text:
Olmeca Tequila Presents Mad Decent Block Party
Mad Decent Block Party is happening on the 8th of February featuring MAJOR LAZER, DILLON FRANCIS and FLOSSTRADAMUS!
With Supporting artists SIBOT & TOYOTA, NISKERONE, DAS KAPITAL, PAUL DEVRO and LIVER
Olmeca Tequila and Mad Decent are blocking off the city streets in Joburg and Cape Town for one day each of world-class entertainment and street-style partying on the 8th and 9th of February 2014. Los Angeles-based record label Mad Decent are well known for their annual Block Party series across the United States and are home to DJ's Major Lazer, Dillon Francis and Flosstradamus – our international headliners for the first-time events happening outside of North America.
Grammy nominated founder, owner and head honcho of Mad Decent Records and lead producer of Major Lazer, Diplo has risen through the ranks and asserted himself as one of the most pioneering producers of all time. Major Lazer has toured major music festivals including Coachella, Pitchfork, Sassquatch and Sonar, as well as worked with the likes of Beyonce, La Roux and Hot Chip. As Major Lazer, Diplo also retreated to Jamaica to work on the production of Snoop Dogg 's previous album "Snoop Lion Reincarnated".
Joining the decks in Jozi and CT is 25 year old Moombahton mastermind Dillon Francis who's made his mark on the global scene with a sexy genre of music he describes as "reggae tone meets Dutch House meets anything you want it to be."
Chicago DJ duo Flosstradamus bring an irresistible sense of fun to every single set, whether it's a sweat-soaked nightclub, in front of thousands at Lollapalooza or right here in February on SA soil. Expect an extra-hype stage show, Flosstradamous is coming… get ready to juke!
With a reputation for throwing some of the best parties in the country, Olmeca and Seed Experiences are collaborating with Mad Decent to ensure that this one raises the bar yet again.
Saturday 8th February 2014, Mary Fitzgerald Square, Newtown 4pm – 2am
Tickets : R 275
Buy Tickets Here : http://bit.ly/1apepum
das kapital, DILLON FRANCIS, FLOSSTRADAMUS, liver, Mad Decent Block Party, MAJOR LAZER, NISKERONE, PAUL DEVRO, SIBOT & TOYOTA
Dont Party at the Town Hall Massacre
"Sweet Release" getting sweeter…
Phorestry (ChapterII)


text:
Le gouvernement de la Moldavie () est le gouvernement de la république de Moldavie. Il est installé à la maison du gouvernement sur la place de la Grande Assemblée nationale à Chișinău, la capitale de la Moldavie. Actuellement, la présidente de la Moldavie est Maia Sandu, tandis que le premier ministre de la Moldavie est Dorin Recean. L'actuel cabinet au pouvoir en Moldavie est le cabinet de Dorin Recean, en fonction depuis le 16 février 2023.

Notes et références

Voir aussi

Articles connexes
 Politique en Moldavie

Liens externes

redpajama_set_name: RedPajamaWikipedia


Kids, just say no to LOL. That's the message coming from Bankstown Grammar School in Sydney, where the carbonated fruit juice is being consumed as a breakfast beverage by children as young as six.
Despite the claims that the side of the packaging make about the soft-drink containing "no bad stuff", each can contains 26mg of sugar, and the design – with its large smiley face and colourful labelling – has been criticised for looking similar to the Red Bull design.
Personally, I think the children are in more danger of having a lexicon similar to that of your average lolcat thanks to the four flavours the drink comes in: 'B current', 'Tropkl', 'Razz bri' and 'Gomango'. I am oddly drawn to the drink though – if they were available in the UK, I'd definitely give a can a try, just for the lols, naturally.


My brand-new 24 gauge Kynar twelve" wide standing seam roof is suffering significant oil-canning in the summer listed here in Vancouver, BC. And, Vancouver 's not likely all that scorching.
Skilled dwelling improvement is ancient and goes again to the start of recorded civilization. A person case in point is Sergius Orata, who in the 1st century B.C. is claimed by the writer Vitruvius (in his famous e book De architectura) to obtain invented the hypocaust.
Upgrading rooms with luxuries, such as incorporating gourmet capabilities to some kitchen or maybe a hot tub spa to some rest room.
Fare This Phoenix-based mostly transportation community company not too long ago produced their debut in Austin. The consumer can ask for rides nearly seven days beforehand, ask for preferred drivers and no surge pricing. Riders must have a referral code from Yet another client to affix the app.
Construction is actually a flourishing Amish marketplace. Amish contractors concentrate on all the things from new home design to commercial properties helpful site to renovation.
This may be an absurd thought, (and my father gained't even entertain the believed) but we're were being hoping to maybe alter the steel roof coloration of every building.
We use 3rd-party information sources to check the owner/principal of every business for personal bankruptcy filings, liens, and considerable point out-amount civil authorized judgments, from the condition by which the owner/principal with the company is located, transpiring within 12 months just before the service Expert's application for membership during the HomeAdvisor community.
Just Ensure that you steer clear of the much less expensive polyester or acrylic paint finishes when it comes to the choice of steel panels in your roof.
Pretty professional! Reaction by Baker Roofing Company: We can't thank you more than enough for your fantastic opinions! We have been satisfied to hear we were able to complete your venture, and hope you can keep us in mind for virtually any future demands. Thanks once more!
A great deal of the GAF architectural shingles have 110 or a hundred thirty mile-an-hour wind warranty. The owner just has to verify that the individual Placing them about the click to find out more roof is Accredited because of the maker to be able to give them People warranties. This also really should Make sure the installation is done appropriately. Also in the event you anonymous use the male down the road that's jogging around in a very pickup truck with two or three his buddies ( because he lower balled the value ) likelihood is he might not be able to provde the same warranties, and people from time to time discover that out the tricky way.
In regions in which You can find little rain, an Just about flat roof with a slight operate-off presents satisfactory defense from an occasional downpour. Drainpipes also remove the need for the sloping roof.
We use 3rd occasion details sources to examine the principal/owner of your business for state degree civil authorized judgments entered against them.


text:
[pjw] REPORT BACK: Climate change: Friday rally #132 since stay-at-home orders
Sat Oct 1 15:44:58 EDT 2022
Next message (by thread): [pjw] MEETING/RSVP: Iraq Affinity Group "meets" Tuesday, October 11, 7 PM
Hello again Iraq Affinity Group supporters
While it's starting to cool off here in late September/early October, it
was still quite humid last night and is supposed to be 80 degrees plus all
this week. Hmm. Last night at the Friday Rally for Peace and Justice, we
had seven people at the rally, five of whom went on the march. There was a
noticeable uptick in positive feedback, though, with many pedestrians
voicing support and cars honking their horns for us. We remained masked up
and distanced for safety, though apparently the CDC has now said people in
hospitals do not have to wear masks (I expect most of them will do so
anyway).
There was a Native American festival in the Square, so as I began the
announcements I noted that Indigenous People's day is coming up soon, that
we are standing on stolen land, that the Ann Huntwork sign calls for US
out of occupied countries and the continent of North America, and that,
inexplicably, the City of Portland has told its advisory groups they are
not allowed to offer land acknowledgments any more. ??? I think they're
worried that a bad land acknowledgment is worse than none, but on the
other hand sometimes people can get participant ribbons just for trying.
ANYWAY, last night was the last time to pre-announce next week's special
Friday rally calling for the US to "Send Aid, Not Bombs" in Afghanistan
and Syria. As mentioned in an earlier email, this event is in
solidarity with actions called by the United National Antiwar Coalition;
our event is now on their website:
https://unac.notowar.net/back-to-the-streets-october-15-23/
And here's the flyer for next week, hope you've been circulating it!
https://www.pjw.info/afghan21yl_flyer.pdf
These were the headlines we talked about at the rally:
--Russia annexed four areas of Ukraine, and the US called them out for
violating international law. See, because the US just invades, sets up its
own giant embassies and military bases, and takes control of the country's
money, which is SOOO different.
https://www.bbc.com/news/world-europe-63095436
--Speaking of how the US takes over and never leaves, in Iraq, at the
behest of the Iraqi government (allegedly) Americans shot down an Iranian
drone to stop them from bombing in Kurdistan.
https://www.kurdistan24.net/en/story/29588-US-shoots-down-Iranian-drone-threatening-Erbil:-CENTCOM
---Earlier, at least one US citizen was killed by an (alleged) Iranian
drone; no information on whether this was someone from the CIA or a
private contractor, it's just said it was not a member of the military.
https://www.reuters.com/world/middle-east/us-confirms-american-citizen-killed-rocket-attack-iraqi-kurdistan-2022-09-29/
--The US has also increased sanctions against Iran's oil sales, which
could be a signal the nuclear deal is off. Or, best case scenario, they're
piling sanctions on so they can take them off when the deal is struck and
make it look like they're giving something up?
https://news.yahoo.com/us-sanctions-traders-iranian-oil-183303845.html
--At the United Nations, Syria called on the US to leave their country,
and... well, nobody did anything about it.
https://www.middleeasteye.net/news/un-general-assembly-syria-calls-us-leave-oman-praises-yemen-peace-efforts
---Meanwhile, Jordan has re-recognized Syria's leadership (many countries
stopped doing so after the civil war started in 2011). The US is not
pleased.
https://english.alaraby.co.uk/news/us-still-against-normalising-ties-assads-syria
--And after the assassination of a top "terrorist" last month, the US is
now saying Afghanistan is harboring more and more terrorists, paving the
way for more intervention. Based on lies.
https://www.voanews.com/a/us-voices-terrorism-concerns-in-afghanistan-opts-for-taliban-engagement-/6767158.html
In local news, while dozens of people have settled out of court for police
brutality inflicted during the racial justice uprising of 2020, the first
civil suit has landed in court, with a woman whose arm was broken seeking
$500,000. It'll be interesting to see how the jury rules, since so many
Portlanders put out Black Lives matter signs in 2020 but have swung back
the pendulum to oppose more cuts to the police.
https://www.opb.org/article/2022/09/27/portland-racial-justice-protester-civil-trial-police-use-of-force/
Along the march route, I relayed a Jerusalem Post article that says
Israel's Defense Forces have OK'd plans to use armed drones for targeted
killings in the West Bank. Remember, there was no drone war until the US
attacked Yemen in 2002. So we can thank US policy for this too.
https://www.jpost.com/israel-news/article-718422
As we got closer to the end point, I talked about the Senate's recent
approval of another $16 billion for Ukraine, $8 billion of which is for
weapons and $3 billion for moving US troops to eastern Europe. You
wouldn't want to spend that money, I don't know, on preventing climate
change when 1/2 of Florida is now under water???
https://www.defensenews.com/congress/2022/09/29/republicans-push-biden-to-use-21-billion-ukraine-aid-set-to-expire/
This week, the Ann Huntwork Peace Memorial Sign made it past the Gucci
store. I'm not sure if it's related but there were delivery trucks parked
up the entire block... which also means nobody driving could see it
anyway. However, we got past that block and halfway between 4th and 5th on
Yamhill when it went out.
Hoping folks who are willing and able can join us next week in particular,
but any Friday to keep the word out that war is not the answer.

