text:
An inside look at the real estate Tiger Woods has been connected to, from a private island in Sweden to a $54 million mansion in one of America's wealthiest ZIP codes
Libertina Brandt
A look at Tiger Woods' real-estate portfolio.
AP Photo/David J. Phillip
Tiger Woods had a net worth of $800 million as of 2018, according to Forbes.
While his name has been connected to a private island in Sweden and a $54 million mansion in one of the wealthiest ZIP codes in the country, not much is known about his personal real-estate investments.
His real-estate related business ventures include investing in a private resort in the Bahamas and a restaurant in Florida.
Business Insider rounded up three residential properties with direct ties to Woods.
Along with his many PGA Tour wins, golf legend Tiger Woods has also accumulated an impressive fortune: In 2018, Forbes estimated that Woods was worth $800 million.
Woods is often seen lounging in his 155-foot-long, $20 million megayacht and private jet. He has also invested in Albany, Bahamas, a private luxury resort, and owns The Woods Jupiter, an American-style restaurant in Jupiter, Florida.
However, public information on his residential real-estate acquisitions is limited.
We do know that after the public scandals surrounding his extramarital affairs and divorce nearly a decade ago, Woods sold some real-estate assets and bought others.
His name has been connected to a private island in Sweden that was designed for him, but which he did not own and which, according to the Los Angeles Times, he never moved into. He also built a $54 million mansion in one of the wealthiest ZIP codes in the country.
Read more: Meet Erica Herman, Tiger Woods' mysterious girlfriend who was at the Masters to celebrate his win
Business Insider rounded up three residential properties that have had direct ties to Woods. Of those three, two have been sold.
Keep reading for the full list.
The Jupiter Island estate: Tiger Woods owns a massive home on Jupiter Island. Jupiter Island is located on the coast of Martin County, Florida, and is about 50 minutes away from West Palm Beach by car.
Source: Forbes, Google Maps
The ultra-exclusive Jupiter Island is home to some seriously wealthy residents. The island has a population of just 907 and a median home value of $5,104,400.
A beach on Jupiter, Island Florida.
Source: Los Angeles Times, United States Census Bureau, Zillow
Woods originally purchased the 12-acre oceanfront property in 2006 for $40 million.
ECHO Fine Properties
He built a $54 million mansion on the estate and moved into the home in 2010.
Source: Golf News Net, The Washington Post
The property boasts a 3.5-acre golf course, a tennis court, an oxygen tank, a full gym, and pools.
Source: Forbes
The Orlando mansion: Tiger Woods used to own a home in Windermere, Florida ...
Source: Business Insider
... but he sold it in 2013 for $2.2 million to Bubba Watson.
REUTERS and Getty Images
The home was the site of Woods' infamous 2009 car crash. The golf pro crashed his car into a fire hydrant in front of his Windermere home just days after reports of his cheating scandal went public.
Source: ESPN
The home includes a movie theater, seven bedrooms, a wet bar, a two-story patio, and a guest house. In 2013, Watson listed the home for $5.6 million. It sold for $4.2 million in 2016.
The block where Tiger Woods' old Windermere home is believed to be located.
Source: Golf Digest, Los Angeles Times
A private island: A Swedish businessman owned and developed a private island in Sweden for Tiger Woods and his then-wife Elin Nordegren ...
Source: Business Insider, Los Angeles Times
... but the couple, who divorced in 2010, never moved in.
VLADI PRIVATE ISLANDS
According to the LA Times, the private island is in Lake Mälaren. It's about an hour from Stockholm, the country's capital.
Via Vladi Private Islands
It boasts a private harbor, a rocky shoreline, several acres of untouched woods, a hunting lodge, a large, open yard, six golf tees, and a private golf course.
The home was designed with a hunting-lodge style.
In 2015, the 62-acre island was listed for $7.1 million. It's unclear how much it was sold for.
But Woods hasn't only purchased real estate for himself.
Andrew Redington/Getty Images)
Source: The Real Deal
In 2007, Woods reportedly bought his mother two plots of land in Jupiter, Florida for around $2.4 million. Public records also show that in 2010, another home in Windermere, Florida was listed under both Tiger Woods' and his father's names. It was assessed at $1.31 million.
Tiger Woods' parents Earl Woods and Tida Woods.
Doug Benc/Getty Images
SEE ALSO: 6 famous homes you can rent right now, from the Malibu beach house in 'Big Little Lies' to the 2-story farmhouse in 'Field of Dreams'
DON'T MISS: A look inside multimillionaire Jeffrey Epstein's real-estate portfolio, where sex trafficking reportedly took place and a $77 million Manhattan mansion may have been acquired for $0
More: Features Real Estate Tiger Woods Arts & Culture
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The community of Fernie is coming together to help Brooke Malakoff, and her family, as she continues her battle with cancer.
Sep. 21, 2012 5:00 p.m.
A trust fund has been set up, a bake sale is in the works, and the Ghostriders are on board. The community of Fernie is coming together to help Brooke Malakoff, and her family, as she continues her battle with cancer.
Brooke was originally diagnosed with a benign tumour on her lung just before Christmas last year. She was told she would need surgery and that it could be taken care of after the holidays. Shortly after the original diagnosis, Brooke's condition worsened, and after heading back to the hospital the doctors determined that the tumour had grown.
The tumour was re-diagnosed as malignant and Brooke was immediately flown to Vancouver to undergo emergency surgery. Brooke's stepfather said the doctors believed without the surgery, she would only have one week to live.
Brooke lost her left lung during surgery, however the operation was successful. She was able to return home to Fernie to finish her last year of high school and go back to her job at Overwaitea. Just a few short months later, Brooke and her family learned that her cancer had returned.
Today, Brooke is undergoing treatment in Vancouver, where she and her mother have been on and off since May. Brooke's stepfather and brother remain in Fernie.
While the family is doing their best to help Brooke keep fighting, the people of Fernie are stepping up to help in any way they can. Cathy Maurier-Prince has opened a CIBC trust fund in Brooke's name, giving anyone the ability to make a deposit of any amount.
Coming up in November, Dawna McDowell is organising a baking table at a community craft fair. She's opening up the table to allow members of the community to contribute baked goods to raise money that will be split between Brooke's trust fund, and another local family.
With no financial goal set, the hope is for the community to continue to fundraise as much as they can for Brooke and her family. Anyone who would like to make a contribution to Brooke's trust fund can visit their local CIBC branch to make a deposit, or email transfer a donation to gtorr@telus.net.


text:
using System.Collections.Generic;

using Sce.Atf;
using Sce.Atf.Adaptation;
using Sce.Atf.Applications;
using Sce.Atf.Dom;

using LevelEditorCore;

namespace LevelEditor.DomNodeAdapters
{

    ///
    /// Root node in a LevelEditor Game document
    public class Game : DomNodeAdapter, IGame
    {
        #region INameable Members

        ///
        /// Gets or sets the name
        public string Name
        {
            get { return (string)DomNode.GetAttribute(Schema.gameType.nameAttribute); }
            set { DomNode.SetAttribute(Schema.gameType.nameAttribute, value); }
        }

        #endregion

        #region IListable Members

        ///
        /// Provides info for the ProjectLister tree view and other controls
        /// <param name="info">Item info passed in and modified by the method</param>
        public void GetInfo(ItemInfo info)
        {
            info.ImageIndex = Util.GetTypeImageIndex(DomNode.Type, info.GetImageList());
            info.Label = Name;
        }

        #endregion

        #region IHierarchical Members

        ///
        /// Returns true iff the specified child can be inserted
        /// <param name="child">Child to be inserted</param>
        /// <returns>True iff the specified child can be inserted as a child of this object</returns>
        /// <remarks>Unlike most other IHierarchical implementations, the specified objects
        /// are NOT inserted as children of this object but rather as children
        /// of the GameObjectFolder child (thus becoming grand children of Game)</remarks>
        public bool CanAddChild(object child)
        {
            return child.Is<GameReference>();
        }

        ///
        /// Inserts the specified child
        /// <param name="child">Child to be inserted</param>
        /// <remarks>Unlike most other IHierarchical implementations, the specified objects
        /// are NOT inserted as children of this object but rather as children
        /// of the GameObjectFolder child (thus becoming grand children of Game).</remarks>
        public bool AddChild(object child)
        {
            GameReference gameref = child.As<GameReference>();
            if (gameref != null)
            {
                IList<GameReference> grefList = GetChildList<GameReference>(Schema.gameType.gameReferenceChild);
                grefList.Add(gameref);
                return true;
            }
            return false;
        }

        #endregion

        #region IGame Members

        ///
        /// Gets the Grid child
        public IGrid Grid
        {
            get { return GetChild<IGrid>(Schema.gameType.gridChild); }
        }

        public IReference<IGame> Parent
        {
            get { return m_parent; }
        }

        public IEnumerable<IReference<IGame>> GameReferences
        {
            get { return GetChildList<IReference<IGame>>(Schema.gameType.gameReferenceChild); }
        }

        public IGameObjectFolder RootGameObjectFolder
        {
            get
            {
                GameObjectFolder rootFolder = GetChild<GameObjectFolder>(Schema.gameType.gameObjectFolderChild);
                if (rootFolder == null)
                {
                    rootFolder = (GameObjectFolder)GameObjectFolder.Create();
                    rootFolder.Name = "GameObjects".Localize("this is the name of a folder in the project lister");
                    SetChild(Schema.gameType.gameObjectFolderChild, rootFolder);
                }
                return rootFolder;
            }

        }

        public IGameObjectGroup CreateGameObjectGroup()
        {
            GameObjectGroup gobGroup = new DomNode(Schema.gameObjectGroupType.Type).As<GameObjectGroup>();
            gobGroup.Name = "GameObjectGroup".Localize("this is the name of a folder in the project lister");
            return gobGroup;
        }

        public IReference<IResource> CreateResourceReference(IResource resource)
        {
            return ResourceReference.Create(resource);
        }

        public IGameObjectFolder CreateGameObjectFolder()
        {
            return GameObjectFolder.Create();
        }

