What is Bayesian Thinking?

It is common knowledge that human beings commit errors in judgment all the time. In areas of uncertainty, most of us go with our gut intuition, and in most cases this intuition turns out to be wrong. Much of this is derived from the fact that humans are poor statistical thinkers, and thus poor Bayesian thinkers. What is Bayesian thinking? Let us start with an illustrative example, called the Monty Hall problem — famously depicted in the Kevin Spacey movie â€Å"21.† There are three doors, and behind each door is either a goat or a car. There will always be two doors with goats and one door with a car. The player first chooses a door without opening, and the game show host whose interests are opposed to the player, proceeds to open a different door. Since the host knows what is behind each door, he always opens a door with a goat. Now that the player is left with the initially chosen door and another closed door, the host offers an opportunity to switch to the other unopened door. Should the player switch? The answer for an intuitive Bayesian, a purely statistical thinker, should be easy. Unfortunately, human beings are not intuitive Bayesians. In fact, most people answer that it doesn't matter if the player switches or not, since the probability of winning a car is 50% between the two doors anyways. They would be wrong. Now, before we examine the correct way to think about this problem, one might ask, so what? Why does it matter if humans are not intuitive Bayesians, or even more broadly, bad statistical thinkers? Simply, Bayesian reasoning corrects some of the issues with bad statistical thinking. Bad statistical thinking leads to bad judgments and decisions, which have a wide variety of consequences in everyday life as well as in arenas such as politics and science. Thus, everyone should become better Bayesian thinkers, because under uncertainty, accurate probabilistic judgments are useful and important.To give a accurate depiction of how Bayesian reasoning works, let us return to the Monty Hall problem, and examine why not only switching doors matters, but that it is beneficial to switch. When the host first opened the door with the goat, something happened: opening the door gave the player extra information, and thus changed the probability of outcomes. By utilizing this extra information, it is no longer a 50% chance for the player to win the car after switching doors, but a ~67% (2/3) chance. Let us suppose that the player picks the door which contains the car. The host opens either the first goat door or the second (it does not matter), and the player switches to the other goat door and loses. Now, suppose the player picks the first goat door instead, which means the host is forced to open the second goat door. Since the only other door contains the car, the player switches and wins. Lastly, suppose the player picks the second goat door. The host is forced to open the first goat door, which again, means the player will win the car after a switch. These are the only three possible scenarios, and so we see that the probability of winning is two out of three if the player switches. Conversely, what if the player doesn't switch? In the first scenario, the player wins the car, but in scenarios two and three, the player obviously loses. Thus, to not switch is to have only a 33% (1/3) chance to win the car.The Monty Hall problem is a rather simple illustration of how Bayesian reasoning works, so in order to gain a more complete understanding, we must explore its principles. In 1763, a paper by Reverend Thomas Bayes was published posthumously called â€Å"An Essay towards solving a Problem in the Doctrine of Chances,† and brought about a paradigmatic shift in statistics: by using ever-increasing information and experience, one can gradually approach the unknown or understand the unknown (of course, his main motive was to prove the existence of God). Fundamentally, Bayesian reasoning believes in the correction of probabilities over time, and that all probabilities are merely estimates of the likelihood of events to occur. Through the further efforts of mathematicians like Lagrange in perfecting the Bayesian framework, we now have a modern and complete theory of probability. First, there are what we call priors, which is the strength of our beliefs, or put it another way, the likelihood that we are to change our beliefs. Then, we have our posteriors, which is the empirical aspect, or the influx of new information. The Bayesian framework then takes these two components and mathematically analyzes how posteriors affect priors. If we know nothing about an event, then all we can do is estimate a probability. However, if there is new information, then the probability must be corrected based on this new information. Over time, as experiences grow through more information, these estimates of probabilities will eventually fit â€Å"reality.† In the Monty Hall case, the moment the the host opened the goat door, that influx of new information, or change in posteriors, immediately influences the player's priors. If the host doesn't open a door, the player merely has a 33% chance to win the car between the three doors, and switching makes no difference. However, since the host removes a door, and specifically the door that contains a goat, these two new posteriors directly influence the original prior from 33% to 66%. One might think that this method of thinking is mysteriously similar to the scientific method, which is certainly true. However, To put it another way, Bayesian thinking is how to use some known information or experience to judge or predict the unknown. For example, event A is â€Å"rainy tomorrow† and event B is â€Å"cloudy tonight†. If you see cloudy tonight, what is the probability of raining tomorrow? If you use the Bayes theorem directly, you only need to know the probability of raining every day, the probability of cloudy nightly, and if one day it rains, then the probability of the cloudy night of the previous night will be substituted into the formula and done. The question is, where do these probabilities come from, and how do we infer the possibility based on the information we have . In fact, most of the valuable problems are backward problems, for example: the stock market, through those few signs can be judged to be a more or less opportunity; the hospital, through which symptoms can determine what is the disease; science Research, through several experimental data, you can construct what theory to explain the model and so on. In general, mathematicians, physicists, etc. are all about backward problems, or they can not predict or judge the outcome with few signs or phenomena, and there is no value (by the way, do not know the reverse Problem-thinking people can not fight in the financial market or the stock market. At present, the most advanced research in the speculative market is almost a process of backward stochastic process and martingale theory. It is known that the incidence of a disease is 0.001, that is, 1 in 1,000 people is sick. There is a reagent that can test whether a patient is sick or not, and its accuracy is 0.99, which means that 99% of the patients may be positive when the patient really gets sick. Its false positive rate is 5%, which means that 5% of the patients may get positive if they do not get sick. There is a positive test result of a patient, what is the probability that he does get sick?We got a staggering result of about 0.019. In other words, even if the test is positive, the probability of getting sick is only increased from 0.1% to 2%. This is the so-called â€Å"false positive†, that is, the positive result is not enough to show that the patient is sick.Why is this? Why is the accuracy of this test up to 99%, but the credibility is less than 2%? The answer is related to its false positive rate. Here we see the power of the Bayesian theorem, that it allows us to deduce the unknown probability from the known probability and the information at hand.The human brain and quantification vs heuristic thinking. The advantage of Bayesian analysis is that it does not require any objective estimation, just guess a priori casually. This is the key, because most of the events that occur in the real world have no objective probability. This is actually very similar to the scientific method: we did not know anything from the beginning, but we are willing to experiment and gradually find out the laws of nature. Bayesian reasoning operates in the same way, through continually the posterior probability in accordance with existing experimental data. Biggest problem with Bayesian reasoning is that human brains cannot quantify information easily. The most commonly raised example is Malcolm Gladwell's â€Å"Outliers†, where many people who are trained enough in certain low-chaotic environments make correct decisions and judgments without using the Bayesian framework at all. Firefighters, for example, do not undergo a Bayesian calculus before deciding whether or not it's safe to pull a child out of a burning building. They just do it because they've done it many times before, and have a rough heuristic estimate on the safety of such an action. Similarly, chess players do not use Bayesian analysis to think many turns ahead; what research has found is that through thousands of hours of practice and becoming familiar and experienced with similar setpieces in the past, gives them an ability to predict moves assuming that the opposing player is also rational. Conversely, high chaotic environments, such as the political sphere, is where Bayesian reasoning thrives due to the high amount of uncertainty.The other criticism are from the frequentists. In general, the probability of teaching in school can be called frequencyism. An event, if performed repeatedly multiple times independently, dividing the number of occurrences by the number of executions yields a frequency. For example, throwing coins, throwing 10000 times, 4976 times positive, the frequency is 0.4976. Then if the implementation of many many, the frequency will tend to a fixed value, is the probability of this time. In fact, to prove it involves the central limit theorem, but it does not start.

Native Americans in the United States and Hardy Individualism Essay

Prompt: Although the development of the Trans-Mississippi West is popularly associated with hardy individualism, it was in fact largely dependent on the federal government. Assess the validity of this statement with specific reference to western economic activities in the 19th century. In the late 1800s and early 1900s, the idea of the far west captivated many. The chance to begin life anew attracted thousands of individuals and families alike to move out west and escape their current life, which was usually full of poverty and for some, full of discrimination. As the west expanded and grew into an important part of the United States, westerners found it somewhat difficult to survive with important resources going scarce. Although the development of the Trans-Mississippi west is mainly associated with hardy individualism, the westÂ’s development as a whole was largely the result of the aid of the federal government by constructing railroads, promoting and protecting the land, and removing the Indian tribes. Railroads were an integral part of the west; without them the West would not be successful. The distance of the west from the rest of the country was large and the only way to reach the west was through a long, tiresome journey by wagon. The Pacific Railroad Act of 1862 paved the way for the expansion of the railroads. The Act gave companies land to build railroads. The faster the company built the railroad, the faster they could get more land, which they usually sold for profit later on. The construction of the railroad benefitted many who were not living in the west, namely Chinese immigrants. With thousands of workers, railroad companies had to ensure their safety to prevent being sued and frowned upon by the public. To prevent that, railroad companies provided many necessities for their workers like shelter.

