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Q: Checkin multiple files with tf.exe in one changeset On one of our builds we are kicking off some automated process which is checking out and checking in some files automatically.
This all works rather well, but at this time we are running the checkin command which looks like the following
tf.exe checkin /force /comment:"foo" /noprompt /bypass /override:"bar"

All of the files with a Pending status will get checked in.
I'd like to make this script a bit more specific and only checkin the files (2 in total) which we actually change during the build, so we know for sure no files will get checked in 'by accident'.
I've already seen we can specify a single filename with the checkin command, but doing so we will get 2 different changesets in TFS, instead of 1. We would really like to have 1 changeset, containing both changed files as the changes belong to eachother.
Any ideas on how to approach this?
Minor addition / Short term solution
For the moment I've solved our 'problem' by specifying the folder where our modified files are located, which kind of looks like this
tf.exe checkin "/my/folder/location/" /recurse /force /comment:"foo" /noprompt /bypass /override:"bar"

Note the folder location and the /recurse parameter added.

A: You simply separate the files by spaces:
tf.exe checkin file1.ext file2.ext /force /Comment:"foo" /noprompt /bypass /override:"bar"

The documentation is not clear about this point but it might be a general specification of an itemspec that it can be multiple items.
See similar question about checkout: Is there a way to check-out multiple files from various folders in TFS in a single operation
As mentioned by others you might run into problems with the command line being longer than the system supports, in which case you might need to look at other solutions.

A: cmd.exe has a limit on how long a command can be. Using the version control API,  or simply 'tf checkin /i' (no arguments) is likely to be a better choice than passing lots of long filenames.
It's normal if a file becomes automatically checked out due to a change, and if ultimately the contents of the file are changed back to it's original state. At that point you would see the message about identical contents upon comparison. You could also use tfpt uu /noget /r * command to ignore Files which are identical to the originals. You'll need to have TFS Power Tools installed for this to work. Note: there is no TFS Power tool 2017.
For more details please refer below two links:

*

*Visual Studio TFS shows unchanged files in the list of pending changes

*Can TFS Pending Changes show files that are truly changed like SourceGear Vault?


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2018 World Cup First Time Winner Betting Tips
2018 World Cup Football
The 2018 World Cup will surely be one of the biggest global betting events of all time. The question is whether you're going to bet on the teams that have won the World Cup before, or take a chance by betting on there being a first-time winner of the 2018 World Cup.
However, if you can't narrow down those possibilities – then you can bet on all the previous winners or all the teams that would be first-time winners in the First Time Winner Betting Market.
2018 World Cup First Time Winner Betting Guide
Yes 3/1, No 1/4
Only 8 teams have previously won the World Cup and, with Italy failing to make the finals, you may think that 1/4 looks short enough for one of the previous 7 winners to win it again.
However, the 7 teams you'll have on your side are Brazil, Germany, France, Spain, Argentina, England and Uruguay – who have collectively won 16 of the 20 previous World Cups, making them a perfect 1/4 shot on those stats.
You also have to consider the best-prices the bookies are offering about those 7 teams. Brazil and Germany are both 5/1, France 6/1, Spain 7/1 and Argentina 9/1 – these five teams being the first five favourites. You then also get England at 16/1 and Uruguay at 33/1 – a 76% market share in total.
Of course, if you fancy there might be a first-time winner of the 2018 World Cup, then you'll have 25 teams running for you. They include Belgium (12/1) and 2016 Euros Champions Portugal (28/1). However, of the rest, only Colombia, Croatia, Russia and Poland are priced-up at less than 100/1. Those teams priced-up at three-figure odds, or four-figure odds in some cases, are Denmark, Switzerland, Mexico, Sweden, Senegal, Serbia, Iceland, Nigeria, Peru, Egypt, Japan, Costa Rica, Iran, Morocco, Australia, South Korea, Tunisia, Panama, and Saudi Arabia.
It's certainly possible to make a case for Belgium and Portugal. Meanwhile, with the host nation winning 5 of the 20 World Cups, Russia must have some sort of chance, even if they don't look a World Cup winning side on paper. Though, as the former Soviet Union, they have at least made a previous semi-final.
However, dig a little deeper and it's hard to find many reasons for backing the others. The only team in the non-winners to have previously made a World Cup Final is Sweden who, admittedly, knocked out 4-time champions Italy in the play-offs. Meanwhile, other teams that have made the semi-finals are Poland, Belgium, Portugal, South Korea, and Croatia.
There have actually been 8 first-time winners of the World Cup, with only France, Spain and England failing to go on to win a second World Cup. Therefore, a first-time winner of the 2018 World Cup may not be as big a pipedream as it may sound. However, odds of 1/4 about either Brazil, Germany, France, Spain, Argentina, England or Uruguay winning the 2018 World Cup look justified on all known form.
2018 World Cup First Time Winner
No @ 1/4 (Bet365)
Published Under: 2018 World Cup Football /
Adam Barnes January 17, 2018 at 10:42 am
Chloe's tip are awesome, that's all i know of for sure.
Share your Thoughts to Adam Barnes Below

redpajama_set_name: RedPajamaCommonCrawl


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Jane Milotte
DOVER — Jane Susan Hamilton Milotte died Thursday, Nov. 24, 2011, at Hyder Family Hospice House in Dover after a long battle with cancer.
She was born May 16, 1946, in Laconia, to William John Hamilton and Evelyn (Fifield) Hamilton.
Jane was a dedicated nurse and was dearly loved by her family, friends and many others. She was a generous and loving person with a wonderful sense of humor, as well as a courageous, stoic and gentle spirit. She loved nature — the mountains, the sea and the stars.
She is survived by her daughter, Christina Milotte and her husband, Jeremy Tooley, of Hagerstown, Md.; her son, Ian Milotte and his girlfriend, Carolyn, of Kittery, Maine; two sisters, Anne Hollingworth and her husband, Guy, of York, Maine, and Mary Lou Bagley and her husband, Bob, of South Berwick, Maine: one brother, John Hamilton and his wife, Jody, of Strafford; and several nieces and nephews and their families.
She was predeceased by her parents; and nephew William Clay.
SERVICES: A memorial service will be held at 1 p.m. Saturday, Dec. 3, at Greystone Manor, 3 Anbewold Lane, York, Maine; (207) 363-1194. For directions, e-mail capntam@yahoo.com. In memory of Jane, donations may be made to the American Cancer Society or an animal rescue of your choice.
© Copyright 2006-2019 GateHouse Media, LLC. All rights reserved • GateHouse Publicrecords04


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Transcript of remarks by SEN
Following is the transcript of remarks by the Secretary for the Environment, Mr Wong Kam-sing, after officiating at the opening ceremony of T·PARK today (May 19):
Reporter: Mr Wong, how can this new facility (T·PARK) help our residents in terms of environmental issues?
Secretary for the Environment: On one hand, it reduces the landfill burden because it can turn each day more than a thousand tonnes of sludge previously landfilled. Today, we can have the new facility to turn the sludge into energy, and that becomes a kind of urban renewable energy, and can reduce our carbon footprint. At the same time, this new facility, a green building by itself, it can have various community facilities, (both) indoors and outdoors, that can help educate and inform people about environmental sustainability. So, it is good for the city to reduce our carbon footprint. At the same time, it can help push people to be more environmentally friendly.
Reporter: Would this new facility affect the region? How would the Government implement policy in order to protect the residents living nearby?
Secretary for the Environment: This facility is up to the modern standard. We have a kind of stringent monitoring system involving our EPD (Environmental Protection Department) staff. At the same time, it will be transparent, that is, the relevant data will be uploaded to the website so that everyone, including the public, can understand that it is up to the standard.
(Please also refer to the Chinese portion of the transcript.)
Ends/Thursday, May 19, 2016
Issued at HKT 18:22
News Archives | Yesterday's News


At Coulstock and Place we offer a complete range of motor services from new motors to motor rewinds and onsite capabilities.
We fully understand than our customers sometimes require motor repairs on site. That's why our dedicated on site team have the capabilities to provide drive motor bearing changes in-situ.
We are also able to offer a range of new gearboxes and pumps as well as a comprehensive gearbox and pump repair service.


