text:
Q: Module compiled with Swift 4.0 cannot be imported in Swift 3.2.2: AudioKit I have just updated xCode to the latest version, and am getting the error when trying to build.
"Module compiled with Swift 4.0 cannot be imported in Swift 3.2.2: /Users/james/Documents/MyApp/Pods/AudioKit/iOS/AudioKit.framework/Modules/AudioKit.swiftmodule/x86_64.swiftmodule"
It was working before I upgraded the latest xCode.

A: The error message is telling you that Xcode 9.1 is using Swift 3.2.2, while the Swift framework you're building against was built for Swift 4.0 (using Xcode 9.0). You need to update your frameworks to versions built for the version of Swift that you're using.

A: First If you update the Xcode the Xcode-> preferences-> LocationsTab and change the command line tolls Xcode version.. Now check here
AudioKitReleases
pod 'AudioKit', '~> 4.0.3'

AudioKit V4.0.3 for Xcode 9.1 Pod version according to your Swift language, and update your AudioKit pod by command pod update AudioKit

A: If you're using AudioKit with CocoaPods, you can run 'pod install' to pull the Swift 4/Xcode 9.1 ver of AK. Then clean (Shift + CMD + K), and clear the derived data. If still having issues, an Xcode restart should fix that.
Or, you can:
(1) Pull the latest AudioKit
(2) Re-compile the framework:
$ cd AudioKit/Frameworks
$ ./build_frameworks.sh

(3) Delete the Derived Data, Clean (Shift+CMD+K), and Restart Xcode
Best of luck,

A: Best way to get rid of this is, go to https://github.com/AudioKit/AudioKit/releases
find the version compatible with the Xcode that you are using and change you pod file version.
I was using Xcode 10.1 so I changed my file pod 'AudioKit',  '4.5'
it solved my problem.

redpajama_set_name: RedPajamaStackExchange


Questions? Contact the CMC Office of Events at (909) 607-4006 or email events@cmc.edu.
Full Registration provides access to all three days of Panels, Plenary Sessions and meals, including the Saturday Exemplar Awards Banquet.
Free registration only includes Saturday Plenary Lunch; all other meals are not included.
Registration provides access to all sessions and meals.
Registration provides access to all sessions and meals, EXCEPT FOR the Awards Banquet. The Awards Banquet can be added (below) for an additional $35 cost.
Saturday-only Registration provides access to all Saturday Panels, Plenary Sessions and luncheon, but EXCLUDES the Saturday Exemplar Awards Banquet. The Exemplar Awards Banquet can be added (below) for an additional $35 cost.
Sunday-only Registration provides access to all Sunday Panels, Plenary Sessions and luncheon. The Exemplar Awards Banquet on Saturday evening can be added (below) for an additional $35 cost.
The Arts Festival will be held on Friday, March 1 from 2:00 - 4:00 p.m. There is no charge for the Arts Festival, but we do ask that you let us know you plan to attend.


A ready-to-use monolayer cell-based assay for MDR1 in-vitro evaluation of drug interactions with the P-gp/MDR1 transporter. The assay is suitable for performing both inhibition and substrate assessments. The kit consists of 24- or 96-well permeable plates seeded with differentiated MDCKII cells expressing MDR1 as well as the parental cell line.
Differentiated MDCKII-MDR1 cell barrier (11 day system) plated on HTS Transwell®-24 and -96 permeable supports.
Integrated Transwells enable easy handling and provide a user-friendly system.
Proprietary conditioning medium allows for up to 11 days of transportation/storage at room temperature.
Cost-effective for any size laboratory.


text:
Cost of bitcoin exceeds psychological level of $16 thousand
The cost of the most popular cryptocurrency — bitcoin-on Thursday is growing by about 4%, for the first time since January 2018, exceeding 16 thousand US dollars, Report says, citing the auction data.
According to the Coinmarketcap portal, which calculates the average price for more than 20 exchanges, as of 13.56 (Baku time), bitcoin is growing in price by 3.45%, to 15,923 US dollars. On the largest cryptocurrency exchange in terms of trading volume, Binance, bitcoin is increasing in price by 4.21% to 16,076 US dollars. Thus, the cost of cryptocurrency for the first time since January 8, 2018, exceeded the psychological level of 16 thousand US dollars.
Bitcoin accounts for 64.6% of the global cryptocurrency market.
The director of the Center for Social Matrix Technologies Socioma, Vladimir Panushkin said the bitcoin exchange rate's growth looks like good revenge after a small corrective rest at the end of last week.
"It is quite possible that by the end of today, the main cryptocurrency will be able to go further," Panushkin added.
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Helicopter crashes on Saudi border, five injured
Azerbaijan responds to Canadian Foreign Minister
Azerbaijan recorded 402 new coronavirus cases, Task Force under the Cabinet of Ministers told Report.
As many as 650people recovered from the infection, while 15patients lost their lives over the past day.
So far, 226,951 people have contracted COVID-19 in Azerbaijan, 214,423 have recovered, while 2,998 died. At present, 9,530 people are receiving treatment.
Azerbaijan has conducted 2,311,417 coronavirus tests over the past period, including 8,574 today.
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Belarus FM, Azerbaijani ambassador moot development of bilateral ties
Minister of Foreign Affairs of Belarus Vladimir Makei met with Azerbaijani Ambassador Latif Gandilov on January 15. Report informs that press service of the Belarusian Ministry of Foreign Affairs told BelTA.
The parties discussed the schedule of upcoming bilateral visits at the highest and high levels.
Special consideration was given to trade and economic cooperation and its results in 2020. The parties exchanged information on the measures taken in the two countries to counter the spread of COVID-19.
The agenda of cooperation of the ministries of foreign affairs for 2021, including cooperation in international organizations, was also reviewed.
The parties also outlined the steps to further develop Belarus-Azerbaijan strategic cooperation in the political, trade, economic, and humanitarian areas.
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Defense Ministry has released a video footage of the Shelli village of the Aghdam region.
Report presents the footage:

redpajama_set_name: RedPajamaCommonCrawl


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text:
Friday 05 November, 2021
Published On: Fri, Dec 16th, 2016
Television | By The Dispatch
Discovery sets 'The Wheel' survivor show for January 13 premiere
Discovery is putting a new spin on the survivalist genre with THE WHEEL, the network's new adventure series from Pilgrim Media Group. THE WHEEL dares six participants to survive in six distinctly grueling landscapes across South America.
With every turn of the wheel, each survivalist is dropped into a new isolated location, exposed to the world's deadliest terrains including freezing tundra, rugged mountains and treacherous rainforest. Participants don't know when or why the wheel will turn – nor that their stint at each spot is determined by the rotation of the moon. Equipped only with light survival packs and SOS devices that can be used at any time to quit the challenge and call for help, they must fend for their lives by procuring food, water and shelter. And when the wheel turns again, they will be thrust into a new location, forced to use a completely different set of skills to survive. THE WHEEL premieres Friday, January 13, at 10 PM ET/PT on the Discovery Channel.
"The Wheel" photo/courtesy of Discovery
THE WHEEL is the ultimate survival challenge: survive not one, but six lethal environments over the course of 60 days. The participants are not typical survival experts, but rather everyday people with something to prove. Unbeknownst to them, the wheel is a mechanism that mirrors the lunar calendar. With every phase of the moon, the wheel will turn and send each person to a new eco-zone. The amount of time at each location is revealed only to viewers, leaving the survivors to agonize and obsess over when the wheel will spin again. As every rotation thrusts them – without warning – into a new harrowing domain, participants are completely at the wheel's mercy.
Will the survivalists have what it takes to endure the harsh terrains? Or will the challenges prove insurmountable? Watch as participants battle the elements and their own mental stamina to see who will survive THE WHEEL.
THE WHEEL is produced for Discovery Channel by Pilgrim Media Group. For Pilgrim Media Group, executive producers are Craig Piligian and Sam Korkis. For Discovery Channel, executive producer is Joseph Boyle and coordinating producer is Brian Peterson. To learn more, go to www.discovery.com, on Facebook at Facebook.com/discovery and on Twitter @Discovery.
The Dispatch - The generic Dispatch designation, used primarily for press releases or syndicated content, but may be used for guest author requesting a generic nomenclature
Discovery ChannelDual SurvivorLone Survivormiracle survivorrain forestReality TVSouth AmericaSurvivorSurvivormanThe Discovery Channel
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Legends of Tomorrow season 5 arrives on Blu-Ray, digital in September
The Flash season 6 Blu-ray, digital release and details emerge


Airbnb will be offering up a free stay in a transparent geodesic dome in Bend, Oregon to watch the solar eclipse which takes place later this month. The dome comes with an observation deck and telescopes, as well as National Geographic Explorers. Astrophysicist Dr. Jedidah Isler and science journalist Babak Tafreshi will be on hand to teach along the way.
Not only that, but on the day of the eclipse August 21, the guests will be sent on a flight to the path of totality. On the two hour flight, the eclipse will be visible by more than a longer than what can be seen on the ground.
The Airbnb contest starts on Tuesday and involves answering a question with a creative answer. The contest will be open until Thursday.


