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8002 II- Mathematical Foundations of Risk(R) Measurement

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8002 exam Dumps Source : II- Mathematical Foundations of Risk(R) Measurement

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PRMIA II- Mathematical Foundations of

Choreographed entanglement dances: Topological states of quantum be counted | killexams.com actual Questions and Pass4sure dumps

evaluation

  • Xiao-Gang Wen
  • branch of Physics, Massachusetts Institute of expertise, Cambridge, MA 02139, united states of america.
  • electronic mail: xgwenatmit.edu
  • See allHide authors and affiliations

    Science  22 Feb 2019:Vol. 363, challenge 6429, eaal3099DOI: 10.1126/science.aal3099

    Xiao-Gang Wen

    department of Physics, Massachusetts Institute of expertise, Cambridge, MA 02139, united states of america.


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    Lyndon LaRouche, Cult determine Who Ran for President 8 instances, Dies at ninety six | killexams.com actual Questions and Pass4sure dumps

    In his efforts to build a global firm, Mr. LaRouche changed into helped by means of his spouse, Helga Zepp-LaRouche, a local of Germany who had been a journalist there. suggestions about his survivors changed into no longer immediately obtainable.

    Mr. LaRouche changed into on the apex of his energy within the mid-Eighties, when he moved his headquarters to a commodious rented property in Northern Virginia, in round Hill, outdoor Leesburg. When neighbors questioned aloud why he had turned the estate into an armed camp, rigged with digital protection and patrolled via men with semiautomatic rifles, Mr. LaRouche went on the assault. He stated that the Leesburg garden club changed into “a nest of Soviet fellow tourists.”

    In 1987, after an F.B.I. investigation, Mr. LaRouche turned into convicted in Virginia on charges of scheming to defraud the interior salary carrier and of intentionally defaulting on more than $30 million in loans from hundreds of his supporters. He changed into sentenced to 15 years in prison and sent to a federal penitentiary in Minnesota.

    The conviction harm his circulate but did not halt it. He turned into released from detention seat in 1994, after serving a 3rd of his sentence. He automatically introduced that he would flee for president in 1996. He ran again in 2000 and 2004. After Barack Obama turned into elected in 2008, Mr. LaRouche warned that the unique president become in “grave and imminent hazard” of being assassinated via the “British Empire,” a well-recognized goal of Mr. LaRouche’s.

    via 2015 he had long turned against Mr. Obama, calling for his impeachment and accusing him in a sole case of orchestrating Turkey’s downing of a Russian fighter jet worried within the contest in Syria. “Obama organized an act of war, and has consequently endangered the us, as well as complete of humanity,” Mr. LaRouche wrote.

    but he could be bipartisan in his assaults. He accused the Bush family of participating with Nazi Germany complete through World warfare II, and spoke of that the invasion of Iraq in 2003 changed into a manufactured from a neoconservative conspiracy, led by means of vice president Dick Cheney, to bamboozle the American people. That view changed into expressed in a collection of pamphlets, titled “babies of devil.”

    but Mr. LaRouche turned into heartened by means of the election of President Trump, notwithstanding he perceived a British conspiratorial hand attaining into the USA to foment efforts to “politically paralyze” the president and convey about his impeachment.

    that could be a mistake, he observed. As his website declared, “now not considering that William McKinley has a president been so clear in his intent to reach back the nation to the monetary artery of life of Alexander Hamilton, to halt the policies of British imperial free trade, and gain a complete dedication to business, manufacturing, scientific advancement and world peace.”


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    II- Mathematical Foundations of Risk(R) Measurement

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    Open Science, Open Source and R | killexams.com actual questions and Pass4sure dumps

    Free software will save psychology from the Replication Crisis.

    "Study reveals that a lot of psychology research really is just 'psycho-babble'".—The Independent.

    Psychology changed forever on the August 27, 2015. For the previous four years, the 270 psychologists of the Open Science Collaboration had been quietly re-running 100 published psychology experiments. Now, finally, they were ready to partake their findings. The results were shocking. Less than half of the re-run experiments had worked.