        #endregion

        internal void SetParent(IReference<IGame> gameref)
        {
            m_parent = gameref;
        }

        private IReference<IGame> m_parent;
    }
}

redpajama_set_name: RedPajamaGithub


text:
Sökresultat - Anqa Publishing
Seven Days of the Heart
av Muhyiddin Ibn 'Arabi
Providing a precious glimpse into the practice of the mystical life within the Sufi tradition, this volume marks the first time any of Ibn 'Arabi's prayers have been translated into another language. The 14 prayers include not only the most astoundi…
Divine Sayings
A collection of 101 hadith sayings, this work is one of the most important and influential early collections of hadith qudsi. Falling into three categories, the first 40 sayings each have a full, unbroken chain of transmission that goes back to God…
Lamp of Mysteries
av Isma'il Anqarawi
Unearthing a hidden treasure from the golden age of Ottoman scholarship, this examination presents original, expert Arabic observations on the light verse of the Quran. Presented in English for the first time, the study reveals profound insights int…
Nightingale in the Garden of Love
av Paul Ballanfat
A detailed overview of the life and teachings of one of the great Ottoman Sufi masters, Mehmed Muhyiddin Uftade, is accompanied by an English translation of a collection of his religious poetry in this tribute to the Turkish and Persian spiritual tr…
Prayer for Spiritual Elevation & Protection
av Muhyiddin Ibn'Arabi
While used for centuries in Sufi circles, the prayer known as 'The Most Elevated Cycle' or 'The Prayer of Protection', had never before been available in English. This book provides a lucid English translation of this beautiful and powerful prayer w…
Ibn al-'Arabi and the Sufis
av Binyamin Abrahamov
Ibn al-'Arabi and the Sufis is a fascinating and groundbreaking analysis of the extent to which various major Sufi figures contributed to the mystical philosophy of Ibn al-'Arabi. While recent scholarship has tended to concentrate on his teachings a…
The Alchemy of Human Happiness
Häftad - 2018 - Arabiska - ISBN: 9781905937592
The quest for happiness and fulfilment lies at the very heart of human life, but for Ibn 'Arabi there is a realm beyond our ordinary understanding of happiness, where the human stands truly fulfilled, in vision of Reality. This is a goal within the…
Ibn 'Arabi & Modern Thought
av Peter Coates
In these global times it is a curious and pertinent fact that the life and writings of Muhyiddin Ibn 'Arabi, which since the 12th century have incalculably influenced the metaphysical structure of much Oriental thought and practice, still remain rel…
Teachings of a Perfect Master
av Henry Bayman
Collected in entirety for the very first time, this study reflects more than 25 years of close contact with the Sufi Masters of Central Anatolia, with most of that time spent in the presence of the peerless Sufi teacher, Mr. Ahmet Kayhan. Out of the…
Unlimited Mercifier
av Stephen Hirtenstein
This unique portrait of the great Andalusian mystic uses his own writings to tell the story of his life and teachings. Chapters of biography are interwoven with chapters portraying the central elements of his thought and are supplemented with photog…
Beshara and Ibn 'Arabi
av Suha Taji-Farouki
Investigating sufi-inspired spirituality in the modern world, this multi-faceted and interdisciplinary volume focuses on Beshara, a spiritual movement that applies the teachings of Ibn 'Arabi in a non-Muslim context. It traces the movement's emergen…
Investigating sufi-inspired spirituality in the modern world, this multi-faceted and interdisciplinary volume focuses on Beshara, a spiritual movement that applies the teachings of Ibn 'Arabi in a non-Muslim context. It traces the movements emergenc…
Secrets of Voyaging
Ibn 'Arabi is one of the greatest writers of mystical literature of any era. The voyages recounted in the enigmatic Secrets of Voyaging offer readers an inexhaustible source of reflection on the nature of the journeys of life. This unique book explo…
Ibn 'Arabi (1165-1240) has long been known as a great spiritual master. His many works of prose and poetry are beginning to be more accessible in translation in Western languages. They possess the remarkable quality of being able to speak to people…
Four Pillars of Spiritual Transformation
Objective and illuminating, this treatise, written by Sufi leader Muhyiddin Ibn 'Arabi, presents a fundamental analysis of spiritual practice. Underscoring the importance of silence, seclusion, hunger, and vigilance, this guide demonstrates that the…
Contemplation of the Holy Mysteries
A major work of mystical literature, this account focuses on 14 visions in the form of dramatic conversations with the divine, interspersed with dazzling visionary episodes regarding the nature of existence, humans' relationship with reality, and th…

redpajama_set_name: RedPajamaCommonCrawl


text:
Special Edition Using Microsoft Office 2007
by Ed Bott
Windows 10 All-In-One For Dummies
Windows 10 All-In-One For Dummies (eBook)
by Woody Leonhard (Author)
The most comprehensive guide to Windows 10, updated with the latest enhancements
If you're new to Windows 10 and want an authoritative and accessible guide to the basics of the widely used operating system, Windows 10 All-in-One For Dummies is the book for you. Written by trusted Windows expert Woody Leonhard, this freshly updated guide cuts through the jargon and covers everything you need to know, including navigating the start menu, personalizing your Windows experience, maximizing Windows apps, and managing security.
Windows 10 All-in-One For Dummies includes all the guidance you need to make the most of this latest update of Windows. It shows you how to set up multiple user accounts, create a Homegroup for easy sharing between devices, backup your files, and troubleshoot common problems.
Covers all the new features and latest enhancements to Windows 10
Makes upgrading to the latest version easier than ever
Lets you work with apps like a pro
Includes tons of tips on protecting your computer, data, privacy, and identity
Whether you use Windows 10 for business, fun and games, or staying in touch with family and friends, Windows 10 All-in-One For Dummies makes it easy.
Categories: Computers & Internet
Publisher: For Dummies
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Better labor standards in trading-partner countries, especially in less industrialized economies, can positively affect U.S. exports and U.S. imports. Better labor rights could increase demand for U.S. exports by boosting the incomes of workers overseas. And better labor standards abroad reduce the cost advantage that some countries may enjoy by paying their workers poorly.
– Trade with less industrialized countries with weak or no worker protections has substantially contributed to the increase in the U.S. trade deficit from 2000 to 2007. If the United States had only traded with less industrialized economies that had some or strong worker rights during those years, its trade deficit in 2007 would have been $123 billion smaller than it actually was.
– In 2000, U.S. exports to countries with strong or some worker rights were 182.3% greater than U.S. exports to countries with limited or no worker rights.
– U.S. imports grew faster from 2000 to 2007 for countries with limited or no labor rights than for countries with some or even strong labor rights.
Labor and environmental rules are just as much a part of the trade equation as copyright protection or investment rules. It isn't a question of blocking trade but of making sure that trade deals are fair to workers, don't trash the environment and provide the right incentives to businesses and governments. McCain's trade policy would serve only one constituency: multinational corporations.


This is a persuasive speech I wrote based on The Book Thief by Markus Zusak from Liesel's point of view speaking to Rudy, convincing him of the power words have. The timeframe is when Rudy is upset with Liesel after she steals a book instead of the food he wanted, and she explains why she did it.
You don't understand, Rudy. I know you think that I should have taken more than just a book when I had the chance, but don't you see? Books hold so much more than food. You say that it's too bad you can't eat books, but what you have to see is the power of the words in those books. Words can be the greatest gift a person can give, words can heal and hurt, words can end or save a life—words can change the world. Don't you see? The Führer did, and he used that power for evil, "Without words, the Führer [would be] nothing. There would be no limping prisoners, no need for consolation or wordly tricks to make us feel better" (521). He attacked by "..plant[ing] words…. He watched them grow, until eventually… It was a nation of farmed thoughts" (475). He started this war with his words, manipulated this country with his words. Though without words, the two of us wouldn't be the people we are today, we wouldn't be standing together right here, right now.
Don't you see? It's not just you, most people don't. Words have power. They can be a weapon, but they can also heal. Remember down in the basement last time? We were there during the first air raid, and I read The Whistler in the warm chaos of that shelter. They listened to that story, and as my voice read the words, the children were soothed, the adults distracted from the raid by "…visions of the whistler running from the crime scene" (381). Could food have done that? Even you were comforted in that moment, though you tried to act like you didn't need it—don't think I didn't notice, I see right through you.
My books give me hope, give me the strength to survive, the will to keep living. My books give you and everyone around me a light in this darkness. When I first came to Himmel Street I was so alone, "…you could still see the bite marks of snow on [my] hands and the frosty blood on [my] fingers" (31). Mama had left me with an unfamiliar family and Werner had just died. Without a family or a home, I walked into that "small, boxlike house" (32) carrying nothing but that book. I hid it under my bed, knowing that no matter what happened I would still have that. Soon after that, Papa taught me how to read, and that's when I truly felt love in my life again, from them and you. Remember that day Sister Maria gave us reading tests in front of the whole class? I stood in front of all of you, trembling as my "…blood loudened. The sentences blurred…and it didn't help that tears…formed in [my] eyes" (77). Do you remember how stupid I proved myself to be compared with you and the rest of them? The humiliation, anger, and frustration I felt after that was something I can still bring to mind. You comforted me that day, and you still do with so much, but what continues to save me are those books. Learning how to read freed me, gave me the sense of identity I craved. I know I should repay you for that time you helped me and many more after that, and I know that I shouldn't have done this, but I still stole this book instead of the food you wanted and I can explain why.
Books are precious, so valuable to humanity, priceless compared to food. I know you're thinking right now, "But why does this all matter? I'm hungry right now, what can books do to fix that?" Well, if only people realized the power the words those books hold have, this war wouldn't have started—our families wouldn't be poor, you wouldn't be hungry—the whole of Germany wouldn't be starving. A bite of food now might curb your hunger for the time being, but what about tomorrow? What about the rest of Germany?
Food keeps you alive, but books give you a reason to keep living—something all of humanity needs in this time of destruction and loss. Why doesn't anyone realize this? We may not have much food, but books will always be there. I'd do anything it takes—I might have to steal for them, but I'll still do it. You and everyone else must realize, words are what change the world. If only people could see that, this war wouldn't be happening and death wouldn't visit this little town so often. They change lives, for better or for worse—ours and this entire country's. Words have power. You have to understand.