text:
Weather in Hallau, CH
Avg. wind speed: 1.8 km/h
Temperature -3 °C 0 °C 2 °C 2 °C 0 °C 0 °C 0 °C
Wind gusts 12.4 km/h 10.6 km/h 15.7 km/h 18.3 km/h 15.1 km/h 16.6 km/h 19.5 km/h
Relative humidity 100 % 87 % 72 % 71 % 82 % 85 % 85 %
Feels like -5 °C -1 °C 0 °C 1 °C -2 °C -3 °C -3 °C
Dewpoint -3 °C -2 °C -2 °C -2 °C -2 °C -2 °C -3 °C
Temperature 0 °C 0 °C 0 °C 1 °C 2 °C 2 °C 0 °C -1 °C
Wind gusts 19.5 km/h 21.7 km/h 21.4 km/h 23.4 km/h 29.6 km/h 27.7 km/h 21.5 km/h 17.2 km/h
Avg. wind speed 8.6 km/h 10.8 km/h 11.7 km/h 13.3 km/h 14.2 km/h 15.7 km/h 10.4 km/h 7.3 km/h
Feels like -3 °C -4 °C -4 °C -3 °C -2 °C -2 °C -4 °C -4 °C
Temperature -2 °C -3 °C -4 °C -2 °C 0 °C 1 °C -1 °C -2 °C
Wind gusts 15.7 km/h 11.9 km/h 9.1 km/h 9.7 km/h 9.9 km/h 11 km/h 9.9 km/h 7.7 km/h
Avg. wind speed 6 km/h 6.8 km/h 2.2 km/h 2.9 km/h 3.3 km/h 6 km/h 3.1 km/h 3.1 km/h
Feels like -4 °C -6 °C -4 °C -2 °C 0 °C -1 °C -1 °C -2 °C
Temperature -3 °C -4 °C -4 °C -1 °C 1 °C 1 °C 0 °C -1 °C
Wind gusts 8.6 km/h 7.8 km/h 7.8 km/h 9.5 km/h 10 km/h 12 km/h 9.3 km/h 16.1 km/h
Avg. wind speed 2.7 km/h 2.7 km/h 2 km/h 3.3 km/h 4.4 km/h 7.1 km/h 2.6 km/h 4.6 km/h
Feels like -3 °C -4 °C -4 °C -1 °C 1 °C -1 °C 0 °C -1 °C
Temperature -1 °C -1 °C -2 °C 0 °C -1 °C 0 °C 0 °C -1 °C
Wind gusts 21 km/h 18.1 km/h 18.8 km/h 5.1 km/h 8.2 km/h 8.8 km/h 9.3 km/h 10 km/h
Avg. wind speed 8 km/h 8.6 km/h 7.5 km/h 2.2 km/h 2 km/h 2.9 km/h 3.1 km/h 2.6 km/h
Feels like -4 °C -4 °C -5 °C 0 °C -1 °C 0 °C 0 °C -1 °C
Temperature -2 / 7 °C 1 / 6 °C 1 / 7 °C
Precipitation 0.1 l/m² 0.1 l/m² 0 l/m²
Data provided by weather station Hallau, 419m (0.4 km)
+4.6 km - Trasadingen
+6.3 km - Siblingen-Randenhaus
+6.8 km - Wutöschingen-Ofteringen
+7 km - Stühlingen/Schwarzwald
Weather in Hallau, 19.01.2020
In the early morning it will be mostly cloudy. Before noon widely overcast, but mainly dry weather will dominate. The afternoon will bring mostly cloudy weather. Morning temperatures will be around -3 °C. During the day they will rise to a maximum of 3 °C. Low temperatures at night will be around -1°C. We have weak northeasterly winds.
On Monday it will be partly cloudy, partly sunny until noon. At noon cloudy, but mostly dry weather will prevail. The afternoon will bring mainly cloudless weather. Morning temperatures will be around -1 °C. During the day they will rise to a maximum of 3 °C. Low temperatures at night will be around -5°C. Northeasterly winds are weak.


Looking for a state registered chiropodist/podiatrist?
State registration is the only qualification recognised by the NHS.
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Here are some of the more common foot conditions.


text:
When Spider Man Didn't Look Legal Age To Buy Beer…
The Cinema Times, Kathmandu- Cinema 'Spider-Man: Homecoming' actor Tom Holland had to turn to 'Thor' star Chris Hemsworth to buy drinks, while they were having 'Avengers: Infinity War' nights out.
According to 'dailystar.co.uk', Hemsworth told that they were long days and they would go out for a couple of beers after filming 'Avengers'. He added, "When he is clean shaven, bless him, Tom Holland looks about 12 and the bar didn't think he was old enough to grab a beer, so we had to get it for him. So while he might be an Avenger and he might help save the world, he doesn't look old enough to get a beer."
Further Hemsworth talking about the high anticipation around Avengers: Infinity War said: "We know the anticipation behind this movie and we know what it means to the fans…to have all these characters in one movie and have the worlds collide. Fans expect a lot and they are going to get it. Everybody has worked so hard and we are so proud of what we have produced."
The most giant superhero 'Avengers: Infinity War' of Marvel Cinematic Universe will hit cinemas on April 27. Directed by Anthony Russo and Joe Russo, it stars Robert Downey Jr, Scarlett Johansson, Chris Pratt and Zoe Saldana alongside Holland and Hemsworth.


text:
Q: How to logrotate HHVM How would youu logrotate HHVM logs?
Would you use /etc/logrotate.conf to do this?
I've tried below but this doesn't seem to be working. Also what do you need to do to reopen logs after rotate is complete?
/var/log/*.log {
    rotate 5
    daily
    compress
    missingok
    notifempty
    delaycompress
    postrotate
        invoke-rc.d hhvm reopen-logs > /dev/null
    endscript
}

This is based on Ubuntu but I want something that we can use on CentOS server?
Is there a way we can find where pid is stored for current HHVM daemon?

A: I haven't used logrotate with HHVM yet, but you should be able to find the pid in /var/run/hhvm/pid assuming some run time config isn't changing that location.


text:
🔥 Casino | Resort World Catskills | Hotel & Casino | Monticello New York
🤑 Resorts World Catskills was sold last month. Here's the first big change Where Gaming Meets Luxury
is New York's newest casino- resort with the closest proximity to Manhattan. Nestled among the beauty of the Catskill Mountains.
🤑 Resorts World Catskills A Casino Where Gaming Meets Luxury
Located in the heart of New York's Catskill region, Resorts World Catskills offers The integrated casino resort includes an story all-suite hotel and a casino.
🤑 Why a $1.2 Billion Gambling Mecca Has Fallen on Hard Times Come Get Away
A Casino Where Gaming Meets Luxury. Resorts World Catskills provides unparalleled gaming like no other in the region. The , square foot casino floor.
🤑 Welcome to Resorts World Catskills Site Index
ALBANY - Resorts World closed its two massive casinos in New York on Monday after New York City as the state has limited large gathering.
🤑 Site Information Navigation
Best Casinos in Catskill, NY - Renee Stanley - Cruise Planners, Cuttle Bug Taxi & Car Service, Steve Van Zandt Enterprises, Tech Valley Hospitality.
🤑 What's the latest with Resorts World Catskills?
Bugün kaydolun ve şimdiye kadarki en iyi fırsatları alın. Sadece güvenilir olanlar.
Now the most exciting place to be is about 90 miles north of New York City. Nestled among the beauty of the Catskill Mountains, Resorts World Catskills Casino.
New York's newest casinos are already struggling for survival, as an The Resorts World Catskills casino is roughly 85 miles northwest of New.
You do not need a Facebook profile to participate. So the story hotel and casino in the Catskills is hoping its partnership with the New York City site will lure city gamblers and tourists to the once booming tourist region in upstate — something that Empire Resorts struggled to do. So the companies bought out the publicly traded Empire Resorts of its ownership of Resorts World Catskills, hoping to create synergy between the two properties and turn around the struggling Sullivan County resort and casino about 90 minutes from the city. Resorts World Catskills was sold last month. Typed comments will be lost if you are not logged in. If you see comments in violation of our community guidelines , please report them. ET Dec.{/INSERTKEYS}{/PARAGRAPH} Share This Story! Andrew Cuomo and the state Legislature next year. While Resorts World Catskills has been less successful, it has something its downstate counterpart lacks: a full-scale gaming license that allows it to offer table games and on-site sports betting. Currently, only four upstate casinos, including the Catskills one, can offer full-scale gambling, and the state has a moratorium on new licenses until More: Why this quiet decision on the future of New York casinos may have a big impact. Please be polite. It employs about 1, people. It's OK to disagree with someone's ideas, but personal attacks, insults, threats, hate speech, advocating violence and other violations can result in a ban. You will need to register before adding a comment. Cancel Send. The Boston Globe said DeSalvio was well received by community leaders and the public for navigating the high-profile project. Welcome to our new and improved comments , which are for subscribers only. A link has been sent to your friend's email address. Resorts World New York City is the highest grossing slots casino in the world, the company said. {PARAGRAPH}{INSERTKEYS}Let friends in your social network know what you are reading about. Post to Facebook. The company and other downstate casinos are looking to get state approval to get a full license, but that would be up to Gov. A link has been posted to your Facebook feed. He also previously spent 10 years heading the marketing department at Foxwoods Resort Casino in Mashantucket, Connecticut. Eller will step down following "a brief transition period," Genting said. Comments Welcome to our new and improved comments , which are for subscribers only. Edward Farrell will remain president of Genting Americas Inc. Check out this story on poughkeepsiejournal. More: Resorts World Catskills, facing potential bankruptcy, has a casino giant as an eager suitor. This is a test to see whether we can improve the experience for you.


