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ONLINE LEARNING.

Online machine learning is a model of induction that learns one instance at a time. The goal in on-line learning is to predict labels for instances. For example, the instances could describe the current conditions of the stock market, and an online algorithm predicts tomorrow's value of a particular stock. The key defining characteristic of on-line learning is that soon after the prediction is made, the true label of the instance is discovered. This information can then be used to refine the prediction hypothesis used by the algorithm. The goal of the algorithm is to make predictions that are close to the true labels.

More formally, an online algorithm proceeds in a sequence of trials. Each trial can be decomposed into three steps. First the algorithm receives an instance. Second the algorithm predicts the label of the instance. Third the algorithm receives the true label of the instance.^[1] The third stage is the most crucial as the algorithm can use this label feedback to update its hypothesis for future trials. The goal of the algorithm is to minimize some performance criteria. For example, with stock market prediction the algorithm may attempt to minimize sum of the square distances between the predicted and true value of a stock. Another popular performance criterion is to minimize the number of mistakes when dealing with classification problems. In addition to applications of a sequential nature, online learning algorithms are also relevant in applications with huge amounts of data such that traditional learning approaches that use the entire data set in aggregate are computationally infeasible.

Because on-line learning algorithms continually receive label feedback, the algorithms are able to adapt and learn in difficult situations. Many online algorithms can give strong guarantees on performance even when the instances are not generated by a distribution. As long as a reasonably good classifier exists, the online algorithm will learn to predict correct labels. This good classifier must come from a previously determined set that depends on the algorithm. For example, two popular on-line algorithms perceptron and winnow can perform well when a hyperplane exists that splits the data into two categories. These algorithms can even be modified to do provably well even if the hyperplane is allowed to infrequently change during the on-line learning trials.

Unfortunately, the main difficulty of on-line learning is also a result of the requirement for continual label feedback. For many problems it is not possible to guarantee that accurate label feedback will be available in the near future. For example, when designing a system that learns how to do optical character recognition, typically some expert must label previous instances to help train the algorithm. In actual use of the OCR application, the expert is no longer available and no inexpensive outside source of accurate labels is available. Fortunately, there is a large class of problems where label feedback is always available. For any problem that consists of predicting the future, an on-line learning algorithm just needs to wait for the label to become available. This is true in our previous example of stock market prediction and many other problems.

A prototypical online supervised learning algorithm

In the setting of supervised learning, or learning from examples, we are interested in learning a function , where  is thought of as a space of inputs and  as a space of outputs, that predicts well on instances that are drawn from a joint probability distribution  on . In this setting, we are given a loss function , such that  measures the difference between the predicted value  and the true value . The ideal goal is to select a function , where  is a space of functions called a hypothesis space, so as to minimize the expected risk:

In reality, the learner never knows the true distribution  over instances. Instead, the learner usually has access to a training set of examples  that are assumed to have been drawn i.i.d. from the true distribution . A common paradigm in this situation is to estimate a function  through empirical risk minimization or regularized empirical risk minimization (usually Tikhonov regularization). The choice of loss function here gives rise to several well-known learning algorithms such as regularized least squares and support vector machines.

The above paradigm is not well-suited to the online learning setting though, as it requires complete a priori knowledge of the entire training set. In the pure online learning approach, the learning algorithm should update a sequence of functions  in a way such that the function  depends only on the previous function  and the next data point . This approach has low memory requirements in the sense that it only requires storage of a representation of the current function  and the next data point . A related approach that has larger memory requirements allows  to depend on  and all previous data points . We focus solely on the former approach here, and we consider both the case where the data is coming from an infinite stream  and the case where the data is coming from a finite training set , in which case the online learning algorithm may make multiple passes through the data.

The algorithm and its interpretations[edit]

Here we outline a prototypical online learning algorithm in the supervised learning setting and we discuss several interpretations of this algorithm. For simplicity, consider the case where , , and  is the set of all linear functionals from  into , i.e. we are working with a linear kernel and functions  can be identified with vectors . Furthermore, assume that  is a convex, differentiable loss function. An online learning algorithm satisfying the low memory property discussed above consists of the following iteration:

where  ,  is the gradient of the loss for the next data point  evaluated at the current linear functional , and  is a step-size parameter. In the case of an infinite stream of data, one can run this iteration, in principle, forever, and in the case of a finite but large set of data, one can consider a single pass or multiple passes (epochs) through the data.

Interestingly enough, the above simple iterative online learning algorithm has three distinct interpretations, each of which has distinct implications about the predictive quality of the sequence of functions . The first interpretation considers the above iteration as an instance of the stochastic gradient descent method applied to the problem of minimizing the expected risk  defined above. ^[2] Indeed, in the case of an infinite stream of data, since the examples  are assumed to be drawn i.i.d. from the distribution , the sequence of gradients of  in the above iteration are an i.i.d. sample of stochastic estimates of the gradient of the expected risk  and therefore one can apply complexity results for the stochastic gradient descent method to bound the deviation , where  is the minimizer of . ^[3] This interpretation is also valid in the case of a finite training set; although with multiple passes through the data the gradients are no longer independent, still complexity results can be obtained in special cases.