Despite an unwelcome visit from The Flu, ConFurence Zero turned out to be a rousing success. A bit more than one-hundred people enjoyed two days of discussions, artwork, and films dedicated to anthropomorphic cartoons, comics and stories. A specific list of the fun could go on into the boring, so here we'll condense just a bit.
The "panels" and round-robin discussions worked well for their unusual format. Several topics examining Furry Fandom's various aspects were popular, such as "My First Furry", "Furries and Gender" and "Real Genetic Research", as well as discussions of art technique, layout and storytelling. Especially liked were the "Species Panels", where fans of specific furry/funny animal types could meet and talk with like minds to exchange stories, findings and anecdotes. Cat fans, fox fans, weasel fans and even bird & reptile fans found plenty of their kin here.
Many other popular events were less structured, and some were outright spontaneous. A late-night video screening of student animated films from CalArts proved to be a big crowd pleaser, and Saturday night the artists' jam room was packed to capacity with pen and paper flying. Among the other well-attended con events were the wide-screen presentation of "Animalympics" and the Furry Filk Contest, which drew seven entries.
For a small convention, the ConFurence Zero art show was amazing. More than ten artist showed nearly two hundred drawings and paintings for sale, and the final cash tally after sale and auction exceeded several larger conventions'! One piece by Susan Van Camp, "Reclining Tigeress", sold for a whopping $450. Other artists made quite a showing as well, including several who had never been in an art show before. Among the art presented were works by Van Camp ("Aniverse"), Jim Groat ("'Morphs"), Deal Whitley ("High Orbit") and Mel. White ("Duncan & Mallory"), as well as fan artists like Steve Martin, Mary Hanson-Roberts, Ken Sample and others.
Mention must be made of the excellent service provided by the Holiday Inn Bristol Plaza, our host. Service was fast, cooperative and efficient; materials needed were handled in a flash, and several rooms were added to the package for our usage at little or no extra cost. It is for this reason and several others that we are staking a claim for future ConFurences at this hotel.
Dates: January 19, 20 & 21 (Fri. – Sun.).
Location: Holiday Inn Bristol Plaza, Costa Mesa (of course!).
Membership: $20 by mail, $25 at-the-door.
For art show and dealer table info, or suggestions, write to: ConFurence, P.O. Box 1958, Garden Grove, CA 92644-1958, or call (714) 530-1312.
Special Thanks go out to the volunteers and other helping hands without whom we would never have pulled this off!


text:
Church Raises Nearly $2 Million To Provide Mental Health Counseling Amid Current COVID-19 Crisis
By Krizzia Paolyn Reyes Feb 23, 2021 02:03 PM EST
(Photo : 'bigbirdz' / Flickr / CC)
A megachurch in Arizona raised nearly $2 million since November last year to pay for counseling services for those who are battling mental health problems amid the ongoing pandemic and other recent stress factors.
According to The Christian Post, the Christ's Church of the Valley (CCV) in Arizona started its "Press On" campaign in November, at the start of the holiday season. The church used Pushpay as its primary source to raise funds to pay for the cost of counseling services for those coping with the stresses of the pandemic and other factors, like the election season.
On the PressOn Campaign page of CCV's website, it writes: "100% of all donations generated in this campaign will benefit people in the Valley who are ready to step into Biblical counseling from chosen, vetted partners."
More than 4,000 people contributed to the campaign and raised $1.98 million, Christian Headlines reported.
A screenshot of the Press On Campaign website by CCV.
Tyler Kurbat, director of marketing and communications for the 12-campus church, said that the idea for the Press On Campaign was first discussed during a meeting at the end of 2019 to discuss the upcoming series called "Let's Talk About It," which emphasizes on mental health during election season. At that time, COVID-19 was not part of the discussion as it wasn't rampant yet.
"It hit me that we had the opportunity to lean into mental health...," Kurbat said. "If you reframe the way you look at depression, right in the middle is the rallying cry and an invitation into hope. If you deemphasize just a couple of letters, 'press on' pulls through"
"It was amazing how God sort of set the table for us, led our leadership to know that this is a topic that is important ...," Kurbat added. "The opportunity was to remind our congregation and also non-believers that you're seen, you're not alone, and that there's help because really there's a tremendous opportunity to destigmatize these topics and provide some actual hope."
And thus, the Press On campaign was made.
Kurbat also said that in an average year, one in five people struggle with mental health problems, but due to the stress from 2020, more people could use the help they are offering.
"This year it's one in two, which means that if it's not you, it's probably someone you love. We have the opportunity to lean into that and provide some support." Kurbat claimed.
Scientists have been tracking an increase in mental health issues since the pandemic started and according to a U.S. Census Bureau survey, more than 42 percent of people in the December poll said they had been feeling anxious and depressed. It was an increase of over 11 percent compared to the previous year, Nature said.
"Mental health [problems are] skyrocketing," Kurbat said. "At the same time, financial uncertainty, financial stresses are also climbing. We had the opportunity as a church to jump in and fill that gap."
With the gathered funds from the campaign, the church will be able to cover 80 percent of the cost of 10 counseling sessions, and half the cost of the following 10 sessions.
Rachel Schmidt, CCV's Life Training pastor who oversees pastoral care, says she will help recommend people to counselors with biblical values.


text:
INTERVIEW: Do as well as I can
Amy Nixon
Craig Roddan on his loan move
Midfielder Craig Roddan spoke to us shortly after signing his one-month loan deal on Thursday morning.
"I knew about six months ago that Carlisle were interested so it was just a case of waiting for the opportunity," he explained. "I'm made up that I've got the chance to come here and play some first team football.
"All I want to do is play, so I'm delighted that Kav has given me the opportunity to do that. As you get a little bit older it does get frustrating playing in the reserves all of the time. Liverpool is a fantastic club and they've helped me a lot, but I need to be playing as much as I can now."
"I played in one of the bounce games at Rangers last month so I already know a few of the lads," he continued. "I came to the game on Tuesday night and I thought it was a really good result so hopefully we can build on that now.
"This is a new challenge and a fresh start for me so it's something I'm really looking forward to. I like to think I'm a creative player, I like to get on the ball and make chances and hopefully help the team score some goals."
"The people at Liverpool have told me to come here and be myself," he told us. "They've said if I do that then the type of player I am should shine through. Kav has told me to relax and enjoy my football and hopefully I can do well for him."
"This is the first time I've been out to play first team football so it's something I'm really looking forward to," he confirmed. "It's also the first time I've been away from Liverpool, so I have had a few nerves, but the lads here have made me feel really welcome. I've trained with them a couple of times already so I already feel like I've settled in."
And on his influences at Anfield, he said: "Alex Inglethorpe and Rodolfo Borrell have been really good with me down there. They've both done a lot for me during my time there and Alex is a brilliant manager. Liverpool is a fantastic place to learn your trade because you're training with players who have real quality. However, this is the next challenge and I just want to do as well as I can."
United Player subscribers can see a video interview with Craig Roddan now. Click HERE to go to the Player platform. Follow the same link for more information on United Player, and to subscribe.
Click HERE to see a clip from this interview on our YouTube channel. Follow the same link for more FREE content right from the heart of the club.
INTERVIEW: We were unlucky not to get the win
Striker Hallam Hope provided a fantastic assist just before half-time on Saturday afternoon which set wide man Harry McKirdy up for his ninth goal of the season as he got on the end of what was a...
INTERVIEW: I have been extremely impressed with the way he has gone about his business
The Emirates FA Cup third round meeting with Cardiff City brought with it the usual increased levels of media interest following United's superb away performance on Saturday afternoon.
INTERVIEW: I can't wait to get started
Wolves midfielder Elliot Watt officially joined the Blues on New Year's Day and, having joined three first-team training sessions ahead of his unveiling at Gresty Road, he immediately spoke of how...
INTERVIEW: It was disappointing and unacceptable
Club captain Adam Collin spoke about the disappointment felt in the dressing on Saturday evening after the unbeaten five-game run was ended at the Jobserve Community Stadium in Colchester.


text:
package me.lucko.luckperms.common.locale;

import com.google.gson.JsonElement;
import com.google.gson.JsonObject;
import com.google.gson.JsonPrimitive;

import me.lucko.luckperms.common.config.ConfigKeys;
import me.lucko.luckperms.common.http.UnsuccessfulRequestException;
import me.lucko.luckperms.common.plugin.LuckPermsPlugin;
import me.lucko.luckperms.common.sender.Sender;
import me.lucko.luckperms.common.util.gson.GsonProvider;

import org.checkerframework.checker.nullness.qual.Nullable;

import okhttp3.Request;
import okhttp3.Response;
import okhttp3.ResponseBody;

import java.io.BufferedReader;
import java.io.BufferedWriter;
import java.io.Closeable;
import java.io.FilterInputStream;
import java.io.IOException;
import java.io.InputStream;
import java.io.InputStreamReader;
import java.nio.charset.StandardCharsets;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.StandardCopyOption;
import java.util.ArrayList;
import java.util.List;
import java.util.Locale;
import java.util.Map;
import java.util.Objects;
import java.util.concurrent.TimeUnit;
import java.util.function.Predicate;

public class TranslationRepository {
    private static final String TRANSLATIONS_INFO_ENDPOINT = "https://metadata.luckperms.net/data/translations";
    private static final String TRANSLATIONS_DOWNLOAD_ENDPOINT = "https://metadata.luckperms.net/translation/";
    private static final long MAX_BUNDLE_SIZE = 1048576L; // 1mb
    private static final long CACHE_MAX_AGE = TimeUnit.HOURS.toMillis(23);

    private final LuckPermsPlugin plugin;

    public TranslationRepository(LuckPermsPlugin plugin) {
        this.plugin = plugin;
    }

    /**
     * Gets a list of available languages.
     *
     * @return a list of languages
     * @throws IOException if an i/o error occurs
     * @throws UnsuccessfulRequestException if the http request fails
     */
    public List<LanguageInfo> getAvailableLanguages() throws IOException, UnsuccessfulRequestException {
        return getTranslationsMetadata().languages;
    }

    /**
     * Schedules a refresh of the current translations if necessary.
     */
    public void scheduleRefresh() {
        if (!this.plugin.getConfiguration().get(ConfigKeys.AUTO_INSTALL_TRANSLATIONS)) {
            return; // skip
        }

        this.plugin.getBootstrap().getScheduler().executeAsync(() -> {
            // cleanup old translation files
            clearDirectory(this.plugin.getTranslationManager().getTranslationsDirectory(), Files::isRegularFile);

            try {
                refresh();
            } catch (Exception e) {
                // ignore
            }
        });
    }

    private void refresh() throws Exception {
        long lastRefresh = readLastRefreshTime();
        long timeSinceLastRefresh = System.currentTimeMillis() - lastRefresh;

        if (timeSinceLastRefresh <= CACHE_MAX_AGE) {
            return;
        }

        MetadataResponse metadata = getTranslationsMetadata();
        if (timeSinceLastRefresh <= metadata.cacheMaxAge) {
            return;
        }

