three test matches against India. He scored 1 run in his 4th test match against Australia and was again out on zero on 5th and 6th test matches.


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As you may already know, there are two ways you can ship a vehicle – using open or enclosed auto transport. Even though open car shipping is a more affordable option and because of this usually more available, in some situations enclosed car transport is much more preferable. For instance, if you need to ship an expensive vehicle or just a vehicle that has some special significance for you, enclosed auto shipping will be a great choice for you. It guarantees the best available protection for your car during the process of auto transport. This method may be more expensive, but the price for this kind of service is absolutely justified.
At San Jose Car Transport we are always happy to provide our customers with exceptional Enclosed Auto Transport services. Our team knows how to take care of any vehicle, including classic cars and sports cars, retro cars, and all other luxury vehicles. Our enclosed carriers ensure the highest level of safety for vehicles San Jose Car Transport ships. No matter if the car's value is great on paper or in your heart, we will make sure your vehicle travels safe and secure.
We use our Enclosed Auto Shipping for Exotic Car Transport services. So if you need San Jose Auto Transport for any kind of exotic vehicle, our crew is always there to help you! We will deliver your vehicle door-to-door across the country in an enclosed trailer, providing the best kind of San Jose Car Shipping.
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This Pullbox contains 1000 feet of Plenum-rated 16/2 cable. Plenum cable is jacketed with a fire-retardant plastic jacket of either a low-smoke polyvinyl chloride (PVC) or a fluoridated ethylene polymer (FEP). Local building codes may require all cables run inside the walls or ceiling be Plenum-rated. This comes in the color red.


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Q: TypeError: (0 , _react.createContext) is not a function on Nextjs using Redux im new in using redux and tried to use it with the help of online tutorials. nut im seeing this error:

Server Error TypeError: (0 , _react.createContext) is not a function

This error happened while generating the page. Any console logs will be displayed in the terminal window.
im providing my codes bellow:
authSlice.js file:
    import { createSlice } from "@reduxjs/toolkit";
import { HYDRATE } from "next-redux-wrapper";

const initialState = {
    authState: false,
    userInfo: {},
    authToken: ""
}

export const authSlice = createSlice({
    name: "auth",
    initialState,
    reducers: {

        setAuthState(state, action) {
            state.authState = action.payload
        }
        , setUserInfo(state, action) {
            state.userInfo = action.payload
        },
        setToken(state, action) {
            state.authToken = action.payload
        },
        setAuth(state, action) {
            state.authState = action.payload.authState;
            state.userInfo = action.payload.userInfo;
            state.authToken = action.payload.authToken;
        }
    },
    extraReducers: {
        [HYDRATE]: (state, action) => {
            return {
                ...state,
                ...action.payload.auth
            }
        }
    }
})

export const { setAuthState, setUserInfo, setToken, setAuth } = authSlice.actions
export const selectAuthState = (state) => state.auth.authState;
export const selectAuthToken = (state) => state.auth.authToken;
export const selectUserInfo = (state) => state.auth.userInfo;

export default authSlice.reducer

store.js codes:
    import { configureStore, ThunkAction, Action } from "@reduxjs/toolkit";
import { authSlice } from "./authSlice";
import { createWrapper } from "next-redux-wrapper";

const makeStore = () =>
    configureStore({
        reducer: {
            [authSlice.name]: authSlice.reducer,
        },
        devTools: true,
    });

const store = makeStore()
export default store

components where i tried to use redux:
    import { useRouter } from "next/navigation";
    import { useDispatch, useSelector } from "react-redux";
    import {
    selectAuthState,
    selectAuthToken,
    selectUserInfo,
    setAuthState,
    setUserInfo,
} from "@/data/authSlice";

function Sidebar() {
    const router = useRouter();
    const dispatch = useDispatch();
    const authState = useSelector(selectAuthState);
    const authToken = useSelector(selectAuthToken)
    const userInfo = useState(selectUserInfo)

    useEffect(() => {
        if (!authState) {
            router.push("/")
        }
    }, [router.asPath]);
// rest of the codes

import axios from "axiosConfig";
import { useDispatch, useSelector } from "react-redux";
import {
  selectAuthState,
  selectAuthToken,
  setAuthState,
  setUserInfo,
} from "@/data/authSlice";

// toastify
// import { toast } from 'react-toastify';
// import 'react-toastify/dist/ReactToastify.css';
// const notify = (text) => {
//     toast.success(text , {position: toast.POSITION.BOTTOM_RIGHT})
// }

const LoginPage = () => {
  const router = useRouter();
  const dispatch = useDispatch();
  const authState = useSelector(selectAuthState);

  useEffect(() => {
    const token = authState;
    if (token) {
      router.push("/Dashboard");
    } else {
    }
  }, [router.asPath]);
// rest of the codes

i tried to reinstall redux and react-redux but didnt helped.

*

*also tried Remove the node_modules

*Remove package-lock.json

*npm install again.
but nothing happend.

redpajama_set_name: RedPajamaStackExchange


Hello again! I talked about this recently on the marketing for owners.com podcast. Local businesses can build relationships with other local businesses and what happens is you then leverage each others' following so one promotes the other business to their customers and in exchange you promote the other business to your customers.
If you can make that a 3 or 4 way thing then you've got a mini network, entire organization such as the BNI and so on a built around networking between businesses, but it doesn't have to be like in that formalized way.
You don't have to get up at 6 o'clock and go and have breakfast with a bunch of other local business to do this just like BN. By the way, I don't like getting up early. I'll just never be the one. But you can make your own. It's not that hard.
For example, you may be a photographer and how about getting together with a real estate agent or realtor or estate agent for other countries with your different terms for the same thing? So, if you are real estate people you probably take photos. Do you want your photos to pop? Do you take them with your iPhone?
Have you got a DSLR? But you have to look up on a manual, how to set up and so on. But how about getting a professional photographer to do that? This is not part of the business, this is part of marketing.
Okay, this is not for you to get a real estate agent as your customer, this is for you to build a relationship with them and to get in front of their customers so you need to strike a deal as part of your marketing.
So, what happens is you go and you say, "I would like to take photographs for you inside a house and to show you how I can make your houses look better. In exchange, I would like to give you some vouchers to give out to your clients and your visitors.
Let's say, these photos were taken by sunset, if you like to see what they can do for you offer a free voucher of family portrait in your own house or something like that then contact." Okay, so what happens you then go to them, you take your photos, you make it look fantastic.
You go to real estate agent and they say, "Hmm nice yes, I will do that." And then in exchange perhaps you say,"Do you have something I can give to my clients so that we can do quick pro coil something for each other.
Yeah, you can think these things out because you know your business better than I do, but how about some deal, some special offering for instance, the real estate agent then puts together some kind of open house or some kind of rents a room and house and puts some nibbles out in a buffet and I think puts all the details or other things and a photographer's clients get a special invite to this cocktail buffet. You can invent things, see what I mean.
For instance, how about commercial landscape gardeners getting together with commercial painters? If the company is prepared to spend that money on the outside in their garden, how about if the actual building doesn't look quite so neat?
The chances are, if they're going to spend it making a garden good they'll probably want to spend money to make the building look good so each does a deal with each other to be able to offer some kind of introduction or some kind of discount or some kind of buy one, you know paint five windows get a free door painted who knows what you do. You can figure these things out out.
Dry cleaners and carpet cleaners not related in the slightest however, they both make people look great and feel fantastic. Feel fantastic in a new close, feel fantastic in a nice clean home. Surely, there is a deal there. If someone is going to pay it have their clothes dry clean instead of just throwing them in the washing machine and tumble dry, then I am sure they going to be proud of the way their house looks.
How about a special voucher that you introduce? You say, "These are our friends, let me give you this." Your carpet cleaners do the same when you get a carpet clean, "here is a voucher if you get two items dry cleaned, one will be free.
Half price on your first cleaning." Think, figure out yourselves. There are ways to make business happen without spending any money. So what is all about? And you make lots of friends. You can expand on this. We'll come back with some other ideas later on.
It's Tuesday. It's time for the tool box tip. Today my recommendation is Sidekick. Sidekick is from pops box and it integrates with your email, with Gmail, with Google apps with Outlook, pretty sure with whatever email are there.
Are there others? With pretty much anything. Remember I've taught, you should be using Gmail anyway. How do I explain? It's free. Oh, yeah by the way, it's free. It integrates.
It also pops up another one saying "they've just clicked on the link". Now you are not going to send this with all your mail shots because you are not doing your mail shots from that. You're doing that from aweber or mail chimp or something. This is for your ordinary emails.
When you send a quote to a customer imagine if you know that they've opened it and they've clicked the link, when they don't reply perhaps you phone them and you know. You are not going to say, "I've been spying on you and I know but you know".
But you know they've clicked, you know they've read it, you know what you are talking about is relevant, it's on their mind. Get sidekick.com. Honestly, I've been using it for the last week or two, it's great. It's really addictive. It's really addictive and it's been a great help. Try it, that's all I ask. This is only recommended for you, doesn't help me. Honestly, all this stuff works.
How could you get businesses on board to promote you?