Ap Chemistry †Gravimetric Lab Essay

By filtering and weighing the carbonate after it has precipitated, the mass and moles of CaCO3 could then be found; with these values, a molar mass of M2CO3 can be found. Using gravimetric analysis, it has been determined that the unknown Group 1 metal carbonate compound is K2CO3 (potassium carbonate). Experimental Sources of Error: A) The first source of error had to do with the precipitation section of the lab. Not only is possible, but it is almost guaranteed that the CaCO3 did not precipitate to its fullest extent. Attaining a precipitate that is 100% pure and is exactly of the composition represented by its chemical formula would be extremely difficult. A second source of error was in the filter paper. No filter paper can be perfect, and it is very likely that it did not filter all of the precipitate, which would then decrease not only the mass of CaCO3, but also the molar mass because only the majority of the correct mass of the precipitate was found; by lowering the mass of a compound, its molar mass will also fall. B) After the precipitate had been filtered and dried, the filer paper that contained the precipitate was mishandled and its contents was scattered all over our lab bench. The dried precipitate had to be then gathered and then put back onto the filter paper; this contributed a large portion of human error to this lab. Spilling the dried precipitate is a source of human because it is almost guaranteed that not all of the precipitate was collected that had been spilled. This would have then lowered the mass of all of the following data, and wholly, our end result (i.e. molar mass). C) Percent error = your result-accepted valueaccepted value x 100 Percent error = 128.79-138.21138.21 x 100 = 6.8157% Considering that any percent error that is under 5% is often times considered accurate, a percent error of 6.8% can be viewed as fairly accurate. It is not too far off to completely disregard, but it is also not close enough to use as fact.

Is War Ethical Essay Example | Topics and Well Written Essays - 1250 words

Is War Ethical - Essay Example Some people like war. They believe that war can change their lives only to realize that it caused them more harm than they had thought. Those who win always get power over the losers. They also get wealth and resources. They make laws to govern the territory. Many people have been asking themselves very many questions about war. These questions may include: is ethical to go to war? When is the right time to start war? Is it right to revenge against those wrong us? Is it ethical to interfere with other states affairs? Anyone who thinks about war must put in consideration what is right and wrong. Anything happening in war is either considered right or wrong depending on the people affected. Those who are the cause of war take every action to be right because it favors their interests. The other victims take the action to be wrong because it affects them negatively. Therefore, war has both positive and negative effects in the human population. War is a brutal activity but it is still th e centre of human history and the changes in social lives. It is because of war that some countries have changed in their economy and the way of live. During the time of war, life becomes hard for weak population (Coates, 2006). The weak people in the society are always the victims of war. In â€Å"Sirens of Bagdad†, Bedouin family is very poor. They live in a village in the Iraqi desert called Kafr Karam. Bedouin is a young man who is determined to be a more educated man than his father who never went to school. He goes to university of Bagdad but his education is cut short by invasion of Americans in the area. The university is destroyed and later closed making him go back home. Therefore, war kills his ambition of being a good person with a well paying job after the university. His dreams are crushed completely making him see no reason to live. This is how war can affect lives of the innocent people in the society (Khadra, 2007). War is always a very bad thing that can hap pen in the society. It compromises no one including the one who causes it. War abuses the human rights. Many people are killed, displaced from their homes while others lose their properties. This is against the human rights. War is way of governing using force instead of using peaceful measures of resolving policies. Some people in the government use their power to control what should be going on in a certain territory. They do not care how commands are going to affect the lives of people living in that territory. Lives of these people are going to be affected greatly. Bedouin, in â€Å"Sirens of Bagdad†, was affected by war caused by the Americans in Bagdad. It made him lose hope in life. It is because of war that he is forced to drop his education since the university he was studying in closed. War also follows him back to his village. He feels so embarrassed when he sees his father half-naked after being raided by the American soldiers. The way he felt was really touching. He says, â€Å"And beyond it, there was nothing but an infinite void, an interminable void, nothingness.† He says that he is forced to see his father’s genitals. What life is he going through? War has ruined his education and now it follows him home where he sees his weak father pulled around by war soldiers. All these actions changed him into a very angry man ready for vengeance (Khadra, 2007). Sometimes it is right to wage war. This especially is when a country has to protect her citizens from any terrorist attack. Every

Research Paper Example | Topics and Well Written Essays - 1500 words

Research Paper Example 7). The post secondary education offers non-degree programs leading to certificates, diplomas and degree levels. The system does not have a second or higher doctorate, but it offers post doctorate research programs. Because of this complexities and levels, the system has been labelled as an obsolete system that requires innovation. Contentious issues exist on the current organization of levels in the education structure, the K-12 structure. Most American learners especially in literature and arithmetic have accused the education structure of inadequacies that that lead to academic incapability. The chief demonstrators of the existence of academic structure issues lay on incompetence in employment places, as well as the escalating numbers of dropouts. This shows that instructors do not utilize appropriate mechanisms to enhance knowledge comprehension and retention, and techniques that stimulate learners’ interest in academics. This dissertation scrutinizes academic reforms, the various facets that it possesses, and suggested modifications. Relevant instructional material and qualified teachers comprise valuable educational components for education at home or school. This enables students to learn and gain skills and knowledge. They also contribute to the ability of students to compete for economic security through job competition. Adequate education provides students with the tools required for engagement in appropriate civic adult life (Rowan, & Miller, p. 5). The consequences of lacking the educational facilities and components have led to decline in the standards of education. The disparities and inadequacies of instructional material, teachers and facilities, indicate a systemic and deep flaw in the national educational system. These flaws include incoherent and fragmented approach to national policymaking. They also indicate a flawed system of school finance based inequitable distribution of resources. Varied individuals suggest differing solutions t o the problem of academic restructuring in America. Additionally, there have been innumerable transformations of the current structure of American education since commencement. The problem lay with defining the perfect system of organizing and delivering of knowledge in academic institutions. This results to a dilemma because of varied ideologies on educational systems from reformists, as well as scholars. In addition, politicians add to the misunderstanding that surrounds the definition of an ideal structure of education worth implementing. Most politicians voice what they perceive as the anticipation of voters (Futrell, p. 9). They lack an understanding of the reality of the inadequacies in the educational systems, and the requirements for educational reforms. This raises debate on the appropriateness of their ideologies (McClure, Wiener, Roza, & Hill, p. 10). There exists a universal conviction in the global community that education provides an imperative resource that crucial fo r involving in life’s triumphs. Education provides the perseverance to acquire skills and the determination to attain objectives and achieve high standards. It permits individuals to comprehend skills of life and the value of possessing such skill; hence the need to ascertain its attainment. Consequently, creating a competitive, educational structure crucially contributes to the wellbeing of

Service Encounter Journal Essay Example | Topics and Well Written Essays - 2000 words

Service Encounter Journal - Essay Example Therefore, the objective of the report is to analyse the service encountered while availing the services of four different organisations namely, Emirates Airlines, Commonwealth Bank, Wrest Point Tasmania and Eagle Boys Pizza. Thus, to enhance the measurement of the service encounters, two service marketing theories namely, level of customer service and the flower of service model will be used (Lovelock, Wirtz, & Chatterjee, 2010). Lastly, the dissatisfactory services encountered from the two organisations will be discussed and recommendations for improving them will be made. Most Satisfactory Encounter The Flower of Service According to Lovelock, Wirtz, & Chatterjee (2010), services can be of two types; facilitating supplementary services and enhancing supplementary services. Facilitating services are essential in the distribution of the service or to provide an aid to the core product. Whereas, augmenting additional services add value for the consumers. He further classified the ser vices; facilitating services included order taking, information, billing and payment and enhancing services included hospitality, exceptions, consultation and safekeeping. These classified services are illustrated through a flower diagram where the centre of the flower is the core product or service surrounded by the petals that included the different services. Thus, the name flower of service emerged through the diagram (Scribd Inc, 2012). The Flower of Service Source: (Scribd Inc, 2012). Theoretical Application in Satisfactory Level: Emirates Airlines Facilitating supplementary services are related to the services provided by the Emirates Airlines with regard to information concerning the timetable of the aircraft, availability and rate of the tickets, and company’s promotional activities among others. The company ensures that customers conveniently can access informations that matters them most. Furthermore, the company uses modern techniques to provide information to the customers, such as information through SMSs and emails. Billing and payment information are generated smoothly and quickly through electronic receipts. Emirates Airlines has facilitated customers by providing the option of debiting the account through online technologies. Enhancing services provided by the company ensures that the hospitality is provided to the optimum level by their onboard staff. With the new Boeing A380, customers are provided with private suites, shower spas and in-flight Wi-Fi among others. They make the customers feel that they are present in their own home. It has further made representatives available either physically or over the phone through 24 hours’ help-line centres where the best available opportunities or benefits to the customers are consulted. Safekeeping has also been given priority by Emirates, which ensures that children are provided with goodie bags and cartoon games for hyper active children, which will engage them for long hours, thus, reducing the tensions felt by parents while travelling with younger children (Emirates, 2012). Theoretical Application in Satisfactory Level: Commonwealth Bank The Commonwealth Bank is regarded as the foremost financial institution in Australia. Its developed services have enabled them to understand customers’ core values and respond to any requests quickly and politely (Commonwealth Bank of

Literature Research Paper Example | Topics and Well Written Essays - 1250 words

Literature - Research Paper Example In the end, marriage and love undermine Helena and Hermias friendship, destroying their chance to have the kind of relationship Woolf and other feminists dream of. The desire of Helena and Hermia to get married, and the relationship Oberon has with his wife Titania, show that "A Midsummer Nights Dream" ultimately reinforces the cultural subordination of women by their husbands and lovers. As Roberts points out about Elizabethan drama in general, "unless we are very careful, these plays reinforce for women their inherited and culturally sustained sense of their own insignificance" (367). The same is certainly true of A Midsummer Nights Dream in specific; the play reinforces traditional gender roles which require women to get married and nothing else. This can be seen in the fact that all of the main female characters only want to get married. Even the dramatis personae describes the women characters as "in love with" their lover, or as "betrothed" to them. As Woolf suggests, the women are only described based on the men they associate with (82). Additionally, "A Midsummer Nights Dream has 13 men to 4 women" (Roberts 367). This shows that the play is more interested in men than women, even if the women characters do play such an important role, relatively speaking, to those of the male characters. Of course, the main female characters are Hermia and Helena. Their goals are both marriage, and both of them seem at first to be good Feminist role models. After all, they have both fled with their preferred lover, denying their fathers wishes by refusing to marry the men their fathers prefer. In the first act, Theseus warns them of the consequences of their disobedience. If Hermia does not marry Demetrius instead of Lysander, she will have either "to die the death, or to abjure / For ever the society of men" (I.i.65-66). Even when threatened with execution or being sent to a nunnery, though, Hermia is unrepetant: She would