The second interpretation applies to the case of a finite training set and considers the above recursion as an instance of the incremental gradient descent method^[4] to minimize the empirical risk:

Since the gradients of  in the above iteration are also stochastic estimates of the gradient of , this interpretation is also related to the stochastic gradient descent method, but applied to minimize the empirical risk as opposed to the expected risk. Since this interpretation concerns the empirical risk and not the expected risk, multiple passes through the data are readily allowed and actually lead to tighter bounds on the deviations , where  is the minimizer of .

The third interpretation of the above recursion is distinctly different from the first two and concerns the case of sequential trials discussed above, where the data are potentially not i.i.d. and can perhaps be selected in an adversarial manner. At each step of this process, the learner is given an input  and makes a prediction based on the current linear function . Only after making this prediction does the learner see the true label , at which point the learner is allowed to update  to . Since we are not making any distributional assumptions about the data, the goal here is to perform as well as if we could view the entire sequence of examples ahead of time; that is, we would like the sequence of functions  to have low regret relative to any vector:

In this setting, the above recursion can be considered as an instance of the online gradient descent method for which there are complexity bounds that guarantee  regret.

It should be noted that although the three interpretations of this algorithm yield complexity bounds in three distinct settings, each bound depends on the choice of step-size sequence  in a different way, and thus we cannot simultaneously apply the consequences of all three interpretations; we must instead select the step-size sequence in a way that is tailored for the interpretation that is most relevant. Furthermore, the above algorithm and these interpretations can be extended to the case of a nonlinear kernel by simply considering  to be the feature space associated with the kernel. Although in this case the memory requirements at each iteration are no longer , but are rather on the order of the number of data points considered so far.

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IMPORTENTS OF COMPUTER..

A computer is a general purpose device that can be programmed to carry out a set of arithmetic or logical operations. Since a sequence of operations can be readily changed, the computer can solve more than one kind of problem.

Conventionally, a computer consists of at least one processing element, typically a central processing unit (CPU), and some form ofmemory. The processing element carries out arithmetic and logic operations, and a sequencing and control unit can change the order of operations in response to stored information. Peripheral devices allow information to be retrieved from an external source, and the result of operations saved and retrieved.

In World War II, mechanical analog computers were used for specialized military applications. During this time the first electronicdigital computers were developed. Originally they were the size of a large room, consuming as much power as several hundred modernpersonal computers (PCs).

Modern computers based on integrated circuits are millions to billions of times more capable than the early machines, and occupy a fraction of the space.^[2] Simple computers are small enough to fit into mobile devices, and mobile computers can be powered by smallbatteries. Personal computers in their various forms are icons of the Information Age and are what most people think of as “computers.” However, the embedded computers found in many devices from MP3 players to fighter aircraft and from toys to industrial robots are the most numerous.

Etymology

The first use of the word “computer” was recorded in 1613 in a book called “The yong mans gleanings” by English writer Richard Braithwait I haue read the truest computer of Times, and the best Arithmetician that euer breathed, and he reduceth thy dayes into a short number. It referred to a person who carried out calculations, or computations, and the word continued with the same meaning until the middle of the 20th century. From the end of the 19th century the word began to take on its more familiar meaning, a machine that carries out computations.

History

Rudimentary calculating devices first appeared in antiquity and mechanical calculating aids were invented in the 17th century. The first recorded use of the word "computer" is also from the 17th century, applied to human computers, people who performed calculations, often as employment. The first computer devices were conceived of in the 19th century, and only emerged in their modern form in the 1940s.

First general-purpose computing device

A portion of Babbage's Difference engine.

Charles Babbage, an English mechanical engineer and polymath, originated the concept of a programmable computer. Considered the "father of the computer", he conceptualized and invented the first mechanical computer in the early 19th century. After working on his revolutionary difference engine, designed to aid in navigational calculations, in 1833 he realized that a much more general design, an Analytical Engine, was possible. The input of programs and data was to be provided to the machine via punched cards, a method being used at the time to direct mechanical looms such as theJacquard loom. For output, the machine would have a printer, a curve plotter and a bell. The machine would also be able to punch numbers onto cards to be read in later. The Engine incorporated an arithmetic logic unit, control flow in the form of conditional branching and loops, and integrated memory, making it the first design for a general-purpose computer that could be described in modern terms as Turing-complete.

The machine was about a century ahead of its time. All the parts for his machine had to be made by hand - this was a major problem for a device with thousands of parts. Eventually, the project was dissolved with the decision of the British Government to cease funding. Babbage's failure to complete the analytical engine can be chiefly attributed to difficulties not only of politics and financing, but also to his desire to develop an increasingly sophisticated computer and to move ahead faster than anyone else could follow. Nevertheless his son, Henry Babbage, completed a simplified version of the analytical engine's computing unit (the mill) in 1888. He gave a successful demonstration of its use in computing tables in 1906.