text:
The Professional Education Group
ONLINE COURSES / PRODUCTS
The Cutting Edge in Courtroom Persuasion
View Course Outline
Russ M. Herman
Herman, Herman & Katz LLP
Russ M. Herman is a senior partner in the New Orleans firm of Herman, Herman & Katz LLP. He is a genuine legend at the bar—an advocate's advocate. He has earned a master's reputation as a dynamic and entertaining CLE speaker.
Herman is a champion from either side of the civil bar. He is a past president of the Association of Trial Lawyers of America (ATLA) and the Louisiana Trial Lawyers Association; a fellow and director of the International Academy of Trial Lawyers; a diplomate of both the National College of Advocacy and the American Board of Professional Liability Attorneys; and a member of the American Board of Trial Advocates (ABOTA).
Legend at the Bar
In 2007, Herman was named Lawyer of the Year by Lawyers Weekly USA. He has received the Pursuit of Justice Award from the Tort & Insurance Practice Section of the American Bar Association and the Leonard Ring Champion of Justice Award and the Lifetime Achievement Award, both from ATLA.
Herman was a lead negotiator for plaintiffs in the $368 billion proposed settlement with the tobacco industry and was on the private counsel team that recovered $4.6 billion for Louisiana through its attorney general. In addition to his impressive track record for plaintiffs, Herman's body of work includes product liability, environmental, anti trust and Civil RICO defense work.
Author of numerous articles and papers covering the art of trial advocacy, Herman has been published in such periodicals as The National Business Institute, The New York Times, and The Wall Street Journal. His book, Courtroom Persuasion: Art, Drama & Science, was published by West Group, Clark Boardman & Callaghan and the ATLA Press in 1997. He is also the creator and performer of ATLA's best-selling, six-volume Video Trial Practice Series, Courtroom Persuasion: Art, Drama & Science.
Dynamic Lecturer
Herman has spoken extensively to audiences in both the professional forum and educational realms. He has lectured at Tulane, Loyola, Georgetown and other law schools. He has appeared on television programs including Good Morning America, Today and CNN Newsline.
© 2021 by The Professional Education Group

redpajama_set_name: RedPajamaCommonCrawl


text:
Celebrity News, Music
Rumors Of Bobby Shmurda's Release From Prison Spread On Social Media – Again
Hot New Hip Hop reported on a shocking development in the case of the incarcerated rapper, Bobby Shmurda. The outlet says the 26-year-old performer will be able to get out of prison sometime in the coming month following his arrest in 2014.
As it was previously reported, Bobby Smurda was sent to prison back in 2014 which left much of the rap world screaming, "Free Bobby!" At that time, Bobby was doing very well in his career and was even at the height of it up until that point.
However, the authorities came for the rapper and he was slapped with a number of serious charges related to drugs, guns, and gang conspiracy. Bobby pled guilty and was given a seven-year sentence with December 2021 set as his release.
Hot New Hip Hop says the parole board denied him parole in September 2020 due to his behavior behind bars. According to the publication, Bobby was busted with drugs and was caught fighting in prison which squashed his chances at getting out sooner rather than later.
https://www.instagram.com/p/CJoZRCyFqMN/
In other words, Bobby's situation looked bleak, as if he wasn't about to get out anytime soon. However, Hot New Hip Hop says Bobby is now going to get out on the 23rd of February, 2021 under the terms of what has been described as a "conditional release."
The conditional release means Bobby will be under parole until the day his prison sentence has been reached. This means that Bobby – who was supposed to get out on the 11th of December, 2021 – will be out of prison in the coming months and then will be on parole-like-conditions until December 2021.
This wouldn't be the first time a rapper has been let out of jail earlier than expected. As it was previously reported, Tekashi 6ix9ine – who was incarcerated on racketeering among other charges – was let out amid the coronavirus pandemic after he and his legal team claimed he was asthmatic.
Tekashi and his lawyer argued that his life was in danger if he were to be denied early release. In late 2020, Bobby's contemporary, Rowdy Rebel, was also let out of jail.
Read More: Rumors Of Bobby Shmurda's Release From Prison Spread On Social Media – Again
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You are at:Home»News»Community News»Deptherapy Ambassador Ben Lee wins Endeavour Fund Award
By Deptherapy on	 February 8, 2018 Community News
At a special Awards Ceremony at Goldsmiths' Hall in London, inspirational Deptherapy Programme Member and Ambassador Ben Lee was one of three wounded ex-servicemen honoured to receive an Endeavour Fund Award in the presence of HRH Prince Harry and Ms Meghan Markle.
Ben Lee, a former Private in the Royal Engineers, who lost both his legs and suffered other catastrophic injuries whilst searching and disposing of IEDs in Afghanistan in 2011, was presented with The Recognising Achievement Award.
The Royal Foundation's Endeavour Fund supports wounded, injured and sick servicemen and women and encourages them to rekindle their fighting spirit through sporting and adventurous endeavours. The Recognising Achievement Award is given to the individual, who in the judges' opinion, has best utilised their endeavour experience to promote and catalyse their recovery.
Ben was introduced to scuba diving through UK diving rehabilitation charity Deptherapy when he completed his PADI Open Water Diver course on a specialist training programme in Egypt in 2016. Double-amputee Ben's achievement is all the more incredible due to the fact that he had drowned in a swimming pool as a child and although successfully resuscitated, the experience had left him with an overwhelming fear of water.
With the close support of his Deptherapy team mates, diving instructors and fellow wounded veterans, Ben overcame his fears and discovered the power of scuba diving to both heal and rehabilitate. Since passing his Open Water course with flying colours at Roots Red Sea, Ben has progressed rapidly through the PADI Continuing Education programme, subsequently completing his Advanced Open Water, Deep, Wreck, PPB and SMB Specialties, as well as his Rescue Diver course. Ben is now a PADI Master Scuba Diver and a trainee Divemaster.
Not only are his personal achievements awe inspiring but on completing his Open Water Diver, Ben stated that he wanted to "give back to the charity." His commitment to helping and inspiring others has made him the 'go-to-guy' for new Programme Members and he has become an active Ambassador for both scuba diving and the Deptherapy charity.
Most recently, to enhance his ability to help others, Ben and his partner Sophie attended a two day Deptherapy Mental Health First Aid Course for the Armed Services' Community.
On receiving The Recognising Achievement Award, Ben said:
"This has been an amazing journey and I am so honoured to have been selected to receive this Award amongst so many incredible individuals… but this Award isn't about me. It is about Deptherapy and all my brothers and sisters in arms that the charity helps and supports. None of this would have been possible without the support of Richard Cullen (Founder and Chairman of Deptherapy) for whom I have the utmost respect.
Deptherapy is a small charity, run entirely by volunteers. I personally don't know how they do so much, with so little income. Richard and the Deptherapy Team have changed my life, just as they are changing the lives of many others."
Deptherapy would like to thank The Royal Foundation's Endeavour Fund for their contribution that enabled the charity to run an additional Open Water Diver training course in 2017 to reduce the ever-growing waiting list of injured and wounded ex-service personnel wanting to join the Deptherapy specialist programme. The charity is now actively seeking funding and sponsorship to continue being able to offer its life-changing programmes in 2018 and beyond.
Find out about the work of Deptherapy & Deptherapy Education at www.deptherapy.co.uk.
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Deptherapy
Deptherapy and Deptherapy Education is an English charity since 2014 that aims to rehabilitate seriously injured UK Armed Services Personnel who have suffered life changing mental or physical injuries, through the medium of scuba diving. The charity also provides education in adaptive scuba diving techniques to dive professionals.
The Great Northern Dive Show Featured on BBC Radio Lancashire with Stephen Lowe


Ever wanted to drive like Lewis Hamilton on one of the world's greatest circuits? Prepare to experience what it is like to be a Formula One driver in this F1 Grand Prix Simulator Race.
Chandler's Ford or Halifax - You'll compete against up to 19 other drivers in an adrenaline-fuelled race to the finish line. Starting off in a FF1600 simulator, you'll have the chance to practice and get to grips with the controls as you qualify for your place on the grid.
Then the fun really beings as you experience the thrill of speeding round the circuit, wheel to wheel with your fellow competitors in a race to take the chequered flag first. Once you have mastered the FF1600 race car, you'll be upgraded to a modern F1 car for more racing, as you battle it out to accumulate points and take home a coveted trophy.
Oxford - With a full grid of up to 16 cars on circuit at any one time, you'll compete against up to 31 other drivers, in an adrenaline-fuelled races to the finish line, with a great choice of F1, GT, Le Mans and Touring car events. All events include a Practice Session, Racing Heats, and Grand Final with a Trophy Presentation for the top three drivers. So get ready, get focused – you're in for a thrilling ride.
On arrival at the centre, you will need to register before receiving a driver's briefing, which includes an introduction to the controls on the simulator and details on how the event will be run. You will then be unleashed onto the circuit for your 20 minutes of practice and qualifying. You'll use this time to learn how to handle the car and get to grips with the best lines into and out of the corners, before getting in that all-important fast lap to determine your position on the grid.
When your grid position has been determined, it's time for your first race to start. With all drivers ready, you will find yourself on the starting grid waiting for the start light procedure to commence. With the engine revs rising all around you, you will need to time your start to perfection as the lights turn to green.
Chandler's Ford or Halifax - Points will be awarded and preparations made for the next race. Once you have mastered the FF1600, you will be upgraded to a modern F1 car.
Oxford - Points will be awarded for drivers' finishing positions from each race. Depending on driver numbers there may be an additional race for each driver, with semi-finals, which will determine the starting grid for the Grand Final.
Trophies will be awarded to the top three drivers of what will be an exciting and memorable race event.
The experience is run on most weekends throughout the year.
We recommend that you book at least six to eight weeks in advance to ensure that dates are available. All bookings are subject to availability.
Please allow at least 3 hours for this experience.
Chandler's Ford or Halifax - you will have a combined 20 minutes of practice and qualifying with at least four Grand Prix races, which will give a minimum driving time of 60 minutes.
Oxford - you will have a combined 15 minutes of practice with at least three 10 minute Grand Prix races, followed by a 15 minute Grand Final which will give a minimum total driving time of 60 minutes. Depending on driver numbers there may be an additional race for each driver, with semi-finals, which will determine the starting grid for the Grand Final.
I am not an F1 fan myself but accompanied a friend to this experience. It was great fun, much better than my expectation. The event was really well organised with clear instructions given to maximise enjoyment.
I would thoroughly recommend this to anyone be they an F1 fan or not!