text:
(Z)-octadec-9-enol
Currently viewing: S-01 | Summary001 Key | Read-across (Category)002 Supporting | (Q)SAR003 No specified study adequacy | No specified result type
Alcohols with chain length carbon number >C15 are not expected to be toxic at the limit of solubility (expert judgement).
No reliable measured data are available for long-term toxicity of (z)-octadec-9-enol to aquatic invertebrates. In an expert statement based on ecotoxicological information available on the alcohols category no toxicity is expected at the limit of solubility.
No aquatic toxicity is expected at chain lengths >C15, therefore a study on long-term toxicity of aquatic invertebrates to pentadecan-1-ol (CAS 629-76-5, C15) has been read across for the purpose of setting indicative aquatic PNECs for use in assessing risk to the sediment and terrestrial compartments, using the equilibrium partitioning method.
A 21-d EC10 of 0.012 mg/l has been determined for the effects of pentadecan-1-ol (CAS 629-76-5, C15) on reproduction of Daphnia magna.
Data are available from 21-day Daphnia magna reproduction tests conducted generally in accordance with standard test guideline OECD 211 with single carbon chain length alcohols: 1-octanol (Kuhn et al., 1989), 1-decanol, 1-dodecanol, 1-tetradecanol, 1-pentadecanol (Schäfers, 2005a-d respectively) 1-octadecanol (Guhl 1992), pentadecanol branched and octadecanol branched (ABC 1999a and 1999c respectively). Some modifications to the test guideline procedures took place in the Schäfers studies in order to reduce losses of test substance due to the extensive and rapid biodegradation of the alcohols. Details of the modifications to the guideline are provided in the IUCLID dossiers and in the CSR.
The results of the tests are given in Table 1. Summary statistics for each test are presented as both NOEC and EC10. The 1-octanol and 1-octadecanol studies are reliability 2, valid with restrictions; the other studies are reliability 1.
Table 1. Long-term (21-d) aquatic toxicity of long chain alcohols to Daphnia magna.
(µg/L)
Exposure based on mean measured concentrations in fresh and old media
551,000 at 25°C
1930 at 20°C
191 at 25°C
7.8 and EC10 12
C15b
0.07 mg/L (estimate)
1.1 at 25°C
0.0056 mg/L (estimate)
N.A. is not available
* no analytical analysis took place.
The study with C18 determines a NOEC of 0.98 mg/L, corresponding to the lowest concentration tested. Should a lower exposure concentration have been tested, it is likely that the NOEC would also be lower.
A pattern of increased toxicity with increasing chain length is apparent up to C14. The NOEC and EC10 values then increase from C14 to C15. This is almost certainly the result of the concentration exceeding the solubility of the substance (Rufli et al. 1998). Based on the trends observed in these data it is expected that the NOEC for long-term effects on mortality and reproduction would be above the solubility limit of linear alcohols with carbon numbers >C15 Schäfers et al. (2009).
This is also discussed in the attached ECOTOXICITY Alcohols C6-24 Category report.
Discussion of trends in the Category of C6-24 linear and essentially-linear aliphatic alcohols:
Linear LCAAs
Data of an acceptable quality are available for 21-day reproduction studies with Daphnia magnafor the single carbon chain length LCAAs 1-octanol (Kuhn et al., 1989), 1-decanol, 1-dodecanol, 1-tetradecanol, 1-pentadecanol (Schäfers, 2005a-d respectively), pentadecanol branched (ABC 1999a) and octadecanol branched (ABC 1999c). The data were obtained generally in accordance with standard test guideline OECD 211. However some modifications to the normal guideline procedures were necessary to reduce losses of test substances due to the extensive and rapid biodegradation of the LCAAs. The following changes to typical protocols were therefore adopted to enable the performance of high-quality and meaningful studies:
Vessels were closed, to reduce entry of bacteria from the atmosphere;
Gentle aeration of test vessels was required as degradative losses of LCAAs resulted in unacceptably low dissolved oxygen concentrations;
Test solution renewals were made daily, with confirmatory analysis on both renewed and initial test solutions;
Static renewal was determined to be the best exposure regime for long chain aliphatic alcohols as this reduced the transfer of LCAAs -degrading or consuming microbes (as compared to flow-through systems, where it becomes increasingly difficult to discourage acclimation and bio film formation; see Brixham Environmental Laboratory, AstraZeneca, 2004);
Saturated alcohol stock solutions were prepared daily for each test concentration. This involved a detailed preparatory method to reduce the possibility of insoluble material being present in the tests (Schäfers, 2005a, b);
Daphnia magna were carefully rinsed with each daily transfer to reduce bacterial cross over to fresh exposure solutions. As Daphnia magna grow in size, this becomes less effective; and,
Dilution water and test vessels were autoclaved prior to use each time (Schäfers et al., 2005a, b, c, d).
Algae have been found to metabolize LCAAs and this is an unavoidable occurrence in long-term studies with Daphnia magna fed with algae. No modifications could be made to counter this without conducting further research into an alternative diet.
In spite of the guideline modifications significant losses of test substance still occurred. It was therefore necessary to report the results both in terms of the mean of the measured concentrations in the fresh media and the mean of the measured concentrations in the fresh and old media. The test substance renewal interval was 24 hours. Survival and reproduction endpoints have been summarised using standard statistical techniques. Conclusions for each test are presented as both NOEC and EC10. The 1-octanol and 1-octadecanol study are reliability 2, valid with restrictions; the other studies are reliability 1.
The effect of LCAAs on Daphnia magna survival is generally less sensitive than the effect on reproduction. A pattern of increased toxicity with increasing chain length is also apparent. In the octanol study, the most sensitive and only reported effect was on time to first brood release which occurred at 1000 µg/L (nominal concentration). For comparison of results across chain lengths and structure activity models the response for survival and reproduction was assumed to be equal to the effect on time to first brood.
The data indicates that for survival and reproduction, the NOEC and EC10 values increase from C14 to C15. This is almost certainly due to exceeding the limit of water solubility as would be expected from conventional toxicological theory (Rufli et al. 1998). Under these circumstances a more accurate interpretation of the results might be obtained by setting the exposure to the solubility of the substance (i.e. 49 µg/L). This has the effect of lowering the toxicity values but they are still higher than those for the C14 substance. This pattern is not in keeping with the trend of reducing short-term toxicity values (i.e. higher toxicity) observed between the C8 and C14 alcohols. Similarly, the NOEC identified for C18 is a limit value of >980 µg/L but a lower value would have been obtained if a lower loading had been tested. A more accurate NOEC would therefore be obtained by expressing it as greater than the water solubility of the test substance, which is 10 µg/L. This statement is supported by data on C15 and C15 branched, where the NOEC was not achieved at the solubility limit.
It must be appreciated that significant uncertainty exists in identifying the true exposure concentrations in the region of the water solubility of a substance. The water solubility values of the LCAAs category decrease with increasing chain length (see section 1.4 for further details.). In a review of aquatic toxicity testing of sparingly soluble compounds Rufli et al. (1998) point out that interpretation of toxicity responses observed above the solubility limit is aggravated by artefacts and that testing should only occur at or below the limit. For LCAAs with carbon numbers greater than C15 there are significant experimental difficulties in producing, maintaining and quantifying exposures of the test substance due to progressively lower solubility, while exceptionally rapid biodegradability would remain unchanged. This explains why there are no data for such substances.
However, based on the trends observed in the available data, it is expected that for linear LCAAs with carbon numbers ≥C15 the NOEC for long-term effects on mortality and reproduction would be above the solubility limit (Schäfers et al. 2009).
Multi-constituent LCAAs
No measured data are available for multi-constituent substances of different carbon chain length LCAAs.


text:
Canadian sports body upholds ban on turbans
Toronto, June 3 (IANS) The World Sikh Organisation has expressed sadness at the decision of Canadian football organisation's decision to uphold a ban on turbans on field.
The board of directors of the Quebec Soccer Federation (QSF) has decided to continue its ban on players wearing turbans on field despite a directive by the Canadian Soccer Association to referees last April to permit head coverings on field, CBC News reported.
The QSF had cracked down on players wearing the turban, including the patka and the keski worn by young Sikhs, since last year.
"It was decided that referees in Quebec will apply FIFA's (Federation of International Football Associations) Law 4, taking into account that the only equipment accepted, and that the only exceptions allowed, are specifically mentioned in Law 4," the QSF was quoted as saying in a statement.
Though FIFA hasn't made yet made clear its ruling on turbans, the QSF has said it would sanction referees allowing the same.
"While girls are permitted to wear headscarves, I fail to see the logic in prohibiting turbans for Sikh boys," Prem Singh Vinning, president of the World Sikh Organisation, was quoted as saying in a statement.
"Sikh children have waited patiently since last year for this issue to resolve so they can rejoin their peers on the pitch. Unfortunately, they will have to wait even longer," he said.
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Shooting intimate scene was easier, thanks to director: Ranveer


What are High-Impact Aroma Chemicals?
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text:
Heliella singularis is een hooiwagen uit de familie Gonyleptidae.

Gonyleptidae

redpajama_set_name: RedPajamaWikipedia


text:
\section{Introduction}
\label{intro}

Generating realistic-looking images is of great value for many applications such as face editing, movie making and image retrieval based on synthesized images.
Consequently, a wide range of methods have been proposed
including Variational Autoencoders (VAE)~\cite{VariationalAE}, Generative Adversarial Networks (GANs)~\cite{GAN-2014nips} and Autoregressive models (e.g., PixelRNN~\cite{Oord2016-pixelRNN}). Recently, GAN models have been particularly popular due to their principle ability to generate sharp images through adversarial training.
For example in~\cite{Radford2015-DCGAN,InfoGAN,WGAN}, GANs are leveraged to generate faces and natural scene images and several methods are proposed to stabilize the training process and to improve the quality of generation.

From an application perspective, users typically have a particular intention in mind  such as changing the background, an object's category, its color or viewpoint. The key idea of our approach is to guide the generation process explicitly by an appropriate  representation of that intention to enable direct control over the generation process.
More specifically, we propose to generate an image by conditioning it on both a reference image and a specified pose. With a reference image as condition, the model has sufficient information about the appearance of the desired object in advance. The guidance given by the intended pose is both explicit and flexible. So in principle this approach can manipulate any object to an arbitrary pose. In this work, we focus on transferring a person from a given pose to an intended pose. There are many interesting applications derived from this task. For example, in movie making, we can directly manipulate a character's human body to a desired pose or, for human pose estimation, we can generate training data for rare but important poses.

\begin{figure*}[t]
  \centering
  \includegraphics[width=0.9\linewidth]{figure-111.pdf}\\
  \caption{Generated samples on DeepFashion dataset~\cite{DeepFashion} (a)(c) and Market-1501 dataset~\cite{Market1501} (b). }
  \label{figure-1}
\end{figure*}

Transferring a person from one pose to another is a challenging task. A few examples can be seen in Figure~\ref{figure-1}. It is difficult for a complete end-to-end framework to do this because it has to generate both correct poses and detailed appearance simultaneously. Therefore, we adopt a divide-and-conquer strategy, dividing the problem into two stages which focus on learning global human body structure and appearance details respectively similar to \cite{Zhang-stackGAN,huang2016stacked,carreira2016human,newell2016stacked}. At stage-I, we explore different ways to model pose information. A variant of U-Net is  employed to integrate the target pose with the person image. It outputs a coarse generation result that captures the global structure of the human body in the target image. A masked L1 loss is proposed to suppress the influence of background change between condition image and target image. However, it would generate blurry result due to the use of L1. At stage-II, a variant of Deep Convolutional GAN (DCGAN) model is used to further refine the initial generation result. The model learns to fill in more appearance details via adversarial training and generates sharper images. Different from the common use of GANs which directly learns to generate an image from scratch, in this work we train a GAN to generate a difference map between the initial generation result and the target person image. The training converges faster since it is an easier task. Besides, we add a masked L1 loss to regularize the training of the generator such that it will not generate an image with many artifacts. Experiments on two dataset, a low-resolution person re-identification dataset and a high-resolution fashion photo dataset, demonstrate the effectiveness of the proposed method.

