text:
Eleciton RESULTS
All Political Parties A-Z
AASD – Afrikan Alliance of Social Democrats
ACDP – African Christian Democratic Party
ACO – African Covenant
ADEC- African Democratic Change
AIC – African Independent Congress
ANC – African National Congress
APC – African People's Convention
ARU – African Renaissance Unity Party
ASC – African Security Congress
ATM – African Transformation Movement
AGANG SA – Agang South Africa
ALJAMA – Al Jama-ah
ATA – Alliance for Transformation for All
AZAPO – Azanian People's Organisation
BRA – Better Residents Association
BLF – Black First Land First
ZACP – Capitalist Party of South Africa
CPM – Christian Political Movement
CSA – Compatriots of South Africa
COPE – Congress of the People
DA – Democratic Alliance
DLC – Democratic Liberal Congress
ECOFORUM – Economic Emancipation Forum
EFF – Economic Freedom Fighters
F4SD – Forum for Service Delivery
FD – Free Democrats
FF+ – Freedom Front Plus
FN – Front National
GOOD – Good
ICOSA – Independent Civic Organisation of South Africa
IFP – Inkatha Freedom Party
IRC – International Revelation Congress
LAND – Land Party
MF – Minority Front
NFP – National Freedom Party
NPA – National People's Ambassadors
NAPF – National People's Front
PAC – Pan Africanist Congress of Azania
PA – Patriotic Alliance
PRM – People's Revolutionary Movement
PAU – Power of Africans Unity
SRWP – Socialist Revolutionary Workers Party
CONTRALESA – SA National Congress of Traditional Authorities
SAMEBA – SA Maintenance and Estate Beneficiaries Association ()
UDM – United Democratic Movement
WF – Women Forward
VFP – Freedom Front Plus (FF+)
Bantu Holomisa (UDM)
Cyril Ramaphosa (ANC)
Julius Malema (EFF)
Kenneth Meshoe (ACDP)
Mangosuthu Buthelezi (IFP)
Mmusi Maimane (DA)
Mosiuoa Lekota (COPE)
Pieter Groenewald (FF+)
South Africa Election Results: 1994
Home Political Parties
African National Congress (ANC)
The African National Congress (ANC) is South Africa's ruling national party and is credited with spearheading the liberation movement which lead to the abolishment of apartheid.
The mighty ANC has governed the nation since the dawn of democracy in 1994. Nationally and provincially, the ANC still maintains its dominance, bordering a two-thirds majority in the National Assembly.
African National Congress Logo
Wikipedia: African National Congress
ANC Website: anc1912.org.za
While the party has seen a slight decline in support in recent years, owing to factional infighting and the rise of opposition parties, particularly in the form of the Economic Freedom Fighter (EFF), the ANC remains the most well supported political organisation in South Africa.
Due to its pivotal role in the liberation movement, exemplified by the appointment of South Africa's first democratically elected president, Nelson Mandela, the ANC has been afforded the majority vote through historical reverence. However, in the last decade the political party's fabric of unity has begun to unwind, demonstrated by fierce factional battles and allegations of wanton corruption and mismanagement.
While the nation still bares the wounds of an oppressive apartheid regime, the electorate has, for so many years, placed its faith in a party which now stands at a crossroads. South Africa's 2019 General Election has been branded as the most important vote of confidence since citizens first took to the democratic polls in 1994.
History of the African National Congress
The South African Native National Congress delegation to England, June 1914. Left to right: Thomas Mapike, Rev Walter Rubusana, Rev John Dube, Saul Msane, Sol Plaatjie. The delegation tried to get the British Government to intervene against the Land Act but the outbreak of the First World War thwarted their hope / Image: Wikimedia Commons
The political organisation now known as the African National Congress was initially founded as the South African Native National Congress (SANNC) on 8 January 1912 in Bloemfontein. The SANNC was formed to fight for the rights of black South Africans. In 1923, the organisation was renamed the African National Congress.
The birthing process for the organisation which would go on to define the struggle for a non-racial society was slow and lacklustre. Due to internal struggles and a dire lack of resources resulting from a steadily intensifying white-minority regime, the ANC was left in limbo during its early years. However, as the government resolved to implement laws aimed at racial segregation and oppression, the ANC found its spark amongst the impending calamity.
The government of the newly formed Union of South Africa had already begun to introduce legislature which would further the systematic oppression of black people in South Africa. As a response to the growing discontent amongst the majority of black South Africans, the founder of the ANC, Pixley ka Isaka Seme, proposed the organisation's original mission statement, saying:
"Forget all the past differences among Africans and unite in one national organisation."
Following an upswell of support, the ANC supported a militant mineworkers' strike in 1929. At the time, the ANC was hoping to forge working relationships with the Communist party. However, J.T. Gumede, then-president of the ANC and the man responsible for deliberations, was voted out of power in the early 1930's.
Again, the progress of the African National Congress stalled. For almost ten years, the organisation lay dormant. It was only in the mid-1940's that the organisation revitalised itself. The ANC, from that point on, would be known as a mass movement responsible for its organisation and participation in strikes, boycotts, and general defiance.
The ANC then began working closely with the South African Communist Party (SACP), a relationship which would prove to stand the test of time despite earlier disputes. In 1943 women were, for the first time, admitted as full members of the ANC. This led to the formation of the ANC Women League. The following year, the ANC Youth League was born.
Together with its subsidiary organisations, the ANC adopted the 'Programme of Action'. This method of protest was an evolution from previous passive boycotts and strikes – in answer to an increasingly oppressive regime, the ANC sought to redouble its efforts.
In 1948, the National Party came to power and began implementing the system of apartheid. The ANC were perfectly positioned to lead the liberation movement.
The Defiance Campaign of the 1950's, spearheaded by the ANC, was in response to the racially oppressive laws implemented by the National Party. The Population Registration Act, the Groups Areas Act, the Reservation of Separate Amenities Act and the Immorality Amendment Act birthed a new wave of intensified resistance campaigns.
In 1955 the ANC sent out 50,000 volunteers into townships and the countryside to collect "freedom demands" from the people of South Africa. These demands were then tabled at a gathering held in Kliptown on 26 June 1955. Intended to lay out the vision of the South African people, the meeting was spearheaded by the African National Congress (ANC) and its allies – the South African Indian Congress, the South African Congress of Democrats and the Coloured People's Congress.
So the Freedom Charter was born – core principles of the anti-apartheid alliance, which demanded that:
The People Shall Govern
All National Groups Shall Have Equal Rights
The People Shall Share In The Country's Wealth
The Land Shall Be Shared Among Those Who Work It
All Shall Be Equal Before The Law
All Shall Enjoy Equal Human Rights
There Shall Be Work And Security
The Doors Of Learning And Of Culture Shall Be Opened
There Shall Be Houses, Security And Comfort
There Shall Be Peace And Friendship
The Freedom Charter closed with this resolution:
"THESE FREEDOMS WE WILL FIGHT FOR, SIDE BY SIDE, THROUGHOUT OUR LIVES, UNTIL WE HAVE WON OUR LIBERTY."
The founding of the Freedom Charter would set the wheels of socio-political turmoil in motion – eventually leading to the liberation of South Africa almost 40 years later.
The apartheid government labelled the Freedom Charter a Communist document, and as such, outlawed its circulation. Subsequently, leaders of the ANC and other ant-apartheid organisations were arrested.
Following the release of those arrested, the ANC prepared to initiate a campaign of protests against pass laws – however, rival party, the Pan-Africanist Congress (PAC), led by Robert Sobukwe, beat them to the punch, organising its own demonstration on 21 March 1960.
21 March 1960 will forever be remembered as one the darkest days in South Africa's history. A group of between 5,000 and 10,000 people converged on the local police station in the township of Sharpeville, offering themselves up for arrest for not carrying their passbooks. Later the crowd grew to about 20 000.
The situation reached a fever pitch and South African Police opened fire on the crowd, killing 69 people and injuring 180 others.
The Sharpeville Massacre was a defining moment in the liberation struggle which sparked a wave of anti-apartheid resistance strategies. The ANC itself embarked on a new era of armed struggle.
uMkhonto we Sizwe (MK), "The Spear of the Nation", was founded in 1961. As the military wing of the ANC, its intention was to disrupt the apartheid government with armed struggle, replacing peaceful protest with acts of sabotage and violence.
MK operatives were trained and deployed to disable valuable state institutions, usually by way of bombings. A series of MK bob attacks rocked the nation in the 1970's and 80's, resulting in the deaths of civilians and military personnel. As such, the ANC and its members were placed on the US terrorism watch list.
During the 1970's, most of the ANC's top leadership structure were behind bars, facing charges relating to terrorism and treason. ANC members not yet detained fled the country or went into hiding as the apartheid government redoubled its efforts to destroy the organisation it had already banned.
The ANC's militaristic approach garnered criticism from the international community. It's reported that the Prime Minister of the United Kingdom at the time, Margaret Thatcher, regarded the ANC, as a "typical terrorist organisation".
The 1980's saw the vicious dying breaths of apartheid, as the National Party launched a series of bloody attacks on ANC safehouses in response to the MK bombings. International attention was focused on South African affairs – leading to sanctions and global boycotts.
On 13 December 1989, Nelson Mandela and F.W. de Klerk met for the first time to discuss the country's political future.
Current Position and 2019 outlook
President Cyril Ramaphosa attends Basic Education Lekgotla, 21 Jan 2019 / Image via GCIS
Following the country's transition into democracy, demonstrated by the ANC's overwhelming victory at the 1994 elections, and every election since then, the party has attempted to adjust into its role of national governance while still defining its agenda as the National Democratic Revolution (NDR).
As part of its manifesto, the ANC is committed to redressing socioeconomic injustices spawned under the oppressive apartheid era. The organisation's commitment to redress is guided by its socialist-democratic administrative policy.
While the ANC has spent most of its time in governance reaping the rewards of political dominance, it's one might moral high ground has taken a battering in recent years. As such, the ANC has seen a marginal loss of support nationally. Matters are worse on the provincial front, exemplified by the election of 22 April 2009, whereby the ANC was unseated by the DA in the Western Cape.
