If you are thinking of sending some greeting via the good old means and are perplexed about what is the proper way to address envelopes, read further for tips to write the same.
When you have a festive season, there is no better way to wish your family and friends prosperity and happiness, than the good old mail. Unlike emails where there is no 'code of conduct' per se, one has to keep in mind certain etiquette while writing the greetings. You need to learn the proper way to address envelopes, which is not difficult but tricky.
Always remember to write the invitations and addresses by hand.
Avoid spelling mistakes, and make sure that your handwriting is legible.
Keep the greeting, invitation, and the envelope neat.
Remember to send the invitation and the greetings well in time.
Don't miss out on any member of the family.
All the addresses should ideally be in capital letters, according to US postal services.
The correct way to address envelopes is very easy. If you are sending a greeting/invitation to a boss, colleague, or someone with whom you have a formal acquaintance, remember to follow the same conduct here. The address should come in this format.
Remember that if the receiver has a middle name, don't use the initial for it, either write it in full or leave it out. If the husband and wife have different surnames, then the right way to address them is - Mrs. Samantha Murphy and Mr. Jack Woods. Remember to always use the complete titles, like Reverend, Rabbi, Lieutenant, General, Mayor, or Senator.
In case of a married couple, if the wife is a doctor or holds a title, her name will come first, for example Dr. Jane Austin and Mr. Peter Austin. If one is to address the aforementioned title as Mr. Adam Smith and Mrs. Adam Smith instead of Mr. and Mrs. Adam Smith, the former one will be incorrect.
It is not incorrect to leave name of the children from the outer envelope, but one should mention the names inside, either exclusively or write Mr. and Mrs. Adam Smith and family.
The proper way to direct an envelope is to make sure that you spell the city and state name completely. Don't abbreviate it. Always remember to mention the zip. This also holds true for the envelopes for wedding invitations.
The correct way to address envelopes for greetings to people with whom you are well acquainted is much easier. It is not mandatory to use the formal title. You can just address it to 'The Smith Family'.
In the case of informal greetings, it is best to not abbreviate the city, state, or the country name. Also, don't forget to write your address on the left lower corner on the reverse side of the envelope.
For season greetings, it is best to hand write the addresses, but in case, one is sending a formal letter or something, a printed address will suffice. The address should be in all capital with no punctuations, this holds true for formal addressing also. Remember to use simple fonts if you are planning to print out the addresses.
These were some tips about addressing envelopes in the correct fashion. Remember, if you are sending seasons greetings, it is best to personalize the greetings, so spend some time handwriting the envelopes. Small gestures go a long way in making the receiver feel warm.


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EMR Integrations
Key Ways to Handle End of Windows 7 for Healthcare Organizations
Top Ways to Handle the End of Windows 7 in the Healthcare Industry
Microsoft will end its support for Windows 7 soon. Learn how this will affect your healthcare organization and what you can do to prevent security problems.
Between the years 2009 and 2018, 189,945,874 healthcare records were either stolen or exposed because of cybersecurity breaches.
If that sounds like a lot, that's because it is. In fact, "it equates to more than 59% of the population of the United States," according to HIPAA Journal. Obviously, among healthcare organizations, cybersecurity has become a serious concern.
And it's about to get worse.
In only a few months, the operating system that nearly all healthcare organizations in the United States utilize — Windows 7 — will lose support from its manufacturer, Microsoft.
Microsoft calls this the "end-of-life" for Windows 7, and it's going to happen on January 14, 2020. The change will affect all businesses and individuals who are currently operating the Windows 7 OS, but healthcare organizations are especially at risk. That's because this loss of support also means that the majority of Windows 7 medical devices will be running an outdated and unprotected version of Windows.
Fortunately, healthcare organizations can make changes now to avoid serious operating system and security problems in January of 2020 We'll discuss how to transition to Windows 10 (the most up-to-date Microsoft operating system) in a moment. For now, let's discuss what it really means that Windows 7 is losing support from Microsoft.
What Do "End-of-Life" and "Loss of Support" Really Mean?
"End-of-life" is the term Microsoft specifically uses to define the period when they will no longer provide software support for a specific application or piece of software. It's the same as "loss of support."
Both terms mean that "Microsoft will no longer provide the following:
Technical support for any issues
Security updates or fixes"
Why Would Continuing to Use Windows 7 Be Bad?
Most of the precautions surrounding Windows 7's end-of-life revolve around cybersecurity.
Though you may not have realized it, for the past ten years, Microsoft has been constantly working on the security, efficiency, and fluidity of its Windows 7 operating system. The Microsoft team constantly provides updates and upgrades for Windows 7 users. Moreover, it monitors and troubleshoots possible cybersecurity issues, catching issues and breaches before they start.
Often, these patches and updates are keeping you and your healthcare organization from being breached by cybercriminals who would love to steal your money or get their hands on your data and hold it for ransom.
When Microsoft ends their support, this dam they've been maintaining goes away, and the influx of cybersecurity troubles may very well be at your doorstep as soon as the first day of the end of support.
How Can You Maintain Security Within your Healthcare Organization as the End-of-Life Day for Windows 7 Nears?
If your healthcare organization is still using Windows 7, you're safe for now. But it's time to start the transition to Windows 10 — Microsoft's latest OS. You'll want to start this shift as soon as possible as the change can instigate a sizable change in pace for your business and a considerable amount of expenses as well.
A good place to start is with your managed services provider. The designated IT specialists within your healthcare establishment will be able to help you transition smoothly and seamlessly from Windows 7 to Windows 10.
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When is the right time to consider elder care options?
When is it time to consider an Adult Family Home or an Assisted Living Facility in Olympia, WA?
Planning ahead can make a challenging situation less complicated, because you will already have Assisted Living Options on your side. Armed with information about the differences between Assisted Living and Adult Family Homes, along with learning about other assisted living options in the Olympia, Tacoma, and Puyallup, WA region, you and your elder loved one will feel confident about your choices.
Whether or not you are looking for assisted living in Olympia WA, it's important to know when it is time to seek professional advice. If you are considering assisted living in Olympia WA or the surrounding area, I'm here to guide you and help you make the right choices.


The city SUV's interior is based on the Vision RS concept that was revealed last year and features two large displays.
After unveiling the front light signature and design sketches of the Kamiq, Skoda has released an image of the new SUV's cabin.
Taking inspiration from the Vision RS concept that was revealed in 2018, the Kamiq follows on from the recently-unveiled Scala hatchback by having the same interior layout that includes two large displays for the instrumentation and infotainment.
The central 'floating' touchscreen stands out from the rest of the dashboard and features a buttonless surround with touch-sensitive sections to navigate through the various options – with the rest of the centre console having a very simple design.
Instead of the normal dials in the instrument binnacle, Skoda can fit the Kamiq with the 10.25-inch Virtual Cockpit display, which comes with customisable displays that drivers can scroll through before finding the one that suits them best.
The ambient lighting fitted as standard comes in copper, red or white and they contrast the upholstery detailing and add another tone to the interior finish.
Skoda is also introducing 'suedia' – a microfiber material that is one of the upholstery options, with further comfort options including a heated windscreen, rear seats and steering wheel.
Based on Volkswagen Group's adaptable MQB platform, the Kamiq comes with plenty of interior space, such as the 400-litre boot that can be increased to 1,395 litres when the rear seats are folded flat and a considerable amount of legroom thanks to a 2,651mm wheelbase.
Skoda will be revealing the Kamiq in full at the 2019 Geneva Motor Show, which is open to the public from March 7 to 17.


Discover Aidan's way to generate money on Amazon selling different people's products (LEGALLY).
When most men and women think about earning money on Amazon by selling other people's items rather private labeling, they generally consider retail arbitrage. But, there's another wonderful way to do this (that unlike retail arbitrage can scale). And that's with wholesalers.
1) Locate hot-selling Amazon products which are being marketed by wholesalers for cheap.
2) Buy a little batch of these items in the wholesalers and ship them to the Amazon FBA Shops.
3) Organize yourself as a vendor for all these items on the Amazon product list.
5) Earn money on Amazon through passive earnings out of free, traffic.
Employing this procedure, you don't have to produce new products. Alternatively, you can sell other people's goods by locating wholesalers which are selling thembuy them and resell them.
What's the Amazon Purchase Box?
The"Purchase Box" is your add-to-cart button on every Amazon product list. What Amazon does is that they rotate qualified sellers with a few"Purchase Box time" Meaning that, daily, you are going to find a turn with the Purchase Box, and you're going to be the vendor which gets the earnings from folks using the add-to-cart button.
It is extremely important to be qualified to acquire the Purchase Box, because about 80percent of sales on Amazon are created utilizing The Purchase Box. Not many men and women go through the listing of different vendors and select one from the listing.
How Can You Become Eligible For Your Purchase Box?
1) Have an Amazon accounts in good standing. If you're fulfilling items through the Amazon FBA app, then you will almost always fulfill this criteria since Amazon are excellent at satisfying orders.
2) Have an expert Amazon seller accounts. Individual vendor accounts aren't qualified for Your Purchase Box.
3) Your items will need to be priced aggressive. Nobody knows just how close the cost should be, but when it is equal/within a couple pennies of the cheapest priced item in Amazon, you ought to be deemed aggressive enough to qualify for the Purchase Box.
What Are the Benefits of Wholesale vs. Private Labeling?
1) You buy from USA wholesalers. In case you've located the notion of importing goods from China to become intimidating, then this really is a means to market on Amazon without doing this.
2) You don't have to do any compensated marketing/advertising. Instead, you piggyback from free traffic. This is only because you're finding things which are currently having preexisting earnings, and list yourself as a vendor that's qualified for the Purchase Box. By doing so, you are going to take a piece of these sales on your own from natural, passive visitors.
This is in strong contrast to personal labeling in which you need to perform a promotion campaign to receive first sales (after you do this though and increase up in the Amazon search results – you'll make passive, continuing sales).
3) You do not have to be innovative and develop your own brandnew. Alternatively, you may simply piggyback from preexisting earnings.
What do you think about the method? Tell us in the comments!


