The quest for stimulated energy release Nuclear isomers provide a form of energy storage . Whereas betadecaying radioactive nuclei can also store MeV energies (per nucleus) for long periods of time, nuclear isomers usually decay electromagnetically, by g-ray emission or internal conversion. This difference may offer improved opportunities for stimulating the decay of isomers . The interaction of visible or ultraviolet photons with nuclei has long been sought. In principle, low-energy (eV) photons should be able to initiate isomer decay, either by stimulated emission to a nearby lower-energy state, or by stimulated absorption to a nearby higher-energy state. In either case, if the stimulated transition is to a short-lived energy level, a cascade of g-rays could be released, with a total energy of several MeV. If this scheme were to be realized, then it would be possible to have a type of nuclear reservoir, where the energy could be released with a photon `switch' . It would not be necessary to wait for the intrinsic half-life to release the energy and, if the isomer were in a nucleus with a stable ground state, there would be no subsequent radioactive waste. Moreover, the potential development of a g-ray laser would be brought a significant step closer52. Such an idealized scenario has not yet been realized, and low-energy photons have yet to be proven to stimulate nuclear transitions; but this does not mean that there are no possibilities. In the search for stimulated transitions, the isomers studied to date have mostly been at low energies (that is, low on a nuclear energy scale). The 45-hour isomer in 229Th, at an excitation energy of 3.5 eV, has received special attention51,53,54 because the nuclear excitation is on a similar energy scale to atomic valence electrons. Although the stimulation of the 3.5-eV transition would release no additional energy, it is of fundamental interest for investigating photon±nucleus interactions . But the MeV isomers discussed here have the potential to release significant quantities of energy. A recent encouraging advance was the de-excitation55, using ,100-keV photons, of the 2.4-MeV, 31-year isomer in 178Hf , although this is still a long way from de-excitation with eV photons. At face value, the use of eV photons to stimulate nuclear transitions requires one or more states to lie within a few eV of the isomer. For low-lying isomers, this presents a general problem. A chance near-degeneracy is needed, such as that found exceptionally in 229Th. For highly excited isomers, however, with energies of several MeV, the isomers become embedded in a high statistical density of excited states. For example, at 5MeV in a nucleus such as 178Hf, there is on average about one state per eV, so the `chance' of a near-degeneracy becomes certain. Notwithstanding this certainty, the isomer would probably have very different spin quantum numbers from the background of statistical states (otherwise it would not be an isomer in the first place). To stimulate isomer de-excitation, additional processes may therefore be required; one such example is the assisted electronbridge mechanism51, in which excitation of atomic electrons can eliminate energy and spin mismatching between the isomeric state of a nucleus and the state to which it decays. There is, therefore, room for chance to play an important role in providing the right conditions for isomer formation , and for allowing transitions to be stimulated by low-energy photons . Many new isomers are predicted to exist in nuclei with about 180 nucleons. The insights already gained into the nucleus, together with the development of experimental facilities, suggest the possibility of wide technological benefits in the future.
2015年3月2日 The History of Astronomy Your teacher is abroad on a conference today, so there is no class on 2 March. However, the two student assistants (Yunfeng Chen and Zhen Guo) will be in the lecture room to answer many of your questions. They will also provide you with hand-outs ( download PDF) that contain information about the lecture course. No homework today. 2015年3月9日 The Night Sky No homework today. 2015年3月16日 The Physics of Astrophysics; Light and other forms of radiation 2015年3月23日 Telescopes and Astronomical Observing Homework 1 handed out. Please write the answers of your homework on a separate piece of paper and hand it in next week. 2015年3月30日 Gravity, Planetary Orbits, and the Sun Deadline for handing in Homework 1. 2015年4月6日 No lecture today (Qingming). 2015年4月13日 The Rocky inner planets: Mercury, Venus, and the Earth-Moon system 2015年4月20日 The Earth; the Moon, Mars, and Space Exploration 2015年4月27日 Asteroids; the Gas Giant Planets, and the Outer Solar system 2015年5月4日 No lecture today (University Day). 2015年5月11日 Interstellar Gas; The Formation and Evolution of Stars 2015年5月18日 Stellar Evolution; Planet Formation 2015年5月25日 Star clusters; Astrophysical Computer Simulations, the Milky Way 2015年6月1日 Galaxies and Cosmology 2015年6月8日 Einstein's Relativity and the Big Bang Theory 2014年5月15日 Life in the Universe Deadline for handing in your essay or research project report: (date to be decided . You can hand in the essay during the class, or you can bring it later to the Kavli Institute for Astronomy and Astrophysics (KIAA). You can give it to Yunfeng Chen, or to Zhen Guo, or to Thijs Kouwenhoven (room 201). If we are both away, you can also give your essay to the KIAA guard, and tell him to give the essay to Thijs Kouwenhoven (柯文采). To be absolute sure that your essay is received on time, you are recommended to ALSO send an email with the essay to the teacher. Make sure that you write your name and student number on the first page of the essay. Of course, you can also hand in your essay or research project report before the deadline. Good luck!