The same loss of support has been witnessed in Gauteng, where the ANC currently holds 53.59% of the vote – far less than their national total of 62.15%. This shows that support for the party, especially in metropolitan areas, is waning.
This dip in support can be traced back to the 2009 elections. Studying the events which led up to the elections provide clues as to the ANC's political problems. In 2008, President Thabo Mbeki resigned as the leader of the party and of the country, amid a fierce internal factional battle. The contentious unseating of Mbeki signified a downward trend in support for the ANC.
Former President Jacob Zuma, who was alleged to have masterminded the ousting of Mbeki, ascended to the throne under a cloud of suspicion. From 2009 until 2017, Zuma's tenure as president of the ANC and South Africa was marred by allegations of nepotism, corruption and state capture.
Zuma himself was eventually 'forced' to step down as the president, amid fears that his time in office may further embattle the ANC's image, which, by that time, had already taken a serious knock. Zuma was replaced by president Cyril Ramaphosa, who is currently attempting to mend the disunified ANC.
It's common knowledge that the ANC is being torn asunder by two prominent caps: the pro-Zuma alliance and the pro-Ramaphosa contingent. This infighting has split the ANC down the middle, with reports of a plot to oust Ramaphosa the latest in a string of internal controversies.
Recent voter surveys conducted by IPSOS, IRR and Afrobarometer show that confidence in the ANC has dropped dramatically – in some cases by more than 10%. If these surveys are anything to go by, South Africa's 2019 General Election will most likely see the largest drop in support for the ANC since 1994, on both a national and provincial level.
President Jacob Zuma addressing the foreign nationals outside Home Affairs offices during the walkabout at Marabastad taxi rank ahead of the Stata of the Nation Address.08/02/2016 Kopano Tlape GCIS
This mooted drop in ANC support relates to the many controversies the ruling party has had to navigate over the past decade.
While it's only natural for a ruling party to come under scrutiny from the media and the electorate, recent misgivings relating to the state of the nation, at the hands of the ANC government, have led to a ballooning discontentment.
While it may be unfair to place all the blame on one representative, Jacob Zuma's tenure as president of South Africa has been cited as a major reason for the loss in party support. Zuma's tenure, which was a result of Mbeki's ousting, has resulted in grievous consequences for the financial and socio economic stability of South Africa.
It's estimated that Zuma's time as president of the country cost the South African economy approximately R1 trillion. As a result of mismanagement and corruption, bred over the past decade, many vital state owned enterprises face insolvency and rely solely on government bailouts. This has, in turn, damaged investor confidence and led to financial downgrades.
Ramaphosa now has the daunting task of undoing all this damage while, at the same time, fights to keep his position within a seriously fractured ANC.
President Ramaphosa's own tenure has gotten off to a shaky start thanks to a technical economic recession, unemployment rate rises and debilitating fuel hikes – not all of which can be blamed on the leader, but the responsibility to mitigate the effects rest squarely on the ANC's shoulders. The president's commitment to amend the Constitution in favour of land expropriation without compensation has opened a can of worms, which has impacted investor confidence and shaken concerns around private property rights to its very core. In an attempt to quell dissent, the president has promised that no land grabs will be tolerated and that the process of reform will be carefully mandated by legislature.
Notable ANC members:
John Langalibalele Dube (1871 – 11 February): Founding president of the South African Native National Congress (SANNC), which became the African National Congress in 1923.
Pixley ka Isaka Seme (c. 1881– June 1951): One of the first black lawyers in South Africa and a founder and President of the African National Congress..
Photo by ALEXANDER JOE / AFP
Oliver Tambo (1917 –1993): President of the ANC from 1967 to 1991, responsible for organizing active guerrilla units deployed to disrupt the apartheid state.
Photo by WALTER DHLADHLA / AFP
Nelson Mandela (1918 –2013): The most iconic liberation leader in recent history, President of the ANC from 1991 to 1997. President of South Africa from 10 May 1994 – 16 June 1999.
Joe Slovo (1926 –1995): Long-time leader of the South African Communist Party (SACP), a leading member of the ANC and a commander of the Umkhonto we Sizwe (MK).
Fromer Secretary General of South African Communist Party (SACP) Chris Hani speaks at a press conference on the third day of the first SACP legal congress inside South Africa.
Chris Hani (1942 – 1993): Leader of the South African Communist Party and chief of staff of uMkhonto we Sizwe. Was assassinated on 10 April 1993.
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THE SOUTH AFRICAN
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text:
"bark eater"
Sloganza™
The Constant Lover
John Hicks November 11, 2011
In terms of sheer volume, I peaked as a reader in my 20s.
In those days, I didn't do much except read. Life was pretty simple. I extended my student years without much effort, drifting along. But always with a book!
As long as I stayed in school, no one seemed to bother me too much. I knew I'd discovered one of the great truths, hidden for so long in plain sight: As long as you appear to be doing something, no matter how pointless or quixotic it is, people will generally leave you alone.
That is what I wanted. I wanted to enjoy my books and my friends and write my not-very-good stories and poems.
I didn't think too much about the future. By the time I was 25, I'd worked so many different kinds of jobs that I could tell how things were going to pan out – cheap rent, low wages, ridiculous adventures. Fine!
I was an arrogant, hard-headed student. I had the convictions of a longtime autodidact. When you're caught up in the romance of literature, you make your own canon.
My favorite writers went against the grain. I felt I'd personally lived each book. In this realm, I was the sole arbiter of what was gunpowder and what was dynamite.
My list of favorites was constantly evolving, of course. Part of the fun was the detective work, connecting the dots between writers and genres and styles. A good writer will always introduce you to other good writers.
This was the vanishing analog world. Print, tape, vinyl. I won't say it was a better time, just a different time. I'm glad I spent my youth there.
And what of those writers I devoured in my 20s?
Some have held up remarkably well. Philip Roth, Flannery O'Connor, Raymond Carver, Hemingway. Even Bukowski and the Beats have been safely tucked away in Norton anthologies.
Joan Acocella, in a recent review of three novels deemed representative of "the new sex" for The New Yorker ("Blue Period," Nov. 7), mentioned another writer I still enjoy reading:
A century [after Jane Austen's Persuasion], many writers felt that sex, to be sexual, had to be more forthrightly depicted, a notorious example being the scene in which Lady Chatterley weaves a garland of flowers into her gamekeeper's pubic hair. Today, however, that passage looks rather old-fashioned, as does a lot of Henry Miller.
Whoa, hold up there, Joan.
Getting freaky botanical-style is out? That's good intel. I suppose it's been replaced by something, uh, digital.
Lady Chatterley's creator, D.H. Lawrence, died in 1930. "Old-fashioned" is a wide net, but I guess it applies to a large number of writers who have been dead almost a century.
But Henry Miller? I happened to be reading Miller again when I came across Acocella's aside.
His first book, Tropic of Cancer (1934) was not published in the U.S. until 1961, and it took three more years for the U.S. Supreme Court, in a landmark obscenity case, to declare it a work of literature.
I'm guessing the U.S. Supreme Court sold more books for Miller than all of the critical appreciations of his work combined.
In The World of Sex (1957), Miller makes no apologies for the "liberal dosage" of four-letter words and ribald interludes that led to his work being banned in the U.S. for decades.
"Only a few discerning souls seem capable of reconciling the supposedly contradictory aspects of a being who has endeavored to withhold no part of himself in his written work," Miller wrote.
The author of Tropic of Cancer was concerned "not with sex, nor with religion, but with the problem of self-liberation."
For Miller, self-liberation meant an impoverished exile in France and an arduous search for enlightenment.
Everything fell into latitude and longitude, so to speak. There were great tracts of fog, which was metaphysics; broad, flaming belts, the religions; burning comets, whose tails spelled hope. And so on … And there was sex. But what was sex? Like the deity, it was omnipresent. It pervaded everything.
Miller's sexy oeuvre – which includes Black Spring, the two Tropics and the three novels known collectively as The Rosy Crucifixion – doesn't strike me as old-fashioned.
Comical and more than a little surreal, yes. Miller explored sexuality with the same kitchen-sink fervor he brought to bear on all facets of existence. The resulting books are an attempt to comprehend nothing less than the entire cosmos.
Is Miller still worth a look in 2011? My answer is an unqualified yes, if for no other reason than this passage from The World of Sex:
A new world is in the making, a new type of man is in the bud. The masses, destined now to suffer more cruelly than ever before, are paralyzed with dread and apprehension. They have withdrawn, like the shell-shocked, into their self-created tombs; they have lost all contact with reality except where their bodily needs are concerned. The body, of course, has long ceased to be the temple of the spirit. It is thus that man dies in the world – and to the Creator. In the course of disintegration, a process which may go on for centuries, life loses all significance.
John Hicks is a regular contributor to B2L2.
John Hicks
against the grain, getting freaky botanical-style, Henry Miller, the vanishing analog world, The World of Sex, Tropic of Cancer
Derek Bridges on November 11, 2011
M-V-P, M-V-P …
Bob Hudson on November 11, 2011
Henry Miller was always merry and bright. In his prime, (from his fifties 'till his death,) he began his mornings with a ten mile bike ride, wrote for eight hours like a work-a-day workman, and spent the evenings with friends and loved ones, eating good meals with wine and conversation. Always wished I could be like him.
Jimmy Gabacho on November 12, 2011
Nice piece to start my day. Here's to ya.
eddy on November 12, 2011
I love Bob Hudson, and love his spirit as a human, but that description is my own personal hell.
Give me the opposite for my 50s til death, and the smile will never leave my face. Well, except for the eating. Gotta have eating.
Leave a Reply to eddy Cancel reply
Havin' a wild weekend.
John Hicks lives outside the city limits, where eagles dare.
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redpajama_set_name: RedPajamaCommonCrawl