    When someone tries to re-run an experiment, and it doesn't work, they summon this a failure to replicate. Scientists had known about failures to replicate for a while, but it was only quite recently that the extent of the problem became apparent. Now, an almost existential juncture loomed. That juncture even gained a name: the Replication Crisis. Soon, people started asking the identical questions about other areas of science. Often, they got similar answers. Only half of results in economics replicated. In pre-clinical cancer studies, it was worse; only 11% replicated.

    Open Science

    Clearly, something had to be done. One option would hold been to conclude that psychology, economics and parts of medicine could not be studied scientifically. Perhaps those parts of the universe were not lawful in any meaningful way? If so, you shouldn't be surprised if two researchers did the identical thing and got different results.

    Alternatively, perhaps different researchers got different results because they were doing different things. In most cases, it wasn't practicable to order whether you'd flee the experiment exactly the identical artery as the original authors. This was because complete you had to travel on was the journal article—a short summary of the methods used and results obtained. If you wanted more detail, you could, in theory, request it from the authors. But, we'd already known for a decade that this approach was seriously broken—in about 70% of cases, data requests ended in failure.

    Even when the authors sent you their data, it often didn't waiton that much. One of the most common problems was that when you re-analysed their data, you ended up with different answers to it! This turned out to be quite common, because most descriptions of data analyses provided in journal articles are incomplete and ambiguous. What you really needed was the original authors' source code—an unambiguous and complete record of every data processing step they took, from the raw data files, to the graphs and statistics in the final report. In Psychology in 2015, you almost never could pickup this.

    If you did eventually manage to replicate the authors' analysis, could you be confident that their results were real? Not necessarily. Perhaps they tested only a few people, who were not particularly representative of the population as a whole. In this case, you might want to re-run the experiment yourself, testing a lot more people. Or perhaps the problem was not with their analysis, or their data, but with the routine by which they collected their data. For the ultimate 20 years, psychology experiments largely hold involved computer-based testing. So, for very many experiments, there is a complete and unambiguous specification of the methods used—the source code for the testing program. But in 2015, this almost never was publicly available either.

    In other words, psychology research at the rise of the Replication juncture was fancy closed-source software. You had to win the authors' conclusions entirely on trust, in the identical artery you hold to reliance that closed-source software performs as described. There essentially was no artery to audit research properly, because you could not access the source code on which the experiment was based—the testing software, the raw data and the analysis scripts.

    A growing number of scientists felt this had to change. The year before, in 2014, I had read Stephen Levy's Hackers, and from there, I went on to read more about Richard Stallman, Eric S. Raymond and Linus Torvalds. For me, it was a revelation. The Free and Open Source Software community, I felt, showed how science could be different. The pervasiveness of Linux showed that tens of thousands of people with different views and goals could collaborate on a involved project for the common good. Just as important, they could accomplish so without necessarily even having to fancy each other complete the time. That was marvelous intelligence for science. Discussions between academics can get...well, let's just narrate "heated".

    So, in the identical artery that computing has its advocates of open-source software, psychology and other sciences started gaining advocates for Open Science. The phrase Open Science had been coined back in 1998 by Steve Mann, but once the Replication juncture hit psychology, a lot more of us began to sit up and win notice. Early on, the Centre for Open Science, a non-profit company started in 2013, had set up the Open Science Framework (OSF). The OSF is a web-based public repository for experiment-related data and code. It's built entirely from free and open-source software.

    As awareness of the Replication juncture grew, peer reviewers started insisting that data and code be made publicly available. Peer review in research is a bit fancy a code review in IT. Scientists route their articles to a journal for consideration. The journal sends the article out to experts in the domain for comment, and the work is accepted for publication only when the journal editor thinks those comments hold been adequately addressed. In 2015, Richard Morey and colleagues started the Peer Reviewers' Openness Initiative, a declaration that they would not recommend any paper for publication unless it met inevitable basic standards of open science. Within three years, more than 500 peer reviewers in psychology had signed that declaration.