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Bear-like Pianist Denis Matsuev a Knockout
By L.H. Tiffany Hsieh
There was a bear on stage at Roy Thomson Hall Wednesday night, and he consumed the black Steinway concert grand like a toy piano.
OK, the Siberian-born pianist Denis Matsuev isn't a bear, but the 34-year-old with all his Russian roar was bear-like in his performance of Rachmaninoff's Piano Concerto No. 3 — lovable and cuddly on the outside, powerful on the inside, and prone to be violent in extreme situations.
Backed by conductor Valery Gergiev and the touring Mariinsky Orchestra (formerly the Kirov Orchestra), the Rach 3 was the centrepiece of an all-Russian program that marked the end of the Mariinsky's two back-to-back concerts in Toronto.
Matsuev, a pianist with inhuman techniques, was more than generous in his delivery of the world's toughest piece of piano music. The sound was big, to say the least, and depending on where you sat in the hall, it often drowned out the entire orchestra with seemingly little effort. At least that was the case sixth row from the stage and off centre to the right.
That being said, Matsuev was a pure knockout. His lyricism was subdued (lovable and cuddly), his sense of harmonics multi-dimensional (powerful), and his blistering climaxes extreme (prone to be violent). Even as he pounded across the keyboard in full force and oversaturated intensity, the lid shaking and all, there was something ecstatic about his playing that made you want to stay with the music instead of tuning out.
Gergiev and the fabled Mariinsky Orchestra did their best to keep up with the soloist, but there was only room for one bear on stage.
The crowd gave Matsuev a persistent standing ovation before receiving a solo encore prior to the intermission. With the orchestra still seated on stage, Matsuev played Figaro's aria from Rossini's Barber of Seville in a flashy Liszt-like transcription.
This is a pianist with a big heart and he holds nothing back. If you like things hot, you'll love Matsuev. If you have a low tolerance for heat, Matsuev is better appreciated in small doses.
The rest of the program consisted of Liadov's The Enchanted Lake and Shostakovich's Symphony No. 15.
Anatol Liadov (1855-1914) was a pupil of Rimsky-Korsakov and teacher to Prokofiev. In The Enchanted Lake, which opened the concert, Gergiev created a romantic soundscape with serene colours and rich textures. Conducting baton-less and without a podium, Gergiev's hands didn't beat times (they musicians know how to count perfectly well by themselves). Rather, his incredibly soft-looking and what seemed like battery-run tripe-jointed fingers fluttered about in the air, sending out vibrations of feelings.
As a listener, Gergiev's hands were intriguing to watch throughout the concert. However, by the end of the concert, in Shostakovich's Symphony No. 15, one wondered whether it was a necessity, a conducting style, or a nervous tic that those fingers fluttered as much and fast as butterflies do.
The Mariinsky Orchestra was a powerhouse in Shostakovich's last symphony, which isn't an easy piece to take in for an average listener. Throughout its barren four movements — the fastest being allegretto — the musicians responded to Gergiev's ever-animated hand gestures and displayed a well-absorbed understanding of the piece's dark inner meaning. The solo cello was especially haunting and beautiful while the percussions offered a striking blend with absolute precision.
Gergiev gave an encore following another standing ovation. After several more bowings, he signaled section principals to exist the stage and waved goodbye at the audience.
Labels: Denis Matsuev, Mariinsky Orchestra, Valery Gergiev
posted by Crystal Chan at 10:20 AM | 0 Comments Links to this post
This Week in Toronto (March 15 - 21)
Dmitri Hvorostovsky and Sondra Radvanovsky
(Photo: Pavel Antonov)
The big news for voice fans this week is the blockbuster concert starring Dmitri Hvorostovsky and Sondra Radvanovsky on Saturday 8 pm March 20 at Roy Thomson Hall. It is part of a concert tour the two are taking to a number of cities in North America, including Montreal and New York. This event is not to be missed! For a little preview, Radvanovsky will be giving a mini-recital live from the concert lobby at The New Classical 96.3 FM tomorrow (Tuesday 16) at 1 pm. You can tune in to your radio, or listen to it in a webcast. I am not positive but the concert may even be available for view on the website. Go to http://www.classical963fm.com/contests/concert-lobby-sondra-radvanovsky (Note: I just had confirmation from Linda Litwack, the publicist of this concert for Show One Productions that the concert will indeed be videocast live on the internet!)
Hvorostovsky is of course a frequent visitor to Toronto - I must have heard him here close to ten times since his first appearance here, around 1992. And I try to catch him wherever I can in my operatic travels. It is rare, however, to find Sondra Radvanovsky singing on local stages. This is particularly strange as she has been living in the GTA for quite a number of years, having married a Canadian. I heard her at the Met and Santa Fe as Violetta in La Traviata, but in Toronto, it was limited to only the LUNA concert of the first year of the Luminato Festival. If memory serves, she sang Casta Diva from Norma and wowed everyone. With her voice, it just takes two seconds and you just know that this is not your usual soprano voice. It is dark, rich, powerful, expressive, with stunning breath control and exemplary agility. It is, in fact, an authentic Verdi soprano, an extremely rare species. She arrives home fresh from a triu mph as Elisabetta in Don Carlo at the Opera Bastille in Paris. I spoke with a friend who was lucky enough to catch her at the performance last Friday. She was in fabulous form, receiving endless ovations from the enthusiastic audience. Here is the photo of a smiling Radvanovsky backstage at the Opera Bastille after Don Carlo last Friday.
(Photo: Pierre Couture)
Another exciting event this week is the appearance of Valery Gergiev and the Mariinsky Orchestra (formerly the Kirov Orchestra), all the way from St. Petersburg. Anytime this orchestra tours, it is always an unforgettable event, so this time it'll be no different. There will be different programs on the two nights. The first, on March 16, has Berlioz's Royal Hunt and Strom from Les Troyens and selections from Romeo et Juliette, and Tchaikovsky Symphony No. 5; the second performance has Denis Matsuev playing Rach 3, and Shostakovich Symphony No. 15! Either program is terrific and if you can, go!
The Aradia Ensemble under conductor Kevin Mallon presents the music of Purcell's Theatre Music (Don Quixote presented with excerpts from Thomas D'Urfey's play Boduca), on Sunday, 8 pm March 21, at the Glenn Gould Studio. The soloists are sopranos Eve Rachel McCleod and Laura Albino, tenor Nils Brown and bass Jason Nedecky. For more information, go to http://www.aradia.ca/
The Glenn Gould School of the Royal Conservatory of Music presents a staged version of Massenet's Cendrillon conducted by Uri Mayer with stage direction by Graham Cozzubbo, with students from the GGS Vocal Program and Opera in Concert Chorus. I once saw this show at Santa Fe Opera, in the Laurent Pelly production, and it was incredibly funny. I am sure with the youthful enthusiasm of the GGS students and the expert baton of Mayer, this will be a very good show. It opens on March 20, 7:30 pm at Koerner Hall, with additional performances on March 21, 23, and 35, at different times. For details and tickets, go to http://performance.rcmusic.ca/viewallconcerts
Labels: Dmitri Hvorostovsky, Mariinsky Orchestra, Sondra Radvanovsky, Valery Gergiev
posted by Joseph So at 7:17 AM | 1 Comments Links to this post


The interaction of Fast Green FCF (FCF) with proteins (including bovine serum albumin (BSA), human serum albumin (HSA), pepsin (Pep) and α-chymotrypsin (Chy), and lysozyme (Lys)) was characterized by enhanced resonance light-scattering (RLS) measurements using a common spectrofluorometer. The enhanced RLS signals of FCF by proteins at 279.0 nm were obtained, and the mechanism of the RLS enhancement was considered in terms of the effects of the pH and ionic strength on the interaction. It was found that the enhanced RLS intensities were in proportion to the concentrations of proteins in the range of nanogram levels, displaying that the present assay is much more sensitive than the reported RLS methods, with the limits of determination being 4.54, 0.6, 22.8, 4.32 and1.75 ng/ml for BSA, HSA, Pep, Chy, and Lys, respectively.


Mumbai: Mumbai Metro Rail Corporation (MMRC) on Wednesday witnessed the breakthrough of the 5th TBM of Colaba-Bandra-SEEPZ Metro-3 corridor at Chhatrapati Shivaji Maharaj International Airport (CSMIA) – T2 metro station.
Wainganga 3, the Tunnel Boring Machine, which was commissioned on 2nd April 2018 from Pali ground completed its tunnel drive of 1.31 Kms in package 7 to see the light at its end at (CSMIA)- T2 metro station. One more time it marks the achievement of an important milestone towards the steady completion of the 33.5-km long Metro-3 corridor. It is the result of consistent and continuous effort by a team of nearly 150 engineers, technicians and workers together to make this reach of tunneling successful.
Wainganga 3 made its way through the rocky strata made of Basalt, volcanic Breccia drilling relentlessly for exactly 318 days to attain the breakthrough. It has charted its course from under existing Mumbai Metro Line 1 at Marol Naka and Sahar Elevated Road connecting to CSMIA T-2. The 92-meter long Wainganga-3 bored, on an average, 4.2 meters daily, shaping the tunnel with 937 RCC cement rings.
Metro-3 corridor in its reach between CSMIA-T2 station to the ramp at Aarey colony will provide access to Versova-Andheri-Ghatkopar Metro-1 at Marol Naka and Swami Samarth Nagar-JogeshwariKanjurmarg-Vikhroli Metro-6 corridor at Aarey Station and also to business hubs like MIDC and SEEPZ which today are not connected by suburban rail.
M/s. Larsen & Toubro and Shanghai Tunnel Engineering Co. Ltd. are consortia partners for the construction of Package-7 which runs from Marol Naka to SEEPZ via MIDC. The total length of the tunnel is 7.07-km out of which 50% of tunneling work (i.e. 3.57 km) has been completed till date.


text:
Faculty Advocate
Faculty Advocate Volume 24:2 (December 19, 2014)
For better formatting and to see attached PDF file
Volume 24: Number 2, December 19, 2014 (labor donated)
Campus Contacts:
Francesca Sammarruca (fsammarr@uidaho.edu), Randy Berriochoa (berrioch@csi.edu), Craig Steenberg (csteenbe@lcsc.edu), John Trombold(NIC) (jmtrombold@yahoo.com), Jim Stockton (jstockto@boisestate.edu), Dave Delehanty (deledavi@isu.edu)
Nick Gier, President (ngier006@gmail.com)
Kim Johnson, Vice-President (kajohnso@nic.edu)
Please Join Us in Protecting Faculty Rights; Increasing Salaries and Benefits
Members of the American Federation of Teachers receive a $1,000,000 professional and legal liability policy, access to legal and moral support, and national/state AFT publications. For application forms please go to www.idaho-aft.org/IftDues.htm.
NIC Faculty Seek Legal Advice on Salary Step System
Update on Sanjay Gupta's Sexual Harassment Case
North Idaho College is the only Idaho campus that has a salary schedule, which guarantees an annual step increase for all faculty who perform at a satisfactory level. This annual increment is mandated by the NIC policy manual, and it is separate from additional funds that increase salaries in every step.
NIC presidents and the NIC Board of Trustees, citing chronic budget problems, have failed to fund these steps in seven of the past 12 years. At a May 2014 meeting Board Member Christie Wood admitted that that funding the steps was a contractual obligation.
The North Idaho Federation of Teachers has hired attorney Douglas Pierce from the firm James, Vernon and Weeks to research this issue. In a recent letter to NIC president Joe Dunlap, Pierce states that the NIC Board and the administration have a fiduciary duty that requires conducting transactions in the best interest of the college. This fiduciary duty is not reducible to minimizing the college's financial costs purely and simply. Pierce reminds Dunlap that abiding by published agreements, including the faculty salary schedule, is in the best interest of the institution.
In a recent meeting of the IFT Executive Board, it was agreed that the IFT will match any legal fees that the NIFT incurs in the process of settling this fundamental issue.
An Update on the Sanjay Gupta Sexual Harassment Case
Damage could result to the career and reputation of any person who is accused of sexual harassment falsely or not in good faith.
—UI Faculty Staff Handbook, Section3220 C-2
The depositions in the Sanjay Gupta case were taken October 13-15, and a Moscow jury trial is set for June of next year. Since then Gupta has received a very strong letter of support from a former investigator for the Equal Employment Opportunity Commission. With legal aid from local, state, and national chapters of the AFT, we are confident that he will prevail in court.
Sanjay Gupta was an assistant professor of plant science, who worked for at the Kimberly Research Station until 2011, when he was accused and later dismissed on charges of sexual harassment. The details of the case were published in this newsletter (see www.idaho-aft.org/Gupta1331.pdf), and we have reprinted the results of Gupta's appeal just below.
Gupta waited eight long months for his appeal to be scheduled. A panel of five faculty met on March 21, 2013, and they voted 3-2 that the evidence against Gupta was not enough to warrant dismissal. Here is a summary of their findings:
Some of the evidence the university administration presented at the termination hearing was known to have been falsified.
There is an alternative and plausible explanation for virtually every single allegation made against Gupta.
The panel worried that the case was investigated during a time of heightened sensitivity because of the two previous high profile cases.
The UI should have a high standard for the evidence it presents against one of its faculty, and it should discard it once it has been proven either false or unreliable.
Interim President Donald Burnett refused to respond to the appeal decision, and he rejected a very reasonable offer of settlement. UI policy requires that the administration respond to any appeal decision within 45 days.
Everywhere Gupta turned for comparable employment, he found that the false charges arrived before he could apply. Gupta was shocked to learn that some of these prospective employers knew about the accusations even before the UI made his suspension official. He is now in a temporary, post-doctoral position with his former employers at the University of Minnesota, who know very well that he is innocent.
Currently, Gupta's applications for research funding are in limbo because of questions about what happened in Idaho. Early in 2011 Gupta received a 4-year $7.8 million research grant that he obtained with five other scientists, and his share of the funds and data generated in his lab are now being used by a woman who testified against him. Part of the lawsuit charges theft of intellectual property.
We will stand by him until his good name is cleared, and when he is able to receive compensation for lost wages, losses on a home in Twin Falls, $30,000 in unpaid attorneys' fees, current legal costs, and emotional damage to him and his wife.


