Title: Do you agree or disagree people are living longer now? Discuss the causes of this phenomenon. Give specific reasons and details to support.
In the ancient society it was not uncommon for a man to die at thirty， having lived a full life. But now we consider a lifespan of thirty years to be very short. It is not unusual for people to live into their eighties and nineties， and some even reach 100.
According to a 2006 United Nations report, the lifespan of Singaporeans has also increased; today the average life expectancy is 80 compared with 61 in 1957. According to statistics in China, The average lifespan has shot up by 36 years and people now live to 71, a dramatic shift from half a century ago.
With the development of human society, people are living longer now. Many factors interacting together to enable the longer life. In my opinion, there are three most important causes: the quality of life has been greatly improved; people could have better medical services and treatments; more and more people realize that regular sports benefit their health.
First, under the development of science and technology, it’s obviously the standards of living have been improved a lot. More entertainment has been provided, hence people have more chances to entertain and keep a good mood everyday, which is essential for being healthy and living longer. The improving quality of our food has also been improved. We could have not only enough food as we want, but also the healthier food. When we preparing food, we no longer consider the cost, but pay more attention to the nutrition of the food. With the development of transportation systems, inland people now could also enjoy seafood and tropical fruit.
Furthermore, governments are paying more and more money on medical establishments. Citizens could have medical services more easily. Because of the convenient medical service more illnesses could be detected at an earlier stage. Also, many illnesses that had been considered fatal could be cured today. The improvement in medicine plays an important role. There’s an undeniable fact that after people discovered the mysteries of gene, more and more newly developed medicine cured a lot of illness that can’t be cured before. Therefore, the better detection and treatments enable people's longer life.
Last but not least important is that people care more for their own health. Every morning you could see people doing sports outside. More and more people have realized the saying "life is locomotion". Regular sports build up a strong body. Naturally, people with stronger body could resist more diseases.
In conclusion, I agree that people are living longer now, and to sum up, the main factor of people’s longer life is the development of science and technology.
Saturday, April 18, 2009
Wednesday, April 1, 2009
post#6-grammar mistakes
In this post I state three most common grammar mistakes which are subject-verb agreement, verb form and also sentences clauses.
For subject-verb agreement, some indefinite pronouns are particularly troublesome Everyone and everybody certainly feel like more than one person and, therefore, students are sometimes tempted to use a plural verb with them. They are always singular, though. Each is often followed by a prepositional phrase ending in a plural word (Each of the cars), thus confusing the verb choice. Each, too, is always singular and requires a singular verb.
Everyone has finished his or her homework.
You would always say, "Everybody is here." This means that the word is singular and nothing will change that.
Each of the students is responsible for doing his or her work in the library.
Don't let the word "students" confuse you; the subject is each and each is always singular.
All verbs have 5 forms. We know that each verb has its own form in different time phase. And I always made a lot of mistakes due to insufficient of reading, Here are some examples:
1) If you sent me home first you can.....It sounds right but we must use 'take' instead of 'sent' here.
2) We will be going to your house at 6 so please wait for us.Use 'coming' and not 'going'
Sentences are fundamental in organizing an essay, there is only a poor essay without goo sentences. We usually use adjective clauses and adverb clauses in our essay and below are my common mistakes:
1) It encourages the idea what juveniles should be given a second chance.The correct answer is 'that' and not 'what'.
2) What she loves is Indian food.
Clause Sentences must connected by coordinators or transitions. Here is 1 example:
1) Although the price of electricity is increasing, but we continue to consume more and more of it. Omit the coordinator 'but'.
For subject-verb agreement, some indefinite pronouns are particularly troublesome Everyone and everybody certainly feel like more than one person and, therefore, students are sometimes tempted to use a plural verb with them. They are always singular, though. Each is often followed by a prepositional phrase ending in a plural word (Each of the cars), thus confusing the verb choice. Each, too, is always singular and requires a singular verb.
Everyone has finished his or her homework.
You would always say, "Everybody is here." This means that the word is singular and nothing will change that.
Each of the students is responsible for doing his or her work in the library.
Don't let the word "students" confuse you; the subject is each and each is always singular.
All verbs have 5 forms. We know that each verb has its own form in different time phase. And I always made a lot of mistakes due to insufficient of reading, Here are some examples:
1) If you sent me home first you can.....It sounds right but we must use 'take' instead of 'sent' here.
2) We will be going to your house at 6 so please wait for us.Use 'coming' and not 'going'
Sentences are fundamental in organizing an essay, there is only a poor essay without goo sentences. We usually use adjective clauses and adverb clauses in our essay and below are my common mistakes:
1) It encourages the idea what juveniles should be given a second chance.The correct answer is 'that' and not 'what'.
2) What she loves is Indian food.