        // perform a refresh!
        downloadAndInstallTranslations(metadata.languages, null, true);
    }

    private void clearDirectory(Path directory, Predicate<Path> predicate) {
        try {
            Files.list(directory)
                    .filter(predicate)
                    .forEach(p -> {
                        try {
                            Files.delete(p);
                        } catch (IOException e) {
                            // ignore
                        }
                    });
        } catch (IOException e) {
            // ignore
        }
    }

    /**
     * Downloads and installs translations for the given languages.
     *
     * @param languages the languages to install translations for
     * @param sender the sender to report progress to
     * @param updateStatus if the status file should be updated
     */
    public void downloadAndInstallTranslations(List<LanguageInfo> languages, @Nullable Sender sender, boolean updateStatus) {
        TranslationManager manager = this.plugin.getTranslationManager();
        Path translationsDirectory = manager.getRepositoryTranslationsDirectory();

        // clear existing translations
        clearDirectory(translationsDirectory, TranslationManager::isTranslationFile);

        for (LanguageInfo language : languages) {
            if (sender != null) {
                Message.TRANSLATIONS_INSTALLING_SPECIFIC.send(sender, language.locale().toString());
            }

            Request request = new Request.Builder()
                    .header("User-Agent", this.plugin.getBytebin().getUserAgent())
                    .url(TRANSLATIONS_DOWNLOAD_ENDPOINT + language.id())
                    .build();

            Path file = translationsDirectory.resolve(language.locale().toString() + ".properties");

            try (Response response = this.plugin.getBytebin().makeHttpRequest(request)) {
                try (ResponseBody responseBody = response.body()) {
                    if (responseBody == null) {
                        throw new IOException("No response");
                    }

                    try (InputStream inputStream = new LimitedInputStream(responseBody.byteStream(), MAX_BUNDLE_SIZE)) {
                        Files.copy(inputStream, file, StandardCopyOption.REPLACE_EXISTING);
                    }
                }
            } catch (UnsuccessfulRequestException | IOException e) {
                if (sender != null) {
                    Message.TRANSLATIONS_DOWNLOAD_ERROR.send(sender, language.locale().toString());
                    this.plugin.getLogger().warn("Unable to download translations", e);
                }
            }
        }

        if (updateStatus) {
            writeLastRefreshTime();
        }

        manager.reload();
    }

    private void writeLastRefreshTime() {
        Path statusFile = this.plugin.getTranslationManager().getRepositoryStatusFile();

        try (BufferedWriter writer = Files.newBufferedWriter(statusFile, StandardCharsets.UTF_8)) {
            JsonObject status = new JsonObject();
            status.add("lastRefresh", new JsonPrimitive(System.currentTimeMillis()));
            GsonProvider.prettyPrinting().toJson(status, writer);
        } catch (IOException e) {
            // ignore
        }
    }

    private long readLastRefreshTime() {
        Path statusFile = this.plugin.getTranslationManager().getRepositoryStatusFile();

        if (Files.exists(statusFile)) {
            try (BufferedReader reader = Files.newBufferedReader(statusFile, StandardCharsets.UTF_8)) {
                JsonObject status = GsonProvider.normal().fromJson(reader, JsonObject.class);
                if (status.has("lastRefresh")) {
                    return status.get("lastRefresh").getAsLong();
                }
            } catch (Exception e) {
                // ignore
            }
        }

        return 0L;
    }

    private MetadataResponse getTranslationsMetadata() throws IOException, UnsuccessfulRequestException {
        Request request = new Request.Builder()
                .header("User-Agent", this.plugin.getBytebin().getUserAgent())
                .url(TRANSLATIONS_INFO_ENDPOINT)
                .build();

        JsonObject jsonResponse;
        try (Response response = this.plugin.getBytebin().makeHttpRequest(request)) {
            try (ResponseBody responseBody = response.body()) {
                if (responseBody == null) {
                    throw new RuntimeException("No response");
                }

                try (InputStream inputStream = new LimitedInputStream(responseBody.byteStream(), MAX_BUNDLE_SIZE)) {
                    try (BufferedReader reader = new BufferedReader(new InputStreamReader(inputStream, StandardCharsets.UTF_8))) {
                        jsonResponse = GsonProvider.normal().fromJson(reader, JsonObject.class);
                    }
                }
            }
        }

        List<LanguageInfo> languages = new ArrayList<>();
        for (Map.Entry<String, JsonElement> language : jsonResponse.get("languages").getAsJsonObject().entrySet()) {
            languages.add(new LanguageInfo(language.getKey(), language.getValue().getAsJsonObject()));
        }
        languages.removeIf(language -> language.progress() <= 0);

        if (languages.size() >= 100) {
            // just a precaution: if more than 100 languages have been
            // returned then the metadata server is doing something silly
            throw new IOException("More than 100 languages - cancelling download");
        }

        long cacheMaxAge = jsonResponse.get("cacheMaxAge").getAsLong();

        return new MetadataResponse(cacheMaxAge, languages);
    }

    private static final class MetadataResponse {
        private final long cacheMaxAge;
        private final List<LanguageInfo> languages;

        MetadataResponse(long cacheMaxAge, List<LanguageInfo> languages) {
            this.cacheMaxAge = cacheMaxAge;
            this.languages = languages;
        }
    }

    public static final class LanguageInfo {
        private final String id;
        private final String name;
        private final Locale locale;
        private final int progress;
        private final List<String> contributors;

        LanguageInfo(String id, JsonObject data) {
            this.id = id;
            this.name = data.get("name").getAsString();
            this.locale = Objects.requireNonNull(TranslationManager.parseLocale(data.get("localeTag").getAsString()));
            this.progress = data.get("progress").getAsInt();
            this.contributors = new ArrayList<>();
            for (JsonElement contributor : data.get("contributors").getAsJsonArray()) {
                this.contributors.add(contributor.getAsJsonObject().get("name").getAsString());
            }
        }

        public String id() {
            return this.id;
        }

        public String name() {
            return this.name;
        }

        public Locale locale() {
            return this.locale;
        }

        public int progress() {
            return this.progress;
        }

        public List<String> contributors() {
            return this.contributors;
        }
    }

    private static final class LimitedInputStream extends FilterInputStream implements Closeable {
        private final long limit;
        private long count;

        public LimitedInputStream(InputStream inputStream, long limit) {
            super(inputStream);
            this.limit = limit;
        }

        private void checkLimit() throws IOException {
            if (this.count > this.limit) {
                throw new IOException("Limit exceeded");
            }
        }

        @Override
        public int read() throws IOException {
            int res = super.read();
            if (res != -1) {
                this.count++;
                checkLimit();
            }
            return res;
        }

        @Override
        public int read(byte[] b, int off, int len) throws IOException {
            int res = super.read(b, off, len);
            if (res > 0) {
                this.count += res;
                checkLimit();
            }
            return res;
        }
    }
}


