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Bit Manipulation Instruction Sets
Bit Manipulation Instructions Sets (BMI sets) are extensions to the x86 instruction set architecture for microprocessors from Intel and AMD. The purpose of these instruction sets is to improve the speed of bit manipulation. All the instructions in these sets are non-SIMD and operate only on general-purpose registers. There are two sets published by Intel: BMI (here referred to as BMI1) and BMI2; they were both introduced with the Haswell microarchitecture. Another two sets were published by AMD: ABM (Advanced Bit Manipulation, which is also a subset of SSE4a implemented by Intel as part of SSE4.2 and BMI1), and TBM (Trailing Bit Manipulation, an extension introduced with Piledriver-based processors as an extension to BMI1, but dropped again in Zen-based processors).
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Topic Review
Sakai
Sakai is a free, community source, educational software platform designed to support teaching, research and collaboration. Systems of this type are also known as Course Management Systems (CMS), Learning Management Systems (LMS), or Virtual Learning Environments (VLE). Sakai is developed by a community of academic institutions, commercial organizations and individuals. It is distributed under the Educational Community License (a type of open source license). Sakai is used by hundreds of institutions, mainly in the United States , but also in Canada , Europe, Asia, Africa and Australia . Sakai was designed to be scalable, reliable, interoperable and extensible. Its largest installations handle over 100,000 users.
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Topic Review
Exclusion of the Null Hypothesis
In inferential statistics, the null hypothesis (often denoted H0) is a default hypothesis that a quantity to be measured is zero (null). Typically, the quantity to be measured is the difference between two situations, for instance to try to determine if there is a positive proof that an effect has occurred or that samples derive from different batches. The null hypothesis is effectively stating that a quantity (of interest) is larger or equal to zero AND smaller or equal to zero. If either requirement can be positively overturned, the null hypothesis is "excluded from the realm of possibilities". The null hypothesis is generally assumed to remain possibly true. Multiple analyses can be performed to show how the hypothesis should be either: rejected or excluded e.g. having high confidence level, thus demonstrating a statistically significant difference. This is demonstrated by showing that zero is outside of the specified confidence interval of the measurement on either side, typically within the real numbers. Failure to exclude the null hypothesis (with any confidence) does logically NOT confirm or support the (unprovable) null hypothesis. (When you have not proven something is e.g. bigger than x, it does not necessarily mean you have made it plausible that it is smaller or equal than x; alternatively you may just have done a lousy measurement with low accuracy. Confirming the null hypothesis two-sided would amount to positively proving it is bigger or equal than 0 AND to positively proving it is smaller or equal than 0; this is something for which you need infinite accuracy as well as exactly zero effect neither of which normally are realistic. Also measurements will never indicate a non-zero probability of exactly zero difference.) So failure of an exclusion of a null hypothesis amounts to a "don't know" at the specified confidence level; it does not immediately imply null somehow, as the data may already show a (less strong) indication for a non-null. The used confidence level does absolutely certainly not correspond to the likelihood of null at failing to exclude; in fact in this case a high used confidence level expands the still plausible range. A non-null hypothesis can have the following meanings, depending on the author a) a value other than zero is used, b) some margin other than zero is used and c) the "alternative" hypothesis. Testing (excluding or failing to exclude) the null hypothesis provides evidence that there are (or are not) statistically sufficient grounds to believe there is a relationship between two phenomena (e.g., that a potential treatment has a non-zero effect, either way). Testing the null hypothesis is a central task in statistical hypothesis testing in the modern practice of science. There are precise criteria for excluding or not excluding a null hypothesis at a certain confidence level. The confidence level should indicate the likelihood that much more and better data would still be able to exclude the null hypothesis on the same side. The concept of a null hypothesis is used differently in two approaches to statistical inference. In the significance testing approach of Ronald Fisher, a null hypothesis is rejected if the observed data is significantly unlikely to have occurred if the null hypothesis were true. In this case, the null hypothesis is rejected and an alternative hypothesis is accepted in its place. If the data is consistent with the null hypothesis statistically possibly true, then the null hypothesis is not rejected. In neither case is the null hypothesis or its alternative proven; with better or more data, the null may still be rejected. This is analogous to the legal principle of presumption of innocence, in which a suspect or defendant is assumed to be innocent (null is not rejected) until proven guilty (null is rejected) beyond a reasonable doubt (to a statistically significant degree). In the hypothesis testing approach of Jerzy Neyman and Egon Pearson, a null hypothesis is contrasted with an alternative hypothesis, and the two hypotheses are distinguished on the basis of data, with certain error rates. It is used in formulating answers in research. Statistical inference can be done without a null hypothesis, by specifying a statistical model corresponding to each candidate hypothesis, and by using model selection techniques to choose the most appropriate model. (The most common selection techniques are based on either Akaike information criterion or Bayes factor).