Analog computers

Sir William Thomson's third tide-predicting machine design, 1879-81

During the first half of the 20th century, many scientific computing needs were met by increasingly sophisticatedanalog computers, which used a direct mechanical or electrical model of the problem as a basis for computation. However, these were not programmable and generally lacked the versatility and accuracy of modern digital computers.

The first modern analog computer was a tide-predicting machine, invented by Sir William Thomson in 1872. Thedifferential analyser, a mechanical analog computer designed to solve differential equations by integration using wheel-and-disc mechanisms, was conceptualized in 1876 by James Thomson, the brother of the more famous Lord Kelvin.

The art of mechanical analog computing reached its zenith with the differential analyzer, built by H. L. Hazen and Vannevar Bush at MIT starting in 1927. This built on the mechanical integrators of James Thomson and the torque amplifiers invented by H. W. Nieman. A dozen of these devices were built before their obsolescence became obvious.

The modern computer

Alan Turing was the first to conceptualize the modern computer, a device that became known as the Universal Turing machine.

The principle of the modern computer was first described by computer scientist Alan Turing, who set out the idea in his seminal 1936 paper, On Computable Numbers. Turing reformulated Kurt Gödel's 1931 results on the limits of proof and computation, replacing Gödel's universal arithmetic-based formal language with the formal and simple hypothetical devices that became known as Turing machines. He proved that some such machine would be capable of performing any conceivable mathematical computation if it were representable as an algorithm. He went on to prove that there was no solution to the Entscheidungsproblem by first showing that the halting problem for Turing machines is undecidable: in general, it is not possible to decide algorithmically whether a given Turing machine will ever halt.

He also introduced the notion of a 'Universal Machine' (now known as a Universal Turing machine), with the idea that such a machine could perform the tasks of any other machine, or in other words, it is provably capable of computing anything that is computable by executing a program stored on tape, allowing the machine to be programmable. Von Neumann acknowledged that the central concept of the modern computer was due to this paper. Turing machines are to this day a central object of study in theory of computation. Except for the limitations imposed by their finite memory stores, modern computers are said to be Turing-complete, which is to say, they have algorithm execution capability equivalent to a universal Turing machine.

Electromechanical computers

Replica of Zuse's Z3, the first fully automatic, digital (electromechanical) computer.

Early digital computers were electromechanical - electric switches drove mechanical relays to perform the calculation. These devices had a low operating speed and were eventually superseded by much faster all-electric computers, originally using vacuum tubes. The Z2, created by German engineer Konrad Zuse in 1939, was one of the earliest examples of an electromechanical relay computer.

In 1941, Zuse followed his earlier machine up with the Z3, the world's first working electromechanical programmable, fully automatic digital computer.The Z3 was built with 2000 relays, implementing a 22 bit word length that operated at a clock frequency of about 5–10 Hz.Program code and data were stored on punched film. It was quite similar to modern machines in some respects, pioneering numerous advances such as floating point numbers. Replacement of the hard-to-implement decimal system (used in Charles Babbage's earlier design) by the simpler binary system meant that Zuse's machines were easier to build and potentially more reliable, given the technologies available at that time. The Z3 was probably a complete Turing machine.

Electronic programmable computer

Purely electronic circuit elements soon replaced their mechanical and electromechanical equivalents, at the same time that digital calculation replaced analog. The engineer Tommy Flowers, working at the Post Office Research Station in Dollis Hill in the 1930s, began to explore the possible use of electronics for the telephone exchange. Experimental equipment that he built in 1934 went into operation 5 years later, converting a portion of the telephone exchange network into an electronic data processing system, using thousands of vacuum tubes. In the US, John Vincent Atanasoff and Clifford E. Berry of Iowa State University developed and tested the Atanasoff–Berry Computer (ABC) in 1942, the first "automatic electronic digital computer". This design was also all-electronic and used about 300 vacuum tubes, with capacitors fixed in a mechanically rotating drum for memory.

Colossus was the first electronic digitalprogrammable computing device, and was used to break German ciphers during World War II.

During World War II, the British at Bletchley Park achieved a number of successes at breaking encrypted German military communications. The German encryption machine, Enigma, was first attacked with the help of the electro-mechanical bombes. To crack the more sophisticated GermanLorenz SZ 40/42 machine, used for high-level Army communications, Max Newman and his colleagues commissioned Flowers to build the Colossus.He spent eleven months from early February 1943 designing and building the first Colossus. After a functional test in December 1943, Colossus was shipped to Bletchley Park, where it was delivered on 18 January 1944 and attacked its first message on 5 February.

