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Big Black and Bulwinkle at Harrison House
Big Black and Bulwinkle at Harrison House | KCET
Ted Quinn
Ted Quinn is an artist, musician and cultural activist, as profiled in the documentary, 'Nowhere Now: the Ballad of Joshua Tree;" Host of the Local Music Showcase on Z107.7 FM in Joshua Tree, California.
As President and Music director for Morongo Basin Arts Council, Quinn produced two compilation CD's, featuring many of the desert's most interesting musicians, while presiding over the 2011 Hwy 62 Art Tours, the area's largest annual Arts event.
Mark Bulwinkle at Harrison House. | Photo: Eva Soltes.
At sunset, pink clouds smear across a faded, aqua blue sky, looking out from inside a capsule-shaped temple, made of earth and straw. The dome ceiling is hung with a flock of plasma-cut, aluminum birds, with a powder finish, in turquoise, gold and copper, like giant, shiny ornaments. The artist, Mark Bulwinkle, had discovered his style while working as an arc welder, post-design school, and enjoyed a rise to fame in the 1970's, left his day job and then proceeded to watch all of his curator friends fall away from AIDS.
Harrison House in Joshua Tree is decorated inside and out with Bulwinkle's "Brake Job Sculptures."
Fifteen years after his last show of new work, Bulwinkle's residency at the home of his old friend, the late composer Lou Harrison, has rekindled the blue flame in his welder's torch.
In the years between, imitators took it upon themselves to make their own rusty, steel-cut "Bulwinkles" and sell them to designers without access to the real thing.
Invited by Harrison House curator, and longtime friend, "the salubrious, multi-talented desert hostess" and acclaimed dancer, Eva Soltes, Bulwinkle paraphrases his other-world host, Lou Harrison, "I am thankful for this opportunity to be free to do my best work."
Harrison House was the final work of Lou Harrison and his lover/collaborator, Bill Colvig. Fabricated from straw bales, it was the first building of it's kind to be permitted in San Bernardino County, resembling a small chapel. It was made for acoustic instruments and voices to ring out and sing within it's adobe walls and high ceilings.
Bill, Eva and Lou at Taliesin. | Photo: Courtesy of Eva Soltes.
Bill and Lou in Japan. | Photo: Eva Soltes.
While Lou Harrison envisioned this house, his final acoustic 'instrument,' as a place for unamplified music, it has been transformed into a living, breathing space for all of the arts by his friend, Eva Soltes, who brings forty-plus years experience, as an artist and arts presenter, to this high desert temple of culture. Soltes has presented countless Bay Area performances, including early work by composer/performer Laurie Anderson.
"I honor Lou for making such an amazing place and for being the miraculous person that he was," Eva says, but the activities that go on at Harrison House are the result of her own dreams and hard work. Harrison House is the venue for Eva's personal vision of how she can "make the world a better place."
Harrison House, under the guidance of Soltes, since Harrison's death in 2003, has presented numerous world-class (and 'world') musicians, ranging from experimental Cambridge guitarist Fred Frith (from the 70's prog band, Henry Cow) to Cameron Powers, oud player and author of the book, "Harmonic Secrets of Arabic Music Scales." There have been Butoh dance workshops with Koichi and Hiroko Tamano, performances by Russian violin duo Movses Pogossian and Varty Manouelian and many more, lovingly hand-selected by Soltes.
In Joshua Tree, a town on the edge of the Mojave desert, with a population of under 10,000, Soltes has found an eager, supportive audience for Harrison, his "House" and the varied sounds of their eclectic friends and contemporaries.
Eva Soltes. | Photo: Vera Topinka.
Harrison had a long career as a composer, having worked alongside fellow 20th Century masters Charles Ives and John Cage. But while most Modern American classical music took Arnold Schoenberg's 12 tone as a jumping off point, Harrison's optimistic compositions found their inspiration in the sounds of nature, rainfall, and the Gamelan music of Indonesia.