text:
The few criticisms for the game centered on its length and pacing. Clements noted that not all players would appreciate a game with a "deliberate, melancholic pace" and short duration, comments echoed by the Edge review.[3][5] Miller noted the lack of complex gameplay elements in Journey, and Shaw was disappointed that the game was only a few hours long, though Douglas said the length was perfect.[1][2][46] Miller concluded the game could be compared to "a musical concert, a well-directed film, or a long-awaited book", while Clements concluded, "completing Journey will create memories that last for years".[1][46]
beliefs of the Roman Catholic Church. More had several children by his first wife. His daughter Margaret was considered to be the smartest woman in England being proficient in Latin, Greek and the classics. All of his children loved him. More indulged in scatological jokes; had countless pets and viewed life as a grand drama with him as an actor upon the stage of affairs.
Margaret Cavendish, The Blazing World. Cavendish's work is frequently interested in the idea of utopia, such as the all-female university she imagines in The Female Academy and The Convent of Pleasure, in which a group of women remove themselves from society in order to devote themselves to a life of pleasure. But The Blazing World, published in 1666 when London was quite literally ablaze with the Great Fire, is her most representative utopian work, a fictional account of a young woman's fantastic voyage to an alternative world, which she accesses via the North Pole. Cavendish's looking-glass utopia anticipates the world of Lewis Carroll's Alice books in a number of startling ways.
Education is regarded as important and is continued to age 21 for all citizens. During their years of schooling, the pupils are introduced to many trades, professions, and arts in order to discover where their talents and interests lie, to enable them to choose the vocation that will bring them the greatest satisfaction in their adult years. At age 21, everyone enters the work force, "the industrial army," where he or she will serve up to the age of 45. Women and men are treated equally with respect to education, career in the work force, compensation. There is some distinction made regarding the occupations of men and women, and there are provisions for pregnancy.
The reader, following the narrator's shifting attitude from admiration to surprise and finally to contempt, is led to believe that the author is bent on demonstrating the vast inferiority of the Erewhonians to his fellow Englishmen; but it gradually becomes clear that he attributes to Englishmen much of the irrationality and the ingenious equivocations that make the Erewhonians look foolish. The difference is only one of degree. The technique is close to that of Swiftian satire. In some places it is confused and inconsistent, but in other passages it is both clever and devastating, worthy of the master.
There is no private property on Utopia, with goods being stored in warehouses and people requesting what they need. There are also no locks on the doors of the houses, and the houses are rotated between the citizens every ten years. Agriculture provides the most important occupation on the island. Every person is taught it and must live in the countryside, farming for two years at a time, with women doing the same work as men. Parallel to this, every citizen must learn at least one of the other essential trades: weaving (mainly done by the women), carpentry, metalsmithing and masonry. There is deliberate simplicity about these trades; for instance, all people wear the same types of simple clothes and there are no dressmakers making fine apparel. All able-bodied citizens must work; thus unemployment is eradicated, and the length of the working day can be minimised: the people only have to work six hours a day (although many willingly work for longer). More does allow scholars in his society to become the ruling officials or priests, people picked during their primary education for their ability to learn. All other citizens, however, are encouraged to apply themselves to learning in their leisure time.
The great society of the future, he believes, will have a worldwide structure, a World State. Nationalities will no longer have significance. The entire human race will become one commonwealth with uniform customs and mores and with a common language. Money will still be used for exchange, and individuals will still hold the right to ownership of personal property, though all of the earth's land and all sources of power will belong to the State — that is, to everyone. He does not subscribe to the idea that all work can be made pleasurable; consequently, he declares that every member of society shall be required to perform work up to a minimum which earns him enough to discharge his obligations. Beyond that, he may choose to work more to earn more or he is free to enjoy his leisure.
Jonathan Swift, Gulliver's Travels. In this work of 1726, which was an immediate bestseller, Lemuel Gulliver actually visits four different fantasy worlds, but the one that's especially interesting here is the world of the Houyhnhnms, horses endowed with reason and speech, and a world in which humans are yobbish Yahoos flinging their muck around. Gulliver interprets the Houyhnhnms' society as a utopian world, though whether Swift is inviting us to agree, or to distance ourselves from Gulliver, remains a contentious point.
England, in the year 1500, was emerging from a century of grim civil wars during which the cultural life of the country had deteriorated to a deplorable state. It was not until 1485 that the civil wars were ended by the victory of Henry Tudor, Earl of Richmond, at Bosworth Field, establishing the Tudor dynasty with the crowning of Henry Tudor as Henry VII. During the next 118 years under the reign of the Tudors, especially through the long reigns of Henry VIII and Elizabeth I, England attained the status of a first-rate European power and produced a flourishing culture scarcely equaled in all the history of Western civilization.
The "Aloha spirit" is universal, and certainly contagious, when you set foot onto the Pacific paradise of Hawaii and the port of Hilo. The small, archipelago state presents a beautiful tapestry of natural sights, including secluded beaches, magnificent waterfalls, and a vast rainforest filled with fauna. Among the extensive list of geographic must-sees is Hilo's National Volcano Park, home of the most active volcano in the world.
A serious dilemma presented itself as a result of this newfound devotion to the ancient sages because of the apparent conflict between pagan classicism and Christian doctrine. It became a matter of deepest concern for all Renaissance thinkers to find an accommodation of the two doctrines — the philosophy of Plato and the teachings of Christ. As a result of their dual allegiance, we get the term which describes the movement, "Christian humanism." The successful adaptation of double devotion is seldom better illustrated than in the works of Thomas More, especially in Utopia.
One highly influential interpretation of Utopia is that of intellectual historian Quentin Skinner.[9] He has argued that More was taking part in the Renaissance humanist debate over true nobility, and that he was writing to prove the perfect commonwealth could not occur with private property. Crucially, Skinner sees Raphael Hythlodaeus as embodying the Platonic view that philosophers should not get involved in politics, while the character of More embodies the more pragmatic Ciceronian view. Thus the society Raphael proposes is the ideal More would want. But without communism, which he saw no possibility of occurring, it was wiser to take a more pragmatic view.
Unlike many games, where different songs have different themes for each character or area, Wintory chose to base all of the pieces on one theme which stood for the player and their journey, with cello solos especially representing the player. Wintory describes the music as "like a big cello concerto where you are the soloist and all the rest of the instruments represent the world around you", though he describes it as not necessarily orchestral due to the inclusion of electronic aspects.[28][31] The cello begins the game as "immersed in a sea of electronic sound", before first emerging on its own and then merging into a full orchestra, mirroring the player's journey to the mountain.[32] While the game's art style is based on several different cultures, Wintory tried to remove any overt cultural influences from the music to make it "as universal and culture-less as possible".[28] Tina Guo features as the cellist for the soundtrack. She is a close friend of Wintory and has since performed "Woven Variations" with him, an eight-minute orchestral variation on the Journey soundtrack.[31] All of the non-electronic instruments in the soundtrack were recorded with a live orchestra.[29]


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text:
\section{Introduction}
3D point cloud understanding is a long-standing problem. Typical tasks include 3D object classification \cite{3Dshapenet}, 3D object detection \cite{2Ddriven, qi2017frustum, DeepSlidingShapes} and 3D semantic segmentation \cite{qi2016pointnet, qi2017pointnetplusplus,Riegler2017OctNet}. Among these tasks, 3D semantic segmentation which assigns semantic labels to points is relatively challenging. Firstly, the sparseness of point cloud in 3D space makes most spatial operators inefficient. Moreover, the relationship between points is implicit and difficult to be represented due to the unordered and unstructured property of point cloud. In retrospect of previous work, several lines of solutions have been proposed to resolve the problem. In \cite{supervoxel} handcrafted voxel feature is used to model geometric relationship, and in \cite{scenecut} 2D CNN features from RGBD images are extracted. Additionally, there is a dilemma between 2D convolution and 3D convolution: 2D convolution fails to capture 3D geometry information such as normal and shape while 3D convolution requires heavy computation.

Recently, PointNet architecture \cite{qi2016pointnet} directly operates on point cloud instead of 3D voxel grid or mesh. It not only accelerates computation but also notably improves the segmentation performance. In this paper, we also work on raw point clouds. We get inspiration from the successful feature detection algorithm Scale-invariant feature transform (SIFT) \cite{sift} which involves two key properties: scale-awareness and orientation-encoding. Believing that the two properties should also benefit 3D shape description, we design a novel module called PointSIFT for 3D understanding that possesses the properties. The main idea of PointSIFT is illustrated in Figure \ref{fig:sift}. Unlike SIFT algorithm which uses handcrafted features, our PointSIFT is a parametric deep learning module which can be optimized and adapted to point cloud segmentation.

\begin{figure*}[!t]
	\centering
	\includegraphics[width=\linewidth]{sift.pdf}
	\caption{Structure of SIFT \cite{sift} and our PointSIFT module.  The left side shows that both of them can capture multi-scale patterns and is adaptive to various scales. The right side shows that orientation is encoded in each key point/pixel.}
	\label{fig:sift}
\end{figure*}

The basic building block of our PointSIFT module is an orientation-encoding (OE) unit which convolves the features of nearest points in 8 orientations. In comparison to K-nearest neighbor search in PointNet++ \cite{qi2017pointnetplusplus} where $K$ neighbors may fall in one orientation, our OE unit captures information of all orientations. We further stack several OE units in one PointSIFT module for representation of different scales. In order to make the whole architecture scale-aware, we connect these OE units by shortcuts and jointly optimize for adaptive scales. Our PointSIFT module receives point cloud with $n$ features each point and outputs points of $n$ features with better representation power. PointSIFT is a general module that can be integrated into various PointNet-based architectures to improve 3D shape representation.

We further build a hierarchical architecture that recursively applies the PointSIFT module as local feature descriptor. Resembling conventional segmentation framework in 2D \cite{fcn} and 3D \cite{qi2017pointnetplusplus}, our two-stage network first downsamples the point cloud for effective calculation and then upsamples to get dense predictions. The PointSIFT module is used in each layer of the whole framework and significantly improves the representation ability of the network.

Experimental results show that our architecture based on PointSIFT module outperforms state-of-the-art methods on S3DIS\cite{s3Dis} (relative $\mathbf{12\%}$ improvement) and ScanNet\cite{scannet} dataset(relative $\mathbf{8.4\%}$ mean IoU improvement).

\section{Related Work}
Deep learning on 3D data is a growing field of research. We investigate segmentation methods on several important 3D representations. Point cloud segmentation is discussed in more detail. After that, we briefly survey the SIFT descriptor where we borrow inspiration.
\subsection{3D Representation}
\paragraph{Volumetric Representation} The first attempt to apply deep learning is through volumetric representation. Many works \cite{voxelnet,qi2016volumetric,3Dshapenet} attempt to voxelize 3D point cloud or scene into regular voxel grids. However, the main challenges in volumetric representation are data sparsity and computational overhead of 3D convolution. A practical choice for resolution of voxel grid can be $32 \times 32 \times 32$, which is far from sufficient to faithfully represent complex shapes or scenes. Further, conversion is required to construct volumetric grids based on handy data format such as point cloud, which suffers from both truncation error and information loss. While some recent work \cite{fpnn, Riegler2017OctNet, octtree_gen} propose techniques to address the sparsity issue (\eg, octree data structure), the performance of volumetric methods is still not on par with methods based on point cloud \cite{qi2016volumetric}.

\paragraph{Polygonal Meshes} Some literature \cite{spectralnetwork,Geodesic, syncspeccnn} focuses on the use of graph Laplacian to process meshes. Further, functional map and cycle consistency \cite{peter_functional_map, peter_cycle_consistency} helps build correspondence between shapes. However, this kind of methods are confined to manifold meshes.

\paragraph{Multi-view Representation} \cite{qi2016volumetric, multi_view, 3D_assistied} make an effort to exploit the strong capacity of 2D CNNs in 3D recognition. In these works, in order for the input to fit in 2D CNNs, the projection of 3D shapes to 2D images is required. The 3D-level understanding of object (or scene) is achieved by combining 2D images taken from various viewpoints. However, such kind of projection results in the loss of most crucial and discriminative geometric details. For example, calculating normal vectors becomes nontrivial, spatial distance is not preserved, and occlusion prevents a holistic understanding of both local and global structure. Failure to include those geometric details could substantially limit the performance in tasks such as shape completion and semantic segmentation.

\subsection{Deep Learning on Point Cloud}

\paragraph{PointNet and follow-up works} Recently, a series of works propose several effective architectures that process point cloud directly. Among those, a big branch of works apply PoinetNet \cite{qi2016pointnet} as an unordered global or local descriptor. PointNet \cite{qi2016pointnet} is a pioneering effort that applies deep learning to unordered point clouds by point-wise encoding and aggregation through global max pooling. PointNet++ \cite{qi2017pointnetplusplus} proposes a hierarchical neural network to capture local geometric details. PointCNN \cite{pointcnn} uses $\mathcal{X}$-Conv layer instead of vanilla mlp (multilayer perceptron) layer to exploit certain canonical ordering of points. Dynamic Graph CNN \cite{dgcnn} (DGCNN) suggests an alternative grouping method to ball query used in PointNet++ \cite{qi2017pointnetplusplus}: KNN w.r.t. Euclidean distance between feature vectors. Superpoint Graphs\cite{SpGraph} (SPG) first partitions point cloud into superpoints and embed every superpoint with shared PointNet. The semantic labels of superpoints are predicted from the PointNet embedding of current superpoint and spatially neighboring superpoints.  While achieving leading results, we feel that segmentation algorithms could benefit from having ordered operators to some extent.

