Let's be honest --- winning back your man will be a lot harder than winning over a new man. But if you want to keep fighting for him, then he must be worth it. If you want to win your man back, you have to reflect on what went wrong, work on improving yourself, and then make a move at the right time. If you want to know how to win your man back and avoid further heartbreak, just follow these steps.
Give yourself some space. If you're constantly around your former flame, you won't be able to step back and actually get some perspective on what went wrong. Though you don't have to ignore him, you shouldn't call him, go out of your way to talk to him, or even Facebook him right after your relationship ends. It's time to be alone for a while, even if that means not going to a party if you'll know he'll be there at first.
Once you're away from the guy, you'll have a less biased perspective about how you really feel and what you loved so much about the relationship.
If you're away from him, he'll start thinking about where you are. You'll already make him want you more just by pulling away.
Think about what went wrong. Before you can try to make things right, you need to be honest about what went wrong in the relationship. Take some time to do some soul-searching and ask yourself what ended the relationship. Where you too moody, too distant, or too flirtatious with others? Think about what you can do to change the dynamic.
You should only try to win him back if you think you can make things different next time. If you ultimately broke up because you were incompatible and just couldn't make things work, then it'll be harder to win your man back than if it was just because you didn't make enough time for him.
Make a list of all the things that went wrong, and highlight the things you can control.
Ask if you were just going through a difficult time in your life or dealing with a major change. You may be more ready for a relationship if you feel more stable and positive.
Change your ways. Once you've figured out what you may have done wrong in the relationship, resist the urge to call the ex and show off your new found self-realization. Actions speak louder than words. Just be the new improved self, and word will spread.
If the relationship ended because you were too clingy, work on not being so attached to him. Play it cool and he'll see that you're a new person.
If the relationship ended because you didn't give him enough attention, start making him feel special. Tell him he looks nice and that you're happy to see him.
If the relationship ended because you spent too much time flirting with other guys, don't let him see you with other guys too much or he'll be reminded of why things didn't work out.
Look and feel your best. Get your nails and hair done. Take a nice warm bubble bath, and pick out a nice outfit. Once you look your best, you will begin to feel your best. You don't need to get a complete makeover if you want to change. You just need to pay extra attention to grooming and hygiene, and the rest will follow.
Spend an extra 10-15 minutes getting ready in the morning, and you'll start to feel better. Maybe you were too busy or stressed out to care about your appearance before.
Get a fun hair cut. Nothing makes you feel like getting a fresh start like a fresh cut.
Stop caring about what other people think. If you're so insecure that you spend all of your time thinking about how others perceive you, then you'll never be able to work on yourself enough to catch your former man's eye again. It doesn't matter if people think you're funny, cute, or charming -- it's all about thinking positive things about yourself.
If you don't care what other people think about you, then you'll exude confidence and your former guy will be impressed that you finally know how to do your own thing.
Don't worry about his friends. Maybe his friends didn't think you were right for your former man or just didn't get along with you. You should be nice and get in their good graces, but don't bend over backwards trying to charm them or you'll lose sight of your target.
Stay busy doing the things you love. If you're busy with schoolwork, friends, and pursuing your interests, you will be able to grow as a person and to continue improving yourself instead of pining after your guy. Plus, if he sees that you're obviously occupied instead of sitting around staring up at the sky, then he'll see that you have a lot to contribute and that you're person with an active and engaging life.
Let your former guy see you doing the things you love, whether it's immersing yourself in your studies or going out for a nice run.
Spend lots of time with your friends. They'll be able to keep you positive and will lend you a fresh perspective.
Don't use a rebound relationship as a means of staying busy. This will only distract and confuse you even more.
Develop and express your individuality. As you continue to pursue your interests and get some space away from the guy, you'll start getting a better sense of yourself as an individual, not just a hurt ex-girlfriend. Take this time to really be your true self and to let the world know who you are.
Dress to impress. Wear clothes that really make you stand out as an individual. You don't have to wear anything garish to get attention -- just be brave and wear a "fun" outfit you were too timid to wear before.
Express your individuality by being creative. Write a story, pick up an acting class, or sign a song at an open mic. Let your former man see you being comfortable with expressing your individuality.
Become a more positive thinker.If you learn to see the good in the world instead of the bad, you'll give off an irresistible positive energy, and your guy will want to be around you again.
Be happy with your appearance. Think of at least three things you love about yourself, and dress to show off your best assets. Make sure to get regular exercise to stay mentally and physically strong.
Be assertive. Speak in a clear and effective manner instead of mumbling or talking softly. Command attention with your voice, and everyone will see that you're confident about what you have to say.
Be more classy. It's fun to be in a party and dance a lot, but that's not always classy behavior. If you're at a party, the classy behavior is to socialize, but speak calmly, not monopolizing all the attention. You can still get a little crazy as long as you don't get sloppy and he'll like you even more for it. No one wants a girl who is falling down drunk or fighting for all of the attention in the room.
Being classy is a form of being mature. Every guy wants a girl who is mature and in control of her emotions instead of a hot mess in public.
Dress elegantly. You have to look classy as well as be classy. You can show some cleavage, but don't wear anything so scandalous or tight that it looks like you're desperate or just trashy. If you look cute but classy, your former flame will naturally notice you.
Let him see you having a great time. Have a big smile on your face and let him see you really enjoying yourself and bringing pleasure to the world and the people around you. You shouldn't have to fake it. If you really want him to want you, then you should actually be having a great time, and be comfortable enough to be happy without him.
Laugh -- a lot. Let him see you laughing and giggling with friends and really having the time of your life.
Be engaged. Be fully engaged in whatever conversation you're having. Let him see you being animated, gesturing, and making great points.
Let him see you engaging in fun activities. If he sees you laughing it up at trivia night, having a great time running with a girlfriend, or hitting up the dance floor, he'll want to spend more time with you.
Avoid desperate behavior like the plague. If you're desperate to get his attention, make him see how much fun you're having, or to look so hot that he falls over, then he'll be able to tell right away. Just work on being your best self and hope he catches you in the process. Don't overdo it, or you'll only make things worse.
Don't try too hard to look like you're having fun when you're around him. Let it be natural.
Don't try too hard to look good around him, either. Dress appropriately for the occasion and don't wear much more makeup than usual or incredibly high heels just to catch his eye. He will notice you this way -- but not in a good way.
Don't cling to him. Let him come to you. If you're at a party, let him come up to say hi first.
Pick the right place and time. Try to get him alone at a time when he's in a good mood and when there isn't a chance his friends will be lurking in the background.
Swallow your pride. If you've made any mistakes, apologize for the past, and be as specific as possible to show that you're self-aware.
Be honest. Tell him that you really miss having him in your life, and that you made a big mistake and want him back.
Say that you want to make it up to him and to show him how much you've changed.
Don't repeat your mistakes. Remind yourself of what went wrong and vow not to do the same thing this time. Remember how hard you've tried to improve yourself.
Don't put too much pressure on yourself. If you're constantly worried about not messing up again, you won't be able to focus on the present.
Start fresh. Think of this as starting a brand-new relationship while having a bank of information to work with. Don't dwell on the past or rehash old arguments.
Be yourself. Though you're an improved version of yourself, you're still you at the end of the day, so remember to be the girl he fell for in the beginning. If you try too hard to prove how different you've become, you'll lose sight of who you are.
Know when to walk away. If you've tried everything and then some, but your former man still won't give you the time of day, then too much damage has been done for you to pursue the relationship further. If he's ignoring you, resisting your advances, or just downright being mean to you, then it's time to walk away before you get even more hurt.
Remember that not very relationship can be salvaged. You've done your best and can applaud yourself for trying.
Walk away with your head high. Don't be embarrassed about opening up and sharing your true feelings.
Jealous people will try to bring you down to their level. Don't let them. Remember you've got a lot going for you.
If you're asked why the guy/girl is acting like that toward you, say honestly that you don't know. Don't talk trash or gossip about them to anyone. In that way, they look like (what they probably are) a loony freak, and you look like an innocent, injured person.
Keep a positive attitude through everything; it looks like you don't care about the whole situation. Don't pass negative comments about what the person is saying, because that shows that you do, indeed, care.
If you find yourself near him, don't sit there thinking about what you should say. Talk to the people around you and he'll be naturally drawn into the conversation.
Remember that happiness is the key to value. Enjoy your life as much as you can and make the best out of every situation. Finding happiness is the definition of success in life.
Any performance that he gives is to catch your attention. So just smile and act like it doesn't bother you if you see him making out with another girl.
If you have trouble ignoring drama, imagine you're sitting in an airplane taking off, watching the cars and buildings get smaller and smaller. You are that small. You're actually smaller. Your problem is only in your head. Imagine how small your head is compared to one of those cars.
No matter what he says, he should never be able to change you so don't go out of your way to make him want you. If he just isn't that into you, then you will find someone who loves you for who you are.
When your ex has a new girlfriend try to be friendly to them. Try no to show jealousy as it will ruin your contact with him.
Don't get revenge by cheating yourself.
Never tell your friends that you are having problems with your man because they'll just end up interfering!
Don't respond to the content of their remarks. The less you say, the less likely you are to get drawn into an argument.
Thanks to all authors for creating a page that has been read 1,146,909 times.


