text:
<!DOCTYPE html>

    <meta charset="utf-8">
    <meta name="viewport" content="width=device-width, initial-scale=1">
    <title>zsearch-trees: Error</title>
    <link rel="shortcut icon" type="image/png" href="../../../../../favicon.png" />
    <link href="../../../../../bootstrap.min.css" rel="stylesheet">
    <link href="../../../../../bootstrap-custom.css" rel="stylesheet">
    <link href="//maxcdn.bootstrapcdn.com/font-awesome/4.2.0/css/font-awesome.min.css" rel="stylesheet">

             Coq bench

clean / extra-dev

dev / zsearch-trees - dev

            « Up

              zsearch-trees

                dev
                Error

 (2021-04-03 04:38:32 UTC)

Context

# Packages matching: installed
# Name              # Installed # Synopsis
base-bigarray       base
base-threads        base
base-unix           base
conf-findutils      1           Virtual package relying on findutils
conf-gmp            3           Virtual package relying on a GMP lib system installation
coq                 dev         Formal proof management system
num                 1.4         The legacy Num library for arbitrary-precision integer and rational arithmetic
ocaml               4.11.1      The OCaml compiler (virtual package)
ocaml-base-compiler 4.11.1      Official release 4.11.1
ocaml-config        1           OCaml Switch Configuration
ocamlfind           1.9.1       A library manager for OCaml
zarith              1.12        Implements arithmetic and logical operations over arbitrary-precision integers
# opam file:
opam-version: "2.0"
maintainer: "dev@clarus.me"
homepage: "https://github.com/coq-contribs/zsearch-trees"
license: "LGPL 2"
build: [
  ["coq_makefile" "-f" "Make" "-o" "Makefile"]
  [make "-j%{jobs}%"]
  [make "install"]
]
remove: ["rm" "-R" "%{lib}%/coq/user-contrib/ZSearchTrees"]
depends: [
  "ocaml"
  "coq" {= "dev"}
]
tags: [ "keyword:binary search trees" "category:Computer Science/Data Types and Data Structures" "category:Miscellaneous/Extracted Programs/Data structures" ]
authors: [ "Pierre Castéran <>" ]
synopsis: "Binary Search Trees."
description:
  "Algorithms for collecting, searching, inserting and deleting elements in binary search trees on nat"
flags: light-uninstall
url {
  src: "git+https://github.com/coq-contribs/zsearch-trees.git#master"
}

Lint

Command

true

Return code

0

Dry install

Dry install with the current Coq version:

Command

opam install -y --show-action coq-zsearch-trees.dev coq.dev

Return code

0

Dry install without Coq/switch base, to test if the problem was incompatibility with the current Coq/OCaml version:

Command

true

Return code

0

Install dependencies

Command

opam list; echo; ulimit -Sv 4000000; timeout 2h opam install -y --deps-only coq-zsearch-trees.dev coq.dev

Return code

0

Duration

26 s

Install

Command

opam list; echo; ulimit -Sv 16000000; timeout 2h opam install -y coq-zsearch-trees.dev coq.dev

Return code

7936

Duration

51 s

Output

# Packages matching: installed
# Name              # Installed # Synopsis
base-bigarray       base
base-threads        base
base-unix           base
conf-findutils      1           Virtual package relying on findutils
conf-gmp            3           Virtual package relying on a GMP lib system installation
coq                 dev         Formal proof management system
num                 1.4         The legacy Num library for arbitrary-precision integer and rational arithmetic
ocaml               4.11.1      The OCaml compiler (virtual package)
ocaml-base-compiler 4.11.1      Official release 4.11.1
ocaml-config        1           OCaml Switch Configuration
ocamlfind           1.9.1       A library manager for OCaml
zarith              1.12        Implements arithmetic and logical operations over arbitrary-precision integers
[NOTE] Package coq is already installed (current version is dev).
The following actions will be performed:
  - install coq-zsearch-trees dev
<><> Gathering sources ><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
[coq-zsearch-trees.dev] synchronised from git+https://github.com/coq-contribs/zsearch-trees.git#master
<><> Processing actions <><><><><><><><><><><><><><><><><><><><><><><><><><><><>
[ERROR] The compilation of coq-zsearch-trees failed at "/home/bench/.opam/opam-init/hooks/sandbox.sh build make -j4".
#=== ERROR while compiling coq-zsearch-trees.dev ==============================#
# context              2.0.7 | linux/x86_64 | ocaml-base-compiler.4.11.1 | file:///home/bench/run/opam-coq-archive/extra-dev
# path                 ~/.opam/ocaml-base-compiler.4.11.1/.opam-switch/build/coq-zsearch-trees.dev
# command              ~/.opam/opam-init/hooks/sandbox.sh build make -j4
# exit-code            2
# env-file             ~/.opam/log/coq-zsearch-trees-2314-0a5907.env
# output-file          ~/.opam/log/coq-zsearch-trees-2314-0a5907.out
### output ###
# [...]
# use "export" whenever possible. [deprecated-hint-without-locality,deprecated]
# File "./search_tree.v", line 66, characters 0-39:
# Warning: The default value for hint locality is currently "local" in a
# section and "global" otherwise, but is scheduled to change in a future
# release. For the time being, adding hints outside of sections without
# specifying an explicit locality is therefore deprecated. It is recommended to
# use "export" whenever possible. [deprecated-hint-without-locality,deprecated]
# File "./search_tree.v", line 72, characters 7-15:
# Error: The reference Z_eq_dec was not found in the current environment.
#
# make[1]: *** [Makefile:721: search_tree.vo] Error 1
# make: *** [Makefile:344: all] Error 2
<><> Error report <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
+- The following actions failed
| - build coq-zsearch-trees dev
+-
- No changes have been performed
# Run eval $(opam env) to update the current shell environment

Installation size

No files were installed.

Uninstall

Command

true

Return code

0

Missing removes

                none

Wrong removes

                none

          Sources are on GitHub. © Guillaume Claret.


Greetings are a kind of manner as well as a way of communication to open up for each other. As you may know, Japanese hospitality is highly valued by other countries. Our hospitality always starts with greetings and ends with greetings. Through greetings, we can feel closer to each other. I think it's also a way to get closer to yourself as well as charm of others.
Please use these phrases with your great smile!


2.5W solar panel replacement for 2.5w solar fountain pump kits.
Connect to pond pump with female connector on panel. Cable not included.
Use ground spike and rest to position panel in best sunlight. Ideal for small solar projects fountains aerators and DC bulbs.


annual Sheep and Goats sur-vey.
report would not be possi-ble.
measured at 115,000 head. The current level of 106,000 head is up 16 percent from one year ago.
Purpose Oklahoma and U.S. Inventory Oklahoma's sheep producers had 75,000 head of all sheet and lambs on hand January 1, 2013, up 7 percent from last year. The 2012 lamb crop was 49,000 head, up 7 percent from the previous year.
Full text USDA-NASS OKLAHOMA FIELD OFFICE Lamb Crop, Oklahoma and United States 2011-2012 1 Lamb crop is defined as lambs born in the Eastern States and lambs docked or branded in the Western States. * Revised. Sheep and Lambs: Number by Class, Oklahoma and United States, January 1, 2012-2013 JANUARY 2013 W elcome to the January 2013 issue of the Annual Sheep and Goat Review! First, I want to thank all partici-pants from the sheep and goat industries for their con-tinued support and coopera-tion – especially the produc-ers who provide data for the annual Sheep and Goats sur-vey. Without their commit-ment and participation, this report would not be possi-ble. Though many of the statistics in this publication can be accessed through our web-site on a monthly basis (www.nass.usda.gov/ Publications), this Oklahoma Field Office summary will be issued each January follow-ing the release of the USDA-NASS annual Sheep and Goats report. Please feel free to contact our office with any com-ments or questions you may have. I hope you find this information useful! Wilbert Hundl, Jr., Director Oklahoma Field Office Oklahoma's sheep producers had 75,000 head of all sheep and lambs on hand January 1, 2013, up 7 percent from last year. The 2012 lamb crop was 49,000 head, up 7 percent from the previous year. OKLAHOMA AND U.S. INVENTORY Data Source: USDA-NASS January Sheep and Goats report. Date Oklahoma United States 2012 2013 2013 as Percent of 2012 2012 2013 2013 as Percent of 2012 1,000 Head 1,000 Head Percent 1,000 Head 1,000 Head Percent All Sheep and Lambs 70 75 107 *5,365 5,335 99 Breeding Sheep 89 59 105 *3,995 3,975 99 Ewes 1 Year + 42 44 105 *3,165 3,140 99 Rams 1 Year + 4 4 100 170 175 103 Replacement Lambs 10 11 110 660 660 100 Market Sheep and Lambs 14 16 114 1,370 1,360 99 Market Sheep 1 Year + 2 1 50 84 75 89 Market Lambs 12 15 125 1,286 1,285 100 Lambs Under 65 pounds 7 9 129 297 320 108 Lambs 65-84 pounds 2 2 100 159 160 101 Lambs 85-105 pounds 2 2 100 *312 305 98 Lambs over 105 pounds 1 2 200 *518 500 97 Date Oklahoma United States 2011 2012 2012 as Percent of 2011 2011 2012 2012 as Percent of 2011 1,000 Head 1,000 Head Percent 1,000 Head 1,000 Head Percent Lamb Crop1 46 49 107 3,510 3,455 98 Goats: Number by Class, Oklahoma and United States, 2012-2013 1 Oklahoma is included in Other States. T he majority of Oklahoma's goats are meat-type. Initial goat estimates, which began in 2005, showed 73,000 meat goats. They peaked in 2008 when the meat-type inventory was measured at 115,000 head. The current level of 106,000 head is up 16 percent from one year ago. Milk goats make up the bulk of the remaining total goats. In 2005, there were 9,000 head esti-mated with a high inventory of 10,000 reached in 2006. The 2012 inventory of 6,100 is down 900 head from last year. Date Oklahoma United States 2012 2013 2013 as Percent of 2012 2012 2013 2013 as Percent of 2012 1,000 Head 1,000 Head Percent 1,000 Head 1,000 Head Percent Angora (1) (1) - 146 136 93 Milk 7.0 6.1 87 360 360 100 Meat 91.0 106.0 116 2,356 2,315 98 ANNUAL SHEEP AND GOAT REVIEW *Revised.