    Open Platforms and R R logo

    There's quiet one major problem to solve. Publishing your scientific source code is essential for open science, but it's not enough. For fully open science, you moreover necessity the platforms on which that code runs to be open. Without open platforms, the future usability of open-source code is at risk. For example, there was a time when many experiments in psychology were written in Microsoft Visual Basic 6 or in Hypercard. Both were closed-source platforms, and neither are now supported by their vendors. It is just not acceptable to hold the permanent archival records of science rendered unusable in this way. Equally, it's a pretty narrow form of Open Science, if only those who can afford to purchase a particular piece of proprietary software are able to access it. complete journal articles published in psychology since around 1997 are in PDF format. Academic libraries would not tolerate these archival files being in a proprietary format such as DOCX. They can and must apply the identical standards of openness to the platforms on which they ground their research.

    Psychology has a long history of using closed-source platforms, perhaps most notably the proprietary data analysis software SPSS. SPSS initially was released in 1968, and it was acquired by IBM in 2010. Bizarrely, SPSS is such a closed platform, current versions can't even open SPSS output files if they were generated before 2007! Although it's quiet the most used data analysis software in psychology, its employ has been declining steeply since 2009. What's been taking up the slack?

    In large part, it's R. R is a GNU project, and so it's free software released under the GNU universal Public Licence. It works much under Linux, but it moreover works just fine on Windows and Mac OS too. R is a very long-standing project with much community support. R is moreover supported by major tech companies, including Microsoft, who maintain the Microsoft R Application Network.

    R is an implementation of the statistical language S, developed at Bell Labs shortly after UNIX, and inspired by Scheme (a jargon of Lisp). In the 1990s, Ross Ihaka and Robert Gentleman, at the University of Auckland, started to develop R as an open-source implementation of S. R reached version 1.0 in 2000. In 2004, the R Foundation released R 2.0 and began its annual international conference: _useR!_. In 2009, R got its own dedicated journal (The R Journal). In 2011, RStudio released a desktop and web-based IDE for R. Using R through RStudio is the best option for most unique users, although it moreover works well with Emacs and Vim. The current major release of R landed in 2013, and there are point releases approximately every six months.

    I first started using R, on a Mac, in 2012. That was two years before I'd heard of the concept of Free Software, and it was about three years before I ran Linux regularly. So, my election to journey from SPSS to R was not on philosophical grounds. It moreover was before the Replication Crisis, so I didn't switch for Open Science reasons either. I started using R because it was just better than SPSS—a lot better. Scientists expend around 80% of their analysis time on pre-processing—getting the data into a format where they can apply statistical tests. R is fantastically marvelous at pre-processing, and it's certainly much better than the most common alternative in psychology, which is to pre-process in Microsoft Excel. Data processing in outdo is infamously error-prone. For example, one in five experiments in genetics hold been screwed up by Excel. Another example: the case for the UK government's policy of monetary austerity was based on an outdo screw up.

    Another much understanding for using R is that complete analyses win the form of scripts. So, if you hold done your analysis completely in R, you already hold a full, reproducible record of your analysis path. Anyone with an internet connection can download R and reproduce your analysis using your script. This means they can achieve the goal of fully open, reproducible science really easily with R. This contrasts with the artery psychologists mainly employ SPSS, which is through a point-and-click interface. It's a fairly common suffer that scientists struggle to reproduce their own SPSS-based analysis after a three-month delay. I struggled with this issue myself for years. Although I was always able to reproduce my own analyses eventually, it often took as long to accomplish so as it had the first time around. Since I moved to R, reproducing my own analyses has become as simple as re-running the R script. It moreover means that now every member of my lab and anyone else I work with can partake and audit each other's analyses easily. In many cases, that audit process substantially improves the analysis.

