Abstract: Experimental and numerical investigations on cylindrical shell panels subjected to underwater explosion loading are presented. Experiments were conducted on panels of size 0.8 × 0.6 × 0.00314 m and shell rise-to-span ratios h/l =0.0, 0.05, 0.1 , using a box model set-up under air backed conditions in a shock tank. Small charges of PEK I explosive were employed. The plastic deformation of the panels was measured for three loading conditions. Finite element analysis was carried out using the CSA/GENSA [DYNA3D] software to predict the plastic deformation for various loading conditions. The analysis included material and geometric non-linearities, with strain rate effects incorporated based on the Cowper-Symonds relation. The numerical results for plastic deformation are compared with those from experiments.


Hope Reins almost lost its sweet horse Buddy last winter due to a severe case of colic. For 72 hours straight, in the frigid 20 degree December air, volunteer teams of 2 comforted the hurting gelding while walking him around the ranch in shifts.
Eventually the critical digestive disorder subsided after countless medications, diagnostic procedures and visits from Hope Reins' star veterinarian, but nothing warms Buddy's tummy like the love of others.
We thank our Equine Wellness Volunteers Jen, Heather, Karen, Vickie and Amy.


'unsafe-eval' is not an allowed source of script - Kantu - Forum for Kantu Task and Test Automation and the free OCR API.
Sorry for all of these posts, I have never coded in my life before.
I don't see a pattern to how these are being rewritten. How do I re-write my command so that this will work? I don't know why this is happening, since the command should not be sent to the website to be executed, but performed inside Kantu.
I also saw that I could add ""content_security_policy": "script-src 'self' 'unsafe-eval'; object-src 'self'" to my "policy" to relax security. Where do I put that in?
Alright, thank you. That means that I cannot do any storeEval commands in my script. Difficult.
You just have to switch to tab (with selectWindow) where storeEval is allowed.


EmpireAdvertisement reaches 4K members who joined the project an also below 100K in Alexa Global Rank as it is close to celebrating 1 month online performance. The admin of the project congratulates every member on growing fast and claims the mainpage currently available in multiple languages is a perfect start for active promotional activity along with the materials available in the member area.
Some hot updates are also published by the admin of BitLeader, who announced the updated of the FAQ section, which should be read through carefully before opening the support ticket. Still the support is there to serve investors needs 24/7. The program is also in search of representatives for the company. Those willing to become ones should send the details to the support and get the response ASAP. All in all the growth of the BitLeader project has been claimed to be "...totally amazing" with new registrations day to day as well as large deposit amounts. The admin thanks every member for the support.
Ethtrade created the branded style for productive offline cooperation. Now you can create the common style in Ethtrade brand for offices, have meetings and conferences by using the entire set of attributes of the company: personal cards, presentation leaflets, advertising banners, promo videos, presentation posters for webinars, etc! Anyone interested can print and use them effectively.
MaxHours Science recently added to HyipNews listing reports adding NixMoney as the new payment gateway, using which one can make deposits and complete withdrawals. So all in all the project currently accepts the following e-currencies: PerfectMoney, Payeer, BitCoin, Adv and NixMoney all supporting instant withdrawal feature.
OIFLTD is experiencing very strong and stable growth with over $2,86,000+ in total deposits and over $1,42,000+ in total withdraw, 2300+ registered members and over 1150+ active investments. The traffic of the website is increasing and going up the ranks on most traffic ranking websites. The program has become that popular, from the admin's points of view mostly due to the truly fast email based support. The support team of OIFLTD sets as a priority to process most of questions through e-mail, however, in case there is some need to speak to someone live, feel free to try to use the Live Chat option available.


One the #AskGaryVee Show, Gary was asked about his social media strategy for 2016. I thought his answer was quite interesting and its something I am into and I believe you guys will find value from it as well. Tell me what you think in the comments below.


John Shima. Vinyl, CD and tapes by John Shima at Norman Records UK.
John Shima follows his recent releases on FireScope with a brand new EP on DMK, titled "Stealth". Previous EP 'Elements Unknown' garnered widespread acclaim for its emotionally charged, electro-tinged techno along the lines of UK stalwarts B12, classic Stasis and Kirk Degiorgio. The new release features 4 tracks of atmospheric deep techno.
John Shima releases his latest record on FireScope Records with a four-track EP entitled Elements Unknown. Described as being a collection of songs that lets you drift away into a head-heavy trance of pure techno and solid musical architecture, giving you a good while away from real life. Available on Vinyl 12".


text:
Blu-ray Review: Paprika (2006)
By Kevin Bechaz / June 29, 2021 September 4, 2022
Anime titles are in no short supply here in Australia but are mostly distributed by a single label. However, the very genre-oriented, Umbrella Entertainment has a couple in their catalogues, such as Satoshi Kon's Tokyo Godfathers and the director's final film, Paprika, which recently landed on shelves.
We enter a reality where therapists have the means to enter the dream of their patients via a machine; but when it's stolen, these two realms begin to merge in a chaotic fashion; and only a young female therapist and her dreamt-up personality, Paprika can stop it.
I generally dream about the same thing every night, a world where everything and everyone I've known co-exist in an urban sprawl where it's forever night. Although, like all of us, it's where I wrestle with my subconscious, tormented by unresolved issues and personifications of guilt. Paprika deals with these themes in a very illustrated manner as you can imagine and the dream realm is represented by a seemingly endless parade of "everything under the sun" to quote one of the filmmakers. The idea of literally entering one's dream at will with an ambiguous device is likely more familiar to audiences thanks to Christopher Nolan's Inception, which borrows from the same premise. As a result of regular visits to the 'Land of Nod' our characters begin to struggle in determining the difference between both worlds, carrying over to the viewer's perspective.
The film's titular hero gives a face to the kaleidoscope of visuals as she helps our two protagonists Chiba Atsuko and Detective Kogawa Toshimi in identifying the "dream terrorist", who seeks dominion over the human psyche. Needless to say, the plot requires your utmost attention and while it can be hard to follow, it's the sheer level of creativity and outsider thinking that makes Paprika so wonderfully unique.
Sourced from Sony's 2007 release, which despite being 14-years-old, has held up well and pushed my TV to its colour limits. While the 1.85:1 image is very crisp, there's a subtle softness during some scenes, mostly likely for added effect and style. The film's equally vibrant soundtrack comes to life via 5.1 DTS HD-MA and 2.0 which also amplifies the very electric theme song during the opening credits.
Audio Commentary with director/co-writer Satoshi Kon, composer Susumu Hirasawa, and associate producers
A Conversation about the "Dream"
The Dream CG World
The Art of Fantasy
Storyboard Comparisons
Parade Scene
The Ruins Scene
Skin Slitting Scene
Bonus content has also been sourced from the previous Sony release, which contains a nice assortment of interviews with a large portion exploring the film's technical side. A Conversation about the "Dream" is a candid discussion with the director, Satoshi Kon, and voice actors, Megumi Hayashibara and Tôru Furuya who you'll notice bear a striking resemblance to their animated characters. If you felt confused while watching Paprika, you'll feel better to hear that the cast was equally so, while making the film. The Dream CG World and The Art of Fantasy dive into the animating process along with the noticeable digital presence that helped to reflect the story's technological angle of harnessing dreams. The attention to detail and the emphasis on true-to-life movement for objects that you would have otherwise not have noticed is very impressive and speaks to Satoshi Kon's dedication to storytelling.
Umbrella's artwork is a vast improvement over the previous edition that was slapped with labels and banners promoting the Blu-ray format, whereas this reversible layout clearly showcases what's in store for the viewer.
Paprika was originally slated for Umbrella's very popular sub-label, Beyond Genres, and while it would have brought some diversity to the line, I'm glad it still got a release. I wouldn't cite the film as a gateway into Anime, but it's a window into the mind of one of Japan's greatest visionary filmmakers, who sadly passed in 2010 at age 46.
(2006), director: Satoshi Kon)
direct blu-ray screen captures

redpajama_set_name: RedPajamaCommonCrawl


Cost to have roofing shingles installed varies greatly by region (and even by zip code).	Get free estimates from roofers in your city.
Cost to have roofing shingles installed varies greatly by region (and even by zip code).
How much does it cost to have roofing shingles installed?
There are a few kinds of roofing shingles readily available. The most common are the asphalt types that cost around $80 to $100 per square. A square covers approximately one hundred square feet. There are also wood shingles or "shakes" (which are the types cut by hand instead of machine), and BuildDirect.com places the average cost of a square of cedar shingles at $160. There are also tile shingles and metal versions too.
For the purpose of this discussion we will explore the costs and materials needed for two types of roofing shingles 1 projects - the asphalt 2 type and the wooden shingle 1 variety.
First we will illustrate the type of property on which the shingles 1 will be installed. This will be the "average" home which offers 2200 square feet distributed across two stores. The footprint 3 of this home is around 24'x45', and the roof is a very simple design without a lot of valleys or angles. If the roof has a moderate pitch of 6/12 (meaning that the roof rises six inches every 12" it runs), we can assume that the roof will take up approximately 1300 to 1500 square feet.
The asphalt 2 types: regardless of whether the homeowner is opting to run a whole new course of shingles 1 over a pre-existing one (which is allowed in many areas) or installing a new roof altogether, they are going to require around 13 to 15 squares of shingles 1. This means a total cost of $1500. There is also the need for roofing felt at $18 per roll, for a total of $90. Attaching all of the materials requires a roofing nail gun using a standard roll of 1 1/4" galvanized nails at $65 each, and roofing cement which is costs approximately $22 per gallon.
Roof sealant: applying sealant is a great option for preventing future leaks. This typically costs $850-$1000 for a standard roof.
Underlayment 5 is a waterproofing membrane always recommended to prevent the ingress of moisture and other elements. It also provides weather-proofing until the roof is installed. It is required depending on the type of roofing material and slope. Check local codes for requirements.
Time: the major factor that most homeowners consider is the cost of the project. Thought the asphalt 2 shingles 1 are both inexpensive and easy to install, the wooden shingles 1 usually offer a significantly longer life span (averaging between 30 and 50 years). Additionally, they are far more environmentally friendly and provide a level of insulation that asphalt 2 options cannot.
How much does it cost to have roofing shingles installed in my city?
Cost to have roofing shingles installed varies greatly by region (and even by zip code). To get free estimates from local contractors, please indicate yours.


text:
package com.siyeh.ig.controlflow;

import com.siyeh.ig.IGInspectionTestCase;

public class SwitchStatementDensityInspectionTest extends IGInspectionTestCase {

  public void test() throws Exception {
    doTest("com/siyeh/igtest/controlflow/switch_statement_density", new SwitchStatementDensityInspection());
  }
}

redpajama_set_name: RedPajamaGithub


text:
Q: What was the fate of Luke's landspeeder? Luke and Ben and the droids make it to Mos Eisley and mind trick their way past the troopers. That's the last we see of the land speeder.
Was it just a loss that Luke wrote off when they had to flee Tatooine on the Falcon? Or did Luke take time to sell it for some extra cash between the cantina mess and the craziness in the hangar?
Any Canon or Legends information? (novelizations, comics, etc.)