text:
I Plone sono un gruppo musicale inglese.

Storia
Nati a Birmingham nel 1994, i Plone erano originariamente un trio composto da Billy Bainbridge, Michael Johnston e Mark Cancellara. Essi debuttarono con il singolo Press a Key (1997), che verrà campionato da John Peel, a cui seguì una loro apparizione nell'antologia We Are Reasonable People (1998) della Warp. Il loro primo album in studio è For Beginner Piano (1999), i cui brani strumentali sembrano risentire l'influsso dei Perrey & Kingsley. Dopo aver tentato senza successo di dare alle stampe un secondo album mai pubblicato, Bainbridge entrò a far parte dei Seeland, mentre Johnston collaborò con i Modified Toy Orchestra, formò gli ZX Spectrum Orchestra e pubblicò singoli da solista con l'alias Mike in Mono. Nel 2020, i Plone pubblicarono Puzzlewood (2020), contenente diverse tracce registrate a più riprese durante il loro periodo di silenzio seguito alla registrazione del secondo album. La veste grafica dell'album fu realizzata da Julian House, co-proprietario della Ghost Box.

Stile musicale
Lo stile dei Plone è un'elettronica giocosa e dall'approccio pop, che è stata definita "qualcosa a metà strada tra i Cluster caramellati di Zuckerzeit e Popcorn di Gershon Kingsley nella versione degli Hot Butter". I Plone vengono inoltre correlati alla scena retrofuturista di Birmingham assieme ai Broadcast e i Pram per la loro tendenza a usare strumenti musicali elettronici d'annata.

Formazione

Formazione attuale
 Billy Bainbridge
 Michael Johnston

Ex componenti
 Mark Cancellara

Discografia

Album in studio
 1999 – For Beginner Piano
 2020 – Puzzlewood

Singoli
 1997 – Press a Key
 1998 – Plock

Note

Collegamenti esterni

redpajama_set_name: RedPajamaWikipedia


text:
\section{Introduction}

In recent years, small satellites have demonstrated their value as Earth observation platforms capable of hosting a variety of metrology payloads. Dividing the workload of a single large satellite between multiple smaller satellites has been shown to reduce cost, provide robustness to single-point-failures, decrease revisit times, and increase information gathering throughput \cite{brown2006value,derrico2012distributed}. These small satellite platforms typically operate in low Earth orbit (LEO). Operating in this orbit regime presents its own unique set of challenges. Since direct contact with a satellite is limited to brief periods over ground stations, longer term task schedules must be generated for the vehicle to continuously operate.
This paper studies the problem of generating task schedules using a semi-Markov decision process formulation.

Satellite task scheduling involves choosing data collection opportunities to maximize an observation objective given a set of target locations to observe, the satellite trajectory, and a planning horizon. Common objectives include maximizing the number of images collected, timeliness of data return, or total monetary reward. There are often constraints on ground or on-board resources. These constraints are particularly relevant to small satellites as the reduction in platform size is associated with reductions in power generation, energy storage, and on-board data storage capacity. Time constrained task-sequencing has been shown to be NP-complete \cite{garey1979computers}; consequently, various formulations and heuristics have been introduced over the years \cite{hall1994maximizing,lemaitre2002selecting,bianchessi2005earth,beaumet2011feasibility}.

Past work has largely focused on rule-based \cite{bensana1999dealing,bianchessi2008planning} or integer programming \cite{augenstein2016optimal,nag2018scheduling,cho2018optimization,shah2019scheduling} formulations of the satellite task planning problem. Rule-based systems are advantageous in that they are computationally efficient, are straight forward to build, and can guarantee specific behaviors. However, rule-based systems and heuristics suffer from the fact that it is not obvious how to formulate the rules to achieve optimal system performance and such systems cannot dynamically trade between multiple disparate operational objectives.
All decision permutations have to be explicitly considered by the designer, which can lead to complex logic to encode operational rules.

Mixed-Integer Linear Programming (MILP) approaches guarantee that a solution is optimal up to the dual bound under the modeling assumptions used to formulate the problem. The guarantee that the resulting plan is best, along with the ability to enforce specific  constraints, makes this approach attractive for many missions. The draw-backs  are that it is computationally intensive and does not efficiently scale with the number of satellites or the number of decision variables. Furthermore, it is an inherently discrete-time model of the system and must rely on bounding-value approximations for power and data resource usage.

 More recent work has started to apply deep reinforcement learning techniques to address challenges related to satellite task scheduling. Reinforcement learning (RL) is a field of machine learning where an agent learns a decision policy through repeated interactions with an environment. The problem is typically modeled as a Markov Decision Process (MDP) \cite{sutton2018reinforcement}, where reasoning is done in terms of the agent's transition and reward functions.

 Ferreira et al. \cite{ferreira2018multiobjective} applied reinforcement learning to manage the optimization of selecting radio control parameters for a satellite communications system in various operational scenarios. In the Earth Observation (EO) domain, Hadj-Salah et al. \cite{hadjsalah2019schedule} applied reinforcement learning to optimize image collection planning in the presence of cloud coverage uncertainty.

 This paper studies a model-based approach with a known reward function. The problem is posed as a semi-Markov Decision Process (SMDP). An SMDP is a variant of an MDP that reduces computational complexity by only evaluating the decision policy at specific times. We introduce two different approximate solution techniques to the problem\textemdash forward search and Monte Carlo Tree Search (MCTS). The viability of this approach is demonstrated by comparison to common single-objective optimization approaches, and results are presented for multi-objective optimization as well.

\section{Markov Decision Process Formulation}

A Markov Decision Process is a general framework \cite{sutton2018reinforcement,kochenderfer2015decision} for modeling and solving decision making problems. The agent (the satellite) chooses an action in the current state, receives a reward, and then transitions to the next state. The process is repeated over the planning horizon. The state space $\mathcal{S}$, action space $\mathcal{A}$, transition function $T$, and reward function $R$, define an MDP
\begin{equation}
	\mathcal{M} = (\mathcal{S},\mathcal{A},\mathit{T},\mathit{R})
\label{eqn:mdp_problem}
\end{equation}

Relevant to the Earth observing satellite tasking problem is a set of \emph{locations} $I$, which have been requested to be collected over a time interval $[0, H]$ with $H$ as the planning horizon. Each image $i \in I$ is defined by an Earth-fixed center point and has an associated reward for collection $r_i$. Over the horizon, each image has a set of windows of \emph{opportunity} $O_i$. Each individual opportunity for image $i$ is denoted $o_i \in O_i$ and has a start time $t_s$ and end time $t_e$ during which the image could be collected. The problem is to decide which images should be collected to maximize the total reward over the planning horizon.

There are a few notable variations of this core problem. The first is the agile satellite scheduling problem. Here, the satellite imaging instrument is fixed in the satellite body-frame and the satellite must use its attitude control system to reorient to point at each location before collecting an image. The second variation adds the complication of ground contact planning; the satellite can only be communicated with while it is in view of a set of ground station locations $G$, and ground contacts must be scheduled to download collected images. The final variation considered here is the imposition of limitations on on-board spacecraft resources, in particular constraints on power and data.

One challenge of modeling the satellite tasking problem as an MDP is that typical planning horizons are long, while the required time step for making decisions is significantly smaller. Planning horizons are, at a minimum, a few hours long\textemdash the average time between ground contacts in Low Earth Orbit. More useful scheduling horizons are on the order of days or weeks. However, opportunities to collect an image or make contact with the ground occur at specific times, when the decision to take an opportunity must be made. Since the duration of these opportunities are minutes or seconds long, the decision making time step has to have the same or finer resolution. The standard MDP formulation therefore has an extremely large state space, making it computationally challenging to solve. The standard formulation results in a difficult exploration problem, where the rewards associated with collecting or downlinking images are greatly delayed compared to the current agent state.

To address the challenge of computational tractability we take advantage of the inherent structure of the problem where opportunities for image collection or communication with the ground only occur at specific times. This property indicates that the problem could be posed as a semi-Markov Decision Process, a variation of the MDP. In a semi-Markov model, the uniform time step assumption inherent to MDPs is removed, and the agent state is instead only considered at times when actions could be taken. See \Cref{fig:mdp_smdp} illustrates this difference. This modeling choice reduces the size of the decision space, making the problem more computationally tractable, and also removes the challenge of delayed rewards. The rest of the section describes the state, action, transition, and rewards of the SMDP formulation, as well as the solution approach.

\begin{figure}
\begin{center}
	\include{mdp_smdp}
\end{center}
\caption{Illustration of the difference of time discretization in MDP and SMDP formulations. In the MDP formulation (top), all time steps are uniform and are taken sequentially. In the SMDP model (bottom) transitions occur at non-uniform times and some time steps are skipped.}
\label{fig:mdp_smdp}
\end{figure}

\subsection{State Space}

The satellite state is defined as
\begin{equation}
s = (t, t_s^p, I_{c}, d, p)
\label{eqn:state_def}
\end{equation}
where $t$ is the time of the most recent action taken, $t_s^p$ is the start time of the last collection or downlink opportunity taken, $I_{c}$ is the set of all images collected by time $t$, $p$ is current spacecraft power, and $d$ is the current on-board data usage. Data and power can be removed from the state definition if resources are not included as part of the problem.

\subsection{Action Space}

At each discrete step $t$, the agent selects the next action to take from the set of feasible actions for that state $A(s)$, where $A(s) \subset \mathcal{A}$. For a semi-Markov model the next time step directly corresponds to the start time $t_s$ of the next action. The possible actions are drawn from an enumeration of possible higher-level functional modes the satellite can assume. For this problem we consider actions of (image) collect, (ground) contact, and sun-point. This action set can be extended to include other common modes like maneuvering.

Actions are defined as
\begin{equation}
a = (t_s, t_e, \ell)
\label{eqn:action_def}
\end{equation}
where $t_s$ is the start-time, $t_e$ is the action end-time, and $\ell$ is the location. The location $\ell \in \left\{i, g, \mathsc{nil}  \right\}$ indicates which image $i \in I$ or ground station $g \in G$ the action is associated with. For sun-point actions the location is $\mathsc{nil}$.  This information is also used to indicate the mode associated with the action. It is assumed that for all collect and contact actions there is also a sun-point action with the same start and end times that is always available to take. This might not always be be useful action to take (e.g. if the satellite is in eclipse) but it is still an option.

For each state, the action space $A(s)$ is the set of all possible actions with start-time after the current time that are feasible for the spacecraft to transition to given the last collection or contact time $t_s^p$:
\begin{equation}
A(s) = \left\{a \; | \; t_s > t, C(t_s^p, t_s) = 1 \right\}
\label{eqn:action_space}
\end{equation}
$C(t_s^p, t_s)$ is the agility constraint function that indicates whether a slew between the pointing configurations for actions with start times $t_s^p$ and $t_s$ is feasible. It is a binary function $C(t_s^p, t_s) \in \left\{0, 1\right\}$ where $1$ denotes feasibility and $0$ infeasibility of the transition.