If you haven't heard of Cachaca before, chances are you have never enjoyed the rejuvenating taste of a traditional Brazilian cocktail called Caipirinha. And that is something we would like to change this summer. Made with muddled lime, sugar, crushed ice and a sugar cane liquor Cachaca (pronounced Kachaza), caipirinhas are super refreshing and easy to make, but this is still one cocktail that is definitely overlooked.
Now, it's not like we ever need an excuse to have a cocktail around here, but with wretchedly high temperatures this summer, we thought sipping on a Caipirinha (or in this case variation of it) might be the perfect way to revive ourselves, get a break from the torching hot sun and introduce you to something new and exciting.
But a regular Caipirinha, as good as it is, just didn't have enough oomph to make our blog…yes, we are special like that 🙂 Not to mention we had some farmer's market goodies waiting for us in the fridge.
Three days ago we made this cocktail syrup with fresh cherries we picked up from the farmer's market. And now, that we had plenty left unused, it was the perfect time to combine our love for cherries, cocktails, and Cachaca with the eagerness to cool down and take some deserved time off…Enter the Cherry Caipirinha Crush.
Not following the traditional muddled lime/sugar Caipirinha recipe, we tweaked the cocktail just a bit with fresh arugula and agave nectar. And while you probably already know from reading our blog that we are not huge fans of muddling fruit, this cocktail just had to make an exception. We tried the mixture with both freshly pressed cherry juice and with muddled cherries. While both ways tasted equally good, the interesting presentation of the "muddled way" was too eye-catching to pass on. And there is nothing wrong with looking good, right?
Simple yet captivating, we hope you can enjoy this cocktail on a hot summer day before cherries start disappearing from the produce rack again.
Fill the tall glass (serving glass) with crushed ice and fine strain the cocktail over. Top with club soda and more crushed ice to form a nice "dome"
If you don't have Cachaca, which is a sugar cane liquor, quality white rum would do the trick.
As we mentioned before, we are huge fans of crushed ice in summer drinks. The easiest and cheapest way to get instant crushed ice is by using this portable hand crank ice crusher you can find on Amazon. This is one gadget you can use all the time so believe me when I say it's worth the investment. However, if you are really reluctant to spend the money for it, cubed ice would still work for this drink, but it just won't look as good!
So cheers to another bar quality cocktail you can make at home!


text:
Q: MESI protocol - what keeps cache line in exclusive mode during atomic operations I am reading a bit about the MESI protocol for cache coherance. I have read that atomic operations in x86-64 such as XCHG acqure the cache line in exclusive mode.
But according to the protocol, the cache line can transition to share or invalid state if another core reads or writes to a momory location in that cache line. So can this happen while a core is executing an atomic operation? And how is it prevented?

A: The CPU core that owns that line simply chooses not to process and respond to requests to share or invalidate that line until after the atomic RMW operation has completed.
The detailed mechanism in modern CPUs is probably based on microcode: One of the uops for xchg [mem], reg probably does a special kind of load that "locks" that cache line (once it's exclusively owned in this cores L1d if it wasn't already), and one of the final uops does a special kind of store that also "unlocks" it, for this internal locking mechanism that's only usable by microcode.
(Opening that up to separate x86 instructions locking and unlocking would create the possibility of deadlocking the system.  Making it internal to one instruction's microcode can ensure that the max lock-hold time is very low and can't be broken up by an interrupt.)
Related: I wrote a more general answer about x86 atomic RMW operations on Can num++ be atomic for 'int num'?

A: In addition to the MESI states, all (?) cache coherence protocols have "transient" states that are used while transitions among the MESI states are taking place. For example, when a cache requests an S to M transition, the requesting cache has to wait until all of the other caches (or equivalent directories) acknowledge that they have invalidated the cache line before the M state can be granted.  During this interval, other transactions referencing the transient cache line have to be deferred -- otherwise a cache would never be able to complete an "upgrade" transaction on a cache line that other cores are reading.  Atomic operations require a read and an update to the same line without allowing any other agent to operate on the middle of the transactions.  Perhaps the most straightforward way to implement this is to extend one or more transient states to "protect" the cache line during the read+write transaction.

redpajama_set_name: RedPajamaStackExchange


text:
\section{Introduction}
Inferring user intents through user behavior data has been extensively studied in industrial applications, such as recommendation systems, search engines, and online advertising \cite{dupret2008user,he2014practical,elkahky2015multi,yu2016dynamic}. One key aspect in these systems is user modeling, which describes the process of building up and modifying a conceptual understanding of the user \cite{fischer2001user}. Essentially, user modeling is to learn a user representation that helps to capture the user's interests and preferences to improve the performance of downstream tasks.

In the literature, there have been many studies that focused on task-specific user modeling, such as user response prediction and personalized recommendation \cite{ren2019lifelong,yu2019adaptive,ji2020two}. However, the user representation learned by a specific task can hardly be generalized to other tasks. As a result, specific user representation models need to be trained in each downstream task, which requires massive labeled data, training time, computing and storage resources. Given these limitations, universal user representations that can serve a variety of downstream tasks are preferred.

Due to the sequential form of user behavior data, recurrent neural networks (RNNs) are usually used to encode the temporal dynamics of user behavior sequences \cite{wu2017recurrent,devooght2017long,zhu2017next,an2019neural}. Unfortunately, these approaches can only process user behavior sequences with a length of tens or hundreds, while the length can reach hundreds of thousands in many social networks and e-commerce services.
Moreover, we have verified by experiments that the performance will become better as the user behavior becomes richer in most downstream tasks (Fig. \ref{comparison_length}).
To solve this problem, many methods drown from natural language processing (NLP) \cite{kumar2016ask,dai2019transformer,yang2019xlnet} are proposed. They model long sequential data based on hierarchical architectures and memory networks \cite{ying2018sequential,pi2019practice,ren2019lifelong}. However, it is still hard for them to encode lifelong user behavior sequences when the length scales up to 1000. And representations they learned through a specific task result in poor generalization capabilities.

In this work, we propose a novel framework called Lifelong User Representation Model (LURM) to model user behaviors since registration. To meet the needs of extremely long sequence modeling, we first introduce a model named Bag of Interests (BoI) to summarize items in behavior sequences similar to Bag of Visual Words. In this way, we can use a sparse vector with super-high dimension to represent user behavior in any time period. Then, a Self-supervised Multi-anchor Encoder Network (SMEN) that maps sequences of BoI features to multiple low-dimensional user representations is proposed. SMEN consists of three modules: a multi-anchor module which can learn different aspects of user preferences, a time aggregation module which can model evolution of user behaviors, and a multi-scale aggregation module which can learn and aggregate BoI features in different scales. Considering the consistency between user behaviors in different time periods, we introduce a contrastive loss function to the self-supervised training of SMEN.
With the designs above, SMEN achieves almost lossless dimensionality reduction.

The main contribution of our work can be summarized as follows:
\begin{itemize}
\item[$\bullet$] In this work, a novel framework named LURM is proposed to model lifelong user behaviors of any length. To the best of our knowledge, it is the first method that has the ability to model lifelong behaviors in the field of universal user representation learning.

\item[$\bullet$] We introduce a sub-model named BoI
which can encode behaviors in any time period, so that lifelong behavior data can be represented by a sequence of sparse vectors.

\item[$\bullet$] We can obtain compressed user representations with little information loss with the help of a designed sub-model named SMEN.

\item[$\bullet$] Extensive experiments are performed on several real-world datasets. The results demonstrate the effectiveness and generalization ability of the learned user representation.
\end{itemize}

\section{Related Works}
\subsection{Universal User Modeling}
Compared with task-specific user modeling that requires more resources, universal user representations are preferred to serve different downstream tasks. In recent years, some works dedicated to learning universal user representations have been proposed.
Ni \textit{et al.} \cite{ni2018perceive} proposed a representation learning method based on multi-task learning, which enabled the network to generalize universal user representations. Extensive experiments showed the generality and transferability of the user representation. However,  the effectiveness of this method may still suffer due to the selection of tasks and the need of labels. To release the burden of labeling, Andrews \textit{et al.} \cite{andrews2019learning} proposed a novel procedure to learn user embedding by using metric learning. They learned a mapping from short episodes of user behaviors to a vector space in which the distance between points captures the similarity of the corresponding users' invariant features. Gu \textit{et al.} \cite{gu2020exploiting} proposed a network named self-supervised user modeling network (SUMN) to encode user behavior data into universal representation. They introduced a behavior consistency loss, which guided the model to fully identify and preserve valuable user information under a self-supervised learning framework. However, these methods can only process user behavior sequences with a length of hundreds, and cannot leverage the rich information brought by lifelong user behaviors.

\subsection{Lifelong User Modeling}
Previous works have shown that considering long-term historical behavior sequences for user modeling can significantly improve the performance of different tasks \cite{ren2019lifelong,pi2019practice,pi2020search}. Ren \textit{et al.} \cite{ren2019lifelong} proposed a hierarchical periodic memory network for lifelong sequential modeling. They built a personalized memorization for each user, which remembers both intrinsic user tastes and multi-facet user interests with the learned while compressed memory. Pi \textit{et al.} \cite{pi2019practice} decoupled the user modeling from the whole CTR prediction system to tackle the challenge of the storage cost and the system latency. Specifically, they proposed a user interest center module for real-time inference and a memory-based network that can be implemented incrementally. Pi \textit{et al.} \cite{pi2020search} also designed a search-based interest model (SIM) with a cascaded two-stage search paradigm to capture the diverse long-term interest with target item. Unfortunately, the length of the user behavior sequence that these methods can handle is still limited. Moreover, these methods are all trained on specific tasks, which limits the generalization ability.

\section{Methodology}
In this work, we are committed to learn universal user representation through truly lifelong user behavior sequences with arbitrary length. For this purpose, we propose a framework named Lifelong User Representation Model (LURM) which consists of two cascaded sub-models: Bag of Interests (BoI) and Self-supervised Multi-anchor Encoder Network (SMEN). The overall architecture of LURM is shown in Fig. \ref{LURMoverflow}.

\subsection{Bag of Interests}
In order to model extremely long lifelong user behavior sequence, we propose to aggregate the content of items under user purchases, clicks, or other behaviors at a certain granularity.
Inspired by Bag of Visual Words (BoVW) \cite{fei2005bayesian}, item is the natural granularity for aggregation. But there are billions of items on the entire e-commerce platform, which means that the item vocabulary is extremely large, making it infeasible in practice. Therefore, we propose a model called Bag of Interests (BoI) to aggregate user behavior data at `interest' granularity. Every `interest' is a cluster of similar items and represents a certain kind of preference. The size of `interest' vocabulary is often selected at a level of about $10^5$ for retaining enough details. As shown in Fig. \ref{LURMoverflow} (a), BoI consists of an item embedding module and a large-scale clustering module. For convenience, we focus on text modality only in this work. It should be noted that our method can be extended to multi-modal data easily.

\begin{figure*}[t]
\begin{center}
\centerline{\includegraphics[scale=0.4]{LURM.pdf}}
\caption{Illustration of our Lifelong User Representation Model (LURM) for user understanding. LURM consists of two sub-model: (a) Bag of Interests (BoI) is used to aggregate user behavior data at `interest' granularity, and is composed of an item embedding module and a large scale clustering module. We apply the BoI model on lifelong behavior data to generate multi-scale BoI feature sequences, (b) Self-supervised Multi-anchor Encoder Network (SMEN) is used to learn compressed user representations from BoI features sequences, and is composed of a multi-anchor module, a time aggregation module, a multi-scale aggregation module and a contrastive learning module.}
\label{LURMoverflow}
\end{center}
\end{figure*}

\subsubsection{Item Embedding Module}
\label{contrastive loss}
Like the BoVW model, an `interest' vocabulary are supposed to be built in our BoI model. Therefore, the embedding of each item is required, so that similar items with close distance in the embedding space can be clustered together. Recently, in natural language and image processing, discriminative approaches based on contrastive learning in the latent space have shown great success in the field of representation learning, achieving state-of-the-art results.
Inspired by these works, a contrastive learning task based on the relation between items drawn from a user is designed to learn item embedding similar to item2vec \cite{barkan2016item2vec}.