The Storkcraft Hoop Glider and Ottoman gives an instant lift to the decor of your nursery. It makes feeding time easy for mother and child while adding a touch of charm to the room. These color combinations are fixed because they are the most popular colors for the Hoop Glider and Ottoman. The soft polyester gives a comfortable feel while being durable. With enclosed metal ball-bearings, this wooden glider allows for incredibly smooth rocking to soothe your baby. Solid wood construction gives these pieces a sturdy feel that you ll appreciate far beyond the baby years.
The Baby Relax Tinsley Rocker will become your new favorite spot to relax and spend time with your baby. This rocker is a great rocking chair for nursery for those looking for that quintessential baby rocker, but with a few added design and comfort perks. The design details such as the complementing two-tone welting and the button-tufting on the backrest give the Baby Relax Tinsley Rocker that polished look. The soft Microfiber fabric is easy to care for and three fabric color options are available, making it easy to match with your nursery decor. The sturdy construction and espresso finished solid wood feet will ensure years of use and the thickly padded seat, arms, and backrest will have you enjoying every minute spent sitting in The Baby Relax Tinsley Rocker with your little one.
Fall in love with the classic styling of The Baby Relax Harlow Wingback Rocker with Nailheads. Featuring the cozy comforts of a wingback chair, but elegantly detailed and mounted on rocker feet, this rocker will be a family favorite for tending to baby. The decorative and individually set antique colored nailheads are found across the base, front arm panels, and wingback side panels for a clean and polished look. The solid wood espresso finished legs complement the soft-to-the-touch upholstery. There are a number of shades to choose from that will enhance the look of your nursery. The sturdy construction and the supportive spring core foam filled seat will ensure maximum comfort and years of use.
Now you can rock anywhere, anytime with the Classic Ready Rocker. When it's feeding time, bedtime or just cuddle time, a comfortable rocker is a must-have to help make bonding with your baby easier. The Classic Ready Rocker turns any chair, bench, or wall into a comfortable rocker. Since it's lightweight and portable, the rocker goes where you go, so you can rock your baby when you need it. Plus, the soft and durable fabric keeps you comfortable while you are doing the comforting. Whether you are in your house or out & about, the Classic Ready Rocker is the perfect solution for rocking on the go! Most people will be rocking with our REGULAR Ready Rocker. The LITE version works best if you wear petite clothing or your feet don't touch the floor in a chair.
Lightweight portable rocker to help parents rock their babies anywhere, anytime.
Whether in bed, around the house or on-the-go, the Ready Rocker works against any sturdy vertical surface.
Durable and safe, made with enhanced soil and stain resistant finish.
Lighter than a 2L bottle of soda and fits into your suitcase, this rocker is ready to travel and store. NO ASSEMBLY REQUIRED!
Dimensions: 25.0 In. X 13.0 In. X 22.0 In. | Material: Polyester plush. Wooden rocking base and handles. | ... Safe 'Parents can put their children on the shaker and they will be surrounded by 360 protection. It is also recommended that you add a cushion to your seat to make your child more comfortable. | Comfort 'soft saddle, soft and comfortable, baby play more peace of mind, solid wood base, can bear 80 kg. Suitable for 1 year to 5 years old baby.
Little ones can go on a make-believe Alpaca adventure. Sweet and huggable, it features solid wood handles and precision-shaped runners for a smooth, secure and gentle rocking motion. Gentle rocking motion. Wooden handles for an easy grip.
The rocking chair is made of multi-layer birch frame and high density sponges cushion with imitation linen fabric, which is not only solid and durable but also easy to clean up.
The wider and soft cushion provides a great comfort to you and your family. And the adjustable footrest angle satisfies various needs and people of various ages.
The side pocket on the armrest is convenient for you to store and get something you need when enjoying your relax time. Anti-slip mat at the bottom is great for protecting your chair and floor.
The curve wood design better matches your furniture and adds elegance to your home set. Don't hesitate to buy one!
The removable cushion fabric and headrest make it easy to clean up so that you can enjoy a clean and healthy environment.
Isn't it just the worst when you lose the most important part to a pad?
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We carry a large variety of replacement parts for your pad or baby product.


So it's kind of like a pivot.
It's not just with your finger.
And you don't want to bounce too high.
you're hardly moving at all.
you just are pecking, pecking, pecking.
So your not thinking of coming up, up, up.
every single arm into a key.
coming off the keys as fast as possible.
Here's an example with my left hand.
I'll do it on C.
pivot coming from your elbow.
Then it can get closer and closer.


Breaking Through Concrete team(Michael Hanson)The Breaking Through Concrete team — David Hanson, Michael Hanson, Charles Hoxie, and Edwin Marty — is taking a 21st century road trip to document the American urban farm movement. Driving across the country and back in a biodiesel-fueled, Internet-enabled short bus they've nicknamed Lewis Lewis, they'll visit 14 diverse projects that are, in distinct ways, transforming our built environments and creating jobs, training opportunities, local economies, and healthy food in our nation's biggest cities. Along the way, David will post stories for Grist (and for one of the team's sponsors, WHYHunger), illustrated by his and Michael's stunning images — material that will ultimately be collected into a book — and Charles' short video snippets.
A bottle of Korbel's christened the front right bumper of Lewis Lewis last Tuesday night in Seattle. We rolled out Wednesday morning, picked up five pounds of farm-direct Sumatra beans from our friends and sponsors at Caffe Vita, and hit I-5.
Flipped the switch to veggie grease south of town and ran on the waste fry-juice for over 370 miles. It doesn't smell nearly as bad as they say, and Lewis Lewis ran smooth as butter.
Pulled off in Eugene, Ore., for a visit to Huerto de la Familia, a community garden project for low-income Latin American families. Sarah Cantril and the Huerto project were recently awarded one of WHY Hunger's 10 annual Harry Chapin Self-Reliance awards, honoring community-based organizations for innovative and sustainable approaches to fighting hunger and poverty.
Norma and Jesús will have a plot for the first time this year. They used to farm in their hometown of San Pablo Tecaleo, Mexico. They guess that they pulled 80 percent of their household food from the farm. They came to America last year to find a better life. This year they hope to grow 70 percent of their veggies here at the Churchill Community Garden.
We slept in Lewis Lewis outside Talent, Ore.
Look for the white short bus emblazoned Lewis Lewis, and if you see us, come and get some coffee. We want to hear where your food comes from.


The start of the Foundation would come into effect after the death of his second wife. She passed away in 2006 and so the task of the Trustees, Hugh Taylor, who succeeded Prof Ray Whitmore in 2003, and Wybe Geertsma (the son-in law of Willis L. Haenke) has now begun.
The Foundation is recognised by the Australian Taxation Office as a Charitable Fund. This means that the Foundation is also a Deductible Gift Recipient.


text:
namespace Demo
{
    class NetworkServer
    {
    public:
    	NetworkServer(MPACK::Network::UDPSocket *sock, int searchPort);
    	~NetworkServer();

        int Run();

    private:
        int m_searchPort;
        MPACK::Network::UDPSocket *m_sock;
    };
}

#endif

redpajama_set_name: RedPajamaGithub


Ed Lull – You're what?! – High Tide Publications, Inc.
An early bio on you said you were a retired Navy Commander – a submariner. I want to know how you morphed from a Navy submariner into, of all things, a poet. There must be some stories there.
It has been a lengthy journey with lots of curves and surprise turns – but stories, yes, there are many I could share with you. I'd probably bore you to death.
Well, try me. What drove you into your first writing project?
In my final year of my English classes at the Naval Academy, the last project (hurdle) I had to get over was a thesis on some topic related to naval history. After weeks of indecision, I settled on researching and writing on England's preparation to meet the Spanish Armada. Not long into the research, I realized the topic was too broad to cover in a thesis, so I selected a segment of that preparation. The paper was titled: "Sir Francis Drake's Expedition to Cadiz." My academics at the Academy were not my strong point; I'd consider myself a mediocre student. However, for that paper, I received a 3.9 (on a 4.0 scale) . I was so fascinated with that period of history, I vowed to return to it, expand on my research, and write a book.
I did. However after graduation, life got in the way and my vow was delayed. The book, "Cabin Boy to Captain: A Sea Story," that incorporates many of Drake's adventures was written in blank verse and published as a historical novel – 50 years later.


From BBSs, through Napster, torrent sites, and stream rippers, there's been no shortage of targets over the past decades.
Despite efforts from the music industry to counter these ever-emerging threats, people who want to access music for free now have more options than ever.
While piracy is bad enough, there's one thing that music insiders fear even more; pre-release leaks. So, when a website trades in these kinds of leaks in the open, alarm bells go off.
This was the case around a year ago when the mysterious "Music Mafia" group entered the stage. The group gained mainstream attention when two unreleased Kanye West tracks appeared online, way ahead of schedule.
The tracks soon made their way to YouTube, with many pointing out Music Mafia as the source.
More than a year has passed and the Music Mafia site is still up and running. In exchange for Bitcoin, it auctions and sells unreleased tracks, demos, and unreleased music videos. Even email addresses and phone numbers of artists are on the menu.
In the site's "assortment" you'll find popular names such as Beyonce, Coldplay, Drake, Hardwell, Justin Bieber and Katy Perry. The list of unreleased material is impressive as well, with dozens of records from David Guetta, Kanye West, and Travis Scott, among others.
Some of the unreleased tracks are auctioned off on a regular basis while others can be bought directly. The most exclusive releases are sold behind the scenes, all traded for Bitcoin.
The music industry sees the anonymous group as a severe threat, something which pushed it onto the RIAA's radar.
A few weeks ago the music group obtained a DMCA subpoena (pdf) from a federal court in the District of Columbia ordering Tonic Domains Corp, the registry of Music Mafia's .to domain, to identify the associated registry.
"We have determined that a user of your system or network has infringed our member record companies' copyrighted sound recordings," RIAA wrote to the registry, requesting information.
Whether Tonic Domains Corp complied with the request is unknown. However, a few weeks have passed now and MusicMafia.to is still up and running as usual, trading unreleased music for Bitcoin.
One of the most intriguing questions is where this group gets its booty. Is it connected to rogue music industry insiders? Are they skilled hackers who get their treasure through digital means?
There are some hints that support the hacker theory. For example, last year Drake posted Music Mafia's website address on Twitter. This tweet was swiftly removed and some suggested that the account had been hijacked.


Pre Arrival Letter to Hotel for Corporate Guest. You know that feeling…you arrive at your hotel exhausted after a long trip, only to find yourself arguing with the front desk representative who has no record of the company arranging your stay.
As a company that values its customers (and their time and consideration).
When you have an employee or guest traveling for business purposes, you want to do everything you can to make sure their trip goes as smoothly as possible.
Sending his letter to the hotel is one step in that direction.
Giving the hotel a heads-up will save your employee a night of frustration and worry, which of course, will in turn keep them performing at peak condition.
Although you will have a detailed conversation beforehand, getting the specifics on paper is of the utmost importance!
This letter is to confirm our recent telephone conversation regarding [individual's] stay at your hotel next week. All charges are to be directed to me at the address on our letterhead.
[individual] will arrive at the Los Angeles airport on [date]. She should be checking in around [time].
The rate for her room will be $_____ plus tax per day. She will probably be staying for days.
Please have flowers in her room when she arrives.