Clause Sentences must connected by coordinators or transitions. Here is 1 example:
1) Although the price of electricity is increasing, but we continue to consume more and more of it. Omit the coordinator 'but'.
post#5-round table discussion
The topic we discuss in tutorial was whether the governments or organizations should regulate the technology of brain study utilizing with computaional modeling.
In recent years, as the development of computer technologies, there were more and more new born things came to our life, such as Internet-banking, online games, interactive media and so on. Brain study utilized computational modeling is one of them, which helps us making a much clearer understanding of how human’s brain processes information.
The phrase “brain study” seems like the mind reader mentioned in folklore and science fiction. It sounds incredible, but now the scientists are very close to achieve this concept. The term brain study utilized computational modeling refers to use extensive computaional resources to study the behavior of human brain by computer simulation. It probably makes a great improvement on current medical technology. With the help of brain study, we can know the reason such as why people got mental diseases and how heroin controls drug taker’s mind, after knowing the reasons we can take corresponding actions to solve the problems. Such a helpful technology, the supporting team think governments should not regulate it. However, our team having an opposite view and I think governments should regulate this technology.
Why do they think brain study should not be regulated? They think for computational modeling, it requires gathering a huge amount of related information in order to do computer simulation. If governments regulate it, the researchers would not have the freedom to gather sufficient information as they wanted. However, I think it is to be emphasized is that freedom does not mean no rules, another way saying is that rules do not refer to no freedom. For giving the right direction of development of this technology, government should regulate it, just like creating a road for vehicles driving with safety. The regulation likes the road, proper road planning leads more vehicles driving on it. Meanwhile, under the rules, the researchers can carry on the research more freedom without people’s doubts.
They also think that there are no adverse effects of brain study, so governments do not need put time on making regulation. If we think carefully about brain study, there is negative side of this technology. For instance, some people would use this technology to illegally obtain and propagate personal information, people would live without privacy.
In my polytechnic final year project, I did a research on neural network. I think it can be considered as a kind of brain study. Neural networks are models of the brain and have been used within Artificial Intelligence to provide alternative explanations to the symbolic explanations of cognition in which one assumes that an intelligent system has certain explicit representations of some aspect of the world and uses these in intelligent behavior [1]. For our project, the neural network was used to recognized car’s number plates; it also can be implemented with many other purposes, if it is highly developed, it would very close to human’s brain, so there is a potential adverse effect. An example, such as the robot in some science fiction movies, the robot may attack humans and destroy the civilization, although it was only showed on movies, none of us can assure these will not happens in the future. Therefore, it is necessary to make regulation on using those technologies for eliminating the potential danger. A proposed rule like the robot’s programmer must write a high priority command “cannot attack human” when they program a robot.
At the end of discuss, the oppsite team changed their view, and agreed with our view. In conclusion, nothing can be accomplished without norms. Therefore the governments should regulate the technology of computational modeling on brain study, it is necessary for the health development of this technology.
In recent years, as the development of computer technologies, there were more and more new born things came to our life, such as Internet-banking, online games, interactive media and so on. Brain study utilized computational modeling is one of them, which helps us making a much clearer understanding of how human’s brain processes information.
The phrase “brain study” seems like the mind reader mentioned in folklore and science fiction. It sounds incredible, but now the scientists are very close to achieve this concept. The term brain study utilized computational modeling refers to use extensive computaional resources to study the behavior of human brain by computer simulation. It probably makes a great improvement on current medical technology. With the help of brain study, we can know the reason such as why people got mental diseases and how heroin controls drug taker’s mind, after knowing the reasons we can take corresponding actions to solve the problems. Such a helpful technology, the supporting team think governments should not regulate it. However, our team having an opposite view and I think governments should regulate this technology.
Why do they think brain study should not be regulated? They think for computational modeling, it requires gathering a huge amount of related information in order to do computer simulation. If governments regulate it, the researchers would not have the freedom to gather sufficient information as they wanted. However, I think it is to be emphasized is that freedom does not mean no rules, another way saying is that rules do not refer to no freedom. For giving the right direction of development of this technology, government should regulate it, just like creating a road for vehicles driving with safety. The regulation likes the road, proper road planning leads more vehicles driving on it. Meanwhile, under the rules, the researchers can carry on the research more freedom without people’s doubts.
They also think that there are no adverse effects of brain study, so governments do not need put time on making regulation. If we think carefully about brain study, there is negative side of this technology. For instance, some people would use this technology to illegally obtain and propagate personal information, people would live without privacy.