Weddings are a time of love and celebration. It's about the moments you share with your loved ones and capturing all of those special moments on your big day! There's something about having so many wonderful photos to look back on and share with the people you are very close with. Trust me, these are the photos you'll regret not taking! So, here are some wedding picture ideas for you to take on your wedding day!
Traditional wedding photos are simply timeless. They capture the essence of happiness, love, and commitment…not to mention, they are completely stunning!
The first dance is one of the most meaningful parts of your wedding day and getting a snapshot of it will leave your heart smiling.
A classic photo is of your wedding rings is a simple and modern picture you're sure to love!
This is an adorable series of photos to make a collage out of. These photos can also be done the same way if you're waiting to do the first look while walking down the aisle. Either way, they are one-of-a kind!
Having your mom or loved one help with the final touches of getting ready is a definite must-have photo from your wedding day. It's something you both can look back on and cherish.
A classic photo of the groom getting ready is very special. It's also nice to share these photos with your wife after the big day so she can see your process of getting ready and vice versa!
The brides first look is one you'll never forget! Also, the first look of seeing your 'maids in theirchiffon bridesmaid dresses is extremely exciting. It's a very special moment shared with your closest family members and friends.
This is one of the most intimate moments of the day. The emotions and love that are captured in these pictures are priceless.
The bride and her beautiful loved ones in their chiffon bridesmaid dresses is bound to be absolutely stunning.
This is a nice moment to capture and thank all of your guests for coming to your wedding! It's a great way to make your thank you cards more personal.
Modern wedding photos add a little more fun and creativity into your big day! Here are some of our favorites.
This is a super fun take on just a traditional picture of your 'maids. Whether they're in short satin gowns or long chiffon bridesmaid dresses, this is a photo your 'maids are sure to LOVE.
Incorporating the season into your wedding will make for fun and festive photos you're bound to love!
If you're getting your guys a fun wedding gift like funky socks, have them show them off in a funny picture that will have everyone laughing.
Make the groom feel like the coolest guy in the world with this fun picture.
The groom is definitely going to feel the love from your 'maids! You can do the same with the bride and groomsmen. They'll make for great side-by-side framed pictures.
Now that the ceremony is over, It's time to celebrate! If you have a fun party favor to add to some of your pictures, it'll be a great group shot that's really fun!
Weddings are a once in-a-lifetime event! It's important to capture the big day so you can reminisce with your hubby and loved ones down the road. Whether you like more of the traditional look, modern feel, or a mix of both, your wedding photos will definitely be charming and fun.
Now that you have some more options to potentially add to your wedding album, we'd love to hear what your favorite pictures are? Tell us in the comments below!


text:
Marquis is a Germany based fetish magazine originally produced by Peter W. Czernich since 1994. For around 25 years, the magazine was published under his auspices. First, the magazine ceased publication in 2015 with issue 63. Then, in 2016, issue 64 appeared after an economic restructuring.[2] Czernich's last issue as editor-in-chief in charge was issue 67, the 25th anniversary issue. Czernich's original publishing venture entitled «O» began as a spin-off of the British Skin Two magazine but quickly developed its own identity. After losing the right to the «O» name, Czernich created Marquis in 1994. It has continued to be published semiannually-to-quarterly ever since.

The Time after Peter W. Czernich
In 2019, the magazine was sold to the U-Line publishing house, which had mainly appeared with its imprint "Ubooks" in the years before. Marquis issue 68 was released in March 2020. Peter W. Czernich remained on board as creative consultant and photographer, but the owner of U-Line Verlag, Andreas Reichardt, was now responsible. In the first issue under new management, the cabaret artist Lisa Eckhart, among others, could be won for an exclusive interview along with a photo shoot in latex

Marquis magazine is published semi- to quarterly. It is mainly made for fetish, latex and fashion lovers. Topics include photography, models, books, films, fashion and lifestyle. The name of the magazine refers to the Marquis de Sade. The first issue was published in 1994 as a successor to the magazine O.

Heavy Rubber
The magazine Heavy Rubber was created as a spin-off in 1996. While Marquis magazine focuses more on fashion, mainstream and glamour, Heavy Rubber is clearly aimed at fetishists. It is all about latex, rubber as a source of erotic pleasure.

References

External links
 Official Site of MARQUIS Magazine

1994 establishments in Germany
Fetish magazines
Magazines published in Germany
German-language magazines
Magazines established in 1994
BDSM literature

redpajama_set_name: RedPajamaWikipedia


Great for romantic get-aways, getting back to nature, relaxing with family and friends, swimming, skiing, and fishing. Hala's Place is across State Highway 304 North from Watts Bar Lake. Public access boat ramp is 1/4 mile. JC Neptoon boats are available for rent at www.bluespringsmarina.com; from Hala's, this is a 5-mile drive north on Hwy 304. Fishing supplies available at Riverside Market, 1 mile. For swimming access, use Watts Bar Park, which has a large marked swimming area and sandy beach, a playground, a large pavilion, and a roomy bathroom facility. This is a 4.5 mile drive from Hala's. Tennessee Wildlife Resources Agency Contact us for more information!


text:
National Aboriginal and Torres Strait Islander Peoples Drug Strategy 2014–2019
This strategy aims to reduce the harmful effects alcohol and other drugs in Aboriginal and Torres Strait Islander communities.
Download National Aboriginal and Torres Strait Islander Peoples Drug Strategy 2014–2019 as PDF - 803 KB , 39 pages
Download National Aboriginal and Torres Strait Islander Peoples Drug Strategy 2014–2019 as Word - 1.6 MB , 38 pages
This strategy aims to:
reduce the harmful effects of alcohol and other drugs on individuals, families and communities
improve the health and wellbeing of Aboriginal and Torres Strait Islander peoples
It is a guide to decide on key issues and priorities for:

redpajama_set_name: RedPajamaCommonCrawl


Igor Ferreira is a difficult man to get hold of. 'Sorry,' he says, 'I have been down at the beach for a few days and I left my phone behind.' The Portuguese designer is no doubt taking a well-earned break, having spent the past year opening and designing a series of ever more delightful stores.
Retail, particularly when it comes to the smaller independents, is unusual in that it allows the non-trained, or amateur, to mix it with the star designer or specialist consultancy – and sometimes come out on top. Often, it is the natural flair and originality of retailers who design their own shops that professional designers and top brands seek to emulate.
Adidas, for example, enlisted Ferreira to design its store in Portugal for the Adicolor series a couple of years ago. The result was a labour of love. Old Adidas catalogues were painstakingly trawled and scissored to create mosaic-montage zones that were coloured according to the relevant sneaker on a plinth. For the floor, Panini football stickers were sorted, again according to colour (for example, Brazil and Sweden for yellow), and then covered with a protective layer. Ferreira has since transformed the store with a rough and ready grungy redesign to become a concept store for Eastpak bags.
Sneaker Delight, in Lisbon's trendy Bairro Alto, was opened by Ferreira in 2000 as Portugal's first specialist sneaker store. The latest of its annual refits is coming to an end as the customers will soon have finished colouring it in. Having come across the work of French illustrator Sqwak in a magazine, Ferreira invited him to Lisbon to create an intricate illustration, in black and white, covering the store's interior. Colour marker pens, kindergarten style, were given to customers who then coloured it in.
While he doesn't have any design or art school training, Ferreira can always phone his dad, who works in set design in The Netherlands. 'He helps me with technical aspects, on how to actually execute the ideas,' he explains. His family left Portugal for political reasons, emigrating to Holland where he grew up, but Ferreira returned ten years ago at the age of 27, opened a series of shops and then gradually got into decorating them himself.
It is a sentiment that has undoubtedly gone down well with the youngsters who frequent No Kidding, a children's clothing shop that he designed for a former girlfriend, and the children's hair salon, Facto Kids, that is attached to a salon he continues to be involved with.
Sneaker Delight has now been sold, but the store's new owner has retained his services for a refit this September. The plan is to do a design around Converse trainers, but Ferreira hasn't yet decided exactly what he will do. The plan is to set up so he can do more retail designs for others – to become a designer rather than an owner-retailer.
'I should get my own website and stuff,' Ferreira says. Yes, he probably should, although at this time of year the attractions of the beach clearly outweigh business and technical ones. And you can be sure Ferreira will be having fun, whether in his designs or otherwise.


We are the leading manufacturer of diamond, CBN, and professional tools for molds and die makers. We produce and OEM for many well-known suppliers around the world. Our company is established in Thailand since 1989 with Taiwanese technical expertise and is a joint venture with a Japanese company.
This caused us to be one of the most fully equipped suppliers of diamond and CBN tools in Thailand. We produce many abrasive products such as electroplated bond, resin bond, metal bond as well as vitrified bond. Our products are well-known worldwide, of which we pride in meeting and even exceed international standards.
Alpha Diamond Tools has expanded to a well-established and modern manufacturing facility. The current factory has a land area of around 5 rai, around 8,000 square meters or 86,000 square feet.
Address. 333/9 Moo 9 Bangna-Trad Km 19.5 Bangchalong, Bangplee, Samutprakarn 10540 Thailand.


Selfridges is a UK based store that deals with fashionable accessories for men, women and kids. Recently, the telegraph reported that their sales had hit more than one billion pounds. To maintain the leading position, this store will have to work on its selling edge. The sales staff's motivation is key. This research aims at identifying the role that music plays in sales motivation. The most powerful tool for any sales person's toolkit is the attitude towards Sales Motivation (Jobber & Lancaster, 2006). Music has been a motivator in many other areas especially in sports and when working out at the gym. Music has the ability to enhance moods of individuals and consequently motivate them.
Can music motivate sales people?
Can music make a sales person change their perception of their job or make them love their job?
Are there tests showing that music enhance positive psychological changes?
Motivation of sales people in an organization that is sales oriented is invariable. When the staff is motivated, they close more sales and vice versa when demotivated. In other words, the level of sales is proportionate to the motivational level of employees and could be the difference between making profits and making losses. Motivation has been defined as the process that ignites and guides people to act and maintain goal oriented behaviors (Vance, 2011). The process of motivation involves social, cognitive and emotional forces that activate behavior. In the life of a human being, the process of motivation presents itself in many aspects, from simple acts like drinking water to quench thirst, to complex tasks pursuing an education to gain knowledge.
During this study, some difficulties were encountered. Motivation is an abstract concept and impossible to substantially determine and measure. Therefore, what is used as a measure of motivation is to a large extent arbitrary. Variations that occur among individuals (ranging from being highly motivated by music to remotely being motivated by music) make compiling of data tedious. The taste of music differs from an individual to another. This makes it tedious to administer music to determine the level of motivation.
Employee motivation plays a crucial role in productivity in today s business world (Thomas, 2009). It is for this reason that there are so many professionals who have taken it upon themselves to research on various ways of motivating employees. Motivation factors can be extrinsic or intrinsic to a sales person.
The company decides mostly on extrinsic motivators which are external to the employee .Extrinsic factors include reward, working environment, and praise among others. Music falls in this category. Motivational intrinsic factors that are internal to the employee are to a large extent decided by the employee, but can be influenced by the employer (Thomas, 2009). This encompasses the sale's staff love for their job and the inner good feeling among others. Music can achieve both. Individual workers have divergent values in regard to their work delivery. (Thomas, 2009). It is therefore up to an employer to identify the factors.
As simple as it sounds, scientists have found that listening to particularly happy or sad music changes the way we perceive the world (Peretz & Zatorre, 2003). Jolij and Meurs administered sad, smiling, and neutral faces to individuals listening to different types of music. While listening to happy music and a neutral face administered, most individuals claimed to have seen a smiley face. Studies prove that, the music we listen to engages a wide range of neurobiological systems that affect one's psychology (Kurlan, 2001), and this is proven by Jolij's and Meurs' experiment. "Seeing anything, not there results to top-down processes in the brain.
However, not all music applies to all situations. One can dial up a mood, mindset or perception on demand by choosing music that elicits a specific emotional response (Bergland, 2012). A sales person might be feeling overwhelmed by dealing with a given customer(s). The song that is best placed is a song that inspires conquering. The words of the song and its tempo elicit confidence in the sales person. For instance, the Jayz's and Linkin Park 's collaborated song, Encore has this message;" I came I saw I conquered". This is highly motivational. On the other hand, when relaxing, a sales person could listen to slow soothing songs such as kiss from a rose by Seal.
Music among other things helps sales persons calm their nerves, boost their morale, inspire their actions, and increase their energy. The mind drives the body and music provides the inspiration and the needed enthusiasm. A recent study by Jacob Jolij, a student of the Psychology Department of the University of Groningen in the Netherlands shows that music has a dramatic effect on explanatory style and perception (Bergland, 2012). Meaning that music can help a sales person have a positive perception of his job which will boost their morale and ultimately their performance.
The human heart wants to synchronize to music; the legs want to swing, metronomically, to a beat (Peretz, & Zatorre, 2003). Studies have shown that only songbirds and humans instinctively respond to music. When an individual coordinates activities they are doing with music the result is added energy. This could be required at a company (Selfridges & co.) during activities as moving stock by sales people. Music is so energizing and amazing. In some regions, music is sometimes banned to prevent athletes from gaining unfair benefits during competitions (Campbell, 1997).One of music's energizing effects comes from its ability to engage the body's sympathetic nervous system (Alcantara, 2011).
Music helps the brain connect its activities. Studies examining the operations of the brain suggest that synchronization of brain signals is crucial for linking cognitive, perceptual, and motor processes (The Globe and Mail, 2013). Music helps people focus on activities better consequently makes workers enjoy in their jobs. Mental and physical endurance can also be enhanced by music's ability to draw attention away from negative aspects of a given task (Campbell, 1997).The brain's attention system includes top and outermost regions of frontal and parietal lobes. They act to sustain neural activity in areas that contribute to issues focused. This reduces the activities from other areas of the brain (Campbell, 1997).
Clearly music is an essential ingredient in the motivation of sales people. For instance if a sales person is to make a call and get hold of a tune playing before the client picks up their call, it will help them relax and calm down by avoiding too much thought. This can be done before they can engage the customer in closing sales. It is recommended that Selfridges & Co. plays music for its sales people at work. Other companies can also incorporate this brilliant idea.