    A third thing that makes R so much is that the core language is supplemented by more than 13,000 packages mirrored worldwide on the Comprehensive R Archive Network (CRAN). Every analysis or graph you can contemplate of is available as a free software package on CRAN. There's even a package to draw graphs in the style of the xkcd cartoons or in the colour schemes of Wes Anderson movies! In fact, it's so comprehensive, in 2013 the authors of SPSS provided users with the faculty to load R packages within SPSS. Their in-house team just couldn't support up with the breadth and depth of analysis techniques available in R.

    R's faculty to support up with the latest techniques in data analysis has been crucial in addressing the Replication Crisis. This is because one of the causes of the juncture was psychology's reliance on out-dated and easily misinterpreted statistical techniques. Collectively, those techniques are known as Null Hypothesis Significance testing, and they were developed in the early 20th century before the advent of low-cost, high-power computing. Today, they increasingly employ more computationally intensive but better techniques, based on Bayes theorem and Monte Carlo techniques. unique techniques become available in R years before they're in SPSS. For example, in 2010, Jon Kruschke published a textbook on how to accomplish Bayesian analysis in R. It wasn't until 2017 that SPSS supported Bayesian analyses.

    Teaching R

    For more than 20 years, teaching statistics in psychology has been synonymous with teaching people how to employ SPSS. However, during the ultimate few years, several universities hold switched to R, and many more are considering it. One terror about this change was that psychology students would find R harder to learn than SPSS, and that they would fancy it less. This turns out to be incorrect. In pioneering work by Dale Barr and colleagues at Glasgow University, psychology undergraduates were taught both SPSS and R. They then got to elect which software to employ in their final assessments. Around two-thirds of the students chose R. Those who chose R moreover scored higher on the exam. They moreover scored lower on a touchstone measure of statistics anxiety. At Plymouth University, unique entrants to their Psychology degrees are now just taught R, with SPSS removed from the curriculum entirely. We've seen an extend in what their students can achieve in statistics, while maintaining towering levels of student satisfaction.

    One of the side benefits of this change, for the R project, is that psychologists attend to be quite marvelous at writing documentation. Andy Field's textbook, Discovering Statistics, much-praised by Psychology undergraduates, has had an R version since 2012. More recently, academics hold started developing teaching materials that are as open as R is. For example, my own teaching materials, Research Methods in R, aimed at first-year psychology undergraduates, are available under a Creative Commons Licence. Just Enough R, written by Ben Whalley and aimed at postgraduate students, is available under the identical licence.

    Open Science in R: an Example catlearn logo

    In my lab at Plymouth University, they work on the psychology of learning, memory and determination making. In many cases, the theories they are testing are expressed in the form of computer models. For example, one of the classic theories of how they learn to group objects into categories (dogs, cats, bagels and so on) is called ALCOVE. This theory takes the form of a neural network model, which makes predictions about how people will classify objects. They compare those predictions to data from actual people making those decisions and evaluate the model on that basis.

    Traditionally, this computational modelling side to psychology has been fairly closed-source. These models of the mind, which are moderately involved programs, typically are released only as a set of mathematical equations with some explanatory text. The code required to reproduce the results reported is seldom fully published. The result is that it can win several days to several months to reproduce the results of these computer models. The amount of time this wastes is substantial.

    Starting in 2016, their lab decided to accomplish something about this issue. Specifically, they released an R package called catlearn, short for models of CATegorization and LEARNing. In the current version, released in July 2018, they hold implemented nine different models. fancy complete R packages, the code is open source. The package moreover includes archives of the complete code for simulations of specific experiments and the data sets for those experiments. We're rise to build a community around the world, with people in the USA, UK, Germany and Switzerland complete having contributed code. It's a really exciting time, and I'm looking forward to the release of version 0.7 later this year. If you'd fancy to contribute, we'd carefor to hear from you—we desperately necessity more marvelous programmers. Prior suffer of psychology is not essential.

    A Final Thought

    The Replication juncture might hold been one of the best things ever to befall to psychology. It became a catalyst for much-needed change to their scientific processes. If they can build 21st-century psychology on the principles of Open Science, I contemplate much and enduring discoveries await us. Those future successes will owe a lot to the pioneering case of the Free and Open-Source Software community. Thanks in advance, Linux Journal readers!

