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🔗 Mathematics 🔗 Statistics 🔗 Systems 🔗 Robotics 🔗 Systems/Control theory

In statistics and control theory, Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by estimating a joint probability distribution over the variables for each timeframe. The filter is named after Rudolf E. Kálmán, one of the primary developers of its theory.

The Kalman filter has numerous applications in technology. A common application is for guidance, navigation, and control of vehicles, particularly aircraft, spacecraft and dynamically positioned ships. Furthermore, the Kalman filter is a widely applied concept in time series analysis used in fields such as signal processing and econometrics. Kalman filters also are one of the main topics in the field of robotic motion planning and control and can be used in trajectory optimization. The Kalman filter also works for modeling the central nervous system's control of movement. Due to the time delay between issuing motor commands and receiving sensory feedback, use of the Kalman filter supports a realistic model for making estimates of the current state of the motor system and issuing updated commands.

The algorithm works in a two-step process. In the prediction step, the Kalman filter produces estimates of the current state variables, along with their uncertainties. Once the outcome of the next measurement (necessarily corrupted with some amount of error, including random noise) is observed, these estimates are updated using a weighted average, with more weight being given to estimates with higher certainty. The algorithm is recursive. It can run in real time, using only the present input measurements and the previously calculated state and its uncertainty matrix; no additional past information is required.

Optimality of the Kalman filter assumes that the errors are Gaussian. In the words of Rudolf E. Kálmán: "In summary, the following assumptions are made about random processes: Physical random phenomena may be thought of as due to primary random sources exciting dynamic systems. The primary sources are assumed to be independent gaussian random processes with zero mean; the dynamic systems will be linear." Though regardless of Gaussianity, if the process and measurement covariances are known, the Kalman filter is the best possible linear estimator in the minimum mean-square-error sense.

Extensions and generalizations to the method have also been developed, such as the extended Kalman filter and the unscented Kalman filter which work on nonlinear systems. The underlying model is a hidden Markov model where the state space of the latent variables is continuous and all latent and observed variables have Gaussian distributions. Also, Kalman filter has been successfully used in multi-sensor fusion, and distributed sensor networks to develop distributed or consensus Kalman filter.

🔗 Psychology

The need for cognition (NFC), in psychology, is a personality variable reflecting the extent to which individuals are inclined towards effortful cognitive activities.

Need for cognition has been variously defined as "a need to structure relevant situations in meaningful, integrated ways" and "a need to understand and make reasonable the experiential world". Higher NFC is associated with increased appreciation of debate, idea evaluation, and problem solving. Those with a high need for cognition may be inclined towards high elaboration. Those with a lower need for cognition may display opposite tendencies, and may process information more heuristically, often through low elaboration.

Need for cognition is closely related to the five factor model domain openness to experience, typical intellectual engagement, and epistemic curiosity (see below).

🔗 Philately 🔗 Netherlands

A postzegelcode is a hand-written method of franking in the Netherlands. It consists of a code containing nine numbers and letters that customers can purchase online from PostNL and write directly on their piece of mail within five days as proof-of-payment in place of a postage stamp.

For mail within the Netherlands the nine letters and numbers are written as a grid of 3x3. For international mail there is fourth additional row that contains P, N, L.

The system was started in 2013. Initially the postzegelcode was more expensive than a stamp because additional handling systems were required. Then for a while the postzegelcode was cheaper. Eventually the tariffs were set to the same price.

In December 2020, 590,000 people sent cards with postzegelcodes.

🔗 Climate change 🔗 Environment 🔗 Transport 🔗 Urban studies and planning

Peak car (also peak car use or peak travel) is a hypothesis that motor vehicle distance traveled per capita, predominantly by private car, has peaked and will now fall in a sustained manner. The theory was developed as an alternative to the prevailing market saturation model, which suggested that car use would saturate and then remain reasonably constant, or to GDP-based theories which predict that traffic will increase again as the economy improves, linking recent traffic reductions to the Great Recession of 2008.

The theory was proposed following reductions, which have now been observed in Australia, Belgium, France, Germany, Iceland, Japan (early 1990s), New Zealand, Sweden, the United Kingdom (many cities from about 1994) and the United States. A study by Volpe Transportation in 2013 noted that average miles driven by individuals in the United States has been declining from 900 miles (1,400 km) per month in 2004 to 820 miles (1,320 km) in July 2012, and that the decline had continued since the recent upturn in the US economy.

A number of academics have written in support of the theory, including Phil Goodwin, formerly Director of the transport research groups at Oxford University and UCL, and David Metz, a former Chief Scientist of the UK Department of Transport. The theory is disputed by the UK Department for Transport, which predicts that road traffic in the United Kingdom will grow by 50% by 2036, and Professor Stephen Glaister, Director of the RAC Foundation, who say traffic will start increasing again as the economy improves. Unlike peak oil, a theory based on a reduction in the ability to extract oil due to resource depletion, peak car is attributed to more complex and less understood causes.

🔗 Biography

Joseph P. Nacchio (born June 22, 1949 in Brooklyn, New York) is an American executive who was chairman of the board and chief executive officer of Qwest Communications International from 1997 to 2002. Nacchio was convicted of insider trading during his time heading Qwest. He claimed in court, with documentation, that his was the only company to demand legal authority for surreptitious mass surveillance demanded by the NSA which began prior to the 11 September 2001 attacks.