\subsection{Transition Function}

$T: \mathcal{S} \times \mathcal{A} \rightarrow \mathcal{S}$ is the transition function used in this work. Given an input state and action, the transition deterministically returns the next state. For the semi-Markov formulation, this means advancing the agent state to $t_s$, the start time of the chosen action\footnote{The end time is equally valid from an implementation point-of-view so long as the times are consistent throughout implementation.}. The transition also updates on-board resource states by propagating their dynamics. For this work, we use a simple linear update for power and data resources. \Cref{eqn:time_update,eqn:past_action_update,eqn:collected_update,eqn:collected_update,eqn:power_update,eqn:data_update} provide the transition that propagates the state $s_t$ to the next state $s_{t+1}$ after taking action $a$:
\begin{equation}
t \gets t_s
\label{eqn:time_update}
\end{equation}
\begin{equation}
t_s^p \gets
\begin{cases}
      t & \text{if $\ell \in I$ or $\ell \in G$} \\
      t_s^p & \text{otherwise}
   \end{cases}
\label{eqn:past_action_update}
\end{equation}
\begin{equation}
I_c \gets
\begin{cases}
      I_c \cup \{\ell\} & \text{if $\ell \in I$ and $\ell \notin I_c$} \\
      \phantom{-} & \text{and $p > p_{\text{min}}$} \\
      \phantom{-} & \text{and $d < d_{\text{max}}$} \\
      I_c & \text{otherwise}
   \end{cases}
\label{eqn:collected_update}
\end{equation}
\begin{equation}
p \gets p + (t_s - t_s^p)\dot{p}_\ell
\label{eqn:power_update}
\end{equation}
\begin{equation}
d \gets d + (t_s - t_s^p)\dot{d}_\ell
\label{eqn:data_update}
\end{equation}

\Cref{eqn:time_update} advances the time. \Cref{eqn:past_action_update} updates the most recent collect or contact action if a collect or contact was taken. \Cref{eqn:collected_update} adds an image to the set of collected images if it had not been previously collected and there are enough available resources to do so. \Cref{eqn:power_update,eqn:data_update} could also be exchanged for non-linear update functions that capture full system dynamics to the desired degree of fidelity. Here, $\dot{p}_\ell$ and $\dot{d}_\ell$ are the power and data generation associated with the action type. The exact values of each depend on the satellite design, but the general characteristics of each mode are summarized in \Cref{tab:resource_states}. During image collections power is consumed and data generated, during ground contacts power is again consumed but data is removed, and finally during sun-point actions power is generated along with data from the continuous recording of spacecraft telemetry.

\begin{table}[ht]
\caption{Resource update characteristics for each action mode.}
\centering
\begin{tabular}{ccc}
\specialrule{.1em}{.05em}{.05em}
 Mode & Power & Data \\
\specialrule{.05em}{.05em}{.05em}
$i$ & $\dot{p}_\ell < 0$ & $\dot{d}_\ell > 0$ \\
$g$ & $\dot{p}_\ell < 0$ & $\dot{d}_\ell < 0$ \\
$\mathsc{nil}$ & $\dot{p}_\ell \ge 0$ &  $\dot{d}_\ell > 0$\\
\specialrule{.1em}{.05em}{.05em}
\end{tabular}
\label{tab:resource_states}
\end{table}

\subsection{Reward Function}

The reward function $R(s,a)$ is the associated reward for taking action $a$ in state $s$. The reward function is designed to encode mission-specific limitations or desired behavior. The specific choice of function comes from expert-knowledge of the mission designers and engineers. In this work, we define reward to be initally $0$ and add to it for each of the conditions listed in \Cref{tab:reward_states} that are satisfied by the input state-action pair $(s,a)$.

\begin{table}[ht]
\caption{Initial position error for different orbit determination techniques}
\centering
\begin{tabular}{cc}
\specialrule{.1em}{.05em}{.05em}
 Reward & Condition \\
\specialrule{.05em}{.05em}{.05em}
$\gamma^{(t_s - t)}r_i$ & if $\ell \in I$ and $\ell \notin I_c$ \\
$1\times 10^{-1} \times (t_s - t)$ & if $\ell = \mathsc{g}$ \\
$1\times 10^{-4} \times (t_s - t)$ & if $\ell = \mathsc{nil}$ \\
$-1\times 10^4$ & $p \leq p_{\text{min}}$ \\
$-1\times 10^4$ & $d \geq d_{\text{max}}$ \\
\specialrule{.1em}{.05em}{.05em}
\end{tabular}
\label{tab:reward_states}
\end{table}

The first condition is the reward for collecting image $i$ if that location has not been previously imaged. The imaging action has a discount factor $\gamma$ applied to properly discount the reward for actions further into in the planning horizon relative to actions closer in time. If omitted, the reward for an action at the very end of the planning horizon would be the same as an action at the start. The second condition provides a reward for downlinking data proportional to the length of the contact. The third condition encourages taking sun-pointed actions by assigning a small reward for the duration of the action. The final two conditions apply a strong penalty if the spacecraft violates safe power or data thresholds.

\subsection{Implementation Challenges}

Even with a semi-Markov model, we could not solve the problem exactly. The state space grows exponentially with just the number of images being scheduled. This neglects the size of state space dimensions in time, previous action, and the discretization of power and data. Because the size of the space grows at least exponentially, finding an exact solution quickly becomes computationally intractable even for small sets of image collection requests. For this reason, we do not consider exact MDP solutions algorithms, such as value iteration or policy iteration, and instead use approximate, online solution methods.

Using online solutions alone was not enough to enable the problem to be solved quickly given the size of the possible action space. Consider the size of the action space $A(s)$ for the initial state. For a problem of $1000$ locations, there are typically $2000$ to $4000$ image collection opportunities in a \SI{24}{\hour} planning horizon, almost all of which would be feasible actions from the initial state. Most of these opportunities are detrimental to take as they skip earlier collection opportunities that could have otherwise been taken. For these reasons, we impose a limit on the number of actions in $A(s)$ to the first $N_a^{\text{max}}$ in time.

Finally, when using an online solution method, the action space is a function of the state and must be recomputed for each state visited, computed a-priori, or cached in some manner. We found that recomputing the action space at each state works for the forward-search solution, but the MCTS approach relies too heavily on simulation rollouts to make recomputation practical. To make MCTS computationally feasible we precompute the action space.

\subsection{Solution Methods}

To solve the problem, we used common algorithms for solving MDPs and SMDPs\textemdash forward search and Monte Carlo Tree Search. While both are discussed in greater depth in other works \cite{sutton2018reinforcement,kochenderfer2015decision,browne2012survey}, we will present a brief review of the techniques here for understanding.

\subsubsection{Forward Search}

Forward search is an online method that exhaustively explores the state and action space from the current state $s$ to some depth $d_{\text{solve}}$.
Shown in \Cref{alg:forward_search}, it uses \textsc{SelectAction} to iterate over all possible actions in the current state, recursively calling itself on future states until the desired depth is reached.

Solving the SMDP proceeds from the initial state $s_0$ by computing all paths of depth $d_{\text{solve}}$, then choosing the action associated with the path of highest reward. The transition function is called to deterministically advance the state. In the next state, the process is repeated, and the algorithm may find a path different than the previous maximizes the reward. This sequence is repeated until the end of the planning period $H$. The resulting series of states and actions is the task plan.

The solve depth $d_{\text{solve}}$ varies based on computational resources and the desired optimality of the solution. The choice of $d_{\text{solve}}$ is also inherently tied to the choice of the actions space size limit $N_a^{\text{max}}$. This can be seen by the total computational complexity of this method, which is $O(|$$N_a^{\text{max}}|^{d_{\text{solve}}})$. Larger values of $d_{\text{solve}}$ will lead to better plans due to greater ``foresight'' of the algorithm when choosing actions but come at increased computational cost.

\begin{algorithm}[htb]
\caption{SMDP Forward Search}
\label{alg:forward_search}
\begin{algorithmic}[1]
\Function{SelectAction}{$s,d_{\text{solve}},\gamma$}
\If{$d = 0$}
	\State \Return (\textsc{nil}, 0)
\EndIf
	\State $(a^\star, v^\star) \gets (\texttt{NIL}, -\infty)$
\For{$a \in A(s)$}
	\State $v \gets R(s,a)$
	\For{$s^\prime \in T(s, a)$}
		\State $(a^\prime, v^\prime) \gets \textsc{SelectAction}(s^\prime, d_{\text{solve}}-1,\gamma )$
		\State $v \gets v + \gamma^{(a.t_s - s.t)}v^\prime$
	\EndFor
	\If{$v > v^\star$}
		\State $(a^\star, v^\star) \gets (a, v)$
	\EndIf
\EndFor
\State \Return $(a^\star, v^\star)$
\EndFunction
\Function{ForwardSearch}{$s_0,d_{\text{solve}},\gamma$}
\State $\pi(s_0) \gets \left[\;\right]$
\State $a, v \gets \textsc{SelectAction}(s_0, d_{\text{solve}},\gamma )$
\State $s \gets T(s_0,a)$
\State $\pi(s_0) \gets \textsc{Append}(\left[(s_0, a)\right])$
\While{$v \neq -\infty$}
	\State $a, v \gets \textsc{SelectAction}(s, d_{\text{solve}},\gamma )$
	\State $s \gets T(s,a)$
	\State $\pi(s_0) \gets \textsc{Append}(\left[(s, a)\right])$
\EndWhile
\State \Return $\pi(s_0)$
\EndFunction
\end{algorithmic}
\end{algorithm}

\subsubsection{Monte Carlo Tree Search}

Monte Carlo Tree Search is an online, sampling-based approach \cite{browne2012survey,kocsis2006bandit,gelly2011monte}. In contrast to forward search, the algorithm's computational complexity does not grow exponentially with the solve depth or size of the action space. The approach entails running many simulations from the current state, updating an estimate of the state-action value function on each iteration. After a fixed number of simulations have been run, the best action found is taken, and the process repeats until the end of the planning horizon is reached.