Hands-On Data Science with R Dr Graham Williams, PhD (ANU, Machine Learning), BSc (Maths, Hons) Chief Data Scientist, Togaware and Australian Taxation Office Adjunct Professor, Australian National University and University of Canberra International Visiting Professor, Chinese Academy of Sciences Our goal is to provide introductory material to cost effectively kick start an organization's entry into Data Science. To that end, we introduce the use of R for doing Data Science. In addition to the extensive material available on our web site we provide a unique offering of in-situ hands-on training . We offer traditional out-of-office training courses, but we find more effective learning can occur hands-on in-situ. We offer one of the world's leading Data Scientists to work alongside and mentor your staff over one or two weeks. We work confidentially on actual projects, with training "on-the-job" provided by a professional with 30 years experience in the industry and author of the best selling book on Data Mining with Rattle and R . Contact Togaware Training at training@togaware.com for details. Our on-line resources, including Hands-On Data Science , weave together a collection of freely available and open source tools for the Data Scientist. The tools are all part of the R Statistical Software Suite. Each chapter is made up of multiple pages, but each page within a chapter is a one page guide that covers a particular aspect of the topic (hence also refered to as the OnePageR guide). They are a great place to start, before engaging our hands-on training experts. Hands-On Data Science can be worked through as a hands-on guide and then used as a reference guide. Each page aims to be a bite sized chunk for hands-on learning, building on what has gone before. Many chapters also have a lecture pack and a laboratory session where a number of tasks can be completed. The R code sitting behind each chapter is also provided and can be easily run standalone to replicate the material presented in the chapter. The material is always under development ! Chapters will change (and hopefully improve) regularly. Links preceded with a * are more well developed. All of the material is provided under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License allowing access to everyone for any purpose (except commercial) and is provided at no cost. You can assist in helping cover the costs of providing this material through a $40 contribution using PayPal. Your support encourages further development of this resource as does feedback, suggestions, and ideas , which are always welcome. Refer to the Data Mining Survival Guide or my book on Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery (Use R) for related material. Many of the initial chapters were developed and tested whilst visiting the Shenzhen Institutes of Advanced Technology as an International Visiting Professor of the Chinese Academy of Sciences. The data used across the chapters is available for download as data.zip . Enjoy! Getting Started as a Data Scientist An Introduction to Data Mining: * Lecture Introducing Data Science, Rattle and R: * Lecture - * Chapter - * R Rattle to R: * Chapter - * R R for the Eager Data Scientist A Template for Preparing Data: * Chapter - * R A Template for Building Models: * Chapter - * R Case Studies: * Chapter - * R Basic R Tips and Tricks Chapter - R Dealing With Data Reading Data into R: * Chapter - * R Exploring and Summarising Data: * Chapter - * R Visualising Data with GGPlot2: * Chapter - * R Transforming Data: * Chapter - * R Descriptive Analytics Cluster Analysis: * Lecture - Chapter - R Association Analysis: * Lecture - Chapter - R Predictive Analytics Decision Trees: * Lecture - * Chapter - * R - * Rattle Ensembles of Decision Trees: * Lecture - * Chapter - * R Support Vector Machines Neural Networks Naive Bayes: Chapter - R Multivariate Adaptive Regression Splines: Chapter - R Evaluating Models: * Chapter - * R Scoring (R) PMML (R) Exporting Models for Deployment Advanced Analytics Text Mining: * Chapter - * R Social Network Analysis: Chapter -R Genetic Programming: Chapter -R Advanced R Strings: Chapter , R Dates and Time: * Chapter - * R Spatial Data * Chapter - * R Big Data * Chapter - * R Exploring Different Plots: Chapter - R Writing Functions: Chapter - R Parallel Processing: Chapter - R Environments: * Chapter - R Expert R Packaging (R) Pulling it Together into a Package Doing R with Style: * Chapter - * R Literate Data Science with KnitR: * Lecture - * Chapter - * R