Soltes has recently completed a documentary, a labor of love for her dearly departed friends, Lou and Bill, entitled, "Lou Harrison; A World of Music," and truly, his compositions embraced a world of sounds, often made on 'found object' instruments devised by Colvig. The film has helped to secure Harrison's place as one of the important composers of 'serious' music, while illustrating his childlike enthusiasm for experimentation.
An engagement at the Castro Theatre in San Francisco brought Harrison - and Soltes - home to the Bay Area, where numerous screenings were added to the initial, sold out, run.
Harrison's pioneering work, along with his openly gay lifestyle, in an era when his sexuality was still considered outside of the mainstream, is finally being recognized by a larger, more receptive audience.
Saturday night's performer, Big Black, is "tellin' the blues," in a combination of hand drumming, free-form improvisation and story-telling/singing.
Big (Danny) Black's long career has included playing with Hugh Masakela at Monterey Pop Festival - back in the Summer of Love, around the time Harrison and Colvig first met; He's shared stages with John Coltrane, Dizzy Gillespie, Richie Havens, Freddie Hubbard, BB King, Oscar Peterson and Muddy Waters. In Zaire, 1974, Big Black played congas in Muhammed Ali's corner, prior to "Rumble in the Jungle" with George Foreman.
Photo: Eva Soltes.
Now, his presence fills the room, as he graciously welcomes British guitarist and Joshua Tree resident, Clive Wright, to jam. A local drum-circle organizer, Sam Sloneker, joins in, watching the master at work.
"No program should open without the heartbeat. Let us pray..." His large black hands commence to pound out a gentle rhythm.
A cell phone rings as an embarrassed audience member hurries to turn it off. "It's all music," Black assures her.
Later, the trio is joined by a long-ago collaborator, John "Juke" Logan, best known for his harmonica contribution to the Walter Hill film, "Crossroads." Juke has recently beaten esophageal cancer and is eager to blow his harp with his old comrade. "Forty years ago, we did a lot of crazy things...We saved the Santa Monica Merry-Go-Round from being demolished..."
Black recalls his early days in "the Carolinas."
"When I was 7 years old, in second grade, there was a beautiful little girl named Georgia Lee, with coal black eyes and a paper sack tan. I'd walk her home from school each day. Then, one day, out of the clear blue sky, Georgia Lee gave me a kiss. I stayed awake at night, thinking about that. 75 years later, I'm still thinking about it. Two weeks later, her family moved away. Years went by but I still thought about her. One day, I wrote a piece about her."
"Everybody's blues is a special kind of blues. It's taught me to accept it all. Tolerance is the name of the game. As human beings, we're low on tolerance. To me, the greatest threat to our existence is 'belief' - the most misused word. It alleviates you of any responsibility. Apply it to religion, it becomes 'sacred.' All the wars in the world...people being killed every day because of what they believe."
"There's a beast in the east/The beast is looking for a feast/The fattest lamb in the flock to fleece..."
The following morning is Sunday drum meditation at Harrison House.
"Speak to me," Big Black sings a capella, 'in the manner of the chapel,' before tapping on his drum.
"In the beginning was the word...Om...Creation. Praise the god of love and the universe within/We offer this love to everyone/Let us all begin."
To make the world a better place...
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Philosophy of Science Association 2016 Symposium
PSA 2016 Symposium:
Philosophy of Statistics in the Age of Big Data and Replication Crises
Friday November 4th 9-11:45 am (includes coffee break 10-10:15)
Location: Piedmont 4 (12th Floor) Westin Peachtree Plaza
Deborah Mayo (Professor of Philosophy, Virginia Tech, Blacksburg, Virginia) "Controversy Over the Significance Test Controversy" (Abstract)
Gerd Gigerenzer (Director of Max Planck Institute for Human Development, Berlin, Germany) "Surrogate Science: How Fisher, Neyman-Pearson, and Bayes Were Transformed into the Null Ritual" (Abstract)
Andrew Gelman (Professor of Statistics & Political Science, Columbia University, New York) "Confirmationist and Falsificationist Paradigms in Statistical Practice" (Abstract)
Clark Glymour (Alumni University Professor in Philosophy, Carnegie Mellon University, Pittsburgh, Pennsylvania) "Exploratory Research is More Reliable Than Confirmatory Research" (Abstract)
Key Words: big data, frequentist and Bayesian philosophies, history and philosophy of statistics, meta-research, p-values, replication, significance tests.