Discipline of planning policy in the UK Assignment

Discipline of planning policy in the UK - Assignment Example This paper is intended to explain the national, regional and local framework for planning policy and practice identifying the main instruments for plan making in the UK. The paper focuses on particular policies relating to sustainable urban regeneration and critically examines the impact which these polices have had on a selected city in England.The salient feature of the UK planning system consists in a paradox – being born and clearly rooted in local government practice (Cherry, 1988, p.72) during the late nineteenth and early twentieth centuries, it tended to be highly centralised over the time, but in contrast with many other countries, there is a lack of a spatial plan at national level (Balchin, Sykora and Bull, 1999, p.89). It may have its origins in the British governmental system which, as Cherry writes (1988, p.183) is generally characterised by three-component, interactive structure providing periodic responses to demand for reform and innovation. The first element is the bureaucracy (local government and the civil service) which is conservative in terms of outlook; the second are the active pressure groups – reformist in nature; and the third element is represented by the elected politicians who decide policy and implement the taken decisions. Given this scenario, planning regulations are categorically a political act and represent the outcome of conflict/degree of compromise between competing views. Plan making itself, being considered not just a technical activity, but deeply political, deriving legitimacy from values expressed in the community, has become a highly sophisticated process of complex bargaining and negotiation, in which powerful interests (including professions) ‘both mediate and promote their preferences’ (Cherry, 1988, p.184). There are three distinctive patterns of policy that dominated the post-war Britain, and which have left their imprint in the field of planning – the concept of welfare state manifested in the redistributive policies and decentralist land use strategies particularly characteristic of the period between the 1940s and 1970s; the significant neo-liberal shift in the 1980s characterised by interventionist practices – market-driven, ad hoc, piecemeal and responsive to particular pressures, with certain limitations on local government practice in terms of strategic role and oversight on town and environmental planning (Cherry, 1988, p.1

Operation management Case Study Example | Topics and Well Written Essays - 1000 words

Operation management - Case Study Example The crashes caused sales to dropped by 60% and this translates to a drop in revenue that ranges from $1.2 million to $1.8 to a mere $400,000. The business cannot continue losing revenue at this rate. It even nearly folded had it been to $200,000 loan from a friend. The crashes also cause other intangible problems that sabotage the business. Its crashes undermine the brand of its company as it will lose credibility in the market. It is also losing the goodwill of its customers of which it already had hard time retaining them during the height of its technical problems. The website of InsuranceAgents.com is not just a mere website for the company to have an online presence of informing the public that it exists. Rather, its website is the platform where the company conducts its business. It is like its office, its store, that if it crashes, it is tantamount to an office or store to be closed that no business can be done. And since no business can be done, no revenues can be made. Meanwhile, expenses continue to incur even if there is no revenue coming in. it goes without saying that the company cannot afford the website to crash because every time it crashes, it will have an economic impact to the company’s bottom line. There are also other intangible loses that the company incurs that may not register on its financial statements every time it incurs a downtime. This is the tarnished image and brand of the company. It could also drive customers away to the competitor. The company is back on the black and is no longer struggling. It is again a good time to consider growth. And since the tough times seems to be over, the company no longer has to bother with problem and just maintain how things are done. Hiring an experienced and competent CIO who will ensure that the company’s website will be up and running all the time. He or she can also immediately address any issue in the IT department without causing any damage of downtime to the

Phenomenological, ground theory and ethnographic differences Essay

Phenomenological, ground theory and ethnographic differences - Essay Example Despite the strong similarities, grounded theory and phenomenology have several differences. One of the differences is based on sources of data and method of data collection. Grounded theory utilizes any data and explanations that contribute to knowledge acquisition in a particular study. In essence, grounded theory admits any information that is relevant to the study. Methods used in data collection include interviews, observations, and secondary sources. On the other hand, a phenomenological approach uses data from people who have real life experience with the question at hand (Grove, Burns & Gray, 2012). The approach discriminates data from other sources. This means that the approach uses historical facts. Thus, data is often extracted using interviews. The discussion shows that the two approaches have a high similarity index. The similarity is visible in data collection and analysis in that both methods seek to make conclusions based on descriptions from the raw information. However, the approaches are different in terms of sources of data. Grounded theory utilizes data from any sources whereas phenomenological data uses data from persons who have experience with the aspect under

NAZI (symbol Swastika ) Essay Example | Topics and Well Written Essays - 1000 words

NAZI (symbol Swastika ) - Essay Example It wasn’t until the 1930’s that the Swastika began to denote evil implications. For example, the swastika was worn as a shoulder patch of a World War I U.S. Army Division and was a common decoration found on a myriad of objects. As Germany was behind other countries of the region in forming a formal nation (1871), its people felt susceptible to military and societal invasion from outside its borders. As an instrument to promote unification and national pride, German nationalists began to use the swastika from the mid 1800’s to represent the history of the Germanic and Aryan people. The swastika could be found on nationalist German ‘volkish’ publications by the end of the 1800’s and by the turn of the twentieth century, the swastika had grown in popularity throughout many German organizations. It was frequently used as the symbol for German nationalism. The Nazi Party’s aspiration to appeal to a wide German audience led them to chose the symbol in 1920. â€Å"Because of the Nazis’ flag, the swastika soon became a symbol of hate, anti-Semitism, violence, death, and murder† (Rosenberg, 2006). As the industrial age swept across Europe in the mid-1800’s it brought society new opportunities but also inadvertently served to increase the individual’s feeling of remoteness and a loss of personal belonging (Mosse, 1964, p. 13). As Germany became modernized, its people began to feel alone in their own culture and began to desire closer association to their community. â€Å"Joining the Volk (the people of Germany) was a way to intellectually rebel against this new, modern world. The Volk was an intermediary between the extremes of individuality and the quest for cosmic identity† (Mosse, 1964, p. 15). The effect of this National Socialist movement was that it served to replace the capitalist philosophy and ended chances for personal upward mobility. Third

Chemistry and Society Essay Example for Free

Chemistry and Society Essay Chemistry is a vast quantity of a person’s everyday life. A person can find chemistry in his or her daily life in the foods that a person eats, air a person breathes, soap, and accurately everything a person comes in contact with. Chemistry is significant in everyday life because chemicals make up everything in life. For example, a person’s body, pet, a desk, the sun, food, and drugs a person may take, to name a few. A person can observe changes in chemistry caused by chemical reactions, such as leaves changing colors, cooking food, and mixing a cleaning product. Knowing chemistry can help a person make day-to-day choices that affect his or her life. For example, if a person should mix certain household chemicals together. Accuracy is the magnitude in which a certain measurement agrees with the standard worth for that measurement (Dictionary, 2011). Precision is how close the measured standards are to each other (Math is fun, 2011). Society depends on accuracy and precision in everyday life. These two relationships are often substituted freely, but both have crucial differences. Businesses entail both accurate and precise measurements to stay in business. Accuracy states that something is constant with an identified rate, whereas precision is the volume of detail something delivers. Society depends on accuracy and precision in many places. One instance is the gas pumps. The gas pump can show accuracy when the gas is flowing, but this is not a precise measurement of how much gas is pumping through the pumps. Gas pumps must not only know how much gas is pumping through the pumps but also how precise the measurement of gas pumped. The gas companies need to know how much is pumped so that the company can charge the right amount for the gas. Sometimes in everyday life a person does not want to be precise. For example, if someone stops and asks for directions to the nearest gas station a person may say that the next gas station is about 10 minutes down the road. Providing directions to someone is a way a person can give accurate information. Another example is, when measuring a room in the house to paint and needing to know how much paint to provide. A person can either measure and provide a precise measurement or provide an accurate measurement on the room to paint. Another precision and accuracy a person may use in every day is how long it takes his or her child to finish the homework. An accurate answer is 20 minutes, but the precise answer would be 20 minutes and 20 seconds. Time is an excellent way to be accurate or precise. References Dictionary.com. (2011). Accuracy. Retrieved November 3, 2011 from http://dictionary.reference.com/browse/accuracy Math is fun. (2011). Accuracy and Precision. Retrieved November 3, 2011 from http://www.mathsisfun.com/accuracy-precision.html