Given a set of users $U=\{u_1, u_2,...,u_{|U|}\}$, each user $u\in U$ corresponds to a behavior sequence $\boldsymbol{S}=\left\{\boldsymbol{x_1}, \boldsymbol{x_2},..., \boldsymbol{x_{|S|}}\right\}$, where $\boldsymbol{x_i} \in \boldsymbol{S}$ denotes the $i$-th item. $|\boldsymbol{U}|$ and $|\boldsymbol{S}|$ denote the number of users and the length of $u$'s behaviors respectively. Generally, the content of an item $\boldsymbol{x}$ can be expressed as $\{w_{1},w_{2},...,w_{|\boldsymbol{x}|}\}$, where $w_i$ denotes a word from a vocabulary $V$, and $|\boldsymbol{x}|$ denotes the number of words in the content of $\boldsymbol{x}$. Firstly, an encoder with average operation is used to generate item embedding $e$:
\begin{equation}
\label{ItemEncoder}
\boldsymbol{e_x}=encoder(w_{1},w_{2},...,w_{|\boldsymbol{x}|})=proj(\frac{1}{|\boldsymbol{x}|}\sum_{i=1}^{|\boldsymbol{x}|}\boldsymbol{W_i}),
\end{equation}
where $\boldsymbol{W_i}\in \mathbb{R}^d$ is the embedding of word $w_i$ and will be learned during training, $proj(\cdot)$ includes two residual blocks, and a $L_2$ normalization layer. To construct the contrastive learning task, positive pairs are sampled from behavior sequences of users randomly. Specifically, two items $(\boldsymbol{x_i},\boldsymbol {y_i})$ are similar, i.e. a positive pair, if they are drawn from the same user behavior sequence and the time interval between the occurrence of these two items is less than $\beta$, where $\beta$ is the window size controlling the interval of the two user behaviors. Without loss of generality, the sampled mini-batch with batch size $n$ can be denoted as $\boldsymbol{\Delta}=\{\boldsymbol{x^1},\boldsymbol{y^1},\boldsymbol{x^2},\boldsymbol{y^2},...,\boldsymbol{x^n},\boldsymbol{y^n}\}$, where $(\boldsymbol{x^i},\boldsymbol{y^i})$ construct a positive pair drawn from the behavior sequence $\boldsymbol{S^{i}_t}$ of the $i$-th user in batch. Then, the contrastive prediction task is defined to identify $\boldsymbol{y^i}$ in $\boldsymbol{\Delta}\setminus \{\boldsymbol{x^i}\}$ for a given $\boldsymbol{x^i}$, and all other items in $\boldsymbol{\Delta}\setminus \{\boldsymbol{x^i},\boldsymbol{y^i}\}$ are negatives. The loss for the positive pair $(\boldsymbol{x^i},\boldsymbol{y^i})$ is written as
\begin{equation}
\label{positive loss}
l(\boldsymbol{x^i},\boldsymbol{y^i})=-\log \frac{e^{g(\boldsymbol{x^i},\boldsymbol{y^i})/\tau}}{\sum_{\boldsymbol{\nu}\in \boldsymbol{\Delta}, \boldsymbol{\nu}\neq \boldsymbol{x^i}}e^{g(\boldsymbol{x^i},\boldsymbol{\nu})/\tau}},
\end{equation}
where $g(\boldsymbol{x},\boldsymbol{y})=\frac{\boldsymbol{e_{x}}^T\boldsymbol{e_{y}}}{\|\boldsymbol{e_x}\|\|\boldsymbol{e_y}\|}=\boldsymbol{e_{x}}^T\boldsymbol{e_{y}}$ denotes the cosine similarity between the embedding $\boldsymbol{e_{x}}$ and the embedding $\boldsymbol{e_{y}}$ , and $\tau$ is the temperature parameter. The final objective is the average loss of all positive pairs in the mini-batch, which can be written as
\begin{equation}
\label{contrastiveloss}
Loss=\frac{1}{2n}\sum_i(l(\boldsymbol{x^i},\boldsymbol{y^i})+l(\boldsymbol{y^i},\boldsymbol{x^i})).
\end{equation}

Noting that, other methods which can be used to generate item embedding are feasible.

\subsubsection{Large-scale Clustering Module}
After the encoder above has been trained, an item embedding set $\boldsymbol{E}=\left\{\boldsymbol{e_i}\right\}_{i\in I}$ is obtained, where $I$ is the complete collection of items at the billion level. In order to retain details as many as possible, the size of the `interest' vocabulary is set to be at $10^4\sim 10^5$ level. In other words, all items should be clustered into $D$ (e.g., $10^5$) categories. Considering the large-scale item set, a subset $\boldsymbol{E'} \subset \boldsymbol{E}$ at the million level is sampled, and an efficient clustering algorithm named HDSC \cite{yi2014single} on $\boldsymbol{E'}$ is employed to cluster similar items into the same `interest'.

After clustering, the cluster centers $\boldsymbol C$ make up an `interest' vocabulary. Therefore each item can be attributed to one/multiple `interest(s)' by hard/soft cluster assignment. Take hard cluster assignment as an example, each user can obtain his/her sparsely high-dimensional BoI feature $\boldsymbol{b_t}\in \mathbb R ^D$ in time period $\boldsymbol{t}$ according to his/her behavior sequence $\boldsymbol{S_t}=\left\{\boldsymbol{x_1}, \boldsymbol{x_2},..., \boldsymbol{x_{|\boldsymbol{S_t}|}}\right\}$ :
\begin{equation}
\label{BoI}
\begin{split}
\boldsymbol{b_t}=[log(1+\Sigma_{i=1}^{|\boldsymbol{S_t}|} \mathbb I_{\boldsymbol{x_i}\in c_1}), log(1+\Sigma_{i=1}^{|\boldsymbol{S_t}|} \mathbb I_{\boldsymbol{x_i}\in c_2}),\\ ..., log(1+\Sigma_{i=1}^{|\boldsymbol{S_t}|} \mathbb I_{\boldsymbol{x_i}\in c_D})]
\end{split}
\end{equation}
where $\boldsymbol{x_i}\in c_j$ means item $i$ is assigned to the cluster $c_j\in \boldsymbol C$, $\mathbb I$ is an indicator function.

\subsection{Self-supervised Multi-anchor Encoder Network}
As mentioned above, a BoI feature $\boldsymbol{b_t}$ for each user is obtained, through the BoI model given behavior data in any time period $\boldsymbol t$. Most directly, a user representation with super-high dimension $\boldsymbol{b_T}$ can be obtained by applying the BoI model to the whole lifelong time $\boldsymbol T$. However, there are two main disadvantages: 1)  it is not friendly to the downstream tasks since the dimension of representation is too high, and 2) it is too crude to aggregate the information in the whole lifelong time without considering variations over time.

Therefore, BoI model is applied at each time period of the whole lifelong time to get a BoI feature sequence. Then, a Self-supervised Multi-anchor Encoder Network (SMEN) is designed to learn compressed user representations from the sequence of BoI features.

The lifelong time $\boldsymbol T$ is divided into $N$ parts at a fixed time interval, i.e. $\boldsymbol T=\left\{\boldsymbol{t_1}, \boldsymbol{t_2}, ..., \boldsymbol{t_N}\right\}$ and $\boldsymbol{t_i} $ denotes the $i$-th time period. In our experiments, the time interval is usually set to be monthly/seasonly/yearly granularity. In this way, a sequence of BoI features $\boldsymbol B=\left\{\boldsymbol{b_{t_1}}, \boldsymbol{b_{t_2}}, ..., \boldsymbol{b_{t_N}}\right\}$ can be obtained, where $\boldsymbol{b_{t_i}}$ denotes the BoI feature corresponding to the $i$-th time period $\boldsymbol{t_i}$. After that, SMEN is used to map $\boldsymbol B$ to low-dimensional user representations. As shown in Fig. \ref{LURMoverflow} (b), SMEN consists of a multi-anchor module, a time aggregation module, a multi-scale aggregation module and a contrastive learning module. Details of the model will be described in the following subsections.

\begin{figure*}[ht]
\begin{center}
\centerline{\includegraphics[scale=0.41]{MultiAnchor.pdf}}
\caption{Illustration of the multi-anchor module in SMEN. The module outputs a group of diverse representations by assigning different portions of each `interest' to different anchors. The $\bullet$ denotes the dot product operation, $\otimes$ denotes the scalar multiply operation, and $\oplus$ denotes vector sum operation.
}
\label{multi anchor}
\end{center}
\end{figure*}

\subsubsection{Multi-anchor Module}
The data of user behavior, which is highly unstructured and complex, implies different preferences of the user. To capture diverse aspects of user preferences, a novel multi-anchor module is proposed. Specifically, suppose there are $M$ anchors, and each of them indicates preference of users in a certain perspective. Let $\boldsymbol{b}$ be a BoI feature, the module converts $\boldsymbol{b}$ to $M$ low-dimensional representations as shown in Fig. \ref{multi anchor}. Each representation is computed as
\begin{equation}
\label{MultiAnchor}
\boldsymbol{r^i}=ReLU(\boldsymbol{\alpha_i}^Tf(\boldsymbol{b}))=ReLU(\sum_j^D\alpha_{ij}f(b_j))=ReLU(\sum_j^D\alpha_{ij}b_j \boldsymbol{W^e_j}),
\end{equation}
where $f(b_j)=b_j\boldsymbol{W^e_j}$ is the `interest' embedding function, $b_j$ is the $j$-th element of $\boldsymbol{b}$, $\boldsymbol{W^e}={(\boldsymbol{W^e_1}, \boldsymbol{W^e_2}, ..., \boldsymbol{W^e_D})}^T$ is the embedding matrix. And $\alpha_{ij}$ is the attention weight between the $i$-th anchor and the $j$-th `interest', which measures the portion assigned to the $i$-th preference from the $j$-th behavior `interest'. The weight $\alpha_{ij}$ is defined as
\begin{equation}
\label{Attention}
\alpha_{ij} = \frac{\exp(\boldsymbol{W^a_i k_j})}{\sum_l\exp(\boldsymbol{W^a_l k_j})},
\end{equation}
where $\boldsymbol{W^a_i}$ is the anchor vector corresponding to the $i$-th anchor, $\boldsymbol{W^a}={(\boldsymbol{W^a_1}, \boldsymbol{W^a_2}, ..., \boldsymbol{W^a_M})}^T$ is the anchor matrix, and $\boldsymbol{k_j}=\boldsymbol{W^p}ReLU(\boldsymbol{W_j^e})$ is the interest vector corresponding to the $j$-th `interest'. $\boldsymbol{W^e}\in \mathbb{R}^{D\times H}$, $\boldsymbol{W^a}\in \mathbb{R}^{M\times H}$, and $\boldsymbol{W^p}\in \mathbb{R}^{H\times H}$ are learned parameters. $\boldsymbol{r^i}$ can be computed efficiently since $\boldsymbol{b}$ is a sparse vector. Due to the different anchor vectors, different attention weights can be generated for each `interest'. Finally, a group of different aggregated representations $\boldsymbol{R}=\{\boldsymbol{r^1}, \boldsymbol{r^2}, ..., \boldsymbol{r^M}\}$ can be obtained ($ \boldsymbol{M}$ indicates the total number of anchors). In this way, user preferences are learned from different perspective.
Specially, experiments prove that SMEN can achieve almost lossless dimensionality reduction mainly due to the multi-anchor module.

