text:
Eumarrah: treat for natural & organic consumers
Section(s): Retail Insight
Walking into Hobart, Tasmania's natural and wholefoods store Eumarrah, I was struck by the warm earthy atmosphere, the use of natural wood, and the breadth of natural and organic foods on display in the state capital's biggest specialist natural foods store.
The city of Hobart, population 220,000, runs up the lower slopes of Mt. Wellington, is a major Antarctic ship base, and is an important showcase for Tasmania's international fine food and wine industry. It's also where the world's first Green political party was founded in 1972.
Eumarrah first opened in 1985 in Barrack Street, Hobart as a response to the then Socialist Workers Party taking over the then Unemployed Peoples Union and its associated shop.
The one-stop shop of around 160sqm ranges a broad selection of organic produce, natural and organic whole foods in bulk, natural and organic groceries, beverages and wine.
"Several of us involved in the UPU wanted to create a community resource and we started from the premise of good food at affordable prices that was environmentally responsible", says founder Alban Johnson.
"We didn't have a lot of experience and made lots of mistakes, and at times lots of losses, and eventually I ended up holding the baby so to speak. Because we were in a small city we could not specialise on just one area, we had to improvise and get a broad selection of foods.
"So we chose natural foods, organic foods, ethnic foods, gluten-free foods, vegetarian and vegan foods, GM-free foods, and even just foods that interested us. Over the years we had a number of shops in the Hobart area, then about 15 years ago decided to start a consolidation process, to have one large store with a wide range of products."
While most of the staff initially were volunteers, over time things changed and we needed greater specialisation and professionalism, Alban says.
He explains that the northern Tasmanian city of Launceston had a shop with a similar history to the UPU except that the SWP didn't take it over, the UPU simply degenerated and then sold the shop to private operators who ran it.
"We bought the shop about five years ago and undertook a huge renovation," Alban says.
There are around 4-5 specialist stores in Hobart city stocking organics, and a group of five gourmet grocers servicing the city consumers and competing with two huge supermarket chains.
"There are many specialist grocers in Hobart but not so many natural food stores and we are proud that Eumarrah Hobart is the largest and Eumarrah Launceston second largest organic and natural food store in Tasmania," Alban says.
Eumarrah stocks about 450 self-serve (bulk) lines plus another 1500 or so other grocery lines, with organics important, but quality, origin, and many other criteria are also considered.
Being in Australia's southernmost city where retailers need most food products shipped into the island state, is a disadvantage outside of fresh produce.
"Surprisingly organic production is not strong in Tasmania, although we do have excellent suppliers like Kindred Organics, Forest Hill Farm, Elgaar Farm, Callington Mill, etc, Alban explains.
"There are also some excellent chemical-free (organic but not certified) producers, mainly of vegetables. So much product comes from the mainland or from overseas. We are lucky in that we are large enough to be able to choose specific suppliers for specific products, rather than just buying everything from a wholesaler."
Sales have been growing strongly since Eumarrah opened the consolidated Hobart store eight years ago. "Regular customers are more important than anything and to us every customer is an individual human being, a person to be respected and treated as an equal," Alban says.
Some of the trends seen at the store include a move towards superfoods, raw foods and paleo foods, while most of the population is moving more slowly in their diet.
"Of course people are more concerned about what they put in their bodies every day, but many are also interested in the environmental and social impact of their food choices," he says.
On the subject of expansion plans for Eumarrah, Alban says, "Watch this space".
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Certification & Legislation
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Retail Insight
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THQ Nordic buys Delta Force and the rest of NovaLogic's back catalogue
"They're back at it again with the acquisition of partly forgotten, but classic game IPs."
In August, Nordic Games rebranded as THQ Nordic—a move which followed its acquisition of several free agent THQ franchises two years prior. Today, the Austrian studio has announced its purchase of NovaLogic and all of its game series.
Although Nordic hasn't announced any new games, it now owns the rights to the likes of the Delta Force series, the Comanche series, the Armored Fist series, and the F-22 series, as well as a number of other game licenses spanning NovaLogic's 30 year profile.
Speaking to the deal, NovaLogic's CEO and founder John Garcia cited Nordic's "agility and acumen" as key to moving his decision to pass the baton and help move his company forward. Nordic's business and product development director Reinhard Pollice said this:
"NovaLogic pioneered military simulations and military-themed multiplayer shooters with vehicular combat and also clearly targeted at an adult audience.
"We are extremely satisfied with the new additions to our portfolio, and also very thrilled about how to continue some of said franchises, we are open for talks in this regard if any developer approaches us with a concept for a potential sequel to any IP."
Alongside the 13 unannounced projects it is said to have in the pipeline—the majority of which are "based on former THQ owned IPs and franchises"—it'll be interesting to see what THQ Nordic does with NovaLogic's games into the future. Do you fancy remakes of anything listed above? Let us know in the comments south of here.

redpajama_set_name: RedPajamaCommonCrawl


Mmmmm. A big word is trust. Not something I feel comfortable with. I mean, if the bus driver says he'll drop me at my road end then I trust him. I trust school will open on time and I trust that the supermarket will arrange supplies so I can feed my children. Those lists are endless. I more mean proper trust. I am in deficit when it comes to that. I feel I am not alone... I feel there are more than a few of us who feel this way. It doesn't matter why, it just matters that we are aware.
And so I know I must sometimes challenge myself. Prod. Push. Stir.
How? How do we do this without tipping ourselves off balance?
Just begin with you. Trust yourself. It's big enough.
We are not required to throw our trust into the street for all to see. We are not needed to lay out a table of trust for all to sample. It is not expected that we might dress in our trusting robes. It can be small. It can be tenuous. It just has to be a leap of faith that whatever we are wrestling with, we can deal with.
Trust yourself. We can handle it. We can grow here. We can pick up. We can flourish. We can follow our own footsteps. We can forgive ourselves. We can allow ourselves mistakes. We can. Trust it.


Take our time to enjoy yours, ..
... Every part of you is special! "
All the treatments at the clinic are designed to work together holistically from facials to massages and slimming packages to name a few. At Heaven we like to get to know our clients first, which is very important as everyone has different needs. A woman going through menopause will need a very different approach to a teenager with skin problems.
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All treatments work in harmony to balance the skin for example Murad is a cosmeceutical brand which doesn't just fluff and buff the surface of the skin but works at a much deeper level, treating problematic skins such as acne, acne-scarring, hormonal ageing, environmental ageing (pigmentation) and sensitivity and more with very impressive results.
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The US Department of Energy announced 16 projects supporting activities in 24 states and the District of Columbia to accelerate the adoption of electric vehicles (EVs) in communities across the nation, and 7 additional projects in seven states to help prepare college students for careers designing and building advanced vehicle technologies.
Clean Cities Initiative Awards $8.5 million. Projects to support community planning for plug-in electric vehicles and charging infrastructure will receive $8.5 million through DOE's Clean Cities initiative to facilitate local public-private partnerships that will develop EV deployment strategies. The funding recipients range from communities with extensive EV planning experience to those that are eager to begin, but have not previously had the resources to do so.
These one-year projects will help communities address their specific needs, which include updating permitting processes, revising codes, training municipal personnel, promoting public awareness, or developing incentives, and each will create a plan that will be publicly available so that other stakeholders can learn best practices.
The Graduate Automotive Technology Education (GATE) Initiative Awards $6.4 million. DOE's Graduate Automotive Technology Education (GATE) initiative will award $6.4 million over the course of five years to support seven Centers of Excellence at American colleges, universities, and university-affiliated research institutions. The awardees will focus on three critical automotive technology areas: hybrid propulsion, energy storage, and lightweight materials.
By funding curriculum development and expansion as well as laboratory work, GATE allows higher education institutions to develop multidisciplinary training. As a result, GATE promotes the development of a skilled workforce of engineering professionals who will overcome technical barriers and help commercialize the next generation of advanced automotive technologies.
DOE announced the availability of funding for these projects in February and April 2011.
This type of project is very useful to reduce our inherent resistance to EVs (and many other changes), specially the die hard naysayers.
Agreed Harvey. We'd like to see these awards tracked for efficacy and public awareness.
This is to "overcome technical barriers".
There is no problem with "inherent resistance to EVs (and many other changes)".
This is not about "public awareness".
All credible predictions of when EVs will be competitive are sadly in the not too near future.
"Study finds that low carbon vehicles will make progress in closing TCO gap by 2030, but will still require financial support for wide adoption. 6 September 2011" GreenCarCongress.
You think 15 years, Harvey?
Obviously EV sales are low because when people ask themselves if they want to spend their own money on an EV, they say ney.
It's not just the initial price; they might spend more, they might spend less, but they turn away from EVs.