A: Canon(ish)
There was a scene in the original screenplay that addressed this. Luke sold it to a 'used speeder dealer'. Beyond this, its fate is unknown.

EXT. TATOOINE - MOS EISLEY - STREET - ALLEYWAY - USED  SPEEDER LOT.
Ben and Luke are standing in a sleazy used speeder lot,  talking with a tall, grotesque, insect-like used speeder dealer. Strange exotic bodies and spindly-legged beasts pass by as the insect concludes the sale by giving Luke some coins.
LUKE: He says it's the best he can do.  Since the XP-38 came out, they're just not in demand.
BEN: It will be enough.
Ben and Luke leave the speeder lot and walk down the dusty
alleyway past a small robot herding a bunch of anteater-like
creatures. Luke turns and gives one last forlorn look at his
faithful speeder as he rounds a corner.

Some of Luke and Ben's dialogue made it into the film, but not the used car speeder dealership, which was deleted, presumably because it adds little to the plot.

hat tip to Megaptera novaeangliae for the reminder.

We learn in the junior novelisation that he sold his speeder for 2000 Imperial credits (described in the original novelisation as "a number of small metal shapes").

Facing the speeder dealer, Luke said, "All right, give it to me, I'll
take it." The speeder dealer, an insectoid alien, had finally agreed
to buy Luke's cherished landspeeder for two thousand credits.

The scene made it into the Marvel comic adaptation, issue #2

Legends
We have some additional info about the creature that purchased the speeder, a vuvrian called Wioslea. The company that she worked for was called Spaceport Speeders.
The negotiations were captured in the Star Wars Radio Play.

Luke Skywalker: I want to sell this speeder.
Obi-Wan Kenobi: If we can get a fair price.
Luke Skywalker: Yeah. I paid twenty-four hundred for it, and I put a lot of work into it. I think it's worth a lot more.
Wioslea: Fifteen hundred is all I can offer you, human.
Luke Skywalker: Fifteen hundred? Look, this landspeeder's in great shape, even if it's not very pretty.
Obi-Wan Kenobi: We can accept no less than two thousand.
Wioslea: Your grip on reality is fragile, humans. It's the newer models that are in demand now, not these old ones. I shall probably have to sell it at a loss.
Obi-Wan Kenobi: But it's still not a fair price.
Wioslea: You touch a responsive chord in my… charitable nature, humans. Sixteen hundred.

And it was confirmed in the EU novel Star Wars Journal: The Fight for Justice that Obi-Wan used a Jedi mind-trick to convince Wioslea to pay precisely what the Jedi needed to pay for passage off the planet.

We left Solo and Chewie at the cantina and headed over to a used
speeder lot with Artoo and Threepio. As usual, the dealer tried to cut
us down in price, and Ben's use of the Force saved us again. We needed
two thousand for Captain Solo, and that's what Ben got us. I'm sure
the dealer's going to wonder why he was so generous when he recovers,
but he can still sell the landspeeder at a profit, so we didn't feel
too badly.

The speeder ended up being purchased from the used speeder lot and used by a racer called Wade Vox in the game Star Wars: Demolition.

We have confirmation from Star Wars Insider #50 that this was indeed Luke's speeder.
At the end of the game Wade is killed and the fate of the speeder is unknown, but presumably it ends up in the possession of Jabba the Hutt.

A: It's shown that he sold it to pay for their passage on the Falcon.

LUKE: Look at this. Ever since the XP-38 came out, they just aren't in
demand.
BEN: It'll be enough.

A: Tatooine Manhunt, a WEG D6 Adventure, has the option of buying Luke's landspeeder from the Speeder Salesman, selling it along with others to the PCs.

Spaceport Speeders is a repulsor vehicle shop that rents, sells, and
buys land- and airspeeders. In fact, Luke Skywalker's landspeeder is
on sale — for 4,000 credits! Other than that, there are only two
landspeeders available — an old Mobquet A-l and a rebuilt Ubrikkian.
The heavy influx of bounty hunters has cleaned the shop out. Three
bumbling, rather inept-looking bounty hunters haggle with the shop
owner about the price of Luke's old vehicle, and Zardra (see the
pullout) eyes the Ubrikkian when the PCs reach this encounter. She
greets them with a chilling smile, and finishes her purchase

Pretty fun thing they added.

redpajama_set_name: RedPajamaStackExchange


text:
Director Adrian Lyne (Lolita, Indecent Proposal) has a thing for issues of fidelity and relationships. Movies like Indecent Proposal and Fatal Attraction demonstrate Lyne wants to explore issues regarding trust, and Unfaithful is no different. Here, Lyne wants to look at the effects of an affair on both partners of a marriage. Superficially, Edward (Richard Gere, The Mothman Prophecies, Dr. T & the Women) and Connie Sumner (Diane Lane, The Glass House, Hardball) have a good marriage. They have a young son (Erik Per Sullivan, Wendigo, Joe Dirt), a nice house, Edward owns a business, and Connie participates in community functions. On a deeper level, the love between the two is gone. They are friends, but it's hard to say whether or not they are in love. Unfaithful is based on the 1969 French film La Femme Infidele.
The slump in their relationship comes to a head on a windy day in New York. Connie literally runs into Paul Martel (Olivier Martinez, Bullfighter, Before Night Falls), a sexy young book dealer. She skins her knee, and Paul invites her up to his apartment for a Band-Aid. Paul represents things that her husband does not. He is spontaneous, lively, and extremely attentive towards Connie. He also obviously has his sights set on seducing Connie. He turns into an obsession for Connie, who begins a steamy affair. All the while, Edward suspects nothing. Unfaithful unfolds in three main acts. In the first, Connie deals with her own emotions towards infidelity. The second is the affair, and the third is the ramifications when Edward discovers what is happening.
The best scenes involve Lane. The screenplay by Alvin Sargent (Anywhere But Here, Bogus) and William Broyles, Jr. (Planet of the Apes, Cast Away) gives Lane ample opportunity to express a wide variety of emotions. She ably expresses the hesitation and near revulsion of a potential affair, the bliss of hedonistic pleasure, and the realization that what she is doing is horribly wrong. Lane is one of the better actresses working today, and for whatever reason, most people still don't know her. The emotions she expresses when she finally gives in to Paul's advances are heartbreaking. She is happy and horrified at the same time. Gere and Martinez pale in comparison to her. Their characters are shallowly written and devoid of personality. This may be what they intended for Edward, but he undergoes a personality change in the last portions of the film that do not make sense. Martinez needs to do nothing more than walk around with his shirt off.
Unfaithful falls apart in the third act. Lyne defenestrates everything he carefully constructed with a ludicrous action by one of the main characters. Lyne wants the viewer to relate to the introspection that each character is undergoing, and to realize how difficult the choices they need to make are. However, the events play off as unintentionally funny. This is partially because of Gere's approach to Edward, which is mostly hands-off. The denouement also takes much too long, forcing the viewer to watch while Lyne tries to convey his message. Lyne shot two endings, and used the first, which is more ambiguous. This is more realistic and brings the level of Unfaithful up a notch, but comes a little too late.
Haro Rates It: Not That Good.
2 hours, 1 minute, Rated R for sexuality, language, and a scene of violence.