    Resources
  • "Study reveals that a lot of psychology research really is just 'psycho-babble'" by Steve Connor, The Independent
  • "Estimating the Reproducibility of Psychological Science" by Alexander A. Aarts, Christopher J. Anderson, Joanna E. Anderson and Peter Attridge, Science
  • Replication Crisis
  • "Is Economics Research Replicable? Sixty Published Papers from Thirteen Journals narrate 'Usually Not'" by Andrew C. Chang and Phillip Li, Finance and Economics Discussion succession 2015-083. Washington: Board of Governors of the Federal Reserve System
  • "Reproducibility: Six red flags for suspect work" by C. Glenn Begley, Nature
  • "The poor availability of psychological research data for reanalysis" by Jelte Wicherts, Judith Kats, Denny Borsboom and Dylan Molenaar, American Psychologist
  • Hackers: Heroes of the Computer Revolution by Steven Levy, Doubleday, 1984 (Wikipedia)
  • Open Science (Wikipedia)
  • COS (Center for Open Science)
  • Open Science Framework
  • Peer Reviewers' Openness Initiative
  • SPSS Statistics (Wikipedia)
  • "The Popularity of Data Science Software" by Robert A. Muenchen, r4stats.com
  • The R Project for Statistical Computing
  • The GNU Operating System
  • GNU universal Public License
  • Microsoft R Application Network (MRAN)
  • RStudio
  • "Cleaning commodious Data: Most Time-Consuming, Least Enjoyable Data Science Task, Survey Says" by Gil Press, Forbes
  • "Gene name errors are widespread in the scientific literature" by vestige Ziemann, Yotam Eren and Assam El-Osta, Genome Biology
  • "Microsoft Excel: The ruiner of global economies?" by Peter Bright, Ars Technica
  • The Comprehensive R Archive Network
  • xkcd
  • "Calling R from SPSS" by Catherine Dalzell
  • Doing Bayesian Data Analysis
  • Bayesian statistics (IBM learning Center)
  • LTC Workshop
  • Discovering Statistics Using R by Andy Field, Jeremy Miles and Zoe Field, Sage Publishing
  • Research Methods in R by Andy Wills (Teaching Materials)
  • Ben Whalley's Just Enough R
  • catlearn GitHub Page
  • Acorn Programs

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    Good Tech Instruments' dehydrate pump monitoring solution takes risk out of semiconductor manufacturing

    The fabrication of semiconductor devices comprises a sequence of processing steps including etching, lithography, diffusion and thin film. How to ensure complete process gear is in tip-top shape is faultfinding to semiconductor manufacturing.

    For example, if the dehydrate pump of the low pressure chemical vapor deposition (LPCVD) system or ion implanter used in the diffusion process provides not enough vacuum or even fails, causing back pressure pushing air and unreasonable particles into the chamber, the entire lot of wafer could travel to waste. As to the thin film and etching processes, inadequate vacuum could moreover result in electrical anomaly or wafer defect, incurring tremendous loss to manufacturers. However, it may be challenging to obviate problems of inadequate vacuum or pump failure.

    LPCVD systems and ion implanters operate under towering vacuum conditions created by dehydrate pumps, consisting of rotor motors and Roots vacuum pumps. In general, monitoring systems provided by dehydrate pump suppliers only support track of conditions including pressure, flow, temperature and electrical current but not vibration, so users accomplish not hold access to complete information and thus hold hardship predicting failure. Specializing in vibration measurement, marvelous Tech Instruments has introduced a dehydrate pump monitoring solution in 2018 to waiton semiconductor manufacturers purge problems that hold long been troubling them.

    Wen-Tze Hsu, technology manager, marvelous Tech Instruments, notes that users await two things from their dehydrate pumps. One is the faculty to prognosticate failure and obviate downtime. The other is dependable data proving that marvelous pumps can maintain long lifetime, which translates to reduction in maintenance costs. Even in the case that the pump is returned to the vendor for service, there must be guarantee that the serviced gear is compliant with maintenance requirements.