In the past week, I've been in the presence of multiple protein pizzas. During the Brighton Protein Pow(d)er Cooking Workshop, we made eighteen protein pizzas – all tremendous and so different! (check out, for example, Diana's, Ben's, Akhil's, Ana's, mine, and Chris'!) Then, a couple of days ago, Robin facebooked her adaptation of my accidental egg white protein pizza and Dai shared with me the base he made by following my sweet potato protein pizza recipe.
Are you wondering what superveg is? Well, superveg is basically powdered vegetables. I know it sounds like an abomination but, like I said last weekend at the workshop and love repeating, I would never sub actual veg for superveg. Instead, I use superveg as a flour substitute. It tastes garlicki, oniony, and in the context of pizza, it works a treat because it adds that umph-umph seasoned veggie flavor.
So the pizza came out a funny shape but that's OK, it tasted fab. And oh! Let me tell you about the toppings! I started by brushing some tomato puree on the pizza (the lazy person's tomato pizza sauce). I sprinkled some Rosemary and Thyme on it (because I'm of the first conviction that all pizzas need rosemary and thyme). Then, I added some sliced mushroom, low-fat mature cheddar (which I'm actually pretty impressed by it because most low-fat cheeses make me feel melancholic and this one didn't), a couple of anchovies. Then, I stuck it under the grill for 10 minutes. When it came out *drumroll* a tablespoon of low fat extra thick cream. The cream was a spur of last minute touch of genius! Un-be-lie-va-ble. You know, it's because cream + mushrooms = nomm so cream + mushrooms + pizzaness = NOMMM!


text:
We're Open For Walk-Ins & By Appointment!
Text Now (847) 565-1096
Quick Move-Ins
Homecare Checklists
Docs & Manuals
For Realtors®
Text Now: (847) 565-1096
Learn more about Lexington Homes. Get updates on news, events, and special promotions in .
Learn more about Lexington Homes. Get updates on news, events, and special promotions.
Yesterday. Today. Always.
Founded in 1973, Lexington Homes is the undisputed leader in Chicagoland homebuilding. No other builder, past or present, in this area is more regarded for their quality, location, integrity, and success.
Under the helm of the company's founding family, Lexington continues to create communities where people most want to live. That's been the key to our success for more than 45 years throughout Chicago and its suburbs.
Heartfelt satisfaction, yours and ours, comes not just from the more than 40,000 homes we've delivered. We build each home as though it's the most important ever, in a place where everyone most wants to call home. That's why three generations of loyal Lexington homeowners have enjoyed life in a well-located happy home.
Learn more about the Lexington Homes. Get updates on news, events, and special promotions.
© Lexington Homes LLC. All Rights Reserved. All contents herein are copyrighted and may not be used without permission.
All Prices Subject to Change Without Notice.
Homebuilder Website by Graphic Language


text:
package org.apache.struts.webapp.examples;

import org.apache.struts.action.*;

/**
 * Custom ActionMapping to demonstrate usage.
 *
 * @version $Rev: 471754 $ $Date: 2006-11-06 08:55:09 -0600 (Mon, 06 Nov 2006) $
 */

public final class CustomActionMapping extends ActionMapping {

    // --------------------------------------------------- Instance Variables

    /**
     * An example String property.
     */
    private String example = "";

    // ----------------------------------------------------------- Properties

    /**
     * Return the example String.
     */
    public String getExample() {

        return (this.example);

    }

    /**
     * Set the example String.
     *
     * @param example The new example String.
     */
    public void setExample(String example) {

        this.example = example;

    }

}

redpajama_set_name: RedPajamaGithub


The Yield Enhancement Network (YEN) already networks innovative farmers. Our idea is for 24 YEN innovators to (i) form ideas for yield enhancement, (iii) robustly test their success on adjacent tramlines and (iii) share findings with farmers and crop scientists. The farmers' initial idea is 'little-and-often nitrogen' since YEN winners have used this approach. However, the unique aspect here is to use 'ADAS Agronōmics technology' to gauge the robustness of their potential yield enhancing ideas, so kick-starting farmers' capability to 'accumulate marginal gains' in yields by specifying confidence levels, even for small effects. Ideas shared between farmers and scientists will also become better trusted. After disseminating this approach, any arable business will be able to test, share and accumulate series of improvements in agronomy, hence perpetually enhancing the industry's productivity, profitability, and environmental efficiency.
(1.) Collect and prioritise farmers ideas for yield enhancement through Ideas Labs', (2.) Prepare and distribute guidelines for farmer yield tests, (3.) Enlist members of 'Idea Groups' who agree to test the same question and appoint academic facilitator and industry supporters for each IG, (4.) Design yield-tests, and prepare farm yield-test database with field information, (5.) collect yield-test data and analyse supporting samples, (6.) Analyse and report data from each test, and (7.) prepare conclusions and messages.
We are gauging the provenance of innovations by using a new way of gauging the confidence that farmers should put in the results of any crop yield comparisons that they may make.


When fitting out your store, items such as dump bins or stacking baskets can help make use of awkward spaces and maximise your display area. Dump bins are a useful addition to any store, and have an adjustable base so that you can place products at the right height for your needs. Freestanding wire shelving units are useful when you have an empty wall or space that requires shelving but will not fit a traditional shelving unit. These sturdy but lightweight wire shelves units are perfect for window displays, temporary promotions, marked-down goods or as shelving for storage areas or warehouses.


suspiciously matches one of the new values).
3. run the program --> rrd_update() will fail.
Note that the rrdfile was created successfully.
4. now run the program again --> rrd_update() works fine.
The problem is apparently a well known problem with rrdtool.
I found messages as early as 2002 referring to this problem.
need to know how librrd is implemented.
This is the patch for rrdtool-1.0.50, as used in FC5.
unless they address severe bugs or security issues.
As for FC5, rrdtool is already updated to 1.2.15.
stumbled upon a very old bug (and apparently, well known).
information here will provide the info they need.


text:
remembering – it maybe comes all down to this
29 Sep 2009 | von Teresa Lengl |
after more than two weeks of great effort and labour, the monumental installation titled "remembering" on the front side of the museum is now almost finished. this work of art consists of about 9,000 colorful backpacks of the same kind as those that are carried by chinese schoolchildren. the vivid colors remind us of cheap plastic toys that nowadays are almost always manufactured in the people's republic of china.
remembering has a deeper and much more disturbing message, though, and must be considered as central in the work of ai weiwei. in 2008, an earthquake shook the ground in the remote province of sichuan in south china. while many buildings were damaged only lightly, it were schools that collapsed in the course of this disaster because they had been built neglecting the building code due to corrupt officials that cared more for their personal livelihood than for the lives of the children that went to study in these buildings every day.
when the earthquake hit the sichuan schools, thousands of children died, buried under the debris of the collapsed structures. in the aftermath, officials tried to hush up this obvious scandal so it was up to brave political activists to research the incident – among them Ai Weiwei. The artist went on numerous journeys to sichuan, talking to the parents of the children that had become victims of greed and corruption just as much as of the devastating force of nature.
when ai tried to appear as a witness in the corruption lawsuit, he was disallowed to do so from the beginning – so he travelled to sichuan for one last time in order to be able to follow the case as an observer, at least. it was then, in the past august of 2009, when members of the chinese secret police faced ai in his sichuan hotel room, arrested the artist, held him captive for about 18 hours and beat him up so badly that he had to undergo emergency surgery five weeks later, just after having arrived in munich to prepare the upcoming exhibition.
according to the neurologists of the university hospital in munich, ai had had a life-threatening intracranial haematoma that might have killed him if he had come to the hospital much later. now, ai has announced that he is going to accuse his country's government for the violence that has been exerted on him.
although these events are certainly alarming, they have also resulted in quite a lot of media attention for the exhibition and the reception of ai's work in germany, so hopefully this time, officials won't be able to put ai weiwei off with a lapidar "so sorry".
Tags: remembering
9 Antworten auf "remembering – it maybe comes all down to this"
von black hattitude 16 Okt 2009 | Antworten
thanks for the great quality of your blog, every time i come here, i'm amazed.
von wallmart 21 Okt 2009 | Antworten
I express my deep gratitude for your hard work
von macafee 21 Okt 2009 | Antworten
I'm always amazed how you can so easily write your message
von medtopix 20 Mrz 2010 | Antworten
The author of the rights and a material really interesting. I will subscribe on RSS
von JabLiaity 27 Mrz 2010 | Antworten
There are many differents mind. But I thk auther is right
von South-Dakota-Insurance-112 30 Mrz 2010 | Antworten
hm. hope to see same more info
15 Jun 2010: New Director of Tate Modern Appointed « Tate Blog
19 Jun 2010: HaggBridge.com » Blog Archive » New Director of Tate Modern Appointed