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text:
Olympics: Flora Duffy To Compete Tomorrow
[Written by Don Burgess]
Flora Duffy is cutting out all the distractions ahead of her biggest moment on the world stage on Saturday. The Bermudian has a legitimate shot of making the podium in the Rio Olympic women's triathlon at 11am tomorrow.
Local residents are likely to catch at least snippets of the race live on TV, but they can watch it in its entirety without commentary if they download the Bermuda Broadcasting Rio 2016 app, available in both the Apple and Google Play stores.
Duffy posted on Facebook Thursday morning: "Thanks for all the good luck messages. I'm really excited to race this Saturday in Rio. I'm going to take the next few days off social media, so if I don't respond to a message that is why. See you all on the flip side."
Duffy — who has a string of international wins — is widely considered a medal contender at these Games, with USA Today predicting Duffy will win the silver while Sports Illustrated has her down for the bronze.
Triathlon Magazine issued their predictions yesterday, and while they predict Duffy could well be leading after the bike, they are saying stronger runners will pass her and keep the Bermudian off the podium.
Most odds makers have her from 6-1 to 8-1 win the gold medal with her being 4.5-1 to win any medal. Duffy is currently the number one ranked woman in the world in triathlon.
Bermuda — which is the smallest nation in the world to hold an Olympic medal due to Clarence Hill's 1976 bronze in boxing — has a legitimate shot to double that total.
Duffy had her specialized bike "blinged" with the Rio Olympic colours for Saturday's triathlon.
If you had asked triathlon experts a year ago, it is likely none of them would have listed Duffy as a medal favourite for the Olympics.
An article in Lava magazine illustrates this, saying "A year or so ago, she was an outlier. A risk-taking cyclist who was often eaten up on the run. At the Bejing Olympic Games, Bermuda's Flora Duffy was a DNF. In London she finished a dismal 45th, a few spots back of some American named "Jorgensen" in 38th and some Swiss girl named "Ryf" in 40th.
"Flora Duffy is pack fill no longer. And certainly not this year. As if a veil had been lifted, Duffy—the reigning XTERRA World Champ and a bike specialist in the mold of the great Craig Walton—swims front back, then at her choosing, takes off on the bike. In the past, some from the bike pack would reel her in on the run. Lately, however, Duffy's run has improved, increasing her chances of staying away to the finish.
"This summer has been a windfall. While she's come close to victory last year and finished 11th at the Rio test event last August, this year she's broken through, in stepladder fashion; fourth at WTC Abu Dhabi and Gold Coast. Third at WTS Cape Town. Second at WTS Leeds. Then finally, her first WTS win at Stockholm. She polished that off with a victory this weekend at the ITU's World Cup stop in Montreal.
"If you wanted to write a textbook on how to peak for an apex event like the Olympics, there it is. So the question becomes not if Duffy can go top 15, or even top 5. It's become "what color hardware can she bring home for Bermuda?" the Lava report added.
So on Saturday morning, the eyes of Bermuda will be on her. Packed sports bars will pry their gaze away from English football; mom and dads with sit in front of the TV with their children hoping that Duffy can make it on the podium and stand there with a medal around her neck.
Regardless of the outcome, Bermuda will be proud that one of our own more than held place amongst the world's elite.
The Olympic Triathlon Women start positions follow below [PDF here]:
Videos: Flora Duffy Finishes 8th In Olympics
Flora Duffy Praised For 'Magnificent Performance'
Olympics: Five Athletes In Action Tomorrow
Olympics: Pimentel & Wollmann Continue Sailing
Olympics: Pimentel & Wollmann Continue In Rio
Olympics: Family On Hand To Support Wollmann
#DonBurgessReports #FloraDuffy #Olympics
Category: All, News, Sports
hmmm says:
Lots of competition for the medals.
Fl..uffy, it's just another race. Believe in yourself, believe that you can get it.
We know there are some tough competitors out there ..the American, some British and others…Guess what they are all thinking…there is tough competition out there, the Bermudian. Show them why they are right to admire you.
Have a great race…we are all very proud of you.
Flora is our best hope of getting a medal at the Olympics in a long time, I am hoping she wins the gold and then like Usain Bolt encourage those who want to do adverts using her to come to Bermuda and shoot them so Bermudians can benefit…
aceboy says:
You make us proud. No matter what happens. Best of luck and we are all rooting for you!
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text:
Chiesa di Santa Caterina (Church of Saint Catherine), Palermo
The Church of Saint Catherine, otherwise known locally as the Church of Santa Caterina delle Donne, is found in the heart of the historic center of Palermo, between Piazza Bellini and Piazza Pretoria.
In 1310, following a testament left by the rich local aristocrat, Benvenuta Magistro Angelo (Mastrangelo), the area, previously occupied by the palace of George of Antioch, admiral of Roger II of Sicily, was secured for the foundation of a Dominican convent, complete with a church. The new nunnery was dedicated to Saint Catherine of Alexandria.
In 1532, the church was expanded, and between 1566 and 1596 was further remodeled under the supervision of the Mother Prioress Maria del Carretto. It was inaugurated on 24 November 1596.
The building represents a synthesis of Sicilian Baroque, Rococo and Renaissance styles, manifested in the combination of texture, sculptures, mixed marble, stucco, and frescoes. For a long time, its design was attributed to Giorgio di Faccio, the architect of another church in Palermo, of San Giorgio dei Genovesi. However, more recent studies reveal the involvement of two other architects, such as Florentine's Francesco Camilliani and Lombardia's Antonio Muttone, who had been involved in the construction of Piazza Pretoria. The dome was designed by Francesco Ferrigno. The inner ornamentation dates back to the 17th and 18th centuries.
During the 19th century, the church was damaged on several occasions, namely: the uprising of 1820-1821, the Sicilian revolution of 1848, the Gancia revolt (1860), and the Seven and a Half Days revolt (1866).
Image Courtesy of Flickr and Andrea Schaffer.
Want to visit this sight? Check out these Self-Guided Walking Tours in Palermo. Alternatively, you can download the mobile app "GPSmyCity: Walks in 1K+ Cities" from iTunes App Store or Google Play. The app turns your mobile device to a personal tour guide and it works offline, so no data plan is needed when traveling abroad.
Chiesa di Santa Caterina (Church of Saint Catherine) on Map
Sight Name: Chiesa di Santa Caterina (Church of Saint Catherine)
Sight Location: Palermo, Italy (See walking tours in Palermo)
Sight Type: Religious
Historical Religious Buildings
Walking Tours in Palermo, Italy
Create Your Own Walk in Palermo
Creating your own self-guided walk in Palermo is easy and fun. Choose the city attractions that you want to see and a walk route map will be created just for you. You can even set your hotel as the start point of the walk.
Palaces of Palermo
For centuries, Sicilian capital Palermo has been a focal point for the construction of noble dwellings and palaces. The latter were and still are a fine manifestation of the historical life in the region. They are a few hundred of them still in place, ranging chronologically from the medieval period to the first decades of the 20th century, and comprising an impressive collection of architectural... view more
Palermo Introduction Walking Tour
Around 8,000 BC, people called Sicani drew pictures in a cave outside Palermo. The scene shows a ritual sacrifice in the theatre. Was it a paleolithic foreshadowing of the temples, churches, and cathedrals to come?
The Sicani were succeeded by the Phoenicians. They made a settlement called Ziz which eventually became Palermo. But first, the Greeks, followed by the Carthaginians, followed by the... view more
Religion has always played an important role in the lives of the Italians – and the Sicilians are no exception. Thus, it is little wonder that the main city of the island, Palermo, boasts a plethora of religious buildings. The collection of historical churches found here ranges from the Arab-Norman-Byzantine to the Medieval, Gothic, Baroque and the Renaissance.
In the course of history, many... view more


The IRS recently approved a unique move by employers and unions in the hospitality industry to replace a 401(k) plan with a new type of traditional pension plan.
For decades, employers have left the pension system in droves and adopted 401(k) plans for their workers. But trustees for a 401(k) plan covering 4,000 members of the Boston-based UNITE HERE! union saw that members weren't using their 401(k). So the trustees made the move to a new type of pension plan.
"Seventy-five percent of the plan's members weren't participating in the 401(k) and we felt the plan was unfair to employees because they incurred all of the investment risk," Brian Lang, president of UNITE HERE Local 26, told Bloomberg BNA. During bargaining negotiations, the union began pushing for a traditional pension that would reduce this risk and be fair to its members. The union also sought a pension that would address some of the concerns of plan sponsors, which typically object to the costs and risks for them inherent in traditional pensions, said Lang, who is a union-trustee of the pension trust.
The union and plan sponsors in 2012 implemented a new pension design that they believed would be fair to both employees and contributing employers. On July 24, 2017, the trustees received word that the IRS had approved tax-qualified status for the plan. The plan continues to maintain the 401(k) but makes matching contributions only for members who held accounts when the contributions were frozen for new participants.
The new plan design, known as the variable defined benefit, "works well in favor of plan sponsors in that it limits their liability going forward," George Greene, vice president for labor relations at Marriott International in New York, told Bloomberg BNA. Greene was involved in collective bargaining negotiations over the new plan.
The VDB "is intended to be 100 percent funded," Jim Stamos, one of the plan's three employer representative-trustees, told Bloomberg BNA. If the plan dips below a 90 percent funded level, the trustees are "required to do whatever it takes to bring it back to full-funding," said Stamos, a retired vice president with Omni Hotels and the former dean of Boston University's School of Hospitality Administration.
Lang said that the new pension was a smart move for the plan sponsors as well as for union members. That's because there's a shortage of hotel industry workers in the Boston area and the availability of the pension serves to attract high-quality skilled workers who want to stay at their jobs, he said.
The VDB is attractive to plan sponsors because it "provides a higher degree of employer contribution stability," Richard Hudson, the plan's enrolled actuary, told Bloomberg BNA. It's also beneficial for participants because investment risk is shared by sponsors and participants. In addition, participants can accrue greater benefits based on positive investment returns on plan assets.
The plan includes two separate benefit accrual calculations—a guaranteed minimum floor benefit and a variable benefit based on the investment performance of plan assets. The annual adjustment to the variable benefit is capped at 4.5 percent above the plan's targeted 5.5 percent investment return. At retirement, participants would be entitled to the accrual calculation providing the greater benefit.
The 5.5 percent expected return is significantly lower than the 7 to 8 percent return that plans typically use. "The lower return expectation gives a higher chance of exceeding the plan's investment benchmark," said Hudson, a principal consulting actuary with Cheiron in New York.
If the plan's funded level falls below 90 percent, the trustees are required to act to bring the fund back to a 100 percent funded level. One of those actions would be to slow down the participants' annual floor benefit accrual until the desired funding level is achieved, Hudson said. Such a slowdown in floor benefit accruals wouldn't affect any benefits that members accrued in prior years, he said.
The IRS determination letter is at http://src.bna.com/reE.


text:
\section{Introduction}
Person re-identification (Re-ID)~\cite{sun2018beyond,liu2016multi,liu2019adaptive} has attracted increasing attention recently, due to its widespread applications in automated tracking~\cite{wang2013intelligent,kim2017omnitrack} and activity analysis~\cite{aggarwal2011human,caba2015activitynet}, \textit{etc}. It aims at identifying a target pedestrian from a gallery set captured from multi-disjoint camera views. Person Re-ID is quite challenging due to large intra-class and small inter-class variations caused by background clutter, occlusion, dramatic variations in illumination, body pose, \textit{etc}. Most existing person Re-ID methods mainly focus on RGB images of pedestrians from visible cameras and formulate the task as a single-modality (RGB-RGB) matching problem. They have achieved remarkable progresses in recent years for addressing appearance discrepancy (large intra-class and small inter-class variations). However, visible (RGB) cameras can not provide useful appearance information under poor illumination environments (\textit{e.g.}, at night), which limits the applicability of person Re-ID in a real scenario.

To handle this issue, recent surveillance systems begin to be equipped with infrared (IR) cameras to facilitate night-time monitoring, which raises a new cross-modality matching task termed RGB-infrared person Re-ID~\cite{wu2017rgb}. RGB-infrared person Re-ID aims to find the corresponding IR (or RGB) images of the same person captured by other spectrum cameras, given a RGB (or IR) image of a target person. Compared with the conventional single-modality person Re-ID, it encounters prominent modality discrepancy derived from the different imaging processes between different spectrum cameras (RGB and infrared images are intrinsically heterogeneous, which have different wavelength ranges), apart from appearance discrepancy. The key solution for RGB-infrared person Re-ID is to bridge large modality gap, and learn modality-invariant and discriminative features from RGB and IR images.