What Kind of Business Do I Want to Form?
When is a Financial Presentation a Compilation?
The following is an article authored by Laura Belval, CPA and John Cotton, CPA, that was published in the June 2013 Arizona Society of CPA's newsletter to help provide some insight into the Board's CPE audit process and the importance of CPE record retention.
The following is an article authored by the Board's Compliance Manager, Ryan P. Edmonson, that was published in the November 2013 Arizona Society of CPA's newsletter to highlight some of the common regulatory pitfalls that lead to compliance issues that can be easily avoided.
The following is an article authored by Timothy R. Coffey, CPA, that was published in the February 2011 Arizona Society of CPA's newsletter that delineates the Board's Peer Review Oversight Advisory Committee's top ten list of common deficiencies noted in non-disclosure compiled financial statements.
For the convenience of those considering forming a new firm or changing the structure of an existing one, we have attached a hyperlink to an article that ran in the Arizona Republic in July, 2006. It does an excellent job of explaining various business structures, the difference between a sole practitioner and a solely-owned firm and the tax difference between the two.
On a monthly basis, the Board database randomly generates a list of names from the current/active list of CPA's certified with the ABA. These registrants are notified that their prior renewal's CPE submission is being audited. If you are wondering what to do should your name be selected, you will find the attached article published by the Arizona Society of CPA's to be very helpful.
The North Carolina State Board of Certified Public Accountant Examiners (NCSB) published an article regarding providing letters of verification for lenders and loan brokers. It contains some excellent insight into the dangers of providing an attest letter for non-attest work.
To assist the professional in determining if a compilation engagement has been performed, once again the North Carolina State Board of Certified Public Accountant Examiners has published an article for the benefit of the profession.
Find out how to avoid a lawsuit by managing your client's expectation of your services. The North Carolina State Board of Certified Public Accountant Examiners has published another important article for CPAs and CPA firms.
The Arizona Society of Certified Public Accountants published in their November 2009 AZCPA issue that CPAs will continue to use the current peer review process until the State Board of Accountancy takes action and the law changes.
The Facilitated State Board Access (FSBA) is a new process that will address the demand for greater peer review transparency. This is a program that the American Institute of CPAs (AICPA) has created to help the profession keep up with the evolving changes in the business and regulatory environments.
The following article describes the differences between different levels of service that a CPA can engage with a client. These are terms that are used on registrations for individual CPAs and firms and used often in peer review and compliance.


After moving back home, Charlene is anxious to prove her merit to her new boss – she's determined to become their top salesperson. So when she goes to meet a promising new client after an already trying day, and discovers the CEO was called out on an emergency, she takes out her frustration on the receptionist.
Unfortunately for Charlotte, when the CEO walks in, she's in the middle of a meltdown.
As if that weren't enough, he happens to be Brad Brigham, her former best friend in high school.
The man she's always had chemistry with.
The man she had already made plans to catch up with.
Publisher's Note: This sweet love story includes elements of domestic discipline.
Charlene Redman's mind was wandering as she made her way through the produce section of the grocery store just down the street from her new house. As she turned the corner to go up the next aisle she ran into a granite statue. It shocked her to find a statue in the middle of a grocery aisle, and she not only lost her balance as she bounced back off, but also dropped the red pepper she was holding. To her amazement the statue quickly reached a hand out and caught the pepper in one hand, while his other hand gently grabbed her arm to help her regain her balance.
Charlene froze. She recognized that voice, and immediately looked up to take a closer look at the statue. Sure enough, she was staring into the smiling eyes of the 18-year-old boy she'd dated her senior year of high school. Except that the cute teenager was now a large, extremely handsome man.
"Me, too," she said quietly.
"I live here," she said with pride in her voice.
She giggled as she shook her head. "No, relax. There's no one out there that'll be looking you up." Looking at how tall and muscular Brad was now, without thinking she blurted out the first thing that came to her mind. "It doesn't look like you'd have much to worry about, anyway." As soon as the words popped out of her mouth, she turned red.
"Nope," he answered, loving her mischievous smile.
"In that case, I'd love to." They talked a couple minutes, and she gave him her address.
The following day was Wednesday, and Charlene spent much of the day recalling her senior year in high school when she and Brad had been an item. She had been attracted to him the first time she saw him, with his thick, dark brown hair and gorgeous brown eyes to match. He was an athlete, and his body showed it. He was six feet tall back then, and strong. Every girl wanted to go out with him, and she couldn't believe it when he asked her out.
She had been so nervous when he came to pick her up that first day she tripped over her own feet and about fell flat on her face. He caught her and held her until she had her balance again, and said something to casually dismiss the whole thing, sparing her from total embarrassment. She soon found he was very easy to talk to, or be with, or grow very fond of. They were together most of their senior year. They were going to colleges on opposite sides of the country after high school and decided they owed it to themselves to experience the whole college life, including dating other people, but vowed they would still be friends.
Then her father got transferred and they moved. She had to switch colleges and attend one closer to their new home that was cheaper. This was before cell phones were so popular, and she hadn't had one before they left. As a result, they lost contact with each other, but she had often wondered what had become of Brad.
He had always wanted to go into the business world in some form, and she felt sure he would. He was extremely smart, and was a natural leader. Anytime they had a group project in school, Brad always seemed to be in charge of whatever group he was in. He was a good organizer and people seemed to look up to him and listen to what he said. She was sure he would be good at whatever sort of business venture he took on. Now, thinking back on that, she was anxious for their date Friday so she could see what he was doing these days.
Brad was across town in his office, thinking back on the same time frame. He'd thought Charlene, or Charlie, as he'd called her then, was the prettiest girl in the school. She was refreshing, though, because she didn't see herself as the prettiest girl in the school, unlike many of the others. She had long hair that was the prettiest reddish brown he'd ever seen. It had more of a reddish tint when the sun hit it, and it had enough curl to make him want to run his hands through it often. He always thought her beautiful green eyes just sparkled with mischief. She hadn't thrown herself at him the way many of the girls in school had, which was something else he liked about her.
Charlene may have been on the petite side, but her personality sure wasn't. She was a feisty little thing, and he'd liked her from the first time he'd met her. There wasn't anything fake about her. She was who she was, and she never apologized for it or tried to be someone different.
She had two older brothers and as a result was a bit of a tomboy, which is why he always thought the nickname Charlie fit her. In his mind that bit of a tomboy in her made her absolutely adorable. She liked most sports, both to play and watch, and understood them. He'd met very few females of any age that understood football and liked to watch it. She loved it, and he loved watching her watch it. They'd spent several Sunday afternoons watching a football game, and she was by far the cutest armchair quarterback he'd ever seen.
But as much as she liked sports, she also liked being a lady. He'd taken her out somewhere special on several occasions just to see her all dressed up. When she 'became a lady', as she put it, she was beautiful.
His secretary, Toni, stuck her head in his office and interrupted his trip down memory lane. "What's got you smiling?" she asked.
Brad shook his head a bit to bring himself back to the present. "I ran into an old friend from high school yesterday. I was doing a little reminiscing, I guess," he confessed.
Thursday morning Charlene woke with a start. The sun was coming through the windows, and she quickly looked at the clock. She was starting work today and had her first calls scheduled. It was almost 7:30, and she had her alarm set for 6:00. Oh, great!
She jumped out of bed and hurried to the bathroom to take a quick shower. She flipped the light switch on and realized why her alarm hadn't gone off. She had no electricity! Thinking quickly, she went to the kitchen and put a big pot of water on to boil, thankful for her gas stove. Closing her curtains, she used the heated water to wash her hair and give herself a sponge bath. With no hot water for a shower that would have to do. She dried her hair the best she could with a towel, and combed it out. She'd have to leave it down this morning to let it dry as long as she could, and pull it back into a ponytail when she got to her first sales call. She got dressed and did her makeup quickly, grabbed a couple rubber bands and a nice professional-looking hair clip and her purse, and ran out the door.
She hated being late. She always liked to arrive at her destination early, then review a customer's file in her car before going in to meet with them. She felt that extra familiarity with the account gave her an edge. Today she'd have to skip that and hope she remembered it from looking it over yesterday afternoon.
When she came to a red light, she reached over to grab the customer's file. Maybe she could glance at it quickly while she waited. She gasped as she realized in her haste to leave she'd forgotten her briefcase. As the light turned green she cursed herself inwardly and turned to go back to her house.
"Oh, so that's what happened," Charlene answered.
Charlene felt better, but it still threw her whole day off. Once she got there she combed her hair and pulled it back into a ponytail. She was putting the rubber band in when it broke, snapping on her fingers, which hurt! She took the other rubber band and got it on without incident. She put the hair clip on over the rubber band, and the hair clip broke. Cursing to herself yet again, she was able to fix the clip enough to get it in her hair. She hoped it lasted, at least through this meeting. Hopefully the meeting would be quick and she'd have time to stop at a store for another hair clip before her next appointment.
As it turned out, the meeting with Mr. Hudson went a little longer than she'd planned, which put her in a rush to make it to her second call on time. Again, there was no time to review the file first, which tended to fluster her a bit. Thank goodness her hair clip was holding up, at least so far. That was a concern at the back of her mind, though, and was a distraction she didn't need. Luckily, that meeting went well, although again it was a little longer than she'd hoped.
She normally made herself eat a good lunch, knowing she'd do better in the afternoon after a relaxing lunch with a salad or something nutritious. She decided to try and make up her lost time today, though, by going through a fast food drive through. She ordered a chef salad with dressing on the side. She had a few food allergies, especially strawberries, pecans and a type of oil often found in bottled salad dressings, so she always ordered the dressing on the side. She kept some packets of salad dressing in her car that were safe for her to have.
Since she'd gotten the late start and skipped breakfast this morning she was hungry. She pulled over at a little park, thinking she'd have just enough time to eat her salad and glance through her next client's file. She carried the salad, some of her dressing, and the customer's file to a picnic table and sat down, her mouth watering. She opened the lid and moaned. They'd given her the wrong salad. This was their summer salad with strawberries and pecans. To make it worse, they'd poured the strawberry salad dressing over the entire thing, making it impossible to pick out the strawberries and pecans and still eat it!
Frustrated and hungry, she dumped the salad in the garbage can on her way back to her car. There were no more fast food places or convenience stores between here and her destination, so she'd just have to wait until after her meeting. She was starving, and searched her car for anything she could eat. Surely she'd have a candy bar or something in it somewhere. All she found, however, was a little package of stale crackers. At this point she was so hungry even they looked good, so she munched on them on her way to her meeting. Hopefully they would be enough to at least keep her stomach from growling and embarrassing her. This was a very important meeting, and she sure wished she had had a good lunch.
She got there, glanced at the file quickly and hurried inside. This was not an established customer, but she hoped it would become one. The company was not large, but her research showed they'd promoted a former Vice-President to the position of CEO and the company had been growing steadily since then. Word in the industry was that this new CEO was very impressive, and this was a company on the move. She hoped to get a foot in the door today. She was meeting with the head of their purchasing department, and on the phone he seemed pretty interested. She hoped today went well.
She smiled as she approached the receptionist and told her she was here to meet with Mr. McMillain.
She was still talking, but Charlene wasn't really listening. She was hungry, and angry. Her day had been terrible, and this was not any different. She did not want to sit here and wait for who knows how long. The later he was getting back, the later the meeting would go and the hungrier she would be. On top of that, her mood now was going from bad to worse, and she would readily admit she had a terrible temper. Once she got upset it was incredibly difficult for her to calm down and be civil. She knew her best shot at getting this account was to reschedule.
Charlene was being hustled along so quickly she hadn't had time to digest what was happening. Brad wasn't the purchasing manager that she was to meet with today. She was sure she'd talked to the manager on the phone, and it wasn't Brad. It was a Mr. McMillain. The receptionist's words came back to her then. She'd said something about the CEO. Did she say he was going to meet with her since Mr. McMillain had been called out? Was Brad the new CEO?
It had just occurred to her that she'd made a complete fool of herself in front of Brad, and probably lost a possible big account, when they reached his office. Sure enough, the door said, Mr. Brigham, CEO. He led her inside and stopped to close and lock his door before leading her to the leather couch, where he deposited her. He remained standing, but looked down at her as if he were studying her.
She was still trying to make sense of everything that had just happened, including the way she'd acted. That really wasn't like her, but Brad probably wouldn't believe that at the moment. The only thing she could think to do was look up at him and very quietly say, "I'm sorry, Brad." She barely got the words out past the lump in her throat.
Brad had been pacing since they'd entered his office, and after hearing her answer to his question he sat down right beside her. She was looking down at the floor, and he reached over and gently used one finger to raise her chin so she was looking at him. He looked directly into her eyes, uncertainty in his own. After several moments he seemed to reach a decision.
"We're going to see if you are sorry, Charlie." He reached over to her and before she knew what was happening she found herself lying over his lap. She had a loose-fitting skirt on, and he quickly flipped it up over her back.
Brad gave her another swat. "I would think it's pretty obvious what I'm doing. I'm giving you a spanking, which you've most definitely earned. And I'm glad to hear it hurts. That means I'm doing it right and it might have the desired effect." He started spanking again, holding her tightly with his left arm around her waist as she wriggled and struggled to get off his lap.
She quickly came up with a plan and kicked her legs to get loose while rolling to the side and off his lap. She pushed up with her hands, fully expecting all those movements at the same time would free her from his hold. She had every intention of standing up, slapping him in the face, and telling him exactly what she'd do if he laid one hand on her ever again, before storming out of his office. In her mind it worked like a charm.
However, in real life she didn't move much at all. Rolling to the side and pushing with her hands did nothing – absolutely nothing. Brad was holding her down with his left arm around her waist, and he was much stronger and had a much better hold than she'd anticipated, because he held her firmly in place. Kicking her legs caused him to lower his aim and the spanking now was aimed at her thighs, and darn if that didn't hurt even worse!
"Ow! Brad, that hurts even more! Stop that right now. I can't take any more." To her horror, she could no longer control the tears that had been threatening, and the couch beneath her was soon wet. To make it even worse, once the tears started, so did the emotions. She was soon sobbing, gasping for breath, and then out of nowhere came a horrible sense of guilt. She felt terrible. She couldn't remember ever acting so badly, and it was in front of Brad, of all people.
Once she said that, and meant it, she lost all her fight. She lay there, draped over his lap while he continued spanking her. She was worn out; physically worn out from fighting, trying to get loose, but also emotionally exhausted. She'd behaved horribly. She'd taken her frustrations out on Brad's receptionist, even though none of her awful day was her fault.
She'd said some horrible things, some about the man she was supposed to meet with, and she'd never met him. He might be a wonderful guy. The receptionist had said he had an emergency. She'd never even stopped to think about that. He could be facing something awful right now with a member of his family, and here she was talking about him like that.
And worst of all, it had occurred to her while Brad was leading her back to his office that the receptionist had said they weren't expecting her this soon. She thought about those words, and remembered the conversation she had with the man she was to meet with. She tried to set the meeting for 1:00, like she did most of her afternoon meetings with first time clients. He had said that would be too early for him and asked for 2:00. She was afraid that wouldn't give them enough time, so he said he could rearrange things and be ready by 1:30. She had agreed, but forgotten about that agreement. She was there at 1:00, complaining that he wasn't ready yet. She had acted just plain horribly!