\subsubsection{Time Aggregation Module}
Through the multi-anchor module, a sequence of representation groups $\boldsymbol{\mathcal{R}}=\{\boldsymbol{R_{t_1}}, \boldsymbol{R_{t_2}}, ..., \boldsymbol{R_{t_N}}\}$ for each user is obtained
In the time aggregation module, each sequence $\boldsymbol{R^i}=\{\boldsymbol{r_{t_1}^i}, \boldsymbol{r_{t_2}^i}, ..., \boldsymbol{r_{t_N}^i}\}$ is aggregated separately to yield a new representation $\boldsymbol{\tilde{r}^i}\in \mathbb R^H$. Thus the variable-size representation sequence $\boldsymbol{\mathcal{R}}$ can be transformed into $M$ fixed-size representations $\boldsymbol{\tilde R}=\left\{\boldsymbol{\tilde{r}^1}, \boldsymbol{\tilde{r}^2}, ..., \boldsymbol{\tilde{r}^M}\right\}$.

There are various methods which can be used to aggregate variable-size sequences, such as average/max pooling and RNNs. Compared to average and max pooling, RNNs are more appropriate to capture variations over time.
Among all RNN-based models, long short-term memory (LSTM) and gated recurrent units (GRU) are most commonly used. Considering that GRU has fewer parameters and is less computationally intensive, GRU is adopted to the time aggregation module in this work.

\subsubsection{Multi-scale Aggregation Module}
Under a time division $\boldsymbol T=\left\{\boldsymbol{t_1}, \boldsymbol{t_2}, ..., \boldsymbol{t_N}\right\}$, we have obtained $M$ representations $\boldsymbol{\tilde R}=\left\{\boldsymbol{\tilde{r}^1}, \boldsymbol{\tilde{r}^2}, ..., \boldsymbol{\tilde{r}^M}\right\}$ through the BoI, multi-anchor module and time aggregation module as introduced above. If the time interval is too small, the input sequence of SMEN, i.e. $\boldsymbol B=\left\{\boldsymbol{b_{t_1}}, \boldsymbol{b_{t_2}}, ..., \boldsymbol{b_{t_N}}\right\}$ will become extremely long, which contains extensive details but causes modeling difficulties due to catastrophic forgetting. On the other hand, details may be lost if the time interval is too large.

To address this trade-off, multi-scale aggregation module is designed. The module captures diverse patterns from user behavior by aggregating several representations generated at different granularities. Generally, user behavior in this work is aggregated monthly and yearly, i.e. $\boldsymbol T=\left\{\boldsymbol{t_1}, \boldsymbol{t_2}, ..., \boldsymbol{t_N}\right\}=\left\{\boldsymbol{t'_1}, \boldsymbol{t'_2}, ..., \boldsymbol{t'_{N'}}\right\}$ (e.g., if the length of time period is 5 years, $\boldsymbol{N}$ is 60 for monthly granularity, and $\boldsymbol{N'}$ is 5 for yearly granularity). Thus two sequences of BoI feature, $\boldsymbol B=\left\{\boldsymbol{b_{t_1}}, \boldsymbol{b_{t_2}}, ..., \boldsymbol{b_{t_N}}\right\}$ and $\boldsymbol B'=\left\{\boldsymbol{b_{t'_1}}, \boldsymbol{b_{t'_2}}, ..., \boldsymbol{b_{t'_{N'}}}\right\}$ at different scales correspondingly, are obtained. Then, two groups of representations $\boldsymbol{\tilde R}=\left\{\boldsymbol{\tilde{r}^1}, \boldsymbol{\tilde{r}^2}, ..., \boldsymbol{\tilde{r}^M}\right\}$ and $\boldsymbol{\tilde R'}=\left\{\boldsymbol{\tilde{r'}^1}, \boldsymbol{\tilde{r'}^2}, ..., \boldsymbol{\tilde{r'}^M}\right\}$ are obtained after the multi-anchor module and time aggregation module with parameter sharing. Finally we aggregate $\boldsymbol{\tilde R}$ and $\boldsymbol{\tilde R'}$ as follow:
\begin{equation}
\label{multiscale}
\boldsymbol{\hat{r}^i}=s^i([\boldsymbol{\tilde{r}^i},\boldsymbol{\tilde{r'}^i}])\odot
\boldsymbol{\tilde{r}^i}+(1-s^i([\boldsymbol{\tilde{r}^i},\boldsymbol{\tilde{r'}^i}]))
\odot\boldsymbol{\tilde{r'}^i},
\end{equation}
where $\odot$ is the Hadamard product, $[\cdot]$ is a operation concatenating vectors along the last dimension, and $s^i(\cdot)$ is the switch function which is implemented as a fully-connected layer followed by a sigmoid function. The switch function controls the fusion between BoI feature sequences of different scales. The output of this module $\boldsymbol{\hat{R}}=[\boldsymbol{\hat{r}^1}, \boldsymbol{\hat{r}^2}, ..., \boldsymbol{\hat{r}^M}]$ is served as the final user representation.

\subsubsection{Contrastive Learning Module}
As mentioned in \ref{contrastive loss}, contrastive loss is also used to learn user representation in this network. In order to obtain a unified vector for contrastive learning, a nonlinear projection head is added, i.e. $h(\cdot)$, as used in SimCLR \cite{chen2020simple}. The function $h(\cdot)$ is computed as
\begin{equation}
\label{projection}
\begin{split}
\boldsymbol{v}\ \ &= a([\boldsymbol{W_1^{h_1}\hat{r}^1},\boldsymbol{W_2^{h_1}\hat{r}^2},...,\boldsymbol{W^{h_1}_M\hat{r}^M}])\\
h(\boldsymbol{v}) &=\boldsymbol{W^{h_2}} a(\boldsymbol{v}+mix(\boldsymbol{v},\boldsymbol{W^{h_3}}).\\
\end{split}
\end{equation}
The function $mix(\boldsymbol{v},\boldsymbol{W^{h_3}})$ allows interaction between different preferences, and is implemented as
\begin{equation}
\label{1}
mix(\boldsymbol{v},\boldsymbol{W^{h_3}})=r_2(\boldsymbol{W^{h_3}}r_1(\boldsymbol{v})),\\
\end{equation}
where $r_1(\cdot)$ reshapes $\boldsymbol{v}$ to a matrix of size $M\times H$, and $r_2(\cdot)$ reshapes $\boldsymbol{W^{h_3}}r_1(\boldsymbol{v})$ to a vector.
 $\boldsymbol{W_i^{h_1}}\in \mathbb{R}^{H\times H}$, $\boldsymbol{W^{h_2}}\in \mathbb{R}^{MH\times MH}$, and $\boldsymbol{W^{h_3}}\in \mathbb{R}^{M\times M}$ are trainable parameters.

Two behavior sequences drawn from the same user are treated as a positive pair, and behavior sequences from other users are negatives. All sequences are continuous sub-sequences randomly sampled from the whole life time $\boldsymbol T$. Finally, the contrastive loss is defined the same as equation (\ref{contrastiveloss}).

\section{Experiments}
In this section, we report experimental results in detail.

\subsection{Datasets}
Model comparisons are conducted on a public datatsets and an industrial dataset. Table \ref{dataset_info} shows the statistics of the two datasets. The public Amazon dataset \cite{ni2019justifying} contains product reviews and metadata like product titles and categories from Amazon. For each user, the reviewed product titles constitute a sequence of review behaviors.
The dataset for self-supervised user modeling on Amazon $D_{pt}^A$ is constructed by collecting users who have behaviors between 1997-01 and 2017-12, while taking behaviors occurred between 2018-01 and 2018-10 to form datasets $\{D_{ds}^A(t)\}_t$ ($t$ refers to tasks) for downstream tasks.
Industrial dataset is collected from visit/order logs on a popular e-commerce platform. We constructed a training dataset $D_{pt}^I$ for self-supervised user modeling by randomly sampling hundred million of users who have behaviors between 2016-06 and 2021-05, while taking behaviors occurred between 2021-07 and 2021-08 to form datasets $\{D_{ds}^I(t)\}_t$ for downstream tasks.

\begin{table}[h]
\centering
\caption{Statistics of the datasets. $Tr\left(\cdot \right)$ indicates the truncation threshold.}
\label{dataset_info}
 \begin{tabular}{cccccc}
     \hline
      Dataset & $\left| U\right|$  &  $Max\left(\left| \boldsymbol{S}\right|\right)$ & $Tr\left(\left| \boldsymbol{x}\right| \right)$ & $\left| V\right|$ \\
       \hline
       Amazon & 43,531,850 & 13,122 & 35 & 103,581\\
       Industrial  & 214,317,285 & 1,952,546 & 24 & 178,422&\\
       \hline
   \end{tabular}
\end{table}

\subsection{Downstream Tasks}
Two kinds of downstream tasks were used to evaluate our method: category preference identification and user profiling prediction. For task $t$, 80\% of the data is selected randomly from $D_{ds}^A(t)$/$D_{ds}^I(t)$ to train downstream models and the rest for performance validation.

Category preference identification refers to the task of predicting whether users have short-term preferences for products in the target category. For Amazon dataset, three categories are included:`Books of Literature', `Games of Sports',  `Outdoor-hunting', and `Musical Instrument'. For the industrial dataset, we consider three categories including clothing, shoe and coffee for evaluation.

User profiling prediction aims to identify user aspects such as gender and age. Experiments are  conducted on the industrial dataset. Two tasks were involved: (1) user age classification ( age is divided into 6 classes) which predicts the age ranges of users, and (2) baby age classification (7 classes). In both tasks, the ground-truth labels come from an online questionnaire.

\subsection{Competitors}
The proposed LURM model is compared against a rich set of user representation learning methods, including TF-IDF, Doc2Vec, TextCNN, HAN, PTUM \cite{wu2020ptum} and SUMN. TF-IDF and Doc2Vec view the whole user behavior sequence as a document. TF-IDF generates a sparse high-dimensional vector, while Doc2Vec learns to represent the document by a dense vector. We also compared LURM with two methods named TextCNN and HAN which learn task-specific user representations and classifiers in a supervised manner.
Finally, we compared LURM with two self-supervised methods for user modeling. PTUM is designed based on Bert and proposes two self-supervision tasks for pre-training.
SUMN proposes a multi-hop aggregation layer to refine user representations and uses behavioral consistency loss to guide the model to extract latent user factors.

\subsection{Training Details}
For item embedding learning, the number of words in each item $\boldsymbol{x}$ was truncated. The principle of setting truncation threshold is that 95\% data values can be covered by the threshold. In experiments, word2vec and average pooling are used to obtain item embedding on Amazon dataset, while the method described in 3.1.1 was used on the industrial dataset. The entire behavior sequences were input into SMEN because the length of input sequence only depends on the time interval. On Amazon dataset, TF-IDF and Doc2Vec also take the entire behavior sequence as input. Meanwhile, the length of input of TextCNN, HAN, Doc2Vec, SUMN and PUTM are limited to 50 due to memory limitation. On industrial dataset, all competitors use data of two months.