Amilia Solly instead of Amelia Solley.
Hally Solley instead of Hetty Solley.
In fact Hetty's proper name was Juliet, although she seems to have been known in the family as Het/Hettie/Hetty. The age/date/place of birth all fit for 'Hally'. I would guess that the 'e' was read as 'a' and the 't's as 'l's, but it is not a variation I would have thought of looking for in a million years!
Throughout the various census returns the use of the letter 'e' in Solley seems to drop in and out of use like a yo-yo within members of the same family in different households. But this is one variation I can usually cope with now.
A year or two ago checking for my maternal grandmother's family in the 1881 Census, when I eventually tracked them down my grandmother's name Louisa was actually spelled 'Lueza'!


The Graduate School offers the administrative structure, leadership, and guidance necessary to support graduate education at Louisiana Tech University. Graduate School is the initial and final stop in the graduate application and admission process. Graduate School works with the Provost, the Graduate Council, the academic deans, the graduate program coordinators, and faculty to help provide students with a superior educational and research environment within which to pursue rigorous, challenging, and relevant graduate and professional degrees.
The "Apply Now" link below will take you to a page containing detailed instructions for preparing and submitting your application. We encourage you to print or save these instructions. Prior to starting an application, you should also review the website and curriculum for any additional requirements for the specific program you desire.
You may start an application and save it, and return to it to complete it. After submitting the application, no further changes can be made by you without notifying the Graduate School via email.


text:
JenniferPlagman
Jennifer Plagman
Jenny Plagman is a trial lawyer who represents and advises employers in a diverse range of labor and employment matters. Her practice focuses on individual and complex disputes arising under the Fair Labor Standards Act, Title VII of the Civil Rights Act of 1964, the Age Discrimination in Employment Act, and the Americans with Disabilities Act. A portion of Jenny's practice also focuses on counseling clients on human resources matters, including compensation practices, worker classification, state law compliance, employment agreements, and workplace sensitivity.
Recently, Jenny successfully defended a Fortune 1000 company in a six-day jury trial in the Eastern District of Texas. She also co-led four-day (California) and three-day (Hawaii) wage and hour bellwether arbitrations, winning each on all counts, which subsequently resolved over 300 pending arbitrations. Jenny successfully represented a global technology company in a highly contested trade secrets litigation; defended a Fortune 500 company in a federal civil rights litigation involving multiple claims of discrimination, retaliation, and Illinois Whistleblower Act violations; and defended an oil and gas company in an executive contract dispute. In addition, she conducted and counseled clients on internal investigations and company audits, drafted executive employment agreements and employee handbooks, and provided key assistance in defending large collective and class actions and National Labor Relations Board (NLRB) issued complaints.
Jenny is a member of the Cook County, Federal, Chicago, and Illinois Bar Associations and the National Employment Law Council. She is also a member of the Board of Directors for the Center for Conflict Resolution and the Chicago Appleseed Center for Fair Courts.
Fortune 1000 company wins age discrimination jury trialJones Day successfully represented a Fortune 1000 company in an age discrimination jury trial in the Eastern District of Texas.
McDonald's wins summary judgment in Title VII class action matterJones Day won summary judgment for McDonald's in a class action against McDonald's Corporation, McDonald's USA, and franchisee defendants alleging systemic sexual harassment under Title VII and the Michigan Elliot-Larsen Civil Rights Act on behalf of a class of over 100 women employees at franchisee's restaurant based on joint employer, agency and apparent agency theories of liability.
McDonald's successfully resolves groundbreaking NLRB litigationJones Day successfully represented McDonald's USA, LLC and McDonald's Restaurants of Illinois, Inc., in the largest consolidated National Labor Relations Board (NLRB) litigation matter ever and one of the most important labor cases in the United States today.
CITGO wins summary judgment of Illinois Whistleblower Act lawsuitJones Day secured summary judgment on behalf of CITGO Petroleum Corporation on all claims from a former employee alleging wrongful termination in violation of the Illinois Whistleblower Act, based upon allegedly raising safety concerns, as well as claims for unpaid compensation under the Illinois Wage Payment & Collection Act.
Advocate Aurora Health wins summary judgment, defeats class certification, and obtains dismissal in FICA refund class actionJones Day client Advocate Aurora Health won summary judgment and defeated class certification in a FICA refund class action case.
RotaDyne secures victory in long-running trade secret and non-compete matterJones Day secured a victory for our client RotaDyne, a portfolio company of client The Robins Group, in a long-running trade secrets and non-compete matter filed against a former employee and his new employer.
Advocate Health Care wins summary judgment against multiple discrimination claimsOn June 28, 2016, Jones Day obtained summary judgment for Advocate Health Care Network on nearly all of Plaintiff's discrimination claims.
Kraft Heinz obtains Eighth Circuit affirmation of defeat of race discrimination and sexual harassment suitJones Day successfully defeated a race discrimination and sexual harassment lawsuit on behalf of Kraft Heinz Foods Company.
Publix wins summary judgment on questions of FLSA regular rate calculationJones Day represented Publix Super Markets, Inc. in defense against a company-wide collective action lawsuit alleging violations of the Fair Labor Standards Act claiming that certain bonuses and other payments were improperly excluded from the calculation of the regular rate of pay of supermarket hourly associates nationwide.
CareerBuilder wins denial of collective certification in FLSA caseOn July 20, 2015, Jones Day secured, for client CareerBuilder, LLC, the denial of collective certification of named plaintiffs' FLSA claims that CareerBuilder unlawfully denied sales employees overtime pay by forcing them to under-report their time worked.
Fortune 500 manufacturer and retailer successfully wins discrimination and whistleblower arbitrationJones Day represented a Fortune 500 manufacturer and retailer against claims for religion, disability, and national origin discrimination and retaliation, and the Illinois Whistleblower Act.
WHM LLC (LXR Luxury Resorts) wage-hour class action trial court win affirmed by Florida Appeals CourtOn April 16, 2015, the Fourth Circuit District Court of Appeals of Florida affirmed, in a per curiam decision, the trial court win on behalf of Jones Day client WHM LLC (LXR Luxury Resorts), and granted WHM's motion for appellate attorney's fees against the plaintiffs.
Nationwide hospitality company audits wage and hour practicesJones Day was hired by a nationwide hospitality company to audit its wage and hour practices in Oklahoma.
WHM LLC obtains summary judgment on Florida wage-hour class actionOn December 9, 2013, Jones Day prevailed on summary judgment on behalf of WHM LLC (LXR Luxury Resorts), a nationwide owner and operator of hotels, dismissing a putative wage class action for unpaid wages under the Florida Minimum Wage Law in its entirety.
May 2022 Podcast
JONES DAY TALKS®: Perspectives and Pathways: Choosing a Firm That's Right for You
May 2021 Commentary
Illinois Restricts Employers From Using Conviction Records in Making Employment Decisions
Illinois Employers Face Significant New Requirements, Penalties Under Equal Pay Act, Business Corporation Act Amendments
Cook County Bar Association Student Preparedness Workshop
The University of Chicago (J.D. 2013); University of Notre Dame (B.A. in Psychology and Sociology cum laude 2010; Alpha Kappa Delta)
"Rising Star," Illinois Super Lawyers (2016-2022)


The average senior is looking for senior portrait photographers around this time of year and many of them are either looking for ways to save money or just don't know how to go about in hiring a photographer. The one question that I always get asked is "Do you provide all the images on CD?" Here at our studio we do not provide that but we do offer the digital images for purchase. The reason why people ask for the images on CD is mainly to print the images themselves on their ink jet photo printer or take the images down the street to their local drugstore that provide decent quality prints at a low price.
When I first started out doing photography, I had a client that wanted all the images on CD. I thought that's what all photographers do for their clients. Let me remind you that I took over 500 images of her daughter. When she got home, she had no idea which photograph to choose thinking that it would be easy for her to do so. In the end, she called me up and wanted me to pick out which photos are the best of her daughter. She also wanted me to print the images using my professional photo print vendor since her inkjet did not match professional quality prints. From then on I decided not to allow my customers to have all of their images. I like to show them the images at my studio on my 42" inch monitor and also provide them time to 'touch and fee' the print products that I have to offer like large size canvas prints, albums, small framed prints, etc.
When you hire a photographer, you're hiring their creative expertise in making your photos the best ever. Many of them are fully trained and understand how to use light to their advantage. Trust them. They are the visualizers and artist who know what they're doing.
Many photographers, like myself, take pride of their work. They want to provide the best quality images to show them off using the professional photo quality print companies. These companies go out of their way to check the accuracy of the colors as well as color correct any photos that need minor adjustments. They also provide a variety of print sizes from small wallet sizes to large 24" x 36" framed prints.
The one product that many seniors love are the albums. Many of them would have over 500 photos and out of that many, there are a ton of shots that they would use in their albums. They would also purchase that one large print for their bedroom or living room wall. Our photographs aren't just photographs- they're art.
So if you're hiring us to do your portraits, know that we use the best quality photo print services- and not your typical drugstore or homemade inkjet photo prints.
ANOTHER new senior couture ambassador is on the horizon!


text:
\section{INTRODUCTION}
In robotics, simultaneous localization and mapping (SLAM) with high-level features finds various real-world tasks such as autonomous navigation and object search \cite{monocularNavigationAutonomous}\cite{object_search}.
Object-oriented features have evolved as a practical choice for semantic SLAM \cite{slam++}, as they are ideal for view-independent loop closure and complete volumetric maps.
Since mono cameras are popular for SLAM on platforms such as hand-held devices and mobile robots \cite{lsdSLAM}\cite{orbSLAM}, semantic feature detections in single RGB images are widely used.
In particular, thanks to the advent of the neural networks, real-time 2D object detection has been improved and exploited in SLAM implementations.
However, existing object detection methods hardly consider the probabilistic observation model as they mainly focus on the accuracy of the detection and category classification \cite{vggnet}\cite{yolo9000}.
In the case of detection algorithms based on point feature matchings, for example, distinct feature extraction and key point matching are main factors for better performance; therefore, obtaining viewpoint-independent features of the object is relatively less considered \cite{monoObjectSLAM2016PointMatch}.