text:
\section{Introduction}
\vspace{-0.1in}

The use of high-dimensional data has become very popular these days, especially in search, natural language processing (NLP), and computer vision.  For example, Winner of 2009 PASCAL image classification challenge  used {4 million} (non-binary) features~\cite{Proc:Wang_CVPR10}. \cite{Report:Sibyl,GoogleBlog,Proc:Weinberger_ICML2009} discussed datasets with billions or even trillions of features.

For text data, the use of extremely high-dimensional representations (e.g., $n$-grams) is the standard practice, for example, \cite{Report:Sibyl,GoogleBlog,Proc:Weinberger_ICML2009}. Binary representations could be sufficient if the order of $n$-grams is high enough. On the other hand, in  current practice of computer vision, it is still more common to use non-binary feature representations, for example, {\em local coordinate coding (LCC)}~\cite{Proc:LCC_NIPS09,Proc:Wang_CVPR10}. It is often the case that in practice high-dimensional non-binary visual features might be appropriately sparsified without hurting the performance. However simply binarizing the features will often incur loss of accuracies, sometimes significantly so.  See Table~\ref{tab_linearSVM} for an illustration.

Our contribution in this paper is the proposal of two types of (nonlinear) ``CoRE'' kernels, where ``CoRE'' stands for ``correlation-resemblance'', for non-binary sparse data. Interestingly, using CoRE kernels leads to improvement in classification accuracies (in some cases significantly so)  on a variety of datasets.

For practical large-scale applications, naive implementations of nonlinear kernels may be too costly (time and/or memory), while linear  learning methods (e.g., linear SVM or logistic regression) are extremely popular in  industry.  The proposed CoRE kernels would be facing the same challenge.  To address this critical issue, we also develop efficient hashing algorithms which approximate the  CoRE kernels by linear kernels. These new hashing algorithms allow us to take advantage of  highly efficient (batch or stochastic) linear algorithms, e.g.,~\cite{Proc:Joachims_KDD06,Proc:Shalev-Shwartz_ICML07,URL:Bottou_SGD,Article:Fan_JMLR08}.

In the rest of this section, we first review the definitions of correlation and resemblance, then we provide an experimental study to illustrate the loss of classification accuracies when  sparse data are binarized.

\vspace{-0.1in}
\subsection{Correlation}
\vspace{-0.1in}

We assume a data matrix of size $n\times D$, i.e., $n$ observations in $D$ dimensions. Consider, without loss of generality, two data vectors $u, v\in\mathbb{R}^D$.   The correlation is simply the normalized inner product:
\begin{align}\label{eqn_rho}
&\rho = \rho(u,v) = \frac{\sum_{i=1}^D u_i v_i}{\sqrt{\sum_{i=1}^D u_i^2\sum_{i=1}^D v_i^2}} = \frac{A}{\sqrt{m_1 m_2}},\\\notag
&\text{where } \ A = \sum_{i=1}^D u_iv_i, \ \
m_1 = \sum_{i=1}^D u_i^2,\ \ m_2 = \sum_{i=1}^D v_i^2
\end{align}
It is well-known that $\rho(u,v)$  constitutes a positive definite and linear kernel, which is one of the reasons why correlation is very popular in practice.

\subsection{Resemblance}
For binary data, the resemblance is commonly used:
\begin{align}\label{eqn_R}
&R = R(u,v) = \frac{a}{f_1+f_2-a},  \\\notag
\text{where }\ &f_1 = \sum_{i=1}^D 1\{u_i \neq 0\},\hspace{0.2in}
f_2 = \sum_{i=1}^D 1\{v_i \neq 0\},\\\notag
 &a = \sum_{i=1}^D 1\{u_i \neq 0\} 1\{v_i\neq 0\}
\end{align}
It was shown in~\cite{Proc:HashLearning_NIPS11} that the resemblance defines a type of positive definite kernel. In this study, we will combine correlation and resemblance to define two new types of nonlinear kernels.

\vspace{-0.1in}
\subsection{Linear SVM Experiment}
\vspace{-0.05in}

Table~\ref{tab_linearSVM} lists the datasets, which are  non-binary and sparse. The table also presents the test classification accuracies using linear SVM on both the original (non-binary) data and  binarized data. The results in the table illustrate the noticeable drop of accuracies by using only binary data.

\begin{table}[h!]
\caption{Classification accuracies (in \%) using linear SVM (LIBLINEAR~\cite{Article:Fan_JMLR08}) on  sparse non-binary datasets. As we always normalize the data (to unit norm),  the correlation kernel $\rho$ is naturally used here. We experiment with the $l_2$-regularized linear SVM and report the best test accuracies from a wide range of ``$C$'' values (where $C$ is the parameter in linear SVM). Using binarized data (i.e., the last column), the test accuracies drop quite noticeably in most cases. \\
Available at the UCI repository, \textbf{Youtube}  is a multi-view dataset, and we choose the largest set of features (audio) for our experiment. \textbf{M-Basic}, \textbf{M-Rotate}, and \textbf{MNIST10k} were used in~\cite{Proc:ABC_UAI10} for testing {\em abc-logitboost} and {\em abc-mart}~\cite{Proc:ABC_ICML09} (and comparisons with deep learning~\cite{Proc:Larochelle_ICML07}). For \textbf{RCV1}, we use a subset of the original testing examples (to facilitate efficient kernel computation  later  needed in the paper).}
\begin{center}{
{\begin{tabular}{l r r r r r}
\hline \hline
Dataset        &\#Train    &\#Test &Linear &Lin. Bin.\\
\hline
M-Basic      &12,000 &50,000   &90.0\%   &88.9\% \\
MNIST10k     &10,000  &60,000  &90.0\%   &88.8\% \\
M-Rotate    &12,000 &50,000  &48.0\%   &44.4\%\\
RCV1       &20,242 &60,000 &96.3\%  &95.6\%\\
USPS        &7,291 &2,007 &91.8\%   &87.4\%\\
Youtube &11,930 &97,934 &47.6\% &46.5\%
\\\hline\hline
\end{tabular}}
}
\end{center}\label{tab_linearSVM}

\end{table}

\begin{figure}[h!]
\begin{center}
\mbox{\includegraphics[width=1.7in]{fig/MBasicAccLinear.eps}\hspace{-0.12in}
\includegraphics[width=1.7in]{fig/MNIST10kAccLinear.eps}
}

\mbox{\includegraphics[width=1.7in]{fig/MRotateAccLinear.eps}\hspace{-0.12in}
\includegraphics[width=1.7in]{fig/RCV1AccLinear.eps}
}

\mbox{\includegraphics[width=1.7in]{fig/USPSAccLinear.eps}\hspace{-0.12in}
\includegraphics[width=1.7in]{fig/YoutubeAccLinear.eps}
}

\end{center}
\vspace{-0.2in}
\caption{Test classification accuracies for both non-binary (solid) and binarized (dashed) data, using $l_2$-regularized linear SVM with a regularization parameter $C$. We present results for a wide range of $C$ values. The best (highest) values are  summarized in Table~\ref{tab_linearSVM}.}\label{fig_linearSVM}\vspace{-0.1in}
\end{figure}

Figure~\ref{fig_linearSVM} provides more detailed results for a wide range of $C$ values, where $C$ is the usual $l_2$-regularization parameter in SVM.