redpajama_set_name: RedPajamaCommonCrawl


Stanford University, Bachelor of Arts in Design, 1990.
Construct 3D, Duke University, May 5th-7th, 2017. Workshop: CAD Design and 3D printing as Community and Culture Building Tools. PechaKucha presentation: Elevating the Process Over the Print/Guiding K-12 Students in 3D Printing Challenges.
Design Thinking Boot Camp, April 4-6, 2017. Tech Museum, San Jose, CA. A K-12 teacher professional development workshop series in collaboration with the Tech Museum and the Krause Center for Innovation. This is the fourth year I will be leading this workshop series, and this year's focus will be on science and biology.
Make Monterey teacher professional development conference in Monterey, CA, May 21st, 2016. Takara conducted a workshop on prototyping scale models of micro parks.
Equity in Making and Creating with Technology, Ignite speaker, May 9th, Stanford Graduate School of Education, Stanford University.
Design Thinking Educator Boot Camp, Tech Museum, San Jose, CA. April 6-8, June 28, July 9, and Dec 5, 2015. Second workshop series was conducted by Takara on April 4th-April 6th, 2016. Takara developed and led these professional development workshops series introducing 3rd through 6th grade teachers to design thinking and STEM journeys.
Everyone is an Inventor, Arts In Your Classroom/Montalvo Arts Integration Conference, Montalvo Arts Center, Saratoga, CA. March 7th, 2015. Teacher professional development training in design thinking. This workshop integrated design thinking methodology into an exploration in building computer controller interfaces for public space needs. Participants created working prototypes that connected to MakeyMakey device and computers.
Design Thinking in High Schools, teacher professional development training for Notre Dame High School, Belmont. Conducted through the Krause Center for Innovation, San Jose, 2014.
3D Printing & 3D Designing in the Classroom, teacher professional development training for Palo Alto Unified Technology Leads, Gunn High School, Palo Alto, 2014.
Design Thinking in K-12, teacher professional development training for Union School District through the Krause Center for Innovation, San Jose, 2013.
Mentor, Art of Creative Engagement, Stanford Creative Ignition Lab, Stanford University, Stanford, CA, 2016.
Guest Lecturer, Stanford University, Stanford, CA Mechanical Engineering, ME101. 2012.
Guest instructor for workshop series Urban Tools, Royal Institute of Art, Stockholm, Architecture Department, Stockholm, Sweden. 2011.
Guest speaker, Stanford University, Stanford, CA. History Department, graduate class of Dr. Gordon Chang. 2009.
Instructor, Stanford University, Stanford, CA. Mechanical Engineering, 116 D, (Advanced Design Sketching, a 1 unit rendering class) 1998-1999.
Guest Lecturer, Stanford University, Stanford, CA. Mechanical Engineering, ME101, 1993-1995.
MakeHer Workshops, Sunnyvale Library, Sunnyvale, CA. 2015 - 2018. Girl and Mom STEM workshops exploring ProtoStorming and design thinking using recycled materials, MakeyMakey, Scratch, paper circuitry and 3D printing.
Design Thinking and Product Development, Krause Center for Innovation, Foothill College, CA. 2014. A teen workshops series exploring design thinking, 3D printing, and product development. Two six hour consecutive Saturday workshops.
Art & Design Thinking Camp, Camp Instructor for a six week summer camp created for East San Jose middle school students in Mayfair, San Jose, CA. Lead Camp Instructor from 2014- 2016 , Camp Instructor from 2017-2018. Supported by San Jose Safe Summer Initiative grants and San Jose TakepART grants.
Art & Design Thinking Camp, Takara designed and conceived of this week-long summer camp pilot program created for East San Jose middle school students. Mayfair Community Center, San Jose, CA. 2013. Supported by a San Jose Safe Summer Initiative Grant.
Teaching Artist-in-Residence, Montalvo Arts Center, Saratoga, CA. 2012. Designed and taught 10 STEAM and Common Core centered art classes to five classes of kindergartners at Lynhaven Elementary for a period of four months, Campbell, CA.
Visual Rhythms/Community Pattern Workshop, the 2012 Steinbeck Festival, Salinas, CA, 2012. A wii remote interactive mural projection project at the Alisal Center for Fine Arts.
You Are Here Street Banner Project, youth designed banners for Alum Rock, San Jose, CA, 2010. Two 5th grade classes participated at Cureton Elementary School.
Community Identity through Pattern and Photography workshop series with three classes of 6th graders at Renaissance Academy @ Fishcer, San Jose, CA, 2011. Selected patterns will be integrated into the VTA hardscaping projects at the 101//Tully and 101/Capitol Expressway pedestrian and bike paths.
Remix and Reflect Recycled Art, Horace Mann Elementary, San Jose, CA. 2010. Third grade workshop series in recycling art using plastics resulting in large collaborative tapestry for the library.
TECHstlye SoftWEAR, Surface & Shape, San Jose Museum of Quilts & Textiles, San Jose, CA, 2010. Collaborative fashion sculpture installation drawing upon online public submission to FusionwearSV of photos and textile designs representing Silicon Valley. Public and youth workshops were conducted at the museum. Project supported with an Applied Materials Excellence in the Arts Grant, Creative Connections James Irvine Foundation Grant and an NEA Access to Excellence Grant through the ZERO1 Art & Technology Network.
Picking Strawberries on Gold Mountain stop motion animation workshop series at Cureton Elementary, San Jose and at Stevens Creek Elementary in Cupertino.
Thinking Outside the Box, stop motion animation workshop series at Cureton Elementary, San Jose, CA.
A Serving of Shapes, a public art exploration into 3D printing. Four public workshops conducted at the de Saisset Museum, Alum Rock Branch Library and Mayfair Community Center in San Jose, CA. 2014. Supported by an Applied Materials Excellence in the Arts Grant and the San Jose Museum of Art Around the Table Initiative.
Patterns of Community, the Steinbeck Festival, Alisal, Salinas, CA. 2012.
Teen Digital Textile Workshops, San Jose Museum of Quilts & Textiles, San Jose, CA, 2010.
Claymation!, Montalvo Arts, Saratoga, CA. Youth animation workshops. 2010.
Lord of the Samurai, Online youth art and history curriculum, the Asian Art Museum, San Francisco, CA, 2009.
Friday Night Events, the de Young Museum, San Francisco, CA. three recycled materials art workshops in 2007-2009.
National Junior Winners of Future Engineers Design Challenge. 2014-2017. Takara has led youth in all five of these NASA/ASME sponsored 3D printing design challenges and had mentored three National Junior Winner and numerous finalists and semifinalists.
San Jose Arts and Cultural Exchange Grant Project, 2017. Awarded for Unraveling the Knot Project to be conducted in Dublin, Ireland and San Jose, CA. Partnering organization are the Alum Rock Educational Foundation and the Chester Beatty Library.
Knight Foundation Grant for Pop-Up Mobile Makerspaces, 2015.
Take pART Grant, 2014 and 2015. A City of San Jose grant supporting arts enrichment in communities. Funding applied to the Art & Design Thinking Summer Camp in Alum Rock, 2014 and 2015.
Applied Materials Excellence in the Arts Grant 2014. Grant supported a Serving of Shapes/a 3D Printing Exploration.
Around the Table Initiative funding from the San Jose Museum of Art supported the Serving of Shapes workshops and installations at the de Saisset Art Museum, Santa Clara University, CA, 2014.
Silicon Valley Community Foundation Grant. Grant funding for the 2014 Art & Design Thinking Summer Camp at Mayfair Community Center, San Jose.
San Jose Safe Summer Initiative. Grant funding for the 2013 pilot program Art & Design Thinking Summer Camp at Mayfair Community Center, San Jose.
Silicon Valley Community Foundation Grant. Grant funding for the Wrap It Up! video interview project to be conducted by Burnett Middle School students and incorporated into the San Jose Japantown Mural Project website. 2012-2013.
Silicon Valley Community Foundation Grant. Grant supported the You Are Here Street Banner Project, a digital textile and photography youth project in Alum Rock, San Jose, CA. 2011.
Target Arts Grant for Remix and Reflect, a collaborative art and science project with Horace Mann Elementary.
Rambus-KCI Innovation Award, Grand Prize Winner for Picking Strawberries on Gold Mountain, Krause Center for Innovation, Los Altos Hills, CA. 2010.
Rambus-KCI Innovation Award Finalist for Thinking Outside the Box, Krause Center for Innovation, Los Altos Hills, CA. 2009.
Alum Rock Education Foundation Mini Grant, San Jose, CA. 2009.
Distinguished Artist Award, City of Cupertino Fine Arts Commission, CA. 2006.
The Guilla Webster McFarland Award in Design, Stanford University, CA. 1990.
TechShop Artist Residency, San Jose, CA. October through December, 2015.
Montalvo Arts Center, Teaching Artist-in-Residence, 2013.
Rhythms and Space, The de Young Museum Artist-in-Residence, San Francisco, CA. December 3rd 2008 -January 4th 2009. The creation of public participatory workshops are integral to this residency.
Stanford Sierra Camp Artist in Residence Program, Stanford Sierra Camp, Fallen Leaf Lake, South Lake Tahoe, CA, 2004.
TEDx Livermore, Where Are We? Who Are We? Finding Our Roots & Identity in Public Art, Livermore, CA, 2014.
Make a Makerspaces and Design Thinking Lab in Your Classroom, co-presented with Steve McGriff, Napa Fall CUE American Canyon, CA, 2014.
Visual Arts & the Common Core/ Journeys of Discovery Via Paper, Pixels and Paint. Arts in your Classroom Professional Development Conference, Santa Clara County of Education, 2013.
Re-imagining the Bus Stop Shelter/a Design Thinking Exploration, CUE 2013, Palm Springs, CA. 2013.
Canvas on the Street: Youth Digital Art Projects for Public Spaces, Napa Fall CUE 2012, American Canyon, CA. 2012.
Tape, Cardboard and Cloud Tools, MERIT Con, Krause Center for Innovation, Foothill College, summer 2012.
Guest lecturer in workshop series Urban Tools, Royal Institute of Art, Stockholm, Architecture Department, Stockholm, Sweden. 2011.
Digital Textiles: Explorations into Geometry and Pattern Design and Claymation Made Simple, Silicon Valley CUE, Harker, San Jose, CA. 2010.
Enlace Mentor for the Enlace Program, Evergreen College, San Jose, CA. The program mentors Latino/Chicano college students and helps them to complete their academic core classes. 2012-2013 academic year and 2013-2014 academic year.