Existing RGB-infrared person Re-ID approaches mainly concentrate on mitigating the inherent modality discrepancy at the pixel-level or the feature-level to extract cross-modality shared features. For alleviating modality discrepancy at the pixel-level, these methods commonly design complex generative adversarial models~\cite{dai2018cross,wang2019learning,wang2020cross,wang2019rgb} to perform image-to-image translation and generate fake counterpart images, which are difficult to optimize and inevitably introduce noisy generated samples due to the ill-posed infrared-to-RGB transforming. On the other hand, for mitigating modality discrepancy at the feature-level, these methods employ one-stream~\cite{wu2017rgb,li2020infrared} or two-stream networks~\cite{ye2018hierarchical,ye2018visible,luo2020dynamic,hao2019hsme,zhu2020hetero,ye2019bi} to extract modality-invariant features by several customized losses. Nevertheless, one-stream network based methods learn a common network model, which lacks the capacity of explicitly modeling individual modalities and neglects modality specific characteristics, leading to crucial information loss. The two-stream network based methods firstly utilize separate branch layers for each modality to abstract modality-specific information, and then use non-branched shared layers for projecting the modality-specific features into a common feature space. They totally separate the process of modeling modality-specific and modality-share information, and could damage the vital cross-modality shared semantic during extracting modality-specific features. Furthermore, all aforementioned methods attempt to directly handle such large modality discrepancy and align two modalities, which are sensitive to the parameters and difficult to converge.

In this work, we propose a novel modality-adaptive mixup and invariant decomposition (MID) approach for RGB-infrared person Re-ID towards learning modality-invariant and discriminative representations. MID designs a modality-adaptive mixup scheme (MAM) to generate appropriate mixed modality images and to reconcile modality gap at the pixel-level, according to the dynamical appearance and modality discrepancies between different RGB and IR images. MID then employs modality-adaptive convolution decomposition (MACD) to simultaneously counter modality discrepancy and enforce cross-modality shared semantics at the feature-level, towards facilitating cross-modality feature learning. Specifically, as shown in Figure \ref{fig2}, MAM is implemented by an actor-critic agent under a deep reinforcement learning framework. The state of the agent is the intermediate feature maps of RGB-infrared image pair, while the relevant action is the mixup ratio at a training step. With the joint optimization of the actor and critic networks, the suitability of the mixup ratio could be progressively boosted, driven by the supervision signal of reward that measures the relative performance improvement of person Re-ID by utilizing the generated mixed modality samples in the evaluation metrics. The actor-critic agent finally performs a dynamical and local linear interpolation policy between different regions of two modality images in a data-adaptive way, and generates augmented mixed modality samples with identity consistency. The mixed modality images mitigate modality discrepancy at the pixel-level and lead to a more continuous modality-invariant latent space. Moreover, MACD is designed to decompose a regular convolution layer into modality-specific basis layers and a modality-shared coefficient layer for handling the discrepancies among RGB, IR and mixed modalities. The former is in charge of modality intrinsic characteristics, while the latter enforces cross-modality shared decomposition coefficient to capture invariant semantics. ResNet-50 model~\cite{he2016deep} is equipped with MACD to learn aligned and discriminative features of pedestrians. Extensive experimental results on two datasets have shown the effectiveness of the proposed approach.

Note that the related work XIV~\cite{li2020infrared} also generates X modality as an auxiliary modality to optimize the model. Nevertheless, XIV produces the limited X modality only from RGB images without considering the characteristics of IR images, failing to generate high-quality mixed modality samples for reducing modality gap and guaranteeing modality-invariant in a more continuous latent space.

The main contributions are summarized as follows: (1) We propose a novel modality-adaptive mixup and invariant decomposition for RGB-infrared person re-identification towards learning modality-invariant and discriminative representation. (2) We propose a modality-adaptive mixup scheme to generate suitable mixed modality samples for reconciling RGB and IR modalities at the pixel-level. (3) We design a modality-adaptive convolution decomposition to capture invariant visual semantics and shrink the modality discrepancy at the feature-level.

\section{Related work}

\begin{figure*}[!t]
	\centering
	\includegraphics[width=1.0\textwidth]{pipeline.png}
	\caption{The overall architecture of the proposed MID. It consists of a modality-adaptive mixup module for generating appropriate mixed modality images and reducing the modality discrepancy at the pixel-level, and a decomposed convolution network for further shrinking the modality discrepancy at the feature-level to learn modality-invariant and discriminative representations.}
	\label{fig2}
\end{figure*}

\subsection{RGB-Infrared Person Re-ID}
Existing RGB-infrared person Re-ID approaches mitigate the modality discrepancy at the pixel-level~\cite{dai2018cross,wang2020cross,fan2020modality} or the feature-level~\cite{wu2017rgb,li2020infrared,ye2018hierarchical,ye2018visible,luo2020dynamic,hao2019hsme}. For alleviating modality discrepancy at the pixel-level, Dai \textit{et al.}~\cite{dai2018cross} proposed a cross-modality generative adversarial network to handle the large-scale cross-modality metric learning problem. Wang \textit{et al.}~\cite{wang2020cross} proposed to generate cross-modality paired-images and perform both global set-level and fine-grained instance-level alignments. Fan \textit{et al.}~\cite{fan2020modality} proposed a modality-transfer generative adversarial network to generate a cross-modality counterpart of a source image in the target modality, for obtaining paired images and producing a general and robust unified feature embedding. Zhang~\textit{et al.}~\cite{zhang2021rgb} proposed a teacher-student GAN model (TS-GAN) to generate the corresponding fake IR images and guide the backbone to learn better feature. For mitigating modality discrepancy at the feature-level, Ye \textit{et al.}~\cite{ye2018hierarchical} proposed a hierarchical cross-modality matching model by jointly optimizing the modality-specific and modality-shared metrics. Ye \textit{et al.}~\cite{ye2018visible} proposed a dual-path network with a bi-directional dual-constrained top-ranking loss to learn discriminative feature representations. Luo~\textit{et al.}~\cite{luo2020dynamic} proposed a dynamic dual-attentive aggregation (DDAG) learning method by mining both intra-modality part-level and cross-modality graph-level contextual cues for RGB-infrared person Re-ID. Zhu~\textit{et al.}~\cite{zhu2020hetero} proposed a hetero-center loss to reduce the intra-class cross-modality variations and utilized a two-stream part-pooling-based model to obtain modality-invariant features.

\subsection{Domain Mixup}
\label{related:mixup}
Mixup is a recent data augmentation scheme to regularize deep learning models via \textit{global and random linear interpolations} between pairs of samples and their labels, which plays an important role in domain adaption \cite{zhang2017mixup, verma2019manifold,xu2020adversarial,zhong2020openmix}. For example, Zhang \textit{et al.} \cite{zhang2017mixup} proposed a simple data augmentation method termed MixUp, which randomly generates virtual training images by linearly interpolating two images and their corresponding labels. Panfilov \textit{et al.}~\cite{panfilov2019improving} investigated two regularization including mixup and adversarial unsupervised domain adaptation to improve the generalization of deep learning based knee cartilage segmentation model. Wu \textit{et al.}~\cite{wu2020dual} proposed a dual mixup regularized learning method for unsupervised domain adaptation, which jointly conducts category and domain mixup regularizations at the pixel-level to enhance the robustness of the model. Xu \textit{et al.}~\cite{xu2020adversarial} presented an adversarial domain adaptation with domain mixup (DM-ADA), which conduct on domain mixup on pixel and feature level to improve the robustness of models. Nevertheless, these existing mixup schemes are not suitable for RGB-Infrared Person Re-ID, which is elaborately analyzed in the next section.

\section{Method}

\subsection{Problem Definition and Overview}
Let $\boldsymbol{x}^{rgb}$ donates RGB image, and $\boldsymbol{x}^{ir}$ donate IR image. The RGB-IR training dataset is represented as $\boldsymbol{X} = \{\boldsymbol{x}^{rgb}_i, \boldsymbol{x}^{ir}_i, y_i\}_{i=1}^N$, where $N$ is the number of pedestrian images. Each image $\boldsymbol{x}^{rgb}$ or $\boldsymbol{x}^{ir}$ corresponds to an identity label $\boldsymbol{Y} =\{y_i\}_{i=1}^N$, where $y \in \{1,2,...,\mathcal{P} \}$ , and $\mathcal{P}$ refers to the number of persons. The objective is to learn a modality-invariant and discriminative representation for identifying the specific pedestrian from RGB and IR images. We propose a novel modality-adaptive mixup and invariant decomposition (MID) approach for RGB-infrared person Re-ID. As shown in Figure \ref{fig2}, MID firstly employs the modality-adaptive mixup scheme to synthesize augmented mixed modality images between RGB and IR images. The three modality images are then fed into a decomposed convolution network to learn modality-invariant and discriminative features, which is termed as $\mathcal{F}$. The first three residual blocks in $\mathcal{F}$ are denoted as $\mathcal{F}_1$, which are equipped with the designed MACD for absorbing modality variations and aligning invariant semantics across modalities. The decomposed convolution network is optimized with an identification loss and a center triplet loss for re-identification.

\subsection{Modality-Adaptive Mixup Scheme}
\label{sec.mixup}
Mixup is an effective data augmentation algorithm that regularizes deep learning models by linear interpolations between pairs of samples and their labels. Nevertheless, existing mixup scheme can not be directly applied to RGB-Infrared Person Re-ID for effectively reconciling RGB and IR modalities at the pixel-level. They commonly generate virtual samples via a simple \textit{global and random} linear interpolation policy. The mixup ratio for interpolating two samples is a scalar, and randomly sampled from a Beta$(\alpha, \alpha)$ distribution with a hyper-parameter $\alpha$. As the inter-modality discrepancy between different RGB and IR images dramatically changes, a random mixup ratio could produce low-quality mixed modality images, which is non-adjacent to real RGB and IR images of the same identity, resulting in manifold intrusion issue. Moreover, as RGB-Infrared Re-ID datasets lack paired RGB and IR images of the same pedestrian and have large intra-modality variations, a scalar mix ratio for globally interpolating RGB and IR images could corrupt the visual semantic and identity information of the mixed modality image, leading to performance degradation.