SOPAC/CSRC uses the Hatanaka, or compressed RINEX, file compression strategy for ALL of its RINEX observation files. Hatanaka files contain a 'd', rather than 'o', as the last filename character. These files are then unix-compressed, which adds a ".Z" extension.
(Windows users: to unix-uncompress these files, use a utility such as Winzip).
SOPAC/CSRC provides standard unix-compressed RINEX observation files for the past 60 days (in addition to Hatanaka-unix compressed files). These are available from the SOPAC http archive (password: your e-mail address) or via ftp.
You can download the crx2rnx Hatanaka to RINEX conversion program from GSI. This program must be run on your computer to hatanaka uncompress the file (after unix-uncompressing it).
The main benefits of moving to this compression strategy for our RINEX Observation files are a significantly decreased storage requirement (Hatanaka-unix-compressed RINEX Obs files can take as little as 25 to 30% of the space that a standard unix-compressed file would require), and a significantly decreased network load (as a result of the storage improvement). In addition, RINEX Observation file transfers from SOPAC/CSRC should be markedly faster for our external users. Finally, the overall reduced storage requirement allows for more data to be on-line.
confirm that your "d" file is uncompressed. Some downloads will drop the .Z extension, but the file remains compressed. Try viewing the file using an editor such as notepad. If you can see normal text, the file is uncompressed. If not, rename the file with a .Z extension, and use a file uncompression utility such as winzip (available from winzip.com) to uncompress the file. This is the most common problem with converting "d" files.
A RINEX 'o' file should be created in your working directory.
Note: On Sun and Linux-based operating systems, you will likely have to change the crx2rnx file's permission modes to executable first: chmod +x crx2rnx.


text:
Cast Member Profile: ABIGAIL LUMSDEN
Abigail Lumsden | Keyboard
Abigail Lumsden embraced music as a way of life starting at age three. Her instruments of choice have always been piano, singing, violin and viola - which expresses both her classical and improvisational sides. She graduated with honors in piano and conducting from two Conservatoria in the Netherlands. She has taught and performed music in the U.S. and Europe all her adult life. ...Continued
The ME Nobody Knows | Open: 08/07/08 Close: 08/10/08 - Roy Arias Theatre 2
Based on the book "THE ME NOBODY KNOWS" - Edited by Stephen M. Joseph
"These are children's voices from the ghetto. In their struggle lies their hope, and ours. They are the voices of change."
Inspired by the anthologized writings of nearly 200 New York City students, aged 7 through 18 and subtitled "Children's Voices From the Ghetto"; Stephen M. Joseph, a teacher, edited the collection, which the children had written about their feelings and their neighborhoods. According to Mr. Joseph, they wrote "for keeps" and "like it is." At odds with a squalid setting and a cynical, materialistic view of the world, themes of hope and renewal emerged. To guarantee uplift, samples from the students' work were interwoven with a ground-breaking score that combined rock music, classical fugues, early rap and jazz.
Critics were overwhelmingly impressed by the vitality, passion, honesty and powers of observation shown by the young writers, as they painted a moving but sometimes ugly picture of inner city life, and there were hit records by The Staple Singers ("This World") and The Fifth Dimension ("Light Sings").
THE ME NOBODY KNOWS
Music by: Gary William Friedman
Lyrics by: Will Holt
Adapted by: Robert H. Livingston and Herb Schapiro
Additional Lyrics by Herb Shapiro
Arrangements and Orchestration by Gary William Friedman
Original New York Production Directed by Robert H. Livingston
Originally produced on Broadway by Jeff Britton
Directed by Laura Rizzo
Co-Directed by Gregory Cilmi
Musical Direction by Abigail Lumsden
Choreography by Carrie Nagy
Produced in part by a grant from Orange Board of Education, Orange, NJ


text:
\section*{Acknowledgement}
We thank Mathieu Garon, Yannick Hold-Geoffroy, Henrique Weber, and Maxime Tremblay for helpful discussions and comments, and Jean-Michel Fortin for his help in capturing images for the novel dataset. This work was partially supported by the FRQNT New Researcher Grant 2016NC189939, the NSERC Discovery Grant RGPIN-2014-05314, and the REPARTI Strategic Network. We gratefully acknowledge the support of Nvidia with the donation of the GPUs used for this research.

\section{Applications}
\vspace{-.2em}
\label{sec:app}
Extrapolating accurate high dynamic range from a single, low dynamic range panorama gives rise to several interesting applications. In this section, we present three different ways of using our network in practical scenarios.

\subsection{Single shot outdoor light probe}

We can use our method to simplify the process of capturing outdoor light probes. Because of its extremely high dynamic range, capturing outdoor light requires a carefully calibrated setup~\cite{stumpfel-afrigraph-04} or specially-designed light probes~\cite{debevec-st-12}. Instead, one could simply take an LDR panorama with an off-the-shelf, point-and-shoot $360^\circ$ camera such as the Ricoh Theta S, and extrapolate the HDR using our network.

This is demonstrated in fig.~\ref{fig:application-theta}. In this example, a novice user shot an LDR panorama with a Theta S at the location where the object is to be inserted. From this LDR panorama, the prediction from our ``Fine-tuning'' network is used as a light probe for image-based lighting to insert virtual objects in the photograph. The image rendered with our prediction produces a plausible rendering result.

\subsection{Visualization in Google Street View imagery}
\label{sec:street-view}

The Google Street View dataset is a huge source of LDR panoramas, captured all over the world by cars equipped with omnidirectional cameras. We leverage our approach to extrapolate HDR from this dataset, and show that the resulting panoramas can be used for image-based lighting in fig.~\ref{fig:application-google}. In this case, we use the domain-adapted version of the network from sec.~\ref{sec:quantitative-evaluation} as it qualitatively yields better results than the network fine-tuned on the theta S data.

\subsection{Image matching in the SUN360 dataset}

We can also use our method for matching panoramas based on intuitive illumination parameters. For instance, we show an example of browsing the SUN360 database~\cite{xiao2012recognizing} based on sun elevation and intensity in fig.~\ref{fig:application-sun360}. For this, we first run our network on all outdoor LDR panoramas in the database. Then, we compute the sun intensity as being the intensity of the brightest pixel in the estimated HDR panoramas. Finally, a target set of parameters is specified (e.g. bright sun at $75^\circ$ elevation as in the first row of fig.~\ref{fig:application-sun360}), and the best images can efficiently be retrieved using nearest neighbor matching. Note that this would be very hard to do without our method, as the sun is always saturated in outdoor panoramas. As with sec.~\ref{sec:street-view}, the domain-adapted version of the network from sec.~\ref{sec:quantitative-evaluation} is used here as well.

\vspace{-.2em}

\section{Approach}
\vspace{-.2em}
Our approach relies on a convolutional autoencoder that learns to reconstruct high dynamic range from low dynamic range panoramas. It is trained on a large dataset of synthetic LDR panoramas. Before the data generation process is described in sec.~\ref{sec:validation-synthetic}, we first describe our CNN architecture, loss function, and training parameters.

\subsection{Network architecture}

As shown in fig.~\ref{fig:cnn-architecture}, we use a convolutional neural network that takes in a $64 \times 128$ LDR panorama $\mathbf{x}_\text{LDR}$ as input stored in the latitude-longitude format. It assumes the panorama has been previously rotated to align the sun with the center of the image\footnote{As in~\cite{hold-geoffroy-cvpr-17}, the sun is detected by computing the center of mass of the largest saturated region in the image.}. From this input, it produces a 64-dimensional encoding of the input through a series of convolutions downstream and splits into two upstream expansions, with two distinct tasks: (1) HDR recovery, and (2) sun elevation prediction. The encoder and decoder are both modeled by four (de)convolution layers. The output of each convolution is added to the input of its corresponding deconvolution layer via skip links (shown with dashed lines in fig.~\ref{fig:cnn-architecture}). Each (de)convolution layer is followed by an (up-sub)sampling step (stride of 2), batch normalization, and the ELU activation function~\cite{clevert-iclr-16}. The sun elevation decoder is composed of three low-dimensional fully-connected layers.

\begin{figure}[!t]
\footnotesize
\centering
\begin{tabular}{cc}
\includegraphics[height=2.8cm,trim={0 3cm 0 0},clip]{figures/architecture/render/render-3D} &
\includegraphics[height=2.8 cm]{figures/architecture/render/render-example} \\
(a) 3D scene & (b) Example rendering
\end{tabular}
\vspace{.5em}
\caption{(a) 3D view of the scene used to compute the transport matrix $\mathbf{T}$ in the rendering loss $\mathcal{L}_\text{render}$ (\ref{eqn:loss-render}), and (b) example rendering obtained with the resulting $\mathbf{T}$ and a sunny HDR panorama. The perspective effect is considerable when the camera is close to the object, as such, the shadow cast on the plane is smaller than the object. The scene was chosen to generate complex lighting effects such as cast shadows, smooth shading, and occlusions.}
\label{fig:spiky-sphere}
\vspace{-1em}
\end{figure}

\subsection{Loss function}

To train the CNN, the following loss function is defined:
\begin{equation}
\mathcal{L}_\text{all}(\mathbf{y}, \mathbf{t}) =
\mathcal{L}_\text{HDR}(\mathbf{y}, \mathbf{t}) +
\lambda_{1}\mathcal{L}_\theta(\mathbf{y}, \mathbf{t}) +
\lambda_{2}\mathcal{L}_\text{render}(\mathbf{y}, \mathbf{t}) \,,
\label{eqn:loss-all}
\end{equation}
where $\mathbf{y}$ is the predicted output, and $\mathbf{t}$ is the ground truth target label. Eq. (\ref{eqn:loss-all}) is composed of three loss functions computed on different labelled data, with the weights $\lambda_{1} = \lambda_{2} = 0.1$ controlling the relative importance of each loss function.
The first element in (\ref{eqn:loss-all}) is the L1 norm between the predicted HDR panorama $\mathbf{y}_\text{HDR}$ and the ground truth $\mathbf{t}_\text{HDR}$:
\begin{equation}
\mathcal{L}_\text{HDR}(\mathbf{y}, \mathbf{t}) = || \mathbf{y}_\text{HDR} - \mathbf{t}_\text{HDR} ||_1 \,.
\end{equation}
The L1 norm is used to be more robust to the very high dynamic range of the HDR panorama. Since the sun pixel intensity can be up to 100,000 times brighter than other pixels, using an L2 norm overwhelmingly penalizes errors on the sun at the expense of the other, lower dynamic range pixels. To help the network in predicting high dynamic range values, the full HDR target $\mathbf{t}^*_\text{HDR}$ is tonemapped using
\begin{equation}
\mathbf{t}_\text{HDR} = \alpha (\mathbf{t}^*_\text{HDR})^{1/\gamma} \,.
\label{eqn:loss-tonemapping}
\end{equation}
We use $\gamma=2.2$ and $\alpha=1/30$, which brings the sun intensity close to 1 when it is bright. The inverse of (\ref{eqn:loss-tonemapping}) is applied to $\mathbf{y}_\text{HDR}$ to convert the network output to full HDR.

The second element in (\ref{eqn:loss-all}) computes the L2 norm between the predicted and ground truth sun elevations $\mathbf{y}_\theta$, $\mathbf{t}_\theta$:
\begin{equation}
\mathcal{L}_\theta(\mathbf{y}, \mathbf{t}) = || \mathbf{y}_\theta - \mathbf{t}_\theta ||_2 \,.
\end{equation}
Our experiments demonstrate that a small gain in performance can be obtained with this additional path in the network (see sec.~\ref{sec:synthetic-quantitative}). Finally, we also incorporate a render loss in the third element in (\ref{eqn:loss-all}):
\begin{equation}
\mathcal{L}_\text{render}(\mathbf{y}, \mathbf{t}) = || \mathbf{T}\mathbf{y}_\text{HDR} - \mathbf{T}\mathbf{t}_\text{HDR} ||_2 \,,
\label{eqn:loss-render}
\end{equation}
where $\mathbf{T}$ is the transport matrix for a lambertian scene (without interreflections). To ensure that interesting lighting effects are captured, a scene made of a complex ``spiky sphere'' on a flat ground plane seen from above is used (see fig.~\ref{fig:spiky-sphere}). This effectively re-weights the pixels in the panoramas according to the fraction of the visible hemisphere for each pixel in the scene. Rendering is performed at $64 \times 64$ resolution, so $\mathbf{T}$ is of dimensions\footnote{The number of columns in $\mathbf{T}$ is equal to the number of pixels in the panorama, so $64 \times 128 = 8,192$. To save memory, it can be divided by 2 because only the top hemisphere is visible in the render.} $64^2 \times 4,096$. Since this is a simple matrix multiplication, gradients can easily be back-propagated through this rendering step.