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Topic Review
Net Neutrality (Last Week Tonight)
"Net Neutrality" is the first segment of the HBO news satire television series Last Week Tonight with John Oliver devoted to net neutrality in the United States. It aired for 13 minutes on June 1, 2014, as part of the fifth episode of Last Week Tonight's first season. During this segment, as well Oliver's follow-up segment entitled "Net Neutrality II", comedian John Oliver discusses the threats to net neutrality. Under the administration of President Barack Obama, the Federal Communications Commission (FCC) was considering two options for net neutrality in early 2014. The FCC proposed permitting fast and slow broadband lanes, which would compromise net neutrality, but was also considering reclassifying broadband as a telecommunication service, which would preserve net neutrality. After a surge of comments supporting net neutrality that were inspired by Oliver's episode, the FCC voted to reclassify broadband as a utility in 2015.
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Topic Review
Windows 10 Version History (Version 2004)
The Windows 10 May 2020 Update (also known as version 2004 and codenamed "20H1") is the ninth major update to Windows 10. It carries the build number 10.0.19041.
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Topic Review
Measuring Network Throughput
Throughput of a network can be measured using various tools available on different platforms. This page explains the theory behind what these tools set out to measure and the issues regarding these measurements. Reasons for measuring throughput in networks. People are often concerned about measuring the maximum data throughput in bits per second of a communications link or network access. A typical method of performing a measurement is to transfer a 'large' file from one system to another system and measure the time required to complete the transfer or copy of the file. The throughput is then calculated by dividing the file size by the time to get the throughput in megabits, kilobits, or bits per second. Unfortunately, the results of such an exercise will often result in the goodput which is less than the maximum theoretical data throughput, leading to people believing that their communications link is not operating correctly. In fact, there are many overheads accounted for in throughput in addition to transmission overheads, including latency, TCP Receive Window size and system limitations, which means the calculated goodput does not reflect the maximum achievable throughput.
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Topic Review
Set Notation
Sets are fundamental objects in mathematics. Intuitively, a set is merely a collection of elements or members. There are various conventions for textually denoting sets. In any particular situation, an author typically chooses from among these conventions depending on which properties of the set are most relevant to the immediate context or on which perspective is most useful.
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Topic Review
Famous Photographical Manipulations
Photographical Manipulation is the alteration of a photograph. The U.S. National Press Photographers Association (NPPA) Digital Manipulation Code of Ethics states: “As journalists we believe the guiding principle of our profession is accuracy; therefore, we believe it is wrong to alter the content of a photograph in any way that deceives the public. As photojournalists, we have the responsibility to document society and to preserve its images as a matter of historical record. It is clear that the emerging electronic technologies provide new challenges to the integrity of photographic images... in light of this, we the National Press Photographers Association, reaffirm the basis of our ethics: Accurate representation is the benchmark of our profession. We believe photojournalistic guidelines for fair and accurate reporting should be the criteria for judging what may be done electronically to a photograph. Altering the editorial content... is a breach of the ethical standards recognized by the NPPA.”
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Topic Review
Medical Expenditure Panel Survey
{{Multiple issues| The Medical Expenditure Panel Survey (MEPS) is a family of surveys intended to provide nationally representative estimates of health expenditure, utilization, payment sources, health status, and health insurance coverage among the noninstitutionalized, nonmilitary population of the United States . This series of government-produced data sets can be used to examine how individuals interact with the medical care system in the United States. MEPS is administered by the Agency for Healthcare Research and Quality (AHRQ) in three components: the core Household Component, the Insurance/Employer Component, and the Medical Provider Component. Only the Household Component is available for download on the Internet. These components provide comprehensive national estimates of health care use and payment by individuals, families, and any other demographic group of interest.
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Topic Review
Odal (Rune)
Template:Infobox rune The Elder Futhark Odal rune (ᛟ), also known as the Othala rune, represents the o sound. Its reconstructed Proto-Germanic name is *ōþalan "heritage; inheritance, inherited estate". It was in use for epigraphy during the 3rd to the 8th centuries. It is not continued in the Younger Futhark, disappearing from the Scandinavian record around the 6th century, but it survived in the Anglo-Saxon Futhorc, and expressed the Old English œ phoneme during the 7th and 8th centuries. Its name is attested as ēðel in the Anglo-Saxon manuscript tradition. The odal rune with serifs (feet) is associated with Nazism and is banned in Germany under laws restricting Nazi symbolism, and other, similar organizations. The rune is encoded in Unicode at code point U+16DF: ᛟ.
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