Science is undergoing a crisis over reliability and reproducibility. High-powered methods are prone to cherry-picking correlations, significance-seeking, and assorted modes of extraordinary rendition of data. The Big Data revolution may encourage a reliance on statistical methods without sufficient scrutiny of whether they are teaching us about causal processes of interest. Mounting failures of replication in the social and biological sciences have resulted in new institutes for meta-research, replication research, and widespread efforts to restore scientific integrity and transparency. Statistical significance test controversies, long raging in the social sciences, have spread to all fields using statistics. At the same time, foundational debates over frequentist and Bayesian methods have shifted in important ways that are often overlooked in the debates. The problems introduce philosophical and methodological questions about probabilistic tools, and science and pseudoscience—intertwined with technical statistics and the philosophy and history of statistics. Our symposium goal is to address foundational issues around which the current crisis in science revolves. We combine the insights of philosophers, psychologists, and statisticians whose work interrelates philosophy and history of statistics, data analysis and modeling.
Philosophy of statistics tackles conceptual and epistemological problems in using probabilistic methods to collect, model, analyze, and draw inferences from data. The problems concern the nature of uncertain evidence, the role and interpretation of probability, reliability, and robustness—all of which link to a long history of disputes of personality and philosophy between frequentists, Bayesians, and likelihoodists (e.g., Fisher, Neyman, Pearson, Jeffreys, Lindley, Savage). Replication failures have led researchers to reexamine their statistical methods. Although nowadays novel statistical techniques use simulations to detect cherry-picking and p-hacking, we see a striking recapitulation of Bayesian-frequentist debates of old. New philosophical issues arise from successes of machine learning and Big Data analysis: How do its predictions succeed when parameters in models are merely black boxes? One thing we learned in 2015 is why they fail: a tendency to overlook classic statistical issues– confounders, multiple testing, bias, model assumptions, and overfitting. The time is ripe for a forum that illuminates current developments and points to the directions of future work by philosophers and methodologists of science.
The New Statistical Significance Test Controversy. Mechanical, cookbook uses of statistical significance tests have long been lampooned in social sciences, but once high-placed failures revealed poor rates of replication in medicine and cancer research, the problem took on a new seriousness. Drawing on criticisms from social science, however, the new significance test controversy retains caricatures of a "hybrid" view of significance testing, common in psychology (Gigerenzer). Well-known criticisms—statistical significance is not substantive significance, p-values are invalidated by significance seeking and violated model assumptions—are based on uses of methods warned against by the founders of Fisherian and Neyman-Pearson (N-P) tests. A genuine experimental effect, Fisher insisted, cannot be based on a single, isolated significant result (a single low p-value); low p-values had to be generated in multiple settings. Yet sweeping criticisms and recommended changes of method are often based on the rates of false positives assuming a single, just-significant result, with biasing selection effects to boot!
Foundational controversies are tied up with Fisher's bitter personal feuds with Neyman, and Neyman's attempt to avoid inconsistencies in Fisher's "fiducial" probability by means of confidence levels. Only a combined understanding of the early statistical and historical developments can get beyond the received views of the philosophical differences between Fisherian and N-P tests. People should look at the properties of the methods, independent of what the founders supposedly thought.
Bayesian-Frequentist Debates. The Bayesian-frequentist debates need to be revisited. Many discussants, who only a decade ago argued for the "irreconcilability" of frequentist p-values and Bayesian measures, now call for ways to reconcile the two. In today's most popular Bayesian accounts, prior probabilities in hypotheses do not express degrees of belief but are given by various formal assignments or "defaults," ideally with minimal impact on the posterior probability. Advocates of unifications are keen to show that Bayesian methods have good (frequentist) long-run performance; and that it is often possible to match frequentist and Bayesian quantities, despite differences in meaning and goals. Other Bayesians deny the idea that Bayesian updating fits anything they actually do in statistics (Gelman). Statistical methods are being decoupled from the philosophies in which they are traditionally couched, calling for new foundations and insights from philosophers.