Colossus was the world's first electronic digital programmable computer. It used a large number of valves (vacuum tubes). It had paper-tape input and was capable of being configured to perform a variety of boolean logical operations on its data, but it was not Turing-complete. Nine Mk II Colossi were built (The Mk I was converted to a Mk II making ten machines in total). Colossus Mark I contained 1500 thermionic valves (tubes), but Mark II with 2400 valves, was both 5 times faster and simpler to operate than Mark 1, greatly speeding the decoding process.

ENIAC was the first Turing-complete device,and performed ballistics trajectory calculations for the United States Army.

The US-built ENIAC (Electronic Numerical Integrator and Computer) was the first electronic programmable computer built in the US. Although the ENIAC was similar to the Colossus it was much faster and more flexible. It was unambiguously a Turing-complete device and could compute any problem that would fit into its memory. Like the Colossus, a "program" on the ENIAC was defined by the states of its patch cables and switches, a far cry from the stored program electronic machines that came later. Once a program was written, it had to be mechanically set into the machine with manual resetting of plugs and switches.

It combined the high speed of electronics with the ability to be programmed for many complex problems. It could add or subtract 5000 times a second, a thousand times faster than any other machine. It also had modules to multiply, divide, and square root. High speed memory was limited to 20 words (about 80 bytes). Built under the direction of John Mauchly and J. Presper Eckert at the University of Pennsylvania, ENIAC's development and construction lasted from 1943 to full operation at the end of 1945. The machine was huge, weighing 30 tons, using 200 kilowatts of electric power and contained over 18,000 vacuum tubes, 1,500 relays, and hundreds of thousands of resistors, capacitors, and inductors.

Stored program computer

A section of the Manchester Small-Scale Experimental Machine, the first stored-program computer.

Early computing machines had fixed programs. Changing its function required the re-wiring and re-structuring of the machine. With the proposal of the stored-program computer this changed. A stored-program computer includes by design an instruction set and can store in memory a set of instructions (a program) that details the computation. The theoretical basis for the stored-program computer was laid by Alan Turing in his 1936 paper. In 1945 Turing joined the National Physical Laboratory and began work on developing an electronic stored-program digital computer. His 1945 report ‘Proposed Electronic Calculator’ was the first specification for such a device. John von Neumann at the University of Pennsylvania, also circulated his First Draft of a Report on the EDVAC in 1945.

Ferranti Mark 1, c. 1951.

The Manchester Small-Scale Experimental Machine, nicknamed Baby, was the world's first stored-program computer. It was built at the Victoria University of Manchester by Frederic C. Williams, Tom Kilburn and Geoff Tootill, and ran its first program on 21 June 1948. It was designed as a testbed for the Williams tube the firstrandom-access digital storage device.Although the computer was considered "small and primitive" by the standards of its time, it was the first working machine to contain all of the elements essential to a modern electronic computer. As soon as the SSEM had demonstrated the feasibility of its design, a project was initiated at the university to develop it into a more usable computer, the Manchester Mark 1.

The Mark 1 in turn quickly became the prototype for the Ferranti Mark 1, the world's first commercially available general-purpose computer.Built by Ferranti, it was delivered to the University of Manchester in February 1951. At least seven of these later machines were delivered between 1953 and 1957, one of them toShell labs in Amsterdam. In October 1947, the directors of British catering company J. Lyons & Company decided to take an active role in promoting the commercial development of computers. The LEO I computer became operational in April 1951  and ran the world's first regular routine office computer job.

Transistor computers

A bipolar junction transistor

The bipolar transistor was invented in 1947. From 1955 onwards transistors replaced vacuum tubes in computer designs, giving rise to the "second generation" of computers. Compared to vacuum tubes, transistors have many advantages: they are smaller, and require less power than vacuum tubes, so give off less heat. Silicon junction transistors were much more reliable than vacuum tubes and had longer, indefinite, service life. Transistorized computers could contain tens of thousands of binary logic circuits in a relatively compact space.

At the University of Manchester, a team under the leadership of Tom Kilburn designed and built a machine using the newly developed transistors instead of valves.^[31] Their first transistorised computer and the first in the world, was operational by 1953, and a second version was completed there in April 1955. However, the machine did make use of valves to generate its 125 kHz clock waveforms and in the circuitry to read and write on its magnetic drum memory, so it was not the first completely transistorized computer. That distinction goes to the Harwell CADET of 1955,^[32] built by the electronics division of the Atomic Energy Research Establishment at Harwell.^[33][34]

The integrated circuit

The next great advance in computing power came with the advent of the integrated circuit. The idea of the integrated circuit was first conceived by a radar scientist working for the Royal Radar Establishment of the Ministry of Defence, Geoffrey W.A. Dummer. Dummer presented the first public description of an integrated circuit at the Symposium on Progress in Quality Electronic Components in Washington, D.C. on 7 May 1952.^[35]

The first practical ICs were invented by Jack Kilby at Texas Instruments and Robert Noyce at Fairchild Semiconductor.^[36] Kilby recorded his initial ideas concerning the integrated circuit in July 1958, successfully demonstrating the first working integrated example on 12 September 1958.^[37] In his patent application of 6 February 1959, Kilby described his new device as “a body of semiconductor material ... wherein all the components of the electronic circuit are completely integrated.”^[38][39] Noyce also came up with his own idea of an integrated circuit half a year later than Kilby.^[40] His chip solved many practical problems that Kilby's had not. Produced at Fairchild Semiconductor, it was made of silicon, whereas Kilby's chip was made of germanium.