\paragraph{Rotation Equivariance and Invariance} Another branch of work focuses on rotation equivariance or invariance. G-CNN \cite{gcnn} designs filter so that the filter set is closed under certain rotations (\eg, 90-degree rotation) to achieve rotation invariance for those fixed rotations. This kind of method achieves exact rotation invariance only for some discrete rotations while introduces huge computational overhead. The time complexity is proportional to the cardinality of equivalence classes under the rotations one want to be equivariant of, making it impractical to consider rotation equivariance for large and general groups. Spherical CNN \cite{SphericalCNN_uva, SphericalCNN_upenn} projects 3D shapes onto spheres and process the signal with spherical filters for rotation equivariant representation. Global max pooling that transforms sphere to a single value further achieves rotation invariance. While spherical CNNs is fully invariant to rotation, the projection of shapes onto sphere introduces large error and is not appropriate for objects with certain topological property or scenes. Besides, the operation that helps achieve rotation invariance substantially limits the model capacity, discouraging its use on segmentation tasks.

\subsection{Scale-Invariant Feature Transform (SIFT)}
SIFT \cite{sift} is a local image pattern descriptor widely used in object recognition, 3D modeling, robotics and various other fields. SIFT and its variants \cite{surf} consist of two entities, a scale-invariant detector and a rotation-invariant descriptor.

We borrow inspiration from both entities in SIFT algorithm. In keypoint detection stage, the SIFT algorithm achieves scale invariance with multi-scale representation which we also use for robustly processing objects of various scale. As for feature description stage, SIFT detects dominant orientations for rotation invariance and comprehensively perceives image pattern in different orientations. Given the fact that ordered descriptors like the descriptor of SIFT or kernels of CNN yield impressive results for 2D images, we expect that having such descriptors may also benefit representation of point cloud. The above two observations from SIFT algorithm lead us to design a scale-aware descriptor that encodes information from different orientations with ordered operations.

\section{Problem Statement}
    We first formulate the task of point cloud semantic segmentation. The given point cloud is denoted as $\mathbf{P}$ which is a point set containing $n$ points $p_{1}, p_{2}, ..., p_{n} \in \mathbb{R}^{d}$ with $d$ dimensional feature. The feature vector of each point $p_i$ can be its coordinate $(x_i, y_i, z_i)$ in 3D space (or plus optional feature channels such as RGB values, normal, a representation vector in intermediate step, etc) .
    The set of semantic labels is denoted as $\mathbf{L}$. A semantic segmentation of a point cloud is a function $\Psi$ which assigns semantic labels to each point in the point cloud. i.e.,
    \begin{equation}
    \Psi: \mathbf{P} \longmapsto L^n
    \end{equation}
    The objective of segmentation algorithms are finding optimal function that gives accurate semantic labels.

    Several properties of point set $P$ have been emphasized in previous work \cite{qi2016pointnet,qi2017pointnetplusplus}. The density of $P$ may not be uniform everywhere, and $P$ can be very sparse. Moreover, $P$ as a set is unordered and unstructured which distinguishes point cloud with common sequential or structured data like image or video.

\section{Our Method}
\label{sec:main}
Our network follows a encode-decode (downsample-upsample) framework similar to general semantic segmentation network \cite{segnet} for point cloud segmentation  (Illustrated in Figure \ref{fig:whole_network}) . In the downsampling stage, we recursively apply our proposed PointSIFT module combined with set abstraction (SA) module introduced in  \cite{qi2017pointnetplusplus} for hierarchical feature embedding. For upsampling stage dense feature is enabled by effectively interleaving feature propagation (FP) module \cite{qi2017pointnetplusplus} with PointSIFT module. One of our main contribution and core component of our segmentation network is the PointSIFT module which is endowed with the desired property of orientation-encoding and scale-awareness.

\subsection{PointSIFT Module}
\label{sec:SITT}
Given an $n \times d$ matrix as input which describes a point set of size $n$ with $d$ dimension feature for every point, PointSIFT module outputs an $n \times d$ matrix that assigns a new $d$ dimension feature to every point.

Inspired by the widely used SIFT descriptor, we seek to design our PointSIFT module as a local feature description method that models various orientations and is invariant to scale.

\begin{figure*}[!h]
	\centering
	\includegraphics[width=\linewidth]{total_network.pdf}
	\caption{Illustration of our two-stage network architecture. The network consists of downsampling (set abstraction) and upsampling (feature propagation) procedures. PointSIFT modules (marked in red) are interleaved with downsampling (marked in blue) and upsampling (marked in green) layers. Both SA and FP module are introduced in \cite{qi2017pointnetplusplus}. The FP-shortcuts are not shown in the figure for better clarity. PointSIFT$(\cdot)$ specifies feature dimensionalities of each orientation-encoding(OE) units, for example, PointSIFT$(64, 64)$ stands for two stacked OE units both having 64 output feature channels. The number beneath layers is the shape of output point set of corresponding layers, for example, $8192 \times 64$ means 8192 points with 64 feature channel each point.}
	\label{fig:whole_network}
\end{figure*}

\subsubsection{Orientation-encoding}
\label{sec:OEC}
Local descriptors in previous methods typically apply unordered operation (\eg, max pooling \cite{qi2017pointnetplusplus, dgcnn}) based on the observation that point cloud is unordered and unstructured. However, using ordered operator could be much more informative (max pooling discards all inputs except for the maximum) while still preserves the invariance to order of input points. One natural ordering for point cloud is the one induced by the ordering of the three coordinates. This observation leads us to the Orientation-encoding(OE) unit which is a point-wise local feature descriptor that encodes information of eight orientations.

\begin{figure}[htbp]
	\centering
	\includegraphics[width=\linewidth]{ori_conv.pdf}
	\caption{Illustration of Orientation-encoding(OE) Unit. (a): Point cloud in 3D space, the input point is at origin. (b) nearest neighbor search in eight octants. (c) convolution along $X, Y, Z$ axis.}
	\label{fig:ori_conv}
\end{figure}

 The input of OE unit is a $d$-dimension feature vector $f_0 \in \mathbb{R} ^{d}$ of point $p_0$. Information from eight orientations are integrated by a two-stage scheme to produce an orientation-aware feature $f_0'$. The OE Unit is illustrated in Figure \ref{fig:ori_conv}.

The first stage of OE embedding is Stacked 8-neighborhood(S8N) Search which finds nearest neighbors in each of the eight octants partitioned by ordering of three coordinates. Since distant points provides little information for description of local patterns, when no point exists within searching radius $r$ in some octant, we duplicate $p_0$ as the nearest neighbor of itself.

We further process features of those neighbors which resides in a $2 \times 2 \times 2$ cube for local pattern description centering at $p_0$. Many previous works ignore the structure of data and do max pooling on feature vectors along $d$ dimensions to get new features. However, we believe that ordered operators such as convolution can better exploit the structure of data. Thus we propose orientation-encoding convolution which is a three-stage operator that convolves the $2 \times 2 \times 2$ cube along $X$, $Y$, and $Z$ axis successively. Formally, the features of neighboring points is a vector $V$ of shape $2 \times 2 \times 2 \times d$, where the first three dimensions correspond to three axes. Slices of vector $M$ are feature vectors, for example $M_{1,1,1}$ represents the feature from top-front-right octant.The three-stage convolution is formulated as:
\begin{equation*}
\label{eq:conv}
\begin{aligned}
V_x &= g(Conv(W_x,V)) \in \mathbb{R}_{1 \times 2 \times 2 \times d} \\
V_{xy} &= g(Conv(W_y,V_x)) \in \mathbb{R}_{1 \times 1 \times 2 \times d} \\
V_{xyz} &= g(Conv(W_z,V_{xy})) \in \mathbb{R}_{1 \times 1 \times 1 \times d}\\
\end{aligned}
\end{equation*}
where $W_x\in \mathbb{R}_{2 \times 1 \times 1 \times d}$, $W_y \in \mathbb{R}_{1 \times 2 \times 1 \times d}$ and $W_z\in \mathbb{R}_{1 \times 1 \times 2 \times d}$ are weights of convolution operator (bias is omitted for clarity). In this paper, we set $g(\cdot) = ReLU(\cdot)$. Finally, OE convolution outputs a $d$ dimension feature by reshaping $V_{xyz} \in \mathbb{R}_{1 \times 1 \times 1 \times d}$. Orientation-encoding Convolution (OEC) integrates information from eight spatial orientations and obtains a representation that encodes orientation information.

\subsubsection{Scale-awareness}

In order for our PointSIFT module to be scale-aware, we follow the long-standing method of multi-scale representation by stacking several Orientation-encoding (OE) units in PointSIFT module, as Figure \ref{fig:sift_module} illustrated. Higher level OE units have larger receptive field than those of lower level. By constructing a hierarchy of OE units, we obtain a multi-scale representation of local regions in the point cloud. The features of various scales are then concatenated by several identity shortcuts and transformed by another point-wise convolution that outputs a $d$ dimensional multi-scale feature. In the process of jointly optimizing feature extraction and the point-wise convolution that integrates multi-scale feature, neural networks will learn to select or attend to appropriate scales which makes our network scale-aware.

The fact that the input and output vector of our PointSIFT module is of the same shape makes it convenient for our module to be integrated into other existing point cloud segmentation architectures. We are looking forward to future applications of PointSIFT module, probably not restricted to point cloud segmentation domain.

\begin{figure}[t]
	\centering
	\includegraphics[width=0.9\linewidth]{new_pointsift.png}
	\caption{PointSIFT Module. Input features first pass through a series of Orientation-encoding(OE) layers, then outputs of OE units are concatenated and transformed by another point-wise convolution to obtain multi-scale feature.}
	\label{fig:sift_module}
\end{figure}

\subsection{Overall Architecture}
\label{sec:all}
We first revisit set abstraction (SA) and feature propagation (FP) module in PointNet++, thus present how to construct our model by SA, FP and pointSIFT modules.