This paper presents a case study of an advocacy campaign by the residents of Tambogrande, an agricultural town located in the San Lorenzo valley of northern Peru, to defend their environment and natural resources. In 1999, the Peruvian government conceded mineral extraction rights in the area to the Manhattan Minerals Corporation, providing permission to extract gold, zinc and copper deposits located underneath Tambogrande through the use of environmentally destructive open-pit mining operations. Residents organized the Tambogrande Defense Front, a grassroots organization to represent their interests. With support from the local church, as well as local, national, and international organizations, the Tambograndinos sought to defend the town from the negative effects that open-pit mines would have on the valley's environment and agricultural productivity. Although the battle lasted over five years and took the life of one of its most important leaders, the Defense Front maintained a unified spirit and, through organizing a municipal consultation and other strategies, achieved a historic political victory when the Peruvian government withdrew the company's concession. The study, based on extensive document reviews and interviews with both participants and expert analysts, identified a number of different general lessons about advocacy, including the importance of strength, perseverance, and commitment.
Olortegui, Rosa Maria, "Tambogrande Vale Mas Que Oro: Tambogrande Is Worth More Than Gold" (2007). Capstone Collection. 1526.


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InsiderEconomyPresident presents National Recovery Plan for the post-COVID era
President presents National Recovery Plan for the post-COVID era
President of Cyprus Nicos Anastasiades said that today marks a historic day, as the foundations for the post- COVID era in Cyprus are being laid.
The President, who presented on Monday Cyprus' National Recovery and Resilience Plan, the blueprint containing projects and reforms with a view to absorb funds from the EU's Recovery and Resilience Fund (Next Generation EU), said that
it is an ambitious and realistic plan not only for the recovery of the economy, but also for the creation of a new vision for Cyprus.
The plan, he added, is a road map for the post-COVID era, and addresses the economic and social repercussions of the pandemic. "It is a complete economic and social transformation. It leads economy, technology, mentalities and institutions to a new era," he pointed out.
It is noted that the plan was approved by the Council of Ministers on the 14th of May and is being submitted today to the European Commission.
The President said that for the implementation of the plan the government will fully utilise the €1.2 billion provided in the Recovery and Resilience Plan, the €1.8 billion provided by the Cohesion Funds, the €1.4 billion that, according to a study will result from private investment or public-private partnerships while the annual expenditure of the next budgets will be adjusted and distributed according to the new growth model.
Therefore, he pointed out, the total amount for the implementation of the Program for the period 2021-2026, amounts, to €4.4 billion, not including the expenditure of the annual budgets.
He noted that as a result of the implementation of the Recovery and Resilience Plan, amounting to €1.2 billion, the whole economy and society will benefit, and added that in addition to the projected increase of the GDP, an additional 7% will be added, while, from the implementation of the Plan, more than 11,000 new and well- paid jobs will be created.
He said that this does not include the new jobs that will be created from the remaining amount of €3.2 billion included in the "Cyprus – Tomorrow" project.
The President said that the plan is built on five foundations, namely, public health and civil protection, rapid transition to the Green Economy, strengthening of the resilience and competitiveness of the economy, the path towards the digital age and the fifth one includes two areas, education, and labour and social welfare and integration.
More details on each area will be announced in the coming days by the competent Ministers.
The President said that the actions of the "Cyprus – Tomorrow" project, include a total of 58 reforms and 76 development investments, including programmes to strengthen private entrepreneurship.
Cyprus residents' trips abroad up 283.5% in April
Two people arrested for arson of six cars in Frenaros
U.S. company Wrike opens office in Cyprus; relocates 150 employees
UK's Hunt to prioritize to half inflation, won't bring forward tax cuts
Thousands of workers across Cyprus stage work stoppage over CoLA (PHOTOS+VIDEOS)
Business administration and law most popular subjects for Cypriot students


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Category: Blue Line Lawyer
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Whitney Houston and the Push to Legalize Drugs: A Perspective From the Street and a Reality Check
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Government Email For Private Use: "We have no secrets"
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A Queer History of the United States
By Michael Bronski
Part of REVISIONING HISTORY
Category: U.S. History
About A Queer History of the United States
Winner of a 2012 Stonewall Book Award in nonfiction
The first book to cover the entirety of lesbian, gay, bisexual, and transgender history, from pre-1492 to the present.
In the 1620s, Thomas Morton broke from Plymouth Colony and founded Merrymount, which celebrated same-sex desire, atheism, and interracial marriage. Transgender evangelist Jemima Wilkinson, in the early 1800s, changed her name to "Publick Universal Friend," refused to use pronouns, fought for gender equality, and led her own congregation in upstate New York. In the mid-nineteenth century, internationally famous Shakespearean actor Charlotte Cushman led an openly lesbian life, including a well-publicized "female marriage." And in the late 1920s, Augustus Granville Dill was fired by W. E. B. Du Bois from the NAACP's magazine the Crisis after being arrested for a homosexual encounter. These are just a few moments of queer history that Michael Bronski highlights in this groundbreaking book.
Intellectually dynamic and endlessly provocative, A Queer History of the United States is more than a "who's who" of queer history: it is a book that radically challenges how we understand American history. Drawing upon primary documents, literature, and cultural histories, noted scholar and activist Michael Bronski charts the breadth of lesbian, gay, bisexual, and transgender history, from 1492 to the 1990s, and has written a testament to how the LGBT experience has profoundly shaped our country, culture, and history.
A Queer History of the United States abounds with startling examples of unknown or often ignored aspects of American history—the ineffectiveness of sodomy laws in the colonies, the prevalence of cross-dressing women soldiers in the Civil War, the impact of new technologies on LGBT life in the nineteenth century, and how rock music and popular culture were, in large part, responsible for the devastating backlash against gay rights in the late 1970s. Most striking, Bronski documents how, over centuries, various incarnations of social purity movements have consistently attempted to regulate all sexuality, including fantasies, masturbation, and queer sex. Resisting these efforts, same-sex desire flourished and helped make America what it is today.
At heart, A Queer History of the United States is simply about American history. It is a book that will matter both to LGBT people and heterosexuals. This engrossing and revelatory history will make readers appreciate just how queer America really is.
Also in REVISIONING HISTORY
Also by Michael Bronski
See all books by Michael Bronski
Playing In The Dark
The Cross of Redemption
Blair Imani
Gender Outlaw
Kate Bornstein
Some of My Best Friends Are Black
Tanner Colby
Charles V. Hamilton
The Souls of Black Folk
W.E.B. Du Bois
Queer Love in Color
Jamal Jordan
"Bronski does a stunning job of sweeping across five hundred years and weaving 'queer' through the history of this nation. Always insightful, and provocative."—John D'Emilio, author of Lost Prophet
"The first book to cover all of LGBT history from 1492 through the present is Michael Bronski's A Queer History of the United States (Beacon Press). It is wonderfully readable and looks at the way we understand the history of the United States. The LGBT population moves from the margins to the mainstream and we see that the history of this country also is our history."—Windy City Times
"Bronski's book provides an excellent overview for readers new to the field of gay history. Summing Up: Essential. All levels/libraries…"—CHOICE Magazine
"…A succinct distillation of the history of lesbians, gays, bisexuals, and transgenders in America… Bronski's impeccable research bolsters his arguments… a useful handbook for LGBT activist groups and other interested members of the gay community."—Boston Globe
"In the age of Twitter and reductive history, we need a complex, fully realized, radical reassessment of history—and A Queer History of the United States is exactly that. Along the way, there are enough revelations and reassessments to fuel dozens of arguments about how we got to where we are today. I don't know when I have enjoyed a history so much."—Dorothy Allison, author of Bastard Out of Carolina
"Bronski has that rare ability to comprehensively synthesize a large body of material without simplifying or distorting it, taking as much care with historical evidence as with the shifts in language necessary to accurately understand it."—Martin Duberman, Distinguished Professor of History Emeritus, City University of New York
"This book is a revelation. Its lively and engaging narrative peels back layers of cultural interconnection—from the creation of corn flakes to curb masturbation to Bette Midler's rise to stardom that started at a gay bathhouse—and much more. Bronski has a Zinn-like grasp of the ties that bind us all together and how to illuminate them on the page."—Jewelle Gomez, activist and author of The Gilda Stories
"Bronski demonstrates with wit, insight, and impeccable scholarship that queer lives are, and always have been, woven into the very fabric of this country. Readable, radical, and smart—a must read."—Alison Bechdel, author of Fun Home
"Elegant, insightfully selective, and unremittingly intelligent, Bronski's survey—of the whys and the ways queer people's work and struggle have been integral in forming what we call 'the United States of America'—is an impressive and useful overview."—Samuel R. Delany, author of Times Square Red, Times Square Blue
"A savvy political, legal, literary (and even fashion) history, Bronski's narrative is as intellectually rigorous as it is entertaining."—Publishers Weekly, starred review
"[A] monumental achievement."—The Bay Area Reporter
One: The Persecuting Society
Two: Sexually Ambiguous Revolutions
Three: Imagining a Queer America
Four: A Democracy of Death and Art
Five: A Dangerous Purity
Seven: Production and Marketing of Gender
Eight: Sex in the Trenches
Nine: Visible Communities/Invisible Lives
Ten: Revolt/Backlash/Resistance
Get the latest updates about Michael Bronski