In my polytechnic final year project, I did a research on neural network. I think it can be considered as a kind of brain study. Neural networks are models of the brain and have been used within Artificial Intelligence to provide alternative explanations to the symbolic explanations of cognition in which one assumes that an intelligent system has certain explicit representations of some aspect of the world and uses these in intelligent behavior [1]. For our project, the neural network was used to recognized car’s number plates; it also can be implemented with many other purposes, if it is highly developed, it would very close to human’s brain, so there is a potential adverse effect. An example, such as the robot in some science fiction movies, the robot may attack humans and destroy the civilization, although it was only showed on movies, none of us can assure these will not happens in the future. Therefore, it is necessary to make regulation on using those technologies for eliminating the potential danger. A proposed rule like the robot’s programmer must write a high priority command “cannot attack human” when they program a robot.
At the end of discuss, the oppsite team changed their view, and agreed with our view. In conclusion, nothing can be accomplished without norms. Therefore the governments should regulate the technology of computational modeling on brain study, it is necessary for the health development of this technology.
post#4-Computer Model Reveals How Brain Represents Meaning
Computer Model Reveals How Brain Represents Meaning
Scientists at Carnegie Mellon University have taken an important step toward understanding how the human brain codes the meanings of words by creating the first computational model that can predict the unique brain activation patterns associated with names for things that you can see, hear, feel, taste or smell.
Researchers previously have shown that they can use functional magnetic resonance imaging (fMRI) to detect which areas of the brain are activated when a person thinks about a specific word. A Carnegie Mellon team has taken the next step by predicting these activation patterns for concrete nouns -- things that are experienced through the senses -- for which fMRI data does not yet exist.
The work could eventually lead to the use of brain scans to identify thoughts and could have applications in the study of autism, disorders of thought such as paranoid schizophrenia, and semantic dementias such as Pick's disease.
The team, led by computer scientist Tom M. Mitchell and cognitive neuroscientist Marcel Just, constructed the computational model by using fMRI activation patterns for 60 concrete nouns and by statistically analyzing a set of texts totaling more than a trillion words, called a text corpus. The computer model combines this information about how words are used in text to predict the activation patterns for thousands of concrete nouns contained in the text corpus with accuracies significantly greater than chance.
Just, a professor of psychology who directs the Center for Cognitive Brain Imaging, said the computational model provides insight into the nature of human thought. "We are fundamentally perceivers and actors," he said. "So the brain represents the meaning of a concrete noun in areas of the brain associated with how people sense it or manipulate it. The meaning of an apple, for instance, is represented in brain areas responsible for tasting, for smelling, for chewing. An apple is what you do with it. Our work is a small but important step in breaking the brain's code."
In addition to representations in these sensory-motor areas of the brain, the Carnegie Mellon researchers found significant activation in other areas, including frontal areas associated with planning functions and long-term memory. When someone thinks of an apple, for instance, this might trigger memories of the last time the person ate an apple, or initiate thoughts about how to obtain an apple.
To construct their computational model, the researchers used machine learning techniques to analyze the nouns in a trillion-word text corpus that reflects typical English word usage. For each noun, they calculated how frequently it co-occurs in the text with each of 25 verbs associated with sensory-motor functions, including see, hear, listen, taste, smell, eat, push, drive and lift. Computational linguists routinely do this statistical analysis as a means of characterizing the use of words.
By using this statistical information to analyze the fMRI activation patterns gathered for each of the 60 stimulus nouns, they were able to determine how each co-occurrence with one of the 25 verbs affected the activation of each voxel, or 3-D volume element, within the fMRI brain scans.
To predict the fMRI activation pattern for any concrete noun within the text corpus, the computational model determines the noun's co-occurrences within the text with the 25 verbs and builds an activation map based on how those co-occurrences affect each voxel.
In tests, a separate computational model was trained for each of the nine research subjects using 58 of the 60 stimulus nouns and their associated activation patterns. The model was then used to predict the activation patterns for the remaining two nouns. For the nine participants, the model had a mean accuracy of 77 percent in matching the predicted activation patterns to the ones observed in the participants' brains.
The model proved capable of predicting activation patterns even in semantic areas for which it was untrained. In tests, the model was retrained with words from all but two of the 12 semantic categories from which the 60 words were drawn, and then tested with stimulus nouns from the omitted categories. If the categories of vehicles and vegetables were omitted, for instance, the model would be tested with words such as airplane and celery. In these cases, the mean accuracy of the model's prediction dropped to 70 percent, but was still well above chance (50 percent).
Plans for future work include studying the activation patterns for adjective-noun combinations, prepositional phrases and simple sentences. The team also plans to study how the brain represents abstract nouns and concepts.
The Carnegie Mellon team included Andrew Carlson, a Ph.D. student in the Machine Learning Department; Kai-Min Chang, a Ph.D. student in the Language Technologies Institute; and Robert A. Mason, a post-doctoral fellow in the Department of Psychology. Others are Svetlana V. Shinkareva, now a faculty member at the University of South Carolina, and Vicente L. Malave, now a graduate student at the University of California, San Diego. The research was funded by grants from the W.M. Keck Foundation and the National Science Foundation.