Is the "Bayesian revolution," like the significance test revolution before it, ushering in the latest in a universal method and surrogate science (Gigerenzer)? If the key problems of significance tests occur equally with Bayes ratios, confidence intervals and credible regions, then we need a new statistical philosophy to underwrite alternative, more self-critical methods (Mayo).
The Big Data Revolution. New data acquisition procedures in biology and neuroscience yield enormous quantities of high-dimensional data, which can only be analyzed by computerized search procedures. But the most commonly used search procedures have known liabilities and can often only be validated using computer simulations. Analyses used to find predictors in areas such as medical diagnostics are so new that often their statistical properties are unknown, making them ethically problematic. Questions about the very nature of replication and validation, and of reliability and robustness enter. Without a more critical analysis of the foibles, the current Human Connectome project to understand brain processes may result in the same disappointments as gene regulation discovery, with its so far unfulfilled promise of reliably predicting personalized cancer treatments (Glymour).[See new topic.]
The wealth of computational ability allows for the application of countless methods with little handwringing about foundations, but they introduce new quandaries. The techniques that Big Data requires to "clean" and process data introduce biases that are difficult to detect. Can sufficient data obviate the need to satisfy long-standing principles of experimental design? Can data-dependent simulations, resampling and black-box models ever count as valid replications or genuine model validations?
The Contributors: While participants represent diverse statistical philosophies, there is agreement that a central problem concerns the gaps between the outputs of formal statistical methods and research claims of interest. In addition to illuminating problems, each participant will argue for an improved methodology: an error statistical account of inference (Mayo), a heuristic toolbox (Gigerenzer), Bayesian falsification via predictive distributions (Gelman), and a distinct causal-modeling approach (Glymour).
Controversy Over the Significance Test Controversy
Deborah Mayo
(Professor of Philosophy, Virginia Tech, Blacksburg, Virginia)
In the face of misinterpretations and proposed bans of statistical significance tests, the American Statistical Association gathered leading statisticians in 2015 to articulate statistical fallacies and galvanize discussion of statistical principles. I discuss the philosophical assumptions lurking in the background of their recommendations, linking also my co-symposiasts. As is common, probability is assumed to accord with one of two statistical philosophies: (1) probabilism and (2) (long-run) performance. (1) assumes probability should supply degrees of confirmation, support or belief in hypotheses, e.g., Bayesian posteriors, likelihood ratios, and Bayes factors; (2) limits probability to long-run reliability in a series of applications, e.g., a "behavioristic" construal of N-P type 1 and 2 error probabilities; false discovery rates in Big Data.
Assuming probabilism, significance levels are relevant to a particular inference only if misinterpreted as posterior probabilities. Assuming performance, they are criticized as relevant only for quality control, and contexts of repeated applications. Performance is just what's needed in Big Data searching through correlations (Glymour). But for inference, I sketch a third construal: (3) probativeness. In (2) and (3), unlike (1), probability attaches to methods (testing or estimation), not the hypotheses. These "methodological probabilities" report on a method's ability to control the probability of erroneous interpretations of data: error probabilities. While significance levels (p-values) are error probabilities, the probing construal in (3) directs their evidentially relevant use.
That a null hypothesis of "no effect" or "no increased risk" is rejected at the .01 level (given adequate assumptions) tells us that 99% of the time, a smaller observed difference would result from expected variability, as under the null hypothesis. If such statistically significant effects are produced reliably, as Fisher required, they indicate a genuine effect. Looking at the entire p-value distribution under various discrepancies from the null allows inferring those that are well or poorly indicated. This is akin to confidence intervals but we do not fix a single confidence level, and we distinguish the warrant for different points in any interval. My construal connects to Birnbaum's confidence concept, Popperian corroboration, and possibly Fisherian fiducial probability. The probativeness interpretation better meets the goals driving current statistical reforms.