For viewpoint-independent features, it is ideal to catch the 3D full shape of the object.
The entire shape can be estimated by 3D scanning or SfM \cite{sfmSurvey}, but these methods lack the reality to the mobile platforms that are typically used for various tasks in real-time.
Even if an object-oriented feature is acquired by inferring a 3D shape from a single 2D view \cite{marrnet}\cite{3D_rec_GAN}, landmark pose and feature optimization should be possible for the complete SLAM formulation.
Since the probabilistic distribution of the object with complex 3D shape is intractable, complete SLAM optimization with numerical analysis is still challenging without approximated distributions.
To this end, \cite{yu2018variational} proposes a variational feature encoding method for objects by exploiting their corresponding single view.
However, this method only covers the voxelized 3D single view from depth cameras or range finders, under the assumption of instance segmentation, which is thus insufficient to be directly applied to the real-world scenarios.
The lack of the consideration of object viewpoint orientation also makes it cumbersome to adopt the algorithm when performing the pose optimization in SLAM.

\begin{figure}[t]
	\centering
	\includegraphics[scale=0.075]{likelihood_inference_new.jpg}
	\caption{
		In the proposed method, an observed view data is assumed to be an RGB image. In order to approximate the object observation model, we train the variational auto-encoder to achieve the object generative model. The encoded features of the 3D object is estimated from the RGB single view. Using the approximated observation model, complete SLAM formulation and optimization becomes possible.
	}
	\label{likelihood_inference}
\end{figure}
\begin{figure*}[t]
	\centering
	\includegraphics[scale=0.19]{graph_model_new.jpg}
	\caption{
		Overview of the proposed Bayesian model for the object generative model. (a) The 3D object shape $\boldsymbol{s}^f$ generation is assumed to be involving the category $\boldsymbol{l}^c$, instance $\boldsymbol{l}^i$ and the object orientation $\boldsymbol{v}$ with respect to the observer. $\boldsymbol{v}$ can be modeled with any representation of the orientation such as quaternion or rotation matrix.
		(b) The latent variables according to each element are adopted for the variational likelihood. Priors of $\boldsymbol{l}^c$ and $\boldsymbol{l}^i$ are also learned form the training data by constructing the additional network. Since $\boldsymbol{v}$ is a real value in our case, we simply let $\boldsymbol{v}$ be the mean of the orientation prior.
		(c) The variational likelihood of the proposed observation model is inferred from the observed single view $\boldsymbol{s}^o$. From this strategy, we can exploit the RGB single view without scanning the entire shape of the object directly.
		The generative model is represented by solid lines, and the dashed lines denote the variational likelihood estimation. For the prior, $\psi$ is learned simultaneously with $\theta$ and $\phi$, which are the parameters of the generative model.
	}
	\label{graph_model}
\end{figure*}
Therefore, we propose a method to approximate an object observation model to a tractable distribution, considering the 3D shape and the viewpoint orientation.
Using the proposed observation model, not only the approximated solutions for the coordinates of the object landmarks but feature values and the orientations can be estimated.
We use the Variational auto-encoder (VAE) \cite{VAE} to infer a variational likelihood of 3D object observation model from RGB single view so that the robot can exploit the observation with RGB mono camera.

Our contributions are two-fold: First, we show that the complete SLAM formulation including feature optimization and automatic loop-closing is available with approximated observation model; second, we propose a real-time encoding method for object observation model with Bayesian generative network, exploiting the RGB single view of the object.

\section{Related Work}
Works on feature-based SLAM typically divide the problem into two parts; front-end for data association and back-end for pose optimization.
Based on the data association using feature matching or tracking algorithm in the front-end, the pose graph optimization is performed at the back-end subsequently.
On semantic SLAM problem, while poses of robot trajectories or of features can be estimated using pose graph optimization methods \cite{g2o}\cite{isam}, obtaining a closed form solution of the optimal feature value is challenging since the high-dimensional features for landmarks follows the intractable distribution.
Due to this complexity, even when object-oriented features are used for classification, features obtained from complex 3D shapes need further learning by using additional algorithms such as SVM \cite{3D_GAN}\cite{ShapeNet}.
Hence, object observation probability is typically considered only on the front-end data association.

Since the object observation model is intractable and hardly considered in SLAM formulation, false-positive data association in the front-end causes tremendous amount of error in the pose graph estimation.
Besides, the optimal pose is solely estimated without considering the feature values, estimating both robot trajectory, object shape and its pose is challenging.
\cite{probDataSLAM} proposes an expectation maximization (EM) formulation for complete SLAM problem considering data association.
However, as they use the existing object detection method for data association \cite{active_deformable}, it is ambiguous to adopt the object observation probability directly to the SLAM formulation.
Also, they rarely consider the viewpoint-independent feature for 3D shape and object orientation in formulation since they adopt edge and corner filter based deformable part detection algorithm.
In \cite{categorySpecificObjectSLAM}, object pose and shape optimization as well as robot trajectory are estimated in back-end using key point matching and PCA-based object observation factor.
Their observation factor, however, barely considers the tractable object observation model, which is hardly adopt to complete SLAM formulation with computing observation probability.
They are bound to perform pose and shape estimation after data association using additional tracking and loop closure algorithm in the front-end.
\cite{yu2018variational} proposes an encoding method of features which imply the full shape of objects from a single view.
The encoded features follow a tractable Gaussian observation distribution and can be estimated approximately together with robot trajectory and landmark coordinates.
However, the proposed algorithm lacks pragmatic aspects as the single view is limited to the depth image under the assumption that the object is already segmented.
Also, the viewpoint orientation of the object is hardly considered in SLAM formulation.

We thus introduce a method to object observation model approximation considering the 3D shape and viewpoint orientation.
Using a tractable observation distribution, data association and landmark feature optimization as well as pose optimization are achievable.
Similar to \cite{generate_chairs}, latent variables of class, instance and viewpoint orientation are assumed to be involved in Bayesian graphical model for the object generation.
In order to exploit the single views obtained as 2D RGB images, we train the network to estimate the variational likelihood of 3D object from 2D single views.
The proposed network is basically VAE, estimated object features can be converted to 3D shapes using decoder as \cite{3D_rec_GAN}\cite{3D_GAN}\cite{generative} to construct a volumetric semantic maps.

\section{Observation Model of Object with 3D Shape and Orientation}

Consider a robot observing the 3D full shape of an object as multiple voxel grids, and using them as a semantic feature $s^f$. Assume that the robot exploits the shape only; not including the other factors such as color, texture or scales of objects.
To obtain the generative model of objects for the approximated observation model, a Bayesian random process can be adopted.
Similar to \cite{generate_chairs},
we assume $\boldsymbol{l} = \{\boldsymbol{l}^\omega\}_{\omega=c,i}$ and $\boldsymbol{v}$ cast the Bayesian dice; $\boldsymbol{l}^c$ and $\boldsymbol{l}^i$ stand for the object category and the instance respectively, and $\boldsymbol{v}$ denotes the viewpoint orientation of the object with respect to the observer. We let $v\in\boldsymbol{v}$ be the Euler angle representation.
The graphical model of the proposed process is shown in Fig.~\ref{graph_model}(a).

Now we can derive the joint distribution for the object observation as $p\left(\boldsymbol{s}^f, \boldsymbol{l}, \boldsymbol{v}\right) = p\left(\boldsymbol{l}, \boldsymbol{v}\right)p\left(\boldsymbol{s}^f|\boldsymbol{l}, \boldsymbol{v}\right)$. $p\left(\boldsymbol{l}, \boldsymbol{v}\right)$ is assumed to be uniform; objects in a scene are observed randomly regardless of their category, instance or pose.
To approximate the complex distributions of the object, we can exploit the
VAE. Variational lower-bound $\mathcal{L}$ of the likelihood $p\left(\boldsymbol{s}^f|\boldsymbol{l}, \boldsymbol{v}\right)$ then can be written as follows:
\begin{align}
	\nonumber
	\mathcal{L}
	\left(
		\theta, \phi, \psi ; \boldsymbol{s}^f, \boldsymbol{l}, \boldsymbol{v}
	\right)
	&=
	-KL
	\left(
		q_\phi
		\left(
			\boldsymbol{z} |\boldsymbol{s}^f
		\right)
		||
		p_\psi
		\left(
			\boldsymbol{z} |\boldsymbol{l,v}
		\right)
	\right)
	\\
	&+
	\mathbb{E}_{\boldsymbol{z}^{\boldsymbol{l,v}}}
	\left[
		\log p_\theta
		\left(
			\boldsymbol{s}^f | \boldsymbol{z}^{\boldsymbol{l,v}}
		\right)
	\right],
	\label{lower_bound}
\end{align}
where $\boldsymbol{z}^{\boldsymbol{l,v}} \sim q_{\phi}\left(\boldsymbol{z}|\boldsymbol{s}^f\right)$.  $\boldsymbol{z}^{\boldsymbol{l,v}}$ can be factorized to $ \boldsymbol{z}^c, \boldsymbol{z}^i, \boldsymbol{z}^{\boldsymbol{v}}$ \cite{VAE}\cite{ladderVAE}, and the KL-divergence in \eqref{lower_bound} is represented as follows:
\begin{align}
	\nonumber
	KL
	\left(
		q_\phi
		\left(
			\boldsymbol{z} |\boldsymbol{s}^f
		\right)
		||
		p_\psi
		\left(
			\boldsymbol{z} |\boldsymbol{l}
		\right)
	\right)
	&=
	\sum_{\omega = c,i}
	KL
	\left(
		q_{\phi^\omega}
		\left(
			\cdot
		\right)
		||
		p_\psi
		\left(
			\boldsymbol{z} | \boldsymbol{l}^\omega
		\right)
	\right)
	\\
	\nonumber
	&+
	KL
	\left(
		q_{{\phi}^{\boldsymbol{v}}}
		\left(
			\cdot
		\right)
		||
		p_\psi
		\left(
			\boldsymbol{z} | \boldsymbol{v}
		\right)
	\right)
	.