This new development heralded an explosion in the commercial and personal use of computers and led to the invention of the microprocessor. While the subject of exactly which device was the first microprocessor is contentious, partly due to lack of agreement on the exact definition of the term "microprocessor", it is largely undisputed that the first single-chip microprocessor was the Intel 4004,^[41] designed and realized by Ted Hoff, Federico Faggin, and Stanley Mazor at Intel.^[42]

Programs

The defining feature of modern computers which distinguishes them from all other machines is that they can be programmed. That is to say that some type of instructions (the program) can be given to the computer, and it will process them. Modern computers based on the von Neumann architecture often have machine code in the form of an imperative programming language.

In practical terms, a computer program may be just a few instructions or extend to many millions of instructions, as do the programs for word processors and web browsers for example. A typical modern computer can execute billions of instructions per second (gigaflops) and rarely makes a mistake over many years of operation. Large computer programs consisting of several million instructions may take teams of programmers years to write, and due to the complexity of the task almost certainly contain errors.

Stored program architecture

Replica of the Small-Scale Experimental Machine (SSEM), the world's first stored-program computer, at the Museum of Science and Industry in Manchester, England

This section applies to most common RAM machine-based computers.

In most cases, computer instructions are simple: add one number to another, move some data from one location to another, send a message to some external device, etc. These instructions are read from the computer's memory and are generally carried out (executed) in the order they were given. However, there are usually specialized instructions to tell the computer to jump ahead or backwards to some other place in the program and to carry on executing from there. These are called “jump” instructions (or branches). Furthermore, jump instructions may be made to happen conditionally so that different sequences of instructions may be used depending on the result of some previous calculation or some external event. Many computers directly support subroutines by providing a type of jump that “remembers” the location it jumped from and another instruction to return to the instruction following that jump instruction.

Program execution might be likened to reading a book. While a person will normally read each word and line in sequence, they may at times jump back to an earlier place in the text or skip sections that are not of interest. Similarly, a computer may sometimes go back and repeat the instructions in some section of the program over and over again until some internal condition is met. This is called the flow of control within the program and it is what allows the computer to perform tasks repeatedly without human intervention.

Comparatively, a person using a pocket calculator can perform a basic arithmetic operation such as adding two numbers with just a few button presses. But to add together all of the numbers from 1 to 1,000 would take thousands of button presses and a lot of time, with a near certainty of making a mistake. On the other hand, a computer may be programmed to do this with just a few simple instructions. 

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THE SCHOOL DISIPLINE.

In its natural sense, discipline is systematic instruction intended to train a person, sometimes literally called a disciple, in a craft, trade or other activity, or to follow a particular code of conduct or "order". Often, the phrase "to discipline" carries a negative connotation. This is because enforcement of order–that is, ensuring instructions are carried out–is often regulated through punishment.

Discipline is a course of actions leading to a greater goal than the satisfaction of the immediate. A disciplined person is one that has established a goal and is willing to achieve that goal at the expense of his or her immediate comfort.

Discipline is the assertion of willpower over more base desires, and is usually understood to be synonymous with self control. Self-discipline is to some extent a substitute for motivation, when one uses reason to determine the best course of action that opposes one's desires. Virtuousbehavior is when one's motivations are aligned with one's reasoned aims: to do what one knows is best and to do it gladly. Continent behavior, on the other hand, is when one does what one knows is best, but must do it by opposing one's motivations.^[1] Moving from continent to virtuous behavior requires training and some self-discipline.

                                                                         School discipline is the system of rules, punishments, and behavioral strategies appropriate to the regulation of children or adolescents and the maintenance of order in schools. Its aim is to control the students' actions and behavior.

An obedient student is in compliance with the school rules and codes of conduct. These rules may, for example, define the expected standards ofclothing, timekeeping, social conduct, and work ethic. The term discipline is also applied to the punishment that is the consequence of breaking the rules. The aim of discipline is to set limits restricting certain behaviors or attitudes that are seen as harmful or going against school policies, educational norms, school traditions, et cetera.

Theory.

School discipline practices are generally informed by theory from psychologists and educators. There are a number of theories to form a comprehensive discipline strategy for an entire school or a particular class.