1 John 1:1 NASB "What was from the beginning, what we have heard, what we have seen with our eyes, what we have looked at and touched with our hands, concerning the Word of Life—and the life was manifested, and we have seen and testify and proclaim to you the eternal life, which was with the Father and was manifested to us— what we have seen and heard we proclaim to you also, so that you too may have fellowship with us; and indeed our fellowship is with the Father, and with His Son Jesus Christ."
There are three basic exegetical problems in this section that we have to deal with. The first is, to what do those neuter clauses refer? They can't refer to logos because that is a masculine noun. In fact there is no neuter noun in the section to which it can refer. And that is not unusual in Greek. Often when there is a collective concept a neuter noun is picked up in order to refer to the collective concept. Some have suggested that the collective concept refers to the witness of the apostles. In other words, "witness" in the Greek is a neuter. It is possible that that is ellipsisised out and that is the understood reference, but it can also simply be that because he is talking about the message, about everything that is involved with the message, that he just simply includes all of these things in one reference by a neuter noun which refers to the message, the testimony, everything that they saw and experienced about the Lord Jesus Christ. The latter is the view that we take. It is referring to the overall apostolic witness about the incarnation, everything that Jesus did, said and taught.
The second thing we have to solve before we can accurately translate it is what "beginning" refers to in the first clause: "what was from the beginning." The phrase in the Greek is the preposition apo [a)po] plus the noun arche [a)rxh]. arche is the word for beginning, the same word that is used in John 1:1 which begins "In the beginning." But there a different preposition is used—en [e)n]. apo means from; en means in or at the time of. Is 1 John 1:1 referring to the same beginning as John 1:1. We don't think so. If we look at how the way arche is used in John's writings, that is, in the Gospel, the epistles and Revelation, it is used eighteen times. In John 1:1 it refers to the absolute beginning of the space-time universe—"In the beginning was the Word." The word "was" is, as in 1 John 1:1, an imperfect active indicative of the verb eimi [e)imi], to be. It refers to continued existence in John 1:1—"In the beginning the logos already was existing." That refers to the fact that when God first created the space-time universe the logos was already in continuous existence. It is a reference to the eternality of Jesus Christ. So John 1:1 refers to an absolute beginning of the space-time universe. In John 2:11 the phrase is used again, and there it refers to the beginning of Jesus' miracles. In John 16:4 it refers to the beginning of His teaching ministry to the disciples NASB "But these things I have spoken to you, so that when their hour comes, you may remember that I told you of them. These things I did not say to you at the beginning, because I was with you." So there it refers to the beginning of His public ministry with the disciples. So "beginning" is a technical term that not always refers to the same beginning. 1 John 2:7 reads NASB "Beloved, I am not writing a new commandment to you, but an old commandment which you have had from the beginning; the old commandment is the word which you have heard." That is not talking about the absolute beginning of the universe or even the beginning of Christ's incarnation, it is referring to the beginning of His teaching them about the spiritual life. 1 John 2:14 NASB "have written to you, fathers, because you know Him who has been from the beginning…" we see there that beginning can refer to eternity past. 1 John 2:24 NASB "As for you, let that abide in you which you heard from the beginning." That is referring to the beginning of Jesus' teaching the disciples. So the word "beginning" is not a technical term for the absolute beginning, it can refer to different beginnings. In 1 John 1:1 it is clear that it is talking about a message. That message, therefore, had to have been communicated at a certain starting point and that was during His teaching ministry to the apostles. So they are talking there about their witness in the beginning of the public ministry of Jesus Christ when the disciples were associated with them, specifically John because that first person plural includes and is a reference to him primarily, so that takes us to the episode in John chapter two when John the apostle was a disciple of John the Baptist. Jesus told John and Andrew to follow Him and they left John the Baptist for the Lord Jesus Christ.
This brings us to why that last phrase in the first verse is not a capital W but should be translated "message," not Word. One of the problems we have when we look at this is that John wants us to think in terms of what he has already said in the Gospel. The Gospel began with the introduction of Jesus Christ as the logos. As soon as we see the word logos in 1 John 1:1 if we are familiar with the Gospel the first thing that should come to mind is that we are thinking about the Gospel. In the Gospel of John the emphasis is on the person of Jesus Christ, the man. John doesn't want us to lose sight of the fact that in this case the man is the message and the message is the man. You can't separate the message from the man in the New Testament.
What we have seen in the introduction here is that the empirical evidence that they are emphasising—what we heard, what we saw, what our hands handled—is the main message of this whole introduction. The thrust of it is related to the incarnation. Jesus Christ had to be both true humanity and undiminished deity to accomplish His work on the cross. He could not be simply a good man. On the other hand, He couldn't be God and not true humanity. If He were not true humanity then He could not have died as a substitute for the sins of humanity. Like had to die for like. But if he was not undiminished deity His death would not have had unlimited value and he would not have had the +R, the perfect righteousness of God. There are two aspects to salvation. The first is the payment for sin, but the sin of every single person has been paid for. Every unbeliever's sin has been paid for but they are not saved. The two aspects: you not only have to have your sins paid for but you have to have perfect righteousness. God's perfect righteousness cannot have fellowship with anyone less than perfect righteousness. On the cross all of our sins were imputed to Christ, and at the instant of faith alone in Christ alone the perfect righteousness of Christ is imputed to us and credited to our account. Therefore when God looks at us He is not looking at the fact that we are –R and that we are sinners, that is covered by the fact that we have received the perfect righteousness of Christ. So two things have to happen at salvation: we have to have our sins paid for but we have to be able to receive perfect righteousness. The perfect righteousness did not come from Christ's humanity, it came from His deity.
The second reason Jesus Christ had to become incarnate is that in the hypostatic union He provided the pattern and the precedent for our new spiritual life in the church age. That spiritual life is based on the indwelling and the filling ministry of God the Holy Spirit.
John uses the word logos here because he knows that it is going to bring to our minds Jesus Christ, but he is not using it in the technical sense. One of the ways we know this is that logos is used seven times in John's epistles, none of the other six are a technical use of logos. In John's Gospel the word is used about 30-40 different times but it is only in the first two verses and the fourteenth verse of the first chapter that it has a technical meaning. So it is unusual for John to use it in a technical sense to begin with. Secondly, we would notice if logos is used technically for the Lord Jesus Christ then we would be able to substitute Jesus Christ for the word logos and it would make sense, e.g. John 1:1.
1 John 1:1, "From the beginning we have heard," akekoamen [a)khkoamen], perfect active indicative of the first person plural of akouo [a)kouw] meaning to hear or to listen to. This is interesting because the first two verbs in the sentence are perfect active indicative and the second two verbs are aorist active indicative. Normally a perfect tense emphasises the results of a past action, but there is a very rare use of the perfect tense which is called the aorist or dramatic perfect. The use of the aorist or dramatic perfect is a rhetorical device to describe an event in a highly vivid way, so that the aoristic or dramatic perfect is used as a simple past tense without concern for present consequences. "What we have heard" is off in the past, it is not an emphasis on present results; but he puts it in the perfect because he is dramatising, he is coming out of the shoot, as it were, with a pun. The second verb is heorakamen [e(wrakamen] which is the perfect active indicative of horao [o(raw] which means to see with comprehension, understand, perceive. This is what we heard and perceived, it is what we understand about Jesus Christ; we saw this with our eyes and we beheld it. The prefect tense, again, is an aoristic or dramatic perfect. The active voice indicates that the subject performs the action. In both of these verbs John, along with the other disciples, heard and saw and perceived with their eyes what was going on and what Jesus was demonstrating before them. The indicative mood expresses the mood of reality.
Then we come to the next two verbs, the aorist middle indicative, first person plural of theaomai [qeaomai] which means to not only on just see but to perceive everything. So it is talking about understanding and perception in horao, and witnessing, what they beheld, in the second. The aorist in the last two verbs is a consummative aorist, it places the stress on the cessation and the completion of the activity. This isn't still going on. They saw it at one time, during those three years of Jesus' public ministry. They saw it all and these are their conclusions. All of these verbs have the same emphasis, that John along with the other disciples saw, heard, beheld, and their hands handled the Word of life. The last verb is the aorist active indicative, first person plural of pselaphao [yhlafaw] which means to feel, touch or handle. All of this means that they had full empirical content with the Lord Jesus Christ and His physical incarnation, and so it wasn't an illusion. It was actual and they were eyewitnesses to it.


text:
A man is dead and a woman injured after gunman opened fire on pair dining at a Houston restaurant
Posted: Jul 9, 2021 10:50 AM CST
Updated: Jul 10, 2021 8:00 AM CST
A man and a woman were finishing dinner at a restaurant at the Downtown Aquarium in Houston, Texas, the night of July 8 when a man seated at the other end of the bar got up, approached the pair and opened fire, officials said.
By Alta Spells, CNN
(CNN) -- A man and a woman were finishing dinner at a restaurant at the Downtown Aquarium in Houston, Texas, Thursday night when a man seated at the other end of the bar got up, approached the pair and opened fire, officials said.
When police arrived at the scene of the Downtown Aquarium, a 28-year-old man was dead and his 24-year-old wife was injured by gunfire. She was taken to a local hospital where she's being treated and is in stable condition, according to police.
Police, via Twitter, called the shooting "a tragic, isolated incident" and said there was "no relationship" between the victims and the suspect.
The suspect was identified as Danny Garcia Cazares, 39, who police said had a previous criminal history and a history of mental illness.
The suspect "immediately started firing rounds" after approaching the pair shortly after 8 p.m., said Matt Slinkard, the Houston Police Executive Chief.
"The deceased male immediately went down, the female was injured, went down as well," he said.
Then the male suspect killed himself, Slinkard said.
Homicide investigators are working to determine what led to a shooting, the chief said.
The-CNN-Wire™ & © 2018 Cable News Network, Inc., a Time Warner Company. All rights reserved.

redpajama_set_name: RedPajamaCommonCrawl


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text:
\section{Introduction}

By analogy to continuous processes, a stochastic process is \emph{absolutely continuous} its paths are absolutely continuous---every path is the integral of some locally integrable ``density function''.

This paper shows that if an absolutely continuous process is adapted, then the density functions of the individual paths can be fitted together to form a predictable process.
In other words, the absolutely continuous process is the time integral of a predictable and pathwise locally integrable process.

Conversely, it is also true that the time integral of a predictable (or progressive) pathwise locally integrable process is adapted and absolutely continuous. Hence, the set of
adapted and absolutely continuous processes is exactly equal to the set of time integrals of predictable (or progressive) pathwise locally integrable processes.

The result is close to
Chung and Williams \cite[1990, Lemma 3.11 (ii)]{Chung-Williams:90}\index{Chung, K. L.}\index{Williams, R. J.}
and Letta \cite[1988, Proposition 3.1]{Letta:88}, and
the proof is almost the same, but spun to a different conclusion.

Chung and Williams show that if $\hat{a}$ is a jointly measurable and pathwise locally integrable process, then its time integral is an adapted process, and there exists a predictable process $a$ such that $\hat{a} = a$ almost everywhere (which will imply that the time integrals of $a$ and $\hat{a}$ are indistinguishable).

The difference is that we do not assume $\hat{a}$ to be jointly measurable.
Thus it is not relevant to ask whether the processes $\hat{a}$ and $a$ are equal almost everywhere.
Instead, we assume that the time integral of $\hat{a}$ is adapted and show directly that it is identical to the time integral of $a$.

\section{Background Information}

Let $(\Omega,\mathcal{F})$ be a measurable space and let $F = (\mathcal{F}_{t})_{t \in [0,\infty)}$ be a filtration.
There is literally no probability measure involved in any of the concepts in this paper.

Let $\mathcal{B}(\Re)$, $\mathcal{B}([0,\infty))$, and $\mathcal{B}([0,t])$, $t \in [0,\infty)$ denote the Borel sigma-algebras on $\Re$, $[0,\infty)$, and $[0,t]$, respectively.

A stochastic \emph{process} is a mapping $X: \Omega \times [0,\infty) \rightarrow \Re$ such that for
every $t \in [0, \infty)$, $X(t): \omega \mapsto X(t)(\omega) = X(\omega,t)$ is measurable with respect to $\mathcal{F}$ and $\mathcal{B}(\Re)$.