Our contribution is three-fold. i) We propose a novel task of conditioning image generation on a reference image and an intended pose, whose purpose is to manipulate a person in an image to an arbitrary pose. ii) Several ways are explored to integrate pose information with a person image. A novel mask loss is proposed to encourage the model to focus on transferring the human body appearance instead of background information.
iii) To address the challenging task of pose transfer, we divide the problem into two stages, with the stage-I focusing on global structure of the human body and the stage-II on filling in appearance details based on the first stage result.
\section{Network architecture}
In this section, we give the details of our network architecture. Since images in the two datasets have different resolutions, we use slightly different network architecture for them. On the Market-1501 dataset, for generator at stage-I G1, the encoder consists of five residual blocks and one fully-connected layer. The decoder is symmetric to the decoder except for the last layer that generates an image of 3 channels. The network is shown in Figure~\ref{fig:G1_net}.
Each residual block in the network consists of two convolution layers with stride=1 followed by one convolution layer with stride=2 for downsampling except the last block, which is shown in Figure~\ref{fig:res_block}.

For generator at stage-II G2, it has similar network architecture to G1 but only three residual blocks in the encoder and decoder, as shown in Figure~\ref{fig:G2_net}

On the DeepFashion dataset, since the input image has a size of 256$\times$256, we use one more residual block than the one used on Market-1501 dataset. That is, we have six residual blocks for encoder and decoder in G1 and four residual blocks for encoder and decoder in G2.

\begin{figure}[hbtp]
\centering
	\includegraphics[width=1\linewidth]{supp-network_architecture_G1.pdf}
    \caption{Network architecture of generator at stage-I.}
    \label{fig:G1_net}
    \vspace{-2mm}
\end{figure}

\begin{figure}[hbtp]
\centering
	\includegraphics[width=0.9\linewidth]{supp-network_architecture_res.pdf}
    \caption{Residual block.}
    \label{fig:res_block}
\end{figure}

\begin{figure}[htbp]
\centering
	\includegraphics[width=0.8\linewidth]{supp-network_architecture_G2.pdf}
    \caption{Network architecture of generator at stage-II.}
    \label{fig:G2_net}
\end{figure}
\section{Related works}
\label{related}
Recently there have been a lot of works on generative image modeling with deep learning techniques. These works fall into two categories.
The first line of works follow an unsupervised setting. One popular method under this setting is variational autoencoders proposed by Kingma and Welling~\cite{VariationalAE} and Rezende~\emph{et al}\onedot~\cite{Rezende-ICML14-VAE}, which apply a re-parameterization trick to maximize the lower bound of the data likelihood. Another branch of methods are autogressive models~\cite{NADE, Oord2016-pixelRNN,Oord2016-PixelCNN} which compute the product of conditional distributions of pixels in a pixel-by-pixel manner as the joint distribution of pixels in an image. The most popular methods are generative adversarial networks (GAN)~\cite{GAN-2014nips}, which simultaneously learn a generator to generate samples and a discriminator to discriminate generated samples from real ones. Many works show that GANs can generate sharp images because of using the adversarial loss instead of L1 loss. In this work, we also use the adversarial loss in our framework in order to generate high-frequency details in images.

The second group of works generate images conditioned on either category or attribute labels, texts or images. Yan~\emph{et al}\onedot~\cite{Yan2016-Attribute2Image} proposed a Conditional Variational Autoencoder (CVAE) to achieve attribute conditioned image generation. Mirza and Osindero~\cite{Mirza-conditionalGAN} proposed to condition both generator and discriminator of GAN on side information to perform category conditioned image generation. Lassner~\emph{et al}\onedot ~\cite{Lassner17ClothNet} generated full-body people in clothing, by conditioning on the fine-grained body part segments. Reed~\emph{et al}\onedot proposed to generate bird image conditioned on text descriptions by adding textual information to both generator and discriminator~\cite{Reed-ICML16} and further explored the use of additional location, keypoints or segmentation information to generate images ~\cite{Reed-NIPS2016,reed2016-techreport}.
With only these visual cues as condition and in contrast to our explicit condition on the intended pose, the control exerted over the image generation process is still abstract.
Several works further conditioned image generation not only on labels and texts but also on images. Researchers~\cite{Yim-CVPR15, Yang-NIPS15, Jia-BMVC16, Huang-faceRotation} addressed the task of face image generation conditioned on a reference image and a specific face viewpoint.
Chen~\emph{et al}\onedot~\cite{chen2014inferring} tackled the unseen view inference as a tensor completion problem, and use latent factors to impute the pose in unseen views.
Zhao~\emph{et al}\onedot~\cite{Zhao-Arxiv16-Multiview} explored generating multi-view cloth images from only a single view input, which is most similar to our task.
However, a wide range of poses is consistent with any given viewpoint making the conditioning less expressive than in our work.
In this work, we make use of pose information in a more explicit and flexible way, that is, using poses in the format of keypoints to model diverse human body appearance. It should be noted that instead of doing expensive pose annotation, we use a state-of-the-art pose estimation approach to obtain the desired human body keypoints.

\section{Influence of $\lambda$ at stage-II}
\label{supp-lambda}

We investigate the influence of $\lambda$ to the performance of our two-stage method.
As is shown in Figure~\ref{lambda}, when $\lambda$ is small, the adversarial loss dominates the training and it is more likely to generate artifacts, e.g., the color of the right arm of person ID. 170 and the faces of person ID. 23 and ID. 53. When $\lambda$ becomes bigger, the generator with a basic L1 loss dominates the training, making the whole model generate blurry results.

\begin{figure}[hb]
  \includegraphics[width=0.9\linewidth]{figure-5-lambda.pdf}
  \caption{Generated results using different values of $\lambda$ at stage-II.}
  \label{lambda}
\end{figure}

\section{Method}
\label{method}

\begin{figure*}
  \centering
  \includegraphics[width=0.9\linewidth]{figure-2-framework0.pdf}\\
  \caption{The overall framework of our Pose Guided Person Generation Network (PG$^2$). It contains two stages. Stage-I focuses on pose integration and generates an initial result that captures the global structure of the human. Stage-II focuses on refining the initial result via adversarial training and generates sharper images.}

\label{figure-framework}
\end{figure*}

Our task is to simultaneously transfer the appearance of a person from a given pose to a desired pose and keep important appearance details of the identity. As it is challenging to implement this as an end-to-end model, we propose a two-stage approach to address this task, with each stage focusing on one aspect. For the first stage we propose and analyze several model variants and for the second stage we use a variant of a conditional
DCGAN to fill in more appearance details. The overall framework of the proposed Pose Guided Person Generation Network (PG$^2$) is shown in Figure~\ref{figure-framework}.

\subsection{Stage-I: Pose integration}
\label{sec:PoseIntegration}
At stage-I, we integrate a conditioning person image $I_A$ with a target pose $P_B$ to generate a coarse result $\hat{I}_B$ that captures the global structure of the human body in the target image $I_B$.

\myparagraph{Pose embedding.} To avoid expensive annotation of poses, we apply a state-of-the-art pose estimator~\cite{poseEstimation} to obtain approximate human body poses. The pose estimator generates the coordinates of 18 keypoints.
Using those directly as input to our model would require the model
to learn to map each keypoint to a position on the human body. Therefore, we encode pose $P_B$ as 18 heatmaps.
Each heatmap is filled with 1 in a radius of 4 pixels around the corresponding keypoints and 0 elsewhere
(see Figure~\ref{posemask}, target pose).
We concatenate $I_A$ and $P_B$ as input to our model. In this way, we can directly use convolutional layers to integrate the two kinds of information.

\myparagraph{Generator G1.}
As generator at stage I, we adopt a U-Net-like architecture~\cite{Res-Unet-autoencoder-seg}, \emph{i.e}\onedot, convolutional autoencoder with skip connections as is shown in Figure~\ref{figure-framework}. Specifically, we first use several stacked convolutional layers to integrate $I_A$ and $P_B$ from small local neighborhoods to larger ones
so that appearance information can be integrated and transferred to neighboring body parts.
Then, a fully connected layer is used such that information between distant body parts can also exchange information.
After that, the decoder is composed of a set of stacked convolutional layers which are symmetric to the encoder to generate an image. The result of the first stage is denoted as $\hat{I}_{B1}$.
In the U-Net, skip connections between encoder and decoder help propagate image information directly from input to output. In addition, we find that using residual blocks as basic component improves the generation performance. In particular we propose to simplify the original residual block~\cite{ResidualNet} and have only two consecutive conv-relu inside a residual block.

\begin{wrapfigure}{r}{0.5\textwidth}
\includegraphics[width=\linewidth]{figure-3-posemask-v2.pdf}
\caption{Process of computing the pose mask.}
\label{posemask}
\end{wrapfigure}
\myparagraph{Pose mask loss.}
To compare the generation $\hat{I}_{B1}$ with the target image $I_B$,
we adopt L1 distance
as the generation loss of stage-I. However, since we only have a condition image and a target pose as input, it is difficult for the model to generate what the background would look like if the target image has a different background from the condition image. Thus, in order to alleviate the influence of background changes, we add another term that adds a pose mask $M_B$ to the L1 loss such that the human body is given more weight than the background.
The formulation of pose mask loss is given in Eq.~\ref{eq:masked_l1} with $\odot$ denoting the pixels-wise multiplication:
\begin{equation}
	{\cal{L}}_{\text{G1}} = \| ({\text{G1}}({I_A}, {P_B}) - I_B) \odot {(1 + M_B)} \|_1  ,
    \label{eq:masked_l1}
\end{equation}
The pose mask $M_B$ is set to 1 for foreground and 0 for background and is computed by connecting human body parts and applying a set of morphological operations such that it is able to approximately cover the whole human body in the target image, see the example in Figure~\ref{posemask}.

