In information technology, a bare machine (or bare-metal computer) is a computer which has no operating system. The software executed by a bare machine, commonly called a bare metal program or bare metal application, is designed to interact directly with hardware. Bare machines are widely used in embedded systems, particularly in cases where resources are limited or high performance is required. == Bare machine computing == Bare Machine Computing is a computing paradigm in which application software runs directly on a bare machine as a single, stand-alone executable, without an operating system or device drivers. The application software has direct access to hardware resources, and there is typically no distinction between user and kernel mode. It is self-managed software that boots, loads and runs without using any other software components. Bare metal programs are typically written in a close-to-hardware language such as C or assembly language. == Advantages == Typically, a bare-metal application will run faster, use less memory and be more power efficient than an equivalent program that relies on an operating system, due to the inherent overhead imposed by system calls. For example, hardware inputs and outputs are directly accessible to bare metal software, whereas they must usually be accessed through system calls when using an OS. It has no OS and therefore has no OS-related vulnerabilities. == Disadvantages == Bare metal applications typically require more effort to develop because operating system services such as memory management and task scheduling are not available. Debugging a bare-metal program may be complicated by factors such as: Lack of a standard output. The target machine may differ from the hardware used for program development (e.g., emulator, simulator). This forces setting up a way to load the bare-metal program onto the target (flashing), start the program execution and access the target resources. == Examples == === Early computers === Early computers, such as the PDP-11, allowed programmers to load a program, supplied in machine code, to RAM. The resulting operation of the program could be monitored by lights, and output derived from magnetic tape, print devices, or storage. Amdahl UTS's performance improves by 25% when run on bare metal without VM, the company said in 1986. === Embedded systems === Bare machine programming is a common practice in embedded systems, in which microcontrollers or microprocessors boot directly into monolithic, single-purpose software without loading an operating system. Such embedded software can vary in structure. For example, one such program paradigm, known as foreground-background or superloop architecture, consists of an infinite main loop in which each task is executed sequentially and must voluntarily return control back to the loop. The loop runs these cooperative background processes that are not time-critical, while interrupt service routines momentarily interrupt the loop to handle time-critical foreground tasks.
Transfer function matrix
In control system theory, and various branches of engineering, a transfer function matrix, or just transfer matrix is a generalisation of the transfer functions of single-input single-output (SISO) systems to multiple-input and multiple-output (MIMO) systems. The matrix relates the outputs of the system to its inputs. It is a particularly useful construction for linear time-invariant (LTI) systems because it can be expressed in terms of the s-plane. In some systems, especially ones consisting entirely of passive components, it can be ambiguous which variables are inputs and which are outputs. In electrical engineering, a common scheme is to gather all the voltage variables on one side and all the current variables on the other regardless of which are inputs or outputs. This results in all the elements of the transfer matrix being in units of impedance. The concept of impedance (and hence impedance matrices) has been borrowed into other energy domains by analogy, especially mechanics and acoustics. Many control systems span several different energy domains. This requires transfer matrices with elements in mixed units. This is needed both to describe transducers that make connections between domains and to describe the system as a whole. If the matrix is to properly model energy flows in the system, compatible variables must be chosen to allow this. == General == A MIMO system with m outputs and n inputs is represented by a m × n matrix. Each entry in the matrix is in the form of a transfer function relating an output to an input. For example, for a three-input, two-output system, one might write, [ y 1 y 2 ] = [ g 11 g 12 g 13 g 21 g 22 g 23 ] [ u 1 u 2 u 3 ] {\displaystyle {\begin{bmatrix}y_{1}\\y_{2}\end{bmatrix}}={\begin{bmatrix}g_{11}&g_{12}&g_{13}\\g_{21}&g_{22}&g_{23}\end{bmatrix}}{\begin{bmatrix}u_{1}\\u_{2}\\u_{3}\end{bmatrix}}} where the un are the inputs, the ym are the outputs, and the gmn are the transfer functions. This may be written more succinctly in matrix operator notation as, Y = G U {\displaystyle \mathbf {Y} =\mathbf {G} \mathbf {U} } where Y is a column vector of the outputs, G is a matrix of the transfer functions, and U is a column vector of the inputs. In many cases, the system under consideration is a linear time-invariant (LTI) system. In such cases, it is convenient to express the transfer matrix in terms of the Laplace transform (in the case of continuous time variables) or the z-transform (in the case of discrete time variables) of the variables. This may be indicated by writing, for instance, Y ( s ) = G ( s ) U ( s ) {\displaystyle \mathbf {Y} (s)=\mathbf {G} (s)\mathbf {U} (s)} which indicates that the variables and matrix are in terms of s, the complex frequency variable of the s-plane arising from Laplace transforms, rather than time. The examples in this article are all assumed to be in this form, although that is not explicitly indicated for brevity. For discrete time systems s is replaced by z from the z-transform, but this makes no difference to subsequent analysis. The matrix is particularly useful when it is a proper rational matrix, that is, all its elements are proper rational functions. In this case, the state-space representation can be applied. In systems engineering, the overall system transfer matrix G (s) is decomposed into two parts: H (s) representing the system being controlled, and C(s) representing the control system. C (s) takes as its inputs the inputs of G (s) and the outputs of H (s). The outputs of C (s) form the inputs for H (s). == Electrical systems == In electrical systems, it is often the case that the distinction between input and output variables is ambiguous. They can be either, depending on circumstance and point of view. In such cases, the concept of port (a place where energy is transferred from one system to another) can be more useful than input and output. It is customary to define two variables for each port (p): the voltage across it (Vp) and the current entering it (Ip). For instance, the transfer matrix of a two-port network can be defined as follows, [ V 1 V 2 ] = [ z 11 z 12 z 21 z 22 ] [ I 1 I 2 ] {\displaystyle {\begin{bmatrix}V_{1}\\V_{2}\end{bmatrix}}={\begin{bmatrix}z_{11}&z_{12}\\z_{21}&z_{22}\\\end{bmatrix}}{\begin{bmatrix}I_{1}\\I_{2}\end{bmatrix}}} where the zmn are called the impedance parameters, or z-parameters. They are so-called because they are in units of impedance and relate port currents to a port voltage. The z-parameters are not the only way that transfer matrices are defined for two-port networks. Six basic matrices relate voltages and currents, each with advantages for particular system network topologies. However, only two of these can be extended beyond two ports to an arbitrary number of ports. These two are the z-parameters and their inverse, the admittance parameters or y-parameters. To understand the relationship between port voltages and currents and inputs and outputs, consider the simple voltage divider circuit. If we only wish to consider the output voltage (V2) resulting from applying the input voltage (V1) then the transfer function can be expressed as, [ V 2 ] = [ R 2 R 1 + R 2 ] [ V 1 ] {\displaystyle {\begin{bmatrix}V_{2}\end{bmatrix}}={\begin{bmatrix}{\dfrac {R_{2}}{R_{1}+R_{2}}}\end{bmatrix}}{\begin{bmatrix}V_{1}\end{bmatrix}}} which can be considered the trivial case of a 1×1 transfer matrix. The expression correctly predicts the output voltage if there is no current leaving port 2, but is increasingly inaccurate as the load increases. If, however, we attempt to use the circuit in reverse, driving it with a voltage at port 2 and calculate the resulting voltage at port 1 the expression gives completely the wrong result even with no load on port 1. It predicts a greater voltage at port 1 than was applied at port 2, an impossibility with a purely resistive circuit like this one. To correctly predict the behaviour of the circuit, the currents entering or leaving the ports must also be taken into account, which is what the transfer matrix does. The impedance matrix for the voltage divider circuit is, [ V 1 V 2 ] = [ R 1 + R 2 R 2 R 2 R 2 ] [ I 1 I 2 ] {\displaystyle {\begin{bmatrix}V_{1}\\V_{2}\end{bmatrix}}={\begin{bmatrix}R_{1}+R_{2}&R_{2}\\R_{2}&R_{2}\end{bmatrix}}{\begin{bmatrix}I_{1}\\I_{2}\end{bmatrix}}} which fully describes its behaviour under all input and output conditions. At microwave frequencies, none of the transfer matrices