\end{align}
We let the prior $p_\psi\left(\boldsymbol{z}|\boldsymbol{l}^{\omega}\right)$ be a Gaussian distribution $\mathcal{N}\left(\boldsymbol{z}; \boldsymbol{\mu}^{\omega}, \boldsymbol{I}\right)$. The corresponding model with latent variable $\boldsymbol{z}$ is displayed in Fig.\ref{graph_model}(b). Here, Gaussians with different $\boldsymbol{\mu}^{\omega}$ should be defined for each label to obtain distinctive features with different shapes. To achieve this, we can construct a prior network for nonlinear function $\boldsymbol{\mu}^{\omega} = f_{\psi}\left(\boldsymbol{l}^{\omega}\right)$ which is trained with VAE simultaneously \cite{yu2018variational}.
The variational likelihood $q_\phi$ is also assumed to be a multivariate Gaussian distribution; $q_{{\phi}^\omega}\left(\cdot\right)= \mathcal{N}\left(\boldsymbol{z}; \boldsymbol{\mu}^{\boldsymbol{s}\omega}, (\boldsymbol{\sigma}^{\boldsymbol{s}\omega})^2\boldsymbol{I}\right)$
and
$q_{{\phi}^{\boldsymbol{v}}}\left(\cdot\right)= \mathcal{N}\left(\boldsymbol{z};\boldsymbol{\mu}^{\boldsymbol{sv}}, (\boldsymbol{\sigma}^{\boldsymbol{sv}})^2\boldsymbol{I}\right)$.
The encoded variables $\boldsymbol{\mu}^{\boldsymbol{s}}=(\boldsymbol{\mu}^{\boldsymbol{s}c}, \boldsymbol{\mu}^{\boldsymbol{s}i})$ then can be viewed as a shape feature, independent to the orientation $\boldsymbol{v}$.
Note that the latent variable $\boldsymbol{z}$ encoded from the variational likelihood $q_\phi$ follows Gaussian priors with $\boldsymbol{\mu}=(\boldsymbol{\mu}^c, \boldsymbol{\mu}^i)$, defined above.

Unlike the category and instance labels $\boldsymbol{l}^c$ and $\boldsymbol{l}^i$, Euler angles $\boldsymbol{v}$ can be directly applied to the Gaussian observation model; therefore we simply let $p_\psi\left(\boldsymbol{z}|\boldsymbol{v}\right) = \mathcal{N}\left(\boldsymbol{z};\boldsymbol{v}, (\sigma^{v})^2\boldsymbol{I}\right)$ with constant $\sigma^{v}$. Unfortunately, Euler angles(both radian and degree) are periodicity, i.e., $0$ and $2\pi$ radians actually represent the same orientation, which makes the network almost impossible to understand the object pose \cite{generate_chairs}. Instead, we let the network to infer $\sin v$ and $\cos v$.
The prior $p_\psi\left(\boldsymbol{z}|\boldsymbol{v}\right)$ then changes as follows:
\begin{align}
\nonumber
	p_\psi\left(z|\cos v\right)
	\simeq
	\mathcal{N}\left(
		v;
		e^{-(\sigma^v)^2/2}\cos v,
		\sigma^{cos}
	\right),
	\\
	\nonumber
	p_\psi\left(z|\sin v\right)
	\simeq
	\mathcal{N}\left(
		v;
		e^{-(\sigma^v)^2/2}\sin v,
		\sigma^{sin}
	\right)
\end{align}
where $\sigma^{cos} = 1/2+1/2e^{-2(\sigma^v)^2}\cos2v-e^{-(\sigma^v)^2}\cos^2v$,
and
$\sigma^{sin} = 1/2+-1/2e^{-2(\sigma^v)^2}\cos2v-e^{-(\sigma^v)^2}\sin^2v$.
For the simple network structure which commonly expects the diagonal covariance, we assume that $\sin v$ and $\cos v$ are independently sampled. The trigonometric functions are obviously not independent, therefore in actual training we give the additional constraint that $\sin^2v + \cos^2v = 1$. With inferred $\sin v$ and $\cos v$,
we easily obtain the rotation matrix of $\boldsymbol{v}$ for SLAM that follows the original prior $p_\psi\left(\boldsymbol{z}|\boldsymbol{v}\right)$.

Since the actual observation of objects mainly comes as the form of an RGB single view, we redefine the variational likelihood as $q_\phi\left(\boldsymbol{z}|\boldsymbol{s}^o\right)$, where $\boldsymbol{s}^o$ is a single view image of the object \cite{yu2018variational}\cite{3D_GAN}. The Bayesian graph model involving $\boldsymbol{s}^o$ is shown in Fig.\ref{graph_model}(c).

\section{Variational Latent Features and Semantic SLAM}
\subsection{Variational Features of Shape and Orientation for EM Formulation}
Suppose a mobile robot navigates the unknown area up to time step $T$. Let robot poses and a set of object observations be $\mathcal{X}=\{\boldsymbol{x}_t\}^T_{t=1}$ and $\mathcal{S}=\{\mathcal{S}_t\}^T_{t=1}$, respectively.
$k$'th object detection
$\boldsymbol{s}_k
=
(
\boldsymbol{s}_k^f, \boldsymbol{s}_k^{\boldsymbol{t}}
) \in \mathcal{S}_t$
on time step $t$ is composed of a full shape $\boldsymbol{s}^f$ and  a 3D coordinate $\boldsymbol{s}^{\boldsymbol{t}}$.
Similar to \cite{probDataSLAM}, assume that we previously have static landmarks $\mathcal{L}=\{l_m=\left(l^p_m, l^c_m\right) \}^M_{m=1}$, where $l^p=\{\boldsymbol{t},\boldsymbol{v}\}$ denotes the landmark pose composed of translation $\boldsymbol{t}$ and orientation $\boldsymbol{v}$. The object label $l^c$ consists of category and instance; $l^c=\boldsymbol{l}=\{\boldsymbol{l}^c, \boldsymbol{l}^i\}$.
We let $\mathbb{D}_t$ be the set of all possible data associations
$\mathcal{D}_t = \{\left(\alpha_k, \beta_k \right) \}^K_{k=1}$ which denotes that the object detection $\boldsymbol{s}_k$ of landmark $l_{\beta_k}$ was obtained at the robot pose $\boldsymbol{x}_{\alpha_k}$.
Also,
$\mathbb{D}_t\left(i,j\right)\subseteq\mathbb{D}_t$ is the set of all possible data association $\mathcal{D}_t^\prime = \{\left(\alpha_k^\prime, \beta_k^\prime \right) \}$ representing that $i$th object detection is assigned to $j$th landmark.