All processes in this paper are understood to be one-dimensional.
The generalization to higher-dimensional processes is trivial.

The process $X$ is \emph{measurable} if it is measurable with respect to $\mathcal{F} \otimes \mathcal{B}([0,\infty)$, and it is \emph{adapted} if for
every $t \in [0, \infty)$, $X(t)$ is measurable with respect to $\mathcal{F}_{t}$.

A stochastic process $X$ is \emph{progressive} if
its restriction to
$\Omega \times [0,t]$ is measurable with respect to
$\mathcal{F}_{t} \otimes {\mathcal{B}}([0,t])$, for every $t \in [0,\infty)$.

Let $\mathcal{M}$ be the smallest
sigma-algebra with respect to which all progressive processes are measurable.
Then a process is progressive if and only if it is measurable with respect to $\mathcal{M}$.

Let $\mathcal{P}$ be the sigma-algebra on $\Omega \times [0,\infty)$ generated by the adapted and continuous processes.
Call it the \emph{predictable} sigma-algebra.
A process is \emph{predictable} if it is measurable with respect to $\mathcal{P}$.

Every predictable process is progressive, and every progressive process is measurable and adapted. Expressed differently,
$\mathcal{P} \subset \mathcal{M} \subset \mathcal{F} \otimes \mathcal{B}([0,\infty))$.

A function $f: [0,\infty) \rightarrow \Re$ is \emph{locally integrable} if it is measurable with respect to
$\mathcal{B}([0,\infty))$ and $\mathcal{B}(\Re)$ and
\[ \int_{0}^{t} |f| \, ds < \infty \]
for all $t \in [0,\infty)$.

A process $a$ is \emph{pathwise locally integrable} if every path of $a$ is locally integrable. In other words, for every $(\omega,t) \in \Omega \times [0,\infty)$,
\[ \int_{0}^{t} |a(\omega,s)| \, ds < \infty \]

If a process is progressive (in particular, if it is predictable) and pathwise locally integrable, then its time integral is an adapted (and absolutely continuous) process. Adaptedness follows from the Tonelli-Fubini theorem.

A function $F: [0,\infty) \rightarrow \Re$ is \emph{absolutely continuous} if there exists a locally integrable function $f: [0,\infty) \rightarrow \Re$ such that
\[ F(t) = \int_{0}^{t} f \, ds \]
for all $t \in [0,\infty)$. If so, then $F$ is differentiable at almost every $t \in (0,\infty)$ with $F'(t) = f(t)$.

A stochastic process $X$ is \emph{absolutely continuous} if every path of $X$ is absolutely continuous.

If $a$ is a progressive process such that
all paths of $a$ are locally integrable, then the mapping $X: \Omega \times [0,\infty) \rightarrow \Re$ defined by
\[ X(\omega,t) = \int_{0}^{t} a(\omega,s) \, ds \]
for all $(\omega,t) \in \Omega \times [0,\infty)$, is an adapted and, of course, absolutely continuous process.

\section{The Theorem}

\begin{theorem}
\label{adconv2-t}
If $X$ is an adapted and absolutely continuous process, then there exists a predictable process $a$ such that
all paths of $a$ are locally integrable and such that
\[ X(\omega,t) = \int_{0}^{t} a(\omega,s) \, ds \]
for all $(\omega,t) \in \Omega \times [0,\infty)$.
\end{theorem}
\begin{proof}
Since $X$ is absolutely continuous, there exists a mapping $\hat{a}: \Omega \times [0,\infty) \rightarrow \Re$ such that for each $\omega \in \Omega$, the mapping $t \mapsto \hat{a}(\omega,t)$
is locally integrable and
\[ X(\omega,t) = \int_{0}^{t} \hat{a}(\omega,s) \, ds \]
for all $(\omega,t) \in \Omega \times [0,\infty)$.

For each $h > 0$, define a mapping $X(h): \Omega \times [0,\infty) \rightarrow \Re$ by
\[ X(h)(\omega,t) =  \left\{ \begin{array}{cc} X(\omega,t)/h & \mbox{ if } 0 \leq t < h  \\
(X(\omega,t) - X(\omega,t-h))/h & \mbox{ if } 0 < h \leq t \end{array} \right. \]
Since $X$ is an adapted process, so is $X(h)$.
For each $\omega \in \Omega$, $X(h)(\omega,t)$ is continuous on $[0,h)$ and on $[h,\infty)$, and
$X(h)(\omega,t) \rightarrow X(\omega,h)/h = X(h)(\omega,h)$ as $t \rightarrow h$, $t < h$. Hence, $X(h)$ is a continuous process. Since it is adapted and continuous, it is predictable.

Let $(h_{n})$ be a sequence of positive numbers such that $h_{n} \rightarrow 0$ as $n \rightarrow \infty$.
Since $X(h_{n})$ is predictable for each $n$, the set
\[ A = \{ (\omega,t) \in \Omega \times (0,\infty) : X(h_{n})(\omega,t) \mbox{ converges} \} \]
is in the predictable sigma-algebra $\mathcal{P}$.

Define $a: \Omega \times [0,\infty) \rightarrow \Re$ by
\[ a(\omega,t) =  \left\{ \begin{array}{cc} \lim_{n} X(h_{n})(\omega,t) & \mbox{ if } (\omega,t) \in A \\
0 & \mbox{ otherwise } \end{array} \right. \]
Then $a$ is a predictable process.

Given $\omega \in \Omega$, the mapping $t \mapsto X(\omega,t): (0,\infty) \rightarrow \Re$ is differentiable at almost all $t$, with derivative $\hat{a}(t)$. Hence,
for almost all $t > 0$, $X(h_{n})(\omega,t) \rightarrow \hat{a}(\omega,t)$
as $n \rightarrow \infty$. This implies that $a(\omega,t) = \hat{a}(\omega,t)$ for almost all $t \in [0,\infty)$.

In particular, all paths of $a$ are locally integrable, and
\[ X(\omega,t) = \int_{0}^{t} \hat{a}(\omega,s) \, ds  = \int_{0}^{t} a(\omega,s) \, ds \]
for all $(\omega,t) \in \Omega \times [0,\infty)$.
\end{proof}

\bibliographystyle{plain}

redpajama_set_name: RedPajamaArXiv


text:
\section{Introduction}

Recent years have witnessed a tremendous growth in the amount of video data on the Internet, resulting in the need for intelligent and autonomous video analysis technology \cite{cai2018private, cai2019trading}.
Behavior recognition, as a fundamental task of video analysis technology, has become increasingly demanding in video-based applications such as human–machine interaction, autonomous driving, and intelligent surveillance \cite{andersson2019deep, ziaeefard2015semantic}.
However, recognizing the motion information of objects is nontrivial due to occlusion, dynamic backgrounds, and moving cameras in video scenarios \cite{li2021occlusion, cai2016collective}.
For example, it is difficult to distinguish between behaviors when faced with dynamic and moving backgrounds.
Therefore, this paper introduces a novel model for capturing temporal structure and spatial features to model motion features.

To comprehensively apply temporal features, current recognition methods mainly focus on two types of temporal motion information: short-range and long-range motions.
Specifically, short-range motion refers to the changes between similar adjacent frames at the pixel level.
This type of motion may provides instant motion information.
The issue is how to distinguish and represent the differences among these frames.
Alternatively, long-range motion refers to the semantic associations between distant frames in videos.
It can integrate short-range motion features to learn the interaction information among them.
The challenge is to search for correlations among distant frames.

For modeling short-range motion features, current methods \cite{simonyan2014two} mainly apply optical flows to directly represent the temporal motion features of short distances,
then fuse them with the spatial features extracted from RGB frames.
These approaches can distinguish the differences between adjacent frames due to the frame gradients represented in the optical flows.
However, the corresponding training processes of optical flows and RGB streams are usually separated, where information fusion occurs only at the end of these approaches.
Thus, such two-stream architectures result in no effective interaction process between spatial and temporal motion features.

For the modeling of long-range motion features, two types of solutions are available.
The first category of methods, such as temporal segment networks (TSN) \cite{wang2016temporal}, directly apply the average pooling and multiscale temporal window integration to represent the long-range features of videos.
Another category applies 3D or (2+1) D convolutional neural networks (CNN) \cite{ji20123d, tran2015learning} to implement joint spatial and temporal motion feature modeling.
These methods model the correlations among distant frames and learn long-range features through many local convolution operations.
However, both types of solutions are still insufficient for modeling detailed long-term temporal information.
The first type of solution may easily lose long-term temporal information due to its simple integration strategy.
The second type requires a massive volume of repetitive local convolution operations, leading to optimization difficulty \cite{he2016deep} as long-range motion features can hardly propagate through a long path.
Thus, it is still a challenging task to capture the effective spatiotemporal information of long-range motion features.

To address the above issues, this paper proposes a behavior recognition approach called the integration of multigranularity (IMG), which integrates two different modules for modeling two types of temporal and spatial motion information.
As shown in Fig.~\ref{fig1}, IMG is composed of a channel attention-based motion feature enhancement module (CMEM) for short-distance motions between adjacent frames,
and a cascaded long-term motion feature integration module (CLIM) for further long-range temporal aggregation at large scales.

\begin{figure}[ht]
\centering
\includegraphics[width = \linewidth]{01.eps}
\caption{Overview of our proposed modules. The CMEM is proposed to expand the differences between short-distance motion features, while the CLIM is proposed to enhance the probability of interaction between long-range features.}
\label{fig1}
\end{figure}

Specifically, the CMEM excites the motion-sensitive part of the original features and avoids polarization through a joint multiscale learning process involving spatial and temporal motion features.
Concretely, residual connection \cite{he2016deep} and channel attention techniques \cite{hu2018squeeze} are applied to enhance the feature-level interactions between spatial and temporal motion information.
In this way, the CMEM can realize effective modeling among short-range spatial and temporal motion features.
Moreover, the CLIM realizes information interaction among the extracted motion features under different receptive fields through a cascaded residual network structure,
which is equivalent to repeated long-range spatiotemporal interactions,
and the residual network structure also prevents the degradation of the network.
Then combined with an adaptive shift module, which increases the interaction frequencies of long-range frames,
The CLIM can achieve long-range temporal aggregation at large scales.
Finally, two modules are combined with a residual network.
Through extensive experiments, our model is able to significantly improve the expression of short-range and long-range motion features,
effectively enhance the behavior recognition accuracy of the final model,
and achieve state-of-the-art results on several datasets.
To summarize, the contributions of our work include the following.

\begin{itemize}
\item We provide a novel behavior recognition method that integrates the two modules listed below to reliably model motion features.
\item The CMEM addresses the poor timeliness and insufficient interaction between spatial features and temporal motion features exhibited by existing methods.
\item The CLIM enhances the interaction frequencies of long-range motion features with an adaptive shift module.
\item Extensive evaluation results are used to demonstrate the advancements of the proposed model.
\end{itemize}

The rest of this paper is organized as follows: chapter 2 briefly introduces the existing behavior recognition methods. Chapter 3 describes our proposed behavior recognition method in detail. Chapter 4 evaluates the effectiveness of our proposed method through experiments. Chapter 5 is the summary of the paper.
\section{Related Work}

In the current field of video behavior recognition, deep learning has gradually become the mainstream approach \cite{zheng2020privacy, cai2021generative}.
For these methods, effectively modeling the motion information of videos is the key to their performance.
The existing methods usually focus on two types of motion information: 1) short-range motion between adjacent frames and 2) long-range temporal aggregation at large scales.