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\section*{INTRODUCTION}
\par A significant challenge in materials discovery is the vast, often untenable, space of potential experiments that could be performed in any given materials optimization effort. Desirable, novel materials exist as needles in enormous proverbial haystacks. Brute force searches of these haystacks, which may represent material compositions, crystal structures, or synthesis parameters, are prohibitively expensive and time-consuming. Therefore, efficient methods for discovering needles are sought to reduce the number of experiments required to discover novel materials that meet a given set of performance specifications  \cite{meredig2014combinatorial,isayev2015materials,rajan2015materials,ramprasad2017machine}.
\par Computational techniques may alleviate this challenge by screening promising candidate materials via predicting whether each candidate from the haystack exceeds some threshold performance (e.g. thermoelectric figure of merit). This haystack may represent a large, high-dimensional design space \cite{gomez2016design, greeley2011computational,isayev2015materials,meredig2014combinatorial, kim2017virtual, borboudakis2017chemically}, or the set of all relevant materials whose performances are unknown.
\par Model accuracy is a well-studied factor in determining whether a materials discovery project using computational techniques will be successful \cite{snoek2012practical, ward2018matminer, ward2016general}. Model accuracy has therefore been widely used as a success metric for machine-learned structure-property relations. A less widely considered factor is the quality of the haystack, or the design space. Depending on the quality of the design space, discovering an improved material may range from trivially easy (if all untested materials are superior) to impossible (if all untested materials are inferior). Jia et al.\cite{jia2019anthropogenic} and Kauwe et al.\cite{kauwe2019can} have shown, respectively, that random and ML-guided materials search strategies may achieve success depending on the underlying difficulty - or quality - of the design space. Thus, there is a critical need for a quantitative method that determines the quality of the design space, which strongly impacts materials discovery success. In other words, we desire a method for finding the best haystacks in which to search for needles.
\par To the best of the authors' knowledge, there is no current work on quantifying the quality of a given design space, where a \emph{high quality} space yields materials discovery success with fewer required experiments \cite{sanchez2018inverse}. While recent studies have begun to explore the effect of choosing different training and test sets for discovery on the practicality of discovering new and useful materials, broadly quantifying these effects in terms of successful materials discoveries remains unexplored \cite{kauwe2019can, meredig2018can, raccuglia2016machine, jia2019anthropogenic}.
\par In this work, we first demonstrate how design space quality is a critical factor in materials development success. We illustrate this by benchmarking sequential learning success versus design space quality through simulated sequential learning on existing materials data sets. Second, we introduce a procedure to initialize the training data and design space which better reflects how known and unknown materials data are distributed \cite{kauwe2019can, meredig2018can,ward2018matminer}. Finally, we present a design space evaluation system using two novel metrics - the PFIC and CMLI scores - for quantifying the quality of a given design space. This design space evaluation system gives insight on the likelihood of success in a given materials development project, and enables data-driven selection between different materials discovery projects.

\section*{METHODS}

\begin{figure}[ht]
\centering
\includegraphics[width=\linewidth]{design_space_eval_schematic.pdf}
\caption{Overall schematic of the methods presented in this work. Data are first initialized into a training set and a design space. This is followed by training a model using the training data, and predicting material performances across the design space candidates. Finally, predictive design space metrics are computed and used to evaluate high quality and low quality design spaces.}
\label{fig:overall_schematic}
\end{figure}

The methods presented in this work are highlighted in Figure \ref{fig:overall_schematic}. Using a novel data partitioning scheme to best represent the nature of in-lab materials discovery, a machine-learned model is trained on a set of training data. Following this, predictions for material properties (e.g., band gap) are computed along with uncertainty estimates across materials in the held-out design space. Finally, these predictions and uncertainties are used to produce \emph{design space metrics} that are indicative of the difficulty of materials discovery for a particular design space. Or, in other words, these metrics evaluate how difficult it is to find a needle in a given haystack.

\subsection*{Sequential learning simulations}
\par To enable the rapid computation of quantitative materials discovery success and design space quality metrics across a broad variety of materials categories, as well as different splits of the training set and design space, \emph{in silico} simulations of sequential learning were used to avoid the overhead of in-lab experimental trials. \emph{In silico} simulations of sequential learning reduce the time required to run experiments to effectively zero.
\par Sequential learning was simulated using two publicly available datasets - the Materials Project and Harvard Clean Energy Project datasets, shown in Table \ref{table:benchmarks}. Across a number of trials, we split each dataset into a training set and design space to assess the quality of the design space on the overall likelihood of sequential learning success. The candidates in the design spaces held out from model training were treated as an approximation to infinite design spaces commonly encountered during in-lab materials development. The methodology of splitting these benchmark datasets into a training set and design space is discussed in the following section.
\par The training set was used to train machine learning models, then the trained models were used to produce predictions with uncertainties for each of the design space candidates' material properties of interest. At each sequential learning iteration, the candidate with the highest performance was then selected to ``measure,'' at which point the true performance of that candidate was revealed to the model, and that candidate was added to the training set. Prior to the next sequential learning iteration, the model was retrained on the training data, including the added design space candidate. Various metrics were recorded at each iteration of sequential learning for analysis, and these metrics are discussed in the following section.
\par Machine learning models were built for five properties in total. The objective was to maximize, minimize, or tune the value of the target property, depending on the case. Table \ref{table:benchmarks} lists the datasets and properties used for simulating sequential learning. A variety of benchmark datasets were chosen to represent different materials classes, as well as both computational and experimental datasets.  The machine learning model inputs were representative of the degrees of freedom that could be adjusted in a laboratory setting, such as the composition and the processing parameters of a material. The output of the machine learning model was the material property to be optimized, such as the band gap.
\par In this work, the open-source \emph{lolo} random forest library was used for the machine learning algorithm \cite{lolo}. Random forests make predictions for a new point based on the values of training data points that are nearby in the input space. Given the clustered nature of many materials datasets, this approach to making predictions make random forests particularly well-suited to materials applications \cite{Ling2017}. However, we stress that the methods introduced in this work are model-agnostic and are thus compatible with a wide variety of modern algorithms, including Gaussian processes and neural networks.
\par 20 identical sequential learning trials were performed for each training set and design space in order to capture the variance due to stochastic model training. To analyze the effect of design space quality on sequential learning success, each trial of the sequential learning process was performed for 50 iterations of sequential learning (where each iteration represents the addition of one new data point to the training set and the retraining of the machine learning model), or until a design space candidate that performed better than the best training data candidate was found. The maximum likelihood of improvement (MLI) and the maximum expected improvement (MEI) were used as acquisition functions to determine the performance of the design space candidates \cite{Ling2017}. 50 to 100 different training data and design space initializations were tested per dataset. Each training data and design space split exhibited a varying level of sequential learning difficulty by virtue of the training-to-design-space ratio and fraction of improved candidates in the design space.
\par Therefore, sequential learning trials were run in order to analyze the relationship between design space quality and success in a materials discovery project without the need for in-lab experiments.