The first comprehensive Canadian survey to measure outcomes of adults who didn't go to public school has found these graduates to be very active and engaged citizens — more likely to volunteer and donate to charity than their government-educated counterparts.
Overall, graduates from separate and independent Catholic schools, secular private schools, evangelical Protestant schools and home schools are at least as likely as their public school peers to be in society working towards the "common good," found the Cardus Education Survey, released to the public on Wednesday.
This survey of more than 2,000 Canadians aged 24-39 helps dispel widely held beliefs about non-governmental school graduates — a sector that is often "ignored," said Ray Pennings, a senior fellow and director of research at Cardus, a Christian think tank that measures the impact of organizations on individuals and governments.
The report surveyed 2,054 graduates, 41% of them government-educated. It did not dwell at length on academic performance.
Using the standards for educational outcomes outlined in provincial statutes, regulation and policy, the study set out to measure how students educated outside of the public realm fared as adults — in their work, family, spiritual and daily life. Controlled for family socioeconomic and religious background, the results showed a measurable impact particularly when it comes to civic engagement and citizenship, Mr. Pennings said.
Evangelical Protestant school grads were more than twice as likely to donate money to many causes —both religious and secular —than their government-educated peers, often shelling out 10% of their income in keeping with Biblical tradition. Religious homeschoolers were also around twice as likely to donate to money, though less likely to give to secular causes.
Non-government-educated graduates were as likely if not more likely to volunteer than public school educated students. Graduates of independent non-religious schools in particular were found to vote more, volunteer more and participate in a wider variety of organizations and causes.
Interestingly, evangelical Protestants and religious homeschoolers reported high levels of social engagement even though "the dominant culture is hostile to their beliefs and values," the report read.


Professionals are required to make more independent decisions and exercise a greater degree of judgment than ever before, heightening liability concerns.
Unfortunately, lawyers, doctors, accountants, financial advisors and other professionals may sometimes find themselves involved in legal proceedings brought by clients who have suffered as a result of their negligent actions.
Professional services practitioners have to be fully prepared to respond to claims of legal liability at any moment, including breach of contract, errors and omissions, and malpractice. The stakes are high.
Our professional liability team has a detailed understanding of the professional industries, including industry-specific principles, terminology, operational norms, and market forces. We are able to immediately and efficiently evaluate the situation and related evidence to assess liability claims. While most professional liability cases settle, our ultimate goal is to achieve the best result for our client, whatever it takes.
The Massachusetts Appeals Court upheld a Superior Court jury's verdict in favor of our clients against their former attorney for charging an unfair and excessive fee. The appeals court also affirmed the trial judge's award of triple damages and attorney's fees under the Consumer Protection Act, commonly known as chapter 93A. See Landry v. Haartz, 83 Mass. App. Ct. 1135.
A member of a health care practice wasn't getting along with the other two members of their LLC, based on money and personality issues. We filed a pre-emptive suit to stop her partners from forcing her out. That allowed time for us to negotiate a settlement favorable to our client, which included withdrawing from the LLC, resolving compensation issues and coming to an agreement on non-compete terms.
Complex securities fraud claims, breach of contract and 93A where investment advisor and his company defrauded investors of millions of dollars.
Medical group against a former physician/employee claiming breach of employment agreement and breach of fiduciary duty; we obtained complete summary judgment in the arbitration before JAMS.
Company in a case involving claims of fraud in a public offering. We defended, and after trial obtained, a judgment in favor of the company.
Accounting firm against claims of vicarious liability and professional malpractice.
Attorney who had sanctions issued against him by trial court; obtained direct appellate review by the Supreme Judicial Court, which vacated the sanctions award.
Lawyers in state and federal courts in actions for malpractice, breach of contract, RICO, breach of fiduciary duty and related claims.
Appearing before state and local grievance and disciplinary committees, serving on bar association ethics and legal malpractice committees, and appearing as expert witnesses in malpractice cases.
Law firm against corporate claims of malpractice in connection with business transactions, securities matters, real estate transactions, patent and trademark matters.
Law firm breakups and dissolutions disputes.
Claims of negligence, misreporting of financial statements, misallocation of expenses, and flawed internal controls.
Claims brought by state regulators or the SEC alleging, among other things, failures in tax consulting or involvement in federal securities offerings.
Disciplinary proceedings by the American Institute of Certified Accounting Professionals.
Subpoenas by third parties seeking audit work papers and testimony regarding audit procedures.


text:
\section*{Appendix}
\label{se: appendix}

Algorithm \ref{alg: train} explains how we train with empowerment. We omit super- and subscripts denoting time, agent and batch indices whenever clear from the context.

\newcommand{\texttt}{\texttt}

\begin{algorithm*}
    \caption{Training joint policy $\boldsymbol \pi_\theta$ with empowerment}\label{alg:training}
    \begin{algorithmic}
    \Require Initialisation of networks $\boldsymbol \pi_\chi$, $\boldsymbol Q_\psi$, $\boldsymbol \omega_\theta$, $q_\theta$ and $p_\nu$, and target networks $\boldsymbol\pi_\phi$, $\boldsymbol Q_\zeta$.
    \ForEach{episode}
        \ForEach{time step}
            \State $\boldsymbol o = O(s)$, $\boldsymbol a \sim \boldsymbol \pi(\boldsymbol a|\boldsymbol o)$, $s'\sim f(s, \boldsymbol a)$
            \State $\tau = \tau \cup \{(\boldsymbol o, \boldsymbol a, \boldsymbol o', r, \hat{\mathcal{I}}, y)\}$
        \EndFor
        \State $\mathcal{D} = \mathcal{D}  \cup \tau $
        \ForEach{agent $i$}

                \State sample minibatch with $S$ tuples from $\mathcal{D}$
                \State $\hat{\mathcal{I}}_{\chi^i, \theta^i}(\boldsymbol o)=$ \texttt{computeLowerBound}($\boldsymbol o$,$\boldsymbol \pi_\chi$, $\boldsymbol \omega_\theta$, $q_\theta$, $p_\nu$) \Comment{Equation \ref{eq: mu}}
            \State $y = r + \hat{\mathcal{I}}_{\chi^i, \theta^i}(\boldsymbol o) + \gamma Q_\zeta^i(\boldsymbol o, \boldsymbol a)$

                \State $\mathcal{L}(\psi^i) = \frac{1}{S}\sum_j
                    (
                        y - Q^i_\psi(\boldsymbol o_j, \boldsymbol a_j)
                    )^2|_{a^i_j \sim \pi^i_\phi}$
                \State $\mathcal{L}(\chi^i) = -\frac{1}{S}\sum_j
                    Q_\psi^i(\boldsymbol o_j, \boldsymbol a_j)|_{a^i_j \sim \pi^i_\chi}$

                \State \texttt{updateCritic}($\mathcal{L}(\psi^i)$, $\psi^i$) \Comment{See \citep{lowe2017multi}}

                \State \texttt{updateActor}($\mathcal{L}(\chi^i)$, $\chi^i$)
                \State \texttt{gradientAscent}($\hat{\mathcal{I}}_{\chi^i, \theta^i}$, $\theta^i$, $\chi^i$)
                \State \texttt{maxLogLikelihood}($\nu$, $\boldsymbol o$, $\boldsymbol a$, $\boldsymbol o'$)  \Comment{See \citep{karl2016deep}}

        \State \texttt{updateTargets}($\phi^i$, $\zeta^i$, $\psi^i$, $\chi^i$)
        \EndFor
    \EndFor
    \end{algorithmic}
    \label{alg: train}
\end{algorithm*}

\begin{table*}[!hb]
    \centering
    \begin{tabular}{l|l}
    \hline
        $\boldsymbol{\pi}$              & Joint policy with $N$ components $[\pi^1, \dots, \pi^N]$. \\
        $O(s)$            & Deterministic observation function $\boldsymbol o = (o^1,\dots,o^N) = O(s)$.\\
        $\boldsymbol a, \boldsymbol o$  & Joint action and observation $(a^1, \dots, a^N)$, $(o^1,\dots,o^N)$.\\
        $\boldsymbol a^{-i}, \boldsymbol o^{-i}$            & Joint action and observation excluding those of agent $i$.\\
        $J^i(\boldsymbol{\pi})$         & Expected return of agent $i$ induced by joint policy $\boldsymbol{\pi}$. \\
        $Q^i(\boldsymbol{o}, \boldsymbol{a})$      & Centralised critic of local policy $\pi^i$. \\
        $\mathcal{E}^{-i\rightarrow i}$ & Transfer empowerment from all agents' actions, excluding agent $i$, towards agent $i$'s action.\\
        $\hat{\mathcal{E}}$ & Lower bound on empowerment by employing variational approximation. \\
        $\hat{\mathcal{I}}_\theta$ & Lower bound on mutual information computed by $\theta$-parameterized neural networks.
    \\ \hline
    \end{tabular}
    \caption{Notation}
    \label{tab:notation}
\end{table*}

\section{Conclusions and Future Work}

Overall, adding transfer empowerment to \ac{MARL} seems to improve the overall performance level of cooperative agents - particularly for harder tasks that rely on an action-oriented communication strategy. This seems to indicate that \ac{TE} helps the learning process to find better solutions to converge on - which remain undiscovered by the baseline \ac{MARL} with similar training time - while also not getting in the way of exploitation to much. An immediate open question, a direction for future work, is of course the question of generalizability of this approach to different scenarios. Other exciting research directions are scenarios with partners unseen at training time, moving in the direction of one-shot adaptation to partners, and scenarios with competitive, or cooperative-competitive mixed scenarios. Using \ac{TE} to bias systems towards control, or information hiding to find optimal solutions.