based on port voltages and currents are convenient to use in practice. Voltage is difficult to measure directly, current next to impossible, and the open circuits and short circuits required by the measurement technique cannot be achieved with any accuracy. For waveguide implementations, circuit voltage and current are entirely meaningless. Transfer matrices using different sorts of variables are used instead. These are the powers transmitted into, and reflected from a port, which are readily measured in the transmission line technology used in distributed-element circuits in the microwave band. The most well-known and widely used of these sorts of parameters is the scattering parameters, or s-parameters. == Mechanical and other systems == The concept of impedance can be extended into the mechanical and other domains through a mechanical-electrical analogy, hence the impedance parameters and other forms of 2-port network parameters can also be extended to the mechanical domain. To do this, an effort variable and a flow variable are made analogues of voltage and current, respectively. For mechanical systems under translation these variables are force and velocity respectively. Expressing the behaviour of a mechanical component as a two-port or multi-port with a transfer matrix is a useful thing to do because, like electrical circuits, the component can often be operated in reverse and its behaviour is dependent on the loads at the inputs and outputs. For instance, a gear train is often characterised simply by its gear ratio, a SISO transfer function. However, the gearbox output shaft can be driven around to turn the input shaft, requiring a MIMO analysis. In this example, the effort and flow variables are torque T and angular velocity ω, respectively. The transfer matrix in terms of z-parameters will look like, [ T 1 T 2 ] = [ z 11 z 12 z 21 z 22 ] [ ω 1 ω 2 ] {\displaystyle {\begin{bmatrix}T_{1}\\T_{2}\end{bmatrix}}={\begin{bmatrix}z_{11}&z_{12}\\z_{21}&z_{22}\end{bmatrix}}{\begin{bmatrix}\omega _{1}\\\omega _{2}\end{bmatrix}}} However, the z-parameters are not necessarily the most convenient for characterising gear trains. A gear train is the analogue of an electrical transformer and the h-parameters (hybrid parameters) better describe transformers because they directly include the turns ratios (the analogue of gear ratios). The gearbox transfer matrix in h-parameter format is, [ T 1 ω 2 ] = [ h 11 h 12 h 21 h 22 ] [ ω 1 T 2 ] {\displaystyle {\begin{bmatrix}T_{1}\\\omega _{2}\end{bm
Hideto Tomabechi
Hideto Tomabechi (苫米地 英人, Tomabechi Hideto; born 1959) is a Japanese cognitive scientist who is an adjunct fellow at Carnegie Mellon University and has had an executive role in several companies. == Early life and education == He grew up in Minato-ku, Tokyo. He graduated from Komaba Toho High School and then joined the University of Massachusetts Amherst. He received his first degree from Sophia University, then joined Mitsubishi Real Estate. Tomabechi was a Fulbright Scholar at Yale University and became member of Yale University Artificial Intelligence Research Center and Yale Cognitive Science Program. Hideto Tomabechi's research topic was: Cognition Models for Language Expressions and Computational Methods (Tomabechi Algorithm). Hideto Tomabechi received his Ph.D. in the field of computational linguistics from Carnegie Mellon University. His 1993 Ph.D. Thesis was entitled "Efficient Unification for Natural Language". == Career timeline == 1992-1998: Director, Justsystem Scientific Institute. 1998: CEO of Cognitive Research Laboratories Inc. 2007: Adjunct Fellow at the Cyber Security & Privacy Research Institute (CyLab) at Carnegie Mellon University. 2020: Visiting professor at Nano & Life Research Center, Waseda University. 2020: Chairman, Resilience Japan, LLC. 2022: Chairman of Japan Society for Foreign Policy. == Brain research == In 1993, Hideto Tomabechi became director of the Development Department. Later, Tomabechi became director of the JustSystems Basic Research Institute Tomabechi researched the basic functions of the human brain and mind. The purpose of brain and consciousness research were to develop the human machine interface. The main areas of research were altered states of consciousness, hypnosis, homeostasis, brain functions, and functions of the human mind in cyberspace. Dr. Tomabechi founded the Bechi Unit, the world's first virtual currency at JustSystems, based on Tomabech Algorithms. == Brainwashing == Tomabechi was the scientist who deprogrammed the leaders of the religious cult responsible for the terrorist attack in the Tokyo subway. The cult (Aum Shinrikyo) brainwashed its people and they carried out the attacks in an influenced state of consciousness.