For generating appropriate mixed modality images and guaranteeing the identity invariance, we propose a modality-adaptive mixup scheme to bridge the modality discrepancy at the pixel-level. It learns a dynamical and local linear interpolation between the different regions of cross-modality images in data-dependent fashion, which is formulated as Markov decision process and implemented by an actor-critic agent under a deep reinforcement learning (RL) framework~\cite{lillicrap2015continuous,li2020deep}. Specifically, the modality-adaptive mixup is computed as follows:
\begin{equation}
	\begin{split}

		\boldsymbol{x}^{mix}_{i,g} &= m_{i,g} \boldsymbol{x}^{rgb}_{i,g} + (1 - m_{i,g}) \boldsymbol{x}^{ir}_{i,g}, \\
		y^{mix}_{i} &= y^{rgb}_{i} = y^{ir}_{i},
	\end{split}
	\label{eq:mixup}
\end{equation}
where $m_{i,g}\in[0,1]$ is the mixup ratio learned from the actor-critic agent with RGB image $\boldsymbol{x}_i^{rgb}$ and IR image $\boldsymbol{x}_i^{ir}$. $\boldsymbol{x}_i^{rgb}$ and $\boldsymbol{x}_i^{ir}$ are divided into $G$ local regions of $\{\boldsymbol{x}_{i,g}^{rgb}\}_{g=1}^G$ and $\{\boldsymbol{x}_{i,g}^{ir}\}_{g=1}^G$ along horizontal axis, respectively. $\boldsymbol{x}^{mix}_{i,g}$ is the $g$-th local region of the generated mixed modality image. $y_i^{mix}$, $y_i^{rgb}$, $y_i^{ir}$ share the same identity.

The mixup ratio dynamically adjusts based on the modality and appearance discrepancies between the corresponding local regions of RGB and IR images, which is performed by the actor-critic agent. The actor network $\mathcal{A}$ in the agent is employed to estimate the mixup ratio $\boldsymbol{m}_i$, and the critic network $\mathcal{Q}$ in the agent predicts the state-action value (Q-value). $\mathcal{A}$ is formulated as follows:
\begin{equation}
	\boldsymbol{m}_i=\sigma\left(\boldsymbol{W}_{1} \delta(\boldsymbol{W}_{0}(\operatorname{Pool}( \operatorname{Conv}([\boldsymbol{F}_i^{rgb}, \boldsymbol{F}_i^{ir}]))))\right),
\end{equation}
where $\boldsymbol{F}_i^{rgb}$ denotes the intermediate feature map of $\boldsymbol{x}^{rgb}_{i}$ extracted from $\mathcal{F}_1$. $\sigma$ and $\delta$ denote the sigmoid function and rectified linear unit (ReLU) activation function, respectively. $\boldsymbol{W}_0$ and $\boldsymbol{W}_1$ denote two Fully Connected (FC) layers. $\operatorname{Conv}$ denotes a $3\times 3$ convolution layer, followed with a batch normalization layer and a ReLU layer. $\operatorname{Pool}$ indicates a global average pooling layer. The critic network in the actor-critic agent has a similar network architecture to the actor network. For guiding the critic network to predict the reliability of the mixup ratio, a reward $\mathcal{R}$ is designed as the supervision signal for optimization. The action, state and reward for the agent are defined as follows:

\textbf{State.} The concatenation of the intermediate feature maps $[\boldsymbol{F}_i^{rgb}; \boldsymbol{F}_i^{ir}]$ of the RGB and IR images extracted from $\mathcal{F}_1$ is viewed as the state of the agent.

\textbf{Action.} The action of the agent is the mix ratio $\boldsymbol{m}_i \in \mathbb{R}^G$ used for linear interpolation bewteen RGB and IR images. It is a continuous vector.

\textbf{Reward.} For a mini-batch input data $\{\boldsymbol{x}^{rgb}_{i}, \boldsymbol{x}^{ir}_{i}\}_{i=1}^{b}$ with $y^{rgb}_{i} = y^{ir}_{i}$, we can obtain the mixed modality images $\{\boldsymbol{x}_i^{mix}\}_{i=1}^{b}$ via dynamic and local linear interpolation in Eq.~(1). We then calculate the similarity matrix $[\boldsymbol{\mathcal{S}}^{rgb,ir}]_{i,j}= \langle\boldsymbol{f}^{rgb}_i, \boldsymbol{f}^{ir}_j\rangle$ between all RGB and IR images, and $[\boldsymbol{\mathcal{S}}^{mix,ir}]_{i,j} = \langle\boldsymbol{f}^{mix}_i, \boldsymbol{f}^{ir}_j\rangle$ between all mixed modality and IR images, where $\boldsymbol{f}_i$ denotes the learned pedestrian representation from the decomposed convolution network $\mathcal{F}$. $\langle\cdot,\cdot\rangle$ denotes Cosine Distance. Thus, the reward is defined as the relative performance improvement of re-identification by using the mixed modality images to enhance the similarity matrix, which is formulated as follows:
\begin{equation}
	\begin{aligned}
		\mathcal{R}=&\mathcal{E}(\boldsymbol{\mathcal{S}}^{rgb,ir}+\boldsymbol{\mathcal{S}}^{mix,ir})-\mathcal{E}(\boldsymbol{\mathcal{S}}^{rgb,ir})\\
		&+\mathcal{E}(\boldsymbol{\mathcal{S}}^{ir,rgb}+\boldsymbol{\mathcal{S}}^{mix,rgb})-\mathcal{E}(\boldsymbol{\mathcal{S}}^{ir,rgb}), \\
	\end{aligned}
\end{equation}

\begin{equation}
	\begin{aligned}
		\mathcal{E}(\boldsymbol{\mathcal{S}})=\operatorname{mAP}(\boldsymbol{\mathcal{S}})+\sum_{k=1}^{K}\frac{1}{k}\operatorname{rank-k}(\boldsymbol{\mathcal{S}}),
	\end{aligned}
\end{equation}
where $\operatorname{mAP}$ and $\operatorname{rank-k}$ are the common evaluation metrics for RGB-Infrared Person Re-ID based on the similarity matrix \cite{ye2018hierarchical,ye2018visible}. $K$ denotes the number of the pedestrian images for one identity in each modality. We adopt the losses $\mathcal{L}_{\mathcal{A}}$, $\mathcal{L}_{\mathcal{Q}}$ to optimize the actor and the critic networks, respectively, which will be introduced in the following subsection. The data-dependent modality-adaptive mixup scheme explicitly exploits the data distribution of two modalities, and can learn appropriate interpolation policy to generate high-quality mixed modality images for reducing modality discrepancy at the pixel-level and facilitating a more continuous modality-invariant latent space.

\subsection{Modality-Adaptive Conv Decomposition}

\label{sec.decompose}

To bridge modality gap at the feature-level, previous works employs one-stream network or two-stream network architecture. Nevertheless, one-stream network based methods lack the capacity of explicitly modeling modality-specific characteristics, resulting in unnecessary information loss. Two-stream network based methods separate the process of modeling modality-specific and modality-share information, vitiating the significant cross-modality shared semantic during extracting modality-specific features. They also require more training parameters.

To address these issues, we propose modality-adaptive convolution decomposition and design a decomposed convolution network $\mathcal{F}$. The decomposed convolution network is built on ResNet-50 model. The modality-adaptive convolution decomposition approximates convolution filters as a linear combination of a small set of dictionary bases, for simultaneously countering modality discrepancy and enforcing cross-modality shared semantic at the feature-level. It is applied to the convolution layers of first three residual blocks $\mathcal{F}_1$ in the decomposed convolution network, each of which is decomposed into modality-specific basis layers and a modality-shared coefficient layer, while both of them remain convolutional. Specifically, $\boldsymbol{W}$ denotes the convolution filters $S \times S \times C_{in} \times C_{out}$ of a convolution layer, where $S$ is the spatial size of the filters, $C_{in}$ and $C_{out}$ denotes the number of input channel and output channel. We decompose $\boldsymbol{W}$ into the modality-specific dictionary bases $\boldsymbol{\alpha} \in \mathbb{R}^{S \times S \times K}$ and the common coefficient across modalities $\boldsymbol{\Psi} \in \mathbb{R}^{K \times C_{in} \times C_{out}}$. In detail, each decomposed convolution layer has three independent modality-specific dictionary bases $\boldsymbol{\alpha}^{rgb},\boldsymbol{\alpha}^{ir},\boldsymbol{\alpha}^{mix}$, and the common coefficient $\boldsymbol{\Psi}$ across modalities:
\begin{equation}
	\boldsymbol{W}^* = [\boldsymbol{\alpha}^{\{rgb\}}\boldsymbol{\Psi}, \boldsymbol{\alpha}^{\{ir\}}\boldsymbol{\Psi}, \boldsymbol{\alpha}^{\{mix\}}\boldsymbol{\Psi}],
\end{equation}
The modality-specific dictionary bases are independently learned from the corresponding modality images to model the modality variations. They convolve spatially each individual input feature channel $[\boldsymbol{F}]_{1:C_{in}} \in \mathbb{R}^{H\times W}$ for modality discrepancy correction. The common coefficient is learned from all the three modality images, and performs $1 \times 1$ convolution for weighting sum the corrected output feature channels, thus promoting cross-modality shared semantic. The decomposed convolution network takes RGB, IR and mixed modality images from the modality-adaptive mixup module as input, and effectively handles the large modality gap at the feature-level for learning modality-invariant features. The network outputs global and local features $\boldsymbol{f}^{rgb}_i$,  $\boldsymbol{f}^{ir}_i$, $\boldsymbol{f}^{mix}_i$ of three modalities by part pooling operation \cite{sun2018beyond}.