Latent dimensions of word/item embedding and all hidden layers were 128 in LURM. In item embedding module, the window size $\beta$ was 5 days, and the temperature $\tau$ was 0.1. In order to retain details as many as possible, the size of the `interest' vocabulary was $10^5$ and the number of anchors $M$ was 10 in our experiments. So the final user embedding dimension was 1280. In addition, the input of month-scale and year-scale were used. For all user representation models, the Adam optimizer with a learning rate of 0.001, and a batch size of 256 was used for training. The hyper-parameters of the supervised competitors were tuned on the validation set. For downstream tasks, a simple MLP classifiers was applied after the user representation was derived.

\subsection{Results}
Table \ref{comparison_1} shows the comparison of category preference identification on the public Amazon dataset. The last two rows show the results of our models under different settings. The first one uses the high-dimensional output of BoI module as input in the downstream tasks, while the second uses the output of LURM. It can be seen that LURM consistently outperforms other unsupervised methods, e.g., about 3.23\%/{1.49\%} average improvements than current SOTA method,  SUMN and 4.41\%/2.09\% average improvements than PUTM in terms of AUC and ACC respectively. Even compared with supervised methods, LURM outperforms TextCNN and HAN about 8.26\%/3.47\% and 3.82\%/2.04\% respectively. In particular, it can be observed that LURM has a significant improvement over BoI.

Table \ref{comparison_2} shows the comparison of two user profiling prediction tasks and three category preference identification tasks on the industrial dataset. In this table, BoI and LURM using 2-month input are compared. When using the same input, LURM(short) achieved better results than other methods on both user profiling prediction tasks and category preference identification tasks. When using longer input, we can obtain more improvements, e.g., about 15.55\%/17.55\% average improvement on user profiling prediction tasks, and 4.19\%/2.91\% average improvement on category preference identification tasks compared with SUMN.

\begin{table}[h]
\centering
\caption{Comparisonn in terms of AUC(\%)/ACC(\%) on the Amazon dataset. Several category preference identification tasks are evaluated. The best score is bold.}
\label{comparison_1}
 \begin{tabular}{cccccc}
     \hline
      Method & Books of Literature  &  Games of Sports & Outdoor-hunting & Musical Instrument \\
       \hline
       TextCNN & 73.84/72.83 & 61.00/69.92 & 66.06/72.76 & 68.28/71.81  \\
       HAN     & 79.12/77.49 & 66.62/70.01 & 71.19/73.42 & 70.01/72.12 \\
       \hline
       TF-IDF  &78.62/77.21 & 65.69/68.69 & 71.90/72.19 & 69.16/68.62  \\
       Doc2Vec &71.21/68.26  & 66.75/66.85 & 71.58/66.36 & 70.29/67.37 \\
     \hline
       PTUM  &  78.66/77.91  & 66.06/68.97  &  70.34/73.01 & 69.50/72.92 \\
       SUMN    &79.57/77.73 & 66.83/70.63 & 72.27/73.44 & 70.61/73.43\\
      \hline
        BoI & 76.29/75.75 & 59.34/67.58 & 67.80/71.28 & 66.36/69.88 \\
       LURM & $\boldsymbol{82.55/79.13}$ & $\boldsymbol{68.02/72.43}$ & $\boldsymbol{77.59/75.19}$ & $\boldsymbol{74.04/74.43}$
       \\
       \hline
   \end{tabular}
\end{table}

\subsection{Ablation Studies and Discussion}
From Table \ref{comparison_1} and Table \ref{comparison_2}, we have observed that LURM has achieved better performance compared to other self-supervised and supervised methods. Its success can be attributed to lifelong user behavior modeling and lossless dimensionality reduction of SMEN.

Fig. \ref{comparison_length} shows the performance of LURM on several downstream tasks of industrial dataset when using inputs of different lengths. It can be seen that the richer the behavior, the better the performance on user profiling prediction tasks. Compared to using only behaviors of two months, an average improvement of 9.75\%/16.03\% can be achieved when using behaviors of 5 years on these tasks. While the benefits of using richer behavioral data become small for category preference identification tasks. This is because these tasks are more focused on the short-term interests of users, and it can be observed that the best performance can be obtained when input with a length of one year is used on these tasks.

We also verified the necessity of multi-scale aggregation module. The third and fourth row of Table \ref{comparison_scale} show the results of LURM with input of monthly/yearly granularity only, respectively. It can be seen that using multi-scale aggregation module can achieve significant improvements on user profile prediction tasks, while the benefits disappear on category preference tasks. The reason is the same as explained in Fig. \ref{comparison_length}, that is, there is no need to model with extremely long user behaviors for category preference identification tasks. The last two rows of Table \ref{comparison_scale} show the results of LURM with 1 anchor, and one anchor with 1280-dimensional output. Taking BoI as a benchmark, it can be seen that LURM achieves almost lossless dimensionality reduction compared to LURM(1anchor-128) and LURM(1anchor-1280), which verifies the effectiveness of the multi-anchor module.

\begin{table}[h]
\centering
\caption{Comparison in terms of AUC/ACC on Industrial dataset. Two profiling prediction tasks and three category preference identification tasks are evaluated. The best score is bold.}
\label{comparison_2}
 \begin{tabular}{cccccc}

     \hline
       Method        & Age         & Baby Age    & Clothing    & Shoe        & Coffee\\
       \hline
       TextCNN       & 86.40/61.02 & 72.19/66.01 & 76.64/76.13 & 84.33/80.24 & 80.49/79.46  \\
       TF-IDF & 87.73/61.75 & 73.08/67.26 & 77.61/78.01 & 85.04/81.15 & 81.75/80.78  \\
       SUMN          & 85.35/60.61 & 74.20/67.63 & 73.86/74.16 & 83.47/79.63 & 79.89/79.42 \\
      \hline
       BoI(short)    & 89.57/63.99 & 82.23/70.22 & 78.93/78.85 & 85.53/81.68 & 82.61/81.41\\
       LURM(short)   & 88.48/61.99 & 82.71/69.29 & 78.37/78.57 & 85.15/81.18 & 81.94/80.78 \\
       BoI           & $\boldsymbol{96.48}/\boldsymbol{80.19}$ & 93.29/83.57&$\boldsymbol{80.83}/\boldsymbol{79.33}$ & $\boldsymbol{86.69}/\boldsymbol{82.16}$ & $\boldsymbol{83.78}/\boldsymbol{81.74}$ \\
       LURM          & 96.09/78.43 & $\boldsymbol{94.59}/\boldsymbol{84.91}$ & 80.37/79.10 & 86.30/81.65 & 83.11/81.18 \\
       \hline
   \end{tabular}
\end{table}

\begin{figure*}[ht]
\begin{center}
\centerline{\includegraphics[scale=0.4]{comparison_length.pdf}}
\caption{Comparison of LURM with  inputs of different lengths on several downstream tasks. The two figures show the results in terms of AUC and ACC respectively. }
\label{comparison_length}
\end{center}
\end{figure*}

\begin{table}[h]
\centering
\caption{Comparison of LURM with different configurations on industrial dataset.}
\label{comparison_scale}
 \begin{tabular}{cccccc}
     \hline
      Method              & Age         &  Baby Age   & Clothing    & Shoe        & Coffee\\
       \hline
       BoI                & 96.48/80.19 & 93.29/83.57 & 80.83/79.33 & 86.69/82.16 & 83.78/81.74 \\
       LURM               & 96.09/78.43 & 94.59/84.91 & 80.37/79.10 & 86.30/81.65 & 83.11/81.18 \\
         \hline
       LURM(monthly)      & 95.82/77.54 & 93.19/82.17 & 80.30/79.02 & 86.26/81.61 & 83.08/81.14 \\
       LURM(yearly)       & 95.61/77.19 & 92.80/80.93 & 80.24/78.88 & 86.17/81.36 & 82.66/80.78 \\
       \hline
       LURM(1anchor-128)  & 93.98/72.78 & 86.13/71.22 & 78.93/78.27 & 85.80/81.11 & 81.98/80.39 \\
       LURM(1anchor-1280) & 94.35/73.71 & 89.21/75.35 & 79.38/78.54 & 86.07/81.32 & 82.34/80.59 \\
       \hline
   \end{tabular}
\end{table}

In addition, we also observed that the gap between BoI and LURM is very different on the two datasets. It may be that the item embedding we learned is better on the industrial dataset and then LURM achieved similar results compared with BoI, indicating the ability of SMEN to compress representation losslessly. On the Amazon dataset, the item embedding is worse. And It can be seen that the representation was improved through the further learning of SMEN.
\section{Conclusion}
In this work, a novel framework named LURM is proposed to model lifelong user behaviors with any length.
With the ability to model lifelong user behaviors,
our method shows promising results on different downstream tasks and datasets. Although our method has made some progress, it still can be improved. In the future research work, we will consider more types of tasks; input data in different modalities, such as images, video, and audio; more dedicated network architecture and so on.