    Users claim precise estimation of gear lifetime to bring down operation and maintenance costs

    Hsu explains in more detail that users are accustomed to assessing machine health by monitoring electrical current, which is a lagging indicator, signification when the current gets too high, the machine could shut down any time. The approach is too risky and does not comply with the principles of predictive maintenance. Hsu moreover speaks about the estimation of gear lifetime, which is generally done based on statistical data and people's experiences. When a dehydrate pump has been operating for a predetermined number of hours, it will be sent back to the vendor for service regardless of its condition. The user may incur unnecessary costs by sending a perfectly marvelous pump for maintenance. Furthermore, the serviced pump returned from the vendor generally goes through an acceptance check based only on electrical current. Such a drill can hardly guarantee third-party maintenance quality, likely to extend the risk of shortened lifetime.

    To address these problems, marvelous Tech Instruments has developed software programs and algorithms, implementing assessments based on ISO 10816 specifications on vibrations of rotodynamic machines, broadband value management and spectrum overall value (SOV) management and transforming involved signals into intuitive pump health indicators for facile viewing. They can effectively complement traditional monitoring systems. Even if the user does not hold posthaste Fourier Transform (FFT) spectrum for energy analysis, marvelous Tech Instruments' solution can quiet provide some insights.

    Good Tech Instruments' monitoring solution supports assessments in accordance with the vibration limits defined in ISO 10816 for Category I pumps. It can exhibit physical vibrations under inevitable bandwidths according to different machine types so that users can instantly know if their dehydrate pump is in a good, normal, warning or uncertain condition. It moreover supports assessments based on the less-noticed sort II variations, meeting the trend of quick and agile management.

    MCM-100 computes frequency spectrum with fine resolution, helping capture minor signs of defect

    SOA is a summary of frequency spectrum analyses that combines measurements of misalignment, dynamic unbalance, axial bending, mechanical loosening and rotary friction – the five major indicators of abnormal vibration – and presents them in easy-to-understand numbers for users to linger on top of machine conditions without hassle. Moreover, marvelous Tech Instruments' solution additionally monitors pitting corrosion (broadband high-frequency vibration) and powder turbulence (low-frequency vibration) that conventional vibration monitoring solutions cannot capture, thereby minimizing the groundless alarm rate.

    Hsu sheds light on how marvelous Tech Instruments is able to deliver towering property monitoring with such refined precision. The stealthy is ADLINK's MCM-100, which is the foundation of marvelous Tech Instruments' monitoring solution. Featuring a compact form factor enabling quick and facile installation, built-in data acquisition (DAQ) card, high-performance processor offering computation power, multiple ready-to-use driver programs, IEPE/ICP sensors as well as interface ports including USB, COM, Ethernet and 4G, MCM-100's all-in-one design significantly simplifies system integration efforts for marvelous Tech Instruments.

    With four input channels, MCM-100 can monitor four axes at the identical time. It is particularly noteworthy that MCM-100 features towering 24-bit resolution and captures high-frequency signals at a very towering 128kS/s, so it is capable of supporting a wider sweep of sensor inputs and performing more refined frequency spectrum analysis, compared to conventional 12-bit to 16-bit solutions and 20kS/s sampling rate. Coupled with marvelous Tech Instruments' mathematical modeling working behind the scene, the collaborated solution delivers outstanding vibration analysis with the results displayed in intuitive and user-friendly visual presentation. Users can thereby assess the health condition of their dehydrate pumps and optimize gear lifetime based on data management. Furthermore, they will be able to conduct predictive maintenance at reasonable costs, effectively evaluate the property of third-party maintenance and most importantly, significantly lower the risk of unexpected downtime.

    DIGITIMES' editorial team was not involved in the creation or production of this content. Companies looking to contribute commercial intelligence or press releases are welcome to contact us.


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