While linear SVM is extremely popular in industrial practice, it is often not as accurate. Our proposed CoRE kernels will be able to produce noticeably more accurate results than linear SVM.

\vspace{-0.1in}
\section{CoRE Kernels}
\vspace{-0.05in}

We propose two types of CoRE kernels, which combine resemblance with correlation, for sparse non-binary data. Both kernels are positive definite. We will demonstrate the effectiveness of the two CoRE kernels using the same datasets in Table~\ref{tab_linearSVM} and Figure~\ref{fig_linearSVM}.\footnote{In order to use LIBSVM precomputed kernel functionality we need to materialize the full kernel matrix, which is extremely expensive to store. Also, it looks the original LIBSVM code limits the size of  kernel matrix. To ensure reproducibility, this paper did not use larger datasets.  }

\vspace{-0.1in}
\subsection{CoRE Kernel, Type 1}
\vspace{-0.05in}

The first type of CoRE kernel is basically the product of correlation $\rho$ and the resemblance $R$.
\begin{align}
K_{C,1} = K_{C,1}(u,v) =  \rho R
\end{align}
Later  we will express $K_{C,1}$ as an (expectation of) inner product, i.e., $K_{C,1}$ is obviously positive definite.

If  the data are fully dense, then $R=1$ and $K_{C,1} = \rho$. On the other hand, if the data are binary, then $\rho = \frac{a}{\sqrt{f_1f_2}}$ and $K_{C,1} = \frac{a}{\sqrt{f_1f_2}}\frac{a}{f_1+f_2-a}$. Recall the definitions of $f_1, f_2, a$ in  (\ref{eqn_R}).
\subsection{CoRE Kernel, Type 2}

The second type of CoRE kernel perhaps  appears less intuitive than the first type:
\begin{align}
K_{C,2} = K_{C,2}(u,v) =  \rho \frac{\sqrt{f_1f_2}}{f_1+f_2-a} = \frac{\rho R }{a/\sqrt{f_1f_2}}
\end{align}
If the data are binary, then $K_{C,2}=R$. We will, later in the paper, also write $K_{C,2}$ as an expectation of inner product to confirm it is also positive definite.

\subsection{Kernel SVM Experiment}

Figure~\ref{fig_kernelSVM} presents the classification accuracies on the same six datasets as in Figure~\ref{fig_linearSVM}, using nonlinear kernel SVM with three different kernels: CoRE Type 1, CoRE Type 2, and resemblance. We can see that resemblance (which only uses binary information of the data) does not perform as well as CoRE kernels.

\begin{figure}[h!]
\begin{center}
\mbox{
\includegraphics[width=1.75in]{fig/MBasicAccCoRE.eps}\hspace{-0.12in}
\includegraphics[width=1.75in]{fig/MNIST10kAccCoRE.eps}
}

\mbox{
\includegraphics[width=1.75in]{fig/MRotateAccCoRE.eps}\hspace{-0.12in}
\includegraphics[width=1.75in]{fig/RCV1AccCoRE.eps}
}

\mbox{
\includegraphics[width=1.75in]{fig/USPSAccCoRE.eps}\hspace{-0.12in}
\includegraphics[width=1.75in]{fig/YoutubeAccCoRE.eps}
}

\end{center}
\vspace{-0.2in}
\caption{Test classification accuracies using nonlinear kernel SVM and three types of kernels: CoRE Type 1, CoRE Type 2, and resemblance. We use LIBSVM pre-computed kernel functionality. Compared with the results of linear SVM in Figure~\ref{fig_linearSVM}, we can see CoRE kernels and  resemblance kernel perform better (or much better, especially M-Rotate).  The best results (highest points on the curves) are summarized in Table~\ref{tab_kernelSVM}. }\label{fig_kernelSVM}
\end{figure}
\begin{table}[h!]
\caption{Best test classification accuracies  (in \%)  for  five different  kernels. The first two columns (i.e., ``linear'' and ``linear binary'') are already shown in Table~\ref{tab_linearSVM}.}
\begin{center}{\small
{\begin{tabular}{l c c c c c}
\hline \hline
Dataset        &Lin.    &Lin. Bin. &Res. & CoRE1 &CoRE2\\
\hline
M-Basic     &90.0   &88.9   &95.9   &\textbf{97.0}   &96.5 \\
MNIST10k      &90.0   &88.8   &95.5   &\textbf{96.6}   &96.0\\
M-Rotate    &48.0   &44.4   &80.3   &\textbf{87.6}   &86.2\\
RCV1       &96.3  &95.6   &96.5   &\textbf{97.0}  &96.9\\
USPS        &91.8   &87.4   &92.5  & \textbf{95.5}   &95.2\\
Youtube &47.6 &46.5 &51.1 &53.1 &\textbf{53.2}
\\\hline\hline
\end{tabular}}
}
\end{center}\label{tab_kernelSVM}

\end{table}

The best results are summarized in Table~\ref{tab_kernelSVM}. It is interesting to compare them  with   linear SVM results in Table~\ref{fig_linearSVM} and Figure~\ref{fig_linearSVM}. We can see that CoRE kernels perform  very well, without using additional tuning parameters. In fact, if we compare the best results in~\cite{Proc:Larochelle_ICML07,Proc:ABC_UAI10} (e.g., RBF SVM, abc-boosting, or deep learning) on MNIST10k, M-Rotate, and M-Basic, we will see that CoRE kernels (with no tuning parameters) can achieve the same (or  similar) performance.

We should mention that our experiments can be fairly easily reproduced because all datasets are public and we use standard SVM packages (LIBSVM and LIBLINEAR) without any modifications. We also provide the results for wide range of $C$ values in Figure~\ref{fig_linearSVM} and Figure~\ref{fig_kernelSVM}. Because we use pre-computed kernel functionality of LIBSVM (which consumes very substantial amount of memory to store the kernel matrices), we only experiment with  training data of moderate sizes, to ensure repeatability.

\subsection{Challenges with Nonlinear Kernel SVM}

\cite[Section 1.4.3]{Book:Bottou_07} mentioned three main computational issues of kernels  summarized as follows:
\begin{enumerate}
\item {\em Computing kernels is very expensive} \\
Computing kernels can account for more than half of the total computing time.
\item {\em Computing the full kernel matrix is wasteful}\\
This is because not all pairwise kernel values will be used during training.\vspace{-0.05in}
\item {\em  The kernel matrix does not fit in memory} \\
The cost of storing the full kernel matrix  in the memory is  $O(n^2)$, which is not realistic for most PCs even for  merely   $10^5$, while the industry has used training data with billions of examples.  Thus, kernel evaluations are often conducted {\em on the fly}, which means the computational cost is dominated by kernel evaluations.
\end{enumerate}
In fact, evaluating kernels on-demand would encounter another serious (and often common) issue if the datasets themselves are too big for the memory.

All these crucial issues motivate us to develop hashing algorithms to approximate CoRE kernels.

\subsection{Benefits of Hashing}

Our goal is to develop good hashing algorithms to (approximately) transform nonlinear kernels into linear kernels. Once we have the new data representations (i.e., the hashed data), we can use highly efficient batch or stochastic linear  methods for training SVM (or logistic regression)~\cite{Proc:Joachims_KDD06,Proc:Shalev-Shwartz_ICML07,URL:Bottou_SGD,Article:Fan_JMLR08}. Another benefit would be in the context of approximate near neighbor search because probabilistic hashing provides a (often good) strategy for space partitioning which will help reduce the search time (i.e., no need to scan all data points). Our proposed hashing methods can be modified to become an instance of {\em locality sensitive hashing (LSH)}~\cite{Proc:Indyk_STOC98} in the space of CoRE kernels.

In this study, we will present hashing algorithms for CoRE kernels based on standard random projection and minwise hashing methods. We first provide a review of these two methods.

\section{Review of Random Projections and Minwise Hashing}

\subsection{Random Projections}

Consider two vectors $u,v\in\mathbb{R}^{D}$. The idea of random projection is simple. We first generate a random vector of i.i.d. entries $r_i$, $i=1$ to $D$, and then compute the inner products as the hashed values:
\begin{align}\label{eqn_P}
P(u) =  \sum_{i=1}^D u_i r_i,\hspace{0.2in} P(v) = \sum_{i=1}^D v_i r_i
\end{align}
For the convenience of theoretical analysis, we adopt $r_i \sim N(0,1)$, which is a typical choice in the literature. Several variants of random projections like~\cite{Proc:Li_Hastie_Church_KDD06,Proc:Weinberger_ICML2009} are essentially equivalent, as analyzed in~\cite{Proc:HashLearning_NIPS11}.

We always assume  the data are normalized, i.e., $\sum_{i=1}^D u_i^2=\sum_{i=1}^D v_i^2 =1$. Note that computing the $l_2$ norms of all the data points only requires scanning the data once which is anyway needed during data collection/processing. For normalized data, it is known that $E\left[P(u)P(v)\right] = \rho$.   In order to estimate  $\rho$, we need to use $k$ random projections to generate $P_j(u), P_j(v), j = 1$ to $k$, and estimate $\rho$ by $\frac{1}{k}\sum_{j=1}^k P_j(u)P_j(v)$, which is also an inner product. This means we can directly use the projected data to build a linear classifier.

\subsection{Minwise Hashing}

The method of minwise hashing~\cite{Proc:Broder} is very popular for computing set  similarities, especially for industrial applications, for example,~\cite{Proc:Broder,Proc:Fetterly_WWW03,Proc:Henzinger_SIGIR06,Article:Urvoy08, Proc:Nitin_WSDM08,Proc:Chierichetti_KDD09,Proc:Gollapudi_WWW09,Proc:Najork_WSDM09,Proc:Buehrer_WSDM08}.

 Consider the space of the column numbers: $\Omega = \{1, 2, 3, ..., D\}$. We assume a random permutation $\pi: \Omega \longrightarrow\Omega$ and apply $\pi$ on the coordinates of both vectors $u$ and $v$. For example, consider $D=4$, $u = [0,\ 0.45,\ 0.89,\ 0]$ and $\pi: 1\rightarrow3,\ 2\rightarrow1,\ 3\rightarrow4,\ 4\rightarrow 2$. Then the permuted vector becomes $\pi(u) = [0.45,\ 0,\ 0,\ 0.89]$. In this example, the first nonzero column of $\pi(u)$ is 1, and the corresponding value of the coordinate is 0.45. For convenience, we introduce the following notation:
\begin{align}\label{eqn_L}
&L(u) = \text{location of  first nonzero entry of } \pi(u)\\\label{eqn_V}
&V(u) = \text{value of  first nonzero entry of } \pi(u)
\end{align}
In this example, we have $L(u) = 1$ and $V(u) = 0.45$.

The following  well-known {\em collision probability}
\begin{align}
\mathbf{Pr}\left(L(u) = L(v)\right) = R(u,v) = R
\end{align}
can be used  to estimate the resemblance $R$. To do so, we  need to generate $k$ permutations $\pi_j$, $j=1$ to $k$.