Lin-Shan Lee
Lin-Shan Lee (Chinese: 李琳山; born 23 September 1952) is a Taiwanese computer scientist. == Education and career == Lee earned a bachelor's degree in electrical engineering from National Taiwan University in 1974, and pursued a doctorate in the same subject at Stanford University, graduating in 1977. He subsequently returned to Taiwan and joined the NTU faculty in 1982. Lee is a 1993 fellow of the Institute of Electrical and Electronics Engineers, recognized "[f]or contributions to computer voice input/output techniques for Mandarin Chinese and to engineering education." The International Speech Communication Association elevated him to fellow status in 2010 "[f]or his contributions to Chinese spoken language processing and speech information retrieval, and his service to the speech language community." In 2016, Lee was elected a member of Academia Sinica.
GLIMMER
In bioinformatics, GLIMMER (Gene Locator and Interpolated Markov ModelER) is used to find genes in prokaryotic DNA. "It is effective at finding genes in bacteria, archea, viruses, typically finding 98-99% of all relatively long protein coding genes". GLIMMER was the first system that used the interpolated Markov model to identify coding regions. The GLIMMER software is open source and is maintained by Steven Salzberg, Art Delcher, and their colleagues at the Center for Computational Biology at Johns Hopkins University. The original GLIMMER algorithms and software were designed by Art Delcher, Simon Kasif and Steven Salzberg and applied to bacterial genome annotation in collaboration with Owen White. == Versions == === GLIMMER 1.0 === First Version of GLIMMER "i.e., GLIMMER 1.0" was released in 1998 and it was published in the paper Microbial gene identification using interpolated Markov model. Markov models were used to identify microbial genes in GLIMMER 1.0. GLIMMER considers the local composition sequence dependencies which makes GLIMMER more flexible and more powerful when compared to fixed-order Markov model. There was a comparison made between interpolated Markov model used by GLIMMER and fifth order Markov model in the paper Microbial gene identification using interpolated Markov models. "GLIMMER algorithm found 1680 genes out of 1717 annotated genes in Haemophilus influenzae where fifth order Markov model found 1574 genes. GLIMMER found 209 additional genes which were not included in 1717 annotated genes where fifth order Markov model found 104 genes."' === GLIMMER 2.0 === Second Version of GLIMMER i.e., GLIMMER 2.0 was released in 1999 and it was published in the paper Improved microbial identification with GLIMMER. This paper provides significant technical improvements such as using interpolated context model instead of interpolated Markov model and resolving overlapping genes which improves the accuracy of GLIMMER. Interpolated context models are used instead of interpolated Markov model which gives the flexibility to select any base. In interpolated Markov model probability distribution of a base is determined from the immediate preceding bases. If the immediate preceding base is irrelevant amino acid translation, interpolated Markov model still considers the preceding base to determine the probability of given base where as interpolated context model which was used in GLIMMER 2.0 can ignore irrelevant bases. False positive predictions were increased in GLIMMER 2.0 to reduce the number of false negative predictions. Overlapped genes are also resolved in GLIMMER 2.0. Various comparisons between GLIMMER 1.0 and GLIMMER 2.0 were made in the paper Improved microbial identification with GLIMMER which shows improvement in the later version. "Sensitivity of GLIMMER 1.0 ranges from 98.4 to 99.7% with an average of 99.1% where as GLIMMER 2.0 has a sensitivity range from 98.6 to 99.8% with an average of 99.3%. GLIMMER 2.0 is very effective in finding genes of high density. The parasite Trypanosoma brucei, responsible for causing African sleeping sickness is being identified by GLIMMER 2.0" === GLIMMER 3.0 === Third version of GLIMMER, "GLIMMER 3.0" was released in 2007 and it was published in the paper Identifying bacterial genes and endosymbiont DNA with Glimmer. This paper describes several major changes made to the GLIMMER system including improved methods to identify coding regions and start codon. Scoring of ORF in GLIMMER 3.0 is done in reverse order i.e., starting from stop codon and moves back towards