Modelling of Meromorphic Retina

Modelling of Meromorphic Retina CHAPTER 1 INTRODUCTION and literature review 1. INTRODUCTION The world depends on how we sense it; perceive it and how we act is according to our perception of this world. But where from this perception comes? Leaving the psychological part, we perceive by what we sense and act by what we perceive. The senses in humans and other animals are the faculties by which outside information is received for evaluation and response. Thus the actions of humans depend on what they sense. Aristotle divided the senses into five, namely: Hearing, Sight, Smell, Taste and Touch. These have continued to be regarded as the classical five senses, although scientists have determined the existence of as many as 15 additional senses. Sense organs buried deep in the tissues of muscles, tendons, and joints, for example, give rise to sensations of weight, position of the body, and amount of bending of the various joints; these organs are called proprioceptors. Within the semicircular canal of the ear is the organ of equilibrium, concerned with the sense of balance. General senses, which produce information concerning bodily needs (hunger, thirst, fatigue, and pain), are also recognized. But the foundation of all these is still the list of five that was given by Aristotle. Our world is a visual world. Visual perception is by far the most important sensory process by which we gather and extract information from our environment. Vision is the ability to see the features of objects we look at, such as color, shape, size, details, depth, and contrast. Vision is achieved when the eyes and brain work together to form pictures of the world around us. Vision begins with light rays bouncing off the surface of objects. Light reflected from objects in our world forms a very rich source of information and data. The light reflected has a short wavelength and high transmission speed that allow us a spatially accurate and fast localization of reflecting surfaces. The spectral variations in wavelength and intensity in the reflected light resemble the physical properties of object surfaces, and provide means to recognize them. The sources that light our world are usually inhomogeneous. The sun, our natural light source, for example, is in good approximation a point sou rce. Inhomogeneous light sources cause shadows and reflections that are highly correlated with the shape of objects. Thus, knowledge of the spatial position and extent of the light source enables further extraction of information about our environment. Our world is also a world of motion. We and most other animals are moving creatures. We navigate successfully through a dynamic environment, and we use predominantly visual information to do so. A sense of motion is crucial for the perception of our own motion in relation to other moving and static objects in the environment. We must predict accurately the relative dynamics of objects in the environment in order to plan appropriate actions. Take for example the following situation that illustrates the nature of such a perceptual task: the batsman a cricket team is facing a bowler. In order to get the boundary on the ball, he needs an accurate estimate of the real motion trajectory of the ball such that he can precisely plan and orchestrate his body movements to hit the ball. There is little more than just visual information available to him in order to solve the task. And once he is in motion the situation becomes much more complicated because visual motion information now represents the relative motion between him and the ball while the important coordinate frame remains static. Yet, despite its difficulty, with appropriate training some of us become astonishingly good at performing this task. High performance is important because we live in a highly competitive world. The survival of the fittest applies to us as to any other living organism, although the fields of competition might have slightly shifted and diverted during recent evolutionary trends. This competitive pressure not only promotes a visual motion perception system that can determine quickly what is moving where, in which direction, and at what speed; but it also forces this system to be efficient. Efficiency is crucial in biological systems. It encourages solutions that consume the smallest amount of resources of time, substrate, and energy. The requirement for efficiency is advantageous because it drives the system to be quicker, to go further, to last longer, and to have more resources left to solve and perform other tasks at the same time. Thus, being the complex sensory-motor system as the batsman is, he cannot dedicate all of the resources available to solve a single task. Compared to human perceptual abilities, nature provides us with even more astonishing examples of efficient visual motion perception. Consider the various flying insects that navigate by visual perception. They weigh only fractions of grams, yet they are able to navigate successfully at high speeds through complicated environments in which they must resolve visual motions up to 2000 deg/s. 1.1 ARTIFICIAL SYSTEMS What applies to biological systems applies also to a large extent to any artificial autonomous system that behaves freely in a real-world environment. When humankind started to build artificial autonomous systems, it was commonly accepted that such systems would become part of our everyday life by the year 2001. Numberless science-fiction stories and movies have encouraged visions of how such agents should behave and interfere with human society. And many of these scenarios seem realistic and desirable. Briefly, we have a rather good sense of what these agents should be capable of. But the construction is still eluding. The semi- autonomous rover of NASAs recent Mars missions or demonstrations of artificial pets are the few examples. Remarkably the progress in this field is slow than the other fields of electronics. Unlike transistor technology in which explosion of density is defined by the Moores law and also in terms of the computational powers the performance of autonomous systems is still not to the par. To find out the reason behind it we have to understand the limitation of traditional approaches. The autonomous system is the one that perceives, takes decision and plans action at a cognitive level, in doing so it must show some degree of intelligence. Returning back to the batsman example, he knows exactly what he has to do to dispatch the ball to the boundary, he has to get into a right position and then hit the ball with a precise timing. In this process, the photons hit the retina and then muscle force is applied. The batsman is not aware that this much is going on into his body. The batsman has a nervous system, and one of its many functions is to instantiate a transformation layerbetween the environme nt and his cognitive mind. The brain reduces and preprocesses the huge amount of noisy sensory data, categorizes and extracts the relevant information, and translates it into a form that is accessible to cognitive reasoning. Thus it is clear here that the there is cluster of process that takes place in a biological cognitive system in a very short time duration. And also that an important part of this whole process is transduction although it is not the one that can solely perform the whole complex task. Thus perception is the interpretationof sensory information with respect to the perceptual goal. The process is shown in the fig-1. 1.2 DIFFERENCE BETWEEN BIOLOGICAL SYSTEMS AND COMPUTERS The brain is fundamentally differently organized than a computer and science is still a long way from understanding how the whole thing works. A computer is really easy to understand by comparison. Features (or organization principles) that clearly distinguish a brain from a computer are: Massive parallelism, Distributed storage, Asynchronous processing, and Self organization. The computer is still a basically serially driven machine with a centralized storage and minimal self organization. The table 1.1 enlists these differences. Table 1.1 Differences in the organization principles and operation of computer and brain The digital computation may become so fast that it may solve the present problems and also it may become possible that the autonomous systems are made by digital components that are as powerful as efficient and as intelligent as we may imagine in our wildest dreams. However there are doubts in it and so we have to switch to an implementation framework that can realize all these things. 1.3 NEURAL COMPUTATIONS WITH THE HELP OF ANALOG INTEGRATED CIRCUITS It was Carver Mead who, inspired by the course â€Å"The Physics of Computation† he jointly taught with John Hopfield and Richard Feynman at Caltech in 1982, first proposed the idea of embodying neural computation in silicon analog very large-scale integrated (aVLSI) circuits. Biological neural networks are examples of wonderfully engineered and efficient computational systems. When researchers first began to develop mathematical models for how nervous systems actually compute and process information, they very soon realized that one of the main reasons for the impressive computational power and efficiency of neural networks is the collective computation that takes place among their highly connected neurons. And in researches, it is also well established that these computations are not undertaken digitally although the digital way is much simpler. Real neurons have a cell membrane with a capacitance that acts as a low-pass filter to the incoming signal through its dendrites; they have dendritic trees that non-linearly add signals from other neurons, and so forth. Network structure and analog processing seem to be two key properties of nervous systems providing them with efficiency and computational power, but nonetheless two properties that digital compute rs typically do not share or exploit. 1.4 LITERATURE REVIEW 1. Biological information-processing systems operate on completely different principles from those with which most engineers are familiar. For many problems, particularly those in which the input data are ill-conditioned and the computation can be specified in a relative manner, biological solutions are many orders of magnitude more effective than those we have been able to implement using digital methods. This advantage can be attributed principally to the use of elementary physical phenomena as computational primitives, and to the representation of information by the relative values of analog signals, rather than by the absolute values of digital signals. This approach requires adaptive techniques to mitigate the effects of component differences. This kind of adaptation leads naturally to systems that learn about their environment. Large-scale adaptive analog systems are more robust to component degradation and failure than are more conventional systems, and they use far less power . For this reason, adaptive analog technology can be expected to utilize the full potential of wafer scale silicon fabrication 2. The architecture and realization of microelectronic components for a retina-implant system that will provide visual sensations to patients suffering from photoreceptor degeneration. Special circuitry has been developed for a fast single-chip CMOS image sensor system, which provides high dynamic range of more than seven decades (without any electronic or mechanical shutter) corresponding to the performance of the human eye. This image sensor system is directly coupled to a digital filter and a signal processor that compute the so-called receptive-field function for generation of the stimulation data. These external components are wireless, linked to an implanted flexible silicon multielectrode stimulator, which generates electrical signals for electro stimulation of the intact ganglion cells. All components, including additional hardware for digital signal processing and wireless data and power transmission, have been fabricated using in-house standard CMOS technology 3. The circuits inspired by the nervous system that either help verifying neuron physiological models, or that are useful components in artificial perception/action systems. Research also aims at using them in implants. These circuits are computational devices and intelligent sensors that are very differently organized than digital processors. Their storage and processing capacity is distributed. They are asynchronous and use no clock signal. They are often purely analog and operate time continuous. They are adaptive or can even learn on a basic level instead of being programmed. A short introduction into the area of brain research is also included in the course. The students will learn to exploit mechanisms employed by the nervous system for compact energy efficient analog integrated circuits. They will get insight into a multidisciplinary research area. The students will learn to analyze analog CMOS circuits and acquire basic knowledge in brain research methods. 4. Smart vision systems will be an inevitable component of future intelligent systems. Conventional vision systems, based on the system level integration (or even chip level integration) of an image (usually a CCD) camera and a digital processor, do not have the potential for application in general purpose consumer electronic products. This is simply due to the cost, size, and complexity of these systems. Because of these factors conventional vision systems have mainly been limited to specific industrial and military applications. Vision chips, which include both the photo sensors and parallel processing elements (analog or digital), have been under research for more than a decade and illustrate promising capabilities. 5. Dr. Carver Mead, professor emeritus of California Institute of Technology (Caltech), Pasadena pioneered this field. He reasoned that biological evolutionary trends over millions of years have produced organisms that engineers can study to develop better artificial systems. By giving senses and sensory-based behavior to machines, these systems can possibly compete with human senses and brings an intersection between biology, computer science and electrical engineering. Analog circuits, electrical circuits operated with continuous varying signals, are used to implement these algorithmic processes with transistors operated in the sub-threshold or weak inversion region (a region of operation in which transistors are designed to conduct current though the gate voltage is slightly lower than the minimum voltage, called threshold voltage, required for normal conduction to take place) where they exhibit exponential current voltage characteristics and low currents. This circuit paradigm pr oduces high density and low power implementations of some functions that are computationally intensive when compared with other paradigms (triode and saturation operational regions). {A triode region is operating transistor with gate voltage above the threshold voltage but with the drain-source voltage lower than the difference between the gate-source voltage and threshold voltage. For saturation region, the gate voltage is still above the threshold voltage but with the drain-source voltage above the difference between the gate-source voltage and threshold voltage. Transistor has four terminals: drain, gate, source and bulk. Current flows between the drain and the source when enough voltage is applied through the gate that enables conduction. The bulk is the body of the transistor.