For modeling the short-range motions between adjacent frames, current methods are mainly implemented by optical flow extraction and two-stream architectures.
The classical two-stream architecture \cite{simonyan2014two} consists of a 2D CNN that learns spatial features from RGB frames and a temporal 2D CNN that models temporal motion information in the form of optical flows.
Then, many successive works extended this model.
Different combination strategies for fusing RGB streams and optical flows have been proposed.
For example, Feichtenhofer \cite{feichtenhofer2016convolutional, feichtenhofer2017spatiotemporal} proposed extracting the features of the temporal dimension by using Conv3D and Pool3D, and fusing the features in the temporal dimension.
After that, a sparse sampling strategy that is used to capture feature associations between frames was proposed through a TSN \cite{wang2016temporal}.
However, the methods mentioned above have two common shortcomings.
One is that these methods require additional computation and storage costs to deal with optical flows.
The second is that the training processes for RGB streams and optical flows are separated, which leads to extremely limited interactions between these two types of features.
Fusion is performed only in the late layers, which makes it difficult to generate effective feature-level interactions.

To solve the problems described above, some works, such as STM \cite{jiang2019stm} have tried to break away from the shackles of the two-stream structure and unify the learning of spatial features and motion information.
However, the main problem is that this approach simply adds spatial features to motion encodings, so it lacks the mutual enhancement within spatiotemporal features.
TEA \cite{li2020tea} extends the structure of STM and avoids the extra costs encountered in a two-stream structure.
However, TEA and the STM share a common problem of using only adjacent temporal features for determining differences to portray the motion features of videos; this technique is prone to pixel-level difference polarization and interframe similarity information loss.
Different from these methods, our proposed CMEM solves the above problems through joint learning and the interaction of spatial and temporal motion features.

For long-range temporal aggregation at large scales, the existing methods usually adopt 3D CNNs.
A 3D CNN expands the dimensions of convolution kernels, and the new dimensions correspond to the temporal dimensions of videos.
The original 3D CNN \cite{ji20123d} was first proposed in 2012 and was used to apply a network to the video behavior classification task.
Then, Tran \cite{tran2015learning} improved upon the 3D CNN structure and proposed the convolutional 3D (C3D) network, which achieved unified spatiotemporal feature modeling by using a 3D CNN on complete video frames.
However, although a 3D CNN is faster than two-stream networks due to the avoidance of additional optical flow inputs, the increase in the dimensionality of the convolution kernels inevitably brings additional computational costs.
For this reason, some researchers have proposed methods for decomposing the 3D convolution operation into 2D spatial convolution and 1D temporal convolution (also known as (2+1) D convolution) \cite{lin2019temporal, qiu2019learning, diba2017temporal, he2019stnet, tran2019video}.
Some researchers have also explored the use of 2D CNNs to replace some convolution layers in 3D CNNs; this is called a mixture of 2D CNN and 3D CNN \cite{tran2018closer, xie2018rethinking} models.
However, after a large number of local convolution operations, the interactions between long-range frames become extremely limited, and it becomes difficult to capture effective spatiotemporal information.

For the above reason, Diba \cite{diba2017temporal} and He \cite{he2019stnet} proposed the temporal 3D ConvNet (T3D) method and spatial temporal network (StNet) respectively.
These two methods model the long-range spatiotemporal relationships in videos through dense connections and self-attention mechanisms respectively.
However, such methods often need to introduce additional parameters or time-consuming operations to improve the interaction of long-range frames.
Our proposed CLIM increases the interaction frequencies of long-range spatiotemporal features through a cascaded residual network structure, combined with an adaptive shift module, which finally achieves long range temporal aggregation at large scales.

\section{Methodology}

The framework of IMG is illustrated in Fig.~\ref{fig2}.
It is composed of a sampling process, backbone network and classifier.
First, a frame sequence with a certain length is extracted from the original video through data preprocessing.
Then, the spatial features and temporal motion features of the video are extracted through the backbone IMG network, including the CMEM and CLIM.
Finally, the obtained features are passed through the classifier to receive the classification result of the behavior.

\begin{figure}[htbp]
\centering
\centerline{\includegraphics[width = \linewidth]{02.eps}}
\caption{The overall framework of the proposed method.}
\label{fig2}
\end{figure}

\subsection{CMEM}

Motion features directly reflect the behaviors of humans in videos.
In previous work, behavior recognition approaches based on optical flows only focused on temporal motion patterns at the pixel-level and the learning processes for motion features and spatial features were separated.
In contrast, our proposed CMEM can recognize motion patterns at the feature-level and unify the learning of motion and spatial features.
Furthermore, the CMEM also introduces channel attention to suppress the background information as irrelevant noise,
and temporal motion information is enhanced as task-relevant information,
which corresponds to video motion patterns.

\begin{figure}[htbp]
\centering
\centerline{\includegraphics[width=0.6\linewidth]{03.eps}}
\caption{The architecture of the CMEM.}
\label{fig3}
\end{figure}

The architecture of the CMEM is shown in Fig.~\ref{fig3}.
The input spatiotemporal feature can be expressed as $X[N,T,C,H,W]$, where $N$ is the batch size and $T$ and $C$ are the length of the frame sequence and the number of feature channels, respectively. $H$ and $W$ correspond to the height and width of the features, respectively.
Given an input feature $X$, the number of the feature channels is reduced by a $1 \times 1$ 2D convolution layer to improve the efficiency of the model.
Then, we obtain $X^r$, which is calculated as:

\begin{equation}
\boldsymbol{X}^{r}=\operatorname{conv}_{\text {prev }} * \boldsymbol{X}, \quad \boldsymbol{X}^{r} \in \mathbb{R}^{N \times T \times C / r \times H \times W}\label{eq1},
\end{equation}

\noindent where $conv_{prev}$ is a $1 \times 1$ 2D convolution layer, $\ast$ indicates the convolution operation, and r is the reduction ratio.
For $X^r$, the spatiotemporal features of the adjacent frames are expressed as $X^r_{(t-1)}$ and $X^r_{(t)}$:

\begin{equation}
\begin{array}{lr}
\boldsymbol{X}_{(t-1)}^{r}=\boldsymbol{X}[N, t, C, H, W], \quad 1 \leq t \leq T, \\ \\
\boldsymbol{X}_{(t)}^{r}=\boldsymbol{X}[N, t, C, H, W], \quad 0 \leq t \leq T-1.

\label{eq2}
\end{array}
\end{equation}

$X^r_{(t-1)}$ and $X^r_{(t)}$ are passed through a $3 \times 3$ convolution layer to prevent excessive mismatches between the two feature vectors.

Unlike the STM\cite{jiang2019stm} and TEA\cite{li2020tea}, the CMEM collaboratively considers the differences and the cosine similarities between adjacent frames for feature-level motion representation. While a difference tends to polarize the variances of some pixel points between adjacent frames, cosine similarity measures the overall similarity between adjacent frames. This makes the training process smoother. The above process can be expressed as Eqs. (\ref{eq3}) and (\ref{eq4}).

\begin{equation}
\resizebox{.8\hsize}{!}{
$\boldsymbol{M}(t)=\operatorname{conv}_{\text {trans }} * \boldsymbol{X}_{(t)}^{r}-\text { conv }_{\text {trans }} * \boldsymbol{X}_{(t-1)}^{r} ,\quad 1 \leq t \leq T$
}
\label{eq3}
\end{equation}

\begin{equation}
\resizebox{.8\hsize}{!}{
$\boldsymbol{P}(t)=\cos \left(\operatorname{conv}_{\text {trans }} * \boldsymbol{X}_{(t)}^{r}, \quad \operatorname{conv}_{\text {trans }} * \boldsymbol{X}_{(t-1)}^{r}\right), \quad 1 \leq t \leq T$
}
\label{eq4}
\end{equation}

Here, $conv_{trans}$ is a 2D $3 \times 3$ convolution layer, $cos(A, B)$ denotes the cosine similarity between A and B, and the formula is shown in Eq. (\ref{eq5}). $\mathrm{M}(\mathrm{t}) \in \mathrm{R}^{\mathrm{N} \times \mathrm{C} / \mathrm{r} \times \mathrm{H} \times \mathrm{W}}$ and $P(t) \in R^{N \times \mathrm{C} / \mathrm{r} \times 1 \times 1}$ denote the two types of different feature-level motion information. Then, we concatenate $M(1), M(2),...,M(T)$ and $P(1),P(2),...,P(T)$ to obtain the motion feature matrices $\boldsymbol{M}=[M(1),M(2),...,M(T)]$ and $\boldsymbol{P}=[P(1),P(2),...,P(T)]$. To ensure that the temporal dimensions of the matrices are the same as those of the input vectors, we fill the ends of the two matrices with 0s.

\begin{equation}
\cos (A, B)=\frac{A \cdot B}{\|A\|\,\|B\|}
\label{eq5}
\end{equation}

After that, the spatial details of the feature maps are less important since the purpose of the CMEM is to enhance the motion-sensitive channels in spatiotemporal features. Hence, the dimensions of $\boldsymbol{M}$ are then changed to $\mathrm{N} \times \mathrm{T} \times \mathrm{C} / \mathrm{r} \times 1 \times 1$ through average pooling:

\begin{equation}
\boldsymbol{M}^{q}=\operatorname{Pool}(\boldsymbol{M}), \quad \boldsymbol{M}^{s} \in \mathbb{R}^{N \times T \times C / r \times 1 \times 1}\label{eq6}
\end{equation}

Both $\boldsymbol{M}^q$ and $\boldsymbol{P}$ reflect the differences between adjacent frames, and they are called video motion features. In addition, $\boldsymbol{M}^q$ and $\boldsymbol{P}$ are complementary, and we can then obtain the complete motion matrix F by spatial weighting in the CMEM:

\begin{equation}
\boldsymbol{F}=\alpha * \boldsymbol{M}^{q}+\beta * \boldsymbol{P}, \quad \boldsymbol{F} \in \mathbb{R}^{N \times T \times C / r \times 1 \times 1},\label{eq7}
\end{equation}

\noindent where $\alpha$ and $\beta$ are hyperparameters that denote the contributions of the motion features to the final attention weights. To enhance the motion-sensitive information of the spatiotemporal features, F is passed through a $1 \times 1$ 2D convolution layer that expands its number of channel to the same as that of the input X. Finally, the motion matrix is normalized by a sigmoid function and a linear transformation to obtain the final attention weights $\boldsymbol{F}^s$:

\begin{equation}
\boldsymbol{F}^{\boldsymbol{s}}=2 * \delta\left(\operatorname{conv}_{\operatorname{exp} } * \boldsymbol{F}-1\right), \quad \boldsymbol{F} \in \mathbb{R}^{N \times T \times C \times 1 \times 1},\label{eq8}
\end{equation}

\noindent where $Conv_{exp}$ denotes a $1 \times 1$ convolution layer, $\ast$ indicates the convolution operation, and $\delta$ denotes the sigmoid function. Then, the attention weights and the input spatiotemporal features are multiplied in the channel dimension to achieve the discovery and enhancement of motion-sensitive information in the spatiotemporal features. In addition, a residual layer is applied to ensure that the background information is not lost during the training process. After introducing the residual structure, the calculation of the input features can be expressed as:

\begin{equation}
\boldsymbol{X}^{o}=\boldsymbol{X}+\boldsymbol{X} \odot \boldsymbol{F}^{\boldsymbol{s}}, \quad \boldsymbol{X}^{o} \in \mathbb{R}^{N \times T \times C \times H \times W}
\label{eq9}
\end{equation}

\noindent where $\boldsymbol{X}^{o}$ is the final output and $\odot$ denotes the channel-wise multiplication operation.