the start codon. Reverse scanning helps in identifying the coding portion of the gene more accurately which is contained in the context window of IMM. GLIMMER 3.0 also improves the generated training set data by comparing the long-ORF with universal amino acid distribution of widely disparate bacterial genomes."GLIMMER 3.0 has an average long-ORF output of 57% for various organisms where as GLIMMER 2.0 has an average long-ORF output of 39%." GLIMMER 3.0 reduces the rate of false positive predictions which were increased in GLIMMER 2.0 to reduce the number of false negative predictions. "GLIMMER 3.0 has a start-site prediction accuracy of 99.5% for 3'5' matches where as GLIMMER 2.0 has 99.1% for 3'5' matches. GLIMMER 3.0 uses a new algorithm for scanning coding regions, a new start site detection module, and architecture which integrates all gene predictions across an entire genome." Minimum description length === Theoretical and Biological Foundation === The GLIMMER project helped introduce and popularize the use of variable length models in Computational Biology and Bioinformatics that subsequently have been applied to numerous problems such as protein classification and others. Variable length modeling was originally pioneered by information theorists and subsequently ingeniously applied and popularized in data compression (e.g. Ziv-Lempel compression). Prediction and compression are intimately linked using Minimum Description Length Principles. The basic idea is to create a dictionary of frequent words (motifs in biological sequences). The intuition is that the frequently occurring motifs are likely to be most predictive and informative. In GLIMMER the interpolated model is a mixture model of the probabilities of these relatively common motifs. Similarly to the development of HMMs in Computational Biology, the authors of GLIMMER were conceptually influenced by the previous application of another variant of interpolated Markov models to speech recognition by researchers such as Fred Jelinek (IBM) and Eric Ristad (Princeton). The learning algorithm in GLIMMER is different from these earlier approaches. == Access == GLIMMER can be downloaded from The Glimmer home page (requires a C++ compiler). Alternatively, an online version is hosted by NCBI [1]. == How it works == GLIMMER primarily searches for long-ORFS. An open reading frame might overlap with any other open reading frame which will be resolved using the technique described in the sub section. Using these long-ORFS and following certain amino acid distribution GLIMMER generates training set data. Using these training data, GLIMMER trains all the six Markov models of coding DNA from zero to eight order and also train the model for noncoding DNA GLIMMER tries to calculate the probabilities from the data. Based on the number of observations, GLIMMER determines whether to use fixed order Markov model or interpolated Markov model. If the number of observations are greater than 400, GLIMMER uses fixed order Markov model to obtain there probabilities. If the number of observations are less than 400, GLIMMER uses interpolated Markov model which is briefly explained in the next sub section. GLIMMER obtains score for every long-ORF generated using all the six coding DNA models and also using non-coding DNA model. If the score obtained in the previous step is greater than a certain threshold then GLIMMER predicts it to be a gene. The steps explained above describes the basic functionality of GLIMMER. There are various improvements made to GLIMMER and some of them are described in the following sub-sections. === The GLIMMER system === GLIMMER system consists of two programs. First program called build-imm, which takes an input set of sequences and outputs the interpolated Markov model as follows. The probability for each base i.e., A,C,G,T for all k-mers for 0 ≤ k ≤ 8 is computed. Then, for each k-mer, GLIMMER computes weight. New sequence probability is computed as follows. where n is the length of the sequence S x {\displaystyle S_{x}} is the oligomer at position x. I M M 8 ( S x ) {\displaystyle IMM_{8}(S_{x})} , the 8 t h {\displaystyle 8^{th}} -order interpolated Markov model score is computed as "where Y k ( S x − 1 ) {\displaystyle Y_{k}(S_{x-1})} is the weight of the k-mer at position x-1 in the sequence S and P k ( S x ) {\displaystyle P_{k}(S_{x})} is the estimate obtained from the training data of the probability of the base