\begin{center}
\begin{table} [H]
\caption{Benchmark datasets used in sequential learning simulations and analysis of predictive design space metrics.}
\begin{tabular}{p{4.4cm}p{1.5cm}rp{4.1cm}p{3.7cm}}
 Dataset & Origin  & Dataset Size & Output Property & Objective \\
 \hline\hline
Harvard Clean Energy Project\cite{hachmann2011harvard} & DFT  & 30,000  & Power conversion efficiency & Maximize  \\
 & &  & HOMO Energy & Maximize \\ \hline
Materials Project\cite{jain2013commentary} & DFT & 30,000  & Formation energy & Minimize \\
 & &  & Band gap & Tune to 1.5 eV \\
 & &  & Total magnetization & Maximize absolute value \\ \hline
Melting Points\cite{Bradley2014} & Experiment & 3041  & Melting point temperature & Maximize \\ \hline
Superconductors\cite{SuperconductorDataset} & Experiment & 585 & Superconducting $T_c$ & Maximize \\ \hline
UCSB Thermoelectrics\cite{gaultois2013data} & Experiment & 1093 & Seebeck coefficient & Maximize \\
 & &  & Electrical resistivity & Minimize \\ \hline
Strehlow \& Cook\cite{strehlow1973compilation} & Experiment & 1449 & Band gap & Tune to 1.5 eV
\end{tabular}
\label{table:benchmarks}
\end{table}
\end{center}

\subsection*{Data initialization}
\par While randomly-chosen splits of training and test data are common throughout machine learning \cite{ward2016general, ward2018matminer}, recent works by Meredig et al. and Kauwe et al. \cite{meredig2018can,kauwe2019can} have shown that the choice of training and test data significantly influence estimates of generalization error in machine-learned models, along with the success of materials discovery.
\par To reflect the clustered nature of materials data \cite{meredig2018can, kauwe2019can}, which are generally derived from human-biased historical results \cite{jia2019anthropogenic}, a cluster-based data initialization method was developed and used in this study. This data initialization method better matched experimental materials discovery projects, since experiments are typically run over similar types of materials, resulting in clustered training data \cite{jia2019anthropogenic}. Figure \ref{fig:data_initializer} shows a diagram of this data initialization method.
\par We also tested random selection of training sets and design spaces, which often yielded trivially solvable sequential learning simulations when initial training data included at least one material nearby to improved, held-out materials. Machine-learned algorithms were able to successfully discover an improved material within five iterations of sequential learning in such design spaces in almost all trials. Existing experimental and computational sequential learning studies have typically explored dozens to hundreds of iterations \cite{granda2018controlling, brandt2017rapid} before discovering novel or improved materials, suggesting that these splits of training and test data were unrealistic, and confirming that a more complex data initialization method was required.
\begin{figure}[ht]
\centering
\includegraphics[width=\linewidth]{clustering_data_initializer.pdf}
\caption{Diagram of the data initialization approach. Data initialization begins with a) a given dataset. The data initializer b) clusters the full dataset, then c) ranks the clusters based on the best performing candidate within. After ranking the clusters, d) the clusters are assigned as either training clusters or design clusters. Finally, the data initializer e) subsamples the training clusters so that some elements from the training clusters are included in the design space.}
\label{fig:data_initializer}
\end{figure}
\par The data initialization method was performed via the following algorithm:

\begin{enumerate}
    \item Data were first clustered into $n$ clusters. The number of clusters, $n$, in this work was tested at 16, 32, 64, and 128.
    \item The $n$ clusters were ranked based on the best performing candidate within each cluster, with respect to the material property of interest.
    \item These clusters were divided into training and design space clusters. Different trials varied the fraction of improved clusters that were assigned as design clusters, thereby varying the quality of the design space.
    \item Training clusters were split into two parts.  Some of the data were included in the training set, and others were included in the design space.  This approach was adopted because typical design spaces include both interpolative (similar to training data) and extrapolative (different from training data) candidates. No data points were included in both the training set and design space.
\end{enumerate}
\par The data initialization algorithm exposed several parameters that were used to modulate the difficulty of the sequential learning problem. These parameters are discussed in more detail in the Supplementary Information. A higher fraction of improved clusters in the design space (i.e. more needles in the haystack) corresponds to an easier materials discovery problem. Additionally, the difficulty of the problem could be influenced by which points from a training cluster were included in the design space.  If the best points in a given training cluster were assigned to the design space, then the materials discovery problem was found to be substantially easier because improved candidates existed near to the training data.  On the other hand, if no improved candidates from training clusters were included in the design space, then it was a more difficult sequential learning problem because extrapolation was required to identify improved candidates.
\par This clustering approach to divide the training set and design space was developed to use in our sequential learning simulations, as well as to calculate predictive design space metrics. While this data initialization approach may seem complex, it was developed with great care to realistically reflect how known and unknown data are distributed in materials datasets \cite{meredig2018can,kauwe2019can,ward2018matminer,jia2019anthropogenic}.

\section*{RESULTS AND DISCUSSION}
\subsection*{Effect of design space quality on sequential learning success}
\par Sequential learning is a workflow that requires iterative testing of materials suggested by a machine learning model. It would be extremely valuable to a researcher to know from the outset of a materials discovery project whether the project is likely to take 10 or 100 experiments to find an improved material. For this reason, this paper quantifies sequential learning success as the number of iterations required to find an improved candidate in the design space (i.e. the number of draws from the haystack until a needle is found). This paper quantifies design space quality as the Fraction of Improved Candidates (FIC), or the fraction of candidates in the design space that perform better than the best training candidate (i.e. the fraction of the haystack that are needles). In practice, design space quality is unknown when searching for an undiscovered material. Therefore, predictive metrics correlated with the FIC are useful to assess design space quality.
\par In order to determine the effect of design space quality on sequential learning success, sequential learning simulations were run using the Materials Project and Harvard Clean Energy Project datasets from Table \ref{table:benchmarks}. These datasets were sub-sampled and divided into a training set and a design space using the data initialization method described in the previous section. The five properties used for these simulations, as well as their objectives, are listed in Table \ref{table:benchmarks}.
\par Figure \ref{fig:iters_to_improvement} shows sequential learning success, or the number of iterations required to find an improved candidate from the design space, as a function of the FIC, or the fraction of improved candidates in the design space, over these benchmark test cases. Each color in this scatter plot corresponds to a different sequential learning objective - maximizing, minimizing, or tuning of some target property from a benchmark dataset. Each data point in this scatter plot represents a separate data intialization of the training data and design space, and the error bars represents the standard deviation over 20 trials of identical data initializations caused by stochastic model training.
\par This figure shows that the number of iterations required to find an improved candidate is highly sensitive to the design space quality, or the FIC. For design spaces with low FIC, many iterations are required on average to find an improved candidate, and the number of iterations required has high variance. For design spaces with high FIC, a comparatively lower number of iterations are required to find an improved candidate. This result emphasizes the contribution of the design space quality to the difficulty of a sequential learning problem. Therefore, design space quality is expected to have a strong impact on the success of sequential learning projects, and materials discovery projects in general. Additional experiments were performed to analyze the relationship between other metrics, such as model quality, and the required number of iterations until an improvement is found. The results of these studies indicate that these other metrics, unlike design space quality, are not as strongly tied to sequential learning success. The details of these tests along with their results are available in the Supplementary Information.
\par Additionally, Figure \ref{fig:iters_to_improvement} highlights how sequential learning nearly always outperforms the baseline random-selection strategy, which is shown by the black curve. Assuming design space candidates are chosen randomly at each sequential learning iteration, the required number of iterations to find an improved candidate can be approximated as the expected number of trials until success for a Bernoulli process. This expected number of trials is given by $1/p$, where $p$ is the fixed per-trial probability of success, which is equal to the FIC. This baseline curve is an approximation and is only accurate for large design spaces where sampling without replacement does not significantly affect the probability of success $p$, or the probability for finding an improved candidate.
\par Figure \ref{fig:iters_to_improvement} also shows a clear outlier corresponding to the formation energy from the Materials Project dataset \cite{jain2013commentary}. Upon investigating this outlier case, it was found that the number of iterations to improvement shows a bimodal distribution. In some trials, the model is able to find an improvement immediately, in the first iteration of sequential learning. In other trials, the iterations to improvement is much larger, between 21 and 46 iterations. Based on this evidence, our hypothesis is that there are several poor-performance candidates near the improved candidates in the design space. The model may get ``lucky'' and discover an improved material immediately, or get ``unlucky'' and discover the bad candidate, misleading the model to not continue exploring that region near the other improved candidates. This outlier highlights the existence, and importance, of antagonistic data-splits in the validation and application of machine learning models for materials discovery. Future work includes further investigation of this effect and determining how to mitigate this outcome in experimental (non-simulated) sequential learning applications.
\par In summary, the fraction of improved candidates in the design space (FIC) was identified as a key metric that is strongly correlated to how many experiments are required to find an improved material (i.e. the number of draws from the haystack until a needle is found), emphasizing the contribution of design space quality to the difficulty of a sequential learning project. The FIC can therefore be used to determine how likely a materials discovery project is to succeed.  However, we emphasize that in general, we do not know the FIC for a given design space \textit{a priori}. Thus, we would like to identify other metrics we can calculate based only on the initial training data that are predictive of the FIC.