}. As the systems mature, human parts replacements would become a major application area of the Neuromorphic electronics. The fundamental principle is by observing how biological systems perform these func tions robust artificial systems are designed. 6. In This proposed work a circuit level model of Neuromorphic Retina, this is a crude electronic model of biologically inspired smart visual sensors. These visual sensors have integrated image acquisition and parallel processing. Having these features neuromorphic retina mimics the neural circuitry of bionic eye. The proposed electronic model contains adaptive photoreceptors as light sensors and other circuit components such as averaging circuits, circuits representing ganglion cells, neuronal firing circuits etc that junction to sense brightness, size, orientation and shape to distinguish objects in closer proximity. Although image-processing features are available with modern robots but most of the issues related to image processing are taken care by software resources. Whereas machine vision with the help of neuromorphic retina is empowered with image processing at the front end. With added hardware resources, processing at the front end can reduce a lot of engineering resources for making electronic devices with sense of vision. 1.5 OBJECTIVES OF THE PRESENT WORK This project work describes a circuit level model of Neuromorphic Retina, which is a crude electronic model of biologically inspired smart visual sensors. These visual sensors have integrated image acquisition and parallel processing. Having these features neuromorphic retina mimics the neural circuitry of bionic eye. The proposed electronic model contains adaptive photoreceptors as light sensors and other neural firing circuits etc at junction to sense brightness, size, orientation and shape to distinguish objects in closer proximity. Although, image processing features are available with modern robots but most of the issues related to image processing are taken care by software resources. Whereas, machine vision with the help of neuromorphic retina is empowered with image processing at the front end. In this paper it has been shown that with added hardware resources, processing at the front end it can reduce a lot of engineering resources as well as time for making electronic devic es with sense of vision. . The objectives of present work are: Modelling of Neuromorphic Retina The photoreceptor block The horrizontal cell block The transistor mesh implemented with cmos technology The integerated block The integrated block of prs, horizontal cells and bipolar cells The spike generation circuit 1.6 Concluding Remarks In this chapter, the function of the artificial system, difference between brain and computer work is described. The present work is focused on designing of neuromorphic retina layer circuits. Many successful studies have been carried out by the researchers to study the behavior and failure of neuromorphic retina. Some investigators have performed the experimental work to study the phenomenon of the neuromorphic retina. Chapter 2 conations the biological neurons and the electronics of neuromorphic retina in this the descriptions of silicon neurons, electrical nodes as neurons, perceptrons, integrate fire neurons, biological significance of neuromorphic systems, neuromorphic electronics engineering methods, process of developing a neuromorphic chip. Chapter 3 describes the artificial silicon retina, physiology of vision, the retina, photon to electrons, why we require the neuromorphic retina?, the equivalent electronic structure, visual path to brain. In chapter 4 designing and implementation of neuromorphic retina in this the description of the photoreceptor block, the horrizontal cell block, the integerated block, the integrated block of photoreceptors, horizontal cells and bipolar cells, the spike generation circuit. In chapter 5 the design analyses and test results of neuromorphic retina layers. The results are summarized in the form of conclusion in Chapter 6 CHAPTER-2 BIOLOGICAL neurons AND neuromorphic electronics 2.1 INTRODUCTION Neuromorphic systems are inspired by the structure, function and plasticity of biological nervous systems. They are artificial neural systems that mimic algorithmic behavior of the biological animal systems through efficient adaptive and intelligent control techniques. They are designed to adapt, learn from their environments, and make decisions like biological systems and not to perform better than them. There are no efforts to eliminate deficiencies inherent in biological systems. This field, called Neuromorphic engineering, is evolving a new era in computing with a great promise for future medicine, healthcare delivery and industry. It relies on plenty of experiences which nature offers to develop functional, reliable and effective artificial systems. Neuromorphic computational circuits, designed to mimic biological neurons, are primitives based on the optical and electronic properties of semiconductor materials 2.1 BIOLOGICAL NEURONS Biological neurons have a fairly simple large-scale structure, although their operation and small-scale structure is immensely complex. Neurons have three main parts: a central cell body, called the soma, and two different types of branched, treelike structures that extend from the soma, called dendrites and axons. Information from other neurons, in the form of electrical impulses, enters the dendrites at connection points called synapses. The information flows from the dendrites to the soma, where it is processed. The output signal, a train of impulses, is then sent down the axon to the synapses of other neurons. The dendrites send impulses to the soma while the axon sends impulses away from the soma. Functionally, there are three different types of neurons: Sensory neurons They carry information from sense receptors (nerves that help us see, smell, hear taste and feel) to the central nervous system which includes the brain and the spinal cord. Motor neurons They carry information from the CNS to effectors (muscles or glands that release all kind of stuff, from water to hormones to ear wax) Interneuron They connect sensory neurons and motor neurons. It has a cell body (or soma) and root-like extensions called mygdale. Amongst the mygdale, one major outgoing trunk is the axon, and the others are dendrites. The signal processing capabilities of a neuron is its ability to vary its intrinsic electrical potential (membrane potential) through special electro-physical and chemical processes. The portion of axon immediately adjacent to the cell body is called axon hillock. This is the point at which action potentials are usually generated. The branches that leave the main axon are often called collaterals. Certain types of neurons have axons or dendrites coated with a fatty insulating substance called myelin. The coating is called the myelin sheath and the fiber is said to be myelinated. In some cases, the myelin sheath is surrounded by another insulating layer, sometimes called neurilemma. This layer, thinner than the myelin sheath and continuous over the nodes of Ranvier, is made up o thin cells called Schwann cells. Now, how do these things work? Inside and just outside of the neurons are sodium ions (Na+) and potassium ions (K+). Normally, when the neuron is just sitting not sending any messages, K+ accumulate inside the neuron while Na+ is kicked out to the area just outside the neuron. Thus, there is a lot of K+ in the neuron and a lot of Na+ just outside of it. This is called the resting potential. Keeping the K+ in and the Na+ is not easy; it requires energy from the body to work. An impulse coming in from the dendrites, reverses this balance, causing K+ to leave the neuron and Na+ to come in. This is known as depolarization. As K+ leave Na+ enter the neuron, energy is released, as the neuron no longer is doing any work to keep K+ in and Na+ out. This energycreates an electrical impulse or action potential that is transmitted from the soma to axon. As the impulse leaves the axon, the neuron repolarizes, that is it takes K+ back in and kicks Na+ out and restores itself to resting potential, ready to send another impulse. This process occurs extremely quickly. A neuron theoretically can send roughly 266 messages in one second. The electrical impulse may stimulate other neurons from its synaptic knobs to propagate the message. Experiments have shown that the membrane voltage variation during the generation of an action potential is generally in a form of a spike (a short pulse figure 2.2), and the shape of this pulse in neurons is rather stereotype and mathematically predictable. 2.2 SILICON NEURONS Neuromorphic engineers are more interested in the physiological rather than the anatomical model of a neuron though, which is concerned with the functionality rather than only classifying its parts. And their preference lies with models that can be realized in aVLSI circuits. Luckily many of the models of neurons have always been formulated as electronic circuits since many of the varying observables in biological neurons are voltages and currents. So it was relatively straight forward to implement them in VLSI electronic circuits. There exist now many aVLSI models of neurons which can be classified by their level of detail that is represented in them. A summary can be found in table 3.1. The most detailed ones are known as ‘silicon neurons. A bit cruder on the level of detail are ‘integrate and fire neurons and even more simplifying are ‘Perceptrons also known as ‘Mc Culloch Pitts neurons. The simplest way however of representing a neuron in electronics is to represent neurons as electrical nodes. Table 2.1 VLSI models of neurons 2.2.1 Electrical Nodesasneurons The most simple of all neuronal models is to just represent a neurons activity by a voltage or a current in an electrical circuit, and input and output are identical, with no transfer function in-between. If a voltage node represents a neuron, excitatory bidirectional connections can be realized simply by resistive elements between the neurons. If you want to add the possibility for inhibitory and mono directional connections, followers can be used instead of resistors. Or if a current represents neuronal activity then a simple current mirror can implement a synapse. Many useful processing networks can be implemented in this manner or in similar ways. For example a resistive network can compute local averages of current inputs. 2.2.2 Perceptrons A perceptron is a simple mathematical model of a neuron. As real neurons it is an entity that is connected to others of its kind by one output and several inputs. Simple signals pass through these connections. In the case of the perceptron these signals are not action potentials but real numbers. To draw the analogy to real neurons these numbers may represent average frequencies of action potentials. The output of a perceptron is a monotonic function (referred to as activation function) of the weighted sum of its inputs (see figure 3.3). Perceptrons are not so much implemented in analog hardware. They have originally been formulated as a mathematical rather than an electronic model and traditional computers are good at those whereas it is not so straight forward to implement simple mathematics into aVLSI. Still there exist aVLSI implementations of perceptrons since they still promise the advantage of a real fully parallel, energy and space conservative implementation. A simple aVLSI implementation of a perceptron is given in the schematics in figure 3.4. This particular implementation works well enough in theory, in practice however it is on one hand not flexible enough (particularly the activation function), on the other already difficult to tune by its bias voltages and prone to noise on the a chip. Circuits that have really been used are based on this one but were more extensive to deal with the problems. 2.2.3 Integrate Fire Neurons This model of a neuron sticks closer to the original in terms of its signals. Its output and its inputs are pulse signals. In terms of frequencies it actually can be modeled by a perceptron and vice versa. It is however much better suited to be implemented in aVLSI. And the spike communication also has distinct advantages in noise robustness. That is also thought to be a reason, why the nervous system uses that kind of communication. An integrate and fire neuron integrates weighted charge inputs triggered by presynaptic action potentials. If the integrated voltage reaches a threshold, the neuron fires a short output pulse and the integrator is reset. These basic properties are depicted in figure 2.5. 2.3 BIOLOGICAL SIGNIFICANCE OF NEUROMORPHIC SYSTEMS The fundamental philosophy of neuromorphic engineering is to utilize algorithmic inspiration of biological systems to engineer artificial systems. It is a kind of technology transfer from biology to engineering that involves the understanding of the functions and forms of the biological systems and consequent morphinginto silicon chips. The fundamental biological unit mimicked in the design of neuromorphic systems is the neurons. Animal brain is composed of these individual units of computation, called neurons and the neurons are the elementary signaling parts of the nervous systems. By examining the retina for instance, artificial neurons that mimic the retinal neurons and chemistry are fabricated on silicon (most common material), gallium arsenide (GaAs) or possibly prospective organic semiconductor materials. 