Reference:
Tom M. Mitchell, Svetlana V. Shinkareva, Andrew Carlson, Kai-Min Chang, Vicente L. Malave, Robert A. Mason, and Marcel Adam Just. Predicting Human Brain Activity Associated with the Meanings of Nouns. Science, 2008
Scientists at Carnegie Mellon University have taken an important step toward understanding how the human brain codes the meanings of words by creating the first computational model that can predict the unique brain activation patterns associated with names for things that you can see, hear, feel, taste or smell.
Researchers previously have shown that they can use functional magnetic resonance imaging (fMRI) to detect which areas of the brain are activated when a person thinks about a specific word. A Carnegie Mellon team has taken the next step by predicting these activation patterns for concrete nouns -- things that are experienced through the senses -- for which fMRI data does not yet exist.
The work could eventually lead to the use of brain scans to identify thoughts and could have applications in the study of autism, disorders of thought such as paranoid schizophrenia, and semantic dementias such as Pick's disease.
The team, led by computer scientist Tom M. Mitchell and cognitive neuroscientist Marcel Just, constructed the computational model by using fMRI activation patterns for 60 concrete nouns and by statistically analyzing a set of texts totaling more than a trillion words, called a text corpus. The computer model combines this information about how words are used in text to predict the activation patterns for thousands of concrete nouns contained in the text corpus with accuracies significantly greater than chance.
Just, a professor of psychology who directs the Center for Cognitive Brain Imaging, said the computational model provides insight into the nature of human thought. "We are fundamentally perceivers and actors," he said. "So the brain represents the meaning of a concrete noun in areas of the brain associated with how people sense it or manipulate it. The meaning of an apple, for instance, is represented in brain areas responsible for tasting, for smelling, for chewing. An apple is what you do with it. Our work is a small but important step in breaking the brain's code."
In addition to representations in these sensory-motor areas of the brain, the Carnegie Mellon researchers found significant activation in other areas, including frontal areas associated with planning functions and long-term memory. When someone thinks of an apple, for instance, this might trigger memories of the last time the person ate an apple, or initiate thoughts about how to obtain an apple.
To construct their computational model, the researchers used machine learning techniques to analyze the nouns in a trillion-word text corpus that reflects typical English word usage. For each noun, they calculated how frequently it co-occurs in the text with each of 25 verbs associated with sensory-motor functions, including see, hear, listen, taste, smell, eat, push, drive and lift. Computational linguists routinely do this statistical analysis as a means of characterizing the use of words.
By using this statistical information to analyze the fMRI activation patterns gathered for each of the 60 stimulus nouns, they were able to determine how each co-occurrence with one of the 25 verbs affected the activation of each voxel, or 3-D volume element, within the fMRI brain scans.
To predict the fMRI activation pattern for any concrete noun within the text corpus, the computational model determines the noun's co-occurrences within the text with the 25 verbs and builds an activation map based on how those co-occurrences affect each voxel.
In tests, a separate computational model was trained for each of the nine research subjects using 58 of the 60 stimulus nouns and their associated activation patterns. The model was then used to predict the activation patterns for the remaining two nouns. For the nine participants, the model had a mean accuracy of 77 percent in matching the predicted activation patterns to the ones observed in the participants' brains.
The model proved capable of predicting activation patterns even in semantic areas for which it was untrained. In tests, the model was retrained with words from all but two of the 12 semantic categories from which the 60 words were drawn, and then tested with stimulus nouns from the omitted categories. If the categories of vehicles and vegetables were omitted, for instance, the model would be tested with words such as airplane and celery. In these cases, the mean accuracy of the model's prediction dropped to 70 percent, but was still well above chance (50 percent).
Plans for future work include studying the activation patterns for adjective-noun combinations, prepositional phrases and simple sentences. The team also plans to study how the brain represents abstract nouns and concepts.
The Carnegie Mellon team included Andrew Carlson, a Ph.D. student in the Machine Learning Department; Kai-Min Chang, a Ph.D. student in the Language Technologies Institute; and Robert A. Mason, a post-doctoral fellow in the Department of Psychology. Others are Svetlana V. Shinkareva, now a faculty member at the University of South Carolina, and Vicente L. Malave, now a graduate student at the University of California, San Diego. The research was funded by grants from the W.M. Keck Foundation and the National Science Foundation.
Reference:
Tom M. Mitchell, Svetlana V. Shinkareva, Andrew Carlson, Kai-Min Chang, Vicente L. Malave, Robert A. Mason, and Marcel Adam Just. Predicting Human Brain Activity Associated with the Meanings of Nouns. Science, 2008
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