• Positive approach is grounded in teachers' respect for students. Instills in students a sense of responsibility by using youth/adult partnerships to develop and share clear rules, provide daily opportunities for success, and administer in-school suspension for noncompliant students. Based on Glasser's Reality Therapy. Research (e.g., Allen) is generally supportive of the PAD program.

• Teacher effectiveness training differentiates between teacher-owned and student-owned problems, and proposes different strategies for dealing with each. Students are taught problem-solving and negotiation techniques. Researchers (e.g., Emmer and Aussiker) find that teachers like the programme and that their behaviour is influenced by it, but effects on student behaviour are unclear.

• Adlerian approaches is an umbrella term for a variety of methods which emphasize understanding the individual's reasons for maladaptive behavior and helping misbehaving students to alter their behavior, while at the same time finding ways to get their needs met. Named for psychiatrist Alfred Adler. These approaches have shown some positive effects on self-concept, attitudes, and locus of control, but effects on behavior are inconclusive (Emmer and Aussiker).Not only were the statistics on suspensions and vandalism significant, but also the recorded interview of teachers demonstrates the improvement in student attitude and behaviour, school atmosphere, academic performance, and beyond that, personal and professional growth.

• Appropriate school learning theory and educational philosophy is a strategy for preventing violence and promoting order and discipline in schools, put forward by educational philosopher Daniel Greenberg and practised by the Sudbury Valley School.

Corporal punishment

Throughout the history of education the most common means of maintaining discipline in schools was corporal punishment. While a child was in school, a teacher was expected to act as a substitute parent, with many forms of parental discipline or rewards open to them. This often meant that students were commonly chastised with the birch, cane, paddle, strap or yardstick if they did something wrong.

Corporal punishment in schools has now disappeared from most Western countries, including all European countries. Thirty-one U.S. states as well as the District of Columbia have banned it, most recently New Mexico in 2011. The other nineteen states (mostly in the South) continue to allow corporal punishment in schools. Paddling is still used to a significant (though declining) degree in some public schools in Alabama, Arkansas, Georgia, Louisiana, Mississippi, Oklahoma, Tennessee and Texas. Private schools in these and most other states may also use it, though many choose not to do so.

Official corporal punishment, often by caning, remains commonplace in schools in some Asian, African and Caribbean countries.

Most mainstream schools in most other countries retain punishment for misbehaviour, but it usually takes non-corporeal forms such as detention and suspension.

Detention.

Detention is one of the most common punishments in schools in the United States, Britain, Ireland, Singapore, Canada, Australia, South Africa and some other countries. It requires the pupil to go to a designated area of the school during a specified time on a school day (typically either break or after school), but also may require a pupil to go to that part of school at a certain time on a non-school day, e.g. "Saturday detention" at some US, UK and Irish schools (especially for serious offenses not quite serious enough for suspension).

In the UK, the Education Act 1997 obliges a (state) school to give parents or guardians at least 24 hours' notice of a detention outside school hours so arrangements for transport and/or childcare can be made. This should say why it was given and, more importantly, how long it will last (Detentions usually last from as short as 10 minutes or less to as long as 5 hours or more).^[7]

Typically, in schools in the US, UK and Singapore, if one misses a detention, then another is added or the student gets a more serious punishment. In UK schools, for offenses too serious for a normal detention but not serious enough for a detention requiring the pupil to return to school at a certain time on a non-school day, a detention can require a pupil to return to school 1-2 hours after school ends on a school day, e.g. "Friday Night Detention".

Suspension

Suspension or temporary exclusion is mandatory leave assigned to a student as a form of punishment that can last anywhere from one day to a few weeks, during which time the student is not allowed to attend regular lessons. In some US, Australian and Canadian schools, there are two types of suspension: In-School Suspension (ISS) and Out-of-School Suspension (OSS). In-school suspension requires the student to report to school as usual but sit in one room all day. Out-of-school suspension bans the student from being on school grounds The student's parents/guardians are notified of the reason for and duration of the out-of-school suspension, and usually also for in-school suspensions. Sometimes students have to complete work during their suspensions, for which they receive no credit.(OSS only) In some UK schools, there is Reverse Suspension as well as normal suspension. A pupil suspended is sent home for a period of time set. A pupil reverse suspended is required to be at school during the holidays. Some pupils often have to complete work while reverse suspended.

Expulsion

Expulsion, exclusion, withdrawing or permanent exclusion bans the student from being on school grounds permanently. This is the ultimate last resort, when all other methods of discipline have failed. However, in extreme situations, it may also be used for a single offense.^[9] Some education authorities have a nominated school in which all excluded students are collected; this typically has a much higher staffing level than mainstream schools. In some US public schools, expulsions and exclusions are so serious that they require an appearance before the Board of Education or the court system. In the UK, head teachers may make the decision to exclude, but the student's parents have the right of appeal to the local education authority. This has proved controversial in cases where the head teacher's decision has been overturned (and his or her authority thereby undermined), and there are proposals to abolish the right of appeal

Expulsion from a private school is a more straightforward matter, since the school can merely terminate its contract with the parents if the pupil does not have siblings in the same school.