2.4 NEUROMORPHIC ELECTRONICS ENGINEERING METHODS Neuromorphic systems design methods involves the mapping of models of perfection and sensory processing in biological systems onto analog VLSI systems which emulate the biological functions at the same time resembling their structural architecture. These systems are mainly designed with complementary metal oxide semiconductors (CMOS) transistors that enable low power consumption, higher chip density and integration, lower cost. These transistors are biased to operate in the sub-threshold region to enable the realizations of high dynamic range of currents which are very important for neural systems design. Elements of adaptation and learning (a sort of higher level of adaptation in which past experience is used to effectively readjust the response of a system to previously unseen input stimuli) are incorporated into neuromorphic systems since they are expected to emulate the behavior of the biological systems and compensate for imperfections in t Modelling of Meromorphic Retina Modelling of Meromorphic Retina CHAPTER 1 INTRODUCTION and literature review 1. INTRODUCTION The world depends on how we sense it; perceive it and how we act is according to our perception of this world. But where from this perception comes? Leaving the psychological part, we perceive by what we sense and act by what we perceive. The senses in humans and other animals are the faculties by which outside information is received for evaluation and response. Thus the actions of humans depend on what they sense. Aristotle divided the senses into five, namely: Hearing, Sight, Smell, Taste and Touch. These have continued to be regarded as the classical five senses, although scientists have determined the existence of as many as 15 additional senses. Sense organs buried deep in the tissues of muscles, tendons, and joints, for example, give rise to sensations of weight, position of the body, and amount of bending of the various joints; these organs are called proprioceptors. Within the semicircular canal of the ear is the organ of equilibrium, concerned with the sense of balance. General senses, which produce information concerning bodily needs (hunger, thirst, fatigue, and pain), are also recognized. But the foundation of all these is still the list of five that was given by Aristotle. Our world is a visual world. Visual perception is by far the most important sensory process by which we gather and extract information from our environment. Vision is the ability to see the features of objects we look at, such as color, shape, size, details, depth, and contrast. Vision is achieved when the eyes and brain work together to form pictures of the world around us. Vision begins with light rays bouncing off the surface of objects. Light reflected from objects in our world forms a very rich source of information and data. The light reflected has a short wavelength and high transmission speed that allow us a spatially accurate and fast localization of reflecting surfaces. The spectral variations in wavelength and intensity in the reflected light resemble the physical properties of object surfaces, and provide means to recognize them. The sources that light our world are usually inhomogeneous. The sun, our natural light source, for example, is in good approximation a point sou rce. Inhomogeneous light sources cause shadows and reflections that are highly correlated with the shape of objects. Thus, knowledge of the spatial position and extent of the light source enables further extraction of information about our environment. Our world is also a world of motion. We and most other animals are moving creatures. We navigate successfully through a dynamic environment, and we use predominantly visual information to do so. A sense of motion is crucial for the perception of our own motion in relation to other moving and static objects in the environment. We must predict accurately the relative dynamics of objects in the environment in order to plan appropriate actions. Take for example the following situation that illustrates the nature of such a perceptual task: the batsman a cricket team is facing a bowler. In order to get the boundary on the ball, he needs an accurate estimate of the real motion trajectory of the ball such that he can precisely plan and orchestrate his body movements to hit the ball. There is little more than just visual information available to him in order to solve the task. And once he is in motion the situation becomes much more complicated because visual motion information now represents the relative motion between him and the ball while the important coordinate frame remains static. Yet, despite its difficulty, with appropriate training some of us become astonishingly good at performing this task. High performance is important because we live in a highly competitive world. The survival of the fittest applies to us as to any other living organism, although the fields of competition might have slightly shifted and diverted during recent evolutionary trends. This competitive pressure not only promotes a visual motion perception system that can determine quickly what is moving where, in which direction, and at what speed; but it also forces this system to be efficient. Efficiency is crucial in biological systems. It encourages solutions that consume the smallest amount of resources of time, substrate, and energy. The requirement for efficiency is advantageous because it drives the system to be quicker, to go further, to last longer, and to have more resources left to solve and perform other tasks at the same time. Thus, being the complex sensory-motor system as the batsman is, he cannot dedicate all of the resources available to solve a single task. Compared to human perceptual abilities, nature provides us with even more astonishing examples of efficient visual motion perception. Consider the various flying insects that navigate by visual perception. They weigh only fractions of grams, yet they are able to navigate successfully at high speeds through complicated environments in which they must resolve visual motions up to 2000 deg/s. 1.1 ARTIFICIAL SYSTEMS What applies to biological systems applies also to a large extent to any artificial autonomous system that behaves freely in a real-world environment. When humankind started to build artificial autonomous systems, it was commonly accepted that such systems would become part of our everyday life by the year 2001. Numberless science-fiction stories and movies have encouraged visions of how such agents should behave and interfere with human society. And many of these scenarios seem realistic and desirable. Briefly, we have a rather good sense of what these agents should be capable of. But the construction is still eluding. The semi- autonomous rover of NASAs recent Mars missions or demonstrations of artificial pets are the few examples. Remarkably the progress in this field is slow than the other fields of electronics. Unlike transistor technology in which explosion of density is defined by the Moores law and also in terms of the computational powers the performance of autonomous systems is still not to the par. To find out the reason behind it we have to understand the limitation of traditional approaches. The autonomous system is the one that perceives, takes decision and plans action at a cognitive level, in doing so it must show some degree of intelligence. Returning back to the batsman example, he knows exactly what he has to do to dispatch the ball to the boundary, he has to get into a right position and then hit the ball with a precise timing. In this process, the photons hit the retina and then muscle force is applied. The batsman is not aware that this much is going on into his body. The batsman has a nervous system, and one of its many functions is to instantiate a transformation layerbetween the environme nt and his cognitive mind. The brain reduces and preprocesses the huge amount of noisy sensory data, categorizes and extracts the relevant information, and translates it into a form that is accessible to cognitive reasoning. Thus it is clear here that the there is cluster of process that takes place in a biological cognitive system in a very short time duration. And also that an important part of this whole process is transduction although it is not the one that can solely perform the whole complex task. Thus perception is the interpretationof sensory information with respect to the perceptual goal. The process is shown in the fig-1. 1.2 DIFFERENCE BETWEEN BIOLOGICAL SYSTEMS AND COMPUTERS The brain is fundamentally differently organized than a computer and science is still a long way from understanding how the whole thing works. A computer is really easy to understand by comparison. Features (or organization principles) that clearly distinguish a brain from a computer are: Massive parallelism, Distributed storage, Asynchronous processing, and Self organization. The computer is still a basically serially driven machine with a centralized storage and minimal self organization. The table 1.1 enlists these differences. Table 1.1 Differences in the organization principles and operation of computer and brain The digital computation may become so fast that it may solve the present problems and also it may become possible that the autonomous systems are made by digital components that are as powerful as efficient and as intelligent as we may imagine in our wildest dreams. However there are doubts in it and so we have to switch to an implementation framework that can realize all these things. 1.3 NEURAL COMPUTATIONS WITH THE HELP OF ANALOG INTEGRATED CIRCUITS It was Carver Mead who, inspired by the course â€Å"The Physics of Computation† he jointly taught with John Hopfield and Richard Feynman at Caltech in 1982, first proposed the idea of embodying neural computation in silicon analog very large-scale integrated (aVLSI) circuits. Biological neural networks are examples of wonderfully engineered and efficient computational systems. When researchers first began to develop mathematical models for how nervous systems actually compute and process information, they very soon realized that one of the main reasons for the impressive computational power and efficiency of neural networks is the collective computation that takes place among their highly connected neurons. And in researches, it is also well established that these computations are not undertaken digitally although the digital way is much simpler. Real neurons have a cell membrane with a capacitance that acts as a low-pass filter to the incoming signal through its dendrites; they have dendritic trees that non-linearly add signals from other neurons, and so forth. Network structure and analog processing seem to be two key properties of nervous systems providing them with efficiency and computational power, but nonetheless two properties that digital compute rs typically do not share or exploit. 1.4 LITERATURE REVIEW 1. Biological information-processing systems operate on completely different principles from those with which most engineers are familiar. For many problems, particularly those in which the input data are ill-conditioned and the computation can be specified in a relative manner, biological solutions are many orders of magnitude more effective than those we have been able to implement using digital methods. This advantage can be attributed principally to the use of elementary physical phenomena as computational primitives, and to the representation of information by the relative values of analog signals, rather than by the absolute values of digital signals. This approach requires adaptive techniques to mitigate the effects of component differences. This kind of adaptation leads naturally to systems that learn about their environment. Large-scale adaptive analog systems are more robust to component degradation and failure than are more conventional systems, and they use far less power . For this reason, adaptive analog technology can be expected to utilize the full potential of wafer scale silicon fabrication 2. The architecture and realization of microelectronic components for a retina-implant system that will provide visual sensations to patients suffering from photoreceptor degeneration. Special circuitry has been developed for a fast single-chip CMOS image sensor system, which provides high dynamic range of more than seven decades (without any electronic or mechanical shutter) corresponding to the performance of the human eye. This image sensor system is directly coupled to a digital filter and a signal processor that compute the so-called receptive-field function for generation of the stimulation data. These external components are wireless, linked to an implanted flexible silicon multielectrode stimulator, which generates electrical signals for electro stimulation of the intact ganglion cells. All components, including additional hardware for digital signal processing and wireless data and power transmission, have been fabricated using in-house standard CMOS technology 3. The circuits inspired by the nervous system that either help verifying neuron physiological models, or that are useful components in artificial perception/action systems. Research also aims at using them in implants. These circuits are computational devices and intelligent sensors that are very differently organized than digital processors. Their storage and processing capacity is distributed. They are asynchronous and use no clock signal. They are often purely analog and operate time continuous. They are adaptive or can even learn on a basic level instead of being programmed. A short introduction into the area of brain research is also included in the course. The students will learn to exploit mechanisms employed by the nervous system for compact energy efficient analog integrated circuits. They will get insight into a multidisciplinary research area. The students will learn to analyze analog CMOS circuits and acquire basic knowledge in brain research methods. 4. Smart vision systems will be an inevitable component of future intelligent systems. Conventional vision systems, based on the system level integration (or even chip level integration) of an image (usually a CCD) camera and a digital processor, do not have the potential for application in general purpose consumer electronic products. This is simply due to the cost, size, and complexity of these systems. Because of these factors conventional vision systems have mainly been limited to specific industrial and military applications. Vision chips, which include both the photo sensors and parallel processing elements (analog or digital), have been under research for more than a decade and illustrate promising capabilities. 5. Dr. Carver Mead, professor emeritus of California Institute of Technology (Caltech), Pasadena pioneered this field. He reasoned that biological evolutionary trends over millions of years have produced organisms that engineers can study to develop better artificial systems. By giving senses and sensory-based behavior to machines, these systems can possibly compete with human senses and brings an intersection between biology, computer science and electrical engineering. Analog circuits, electrical circuits operated with continuous varying signals, are used to implement these algorithmic processes with transistors operated in the sub-threshold or weak inversion region (a region of operation in which transistors are designed to conduct current though the gate voltage is slightly lower than the minimum voltage, called threshold voltage, required for normal conduction to take place) where they exhibit exponential current voltage characteristics and low currents. This circuit paradigm pr oduces high density and low power implementations of some functions that are computationally intensive when compared with other paradigms (triode and saturation operational regions). {A triode region is operating transistor with gate voltage above the threshold voltage but with the drain-source voltage lower than the difference between the gate-source voltage and threshold voltage. For saturation region, the gate voltage is still above the threshold voltage but with the drain-source voltage above the difference between the gate-source voltage and threshold voltage. Transistor has four terminals: drain, gate, source and bulk. Current flows between the drain and the source when enough voltage is applied through the gate that enables conduction. The bulk is the body of the transistor.