\Cref{alg:mcts} presents the method. In the algorithm, $Q(s,a)$ is the estimate of state-action value and $N(s,a)$ is the number of times that the pair has been observed during simulation. The function $Q_0(s,a)$ and $N_0(s,a)$ allow the algorithm to be seeded if prior knowledge is available. In this work, they are both set to 0. The hyperparameter $c$ controls the amount of exploration during the search, and $N_{\text{sim}}^{\text{max}}$ sets is maximum number of simulations performed for each solution step. Exploration done at one step helps inform subsequent steps because $Q(s,a)$ persists between steps.

\begin{algorithm}[htb]
\caption{SMDP Monte Carlo Tree Search}
\label{alg:mcts}
\begin{algorithmic}[1]
\Function{Simulate}{$s,d_{\text{solve}},\gamma$}
\If{$d = 0$}
	\State \Return 0
\EndIf
\If{$s \notin V$}
	\For{$a \in A(s)$}
		\State $N(s,a),Q(s,a) \gets N_0(s,a),Q_0(s,a)$
	\EndFor
	\State $V \gets V \cup \left\{s\right\}$
	\State \Return $\textsc{Rollout}(s, d_{\text{solve}},\gamma)$
\EndIf
\State $a \gets \text{argmax}_a Q(s,a) + c\sqrt{\frac{\log \sum_{a \in A(s)}N(s, a)}{N(s,a)}}$
\State $r \gets R(s, a)$
\State $s^\prime \gets T(s, a)$
\State $q \gets \gamma^{(a.t_s - s.t)}\textsc{Simulate}(s^\prime, d_{\text{solve}}-1,\gamma)$
\State $N(s,a) \gets N(s,a) + 1$
\State $Q(s,a) \gets Q(s,a) + \frac{q-Q(s,a)}{N(s,a)}$
\State \Return $q$
\EndFunction
\Function{Rollout}{$s,d_{\text{solve}},\gamma$}
\If{$d = 0$}
	\State \Return 0
\EndIf
\State $a \sim A(s)$
\State $r \gets R(s,a)$
\State $s^\prime \gets T(s, a)$
\State \Return $r + \gamma^{(a.t_s - s.t)}\textsc{Rollout}(s^\prime, d_{\text{solve}}-1,\gamma)$
\EndFunction
\Function{MonteCarloTreeSearch}{$s_0,d_{\text{solve}},\gamma$}
\State $\pi(s_0) \gets \left[\;\right]$
\State $n \gets 0$
\For{$n \leq N_{\text{sim}}^{\text{max}}$}
	\State $\textsc{Simulate}(s_0,d_{\text{solve}},\gamma)$
	\State $n \gets n + 1$
\EndFor
\State $a \gets \text{argmax}_a Q(s,a)$
\State $s \gets T(s_0,a)$
\State $\pi(s_0) \gets \pi(s_0) \cup \left\{(s_0, a)\right\}$
\While{$v \neq -\infty$}
	\State $n \gets 0$
	\For{$n \leq N_{\text{sim}}^{\text{max}}$}
		\State $\textsc{Simulate}(s,d_{\text{solve}},\gamma)$
		\State $n \gets n + 1$
	\EndFor
	\State $a \gets \text{argmax}_a Q(s,a)$
	\State $s \gets T(s,a)$
	\State $\pi(s_0) \gets \textsc{Append}(\left[(s, a)\right])$
\EndWhile
\State \Return $\pi(s_0)$
\EndFunction
\end{algorithmic}
\end{algorithm}

\section{Baselines}

We compare our methods against serval approaches used previously to solve the satellite task scheduling problem. For this work, we consider three of the most common approaches to provide a performance baseline for comparison\textemdash a rule-based method, a graph search approach, and a MILP formulation.

\subsection{Rule-Based Planning}

The rule-based method is simply hand-coded logic based on expert knowledge of the system and desired behavior. We use an adaptation of a prior implementation \cite{bianchessi2008planning}. Shown in \Cref{alg:rule_solution}, the rule-based planner is simple in nature. It looks at the next feasible action in time. If this feasible action is a collect or contact, and there are enough resources available, it will take this action. Otherwise, it will sun-point for an equivalent duration. The algorithm terminates when no action can be taken at the current state.

\begin{algorithm}[thb]
\caption{Rule-Based Task Planner}
\label{alg:rule_solution}
\begin{algorithmic}[1]
\Function{NaiveSelectAction}{$s$}
\State // Let $a$ be the next action in time from $A(s)$
\If{$a \in G \cup I$}
	\State $s^\prime \gets T(s,a)$
	\If{$s^\prime.p > p_{min}$ and $s^\prime.d \leq d_{max}$}
		\State // Take action if feasible given resources
		\State \Return $a$
	\Else
		\State // Sunpoint for equivalent duration
		\State \Return $a_{sunpoint}(t_s)$
	\EndIf
\Else
	\State \Return $a_{sunpoint}(t_s)$
\EndIf
\EndFunction
\Function{RuleBasedPlanner}{$s_0,d_{\text{solve}},\gamma$}
\State $\pi(s_0) \gets \left[\;\right]$
\State $a \gets \textsc{NaiveSelectAction}(s_0)$
\State $s \gets T(s_0,a)$
\State $\pi(s_0) \gets \pi(s_0) \cup \left\{(s_0, a)\right\}$
\While{$A(s) \neq \emptyset$}
	\State $a \gets \textsc{NaiveSelectAction}(s)$
	\State $s \gets T(s,a)$
	\State $\pi(s_0) \gets \textsc{Append}(\left[(s, a)\right])$
\EndWhile
\State \Return $\pi(s_0)$
\EndFunction
\end{algorithmic}
\end{algorithm}

\subsection{Graph Search}

The graph search method works by first encoding the action space and feasible transitions as a directed acyclic graph then computing the longest weighted path that traverses the graph. In this approach, each possible collect opportunity is a node and feasible transitions between collects are edges. The optimal plan is then simply the longest weighted path through the graph, where the weights are the rewards for collecting each image.

\Cref{alg:graph_solution} allows for the direct calculation of this path through dynamic programming. Here, $R_i$ is the total reward collected for the plan found passing through node $i$, and $n_i$ is the prior node associated with the highest reward. We first initialize all opportunities $o_i$ collecting image $j$ to the reward function $r_j$ associated to collecting that image. The optimal path is found by iterating through nodes in increasing time order, updating the highest value path through each node, then back-tracking from the highest value node found with $\text{argmax}_i R_i$. The agility constraint function $C(t_s^p, t_s)$ is extended to return feasibility of the transition between collection opportunities $C(o_1,o_2)$. If $C(o_1,o_2) = 0$, then it is not possible to take both opportunities $o_1$ and $o_2$. The tasking plan is the reverse of all nodes (collection opportunities) traversed while back-tracking. This approach is based on the work of Augenstein \cite{augenstein2014optimal}.

\begin{algorithm}[htb]
\caption{Graph-based Task Planner}
\begin{algorithmic}[1]
\Function{PropagateWeights}{}
	\For{$o_i \in O_j, j \in I$}
		\State $R_i \gets r_j$
		\State $n_i \gets \mathsc{nil}$
	\EndFor
	\For{$o_i \in O_j, j \in I$}
		\For{$o_k \in O_\ell, \ell \in I$}
			\If{$C(o_i,o_k) = 1 \And R_i + r_\ell > R_k$}
				\State $R_k \gets R_i + r_\ell$
				\State $n_k \gets i$
			\EndIf
		\EndFor
	\EndFor
\EndFunction
\Function{ExtractPath}{}
	\State $\pi(s_0) \gets \left[\;\right]$
	\State $n \gets \text{argmax}_x R_x$
	\While{$n \neq \mathsc{nil}$}
		\State $\pi(s_0) \gets \textsc{append}\left(\left[o_n\right]\right)$
		\State $n \gets p_n$
	\EndWhile
	\State $\pi(s_0) \gets \textsc{Reverse}(\pi(s_0))$
	\State \Return $\pi(s_0)$
\EndFunction
\end{algorithmic}
\label{alg:graph_solution}
\end{algorithm}

\subsection{Mixed-Integer Linear Programming}

The third reference approach is a mixed-integer linear programming (MILP) formulation based on the work of Nag et al. \cite{nag2018scheduling}. This approach attempts to solve
\begin{equation*}
\begin{aligned}
{\text{maximize}} \hspace{0.5cm} & \sum_{x_{ij}} r_ix_{ij} \; \forall o_j \in O_i, i \in I \\
\text{subject to} \hspace{0.5cm} & x_{ij} \in \{0, 1\} \\
& \sum_{o_j \in O_i} x_{ij} \leq 1 \; \forall i \in I \\
& x_{ij} + x_{k\ell} \leq 1 \; \forall o_j \in O_i, i \in I \\
& \hspace{2.075cm} \forall o_\ell \in O_k, k \in I \\
& \hspace{2.075cm} \text{s.t.} \; C(o_j,o_\ell) = 0 \\
\end{aligned}
\label{eqn:mlip_solution}
\end{equation*}
Here, the problem is formulated in terms of in terms of binary variable $x_{ij}$ which indicates whether the image $i$ is collected at opportunity $o_j$ ($x_{ij}=1$) or not ($x_{ij}=0$). The objective represents the total reward from all collect opportunities across all requested images and stations. The first constraint is the integer constraint on the binary decision of whether the collect is taken or not. The second constraint imposes a limit of at most one collect taken per each image to ensure that the solution attempts to capture all images. The final constraint enforces that all possible transitions between collection opportunities must obey constraints on spacecraft agility. The task schedule is the set of collection opportunities selected.

\section{Experiments}

To evaluate the performance of the SMDP planning solution, we create  hypothetical sets of imaging locations that are randomly drawn the the LandSat-2 imaging catalog. The Landsat program regularly collects images of the entire globe along the pre-defined WRS-2 grid. The data set contains 16,896 coastal or land image locations. All locations are assigned an equal reward value of $r_i = 1$.

We consider two variations of the satellite tasking problem. First, we consider the performance in the more common resource-free problem addressed by the baseline approaches. We also consider the formulation where spacecraft resources of power and data are included. In the later case, the rule-based approach is used to provide the comparison as the graph and MILP formulations cannot easily extended to incorporate resources into the planning process.

All scenarios consider a single spacecraft in a \SI{500}{\kilo\meter} circular polar orbit. We assume that the spacecraft has a \SI{1}{\degree\per\second} maximum slew rate between pointing-vectors. The spacecraft model has a 10\% duty cycle on power, one image collection uses 1\% of total data storage capacity. Spacecraft also generates data at constant rate of 0.0001\% the maximum capacity every second to model on-board telemetry logging. Data can be downlinked at 4 times the rate at which it is generated during image collection. We set $p_{\text{min}}$ to 30\% of the total energy capacity and $d_{\text{max}}$ to 75\% of total data storage. The spacecraft dynamics are propagated using the SGP4 propagator \cite{vallado2006revisiting}. The planning horizon is set to be 1 day long for all cases. For all planning simulations, the image collection, ground contact opportunities, and action space were precomputed and reused for all solutions. All simulations were run on a workstation with dual 14-core 2.6 GHz Intel E5-2690 processors with each core running at most one simulation at a time.

\begin{figure*}[tb!]
\centering
\includegraphics[height=3in]{map_hp_search.pdf}
\caption{Random sampling of 1000 locations from the Landsat grid data set. This specific sampling was used to conduct hyperparameter searches.}
\label{fig:landsat_locations}
\end{figure*}

\subsection{Hyperparameter Selection}

Both SMDP solution algorithms rely on a number of hyperparameters that affect their performance (in terms of speed and optimality). Before comparing the SMDP algorithms, it is important to understand the parameters used to control the algorithms. To discover the best possible set of parameters, we performed a brute-force grid search of a select few parameters to find the best combination to use for the resource-free and resource-modeled problems. Grid search was used to find the best parameter combination. The same set of 1000 locations shown in \Cref{fig:landsat_locations} was used for all searches.