The proposed hashing algorithms for CoRE kernels combine  random projections and minwise hashing.
\section{Hashing  CoRE Kernels}\label{sec_hashing}

The goal is to develop unbiased estimators of $K_{C,1}$ and $K_{C,2}$ which can be written as  inner products.  We assume  that we have conducted random projections and minwise hashing $k$ times. In other words, for each data vector $u$, we have the hashed values $P_j(u)$, $L_j(u)$, $V_j(u)$,  $j=1$ to $k$. Recall the definitions of $P_j$, $L_j$, $V_j$ in (\ref{eqn_P}), (\ref{eqn_L}), and (\ref{eqn_V}), respectively.

\subsection{Hashing Type 1 CoRE Kernel}

Our proposed estimator of $K_{C,1}$ is
\begin{align}
\hat{K}_{C,1}(u,v) = \sum_{j=1}^k P_j(u)P_j(v)1\{L_j(u) = L_j(v)\}
\end{align}

\begin{theorem}\label{thm_Kc1}
\begin{align}
E\left(\hat{K}_{C,1}\right) = K_{C,1}
\end{align}
\begin{align}\label{eqn_VarKc1}
Var\left(\hat{K}_{C,1}\right) =\frac{1}{k}\left\{\left(1+2\rho^2\right)R -  \rho^2 R^2\right\}
\end{align}
\textbf{Proof:}\ \ See Appendix~\ref{app_thm_Kc1}.$\hfill\Box$
\end{theorem}

A simple argument can  show that $\hat{K}_{C,1}$ could be written as an inner product; and hence $K_{C,1}$ is positive definite. Although this fact is obvious since $K_{C,1}$ is a product of two positive definite kernels, we would like to present a constructive proof because the construction is basically the same procedure for expanding the hashed data before feeding them to an SVM solver.

 Recall, $L_j$ is the location of the first nonzero after minwise hashing.  Basically, we can view $L_j(u)$  equivalently as a vector of length $D$ whose coordinates are all zero except the $L_j(u)$-th coordinate. The value of the only nonzero coordinate will be $P_j(u)$. For example, suppose $D=4$, $L_j(u) = 2$, $P_j(u)=0.1$. Then the equivalent vector would be $[0,\ 0.1,\ 0,\ 0]$. This way, we can write $\hat{K}_{C,1}$ as an inner product of two $D$-dimensional sparse vectors.

Note that the  input data format of standard SVM packages is  the sparse format. For linear SVM, the  cost is essentially determined by the number of nonzeros (in this case, $k$), not much to do with the dimensionality (unless it is too high). If $D$ is too high, then we can adopt the standard trick of {\em $b$-bit minwise hashing}~\cite{Proc:Li_Konig_WWW10,Proc:HashLearning_NIPS11} by only using the lowest $b$ bits of $L_j(u)$.

\subsection{Hashing Type 2 CoRE Kernel}

Our second proposal is
\begin{align}
\hat{K}_{C,2} = \frac{\sqrt{f_1f_2}}{k}\sum_{j=1}^k V_j(u) V_j(v)1\{L_j(u) = L_j(v)\}
\end{align}
Recall that we always assume  the  data ($u$, $v$) are normalized. For example, if the data are binary, then we have $u_i = \frac{1}{\sqrt{f_1}}$, $v_i = \frac{1}{\sqrt{f_2}}$. Hence the values $V_j(u)$ and $V_j(v)$ are small  (and we need the term $\sqrt{f_1f_2}$).
\begin{theorem}\label{thm_Kc2}
\begin{align}
E\left(\hat{K}_{C,2}\right) = K_{C,2}
\end{align}
\begin{align}\label{eqn_VarKc2}
&Var\left(\hat{K}_{C,2}\right)\\\notag
=& \frac{1}{k}\frac{f_1f_2}{f_1+f_2-a}\left(\sum_{i=1}^D u_i^2v_i^2-\frac{\left(\sum_{i=1}^D u_iv_i\right)^2}{(f_1+f_2-a)}\right)
\end{align}
\textbf{Proof:}\ \ See Appendix~\ref{app_thm_Kc2}.$\hfill\Box$
\end{theorem}

Once we understand how to express $\hat{K}_{C,1}$ as an inner product, it should be easy to see that $\hat{K}_{C,2}$ can also be written as an inner product. Again, suppose $D=4$, $L_j(u) = 2$, and $V_j(u)=0.05$. We can consider an equivalent vector $[0,\ 0.05f_1, 0,\ 0]$. In other words, the difference between $\hat{K}_{C,1}$ and $\hat{K}_{C,2}$ is what value we should put in the nonzero location. One advantage of $\hat{K}_{C,2}$ is that it only requires the permutations and thus eliminates the cost of random projections.

As expected, the variance of $\hat{K}_{C,2}$ would be  large if the data are heavy-tailed. However, when the data are binary or  appropriately normalized (e.g.,   TF-IDF), $Var\left(\hat{K}_{C,2}\right)$ is actually quite small. For example, when the data are binary, i.e., $u_i = \frac{1}{\sqrt{f_1}}$, $v_i=\frac{1}{\sqrt{f_2}}$, we have
$Var\left(\hat{K}_{C,2}\right)= \frac{1}{k}\left(R-R^2\right)$, which is (considerably) smaller than  $Var\left(\hat{K}_{C,1}\right) =\frac{1}{k}\left\{\left(1+2\rho^2\right)R -  \rho^2 R^2\right\}$.

\subsection{Experiment for Validation}

To validate the theoretical results in Theorem~\ref{thm_Kc1} and Theorem~\ref{thm_Kc2}, we provide a set of experiments in Figure~\ref{fig_MSE}. Two pairs of word vectors are selected: ``A--THE'' and ``HONG--KONG'', from a chuck of web crawls. For example, the vector ``HONG'' is a vector whose $i$-th entry is the number of occurrences of the word ``HONG'' in the $i$-th document. For each pair, we apply the two proposed hashing algorithms on the two corresponding vectors to estimate $K_{C,1}$ and $K_{C,2}$. With sufficient repetitions, we can empirically compute the mean square errors (MSE = Var + Bias$^2$), which should match the theoretical variances if the estimators are indeed unbiased and  the variance formulas, (\ref{eqn_VarKc1}) and (\ref{eqn_VarKc2}), are correct.

The number of word occurrences is a  typical example of highly heavy-tailed data. Usually when  text data are used in machine learning tasks, they have to be appropriately weighted (e.g., TF-IDF) or simply binarized. Figure~\ref{fig_MSE} presents the results on the original raw data as well as binarized data, to verify the formulas for these two extreme cases, for $k=1$ to 1000.

Indeed, the plots show that the empirical MSEs essentially overlap the theoretical variances. In addition, the MSEs of $\hat{K}_{C,2}$ is significantly larger than the MSEs of $\hat{K}_{C,1}$ on the raw data, as expected. Once the data are binarized, the MSEs of $\hat{K}_{C,2}$ become smaller.

\begin{figure}[h!]
\begin{center}

\mbox{
\includegraphics[width=1.75in]{fig/A_THE_MSE.eps}\hspace{-0.12in}
\includegraphics[width=1.75in]{fig/A_THE_MSE_Bin.eps}
}
\mbox{
\includegraphics[width=1.75in]{fig/HONG_KONG_MSE.eps}\hspace{-0.12in}
\includegraphics[width=1.75in]{fig/HONG_KONG_MSE_Bin.eps}
}

\end{center}
\vspace{-0.2in}
\caption{Mean square errors (MSE = Var + Bias$^2$) on two pairs of word vectors for validating Theorems~\ref{thm_Kc1} and~\ref{thm_Kc2}. The empirical MSEs (solid curves) essentially overlap the theoretical variances (dashed curves), (\ref{eqn_VarKc1}) and (\ref{eqn_VarKc2}). When using the raw counts (left panels), the MSEs of $\hat{K}_{C,2}$ is significantly higher than the MSEs of $\hat{K}_{C,1}$. However, when using binarized data (right panels), the MSEs of $\hat{K}_{C,2}$ become noticeably smaller.}\label{fig_MSE}
\end{figure}

\section{Hashing CoRE Kernels for SVM}

In this section, we provide a set of experiment for using  the  hashed data  as input for a linear SVM solver (LIBLINEAR). Our goal is to  approximate the performance of (nonlinear) CoRE kernels with linear kernels. In Section~\ref{sec_hashing}, we  have explained how to express the estimators $\hat{K}_{C,1}$ and $\hat{K}_{C,2}$ as inner products by expanding the hashed data. With $k$ permutations and $k$ random projections, the number of nonzeros of the expanded data is precisely $k$. To reduce the dimensionality, we use only the lowest $b$ bits of the locations. In this study, we experiment with $b=1$, 2, 4, 8.

Figure~\ref{fig_MRotateAcc} presents the results on the \textbf{M-Rotate} dataset. As shown in Figure~\ref{fig_linearSVM} and Table~\ref{tab_linearSVM}, using the linear kernel can only achieve an accuracy of $48\%$. This means, if we use random projections (or variants, e.g., ~\cite{Proc:Li_Hastie_Church_KDD06,Proc:Weinberger_ICML2009}), which approximate inner products, then the most we can achieve would be about $48\%$. For this dataset, the performance of CoRE kernels (and resemblance kernel) is astonishing, as shown in Figure~\ref{fig_kernelSVM} and Table~\ref{tab_kernelSVM}. Thus, we choose this dataset to demonstrate that our proposed hashing algorithms combined with linear SVM can also achieve the performance of (nonlinear) CoRE kernels.\\

To better explain the procedure, we use the same examples as in Section~\ref{sec_hashing}. Suppose  we apply $k$ minwise hashing and $k$ random projections on the data and we consider without loss of generality the data vector $u$.  For the $j$-th projection and $j$-th minwise hashing, suppose $L_j(u) = 2, V_j(u) = 0.05, P_j(u)  = 0.1$.  Recall $L_j$  and $V_j$ are, respectively, the location and the value of the first nonzero entry after minwise hashing. $P_j$ is projected value obtained from random projection.

In order to use linear SVM to approximate kernel SVM with Type 1 CoRE kernel, we expand the $j$-th hashed data as  a vector $[0,\ 0.1,\ 0, \ 0]$ if $b=2$, or $[0,\ 0.1]$ if $b=1$. We then concatenate $k$ such vectors to form a vector of length $2^b\times k$ (with exactly $k$ nonzeros). Before we feed the expanded hashed data to LIBLINEAR, we always normalize the vectors to have unit norm.  The experimental results are presented in the right panels of Figure~\ref{fig_MRotateAcc}.