}. As the systems mature, human parts replacements would become a major application area of the Neuromorphic electronics. The fundamental principle is by observing how biological systems perform these func tions robust artificial systems are designed. 6. In This proposed work a circuit level model of Neuromorphic Retina, this is a crude electronic model of biologically inspired smart visual sensors. These visual sensors have integrated image acquisition and parallel processing. Having these features neuromorphic retina mimics the neural circuitry of bionic eye. The proposed electronic model contains adaptive photoreceptors as light sensors and other circuit components such as averaging circuits, circuits representing ganglion cells, neuronal firing circuits etc that junction to sense brightness, size, orientation and shape to distinguish objects in closer proximity. Although image-processing features are available with modern robots but most of the issues related to image processing are taken care by software resources. Whereas machine vision with the help of neuromorphic retina is empowered with image processing at the front end. With added hardware resources, processing at the front end can reduce a lot of engineering resources for making electronic devices with sense of vision. 1.5 OBJECTIVES OF THE PRESENT WORK This project work describes a circuit level model of Neuromorphic Retina, which is a crude electronic model of biologically inspired smart visual sensors. These visual sensors have integrated image acquisition and parallel processing. Having these features neuromorphic retina mimics the neural circuitry of bionic eye. The proposed electronic model contains adaptive photoreceptors as light sensors and other neural firing circuits etc at junction to sense brightness, size, orientation and shape to distinguish objects in closer proximity. Although, image processing features are available with modern robots but most of the issues related to image processing are taken care by software resources. Whereas, machine vision with the help of neuromorphic retina is empowered with image processing at the front end. In this paper it has been shown that with added hardware resources, processing at the front end it can reduce a lot of engineering resources as well as time for making electronic devic es with sense of vision. . The objectives of present work are: Modelling of Neuromorphic Retina The photoreceptor block The horrizontal cell block The transistor mesh implemented with cmos technology The integerated block The integrated block of prs, horizontal cells and bipolar cells The spike generation circuit 1.6 Concluding Remarks In this chapter, the function of the artificial system, difference between brain and computer work is described. The present work is focused on designing of neuromorphic retina layer circuits. Many successful studies have been carried out by the researchers to study the behavior and failure of neuromorphic retina. Some investigators have performed the experimental work to study the phenomenon of the neuromorphic retina. Chapter 2 conations the biological neurons and the electronics of neuromorphic retina in this the descriptions of silicon neurons, electrical nodes as neurons, perceptrons, integrate fire neurons, biological significance of neuromorphic systems, neuromorphic electronics engineering methods, process of developing a neuromorphic chip. Chapter 3 describes the artificial silicon retina, physiology of vision, the retina, photon to electrons, why we require the neuromorphic retina?, the equivalent electronic structure, visual path to brain. In chapter 4 designing and implementation of neuromorphic retina in this the description of the photoreceptor block, the horrizontal cell block, the integerated block, the integrated block of photoreceptors, horizontal cells and bipolar cells, the spike generation circuit. In chapter 5 the design analyses and test results of neuromorphic retina layers. The results are summarized in the form of conclusion in Chapter 6 CHAPTER-2 BIOLOGICAL neurons AND neuromorphic electronics 2.1 INTRODUCTION Neuromorphic systems are inspired by the structure, function and plasticity of biological nervous systems. They are artificial neural systems that mimic algorithmic behavior of the biological animal systems through efficient adaptive and intelligent control techniques. They are designed to adapt, learn from their environments, and make decisions like biological systems and not to perform better than them. There are no efforts to eliminate deficiencies inherent in biological systems. This field, called Neuromorphic engineering, is evolving a new era in computing with a great promise for future medicine, healthcare delivery and industry. It relies on plenty of experiences which nature offers to develop functional, reliable and effective artificial systems. Neuromorphic computational circuits, designed to mimic biological neurons, are primitives based on the optical and electronic properties of semiconductor materials 2.1 BIOLOGICAL NEURONS Biological neurons have a fairly simple large-scale structure, although their operation and small-scale structure is immensely complex. Neurons have three main parts: a central cell body, called the soma, and two different types of branched, treelike structures that extend from the soma, called dendrites and axons. Information from other neurons, in the form of electrical impulses, enters the dendrites at connection points called synapses. The information flows from the dendrites to the soma, where it is processed. The output signal, a train of impulses, is then sent down the axon to the synapses of other neurons. The dendrites send impulses to the soma while the axon sends impulses away from the soma. Functionally, there are three different types of neurons: Sensory neurons They carry information from sense receptors (nerves that help us see, smell, hear taste and feel) to the central nervous system which includes the brain and the spinal cord. Motor neurons They carry information from the CNS to effectors (muscles or glands that release all kind of stuff, from water to hormones to ear wax) Interneuron They connect sensory neurons and motor neurons. It has a cell body (or soma) and root-like extensions called mygdale. Amongst the mygdale, one major outgoing trunk is the axon, and the others are dendrites. The signal processing capabilities of a neuron is its ability to vary its intrinsic electrical potential (membrane potential) through special electro-physical and chemical processes. The portion of axon immediately adjacent to the cell body is called axon hillock. This is the point at which action potentials are usually generated. The branches that leave the main axon are often called collaterals. Certain types of neurons have axons or dendrites coated with a fatty insulating substance called myelin. The coating is called the myelin sheath and the fiber is said to be myelinated. In some cases, the myelin sheath is surrounded by another insulating layer, sometimes called neurilemma. This layer, thinner than the myelin sheath and continuous over the nodes of Ranvier, is made up o thin cells called Schwann cells. Now, how do these things work? Inside and just outside of the neurons are sodium ions (Na+) and potassium ions (K+). Normally, when the neuron is just sitting not sending any messages, K+ accumulate inside the neuron while Na+ is kicked out to the area just outside the neuron. Thus, there is a lot of K+ in the neuron and a lot of Na+ just outside of it. This is called the resting potential. Keeping the K+ in and the Na+ is not easy; it requires energy from the body to work. An impulse coming in from the dendrites, reverses this balance, causing K+ to leave the neuron and Na+ to come in. This is known as depolarization. As K+ leave Na+ enter the neuron, energy is released, as the neuron no longer is doing any work to keep K+ in and Na+ out. This energycreates an electrical impulse or action potential that is transmitted from the soma to axon. As the impulse leaves the axon, the neuron repolarizes, that is it takes K+ back in and kicks Na+ out and restores itself to resting potential, ready to send another impulse. This process occurs extremely quickly. A neuron theoretically can send roughly 266 messages in one second. The electrical impulse may stimulate other neurons from its synaptic knobs to propagate the message. Experiments have shown that the membrane voltage variation during the generation of an action potential is generally in a form of a spike (a short pulse figure 2.2), and the shape of this pulse in neurons is rather stereotype and mathematically predictable. 2.2 SILICON NEURONS Neuromorphic engineers are more interested in the physiological rather than the anatomical model of a neuron though, which is concerned with the functionality rather than only classifying its parts. And their preference lies with models that can be realized in aVLSI circuits. Luckily many of the models of neurons have always been formulated as electronic circuits since many of the varying observables in biological neurons are voltages and currents. So it was relatively straight forward to implement them in VLSI electronic circuits. There exist now many aVLSI models of neurons which can be classified by their level of detail that is represented in them. A summary can be found in table 3.1. The most detailed ones are known as ‘silicon neurons. A bit cruder on the level of detail are ‘integrate and fire neurons and even more simplifying are ‘Perceptrons also known as ‘Mc Culloch Pitts neurons. The simplest way however of representing a neuron in electronics is to represent neurons as electrical nodes. Table 2.1 VLSI models of neurons 2.2.1 Electrical Nodesasneurons The most simple of all neuronal models is to just represent a neurons activity by a voltage or a current in an electrical circuit, and input and output are identical, with no transfer function in-between. If a voltage node represents a neuron, excitatory bidirectional connections can be realized simply by resistive elements between the neurons. If you want to add the possibility for inhibitory and mono directional connections, followers can be used instead of resistors. Or if a current represents neuronal activity then a simple current mirror can implement a synapse. Many useful processing networks can be implemented in this manner or in similar ways. For example a resistive network can compute local averages of current inputs. 2.2.2 Perceptrons A perceptron is a simple mathematical model of a neuron. As real neurons it is an entity that is connected to others of its kind by one output and several inputs. Simple signals pass through these connections. In the case of the perceptron these signals are not action potentials but real numbers. To draw the analogy to real neurons these numbers may represent average frequencies of action potentials. The output of a perceptron is a monotonic function (referred to as activation function) of the weighted sum of its inputs (see figure 3.3). Perceptrons are not so much implemented in analog hardware. They have originally been formulated as a mathematical rather than an electronic model and traditional computers are good at those whereas it is not so straight forward to implement simple mathematics into aVLSI. Still there exist aVLSI implementations of perceptrons since they still promise the advantage of a real fully parallel, energy and space conservative implementation. A simple aVLSI implementation of a perceptron is given in the schematics in figure 3.4. This particular implementation works well enough in theory, in practice however it is on one hand not flexible enough (particularly the activation function), on the other already difficult to tune by its bias voltages and prone to noise on the a chip. Circuits that have really been used are based on this one but were more extensive to deal with the problems. 2.2.3 Integrate Fire Neurons This model of a neuron sticks closer to the original in terms of its signals. Its output and its inputs are pulse signals. In terms of frequencies it actually can be modeled by a perceptron and vice versa. It is however much better suited to be implemented in aVLSI. And the spike communication also has distinct advantages in noise robustness. That is also thought to be a reason, why the nervous system uses that kind of communication. An integrate and fire neuron integrates weighted charge inputs triggered by presynaptic action potentials. If the integrated voltage reaches a threshold, the neuron fires a short output pulse and the integrator is reset. These basic properties are depicted in figure 2.5. 2.3 BIOLOGICAL SIGNIFICANCE OF NEUROMORPHIC SYSTEMS The fundamental philosophy of neuromorphic engineering is to utilize algorithmic inspiration of biological systems to engineer artificial systems. It is a kind of technology transfer from biology to engineering that involves the understanding of the functions and forms of the biological systems and consequent morphinginto silicon chips. The fundamental biological unit mimicked in the design of neuromorphic systems is the neurons. Animal brain is composed of these individual units of computation, called neurons and the neurons are the elementary signaling parts of the nervous systems. By examining the retina for instance, artificial neurons that mimic the retinal neurons and chemistry are fabricated on silicon (most common material), gallium arsenide (GaAs) or possibly prospective organic semiconductor materials. 2.4 NEUROMORPHIC ELECTRONICS ENGINEERING METHODS Neuromorphic systems design methods involves the mapping of models of perfection and sensory processing in biological systems onto analog VLSI systems which emulate the biological functions at the same time resembling their structural architecture. These systems are mainly designed with complementary metal oxide semiconductors (CMOS) transistors that enable low power consumption, higher chip density and integration, lower cost. These transistors are biased to operate in the sub-threshold region to enable the realizations of high dynamic range of currents which are very important for neural systems design. Elements of adaptation and learning (a sort of higher level of adaptation in which past experience is used to effectively readjust the response of a system to previously unseen input stimuli) are incorporated into neuromorphic systems since they are expected to emulate the behavior of the biological systems and compensate for imperfections in t