We also showed how an efficient computation of empowerment could be combined with \ac{RL} for the \ac{MARL} framework, opening the door for more complex scenarios such as humans interacting with robots. In general, the results in this study are promising for the overall agenda to develop a framework of social intrinsic motivations based on empowerment (or similar measures) to bias an agent towards general social concepts, such as reliable reactivity, or lead-following. The fact that it is based on similar, single-agent intrinsic motivations is also interesting, as it might offer insights into how to transition from single to social agent behavior with only gradual adaptation.

\section{Experimental Results}
We adopt the simulator developed for testing multi-agent reinforcement learning algorithms \footnote{https://github.com/openai/multiagent-particle-envs} that allows creating cooperative and competitive environments. Agents have a continuous observation space and a discrete actions space.

\begin{figure*}[!hb]
    \centering
    \begin{subfigure}[b]{0.3\textwidth}
         \centering
         Simple
         \includegraphics[width=\textwidth]{Figures/learning_curve_scenario_simple_speaker_listener.png}
     \end{subfigure}
     \begin{subfigure}[b]{0.3\textwidth}
         \centering
         Challenging
         \includegraphics[width=\textwidth]{Figures/learning_curve_scenario_challening_speaker_listener.png}
     \end{subfigure}
     \begin{subfigure}[b]{0.3\textwidth}
         \centering
         Hard
         \includegraphics[width=\textwidth]{Figures/learning_curve_scenario_hard_speaker_listener.png}
     \end{subfigure}
        \caption{Learning curves for the the three tasks. The rewards are averaged over the steps in an episode to obtain the return. The returns are averaged over three training runs.}
        \label{fig:loss}
\end{figure*}

\subsection{Scenarios}
We use a cooperative two-dimensional environment consisting of two agents. The (disembodied) speaker agent can, each time step, choose a communication action that is broadcast to the listener. The listener can choose from 5 physical actions, moving up, down, left and right, or doing nothing. The environment contains a series of $L$, randomly placed, landmarks. Only the speaker has a signal informing it which of the $L$ landmarks is the target. The objective for the randomly placed listener is to reach the target landmark by decoding the speaker's message. The speaker can send a symbol chosen from a set of $C$ distinct symbols. The team reward is the negative squared distance between the listener and the target landmark, which is given out every time step. The game ends after 100 time steps. To perform well the listener has to quickly move onto the landmarks. We developed three tasks in the environment with increasing difficulty. Fig. \ref{fig:tasks} visualises the tasks.

\begin{figure}[!h]
    \centering
    \begin{subfigure}[b]{0.15\textwidth}
         \centering
         Simple
         \includegraphics[width=\textwidth]{Figures/simple.png}
     \end{subfigure}
     \begin{subfigure}[b]{0.15\textwidth}
         \centering
         Challenging
         \includegraphics[width=\textwidth]{Figures/challenging.png}
     \end{subfigure}
     \begin{subfigure}[b]{0.15\textwidth}
         \centering
         Hard
         \includegraphics[width=\textwidth]{Figures/hard.png}
     \end{subfigure}
        \caption{Visualizations of the three tasks. Small dark circles indicate landmarks and obstacles, and the big circles are the listener and speaker. The listener observes the relative distance to the landmarks indicated by the arrows. The speaker's massages are one-hot vectors displayed by the speaker boxes.}
        \label{fig:tasks}
\end{figure}

\paragraph{Simple}
The number of symbols $K=|C| = 3$ equals the number of landmarks $L=3$. The speaker observes the color of the target landmark, while the listener sees a distance vector pointing to each colored landmark.

This scenario could be solved well by an indexical communication strategy, where the speaker simply has to consistently assign a symbol to each landmark color, and then simply relay the information to the speaker, who then has to minimise the distance to the landmark of that color.

\paragraph{Challenging}
The second task involves more landmarks $L=6$ than distinct symbols $K=|C|=5$. The speaker observes the target \emph{position} and the listener's position, while the listener observes the landmarks' positions and the messages sent by the speaker.
Here, an \textit{action-oriented strategy}, e.g., indicating movement direction, is likely optimal because the speaker cannot use each symbol for a landmark uniquely, nor do the landmarks have any identifying features that are easy to community, i.e. they are not colored anymore. Using symbols to direct the listener now requires the speaker to observe and react to the listener's position with an updated signal, and put more cognitive demands on the speaker, who could simply relay its internal signal in the simple scenario.

\paragraph{Hard}
The last task adds $M=6$ obstacles, and the reward includes a penalty if the listener hits an obstacle. Furthermore, the landmarks' positions are now unobserved by the listener. These two features increase the difficulty because a higher precision is required. First, the listener has to avoid obstacles, and second, the listener is even more dependent on the speaker because it does not see the landmarks.

\paragraph{Reward}
The reward function is determined from the position, $\boldsymbol{p}=[p_x,p_y]$, of the listener (agent 1) $\boldsymbol{p}^1$, target $\boldsymbol{p}^g$ and obstacles $\boldsymbol{p}^o$. The state is defined as $s_t = [\boldsymbol{p}_t^1, \boldsymbol{m}_t, \boldsymbol{p}^g, \boldsymbol{p}^{o, 1}, \dots, \boldsymbol{p}^{o, M}, \boldsymbol{p}^{l, 1}, \dots, \boldsymbol{p}^{l, L}]$ for $M$ obstacles, and $L$ landmarks. The reward function is:
\begin{align}
    r(s_t, \boldsymbol{a}_t) &= -\|\boldsymbol{p}^1_{t+1} - \boldsymbol{p}^g\| + \text{penalty} \\
     \text{penalty} &=
     \begin{cases}
        -1 & ~ \text{if } \quad \exists j \in [1,...,M]: \|\boldsymbol{p}^1_{t+1} - \boldsymbol{p}^{o,j}\| <0.15\\
        0  & ~ \text{otherwise}
    \end{cases}
\end{align}
The observation for the speaker and listener $o_t^0 = [\boldsymbol{p}^1_t, \boldsymbol{p}^g, \boldsymbol{p}^{l, 1}, \dots, \boldsymbol{p}^{l, L}]$ and $o_t^1 = [\boldsymbol{v}^1_t, \boldsymbol{m}^0_t]$, respectively. $v$ is the velocity and $m$ the message, represented by a one-hot vector.
The listener's action is a force vector $a^1=[f_x, f_y]$, while the speaker's action is a message $a^0=[c^0, \dots, c^{K}]$ with vocabulary size $K$.
The listener's position is updated according to the following equation:
\begin{align}
        \begin{bmatrix}
            \boldsymbol{p} \\
            \boldsymbol{v} \\
            \boldsymbol{\dot{v}}
        \end{bmatrix}_{t+1}
        =
        \begin{bmatrix}
            \boldsymbol{p} + \boldsymbol{v} \Delta t\\
            \zeta \boldsymbol{v} + \boldsymbol{\dot{v}} \Delta t \\
            \frac{\boldsymbol{u}}{\text{mass}}
        \end{bmatrix}_t
\end{align}
with damping coefficient $\zeta=0.5$ and $\text{mass}=1$.
The speaker's messages, will be added to the state at the next time-step: $\boldsymbol{m}_t = \boldsymbol{a}^0_{t-1}$.
As is common when working with policies parameterised by neural networks, the actions are one-hot vectors, obtained by Gumbel-Softmax function \citep{pml1Book}. For example, the actions of the speaker are converted into
\begin{align}
    \text{one-hot}(\boldsymbol{a^0}) = [\mathbb{I}(a^0_1=\max(\boldsymbol{a^0})),\dots, \mathbb{I}(a^0_K=\max(\boldsymbol{a^0}))].\nonumber
\end{align}

\begin{table}[!hb]
\centering
\caption{The values show the average distance between the listener and target landmark and the percentage of collisions with obstacles. The results are computed for 100 episodes after training with 10k episodes.}
\begin{tabular}{lccccc}
\toprule
    &  \multicolumn{1}{c}{Simple} & \multicolumn{1}{c}{Challenging}&\multicolumn{2}{c}{Hard}\\
    \midrule
{}              & {Average}  & {Average}          & {Average} & {Obstacle} \\
{}              & {distance} &   {distance}         & {distance} & {hit } &\\
\midrule
{basel.}      & 0.221     &  0.440                   & 0.520        & 0.603     \\
{SI}  & 0.716     &  0.949                     & 1.076        & 0.220     \\
{\ac{TE}}   & 0.414     &  0.460                    & 0.440        & 0.266     \\
\bottomrule
\end{tabular}
\label{tab:comm}
\end{table}

\subsection{Comparison and Implementation details}
We compare our method with MADDPG \citep{lowe2017multi} (baseline) and social influence \citep{jaques2019social} (social infl.). Our method (empowerment) is built on top of the MADDPG, a centralized actor-critic method. Social influence is a decentralized method.
The agents' policies are parameterized by a two-layer ReLU MLP with 64 units per layer. The messages sent between agents are soft approximations to discrete messages, calculated using the Gumbel Softmax estimator.
All models are trained for 10k episodes, of which an episode consists of 25 interactions. Source code can be found at \url{https://github.com/tessavdheiden/social_empowerment}.

\section{Results and Discussion}
\subsection{Learning Curves}
Our two main hypothesis are that adding \ac{TE} to \ac{MARL} produces faster adaptation (needs less training steps),  and achieves better, overall results. To answer both of the questions, we compare the learning curves, over 10k training steps, averaged over three runs, for both the \ac{MARL} baseline, and with the addition of \ac{TE} (empowerment) and \ac{SI} (social).
Figure \ref{fig:loss} shows the averaged return after a given number of training steps. A higher score is better, it shows that the listener is closer to the target. Since the listener starts away from the target a score of 0 is impossible, all scores are  negative.