\subsection{Loss Function and Optimization}
\label{sec.loss}

We adopt the identification loss $L_{id}$ with label smoothing regularization \cite{ainam2019sparse} and the center triplet loss $L_{ct}$ \cite{he2018triplet, hardtripletcenter} to optimize the decomposed convolution network for re-identification. The identification loss is calculated as follows:
\begin{equation}
	L_{id}=\sum_{i=1}^{\mathcal{P}}-q_i\log\left(p_i\right)
	,\; q_i=\left\{\begin{array}{cl}1-\frac{\mathcal{P}-1}{\mathcal{P}} \xi, & y=i \\ \frac{\xi}{\mathcal{P}}, & y \neq i\end{array}\right.
\end{equation}
where $\mathcal{P}$ is the number of identities in the training set, $y$ is the ground-truth ID and $p_i$ denotes the ID prediction logits of the $i^{th}$ class. $\xi$ is the smoothing parameter, which is beneficial to prevent the network from over-fitting to training IDs. The three identification losses $L_{id}^{rgb}$ , $L_{id}^{ir}$ , $L_{id}^{mix}$ are used to supervise the global and local features of three modalities $\boldsymbol{f}^{rgb}_i$,  $\boldsymbol{f}^{ir}_i$, $\boldsymbol{f}^{mix}_i$, respectively. For the center triplet loss, we randomly sample $P$ identities and $K$ images of each identity for RBG and IR modality to form a mini-batch with $PK$ images termed $\{\boldsymbol{x}^{rgb}_{p,k}, \boldsymbol{x}^{ir}_{p,k}, y_{p}\}_{p=1, k=1}^{P,K}$. We calculate the feature centers of each pedestrian for the three modality images in a mini-batch, $\boldsymbol{c}^{\{rgb,ir,mix\}}_p = \frac{1}{K} \sum_{k=1}^K \boldsymbol{f}^{\{rgb,ir,mix\}}_k$. Thus, the center triplet loss is defined as follows:
\begin{equation}
	\begin{aligned}
		L_{ct}^{\alpha,\beta}=
		&\sum_{i=1}^{P}[\rho+
		\|c^{\alpha}_p-\boldsymbol{c}^{\beta}_p\|_{2}-\min\limits_{\begin{subarray}{\boldsymbol{c}}n\in\{\alpha,\beta\} \\ j\neq p\end{subarray}}\|\boldsymbol{c}^{\alpha}_p-\boldsymbol{c}^{n}_{j}\|_{2}]_{+}
		& \\
		&+\sum_{i=1}^{P}[\rho+
		\|\boldsymbol{c}^{\beta}_p-\boldsymbol{c}^{\alpha}_p\|_{2}-\min\limits_{\begin{subarray}{\boldsymbol{c}}n\in\{\alpha,\beta\} \\ j\neq p\end{subarray}}\|\boldsymbol{c}^{\beta}_p-\boldsymbol{c}^{n}_{j}\|_{2}]_{+},
	\end{aligned}
	\label{loss:ct}
\end{equation}
where $\rho$ is a margin parameter, $[z]_+ = max(z, 0)$, $\alpha,\beta \in \{rgb,ir,mix\}$ which denote two different modalities. We utilize three center triplet losses $L_{ct}^{rgb,ir}, L_{ct}^{rgb,mix}, L_{ct}^{ir,mix}$ to supervise the three modality features $\boldsymbol{f}^{rgb}_i$,  $\boldsymbol{f}^{ir}_i$, $\boldsymbol{f}^{mix}_i$. Therefore, the total loss $L_{dcn}$ for the decomposed convolution network is the combination of these identification and center triplet losses:
\begin{equation}
	\begin{aligned}
		L_{dcn} = &\lambda_{1}L_{ct}^{rgb,ir} + \lambda_{2}L_{ct}^{rgb,mix} + \lambda_{3}L_{ct}^{ir,mix} \\  &+  \lambda_{4}L_{id}^{rgb} + \lambda_{5}L_{id}^{ir}+ \lambda_{6}L_{id}^{mix},
		\label{eq.loss}
	\end{aligned}
\end{equation}
where $\lambda_{1-6}$ is the trade-off parameters.

Different from standard reinforcement learning algorithm, the proposed actor-critic agent does not have explicit sequential relationship along different training steps. The action of the interpolation policy between RGB and IR images is conditioned on the state of their intermediate features in a one-shot fashion, which is essentially a one-step Markov decision process. The action space is continuous, thus the optimal action could be found by gradient ascent method following the solution of continuous Q-value prediction \cite{lillicrap2015continuous,li2020deep}. The loss for actor network is defined as follows:
\begin{equation}
	L_{\mathcal{A}}=-\mathcal{Q}(\mathcal{A}([\boldsymbol{F}^{rgb}, \boldsymbol{F}^{ir}]), [\boldsymbol{F}^{rgb}, \boldsymbol{F}^{ir}]),
\end{equation}
The actor network $\mathcal{A}$ is updated to achieve higher Q-value, which implies higher rank-$k$ accuracy and mAP for person Re-ID. The critic network is optimized to predict an accurate Q-value estimation, thus MSE loss is employed as follows:
\begin{equation}
	L_{\mathcal{Q}}=\|\mathcal{Q}(\mathcal{A}([\boldsymbol{F}^{rgb}, \boldsymbol{F}^{ir}]), [\boldsymbol{F}^{rgb}, \boldsymbol{F}^{ir}])-\mathcal{R}\|^{2},
\end{equation}
For the entire MID, we combine the advantage of supervised and reinforcement learning, alternately optimizing the decomposed convolution network and the actor-critic agent.

\section{Experiments}

\begin{table*}[!ht]
	\centering
	\caption{Performance (\%) comparison to the state-of-the-art methods on RegDB and SYSU-MM01 datasets.}
	\resizebox{\linewidth}{!}{
	\begin{tabular}{l|cc|c|cc|c|cc|c|cc|c}
		\hline
		\hline
		\multicolumn{1}{c|}{\multirow{3}[0]{*}{Methods }} & \multicolumn{6}{c|}{RegDB}                                     & \multicolumn{6}{c}{SYSU-MM01} \\
		& \multicolumn{3}{c}{Visible to Thermal} & \multicolumn{3}{c}{Thermal to Visible} & \multicolumn{3}{|c}{All}       & \multicolumn{3}{c}{Indoor} \\
		& \multicolumn{1}{c}{r1} & \multicolumn{1}{c}{r10} &  \multicolumn{1}{c}{mAP} & \multicolumn{1}{c}{r1} & \multicolumn{1}{c}{r10}  & \multicolumn{1}{c|}{mAP} & \multicolumn{1}{c}{r1} & \multicolumn{1}{c}{r10}  & \multicolumn{1}{c}{mAP} & \multicolumn{1}{c}{r1} & \multicolumn{1}{c}{r10}  & \multicolumn{1}{c}{mAP} \\
		\hline
		\hline
		Zero-Pad~\cite{wu2017rgb} & 17.75 & 34.21  & 18.90 & 16.63 & 34.68  & 17.82 & 14.80 & 54.12           & 15.95 & 20.58 & 68.38 & 26.92 \\
		HCML~\cite{ye2018hierarchical}     & 24.44 & 47.53  & 20.80 & 21.70 & 45.02  & 22.24 & 14.32 & 53.16           & 16.16 & 24.52 & 73.25  & 30.08 \\
		MAC~\cite{ye2019modality}      & 36.43 & 62.36  & 37.03 & 36.20 & 61.68  & 39.23 & 33.26 & 79.04           & 36.22 & 36.43 & 62.36 & 37.03 \\
		MSR~\cite{feng2019learning}      & 48.43 & 70.32  & 48.67 &  -    &   -    &   -   & 37.35 & 83.40           & 38.11 & 39.64 & 89.29  & 50.88 \\
		D-HSME~\cite{hao2019hsme}   & 50.85 & 73.36  & 47.00 & 50.15 & 72.40  & 46.16 & 20.68 & 62.74           & 23.12 &   -   &   -    & - \\
		EDFL~\cite{liu2020enhancing}     & 52.58 & 72.10  & 52.98 & 51.89 & 72.09  & 52.13 & 36.94 & 85.42           & 40.77 &   -   &  -      & - \\
		AlignGAN~\cite{wang2019rgb} & 57.90 &    -   & 53.60 & 56.30 &   -    & 53.40 & 42.40 & 85.00           & 40.70 & 45.90 & 87.60  & 54.30 \\
		TS-GAN~\cite{zhang2021rgb} & - & - & - & - & - & - & 49.8 & 87.3 & 47.4 & 50.4 & 90.8  & 63.1 \\
		XIV~\cite{li2020infrared}      & 62.21 & 83.13  & 60.18 &   -   & -      &  -    & 49.92 & 89.79           & 50.73 &   -   &  -      & - \\
		DDAG~\cite{luo2020dynamic},     & 69.34 & 86.19  & 63.46 & 68.06 & 85.15  & 61.80 & 54.75 & 90.39           & 53.02 & 61.02 & 94.06 & 67.98 \\
		Hi-CMD~\cite{choi2020hi}   & 70.93 & 86.39  & 66.04 &   -   & -      &  -    & 34.94 & 77.58           & 35.94 &   -   &   -     & - \\
		TSLFN~\cite{zhu2020hetero}   & - & -  & - & - & -  & - & 56.96 & 91.50           & 54.95 & 59.74 & 92.07  & 64.91 \\
		HAT~\cite{ye2020visible}      & 71.83 & 87.16  & 67.56 & 70.02 & 86.45  & 66.30 & 55.29 & \underline{92.14}          & 53.89 & 62.10 & 95.75  & 69.37 \\
		$G^2$DA~\cite{wan2021g} & 71.72 & 87.13 & 65.90 & 69.50 & 84.87 & 63.88 & \underline{57.07} & 90.99  & 55.05 & \underline{63.70} & 94.06 & 69.83 \\
		NFS~\cite{chen2021neural} & \underline{80.54} & \underline{91.96} & \underline{72.10} & \underline{77.95} & \underline{90.45} &  \underline{69.79}      & 56.91 & 91.34 & \underline{55.45} & 62.79 & \textbf{96.53} & \underline{69.79} \\
		\hline
		MID (ours)  & \textbf{87.45} & \textbf{95.73} & \textbf{84.85} & \textbf{84.29} & \textbf{93.44} & \textbf{81.41} & \textbf{60.27} & \textbf{92.90}  & \textbf{59.40} & \textbf{64.86} & \underline{96.12}  & \textbf{70.12} \\ \hline \hline
	\end{tabular}%
 	}
	\label{tab:state-of-the-art}%
	\vspace{-0.5cm}
\end{table*}%

\subsection{Experimental Setting}

\noindent \textbf{Datasets.} We evaluate the proposed MID using two public RGB-Infrared datasets: RegDB~\cite{nguyen2017person} and SYSU-MM01~\cite{wu2017rgb}. RegDB dataset contains 412 pedestrians. Each pedestrian has 10 visible images and 10 thermal images. Following the evaluation protocol~\cite{ye2018hierarchical,ye2018visible}, this dataset is randomly split into two parts, 206 identities for training and the other 206 identities for testing, with two different testing modes, \textit{i.e.}, visible to thermal mode and thermal to visible mode. The reported results are the average of 10 random training/test splits on RegDB dataset. SYSU-MM01~\cite{wu2017rgb} is the largest existing RGB-infrared dataset, which was captured with 4 visible and 2 infrared cameras. The training set contains 395 persons with 22,258 RGB images and 11,909 IR images, while the testing set contains 96 persons with 3,803 IR images and 301 RGB images. We adopt the all-search mode and indoor-search mode \cite{wu2017rgb, luo2019spectral} to evaluate the performance.