located at position x in the k t h {\displaystyle k^{th}} -order model." The probability of base S x {\displaystyle S_{x}} given the i previous bases is computed as follows. "The value of Y i ( S x ) {\displaystyle Y_{i}(S_{x})} associated with P i ( S x ) {\displaystyle P_{i}(S_{x})} can be regarded as a measure of confidence in the accuracy of this value as an estimate of the true probability. GLIMMER uses two criteria to determine Y i ( S x ) {\displaystyle Y_{i}(S_{x})} . The first of these is simple frequency occurrence in which the number of occurrences of context string S x , i {\displaystyle S_{x,i}} in the training data exceeds a specific threshold value, then Y i ( S x ) {\displaystyle Y_{i}(S_{x})} is set to 1.0. The current default value for threshold is 400, which gives 95% confidence. When there are insufficient sample occurrences of a context string, build-imm employ additional criteria to determine Y {\displaystyle Y} value. For a
Qapital
Qapital is a personal finance mobile application (app) for the iOS and Android operating systems, developed by Qapital, LLC. The app is designed to motivate users to save money through a gamification of their spending behavior. It moves money from a user's checking account to a separate Qapital account, when certain rules are triggered. Its database is used by psychology professor Dan Ariely to study consumer behavior. Qapital was released in Sweden in 2013, then in the US in early 2015. The application was later withdrawn from the Swedish market in April 2015, in order to focus on the US market. == History == The idea for Qapital was conceived by ex-bankers in Sweden. The software was designed by twin brothers Daniel and Andreas Källbom of Studio Källbom and released in Sweden in December 2013. The original software was a personal finance dashboard, similar to Mint.com, to show its users how they spent their money. Qapital introduced the app into the US market with a different design in 2014 and started focusing exclusively on the US market. The app was re-designed to focus on building savings rather than managing personal finances. The Swedish version shut down in April 2015. The app was initially restricted to the iOS platform, but an Android version was released at the end of 2015. Shortly after its US launch, Qapital invited psychology professor Dan Ariely to join its team as its "chief behavioral economist". He uses the app's database to conduct research into behavioral economics and Qapital in turn uses Ariely's research in design and programming decisions. In 2017, Qapital added checking and debit card services to the app. == Concept and features == Qapital is a free personal finance app for iOS and Android devices, intended to encourage its users to save money. Qapital directs each of its users to set savings goals, then automatically transfers money from their checking account to an account for savings, when a rule established in the app is met. It uses the "if this then that" (IFTTT) rule-based web-service. For example, one rule could be that if a user purchases a cup of coffee, then the app will round up the charge to the nearest dollar and deposit the difference into savings. Users connect their bank accounts to Qapital, so it knows when purchases are made. When a rule is met, money for savings are transferred to a Qapital account operated in partnership with Lincoln Savings Bank. As of 2015, Qapital can connect to more than 180 other apps, such as Facebook, Twitter, Dropbox and Instagram. For example, connecting to Jawbone allows the user to set a rule that if they take a certain number of steps during the day, a set amount of money is transferred to savings. The app also allows users to monitor activity among their other financial accounts, such as deposits and withdrawals. == Reception == In an October 2015 review, PC Magazine gave Qapital four out of five marks and an editor rating of "excellent." The review praised the app for having a "lovely design" and criticized it for being a, "bit simplistic in some of its rules." Bankrate, in a May 2015 review, gave the app a score of 3/5 for "ease of use," 5/5 for "features," 4/5 for "effectiveness," 4/5 for "value," for a total score of 16/20. The reviewer criticized Qapital's savings account for providing a low-interest rate, but concluded that its numerous features make the app "intriguing" and "it would be difficult to find a standard bank app more fun to use than Qapital."