He was convicted of 19 counts of insider trading in Qwest stock on April 19, 2007 – charges his defense team claimed were U.S. government retaliation for his refusal to give customer data to the National Security Agency in February, 2001. This defense was not admissible in court because the U.S. Department of Justice filed an in limine motion, which is often used in national security cases, to exclude information which may reveal state secrets. Information from the Classified Information Procedures Act hearings in Nacchio's case was likewise ruled inadmissible.

On July 27, 2007, he was sentenced to six years in federal prison, and after appeals failed he reported to Federal Correctional Institution, Schuylkill in Schuylkill County, Pennsylvania on April 14, 2009 to serve his sentence. Nacchio finished serving his sentence on September 20, 2013.

🔗 Computer science

In information theory, linguistics and computer science, the Levenshtein distance is a string metric for measuring the difference between two sequences. Informally, the Levenshtein distance between two words is the minimum number of single-character edits (insertions, deletions or substitutions) required to change one word into the other. It is named after the Soviet mathematician Vladimir Levenshtein, who considered this distance in 1965.

Levenshtein distance may also be referred to as edit distance, although that term may also denote a larger family of distance metrics known collectively as edit distance. It is closely related to pairwise string alignments.

Tilt! How to get bias into a Wikipedia article

To all you budding propagandists in Wikiland: too many of you are working like a bunch of amateurs. Sorry to be so negative, but you have to understand that getting bias into the Wikipedia is a skill; it requires practice, finesse and imagination. It has to be learned; it is not a natural thing, though some have more talent for it than others.

I have been following the Middle East Wikipedia battleground for a few years now, and have been very impressed with the skill of some editors in introducing bias into articles. To ingenuous editors, some of these techniques may seem innocuous enough; in many cases, it is hard to see how proposed edits are biasing an article one way or another. It is, in fact, only in the last few months that I have been able to define what these techniques are and how they work to introduce bias.

The first thing you need to know as a budding propagandist is this: there are two levels at which bias is introduced into the Wikipedia: at the article level, and at the topic level. You need to set your sights high: you don't want to merely bias a single article, you want the entire Wikipedia on your side. Without understanding the importance of topic bias, it is hard to understand many of the article-level techniques, so I will start with the topic level.

🔗 Espionage 🔗 Military history 🔗 Military history/Intelligence 🔗 Sweden 🔗 Military history/Nordic military history 🔗 Military history/National militaries 🔗 Military history/European military history

The National Defence Radio Establishment (Swedish: Försvarets radioanstalt, FRA) is a Swedish government agency organised under the Ministry of Defence. The two main tasks of FRA are signals intelligence (SIGINT), and support to government authorities and state-owned companies regarding computer security.

The FRA is not allowed to initialize any surveillance on their own, and operates purely on assignment from the Government, the Government Offices, the Armed Forces, the Swedish National Police Board and Swedish Security Service (SÄPO). Decisions and oversight regarding information interception is provided by the Defence Intelligence Court and the Defence Intelligence Commission; additional oversight regarding protection of privacy is provided by the Swedish Data Protection Authority.

🔗 Biography 🔗 California 🔗 California/San Francisco Bay Area 🔗 Mathematics 🔗 Iran 🔗 Women scientists 🔗 Biography/science and academia 🔗 Stanford University

Maryam Mirzakhani (Persian: مریم میرزاخانی‎, pronounced [mæɾˈjæm miːɾzɑːxɑːˈniː]; 12 May 1977 – 14 July 2017) was an Iranian mathematician and a professor of mathematics at Stanford University. Her research topics included Teichmüller theory, hyperbolic geometry, ergodic theory, and symplectic geometry. In 2005, as a result of her research, she was honored in Popular Science's fourth annual "Brilliant 10" in which she was acknowledged as one of the top 10 young minds who have pushed their fields in innovative directions.

On 13 August 2014, Mirzakhani was honored with the Fields Medal, the most prestigious award in mathematics. Thus, she became both the first, and to date, the only woman and the first Iranian to be honored with the award. The award committee cited her work in "the dynamics and geometry of Riemann surfaces and their moduli spaces".

On 14 July 2017, Mirzakhani died of breast cancer at the age of 40.

🔗 Biography 🔗 Computing 🔗 Computing/Computer hardware 🔗 Business 🔗 Women scientists 🔗 Biography/science and academia 🔗 Electrical engineering 🔗 Taiwan 🔗 Women in Business

Lisa Su (Chinese: 蘇姿丰; Pe̍h-ōe-jī: So͘ Chu-hong; born 7 November 1969) is a Taiwanese-born American business executive and electrical engineer, who is the president, chief executive officer and chair of AMD. Early in her career, Su worked at Texas Instruments, IBM, and Freescale Semiconductor in engineering and management positions. She is known for her work developing silicon-on-insulator semiconductor manufacturing technologies and more efficient semiconductor chips during her time as vice president of IBM's Semiconductor Research and Development Center.

Su was appointed president and CEO of AMD in October 2014, after joining the company in 2012 and holding roles such as senior vice president of AMD's global business units and chief operating officer. She currently serves on the boards of Cisco Systems, Global Semiconductor Alliance and the U.S. Semiconductor Industry Association, and is a fellow of the Institute of Electrical and Electronics Engineers (IEEE). Recognized with a number of awards and accolades, she was named Executive of the Year by EE Times in 2014 and one of the World's Greatest Leaders in 2017 by Fortune. She became the first woman to receive the IEEE Robert Noyce Medal in 2021.