Now assume that observation events are iid, and we have
\begin{align}
\nonumber
	p\left(\mathcal{S}|\mathcal{X},\mathcal{L},\mathcal{D}\right)
	=
	\prod_{k}
	p(
	\boldsymbol{s}_k^t|\boldsymbol{x}_{\alpha_k}, \boldsymbol{t}_{\beta_k}
	)
	p(
	\boldsymbol{s}_k^{f}|\boldsymbol{x}_{\alpha_k}, \boldsymbol{v}_{\beta_k}
	)
	p(
	\boldsymbol{s}_k^{f}|\boldsymbol{l}_{\beta_k}
	).
\end{align}
Similar to \cite{yu2018variational}, using variational lower bound in \eqref{lower_bound} to approximate the observation model $p\left(\boldsymbol{s}^f|\boldsymbol{l},\boldsymbol{v}\right)$, the EM formulation for the complete semantic SLAM can be derived as follows:
\begin{align}
\label{emWeight}
	w^t_{ij}
	&\simeq
	\frac
	{
		\sum_{\mathcal{{D}}_t^\prime \in \mathbb{D}_t\left(i,j\right)}
		\prod_{k}
		p\left(
		\boldsymbol{s}^p_k|\boldsymbol{x}_{\alpha_k^\prime},l^p_{\beta_k^\prime}
		\right)
		p_\psi\left(
		\boldsymbol{\mu}^{\boldsymbol{s}}_k
		|
		\boldsymbol{l}_{{\beta}^\prime_k}
		\right)
	}
	{
		\sum_{\mathcal{{D}}_t \in \mathbb{D}_t}
		\prod_{k}
		p\left(
		\boldsymbol{s}^p_k|\boldsymbol{x}_{\alpha_k},l^p_{{\beta}_k}
		\right)
		p_\psi\left(
		\boldsymbol{\mu}^{\boldsymbol{s}}_k
		|
		\boldsymbol{l}_{{\beta}_k}
		\right)
	}
	\\
	\label{poseOptimize}
	\mathcal{X}, l^p
	&=
	\argmin_{\mathcal{X}, l^p}
		\sum_{t,k,j}
			-w^t_{kj}
			\log
				p\left(\boldsymbol{s}^{\boldsymbol{t}}_k|\boldsymbol{x}_t, \boldsymbol{t}_j\right)
				p\left(\boldsymbol{\mu}^{\boldsymbol{sv}}_k|\boldsymbol{x}_t, \boldsymbol{v}_j\right),
	\\
	\label{shapeOptimize}
	\boldsymbol{l}
	&=
	\argmin_{l^c}
	\sum_{t,k,j}
	-w^t_{kj}
	\log
	p_\psi\left(\boldsymbol{\mu}^{\boldsymbol{s}}_k | \boldsymbol{l}_j \right),
\end{align}
where
$p_\psi\left(\boldsymbol{z}|\boldsymbol{l}\right)
=
p_\psi(\boldsymbol{z}|\boldsymbol{l}^c)
p_\psi(\boldsymbol{z}|\boldsymbol{l}^i)
$.
For more details of the approximated EM formulation with variational lower bound, please refer to \cite{yu2018variational}.
Therefore, the approximated solution considering the 3D shape and its orientation of the semantic SLAM can be obtained, even if only the RGB single view $\boldsymbol{s}^o$ of objects is observed.
\begin{figure*}[t]
	\centering
	\includegraphics[scale=0.15]{proposed_networks_new.jpg}
	\caption{
		Proposed network architecture.
		The encoder part for the variational likelihood estimation is constructed using darknet-19 which is used in YOLOv2. We add an additional 2D convolution layer at the end of the darknet-19 so that the encoder becomes a fully convolution network.
		The decoder is composed of a dense layer and several 3D convolution-transpose layers. The prior networks which is trained with VAE simultaneously consist of fully connected layers. The end-to-end training of the proposed networks is achievable.
	}
	\label{proposed_networks}
\end{figure*}

\subsection{Pose and Feature Optimization of 3D Object}

For the case of landmarks, object-oriented SLAM basically deals with finite static targets \cite{slam++}\cite{probDataSLAM}\cite{categorySpecificObjectSLAM}; therefore, to get optimal labels $l^c=\boldsymbol{l}=(\boldsymbol{l}^c, \boldsymbol{l}^i)$ of \eqref{shapeOptimize}, it is necessary to perform maximum likelihood estimation (MLE) by substituting the all static landmark labels that are previously known.
However, since we adopt the VAE which is a probabilistic generative model, we can expand the static and discrete landmark features(denoted as labels) to the continuous ones(as latent variables). Although the network learns from the finite datasets and observes the finite 3D shapes, it can take advantage of nonlinear regression which fits the encoded latent variables into the continuous latent space. \cite{GAN},  \cite{DCGAN} and \cite{3D_GAN} also show that `walking in the latent space' of 2D images or 3D objects would be available. In the same vein, we can assume the continuous feature observation model.
In other words, the prior of $\boldsymbol{z}$ in \eqref{shapeOptimize} is defined not as $p_\psi\left(\boldsymbol{z}|\boldsymbol{l}\right)$, but as $p_\psi\left(\boldsymbol{z}|\boldsymbol{\mu}\right)$.
We therefore find not the optimal labels $\boldsymbol{l}$ of the object, but optimal shape features $\boldsymbol{\mu}=(\boldsymbol{\mu}^c, \boldsymbol{\mu}^i)$. To achieve this task, we simply derivate \eqref{shapeOptimize} which is a weighted sum of the logarithm of Gaussian. The optimal $\boldsymbol{\mu}$ then can be represented as the following:
\begin{align}
	\boldsymbol{\mu}_j = \sum_{t,k,j} -w^t_{kj} \boldsymbol{\mu}^{\boldsymbol{s}C}_k
	\label{optimalShape}
\end{align}
Consequently, without considering the static landmarks used in training, the optimal shape feature of $j$th observation is easily computed by replacing \eqref{shapeOptimize} with \eqref{optimalShape}.
For the pose estimation of \eqref{poseOptimize}, the optimal pose can be calculated with nonlinear pose graph optimizer such as \cite{g2o}\cite{isam}.

After iterating \eqref{emWeight} for \eqref{shapeOptimize} for EM algorithm, we can obtain the approximated solutions of not only the poses of the robot trajectory and the objects, but also the optimized features of 3D shapes. The volumetric full shapes of objects also can be obtained by decoding
$\boldsymbol{\mu}_j$.

\section{Training Details}
\subsection{Training Datasets and Data Augmentation}
The proposed network is assumed to have a single image that contains exactly one object as an input.
In order to train this network, we use 2D object images from PASCAL3D \cite{pascal3D} and ObjectNet3D \cite{objectnet3d} dataset cropped by the ground truth bounding box. The annotated 3D shapes are also used as the ground truth 3D shape, after being converted to the voxelized grid form.
We arbitrary choose 40 object categories in practice from the training datasets, which contain almost 100 categories in total. All instances in each class are fully used.
While training, we use data augmentation for 2D images to avoid overfitting similar to \cite{yolo9000}. Since our network should infer the orientation of the 3D object from the 2D image, random rotation and flipping are discarded which can change the object orientation.

\begin{figure*}[t]
	\centering
	\includegraphics[scale=0.25]{trjAndShape.jpg}
	\caption{
		Overview of the proposed system.
		Basically we estimate the visual odometry for every sequences.
		For each of the keyframes, object detection and feature encoding are performed simultaneously.
		Using the encoded features and the approximated observation model, SLAM optimization considering the object shape and the orientation is conducted.
		To solve the complete SLAM formulation, only the encoding process using the trained encoder is necessary.
		However, the full shape reconstruction of each observed object can be obtained at anytime.
	}
	\label{trjAndShape}
\end{figure*}
\subsection{Loss Function}
The negative variational lower bound from \eqref{lower_bound} is defined as the VAE loss \cite{VAE}\cite{3D_GAN}. The network is basically an auto-encoder, thus the expectation term in \eqref{lower_bound} can be regarded as the shape reconstruction loss $L^{rc}$. In our encoding scheme, network learns various tasks: encoding the shape features of category as well as instance level, and the orientations represented in trigonometric form. Since the network tries to deal with these tasks simultaneously, it hardly reaches the optimal point especially for the shape reconstruction. To prevent the network from plunging into the local minimum, we penalize the false negatives of the predicted voxel grids by defining $L^{rc}$ as follows \cite{generative}:
\begin{align}
\nonumber
	&L^{rc} =
	\\
	\nonumber
	&\sum_j
	-\gamma \boldsymbol{s}_j^{ft} \log\left(\boldsymbol{s}_j^{fp}\right)
	-\left(1-\gamma\right)\left(1-\boldsymbol{s}_j^{ft}\right)\log \left(1-\boldsymbol{s}_j^{fp}\right),
\end{align}
where $\boldsymbol{s}_j^{fp}$ and $\boldsymbol{s}_j^{ft}$ are the $j$th component of the binary variables for the predicted and the target 3D shape $\boldsymbol{s}^f$, respectively. By setting $\gamma$ up to $0.5$, our network avoid the local minimum that simply predicts the empty voxel grids.

For the pose estimation of the object shape, we choose the Euler angle composed of azimuth, elevation and in-plane rotation angles in radian. Since the deviation $\sigma^v$ of each angle for the approximated observation model can be manually selected, we set $\sigma^v = 0.05\text{}rad \simeq 2.87^{\circ}$.

\subsection{Networks}
Recent researches of the monocular SLAM typically aim the real-time performance.
To meet this trend, we adopt darknet-19 for our encoder, which is capable of real-time operation \cite{yolo9000}.
The encoder is then constructed by adding a convolution layer followed by a global max-pooling layer on top of the darknet-19 network.
When the encoder is solely used for feature encoding except the decoder for 3D shape reconstruction, it can operate at about $30$ fps.
In practice, basically 10 to 15 objects are observed in a single scene; when the images of objects are cropped from a scene go parallel to the encoder, the encoding process still can operate at about $30$ fps.
Since our network learns about the objects and their pose from the 2D RGB images, we pre-train the network for two tasks in order; object category classification of the Imagenet dataset \cite{imagenet}, and orientation classification of the Render for CNN dataset \cite{renderforcnn}.