The output of G1 is blurry because the L1 loss encourages the result to be an average of all possible cases~\cite{Isola-CVPR16-Pix2pix}.
However, G1 does capture the global structural information specified by the target pose, as shown in Figure~\ref{figure-framework}, as well as other low-frequency information such as the color of clothes. Details of body appearance, i.e. the high-frequency information, will be refined at the second stage through adversarial training.
\input{3.2-refiner.tex}

\subsection{Network architecture}
We summarize the network architecture of the proposed model PG$^2$. At stage-I, the encoder of G1 consists of $N$ residual blocks and one fully-connected layer
, where $N$ depends on the size of input. Each residual block consists of two convolution layers with stride=1 followed by one sub-sampling convolution layer with stride=2 except the last block.
At stage-II, the encoder of G2 has a fully convolutional architecture including $N$-$2$ convolution blocks. Each block consists of two convolution layers with stride=1 and one sub-sampling convolution layer with stride=2. Decoders in both G1 and G2 are symmetric to corresponding encoders. Besides, there are shortcut connections between decoders and encoders, which can be seen in Figure~\ref{figure-framework}. In G1 and G2, no batch normalization or dropout are applied. All convolution layers consist of 3$\times$3 filters and the number of filters are increased linearly with each block. We apply rectified linear unit (ReLU) to each layer except the fully connected layer and the output convolution layer. For the discriminator, we adopt the same network architecture as DCGAN~\cite{Radford2015-DCGAN} except the size of the input convolution layer due to different image resolutions.

\subsection{Stage-II: Image refinement}
Since the model at the first stage has already synthesized an image which is coarse but close to the target image in pose and basic color, at the second stage, we would like the model to focus on generating more details by correcting what is wrong or missing in the initial result. We use a variant of conditional DCGAN~\cite{Radford2015-DCGAN} as our base model and condition it on the stage-I generation result.

\myparagraph{Generator G2.}
Considering that the initial result and the target image are already structurally similar, we propose that the generator G2 at the second stage aims to generate an appearance difference map that brings the initial result closer to the target image. The difference map is computed using a U-Net similar to the first stage but with the initial result $\hat{I}_{B1}$ and condition image $I_A$ as input instead. The difference lies in that the fully-connected layer is removed from the U-Net. This helps to preserve more details from the input because a fully-connected layer compresses a lot of information contained in the input. The use of difference maps speeds up the convergence of model training since the model focuses on learning the missing appearance details instead of synthesizing the target image from scratch.
In particular, the training already starts from a reasonable result.
The overall architecture of G2 can be seen in Figure~\ref{figure-framework}.

\myparagraph{Discriminator D.}
In traditional GANs, the discriminator distinguishes between real groundtruth images and fake generated images (which is generated from random noise). However, in our conditional network, G2 takes the condition image $I_{A}$ instead of a random noise as input. Therefore, real images are the ones which not only are natural but also satisfy a specific requirement. Otherwise, G2 will be mislead to directly output $I_{A}$ which is natural by itself instead of refining the coarse result of the first stage $\hat{I}_{B1}$.
To address this issue, we pair the G2 output with the condition image to make the discriminator D to recognize the pairs' fakery, $i.e.$, $(\hat{I}_{B2},I_{A})$ vs $(I_{B},I_{A})$. This is diagrammed in Figure~\ref{figure-framework}.
The pairwise input encourages D to learn the distinction between $\hat{I}_{B2}$ and $I_{B}$ instead of only the distinction between synthesized and natural images.

Another difference from traditional GANs is that noise is not necessary anymore since the generator is conditioned on an image $I_A$, which is similar to~\cite{Mathieu-ICLR16}.
Therefore, we have the following loss function for the discriminator D and the generator G2 respectively,
\begin{eqnarray}
    {\cal{L}}^{\text{D}}_{adv} &=& {\cal{L}}_{bce}({{\text{D}}(I_A, I_{B}),1}) + {\cal{L}}_{bce}({{\text{D}}(I_A, {\text{G2}}(I_A,\hat{I}_{B1})),0}),
	\label{eq:adversarial_D_loss}\\
    {\cal{L}}^{\text{G}}_{adv} &=& {\cal{L}}_{bce}({{\text{D}}(I_A, {\text{G2}}(I_A, \hat{I}_{B1})),1}),
	\label{eq:adversarial_G_loss}
\end{eqnarray}
where ${\cal{L}}_{bce}$ denotes binary cross-entropy loss.
Previous work~\cite{Isola-CVPR16-Pix2pix, Mathieu-ICLR16} shows that mixing the adversarial loss with a loss minimizing Lp distance can regularize the image generation process. Here we use the same masked L1 loss as is used at the first stage such that it pays more attention to the appearance of targeted human body than background,
\begin{equation}
	{\cal{L}}_{\text{G2}} = {\cal{L}}^{\text{G}}_{adv} + \lambda \| ({\text{G2}}({I_A}, \hat{I}_{B1}) - I_B) \odot {(1 + M_B)} \|_1 ,
    \label{eq:s2_masked_l1}
\end{equation}
where $\lambda$ is the weight of L1 loss. It controls how close the generation looks like the target image at low frequencies. When $\lambda$ is small, the adversarial loss dominates the training and it is more likely to generate artifacts; when $\lambda$ is big, the the generator with a basic L1 loss  dominates the training, making the whole model generate blurry results\footnote{The influence of $\lambda$ on generation quality is analyzed in supplementary materials.}.

In the training process of our DCGAN, we alternatively optimize discriminator D and generator G2. As shown in the left part of Figure~\ref{figure-framework}, generator G2 takes the first stage result and the condition image as input and aims to refine the image to confuse the discriminator. The discriminator learns to classify the pair of condition image and the generated image as fake while classifying the pair including the target image as real.

\section{Person image generation of arbitrary poses}
\label{supp-seq}

\begin{figure}
\centering
  \includegraphics[width=\linewidth]{supp_seq_df1.pdf}
  \caption{Generated results for one person with various target poses on the DeepFashion dataset.}
  \vspace{-0.2cm}
  \label{seq_df1}
\end{figure}

\begin{figure}
\centering
  \includegraphics[width=\linewidth]{supp_seq_df2.pdf}
  \caption{Generated results for one person with various target poses on the DeepFashion dataset.}
  \vspace{-0.2cm}
  \label{seq_df2}
\end{figure}

\begin{figure}
\centering
  \includegraphics[width=\linewidth]{supp_seq_market.pdf}
  \caption{Generated results for one person with various target poses on the Market-1501 dataset.}
  \vspace{-0.2cm}
  \label{seq_market}
\end{figure}

In this section, we give more generation results conditioned on one person image and a set of different poses. Note that these poses are computed by a pose estimator on random person images in the dataset.
From Figure~\ref{seq_df1} and~\ref{seq_df2},~\ref{seq_market}, we can see our model is able to generate photo-realistic results conditioned on multiple poses.
\section{Generated results}
\label{supp-good}
In Figure~\ref{good_DF} and Figure~\ref{good_market}, we show more pose guided person image generation results.

\begin{figure}[th]
\centering
  \includegraphics[width=1\linewidth]{supp_good_DF.pdf}
  \caption{Generated results on the DeepFashion dataset.}
  \label{good_DF}
\end{figure}

\begin{figure}[th]
\centering
  \includegraphics[width=0.8\linewidth]{supp_good_market.pdf}
  \caption{Generated results on the Market-1501 dataset.}
  \label{good_market}
\end{figure}
\subsection{Qualitative results}
As mentioned above, we investigate three aspects of our proposed PG$^2$ network. Different pose embeddings and losses are compared within stage-I and then
we demonstrate the advantage of our two-stage model over a one-stage model.

\myparagraph{Different pose embeddings.}
To evaluate our proposed pose embedding method, we implement two alternative
methods. For the first, coordinate embedding (CE), we pass the keypoint coordinates  through two fully connected layers and concatenate the embedded feature vector with the image embedding vector at the bottleneck fully connected layer. For the second, called heatmap embedding (HME), we feed the 18 keypoint heatmaps to an independent encoder and extract the fully connected layer feature to concatenate with image embedding vector at the bottleneck fully connected layer.

Columns 4, 5 and 6 of Figure~\ref{big-visualization} show qualitative results of the different pose embedding methods when used in stage-I, that is of G1 with CE (G1-CE-L1), with HME (G1-HME-L1) and our G1 (G1-L1).
All three use standard L1 loss. We can see that G1-L1 is able to synthesize reasonable looking images that capture the global structure of a person, such as pose and color. However, the other two embedding methods G1-CE-L1 and G1-HME-L1 are quite blurry and the color is wrong. Moreover, results of G1-CE-L1 all get wrong poses. This can be explained by the additional difficulty to map the keypoint coordinates to appropriate image locations making training more challenging.
Our proposed pose embedding using 18 channels of pose heatmaps is able to guide the generation process effectively, leading to correctly generated poses.
Interestingly, G1-L1 can even generate reasonable face details like eyes and mouth, as shown by the DeepFashion samples.