\noindent\textbf{Evaluation Metrics.} The standard Cumulative Matching Characteristic (CMC) at Rank-$k$ and the mean Average Precision (mAP) are adopted as evaluation metrics.

\noindent\textbf{Implementation Details:} The proposed method is implemented by the PyTorch framework with one NVIDIA Tesla V100 GPU. Each mini-batch contains 96 images of 8 identities (each person has 4 RGB images, 4 IR images, and 4 generated mixed modality images). ResNet-50~\cite{he2016deep} model is adopted as the backbone network. Part-pooling~\cite{sun2018beyond} is added after the backbone. The first three residual blocks of ResNet-50 model are equipped with modality-adaptive convolution decomposition. The stride of the last convolution layer is set to 1. The margin $\rho$ is set to 0.3. The parameter $\mu$ and  $\xi$ are set to 1 and 0.1, respectively. The trade-off parameters $\lambda_{1,4,5}$ are set to 1, $\lambda_{2,3}$ are set to 0.5, and $\lambda_6$ is set to 0.1 in Eq.~(\ref{eq.loss}). We adopt Adam Optimizer to train the actor-critic agent. And we utilize the stochastic gradient descent (SGD) optimizer for MACD with the momentum of 0.9, the initial learning rate of 0.05, 0.02 on RegDB and SYSU-MM01 datasets, respectively. The learning rates decayed by 0.1 after 20 and 45 epochs. The whole MID framework is trained for 60 epochs on RegDB dataset which takes 1 hour, and for 100 epochs on SYSU-MM01 dataset which takes 6 hours.

\subsection{Comparison to State-of-the-Art Methods}

\textbf{RegDB:} We present a comparison the proposed MID with 11 state-of-the-art approaches on RegDB dataset in Table~\ref{tab:state-of-the-art}. Experiments conducted on RegDB dataset show that the proposed MID achieves the best performance under different testing modes over all state-of-the-art methods by large margins. For Visible to Thermal mode, MID achieves 87.45\% rank-1 accuracy and 84.85\% mAP, outperforming the 2nd best NFS~\cite{chen2021neural} by 6.91\% rank-1 accuracy and 12.75\% mAP, respectively. For Infrared to RGB mode, MID also obtains 84.29\% rank-1 accuracy and 84.29\% mAP, improving the 2nd best method NFS~\cite{chen2021neural} by 6.34\% rank-1 accuracy and 11.62\% mAP, respectively. The boosting demonstrates that the proposed MID is able to learn modality-invariant and discriminative features from cross-modality RGB and infrared images.

\textbf{SYSU-MM01:} Table~\ref{tab:state-of-the-art} also reports the performance of the proposed MID with 11 state-of-the-art methods on SYSU-MM01 dataset. Experiments results show that MID obtains the best performance under both All-search and Indoor settings. For all-search mode,  MID achieves 60.27\% rank-1 accuracy and 59.40\% mAP, outperforming the 2nd best method NFS~\cite{wan2021g} by 3.20\% rank-1 accuracy and 4.35\% mAP score. For indoor-search mode,  MID also obtains the best rank-1 accuracy and mAP score. The comparisons demonstrate that MID can effectively reduce inter-modality and intra-modality discrepancies.

\subsection{Ablation Study}
\label{sec.ablation}

\begin{table}[!t]

	\footnotesize
	\begin{center}
		\caption{Ablation studies on the effectiveness of each component of the proposed MID.
		\scalebox{1}{
			\centering
			\begin{tabular}{ccc|c|c|c|c}
				\hline
				\hline
				\multicolumn{1}{c}{\multirow{2}[0]{*}{$\mathcal{B}$}} & \multicolumn{1}{c}{\multirow{2}[0]{*}{$\mathcal{M}$}} & \multicolumn{1}{c|}{\multirow{2}[0]{*}{$\mathcal{D}$}} & \multicolumn{2}{c|}{RegDB} & \multicolumn{2}{c}{SYSU-MM01} \\
				&& & \multicolumn{1}{c}{r1} & \multicolumn{1}{c|}{mAP} & \multicolumn{1}{c}{r1} & \multicolumn{1}{c}{mAP}\\ \hline
				 $\checkmark$ & $\times$ &$\times$  & 76.34 & 70.81 & 49.46 & 49.32 \\
				 $\checkmark$ & $\checkmark$ &$\times$ & 83.43 & 79.13 & 56.22 & 57.12 \\
				 $\checkmark$ & $\checkmark$&$\checkmark$  & 87.45 & 84.85 & 60.27 & 59.40 \\
				\hline
				\hline
			\end{tabular}
		}
		\label{tab:module-analysis}
		\vspace{-0.5cm}
	\end{center}
\end{table}

\textbf{Effectiveness of each component of MID.} We conduct ablation studies on RegDB and SYSU-MM01 datasets to investigate the effectiveness of the components of MID in Table~\ref{tab:module-analysis}, including modality-adaptive mixup scheme ($\mathcal{M}$) and modality-adaptive convolution decomposition ($\mathcal{D}$). $\mathcal{B}$ denotes a vanilla ResNet-50 model trained with identity loss and normal triplet loss. When introducing the modality-adaptive mixup scheme, the performance is improved by 7.09\% rank-1 accuracy and 8.32\% mAP on RegDB dataset, and by 6.76\% rank-1 accuracy and 7.80\% mAP on SYSU-MM01 dataset. The boosting indicates the dynamical and local linear interpolation policy of $\mathcal{M}$ can generate appropriate mixed modality images and mitigate the inherent modality discrepancy at the pixel-level for promoting a more continuous modality-invariant latent space. By adding the modality-adaptive convolution decomposition, the performance is improved by 4.02\% rank-1 accuracy and 5.72\% mAP on RegDB dataset, and by 4.05\% rank-1 accuracy and 2.28\% mAP on SYSU-MM01 dataset. This demonstrates that the decomposed convolution network with $\mathcal{D}$ can capture invariant visual semantics and further shrink the modality discrepancy at the feature-level.

\begin{table}[!t]
	\small
	\centering
	\caption{Influence of different mixup schemes for MID.}
	\begin{tabular}{c|cccc}
		\hline \hline
		\multicolumn{1}{c|}{\multirow{2}[0]{*}{Mixup Scheme}} & \multicolumn{2}{c}{RegDB} & \multicolumn{2}{c}{SYSU-MM01} \\
		& \multicolumn{1}{l}{r1} & \multicolumn{1}{l}{mAP} & \multicolumn{1}{l}{r1} & \multicolumn{1}{l}{mAP} \\
		\hline
		Fix & 83.96 & 77.83 & 50.43 & 48.64 \\
		Beta & 83.53 & 78.04 & 51.46 & 50.09 \\
		MAM & 87.45 & 84.85 & 60.27 & 59.40 \\
		\hline \hline
	\end{tabular}%
	\label{tab:mixup-policy}%
\end{table}%

\begin{table}[!t]
	\small
	\centering
	\caption{Influence of different number $G$ of the partitioned local regions for mixup.}

	\begin{tabular}{c|cccc}
		\hline \hline
		\multicolumn{1}{c|}{\multirow{2}[0]{*}{Mixup Scheme}} & \multicolumn{2}{c}{RegDB} & \multicolumn{2}{c}{SYSU-MM01} \\
		& \multicolumn{1}{l}{r1} & \multicolumn{1}{l}{mAP} & \multicolumn{1}{l}{r1} & \multicolumn{1}{l}{mAP} \\
		\hline
		w/o partition     & 83.93 & 79.04 & 53.46 & 52.09 \\
		\hline
		5     & 86.22 & 82.53 & 58.22 & 59.33 \\
		6     & 87.45 & 84.85 & 60.27 & 59.40 \\
		7     & 85.45 & 81.79 & 57.33 & 56.27 \\
		\hline \hline
	\end{tabular}%
	\label{tab:mixup-number}%
	\vspace{-0.5cm}
\end{table}%

\textbf{Analysis of modality-adaptive mixup scheme.} Table~\ref{tab:mixup-policy} shows the influence of different mixup schemes. Fix refers to a fixed mixup ratio of 0.5 for interpolating RGB and IR images, Beta refers to a mixup ratio randomly sampled from Beta prior distribution. MAM refers to our modality-adaptive mixup scheme. Fix and Beta belong to data-independent mixup schemes, while MAM is data-dependent. We can observe that the data-independent mixup schemes Fix and Beta obtain performance degradation over the data-dependent modality-adaptive mixup scheme. The comparison demonstrates that the proposed MAM can learn the dynamical modality discrepancy between different pair of RGB and IR images, and adaptively adjust the mixup ratio to generate appropriate mixed modality images for promoting a more continuous modality-invariant latent space and extracting more effective RGB-IR representations.

The results in Table~\ref{tab:mixup-number} show the influence of different number $G$ of the partitioned local regions for the modality-adaptive mixup scheme on RegDB dataset. W/o partition refers to the employ of a global mixup ratio to interpolate the whole RGB and IR images. We can observe that local linear interpolation policy ($\boldsymbol{m} \in \mathbb{R}^G$) obtains obvious performance improvement over global linear interpolation policy ($\boldsymbol{m} \in \mathbb{R}^1$), which indicates that adaptively mixing different local regions of RGB and IR images could reduce intra-modality discrepancy, and produce high-quality mixed modality images with identity consistency and fewer confusing information (\textit{e.g.}, occlusion, blurring and ghosting) for promoting cross-modality feature learning. Moreover, Table~\ref{tab:mixup-number} shows that when adopting 6 partitioned local regions, the proposed MID with $\boldsymbol{m} \in \mathbb{R}^6$ obtains the best  re-identification performance.

\begin{table}[!t]
	\small
	\centering
	\caption{Influence of different number of the decomposed residual blocks $n_d$.}
	\begin{tabular}{c|cccc}
		\hline \hline
		\multicolumn{1}{c|}{\multirow{2}[0]{*}{Mixup Scheme}} & \multicolumn{2}{c}{RegDB} & \multicolumn{2}{c}{SYSU-MM01} \\
		& \multicolumn{1}{l}{r1} & \multicolumn{1}{l}{mAP} & \multicolumn{1}{l}{r1} & \multicolumn{1}{l}{mAP} \\
		\hline
		0     & 83.43 & 79.13 & 56.22 & 57.12 \\
		1     & 85.94 & 82.69 & 56.71 & 58.45 \\
		2     & 86.71 & 83.54 & 57.52 & 58.49 \\
		3     & 87.45 & 84.85 & 60.27 & 59.40 \\
		4     & 84.38 & 82.27 & 56.75 & 57.25 \\
		5     & 82.58 & 80.99 & 54.51 & 56.58 \\
		\hline \hline
	\end{tabular}%
	\label{tab:number-decomposed}%
\end{table}%

\textbf{Analysis of modality-adaptive convolution decomposition.} Table~\ref{tab:number-decomposed} shows the influence of the number of residual blocks equipped with modality-adaptive convolution decomposition. From Table~\ref{tab:number-decomposed}, we can observe that when $n_d$ increases from 0 to 3, the proposed method obtains 4.02\% and 4.05\% improvements in rank-1 accuracy on RegDB and SYSU-MM01 datasets, respectively. When $n_d$ increases from 3 to 5, the performance decreases by 4.87\% and 5.76\% rank-1 accuracy on RegDB and SYSU-MM01 datasets. MID obtains the best performance when $n_d=3$, indicting that suitable decomposed residual blocks can capture more invariant visual semantics and improve the capacity of the learned modality-shared representations.