The Jobs Number Is BS Says Former Head Of BLS Submitted by Tyler Durden on 07/18/2013 15:57 -0400 Ben Bernanke Ben Bernanke BLS Bureau of Labor Statistics Great Depression HFT Monetary Policy Obama Administration Quantitative Easing Real estate Real Unemployment Rate Reality Recession recovery Unemployment After every non-farm payroll report we provide our own breakdown of what the real unemployment rate is in a country in which the labor force participation rate has not been adjusted to normalize for the Second Great Depression. In the most recent such endeavor we found the "Real Unemployment Rate" to be 11.3%. To wit : As of May, assuming realistic LFP assumptions, the real U-3 unemployment rate should have been not 7.6% but 11.3%. Today, courtesy of the Post's John Crudele we find that our estimate was spot on not just from anyone, but the former head of the BLS himself: Keith Hall . Keith Hall believes the US economy is a lot sicker than the 7.6 percent unemployment rate would lead you to believe. And he should know. Hall was, from 2008 until last year, the guy in charge of Washington’s Bureau of Labor Statistics, the agency that compiles that rate. “Right now misleadingly low,” says Hall, who believes a truer reading of those now wanting a job but without one to be more than 10 percent. The fly in the ointment is the BLS employment-to-population ratio, which is currently at 58.7 percent. “It’s lower than it was when the recession ended. I think that’s a remarkable statistic,” says Hall, a senior research fellow at the Mercatus Center at George Mason University in Fairfax, Va. That level tells Hall the real unemployment rate is actually about 3 percentage points higher than the BLS number. If the jobless rate is unacceptable at 7.6 percent, it’d be shockingly bad if he is right and the true rate is 10.6 percent. ... Hall reckons there are millions of U-6 people on top of the 4.5 million long-term unemployed. "This has been a very slow, very bad recovery,” he says. “And I think the numbers have really struggled as a result. In fact, I’ve been very disappointed in the coverage of the numbers." It is not just the artificial manipulation in the labor force participation rate, which we first brought up in 2010 and only became a mainstream theme this past year. There is also the monthly seasonal adjustment factor which provides the much needed smoothing function whose only job is to provide a "credible" number to be used by the HFT algos to ramp stock momentum almost exclusively to the upside: after all the only thing the Fed has left is to promote confidence in the economy using the only transmission mechanism subject to the Fed's central-planning: the manipulated monetary policy vehicle known as the SP500. There are other problems with numbers coming out of BLS, according to Hall. And they will just add to the confusion. All parts of Washington’s data-collecting machine adjust to smooth out the bumps caused by the seasons of the year. But the recession that started five years ago was so severe and the recovery so anemic that the seasonal adjustments have been thrown off. Crudele, a long-time skeptic of manipulated data, points out some other obvious divergences between the Fed's propaganda and reality: The Fed, and particularly Chairman Ben Bernanke, are acting rather strange these days. One minute Bernanke is suggesting that his highly controversial and very dangerous money-printing operation, called quantitative easing, will be tapered off in the near future because the economy is doing better. The next minute Bernanke is talking about how QE will continue if the economy isn’t strong enough to stand on its own. The Fed chief often points to the housing and the stock markets as evidence of economic improvement, although those are nonsense indicators. The stock market is only rising because Bernanke is printing so much money. And housing is improving, with prices rising sharply in spots, because big-time investors who can’t find anywhere else to park their assets profitably are scooping up big-city real estate by the bundle. Finally, while Hall isn't a whistleblower in the pure sense of the word, and hasn't disclosed any specific illegal data manipulation by the BLS, the fact that such systematic data "massaging" has been acknowledged by the former head of the statistical agency should be enough for the BLS and the Obama administration to hang their heads in shame. Of course, they won't - to a big part because nobody in the mainstream media will actually call them out on it - and will instead point to the SP hitting all time manipulated high after all time manipulated high as proof the economy is doing great. Alas, to their chagrin, nobody believes that particular lie anymore. 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