The decoder of our network follows the structure introduced in \cite{3D_GAN}.
To make the decoder familiar with the 3D object shape, we construct the VAE using an additional 3D encoder together, and pre-train the network using ModelNet40 dataset \cite{ShapeNet}.
In this pre-training of the decoder, a prior network consists of $3$ dense layers are pre-trained together.
After pre-trainings
, we combine these networks and train the proposed observation model. The proposed network structure is displayed in Fig.~\ref{proposed_networks}.

\begin{figure*}[t]
	\centering
	\includegraphics[scale=0.4]{trjCompare3Rows.jpg}
	\caption{
		Comparison of the estimated robot trajectory from SLAM. From first to last rows: results of the KITTI 00, 05 and 06 sequences respectively. (a-c): visual odometry, estimation results with shape features only, and results with shape and orientation together, respectively.
		Since most of the objects in KITTI dataset are car category and have similar shapes to each other, SLAM with only shape feature hardly performs the complete loop closing.
		Results with features obtained from imagenet pre-trained darknet=19 and vggnet are not displayed as they have no significant difference to the visual odometry results.
	}
	\label{trjCompare}
\end{figure*}
\begin{table*}[h]
	\caption{Comparison of the Robot Trajectory Estimation}
	\label{ATEandRPE}
	\begin{center}
		\begin{tabular}{c c c c c c c c c c c c}
			\hline
			Encoding & \multicolumn{2}{c}{KITTI00} & &\multicolumn{2}{c}{KITTI05} & &\multicolumn{2}{c}{KITTI06} & &\multicolumn{2}{c}{TUMf3} \\
			\cline{2-3}\cline{5-6}\cline{8-9}\cline{11-12}
			Methods & ATE(m) & RPE(m) & & ATE(m) & RPE(m) & & ATE(m) & RPE(m) & & ATE(m) & RPE(m)\\
			\hline
			visual odometry &  234.94 & 249.21 & & 33.52 & 44.28 & & 28.11 & 31.56 & & 3.00 & 2.90 \\
			ours(w/o object orientation) & 44.34 & 46.82 & & 43.07 & 39.69 & & 33.40 & 30.69 & & 2.87 & 2.97 \\
			ours(with object orientation) & 31.55 & 29.33 & & 25.11 & 29.45 & & 20.83 & 18.48 & & 2.87 & 2.86 \\
			\hline
		\end{tabular}
	\end{center}
\end{table*}
\section{Experiments}
\subsection{Object Detection and Feature Encoding}
In order to achieve the real-time object detection and obtain the bounding boxes of objects in 2D scene with multiple objects, we adopt YOLOv2 \cite{yolo9000}.
Object images obtained by cropping bounding boxes in the scene are passed through our network in parallel.
Since the encoder is solely used for the feature encoding, it takes almost twice as much time as \cite{yolo9000} to perform the object detection and feature encoding altogether.
For the efficiency of SLAM, we simply select every 15th frame as a keyframe where detection and feature encoding is performed.

\subsection{Pose and Feature Graph Optimization}
For the initial guess of robot trajectory $\mathcal{X}$ in \eqref{poseOptimize}, we use visual odometry based on 8 point algorithm \cite{visualOdometry}. Additional optimizations for odometry correction such as local bundle adjustment are not used.
To avoid the scale problem, similar to \cite{probDataSLAM} we assume that the real-world scale can be known from the device such as IMU. Note that we exploit the IMU data not for the robot pose estimation, but only for the real-world scale prediction. The centers of the detected objects are defined with 3D coordinates, given by the median values of each coordinates of the visual features within the object's bounding box.

In the pose and feature graph, the robot node contains $SE3$ pose $\boldsymbol{x}$. The object node also contains $SE3$ pose $\left(\boldsymbol{t}, \boldsymbol{v} \right)$, and its encoded feature $\boldsymbol{\mu^s}$.
For the edges between features, all observed object pairs are fully connected since all the possible data association should be considered in \eqref{emWeight}. In practice for computational reasons, however, we disconnect the edges between two observed objects when the weight $w$ between them is under the threshold $\delta$.

\subsection{Optimization Results for SLAM}
We use KITTI \cite{kitti} and TUM \cite{tumDataset} datasets for SLAM experiments in various environments.
The object images obtained by the object detection are encoded to features with the proposed method.
For the evaluation of the inferred shape features and the orientations of objects, we compare the optimization results using the aspects above with the results using shape features only.
In order to compare SLAM results with other feature extraction methods, we use three different encoders: encoder for 2D-3D autoencoder, darknet-19 \cite{yolo9000} pretrained for imagenet \cite{imagenet} classification, and vggnet16 \cite{vggnet}.
We train the 2D-3D autoencoder to infer the 3D shape from the 2D image of the object similar to the proposed network, but without any other probabilistic constraints.

The optimization results for the robot trajectory are shown in Fig.~\ref{trjCompare}. Although any additional loop closing techniques are not adopted, the proposed method naturally consider loop closing points in a large space in the SLAM optimization process.
The optimizations with the shape features without the orientations of the objects, failed in places with a lot of vehicles having similar shapes.
Since the obtained features from the 2D-3D autoencoder, pretrained darknet-19 and vggnet follow non-linear distributions, optimization results with these features have no difference with the odometry.
The absolute position errors with respect to ground truth are shown in TABLE \ref{ATEandRPE}. For TUMf3 datasets, which is a sequence of images mainly focusing on a desk, complete optimization is unavailable since our method exploits various objects in the images.

3D reconstructions for the observed objects can be performed after the pose and feature optimization.
After performing SLAM on the KITTI00 sequences, we obtain the 3D shape of the corresponding features with the trained decoder. The reconstruction results for an arbitrary scene in this sequence is shown in Fig.~\ref{trjAndShape}.

\section{CONCLUSION}
We have shown that a variational object observation model enables the complete probabilistic semantic SLAM.
The high-dimensional features such as the 3D shape of objects follow an intractable distribution; therefore in the existing SLAM formulation it is scarcely considered to the optimize the feature as well as the poses of the object.
To overcome this problem, we approximate the observation model of the 3D object to a tractable distribution using a generative model.
Since the robot with a mono camera basically observes an RGB single view of an object, the proposed algorithm infers a variational likelihood of the full shape from the single view.
Pose and feature optimization can be simultaneously performed using the encoded features, and the 3D full shape can also be obtained through the decoding process.
Our experiments suggest that the graph optimization and automatic loop closing is performed smoothly.
To evaluate the approximated observation model and the encoded features, we conduct SLAM and shape reconstruction for the environments with various objects.

\section*{Acknowledgement}
We would like to express our gratitude to Jihoon Moon for his comments and proofreading on this manuscript.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


Partly to allow extra time for growth, additional hole phases are inserted in most cell cycles—a G 1 part between M part and S section and a G2 phase between S phase and mitosis Thus, the eucaryotic cell cycle is historically divided into four sequential phases: G1, S, G2, and M ( Figure 17-3 ). G1, S, and G2 collectively are called interphase In a typical human cell proliferating in tradition, interphase might occupy 23 hours of a 24 hour cycle, with 1 hour for M part. A distinctive e-commerce shop comes with innumerable features and Gowebbi gives you the opportunity to cherry-pick those features suiting your marketing and selling obligations. When connected you can easily use your online store data to inform your sales and marketing. In their overall performance during the course of the last few solar cycles, precursor methods have clearly been superior to extrapolation methods.
Single Biggest Advantage of Selling Physical vs. Digital Products: Value concept. This is because the products look and feel like 'Amazon products' to the user, even when they are being sold through a third-party such as yourself. For ecommerce websites, online marketing is much more important, because offline advertising—while certainly still possible—is typically not a favored option.
10 Mind Numbing Facts About 7 Figure Cycle.
Bonanza focuses on fashion and home and garden products and has a marketplace for sellers to offer similar types of products to their audience. The Vee Model endorses the INCOSE Systems Engineering Handbook (INCOSE 2012) definition of life cycle stages and their purposes or activities, as shown in Figure 2 below. Add your store's info and connect it to a payment processor service.
In 2006, Amazon reported that cross-selling and upselling contributed as much as 35% of their revenue. For example, if you have two customers named Bob and Jane, testing software ensures that Bob sees sales letter "A" at the same time Jane sees sales letter "B"-both on the same URL. The 100k factory revolution really comes with a training which is being delivered in an over the shoulder" way.
Figure 22. In broad river valleys, small local ground-water flow systems associated with terraces overlie more regional ground-water flow systems. Zapier's free Ultimate Guide to Email Marketing gives you the tips and tools to send great emails to your customers and keep them engaged. I started out of college, entry level sales, literally pounding the payment working for a payroll company.
Yet there have been relatively few attempts to date to use this data series for solar cycle prediction. As a result, e-commerce businesses need to ensure that the right product is delivered to the customer, in good condition, and within the period that the customer expected. Figure 21. In mountainous terrain, ground water can discharge at the base of steep slopes (left side of valley), at the edges of flood plains (right side of valley), and to the stream.
7 Places That You Can Find 7 Figure Cycle.
While a regular e-commerce site only has products listed for one business, visitors to sites like eBay or Amazon will see a wide variety of goods. In January 2018, Aidan Booth and Steve Clayton, two online entrepreneurs who have years of experience in building out businesses, are releasing a online course. The project will be an ideal opportunity for major regional and international manufacturers to store their goods, products and spare parts in state-of-the-art, fully-equipped, technology-enabled warehouses, to be shipped later to the local markets in record time via e-commerce.
And just like any other business the 7 figure cycle has a lot of unique benefits which when exploited well makes you a lot of money. If your ecommerce store is worth $450k according to the broker, be flexible on that price, but you might want to set a hard minimum of $430k (or whatever you are willing to walk away with) after the broker takes out their commission fee for marketing your business.
Aidan Booth met Steve Clayton when he was looking to lay out structured plans for his online Business which had plenty of websites (a factory of websites). If you're running a long-established retail business, chances are you now have a website gathering orders from your customers and processing them through e-commerce. Not many can compete with huge businesses like Amazon or eBay, but anyone can open an online store and start trading within a matter of minutes.
I have also found that with only 1k email audience, I was still able to build a 6-figure business because of the PR and partnerships we have, even more reason to continue pumping juice into the partnerships traffic source!! As a result, environmental conditions at the interface between ground water and surface water reflect changes in the broader landscape.
I think that a lot of people view the check-out process as an after-thought and spend a significantly greater amount of time and energy on the landing page! So let's talk about ways to nurture your leads through the sales process in an eCommerce fashion. I want to start onling Trading of products like ladies & gents footwear,clothes,kids clothes & footwear and toys, handicraft.