\subsection{CLIM}

In this section, we first propose an adaptive shift module to reconstruct frame sequences, as this module provides the basis for more flexibility and reliability among the long-range interactions. Then, this module is utilized to achieve long-term motion feature integration.

\subsubsection{Adaptive shift Module}

Lin \cite{lin2019temporal} proposed a temporal shift module (TSM) via the creative use of the shift operator; this module can realize the rearrangement of frame sequences. Given an input $X[N,T,C,H,W]$, the frame shift module in the TSM can be expressed as:

\begin{equation}
\begin{array}{lr}
\boldsymbol{X}[N, t, C, H, W]=\boldsymbol{X}[N, t+1, C, H, W] \\
\quad 1 \leq t \leq T-1, \quad 1 \leq c \leq C / 8,\quad \text { left shift }
\label{eq10}
\end{array}
\end{equation}

\begin{equation}
\begin{array}{lr}
\boldsymbol{X}[N, t, C, H, W]=\boldsymbol{X}[N, t-1, C, H, W]\\
\quad 2 \leq t \leq T, \quad C / 8<c \leq C / 4,\quad \text { right shift }\label{eq11}
\end{array}
\end{equation}

\begin{equation}
\begin{array}{lr}
\boldsymbol{X}[N, t, C, H, W]=\boldsymbol{X}[N, t, C, H, W]\\
\quad 1 \leq t \leq T, \quad C / 4<c \leq C,\quad \text { unchanged }\label{eq12}
\end{array}
\end{equation}

\noindent where $N$ is the batch size, $T$ is the length of the frame sequence, and $C$ is the number of feature channels. $H$ and $W$ correspond to the height and width of the feature, respectively.

The process of frame shifting is equivalent to the following transformation: fixing the dimensions of the $N$, $H$ and $W$ of the original features, and replacing some features in the $T$ and $C$ dimensions \cite{li2020tea}. This process is exactly equivalent to the convolution process expressed by Eq. (\ref{eq13}).

\begin{equation}\label{eq13}
\begin{array}{lll}
\mathbf{X}_{\mathrm{rp}}=\text { Reshape }(\mathbf{X}), & \mathbf{X} \in \mathbb{R}^{N \times T \times C \times H \times W}, & \mathbf{X}_{\mathrm{rp}} \in \mathbb{R}^{N H W \times C \times T} \\
\mathbf{X}_{\mathrm{ss}}=\mathbf{K} * \mathbf{X}_{\mathrm{rp}}, & \mathbf{X}_{\mathrm{ss}} \in \mathbb{R}^{N H W \times C \times T}, & \mathbf{K} \in \mathbb{R}^{C \times 1 \times 3} \\
\mathbf{K}[\mathrm{c}, 1, k]=1, & 1 \leq \mathrm{c} \leq C / 8, & k=3 \\
\mathbf{K}[\mathrm{c}, 1, k]=0, & 1 \leq \mathrm{c} \leq C / 8, & k=1,2 \\
\mathbf{K}[\mathrm{c}, 1, k]=1, & C / 8<\mathrm{c} \leq C / 4, & k=1 \\
\mathbf{K}[\mathrm{c}, 1, k]=0, & C / 8<\mathrm{c} \leq C / 4, & k=2,3 \\
\mathbf{K}[\mathrm{c}, 1, k]=1, & C / 4<\mathrm{c} \leq C, & k=2 \\
\mathbf{K}[\mathrm{c}, 1, k]=0, & C / 4<\mathrm{c} \leq C, & k=1,3
\end{array}
\end{equation}

The dimentions of the input are modified by $Reshape(X)$; after transformation, we obtain $X_{rp} \in R^{NHW\times C\times T}$. $X_{ss} $is the result of convolution, $\ast$ indicates the convolution operation, $K$ denotes the convolution kernel, and $K[c,1,k]$ denotes the point in the convolution kernel with coordinates $(c,1,k)$.

Therefore, the frame shifting operation in the TSM \cite{lin2019temporal} can be regarded as a special convolution transformation. Based on this, we propose an adaptive shift module, and the details of the module are shown in Fig.~\ref{fig4}, where permute denotes vector rearrangement and reshape denotes vector transformation.

\begin{figure}[htbp]
\centering
\centerline{\includegraphics[width=0.4\linewidth]{04.eps}}
\caption{The structure of the adaptive shift module.}
\label{fig4}
\end{figure}

Compared with the fixed frame sequence reconstruction process in the TSM \cite{lin2019temporal}, the process in the adaptive shift module no longer relies on hyperparameters and empirical values. This module can be easily integrated into a deep network and participate in the backpropagation process, providing a basis for more flexible and reliable spatiotemporal interactions among long-range frames.

\subsubsection{CLIM}

Previous behavior recognition methods \cite{tran2015learning, sun2015human} usually stacked local temporal convolutions to achieve long-range temporal modeling. However, after a large number of local convolutions, the interactions between long-range frames become limited and it is difficult to capture effective spatiotemporal information. Therefore, the CLIM is proposed to achieve more effective long-term motion information modeling, and this module is inspired by Res2Net \cite{gao2019res2net}.

The architecture of the CLIM is shown in Fig.~\ref{fig5}. The CLIM divides the input spatiotemporal features into several subsets based on their channels. For each subset, the features are further extracted by a set of local convolutions, including one adaptive shift layer and a $3 \times 3$ 2D convolution layer. We use the residual structures between the adjacent subsets of the CLIM, and these residual blocks not only allow the CLIM to form various receptive fields but also increase the frequency of spatiotemporal feature interactions since the adaptive shift layer makes the frame sequence change through local convolution. At the same time, spatiotemporal interactions do not lead to redundant parameters and time-consuming operations. Finally, the integration of long-term motion features is completed by concatenating the features extracted from each subset.

Given an input feature $X[N,T,C,H,W]$, a typical processing method is to extract the temporal and spatial information \cite{lin2019temporal, qiu2019learning, diba2017temporal, he2019stnet, tran2019video} from this feature via a local temporal convolution and a spatial convolution; this process is also called (2+1)D convolution. Based on the convolution and the Res2Net architecture, we propose a new spatiotemporal feature interaction method. First, $X$ is divided into four equal slices $X0,X1,X2,X3$ in the channel dimension, and the shape of each is $[N,T,C/4,H,W]$. Except for the first slice, the remaining three slices are passed through a convolution group consisting of 2D CNN and adaptive shift modules.

\begin{figure}[htbp]
\centering
\centerline{\includegraphics[width=0.3\textwidth]{05.eps}}
\caption{The structure of the proposed CLIM.}
\label{fig5}
\end{figure}

The slices pass through the convolution group and then interact with each other through residual blocks of spatiotemporal features, thus forming a cascaded residual structure. This process can be expressed as Eq. (\ref{eq14}).

\begin{equation}
\begin{aligned}
\boldsymbol{X}_{i}^{\prime} &=\boldsymbol{X}_{i}, & i=0 \\
\boldsymbol{X}_{i}^{\prime} &=\operatorname{SSM}\left(\operatorname{conv}_{s p t} * \boldsymbol{X}_{i}\right), & i=1 \\
\boldsymbol{X}_{i}^{\prime} &=\operatorname{SSM}\left(\operatorname{conv}_{s p t} *\left(\boldsymbol{X}_{i}+\boldsymbol{X}_{i-1}^{\prime}\right)\right), & i=2,3
\end{aligned}
\label{eq14}
\end{equation}

\noindent where $SSM(X)$ denotes the process conducted by the adaptive shift layer on features, $conv_{spt}$ denotes a $3 \times 3$ 2D convolution layer, $\ast$ denotes the convolution operation, $X_i$ denotes the slice of the $i_{th}$ input, and $X^{'}_{i}$ denotes the output corresponding to the $i_{th}$ slice $(X^{'}\in R^{N\times T\times C/4\times H\times W},i=0,1,2,3)$.

The cascade structure of the CLIM provides multiple scales of receptive fields in the feature extraction process. In the CLIM, the receptive fields of different slices are enlarged to different degrees through residual blocks. For example, the receptive field of the second slice is twice as large as that of the first slice, and the perceptual field of the fourth slice is four times as large as that of the first slice. Furthermore, due to the continuous reconstruction of frame sequences by the adaptive shift module, the CLIM effectively enhances the probability of spatiotemporal interaction for long-range frames.

\subsection{IMG Motion Feature Module}
Finally, the CMEM and CLIM are concatenated with ResNet blocks to establish our IMG motion feature module. The approach is illustrated in the backbone of Fig.~\ref{fig2}. To unify the dimensions, $1 \times 1$ convolutions are added to the head and tail of our IMG module. Then, we stack the IMG module to construct the whole behavior recognition network.

In addition, the cross-entropy loss is utilized as our loss function. It can be expressed as:

\begin{equation}
L=\frac{1}{N} \sum_{i} L_{i}=-\frac{1}{N} \sum_{i} \sum_{c=1}^{M} y_{i c} \log \left(p_{i c}\right)
\end{equation}

\noindent where M denotes the number of classes, N denotes the number of samples, $y_{i c}$ equals 1 if the label of the $i_{th}$ sample is $c$, and $p_{i c}$ denotes the predicted probability of class $c$.