Much handwringing stems from hunting for an impressive-looking effect, then inferring a statistically significant finding. The actual probability of erroneously finding significance with this gambit is not low, but high, so a reported small p-value is invalid. Flexible choices along "forking paths" from data to inference cause the same problem, even if the criticism is informal (Gelman). However, the same flexibility occurs with probabilist reforms, be they likelihood ratios, Bayes factors, highest probability density (HPD) intervals, or lowering the p-value (until the maximal likely alternative gets .95 posterior). But lost are the direct grounds to criticize them as flouting error statistical control. I concur with Gigerenzer's criticisms of ritual uses of p-values, but without understanding their valid (if limited) role, there's a danger of accepting reforms that throw out the error control baby with the "bad statistics" bathwater.
Surrogate Science: How Fisher, Neyman-Pearson, and Bayes Were Transformed into the Null Ritual
(Director of Max Planck Institute for Human Development, Berlin, Germany)
If statisticians agree on one thing, it is that scientific inference should not be made mechanically. Despite virulent disagreements on other issues, Ronald Fisher and Jerzy Neyman, two of the most influential statisticians of the 20th century, were of one voice on this matter. Good science requires both statistical tools and informed judgment about what model to construct, what hypotheses to test, and what tools to use. Practicing statisticians rely on a "statistical toolbox" and on their expertise to select a proper tool. Social scientists, in contrast, tend to rely on a single tool.
In this talk, I trace the historical transformation of Fisher's null hypothesis testing, Neyman-Pearson decision theory, and Bayesian statistics into a single mechanical procedure that is performed like compulsive hand washing: the null ritual. In the social sciences, this transformation has fundamentally changed research practice, making statistical inference its centerpiece. The essence of the null ritual is:
Set up a null hypothesis of "no mean difference" or "zero correlation." Do not specify the predictions of your own research hypothesis. 2. Use 5% as a convention for rejecting the null. If significant, accept your research hypothesis. Report the result as p < .05, p < .01, or
p < .001, whichever comes next to the obtained p-value. 3. Always perform this procedure.
I use the term "ritual" because this procedure shares features that define social rituals: (i) the repetition of the same action, (ii) a focus on special numbers or colors, (iii) fears about serious sanctions for rule violations, and (iv) wishful thinking and delusions that virtually eliminate critical thinking. The null ritual has each of these four characteristics: mindless repetition; the magical 5% number, fear of sanctions by editors or advisors, and delusions about what a p-value means, which block researchers' intelligence. Starting in the 1940s, writers of bestselling statistical textbooks for the social sciences have silently transformed rivaling statistical systems into an apparently monolithic method that could be used mechanically. The idol of a universal method for scientific inference has been worshipped and institutionalized since the "inference revolution" of the 1950s. Because no such method has ever been found, surrogates have been created, most notably the quest for significant p-values. I show that this form of surrogate science fosters delusions and argue that it is one of the reasons of "borderline cheating" which has done much harm, creating, for one, a flood of irreproducible results in fields such as psychology, cognitive neuroscience and tumor marker research.
Today, proponents of the "Bayesian revolution" are in a similar danger of chasing the same chimera: an apparently universal inference procedure. A better path would be to promote an understanding of the various devices in the "statistical toolbox." I discuss possible explanations why a toolbox approach to statistics has been so far successfully prevented by journal editors, textbook writers, and social scientists.
Confirmationist and Falsificationist Paradigms in Statistical Practice
(Professor of Statistics & Political Science, Columbia University, New York)
There is a divide in statistics between classical frequentist and Bayesian methods. Classical hypothesis testing is generally taken to follow a falsificationist, Popperian philosophy in which research hypotheses are put to the test and rejected when data do not accord with predictions. Bayesian inference is generally taken to follow a confirmationist philosophy in which data are used to update the probabilities of different hypotheses. We disagree with this conventional Bayesian-frequentist contrast: We argue that classical null hypothesis significance testing is actually used in a confirmationist sense and in fact does not do what it purports to do; and we argue that Bayesian inference cannot in general supply reasonable probabilities of models being true. The standard research paradigm in social psychology (and elsewhere) seems to be that the researcher has a favorite hypothesis A. But, rather than trying to set up hypothesis A for falsification, the researcher picks a null hypothesis B to falsify, which is then taken as evidence in favor of A. Research projects are framed as quests for confirmation of a theory, and once confirmation is achieved, there is a tendency to declare victory and not think too hard about issues of reliability and validity of measurements.