\begin{figure}[ht]
\centering
\includegraphics[width=0.6\textwidth]{iters_to_improvement.pdf}
\caption{Number of iterations required to find an improved candidate versus the fraction of improved candidates in the design space. Sequential learning simulations were run over five different benchmark simulations, represented by the different colors. Each point represents a different design space and training data initialization, and the error bars on each point represents the variance in number of iterations it takes to find an improved material over 20 sequential learning simulations with the same data initialization but stochastically trained random forest models. The black line represents the expected number of iterations to find an improved candidate via random search strategy, $1/p$.}
\label{fig:iters_to_improvement}
\end{figure}

\subsection*{Predictive metrics to evaluate design space quality}
\par Given the strong relationship between design space quality and sequential learning success (the number of iterations required to find an improved candidate), design space quality can be used as a key indicator to determine the difficulty of a sequential learning problem. However during in-lab materials discovery, design space quality, or the FIC (fraction of improved candidates in the design space), is not known. Therefore, predictive design space metrics to assess the design space quality are desirable.
\par Several predictive design space metrics were examined in this work. Each metric leveraged machine-learned predictions, and some of these metrics leveraged uncertainty estimates. These metrics were then compared against the FIC, the true design space quality. These metrics were calculated for 10 properties over six different benchmark datasets from Table \ref{table:benchmarks} which represent different materials classes, as well as both computational and experimental datasets. Each dataset had 50 to 100 different training data and design space initializations, where each split had varying FIC, or design space quality. Critically, sequential learning iterations were not required to calculate these predictive design space metrics and the FIC, since these quantities can be computed without any iterative data acquisition. Table \ref{table:tested_metrics} presents the Pearson correlations of the examined predictive design space metrics against the true design space quality, or the FIC. Additional details regarding the testing of these predictive metrics, along with their explicit definitions, can be found in the Supplementary Information.

\begin{table} [H]
\centering
\caption{Correlations of predictive metrics with true design space quality. Predictive metrics were tested across the datasets in Table \ref{table:benchmarks} with 10 trials for each data initialization. Metrics in boldface yielded the highest correlations with true design space quality.}
\begin{tabular}{r|c}
 Predictive Metric & Pearson Correlation with True Design Space Quality (FIC)\\
 \hline \hline
 \textbf{Predicted fraction of improved candidates (PFIC)} & \textbf{0.38} \\
 PFIC minus 1$\sigma$ uncertainty & 0.20 \\
 \textbf{Top-10 Cumulative MLI (CMLI)} & \textbf{0.31} \\
 Top-5 CMLI & 0.27 \\
 Predicted fraction of improved, interpolated candidates & 0.11 \\
 Ratio of extrapolated to all design candidates & 0.29 \\
\end{tabular}
\label{table:tested_metrics}
\end{table}

\par As shown in Table \ref{table:tested_metrics}, the PFIC and top-10 CMLI score achieved the highest correlations with true design space quality. Consequently, these two predictive metrics are further discussed in the following sections of this work.

\subsubsection*{Predicted Fraction of Improved Candidates (PFIC) score}
\par The PFIC score is defined as the fraction of design space candidates that are predicted by the model to have improved performance over the best training data point. A machine learning model was fit to the training data to calculate the PFIC score. In this work, a random forest \cite{lolo} was used, where at each leaf node in this random forest, a linear model was fit to the training data. The number of trees was set to the training set size, the maximum tree depth was set to 30, the minimum number of samples required to split an internal node was set to 20, and regularization of the linear model coefficients was implemented.
\par After training this random forest, the model then predicted the performance of all of the candidates in the design space. The PFIC score can then be calculated by:
\begin{equation} \label{PFIC_eq}
    \text{PFIC} = \frac{N_{p(x_{i}) > b}}{|X|}
\end{equation}
In Equation \ref{PFIC_eq}, $X$ is the design space, $x_{i}$ represents each candidate in the design space, $p(x_{i})$ represents the predicted performance of that candidate, $b$ is the performance of the best training data point, $|X|$ is the design space size, and $N_{\alpha}$ is the number of candidates satisfying condition $\alpha$. Therefore, the numerator of this equation represents the number of design space candidates predicted to be improvements over the best training data point, and the denominator represents the design space size. Equation \ref{PFIC_eq} assumes a maximization objective. A minimization objective can be achieved by subtracting the PFIC score from 1.
\par Fitting a linear model at each leaf node of the random forest was a critical choice for calculating the PFIC score. In typical random forest implementations, each leaf predicts a constant value equal to the average of the values of the training data at that leaf. Random forests that use this averaging approach are unable to predict values outside the range of the training data, so no candidates could be predicted to be an improvement over the best training data point. Other regression models that can extrapolate beyond the range of the training data to predict improvements could be paired with the PFIC score. Such regression models include Gaussian process regressors, support vector regressors, neural networks, kernel ridge regressors, and polynomial regressors. While these alternative algorithms were not investigated in this study, they would be of interest for future work.

\begin{figure}[ht]
\centering
\includegraphics[width=0.6\textwidth]{PFIC_score.pdf}
\caption{PFIC score evaluated over benchmark design spaces versus the actual fraction of improved candidates in the design space. Each point represents a different design space and training data initialization, and the error bars on each point represents the variance in PFIC score over 20 identical data initializations. The black line indicates an example threshold value of $t_{PFIC} = 0.2$.}
\label{fig:pfic_score}
\end{figure}

\begin{figure}[ht]
\centering
\includegraphics[width=0.5\textwidth]{PFIC_prec_recall.pdf}
\caption{Precision and recall of the PFIC score as a function of the PFIC threshold, $t_{PFIC}$, in identifying high quality design spaces, where high quality is defined as containing at least 4\% improved candidates in the design space. This 4\% corresponds to a baseline of 25 iterations until an improvement is found via random search over the design space. The dashed black line indicates an example threshold value of $t_{PFIC} = 0.2$.}
\label{fig:pfic_prec_rec}
\end{figure}

\begin{figure}[ht]
\centering
\includegraphics[width=0.4\textwidth]{PFIC_ROC.pdf}
\caption{ROC curve for PFIC score in identifying design spaces with fraction of improved candidates greater than 4\%. The AUC score for this curve is 0.62.}
\label{fig:pfic_roc}
\end{figure}

\par The PFIC score was tested on 10 different benchmarks from Table \ref{table:benchmarks}. The results of these tests are shown in Figure \ref{fig:pfic_score}. This figure shows the PFIC score as a function of the FIC, the true design space quality, across the benchmark datasets. Again, each point represents a separate data initialization, and the error bars represent the standard deviation over 20 trials of the same data initialization caused by stochastic model training. These results demonstrate that the PFIC score can be used to surface design spaces with high FIC at the outset of a project. In other words, the PFIC score could be used to highlight sequential learning projects that are likely to succeed because the haystack is full of needles. While this score does not correctly surface all high quality design spaces (i.e. there are some false negatives), it is still able to identify many high quality design spaces.
\par Figure \ref{fig:pfic_prec_rec} shows the precision and recall of the PFIC score in identifying high quality design spaces as a function of the PFIC threshold, $t_{PFIC}$. In this figure, we defined a high quality design space as containing at least 4\% improved candidates in the design space. This 4\% corresponds to a baseline of 25 iterations until an improvement is found via random search over the design space, which is within the typical range for sequential learning applications \cite{granda2018controlling, brandt2017rapid}. Precision is given by $\frac{TP}{TP+FP}$ and recall is given by $\frac{TP}{TP+FN}$, where $TP$ represents the true positives, or the design spaces correctly categorized as high quality, $FP$ represents the false positives, or the design spaces incorrectly categorized as high quality, and $FN$ represents the false negatives, or the high quality design spaces incorrectly categorized as low quality. Therefore, the precision represents the fraction of the design spaces identified as high quality by the PFIC score that are actually high quality, while recall represents the fraction of all high quality design spaces that were surfaced as high quality by the PFIC score. Figure \ref{fig:pfic_prec_rec} shows that as $t_{PFIC}$ increases, the precision improves while recall worsens. The notch in precision between $t_{PFIC}=0.2$ and $t_{PFIC}=0.3$ is caused by one sequential learning trial with $FIC<0.04$ and $PFIC>0.2$. Between $t_{PFIC}=0.2$ and $t_{PFIC}=0.25$, the precision continues to decrease, as other sequential learning trials with $FIC>0.04$ fall below $t_{PFIC}$. The precision shoots up to 1.0 once the one sequential learning trial with $t_{PFIC}<0.04$ finally falls below $t_{PFIC}$ at around $t_{PFIC}=0.25$.
\par Figure \ref{fig:pfic_roc} shows the ROC curve for the PFIC score in identifying high quality design spaces. The ROC curve plots the recall against the false positive rate, which is given by $\frac{FP}{TN+FP}$, where $FP$ represents the false positives, or the design spaces incorrectly categorized as high quality, and $TN$ represents the true negatives, or the low design spaces correctly categorized as low quality. The AUC score for this curve is 0.62. A perfect classifier has an AUC score of 1.0, while a classifier that is not capable of distinguishing between the high and low quality design spaces has an AUC score of 0.5. The PFIC score can thus be used to surface materials discovery problems with high quality design spaces.