\section{Experiments}

\subsection{Datasets and Evaluation Metrics}

Our proposed approach is evaluated on three datasets: HMDB51 \cite{kuehne2011hmdb}, UCF101 \cite{soomro2012ucf101} and Something-Something \cite{goyal2017something}. Among them, HMDB51 can be divided into 51 classes with a total of 6849 video clips, and each class has at least 101 samples. UCF101 can be divided into 101 classes with a total of 13320 video samples. Compared with the previous two datasets, Something-Something has larger numbers of samples and behavioral classes, with a total of 108,499 videos and 174 classes of behaviors.

HMDB51 and UCF101 are commonly used in behavior recognition. Something-Something contains more training data, and its videos are clearer; therefore, we train our model on this dataset. The other two datasets are fine-tuned on the model pretrained on Something-Something.

Accuracy is an important metric for measuring the results obtained on a multiclass classification task such as behavior recognition. For a multiclass classification task, if the total number of samples is $N$, the total number of classes is $M$, and $r_i$ samples are correctly predicted for class i in the predicted results, the accuracy can be described as:

\begin{equation}
\operatorname{Accuracy}=\frac{\sum_{i=1}^{M} r_{i}}{N} \quad(i=1,2, \ldots, M)\label{eq16}
\end{equation}

More categories are contained in Something-Something, and some classes are highly similar. Therefore, our model uses both top-1 accuracy and top-5 accuracy as evaluation metrics.

\subsection{Implementation Details}

Previous work \cite{lin2019temporal, qiu2019learning, diba2017temporal, he2019stnet, tran2019video} has demonstrated that pretrained models can significantly improve the training efficiency and reliability of the developed models. Therefore, we apply the publicly released Res2Net50 model to initialize the parameters.

In addition, although Res2Net performs better than ResNet on various image-based tasks, \cite{li2020tea} suggested that no significant improvement is achieved for video behavior recognition tasks. Thus, Res2Net is utilized as our backbone.

Two types of model training methods are available. The first is similar to the TSM \cite{lin2019temporal}, STM \cite{jiang2019stm}, TEA \cite{li2020tea}, etc. All batch normalization layers in the model are enabled during training. For the Something-Something dataset, the batch size, initial learning rate, weight decay and dropout rate are set to 32, 0.01, 1e4 and 0.5, respectively. The model training process adopts a stochastic gradient descent strategy. The number of training epochs is 50, and the learning rate is decreased by a factor of 10 at 30, 40, and 45 epochs.

Another strategy is aimed at UCF101 and HMDB51, where the training process is based on the Something-Something pretrained model. Therefore, the hyperparameters are somewhat distinct from those of the previous training method. To maintain the reliability of training, the parameters of each batch normalization layer are frozen except for those of the first layer. In this mode, the batch size, initial learning rate, weight decay and dropout rate are set to 32, 0.001, 5e4 and 0.8, respectively. The learning rate is decreased by a factor of 10 at 10 and 20
epochs. The training process stops after 25 epochs.

\subsection{Experimental Results}

\subsubsection{Comparisons with the State-of-the-Art Methods}
\begin{table*}[hbt]
\begin{center}
\caption{Comparison among the results obtained by IMG and other state-of-the-art methods on Something-Something.}
\resizebox{\textwidth}{!}{
\begin{tabular}{ccccccc}
\hline
\textbf{Method} & \textbf{Backbone} & \textbf{FLOPs} & \textbf{Frames} $\times$ \textbf{Crops} $\times$ \textbf{Clips} & \textbf{Pre-train} & \textbf{Val Top-1($\%$)} & \textbf{Val Top-5($\%$)} \\
\hline
\text { I3D-RGB }\cite{carreira2017quo} & \text { 3D ResNet50 } & 153 \text{G} $\times$ 3 $\times$ 2 & 32 $\times$ 3 $\times$ 2 & \multirow{3}*{ImageNet+Kinetics400} & 41.6 & 72.2 \\
\text { NL I3D-RGB }\cite{wang2018videos} & \text { 3D ResNet50 } & 168 \text{G} $\times$ 3 $\times$ 2 & 32 $\times$ 3 $\times$ 2 & ~ & 44.4 & 76.0 \\
\text { NL I3D+GCN-RGB }\cite{wang2018videos} & \text { 3D ResNet50+GCN } & 303 \text{G} $\times$ 3 $\times$ 2 & 32 $\times$ 3 $\times$ 2 & ~ & 46.1 & 46.8 \\
\hline
\text { STM-RGB }\cite{jiang2019stm} & \text { ResNet50 } & 33 \text{G} $\times$ 3 $\times$ 10 & 8 $\times$ 3 $\times$ 10 & \text { ImageNet } & 49.2 & 79.3 \\
\hline
\text { TSM-RGB }\cite{lin2019temporal} & \multirow{3}*{ResNet50}  & 33 \text{G} $\times$ 1 $\times$ 1 & 8 $\times$ 1 $\times$ 1 &  \multirow{3}*{ImageNet+Kinetics400} & 43.4 & 47.3 \\
\text { TSM-RGB }\cite{lin2019temporal} & ~ & 65 \text{G} $\times$ 1 $\times$ 1 & 16 $\times$ 1 $\times$ 1 & ~ & 44.8 & 74.5 \\
\text { TSM-(RGB+FLOW) }\cite{lin2019temporal} & ~ & \text{N} / \text{A} & 16 $\times$ 1 $\times$ 1+16 $\times$ 1 $\times$ 1 & ~ & 50.2 & 79.5 \\
\hline
\text{TEA}\cite{gao2019res2net} & \multirow{2}*{ Res2Net50 }
 & 35 \text{G} $\times$ 1 $\times$ 1 & 8 $\times$ 1 $\times$ 1 & \multirow{2}*{ ImageNet } & 48.9 & 78.1 \\
\text{TEA}\cite{gao2019res2net} & ~ & 35 \text{G} $\times$ 3 $\times$ 10 & 8 $\times$ 3 $\times$ 10 & ~ & 51.7 & 80.5 \\

\hline
\hline
CMEM    &  \multirow{2}*{Res2Net50}  & 34G$\times$ 1$\times$ 1  & 8 $\times$ 1 $\times$ 1 & \multirow{2}*{ImageNet} & 49.1 & 78.2 \\
CMEM    &  ~  & 34G$\times$ 3$\times$ 10  & 8 $\times$ 3 $\times$ 10 & ~ & 52.4 & 80.9 \\
\hline
IMG    &  \multirow{2}*{Res2Net50}  & 37G$\times$ 1$\times$ 1  & 8 $\times$ 1 $\times$ 1 & \multirow{2}*{ImageNet} & \textbf{49.7} & \textbf{78.4} \\
IMG    &  ~  & 37G$\times$ 3$\times$ 10  & 8 $\times$ 3 $\times$ 10 & ~ & \textbf{52.9} & \textbf{81.4} \\
\hline
\end{tabular}
}
\label{tab_all}
\end{center}
\end{table*}

To analyze the effectiveness of our proposed model and to demonstrate that the CLIM has an enhancing effect on the CMEM, the accuracy of our proposed model is compared with that of the current state-of-the-art model on Something-Something. The comparison domains include the backbones, floating point operations(FLOPs), data processing strategies and pretrained models. To ensure the credibility of the comparison experiments, the model results are validated by using 2 different data processing strategies, and the main purpose is to balance the speed and accuracy of the model. One is an efficient strategy that uses one central crop for each video frame. The other is a precise strategy in which 10 different clips are randomly sampled from the video, and the final score is calculated by averaging all clips' scores. The results are shown in Table~\ref{tab_all}.

Compared with these models, IMG achieves state-of-the-art performance in terms of top-1 accuracy and top-5 accuracy under the same strategy. When the efficient strategy is used to process the data, the top-1 accuracy and top-5 accuracy are 49.7\% and 78.4\%, respectively, and when the exact strategy is used, the top-1 accuracy and top-5 accuracy reach 52.9\% and 81.4\%. The FLOPs exhibit a slight increase in our proposed method compared to those produced by TEA \cite{li2020tea}, but our performance is increased significantly (49.7\% $vs.$ 48.9\%).

We model the short-distance motions between adjacent frames and the large-scale associations of long time sequences with our proposed CMEM and CLIM. It can be concluded that the CMEM mainly learns motion patterns from adjacent frames and carries out the interactions between spatiotemporal features. As the network deepens and the receptive field of the network increases, the CMEM can learn motion information under a wider range of videos. However, its modeling of motion features in a long temporal field still has some room to improve, which means that the CMEM lacks the ability to discover the large-scale associations of long time sequences.

Based on this, the CLIM further improves the effectiveness of the CMEM with a cascaded residual network structure and adaptive shift, thus greatly increasing the interaction frequencies of the long-range frames in the model and enhancing the model's ability to discover the large-scale associations of long time sequences.

Finally, to validate our method on more benchmark datasets, comparison results obtained on UCF101 and HMDB51 are reported in Table~\ref{tab5}.

\begin{table}[htbp]
\begin{center}
\caption{Comparison among the results obtained by IMG and other state-of-the-art methods on HMDB51 and UCF101.}
\begin{tabular}{cccc}
\hline
\textbf{Method} & \textbf{Backbone} & \makecell[c]{\textbf{HMDB51} \\ \textbf{Top-1(\%)}} & \makecell[c]{\textbf{UCF101} \\ \textbf{Top-1(\%)}}\\
\hline
TSM\cite{lin2019temporal}     & ResNet50     & 70.7  & 94.5 \\
STM\cite{jiang2019stm}    & ResNet50    & 72.2  & 96.2\\
TEA\cite{li2020tea}    & Res2Net50    & 73.3  & 96.9\\
\hline
\hline
IMG   & Res2Net50    &  \textbf{74.5}  & {95.3}\\
\end{tabular}
\label{tab5}
\end{center}
\end{table}

The results show that our proposed IMG achieves 74.5\%~on HMDB51, outperforming other methods. Nevertheless, it can be observed that the result on UCF101 is slightly decreased. From our analysis, this is because some behavioral categories in UCF101 have almost no long-time associations (e.g., playing the guitar or flute). Thus, the introduction of the CLIM instead increases the complexity of the model and reduces its robustness.

In summary, our proposed IMG behavior recognition network achieves effective modeling of the spatial and motion information in videos while avoiding excessive parameter scales and improves the overall model's behavior recognition accuracy to achieve the current state-of-the-art performance.