\myparagraph{Pose mask loss vs. L1 loss.}
Comparing the results of G1 trained with L1 loss (G1-L1) and G1 trained with poseMaskLoss (G1-poseMaskLoss) for the Market-1501 dataset, we find that pose mask loss indeed brings improvement to the performance (columns 6 and 7 in Figure~\ref{big-visualization}).
By focusing the image generation on the human body, the synthesized image gets sharper and the color looks nicer.
We can see that for person ID 164, the person's upper body generated by G1-L1 is more noisy in color than the one generated by G1-poseMaskLoss. For person ID 23 and 346, the method with pose mask loss generates more clear boundaries for shoulder and head.
These comparisons validate that our pose mask loss effectively alleviates the influence of noisy backgrounds and guides the generator to focus on the pose transfer of the human body.
The two losses generate similar results for the DeepFashion samples because the background is much simpler.

\myparagraph{Two-stage vs. one-stage.}
In addition, we demonstrate the advantage of our two-stage model over a one-stage model. For this we use G1 as generator but train it in an adversarial way to directly generate a new image given a condition image and a target pose as input. This one-stage model is denoted as G1+D and our full model is denoted as G1+G2+D.
From Figure~\ref{big-visualization}, we can see that our full model is able to generate photo-realistic results, which contain more details than the one-stage model. For example, for DeepFashion samples, more details in the face and the clothes are transferred to the generated images. For person ID 245, the shorts on the result of G1+D have
lighter color and more blurry boundary than G1+G2+D. For person ID 346, the two-stage model is able to generate both the right color and textures for the clothes, while the one-stage model is only able to generate the right color.
On Market-1501 samples, the quality of the images generated by both methods decreases because of the more challenging setting. However, the two-stage model is still able to generate better results than the one-stage method. We can see that for person ID 53, the stripes on the T-shirt are retained by our full model while the one-stage model can only generate a blue blob as clothes. Besides, we can also clearly see the stool in the woman's hands (person ID 23).

\begin{figure*}[htp]
  \centering
  \includegraphics[width=0.95\linewidth]{big.pdf}\\
  \caption{Test results on DeepFashion (upper 3 rows, images are cut for the sake of display) and Market-1501 dataset (lower 3 rows). We test G1 in two aspects: (1) three pose embedding methods, i.e., coordinate embedding (CE), heatmap embedding (HME) and our pose heatmap concatenation in G1-L1, and (2) two losses, i.e., the proposed poseMaskLoss and the standard L1 loss. Column 7, 8 and 9 show the differences among our stage-I (G1), one-stage adversarial model (G1+D) and our two-stage adversarial model (G1+G2+D). Note that all three use poseMaskLoss. The IDs are assigned randomly when splitting the datasets.}
  \label{big-visualization}
\end{figure*}

\section{Experiments}
\label{experiments}
We evaluate the proposed PG$^2$ network on two person datasets (Market-1501~\cite{Market1501} and DeepFashion~\cite{DeepFashion}), which contain person images
with diverse poses.
We present quantitative and qualitative results for three main aspects of
 PG$^2$: different pose embeddings; pose mask loss vs. standard L1 loss; and two-stage model vs. one-stage model.
We also compare with the most related work~\cite{Zhao-Arxiv16-Multiview}.

\subsection{Datasets}

The DeepFashion (In-shop Clothes Retrieval Benchmark) dataset~\cite{DeepFashion}
consists of 52,712 in-shop clothes images, and ~200,000 cross-pose/scale pairs. All images are in high-resolution of  256$\times$256.
In the train set, we have 146,680 pairs each of which is composed of two images of the same person but different poses. We randomly select 12,800 pairs from the test set for testing.

We also experiment on a more challenging re-identification dataset Market-1501~\cite{Market1501}
containing 32,668 images of 1,501 persons captured from six disjoint surveillance cameras.
Persons in this dataset vary in pose, illumination, viewpoint and background, which makes the person generation task more challenging. All images have size 128$\times$64 and are split into train/test sets of 12,936/19,732 following~\cite{Market1501}. In the train set, we have 439,420 pairs each of which is composed of two images of the same person but different poses. We randomly select 12,800 pairs from the test set for testing.

\myparagraph{Implementation details}
On both datasets, we use the Adam~\cite{Adam} optimizer with $\beta_1=0.5$ and $\beta_2=0.999$. The initial learning rate is set to $2e$-$5$.
On DeepFashion, we set the number of convolution blocks $N=6$. Models are trained with a minibatch of size $8$ for $30k$ and $20k$ iterations respectively at stage-I and stage-II.
On Market-1501, we set the number of convolution blocks $N=5$. Models are trained with a minibatch of size $16$ for $22k$ and $14k$ iterations respectively at stage-I and stage-II.
For data augmentation, we do left-right flip for both datasets\footnote{More details about parameters of the network architecture are given in supplementary materials.}.

\input{5-experiment_comparison.tex}

\vspace{-1mm}
\subsection{Quantitative results}
\vspace{-1mm}

\begin{table}
\centering
\footnotesize
\caption{Quantitative evaluation. For all measures, higher is better.}
\begin{tabular*}{13.5cm}
{@{\extracolsep{\fill}} l c c c c c c }
\toprule
& \multicolumn{2}{c}{DeepFashion} & \multicolumn{4}{c}{Market-1501} \\
\cmidrule{2-3} \cmidrule{4-7}
Model & SSIM & IS & SSIM & IS & mask-SSIM & mask-IS \\
\midrule[0.6pt]
	G1-CE-L1 & 0.694 & 2.395 &0.219 & 2.568 & 0.771 & 2.455\\
    G1-HME-L1 & 0.735 & 2.427 & 0.294 & 3.171 &0.802 & 2.508 \\
    G1-L1 & 0.735 & 2.427 & 0.304 & 3.006 & 0.809 & 2.455 \\
	G1-poseMaskLoss & 0.779 & 2.668 & 0.340 & 3.326 & 0.817 & 2.682 \\
    G1+D & 0.761 & 3.091 & 0.283 & 3.490 & 0.803 & 3.310 \\
	G1+G2+D & 0.762 & 3.090 &0.253 & 3.460 & 0.792 & 3.435 \\
\bottomrule[1pt]
\end{tabular*}
\vspace{-1mm}
\label{tab:quantitative}
\end{table}
We also give quantitative results on both datasets. Structural Similarity (SSIM)~\cite{ImageQuality} and the Inception Score (IS)~\cite{ImprovedTechniquesForGANs} are adopted to measure the quality of synthesis. Note that in the Market-1501 dataset, condition images and target images may have different background. Since there is no information in the input about the background in the target image, our method is not able to imagine what the new background looks like.
To reduce the influence of background in our evaluation, we propose a variant of SSIM, called mask-SSIM. A pose mask is added to both the synthesis and the target image before computing SSIM. In this way we only focus on measuring the synthesis quality of a person's appearance.
Similarly, we employ mask-IS to eliminate the effect of background.
However, it should be noted that image quality does not always correspond to such image similarity metrics.
For example, in Figure~\ref{big-visualization}, our full model generates sharper and more photo-realistic results than G1-poseMaskLoss, but the latter one has a higher SSIM.
This is also observed in super-resolution papers~\cite{Johnson-ECCV16-Superres,Shi-CVPR16-Superres}.

The advantages are also clearly shown in the numerical scores in Table~\ref{tab:quantitative}. E.g. the proposed pose embedding (G1-L1) consistently outperforms G1-CE-L1 across all measures and both datasets. G1-HME-L1 obtains similar quantitative numbers probably due to the similarity of the two embeddings. Changing the loss from L1 to the proposed poseMaskLoss (G1-poseMaskLoss) consistently improves further across all measures and for both datasets. Adding the discriminator during training either after the first stage (G1+D) or in our full model (G1+G2+D) leads to comparable numbers, even though we have observed clear differences in the qualitative results as discussed above. This is explained by the fact that blurry images often get good SSIM
despite being less convincing and photo-realistic~\cite{Johnson-ECCV16-Superres,Shi-CVPR16-Superres}.

\vspace{-3mm}
\subsection{User study}
\vspace{-1mm}

\begin{table}
\centering
\footnotesize
\caption{User study results from AMT}
\begin{tabular*}{7cm}
{@{\extracolsep{\fill}} l  c c  c c }
\toprule
& \multicolumn{2}{c}{DeepFashion} & \multicolumn{2}{c}{Market-1501} \\
\cmidrule{2-3}  \cmidrule{4-5}
Model & R2G\tablefootnote{R2G means \#Real images rated as generated / \#Real images} & G2R\tablefootnote{G2R means \#Generated images rated as Real / \#Generated images} & R2G & G2R \\
\midrule[0.6pt]
	G1+D & 7.8\% & 9.3\% & 17.1\% & 11.1\% \\
    G1+G2+D & 9.2\% & 14.9\% & 11.2\% & 5.5\% \\
\bottomrule[1pt]
\end{tabular*}
\label{tab:userstudy}
\end{table}

We perform a user study on Amazon Mechanical Turk (AMT) for both datasets. For each one, we show 55 real images and 55 generated images in a random order to 30 users. Following \cite{Isola-CVPR16-Pix2pix,Lassner17ClothNet}, each image is shown for 1 second. The first 10 images are used for practice thus are ignored when computing scores.  From the results reported in Table. \ref{tab:userstudy}, we can get some observations that (1) On DeepFashion our generated images of G1+D and G1+G2+D manage to confuse users on 9.3\% and 14.9\% trials respectively (see G2R), showing the advantage of G1+G2+D over G1+D;
(2) On Market-1501, the average score of G2R is lower, because the background is much more cluttered than DeepFashion; (3) On Market-1501, G1+G2+D gets a lower score than G1+D, because G1+G2+D transfers more backgrounds from the condition image, which can be figured out in Figure. \ref{experiments}, but in the meantime it brings extra artifacts on backgrounds which lead users to rate 'Fake'; (4) With respect to R2G, we notice that Market-1501 gets clearly high scores (>10\%) because human users sometimes get confused when facing low-quality surveillance images.