\textbf{Visualization of results.} To further verify the benefit of the modality adaptive mixup scheme, we visualize some mixed modality images in Figure~\ref{fig:visual}(a). Compared with the vanilla mixup scheme \cite{zhang2017mixup}, the modality adaptive mixup scheme is able to generate high-quality mixed modality images without the interference of occlusion, blurring and ghosting, \textit{etc}. Moreover, we visualize some retrieval results in Figure~\ref{fig:visual}(b), which demonstrate the effectiveness of the proposed MID for identifying the same pedestrians and distinguishing different pedestrians.

\begin{figure}[!t]
	\centering
	\includegraphics[width=0.47\textwidth]{visual.png}
	\centering
	\caption{(a) Visualization of the generated mixed modality images by the vanilla mixup scheme and the modality-adaptive mixup scheme; (b) Visualization of some retrieval results on RegDB dataset.}
	\label{fig:visual}
	\vspace{-0.5cm}
\end{figure}

\section{Conclusions}
In this work, we propose a novel modality-adaptive mixup and invariant decomposition (MID) approach for RGB-infrared person re-identification to mitigate the inherent modality discrepancy between RGB and IR images at the pixel-level and feature-level. MID firstly introduces modality-adaptive mixup scheme to generate appropriate mixed modality images by the actor-critic agent for reducing modality gap at the pixel-level and facilitating a more continuous modality-invariant latent space. MID then designs modality-adaptive convolution decomposition to simultaneously counter modality discrepancy and enforce cross-domain shared semantics at the feature-level, for learning effective modality-shared representation. Experimental results on two cross-modality person re-identification datasets have demonstrated the superiority of the proposed method.

\section{Acknowledgments}
This work was supported by the National Key R\&D Program of China under Grant 2020AAA0105702, National Natural Science Foundation of China (NSFC) under Grant U19B2038 and 62106245, the University Synergy Innovation Program of Anhui Province under Grants GXXT-2019-025, and the Fundamental Research Funds for the Central Universities under Grant WK2100000021.

redpajama_set_name: RedPajamaArXiv


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text:
authors & translators
Graham Speake
Renewal in Paradise
In this second edition of his acclaimed study of Mount Athos (for which he was awarded the 2002 Criticos Prize) the author takes the opportunity to revise and update his text and also to add a completely new chapter documenting the changes that have occurred in the twelve years since its first publication. The renewal that took place in the last quarter of the twentieth century can now be seen in its historical context and has been succeeded by a period of spiritual and cultural maturity. Not for the first time, in a world beset by political upheaval and economic turmoil, the Mountain stands as a symbol of continuity and stability, a spiritual powerhouse offering refreshment to all who turn to it.
This is a history of Mount Athos from pagan antiquity to the present day. It tells the story of the first monks who were hermits, living in caves and simple huts, often in the most inaccessible parts of the peninsula. The first monasteries were founded in the tenth century with support from the Byzantine emperors. Both traditions survive on Athos today, the anchorites in their desert cells and the monks in the twenty ruling monasteries, coexisting more or less happily as they have always done.
The book describes the effects on the Mountain of political events in the outside world—the Latin conquest, Ottoman rule, the Greek War of Independence, two world wars, and the incorporation of Greece into the European Union—and how little the monks' way of life was changed by them. Far more important for them were the religious movements and controversies that often divided the Mountain and demonstrated its spiritual influence over the Orthodox world.
What emerges at the end is a portrait of the Holy Mountain today that despite all the storms created by political change, economic crisis, and religious controversy (not least in the last twelve years) is still recognizable as fundamentally the same monastic preserve that welcomed the first monks and hermits more than a thousand years ago.
From Reviews of the First Edition
'. . . the most comprehensive survey to date of the history, culture, theology and spirituality of Mount Athos . . . As a survey of Athos in all its dimensions, it could hardly be bettered . . . an excellent, sumptuously illustrated, survey which can be unhesitatingly recommended.' Marcus Plested, Journal of Ecclesiastical History
'Speake's book is a triumph . . . And the pictures are wonderful . . . It is a judicious and careful work, a delight to read and look through, fully worthy of the acclaim with which it has been received.' Andrew Louth, Journal of Hellenic Studies
'Speake covers everything: the history, the daily life of the monks and hermits, the miracles, the persecutions, the icons, the saints. I've never read a more sympathetic study of the world's oldest continuous democracy.' The Independent on Sunday
'Graham Speake's Mount Athos: Renewal in Paradise . . . is as close to an exhaustive survey as any writer has come in English . . . travellers and pilgrims alike, as well as those who prefer to stay at home with their well-stocked larders, will relish this tour of Athos's history, its relics and treasures, and, in part, its consciousness.' Matthew Spencer, Literary Review
From a Review of this Second Edition
' There are many books on Mount Athos — pilgrims' guides, photo albums, theological and historical treatises, narratives, etc. Each one has its own value. Each one tries to describe the Holy Mountain, to depict the monastic life, the relationship of its picturesque nature with its inestimable cultural heritage, to promote its spiritual profundity, and to discover its hidden mystery. Many of these books are written in English, and quite a few of them are translations from the original Greek. In all of them one can detect the difficulty of Western thought and language in trying to describe the spiritual wealth of the Holy Mountain, as if the Holy Mountain cannot be translated. The present book seems to transcend this obstacle. It is one of the very few English publications whose content quenches the thirst of every Greek Orthodox reader, even of an Athonite monk. Without wishing to exaggerate, the way book is written reminds me of the way Sir Steven Runciman talked about the Holy Mountain. Perhaps their common secret is their love and humble attitude before its mystery.' Metropolitan Nikolaos of Mesogaia & Lavreotiki, Annual Report of the Friends of Mount Athos
302 pages, 23.3 x 15.5 cm, 78 colour and black and white photographs, 2014
ISBN 978-960-7120-34-2 paper bound €25.00
Extracts from this book for viewing
Mount Athos: Cover
From Chapter 2
The first edition of Mount Athos: Renewal in Paradise was published by Yale University Press in 2002.
Denise Harvey (Publisher), 340 05 Limni, Evia, Greece. VAT Reg. No. EL013143429.
orders@deniseharveypublisher.gr - Tel./Fax (0030) 22270 31154
Tim Peat Web Design & Development


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The 2015 Annual Report and Form 20-F can be downloaded from www.shell.com/annualreport.
Printed copies of the 2015 Annual Report and Form 20-F will be available from April 21, 2016, and can be requested, free of charge, at www.shell.com/annualreport.
The Annual Report and Accounts will be submitted to the Annual General Meeting to be held on May 24, 2016.


text:
Newslookup.com -› Contributed Forum Index -› Automation
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120 million workers will need to be retrained due to AI, says IBM study...
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CNET | 2019-09-06 21:03 UTC
But many CEOs tell IBM they don't have the resources needed to close the skills gap brought on by emerging technologies.
By Anonymous Submission on 2019-09-06 21:33 UTC
Male, blue-collar workers aged 25-54 most at risk to be replaced by robots...
Washington Examiner | 2018-05-15 22:13 UTC
A new book The Future of Work predicts men between the ages of 25 and 54 years old who work blue-collar jobs will be the first group of people to be replaced by robots.
A study finds nearly half of jobs are vulnerable to automation...
The Economist | 2018-04-24 16:00 UTC
Job-grabbing robots are no longer science fiction. In 2013 Carl Benedikt Frey and Michael Osborne of Oxford University used—what else?—a machine-learning algorithm to assess how easily 702 different kinds of job in America could be automated. They concluded that fully 47% could be done by machines
Medill News Service: Half of all jobs can today be automated — and within 50 years, all of them can be...
Marketwatch.com | 2018-04-11 17:03 UTC
It is no longer in question whether all jobs will be automated, the head of the Institute for New Economic Thinking said Tuesday. The real question rests in when that automation will occur and which sectors of society will be affected sooner than others.
A long disruption is ahead, with low-paying jobs...
Submitted to Category: /contrib/business/, /contrib/economy, /contrib/automation
Axios | 2018-04-04 19:58 UTC
The last few months have seen a rash of studies on a coming automation apocalypse, and analysts are moving to a few targeted worries about the jobs future: Their big picture: There may be a long, deep economic disruption lasting decades and taking million


text:
require_relative '../expectation'

module Csv2hash
  module Parser
    module Collection
      include Parser
      include Expectation

      def fill!
        self.data = {}.tap do |data_computed|
          data_computed[:data] ||= []
          self.data_source.each_with_index do |line, y|
            next if unexpected_line?(line, y)
            data_computed[:data] << {}.tap do |data_parsed|
              fill_it data_parsed, line
            end
          end
        end
      end

      def fill_it parsed_data, source_data
        definition.cells.each do |cell|
          if cell.rules.fetch :mappable
            x = cell.rules.fetch :position
            if (nested = cell.rules.fetch :nested)
              parsed_data[nested] ||= {}
              parsed_data[nested][cell.rules.fetch(:key)] = treat(source_data[x])
            else
              parsed_data[cell.rules.fetch(:key)] = treat(source_data[x])
            end
          end
        end
      end

    end
  end
end