\begin{table}[ht]
\centering
\begin{tabular}{cc}
\specialrule{.1em}{.05em}{.05em}
 Parameter & Considered values \\
\specialrule{.05em}{.05em}{.05em}
$\gamma$ & $0.99, 0.9925, 0.995, 0.999, 0.9995, 0.9999$ \\ \vspace{1pt}
$d_{\text{solve}}$ & $3, 5, 7$ \\ \vspace{1pt}
$N_a^{\text{max}}$ & $3, 4, 5$ \\
\specialrule{.1em}{.05em}{.05em}
\end{tabular}
\caption{Hyperparameter search space for forward search.}
\label{tab:hp_search_fs}
\end{table}

\begin{table}[ht]
\centering
\begin{tabular}{cc}
\specialrule{.1em}{.05em}{.05em}
 Parameter & Considered values \\
\specialrule{.05em}{.05em}{.05em}
$\gamma$ & $0.99, 0.995, 0.999$ \\ \vspace{1pt}
$d_{\text{solve}}$ & $5, 10, 15, 20$ \\ \vspace{1pt}
$c$ & $0.1, 1.0, 3.0, 5.0, 10.0$ \\ \vspace{1pt}
$N_{\text{sim}}^{\text{max}}$ & $50, 100, 200, 500$ \\ \vspace{1pt}
$N_a^{\text{max}}$ & $3, 4, 5$ \\
\specialrule{.1em}{.05em}{.05em}
\end{tabular}
\caption{Hyperparameter search space for MCTS. Ten simulations were performed per point in the parameter space.}
\label{tab:hp_search_mcts}
\end{table}

\begin{table}[ht]
\centering
\begin{tabular}{cccc}
\specialrule{.1em}{.05em}{.05em}
$N_a^{\text{max}}$ & $\gamma$ & $d_{\text{solve}}$ & Reward \\
\specialrule{.05em}{.05em}{.05em}
\multicolumn{1}{l}{\textit{w/o Resources}} & \phantom{-} & \phantom{-} & \phantom{-} \\
3 & 0.999 & 3 & 450 \\
4 & 0.999 & 3 & 449 \\
5 & 0.999 & 3 & 447 \\
\multicolumn{1}{l}{\textit{w/ Resources}} & \phantom{-} & \phantom{-} & \phantom{-} \\
3 & 0.99 & 7 & 225 \\
4 & 0.9925 & 3 & 223 \\
5 & 0.995 & 3 & 221 \\
\specialrule{.1em}{.05em}{.05em}
\end{tabular}
\caption{Hyperparameter outcomes for forward search.}
\label{tab:hp_search_fs_results}
\end{table}

\begin{table}[ht]
\centering
\begin{tabular}{cccccc}
\specialrule{.1em}{.05em}{.05em}
$N_a^{\text{max}}$ & $\gamma$ & $d_{\text{solve}}$ & $c$ & $N_{\text{sim}}^{\text{max}}$ & Reward \\
\specialrule{.05em}{.05em}{.05em}
\multicolumn{1}{l}{\textit{w/ Resources}} \\
3 & 0.995 & 10 & 3.0 & 500 & 420 \\
4 & 0.995 & 10 & 5.0 & 500 & 415 \\
5 & 0.995 & 10 & 3.0 & 500 & 406 \\
\multicolumn{1}{l}{\textit{w/o Resources}} \\
3 & 0.99 & 20 & 3.0 & 200 & 215 \\
4 & 0.99 & 20 & 3.0 & 50 & 204 \\
5 & 0.999 & 15 & 3.0 & 100 & 195 \\
\specialrule{.1em}{.05em}{.05em}
\end{tabular}
\caption{Hyperparameter outcomes for MCTS. Reward is the mean value over 10 simulations.}
\label{tab:hp_search_mcts_results}
\end{table}

\Cref{tab:hp_search_fs_results,tab:hp_search_mcts_results} show   the hyperparameter combination with the highest mean reward for each value of $N_a^{\text{max}}$ considered. It is interesting that the planning reward does not increase as the limit on the action space size increases. Instead, the reward decreases slightly. In the case of forward search, this is attributed to the fact that the larger decision space adds actions that are further out in time. These actions, while possibly better in the short term, ultimately advance the solution more quickly in time and reduces the total number of requests that can be collected.

In the case of MCTS, there are two explanations for the decreasing reward with action space size. When resource management is not part of the problem, the larger action space means that more iterations are required to achieve similar levels of exploration across the decision tree. The maximum number of simulations is capped at 500. Consequently, as the action space size increases, the algorithms are unable to explore as thoroughly and performs worse.

When resource management is part the problem formulation, having a greater look-ahead dominates the MCTS planning outcomes as evidenced by greater $d_{\text{solve}}$ values. While one would expect that more rollout simulations should lead to better planning outcomes in MCTS, it is likely that the increased solve depth causes enough statistical variance that 10 iterations per parameter combination are not enough to find the optimal hyperparameter combination.

\subsection{Planning without Resources}

\Cref{fig:smdp_results_no_resources_1000} shows how rule-based, graph, MILP, SMDP forward search, and SMDP MCTS formulations perform when only the single objective of maximizing overall reward is considered. As expected, the rule-based method is the least performant but most computationally efficient. The MILP approach consistently performs the best out of all methods but is also one of the slowest. The next best method is the graph search approach. SMDP forward search manages to be nearly as efficient as the graph formulation, while having computational efficiency near that of the rule-based approach. It also significantly outperforms the reward of the rule-based approach in all cases. The comparative efficiency of forward search is hightlighted in the right panel of \Cref{fig:smdp_results_no_resources_1000}, where it has the best efficiency rating behind the rule-based method for reward per second of computation time. Even though the action space is precomputed, the MCTS solution is still computationally expensive relative to the rule-based and forward search solutions due to the number of iterations required to thoroughly sample the state-action space.

\begin{figure*}
\begin{center}
\includegraphics[width=\textwidth]{group_resource_false_1000.pdf}
\end{center}
\caption{Performance of planning approaches without considering resources for 10 random samplings of 1000 locations of from Landsat data. Total reward (left), simulation runtime (middle), and time-normalized reward (right). The time-normalized reward is the simulation reward divided by the runtime.}
\label{fig:smdp_results_no_resources_1000}
\end{figure*}

\subsection{Planning with Resources}

When resources are considered as part of the scheduling problem, task planning becomes significantly more challenging. The system must balance multiple objectives of imaging, downlinking data, and recharging to maximize the overall reward. \Cref{fig:smdp_results_yes_resources_1000} shows that out of the three solution methods considered the SMDP MCTS approach is the best at handling this challenge and forward search the worst. While it might be surprising that a rule-based method can outperform a more complex approach like forward search, it is important to remember that with a short solve depth the algorithm becomes myopic\textemdash paths that initially look promising can end up steering the solution towards ultimately inefficient outcomes. It is expected that this could be alleviated by increasing the solve depth. However, due to the exponential complexity of the forward search algorithm in solution depth, increasing the solve depth is not a computationally practical solution. Monte Carlo Tree Search can support significantly greater solve depths. Consistent with the hypothesis that solve depth is the driving factor behind making efficient trades between multiple objectives, MCTS performs best with resource management. The rule-based method, while only having an effective solve depth of a single step, ends up being better on-average than forward search.

\begin{figure*}
\begin{center}
\includegraphics[width=\textwidth]{group_resource_true_1000.pdf}
\end{center}
\caption{Performance of planning approaches considering resource management for 10 random samplings of 1000 locations of from Landsat data. Total reward (left), simulation runtime (middle), and time-normalized reward (right). The time-normalized reward is the simulation reward divided by the runtime.}
\label{fig:smdp_results_yes_resources_1000}
\end{figure*}

We ran simulations for 200, 500, and 2000 location sets both with and without considering resource management. Plots from these simulations are included as part of the Appendix.

\section{Conclusions}

This paper introduced a formulation of the satellite task scheduling problem as a semi-Markov Decision process. Two viable solution methods for the problem, forward search and Monte Carlo Tree Search, were provided. Both techniques are shown capable of generating task optimized task schedules. Forward search is shown to be a simple and computationally efficient approach that is most effective in planning when only considering a single objective. MCTS is shown to be able to effectively balance multiple objectives while planning and captures the most reward out of the methods considered. In future work, it would desirable to extend the MILP formulation to also include resources to provide an additional baseline for comparison. Additionally, the SMDP approaches would benefit from a more thorough investigation of hyperparameters and their effect on scheduling outcomes. It would also be interesting to consider, higher fidelity resource models and transition functions, as well as extend the action space to include maneuvering.

\appendices{}

Interestingly, when considering planning with multiple objectives, forward search proves to be more efficient than the rule-based method when the problem size is small (100), but quickly becomes the least effective as the problem size grows. As with the general shortcomings of forward search in multi-objective optimization, it is thought the short solve depth of the forward search method is unable to detect and make efficient trades between the multiple objectives.

\begin{figure*}
\begin{center}
\includegraphics[width=\textwidth]{group_resource_false_200.pdf}
\end{center}
\caption{Performance of planning approaches without considering resources for 10 random samplings of 200 locations of from Landsat data. Total reward (left), simulation runtime (middle), and time-normalized reward (right). The time-normalized reward is the simulation reward divided by the runtime.}
\label{fig:smdp_results_no_resources_200}
\end{figure*}

\begin{figure*}
\begin{center}
\includegraphics[width=\textwidth]{group_resource_false_500.pdf}
\end{center}
\caption{Performance of planning approaches without considering resources for 10 random samplings of 500 locations of from Landsat data. Total reward (left), simulation runtime (middle), and time-normalized reward (right). The time-normalized reward is the simulation reward divided by the runtime.}
\label{fig:smdp_results_no_resources_500}
\end{figure*}

\begin{figure*}
\begin{center}
\includegraphics[width=\textwidth]{group_resource_false_2000.pdf}
\end{center}
\caption{Performance of planning approaches without considering resources for 10 random samplings of 2000 locations of from Landsat data. Total reward (left), simulation runtime (middle), and time-normalized reward (right). The time-normalized reward is the simulation reward divided by the runtime.}
\label{fig:smdp_results_no_resources_2000}
\end{figure*}

\begin{figure*}
\begin{center}
\includegraphics[width=\textwidth]{group_resource_true_200.pdf}
\end{center}
\caption{Performance of planning approaches considering resource management for 10 random samplings of 200 locations of from Landsat data. Total reward (left), simulation runtime (middle), and time-normalized reward (right). The time-normalized reward is the simulation reward divided by the runtime.}
\label{fig:smdp_results_yes_resources_200}
\end{figure*}

\begin{figure*}
\begin{center}
\includegraphics[width=\textwidth]{group_resource_true_500.pdf}
\end{center}
\caption{Performance of planning approaches considering resource management for 10 random samplings of 500 locations of from Landsat data. Total reward (left), simulation runtime (middle), and time-normalized reward (right). The time-normalized reward is the simulation reward divided by the runtime.}
\label{fig:smdp_results_yes_resources_500}
\end{figure*}

\begin{figure*}
\begin{center}
\includegraphics[width=\textwidth]{group_resource_true_2000.pdf}
\end{center}
\caption{Performance of planning approaches considering resource management for 10 random samplings of 2000 locations of from Landsat data. Total reward (left), simulation runtime (middle), and time-normalized reward (right). The time-normalized reward is the simulation reward divided by the runtime.}
\label{fig:smdp_results_yes_resources_2000}
\end{figure*}