\input{5-experiments-comp2.tex}

\subsection{Further analysis}
Since our task with pose condition is novel, there is no direct comparison work. We only compare with the most related one\footnote{Results of VariGAN are provided by the authors.} ~\cite{Zhao-Arxiv16-Multiview}, which did multi-view person image synthesis on the DeepFashion dataset. It is noted that~\cite{Zhao-Arxiv16-Multiview} used the condition image and an additional word vector of the target view e.g. ``side'' as network input. Comparison examples are shown in Figure~\ref{comp-zhao}. It is clear that our refined results are much better than those of~\cite{Zhao-Arxiv16-Multiview}. Taking the second row as an example, we can generate high-quality whole body images conditioned on an upper body while the whole body synthesis by~\cite{Zhao-Arxiv16-Multiview} only has a rough body shape.

Additionally, we give two failure DeepFashion examples by our model in Figure~\ref{failure}. In the top row, only the upper body is generated consistently. The ``pieces of legs'' is caused by the rare training data for such complicated poses. The bottom row shows inaccurate gender which is caused by the imbalance of training data for male / female.
Besides, the condition person wears a long-sleeve jacket of similar color to  his inner short-sleeve, making the generated cloth look like a mixture of both.

\begin{figure}
\centering
\begin{minipage}{.43\textwidth}
  \centering
  \includegraphics[width=\linewidth]{figure-comp-zhaobo.pdf}
  \captionof{figure}{Comparison examples with ~\cite{Zhao-Arxiv16-Multiview}.}
  \label{comp-zhao}
\end{minipage}%
\hspace{0.2cm}
\begin{minipage}{.49\textwidth}
  \centering
  \includegraphics[width=\linewidth]{figure-failure2.pdf}
  \captionof{figure}{Our failure cases on DeepFashion.}
  \label{failure}
\end{minipage}
\end{figure}

\section{Failure results}
\label{supp-bad}
In figure~\ref{seq_failure2} and Figure~\ref{seq_failure1}, we show some failure cases of our method. The main reasons of failure cases include dramatic scale change, inaccurate pose input, and the rare case of samples.

\begin{figure}[th]
\centering
  \includegraphics[width=1\linewidth]{supp-failure2.pdf}
  \caption{Failure cases on the DeepFashion dataset.}
  \label{seq_failure2}
\end{figure}

\begin{figure}[th]
\centering
  \includegraphics[width=0.9\linewidth]{supp-failure1.pdf}
  \caption{Failure cases on the Market-1501 dataset.}
  \label{seq_failure1}
\end{figure}
\section{Conclusions}
In this work, we propose the Pose Guided Person Generation Network (PG$^2$) to address a novel task of synthesizing person images by conditioning it on a reference image and a target pose. A divide-and-conquer strategy is employed to divide the generation process into two stages. Stage-I aims to capture the global structure of a person and generate an initial result. A pose mask loss is further proposed to alleviate the influence of the background on person image synthesis. Stage-II fills in more appearance details via adversarial training to generate sharper images.
Extensive experimental results on two person datasets demonstrate that our method is able to generate images that are both photo-realistic and pose-wise correct.
In the future work, we plan to generate more controllable and diverse person images conditioning on both pose and attribute.

\subsubsection*{Acknowledgments}
We gratefully acknowledge the support of Toyota Motors Europe, FWO Structure from Semantics project, KU Leuven GOA project CAMETRON, and German Research Foundation (DFG CRC 1223).
We would like to thank Bo Zhao for his helpful discussions.

redpajama_set_name: RedPajamaArXiv


My bike just came less than a week ago and I decided to assemble it on 12/7/18. It has only been 4 days of use (not constant) and the left pedal flew off mid- ride which almost led me to get run over. I got back home only to see the inside threading of the crank arm was completely shaved off. I do not hate this bike but if I am paying 200$ and this is what is happening then I think I have to bring this to your attention for the safety of future costumers.
Hi Brandon, Sorry to hear about this issue, we would love to lend a hand. Feel free to email us at info@6ku.com, thanks.
my pedal that goes inside the hole (side connected to the crank) shaved off the threading inside. my pedal is all loose and now the bike is unusable. I just got the bike like a week ago too. very disappointed that I have to spend more money after dropping 200$ on a 6ku bike just to buy another crank set the following week. very disappointed.
Sebas, Sorry to hear about the issue at hand. We can certainly lend a hand. Feel free to email us with your order number and some picture of the defective item at hand. We can proceed from there.


text:
Q: powershell script Ad script group I have the below ps script to Import users details from a domain/ forest from a domain local group, everything is working, but i need to include two more details, user mail is and user domain in the excel.  How can I do this?
Get-ADGroupMember "test" | Select-Object samaccountname, name, distinguishedname | Export-CSV -path "c:\test.csv" -notypeinformation

A: Some properties are not included in the default property set of a user object. In that case you need to query the user with the additional (or all) properties, e.g.:
Get-ADGroupMember "test" `
  | Get-ADUser -Properties * `
  | select samaccountname, name, distinguishedname, mail  `
  | Export-CSV "C:\test.csv" -NoTypeInformation

AFAIK the (DNS) domain name is not an AD attribute, but you could derive it from the distinguished name:
(Get-ADUser "name").distinguishedName -replace '^.*?,dc=' -replace ',dc=', '.'

so you could add another property in the select statement like this:
@{n="domain";e={$_.distinguishedName -replace '^.*?,dc=' -replace ',dc=', '.'}}

As for the referral error: the group seems to be containing members from another domain. AFAIK all of the following requirements must be met to be able to run AD PowerShell cmdlets against other domains in the same forest:

*

*The Active Directory Web Services must be running on at least one of the DCs of the remote domain, and the port must be accessible from the local domain.

*Your account must have admin privileges on the remote DCs (e.g. by being a member of the Enterprise Admins group).


Every company has a brand. Your brand is simply what your customers think about you and how they spread that brand to others through word of mouth. Maybe you think you don't have a brand. If so, that isn't likely. Invisibility is a brand, too — and it's one you want to change.
Creating or controlling your brand can seem daunting. After all, brand marketers spend millions of dollars each year on television, print and Internet advertising. How can the average marketer afford that? The good news is that you don't have to. Your brand only matters to the people that you want to buy your products, either now or in the future. This doesn't need to happen on a mass scale. You can micro-target to your intended customer base and be just as effective. One of the easiest ways to micro-target is to use 1:1 printing.
Take the example of Guardian Home Care Holdings. It used 1:1 printing to reduce the cost of acquisition of new employees by replacing its traditional recruiting and newspaper advertising with personalized prospecting postcards. Guardian sent mailers to a highly targeted list of health care workers. The mailers were personalized based on gender, race, specialty and other demographics impacting recipients' employment concerns. The program reduced the cost of finding new hires from around $3,000 to less than $100, but there was a branding impact, too. Recipients were so impressed with the mailers that they often brought them to interviews, telling recruiters that the high-tech, personalized approach gave them the impression that Guardian was the kind of place they wanted to work. Guardian was seen as an innovative, progressive company that is on the leading edge of health care.
Another company reaping positive branding from its 1:1 marketing is Howard Hanna Real Estate. Every time a Howard Hanna agent sells a house, the company's system sends out an automatic, personalized notification to all of the neighbors in the local area. Agents also have the ability to take even greater control of the personalization, adding their own images and text to make the message even more relevant to the homeowners receiving it. Recipients know that if you want to sell your house, one of Howard Hanna is the place to go.
Thus, we have two different branding messages, created by two very different companies, with the 1:1 marketing approach in common. The volumes of these mailers are relatively low, targeting only the companies' intended prospect bases, and allowing companies to precisely manage the message and control their brands for a very manageable cost. The benefit is a double bang for the buck, higher response rates and powerful impact on company branding, as well.


German scientists have demonstrated that a complementary therapy combining psychology and the teaching of relaxation techniques can substantially reduce symptoms in those with irritable bowel syndrome (IBS) when combined with regular treatment.
The results come from a randomised controlled trial (RCT) of the therapy comparing it to 'enhanced medical care' (EMC) which consisted of regular treatments IBS patients receive plus two counselling sessions. RCTs are considered the most reliable type of study in medical science.
Functional relaxation (FR) is a therapy originally conceived by Marianne Fuchs decades ago and can be described as a form of psychotherapy focused on the body that involves teaching the patient relaxation techniques aimed at maintaining the balance of the nervous system. By achieving this it is presumed symptoms of IBS may improve because tension in the muscles of the gut are relaxed, relieving pain and promoting normal bowel habits.
"The medium of functional relaxation is the body (i.e., body, soul, and mind) and its 'rhythm,' which penetrates and supports the individual human sphere. One who is in ageement with his/her body is also in agreement with its surroundings; one who sets up tensions between the world and the self loses personal rhythm. In body language, this loss is manifested in the form of tensions. Functional relaxation approaches such tensions individually, beginning at the spine and the joints and continuing through the inner cavities and space."
The German clinical trial of FR in IBS patients involved 39 participants receiving FR sessions over a five week period and 39 participants receiving EMC over the same period. A questionnaire measuring the severity of symptoms was completed by all participants before the treatment began, after the treatment period, and a final time three months after completion of treatment.
The result, published in the online edition of the Journal of Alternative and Complementary Medicine show that FR was significantly more effective than EMC and that the benefits, both physical and mental, the patients who received FR achieved remained after three months.
The researchers conclude that functional relaxation is a complementary therapy that has significant and long-term benefits for those suffering from IBS.