redpajama_set_name: RedPajamaArXiv


text:
THE AMBROSE EHIRIM FILES
INFLUENCERS BECOME THE NEW LIBERATED POWER IN AFRICA
Joel Konopo image via Nieman Lab
BY JOEL KONOPO
BOTSWANA, NIEMAN LAB
"I believe 2019 is the year that a majority of young disenfranchised Africans and digital influencers will use the power of hashtag movements to demand greater responsibility from their leaders."
--------JOEL KONOPO
2017 and 2018 proved the value — and power — of collaboration among journalists in Africa. But while the Paradise Papers and, of late, the Implant Files have been major highlights, there still has not been a major shift in the mindset of African leaders towards corruption. Take for example my country, Botswana: Ian Kirby, president of the Botswana Court of Appeal, was linked to several offshore companies in the British Virgin Islands — but simply dismissed the exposé, saying it offered nothing new. In the Democratic Republic of Congo, no action was taken against President Joseph Kabila's twin sister and media mogul Jaynet Kabila after she was linked to a company incorporated on the Pacific island of Niue months before her brother was elected president.
Africa has become impassive to social, economic, and political malaise. Weak institutions allow corruption suspects to go scot-free. Sometimes they get a promotion. This isn't new, but this disorder has reached a breaking point. Unions and independent media are often no match to well-resourced state media, which effectively control the narrative, especially among the rural poor. For decades, African journalists have watched helplessly as politicians decimate private media and invest heavily in state-sponsored outlets. For a long time, there hasn't been a homegrown messiah who can step forward and provide an effective counter-narrative to state propaganda.
Then social media stepped in. It's difficult for African governments to ignore 500 million liberated voices. Digital influencers have rallied youth to discover their voices and demand end to economic crisis. Tanzania and Uganda are already feeling the heat: In July, Uganda imposed a controversial social media tax, putting undue burden on the poorest and clamping down on freedom of expression. Tanzania, which openly supports its neighbor's draconian law, is monitoring the situation with a keen eye.
While I foresee most African countries imposing similar censorship laws — especially as they become jittery in the run up to watershed general elections next year — I believe 2019 is the year that a majority of young disenfranchised Africans and digital influencers will use the power of hashtag movements to demand greater responsibility from their leaders. This will be hard to ignore. Africa has the world's largest population of young people, and its youths account for 60 percent of all of the continent's jobless, according to the World Bank.
Like politicians, we journalists will be spectators, as we increasingly lose the momentum of controlling the news agenda to influencers. Digital influencers and emancipated youth will never let go of the power and influence they have over journalists and politicians, in spite of government pushback.
The number of digital influencers is increasing at an alarming rate in most repressive regimes, a sign of exasperation among youth eager to take advantage of social media to tell their stories in a way that previously would have been impossible or very dangerous.
Take the example of Zimbabwe. Turmoil and the suppression of independent journalists emboldened influencers who played a huge role in nudging the 96-year-old Robert Mugabe out of power in 2017; they continue to pressure the new political structure for egalitarian politics today. The popularity of Zimbabwean "word artist" Samm Farai Monro known as Comrade Fatso has turned a tide against state-sponsored disinformation from The Herald newspaper and national broadcaster, the ZBC. Comrade Fatso uses satire and theater to tell a story of dispossession and disenfranchisement in his country, and he commands an impressive audience on YouTube, especially among millions of Zimbabweans in diaspora.
Ugandan musician-turned-politician Bobi Wine has made a revolutionary confrontation against the leadership of President Yoweri Museveni (in power since 1986) by relying on Facebook and WhatsApp as mediums of political campaigning. As an independent parliamentarian, he uses social media to question Museveni's dynasty and has received massive support from disenfranchised urban youth. Museveni has jailed him several times.
South Africa, Kenya, and Nigeria are not in short supply of social media influencers. Linda Ikeji, Nigerian blogger Linda Ikejicommands millions of followers on social media, giving her much leverage even among politicians. In Kenya, Robert Alai challenges corruption in his country and has 1.14 million followers on Twitter.
African media has one advantage: It is not as polarized along ideological lines. In the African media context, there isn't much room for hedonistic ideological pursuit in media outlets. If there is a left-right agenda in African private media, it will be fluid and mostly against state propaganda. There is an opportunity to safeguard quality journalism.
I am of the view that independent journalists in Africa need to do more to continue to amplify their voice to counter well-resourced state-controlled media. If we fall short, we will lose public trust as we see intensified disruption brought about by social media influencers.
Africa African Union Journalism Society
Igbo Journal Review
Ideas For The Igbo Nation
Igbo Affair
Anti Igbo Pogrom
Igbo Presidency: Unwelcome Mandate
Igbo Union Pioneers: M.I. Okpara (L) and Nnamdi Azikiwe (C) at a political rally, 1959. Michael Iheonukara Okpara was born in Umuahia, a political leader and Premier of Eastern Nigeria during the First Republic from 1959 to 1966. Okpara attended the Uzuakoli Methodist College, where he won a scholarship to study medicine at Yaba Higher College, Lagos. Completing his medical studies at the Nigerian School of Medicine, he worked briefly as a government medical officer in Lagos before returning to Umuahia to set up a private practice. After the Pogrom and Biafra War, Okpara lived in Ireland, on exile, until his return in 1979. He died December 17, 1984. Nnamdi Azikiwe was born in Zungeru, a political leader and was Nigeria's last Governor-General from 1960 to 1963 and the first president from 1963 to 1966. He was the father of modern Nigeria nationalism. Zik attended Hope Wadell Training Institute, Calabar, and Methodist Boys' High School Lagos, then to the United States securi…
"Sorrow, Tears and Blood"
"Everybody run run run. Everybody scatter scatter
Some people lost some bread, someone just die.
Police dey come, army dey com
Confusion everywhere,
Several minutes later, all don cool down brother
Police don go away, army don disappear
Them leave sorrow, tears and blood
My people self dey fear too much
We fear for the thing we no see
We fear for the air around us
We fear to fight for freedom
We fear to fight for justice
We fear to fight for happiness
We fear to fight for progress
We always get reasons to fear
We no want die
We no want quench
Mama dey for house
My pickin dey for house
I get one wife
I get one car
I get one house
Papa dey for house
I no want quench
I want enjoy
So policeman go slap your face
You no go talk
Army man go whip your yansh
You go dey look like donkey
Rhodesia dey do them own
Our leaders dey yab for nothing
South Africa dey do them own
Them leave sorrow tears and blood
(Them regular trademarks...")
I have collected every bit of Fela's album from the Highlife Koola Lobitos years…
The Long War Against Slavery
VICTIMS OF PORTUGUESE SLAVE HUNTERS: This version of the illustration published in "Heroes of the Dark Continent" by J.W. Buel in 1890.The illustration was taken from "Mission d'exploration du Haut-Niger: Voyage au Soudan Francais" by Joseph Simon Gallieni (published Paris, 1885). Although Buel's caption reads "Victims of Portuguese Slave Hunters," the slave traders shown are not Portuguese but Mande-speaking Africans. The location depicted is approximately in the area of present-day Mali.
BY CASEY CEP
"Here's to the next insurrection of the negroes in the West Indies," Samuel Johnson once toasted at an Oxford dinner party, or so James Boswell claims. The veracity of Boswell's biography—including its representation of Johnson's position on slavery—has long been contested. In the course of more than a thousand pages, little mention is made of Johnson's long-term servant, Francis Barber, who came into the writer's house as a child after being tak…
The Tragedy of the Igbo Intellectual
Wake up everybody
No more sleeping in bed
No more backward thinking
Time for thinking ahead
The world's change so very much
For what it use to be
There is so much hatred
War and poverty
----Harold Melvin and the Blue Notes
If you fast-forward the Kenneth Gamble-Leon Huff production on The Sound of Philadelphia Records label released by Harold Melvin and the Blue Notes with that powerful voice of Theodore Pendergrass way back in 1973 now, there will be a whole lot to tell the world has not gotten any better based on if one should ask me coming that afar thirty-something years ago when technology had just begun in making life better from every aspect of society.
We haven't seen much change over the years because the entire world is still compounded by the same old problems; problems now worse than ever seen before. Thirty-something years ago when the Blue Notes envisioned a fragile world if I still remember, was after the pogrom in a fast pace took place in Nigeria when pregnant women were evisce…
What My Father Taught Me About Biafra And My heritage
Ijeoma Ajibade and his father. Image: Ijeoma Ajibade via Independent
BY IJEOMA AJIBADE
My earliest memories of Biafra are the same as my earliest memories of my father. I can remember sitting next to him on a bed and I touched his arm. He turned to me and he said: "Can't you see your father is crying." It was many years later that I realised he was crying because of Biafra. That was 50 years ago today. I didn't see my father cry again. He was mourning the loss of the Biafra dream.
For me and for many of the diaspora, Biafra is a presence that haunts us. It is a part of our history that is not spoken about and yet we try to make sense of it by reading, watching plays and attending lectures. All of this in an attempt to understand this dream that was on the cusp of being realised and yet failed so painfully.
I was two when the war began and four when it ended. This was a civil war in Nigeria fought between the Nigerian government and the eastern region of Nigeria. Predominantly the home of …
We, The Biafrans
French [hysician Bernard Kouchner in Biafra in 1968 during the operations of the International Red Cross, the IRC, sought to provide medical assistance to women and children in need. Kouchner laid the cornerstone of the "Doctors Without Borders" charity. Image:D. Faget/AFP/Getty
BY DARE BABARINSA
It is 50 years now that the Civil War ended and the soldiers returned home. Thousands never returned. They earned their eternal rest on the battlefields and their comrades in hasty retreat or full flight never had the opportunity to retrieve the bodies. They are represented on the Heroes Acres in Abuja with the statue of the Unknown Soldier.
My elder brother, Adeyinka, my father's first son, returned home with a big gash on his handsome face. He had been shot in the head on a battle field after the bloody crossing from Asaba. For Adeyinka, the army was the ultimate. He was recruited into the military shortly after Nigeria's independence from Britain and was posted to the Congo follow…
Tinkering On Policy Options For Nigeria's Worsening Poverty Index
A woman walks on plastic waste in Lagos. Image: Yasuyoshi Chiba/AFP/Getty
BY OMONIYI SALAUDEEN
As an annual ritual, Thursday, October 17 marked yet another International Day for the Eradication of Poverty. This year's event with the theme: "Acting together to empower children, their families and communities to end poverty" drew more attention on the most vulnerable. The objective of the anniversary is to protect the civil, political, economic, social and cultural rights of every individual to earn a decent living regardless of race, religion or abilities.
The commemoration of October 17 each year is to demonstrate how to achieve greater participation by enabling people from all walks of life to come together to respect the human rights and dignity of people living in poverty. In particular, the participation of children and young people has always been encouraged and supported as an integral part of October 17 observances at the United Nations and around the world. This is in recognitio…
Civil War: 50 Years After
Chukwuemeka Odumegwu Ojukwu and Yakubu Gowon shake hands in Aburi, Ghana, Januar 5, 1967, during the Aburi Accord, presided by Joseph Ankrah.
BY EMMANUEL OLADESU
The Nigerian civil war, otherwise known as the Biafran war, ended on January 12, 1970, following the surrender of secessionist forces, led by Col. Philip Effiong, who held forte for his Commander, Lt. Col. Emeka Odimegwu-Ojukwu. Could the war have been averted? Deputy Editor EMMANUEL OLADESU examines the lessons of the war and its implications for nation-building.
Fifty years after, the pain lingers. The scars have not faded. The agony is still bold on the faces of victims, relations and loved ones. Although the war has ended, reconciliation has not been fully accomplished.
To many commentators, full integration of the Southeast appeared theoretical, owing to hypocritical commitment on the part of the Federal Government. The war-time military Head of State, Gen. Yakubu Gowon, proclaimed "no victor, no vanquished." But, there wa…
Nollywood Review: "Girls Cot"
I'm not sure if I should say one is now hooked to Nigerian movies, the Nollywood and fourth-ranked movie industry on the Planet. I have been watching quite a lot lately but I do have a problem with the plots and the titles. They all seem to be the same. Like "Girls Cot" which marks the genuine return of Genevieve Nnaji after a long sabbatical. It has the same resemblance of Nollywood's previous projects which much appears they have ran out of stories and better creative stuff.
Nollywood should come out bold and start telling stories of human events and tragic moments of our time and beyond. Like Hollywood's "Shindler's List," "We Were Soldiers," "Pearl Harbor," "Gangs of New York," "Platoon," "Hotel Rwanda," "The Pianist," "The Holocaust," "Flags of Our Fathers," "Letters From Iwo Jima," "Malcolm X," "Amistad," and many more films of that nature, it would be worthy of Nollywood to start producing movies in the same manner with scenes like "Blood on the Niger," "Aba Mar…
EHIRIM FILES INTERVIEW EXCLUSIVE: The Otokoto Saga
Illustration depicts Innocent Ekeanyanwu carrying a beheaded 11-year-old Ikechukwu Anthony Okonkwo in a plastic bag walking to catch the bus to Lagos
Earlier this year, newspapers around BiafraNigeria carried headlines about a crime syndicate, the Otokoto mafia, which spooked Owerri Township and its neighboring suburbs with acts of murder, mutilaton, and illegal human parts trafficking.
On January 23, 2003, Justice Chioma Nwosu Iheme, the presiding judge in Owerri in the Otokoto case, handed down judgment and condemned Chief Vincent Duru Otokoto, the patriarch of the Otokoto family, and six others to death.
Last week in Los Angeles, Maxwell Vincent Duru Otokoto, the first son of the convicted Chief Duru Otokoto, sat down with BiafraNigeriaWorld's Los Angeles Bureau Chief, Ambrose Ehrim. According to Maxwell Otokoto, his father's conviction was a set-up and conspiracy. This is the other side of the Otokoto story.
BNW: Before we proceed in this interview, members of BNW and Ikenga …