\subsection{Training details}

To train the CNN, we use the ADAM optimizer~\cite{kingma-iclr-15} with a minibatch size of $128$, initial learning rate of $0.001$, and momentum parameter of $\beta_1=0.9$. Training 500 epochs takes roughly 50 hours on an Nvidia GeForce 1060 GPU. At test time, inference takes approximately 5ms.

\begin{figure}[!t]
\centering
\includegraphics[width=.9\linewidth]{figures/synthetic/quantitative/quant}
\caption{Quantitative comparison of results obtained with the LDR panorama (leftmost column) and different combinations of losses (other columns) for our approach on the synthetic test set. Different error metrics are reported, from top to bottom: the mean absolute error on the sky panorama $\mathcal{E}_\text{HDR}$, the RMSE on the sun elevation $\mathcal{E}_\theta$, the RMSE on the sun intensity $\mathcal{E}_\text{sun}$, and the RMSE on the render $\mathcal{E}_\text{render}$. $\mathcal{L}_\text{all}$ yields the lowest error on all metrics. }
\label{fig:synthetic-quantitative}
\vspace{-1em}
\end{figure}

\begin{figure*}[!t]
\centering
\footnotesize
\setlength{\tabcolsep}{1pt}
\newcolumntype{R}{c@{\extracolsep{1pt}}c@{\extracolsep{5pt}}}%
\begin{tabular}{cRRR}
& Panorama & Render & Panorama & Render & Panorama & Render \\
\rotatebox{90}{\hspace{.1em}Gnd truth HDR}&
\includegraphics[height=1.82cm]{figures/synthetic/toshow/20141108124825_gt} &
\includegraphics[height=1.82cm]{figures/synthetic/toshow/20141108124825_render_gt} &
\includegraphics[height=1.82cm]{figures/synthetic/toshow/20160613143930_gt} &
\includegraphics[height=1.82cm]{figures/synthetic/toshow/20160613143930_render_gt} &
\includegraphics[height=1.82cm]{figures/synthetic/toshow/20160519143258_gt} &
\includegraphics[height=1.82cm]{figures/synthetic/toshow/20160519143258_render_gt} \\
\rotatebox{90}{\hspace{.1em}LDR panorama}&
\includegraphics[height=1.82cm]{figures/synthetic/toshow/20141108124825_ldr} &
\includegraphics[height=1.82cm]{figures/synthetic/toshow/20141108124825_render_ldr} &
\includegraphics[height=1.82cm]{figures/synthetic/toshow/20160613143930_ldr} &
\includegraphics[height=1.82cm]{figures/synthetic/toshow/20160613143930_render_ldr} &
\includegraphics[height=1.82cm]{figures/synthetic/toshow/20160519143258_ldr} &
\includegraphics[height=1.82cm]{figures/synthetic/toshow/20160519143258_render_ldr} \\
\rotatebox{90}{\hspace{.1em}Predicted HDR}&
\includegraphics[height=1.82cm]{figures/synthetic/toshow/20141108124825_pred} &
\includegraphics[height=1.82cm]{figures/synthetic/toshow/20141108124825_render_pred} &
\includegraphics[height=1.82cm]{figures/synthetic/toshow/20160613143930_pred} &
\includegraphics[height=1.82cm]{figures/synthetic/toshow/20160613143930_render_pred} &
\includegraphics[height=1.82cm]{figures/synthetic/toshow/20160519143258_pred} &
\includegraphics[height=1.82cm]{figures/synthetic/toshow/20160519143258_render_pred}
\end{tabular}
\caption{Qualitative results on the synthetic dataset. \emph{Top row}: the ground truth HDR panorama, \emph{middle row}: the LDR panorama, and \emph{bottom row}: the predicted HDR panorama obtained with our method. To illustrate dynamic range, each panorama is shown at two exposures, with a factor of 16 between the two. For each example, we show the panorama itself (left column), and the rendering of a 3D object lit with the panorama (right column). The object is a ``spiky sphere'' on a ground plane, seen from above. Our method accurately predicts the extremely high dynamic range of outdoor lighting in a wide variety of lighting conditions. A tonemapping of $\gamma=2.2$ is used for display purposes. Please see additional examples on our project page. }
\label{fig:synthetic-qualitative}
\vspace{-1em}
\end{figure*}

\begin{figure}[!t]
\centering
\footnotesize
\includegraphics[width=\linewidth]{figures/synthetic/timeofday/plot}
\caption{HDR predictions over the course of one day from the synthetic test set. We plot the ground truth sun intensity (blue), along with the predictions from our network (orange) over the course of one day. Our method achieves a relatively stable temporal coherence, even if it is working on one panorama at a time. }
\label{fig:synthetic-time}
\vspace{-2em}
\end{figure}

\section{Conclusion}

We present a full end-to-end learning approach to estimate the extremely high dynamic range of outdoor lighting from a single, LDR $360^\circ$ panorama. Our main insight is to exploit a large dataset of synthetic data composed of a realistic virtual city model, lit with real world HDR sky light probes~\cite{hdrdb}, to train a deep convolutional autoencoder. The resulting network is evaluated on a wide range of experiments on synthetic data, as well as on a novel dataset of real LDR panoramas and corresponding HDR ground truth. The applicability of the approach is also demonstrated on three novel applications.

Despite its success, our approach is not without limitations. First, we note a certain sensitivity to the tonemapping function of the input LDR. Our qualitative experiments demonstrate that domain adaptation helps in adjusting to the wide variability in camera response functions and image post-processing operations applied to panoramas in the SUN360 dataset~\cite{xiao2012recognizing}, but these are complicated by the fact that no ground truth is available. A second limitation is that our approach is limited to outdoor scenes and the sun, when visible, needs to be centered in the panorama. While the sun is typically relatively easy to detect in LDR panoramas, the simple sun detection method of~\cite{hold-geoffroy-cvpr-17} may fail, resulting in unlikely results. A possible extension of this work could be the inclusion of sun azimuth estimation from the FC-64 latent layer. Finally, the resolution of the output is limited at $64 \times 128$, which, while sufficient for relighting applications, cannot extrapolate the HDR information in a full-resolution LDR background image. A potential way to leverage high resolution images is applying a fully convolution network~\cite{long2015fully} by converting all fully-connected layers to convolutions. Future work includes the adaptation of the network to learn high resolution HDR textures from limited field-of-view LDR images.

\section{Introduction}

Outdoor lighting has an intrinsic dynamic range that is much higher than what conventional cameras can capture. While professional cameras boast dynamic ranges of up to 16 bits, we are still a long way from the full 22 bits needed to properly model outdoor lighting~\cite{stumpfel-afrigraph-04}. Therefore, to accurately capture the full dynamic range of outdoor illumination, one must resort to acquiring multiple exposures~\cite{debevec-siggraph-97}, imaging a specially-designed light probe~\cite{debevec-st-12}, or using custom-designed photographic hardware~\cite{manakov-siggraph-13,tocci-tog-11b}.

An attractive alternative is to apply inverse lighting algorithms on low dynamic range imagery, which shifts the burden from capture to processing. These algorithms attempt to inverse the image formation process in order to recover lighting information, either in a physics-based~\cite{lombardi-eccv-12} or data-driven~\cite{lalonde-ijcv-12,hold-geoffroy-cvpr-17} way. A main limitation of these algorithms is that they are inherently limited to the information available within the image. An image may not always contain sufficient information to recover the lighting reliably.

In this work, we propose to directly learn the relationship between the low dynamic range (LDR) information available in an outdoor $360^\circ$ panorama and the high dynamic range (HDR) of outdoor lighting. Our method takes as input a single LDR omnidirectional panorama, and converts it to HDR automatically, filling in saturated pixels with plausible values. Recovering HDR from LDR is known as inverse tonemapping: this is typically achieved by inverting the camera response function~\cite{rempel-siggraph-07}. While there exists a wide variety of such techniques in the literature, these methods are not tuned for outdoor lighting as they do not expect such extreme variations in dynamic range, and fail to recover plausible results as will be demonstrated in the paper.

Our work proposes the following three key contributions. First, we present a full end-to-end learning approach which directly regresses HDR from LDR information in an outdoor panorama. Surely this is a challenging task: the sun can be 17 f-stops brighter than the rest of the sky~\cite{stumpfel-afrigraph-04}! To learn this relationship, we rely on a large set of HDR sky environment maps~\cite{hdrdb}, which we use as light sources to render a high quality synthetic city model to form a large corpus of synthetic panoramas. From this dataset, we train an LDR-to-HDR deep convolutional autoencoder, and show, through extensive experiments on synthetic and real data, that it succeeds in accurately predicting the extreme dynamic range of outdoor lighting. Our second key contribution is a novel dataset of real LDR panoramas and associated HDR ground truth. We use this novel dataset to see how the Convolutional Neural Network (CNN) can be adapted to work on the challenging case of real data. Our third key contribution is to show the applicability of our approach on three novel applications: single shot light probe, visualizing virtual objects and image matching in large LDR panorama database. Given the availability of large datasets of outdoor panoramas (e.g. SUN360~\cite{xiao2012recognizing} and Google Street View) and the recent interest in using HDR panoramas for virtual and mixed reality applications~\cite{rhee2017mr360}, our work is timely in enabling the use of LDR data, which is easy to capture, in HDR applications. Code and data are available on our project page.

\vspace{-.5em}

\section{Experiments on real data}

We now present extensive experiments which validate that our approach is applicable to real-world data. First, we present a novel dataset of real outdoor LDR panoramas with corresponding HDR ground truth. This dataset is used first in a quantitative comparison of several training approaches; and second in a comparison to previous work.

\subsection{Dataset of real photographs}

We collected a novel dataset of LDR panoramas with their corresponding HDR skies. The LDR panoramas were captured with a Ricoh Theta S camera, a consumer grade point-and-shoot $360^\circ$ camera. The HDR skies were captured with a Canon 5D Mark iii mounted on a tripod, equipped with a Sigma 8mm fisheye lens, and placed at the same location as the Theta camera. To properly image the true dynamic range of outdoor lighting, we installed a ND 3.0 filter behind the lens and captured 7 exposures ranging from $1/8000$ to 8 seconds at f/16, following~\cite{stumpfel-afrigraph-04}. The fisheye lens was radiometrically and geometrically calibrated~\cite{scaramuzza-iros-06}, so that the resulting HDR image could be warped to a latitude-longitude panorama.