The learning speed seems to be comparable between models in difference scenarios, i.e. it takes about the same time for the different algorithm to reach their peak, final performance. Only \ac{SI} seems to learn slower in both the simple and challenging task.
Performances seem to mostly stabilise after some point, so we can also take a closer look at the performances of the trained agents after 10k training steps.

\begin{figure}
     \centering
     \begin{subfigure}[b]{0.23\textwidth}
         \centering
         Baseline
         \includegraphics[width=\textwidth]{Images/baseline_agent_1_actions.png}
     \end{subfigure}
     \begin{subfigure}[b]{0.23\textwidth}
         \centering
         Empowerment
         \includegraphics[width=\textwidth]{Images/empowerment_agent_1_actions.png}
     \end{subfigure}
        \caption{The action distributions for the listener in the hard task for both the baseline and empowerment. The colors indicate each a different episode. 0 is the wait action, 1 - 4 are cardinal accelerations.}
        \label{fig:actions}
\end{figure}

\subsection{Final Scores}
Details of the results are presented in Table \ref{tab:comm}. It shows the average distance and the percentage of collisions with an obstacle for the final agents, computed for 100 episodes.

The baseline obtains the top performance, with lowest distance of $0.221$, in the simple task, requiring a simple, lexical strategy. Here empowerment performs worse with $0.414$, with social influence performing even worse with $0.716$. This is an indication of the idea that an added incentive might get in the way of exploiting an easy to find, simple strategy.

In the challenging task, which requires an action-oriented strategy empowerment seems to perform similar to the baseline, while it clearly outperforms the baseline for the hard task, both in terms of average distance, and in terms of obstacles hit. The difference in hit obstacles is particularly large, indicating that \ac{TE} helps with the higher reactivity required within an episode to navigate around the obstacles. Social influence seems to struggle with both tasks, again likely due to an interference between the added reward and the best exploitation strategy.

In contrast, the better performance of empowerment in the hard challenge, compared to the baseline, must be due to empowerment helping to discover a better overall strategy - as the baseline implementation would be fully capable of producing a strategy identical to the one performance by the \ac{TE} framework, had it discovered it.

To illuminate this difference, we can take a look at the action distribution for the speaker and listener agents over several episodes, using the agents after 10k training episodes. Fig.~\ref{fig:actions} shows how often the five available actions were used. Action 0 for the listener is the waiting action - and we see that this one is not used by the baseline. We speculate that it might be difficult for the listener to learn when to use this action, as it is detrimental in most cases. The bias towards reactivity induced by \ac{TE} might help to keep this rare action as an option - following a symbol by the speaker that might become a ``stop'' signal.

\begin{figure}
     \centering
     \begin{subfigure}[b]{0.15\textwidth}
        \centering
        Baseline
        \includegraphics[width=\textwidth]{Images_controllability/positions_listener_baseline.png}
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{0.15\textwidth}
         \centering
         Empowerment
         \includegraphics[width=\textwidth]{Images_controllability/positions_listener_empowerment.png}
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{0.15\textwidth}
        \centering
        Social influence
        \includegraphics[width=\textwidth]{Images_controllability/positions_listener_social.png}
     \end{subfigure}
        \caption{The listener's positions plotted for 10 time steps, given a speaker's message $\boldsymbol{m}^0$, a one-hot vector. The subscript denotes the component in $\boldsymbol{m}^0$  that is equal to a 1.}
        \label{fig:positions}
\end{figure}

We can also take three trained listener agents and compare what they will do when we provide them with a fixed speaker signal over several time steps, to figure out what those symbols directed them to do. Fig.~\ref{fig:positions} shows the trajectories resulting from this, with each color denoting a different forces symbol by the speaker. The baseline has a relatively good separation into cardinal actions, but transfer empowerment leads to nearly perfect control by the speaker over the actions of the listener. Note that one signal results in the wait action, leading to no visible trajectory for empowerment.

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

In this paper we investigate if and how social intrinsic motivation can improve \ac{MARL}. \ac{MARL} holds considerable promise to help address a variety of cooperative multi-agent problems - both for problem solving and simulation of multi-agent systems. However, one problem with \ac{MARL} is that agents develop strong policies that are overfitted to their partners' behaviors. Specifically, with centralised training, agents can adopt strategies that expect other agents to act in a certain way rather than reacting to their actions. Such systems are undesirable as they may fail when their partners alter their strategies or have to collaborate with novel partners, either during the learning or deployment phase. Our aim is to avoid this specific lack of robustness and find a guiding principle that makes agents stay reactive to other agents' policy changes.

We want to introduce an additional reward to bias learning towards socially reactive strategies which should fulfil the following constraints: 1) it should, with minimal adaptation, apply to a wide range of problems with different sensor-actuator configurations to preserve the universality of the RL framework, and 2) it should not negatively affect the performance, i.e., once good policies are found, it should not harm exploitation. Fulfilling the above criteria would provide a general-purpose multi-agent learning algorithm for various cooperative tasks - as well as provide insights into general principles that would enable and improve the development of various forms of social interaction.

To address this challenge, we turn towards the idea of using \ac{IM} - a school of computational models \citep{oudeyer2009intrinsic} that try to capture the essential motivations behind the behavior of (biological) agents - and then use them for behavior generation to obtain plausible and beneficial behavior. The core idea here is to ask if the principles that create single agent behavior can also be used to enhance multi-agent behavior. In this paper specifically, we look at Empowerment, an \ac{IM} that captures how much an agent is able to affect the world it can itself perceive. Its information-theoretic formulation as the channel capacity between an agent's actions and its own sensors makes it a versatile measure that can be applied to a wide range of models where agent's are defined - satisfying constraint 1. Existing work on coupled empowerment maximisation \citep{salge2017empowerment,guckelsberger2018new} extends the formalism to a multi-agent setting. In this paper, we focus specifically on the idea of \ac{TE}, which tries to capture how much one agent can potentially influence the actions of another.

Keeping the \ac{TE} high between two agents means they are in a state were one of them is reliably reacting to the other. Adding this as an additional reward mechanism during training should help to avoid the brittleness of over-fitting we outlined before. We provide here quantitative evidence for our hypothesis that adding transfer empowerment as an additional reward increases upon the performance of state-of-the art \ac{MARL} methods. Constraint 2 will be evaluated empirically. We also compare this approach to a similar idea of social influence by \citet{jaques2019social}. First, we will introduce the concepts of \ac{MARL} and \ac{IM} in more detail. We will then define the specific formalism for \ac{TE} used, and then simulate three increasingly harder, multi-agent, cooperation scenarios. We will also look at how the better reward was obtained, and discuss the difficult switch from a indexical to an action oriented communication strategy.

\section{Methodology}

This section describes an additional heuristic that biases the learning process in obtaining policies that are reactive to other agents' actions. First, we introduce our specific version of transfer empowerment, which rewards the idea of an agent being responsive to adaptations in the other's policy. Then we explain how to train agents in a multi-agent environment.

\subsection{Transfer Empowerment} \label{se: transfer}
Consider two agents, $j$ and $k$, both taking actions and changing the overall state. Each time agent $k$ acts, the state of agent $j$ is modified, and $ j$'s policy indirectly conditions on $ k$'s actions. The objective of coordination is that by changing the actions of agent $k$, agent, $j$ also \emph{reliably} adapts its actions. Here we look at transfer empowerment, namely the \emph{potential} causal influence that one agent has on another. It is defined for pairs of agents by the channel capacity between one agent's action $a^k_t$ and another agent's action  $a^j_{t+1}$ at subsequent time steps and conditioned on the current state $s_t$, which can be computed by maximizing the mutual information $\mathcal{I}$ between those values, with regards to $\omega^k$ :

\begin{equation}
    \mathcal{E}^{k\rightarrow j}(s_t) = \underset{\omega^k}{\max~} \mathcal{I}
    \left(A^j_{t+1}, A^k_t \middle| s_t \right).
\end{equation}

Here, $\omega^k(a^k_t |s_t)$ is the \emph{hypothetical} policy of agent $k$, that takes an action $a^k_t$ after observing state $s_t$ and influencing $a^{j}_{t+1}$ at a later time step. Note that the policy $\omega^k(a^k_t |s_t)$ that maximises the mutual information is not necessarily used for action generation, but simply to compute the channel capacity by looking at all potential policies for the one with the highest mutual information $\mathcal{I}$ .

Our version of transfer empowerment differs slightly from the one introduced by \citet{salge2017empowerment}, as we consider the potential information flow, or channel capacity, between two agents' \emph{actions} in subsequent time steps.
\citet{salge2017empowerment} on the other hand, consider the empowerment between one agent's action and another agent's \emph{sensor} state.
Transfer empowerment to another agent's sensory state captures the direct influence to change the other agent's environment. Using transfer empowerment to another agent's \emph{action}, as we do here, focuses on just the influence that affects the other agent's decision. This influence has to flow through the second agent's sensor and be mediated by their policy. In other words, they have to observe and react to the first agent's actions, which aligns with our goal of biasing a policy towards more reactive strategies.

Transfer empowerment has ties with, but is different from, social influence \citep{jaques2019social}. Social influence is the mutual information between agents' actions. It is high when both action variables have a particular entropy, e.g., policies taking different actions. However,  towards the end of the training, a high entropy policy distribution might be suboptimal. Our method, on the other hand, considers the \textit{potential} and not \textit{actual } information flow, so agents only calculate how they \textit{could} influence and react to each other, rather than carrying out its potential. As such, action sets can have very narrow distributions; as long as the system would still be reactive \textit{if}, those actions change. Therefore it does not interfere with obtaining optimal policies.

\subsection{Multi-Agent Training}

Training with transfer empowerment results in joint policies that are reactive to their partner's actions, because for the value to be high, it requires considering the decisions of others. As such, transfer empowerment rewards a very general idea of coordination that requires paying attention to each other, and reliably reacting to a variation in their actions. While empowerment does not measure how this reaction looks, or even if it is good, combined with the actual reward should lead to the selection of a strategy that both solves the problem while also avoiding the brittleness that comes from not being reactive to the information from other agents' policies. Specifically, we will modify the agents' reward function so that it becomes:

\begin{equation}
\tilde{r}(s_t, \boldsymbol{a_t}) = r(s_t, \boldsymbol{a_t}) + \sum_{s_{t+1}}
P(s_{t+1}|s_t, \boldsymbol{a_t}) ~\sum_{j=1}^N \mathcal{E}^{-j \rightarrow j}(s_{t+1}),
\end{equation}

where $-j$ means all agents excluding agent $j$. To simplify notation, we will use $j$ instead of $-j\rightarrow j$ in the superscript. The new RL objective becomes:
\begin{align*}
    J(\boldsymbol{\pi}) = \mathbb{E}_{\tau \sim \boldsymbol{\pi}}
        \left[
            \sum_{t=0}^T \gamma^t \tilde{r}(s_t, \boldsymbol{a_t})
        \right].
\end{align*}

This new return motivates the potential influence of information between agents' actions, thereby stimulating them to act informatively and react reliably.