\subsubsection{Ablation Study}

To ensure the effectiveness of our proposed CMEM module and its significant ability to model spatiotemporal motion features, we design an ablation experiment to confirm the performance differences yielded by the CMEM module; this experiment is conducted on different backbone networks by comparing the behavior recognition performances of different combinations of the 2D CNN and the CMEM. The results are shown in Table~\ref{tab1}.

\begin{table}[htbp]
\begin{center}
\caption{Ablation results.}
\begin{tabular}{ccc}
\hline
\textbf{Method}& \textbf{Val Top-1(\%)} & \textbf{Val Top-5(\%)}\\
\hline
(2+1) D ResNet     & 45.9  & 75.2 \\
(2+1) D Res2Net    & 46.2  & 75.6\\
\hline
\hline
CMEM + ResNet    & 48.7  & 78.1\\
CMEM + Res2Net    &  \textbf{49.1}  & \textbf{78.2}\\
\end{tabular}
\label{tab1}
\end{center}
\end{table}

Four types of combinations are tested in the experiment, namely, the ResNet residual block, ResNet+CMEM residual block, Res2Net residual block, and Res2Net+CMEM residual block. The structure of each residual block is shown in Fig.~\ref{fig6}.

\begin{figure}[htbp]
\centering
\centerline{\includegraphics[width=0.5\textwidth]{06.eps}}
\caption{Four types of combinations are applied in the ablation experiment.}
\label{fig6}
\end{figure}

From the results, we can conclude that our proposed CMEM can effectively improve the behavior recognition accuracy of the model. Res2Net is more complex than ResNet, yet the corresponding behavior recognition performance is not significantly improved. In contrast, the performance is greatly improved by introducing the CMEM in both backbone networks, and the CMEM+Res2Net achieves the best behavior recognition results, with top-1 and top-5 accuracies that reach 49.1\%~ and 78.2\%~, respectively. Thus, it can be concluded that our proposed CMEM can achieve significantly improved the behavior recognition performance over that of the base ResNet and Res2Net residual structures.

Although we use Res2Net as the backbone network, it does not clearly improve the behavior recognition accuracy over that achieved with ResNet. By comparing the results in the first two rows or the last two rows of Table~\ref{tab1}, it is obvious that although Res2Net achieves much higher performance than ResNet in many image processing tasks such as image classification and image object detection \cite{gao2019res2net}, the difference between the backbone networks does not significantly affect the accuracy of behavior recognition. Thus, we can conclude that the performance improvement is due to the spatiotemporal motion modeling capability of the CMEM, which excites the motion-sensitive part of the original features and improves the model's ability to represent short-range motion information.

\subsubsection{Evaluation of the adaptive Shift Module}

The adaptive shift module is equivalent to the frame reconstruction process in the TSM under specific cases. To verify this fact to fully utilize the performance of the adaptive shift module, we design different CLIM convolution sets (2D CNN+shift) as a way to verify the effectiveness of the adaptive shift module. The results are shown in Table~\ref{tab_CLIM}.

\begin{table}[h]
\begin{center}
\caption{Evaluation of the adaptive shift module.}
\begin{tabular}{cccc}
\hline
\textbf{Method}& \textbf{Backbone}& \textbf{Val Top-1(\%)} & \textbf{Val Top-5(\%)}\\
\hline
TSM-RGB & ResNet50 & 43.4 & 73.2 \\
\hline
\hline
Structure(a) & Res2Net50     & 44.0  & 73.4 \\
Structure(b) & Res2Net50     & 44.5  & 73.8 \\
Structure(c) & Res2Net50     & 46.8  & 76.0 \\
Structure(d) & Res2Net50     & \textbf{47.6} & \textbf{76.5} \\
\end{tabular}
\label{tab_CLIM}
\end{center}
\end{table}

The table includes the backbone network used by each method and the corresponding accuracy metrics, which indicate the different CLIM convolution groups. The structure of each convolution group is shown in Fig.~\ref{fig7}. $s\raisebox{0mm}{-}shift\raisebox{0mm}{-}random$ indicates that the parameters of the convolution layer of the adaptive shift module are initialized randomly, $s\raisebox{0mm}{-}shift\raisebox{0mm}{-}freeze$ indicates that its parameters are frozen, and $s\raisebox{0mm}{-}shift\raisebox{0mm}{-}pret$ indicates that its parameters are initialized to the values in Eq. \eqref{eq13}.

\begin{figure}[htbp]
\centering
\centerline{\includegraphics[width=0.5\textwidth]{07.eps}}
\caption{Four CLIM convolution groups. }
\label{fig7}
\end{figure}

To avoid the influence of the CMEM module on the experimental results, we use the backbone shown in Fig.~\ref{fig8} for this experiment.

The results show that the adaptive shift module can effectively improve the accuracy of the behavior recognition method (up to 47.6\% top-1 accuracy and 76.5\% top-5 accuracy on Something-Something), which positively reflects the superior ability of the CLIM in terms of modeling long-term motion features.
\begin{figure}[htbp]
\centering
\centerline{\includegraphics[width=0.5\textwidth]{08.eps}}
\caption{The backbone utilized in this experiment.}
\label{fig8}
\end{figure}

By comparing the results of structure (a) and structure (b) (44.0\% $vs.$ 44.5\% for top-1 accuracy and 73.4\% $vs.$ 73.8\% for top-5 accuracy), we find that the results are very similar, which supports the fact that the adaptive shift module with randomly initialized parameters is similar to a 1D convolution in the temporal dimension, meaning that they have close semantic representations.

From the results of structure (b) and structure (c) (44.5\% $vs.$ 46.8\% for top-1 accuracy and 73.8\% $vs.$ 76.0\% for top-5 accuracy), it can be found that the adaptive shift module based on the frame sequence reconstruction strategy in the STM exhibits better performance than the adaptive shift module with randomly initialized parameters. One possible explanation for this phenomenon is that the STM's frame sequence reconstruction strategy is a local optimum obtained after a number of different strategy choices. In contrast, the adaptive shift module with randomly initialized parameters tends to have difficulty converging.

To take full advantage of the local optimal results of the STM, the parameters of $s\raisebox{0mm}{-}shift\raisebox{0mm}{-}freeze$ are used for the initialization of structure (d). The adaptive shift module achieves the best results on Something-Something (47.6\% accuracy for top-1 accurate and 76.5\% accuracy for top-5 accurate). Thus, the CLIM can achieve more frequent long-distance frame interactions and improves the model's ability to model long-term motion information.

In summary, our proposed adaptive shift module is close to the 1D temporal convolution layer in the (2+1)D convolution at the semantic level, which can effectively extract of temporal information in features. Through a specific initialization of its parameter settings, the adaptive shift module can achieve a further performance improvement.

\section{Conclusion}

In this paper, we propose a behavior recognition method based on the integration of multigranular motion features that is composed of a CMEM and a CLIM. Specifically, the CMEM achieves short-range motion encoding between adjacent frames through simultaneous learning of spatial and temporal motion features. The CLIM enables more frequent long-range frame interactions, thus making up for the shortcomings of the CMEM. The two proposed methods are then concatenated to achieve joint spatial and temporal motion feature learning in both short-range and long-range situations.


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To see all 90 photos they published start here; you'll never run out of inspiration for your own wedding!
Congrats, and thanks again to Erin and Sam for sharing your special day with the world!


American hip hop outfit CunninLynguists issued their fifth album "Oneirology", which means study of dreams, and you can stream the whole album down here via the band's Bandcamp page. It features guest appearances from Big K.R.I.T., Freddie Gibbs and Tonedeff. Concerning Big K.R.I.T., his awesome album "Return Of 4Eva" is still downloadable for free through the album web page.


Welcome to our Rams digest for Sunday, March 24.
There's lots to get to, so without further ado here are this morning's top stories.
Could Birmingham points deduction impact Rams?
Birmingham City have been plunged into a Championship relegation fight after their punishment for breaching EFL financial regulations was confirmed.
It was found that Birmingham had incurred "adjusted losses" of almost £48.8m between 2015 and 2018 - nearly £10m over the allowed limit.
Read about how that could affect Derby HERE .
Fikayo Tomori has proved to be one of Derby's most consistent performers this season.
More from Fikayo HERE .
Of course, before that, Derby have the small matter of nine games left to play as they bid to book a place in the Sky Bet Championship play-offs.
Your pick of free agents HERE .
Richard Keogh played the full 90 minutes in the Republic of Ireland's 1-0 European Championships qualifying win over Gibraltar on Saturday.
The Derby defender earned his 22nd cap at the Victoria Stadium, sporting a cast on his right arm having been cleared to play after breaking his hand in his club side's 0-0 draw with Stoke City last time out .
Ireland got their Group D campaign up and running with a comfortable victory, ex-Ram Jeff Hendrick netting the winner four minutes after half-time.
Hendrick, who joined Burnley in August 2016 for £10.5million, finished coolly just inside the box, turning home from David McGoldrick's cross from the left.


I've been so busy reading about things like vaccines, UFOs, and government corruption. Dave and I are constantly finishing each other's sentences when we talk about these things, and the most recent thought-sharing instance was that all of those topics feel the same in some ways. Note: I have been the instigator of the vaccine talks, and he has initiated the other stuff (it's not that I don't care about UFOs, but… it's not what I read/watch at night for fun). A lot weighs on me when we talk about vaccines, and it's not something I really want to post my opinions about. I'm not an activist-type person. I keep my controversial-topic opinions to myself, and I don't tell people what they should do.
Although, I do bend that rule if someone asks my opinion about things like hydrogenated oils and high fructose corn syrup. Those things, although controversial, don't elicit the aggressive responses that vaccines do. So, I'm not going to talk about it here. I'm just going to say that I don't think anyone should blindly follow any stance—research it. We watched the documentary Bought this evening. I highly recommend it. I also recommend reading what the CDC website has to say about vaccines, as well as talking to your doctor about it.
Just don't jump on any bandwagons without reviewing all sides of the story.
Actually, I recommend it so much that I'm linking to it (it's not free, but it costs less than 5 bucks to rent it). If you have any interest in vaccines or GMOs, you'll want to watch this.
I've been meaning to watch Bought, so I'm glad to hear that you liked it!! I'm sure it'll just confirm the choices we've made in vaxing, but I'm excited to watch!


Tasmania is a state that has much to offer visitors in the winter time. With vast world heritage listed forests it provides a wonderful escape for the nature lover. While visiting for a 10 day tour of the whole of the state, we found Tahune Airwalk, which is one of the most visited tourist attractions.
Located in Geevston in the southern area of Tasmania, which is also famous for its produce, namely apples, the Tahune Airwalk features many of Tasmania's unique Huon Pines, which have a glorious and distinctive aroma. The park has a 20 minute ground level walk over boardwalks through the Huon Pines and across the Huon River below. There are also two cable foot bridges that cross over the Huon and Picton Rivers.
The walk up to the air walk is 112 steps, but is gradual, so it doesn't feel steep, and we managed it with two small children. As it is in the forest, it can be damp, but is covered with wood steps and boardwalks. The whole airwalk takes about an hour to complete, with plenty of seating for stops and handrails for ease of movement. The airwalk sits at various heights, depending on the section, it can sit anywhere from 20 metres above the ground up to 50 metres above the river, giving the best views of the forest, and the freshest air!
The facilities that sit on the edge of 1.6 million hectares of forest also include a restaurant, toilets and gift shop featuring many Tasmanian wooden gifts. The restaurant has a balcony area, which makes eating lunch in the treetops very special, and why would you sit inside, when right outside is so spectacular? This a walk you simply could not forget and will go down as one of my absolute favourites of all time.