\subsubsection*{Cumulative Maximum Likelihood of Improvement (CMLI) score}
\par The CMLI score is defined as the predicted probability that at least one candidate, out of the top $n$ candidates in the design space with the highest likelihoods of improvement, performs better than the best training data point, where $n$ is tunable by the experimenter. Calculating this score required using a machine learning model to predict the performance and estimate the uncertainty for each design space candidate.
\par This study used a random forest with constant-value leaves as the underlying machine learning algorithm \cite{lolo}. The uncertainty estimates were calculated using a combination of jackknife-based methods and an explicit bias model \cite{Ling2017,efron2012model}. However, other algorithms such as Gaussian process regressors could also be used to compute the CMLI score.
\par First, the top $n$ candidates with the highest likelihoods of improvement must be identified. The likelihood of improvement $L$ of a given design space candidate $x_{i}$ is given as:
\begin{equation} \label{Likelihood_L_eq}
    L(x_{i}) = \int_{b}^{\infty} N(p(x_{i}), \sigma(x_{i}))dx
\end{equation}
In Equation \ref{Likelihood_L_eq}, $N(\mu, \sigma)$ is a normal distribution, with mean $\mu$  and standard deviation $\sigma$. The mean  $\mu$ is given by the predicted performance of candidate $x_{i}$ by the machine learning model, and the standard deviation $\sigma$ is given by the estimated uncertainty. Therefore, this integral represents the probability that design candidate $x_{i}$ is an improvement over the best training data point. Equation \ref{Likelihood_L_eq} assumes a maximization objective. Calculating the likelihood of improvement $L$ for a minimization objective would require changing the integration limits to $-\infty$ and $b$. While normally-distributed uncertainties were used in this study, $L$ could be readily computed for other uncertainty distributions as long as they are integrable.
\par After the top $n$ candidates are identified, the CMLI score can be computed by the following equation:
\begin{equation} \label{CMLI_eq}
    \text{CMLI} = 1 − \prod_{i=1}^{n} (1 − L(x_{i}))
\end{equation}
\par Equation \ref{CMLI_eq} takes the product of the likelihoods that candidates $x_{i}$ are \emph{not} an improvement, $1-L(x_{i})$. This product is over the top $n$ candidates from the design space with the highest likelihoods $L$ of performing better than the best training data point. This equation assumes that these likelihoods are independent for the top $n$ candidates. Therefore, this score assesses the likelihood that at least one of these top $n$ candidates is an improvement. In many cases, the model predictions are not independent, so the CMLI score has a tendency to overestimate the true design space quality. As a result, using $n = |X|$ results in CMLI scores near unity for large design spaces. This study uses CMLI score with $n$ at a value of 10. However, we have tested other values of $n$ which did not substantially affect the benchmarked test results.

\begin{figure}[ht]
\centering
\includegraphics[width=0.6\textwidth]{CMLI_score.pdf}
\caption{CMLI score for top-10 candidates evaluated over benchmark design spaces versus the actual fraction of improved candidates in the design space. Each point represents a different design space and training data initialization, and the error bars on each point represents the variance in CMLI score over 20 identical data initializations. The black line indicates an example threshold value of $t_{CMLI} = 0.7$.}
\label{fig:cmli_score}
\end{figure}

\par The CMLI score was tested on 10 benchmark datasets from Table \ref{table:benchmarks}. The results from these tests are shown in Figure \ref{fig:cmli_score}. This figure shows the CMLI score versus the actual fraction of improved candidates across these benchmark datasets. This figure shows that the benchmark cases with low CMLI scores generally correspond to lower FICs, and that the CMLI score can be used to flag low quality design spaces at the outset of a project. Therefore, this score can be used to filter out sequential learning projects that may be difficult due to scarcity of needles in the haystack. While this score is not able to flag all low quality design spaces, it is able to identify some design spaces with low FIC.

\begin{figure}[ht]
\centering
\includegraphics[width=0.5\textwidth]{CMLI_prec_recall.pdf}
\caption{Precision and recall of the CMLI score as a function of the CMLI threshold, $t_{CMLI}$, in identifying low quality design spaces, where low quality is defined as containing less than 4\% improved candidates in the design space. This 4\% corresponds to a baseline of 25 iterations until an improvement is found via random search over the design space. The dashed black line indicates an example threshold value of $t_{CMLI} = 0.7$.}
\label{fig:cmli_precision_recall}
\end{figure}

\begin{figure}[ht]
\centering
\includegraphics[width=0.4\textwidth]{CMLI_ROC.pdf}
\caption{ROC curve of CMLI score in identifying design spaces with fraction of improved candidates less than 4\%. The AUC score for this curve is 0.65.}
\label{fig:cmli_roc}
\end{figure}

\par Figure \ref{fig:cmli_precision_recall} shows the precision and recall of the CMLI score in identifying low quality design spaces as a function of the CMLI threshold, $t_{CMLI}$. In this figure, we defined a low quality design space as containing less than 4\% improved candidates in the design space. For the CMLI score identifying low quality design spaces, the precision represents the fraction of design spaces identified as low quality by the CMLI score that are actually low quality, while the recall represents the fraction of all low quality design spaces that were surfaced as low quality by the CMLI score. Figure \ref{fig:cmli_roc} shows the ROC curve for the CMLI score in identifying low quality design spaces. The AUC score for this curve is 0.65, where a perfect classifier has an AUC score of 1.0, while a classifier that is not capable of distinguishing between the high and low quality design spaces has an AUC score of 0.5. The CMLI score can thus be used to flag materials discovery problems with low quality design spaces.

\subsection*{Design space evaluation system}
\par We have investigated two metrics, the PFIC and CMLI scores, that can identify high quality and low quality design spaces respectively. Therefore, these two can be combined into a system for assessing design space quality. In the below description of our design space evaluation system, we refer to ML Models A and B. Model A must be capable of estimating uncertainty, and Model B must be able to predict output values that extrapolate outside the range of the training data, in order to predict improved design space candidate performance. In this work, we used two distinct models, but a single algorithm could potentially satisfy the requirements for both Models A and B.
\begin{enumerate}
    \item Build a machine learning Model A with uncertainty estimates on the training data
    \item Use Model A to make predictions and uncertainty estimates for each design space candidate
    \item Use the predictions and uncertainty estimates from Model A to calculate the CMLI score for the top $n$ candidates
    \item Build a machine learning Model B on the training data
    \item Use this Model B to make predictions for each design candidate
    \item Use the predictions from Model B to evaluate the PFIC score
    \item Set thresholds $t_{CMLI}$ and $t_{PFIC}$ based on desired precision and recall
    \item Use the predictive metrics to assess design space quality
\end{enumerate}

\begin{figure}[ht]
\centering
\includegraphics[width=0.7\textwidth]{ds_hist.pdf}
\caption{Distribution of design space qualities as classified by design space evaluation system with $t_{PFIC} = 0.2$ and $t_{CMLI} = 0.7$. The y-axis shows the normalized count of design spaces such that the area sums to 1. }
\label{fig:evaluation_system_dist}
\end{figure}