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ISSAC NEWTON

Sir Issac Newton was a great English scientest. he showed his skills in mathematics ,science and many other things.

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MATHEMAGIC

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OLICE ASKED TO THE BRAIN THAT WHAT IS YOUR PROBLRM? BRAIN SAID SOMEBODY KILLDED NOBODY.THAT TIME STUPIT WAS AT BATHROOM.BLOODY WAS READING NEWS PAPER.THE POLICE SAID DONT YOU HAVE BRAIN. HE SAID THAT YES IAM BRAIN.ARE YOU A STUPIT? NO IAM NOT A STUPIT HE WAS IN BATHROO0

OLICE ASKED TO THE BRAIN THAT WHAT IS YOUR PROBLRM? BRAIN SAID SOMEBODY KILLDED NOBODY.THAT TIME STUPIT WAS AT BATHROOM.BLOODY WAS READING NEWS PAPER.THE POLICE SAID DONT YOU HAVE BRAIN. HE SAID THAT YES IAM BRAIN.ARE YOU A STUPIT? NO IAM NOT A STUPIT HE WAS IN BATHROOOLICE ASKED TO THE BRAIN THAT WHAT IS YOUR PROBLRM? BRAIN SAID SOMEBODY KILLDED NOBODY.THAT TIME STUPIT WAS AT BATHROOM.BLOODY WAS READING NEWS PAPER.THE POLICE SAID DONT YOU HAVE BRAIN. HE SAID THAT YES IAM BRAIN.ARE YOU A STUPIT? NO IAM NOT A STUPIT HE WAS IN BATHROOOLICE ASKED TO THE BRAIN THAT WHAT IS YOUR PROBLRM? BRAIN SAID SOMEBODY KILLDED NOBODY.THAT TIME STUPIT WAS AT BATHROOM.BLOODY WAS READING NEWS PAPER.THE POLICE SAID DONT YOU HAVE BRAIN. HE SAID THAT YES IAM BRAIN.ARE YOU A STUPIT? NO IAM NOT A STUPIT HE WAS IN BATHROOM

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"MOHANDAS KARAMCHAND GANDHI"

Who Was Mohandas Karamchand Gandhi?

Dates:

October 2, 1869 - January 30, 1948

Also Known As:

Mohandas Karamchand Gandhi, Mahatma ("Great Soul"), Father of the Nation, Bapu ("Father"), Gandhiji

Gandhi's Childhood

Off to London

In addition to learning to live a very simple and frugal lifestyle, Gandhi discovered his life-long passion for vegetarianism while in England. Although most of the other Indian students ate meat while they were in England, Gandhi was determined not to do so, in part because he had vowed to his mother that he would stay a vegetarian. In his search for vegetarian restaurants, Gandhi found and joined the London Vegetarian Society. The Society consisted of an intellectual crowd who introduced Gandhi to different authors, such as Henry David Thoreau and Leo Tolstoy. It was also through members of the Society that Gandhi began to really read theBhagavad Gita, an epic poem which is considered a sacred text to Hindus. The new ideas and concepts that he learned from these books set the foundation for his later beliefs.

Gandhi successfully passed the bar on June 10, 1891 and sailed back to India two days later. For the next two years, Gandhi attempted to practice law in India. Unfortunately, Gandhi found that he lacked both knowledge of Indian law and self-confidence at trial. When he was offered a year-long position to take a case in South Africa, he was thankful for the opportunity.

Gandhi Arrives in South Africa

Gandhi had only been in South Africa for about a week when he was asked to take the long trip from Natal to the capital of the Dutch-governed Transvaal province of South Africa for his case. It was to be a several day trip, including transportation by train and by stagecoach. When Gandhi boarded the first train of his journey at the Pietermartizburg station, railroad officials told Gandhi that he needed to transfer to the third-class passenger car. When Gandhi, who was holding first-class passenger tickets, refused to move, a policeman came and threw him off the train.

That was not the last of the injustices Gandhi suffered on this trip. As Gandhi talked to other Indians in South Africa (derogatorily called "coolies"), he found that his experiences were most definitely not isolated incidents but rather, these types of situations were common. During that first night of his trip, sitting in the cold of the railroad station after being thrown off the train, Gandhi contemplated whether he should go back home to India or to fight the discrimination. After much thought, Gandhi decided that he could not let these injustices continue and that he was going to fight to change these discriminatory practices.

Gandhi, the Reformer

In 1896, after living three years in South Africa, Gandhi sailed to India with the intention of bringing his wife and two sons back with him. While in India, there was a bubonic plague outbreak. Since it was then believed that poor sanitation was the cause of the spread of the plague, Gandhi offered to help inspect latrines and offer suggestions for better sanitation. Although others were willing to inspect the latrines of the wealthy, Gandhi personally inspected the latrines of the untouchables as well as the rich. He found that it was the wealthy that had the worst sanitation problems.