To approximate Type 2 CoRE kernel, we expand the $j$-th hashed data of $u$ as $[0,\ 0.05f_1,\ 0,\ 0]$ if $b=2$, or $[0,\ 0.05f_1]$ if $b=1$, where $f_1$ is the number of nonzero entries in the original data vector $u$. Again, we concatenate $k$ such vectors. The experimental results are presented in the middle panels of Figure~\ref{fig_MRotateAcc}.

To approximate resemblance kernel, we expand the $j$-th hashed data of $u$ as $[0,\ 1,\ 0,\ 0]$ if $b=2$ or $[0,\ 1]$ if $b=1$ and we concatenate $k$ such vectors.

The results in Figure~\ref{fig_MRotateAcc} are exciting because linear SVM on the original data can only achieve an accuracy of $48\%$. Our proposed hashing methods + linear SVM can achieve $>86\%$. Using only the original $b$-bit minwise hashing, the accuracy can still reach about $80\%$. Again, we should  mention that other hashing algorithms which aim at approximating the inner product (such as random projections and variants) can at most achieve the same result as using linear SVM on the original data.

\section{Discussions}

There is a line of work called {\em Conditional Random Sampling (CRS)}~\cite{Proc:Li_Church_Hastie_NIPS06} which was also designed for sparse non-binary data. Basically, the idea of CRS is to keep the first $k$  nonzero entries after applying one permutation on the data. \cite{Proc:Li_Church_Hastie_NIPS06}  developed the trick to construct an (essentially) equivalent random sample for each pair. Although CRS is applicable to non-binary data, it is not suitable for training linear SVM (or other applications which require the input data to be in a metric space), because the hashed data of CRS are not appropriately aligned, unlike our method.

Table~\ref{tab_kernelSVM} shows that Type 1 CoRE kernel can often achieve better results than Type 2 CoRE kernel, in some cases quite substantially. This helps justify the need for developing hashing methods for Type 1 CoRE kernel, which require random projections in addition to random permutations.

There are many promising extensions. For example, we can construct new kernels based on CoRE kernels (which currently do not have tuning parameters), by using the exponential function and  introducing an additional tuning parameter $\gamma$, just like RBF kernel. This will allow more flexibility and potentially further improve the performance.

\begin{figure*}[h!]
\begin{center}
\mbox{
\includegraphics[width=2.3in]{fig/MRotateApproxCoRE1Accb1.eps}\hspace{-0.12in}
\includegraphics[width=2.3in]{fig/MRotateApproxCoRE2Accb1.eps}\hspace{-0.12in}
\includegraphics[width=2.3in]{fig/MRotateApproxResAccb1.eps}
}
\mbox{
\includegraphics[width=2.3in]{fig/MRotateApproxCoRE1Accb2.eps}\hspace{-0.12in}
\includegraphics[width=2.3in]{fig/MRotateApproxCoRE2Accb2.eps}\hspace{-0.12in}
\includegraphics[width=2.3in]{fig/MRotateApproxResAccb2.eps}
}

\mbox{
\includegraphics[width=2.3in]{fig/MRotateApproxCoRE1Accb4.eps}\hspace{-0.12in}
\includegraphics[width=2.3in]{fig/MRotateApproxCoRE2Accb4.eps}\hspace{-0.12in}
\includegraphics[width=2.3in]{fig/MRotateApproxResAccb4.eps}
}
\mbox{
\includegraphics[width=2.3in]{fig/MRotateApproxCoRE1Accb8.eps}\hspace{-0.12in}
\includegraphics[width=2.3in]{fig/MRotateApproxCoRE2Accb8.eps}\hspace{-0.12in}
\includegraphics[width=2.3in]{fig/MRotateApproxResAccb8.eps}
}
\end{center}
\vspace{-0.2in}
\caption{Classification accuracies on the M-Rotate dataset using our proposed hashing methods and  linear SVM (LIBLINEAR). The red (if color is available) dot curves are the results of  kernel SVM  on the original data (i.e., the same curves from Figure~\ref{fig_kernelSVM}), using Type 1  CoRE kernel (left panels), Type 2  CoRE kernel (middle panels), and resemblance kernel (right panels), respectively. We apply both $b$-bit minwise hashing (with $b=1, 2, 4, 8$) and random projections $k$ times  and feed the (expanded) hashed data to linear SVM. }\label{fig_MRotateAcc}
\end{figure*}

\clearpage

Another interesting  line of extensions would be  applying other hashing algorithms on our generated hashed data. This is possible again because we can view our estimators as inner products and hence we can apply other hashing algorithms which approximate inner products on top of our hashed data. The advantage is the potential further data compression. Another advantage would be in the context of sublinear time approximate near neighbor search (when the target similarity is the CoRE kernels).

For example, we can apply another layer of random projections on top of the hashed data and then store the signs of the new projected data~\cite{Proc:Charikar,Article:Goemans_JACM95}. These signs, which are bits, provide good indexing \& space partitioning capability to allow sublinear time approximate near neighbor search under the framework of LSH~\cite{Proc:Indyk_STOC98}. This way, we can search for near neighbors in the space of CoRE kernels (instead of inner products).

\section{Conclusion}

Current popular hashing methods, such as random projections and variants, often focus on approximating  inner products and large-scale linear classifiers (e.g., linear SVM). However, linear kernels often do not achieve  good performance. In this paper, we propose two types of CoRE kernels which outperform linear kernels, sometimes by a large margin, on sparse non-binary data (which are common in practice). Because CoRE kernels are nonlinear, we accordingly develop new hash methods to approximate CoRE kernels. The hashed data can be fed into highly efficient linear classifiers. Our experiments confirm the findings. We expect this work will inspire a new line of research on hashing algorithms and large-scale learning.

redpajama_set_name: RedPajamaArXiv


text:
Felipe Braga Alfredo, né le  à Belo Horizonte dans l'État du Minas Gerais au Brésil, est un joueur brésilien de basket-ball.

Biographie

Références

Liens externes

Joueur brésilien de basket-ball
Joueur de l'Élan béarnais Pau-Lacq-Orthez
Joueur d'Angers BC 49
Naissance en juillet 1994
Naissance à Belo Horizonte

redpajama_set_name: RedPajamaWikipedia


People with skin issues like to efficiently cover imperfections, that's a fact. While I don't suffer with any acne issues, I was pretty miserable with my freckles. Over the years, I've seem to partly resign my hatred toward my skin. Fun part about makeup is definitely option to be or look like someone else. Now and then, I utterly enjoy wearing full coverage foundation and still walk into a store barefaced on another occasion. Whereas there's loads of liquid full cover foundations on the market, it's still hard to find a full coverage powder (not counting mineral makeup). Amway's Artistry line offers just what I've missed - a full coverage, perfecting, matte powder. Supposedly perfect for topping up your liquid foundation without caking and luminous shells to brighten entire complexion.
Packaging is pretty glorious with golden top, inside mirror and special compartment for sponge. Quite comparable to high end makeup packaging. Product itself can be bought alone as a refill, meaning less costs over time. While packaging is sturdy, the price itself for it (23,50€, here) is pretty self-explanatory. Powder is around 29,70€ (here). For such a high price, I expect the best - it's almost in the same price range as Dior or Chanel. If we focus on powder itself, we'll find that initial claims are fairly true. Yes, it has a pretty darn medium-full coverage, while it mattifies. Also, it doesn't cake up and almost acts like a mineral powder. On the other hand, I haven't noticed any perfecting nor brightening skills. It also has SPF 20.
I'm stunned that they also have a massive variety of colors-from neutral and yellow to pink undertones. Finally a powder that's light enough for my skintone, yet it doesn't oxidise throughout the day. My perfect pick is Chablis, the lightest and coolest powder they have. Now, the main problem is compatibility with my dry skin. While I don't need dry and matte effect on my skin, powder offers right that. I would recommend powder to all of you, who have oily skin type and suffer with problematic areas. Powder sets liquid foundation wonderfully but in my ''dry skin case'', I get exaggerated dry areas. Nonetheless, powder is a perfect match for oily skin types. While I will fairly enjoy using this powder over certain hyper pigmentation spots, I won't use on my T-zone. As for the price, I can't really say if it's fair. Powder itself is made in Japan which make me think about exquisite quality, but not too sure how many people will actually pay around 50€ for a single powder. The choice is after all, on your side. As far as I know, Amway Artistry is sold through Amway Insiders - similar concept to Avon and Oriflame. Which makes it also a bit hard to get.
This sounds like a lovely product and the packaging is so luxurious!


text:
Q: Printing to a label (Swift 2) I have some text printing to the console. But I want the text to be printed to a label on the viewcontroller.   I just have some simple code "print(ab)". How should i get it to print to the label?

A:
Create new Label on your ViewController with storyboard and Proper alignment/size so it is visible properly and can able to show full text message with proper length, press ctrl and drag it that line to the view controller.Then it pops up a box and mark it as a action and give a name to that Label "yourMessageLabel"

Then it automatically create code like
 @IBOutlet weak var yourMessageLabel: UILabel!
var yourMessageText = "Message to be display on Console and View"

override func viewDidLoad(){
   super.viewDidLoad()

 // do other stuff

 // assign message text to Label to be display on your View

 self.yourMessageLabel.text = (self.yourMessageText as? String)

// print message on console

 print(self.yourMessageText)

}

A: I would advise you to go through Apple Documentation. However, this is the programmatic way of adding a label and text on it:
override func viewDidLoad()
{
  super.viewDidLoad()
  var label = UILabel(frame: CGRectMake(<Your_Frame>))
  label.textAlignment = NSTextAlignment.Center
  label.text = "Your label text" // This is what you are interested in
  self.view.addSubview(label)
}

If you are working in storyboard then follow the suggestion by Zoff.

A: *

*Go to you storyboard, drag & drop a button.

*Double click your view controller.Then your view controller open up in a separate window.