We use a ColorChecker Digital SG chart to match the colors between the LDR and HDR panoramas. To compensate for possible misalignments, the two panoramas are first geometrically aligned by finding SIFT feature correspondences and using RANSAC to find the optimal rotation matrix between the panoramas. This procedure is followed by a step of pixel-wise optical flow refinement to account for lens calibration errors.

Using this technique, we captured pairs of real LDR/HDR panoramas over 13 different days, for a total of 404 pairs. Our dataset contains a variety of different illumination and weather conditions, as shown in fig.~\ref{fig:real-dataset}. The dataset is separated into non-overlapping training and test subsets of 8 and 5 days (490 and 318 panoramas---obtained by flipping each panorama horizontally) respectively. The same split is used in the following experiments to allow for comparisons between techniques. Please see additional results on our project page.

\begin{figure}[!t]
\footnotesize
\centering
\includegraphics[width=.49\linewidth]{figures/realdataset/sunny1}
\includegraphics[width=.49\linewidth]{figures/realdataset/cloudy1}
\includegraphics[width=.49\linewidth]{figures/realdataset/overcast1}
\includegraphics[width=.49\linewidth]{figures/realdataset/overcast13}
\caption{Examples from our real dataset. For each case, we show the LDR panorama captured by the Ricoh Theta S (left), and the corresponding HDR panorama captured by the Canon 5D Mark iii (right, shown at a different exposure to illustrate the high dynamic range). Please see additional examples on our project page. }
\label{fig:real-dataset}
\vspace{-1.5em}
\end{figure}

\begin{figure*}[!t]
\centering
\footnotesize
\setlength{\tabcolsep}{1pt}
\begin{tabular}{cc}
\includegraphics[width=.45\linewidth, trim={0 1cm 0 0}, clip]{figures/real/quantitative/violin/violin-sky-vertical} &
\includegraphics[width=.45\linewidth, trim={0 1cm 0 0}, clip]{figures/real/quantitative/violin/violin-render-vertical} \\
(a) MAE on the sky & (b) RMSE on the render
\end{tabular}
\vspace{.5em}
\caption{Quantitative comparison of models on the real dataset. For each model, we show a full distribution of errors (curved shapes), as well as the 25th, 50th and 75th percentiles (gray vertical bar). }
\label{fig:quant-violin}
\vspace{-2em}
\end{figure*}

\subsection{Adapting to real data}
\label{sec:quantitative-evaluation}

\begin{table}[!t]
\centering
\begin{tabular}{lcccc}
\toprule
Method & $\mathcal{E}_\text{HDR}$ & $\mathcal{E}_\text{render}$ & $\mathcal{E}_\theta$ & $\mathcal{E}_{\text{sun}}$ \\
\midrule
LDR  			& 5.30 & 1.34 & 0.21 & 0.54 \\
Baseline	 	& 5.34 & 1.19 & 0.10 & 0.43 \\
Color 			& 5.32 & 1.16 & 0.10 & 0.46 \\
Tone 			& \textbf{3.59} & \textbf{1.06} & \textbf{0.08} & \textbf{0.31} \\
Domain adaptation~\cite{ganin-icml-15}		& 7.53 & 1.30 & 0.10 & 0.36 \\
\midrule
Fine-tuning		& \textbf{2.55} & \textbf{0.64} & \textbf{0.07} & \textbf{0.22} \\
\bottomrule
\end{tabular}
\vspace{.5em}
\caption{Analyzing the impact of training data.
 Quantitative comparison of models trained on different synthetic inputs, and tested on real JPG panoramas. Refer to fig.~\ref{fig:quant-violin} for qualitative examples. The model is trained on: linear data (``Baseline''), linear data with color changes (``Color''), and non-linear data where a camera response function is applied along with color changes (``Tone''). The network trained on the ``Tone'' data achieves better performance when testing on real panoramas. Domain adaptation~\cite{ganin-icml-15} does not perform well on the real dataset; we believe this is because we do not have enough Theta S images. Fine-tuning with the ground truth training data (``Fine-tuning'') performs best, see sec.~\ref{subsubsection-finetuning}.}

\label{tab:quant-augmentation}
\end{table}

\subsubsection{Augmenting the training dataset}

Evaluating the network trained on linear data on the real world data yields the performance shown in the first row of table~\ref{tab:quant-augmentation}. To improve these results, we first attempt to augment the synthetic dataset used for training, in order to more closely match real world capture conditions.

\begin{table}[!t]
\centering
\begin{tabular}{clcccc}
\toprule
& Method & $\mathcal{E}_\text{HDR}$ & $\mathcal{E}_\text{render}$ & $\mathcal{E}_\theta$ & $\mathcal{E}_{\text{sun}}$ \\
\midrule
\multirow{3}{*}{\rotatebox{90}{JPGs}}
& LDR  			& 5.30 & 1.34 & 0.21 & 0.54 \\
& Baseline	 	& 5.34 & 1.19 & 0.10 & 0.43 \\
& Tone 			& 3.59 & 1.06 & 0.08 & 0.31 \\
\midrule
\multirow{3}{*}{\rotatebox{90}{$\gamma=2.2$}}
& LDR  			& 6.33 & 1.53 & 0.19 & 0.54 \\
& Baseline	 	& 6.63 & 1.45 & 0.13 & 0.49 \\
& Tone 			& 7.06 & 1.50 & 0.08 & 0.43 \\
\midrule
\multirow{3}{*}{\rotatebox{90}{RF}}
& LDR  			& 4.64 & 1.46 & 0.18 & 0.54 \\
& Baseline	 	& 4.33 & 1.18 & 0.08 & 0.36 \\
& Tone 			& 6.64 & 1.35 & \textbf{0.07} & 0.35 \\
\midrule
\multirow{3}{*}{\rotatebox{90}{RF+WB}}
& LDR  			& 3.31 & 1.34 & 0.23 & 0.54 \\
& Baseline	 	& \textbf{2.99} & \textbf{1.03} & 0.08 & \textbf{0.30} \\
& Tone 			& 6.55 & 1.41 & 0.08 & 0.38 \\
\bottomrule
\end{tabular}
\vspace{.5em}
\caption{Analyzing the impact of modeling the camera. Radiometric camera models of increasing fidelity are applied to the JPG inputs and compared. Camera models include: none (``JPGs''), $\gamma = 2.2$, calibrated inverse response function (``RF''), and combining the ``RF'' with white balance adjustment (``RF+WB''). The three methods compared are: using the input LDR directly (``LDR''), network trained on synthetic linear data (``Baseline''), and network trained on synthetic data augmented with color shifts and non-linear response functions (``Tone'', see also table~\ref{tab:quant-augmentation}). }
\label{tab:quant-linearization}
\vspace{-1em}
\end{table}

\begin{table}[!t]
\centering
\begin{tabular}{lcccc}
\toprule
Method & $\mathcal{E}_\text{HDR}$ & $\mathcal{E}_\text{render}$ & $\mathcal{E}_\theta$ & $\mathcal{E}_{\text{sun}}$ \\
\midrule
LDR  							& 5.30 & 1.34 & 0.21 & 0.54 \\
\cite{banterle2007framework} 	& 16.5 & 9.24 & 0.16 & 0.30 \\
\cite{kuo2012content}			& 24.63 & 19.30 & 0.18 & 0.30 \\
\cite{meylan2006reproduction}	& 29.1 & 29.25 & 0.17 & 0.28 \\
\cite{rempel-siggraph-07}		& 29.1 & 29.26 & 0.17 & 0.29 \\
\cite{hold-geoffroy-cvpr-17}	& 5.4  & 21.8 & \textbf{0.06} & 1.70 \\
Ours							& \textbf{2.55} & \textbf{0.64} & 0.07 & \textbf{0.22} \\
\bottomrule
\end{tabular}
\vspace{.5em}
\caption{Comparison with previous work. The main parameters of each competing method are cross-validated on the ground truth training data, while our approach is fine-tuned using the same data. The existing inverse tonemapping methods yield unreasonable estimates when regressing the extreme HDR of outdoor lighting.}
\label{tab:quant-comparison}
\vspace{-1.5em}
\end{table}

\vspace{-1em}
\paragraph{Modeling the white balance} Digital cameras often apply significant color adjustments to images. We simulate this by applying a random additive shift to the hue and saturation channels $(s_h,s_s)$, where $s_h \sim \mathcal{N}(0, 10)$ and $s_s \sim \mathcal{N}(0, 0.1)$. Adding color shifts to the training data only marginally improves performance (table~\ref{tab:quant-augmentation}).

\vspace{-1em}
\paragraph{Modeling the response function of the camera}
Real cameras have non-linear response functions. To simulate this, we randomly sample real camera response functions from the Database of Response Functions (DoRF)~\cite{grossberg2003space}, and apply them to the linear synthetic data before training. Training on this data yields a network which performs significantly better than the previous versions (see table~\ref{tab:quant-augmentation}). This also has the interesting side effect of resulting in better sun elevations.

\vspace{-1em}
\paragraph{Domain adaptation with unlabelled data}
We apply unsupervised domain adaptation~\cite{ganin-icml-15} to adapt the synthetic model to real world images. This is achieved by adding a domain classifier connected to the FC-64 latent layer of the network via a \emph{gradient reversal layer}, which ensures the encoding feature is domain-invariant to synthetic and real data. The discriminator contains two fully-connected layers of 32 and 2 nodes, followed by ELU and softmax activation respectively. This model is trained by adding unlabelled real data to each minibatch, which now contains 50\% of synthetic data with known label $\mathbf{t}$, and 50\% of real-world LDR panoramas sampled from the SUN360 database~\cite{xiao2012recognizing}, Google Street View, and training images (where ground truth is ignored) from our real dataset. Unfortunately, applying this domain-adaptated model does not yield satisfying quantitative results (table~\ref{tab:quant-augmentation}), probably because there are not enough unlabelled theta S panoramas. However, we use this model when testing on SUN360 and Google Street View imagery in sec.~\ref{sec:app} with improved results.

\vspace{-1em}
\subsubsection{Adapting the input panorama}

Aside from augmenting the training data, another option is to adapt the input panorama $\mathbf{x}_\text{LDR}$. For this, we apply different camera models to the JPG files captured by the theta S camera, ranging from: 1) none (``JPGs''); 2) a simple $\gamma = 2.2$ as is commonly done in the literature; 3) a per-channel inverse response function calibrated using a Macbeth color checker (``RF''); and 4) the inverse response function followed by a white balance transformation (``RF+WB''). These options are compared in table~\ref{tab:quant-linearization}. We observe that
linearizing the input data with the ``RF+WB'' model performs best. If such information is unavailable, the model trained on augmented synthetic data (``Tone'') performs best on the input JPG images themselves.

\begin{figure}[!t]
\footnotesize
\centering
\setlength{\tabcolsep}{1pt}
\begin{tabular}{ccc}
\rotatebox{90}{Background image} &
\includegraphics[width=.45\linewidth]{figures/app/thetas/clear_parking}  &
\includegraphics[width=.45\linewidth]{figures/app/thetas/clear_yellow} \\
\rotatebox{90}{Render with GT} &
\includegraphics[width=.45\linewidth]{figures/app/thetas/clear_parking_gt}  &
\includegraphics[width=.45\linewidth]{figures/app/thetas/clear_yellow_gt} \\
\rotatebox{90}{Render with prediction} &
\includegraphics[width=.45\linewidth]{figures/app/thetas/clear_parking_pred} &
\includegraphics[width=.45\linewidth]{figures/app/thetas/clear_yellow_pred}
\end{tabular}
\vspace{.5em}
\caption{Capturing outdoor light probes with a single shot. A single LDR panorama is shot at the point of object insertion (top). Our approach correctly extrapolates the high dynamic range from the panorama, resulting in a realistic render (last row). Please see additional results on our project page. }
\label{fig:application-theta}
\vspace{-1.5em}
\end{figure}

\vspace{-1em}
\subsubsection{Adapting with ground truth HDR}
\label{subsubsection-finetuning}
We observe that with a small amount of labelled data, the model can be fine-tuned to gain a better performance. We use JPG panoramas from the novel real dataset (8 days of training subset) to fine-tune the best model trained with JPGs (``Tone''), and report the results in the last row of table~\ref{tab:quant-augmentation}. This option far outperforms the others, and can learn to robustly predict HDR from LDR. Fig.~\ref{fig:quant-violin} shows the distribution of errors for each method evaluated in this section. In addition, it also shows qualitative examples corresponding to the 25th, 50th and 75th percentile of errors for the fine-tuned network to illustrate the meaning of these numbers.