\subsubsection{Revisit SA and FP Module in PointNet++}

Set abstraction (SA) and feature propagation (FP) modules are proposed in PointNet++ \cite{qi2017pointnetplusplus} that correspond to downsampling and upsampling of point cloud respectively. We give a very brief introduction of SA and FP module here.

A set abstraction module takes in $N \times d$ input standing for a point cloud of $N$ points and $d$ dimensional feature each point. The output is $N' \times d'$ which corresponds to $N'$ downsampled points each with $d'$ dimensional feature. The downsampling is implemented by finding $N'$ centroids with farthest point sampling, assigning points to centroids and then calculate embedding of centroids by feeding feature of assigned points through shared PointNet.

The feature propagation module uses linear interpolation weighted by distances to upsample the point cloud. It receives input point set of size $N$ and outputs an upsampled set of size $N'$ where feature dimensionality is kept same. The upsampling process takes points that are dropped during downsampling, and assign features to them based on features of $k$ nearest points that are not dropped weighted by Euclidean distance in 3D space.

\subsubsection{Architecture Details}

 The input of our architecture is the 3D coordinates (or concatenating with RGB value) of $8192$ points.  In the downsampling stage, following \cite{qi2017pointnetplusplus}, multi-layer perceptron (MLP) is used to transform the input 3D (or 6D) vectors into features with $64$ dimensions. Three consecutive downsampling (set abstraction, SA) operations shrink the size of the point set to $1024$, $256$, $64$ respectively. For the upsampling part, we use feature propagation(FP) module as proposed by \cite{qi2017pointnetplusplus} for dense feature and prediction. The point set is lifted to $256$, $1024$, $8192$ points respectively by three FP layers which are aligned to its counterpart in the downsampling stage. Our PointSIFT module is inserted between all adjacent SA and FP layers. Finally, point features of the last upsampling layer pass through a fully connected layer for semantic label prediction.

Moreover, we insert FP modules not only between downsampling layers but also from downsampling layers to its counterpart in the encoding stage. We call these links FP-shortcuts for they resemble shortcuts by linking corresponding downsampling and upsampling layers and complements low-level information that might be lost during downsampling. The FP-shortcuts lead to much faster convergence which is proved by many prior works \cite{pointcnn, qi2017pointnetplusplus} that use such shortcut flavor connections . The prediction accuracy is also improved by a considerable margin which is also reported in many works, \eg, residual networks \cite{resnet}.

\begin{figure*}[htbp]
	\centering
	\includegraphics[width=\linewidth]{experiment.pdf}
	\caption{Visualization of results on S3DIS dataset\cite{s3Dis}}
	\label{fig:experiment}
\end{figure*}

\section{Experiments}
\label{experiments}

Our experiment consists of two parts: verifying the effectiveness of the OE unit and PointSIFT module (Section \ref{analysis}) and introducing the results on semantic segmentation benchmark datasets (Section \ref{result}). In what follows, SA and FP are set abstraction and feature propagation module respectively, which are proposed by \cite{qi2017pointnetplusplus}.

\subsection{Effectiveness of PointSIFT Module}
\label{analysis}
\paragraph{Orientation-encoding Convolution (OEC)}

\begin{figure}[htbp]
	\centering
	\includegraphics[width=\linewidth]{cp_cube}
	\caption{Accuracy for different searching methods. We use Savitzky–Golay filter for smoothing all the lines.}
	\label{fig:cp_net}
\end{figure}
We apply stacked 8-neighborhood search in OE unit, which is fundamentally different from ball query search proposed in PointNet++ \cite{qi2017pointnetplusplus}. The main difference lies in the fact that S8N search finds neighbors in each of 8 octants, while ball query searches for global nearest neighbors. As Figure \ref{fig:weakness} suggests, the global nearest neighbor search can result in a neighbor set of homogeneous points which is less informative than searching in 8 directions.

To justify our S8N search, we substitute ball query with S8N search in a lightweight version of PointNet++ \cite{qi2017pointnetplusplus} and compare the performance. The detailed architecture of the network is elaborated in Table \ref{table:cp_net}. For fairness, we set the number of neighbors found by the two neighbor search method to be the same. The results are shown in Figure \ref{fig:cp_net} which demonstrates the effectiveness of our S8N grouping plus OE Convolution.

\begin{table*}[htbp]
\setlength{\abovecaptionskip}{10pt
	\caption{Effectiveness of PointSIFT Module.}
    \centering
	\begin{tabular}{lcccc}
		\toprule
		downsampling step & first & second & third & fourth \\
		\toprule
		point cloud size & 8192 & 1024 & 256 & 64 \\
		\midrule
		captured point cloud size of Pointnet++\cite{qi2017pointnetplusplus} & 6570 & 1010 & 255 & 64 \\
		\midrule
		captured point cloud size of PointSIFT framework & 8192 & 1024 & 256 & 64 \\
		\bottomrule
	\end{tabular}
	\label{table:bq}
\end{table*}

\if 0
Our PointSIFT module also consumes less memory since less number of neighbors is required, saving GPU memory for more layers. Actually, GPU memory size is one of the factors that prevent many point cloud-based model from going really deep. For instance, PointNet++ \cite{qi2017pointnetplusplus} and DGCNN \cite{dgcnn} take up 10GB GPU memory with less than 10 layers. On the contrary, our model only need 8GB.
\fi

\begin{figure}[!t]
	\centering
	\includegraphics[width=0.45\linewidth]{weakness.png}
	\caption{In this case, using K nearest neighbors, all chosen points are from one direction (red points). If we select points in different directions (green points), the representation ability will be better.}
	\label{fig:weakness}
\end{figure}

Another observation that justifies the PointSIFT module is that more points from individual input point cloud contribute to the final representation for PointSIFT. In PointNet++ \cite{qi2017pointnetplusplus}, the grouping layer fails to assign some points in the point cloud to any centroid and thus lost the information from the unassigned points. We claim the PointSIFT Module can almost avoid information loss in the downsampling process, which is beneficial to semantic segmentation. To prove this, we conduct an experiment on ScanNet \cite{scannet} dataset compared to PointNet++ \cite{qi2017pointnetplusplus}. Given an input of size 8192, the first step is downsampling 8192 points to 1024 points. The method of \cite{qi2017pointnetplusplus} selects 1024 centroids and groups 32 nearest points inside the searching radius.

Our results show that in PointNet++\cite{qi2017pointnetplusplus}, 1622 points on average are not grouped in 8192 points. That is, information of about $20\%$ points are not integrated into the downsampled representation. On the contrary, our PointSIFT module involves more points in computation by performing multiple orientation-encoding(OE) convolutions in OE units. By inserting PointSIFT module before each downsampling layer, our results show that all points can be processed and make a contribution to final predictions. The results are reported in Table \ref{table:bq}.

\begin{table*}[htbp]
\renewcommand\arraystretch{1.2}
\setlength{\abovecaptionskip}{10pt
\caption{IoU for all categories of S3DIS\cite{s3Dis} dataset.}
\resizebox{\linewidth}{!}{

	\centering
	\begin{tabular}{l|ccccccccccccc}
	\hline
		Method & ceiling & floor & wall & beam & column & window & door & chair & table & bookcase & sofa & board & clutter \\
	\hline
		PointNet\cite{qi2016pointnet} & 88.0 & 88.7 & 69.3 & 42.4 & 23.1 & 47.5 & 51.6 & 42.0 & 54.1 & 38.2 & 9.6 & 29.4 & 35.2 \\
		SegCloud\cite{SEGCloud} & 90.06 & 96.05 & 69.86 & 0.00 & 18.37 & 38.35 & 23.12 & \textbf{75.89} & 70.40 & 58.42 & 40.88 & 12.96 & 41.60 \\
		SPGraph\cite{SpGraph} & 89.9 & 95.1 & 76.4 & \textbf{62.8} & \textbf{47.1} & 55.3 & 68.4 & 73.5 & 69.2 & \textbf{63.2} & 45.9 & 8.7 & 52.9 \\
	\hline
		Ours & \textbf{93.7} & \textbf{97.9} & \textbf{87.5} & 59.3 & 31.0 & \textbf{73.7} & \textbf{80.7} & 75.1 & \textbf{78.7} & 40.8 & \textbf{66.3} & \textbf{72.2} & \textbf{65.1} \\
	\hline
	\end{tabular}}
	\label{table:id3D_cat}
\end{table*}
\subsubsection{Effectiveness of Scale Awareness}
We design a toy experiment to verify the effectiveness of scale-awareness in our framework. The experiment setting is that we generate 10000 simple shapes (\eg, spheres, cuboids) with different scales, train our framework on generated data. Then we test if the activation magnitude of PointSIFT modules in different layer for certain shape is aligned to the scale of the shape. As aforementioned, different layers in PointSIFT module correspond to different scales. So if such alignment exists we can conclude that the network is aware of scale.
\if 0
 \begin{wrapfigure}{r}[0cm]{0pt}
 	\includegraphics[width=0.25\linewidth]{cube3.pdf}
 	\caption{Blue points represent centroids; Yellow cloud represents the ball searching area. In this case, many pink points are not covered by the yellow cloud. Because of random selection, some points within the yellow cloud may also not be chosen.}
 	\label{fig:hole}
 \end{wrapfigure}
\fi
It turns out that 89\% of the time, the position of PointSIFT module with the highest activation in the hierarchy is aligned with the relative scale of input shape w.r.t max and min scale. This toy experiment demonstrates that the proposed PointSIFT framework is aware of scale in some sense.