*Then Go to the Storyboard, press ctrl and drag it that line to the view controller.Then it pops up a box and mark it as a action and give a name to that button

*Then it automatically create code like
  @IBAction func yourButtonName(sender: AnyObject) { }

*Then edit it as this
  @IBAction func yourButtonName(sender: AnyObject) {

    yourButtonName.text = "jv"

  }

redpajama_set_name: RedPajamaStackExchange


Below is a list of the albums that are releasing this past fortnight; which albums are you thinking of listening to and purchasing? Which albums are you going to recommend to others and which would you bypass? Let us know in the comments below.
Below is a list of the albums that are releasing this week; which albums are you thinking of listening to and purchasing? Which albums are you going to recommend to others and which would you bypass? Let us know in the comments below.
An Update And The Year Of 2014 In Review!
Wow, 2014 has flown by don't you think? This is a special post, detailing a few of the important and impacting events that occurred this year for me personally, and briefly highlighting the rest; relaying them back to how God has shaped me as a person and a believer today. As we are gearing up to the end of the year I thought that a post like this- a special post after a week of hiatus (yes, this is our first post since 6 days ago!), is fitting, seeing that there have been quite a lot of changes of late in my life and the life of the site.


text:
More Tales Of Great Players
By RICH BARBER
Adapted from remarks delivered at Founder's Night 2007:
My own introduction to our club came through a literary line of humorists starting nearly a decade before I was admitted. In the fall of 1959, as a rugby player at Dartmouth, I was introduced to our "Coach," Corey Ford, on a road trip to New York to play against his alma mater, Columbia. This fortunate meeting changed the life of a young pre-med/history major. Through the efforts of Dean Thaddeus Seymour, son of Player Whitney North Seymour, and brother to Player "Mike" Seymour, Jr., I was made a Dartmouth Fellow, assigned as research assistant to Ford. For the next five years our scholarly efforts led us many places, always passing through The Players with side trips to fellow club members.
Corey Ford.
Corey had joined the club in 1926, at the age of 24. It was the same year that his work appeared in the inaugural issue of The New Yorker. When Player Rea Irvin created its signature top-hatted fop gazing through his lorgnette at a butterfly, it was Corey who gave him his name, "Eustace Tilley," and wrote his background and history. Corey's illustrious career began at Columbia where he was the editor of the college humor magazine, the Jester, and a stringer for The New York Herald. He also wrote the book and lyrics for the Varsity Show in 1923, which yielded Columbia's football song, "Roar Lion Roar," and eclipsed the Broadway run of the previous year's show which he had panned, written by his upstart classmates, Oscar Hammerstein II, Lorenz Hart and Richard Rodgers.
We often stayed in rooms at The Gramercy Park Hotel, and "breakfasted at The Club." It still sported a bay window in the old dining room, and garden in back.
These were the last years of Franklin Pierce Adams, who lived just a few blocks away on 13th Street. He came in often, especially for Sunday breakfast. One morning all the "good" newspapers had been grabbed up – the city still had multiple dailies then. A lone fat copy of The New York Times remained. Adams leafed through rapidly, growing increasingly agitated. Finally he threw it across the room, growling, "Nothing but f---ing news."
I was invited by Corey to "Founder's Night," for which my dad bought me my first tux, stating, "a gentleman should wear his own." Howard Lindsay presided. Van Wyck Brooks gave the address, seated in front of the fireplace in the Great Hall and in front of the masks of Comedy and Tragedy designed as a gift to Edwin Booth and his club by Augustus Saint-Gaudens. I stood between Corey Ford and Russel (Buck) Crouse. As he received the Loving Cup, Crouse took it and pressed it to his forehead, holding it there for a moment, before passing it along to me. Ford told me later that Buck had stopped drinking many years before, admitting, "One drink and it was like I was hit in the ass with a banjo." It was an impressive assembly. Heywood Hale Broun stood next to Ford. Next to him was Brooks Atkinson. The list went on.
Frank Sullivan.
On our return trip to Hanover, we stopped in to have dinner with Player Frank Sullivan in Saratoga Springs. Sul and Ford had shared an apartment on Beekman Place during Prohibition and the Thirties. Sul became famous for his droll humor pieces, especially Mr. Arbuthnot, the Cliché Expert, and his annual New Yorker Christmas poem. It was a sort of Cox & Box arrangement with Corey, the early riser, greeting Frank, the night owl, coming home from covering the Roaring Twenties and speak-easies for his column. William Faulkner stayed with them for part of an impecunious season and wrote As I Lay Dying on a card table in their living room, sleeping on the couch.
In the summer of 1964, Ford was given an assignment by DeWitt Wallace of Reader's Digest to cover the Republican Convention in San Francisco. And I was given my first real assignment to report on the reaction of foreign correspondents to the nomination of a "true conservative." The selection of Barry Goldwater was pretty much a sure thing. I had met him numerous times when he came to see Julius Monk's Dime A Dozen at the Plaza. I was seeing Susan Browning, one of the stars of the show, and Barry loved to bring friends and laugh at himself and the parody song, "Back With Barry's Boys," that was a featured piece. We stayed with Maggie and Merritt Ruddock, the mayor of Belevedere Island. They were old friends from OSS days, and Maggie had recently divorced Player John Falter. We would sneak over the hill to Johnno's studio whenever we could for Scotch and gossip. Maggie was beautiful, statuesque, but a bit cold. Merritt was the picture of a British Colonel with a wicked, dry sense of humor. He and Johnno had become great good friends, much to Maggie's displeasure. The Ruddocks' luxurious five-story home, on a lot that cascaded down the island's hillside facing the Golden Gate Bridge, was ideal for our assignments as they were hosting several events for the foreign press.
Bing Crosby.
Following the predictable outcome of the convention, we headed north to the middle fork of the Feather River for a fishing trip with Frank Dufresne and an old friend of both his and Corey's, Player Bing Crosby. The guides picked us up in their trucks loaded with gear. Suddenly one of them just had to return home to fetch something indispensable. We were all invited inside when we got there, and I realized he wanted to show off Mr. Crosby. His boys were not impressed. Bing disappeared back to the truck and fished his toupee out of his duffel. Back inside, things were set to rights and the boys were thrilled – an extraordinary gesture. On the trip, Bing was full of stories and puns and good cheer, whether showing me how to cut fur branches to make a mattress or cooking. Ford said later that Crosby was much funnier in person than Bob Hope, with a keen sense of wordplay.
Kathryn Crosby once said she didn't worry about Bing cheating on her: "He would just be off fishing or hunting with Frank." Dufresne was the first U.S. Game Commissioner of Alaska, and Rachel Carson's boss. He encouraged her to write Silent Spring. Frank had traveled by kayak the full extent of the Aleutian Chain of islands with Aleuts, and spoke their language. Largely because of him they helped fend off Russia's attempt to retake the peninsula. He also wrote two outdoors classics, My Way Was North and No Room for Bears, and almost singlehandedly saved the Kodiak bear from extinction.
Back in New England, I taught another year at Exeter, Ford and I finished the book for Little, Brown, and, after a few more adventures, including a stint with the Marines, I pitched up in New York publishing where Ford and his friends Sullivan, Marc Connelly and Falter, along with Roy Doliner and Jack Tebbbel, decided I should become a Player.
For a number of years I was the youngest Player and a proverbial bump on a log, awed and delighted at the company in the grill. It was a heady time, with Robards, Lemmon, Kiley, Ferrer, O'Brien, and Cagney often at the bar, Peter Turgeon spinning tales of a young chorus boy cutting a swath through the leading ladies of Broadway. Returning from a road-tour he strode up to the bar, and when the young new tender poured him a shot said, "Fill it up, lad, don't you know I'm an alcoholic?"
Years later, at the second Cagney Pipe Night, women had finally been admitted and I brought my wife. It was truly an all-star event, even down to police barricades around Gramercy Park. The Board decided to corral all the stars in the library before dinner, but Cagney was having none of it. "This is my club," he said, and he and Billie came down, where he sat in Booth's chair between the Great Hall and the Sargent (now Kinstler) Room, greeting all graciously. I approached with my wife, excused myself, and asked to introduce Elaine. Cagney squinted up at me, and said, "You're Ford's boy, aren't you?," reaching out to shake my hand and holding Elaine's for a few extra moments. What a memory. What kindness.
After the festivities many of us gathered around the bar in the Great Hall. I was talking with Jack Lemmon, who was keeping an eye on Sir/Lord Laurence Olivier. We had learned that if Jack had such an assignment, it curbed the intake. My wife had joined Lord Larry's group and he put his arm round her waist. His hand moved down and he patted her ass. Jack started to move forward, but I restrained him, "She'll dine out on this for years."
Michael Powell.
Some years later I brought "Mickey" Powell of The Red Shoes to lunch, to interview him for a newletter I was writing called Inside Bookselling with Player Brock Brower. Mickey was a member of The Garrick, and wanted to sit at the long table. Peter Turgeon, Rollie Winters, Marty Gabel, Ben Lucien Burman, and a group of the Wednesday regulars were delighted. Mickey had just come from a meeting at his publisher, Knopf, and was still bristling over their attempt to title his biography My Life in Film. "I finally impressed upon them that I love movies, I don't love film," he stated. The table was charmed, and thankfully, his title prevailed.
Rich Barber is a literary agent, writer, and President of A. Richard Barber & Associates, a marketing consultant to publishers. A Player for 53 years, he was a board member as well as First Vice President to Johnnie Planco. He has co-authored two books, on Revolutionary history and on humor.
Rich Barber
A Classic Player: Walter Hampden, 1879-1955
Russel Crouse As M.C.
Jolson And My Dad