\acknowledgments
The authors would like to acknowledge Lucas Riggi for providing the initial algorithm for computing imaging opportunities, Sreeja Nag for providing the Landsat image location data set, Christian Lenz for his continual support and encouragement in undertaking this research. We would also like to thank Amir Maleki for his insightful feedback while preparing this publication.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


text:
by Dr Mark Corden, last update June 25, 2019
A 5 year old presents to you after being picked up from a day at Grandma's house, she has a pruritic, red, blanching, papular rash to both hands. After some questioning she tells you she was helping Grandma in the garden.
What is the cause of her contact dermatitis?
Grevillea species; specifically Grevillea Robyn Gordon.
An Australian native species common in domestic gardens across the country due to its striking colour. It is the most common botanical cause of contact dermatitis and bullous eruptions in Australia. [Reference]
A teenage girl is brought in via ambulance with vomiting and severe abdominal pain after eating some leaves and flowers from this plant.
What is it? What toxic effects is she at risk of?
Oleander trees are equally ubiquitous in gardens in Australia. Each part of the plant, flowers, leaves and stems, contain poisonous cardiac glycosides; similar to digitalis.
Early effects are gastrointestinal: nausea, vomiting, abdominal pain and diarrhoea. Cardiac involvement is common, varying from ST and T changes up to 3rd degree heart block, VF and cardiac death. Weakness, dizziness, confusion and coma have been reported amongst other features
ECG Library – Digoxin Toxicity
TOX Library – Digoxin toxicity: Management
CCC – Digoxin Toxicity
A mother brings her 18 month old daughter in to ED, it's several hours after her bedtime but she is wide awake and running laps around your department! The mother claims the daughter accidentally got fed some diet tablets out of her handbag by the mischievous 3 year old brother. What might they be?
Phentermine.
Any of the sympathomimetic diet agents would give a similar presentation. The child was admitted given the potential risk of seizures and choreiform movements. Unsurprisingly her lack of appetite necessitated an NG for hydration support!
Why don't we give aspirin to children?
Reye Syndrome
First described by Ralph Douglas Kenneth Reye (1912-1977) in a report published in the Lancet in 1963 on a series of 21 children admitted to the Royal Alexandra Hospital for Children in New South Wales between 1951 and 1962 with acute encephalopathy and fatty changes in the liver.
Although the original report did not mention salicylates, and that multiple subsequent studies have attempted to investigate a causal link between aspirin and encephalopathy in children with debatable findings, it is now culturally not appropriate to give aspirin to children.
What is certain is that the incidence of Reye syndrome has decreased since the 1970s and 80s. Whether this is due to a reduction in the use of aspirin to treat fever in children with viral illnesses, or to an increase in earlier diagnosis of other inborn errors of metabolism is uncertain. However, now the only disease whereby aspirin is given to children is Kawasaki disease.
Reye RD, Morgan G, Baral J. Encephalopathy and fatty degeneration of the viscera. A Disease entity in childhood. Lancet. 1963 Oct 12;2(7311):749-52.
Tall, obese, adolescent boys with small external genitalia are most commonly affect by what orthopaedic condition?
Slipped Capital Femoral Epiphysis (SCFE)
About Dr Mark Corden
Dr Mark Corden BSc, MBBS, FRACP. Paediatric Emergency Physician working in Northern Hospital, Melbourne. Loves medical history and trivia, and assumes everyone around him does also. | LinkedIn


text:
Purchase - $8.00 CEPR Subscribers Download
Fertility and Financial Incentives in France
CEPR Discussion Paper No. 4064
See all articles by Guy Laroque
Guy Laroque
National Institute of Statistics and Economic Studies (INSEE) - Center for Research in Economics and Statistics (CREST)
Bernard Salanie
Columbia University - Graduate School of Arts and Sciences - Department of Economics; CESifo (Center for Economic Studies and Ifo Institute)
Commentators have often linked the increase in births in France since 1995 to more generous family benefits. We study here empirically the link between fertility and financial incentives by estimating and simulating a joint structural model of participation and fertility on a sample of French women. Our results suggest that fertility responds to incentives in a non-negligible way. The results, however, also have some puzzling features: financial incentives appear to have much stronger effects on low-parity births.
Keywords: Incentives, fertility
JEL Classification: H30, J10
Laroque, Guy and Salanie, Bernard, Fertility and Financial Incentives in France (September 2003). CEPR Discussion Paper No. 4064. Available at SSRN: https://ssrn.com/abstract=469082
National Institute of Statistics and Economic Studies (INSEE) - Center for Research in Economics and Statistics (CREST) ( email )
15 Boulevard Gabriel Peri
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+33 1 4117 7718 (Phone)
+33 1 4117 7666 (Fax)
Bernard Salanie (Contact Author)
Columbia University - Graduate School of Arts and Sciences - Department of Economics ( email )


It was her dedication to yoga that helped Phyllicia Bonanno, better known as @yogi_goddess on the 'gram, really come into her own. What started out as a physical practice quickly turned into a creative outlet and eventually, a way of life—now the yogi and fine artist teaches yoga and does reiki and sound healing (you can even catch her at our Soho studio!), not to mention teaches art to elementary school kids. She even co-founded an art collective called Sisters of Yoga to celebrate, support and spread awareness about the underrepresented diverse community of yoga teachers and students. A total trailblazer, if you ask us. Phyllicia chatted with us about her journey to yoga and self-acceptance, plus, she curated the perfect playlist for practice!