Solvents remove even hardened paint from a brush.
Dried deck stain results in a stiff, impossible-to-use paintbrush. Like most oil-based paints, water won't remove the hardened stain from the bristles. A paint-thinning solvent dissolves the paint without damaging the brush. It may take several thorough cleanings to remove all the hard stain from the brush since it likely has worked its way deep into the bristles, but a few minutes of effort can save a quality brush for future painting.
Fill a glass jar with a paint solvent, such as paint thinner or turpentine. Use a jar large enough to dip the entire brush.
Dip the paintbrush into the solvent. Move the brush around in the solvent to loosen the paint.
Lift the brush from the solvent. Add more solvent to the jar to freshen it.
Swish the brush around in the solvent two more times, adding more solvent as necessary. Scrape the edges of the bristles on the rim of the jar between dipping to help loosen the stain inside the bristles.
Draw a fine tooth comb through the bristles to pull out any remaining stain residue and to reshape the bristles appropriately. Brush combs made specifically for paintbrushes are available at hardware stores.
Rinse the solvent from the brush with clear water. Shake the excess moisture from the bristles.
Wrap the bristles in a paper towel, which absorbs moisture and helps the bristles retain their shape. Air dry the brush in a well-ventilated area.
Keep two brushes available and clean them promptly after each use. If one begins to get stiff while you are painting the deck, wash it and allow it to dry while you use the second brush.
Paint solvents are extremely flammable. Follow all package safety instructions and do not use them near open flame.
Harrington, Jenny. "How to Soften Deck Stain on a Brush." Home Guides | SF Gate, http://homeguides.sfgate.com/soften-deck-stain-brush-35155.html. Accessed 18 April 2019.


text:
Q: list Index Error (even though it exists)? I'm running a for loop to grab content out of some XML and it works fine, until I reach around the 29th iteration. At that point it gives me this error:
File "C:\Program Files (x86)\Google\google_appengine\lib\webapp2\webapp2.py", line 572, in dispatch
  return self.handle_exception(e, self.app.debug)
File "C:\Program Files (x86)\Google\google_appengine\lib\webapp2\webapp2.py", line 570, in dispatch
  return method(*args, **kwargs)
File "J:\Art & Graphic Design\Graphic Design\Websites\lawvoter-dev\cron_congressman.py", line 64, in get
  birthday      = re.findall("<birthday>(.*)</birthday>",element)[0]
IndexError: list index out of range

The code is:
for element in members:
            title         = re.findall("<title>(.*)</title>",element)[0]
            role          = re.findall("<role_type_label>(.*)</role_type_label>",element)[0]
            name_sortable = re.findall("<name_sortable>(.*)</name_sortable>",element)[0]
            firstname     = re.findall("<firstname>(.*)</firstname>",element)[0]
            lastname      = re.findall("<lastname>(.*)</lastname>",element)[0]
            gender        = re.findall("<gender_label>(.*)</gender_label>",element)[0]
            birthday      = re.findall("<birthday>(.*)</birthday>",element)[0]
            party         = re.findall("<party>(.*)</party>",element)[0]
            state         = re.findall("<state>(.*)</state>",element)[0]
            description   = re.findall("<description>(.*)</description>",element)[0]
            start_date    = re.findall("<startdate>(.*)</startdate>",element)[0]
            end_date      = re.findall("<enddate>(.*)</enddate>",element)[0]
            website       = re.findall("<website>(.*)</website>",element)[0]
            bioguideid    = re.findall("<bioguideid>(.*)</bioguideid>",element)[0]
            osid          = re.findall("<osid>(.*)</osid>",element)[0]
            pvsid         = re.findall("<pvsid>(.*)</pvsid>",element)[0]
            twitterid     = re.findall("<twitterid>(.*)</twitterid>",element)[0]
            youtubeid     = re.findall("<youtubeid>(.*)</youtubeid>",element)[0]

            member = Congressman(title=title, role=role, name_sortable=name_sortable, firstname=firstname, lastname=lastname, gender=gender, birthday=birthday, party=party, state=state,
                                 description=description, start_date=start_date, end_date=end_date, website=website, bioguideid=bioguideid, osid=osid, pvsid=pvsid, twitterid=twitterid, youtubeid=youtubeid)
            member.put()

I really don't get why this error is popping up? It always works fine for the first 29 iterations? The each element in the datamodel is also set to "default=None" just in case. However, when I look at the XML itself, and go to the exact line where the error happens, the value is actually there. Anyone know why it would give an error even though the value exists?

A: it looks like
birthday      = re.findall("<birthday>(.*)</birthday>",element)[0]

returns an empty list and you are trying to extract the first element which is not in the list and it throws the
IndexError: list index out of range

like here:
>>> l = []
>>> l[0]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
IndexError: list index out of range
>>>

EDIT:
import re, logging

def findelement(item, element):
    i = re.findall(item, element)
    if not i:
        logging.info('no item found for %s with element %s' %(item, element))
        return ''
    return i[0]

for element in members:
    title = findelement("<title>(.*)</title>", element)
    ...


text:
Dan Gilbert, David Griffin, and the Internal Power Struggle that Doomed the Cavs
Sam Penix
The Cleveland Cavaliers currently sit at 4-16, the second-worst record in the NBA. The future looks bleak, and the cause for this stunning shift from the past four seasons is the departure of LeBron James. However, the situation goes much deeper than that. Even when James was in Cleveland, there was turmoil within the organization.
It's worth taking a look back and seeing how the team got to this point. This ordeal involves many moving parts, but the one constant is owner Dan Gilbert and his involvement with the team. Gilbert is a very hands-on owner, and that fact has been both a blessing and a curse for Cavs fans.
This story begins in 2010, when LeBron James announced he would be taking his talents to South Beach and joining the Miami Heat. James was the hometown kid and the only reason the Cavaliers were relevant at the time, so Gilbert's frustration with James' decision was justified.
However, Gilbert may have gone too far when he wrote his infamous letter, in comic sans font nonetheless. Gilbert's letter may have endeared him to the city of Cleveland, as it said what many fans felt, but it also irreparably destroyed Gilbert's relationship with James.
With Chris Grant as general manager, the Cavs were a terrible team for the next four years. However, not everything was bad, as Cleveland accumulated plenty of high draft picks, which turned into players such as Kyrie Irving, Tristan Thompson, and Anthony Bennett. Grant was fired in February of 2014, and after the season, there were rumors around the NBA that James wanted to return to the Cavaliers. New GM David Griffin spent two weeks clearing cap space, and on July 12th, James signed with the Cavs.
Gilbert and James publicly made up, but their relationship was never truly fixed. The two were not often seen together, and by this point, James' influence (both in basketball terms and socially) had grown far beyond what it was during his first stint in Cleveland. The NBA's worst-kept secret is that James has a major say in both coaching and personnel decisions. Two clashing personalities who had already had a major falling-out was destined to end poorly, and although things seemed peachy from the outside, Gilbert and James just could not coexist indefinitely.
Griffin made quite a few moves, such as acquiring J.R. Smith, Iman Shumpert, and Timofey Mozgov in 2015. The salary cap was a major issue, but Gilbert was more than willing to spend the money needed to keep the team in contention for a title. Griffin was able to maneuver around the luxury tax just enough to retain key players and make deals for new ones like Kyle Korver. The Cavs may have given up most of their future assets, but such is the price of instant success.
After the Cavaliers fell to the Golden State Warriors in the 2016-2017 NBA Finals, a change was made. In July, assistant GM Koby Altman was promoted to general manager, after Griffin and the team could not reach an agreement on a new contract. Gilbert was chastised by fans for letting go of Griffin, and rightly so. Griffin is the best general manager the team has had in a very long time. However, Griffin and Gilbert had butted heads, and with Gilbert wanting a "yes man", Altman was hired.
Labeling Altman as a "yes man" may be unfair, but as fans, we don't have a choice, based on what we know. The Athletic's Joe Vardon wrote a tell-all piece coupled with an interview with James that outlined what exactly happened. The biggest move to date that Altman made was trading Kyrie Irving, and according to James himself, that trade wasn't even Altman's doing.
There has long been speculation that either Irving wanted out of Cleveland because he knew that James would leave, or that James left Cleveland because Irving was traded. But if James is to be trusted, the answer is somewhere in the middle. This excerpt from Vardon's article is pretty self-explanatory:
James was adamant on the call — do not trade Irving, especially to the Celtics. By the end of the call, according to four separate accounts of people present for the conversation, Altman told James the trade would not occur.
James suggested he didn't feel he was lied to by Altman, so much as Altman was overruled by owner Dan Gilbert.
"You realize at that point in time, take nothing from Koby, because Koby (was just named GM), but at that point in time, you realize that Koby's not the only one running the team, as (former GM David Griffin) had done, and that's why Griff was let go pretty much," James said.
Cavs front-office officials declined to be quoted for this story but disputed that Altman gave James any indication the trade would not occur. They also said Altman asked James whether he would commit to the Cavs long-term if Irving were not traded, and James said no.
Altman may have been the architect of the Irving trade, but it's clear he didn't make the final call. Perhaps Altman fully intended to "okay" the trade before talking to James. But James' influence within the organization gave Altman pause. Trading Irving would displease James, but since James had declined to commit to the Cavs long-term either way, Gilbert felt as if he had no choice. It's hard to blame him, but that doesn't mean the trade was good, either at the time or in hindsight. Irving would still have been under contract through 2018-2019, unable to force his way to another team.
Another interesting question is if James was so against trading Irving, why didn't he do more to convince him to stay? It's a question we will likely never know the answer to, leaving fans wondering if things would have turned out differently had Irving remained a Cavalier.
Just over a year ago, the Cleveland Cavaliers were an organization with three main contestants for power; Gilbert, Griffin, and James. Yet only one had the final say as the actual owner of the team, and he is the reason the latter two are now gone. There is a very real possibility that James had his sights on Los Angeles long before he hit free agency. But Gilbert's firing Griffin and overruling James' wishes during the Irving ordeal certainly didn't help matters. There is no reason to think that Gilbert has relinquished complete control of the team to Altman, but maybe things will work out better this time. Maybe.
JUST IN: Alec Burks will play vs. Celtics
Related TopicsDan GilbertDavid GriffinKoby AltmanKyrie IrvingLeBron James