Instead, we recommend a falsificationist Bayesian approach in which models are altered and rejected based on data. The conventional Bayesian confirmation view blinds many Bayesians to the benefits of predictive model checking. The view is that any Bayesian model necessarily represents a subjective prior distribution and as such could never be tested. It is not only Bayesians who avoid model checking. Quantitative researchers in political science, economics, and sociology regularly fit elaborate models without even the thought of checking their fit.
We can perform a Bayesian test by first assuming the model is true, then obtaining the posterior distribution, and then determining the distribution of the test statistic under hypothetical replicated data under the fitted model. A posterior distribution is not the final end, but is part of the derived prediction for testing. In practice, we implement this sort of check via simulation.
Posterior predictive checks are disliked by some Bayesians because of their low power arising from their allegedly "using the data twice". This is not a problem for us: it simply represents a dimension of the data that is virtually automatically fit by the model.
What can statistics learn from philosophy? Falsification and the notion of scientific revolutions can make us willing to check our model fit and to vigorously investigate anomalies rather than treat prediction as the only goal of statistics. What can the philosophy of science learn from statistical practice? The success of inference using elaborate models, full of assumptions that are certainly wrong, demonstrates the power of deductive inference, and posterior predictive checking demonstrates that ideas of falsification and error statistics can be applied in a fully Bayesian environment with informative likelihoods and prior distributions.
Exploratory Research is More Reliable Than Confirmatory Research
Clark Glymour
(Alumni University Professor in Philosophy, Carnegie Mellon University, Pittsburgh, Pennsylvania)
Ioannidis (2005) argued that most published research is false, and that "exploratory" research in which many hypotheses are assessed automatically is especially likely to produce false positive relations. Colquhoun (2014) with simulations estimates that 30 to 40% of positive results using the conventional .05 cutoff for rejection of a null hypothesis is false. Their explanation is that true relationships in a domain are rare and the selection of hypotheses to test is roughly independent of their truth, so most relationships tested will in fact be false. Conventional use of hypothesis tests, in other words, suffers from a base rate fallacy. I will show that the reverse is true for modern search methods for causal relations because: a. each hypothesis is tested or assessed multiple times; b. the methods are biased against positive results; c. systems in which true relationships are rare are an advantage for these methods. I will substantiate the claim with both empirical data and with simulations of data from systems with a thousand to a million variables that result in fewer than 5% false positive relationships and in which 90% or more of the true relationships are recovered.
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One thought on "Philosophy of Science Association 2016 Symposium"
A Nosek twitter reminds me that Johnson is another one who argues that all we need is to lower p-values, never mind biasing selection effects, p-hacking, cherry picking etc. No problem there. His uniformly most powerful Bayesian tests are interesting but allow inferring max likely alternatives–even though they get severity.5
Do you think @DanTGilbert et al's reproducibility estimate of RPP was too high? These folks definitely did. https://t.co/wqjDSKDX4M pic.twitter.com/kQyntUAh7k
— Brian Nosek (@BrianNosek) October 27, 2016

redpajama_set_name: RedPajamaCommonCrawl


"When It's Dark Out" is the fourth studio album (second label released album) to come from American hip-hop recording artist G-Eazy. It was released on digital retailers on December 4th via RCA Records and Sony Music Entertainment.
It arrives after the first promotional track "You Got Me" was released on digital retailers on August 12th and the lead single "Me, Myself & I" with Bebe Rexha on October 14th.
He also collaborated with Pia Mia on her single "F**k With U", and most recently with Aussie emerging artist Grace on her proper debut track "You Don't Own Me".
Upon the albums digital retailer pre-order, G-Eazy released the promotional track "Random" on 30 October 2015. "Order More" was released as next countdown single on November 13th. "Drifting" was released on November 20th.