\vspace{-.5em}
\subsection{Comparison with previous work}

Since there are very many options in the inverse tone-mapping (iTMO) literature, we compare against the following set of representative methods: \cite{banterle2007framework,kuo2012content,meylan2006reproduction,rempel-siggraph-07}. We also experimented with the approach of Hold-Geoffroy et al.~\cite{hold-geoffroy-cvpr-17}. They propose to fit a physically-based sky model~\cite{hosek-siggraph-12} to the unsaturated portion of the sky, and extrapolate the sun color via an additional sun model~\cite{hosek-cga-13}.

Table.~\ref{tab:quant-comparison} shows the errors for each method computed on our test set. For fairness, the parameters of each method (except \cite{hold-geoffroy-cvpr-17} which does not require tuning) are cross-validated on the training set. Our method is the fine-tuned model on the same training set. As table~\ref{tab:quant-comparison} shows, the existing iTMO methods fail at this task. This is probably due to the fact that they were not designed to work with the extremely high dynamic range of outdoor lighting. We found the method of Hold-Geoffroy et al.~\cite{hold-geoffroy-cvpr-17} to consistently overshoot in estimating the sun intensity.

\section{Related work}

\paragraph{Image-based lighting}
The seminal work of Debevec~\cite{debevec-siggraph-98} on image-based rendering demonstrated that capturing lighting can be achieved by acquiring several photographs of a mirrored sphere at different exposures. Since then, it has been demonstrated that the same can be done from a single shot of a metallic/diffuse hybrid sphere~\cite{debevec-st-12}. Similarly, specialized probes also exist for real-time applications~\cite{calian-sigtr-13}. Another method proposes to stitch multiple photographs taken with different exposures and viewpoints using a smartphone~\cite{kan-egshort-15}. The resulting light probes are useful for virtual object insertion, but also in virtual or mixed reality applications~\cite{rhee2017mr360}. In contrast, our approach uses a single LDR shot, taken from a commodity $360^\circ$ camera.

\vspace{-1.1em}
\paragraph{Inverse tone mapping}
Algorithms for reproducing HDR from LDR images have been proposed in recent years, which are known as inverse tone mapping operators (iTMOs). To reproduce the HDR content from an LDR image is an ill-posed problem since the information is missing in the saturated regions. Banterle et al.~\cite{banterle-cgita-06, banterle2007framework} apply the inverse of the Reinhard tone mapping function~\cite{reinhard2002photographic} to the LDR image, then they create an \emph{expand map} by density estimation of the bright areas to guide the dynamic range expansion. Rempel et al.~\cite{rempel-siggraph-07} proposed a similar expand map combined with an edge stopping function to expand the dynamic range while increasing the contrast around edges. Kuo et al.~\cite{kuo2012content} use different inverse tone mapping parameters based on the scene content. Meylan et al.~\cite{meylan2006reproduction, meylan2007tone} explicitly focus on the specular highlight region; they use different linear functions to expand the diffuse and specular region in the image. The main concern of most of these techniques is to display LDR content onto HDR devices~\cite{akyuz2007hdr}, and as such these approaches are ill-suited for the case of outdoor lighting.

\vspace{-1.1em}
\paragraph{Deep learning}
Deep CNNs are often used in image recognition and classification, but recent work has shown that they can also be used to estimate missing information from images. Pathak et al.~\cite{pathakCVPR16context} proposed to use CNNs to predict the missing content of a scene based on the surrounding pixels by minimizing a reconstruction loss and an adversarial loss~\cite{goodfellow2014generative}. Zhang et al.~\cite{zhang2016colorful} employed CNNs to recover color from grayscale images. Sangkloy et al.~\cite{sangkloy2016scribbler} proposed an adversarial image synthesis architecture to constrain the generation by user input.
CNNs are also used for generating a high resolution image from a low resolution image~\cite{denton2015deep}, predicting the missing depth information from a single RGB image~\cite{eigen2014depth}, producing a complete 3D voxel representation from a single depth image~\cite{song2016ssc}. In contrast, our approach recovers missing \emph{dynamic range}.

\begin{figure}[!t]
\centering
\includegraphics[width=\linewidth]{figures/architecture/table-skiplinks.pdf}
\caption[]{The proposed CNN architecture. The encoder (top half) compresses the input LDR panorama to a 64-dimensional vector through 4 convolutional layers and splits into two heads: one to reconstruct the HDR panorama $\mathbf{y}_\text{HDR}$ through a series of deconvolutional layers; and the second, composed of two fully-connected layers, to predict the sun elevation $\mathbf{y}_\theta$. The output of each convolutional layer is added to the input of its deconvolutional counterpart via skip links (dashed lines). A stride of 2, batch normalization, and ELU~\cite{clevert-iclr-16} are used on all (de)convolutional layers.}
\label{fig:cnn-architecture}
\vspace{-1em}
\end{figure}

\section{Validation on Synthetic Data}
\label{sec:validation-synthetic}

\subsection{Data generation}

Even though small datasets of HDR panoramas exist\footnote{See for example: \url{http://www.hdrlabs.com}.}, none do in sufficiently large quantity to train a deep neural network. Therefore, we have obtained access to the Laval HDR Sky Database~\cite{hdrdb}, which contains approximately 38,000 unsaturated, HDR omnidirectional photos of the sky, gathered over 103 different days over the course of 3 years. We use a subset of 9,732 HDR skies to generate renders of a realistic virtual 3D model of a small city. The 3D model was obtained from the Unity Store, and contains over 100 modular buildings with different styles and materials, including realistic roads, sidewalks, and foliage.

To generate a panorama, the sky is first rotated to center the sun in the middle of the panorama based on the known ground truth sun position. Next, a random camera position is sampled in manually defined regions in the 3D city model corresponding to open spaces. The HDR sky is then set as the sole virtual light source, and rendering is performed. For data augmentation, each HDR sky is flipped horizontally and re-exposed with factors $1.75^x$, where $x=\{-1,0,1\}$. We employ the physically-based rendering engine Mitsuba~\cite{Mitsuba}, and render with a virtual omnidirectional camera, saving the output directly to a latitude-longitude panorama format in EXR. The LDR panorama $\mathbf{x}_\text{LDR}$ is obtained by converting this output to an 8-bit JPG file. Example images generated by this process are shown in fig.~\ref{fig:synthetic-qualitative}.

From the resulting dataset of synthetic panoramas, $69\%$ (70 days, 39,198 images) of the samples are used for training, $15\%$ (16 days 8,730 images) for validation and early stopping, and $16\%$ (17 days, 10,458 images) for test. Note that care is taken to split the dataset according to \emph{days}, since HDR sky images are taken at the frequency of one every two minutes in \cite{hdrdb}, so two consecutive photos from the same day are extremely similar.

\subsection{Quantitative experiments}
\label{sec:synthetic-quantitative}

The model is first evaluated on the synthetic test set. Different loss functions are compared together in fig.~\ref{fig:synthetic-quantitative}. We evaluate the performance using different error metrics: the mean absolute error (MAE) on the HDR sky $\mathcal{E}_\text{HDR}$, RMSE on the sun elevation $\mathcal{E}_\theta$, and RMSE on the ``spiky sphere'' render $\mathcal{E}_\text{render}$ (fig.~\ref{fig:spiky-sphere}). We also compute the RMSE on the predicted sun intensity $\mathcal{E}_\text{sun}$, which is approximated by the intensity of the brightest pixel in the HDR image.

Fig.~\ref{fig:synthetic-quantitative} shows that the model trained solely with $\mathcal{L}_\text{HDR}$ already provides a good result when compared to using the LDR panorama directly. While various combinations of losses improve upon the baseline, combining the three losses in $\mathcal{L}_\text{all}$ (\ref{eqn:loss-all}) yields the lowest error on all metrics. Fig.~\ref{fig:synthetic-qualitative} shows qualitative results obtained by the network. Interestingly, predicting the sun elevation and estimating the HDR benefit from each other, as including them in the loss function results in improved performance at both these tasks.

\subsection{Temporal coherence}

We select one full day from sunrise to sunset in the HDR sky dataset~\cite{hdrdb}, and relight the city model from a fixed camera position using that day. Our model is used to regress the HDR from the resulting LDR panoramas one at a time. Fig.~\ref{fig:synthetic-time} shows that even if we do not enforce temporal consistency, our network successfully adapts to time changes and corresponding variations in sun intensity, as the prediction closely follows the ground truth.

redpajama_set_name: RedPajamaArXiv


text:
Q: Adding values to table1 column based on dynamic conditions from table2 I have 2 tables, table1 and table2 and I am trying to add a column to table2 based on values a from table1 and some sort of dynamic conditional statement on c from table2 to get e in table3. This has to be dynamic because table1 will change in both row count and values in columns a and b.
I am not sure how to approach this problem and any help would be appreciated.
table1
 a   |  b
-----+-----
   0 | 1
   5 | 2
  10 | 3
  20 | 4

table2
 c   |  d
-----+-----
  2 | 100
  3 | 101
  5 | 102
  7 | 103
 11 | 104
 13 | 105
 17 | 106
 19 | 107
 23 | 108

table3
 c   |  d   |   e
-----+------+------
  2 | 100  | 1
  3 | 101  | 1
  5 | 102  | 2
  7 | 103  | 2
 11 | 104  | 3
 13 | 105  | 3
 17 | 106  | 3
 19 | 107  | 3
 23 | 108  | 4

A: Basic goal is: For each row in table2, find the row with greatest a in table1 that is less than or equal to table2.c
Try this outer join:
Working test case / fiddle with PG
SELECT t2.*
     , t1.b AS e
  FROM table2 AS t2
  JOIN table1 AS t1
    ON t1.a  <= t2.c
  LEFT JOIN table1 AS t1b
    ON t1b.a <= t2.c
   AND t1.a  < t1b.a
 WHERE t1b.a IS NULL
;

Result:

The setup:
CREATE TABLE table1 (a int, b int);
INSERT INTO table1 VALUES
  (   0 , 1 )
, (   5 , 2 )
, (  10 , 3 )
, (  20 , 4 )
;

CREATE TABLE table2 (c int, d int);

INSERT INTO table2 VALUES
  ( 2 , 100)
, ( 3 , 101)
, ( 5 , 102)
, ( 7 , 103)
, (11 , 104)
, (13 , 105)
, (17 , 106)
, (19 , 107)
, (23 , 108)
;

redpajama_set_name: RedPajamaStackExchange


text:
Q: How can you create a analytics program that can track API calls I want to create a tracker for users on my website that shows what API calls they are using and how long it takes for them to load. Ideally, I would be able to put each user's session under their ID number to be able to see what everyone is using for analytics purposes and to see what can be improved timing wise. Is this something that can be done through Google Analytics or would I have to write it completely. Thanks for any help you can provide!