text:
Q: Change DateTime format in XML generated by SQL Server I am creating XML from SQL Server database tables, using for XML path to generate a node for each column in the table. I need to be able to store any DateTime column a format like "02/15/2016 07:39:45 PM" instead of the standard "2016-02-15T07:39:45" format. This needs to be flexible enough to make the change to any DateTime column in the database, some tables have more of these columns than others, but, if it helps, every column name for a DateTime columns ends with "_DT". So, Ideally, the XML returned would have the formatting applied to any DateTime field, regardless of its position, and how many are included

A: Probably you might need to convert the datetime data type value into varchar data type in order to display the date value to a format which you wanted.


text:
700 Club Interactive - January 14, 2020
Widow of Chris Kyle ("American Sniper"), Taya Kyle discusses her life and work with families of military service members and first responders as well as her new book, "American Spirit."
Mentioned in the Video
Childhood Michael Jordan Fan Now Coaching MJ's Team
In a game of rebounds - James Borrego has the knack - for bouncing back! His resiliency and persistence turned heads.
Man's New Identity Leads to Extreme Weight Loss
"Yeah, when people see pictures of me when I was younger and they look back at those pictures, they really can't believe that's who they're looking at, that it's me." From an early age, Justin had a passion for food. He recalls, "Food was like my best friend.
On The Home Front - January 14, 2020
Taya Kyle Uncovers "American Spirit"
Healthy Living - January 14, 2020
The 700 Club - January 22, 2020
Omar and Nancy thought a retail business would be a long-term income, but it turned into ongoing debt. After regrouping, they made a decision they...
Running your own business can be challenging. Jaime and Jenny often struggled to pay bills on time, so they made a decision that brought abundance...
When Jamal lost his job, he had to work part-time jobs and still couldn't provide for his family. Then a financial challenge and a step of faith...
Sales dried up in Keith and Terry's business, and they practically lost everything—even their home. In this tough time, they were reminded of a money...
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Military Couple Faces Financial Strain
George and Cori met while serving in the Marine Corps. Later as a married couple, they battled a personal crisis—a seriously ill child, mounting...
Cartoon Transforms Bully
Anastasia was the school bully, saying that's the way others treated her. But after she bullied Sasha, Sasha's mom came up with a brilliant plan that...
God Is Always With You—Even When Life Looks Hopeless
Greg and Sharon found themselves in deep financial difficulty operating their beach motel and saw no way out. But they trusted in God to provide—and...
Your Questions, Honest Answers


text:
"Father" is the third single from LL Cool J's seventh album, Phenomenon.  It was released on January 13, 1998 for Def Jam Recordings and was produced by the Trackmasters.

"Father", which was an auto-biographical song that detailed LL's abuse from his mother's boyfriend, would prove to be the most successful single from the album, it made it to #18 on the Billboard Hot 100 and became his eighth single to reach #1 on the Hot Rap Singles. It sold 500,000 copies. The song's main sample is "Father Figure" by George Michael.

The music video was directed by Samuel Bayer.

Track listing

A-Side
"Father" (Radio Edit)- 3:57
"Father" (Instrumental)- 4:42

B-Side
"4,3,2,1" (Remix Instrumental)- 5:00
"4,3,2,1" (Instrumental)- 4:15

Charts

Weekly charts

End of year charts

References

1998 singles
LL Cool J songs
Music videos directed by Samuel Bayer
Songs written by LL Cool J
1997 songs
Songs written by George Michael
Song recordings produced by Trackmasters
Def Jam Recordings singles
Songs written by Samuel Barnes (songwriter)
Songs written by Jean-Claude Olivier

redpajama_set_name: RedPajamaWikipedia


text:
Ridgefield Daily Voice serves Ridgefield, CT
Trumbull-Monroe
serves Easton, Monroe, Redding & Trumbull
serves Norwalk & Rowayton
serves Weston & Westport
Strong Wind Gusts Will Be Followed By Storm Bringing Snowfall To Region; First Projected Totals
Joe Lombardi
Facebook @joe_lombardi Email me Read More Stories
12/06/2021 8:00 a.m.
Most of the region will generally see a coating to 1 inch of snowfall, with locally higher totals of 1 to 3 inches possible in higher elevations. Photo Credit: AccuWeather
Most of the region will see 30 to 50 mph wind gusts on Monday, Dec. 5 (light purple) with 50 to 70 mph gusts in some spots (dark purple). Photo Credit: AccuWeather
Most of the Northeast will see rain (in green) from the storm system that will sweep through the region on Monday, Dec. 6. Photo Credit: AccuWeather
Temperatures will plummet on Tuesday, Dec. 7, setting the stage for Wednesday's storm. Photo Credit: AccuWeather
More Articles Weather
Nor'easter Nears: Track Shifts For Blockbuster Storm With Proje…
Nor'easter Nears: Brand-New Projections For Snowfall Accumulati…
Eversource Prestaging Crews In Advance Of Nor'easter Set To Bri…
An active weather pattern will start with strong gusty winds that could cause power outages followed by the first storm of the season bringing accumulating snowfall for most of the region.
Accumulation totals for the midweek storm, the second of two systems that will sweep through the region in the coming days, have now been released.
That time frame for that system is early Wednesday morning, Dec. 8, into Wednesday evening.
Most of the region is expected to generally see a coating to 1 inch of snowfall, with locally higher totals of 1 to 3 inches possible in higher elevations. (See the first image above.)
There is still some uncertainty surrounding the storm's track, so there is a chance those projections could change prior to Wednesday.
But before that system sweeps through, another storm bringing rain, showers, and strong wind with gusts up to 50 miles per hour will be followed by a sharp drop in temperatures, setting the stage for the potential for the midweek storm being a snowmaker.
That first storm, a frontal system, arrived Monday morning, Dec. 6. Rainfall from the initial part of the system will taper off in the middle of the morning. Skies will remain cloudy throughout the day, with a brief rise in temperatures, with the high reaching the upper 50s.
The second round of the storm will come just after nightfall on Monday. Along with rain and showers, most of the region will see 30 to 50 mph wind gusts on Monday (light purple in the second image above) with 50 to 70 mph gusts in some spots (dark purple), according to AccuWeather. Scattered power outages are possible.
Precipitation from that storm will mainly be rain (in green in the third image above).
Scattered showers are expected to linger through the early overnight hours on Tuesday, Dec. 7.
Skies will then clear on Tuesday as temperatures plummet, with a high of only about 40 degrees on a mostly sunny day. For Tuesday's temperatures with the wind-chill factor, click on the fourth image.
Check back to Daily Voice for updates.
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Not in Ridgefield?


text:
Q: how to sum list values from API in flutter Does anyone here know/have references/examples of how to add up the values in the list in Flutter. Thanks

A: use sum:
import 'package:collection/collection.dart';

void main() {
  final list = [1, 2, 3, 4];
  final sum = list.sum;
  print(sum); // prints 10
}

Your question is similar to the question here, refer to it for more information

A: you can use .fold() method

*

*fold method:

T fold<T>(T initialValue, T Function(T, Data) combine)

example for sum list of object:
void main() {
  List<Data> listData = [
    Data(count: 10, name: 'a'),
    Data(count: 12, name: 'bc'),
    Data(count: 21, name: 'abc'),
  ];

  int sum = listData.fold(0, (int preValue, data) => preValue + data.count);

  print(sum);// 43
}

class Data {
  int count;
  String name;
  Data({required this.count, required this.name});
}


text:
Rediff.com » News » Shilpa Shetty
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From Shilpa Shetty's lunch in Venice to Anushka Sharma's pottery class in London,...
Bollywood stars like Anushka Sharma, Kareena Kapoor, Shilpa Shetty and more are vacationing all around the world. See their pics here. bollywood... ...
https://www.hindustantimes.com/bollywood/from-shilpa-shetty-s-lunch-in-venice-to-anushka-sharma-s-pottery-class-in-london-check-out-celebs-vacation-pics/story-7kHG3nGu5dDOfazZF4G3FL.html
Fight the FAT battle! Here's how
Yoga acharya Shameem Akthar takes at a look at what you can do to avoid the pitfall of obesity. *Photograph: Kind courtesy silviarita/Pixabay.com... ...
http://www.rediff.com/getahead/report/fight-the-fat-battle-heres-how/20190715.htm
John Cena transforms Shilpa Shetty into Stone Cold, she replies 'This is hilarious'....
John Cena has shared a morphed image of Shilpa Shetty with her face superimposed on the body of WWE wrestler Stone Cold. bollywood Updated: Jul 13,... ...
https://www.hindustantimes.com/bollywood/john-cena-transforms-shilpa-shetty-into-stone-cold-she-replies-this-is-hilarious-see-pic/story-uZZoy3XUc5ACdBXtM9MmAM.html
Super 30 Candid Shots of Bollywood
Bollywood stars may look very glamorous in their movies and at parties, but just once in a while, they let us peek into their regular lives when... ...
http://www.rediff.com/movies/special/super-30-candid-shots-of-bollywood/20190712.htm
Shilpa to make her comeback on the big screen
There's no arguing that Shilpa Shetty Kundra can give the youngsters a run for their money in the fitness space. The stunning actress turned author... ...
https://timesofindia.indiatimes.com/entertainment/hindi/bollywood/photo-features/celebs-who-made-headlines-in-2016/shilpa-shetty-kundra-to-make-her-comeback-on-the-silver-screen/photostory/70157615.cms
Dishkiyaoon
Dishkiyaoon is an Bollywood film...
Shilpa Shetty Photos
Latest Shilpa Shetty Photos, Shilpa...