\subsection{Efficient Implementation}
We now introduce an efficient implementation to estimate empowerment. We use $a$ for agent $k$'s action at time $t$ and $a'$ for agent $j$'s action at time $t+1$. Mutual information is defined as:

\begin{align*}
    \mathcal{I}(A,A'|s)
    &=\text{KL}(p(a,a'|s) \| p(a|s)p(a'|s)) \\
    &=\sum_a \sum_{a'}  p(a,a'|s) \ln \frac{p(a,a'|s)}{p(a|s)p(a'|s)} ,
\end{align*}
where $\text{KL}$ is the KL divergence. We can substitute $p(a,a'|s)$ and cancel out terms:

\begin{align*}
    &\sum_a \sum_{a'}  p(a,a'|s) \ln \frac{p(a,a'|s)}{p(a|s)p(a'|s)} \\
    &= \sum_a \sum_{a'} p(a,a'|s) \ln \frac{p(a|a',s)\cancel{p(a'|s)}}{p(a|s)\cancel{p(a'|s)}} \\
    &= \sum_a \sum_{a'} p(a,a'|s) \ln \frac{p(a|a',s)}{p(a|s)}.
\end{align*}

By choosing a variational approximator $q(a|a',s)$, with the property $\text{KL}(p(a|a',s)||q(a|a',s)) \geq 0$, we obtain a lower bound on the mutual information:

\begin{align*}
    \mathcal{I}(A,A'|s) &\geq
    \hat{\mathcal{I}}(A,A'|s) \\
    &:= \sum_a \sum_{a'} p(a,a'|s) \ln \frac{q(a|a',s)}{p(a|s)} \\
    &= \sum_a \sum_{a'} p(a,a'|s)
        \left(
            \ln q(a|a',s) - \ln p(a|s)
        \right)\\
    &= \mathbb{E}_{p(a,a'|s)}
        \left[
            \ln q(a|a',s) - \ln p(a|s)
        \right].
\end{align*}

The gradient of the lower bound can be approximated by Monte-Carlo sampling.
Furthermore, the overall training procedure can be implemented efficiently when representing the distributions by neural networks and using gradient ascent.
So the gradient computed over $S$ samples:

\begin{align*}
    \nabla_\theta \hat{\mathcal{I}}_\theta(A,A'|s) &=
    \nabla_\theta \mathbb{E}_{p(a,a'|s)}
        \left[
            \ln q_\theta(a|a',s) - \ln \omega_\theta(a|s)
        \right] \\
    &\approx
    \frac{1}{S} \sum_{m=1}^S \nabla_\theta \left(\ln q_\theta(a_m|a_m',s) - \ln \omega_\theta(a_m|s)\right),
\end{align*}
where we substituted $p(a|s)$ with $\omega_\theta(a|s)$ and $q(a|a',s)$ with $q_\theta(a|a',s)$, to denote functions parametrized by $\theta$.

\subsection{Partial Observable}
The objective in the previous section was to estimate the empowerment value for a particular state $s$. However, our main goal is to train policies to be reactive towards the actions of their partners.
Let a policy for agent $j$ be $\pi^j_\chi$ with parameters $\chi$. As each policy is conditioned on its local observations, the lower bound on mutual information for agent $j$ becomes:

\begin{align}
\hat{\mathcal{I}}^j_{\theta, \chi}&(A,A'|\boldsymbol o) = \nonumber\\
        &\mathbb{E}_{\boldsymbol o'\sim p_\nu, a^j\sim\pi^j_{\chi}, a^k\sim\omega^k_{\theta}}
        \left[
            \ln q_\theta(\boldsymbol a|\boldsymbol o, \boldsymbol o', a'^j) -\ln\boldsymbol\omega_\theta^{-j}(\boldsymbol a^{-j}|\boldsymbol o^{-j})
        \right],
        \label{eq: mu}
\end{align}

where samples are generated by a learned transition model $\boldsymbol o' \sim p_\nu(\boldsymbol o'|\boldsymbol o, \boldsymbol a)$. The actions are selected by the target policy $a^j\sim\pi^j_\chi(a^j|o^j)$ and behavior policy $a^k\sim\omega^k_\theta(a^k|o^k)$ and the joint action is $\boldsymbol a = (a^1,\dots, a^j,\dots,a^N)$ where $a^j\sim\pi_\chi$, $a^k\sim \omega_\theta$  and $k\neq j$.

Notice that the actions come from $\boldsymbol \omega_\theta$ and $\pi_\chi$. The former is the joint behavior policy and the latter is the target policy of agent $j$. The behavioral policy is only used to train agent $j$'s policy with empowerment but will not generate extrinsic environmental rewards.

This training procedure has two interesting properties.
First, it estimates a state's empowerment value. This is done by increasing the diversity of agents' actions while ensuring that these are retrievable from agent $j$'s actions. Actions that affect $j$'s policy, e.g., informative, are chosen more often than those with a lower effect.
Second, it trains agent $j$'s policy to be reactive towards the actions of its partners, because we compute the gradient of mutual information w.r.t. $\chi$ to directly optimize $\pi_\chi$. We provide the description of the full algorithm in the Appendix, which also describes how our method applies to settings with more than 2 agents.

Altogether, empowerment prefers states that allow for information flow between agents, altering policies to be more responsive. We will experimentally verify this in the next section.

\section{Model}
First, let us define a general model that captures multi-agent scenarios and lets use define transfer empowerment. Let us consider a Dec-POMDP, an extension of the MDP for multi-agent systems, being both decentralized and partially observable \citep{nair2003taming}. This means that each of the $N$ agents conditions the choice of its action on its partial observation of the world. It is defined by the following tuple: $\langle S, \boldsymbol{A}, T, O, \boldsymbol{O}, R, N \rangle$. $S$ is the set of states and $\boldsymbol{A}=\times_{i \in [1, .., n]} \boldsymbol{A}^i$ the set of joint actions. At each time step, the state transition function $P(s_{t+1}|s_t, \boldsymbol{a}_t)$ maps the joint action and state to a new state. As the game is partially observable, we have a set of joint local observations, $\boldsymbol{O}=\times_{i \in [1, .., n]} \boldsymbol{O}^i$ and an observation function $O$. Each agent $i$ selects an action using their local policy $\pi^i(a_t^i|o^i_t)$.

We consider fully cooperative tasks, so agents share a reward $r(s_t, \boldsymbol{a_t})$ which conditions on the joint action and state.
The goal is to maximise the expected discounted return  $J(\boldsymbol{\pi}) = \mathbb{E}_{\tau\sim\pi}\left[R(\tau)\right]=\mathbb{E}_{\tau\sim\pi}\left[\sum_{t=0}^T \gamma^t r_t \right]$, with discount factor $\gamma \in [0,1]$ and horizon $T$. The expectation is taken w.r.t. the joint policy $\boldsymbol{\pi}=[\pi^1,\dots,\pi^N]$ and trajectory $\tau=(\boldsymbol{o}_0, \boldsymbol{a}_0, \dots, \boldsymbol{o}_T)$.
\section{Related work}
\subsection{Multi-Agent Reinforcement Learning}
There is a large body of research on constructing agents that are robust to their partners. In self-play, for example, agents train against themselves rather than a fixed opponent strategy to prevent developing exploitable strategies \citep{tesauro1994td}.
Population based-training goes one step further by training agents to play against a population of other agents rather than only a copy of itself. For instance, some methods train an ensemble of policies with a variety of collaborators and competitors \citep{max2018human, lowe2017multi}.
By using a whole population rather than only a copy of itself, the agent is forced to deal with a wide variety of potential strategies instead of a single strategy.
However, it requires a great deal of engineering because the policy parameters suitable for the previous environment are not necessarily the next stage's best initialization.

Some works combine the minimax framework and \ac{MARL} to find policies that are robust to opponents with different strategies. Minimax is a concept in game theory that can be applied to find an approach that minimizes the possible loss in a worst-case scenario \citep{osborne2004introduction}. \citet{li2019robust} use it during training to optimize the reward for each agent under the assumption that all other agents act adversarial.
We are interested in methods that can deal with perturbations in the training partners' behavior, which differs from dealing with partners with various strategies.

Recent works look at settings in which one RL agent, that is trained separately, must join a group of new agents \citep{lerer2018learning, tucker2020adversarially, carroll2019utility}.  For example, \citet{carroll2019utility} build a model of the other agents which can be used to learn an approximate best response using RL.
\citet{lupu2021trajectory} propose to generate a large number of diverse strategies and then train agents that can adapt to other agents' strategies quickly using meta-learning.
A related problem is zero-shot coordination \citep{hu2020other} in which agents need to cooperate with unseen partners at test time.
The focus of our paper is not to perform well with novel partners at test-time or build complex opponent models. Our aim is to train agents together to remain attentive and reactive towards their partners' policies.

\subsection{Intrinsic Social Motivation}
Due to centralized training in MARL, agents might adopt non-reactive strategies that may struggle with other agents' changing behaviors. Social intrinsic motivation can give an additional incentive to find reactive policies towards other agents.

\ac{IM} in \ac{RL} refers to reward functions that allow agents to learn interesting behavior, sometimes in the absence of an environmental reward \citep{chentanez2005intrinsically}.
Computational models of \ac{IM} are generally separated into two categories
\citep{baldassarre2013intrinsically}, those that focus on curiosity \citep{burda2018large, pathak2017curiosity} and exploration \citep{gregor2016variational, eysenbach2018diversity}, and those that focus on competence and control \citep{oudeyer2009intrinsic, karl2017unsupervised}. The information-theoretic Empowerment formalism \citep{klyubin2005all} is in the latter category, trying to capture how much an agent is in control of the world it can perceive. Empowerment has produced robust behavior linked to controllability, operationality and self-preservation - in both robots \citep{van2020social, karl2017unsupervised, leu2013corbys} and simulations \citep{guckelsberger2016intrinsically}, with \citep{de2018unified} and without \citep{guckelsberger2018new} reinforcement learning and neural network approximations \citep{karl2017unsupervised}.