A: In terms of tracking, I believe it could be achievable with the userTiming tracking within Google Analytics
The real-time reporting, on the other hand, will be much trickier, as I believe the reporting is at the aggregated level.
The same GA document references a mozilla document on
navigation timing, which could be of interest to you (but I am not familiar with it myself, unfortunately). It seems that it might do what you need.


Based on a new study, the same equipment that was integral to the work of this year's winners of the Nobel Prize for Physics — gravitational wave detectors — might be able to provide valuable insight into another enigmatic field of research: dark matter.
Berti and an international team of researchers produced calculations that suggest that some kinds of dark matter could form clouds around black holes. The going theory is that these clouds emit gravitational waves that could be detected by certain advanced equipment. "Surprisingly, gravitational waves from sources that are too weak to be individually detectable can produce a strong stochastic background," explained co-author Richard Brito.
Based on their research, which has been published in Physical Review Letters, the scientists believe close analysis of data collected by the Laser Interferometer Gravitational-Wave Observatory (LIGO) could confirm or deny the presence of ultralight dark matter.
Dark matter is estimated to be five times as abundant as ordinary matter, and yet, no one has been able to directly detect it. It has the potential to unlock all kinds of secrets about the universe, so the great amount of interest in this topic by scientists and astrophysicists isn't surprising.
Already, this interest has led to new theories on the properties of dark matter and projects like the Large Synoptic Survey Telescope, an immensely powerful digital camera set to go online in 2020. Recently released results from the Dark Energy Survey were even said to have the potential to rewrite physics as we know it.


I recently used gummy bears in a recipe, with the few I had left over I tried something new. The result is a quick and fun little recipe: The Polar gummy bears!
Grab a regular ice cube tray and simply drop a gummy bear in each tray. Top off with water. Easy!
Place in freezer and cool for about 3 hours (until solid). Take out of the freezer and that's it, you've got yourself a bunch of Polar gummy bears! Can you guess what's their favorite environment?
Just drop a Polar Gummy bear in a shot of fine vodka! Sip and enjoy!
A recipe by @CocktailTales !
My quest : creating the tools to transform anyone into a cocktail superhero!


Who is organising the Citizens' Panel?
The Citizens' Panel is a joint project between The Senator George J Mitchell Institute for Global Peace, Security and Justice at Queen's University, Belfast and the Institute of Irish Studies at the University of Liverpool.
Both Universities are working in partnership with the Initiative for Civic Space to promote participatory democracy and citizen engagement in decision-making in society.
What is the Initiative for Civic Space and what does it do?
The Initiative for Civic Space (ICS) promotes inclusive and respectful dialogue between all citizens irrespective of political opinion or community background. ICS is neutral on the constitutional issue.
We create a space for the voices of people to be heard. We adhere to the principles of inclusivity, respect and the peaceful resolution of our differences based on the promotion of rights for all. We provide opportunities for citizens to influence public debate and policy-making through informed deliberation and decision-making.
Many people are 'switched off' from politics today. This can be for a variety of reasons – lack of interest; cynicism; a sense of powerlessness and inability to influence public debate and policy-making; a feeling that elected politicians and political parties do not represent the views of many people in society.
Yet, when people are given the opportunity, the time and the information to deliberate and decide on issues that affect them, they usually develop sensible and pragmatic solutions.
The Citizens' Panel will bring together a broadly representative sample of our whole community – including people like you – to deliberate and decide on the type of formal citizen engagement that would best suit the needs of our society.
What will the Panel involve?
The 50 members of the Citizens' Panel will spend time together learning about the issues, sharing their views and discussing options in facilitated small groups. The Panel will be run by an independent organisation called Involve, which specialises in creating opportunities for people to contribute to decision-making on issues that affect them.
There is absolutely no requirement for you to have any prior knowledge of the topic. All the information you need to take part will be provided in an interactive and easy way. We promise that you won't have to read a bunch of technical or detailed reports!
Towards the end of the day you will be asked to consider everything you've heard, learnt and discussed and work with the other Panel members to vote to decide how citizens can formally influence public and political debate and policy making.
When and where will the Panel be held?
The Panel will meet in Queen's University, Belfast on Saturday 11 May, from 9:30am – 5pm. You must be able to attend for the entire day.
Who can register their interest in joining the Citizens' Panel?
To register your interest you must be over 16 years of age and a permanent resident of Northern Ireland living in a household that has received this invitation.
Elected representatives of the Westminster Parliament, the NI Assembly or a local council cannot register their interest.
Will I be identified as a member of the Panel?
The names of the members of the Citizens' Panel will not be published. We will be taking photos throughout the event and will seek your consent to be included in photos at the beginning of the day. It is entirely up to you whether you give consent, and it will not affect your participation in the Panel. You can also withdraw this consent at any time.
How accessible will the Panel be to people with special needs?
We will make every provision to make the Panel accessible to everyone. The venue will be wheelchair accessible and people selected to be on the Panel will be contacted individually and asked if there are any specific measures that could be taken to support their participation (e.g. the availability of hearing loops or ISL/BSL interpreters, the provision of childcare or respite care for those with a caring responsibility, or specific dietary requirements}. If you wish to discuss your particular needs before registering your interest please contact us on the number below.
How was I selected to receive this invitation?
Your household was one of 5000 households randomly selected from Royal Mail's address database in NI.
How do I register my interest in joining the Citizens' Panel?
To register your interest, please visit www.sortitionfoundation.org/rsvp or call Freephone 0800 009 6486 (9am-6pm, Monday-Saturday). The deadline for registering your interest is Monday 15 April.
The Sortition Foundation is an independent, not-for-profit organisation that promotes the use of stratified, random selection in decision-making. Its aim is to ensure that the Citizens' Panel is broadly representative of the entire NI community.
After I register my interest, what happens next?
Once registration has closed, we will select 50 people at random to join the Citizens' Panel. This selection will be weighted to make sure that the final Panel is broadly representative of the entire community.
If you are selected we will contact you by phone on Tuesday 16 April, and subsequently by email and/or letter to provide you with further information. We will then be back in touch to let you know more about the Panel, organise travel and accommodation arrangements (if required) and discuss any dietary or accessibility requirements you may have.
What will happen after the Panel?
As part of the Panel, we will record your ideas and recommendations. We will produce a report containing your recommendations on how you think citizens should be more involved in decision-making. The report will be presented to political parties and the two governments, and we will build on your work to encourage further citizen engagement in bringing about a more inclusive and democratic society.


If buying, selling, or leasing a commercial property was as simple as just signing on the dotted line, anyone could do it. But it can be a complicated, risky process, and requires the assistance of a professional.
When it's time to expand your business or make an investment there are many decisions to be made. A commercial member of the Ottawa Real Estate Board (OREB) Commercial Services Network (CSN) can help combat the uncertainty in any commercial transaction.
Here are five tips in navigating your next purchase of commercial real estate.
What is your dream commercial property? Where is it located and how many square feet do you think you might need? OREB's Commercial Realtors can help identify and assess the needs of your organization.
Once you've made the decision to move forward with a purchase, or lease of commercial property, CSN members can help you find the right space and ultimately to negotiate and close the deal.
"CSN members can assemble a team of experts to guide clients through things like environmental assessments, and ever-changing mortgage rules, in order to protect our clients' interests," says Mitch Gauzas, CSN member.
Give yourself ample time to gather information on available properties. Keep in mind that your business may expand in the future. A member of the CSN will be able to assist you to find the perfect space.
In 2018, commercial Realtors listed 15,331 commercial properties on the MLS System through their exclusive access, and were able to review historical data on thousands of listings for comparison.
If your organization decides to purchase property as an investment, the key is to determine your return on investment. You don't want to move too far into the process before realizing that maintenance, repairs and financing costs will erode an unacceptable portion of a property's income. Realtors can assist in the process and perhaps identify other properties that could yield a higher return.
"Through our membership in the Canadian Real Estate Association, we have access to quarterly commercial snapshots of key economic indicators for commercial real estate, as well as financial models, and calculator tools," says Mitch Gauzas, CSN chair-elect.
Going solo without representation can leave parties in a vulnerable position when entering into negotiations. Realtors are required, through a strict Realtor Code of Ethics, to protect their clients' negotiating position and have the expertise to do so. Before going through the negotiation process, make sure your Realtor understands your goals and your budget.
"Many Realtors are highly trained negotiators and have taken certification and designation courses through OREB's continuing education program," says Mike Lewicki, broker and CSN member. "CSN Members also have access to a variety of resources offered through our exclusive memberships in both the Ontario and Canadian Real Estate Associations, such as WEBForms which offers the most up-to-date legal real estate forms and clauses to Realtors," says Lewicki.
What is a Commercial Realtor?
"Realtor" is not a job description. It is a trademark of the Canadian Real Estate Association and stands for service, competence and high ethical practice. Ottawa's Commercial Realtors are members of a local Commercial Services Network. They advise, recommend, caution and counsel their clients with a level of expertise that brings value to any transaction. They offer services in providing appraisals, sales and acquisitions, representing tenants and landlords, investment purchases and property management.
Commercial listings are also uploaded to Realtor.ca where they reach a global audience. Realtor.ca is a marketing site operated by the Canadian Real Estate Association. It can be access by anyone. Realtor.ca boasts 264 million visitors in 2018 and 5.3 billion page views. Only Realtors have access to the MLS System, and only Realtors can post listings on the MLS System.


text:
Mendix now available on Microsoft Azure
By Sead Fadilpašić 18 February 2016
Platform-as-a-service provider Mendix and Microsoft today announced they're partnering up, bringing Mendix's services to the Azure cloud platform.
According to the press release following the announcement, Mendix will support both Microsoft Azure and the Azure Marketplace.
Mendix says the new partnership will allow Azure customers to seamlessly access Mendix's rapid application development capabilities, adding that two versions are available – free and commercial one.
The Free Edition features a full-production platform that allows building unlimited apps, with up to 10 users each. Exact pricing for the Pro Edition was not disclosed, as it depends on the number of application users and 'other factors'.
"The apps of the future cannot be built with the tools of the past," said Derek Roos, CEO and co-founder of Mendix. "By working together, Mendix and Microsoft are providing a comprehensive cloud stack to power the digital enterprise of the future and unleash a tidal wave of digital innovation."
"Microsoft Azure is committed to offering best-in-class tools for every developer, so that IT teams can better respond to growing business demands," said John Gossman, Architect, Microsoft Azure. "We are excited that the Mendix platform is now available in Microsoft Azure Marketplace. The combination of Mendix's rapid application development capabilities and Microsoft Azure's public cloud services enables digital innovation at astonishing speed and scale."
Moan my IP number makes quite an impression
Many remote workers dogged by regular connectivity issues
MoD under fire over multiple data breaches