We made arrangements to meet up and I thought I would pop in a new stamp set and some card so we could could chat and craft at the same time. Here were 2 of the cards I made as a result.
I think these images are just TOO cute. They are from one of the new stamp sets which feature in the Annual Catalogue which launches on 1st June 2016. This set also features a frog and the cutest pair of ladybirds - watch out for it.
After we had finished crafting, I could not resist a photo taken in this amazing garden chair.
It was amazingly comfortable, but you would need a large garden to enjoy it at it's best. I am also not sure what you would do with it when it rains!
free for my new catalogue launch, Buy one Get One Free Event.
More details coming really soon.


text:
Review: Immortally Yours by Lynsay Sands
One hundred and twenty-five years is a long time to nurse a crush. That's how long it's been since Beth Argenis first met Cullen "Scotty" MacDonald and he instantly became the star of her most X-rated dreams. Back then, he was rescuing her from a Rogue Immortal. Now Beth's a Rogue Hunter—a damn good one. She doesn't need saving anymore, despite what Scotty thinks. What she does need is the fierce, wild desire that finally erupts between them.
Scotty has hesitated to claim Beth as his own. But one explosive kiss confirms what he's long suspected: She's his life mate. But Beth is tough, fearless, beautiful…and in immortal danger. Unless he wants to lose her forever, he'll have to rethink everything he once believed about love and destiny as well as confront an enemy who's terrifyingly close.
Scotty is an 800 year old vampire and kinda still has some weird thoughts when it comes to things. The fact that on two separate occasions he wanted Beth's mind wiped because of the awful things she went through is a real testament to that. He's a Rogue Hunter and goes after other vampires that break the rules. He met Beth when he saved her from a Rogue and knew she was his mate but she needed time to get over everything she'd been through so he didn't claim her.
Beth on the other hand thought Scotty didn't like her since he was always standoffish and not very friendly but she couldn't stop fantasizing about him or dreaming about him. She had a very bad life as a mortal before she was forcibly turned into a vampire against her will. The story was tragic and awful. What she endured because of her father was heartbreaking.
It became very clear that there were other reasons Scotty wanted Beth's mind wiped and he didn't want to claim her as his lifemate. The fact that she had been a prostitute as a mortal was the biggest reason and it seemed like he just couldn't put her past behind him even though she had done just that. It wasn't until she had explained to him why she didn't want her mind wiped and that her life as a mortal made her who she is now, that he finally understood her reasoning and how wiping her mind would change her personality completely. Scotty was a great character and a good guy. He cares about Beth a lot and he shows it. Plus there's just something extra sexy about a man with a Scottish accent.
While they're trying to sort out their relationship, they're also trying to keep Beth safe from someone who wants her dead and keeps setting traps for her. It's a good thing they're immortal because some of the things that happen to them would surely have killed a normal human being.
While this was a good book in the Argeneau series and can be read as a standalone, it really didn't do much for the series itself. It was a fun read though.
I give this book 4 out of 5 stars.


text:
Projects>Years>All
Mendeli Street Hotel
Hotels/Tel Aviv/2013
The Mendeli Street hotel was designed with the intention to integrate it as a role player within the ecstatic identity of Tel Aviv: a pan-cultural, warm, welcoming, cheeky, blunt and beating 24/7.
The Mendeli Street hotel is infused with Tel-Aviv's personality and perfectly fits to the urban vibes of this Mediterranean city.
Open since the late sixties, the Mendeli Street hotel re-opened in November 2013 after a massive renovation and immediately became the hottest hotel in Tel-Aviv.
The changeover included a complete interior and exterior renovation (66 rooms), including a totally new identity and branding to position it where it is truly deserved.
Two initial values were set to feed on the design: The Orient and the Global, values which they forged and rephrased into "The Gloriental", a thematic approach that runs throughout the building from top to bottom.
The ground floor is celebrated by "Five" (a tribute to the number of the building address: 5 Mendeli St.) the newest chef restaurant who indulges the city guests with his unique Mediterranean cuisine.
The interiors are a mix of furniture, details, art works which convey the idea of contemporary urbanism, fresh and up-to-date atmosphere: pieces of colorful and modern furniture and limited edition pieces from different Israeli designers. On the back wall of the welcoming desk, the contemporary art of the Israeli street artist "Know Hope" can be admired.


These days, I don't have as much time to read as I'd like. It's mostly a few stolen moments each night before passing out. So for me to finish a piece of fiction means it has to surpass great obstacles, namely my physical fatigue and attention-deficit disorder.
I enjoyed Jhumpa Lahiri's Pulitzer winning short story collection Interpreter of Maladies, missed her follow-up novel The Namesake (and now if I ever read it all my mind will be fixated on the image of Kumar as the lead character, damn you Hollywood movie trailers), but picked up her latest collection of semi-linked short stories, Unaccustomed Earth.
It is superb. It is about the Indian-American experience, but it is also about the Asian-American experience, and about the American experience. Like the best of books, it is both specific and universal.


text:
Englishman River Falls Provincial Park is a provincial park in British Columbia, Canada. It is located west of Parksville and borders the small community of Errington on Vancouver Island.

Features

Short year round access hiking trails and bridges access both upper and lower falls on the Englishman River. In 2019 new trails were under development in partnership with a community group. The base of the falls is a popular swimming hole with 15 to 20 ft cliff jumping. Salmon spawning can be observed in season.

The campground has 107 sites, most of which are vehicle access and can be reserved during summer. There is a large day use area. The park covers 97 hectares and includes old-growth and second growth Douglas fir, cedar, hemlock and maple forest surroundings.

A First Nations legend mentions the skeleton of a white man found near the falls, hence its name.

Nearby
The Englishman River Regional Park and a city part can be found nearby along the same river.

See also
 List of British Columbia Provincial Parks
 Mount Arrowsmith Biosphere Region

References

External links

BC Parks: Englishman River Falls Provincial Park
BC Parks brochure

Regional District of Nanaimo
Provincial parks of British Columbia
Mid Vancouver Island
Waterfalls of British Columbia
Canyons and gorges of British Columbia
1940 establishments in British Columbia
Protected areas established in 1940

redpajama_set_name: RedPajamaWikipedia


text:
Home/ F1 & Motorsport News & Views /
Danke, Sebastian Vettel | The Greatest F1 Drivers of All Time
The F1 world was shocked when Sebastian Vettel took to social media to announce his retirement.
Sebastian Vettel has done it all – from breaking records to driving for some of F1's most iconic teams. F1 fans, drivers, and commentators alike took to social media, hailing him as the moral compass of the sport.
We're using this edition of the 'Greatest F1 Drivers of All Time' to recap the career of Sebastian Vettel as we prepare to say 'goodbye' to the former World Champion.
Journey to Red Bull
While Sebastian Vettel is famous for his time at Red Bull, his F1 career started with another team. He made his first appearance on the F1 grid in 2006 for BMW Sauber for a practice session. He set the fastest time in FP2 and became the then youngest participant in a Grand Prix Weekend at 19 years old.
Vettel took on a test driver role with BMW before replacing Robert Kubica in 2007 after his crash at the Canadian Grand Prix. His first race saw him become the youngest driver to score a point in F1.
He was quickly snapped up as a full-time driver for Toro Rosso, the Red Bull Junior Team. Sebastian's start with Toro Rosso was rocky as he failed to finish the first four races of the 2008 season.
Everything changed at the Italian Grand Prix when wet racing conditions saw Vettel win the race by 12 seconds to become the youngest race winner at 21 years and 74 days. His impressive win saw his native German media nickname him "Baby Schumi" and the win was the first for either of Red Bull's F1 teams.
The Youngest F1 World Champion
It was no surprise when Sebastian stepped up to Red Bull in 2009 after Coulthard retired. The German driver went on to win four consecutive World Champion titles from 2010 to 2013. Red Bull and Sebastian appeared like a match made in heaven.
He took Red Bull's first pole position and win at the Chinese Grand Prix and had an impressive season before finishing second behind Button for the World Championship.
Vettel famously clashed with his Red Bull teammate, Mark Webber, but the pair won Red Bull their first World Constructors' Championship in 2010. Sebastian came back from a 15-point deficit to Fernando Alonso and a 7-point gap to his teammate to win the Championship at the Abu Dhabi Grand Prix.
The footage of Sebastian crossing the finish line – with the "DU BIST WELTMEISTER" radio message - is some of the most iconic in F1 history.
Vettel went on to win the 2011, 2012, and 2013 World Championships before exiting his Red Bull contract a year early in 2014.
Ferrari Title Fights Against Hamilton
Every F1 driver dreams of stepping into a Ferrari. Sebastian Vettel famously said, "everyone's a Ferrari fan". After a disappointing season with Red Bull, Sebastian moved to Ferrari for the 2015 season.
Vettel won the Malaysian Grand Prix, the first win for Ferrari in over 2 years. He paid tribute to his hero, Michael Schumacher, and finished 3rd in the championship with 13 podiums and 3 wins.
2017 and 2018 saw Vettel become Hamilton's nearest rival. He had a lead in the 2017 season over Hamilton until reliability issues gave Hamilton the edge.
The 2018 season was famously nicknamed the "Fight for Five' as both Vettel and Hamilton sought their fifth World Championship. Tensions with Ferrari led to a difficult 2019 and 2020 season with Vettel finishing the 2020 season in 13th place.
Journey's End at Aston Martin
After leaving Ferrari, Sebastian Vettel replaced Sergio Perez as Racing Point became Aston Martin. Sebastian secured the team's first podium with a second-place finish at the Azerbaijan Grand Prix. He went on to win the inaugural Overtake Award and finished 12th in the Drivers' standing.
He'll now finish his F1 career with Aston Martin. Sebastian has focused on promoting his various social justice and environmental campaigns, including promoting women in motorsports and protecting wildlife.
Sebastian Vettel's record as an F1 driver stands for itself. He holds the most consecutive race wins (9 in a row) and the highest number of race wins in a single season (13).
These records continue to remain unbroken. Vettel has taken 122 podiums, 57 pole positions, 53 wins, and 38 fastest lap points.
We're crossing our fingers that we can see Sebastian on the podium one last time before he retires. The German driver will finish his F1 career with 299 race starts.
Celebrate his record-breaking F1 career with our range of Sebastian Vettel merchandise, including the 2022 Vettel Driver Cap.
Danke, Seb.
Back to F1 & Motorsport News & Views


What are the postcodes of the district Geuzenbuurt? On this page you will find postcode information about the district Geuzenbuurt in Amsterdam.	There are 3 neighbourhoods in the district Geuzenbuurt.
These are all postcode numbers of Geuzenbuurt, see per postcode number the underlying postcodes. In total Geuzenbuurt 1 has	postcode number.
These are all neighbourhoods in the district Geuzenbuurt, see the corresponding postcodes and	streets for each neighbourhood. Geuzenbuurt has a total of 3 neighbourhoods.
Some postcodes of the district Geuzenbuurt. How many postcodes Geuzenbuurt has? Geuzenbuurt has 127 postcodes. Via the 4-digit	postcode number you can find every postcode of the district Geuzenbuurt.
How many streets does Geuzenbuurt have? Geuzenbuurt has 18 streets. Below are a number of streets of the district Geuzenbuurt.
Geuzenbuurt counts 4 175 households with 6 835 inhabitants. The households are consists out of 60% single	households, 18% households with children and 22% households without children. Of the inhabitants in Geuzenbuurt, 1 230 are	married, that is 18% of the inhabitants of Geuzenbuurt.
The overview of stations and supermarkets in Geuzenbuurt and surroundings.
These 5 stations with postcode and address can be found near Geuzenbuurt.
These are the 5 supermarkets closest to the district Geuzenbuurt.