Figure \ref{fig:evaluation_system_dist} shows the distribution of design spaces as classified by this design space evaluation system with example thresholds of $t_{PFIC} = 0.2$ and $t_{CMLI} = 0.7$. The x-axis is the fraction of improved candidates in the design space, and the y-axis is the density of the design spaces. The black bars show the overall distribution of design spaces. The orange bars represent the design spaces that were classified as high quality by the design space evaluation system, the blue bars represent design spaces classified as low quality, and the grey bars represent design spaces that were classified as unknown quality.
\par In this example, we have defined high quality design spaces as having greater than 4\% of improved candidates in the design space, and low quality design spaces as having less than 4\% of improved candidates. Again, this 4\% corresponds to a baseline of 25 iterations until an improvement is found via random search over the design space, which is within the typical range for sequential learning applications \cite{granda2018controlling, brandt2017rapid}. Ideally, all design spaces with FIC $>0.04$ should be classified as high quality, those with FIC $<0.04$ should be classified as low quality, and there should not be any unknown quality design spaces. The distribution of all design spaces in black show that most design spaces have less than 10\% of improved candidates. This mirrors real materials development projects; very few projects have design spaces with many improvements, with the exception of nascent projects where not many materials have been explored. Additionally, the distribution of unknown quality design spaces closely follows the distribution of all design spaces. While the design space evaluation system is not perfect, Figure \ref{fig:evaluation_system_dist} shows that the design spaces classified as low quality are generally below 0.04 on the x-axis, while the design spaces classified as high quality are generally above 0.04 on the x-axis. Therefore, this system is useful in distinguishing between low and high quality design spaces.
\par With these example thresholds, we are able to identify high quality design spaces with a precision of 0.94 and recall of 0.06. This means that 94\% of the design spaces identified as high quality by the design space evaluation system are actually high quality. Additionally, this means that we are able correctly surface 6\% of all the high quality design spaces. Meanwhile, we are able to flag low quality design spaces with a precision of 0.96 and a recall of 0.23. This means that 96\% of the design spaces flagged as low quality are actually low quality, and that we are able to correctly identify 23\% of all low quality design spaces. In this example, the design space evaluation system is lossy - many design spaces are categorized as unknown quality. However, we can be fairly confident in design spaces that are categorized as high or low quality. Additionally, the thresholds for the predictive metrics can be tuned to achieve the desired precision and recall scores for both high quality and low quality design spaces.
\par This design space evaluation system uses both the PFIC and CMLI scores to identify high quality and low quality design spaces. Given the strong impact of design space quality on the difficulty of the overarching sequential learning project, this design space evaluation system is a useful tool for assessing the probability of success for any given materials development project.

\section*{CONCLUSION}
\par While many previous studies have explored the promise of machine learning methods for accelerating materials development, the use of machine learning to assess the difficulty of a materials development project \textit{a priori} represents a new contribution. However, it would be extremely valuable to know how hard a materials development project would be at the outset.  This information could be used to determine which projects to invest in and how to allocate resources across a research and development portfolio.  For groups investing in multiple projects simultaneously, information on project difficulty could be used to balance the project portfolio between high-risk and low-risk projects.
\par In this work, we demonstrated the importance of design space quality on materials discovery success, introduced a new data initialization method to reflect in-lab materials discovery, and finally, defined novel predictive metrics to determine the quality of a design space at the outset of a materials discovery project.
\par After evaluating these design space metrics across a variety of computational and experimental materials datasets, we identify two scores - PFIC and CMLI - which correlate with the true design space quality. These two metrics are then combined into a high-precision, model-agnostic design space evaluation system. Our work is thus a first step towards determining, \emph{a priori}, the difficulty of a materials development project.  While all the trials run in this study used a simulated sequential learning workflow, these predictive metrics could also be used in more traditional development settings where experimental test order was determined by a scientist instead of a machine learning algorithm.  In all cases, the fraction of improved candidates in the design space will be strongly related to the difficulty of a materials development project.
\par Future work may include testing additional predictive metrics to reveal further insights on design space quality. Future work may also include correlating predictive design space metrics directly to sequential learning success (iterations until an improvement is found) rather than the FIC, in order gain further insight on how these predictive metrics can be used to prioritize sequential learning projects. These metrics may also be calculated at every iteration of sequential learning to analyze how these metrics change when new data points are added to the training data. Additionally, further experiments may be conducted to determine how model accuracy, algorithmic choice, and uncertainty quantification approach affect the accuracy of our predictive design space metrics. Finally, the metrics discussed in this work concern the optimization of single materials properties, and future work may address the challenge of multi-objective optimization.

redpajama_set_name: RedPajamaArXiv


text:
Netanyahu's Palestine strategy rapped by coalition partners
The censure exposed deep rifts in the government but there appeared to be little sign of any imminent collapse.
08 Haziran 2014 Pazar 23:57
Israeli Prime Minister Benjamin Netanyahu's handling of the now-moribund Palestinian peace negotiations drew criticism from both sides of his own coalition on Sunday, and one partner said relations with the United States had plunged to an all-time low.
The censure, aired at a showcase policy conference attended by foreign dignitaries as well as the country's senior politicians, exposed deep rifts in the government but there appeared to be little sign of any imminent collapse.
The next Israeli election is scheduled for 2017 and opinion polls have found no serious potential challenger to the third-term, conservative premier.
Finance Minister Yair Lapid, whose centrist Yesh Atid party is the second-biggest coalition member, said Israel had been too opaque about the extent to which it might cede the occupied West Bank to a future Palestinian state in the U.S.-sponsored talks.
The administration of President Barack Obama has blamed the diplomatic impasse on both its Israeli ally and Palestinian President Mahmoud Abbas, whose surprise reconciliation with rival Hamas in late April prompted Netanyahu to call off peacemaking.
Washington has since further jarred Israel by offering to work with the new Palestinian unity government soon after it was sworn in last week. Netanyahu is shunning Abbas, demanding he first spurn Hamas.
Addressing the annual Herzliya Conference near Tel Aviv, Lapid said there was an "unprecedented crisis" in Israeli-U.S. relations "brought about by problematic and sometimes disrespectful conduct on our part".
He faulted government funding for Jewish settlements in far-flung parts of the West Bank which he saw going to the Palestinians in the future.
Taking the stage next, Economy Minister Naftali Bennett, whose ultranationalist Jewish Home party is the third-biggest in the coalition and opposes Palestinian statehood, accused the government of sapping Israel's security by "constantly freeing terrorists" from jail in past goodwill gestures to Abbas.
WALKOUT THREATS
Bennett urged Netanyahu to adopt his vision of sweeping Israeli annexations of West Bank settlement blocs and leaving Palestinians with limited self-rule elsewhere in the territory.
Lapid, however, preempted Bennett's call by saying that should Israel try to annex any settlements unilaterally, Yesh Atid "will not only quit the government, it will topple it".
The threat was echoed by Justice Minister Tzipi Livni, head of the centrist Hatnua party and the cabinet member formerly in charge of negotiations with the Palestinians.
Livni, who has taken flak from ministers in Netanyahu's rightist Likud party for meeting independently with Abbas in London in mid-May, sounded an unusually harsh critique of the settlements.
"The settlements do not provide security for Israel. They consume security ... They harm Israeli security," she said at the conference.
Livni argued that the Netanyahu government should "work with the Palestinian government in accordance with Israeli interests". She said that Abbas, who lost control of the Gaza Strip to Hamas in 2007, must be told to re-impose his authority over the coastal territory.
Dovish Israeli President Shimon Peres also appeared to be parting with Netanyahu's hardline on Abbas in a very public fashion by meeting the Palestinian leader at a prayer for peace hosted by Pope Francis at the Vatican on Sunday.
The office of Netanyahu, who was not scheduled to speak at Herzliya, declined to respond to the comments on Sunday.
Güncelleme Tarihi: 08 Haziran 2014, 23:57
#Israel,
#Netanyahu,
#Israeli-Palestinian talks,
#palestine unity government


Tom Cruise proved his mettle as an actor way back in 1992 when he starred in A Few Good Men. With Valkyrie, he is a pleasure to watch as the headstrong Col Stauffenberg, who just couldn't go against his raging conscience.
Historians say there were between 17 to 42 failed attempts to kill Hitler, and Valkyrie, which originated in Hitler's own army was the boldest one of all. Attempted on July 20, 1944, Stauffenberg came up with the plan to use Hilter's Reserve Army (Operation Valkyrie) against his own regime. Ambitious though it was, Hitler was lucky and survived the attempt.
The movie makes you realize how outraged people were with Hitler even in his own ranks. Tom Cruise and other notable actors play their roles to perfection as the conspirators who will ultimately pay with their lives.
Interestingly, as you watch the movie, the director is able to create a suspense so strong, it makes you forget that you actually know the outcome of this story. Hitler was not assassinated. But as you watch Stauffenberg and his men, you wish from your heart that they would succeed. Unfortunately, that doesn't happen and WWII gets bloodier, with Hitler committing suicide only nine months later.
I would recommend this movie for any person who is remotely interested in WWII history. The director also spares you any discomfort by not showing any ghastly images from the era, and the filmy is mostly confined to the white corridors of power.
For some facts on the Stauffenberg plot, see this link.
I saw this movie last month... Loved it totally !