Many solutions Purposefully create a closed system, which can be tedious to move away from as soon as you begin. However, with a platform called Ecwid, you no longer have to worry about this. Actually, the whole goal of the business, which began in 2009, is to quickly and quickly create an internet store system on your existing websites.
This raised my Attention, since I understand the struggles that come along with trying to migrate sites, especially in regards to ecommerce. So, I wanted to gather an in-depth review of this Ecwid system to see whether it's worth the time.
It is quite Impressive what Ecwid has done since 2009, with over 1 million retailers getting signed up. What's even more cool is that the Ecwid retailers span over 175 different countries, showing that this is not just a platform for most people in the united states or Europe. Even companies in developing countries have a chance to get an internet store going, and you do not even have to trash your previous site if you have one.
The usership Continues to grow, especially considering Ecwid is now PCI DSS Level 1 accredited. If you're not sure what this is, it is pretty much the gold standard for security in the ecommerce world. You can rest easy knowing you and your customers' data is safe and secure.
In addition to The overall merchant count, over 20,000 active users are utilizing the Ecwid Shopping Cart for WordPress. The difference between that 20,000 along with the 1 million is the simple fact that you don't require a WordPress site in order to implement Ecwid. So, the 20,000 simply identifies WordPress users with the plugin.
Ecwid is a hosted Alternative for building an internet store. Unlike competing products like Shopify, Bigcommerce and Volusion, which permit you to construct a new website with e-commerce performance, Ecwid is geared more towards people who already have a website and simply wish to add an internet store to it.
Much like other E-commerce programs, Ecwid allows you to install'catalogs' of products, and include photographs, pricing, weight etc. for each. You can define shipping prices, accept credit card payments etc — all the crucial things that you would expect to be able to perform using an e-commerce solution.
However it differs Significantly from competing products like Shopify, Volusion, Squarespace and Bigcommerce since it isn't really designed to allow you to produce a fully-featured, standalone e-commerce site; rather, it is designed to allow you add an online store to an present online existence.
The Product operates by providing you a widget that gets put on other sites — hence the title Ecwid: it is short for'E-commerce Widget'. You get a couple lines of HTML code (the widget) to add to an present site or societal networking profile, and your shop is displayed wherever you have inserted this code.
Ecwid makes Strides in the market by catering to businesses which don't need to start a brand-new website. The primary attribute is that you can choose your old site and immediately integrate the Ecwid system within a couple of minutes.
You can also Mirror the machine on various other sites if you plan on selling elsewhere online. Handle all of these websites from 1 place rather than switching between multiple dashboards.
Another pleasant Feature is the syncing between multiple devices. Sell on-the-go together with your smartphone. This usually means that you can go to a tradeshow to generate a number of sales and all of these sales are reflected on the backend of your site.
The system is Also designed to integrate nicely with social networks, and that means you may sell your things on Facebook if needed. One part of Ecwid that we particularly enjoy is the delivery facet. With real-time shipping integrations you constantly know what's happening with your orders and at which they are being sent to.
With Ecwid's POS Performance, regardless of whether or not a customer purchases a product in store, online, on their telephone or through Facebook, everything stays in sync – i.e., the retailer's catalog, inventory and client / trade information.
There is a couple of methods that you can take advantage of this feature: the easiest is probably to use'Paypal Here.' This involves downloading the Ecwid iOS app from the Apple's App shop, connecting it to the Paypal Here service, and then taking payments using a mobile card reader (pictured above).
If you're based outside The US / UK, or need to utilize another mobile device type, the good thing is that Ecwid also works together with the Vend, Clover, NCR Silver and Square POS systems. These all ease utilizing a larger selection of hardware at a physical retail location (full-sized card readers, receipt printers, cash drawers pill racks etc.) and are usable in more nations.
Ecwid Storefronts are responsive, meaning they will display correctly across apparatus — desktopcomputer, tablet and mobile. In an era where smartphone internet surfing is eclipsing desktop surfing, this is vital.
Granted, it is a pretty Standard plan, in which attributes such as discount coupons and service are not available, and it merely lets you sell up to 10 products in two categories — but this could really be sufficient for some customers, and using it is a fantastic way to try the system out.
But, A key point to note about the free program is the fact that it isn't great from an SEO point of view — you have to be on a paid plan to ensure your product pages speak to hunt engines accurately (more on SEO below).
Ecwid is ahead of the Pack in that its storefronts can be automatically translated into nearly 50 distinct languages — clients can see your store in their own language, without you having to worry about producing alternate versions of it. Ecwid provides this performance by detecting visitors' language automatically based on their browser settings / IP address.
Competing products like Shopify And Bigcommerce are nevertheless to provide this type of performance — that they force you to rely on third party programs, the production of multiple shops or Google Translate to supply different language variations of your shop, which is less than ideal. So a definite win for Ecwid here.
As far as I could tell However, you can't manually change the URL of a product — you have to make do with the one which Ecwid generates for you. This isn't perfect because key words in URLs are used by some search engines to categorise content through indexing.
1 place where Ecwid does not perform so Well on the search engine optimization front involves AMP — accelerated cellular pages. As its name suggests, AMP pages load faster on cellular devices. This encourages people to remain longer on your webpage, thus increasing'live time' –something which is considered by many search engine optimization experts consider to be rewarded with Google with preferential treatment in search results. Additionally, Google sometimes highlights AMP webpages in carousels in search results, giving AMP articles an extra little boost.
The Export option allows you to export product information, orders and customers (again, in CSV format) and means that if you ever feel the need to migrate your store to another e-commerce platform, then you shouldn't have any major difficulties doing so.
WordPress does not Supplied any e-commerce functionality from this box, so anyone wanting to market on the stage will need a third-party alternative like Ecwid. There's an Ecwid plugin accessible, so adding an Ecwid shop to your WordPress site is very straightforward — you can get up and running with a couple clicks.
Squarespace includes Increasingly good e-commerce features, but the amount of payment gateway options is quite limited — you have to utilize Stripe or Paypal. (And with Stripe only works if you're selling products from particular countries). The other principal issue with the built-in Squarespace selling software is they don't facilitate dropshipping, and full export of products is not currently possible.
As With Squarespace, there's currently no dropshipping option for Wix users, therefore again Ecwid can offer a good workaround. A dedicated app is provided by Ecwid to allow you integrate the product on a Wix store easily.
Ecwid provides a pretty Limited number integrations with other apps that are well-known. Integrations do exist with key services Mailchimp, Xero and Freshbooks; but to be honest the range of integrations could be extensive — competing products like Shopify provide a much broader assortment of programs. Notable omissions in the Ecwid store include Quickbooks and Zendesk, such as (that said, a Quickbooks integration is coming shortly ).
As with any e-commerce tool, you'll need to Spend a reasonable amount of time getting your head around creating catalogues and product variants, setting up transport prices etc…but there is nothing here that should represent also a lot of a learning curve.
If You are stuck however, you can always get in touch with Ecwid's support staff — more on this later — or, if you're on a'Business' or'Unlimited', you can make the most of this included customization service (you get 2 hours and 12 hours personalization period from Ecwidon these plans).
I am not a Attorney, so please be aware that the below observations should not be interpreted as legal information, but I'm planning to do my best to spell out some of the key GDPR issues facing Ecwid consumers under.
With the introduction of That the EU's new GDPR legislation, there are many legal measures that site owners now should take to ensure that they are adequately protecting EU customers' and visitors' privacy. There are serious financial penalties for not doing so (to the point where it is typically a good idea to consult an attorney on what to do); and even when your company isn't located in the EU, you still will need to obey the regulations where any website visits from the EU are concerned.
There does not Appear to be Anything that stops you meeting the first few requirements readily enough with Ecwid, although you will need to spend time and potentially money generating adequate notices and crafting data capture forms so that they are GDPR compliant.
So for Instance, If you Use a Facebook Ads cookie in combination with your Ecwid store, you will be breaking GDPR laws unless you've got a banner set up which does all of the above.
Nowout of the box at Least there isn't a way to take care of the cookie consent issue with Ecwid. Nor is there an adequate app available from the Ecwid app shop (the only option on front that I can locate, the'EU cookie banner', does not appear to provide the essential performance — despite costing $1.99 per month to use).
It Appears to me that the only alternative available to Ecwid users is to use a specialist product like CookiePro, which does supply all the necessary performance to generate cookie consent fully GDPR compliant. Despite being a strong product however, Cookiepro does need quite a great deal of configuration and you will want to put quite a lot of time apart to set this up.
I Truly like The simple fact that you can run a web site without paying for anything. This makes the setup process that much easier, since you just need to punch in your email and create a password to go straight to the backend port.
The system has Wizards for each platform you want to set your store on. If you have a WordPress site, simply walk through three steps to get up and running. You can even click on choices like Facebook and Joomla, based on the place you want to sell.
The backend Design is perfect, since it walks you through every website creation and integration step with some nice photos and text. Once you get your store implemented on a site or social page it appears fairly slick.
The design Depends somewhat on the theme you have on your website, but as you can see below, it shows photos well and looks clean and easy to navigate. I particularly enjoy the Shopping Bag area, which can be prominent and easy to find for clients when they wish to market.
Although many Sites are going to be cellular friendly, it;s fine understanding that Ecwid includes a native shopping program for both iOS and Android. Merchants now get a native shopping application for iOS and Android.
The final Design thing which makes sense to mention is your vocabulary tool. Since conducting an internet store generally requires you to reach out to individuals from throughout the planet, you're bound to stumble upon a few clients who would like to read product pages and other content in their native language. Ecwid supports 45 languages, so if a user would like to translate from English to Spanish, that's no problem at all.
A Few of the apps Can give you a hand with reviews and rating, rewards programs, automatic ad tools, analytics, custom layout themes and more.
Ecwid is a Third-party remedy to your current site, so you would typically have to employ additional SEO and advertising tools to really get the word out. That said, Ecwid provides quality SEO features to help merchants increase their visibility in Google searches.
Moreover, Google now indexes AJAX websites, so all Ecwid shops are indexed by Google without any extra effort. It gets better with WordPress sites, since you gain access to additional SEO plugins and also the default search engine performance around the dashboard.
Each measure is Clear and concise, so that you may just go in there and type in a product name, price and image then save the product for afterwards. Of course you can always open the More Powerful Options, however they separate these for when you are ready to get started ramping up your own store.
Ecwid offers A Chat With Sales feature which is always one of my favorite ways to talk to companies. Simply send them a question and you can work on something else until they respond.
It's not always Easy to locate an ecommerce platform which doesn't charge fees, but that is just what you get with Ecwid. They never control any kind of trade fee with any of those plans they provide, making it desirable for any store that is hoping to keep costs down and to improve margins.
Keep in mind that Ecwid is not a payment processor, which means you have to set up something like PayPal or Stripe to process all the trades that come through your website. It's rather simple to implement these systems, since Ecwid integrates with over 40 global payment options. However, each of these processors have their own transaction fees so that you have to prepare for these.
· The'starter website' option is a good stopgap measure for merchants who want a store fast, but have yet to create a complete website to add the Ecwid widget to.
Ecwid comes With four plans, among which is completely free. They promote the free plan like crazy, and mention that a number of other platforms drag you in free trial, where you will eventually need to pay.
The Free plan Allows you to sell 10 products, while the Venture program ups that depend to 100 goods and you get things like an iPhone direction program. The Business program goes up to 2500 merchandise for a superb price and you get live chat service. Priority support comes along with the Unlimited program and you might also sell an unlimited number of merchandise.
Ecwid is one of The most economical solutions on the marketplace. You can see that it is actually more affordable than many of the other platforms we have reviewed in the past, so if you're looking for value, this could be the perfect plugin for you.


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Rainbow Six Extraction Will Target 4K/60 FPS on PS5 and Xbox Series X, 1080p/30 FPS on PS4 and Xbox One
Ubisoft confirms target frame rates and resolutions for its upcoming co-op first person shooter on current- and last-gen consoles.
Rainbow Six Extraction is launching later this month, and in the lead-up to its imminent launch, Ubisoft has been revealed new details on the game at a regular clip. Depending on which platform you plan on playing it on though, you might be wondering how well you can expect it to run. Thankfully, Ubisoft has provided specific details on that front.
Speaking with MP1st, Rainbow Six Extraction's technical director Bruno Lalonde confirmed the game's target resolutions and frame rates across all consoles. On PS4 and Xbox One, the game will target a 1080p resolution and 30 FPS performance. Meanwhile, on PS5 and Xbox Series X, the target will be 4K resolution and 60 FPS gameplay. High-end PCs, meanwhile, will be able to run the game at upto 120 FPS. There's no word yet on what Xbox Series S players can expect.
Rainbow Six Extraction launches on January 20 for PS5, Xbox Series X/S, PS4, Xbox One, PC, and Stadia. You can check out the game's PC requirements through here. The game will also launch for Xbox Game Pass and PC Game Pass day 1.
Tagged With: pc, ps4, ps5, Rainbow Six Extraction, Ubisoft, Xbox One, Xbox Series S, Xbox Series X
Tom Clancy's Rainbow Six Extraction
Publisher:Ubisoft
Developer:Ubisoft
Platforms:PS4, PS5, Xbox One, Xbox Series X, PC, Stadia
Yomawari 3 to Receive an Exclusive Limited Edition, Characters and Gameplay Features Revealed
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