The library will be open regular hours on Monday, Tuesday and Wednesday and will be closed Thursday, Friday and Saturday this week due to the Thanksgiving Holiday. Don't forget that the online catalog, Overdrive, Tumblebooks, and Hoopla are available at any time, even when the library is closed. The staff at the library wishes everyone a Happy Thanksgiving!
previous post: Welcome to the new and improved Carver Public Library website!


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redpajama_set_name: RedPajamaGithub


Many companies have unfulfilled growth potential. MEGAFON's business-to-business surveys put the focus on the competition parameters that are essential in order to reach that potential and create lasting, profitable customer relationships.
If you wish to be at the forefront of your industry, you need insight into the demands and needs of your customers. MEGAFON's business-to-business surveys can help your organisation gain a better understanding of the consumers, which is the first step in developing new products and services. This will ensure that you are never overtaken by exploding start-ups or well-established competitors.
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An analysis has only value if implemented properly. MEGAFON offers strategic consultancy throughout the process, and we make sure that you have the best possible means of processing the survey results further.


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2.4.1: Archive (before 2010) .
2.4.1.1: 2009 .
2.4.1.5.1: XFEL 25.11. .
2.4.1.5.2: Part.phys. 21. 11. .
2.4.1.5.3: DESY 31.10. .
Standort: 2.4.1.5.4: ILC 31.10. .
2.4.1.5.8: Part.phys. 01.04. .
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URL: https://www.desy.de/news/news/archive_before_2010/2005/ilc_3110/index_eng.html
Archive (before 2010) /
ILC 31.10.
ILC-Research: Emmy-Noether-Fellowship for Dr. Jenny List
Jenny List was born in 1974 and studied physics at the University of Hamburg. She earned her doctorate at the RWTH Aachen and subsequently worked as a junior scientist on the H1 experiment at DESY and on the ATLAS experiment at CERN.
Physicist Dr. Jenny List has recently received one of the sought-after Emmy-Noether-Fellowships of the Deutsche Forschungsgemeinschaft (DFG). With the subsidies granted for 5 years, she will establish a project group at DESY that will study, among other things, the possibilities to detect dark matter at the International Linear Collider ILC. The Emmy-Noether-Fellowship gives the opportunity to outstanding scientists like Jenny List to qualify for professorship within a short time as a selfdependent head of a team of junior scientists.
Research for the ILC at DESY offers excellent perspectives: the planned linear collider opens possibilities to discover the particles that might be the components of dark matter - the so called WIMPs (weakly interacting massive particles). In this case - like in many other measurements planned at the ILC - the polarization of the electron and positron beam will play a decisive role. Dr. Jenny List and her team want to specify the accelerator and detector requirements for the best possible verification of WIMPs and to develop a prototype of a Compton Polarimeter that will measure the beam polarization with a precision that could not be reached so far.


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Top Gaming Consoles are thrilled to present the excellent Xbox One Battery Charging Dock Kingtop Dual Charger Station with 2 x1200mAh Rechargeable Batteries and USB Cable For Xbox One One S Newest One X Xbox One Elite Controllers.
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Link the dock to Xbox One console or PC with the USB cable to power the dock.
Able to fully charge two batteries within two hours.
Two batteries can be charged all at once.
Connecting the cable to controller will likewise allow charging while still being used.
Xbox One Battery Dual Charging Dock specially designed for charging the batteries for Xbox One / Xbox One S /Xbox One X/ Xbox One Elite Controllers ,charges two batteries simultaneously,a must need for any dedicated Xbox One gamers Short charging time for long playtime, fully charge the batteries just within 2 hours and give up to 11 hours playtime,the distinct dual colored LED light indicator will showing charge status(Charging: Red; Charged: Blue) Come with 2 x1200mAh Rechargeable Batteries and 1xUSB Cable,connecting the cable with the controller directly will also allow charging. Safety Guaranteed Xbox One Controller Battery Charger Station, highly protective security with over-charge protection and secure circuitry,Stops charging automatically when the battery is fully charged 100% Satisfaction Guaranteed Warranty ! Try it out today and enjoy your gaming time as long as you want.,If you're not fully satisfied just contact us for a new replacement or a full payment refund within 90 days.
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