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''THE PI DAY''......

Pi Day is an annual celebration commemorating the mathematical constant ? (pi). Pi Day is observed on March 14 (or 3/14 in the U.S. month/daydate format), since 3, 1, and 4 are the three most significant digits of ? in the decimal form. In 2009, the United States House of Representativessupported the designation of Pi Day.

In the year 2015, Pi Day will have special significance on 3/14/15 at 9:26:53 a.m. and p.m., with the date and time representing the first 10 digits of?.

Pi Approximation Day is observed on July 22 (or 22/7 in the day/month date format), since the fraction ²²?₇ is a common approximation of ?.

History

The earliest known official or large-scale celebration of Pi Day was organized by Larry Shaw in 1988 at the San Francisco Exploratorium where Shaw worked as a physicist, with staff and public marching around one of its circular spaces, then consuming fruit pies The Exploratorium continues to hold Pi Day celebrations.

On March 12, 2009, the U.S. House of Representatives passed a non-binding resolution (HRES 224), recognizing March 14, 2009, as National Pi Day

For Pi Day 2010, Google presented a Google Doodle celebrating the holiday, with the word Google laid over images of circles and pi symbols

Observance

Pi Day has been observed in many ways, including eating pie, throwing pies and discussing the significance of the number ?. Some schools hold competitions as to which student can recall Pi to the highest number of decimal places

The Massachusetts Institute of Technology (MIT) has often mailed its application decision letters to prospective students for delivery on Pi Day.Starting in 2012, MIT has announced it will post those decisions (privately) online on Pi Day at exactly 6:28 pm, which they have called "Tau Time", to honor the rival numbers Pi and Tau equally

The town of Princeton, New Jersey, hosts numerous events in a combined celebration of Pi Day and Albert Einstein's birthday, which is also March 14. Einstein lived in Princeton for more than twenty years while working at the Institute for Advanced Study. In addition to pie eating and recitation contests, there is an annual Einstein look-alike contest

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ozone layer

The ozone layer was discovered in 1913 by the French physicists Charles fabry and Henri bussion. Its properties were explored in detail by the British meterologist G.M.B dobson, who developed a simple spectrophoto meter that could be used to measure stratospheric ozone from the ground. Between 1928 and 1958 Dobson established a worldwide network of ozone monitoring stations, which continue to operate to this day. The Dobson unit  a  conveninent measure of the amount of ozone over head is named in his houour.

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solar energy

Solar energy radiant light and heat from the sun, is harnessed using a range of ever evolving technologies such as                      solar heating, solar photovoltaics,solar architechure and artifical photosynthesis.

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ENERGY CONSERVATION

Energy conservation refers to reducing energy through using less of an energy service. Energy conservation differs from efficient energy use , which refers to using less energy for a constant.

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How do plants animals depend on each other?

Plants provide food and shelter for animals, and as they photosynthesize, regulate the levels of oxygen and carbon dioxide in the atmosphere.

As food producers, plants are eaten by herbivores, which in turn become food for the omnivores and carnivores. Plants are also the homes of many animals, small and big. Plants provide shelter from predators and harsh factors of the environment, like the hot sun, cold snow and torrential rain. When plants photosynthesize, they take in carbon dioxide and give out the fresh oxygen that all the animals need for respiration. Plants are crucial for the health of all animals.

On the other hand, plants depend on animals for nutrients, pollination and seed dispersal, and as the animals consume plants, they regulate the numbers of different species of plants. While plants provide oxygen for the animals as they photosynthesize, animals respire and give out carbon dioxide for plants to make food with. It is an interdependent relationship here. This is not to say that plants do not respire themselves. They do, it is just that the amount of carbon dioxide they give out is not enough for the plants to make enough food with. As such, plants need animals.

Also, when animals die, they decompose and become natural fertilizers for plants. Being pretty much immobile, plants also depend on animals to pollinate them for reproduction. And when the fertilized plants eventually produce seeds encased in fruits, animals eat them or carry them along on their fur, dispersing the seeds far and wide, ensuring the continuity of the plant species.

Animals depend on plants for carbon dioxide through photosynthesis. Also because some animals need plants for food. Plants depend on animals for seed dispersal and pollination.
Many animals depend on plants. Most animals just eat them, but some animals will you plants as shelter, to hide from predators, and many other reasons.


and plants depend on animals for energy. when an animal dies it provides nutrients for the plants to feed off of.

 Ways animals and plants depend on each other is the circle of life. Plants do the Photosynthesis if they don't they die. If plants die , herbavors die of hunger. Then meat eaters die of hunger.

Plants depend on animals to die ito the earth and create fertilizer and plants depend on animals to spread pollen and make even more plants. For example, bees. With pollenation.

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