Empowerment has also been applied to multi-agent simulations, under the term of coupled empowerment maximization \citep{guckelsberger2016intrinsically}, in which it was used to produce supportive and antagonistic behavior. Of particular interest is the idea of transfer empowerment - a measure that quantifies behaviors such as collaboration, coordination, and lead-taking \citep{salge2017empowerment}.

Similar techniques quantify the interaction between agents for improving coordination between agents. \citet{barton2018measuring} analyze the degree of dependence between two agents' policies to measure coordination, specifically by using Convergence Cross Mapping (CCM).
\citet{strouse2018learning} show how agents can share (or hide) intentions by maximizing the mutual information between actions and a categorical goal.
One notably relevant work is by \citet{jaques2019social} called social influence, which is the influence of one agent on the policies of other agents, measured by the mutual information between action pairs of distinct agents.
Similarly, \citet{mahajan2019maven}, compute the mutual information between agents' trajectories and a latent variable that captures the joint behavior. \citet{wang2019influence} compute the mutual information between the transition dynamics of agents.

\iffalse
Influence-based abstraction [1] analyses what an agent needs to remember about the past, to correctly predict the "influence sources" that will affect its local decision making and has recently been applied to deep RL [2].
[1]  Influence-based abstraction for multiagent systems
[2] nfluence-Based Abstraction in Deep Reinforcement Learning
\fi

In contrast to social influence (SI), transfer empowerment considers the \textit{potential} mutual information or channel capacity. When optimizing for \textit{actual} mutual information, its value is bounded from above by the lowest entropy of both agent's action variables. SI might easily interfere with an exploitation strategy and may need regularization once a good strategy is found. On the other hand, empowerment does not have this limitation and the action sets could have very narrow distributions, while still being reactive.

redpajama_set_name: RedPajamaArXiv


text:
Tag archive for 'Madison Square Garden'
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Top Benefits of Pre-planning Your Funeral
October 12, 2021, No Comments on Top Benefits of Pre-planning Your Funeral


text:
package org.apache.wicket.security;

import java.util.HashMap;
import java.util.Map;

import org.apache.wicket.Component;
import org.apache.wicket.RestartResponseAtInterceptPageException;
import org.apache.wicket.Session;
import org.apache.wicket.authorization.UnauthorizedActionException;
import org.apache.wicket.authorization.UnauthorizedInstantiationException;
import org.apache.wicket.markup.html.form.TextField;
import org.apache.wicket.security.actions.WaspAction;
import org.apache.wicket.security.checks.ComponentSecurityCheck;
import org.apache.wicket.security.components.ISecureComponent;
import org.apache.wicket.security.components.ISecureContainer;
import org.apache.wicket.security.components.ISecurePage;
import org.apache.wicket.security.components.SecureComponentHelper;
import org.apache.wicket.security.components.markup.html.form.SecureTextField;
import org.apache.wicket.security.models.ISecureModel;
import org.apache.wicket.security.pages.container.MySecurePanel;
import org.apache.wicket.security.pages.secure.HomePage;
import org.apache.wicket.security.pages.secure.PageA;
import org.apache.wicket.security.pages.secure.PageB;
import org.apache.wicket.security.pages.secure.PageC;
import org.apache.wicket.security.pages.secure.PageC2;
import org.apache.wicket.security.pages.secure.PageD;
import org.apache.wicket.security.strategies.ClassAuthorizationStrategy;
import org.apache.wicket.security.strategies.SecurityException;
import org.apache.wicket.util.tester.FormTester;
import org.apache.wicket.util.tester.TagTester;

/**
 * Some general tests.
 *
 * @author marrink
 */
public class GeneralTest extends WaspAbstractTestBase
{

	/**
	 * Test reading and writing from and to a secure textfield.
	 */
	public void testReadWrite()
	{
		doLogin();
		Map<String, WaspAction> authorized = new HashMap<String, WaspAction>();
		authorized.put(SecureComponentHelper.alias(PageA.class), application.getActionFactory()
			.getAction("access render enable"));
		login(authorized);
		mock.setupRequestAndResponse();
		mock.processRequestCycle();
		mock.clickLink("link", false);
		mock.assertRenderedPage(PageA.class);
		mock.assertInvisible("secure");
		authorized.put(SecureComponentHelper.alias(mock.getLastRenderedPage().get("secure")),
			application.getActionFactory().getAction("access render"));
		login(authorized);
		mock.startPage(PageA.class);
		mock.assertVisible("secure");
		TagTester tag = mock.getTagByWicketId("secure");
		assertTrue(tag.hasAttribute("disabled"));
		try
		{
			mock.getComponentFromLastRenderedPage("secure").setDefaultModelObject(
				"writing in textfield");
			fail("should not be able to write in textfield");
		}
		catch (UnauthorizedActionException e)
		{
		}
		authorized.put(
			SecureComponentHelper.alias(mock.getComponentFromLastRenderedPage("secure")),
			application.getActionFactory().getAction("access render enable"));
		login(authorized);
		mock.startPage(PageA.class);
		mock.assertVisible("secure");
		tag = mock.getTagByWicketId("secure");
		assertFalse(tag.hasAttribute("disabled"));
		String writings = "now we are getting somewhere";
		mock.getComponentFromLastRenderedPage("secure").setDefaultModelObject(writings);
		assertEquals(writings, mock.getComponentFromLastRenderedPage("secure")
			.getDefaultModelObject());
		mock.startPage(mock.getLastRenderedPage());
		mock.assertRenderedPage(PageA.class);
		tag = mock.getTagByWicketId("secure");
		assertTrue(tag.getAttributeIs("value", writings));
	}

	/**
	 * Test behaviour of different secure classes in the
	 * {@link ClassAuthorizationStrategy}.
	 */
	public void testInstantiation()
	{
		doLogin();
		assertEquals(ISecureComponent.class, getSecureClass());
		mock.setupRequestAndResponse();
		new PageB();
		// even though we added the same securitycheck to a regular textfield as
		// a
		// secureTextfield has there is no instantiation check because it is not
		// an
		// IsecureComponent
		Map<String, WaspAction> authorized = new HashMap<String, WaspAction>();
		authorized.put(SecureComponentHelper.alias(PageA.class), application.getActionFactory()
			.getAction("access"));
		login(authorized);
		new PageA(); // still allowed here
		authorized.clear();
		authorized.put(SecureComponentHelper.alias(SecureTextField.class), application
			.getActionFactory().getAction("access"));
		logoff(authorized);
		try
		{
			new PageA();
			fail("somehow page was instantiated");
		}
		catch (UnauthorizedInstantiationException e)
		{
			// because we are not allowed to instantiate a SecureTextField
			// anymore
		}
		mock.processRequestCycle();
		try
		{
			tearDown();
			setSecureClass(ISecurePage.class);
			setUp();
		}
		catch (Exception e)
		{
			log.error(e.getMessage(), e);
			fail(e.getMessage());
		}
		doLogin();
		// now we only check pages so all components will be created, this does
		// not affect
		// the render check though
		mock.setupRequestAndResponse();
		logoff(authorized);
		authorized.clear();
		authorized.put(SecureComponentHelper.alias(PageA.class), application.getActionFactory()
			.getAction("access"));
		// need to enable page a again
		login(authorized);
		new PageA(); // only pages are checked so now securetextfield is
		// allowed.
		mock.processRequestCycle();
	}

	/**
	 * More instantiation testing.
	 */
	public void testAdvancedInstantiationChecks()
	{
		doLogin();
		mock.startPage(PageC.class);
		// even if we are logged in and have access rights it will redirect us
		// to the access denied page because it need render rights
		mock.assertRenderedPage(application.getApplicationSettings().getAccessDeniedPage());
		Map<String, WaspAction> authorized = new HashMap<String, WaspAction>();
		authorized.put(SecureComponentHelper.alias(PageC.class), application.getActionFactory()
			.getAction("access render"));
		login(authorized);
		mock.startPage(PageC.class);
		mock.assertRenderedPage(PageC.class);
		// PageC2 will render because it has all the required rights.
		mock.startPage(PageC2.class);
		mock.assertRenderedPage(PageC2.class);
		mock.startPage(HomePage.class);
		logoff(authorized);
		// but if we remove the render rights for PageC, PageC2 misses those
		// rights and won't be able to be instantiated anymore
		mock.startPage(PageC2.class);
		mock.assertRenderedPage(application.getApplicationSettings().getAccessDeniedPage());
	}

	/**
	 * Test methods of {@link SecureComponentHelper}.
	 */
	public void testSecureComponentHelper()
	{
		TextField<String> field = new TextField<String>("id");
		assertNull(SecureComponentHelper.getSecurityCheck(field));
		assertTrue(SecureComponentHelper.isActionAuthorized(field, "whatever"));
		ComponentSecurityCheck check = new ComponentSecurityCheck(field);
		// this did not register the check with the component
		assertNull(SecureComponentHelper.getSecurityCheck(field));
		SecureComponentHelper.setSecurityCheck(field, check);
		assertEquals(check, SecureComponentHelper.getSecurityCheck(field));
		try
		{
			// can not check this because component is not attached to a page
			// see SecureComponentHelper.alias(Component)
			SecureComponentHelper.isActionAuthorized(field, "whatever");
			fail();
		}
		catch (RestartResponseAtInterceptPageException e)
		{
		}
		catch (SecurityException e)
		{
		}
		assertFalse(SecureComponentHelper.hasSecureModel(field));
		field.setModel(new ISecureModel<String>()
		{
			private static final long serialVersionUID = 1L;

			public void detach()
			{
				// noop
			}

			public void setObject(String object)
			{
				// noop
			}

			public String getObject()
			{
				return "test";
			}

			public boolean isAuthorized(Component component, WaspAction action)
			{
				return false;
			}