Welcome to our new online training course in Reptile and Venomous Snake Control. This course will still require a practical face to face class where we will cover the skills of identification of reptiles as well as the practical handling skills for a variety of reptiles. The course covers all the same materials as a face to face course, however it can be done in the comfort of your own home and can be completed over a few evenings rather than one single sessions.
We will require you to print off your certificate of completion for the theory units and bring it to your practical assessment day.


There are times when I just don't feel like cooking, but I want dinner. I'm sure it happens to everyone. At those times I want something that takes a minimum of fuss but packs a flavor punch. Something that doesn't take too much time and leaves little mess to clean up. Something that won't interfere with a busy night spent on the couch catching up with my shows.
I don't remember exactly when I first tossed a can of salmon into brown rice, but it was super easy and tasted good just with the salmon and rice. Over the years I have refined it bit by bit - first I started adding dill to the rice, then peas. This time I added one of my more recent discoveries, gremolata, which pushed it completely over the top. More on that later.
It does take 45 minutes to cook the rice, but you don't have to be in the kitchen the whole time. I usually put the rice on, go relax for half an hour, and then prepare the salmon and the garnish.
Rinse rice and place in a 3-quart saucepan with the water. Bring to a roiling boil; let boil for a minute. Cover and turn heat as low as it will go. Cook for 45 minutes. Do not lift the lid while the rice is cooking. After the 45 minutes, lift the lid just long enough to throw the frozen peas into the pan with the rice; cover and let stand for 5 minutes.
About 15 minutes before the rice is done, remove the salmon from the can and place it in a large mixing bowl. You do not have to remove the bones from the salmon, but you can if you don't want them in there. I usually remove the large backbone, but the smaller bones are too much trouble and I can't tell they're there so I usually don't bother.
Once the rice and peas are finished, add them to the bowl with the salmon. Add the dill, olive oil, lemon juice, garlic powder, salt and pepper and mix well.
Serve garnished with chopped parsley or gremolata.
http://foodmarketnews.blogspot.com and I would appreciate if you could kindly have a look at my blog too. It's updated on a daily basis.


A snowy winter's night. Three small children are chased from their home by the forces of a merciless darkness. Ten years later, Kate, Michael and Emma are no closer to the truth about what separated their family.
The answer lies with an enchanted atlas.


As the holidays approach, parents are thinking about their kid's upcoming week long breaks from school and many are panicking. What do you do with a kid when they're off for an entire week or more?
There are the usual things like playing with friends, movies, etc… but wouldn't it be nice to actually connect with your kid?
During the school year, most days seem to be spent rushing around: getting ready for school/work, hurrying home to make dinner, dashing off to one activity or another. Holiday breaks can be a nice opportunity to take it a little slower and spend more quality time with your child.
As my tween is about to head into her Thanksgiving break, I'm compiling a few things for us to do. Here's my list of the easiest, low stress ways to connect with your kid on a school break.
Cook – This is my number one go to activity because of the learning potential. Not only does cooking or baking with your kid reinforce the skills needed to make a meal, kids of all ages can learn something or reinforce existing skills like measuring, following directions and reading nutritional values. While cooking together, you also teach your child about effective communication.
Crafts – Getting crafty with a kid can be tough for those of us that are control freaks, I know. I get it. Regardless, one of the best ways to reinforce fine motor skills and concentration, while also allowing for some fantastic no pressure chatting. It's amazing the things that will come out of my kid's mouth when her hands are busy.
Chores – Yup, things still need to get done during the week and cleaning or getting things done around the house provides another opportunity to connect with your kid. Just remember to keep it fun, stay together and resist the urge to turn into a drill sergeant. Some examples of chores to do with your kid are weeding, dusting, hanging holiday decorations or bathing the dogs.
Culture – Spend a few hours in a museum or library. There is something special in the air at those quiet places that allows for real connection. Time spent together in either of these two types of places is a low cost, or free, way to expand your child's mind. Connect with your kid on another level.
Volunteer – This doesn't fit my C word scheme but it's too good to leave out. One of the best volunteering experiences my kid and I have had was spending a few hours working at a food bank. At a food bank, they sort and check all the donated food that then gets distributed to food pantries. Volunteering is a character builder for anyone, but especially children and teens. It opens the door for conversations on gratitude, social issues and the importance of being a citizen that gives back.
Don't let your kid waste his or her holiday break in front of the TV or computer. Use the time off to your advantage to connect with your kid.
How do you connect with your kid during their holiday breaks? What do you have planned?


text:
Riverview Bistro in Stratford, Connecticut, signed up with Energize Connecticut in March 2014.
Embracing Energy Efficiencies
February 2016 Bryan Reesman
Margins are tight and unforgiving in the restaurant business, but an obvious opportunity for savings comes in the form of energy efficiency, and numerous state-funded programs offer cost-cutting incentives to help businesses become more sustainable.
The United Illuminating Company (ui) in Connecticut handles 20 percent of that state's energy supply and provides restaurants with a 36-month history of their energy consumption, including hours of the day and the days of the week, to show where savings can be earned. If restaurants join the program, incentives may include up to 50 percent of costs covered by the state and zero percent financing, for those businesses with strong credit. When the state-funded Energize Connecticut program started in 2000, it provided opportunities for restaurants to improve efficiency in the areas of refrigeration controls, HVAC, and lighting. More recently, innovations like heat-recovery grills and advanced LED lights that can match the color and tone of existing installed lights have come to market.
David Petrone, chef and owner of Riverview Bistro in Stratford, Connecticut, which opened in 2012, signed up with Energize Connecticut in March 2014. The state covers about 50 percent of the tab and allows him to pay the rest interest-free over three and a half years. Petrone opted to implement the entire efficiency package, from LED lighting to controlled thermostats and sustainable water faucets. The restaurant's monthly payment is roughly $700, which is about half its monthly savings of around $1,300.
"Restaurants do not have the highest profit margin, so it's a dollar-and-cents business," Chef Petrone says. "You have to find every angle you can to save as much as you can. In my case, I had to look at the real operating costs that I could play with—gas, electric, water—because those are things you can control to a certain degree."
For instance, he replaced the 400-watt lightbulbs used in the restaurant's parking lot with a 60-watt LED panel that was brighter. In the kitchen, he transitioned from four 34-watt bulbs to two bulbs plus mirrors.
Another Connecticut operator who has worked with UI is Brian Faye, co-owner of Lenny and Joe's, which has locations in New Haven, Madison, and Westbrook. The New Haven restaurant is the newest unit and the first to become energy efficient. "It's a great thing to do to help out, and the cost [savings] are great, too," Faye says, adding that UI "came in and redid all of our light bulbs. We also purchased an energy-efficient refrigerator/freezer, and we redid all of the parking lot lights. It's been great."
Since completing the UI–recommended efficiencies project in October 2013 (they started working on it that February), the 10,000-square-foot location has saved an estimated $10,000 to $11,000 a year. In a scenario that has been common for UI, Faye admits he ignored the program the first couple of times he was approached. "Then I met with them, they explained everything, and I decided to go forward," Faye says. "If something seems too good to be true it probably is, but they said they'd pay for everything and redo it all."
And they did. "I didn't really have to do anything," Faye says.
Similar programs to help restaurants conserve costs and operate more efficiently exist around the country, such as those from ComEd in the Chicago and northern Illinois markets. Exploring incentive programs to conserve energy is certainly a conversation worth having with local utility providers, and national programs are worth exploring as well, such as the Green Restaurant Association, a non-profit based in Boston that provides tools for restaurants to become more energy efficient and environmentally friendly. Similarly, the National Restaurant Association's Conserve program provides do-it-yourself tips for operators and works with organizations including Energy Star, the Food Service Technology Center, and the Food Waste Reduction Alliance.