\newcommand{\tabincell}[2]{$\left\{ \begin{tabular}{#1}#2 \end{tabular}\right\}$}

\begin{table*}[htbp]
\setlength{\belowcaptionskip}{10pt}%
	\centering
	\begin{tabular}{ccccc}
	\hline
		layer name & output size & baseline model & ball query sampling & PointSIFT sampling \\
	\hline
		conv\_1 & 1024$\times$128 & \multicolumn{3}{c}{SA module} \\
	\hline
		conv\_2 & 256$\times$256 & SA module &\tabincell{c}{ball query sampling \\ point-wise convolution\\ SA module} & \tabincell{c}{PointSIFT module \\ (128, 128) \\ SA module} \\
	\hline
		conv\_3 & 64$\times$512 & SA module & \tabincell{c}{ball query sampling \\ point-wise convolution\\ SA module} & \tabincell{c}{PointSIFT module \\ (256, 256) \\ SA module} \\
	\hline
		pf\_conv\_3 & 256$\times$512 & FP module & \tabincell{c}{FP module \\ ball query sampling \\ point-wise convolution } & \tabincell{c}{FP module \\ PointSIFT module \\ (512, 512)} \\
	\hline
		pf\_conv\_2 & 1024$\times$256 & FP module  & \tabincell{c}{FP module \\ ball query sampling \\ point-wise convolution } & \tabincell{c}{FP module \\ PointSIFT module \\ (256, 256)} \\
	\hline
		pf\_conv\_1 & 8192$\times$128 & \multicolumn{3}{c}{FP module} \\
	\hline
		fc & 8192$\times$21 & \multicolumn{3}{c}{fully connected layer} \\
	\hline
	\end{tabular}
	\caption{Architectures for comparison of different sampling methods. After ball query sampling, point-wise convolution takes $32\times1$ kernels for extracting features.}

	\label{table:cp_net}
\end{table*}

\if 0
\paragraph{Effectiveness scale-awareness feature}
In this section, we give more details to explain why our network can be scale-aware. We randomly generate 10000 points cloud shape (\eg, circle, rectangle) with different size. Taking a simple PointSIFT-based network into these data. We visualize the heatmap of first three orientation-encoding units in Figure \ref{fig:scale_aware}. The intensities in heatmap are the sum of output feature. We found the boundary of circle is more and more clear. As we know, boundary region is the more important information to recognize the shape of a circle. So, we say

. From Figure . We take two circular objects with different radius as example. We can clearly see the differences among three OE convolutional layers.  When taking larger scale, more areas in the point cloud will be activated.
\begin{figure*}[!t]
	\centering
	\includegraphics[width=0.7\linewidth]{image/scale_aware.pdf}
	\caption{blahblahblah??..}
	\label{fig:scale_aware}
\end{figure*}

\fi

\subsection{Results on Semantic Segmentation Benchmark Datasets}
\label{result}

\begin{table}[t]
	\renewcommand\arraystretch{1.2}
    \caption{ScanNet\cite{scannet} label accuracy and mIoU}
	\centering
	\begin{tabular}{l|c|c}
	\hline
		Method & Accuracy \% & mean IoU \\
	\hline
		3DCNN\cite{scannet} & 73.0 & -\\

		PointNet\cite{qi2016pointnet} & 73.9 & - \\

		PointNet++\cite{qi2017pointnetplusplus} & 84.5 & 38.28 \\
			PointCNN\cite{pointcnn} & 85.1 & - \\
			\hline
			Ours      & \textbf{86.2}  & \textbf{41.5} \\
	        \hline
	\end{tabular}
	\label{table:scannet}
\end{table}

\begin{table}[t]
\renewcommand\arraystretch{1.2}
\setlength{\abovecaptionskip}{10pt
\caption{Overall accuracy and meaning intersection over union metric of S3DIS\cite{s3Dis} dataset.}
	\centering
	\begin{tabular}{l|c|c}
	\hline
		Method & Overall Accuracy (\%) & mean IoU (\%) \\
	\hline
		PointNet\cite{qi2016pointnet} & 78.62 & 47.71 \\

		SegCloud\cite{SEGCloud} & - & 48.92 \\

		SPGraph\cite{SpGraph} & 85.5 & 62.1 \\

		PointCNN\cite{pointcnn} & - & 62.74 \\

	\hline
		Ours & \textbf{88.72} & \textbf{70.23} \\
	\hline
	\end{tabular}

	\label{table:id3D_all}
\end{table}

\paragraph{ScanNet}
ScanNet \cite{scannet} is a scene semantic labeling task with a total of 1513 scanned scenes. We follow \cite{qi2017pointnetplusplus, pointcnn}, use 1201 scenes for training and reserve 312 for testing without RGB information. The result is reported in Table \ref{table:scannet}. Compared with other methods, our proposed PointSIFT method achieves better performance in the sense of per-voxel accuracy. Moreover, our method outperforms PointNet++ even though we do not use multi-scale grouping (MSG) in SA module which helps address the issue of non-uniform sampling density. The result demonstrates the advantage of our PointSIFT module in point searching and grouping.

\paragraph{Stanford Large-Scale 3D Indoor Spaces}
The S3DIS dataset\cite{s3Dis} takes six folders of RGB-D point cloud data from three different buildings (including 271 rooms). Each point is annotated with labels from 13 categories. We prepare the training dataset following \cite{qi2016pointnet} : we split points by room and sample rooms into 1m $\times$ 1m blocks. As have been used in \cite{s3Dis,qi2016pointnet}, we use k-fold strategy for train and test. Overall Accuracy and mIoU are shown in Table \ref{table:id3D_all}. Our PointSIFT architecture outperforms other methods. Per-category IoUs are shown in Table \ref{table:id3D_cat}, our method improves remarkably on results of semantic segmentation task for this dataset and wins in most of the categories. Our method can achieve great results in some hard categories that other methods perform poorly, \textit{i.e.} about 11 mIoU points on the sofa and 42 mIoU points on board. Some results are visualized in Figure \ref{fig:experiment}.

\section{Conclusion}
We propose a novel PointSIFT module and demonstrated significant improvement over state-of-art for semantic segmentation task on standard datasets. The proposed module is endowed with two key properties: First, orientation-encoding units capture information of different orientations. Second, multi-scale representation of PointSIFT modules enables the processing of objects with various scale. Moreover, an effective end-to-end architecture for point cloud semantic segmentation is proposed based on PointSIFT modules. We also conduct comprehensive experiments to justify the effectiveness of the proposed PointSIFT modules.

\if 0
\paragraph{Future Work} SIFT feature can enable rotation-invariant description, which is still less effective by our pointSIFT. Therefore, exploring rotation-invariant property in our pointSIFT architecture is our future work.
\fi

{\small
\bibliographystyle{ieee}

redpajama_set_name: RedPajamaArXiv


Enters her senior season with the Bearcats this season ... Member of the 4x100m team that placed 3rd at the 2017 NWC Championships ... Earned 5th place in the 1600m relay at the 2016 NWC Championship and 11th in the 400m dash ... Placed 13th in the 400 meter race at the NWC Championships in 2015 ... Recipient of the Myrtle L. Atkinson Scholarship ... Participated in sprints and the long jump at Earl Wooster High School ... Was named most valuable athlete and runner of the year during senior year ... Also participated in soccer for 3 years as a defender ... Member of the National Honor Society ... Is majoring in English with a minor in Classics at Willamette ... Daughter of John Peirce and Michelle Chavez Peirce.


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text:
Dear Colleague Letter: Self-Monitoring and Self-Assessing Intelligent Systems Research for the CISE/IIS Robust Intelligence Core Program
Through this Dear Colleague Letter (DCL), the Division of Information and Intelligent Systems (IIS) within the Directorate for Computer and Information Science and Engineering (CISE) announces its intention to support research on self-monitoring and self-assessing intelligent systems through its Robust Intelligence (RI) core program.
Recent years have brought about an explosion of interest in intelligent systems research and applications, including research on human language processing systems, computer vision systems, machine learning, robotics, software agents, cognitive systems, and systems that automatically monitor data and make predictions (including classification).
However, achieving truly robust behavior of intelligent systems requires that they self-assess, recognize their own limits, accurately predict their own breakdown, and act appropriately. Robust intelligent systems must encode and act in awareness of their own performance limits and uncertainties. This self-assessment criterion goes far beyond performance assessments typically reported during evaluation (e.g., rates of classification, word-error, and task completion), which provide very incomplete assessment of the real-world utility of intelligent systems. Only with attention to these additional challenges can we construct systems that are robust even when sensor inputs and user commands are incomplete, erroneous, or manipulated by an adversary; and when the system learns, adapts, self-modifies, or encounters novel situations after deployment.
The Robust Intelligence core program within the IIS division welcomes proposals (as part of the existing RI program solicitation, available at https://www.nsf.gov/publications/pub_summ.jsp?ods_key=nsf15574) that advance self-monitoring, self-assessment, self-repair, and user interaction to ensure that intelligent systems behave as intended. These advances include (but are not limited to) research that specifically addresses: 1) performance uncertainty measures, 2) self-diagnosis, 3) how to ensure system behavior after deployment, 4) system self-inspection and self-repair, and 5) behavioral modification when the system is not behaving as expected.
With recent growing interest in these areas within the Robust Intelligence research community, this DCL explicitly calls attention to the inclusion of these topics within the scope of the existing IIS RI program. Proposals submitted in response to this DCL will be reviewed in competition with all other proposals submitted to the RI program; this is neither a special competition nor a new program, and proposals will be funded as part of the core RI program budget. Proposals submitted in response to this DCL should be synergistic with RI approaches and must contain clear RI research components as described by the RI program solicitation; if they do not, they may be returned without review. Proposal ideas that do not fit within the RI program solicitation but appear to address topics related to this DCL should be submitted to other relevant NSF programs.
Proposals relevant to this DCL should be submitted to the regular RI core program solicitation, available at https://www.nsf.gov/publications/pub_summ.jsp?ods_key=nsf15574, following all proposal preparation instructions specified therein. For further information, interested PIs may contact Todd Leen (Program Director, CISE/IIS, telephone (703) 292-7242, email tleen@nsf.gov) and Hector Munoz-Avila (Program Director, CISE/IIS, telephone (703) 292-7129, email hmunoz@nsf.gov).
Jim Kurose


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Anyone can come up with a number and say they are suing for that amount, it takes a skilled personal injury lawyer to determine the correct amount and get the insurance company to realize it will be less expensive for them to pay than deal with a lengthy trial. Once your attorney has determined exactly how much of a settlement offer to make, they will not fall prey to the high-pressure tactics of the insurance company to get victims to accept less.
Your attorney will present the insurance company with the offer, and make it clear that nothing less than that amount will be accepted or they can expect to prepare for a lengthy trial at their expense too. Your attorney is fighting on your behalf, and they will not let their emotions dictate how this case plays out. They will conduct business in a professional manner and convince the judge and jury as to why you deserve nothing less than the full settlement award.
Don't allow too much valuable time to pass after being injured, call today and speak with a Little Rock car accident attorney about a free consultation. This is your one opportunity to get professional representation and then position yourself to getting compensated for those injuries that you sustained in the car accident.