Logic, Perception, and Enculturation Essay -- The Nature of Logic

Think about it. How important is thinking? Americans spend all of their day thinking and misthinking of multiple decisions and ideas. Thinking is a very important process of how our thoughts, when transferred verbally or written on paper, can produce a clearer understanding of our views. The nature of logic as it relates to critical thinking, and my perceptual process have been influenced through sources of enculturation.   Ã‚  Ã‚  Ã‚  Ã‚  The nature of logic as understood is when you have a situation, belief, tradition, etc. that is examined and reviewed in great detail to discover the reasoning behind a behavior. Critical thinking as I understand it is when you view a situation in multiple ways to get a accurate answer or results. The nature of logic relates to critical thinking by examining the situation and thoughts to get a clearer decision of possible outcomes, or reasoning. For instance, before heading to work you watch the news and their morning traffic update for possible accidents and road closures because you have an hour commute. On this particular morning you hear the traffic reporter mention that your daily route to work has been closed due to a huge tractor-trailer accident. Logically, and using critical thinking you are able to come up with two alternate routes for getting to work on time. Using further logic and critical thinking you watch the news for further traffic details to mak e a final decision on one of two alternate choices. You hear that one of your choic...

Anger and Aggression Essay -- essays research papers fc

Everyone has felt anger or aggression many times in there life. It happens all of the time. We all face the same challenge of trying to control our temper. It may be easier for some people than it is for others. Many studies show that it is healthy for a person to let out their anger once in a while. They believe that it will help in your relationship with others and that it will increase your self-esteem. They also believe that holding anger in is bad and unhealthy for your body. If you let the anger build up it could go from just being a verbal argument to a point in which someone or something is hurt or destroyed. To control your anger you should release your aggression in a way that is not harmful to others or yourself. People that look into a problem more closely can control their anger better. These people get all of the facts and make a proactive decision. Also by looking into the problem your may find out that it wasn't as bad as you first thought. Looking into the probl em will also help you look at the consequences of the action you are going to take. Researchers also believe that tv and movies have an impact on the ways we release our aggression. They believe that in some way we are all influenced in some way by movies and tv shows that we watch. If we can learn to control our anger we will see that our life, and everyone else's life is a lot safer and more peaceful. Some people may ask, "What causes a person to feel angry?" There are two answers to the question. The first is that you may feel angry with yourself or something that you may have done. The second is that you may be angry at another person or object. Some people may refer to feeling angry with yourself as internal anger and anger towards another person as external anger. An example of internal anger is that you did not do as good as you wanted to do on your test. An example of external anger is getting into an argument with a friend. There are different ways of dealing with your anger. The best way is to go right to the person that you are feeling angry with and talk to them about it. Although this is the best way it may not always be an option for the situation you are in. If you are angry with yourself you should find a friend and talk to them about it and get it off your mind so you don't build up your anger. Bu... ...elp the teen control their aggressions and help them let it out in healthy ways. They can show them different ways to deal with anger. They can use prevention and try to stop the problem before it gets to far. They can also use crisis management such as a sitting down and talking about the problem with the child. Another method is time-outs this will help give the parents and the child a chance to cool down. The best method for parents is to be a good role model for the children. If they show good ways of dealing with anger their children may do the same. A parent is the person who children look up to as they are growing up. Bibliography 1. Dealing with anger http://www.allsands.com/Lifestyles/dealingwitha_apn_gn.htm 2. Dealing with anger http://www.counsel.ufl.edu/selfHelp/dealingWithAnger.asp 3. Adolescent Anger and Aggression http://www.mi-pathways.org/brochures/adolescent_agression.html