			public boolean isAuthenticated(Component component)
			{
				return false;
			}

		});
		assertTrue(SecureComponentHelper.hasSecureModel(field));
	}

	/**
	 * Test mixing security check with secure model.
	 */
	public void testComponentSecurityCheckAndISecureModel()
	{
		doLogin();
		mock.startPage(PageD.class);
		mock.assertRenderedPage(PageD.class);
		mock.assertInvisible("componentcheck"); // no render rights
		mock.assertVisible("modelcheck");
		mock.assertInvisible("both"); // component says no
		mock.assertInvisible("bothcheck");// component says no, model says yes
		try
		{
			mock.getComponentFromLastRenderedPage("modelcheck").setDefaultModelObject("foobar");
			fail("should not be able to write to model");
		}
		catch (UnauthorizedActionException e)
		{
		}
		Map<String, WaspAction> authorized = new HashMap<String, WaspAction>();
		authorized.put(SecureComponentHelper.alias(SecureTextField.class), application
			.getActionFactory().getAction("access render"));
		authorized.put("model:modelcheck", application.getActionFactory().getAction(
			"access render enable"));
		login(authorized);
		mock.startPage(PageD.class);
		mock.assertRenderedPage(PageD.class);
		mock.assertVisible("componentcheck");
		mock.assertVisible("modelcheck");
		mock.assertVisible("both");
		mock.assertVisible("bothcheck");
		mock.getComponentFromLastRenderedPage("modelcheck").setDefaultModelObject("foobar");
		assertEquals("foobar", mock.getComponentFromLastRenderedPage("modelcheck")
			.getDefaultModelObject());
		((ISecureComponent) mock.getComponentFromLastRenderedPage("both")).setSecurityCheck(null);
		authorized.clear();
		authorized.put("model:modelcheck", application.getActionFactory()
			.getAction("access render"));
		authorized.put("model:bothcheck", application.getActionFactory().getAction("access"));
		login(authorized);
		mock.startPage(mock.getLastRenderedPage());
		mock.assertRenderedPage(PageD.class);
		TagTester tag = mock.getTagByWicketId("modelcheck");
		assertTrue(tag.getAttributeIs("value", "foobar"));
		mock.assertVisible("componentcheck");
		mock.assertVisible("modelcheck");
		mock.assertInvisible("both"); // model says no
		mock.assertInvisible("bothcheck"); // model says no, component says yes
		try
		{
			mock.getComponentFromLastRenderedPage("modelcheck").setDefaultModelObject("blaat");
			fail("should not be able to write to model");
		}
		catch (UnauthorizedActionException e)
		{
		}
	}

	/**
	 * Test workings if we are not using a page strategy but a panel replace strategy.
	 */
	public void testPanelReplacement()
	{
		try
		{
			tearDown();
			setHomePage(org.apache.wicket.security.pages.container.HomePage.class);
			setSecureClass(ISecureContainer.class);
			setUp();
		}
		catch (Exception e)
		{
			log.error(e.getMessage(), e);
			fail(e.getMessage());
		}
		mock.startPage(getHomePage());
		mock.assertRenderedPage(getHomePage());
		mock.clickLink("link");
		mock.assertRenderedPage(getLoginPage());
		FormTester form = mock.newFormTester("signInPanel:signInForm");
		form.setValue("username", "test");
		form.setValue("password", "test");
		form.submit();
		mock.assertRenderedPage(getHomePage());
		assertFalse(Session.get().isTemporary());
		mock.assertInvisible("panel");
		// note by adding a second panel visible if the main panel is invisible
		// we could tell the user he is not authorized or something like that
		Map<String, WaspAction> authorized = new HashMap<String, WaspAction>();
		authorized.put(SecureComponentHelper.alias(MySecurePanel.class), application
			.getActionFactory().getAction("access render"));
		login(authorized);
		mock.startPage(mock.getLastRenderedPage());
		mock.assertVisible("panel");
		mock.clickLink("link");
		mock.assertVisible("panel");
	}

	/**
	 * Test what happens if someone goofed up and made an illogical choice for a login
	 * page.
	 */
	public void testBogusLoginPage()
	{
		try
		{
			tearDown();
			setHomePage(HomePage.class);
			setLoginPage(PageD.class);
			setSecureClass(ISecurePage.class);
			setUp();
		}
		catch (Exception e)
		{
			log.error(e.getMessage(), e);
			fail(e.getMessage());
		}
		mock.startPage(getHomePage());
		log.info("" + mock.getLastRenderedPage().getClass());
		mock.assertRenderedPage(getLoginPage());
		// even though we accidentally made it a secure page!
	}
}

redpajama_set_name: RedPajamaGithub


Assistant Professor of Marketing Daniel McCarthy in partnership with Peter Fader, a marketing professor at Wharton, presented a new method of valuing publicly-traded retailers that focused on customer retention.
Professor of Organization & Management and Academic Director of Social Enterprise @ Goizueta Peter Roberts talks about how startup accelerators have become more popular in emerging markets in the Harvard Business Review.
Associate Professor of Organization and Management Melissa Williams notes nonverbal behaviors can paint a different picture of leadership, particularly for women in management positions.
In previous eras, the stock market may have reacted differently to a seemingly endless drumbeat of geopolitical headlines sure to rattle it. These days, the reaction is little more than a yawn, or momentary dip only to rebound within days, said Jeffrey Busse, an associate professor of finance at the Goizueta Business School.


275 lb. Holds 16 long guns. Contains a basic door organizer. UL listed. Tough, powder coated black finish. CA DOJ approved. Spring loaded re locker. 6 large 1" diameter bolts, thick composite door. 30 minute fire protection.


Quite an excellent start! I'll be looking for more!!
Josephine, nice work. Certainly an inviting image. Makes me want to go fishing.
Hey Josephine!!!! I've missed your posts and your paintings. Welcome back. This one is sure a lovely entry right out of the box. Beautiful work. Great to have you back.
Hello! It's a nice start! Neat painting! Post many more on the way.
Wonderful Jo, and welcome back!


text:
Sunday, 22nd January, 2023
Master plan for Expo 2020 Dubai unveiled
(Dubai) – Organisers have recently unveiled the master plan for Expo 2020 Dubai site, one of the most anticipated mega developments that will generate thousands of jobs and benefit the economy.
Expected to bring together more than 180 countries and 25 million visitors, the site in Jebel Ali will host the exhibition of products, trade and innovation from around the world during a six-month period from October 2020 to April 2021. It will be linked to three international airports and feature a dedicated transport network.
The master plan was showcased at the 15th edition of the Arab Media Forum, allowing visitors to be among the first to take a glimpse of how the Expo 2020 Dubai site will look and feel once completed.
The gated Expo site itself will spread across two square kilometres (200 hectares), revolving around the Al Wasl meeting plaza – the project's centrepiece, which will also feature a falcon-inspired UAE Pavilion.
Three themed districts called Opportunity, Mobility and Sustainability will be built, spreading outwards from the meeting plaza. Each zone will have a pavilion where the latest ideas, innovations and developments will be showcased.
The winning design for the UAE Pavilion was also revealed recently by the National Media Council.
"The falcon-inspired concept of the UAE Pavilion captures the values and principles that the nation was founded on and which reflect the theme of Expo 2020 Dubai, namely inclusion, connection and tolerance," organisers said.
Only about four years away, the global event is forecast to generate more than 277,000 jobs. Most of the hiring, or about 90 per cent of the positions, is expected to take place between 2018 and 2021.
May 12, 2016 /by Asiya Mahar
Tags: Diplomat Magazine, International Diplomat Magazine, master plan for Expo 2020 Dubai site
https://thediplomatmagazine.com/wp-content/uploads/2016/05/Master-plan-for-Expo-2020-Dubai-unveiled.jpg 320 550 Asiya Mahar http://thediplomatmagazine.com/wp-content/uploads/2020/02/diplomat-logo-v5.jpg Asiya Mahar2016-05-12 18:43:142016-05-12 18:43:14Master plan for Expo 2020 Dubai unveiled
2nd Round of Talks Held Between Iran & World Powers
Abu Dhabi Sustainability Week ends
King Hamad of Bahrain gifts a university to Pakistan
UAE pavilion visitors experience walk in desert city
Fuel Prices to be deregulated from 1 August
UAE-Russia Joint Ministerial Committee Meeting Held
President of Nepal receives Sheikh Abdullah Bahrain-US military cooperation discussed


text:
How to Draw Faces: Learn to Draw People from Complete Scratch
Vendor: Jasmina Susak
Learn how to draw proportional, realistic portraits in this easy to follow, step-by-step tutorial Learn from an experienced artist and art teacher, Jasmina Susak, who will guide you in drawing process from complete scratch to the finished portrait.Not only will you learn how to draw facial features and skin, but hair too in this highly detailed tutorial.This tutorial was made for intermediate artists, but beginners should also give it a try since Jasmina will share a lot of tips and tricks that will help you understand the most important things behind a realistic style of drawing.Take this book, enhance your drawing skills, and get your portraits to the next level
Author: Susak, Jasmina
Publisher: Independently Published
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Written by a famous American painter and teacher, whose landscapes are found in many of the world's most noted museums, this book is known as one of the art student's...
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The unfading popularity of Gustav Klimt (1862-1918) attests not only to the particular appeal of his luxuriant painting but also to the universal themes with which he worked: love, feminine...
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What is it about a woman reading that has captivated hundreds of artists over the centuries? Stefan Bollmann's Women Who Read Are Dangerous explores this popular subject in more than...
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Science of Creature Design: Understanding Animal Anatomy
What is creature design? We all have a notion--mostly consisting of evocative images of otherworldly beings galloping, swimming, flying, and often attacking the hero of an epic film or story....
Mega Dns
Graffiti for Beginners: An Easy Introduction to Drawing Graffiti Letters
Learn how to draw graffiti letters! Graffiti for Beginners is an easy-to-follow introduction that presents you with the basics behind graffiti lettering. The two funky yet classic graffiti alphabets created...
Thomas Kinkade Special Collector's Edition 2022 Deluxe Wall Calendar with Print: Bridges of Hope
Thomas Kinkade Special Collector's Edition: Bridges 2022 Wall Calendar includes a 12-month calendar featuring thirteen images of beautiful, full-color paintings by Thomas Kinkade Studios, and a ready-to-frame gift print with...
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A completely new way of looking at and understanding Surrealism, with a focus on the worldwide sweep of the movement This groundbreaking book challenges conventional narratives of Surrealism, tracing its...


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Particularly children who would like to color exactly the precise illustration, there's absolutely no service. Coloring has actually ended up being extremely popular over the previous couple of years, and coloring books now are extremely available.
Kids truly like to poke around in various drawers and cabinets due to the fact that of their interest. An excellent deal of kids change exactly what they would actually like to color typically. Of course it likewise enables kids and moms and dads to really dedicate some quality time together.
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