text:
HomeMarch 2016A New Way of Doing 'Health' Policy!
A New Way of Doing 'Health' Policy!
In the immediate aftermath of the recent election, there appears to be widespread support for the suggestion that 'a new way of doing politics' ought to be explored. It will be interesting to see if anything comes of it, writes Denis Doherty.
Denis Doherty
During most of our history since Independence, the health of our people has been compromised because our politicians have been preoccupied with the party politics of health. The recent election campaign, like others before it, fixated on waiting lists and trolley numbers. These are but symptoms of deep malaise in a system that has been in crisis for a long time.
Seven years have been wasted pretending we were moving to a single tier health service funded by universal health insurance. A promised time- frame of four years for achieving all that, and at no extra cost, was irresponsible. No progress has been made on implementing universal health insurance and the granting of medical cards to some children and the over seventies is all there is to show by way of progress in developing universal health care.
Seven years have been wasted pretending we were moving to a single tier health service funded by universal health insurance.
The way we have done health policy up until now has not worked. The worst thing that could happen now is that an insecure government would feel unable to tackle the real issues in the health system that need to be dealt with urgently and that 'health' will continue to serve as a convenient political football from now until the next election.
That situation can be avoided but will require political leadership of a kind that has been absent for a very long time in Ireland, arguably back to the days of Donogh O'Malley in the Department of Education in the mid 1960s.
A severe shock to the political system can occasionally result in enduring benefits. In 1945, Britain emerged victorious but severely bruised, from the war. The wartime government led by Winston Churchill was very popular and was expected to win the election that year. In the event, the Labour Party, led by Clement Attlee, won a landslide victory. They promised free education and a national health service and delivered on their promises.
In 1948, the Health Secretary, Aneurin Bevan, launched the NHS, which has endured to this day. The NHS was based on two key principles of universal coverage and free of charge at the point of use. The NHS, though not beyond criticism, is widely admired as a single tier health system that provides universal coverage and is also cost effective.
Harvard University Professor, Rosabeth Moss Kanter, is a revered leadership expert. She strongly believes that without vision and values, leadership is hollow, that you have to build expectations and make sure people are motivated. "If they feel it's futile that's when they give up". Work. CIPD. Spring 2016
It is not surprising then that the NHS has endured. Universal coverage and free of charge at the point of use are principles that require little explanation and are easy to relate to. British people are proud of their health service and are willing to celebrate their pride in it. A good, recent example of that was the tribute paid to the NHS in the spectacular opening ceremony of the London Olympics in 2012. I cannot recall any comparable occasion when tributes were paid to our health services.
Political leadership, based on vision and values, remotely comparable to those that underpin the NHS, has not been experienced here.
Is it any wonder that, sadly, too many talented workers in our health services have either given up or are at risk of giving up? They are fed up with slogans and pie in the sky promises that everyone knows are not to be taken seriously.
If we go back as far as 1968, 'party politics' has taken precedence over the health of the Irish people where the Fitzgerald Report to the Hanley Report were concerned. For thirty years, from the early 1970s, Comhairle na nOspidéal struggled, with only limited success, to overcome not only party politics but medical politics also, in attempting to bring about overdue changes to our hospital services.
When our health services were already in crisis at the beginning of this century, we took to proclaiming that we were embarking on building a world-class health service and it didn't seem to matter that no one believed that was achievable based on the reality of the Ireland with which they were familiar. At the same time, we claimed we were embarking on developing networks of centres of excellence when all reasonable people would, to begin with, gladly settle for networks of centres of competence.
Great strides have been made in improving many aspects of our health services over the years but the overall sense is one of a service that has been in a state of permanent crisis for as long as most people can remember.
Political leadership, based on vision and values, remotely comparable to those that underpin the NHS, has not been experienced here. If our politicians are serious about a new way of doing politics they will do the Irish people a great service by prioritising health policy.
Experts, in leadership, such as Rosabeth Moss Kanter, who is not a stranger to our health services, in public health, in health economics, in finance and in politics would relish the challenge of designing a latter day equivalent of the initiative that gave birth to the enduring NHS.
Previous articleWalking in the shoes of the patient in Ireland
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text:
-//-Mark Fitzsimmons-//-
Can you tell me what it is your work consists of?
Content-wise almost all of it is angry. It is either angry with myself or angry with whatever powers that be that may have caused me to have the position that I do. I hopefully bring a soft edge to it. The content has to do mostly with my military service. I think the most successful pieces deal specifically with thes
e intimate moments that I have with myself. And using that I want to show everybody what military service does, I don't want to celebrate the fact that young men and women are going to countries that they can't pick out on a map to shoot and be shot at. I want people to see the despicable nature of war and what we bring home. It is an admirable thing to want to sacrifice and be a part of something bigger than yourself, but we need to be honest about what that bigger thing is.
What kind of responses to your work excite you?
Uh when somebody has definitely been moved. You can spend a lot of time in a studio working and being very confidant, and then when it comes to actually showing people the work I still have no idea, I don't know how they're going to react to something.
When somebody is coming up to me after one of my installations and performance pieces, total strangers giving me genuine hugs, it's really exciting and relieving and at the same time comforting because art allows me to connect with people in ways that I haven't in over a decade.
What is it that is relieved?
That I am able to communicate with someone else. That they heard me. Did you go to the protest on November 11 2016? So, this was when they blocked the highway. Somebody told me to jump off a bridge for this. It was a Puerto Rican trump supporter. When this guy told me to jump off a bridge, he heard me, and he understood that I appreciated the right to protest. It was nice. That interaction.
When he told me to jump off the bridge I just started walking away and when I turn back around I'm like dude we cannot have a conversation if you're just going to be hateful like that. And he was like that wasn't hate, I didn't say how high the bridge had to be! I wish it wasn't such a negative response, but he still felt a strong emotion.
I think my favorite reactions comes from a performance piece which has to do with these pillows. This pillow piece, a lot of people cried watching it. This terrarium piece, it made people want to run away.
What's the terrarium piece?
So, the most horrific symptom I ever got outside of the suicidal thoughts that happened from PTSD, sometimes I get a thought that just becomes repetitive or cyclic. So, if I was having it right now it could be I'm talking with Delia I'm talking with Delia I'm talking with Delia. Or, I'm in my studio so I'm talking with Delia and I'm talking with Delia because I'm in my studio. It's frightening, it fucks me up.
Terrariums being a completely encapsulated ecosystem and these thoughts and memories can sometimes be that where you get stuck in it and you don't really see a way out of that specific memory. So, I got up on a stage, and started making these terrariums and I would speak with that cyclic and repetitive talk. So as soon as I was getting ready to perform, as I was walking to the stage I was like okay I'm walking to the stage I'm walking to the stage I'm going to get on the stage I'm going to get on the stage I'm about to start performing I'm about…and I get on the stage, okay I'm grabbing a bottle, placing the bottle on the table so then I need rocks I'm going to put rocks in the bottle rocks are going in the bottle I'm going to put rocks in the bottle.
Kept going like that with every ingredient, I did it like that for about 2.5-3 hours. It became almost a mantra.
Can you tell me what the pillow project is?
Its current iteration is titled "Hard Comforts." I'll set up a small installation in a corner that has a ton of pillows. I'll be in military uniform and sit down, calling military cadences with an audio recording of soldiers marching. Sewing the entire time.
I mean sewing is attributed to home-making. I really thought it was hilarious that a guy that looks like me, blond hair, blue eyes, muscular to be sewing. I thought it was even more hilarious to have a soldier make it. Just because we went over there does not put us on another level, we are still the same person, we are still American citizens.
So the juxtaposition, challenging the assumptions of what a war veteran is, challenging the assumptions of what masculinity is, that's what I really wanted to do. The cadences came in because the most horrific thing I saw in the army wasn't the casualties, it wasn't even the loss of friends, it was when you've graduated basic training and we're all marching down the avenue and it's lined by our parents and we're singing these songs that on one hand are really gung-ho military, and then they go to ones that are like, I could die tomorrow, I probably am going to die, and they are these really incredibly depressing stories and the parents gave us thunderous applause when we stopped.
It was fucked up. I mean they are cheering that people in this formation, possibly even their children are going to die.
And then after the fact, we're trying to rebuild our lives, we're trying to re-integrate and connect with civilians but we can't really. And on top of that we had just this really close knit family that was on speed dial any hour of the day and people saw it as a moral obligation to help you even if they fucking hated you.
The day before I was discharged I had almost 800 men at my disposal if I needed anything and the very next day, gone. And that is true for just about every person who gets out of the military. So we have no home. No family that we understand, no friends that we understand and we're cut loose and then we get someone coming up and thanking us for our service which further alienates us from the community because they are like you're doing something that none of us can do. While that may be praiseworthy, it may not be the best thing for some of us to hear.
When did you first identify as an artist?
Oh. 10/11. I knew that I wanted to be an artist the moment that I saw Jurassic Park. I saw the illusion of dinosaurs on the screen and was just in heaven. And then several things happened in life, with my mother passed(ing) away being the watershed.
I shied away from the more technical stuff in art, I also wanted to make something more expressive. I could make a dinosaur and people would be like woah that's cool but I don't think people would be like, who made that? So from a young kid I wanted to make stuff, and I wanted you to know that I made that. Then when I got older I was trying to do art but didn't know how to break it. And I had the opportunity to teach a class in Southern Missouri, to teach color theory to a bunch of incredibly right wing Christian people.
I only did it once because they wanted me to teach from a Christian standpoint. I was like… I don't know how to teach color theory that way.
Why did you want to teach them?
There are two things I think I am. I assume I'm an artist, but I know I'm through and through I'm a teacher. I have much bigger, grander plans for teaching. I wanted to teach them different ways of looking at the physical world.
What do you think you can bring to education?
Critical thinking. I think it's sorely lost in our world, in our country. I think about some of the art education students here and they are completely boxed in to the rules. Technique is definitely important. You can't break the rules if you don't know technique.
Most specifically I would want to focus on five and younger and teach reading with kids and then have a course in scientific inquiry and artistic expression for toddlers and pre-k. Because that's where a lot of our curiosities will be bolstered and maintained, before they turn five.
For somebody no longer 5 and younger, how do we get people to be disruptive in their work?
Quit being afraid. I'm going to steal something I heard Jim Carrey say. He said through that door there's everything. I've walked through that door and several different doors and it's always so liberating.
There's no need to be afraid. If you start making something with fear it becomes disingenuous and if there is one thing that every single human being catches is when you're being disingenuous.
What do you think the greatest thing that art has ever done for you is?
Oh it kept me sane. And it kept me me. The first crack in the conditioning was when we were marching down the street in basic training, the final crack, I was stationed in Germany. I was complaining about the military, but I still had a very strict military bearing and didn't really pay attention to anything outside of how to be a good soldier.
Then I was stumbling back on the train from Munich and I got stuck in Nuremberg for three hours I'm like alright I'm going to go eat lunch. So I'm walking down the street and I look up and see a sculpture I'm like that looks like Albrecht Dürer. And I'm like that's weird I don't know why that's here and I turn into a plaza and there are all these sculptures that are dedicated to Albrecht Dürer I'm like this is weird. Well there's food I'm going to go in there get some food, I'm half drunk.
It's ten o'clock in the morning. The waiter speak English, I go what's with all this stuff? He goes "Albrecht Dürer is from Nuremberg, yeah you're actually eating in his studio". It reminded me that there is something else outside of the military and there was another passion that I had. So then I started working toward it again, playing with drawings, playing with writing, with whatever I could.


text:
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See previous year Annual Reports here.
The mission of the San Francisco Public Defender's office is to protect and defend the rights of our indigent clients, through effective, vigorous, compassionate, and creative legal advocacy.
We aspire to provide the highest level of legal advocacy for each of our clients, and to be a nationwide model for the delivery of indigent defense services.
Racial Action Plan
Racial Equity Action Plan – Phase I: December 30, 2020
In response to the City's growing racial disparities, and as a means to address the history of structural and institutional racism in San Francisco's delivery of services to the public and its own internal practices and systems, San Francisco legislated the creation of the Office of Racial Equity (Ordinance No. 188-19) in July 2019. For more information, please visit the Office of Racial Equity's website: https://www.racialequitysf.org/
The Office of Racial Equity has called for all City's Departments to prepare and submit a 3-year plan for Phase 1. Like all City Departments, we have work to do to ensure that we examine racial disparities and develop effective changes, programs, initiatives, and policies to address them. Our Plan describes many of the steps to be taken in this first phase, to apply racial equity principles to inspect and dismantle unfair and harmful structural and institutional policies and practices, while creating an actively anti-racist workplace.
The Public Defender's Office (PDR) has prepared its Racial Equity Action Plan –Phase I: December 30, 2020, and will submit it the Plan to the Office of Racial Equity. This is a working document and we anticipate updating during the 3-year period. We welcome this opportunity for continued growth while maintaining our ardent commitment to racial justice work.
What Is A Public Defender?
A public defender is a lawyer appointed to represent people who otherwise cannot reasonably afford to hire an attorney. The Constitution guarantees everyone the right to an attorney, even if they cannot afford one.
Do Public Defenders Defend Criminals?
Public Defenders defend people. Everyone is innocent until proven guilty.
Even when a person is guilty of a crime, they still have rights that must be followed.
Can I Hire A Public Defender To Sue My Landlord?
Public Defenders practice criminal defense law. Rent related issues and lawsuits are considered civil matters and handled by civil attorneys. We can only provide our services to people who cannot afford a lawyer of their own as determined by Gideon v. Wainwright.
Learn More About Public Defenders
Read our FAQ for commonly asked questions regarding our office, our attorneys, and what we do and why we do it.
Bayview Mobilization for Adolescent Growth in our Communities (BMAGIC)
BMAGIC's vision is to create and maintain a deeper unified road-map to social change that advances the educational, health, economic, and juvenile justice of disadvantaged youth and their families in Bayview Hunters Point.
More Mobilization for Adolescent Growth in our Communities (Mo'MAGIC)
Mo'MAGIC aims is to enrich community resources through social and educational development; to create leadership and career opportunities utilizing programs and services for youth, that also serve as early intervention activities that help to reduce juvenile crime in the Fillmore/Western Addition.
Gifts and Donor Information
Donor Name
Financial Interest
Bar Association of San Francisco May 2012 Cash $1,451.98 None
Bar Association of San Francisco June 2012 Cash $1,883.46 None
Bar Association of San Francisco Jan 2013 Cash $2,624.48 None
Bar Association of San Francisco Mar 2013 Cash $2,698.27 None
Champions Public Defense
Defenders of the Year
Youth Champion Award
Best Documentary Award
Trailblazer Award
National Youth Law Award
Athenian Award
Public Transparency Award