Glottochronology
Glottochronology (from Attic Greek γλῶττα 'tongue, language' and χρόνος 'time') is the part of lexicostatistics which involves comparative linguistics and deals with the chronological relationship between languages. The idea was developed by Morris Swadesh in the 1950s in his article on Salish internal relationships. He developed the idea under two assumptions: there indeed exists a relatively stable basic vocabulary (referred to as Swadesh lists) in all languages of the world; and, any replacements happen in a way analogous to radioactive decay in a constant percentage per time elapsed. Using mathematics and statistics, Swadesh developed an equation to determine when languages separated and give an approximate time of when the separation occurred. His methods aimed to aid linguistic anthropologists by giving them a definitive way to determine a separation date between two languages. The formula provides an approximate number of centuries since two languages were supposed to have separated from a singular common ancestor. His methods also purported to provide information on when ancient languages may have existed. Despite multiple studies and literature containing the information of glottochronology, it is not widely used today and is surrounded with controversy. Glottochronology tracks language separation from thousands of years ago but many linguists are skeptical of the concept because it is more of a 'probability' rather than a 'certainty.' On the other hand, some linguists may say that glottochronology is gaining traction because of its relatedness to archaeological dates. Glottochronology is not as accurate as archaeological data, but some linguists still believe that it can provide a solid estimate. Over time many different extensions of the Swadesh method evolved; however, Swadesh's original method is so well known that 'glottochronology' is usually associated with him. == Methodology == The original method of glottochronology presumed that the core vocabulary of a language is replaced at a constant (or constant average) rate across all languages and cultures and so can be used to measure the passage of time. The process makes use of a list of lexical terms and morphemes which are similar to multiple languages. Lists were compiled by Morris Swadesh and assumed to be resistant against borrowing (originally designed in 1952 as a list of 200 items, but the refined 100-word list in Swadesh (1955) is much more common among modern day linguists). The core vocabulary was designed to encompass concepts common to every human language such as personal pronouns, body parts, heavenly bodies and living beings, verbs of basic actions, numerals, basic adjectives, kin terms, and natural occurrences and events. Through a basic word list, one eliminates concepts that are specific to a particular culture or time period. It has been found through differentiating word lists that the ideal is really impossible and that the meaning set may need to be tailored to the languages being compared. Word lists are not homogenous throughout studies and they are often changed and designed to suit both languages being studied. Linguists find that it is difficult to find a word list where all words used are culturally unbiased. Many alternative word lists have been compiled by other linguists and often use fewer meaning slots. The percentage of cognates (words with a common origin) in the word lists is then measured. The larger the percentage of cognates, the more recently the two languages being compared are presumed to have separated. === Glottochronologic constant === Determining word lists rely on morpheme decay or change in vocabulary. Morpheme decay must stay at a constant rate for glottochronology to be applied to a language. This leads to a critique of the glottochronologic formula because some linguists argue that the morpheme decay rate is not guaranteed to stay the same throughout history. American Linguist Robert Lees obtained a value for the "glottochronological constant" (r) of words by considering the known changes in 13 pairs of languages using the 200 word list. He obtained a value of 0.8048 ± 0.0176 with 90% confidence. For his 100-word list Swadesh obtained a value of 0.86, the higher value reflecting the elimination of semantically unstable words. === Divergence time === The basic formula of glottochronology proposed by Morris Swadesh is: t = − ln ( c ) 2 ln ( r ) {\displaystyle t=-{\frac {\ln(c)}{2\ln(r)}}} t = a given period of time from one stage of the language to another (measured in millennia), c = proportion of wordlist items retained at the end of that period and r = rate of replacement for that word list. By testing historically verifiable cases in which t is known by nonlinguistic data (such as the approximate distance from Classical Latin to modern Romance languages), Swadesh arrived at the empirical value of approximately 0.14 for L, (c?) which means that the rate of replacement constitutes around 14 words from the 100-wordlist per millennium. This is represented in the table below. === Results === Glottochronology was applied to a range of language families, including Salishan, Indo-European, Japonic, Afro-Asiatic, Chinese and Mayan and other American languages. For Amerind, correlations have been obtained with radiocarbon dating and blood groups as well as archaeology. === Example Wordlist === Below is an example of a basic word list composed of basic Turkish words and their English translations. == Discussion == The concept of language change is old, and its history is reviewed in Hymes (1973) and Wells (1973). In some sense, glottochronology is a reconstruction of history and can often be closely related to archaeology. Many linguistic studies find the success of glottochronology to be found alongside archaeological data. Glottochronology itself dates back to the mid-20th century. An introduction to the subject is given in Embleton (1986) and in McMahon and McMahon (2005). Glottochronology has been controversial ever since, partly because of issues of accuracy but also because of the question of whether its basis is sound (for example, Bergsland 1958; Bergsland and Vogt 1962; Fodor 1961; Chrétien 1962; Guy 1980). The concerns have been addressed by Dobson et al. (1972), Dyen (1973) and Kruskal, Dyen and Black (1973). The assumption of a single-word replacement rate can distort the divergence-time estimate when borrowed words are included (Thomason and Kaufman 1988). The presentations vary from "Why linguists don't do dates" to the one by Starostin discussed below. Since its original inception, glottochronology has been rejected by many linguists, mostly Indo-Europeanists of the school of the traditional comparative method. Criticisms have been answered in particular around three points of discussion: Criticism levelled against the higher stability of lexemes in Swadesh lists alone (Haarmann 1990) misses the point because a certain amount of losses only enables the computations (Sankoff 1970). The non-homogeneity of word lists often leads to lack of understanding between linguists. Linguists also have difficulties finding a completely unbiased list of basic cultural words. it can take a long time for linguists to find a viable word list which can take several test lists to find a usable list. Traditional glottochronology presumes that language changes at a stable rate. Thus, in Bergsland & Vogt (1962), the authors make an impressive demonstration, on the basis of actual language data verifiable by extralinguistic sources, that the "rate of change" for Icelandic constituted around 4% per millennium, but for closely connected Riksmal (Literary Norwegian), it would amount to as much as 20% (Swadesh's proposed "constant rate" was supposed to be around 14% per millennium). That and several other similar examples effectively proved that Swadesh's formula would not work on all available material, which is a serious accusation since evidence that can be used to "calibrate" the meaning of L (language history recorded during prolonged periods of time) is not overwhelmingly large in the first place. It is highly likely that the chance of replacement is different for every word or feature ("each word has its own history", among hundreds of other sources:). That global assumption has been modified and downgraded to single words, even in single languages, in many newer attempts (see below). There is a lack of understanding of Swadesh's mathematical/statistical methods. Some linguists reject the methods in full because the statistics lead to 'probabilities' when linguists trust 'certainties' more. A serious argument is that language change arises from socio-historical events that are, of course, unforeseeable and, therefore, uncomputable. == Modifications == Somewhere in between the original concept of Swadesh and the rejection of glottochronology in its entirety lies the idea that glottochronology as a formal method of linguistic